From 51613b2251853365d47b068678e06565f8c7d2ce Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Tue, 20 Feb 2024 11:46:13 +0000
Subject: [PATCH 01/45] Updated inspection method

---
 assess.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/assess.py b/assess.py
index ddad85b..7e7978e 100644
--- a/assess.py
+++ b/assess.py
@@ -339,7 +339,8 @@ def error_check(args, logger):
     print('')
     print(f'Identified {errs["Warning"]} files with Warnings')
 
-    logger.info(f'Found {len(savedcodes)} proj_codes with matching errory type "{args.inspect}"')
+    if args.inspect:
+        logger.info(f'Found {len(savedcodes)} proj_codes with matching errory type "{args.inspect}"')
 
     if args.write:
         if args.blacklist:
@@ -349,7 +350,8 @@ def error_check(args, logger):
         else:
             logger.info('No repeat_id supplied, proj_codes were not saved.')
     else:
-        logger.info(f'Skipped writing {len(savedcodes)}')
+        if len(savedcodes) != 0:
+            logger.info(f'Skipped writing {len(savedcodes)}')
 
 def output_check(args, logger):
     """Not implemented output log checker"""

From cec7630e017d40ead49b8d28ec7619ef21297a41 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Tue, 20 Feb 2024 11:46:47 +0000
Subject: [PATCH 02/45] Aded backtrack and xkshape warning

---
 group_run.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/group_run.py b/group_run.py
index 35dc74f..ac7f3fe 100644
--- a/group_run.py
+++ b/group_run.py
@@ -113,6 +113,11 @@ def main(args):
         sb += f' -b {args.bypass}'
     if args.quality:
         sb += ' -Q'
+    if args.backtrack:
+        sb += ' -B'
+
+    if 'X' in args.bypass:
+        logger.warning('Running with XK Shape Bypass flag "X" is experimental and should only be used with approval.')
 
     if args.repeat_id:
         sb += f' -r {args.repeat_id}'
@@ -153,7 +158,7 @@ def main(args):
 
     parser.add_argument('-v','--verbose',dest='verbose' , action='count', default=0, help='Print helpful statements while running')
     parser.add_argument('-d','--dryrun',  dest='dryrun',  action='store_true', help='Perform dry-run (i.e no new files/dirs created)' )
-
+    parser.add_argument('-B','--backtrack', dest='backtrack', action='store_true', help='Backtrack to previous position, remove files that would be created in this job.')
     parser.add_argument('-Q','--quality', dest='quality', action='store_true', help='Quality assured checks - thorough run')
 
     args = parser.parse_args()

From ab5772d15277dabfe0047c36bd149385481d8a37 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Tue, 20 Feb 2024 11:47:10 +0000
Subject: [PATCH 03/45] Added backtrack

---
 single_run.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/single_run.py b/single_run.py
index 3349ec7..b36767a 100644
--- a/single_run.py
+++ b/single_run.py
@@ -161,7 +161,7 @@ def main(args):
             else:
                 args.proj_dir = f'{args.workdir}/in_progress/{args.proj_code}'
 
-            if blacklisted(args.proj_code, args.groupdir, logger):
+            if blacklisted(args.proj_code, args.groupdir, logger) and not args.backtrack:
                 raise BlacklistProjectCode
 
             if args.phase in drivers:
@@ -203,7 +203,7 @@ def main(args):
 
     parser.add_argument('-v','--verbose', dest='verbose', action='count', default=0, help='Print helpful statements while running')
     parser.add_argument('-d','--dryrun',  dest='dryrun',  action='store_true', help='Perform dry-run (i.e no new files/dirs created)' )
-
+    parser.add_argument('-B','--backtrack', dest='backtrack', action='store_true', help='Backtrack to previous position, remove files that would be created in this job.')
     parser.add_argument('-Q','--quality', dest='quality', action='store_true', help='Quality assured checks - thorough run')
 
     args = parser.parse_args()

From 934f0931f2330d21b7f778c7738271bd0af32c8d Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Tue, 20 Feb 2024 11:48:15 +0000
Subject: [PATCH 04/45] Added XKShape tolerance and get_concat_dims for
 multiple validation options

---
 pipeline/errors.py   |  10 +++
 pipeline/logs.py     |   1 +
 pipeline/validate.py | 186 +++++++++++++++++++++++++++++++++----------
 3 files changed, 156 insertions(+), 41 deletions(-)

diff --git a/pipeline/errors.py b/pipeline/errors.py
index dede0c9..5fcd95a 100644
--- a/pipeline/errors.py
+++ b/pipeline/errors.py
@@ -33,6 +33,16 @@ def __init__(self,verbose=0, proj_code=None, groupdir=None):
             self.__class__.__module__ = 'builtins'
     def get_str(self):
         return 'MissingVariableError'
+    
+class XKShapeToleranceError(KerchunkException):
+    """Attempted validation using a tolerance for shape mismatch on concat-dims, shape difference exceeds tolerance allowance."""
+    def __init__(self,tolerance=0, diff=0, dim='',verbose=0, proj_code=None, groupdir=None):
+        self.message = f"Shape difference ({diff}) exceeds allowed tolerance ({tolerance}) for dimension ({dim})"
+        super().__init__(proj_code, groupdir)
+        if verbose < 1:
+            self.__class__.__module__ = 'builtins'
+    def get_str(self):
+        return 'XKShapeToleranceError'
 
 class BlacklistProjectCode(KerchunkException):
     def __init__(self, verbose=0, proj_code=None, groupdir=None):
diff --git a/pipeline/logs.py b/pipeline/logs.py
index 2dff9f2..097aa2b 100644
--- a/pipeline/logs.py
+++ b/pipeline/logs.py
@@ -34,6 +34,7 @@ def __init__(self, switch='FDSC'):
         self.skip_softfail = ('S' in switch)
         self.skip_data_sum = ('C' in switch)
         self.skip_memcheck = ('M' in switch)
+        self.skip_xkshape  = ('X' in switch)
 
     def __str__(self):
         return self.switch
diff --git a/pipeline/validate.py b/pipeline/validate.py
index 893af3a..f85cccd 100644
--- a/pipeline/validate.py
+++ b/pipeline/validate.py
@@ -39,6 +39,22 @@ def get_vslice(shape: list, dtypes: list, lengths: list, divisions: list, logger
     logger.debug(f'Slice {vslice}')
     return vslice
 
+def get_concat_dims(xfiles, detailfile=None):
+    concat_dims = {'time':0}
+    if os.path.isfile(detailfile):
+        with open(detailfile) as f:
+            details = json.load(f)
+    # Initialise concat dims
+    if 'concat_dims' in details:
+        concat_dims[details['concat_dims']] = 0
+
+    for xf in xfiles:
+        # Open netcdf in lowest memory intensive way possible.
+        ds = xr.open_dataset(xf)
+        for dim in concat_dims.keys():
+            concat_dims[dim] += ds[dim].shape[0]
+    return concat_dims
+
 ## 2. File Selection Tools
 
 def get_netcdf_list(proj_dir: str, logger, thorough=False):
@@ -207,14 +223,14 @@ def open_netcdfs(args, logger, thorough=False):
         if len(xobjs) == 0:
             logger.error('No valid timestep objects identified')
             raise NoValidTimeSlicesError(message='Kerchunk', verbose=args.verbose)
-        return xobjs, indexes, len(xfiles)
+        return xobjs, indexes, xfiles
     else:
         if not args.bypass.skip_memcheck:
             check_memory(xfiles, [i for i in range(len(xfiles))], args.memory, logger)
         else:
             logger.warning('Memory checks bypassed')
         xobj = xr.concat([xr.open_dataset(fx) for fx in xfiles], dim='time', data_vars='minimal')
-        return xobj, None, len(xfiles)
+        return xobj, None, xfiles
 
 ## 3. Validation Testing
 
@@ -297,25 +313,74 @@ def compare_data(vname: str, xbox, kerchunk_box, logger, bypass=False):
         logger.error('Validation Error')
         raise ValidationError
 
-def validate_shapes(xobj, kobj, step: int, nfiles: list, xv: str, logger):
+def validate_shape_to_tolerance(nfiles: int, xv, dims, xshape, kshape, logger, detailfile=None):
+    tolerance = 1/(nfiles*5)
+    logger.info(f'Attempting shape bypass using concat-dim tolerance {tolerance*100}%')
+    try:
+        logger.debug('Finding concat dims recorded in details for this proj_code')
+        with open(detailfile) as f:
+            concat_dims = json.load(f)['concat_dims']
+    except KeyError:
+        logger.debug('Unable to determine concat_dims, defaulting to time-only')
+        concat_dims = ['time']
+        check_dims = []
+        for cdim in concat_dims:
+            # Match to index in xobj
+            for index, dim in enumerate(dims):
+                if dim == cdim:
+                    check_dims.append(index)
+        tolerance_error = False
+        general_shape_error = False
+        for cdim in range(len(xshape)):
+            if cdim in check_dims:
+                if abs(xshape[cdim] - kshape[cdim]) / kshape[cdim] > tolerance:
+                    tolerance_error = XKShapeToleranceError(
+                        tolerance=tolerance,
+                        diff=abs(xshape[cdim] - kshape[cdim]) / kshape[cdim],
+                        dim=dims[cdim]
+                    )
+            else:
+                if xshape[cdim] != kshape[cdim]:
+                    general_shape_error = ShapeMismatchError(var=xv, first=kshape, second=xshape)
+        if general_shape_error:
+            raise general_shape_error
+        elif tolerance_error:
+            raise tolerance_error
+        else:
+            pass
+
+def validate_shapes(xobj, kobj, step: int, nfiles: list, xv: str, logger, proj_code, bypass_shape=False, detailfile=None, concat_dims={}):
     """Ensure shapes are equivalent across Kerchunk/NetCDF per variable
      - Accounts for the number of files opened vs how many files in total."""
     xshape = list(xobj[xv].shape)
     kshape = list(kobj[xv].shape)
 
-    if 'time' in xobj[xv].dims:
-        try:
-            xshape[0] *= nfiles
-        except TypeError:
-            logger.warning(f'{xv} - {nfiles}*{xshape[0]} failed to assign')
-        except:
-            pass
-
-    logger.debug(f'{xv} : Comparing shapes {xshape} and {kshape} - {step}')
+    # Perform dimension adjustments if necessary
+    logger.debug(f'{xv} - raw shapes - K: {kshape}, X: {xshape}')
+    if concat_dims:
+        for index, dim in enumerate(xobj[xv].dims):
+            if dim in concat_dims:
+                xshape[index] = concat_dims[dim]
+    else:           
+        if 'time' in xobj[xv].dims:
+            try:
+                xshape[0] *= nfiles
+            except TypeError:
+                logger.warning(f'{xv} - {nfiles}*{xshape[0]} failed to assign')
+            except:
+                pass
+    logger.debug(f'{xv} - dimension-adjusted shapes - K: {kshape}, X: {xshape}')
     
-    if xshape != kshape:
-        logger.info(f'Kerchunk/NetCDF mismatch for variable {xv} with shapes - K {kshape} vs N {xshape}')
+    if len(xshape) != len(kshape):
         raise ShapeMismatchError(var=xv, first=kshape, second=xshape)
+    elif xshape != kshape and bypass_shape: # Special bypass-shape testing
+        if concat_dims == {}:
+            logger.info('Attempting special bypass using tolerance feature')
+            validate_shape_to_tolerance(nfiles, xv, xobj[xv].dims, xshape, kshape, logger, detailfile=detailfile)
+        else:
+            raise TrueShapeValidationError
+    else:
+        pass
 
 def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv: int, logger, bypass=BypassSwitch()):
     """Validate this data selection in xvariable/kvariable objects
@@ -381,7 +446,7 @@ def validate_data(xobj, kobj, xv: str, step: int, logger, bypass=BypassSwitch())
     # Attempt 128 divisions within selection - 128, 64, 32, 16, 8, 4, 2, 1
     return validate_selection(xvariable, kvariable, xv, 128, 128, logger, bypass=bypass)
 
-def validate_timestep(args, xobj, kobj, step: int, nfiles: list, logger):
+def validate_timestep(args, xobj, kobj, step: int, nfiles: int, logger, concat_dims={}):
     """Run all tests for a single file which may or may not equate to 1 timestep"""
 
     # Run Variable and Shape validation
@@ -394,7 +459,10 @@ def validate_timestep(args, xobj, kobj, step: int, nfiles: list, logger):
         logger.info(f'Passed Variable tests')
         print()
         for xv in xvars:
-            validate_shapes(xobj, kobj, step, nfiles, xv, logger)
+            validate_shapes(xobj, kobj, step, nfiles, xv, logger, args.proj_code,
+                            bypass_shape=args.bypass.skip_xkshape, 
+                            detailfile=f'{args.proj_dir}/detail-cfg.json',
+                            concat_dims=concat_dims)
             logger.info(f'{xv} : Passed Shape test')
         logger.info(f'Passed all Shape tests')
         print()
@@ -430,9 +498,51 @@ def run_successful(args, logger):
         os.system(f'mv {kfile} {newfile}')
         os.system(f'touch {kfile}.complete')
 
-def run_backtrack():
-    """Not currently implemented"""
-    raise NotImplementedError
+def run_backtrack(args, logger):
+    """Backtrack progress on all output files. If quality is specified as well, files are removed rather than backtracked"""
+
+    if args.groupID:
+        complete_dir = f'{args.workdir}/complete/{args.groupID}'
+    else:
+        complete_dir = f'{args.workdir}/complete/single_runs'
+
+    if args.proj_dir:
+        proj_dir = args.proj_dir
+    else:
+        proj_dir = f'{args.workdir}/in_progress/{args.groupID}/{args.proj_code}'
+
+    logger.info("Removing 'complete' indicator file")
+
+    for f in glob.glob(f'{proj_dir}/*complete*'):
+        os.remove(f)
+    if args.quality:
+        logger.info("Removing Kerchunk file")
+        for f in glob.glob(f'{complete_dir}/{args.proj_code}*'):
+            os.remove(f)
+    else:
+        logger.info("Backtracking Kerchunk file")
+        for x, file in enumerate(glob.glob(f'{complete_dir}/{args.proj_code}*')):
+            os.rename(file, f'{proj_dir}/kerchunk-1{list("abcde")[x]}.json')
+
+    logger.info(f'{args.proj_code} Successfully backtracked to pre-validation')
+    
+def attempt_timestep(args, xobj, kobj, step, nfiles, logger, xfiles, depth=0, concat_dims={}):
+    try:
+        validate_timestep(args, xobj, kobj, step, nfiles, logger, concat_dims=concat_dims)
+    except ShapeMismatchError as err:
+        if depth == 2:
+            raise TrueShapeValidationError
+        else:
+            return True
+    except XKShapeToleranceError as err:
+        if depth < 1:
+            # Try new routine to just get the key variable sizes
+            concat_dims = get_concat_dims(xfiles, detailfile=f'{args.proj_dir}/detail-cfg.json')
+            attempt_timestep(args, xobj, kobj, step+1, nfiles, logger, xfiles, depth=depth+1, concat_dims=concat_dims)
+        else:
+            return True
+    except Exception as err:
+        raise err
 
 def validate_dataset(args):
     """Perform validation steps for specific dataset defined here
@@ -442,7 +552,19 @@ def validate_dataset(args):
     logger = init_logger(args.verbose, args.mode,'validate')
     logger.info(f'Starting tests for {args.proj_code}')
 
-    xobjs, indexes, nfiles = open_netcdfs(args, logger, thorough=args.quality)
+    if hasattr(args, 'backtrack'):
+        if args.backtrack:
+            run_backtrack(args, logger)
+            return None
+
+    if not args.proj_dir:
+        if args.groupID:
+            args.proj_dir = f'{args.workdir}/in_progress/{args.groupID}/{args.proj_code}'
+        else:
+            args.proj_dir = f'{args.workdir}/in_progress/{args.proj_code}'
+
+    xobjs, indexes, xfiles = open_netcdfs(args, logger, thorough=args.quality)
+    nfiles = len(xfiles)
     if len(xobjs) == 0:
         raise NoValidTimeSlicesError(message='Xarray/NetCDF')
     if indexes == None:
@@ -454,38 +576,20 @@ def validate_dataset(args):
         raise MissingKerchunkError
 
     ## Set up loop variables
-    fullset   = False
-
-    if args.quality:
-        fullset = True
+    fullset = bool(args.quality)
 
     if not fullset:
         logger.info(f"Attempting file subset validation: {len(indexes)}/{nfiles}")
         for step, index in enumerate(indexes):
             xobj = xobjs[step]
             logger.info(f'Running tests for selected file: {index} ({step+1}/{len(indexes)})')
-
-            try:
-                validate_timestep(args, xobj, kobj, step+1, nfiles, logger)
-            except ShapeMismatchError:
-                fullset = True
-                break
-            except ChunkDataError as err:
-                raise err
-                fullset = True
-                break
+            fullset = attempt_timestep(args, xobj, kobj, step+1, nfiles, logger, xfiles)
 
     if fullset:
         print()
         logger.info(f"Attempting total validation")
         xobjs, indexes, nfiles = open_netcdfs(args, logger, thorough=True)
-        try:
-            validate_timestep(args, xobjs, kobj, 0, 1, logger)
-        except ShapeMismatchError:
-            raise TrueShapeValidationError
-        except Exception as err:
-            raise err
-    
+        fullset = attempt_timestep(args, xobjs, kobj, 0, 1, logger, xfiles)
     
     logger.info('All tests passed successfully')
     print()

From df6fddbfbb4ff41b8c9eef58e3060717a84358c3 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Tue, 20 Feb 2024 11:48:30 +0000
Subject: [PATCH 05/45] Updated version

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 572a8db..cdf980a 100644
--- a/setup.py
+++ b/setup.py
@@ -2,6 +2,6 @@
 
 setup(
     name='pipeline',
-    version='1.1',
+    version='1.0.2',
     packages=['pipeline']
 )

From d6edc55737fb044f059876cc0e3af0ed0bc0a2e9 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Tue, 20 Feb 2024 11:49:46 +0000
Subject: [PATCH 06/45] Updated setup and added cci env variables

---
 extensions/templates/setup-cci.sh   | 7 +++++++
 extensions/templates/setup-cmip6.sh | 6 +++++-
 2 files changed, 12 insertions(+), 1 deletion(-)
 create mode 100755 extensions/templates/setup-cci.sh

diff --git a/extensions/templates/setup-cci.sh b/extensions/templates/setup-cci.sh
new file mode 100755
index 0000000..bceeba7
--- /dev/null
+++ b/extensions/templates/setup-cci.sh
@@ -0,0 +1,7 @@
+module load jaspy
+
+export WORKDIR=/gws/nopw/j04/esacci_portal/kerchunk_conversion/
+export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
+export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
+
+source $KVENV/bin/activate
\ No newline at end of file
diff --git a/extensions/templates/setup-cmip6.sh b/extensions/templates/setup-cmip6.sh
index b7b57a0..9d4e76e 100755
--- a/extensions/templates/setup-cmip6.sh
+++ b/extensions/templates/setup-cmip6.sh
@@ -1,4 +1,8 @@
+module load jaspy
+
 export WORKDIR=/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline
 export GROUPDIR=/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233
 export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
-export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
\ No newline at end of file
+export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
+
+source $KVENV/bin/activate
\ No newline at end of file

From 36cad7eaa0c4500e974e3281e53585394beb5b8f Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Tue, 20 Feb 2024 12:23:26 +0000
Subject: [PATCH 07/45] Changed variable difference message between files

---
 pipeline/scan.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pipeline/scan.py b/pipeline/scan.py
index 67a6772..e128fe0 100644
--- a/pipeline/scan.py
+++ b/pipeline/scan.py
@@ -211,7 +211,7 @@ def scan_dataset(args, files: list, proj_dir: str, proj_code: str, logger):
             if not std_vars:
                 std_vars = vars
             if vars != std_vars:
-                logger.warning('Variables differ between files')
+                logger.warning(f'Variables differ between files - {vars} vs {std_vars}')
                 is_varwarn = True
             logger.info(f'Data saved for file {count+1}')
         except ExpectTimeoutError as err:

From b8de738376d83f3693fe3bfb1c23ed59602aecb6 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 21 Feb 2024 16:45:50 +0000
Subject: [PATCH 08/45] Integration of blacklist into progress tracker

---
 assess.py | 48 ++++++++++++++++++++++++++++++++++++++++--------
 1 file changed, 40 insertions(+), 8 deletions(-)

diff --git a/assess.py b/assess.py
index aa22755..6eaf100 100644
--- a/assess.py
+++ b/assess.py
@@ -177,7 +177,7 @@ def extract_keys(filepath: str, logger, savetype=None, examine=None, phase=None,
             path = '/'.join(filepath.split('/')[:-1]) + '/'
             code = get_code_from_val(path, ecode)
 
-            if matchtype:
+            if matchtype and not blacklisted(code, f'{filepath}/../', logger):
                 # Save matching types
                 savedcodes.append((efile, code, log))
                 if examine:
@@ -197,9 +197,9 @@ def merge_old_new(old_codes, new_codes, index_old='', index_new='', reason=None)
                 proj_code = code[index]
             if reason:
                 if ord != 0 and len(code) > 1:
-                    value = code[1]
-                else:
                     value = reason
+                else:
+                    value = code[1]
             merge_dict[proj_code] = value
     merged = []
     for mkey in merge_dict.keys():
@@ -214,7 +214,7 @@ def save_sel(codes: list, groupdir: str, label: str, logger, overwrite=0):
     
     Requires a groupdir (directory belonging to a group), list of codes and a label for the new file.
     """
-    if len(codes) > 1:
+    if len(codes) > 0:
         codeset = '\n'.join([code[1].strip() for code in codes])
         if os.path.isfile(f'{groupdir}/proj_codes_{label}.txt'):
             if overwrite == 0:
@@ -283,14 +283,20 @@ def progress_check(args, logger):
         return None
     else:
         logger.info(f'Discovering dataset progress within group {args.groupID}')
-        redo_pcodes = []
+        ignore_pcodes = []
+        blacklist = get_blacklist(args.groupdir)
+        if blacklist:
+            ignore_pcodes = [b.split(',')[0] for b in blacklist]
+            logger.info(f'blacklist: {len(blacklist)} datasets')
+
         for index, phase in enumerate(phases[:-1]): # Ignore complete check as this happens as a byproduct
-            redo_pcodes, completes = find_codes(phase, args.workdir, args.groupID, checks[index], ignore=redo_pcodes)
+            redo_pcodes, completes = find_codes(phase, args.workdir, args.groupID, checks[index], ignore=ignore_pcodes)
             logger.info(f'{phase}: {len(redo_pcodes)} datasets')
             if completes:
                 logger.info(f'complete: {len(completes)} datasets')
             if phase == args.phase:
                 break
+            ignore_pcodes += redo_pcodes
         if args.phase == 'complete':
             redo_pcodes = completes
         
@@ -361,7 +367,8 @@ def output_check(args, logger):
 
 def add_to_blacklist(savedcodes, groupdir, reason, logger):
     blackfile = f'{groupdir}/blacklist_codes.txt'
-
+    if not os.path.isfile(blackfile):
+        os.system(f'touch {blackfile}')
     logger.debug(f'Starting blacklist concatenation')
 
     merged = ''
@@ -369,12 +376,32 @@ def add_to_blacklist(savedcodes, groupdir, reason, logger):
         blackcodes = [r.strip().split(',') for r in f.readlines()]
 
     merged = merge_old_new(blackcodes, savedcodes, index_old=0, index_new=1, reason=reason)
-    blacklist = '\n'.join([f'{m},{reason}' for m in merged])
+    print(merged)
+    blacklist = '\n'.join([f'{m}' for m in merged])
     
     with open(blackfile,'w') as f:
         f.write(blacklist)
     logger.info(f'Blacklist now contains {len(merged)} codes.')
 
+def get_blacklist(groupdir):
+    blackfile = f'{groupdir}/blacklist_codes.txt'
+    if os.path.isfile(blackfile):
+        with open(blackfile) as f:
+            blackcodes = [r.strip().split(',')[0] for r in f.readlines()]
+    else:
+        return None
+    return blackcodes
+
+def blacklisted(proj_code: str, groupdir: str, logger):
+    blacklist = get_blacklist(groupdir)
+    if blacklist:
+        for code in blacklist:
+            if proj_code in code:
+                return True
+        return False
+    else:
+        logger.debug('No blacklist file preset for this group')
+        return False
 
 def retro_errors(args, logger):
     """Retrospective analysis of errors for all project codes within this group.
@@ -445,7 +472,12 @@ def assess_main(args):
     logger = init_logger(args.verbose, args.mode, 'assessor')
 
     args.workdir  = get_attribute('WORKDIR', args, 'workdir')
+
+    if args.groupID == 'A':
+        os.system(f'ls {args.workdir}/groups/')
+        return None
     args.groupdir = f'{args.workdir}/groups/{args.groupID}'
+
     if ',' in args.inspect:
         args.inspect = args.inspect.split(',')
 

From c1a6ae64eee01ec4c260034607f7d196b21d7f6a Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 21 Feb 2024 16:46:21 +0000
Subject: [PATCH 09/45] Added dryrun carrythrough

---
 group_run.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/group_run.py b/group_run.py
index 134c98e..0591c0a 100644
--- a/group_run.py
+++ b/group_run.py
@@ -115,6 +115,8 @@ def main(args):
         sb += ' -Q'
     if args.backtrack:
         sb += ' -B'
+    if args.dryrun:
+        sb += ' -d'
 
     if 'X' in args.bypass:
         logger.warning('Running with XK Shape Bypass flag "X" is experimental and should only be used with approval.')

From 5b18988536411257e2dca9c48f2831a5a48130ca Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 21 Feb 2024 16:47:09 +0000
Subject: [PATCH 10/45] Overhauled dimension handling and added extra
 functions, scanning now utilises dimension checks

---
 pipeline/compute/serial_process.py | 228 ++++++++++++++++++++---------
 1 file changed, 162 insertions(+), 66 deletions(-)

diff --git a/pipeline/compute/serial_process.py b/pipeline/compute/serial_process.py
index 6a51d3c..bbea74c 100644
--- a/pipeline/compute/serial_process.py
+++ b/pipeline/compute/serial_process.py
@@ -6,8 +6,12 @@
 from datetime import datetime
 import fsspec
 import xarray as xr
+import numpy as np
+import base64
 
-from pipeline.logs import init_logger, BypassSwitch
+from pipeline.logs import init_logger, BypassSwitch, FalseLogger
+from pipeline.errors import IdenticalVariablesError, ValidationError, ConcatenationError, SoftfailBypassError
+from pipeline.validate import validate_data, open_kerchunk, validate_selection
 
 class KerchunkDriverFatalError(Exception):
 
@@ -25,6 +29,7 @@ def __init__(self, clogger, bypass_driver=False):
         self.bypass_driver = bypass_driver
 
     def convert_to_zarr(self, nfile, ctype, **kwargs):
+        #ctype = 'hdf5'
         try:
             if ctype == 'ncf3':
                 return self.ncf3_to_zarr(nfile, **kwargs)
@@ -62,25 +67,24 @@ def __init__(self,
                  proj_code, 
                  cfg_file=None, detail_file=None, workdir=WORKDIR, 
                  issave_meta=False, thorough=False, forceful=False, 
-                 verb=0, mode=None, version_no=1,
-                 concat_msg=CONCAT_MSG, bypass=BypassSwitch(), groupID=None):
+                 verb=0, mode=None, version_no='trial-',
+                 concat_msg=CONCAT_MSG, bypass=BypassSwitch(), 
+                 groupID=None, limiter=None, dryrun=True):
         """Initialise indexer for this dataset, set all variables and prepare for computation"""
         super().__init__(init_logger(verb, mode, 'compute-serial'), bypass_driver=bypass.skip_driver)
 
-        self.bypass = bypass
-
         self.logger.debug('Starting variable definitions')
+        self.bypass     = bypass
+        self.limiter    = limiter
+        self.workdir    = workdir
+        self.proj_code  = proj_code
+        self.version_no = version_no
+        self.concat_msg = concat_msg
 
-        self.workdir   = workdir
-        self.proj_code = proj_code
-
+        self.dryrun = dryrun
         self.issave_meta = issave_meta
         self.updates, self.removals, self.load_refs = False, False, False
 
-        self.version_no = version_no
-
-        self.concat_msg = CONCAT_MSG
-
         self.verb = verb
         self.mode = mode
         if mode != 'std':
@@ -95,6 +99,9 @@ def __init__(self,
         with open(detail_file) as f:
             self.detail = json.load(f)
 
+        if 'virtual_concat' not in self.detail:
+            self.detail['virtual_concat'] = False
+
         if groupID:
             self.proj_dir = f'{self.workdir}/in_progress/{groupID}/{self.proj_code}'
         else:
@@ -136,6 +143,8 @@ def __init__(self,
         if thorough:
             os.system(f'rm -rf {self.cache}/*')
 
+        self.thorough = thorough
+
         self.combine_kwargs = {'concat_dims':['time']} # Always try time initially
         self.create_kwargs  = {'inline_threshold':1000}
         self.pre_kwargs     = {}
@@ -143,11 +152,16 @@ def __init__(self,
         self.set_filelist()
         self.logger.debug('Finished all setup steps')
 
+    def collect_details(self):
+        self.detail['combine_kwargs'] = self.combine_kwargs
+        return self.detail
+
     def set_filelist(self):
         """Get the list of files from the filelist for this dataset"""
         with open(self.filelist) as f:
             self.listfiles = [r.strip() for r in f.readlines()]
-        self.limiter = len(self.listfiles)
+        if not self.limiter:
+            self.limiter = len(self.listfiles)
 
     def add_download_link(self, refs):
         timecount = 0
@@ -189,50 +203,89 @@ def add_kerchunk_history(self, attrs):
         attrs['kerchunk_creation_date'] = now.strftime("%d%m%yT%H%M%S")
         return attrs
     
-    def check_identicals(self, refs):
-        from pipeline.validate import validate_data, open_kerchunk
-        from pipeline.errors import ValidationError, ConcatenationError
-        from pipeline.logs import FalseLogger
-        if 'identical_dims' in self.detail:
-            self.logger.info("Identified identical_dims for concatenation")
-            self.combine_kwargs['identical_dims'] = self.detail['identical_dims']
-        elif len(refs) == 1 or type(refs) == dict:
-            pass
-        else:
-            self.logger.info("Detecting identical dims across time dimension")
-            identical_dims = []
-            concat_dims = []
-            normal_dims = []
-            ds_examples = []
-            for example in range(2):
-                ds_examples.append(open_kerchunk(refs[example], FalseLogger()))
-            for var in ds_examples[0].variables:
-                if 'time' not in ds_examples[0][var].dims:
-                    try:
-                        validate_data(ds_examples[0], ds_examples[1],
-                                      var, 0, self.logger, bypass=self.bypass)
-                        identical_dims.append(var)
-                    except ValidationError:
-                        self.logger.warning(f'Non-identical variable: {var} - if this variable should be identical across the files, please rerun.')
-                        concat_dims.append(var)
-                    except Exception as err:
-                        self.logger.warning('Non validation error is present')
-                        raise err
-                else:
+    def find_concat_dims(self, ds_examples):
+        concat_dims = []
+        for dim in ds_examples[0].dims:
+            try:
+                validate_selection(ds_examples[0][dim], ds_examples[1][dim], dim, 1, 1, self.logger, bypass=self.bypass)          
+            except ValidationError:
+                self.logger.debug(f'Non-identical dimension: {dim} - if this dimension should be identical across the files, please inspect.')
+                concat_dims.append(dim)
+            except SoftfailBypassError as err:
+                self.logger.error(f'Found Empty dimension {dim} across example files - assuming non-stackable')
+                raise err
+            except Exception as err:
+                self.logger.warning('Non validation error is present')
+                raise err
+        if len(concat_dims) == 0:
+            self.detail['virtual_concat'] = True
+        self.combine_kwargs['concat_dims'] = concat_dims
+
+    def find_identical_dims(self, ds_examples):
+        identical_dims = []
+        normal_dims = []
+        for var in ds_examples[0].variables:
+            identical_check = True
+            for dim in self.combine_kwargs['concat_dims']:
+                if dim in ds_examples[0][var].dims:
+                    identical_check = False
+            if identical_check:
+                try:
+                    validate_selection(ds_examples[0][var], ds_examples[1][var], var, 1, 1, self.logger, bypass=self.bypass)
+                    identical_dims.append(var)
+                except ValidationError:
+                    self.logger.debug(f'Non-identical variable: {var} - if this variable should be identical across the files, please rerun.')
+                    normal_dims.append(var)
+                except SoftfailBypassError as err:
+                    self.logger.warning(f'Found Empty variable {var} across example files - assuming non-identical')
                     normal_dims.append(var)
-            if identical_dims:
-                self.logger.debug(f'Found {identical_dims} identical over time axis')
-                self.combine_kwargs['identical_dims'] = identical_dims
-            if concat_dims:
-                self.logger.debug(f'Found {concat_dims} additional concatenations as well as time')
-                self.combine_kwargs['concat_dims'] += concat_dims
-            self.logger.debug(f'Found {normal_dims} that stack as expected over time')          
+                except Exception as err:
+                    self.logger.warning('Unexpected error in checking identical dims')
+                    raise err
+            else:
+                normal_dims.append(var)
+        if len(identical_dims) == len(ds_examples[0].variables):
+            raise IdenticalVariablesError
+        self.combine_kwargs['identical_dims'] = identical_dims
+        if self.combine_kwargs["concat_dims"] == []:
+            self.logger.info(f'No concatenation dimensions identified - {normal_dims} will be concatenated using a virtual dimension')  
+        else:
+            self.logger.debug(f'Found {normal_dims} that vary over concatenation_dimensions: {self.combine_kwargs["concat_dims"]}')          
 
     def combine_and_save(self, refs, zattrs):
         """Concatenation of ref data for different kerchunk schemes"""
         self.logger.info('Starting concatenation of refs')
-        self.check_identicals(refs)
-
+        if len(refs) > 1:
+            # Pick 2 refs to use when determining dimension info.
+            if len(refs) == 1 or type(refs) == dict:
+                pass
+            else:
+                # Concatenation Dimensions
+                if 'combine_kwargs' in self.detail:
+                    self.combine_kwargs = self.detail['combine_kwargs']
+                else:
+                    self.logger.info("Determining concatenation dimensions")
+                    print()
+                    self.find_concat_dims([
+                        open_kerchunk(refs[0], FalseLogger()),
+                        open_kerchunk(refs[-1], FalseLogger())
+                    ])
+                    if self.combine_kwargs['concat_dims'] == []:
+                        self.logger.info(f"No concatenation dimensions available - virtual dimension will be constructed.")
+                    else:
+                        self.logger.info(f"Found {self.combine_kwargs['concat_dims']} concatenation dimensions.")
+                    print()
+
+                    # Identical (Variables) Dimensions
+                    self.logger.info("Determining identical variables")
+                    print()
+                    self.find_identical_dims([
+                        open_kerchunk(refs[0], FalseLogger()),
+                        open_kerchunk(refs[-1], FalseLogger())
+                    ])
+                    self.logger.info(f"Found {self.combine_kwargs['identical_dims']} identical variables.")
+                    print()
+                
         if self.use_json:
             self.logger.info('Concatenating to JSON format Kerchunk file')
             self.data_to_json(refs, zattrs)
@@ -240,6 +293,38 @@ def combine_and_save(self, refs, zattrs):
             self.logger.info('Concatenating to Parquet format Kerchunk store')
             self.data_to_parq(refs)
 
+    def construct_virtual_dim(self, refs):
+        vdim = 'file_number'
+        for idx in range(len(refs)):
+            ref = refs[idx]
+            zarray = json.dumps({
+                "chunks": [1],
+                "compressor": None,
+                "dtype":"<i8",
+                "fill_value": 4611686018427387904,
+                "filters": None,
+                "order": "C",
+                "shape": [1],
+                "zarr_format": 2
+            })
+            zattrs = json.dumps({
+                "_ARRAY_DIMENSIONS": [vdim],
+                "axis": "F",
+                "long_name": vdim,
+                "standard_name": vdim
+            })
+            values = b"base64:" + base64.b64encode(np.array([idx]).tobytes())
+
+            if 'refs' in ref:
+                ref['refs'][f'{vdim}/.zarray'] = zarray
+                ref['refs'][f'{vdim}/.zattrs'] = zattrs
+                ref['refs'][f'{vdim}/0'] = values
+            else:
+                ref[f'{vdim}/.zarray'] = zarray
+                ref[f'{vdim}/.zattrs'] = zattrs
+                ref[f'{vdim}/0'] = values
+        return refs, vdim
+
     def data_to_parq(self, create_refs):
         """Concatenating to Parquet format Kerchunk store"""
         from kerchunk.combine import MultiZarrToZarr
@@ -272,6 +357,9 @@ def data_to_json(self, refs, zattrs):
         # Already have default options saved to class variables
         if len(refs) > 1:
             self.logger.debug('Concatenating refs using MultiZarrToZarr')
+            if self.detail['virtual_concat']:
+                refs, vdim = self.construct_virtual_dim(refs)
+                self.combine_kwargs['concat_dims'] = [vdim]
             mzz = MultiZarrToZarr(list(refs), **self.combine_kwargs).translate()
             if zattrs:
                 zattrs = self.add_kerchunk_history(zattrs)
@@ -285,10 +373,12 @@ def data_to_json(self, refs, zattrs):
         # Override global attributes
         mzz['refs'] = self.add_download_link(mzz['refs'])
 
-        with open(self.outfile,'w') as f:
-            f.write(json.dumps(mzz))
-
-        self.logger.info(f'Written to JSON file - {self.proj_code}/kerchunk-1a.json')
+        if not self.dryrun:
+            with open(self.outfile,'w') as f:
+                f.write(json.dumps(mzz))
+            self.logger.info(f'Written to JSON file - {self.outfile}')
+        else:
+            self.logger.info(f'Skipped writing to JSON file - {self.outfile}')
 
     def correct_metadata(self, allzattrs):
         # General function for correcting metadata
@@ -393,19 +483,25 @@ def check_time_attributes(self, times):
 
     def save_metadata(self,zattrs):
         """Cache metadata global attributes in a temporary file"""
-        with open(f'{self.cache}/temp_zattrs.json','w') as f:
-            f.write(json.dumps(zattrs))
-        self.logger.debug('Saved global attribute cache')
+        if not self.dryrun:
+            with open(f'{self.cache}/temp_zattrs.json','w') as f:
+                f.write(json.dumps(zattrs))
+            self.logger.debug('Saved global attribute cache')
+        else:
+            self.logger.debug('Skipped saving global attribute cache')
 
     def save_cache(self, refs, zattrs):
         """Cache reference data in temporary json reference files"""
         self.logger.info('Saving pre-concat cache')
-        for x, r in enumerate(refs):
-            cache_ref = f'{self.cache}/{x}.json'
-            with open(cache_ref,'w') as f:
-                f.write(json.dumps(r))
+        if not self.dryrun:
+            for x, r in enumerate(refs):
+                cache_ref = f'{self.cache}/{x}.json'
+                with open(cache_ref,'w') as f:
+                    f.write(json.dumps(r))
+            self.logger.debug('Saved kerchunk data cache')
+        else:
+            self.logger.debug('Skipped saving metadata cache')
         self.save_metadata(zattrs)
-        self.logger.debug('Saved metadata cache')
         self.issave_meta = False
         # Save is only valid prior to concatenation
         # For a bizarre reason, the download link is applied to the whole cache on concatenation,
@@ -481,12 +577,12 @@ def create_refs(self):
             zattrs, refs = self.load_cache()
             zattrs = self.correct_metadata(zattrs)
         try:
-            if self.issave_meta:
+            if self.issave_meta and (not self.load_refs):
                 self.save_cache(refs, zattrs)
             if self.success:
                 self.combine_and_save(refs, zattrs)
         except Exception as err:
-            if self.issave_meta:
+            if self.issave_meta and (not self.load_refs or self.thorough):
                 self.save_cache(refs, zattrs)
             raise err
 

From 1688b01d35a00ce40592990e57c0642f568ddcdc Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 21 Feb 2024 16:47:50 +0000
Subject: [PATCH 11/45] Added functionality for dimension highlighting and
 concatenation testing within scanning phase

---
 pipeline/scan.py | 143 +++++++++++++++++++++++++++++++++++------------
 1 file changed, 108 insertions(+), 35 deletions(-)

diff --git a/pipeline/scan.py b/pipeline/scan.py
index e128fe0..0505dac 100644
--- a/pipeline/scan.py
+++ b/pipeline/scan.py
@@ -18,7 +18,8 @@
 import numpy as np
 
 from pipeline.logs import *
-from pipeline.errors import ExpectTimeoutError, FilecapExceededError
+from pipeline.errors import ExpectTimeoutError, FilecapExceededError, ConcatFatalError
+from pipeline.compute.serial_process import Converter, Indexer
 
 def format_float(value: int, logger):
     """Format byte-value with proper units"""
@@ -40,10 +41,9 @@ def safe_format(value: int, fstring: str):
     except:
         return ''
 
-def map_to_kerchunk(args, nfile: str, ctype: str, logger):
+def trial_kerchunk(args, nfile: str, ctype: str, logger):
     """Perform Kerchunk reading on specific file"""
     logger.info(f'Running Kerchunk reader for {nfile}')
-    from pipeline.compute.serial_process import Converter
 
     quickConvert = Converter(logger, bypass_driver=args.bypass.skip_driver)
 
@@ -71,13 +71,30 @@ def map_to_kerchunk(args, nfile: str, ctype: str, logger):
     else:
         logger.info(f'Scan successful with {ctype} driver')
         return tdict['refs'], ctype, t_len
-
-def get_internals(args, testfile: str, ctype: str, logger):
+    
+def load_from_previous(args, cache_id, logger):
+    cachefile = f'{args.proj_dir}/cache/{cache_id}.json'
+    if os.path.isfile(cachefile):
+        logger.info(f"Found existing cached file {cache_id}.json")
+        with open(cachefile) as f:
+            refs = json.load(f)
+        return refs
+    else:
+        return None
+        
+def perform_scan(args, testfile: str, ctype: str, logger, savecache=False, cache_id=None):
     """Map to kerchunk data and perform calculations on test netcdf file."""
-    refs, ctype, time = map_to_kerchunk(args, testfile, ctype, logger)
+    if cache_id:
+        refs = load_from_previous(args, cache_id, logger)
+        ctype = None
+        time = 0
+        if not refs:
+            refs, ctype, time = trial_kerchunk(args, testfile, ctype, logger)
+    else:
+        refs, ctype, time = trial_kerchunk(args, testfile, ctype, logger)
     if not refs:
         return None, None, None
-    logger.info(f'Starting summation process for {testfile}')
+    logger.debug(f'Starting Analysis of references')
 
     # Perform summations, extract chunk attributes
     sizes = []
@@ -88,10 +105,27 @@ def get_internals(args, testfile: str, ctype: str, logger):
             try:
                 sizes.append(int(refs[chunkkey][2]))
                 chunks += 1
-                vars[chunkkey.split('/')[0]] = 1
             except ValueError:
                 pass
-    return np.sum(sizes), chunks, sorted(list(vars.keys())), ctype, time
+        if '/.zarray' in chunkkey:
+            var = chunkkey.split('/')[0]
+            chunksize = 0
+            if var not in vars:
+                if type(refs[chunkkey]) == str:
+                    chunksize = json.loads(refs[chunkkey])['chunks']
+                else:
+                    chunksize = dict(refs[chunkkey])['chunks']
+                vars[var] = chunksize
+
+    # Save refs individually within cache.
+    if savecache:
+        cachedir = f'{args.proj_dir}/cache'
+        if not os.path.isdir(cachedir):
+            os.makedirs(cachedir)
+        with open(f'{cachedir}/{cache_id}.json','w') as f:
+            f.write(json.dumps(refs))
+
+    return np.sum(sizes), chunks, vars, ctype, time
 
 def eval_sizes(files: list):
     """Get a list of file sizes on disk from a list of filepaths"""
@@ -162,29 +196,34 @@ def perform_safe_calculations(std_vars: list, cpf: list, volms: list, files: lis
 
     return avg_cpf, num_vars, avg_chunk, spatial_res, data_represented, num_files, total_chunks, addition, estm_time
 
-def scan_dataset(args, files: list, proj_dir: str, proj_code: str, logger):
+def write_skip(proj_dir, proj_code, logger):
+    details = {'skipped':True}
+    with open(f'{proj_dir}/detail-cfg.json','w') as f:
+        f.write(json.dumps(details))
+    logger.info(f'Skipped scanning - {proj_code}/detail-cfg.json blank file created')
+
+def scan_dataset(args, files: list, logger):
     """Main process handler for scanning phase"""
+    proj_code = args.proj_code
+    proj_dir  = args.proj_dir
+    detailfile = f'{proj_dir}/detail-cfg.json'
     logger.debug(f'Assessment for {proj_code}')
 
     # Set up conditions, skip for small file count < 5
     escape, is_varwarn, is_skipwarn = False, False, False
     cpf, volms, times = [],[],[]
     trial_files = 5
+
     if len(files) < 5:
-        details = {'skipped':True}
-        if args.dryrun:
-            logger.info(f'DRYRUN: Skip writing to {proj_dir}/detail-cfg.json')
-        else:
-            with open(f'{proj_dir}/detail-cfg.json','w') as f:
-                f.write(json.dumps(details))
-            logger.info(f'Skipped scanning - {proj_code}/detail-cfg.json blank file created')
+        write_skip(proj_dir, proj_code, logger)
         return None
     else:
         logger.info(f'Identified {len(files)} files for scanning')
     
     # Perform scans for sample (max 5) files
     count    = 0
-    std_vars = None
+    std_vars   = None
+    std_chunks = None
     ctypes   = []
     filecap = min(100,len(files))
     while not escape and len(cpf) < trial_files:
@@ -195,11 +234,25 @@ def scan_dataset(args, files: list, proj_dir: str, proj_code: str, logger):
         if '.' in scanfile:
             extension = f'.{scanfile.split(".")[-1]}'
         else:
-            extension = '.nc'
+            extension = 'ncf3'
 
         try:
             # Measure time and ensure job will not overrun if it can be prevented.
-            volume, chunks_per_file, vars, ctype, time = get_internals(args, scanfile, extension, logger)
+            volume, chunks_per_file, varchunks, ctype, time = perform_scan(args, scanfile, extension, logger, savecache=True, cache_id=str(count))
+
+            vars = sorted(list(varchunks.keys()))
+            if not std_vars:
+                std_vars = vars
+            if vars != std_vars:
+                logger.warning(f'Variables differ between files - {vars} vs {std_vars}')
+                is_varwarn = True
+
+            if not std_chunks:
+                std_chunks = varchunks
+            for var in std_vars:
+                if std_chunks[var] != varchunks[var]:
+                    raise ConcatFatalError(var=var, chunk1=std_chunks[var], chunk2=varchunks[var])
+
             if count == 0 and time > get_seconds(args.time_allowed)/trial_files:
                 raise ExpectTimeoutError(required=format_seconds(time*5), current=args.time_allowed)
 
@@ -208,14 +261,11 @@ def scan_dataset(args, files: list, proj_dir: str, proj_code: str, logger):
             ctypes.append(ctype)
             times.append(time)
 
-            if not std_vars:
-                std_vars = vars
-            if vars != std_vars:
-                logger.warning(f'Variables differ between files - {vars} vs {std_vars}')
-                is_varwarn = True
-            logger.info(f'Data saved for file {count+1}')
+            logger.info(f'Data recorded for file {count+1}')
         except ExpectTimeoutError as err:
             raise err
+        except ConcatFatalError as err:
+            raise err
         except Exception as e:
             if args.bypass.skip_scanfile:
                 logger.warning(f'Skipped file {count} - {e}')
@@ -270,6 +320,28 @@ def scan_dataset(args, files: list, proj_dir: str, proj_code: str, logger):
             # Replace with dumping dictionary
             f.write(json.dumps(details))
         logger.info(f'Written output file {proj_code}/detail-cfg.json')
+    logger.info('Performing concatenation attempt with minimal files')
+    try:
+        assemble_trial_concatenation(args, logger)
+    except Exception as err:
+        logger.error('Error in concatenating files')
+        raise err
+
+def assemble_trial_concatenation(args, logger):
+
+    cfg_file    = f'{args.proj_dir}/base-cfg.json'
+    detail_file = f'{args.proj_dir}/detail-cfg.json'
+
+    idx_trial = Indexer(args.proj_code, cfg_file=cfg_file, detail_file=detail_file, 
+                    workdir=args.workdir, issave_meta=True, thorough=args.quality, forceful=args.forceful,
+                    verb=args.verbose, mode=args.mode,
+                    bypass=args.bypass, groupID=args.groupID, limiter=2)
+    
+    idx_trial.create_refs()
+    with open(detail_file,'w') as f:
+        f.write(json.dumps(idx_trial.collect_details()))
+    logger.debug('Collected new details into detail-cfg.json')
+
 
 def scan_config(args):
     """Configure scanning and access main section"""
@@ -288,16 +360,17 @@ def scan_config(args):
     args.workdir  = get_attribute('WORKDIR', args, 'workdir')
     args.groupdir = get_attribute('GROUPDIR', args, 'groupdir')
 
-    proj_code = args.proj_code
-
     if args.groupID:
-        proj_dir = f'{args.workdir}/in_progress/{args.groupID}/{proj_code}'
+        args.proj_dir = f'{args.workdir}/in_progress/{args.groupID}/{args.proj_code}'
     else:
-        proj_dir = f'{args.workdir}/in_progress/{proj_code}'
+        args.proj_dir = f'{args.workdir}/in_progress/{args.proj_code}'
 
-    logger.debug(f'Extracted attributes: {proj_code}, {args.workdir}, {proj_dir}')
+    logger.debug(f"""Extracted attributes: {args.proj_code}, 
+                                           {args.workdir}, 
+                                           {args.proj_dir}
+    """)
 
-    filelist = f'{proj_dir}/allfiles.txt'
+    filelist = f'{args.proj_dir}/allfiles.txt'
     
     if not os.path.isfile(filelist):
         logger.error(f'No filelist detected - {filelist}')
@@ -306,10 +379,10 @@ def scan_config(args):
     with open(filelist) as f:
         files = [r.strip() for r in f.readlines()]
 
-    if not os.path.isfile(f'{proj_dir}/detail-cfg.json') or args.forceful:
-        scan_dataset(args, files, proj_dir, proj_code, logger)
+    if not os.path.isfile(f'{args.proj_dir}/detail-cfg.json') or args.forceful:
+        scan_dataset(args, files, logger)
     else:
-        logger.warning(f'Skipped scanning {proj_code} - detailed config already exists')
+        logger.warning(f'Skipped scanning {args.proj_code} - detailed config already exists')
 
 if __name__ == '__main__':
     print('Kerchunk Pipeline Config Scanner - run using master scripts')
\ No newline at end of file

From 66ef1d462422d5e3f62a54e47d9bcf3ff5b40f0c Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 21 Feb 2024 16:48:42 +0000
Subject: [PATCH 12/45] Added multiple nan-type checks

---
 pipeline/validate.py | 31 ++++++++++++++++++-------------
 1 file changed, 18 insertions(+), 13 deletions(-)

diff --git a/pipeline/validate.py b/pipeline/validate.py
index f85cccd..8b3eace 100644
--- a/pipeline/validate.py
+++ b/pipeline/validate.py
@@ -268,7 +268,7 @@ def compare_data(vname: str, xbox, kerchunk_box, logger, bypass=False):
      - Expect TypeErrors from summations which are bypassed.
      - Other errors will exit the run.
     """
-    logger.debug('Starting xk comparison')
+    logger.debug(f'Starting xk comparison')
 
     try: # Tolerance 0.1% of mean value for xarray set
         tolerance = np.abs(np.nanmean(kerchunk_box))/1000
@@ -277,36 +277,36 @@ def compare_data(vname: str, xbox, kerchunk_box, logger, bypass=False):
 
     testpass = True
     if not np.array_equal(xbox, kerchunk_box):
-        logger.warn(f'Failed equality check for {vname}')
+        logger.warning(f'Failed equality check for {vname}')
         raise ValidationError
     try:
         if np.abs(np.nanmax(kerchunk_box) - np.nanmax(xbox)) > tolerance:
-            logger.warn(f'Failed maximum comparison for {vname}')
+            logger.warning(f'Failed maximum comparison for {vname}')
             logger.debug('K ' + str(np.nanmax(kerchunk_box)) + ' N ' + str(np.nanmax(xbox)))
             testpass = False
     except TypeError as err:
         if bypass:
-            logger.warn(f'Max comparison skipped for non-summable values in {vname}')
+            logger.warning(f'Max comparison skipped for non-summable values in {vname}')
         else:
             raise err
     try:
         if np.abs(np.nanmin(kerchunk_box) - np.nanmin(xbox)) > tolerance:
-            logger.warn(f'Failed minimum comparison for {vname}')
+            logger.warning(f'Failed minimum comparison for {vname}')
             logger.debug('K ' + str(np.nanmin(kerchunk_box)) + ' N ' + str(np.nanmin(xbox)))
             testpass = False
     except TypeError as err:
         if bypass:
-            logger.warn(f'Min comparison skipped for non-summable values in {vname}')
+            logger.warning(f'Min comparison skipped for non-summable values in {vname}')
         else:
             raise err
     try:
         if np.abs(np.nanmean(kerchunk_box) - np.nanmean(xbox)) > tolerance:
-            logger.warn(f'Failed mean comparison for {vname}')
+            logger.warning(f'Failed mean comparison for {vname}')
             logger.debug('K ' + str(np.nanmean(kerchunk_box)) + ' N ' + str(np.nanmean(xbox)))
             testpass = False
     except TypeError as err:
         if bypass:
-            logger.warn(f'Mean comparison skipped for non-summable values in {vname}')
+            logger.warning(f'Mean comparison skipped for non-summable values in {vname}')
         else:
             raise err
     if not testpass:
@@ -395,8 +395,8 @@ def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv:
     logger.debug(f'Attempt {repeat} - {currentdiv} divs for {vname}')
 
     vslice = []
+    shape = {}
     if divs > 1:
-        shape = xvariable.shape
         logger.debug(f'Detected shape {shape} for {vname}')
         dtypes  = [xvariable[xvariable.dims[x]].dtype for x in range(len(xvariable.shape))]
         lengths = [len(xvariable[xvariable.dims[x]])  for x in range(len(xvariable.shape))]
@@ -415,7 +415,12 @@ def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv:
 
     try:
         kb = np.array(kbox)
-        isnan = np.all(kb!=kb)
+        if np.all(kb!=kb):
+            isnan = True
+        elif np.all(kb == np.mean(kb)):
+            isnan = True
+        else:
+            isnan = False
     except Exception as err:
         if bypass.skip_boxfail:
             logger.warning(f'{err} - check versions')
@@ -433,18 +438,18 @@ def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv:
             # Recursive search for increasing size (decreasing divisions)
             validate_selection(xvariable, kvariable, vname, divs, int(currentdiv/2), logger, bypass=bypass)
         else:
-            logger.warn(f'Failed to find non-NaN slice (tried: {int(math.log2(divs))}, var: {vname})')
+            logger.warning(f'Failed to find non-NaN slice (tried: {int(math.log2(divs))}, var: {vname})')
             if not bypass.skip_softfail:
                 raise SoftfailBypassError
 
-def validate_data(xobj, kobj, xv: str, step: int, logger, bypass=BypassSwitch()):
+def validate_data(xobj, kobj, xv: str, step: int, logger, bypass=BypassSwitch(), depth_default=128):
     """Run growing selection test for specified variable from xarray and kerchunk datasets"""
     logger.info(f'{xv} : Starting growbox data tests for {step}')
 
     kvariable, xvariable = match_timestamp(xobj[xv], kobj[xv], logger)
 
     # Attempt 128 divisions within selection - 128, 64, 32, 16, 8, 4, 2, 1
-    return validate_selection(xvariable, kvariable, xv, 128, 128, logger, bypass=bypass)
+    return validate_selection(xvariable, kvariable, xv, depth_default, depth_default, logger, bypass=bypass)
 
 def validate_timestep(args, xobj, kobj, step: int, nfiles: int, logger, concat_dims={}):
     """Run all tests for a single file which may or may not equate to 1 timestep"""

From 22a74d766c2b049f534177be4a77ad94fa50b6c1 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 21 Feb 2024 16:50:23 +0000
Subject: [PATCH 13/45] Added concat fatal error for l2 data

---
 pipeline/errors.py | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/pipeline/errors.py b/pipeline/errors.py
index 144c013..a7d7908 100644
--- a/pipeline/errors.py
+++ b/pipeline/errors.py
@@ -32,18 +32,17 @@ def __init__(self,verbose=0, proj_code=None, groupdir=None):
         if verbose < 1:
             self.__class__.__module__ = 'builtins'
     def get_str(self):
-        return 'MissingVariableError'
+        return 'KerchunkDriverFatalError'
 
-    
-class KerchunkDriverFatalError(KerchunkException):
-    """All known drivers failed when performing conversion - NetCDF3/HDF5/Tiff"""
+class IdenticalVariablesError(KerchunkException):
+    """All variables found to be suitably identical between files as to not stack or concatenate"""
     def __init__(self,verbose=0, proj_code=None, groupdir=None):
-        self.message = "All drivers failed when performing conversion"
+        self.message = "All variables are identical across files"
         super().__init__(proj_code, groupdir)
         if verbose < 1:
             self.__class__.__module__ = 'builtins'
     def get_str(self):
-        return 'MissingVariableError'
+        return 'IdenticalVariablesError'
     
 class XKShapeToleranceError(KerchunkException):
     """Attempted validation using a tolerance for shape mismatch on concat-dims, shape difference exceeds tolerance allowance."""
@@ -214,3 +213,13 @@ def __init__(self, message="Variables could not be concatenated over time and ar
             self.__class__.__module__ = 'builtins'
     def get_str(self):
         return 'ConcatenationError'
+    
+class ConcatFatalError(KerchunkException):
+    """Chunk sizes differ between refs - files cannot be concatenated"""
+    def __init__(self, var=None, chunk1=None, chunk2=None, verbose=0, proj_code=None, groupdir=None):
+        self.message = f"Chunk sizes differ between refs for {var}: {chunk1} - {chunk2} - files cannot be concatenated"
+        super().__init__(proj_code, groupdir)
+        if verbose < 1:
+            self.__class__.__module__ = 'builtins'
+    def get_str(self):
+        return 'ConcatFatalError'

From aa96b5757178e5a1a32eacca1ee4766b01d80c2b Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 21 Feb 2024 16:50:47 +0000
Subject: [PATCH 14/45] Integration with other pipeline functions

---
 single_run.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/single_run.py b/single_run.py
index 2f4f991..8dbe4ab 100644
--- a/single_run.py
+++ b/single_run.py
@@ -64,7 +64,7 @@ def run_compute(args, logger):
         return Indexer(args.proj_code, cfg_file=cfg_file, detail_file=detail_file, 
                 workdir=args.workdir, issave_meta=True, thorough=args.quality, forceful=args.forceful,
                 verb=args.verbose, mode=args.mode,
-                version_no=version_no, concat_msg=concat_msg, bypass=args.bypass, groupID=args.groupID).create_refs()
+                version_no=version_no, concat_msg=concat_msg, bypass=args.bypass, groupID=args.groupID, dryrun=args.dryrun).create_refs()
     else:
         logger.error('Output file already exists and there is no plan to overwrite')
         return None
@@ -110,7 +110,8 @@ def main(args):
     logger = init_logger(args.verbose, args.mode, 'main')
 
     args.workdir  = get_attribute('WORKDIR', args, 'workdir')
-    args.groupdir = get_attribute('GROUPDIR', args, 'groupdir')
+    if args.groupID:
+        args.groupdir = f'{args.workdir}/groups/{args.groupID}'
 
     args.bypass = BypassSwitch(switch=args.bypass)
 

From 1abd97c8bec91957f78665dafae886dc67de8ae1 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 12:28:29 +0000
Subject: [PATCH 15/45] Added blacklist/virtual support, reading log and status
 files

---
 assess.py | 705 +++++++++++++++++++++++++++++-------------------------
 1 file changed, 382 insertions(+), 323 deletions(-)

diff --git a/assess.py b/assess.py
index 6eaf100..8545452 100644
--- a/assess.py
+++ b/assess.py
@@ -3,189 +3,73 @@
 import glob
 import json
 import sys
+from datetime import datetime
+
+from pipeline.logs import init_logger
+from pipeline.utils import get_attribute, format_str, mem_to_val, get_codes
+import pipeline.errors as errs
+
+# Hints for custom errors
+HINTS = {}
+for obj in dir(errs):
+    object = getattr(errs, obj)
+    try:
+        inst = object()
+        HINTS[inst.get_str()] = inst.message
+    except:
+        pass
 
-from pipeline.logs import init_logger, get_attribute
-from pipeline.errors import MissingVariableError
-
-# Hints for errors
-HINTS = {
-    'TrueShapeValidationError': "Kerchunk array shape doesn't match netcdf - missing timesteps or write issue?",
-    'slurmstepd': "Ran out of time in job",
-    'INFO [main]': "No error recorded",
-    'MissingKerchunkError': "Missing the Kerchunk file",
-    'KerchunkDriverFatalError': "Kerchunking failed for one or more files",
-    'ExpectTimeoutError': "Time remaining estimate exceeded allowed job time (scan)",
-    'BlackListProjectCode': "Problematic Project currently on blacklist so being ignored"
-}
 
 phases = ['scan', 'compute', 'validate', 'complete']
 checks = ['/detail-cfg.json','/*kerchunk*','/*.complete']
 
-def format_str(string: str, length: int):
-    """Simple function to format a string to a correct length"""
-    while len(string) < length:
-        string += ' '
-    return string[:length]
+def get_rerun_command(phase: str, ecode: str, groupID: str, repeat_id: str):
+    """Print a rerun command for inspecting a single dataset using single_run.py"""
+    print(f'python single_run.py {phase} {ecode} -G {groupID} -r {repeat_id} -vvv -d')
 
-def find_codes(phase: str, workdir: str, groupID: str, check: str, ignore=[]):
-    """Find project codes for datasets that failed at various stages of the pipeline
-    
-    Checks 'in-progress' and 'complete' directories for datasets and config files to determine progress of all datasets.
-    
-    Parameters
-    ----------
-    phase : str
-        Check config and output files corresponding to a pipeline phase.
-    workdir : str
-        Path to current working directory of the pipeline.
-    groupID : str
-        Check pipeline for a specific group ID.
-    check : str
-        File type or specific file required for this phase to be considered complete
-    ignore : list (str-like)
-
-    Returns
-    -------
-    redo_pcodes : list (str-like)
-        List of project codes to re-run for this phase.
-    complete : list (str-like)
-        List of project codes considered to be complete for the whole pipeline.
+def get_index_of_code(workdir, groupID, repeat_id, code):
+    """Get the index of a project code within some repeat set of codes"""
+    proj_codes = get_codes(groupID, workdir, f'proj_codes_{repeat_id}')
+    pindex = 0
+    pcode = proj_codes[pindex]
+    while pcode != code:
+        pindex += 1
+        pcode = proj_codes[pindex]
+    return pindex
+
+def examine_log(workdir: str, proj_code: str,  phase: str, groupID=None, repeat_id=None):
+    """Open and examine a log file from a previous run of a given phase
+    - Show full error log
+    - Suggest rerun/examination command
     """
-    checkdir = f'{workdir}/in_progress/{groupID}/'
-    proj_codes = os.listdir(checkdir)
-
-    #if phase == 'validate':
-        #checkdir = f'{args.workdir}/complete/{args.groupID}/'
-    redo_pcodes = []
-    complete = []
-    for pcode in proj_codes:
-        check_file = checkdir + pcode + check
-        #if phase == 'validate':
-            #print(check_file)
-        if pcode not in ignore:
-            if glob.glob(check_file):
-                if phase == 'validate':
-                    complete.append(pcode)
-                else:
-                    pass
-            else:
-                redo_pcodes.append(pcode)
-    return redo_pcodes, complete
+    if groupID:
+        proj_dir = f'{workdir}/in_progress/{groupID}/{proj_code}'
 
-def get_code_from_val(path: str, index: str, filename='proj_codes'):
-    """Takes some index value from command line and fetches the corresponding project code.
-    
-    Project codes stored in proj_codes.txt under each job id.
-
-    Returns
-    -------
-    proj_code : str
-        Project code that matches the provided index
-    """
-    path = path.split('*')[0]
-    if os.path.isfile(f'{path}/{filename}.txt'):
-        with open(f'{path}/{filename}.txt') as f:
-            try:
-                code = f.readlines()[int(index)].strip()
-            except IndexError:
-                print('code',index)
-                code = 'N/A'
+    phase_log = f'{proj_dir}/phase_logs/{phase}.log'
+    if os.path.isfile(phase_log):
+        with open(phase_log) as f:
+            log = [r.strip() for r in f.readlines()]
     else:
-        code = 'N/A'
-    return code
-
-def get_rerun_command(phase: str, ecode: str, groupID: str, repeat_id: str):
-    print(f'python single_run.py {phase} {ecode} -G {groupID} -r {repeat_id} -vvv -d')
+        print(f'Phase log file not found: {phase_log}')
+        return None
 
-def examine_log(log: str, efile: str, code: str, ecode=None, phase=None, groupID=None, repeat_id=None):
     print()
     print('\n'.join(log))
-    print(f'{efile} - {code}')
+    print(f'Project Code: {proj_code}')
     print('Rerun suggested command:    ',end='')
-    if phase and ecode:
-        get_rerun_command(phase, ecode, groupID, repeat_id)
+
+    ecode = get_index_of_code(groupID, repeat_id, proj_code)
+    get_rerun_command(phase, ecode, groupID, repeat_id)
+
     paused=input('Type "E" to exit assessment: ')
     if paused == 'E':
         sys.exit()
 
-def extract_keys(filepath: str, logger, savetype=None, examine=None, phase=None, groupID=None, repeat_id=None):
-    """Extract keys from error/output files, collect into groups and examine a particular type if required.
-    
-    Parameters
-    ----------
-    filepath : str
-        String path to the error/output files
-    logger : Logging.object
-        Logger object for warning/info/debug messages.
-    savetype : str or bool
-        Error code to compare to error files and save matching codes to savedcodes array.
-    examine : bool
-        Boolean switch for halting if matching code is found.
-
-    Returns
-    -------
-    savedcodes : list
-        List of tuples for each file which abides by the savetype. Stored values in each tuple are:
-         - efile (str) : Path to error/output file
-         - code (str)  : Project code of error file
-         - log (str)   : Full error log for this project code
-    keys : dict
-        Dictionary of number of occurrences across all error logs of specific error codes.
-    total : int
-        Total number of error files found under the path provided. 
+def merge_old_new(old_codes, new_codes, index_old='', index_new='', reason=None):
+    """Merge an existing list of project codes with a new set
+    - Uses indexes if not a 1d list
     """
-    keys       = {'Warning': 0}
-    savedcodes = []
-    listfiles  = glob.glob(filepath)
-    total      = len(listfiles)
-    logger.info(f'Found {len(listfiles)} files to assess')
-
-    for efile in listfiles:
-        logger.debug(f'Starting {efile}')
-        log = []
-        show_warn = False
-        with open(os.path.join(filepath, efile)) as f:
-            for r in f.readlines():
-                log.append(r.strip())
-                if 'WARNING' in r.strip():
-                    show_warn = (savetype == 'Warning')
-                    keys['Warning'] += 1
-        logger.debug(f'Opened {efile}')
-        # Extract Error type from Error file last line
-        if len(log) > 0:
-            if type(log[-1]) == str:
-                key = log[-1].split(':')[0]
-            else:
-                key = log[-1][0]
 
-            if '/var/spool/slurmd' in key:
-                key = 'SlurmMemoryError'
-
-            if key == 'slurmstepd':
-                key = 'SlurmTimeoutError'
-
-            logger.debug(f'Identified error type {key}')
-            # Count error types
-            if key in keys:
-                keys[key] += 1
-            else:
-                keys[key] = 1
-
-            matchtype = (key == savetype or (type(savetype) == list and key in savetype) or show_warn)
-
-            ecode = efile.split('/')[-1].split('.')[0]
-            path = '/'.join(filepath.split('/')[:-1]) + '/'
-            code = get_code_from_val(path, ecode)
-
-            if matchtype and not blacklisted(code, f'{filepath}/../', logger):
-                # Save matching types
-                savedcodes.append((efile, code, log))
-                if examine:
-                    # Examine logs if matching types or warnings is on
-                    examine_log(log, efile, code, ecode=ecode, phase=phase, groupID=groupID, repeat_id=repeat_id)
-    return savedcodes, keys, len(listfiles)
-
-def merge_old_new(old_codes, new_codes, index_old='', index_new='', reason=None):
     merge_dict = {}
     indices = [index_old, index_new]
     for ord, codeset in enumerate([old_codes, new_codes]):
@@ -209,7 +93,7 @@ def merge_old_new(old_codes, new_codes, index_old='', index_new='', reason=None)
             merged.append(f'{mkey}')
     return merged
 
-def save_sel(codes: list, groupdir: str, label: str, logger, overwrite=0):
+def save_selection(codes: list, groupdir: str, label: str, logger, overwrite=0):
     """Save selection of codes to a file with a given repeat label. 
     
     Requires a groupdir (directory belonging to a group), list of codes and a label for the new file.
@@ -218,82 +102,206 @@ def save_sel(codes: list, groupdir: str, label: str, logger, overwrite=0):
         codeset = '\n'.join([code[1].strip() for code in codes])
         if os.path.isfile(f'{groupdir}/proj_codes_{label}.txt'):
             if overwrite == 0:
-                logger.info(f'Skipped writing {len(codes)} to proj_codes_{label} - file exists and overwrite not set')
+                print(f'Skipped writing {len(codes)} to proj_codes_{label} - file exists and overwrite not set')
             elif overwrite == 1:
-                logger.info(f'Adding {len(codes)} to existing proj_codes_{label}')
+                print(f'Adding {len(codes)} to existing proj_codes_{label}')
                 with open(f'{groupdir}/proj_codes_{label}.txt') as f:
                     old_codes = [r.strip() for r in f.readlines()]
-                merged = merge_old_new(old_codes, codes, index_new=1)
+                merged = merge_old_new(old_codes, codes)
                 # Need check for duplicates here
                 with open(f'{groupdir}/proj_codes_{label}.txt','w') as f:
                     f.write('\n'.join(merged))
             elif overwrite == 2:
-                logger.info(f'Overwriting with {len(codes)} in existing proj_codes_{label} file')
+                print(f'Overwriting with {len(codes)} in existing proj_codes_{label} file')
                 with open(f'{groupdir}/proj_codes_{label}.txt','w') as f:
                     f.write(codeset)
         else:
             with open(f'{groupdir}/proj_codes_{label}.txt','w') as f:
                 f.write(codeset)
-            logger.info(f'Written {len(codes)} to proj_codes_{label}')
+            print(f'Written {len(codes)} to proj_codes_{label}')
     else:
-        logger.info('No codes identified, no files written')
+        print('No codes identified, no files written')
 
-def show_options(option: str, groupdir: str, operation: str, logger):
+def show_options(args, logger):
     """Use OS tools to list contents of relevant directories to see all jobids or labels.
     
     List output or error directories (one per job id), or list all proj_codes text files."""
-    if option == 'jobids':
-        logger.info('Detecting IDs from previous runs:')
-        if operation == 'outputs':
-            os.system(f'ls {groupdir}/outs/')
+
+    # Load the blacklist here 
+    if args.option in ['blacklist', 'virtuals']:
+        blackset = get_codes(args.groupID, args.workdir, 'blacklist_codes')
+        if blackset:
+            blackcodes = {b.split(',')[0]: b.split(',')[1] for b in blackset}
         else:
-            os.system(f'ls {groupdir}/errs/')
-    elif option == 'labels':
-        logger.info('Detecting labels from previous runs:')
-        labels = glob.glob(f'{args.workdir}/groups/{args.groupID}/proj_codes*')
+            blackcodes = {}
+
+    if args.option == 'jobids':
+        print('Detecting IDs from previous runs:')
+        if args.operation == 'outputs':
+            os.system(f'ls {args.groupdir}/outs/')
+        else:
+            os.system(f'ls {args.groupdir}/errs/')
+    elif args.option == 'labels':
+        print('Detecting labels from previous runs:')
+        labels = glob.glob(f'{args.groupdir}/proj_codes*')
         for l in labels:
             pcode = l.split('/')[-1].replace("proj_codes_","").replace(".txt","")
             if pcode == '1':
                 pcode = 'main'
-            logger.info(f'{format_str(pcode,20)} - {l}')
+            print(f'{format_str(pcode,20)} - {l}')
+    elif args.option == 'blacklist':
+        print('Current Blacklist:')
+        for b in blackcodes.keys():
+            print(f'{b} - {blackcodes[b]}')
+    elif args.option == 'virtuals':
+        print('Finding datasets with a virtual stacking dimension')
+        codefile = f'{args.groupdir}/proj_codes_1.txt'
+        if os.path.isfile(codefile):
+            with open(codefile) as f:
+                proj_codes = [r.strip() for r in f.readlines()]
+            for p in proj_codes:
+                virtual = False
+                if p in blackcodes:
+                    continue
+                detailfile = f'{args.workdir}/in_progress/{args.groupID}/{p}/detail-cfg.json'
+                if os.path.isfile(detailfile):
+                    with open(detailfile) as f:
+                        details = json.load(f)
+                    if 'virtual_concat' in details:
+                        if details['virtual_concat']:
+                            virtual = True
+                else:
+                    print(f'Missing detailfile for {p}')
+                            
+                if virtual:
+                    print(f'Project code: {p} - Virtual present')
+                else:
+                    print(f'Project code: {p}')
+        else:
+            logger.warning(f'No proj_codes file found for {args.groupID}')
     else:
-        logger.info(f'{option} not accepted - use "jobids" or "labels"')
+        print(f'{args.option} not accepted - use "jobids" or "labels"')
 
 def cleanup(cleantype: str, groupdir: str, logger):
     """Remove older versions of project code files, error or output logs. Clear directories."""
-    if cleantype == 'labels':
+    if cleantype == 'labels' or cleantype == 'all':
         projset = glob.glob(f'{groupdir}/proj_codes_*')
         for p in projset:
             if 'proj_codes_1' not in p:
                 os.system(f'rm {p}')
-    elif cleantype == 'errors':
+    if cleantype == 'errors' or cleantype == 'all':
         os.system(f'rm -rf {groupdir}/errs/*')
-    elif cleantype == 'outputs':
+    if cleantype == 'outputs' or cleantype == 'all':
         os.system(f'rm -rf {groupdir}/outs/*')
-    else:
-        pass
-
+        
 def progress_check(args, logger):
+    """Give a general overview of progress within the pipeline
+    - How many datasets currently at each stage of the pipeline
+    - Errors within each pipeline phase
+    - Allows for examination of error logs
+    - Allows saving codes matching an error type into a new repeat group
+    """
+    blacklist  = get_codes(args.groupID, args.workdir, 'blacklist_codes')
+    proj_codes = get_codes(args.groupID, args.workdir, f'proj_codes_{args.repeat_id}')
+
+    groupdir = f'{args.workdir}/groups/{args.groupID}'
+
+    done_set = {}
+    extras = {'unknown': {'no data': 0}, 'blacklist': {}}
+
+    for b in blacklist:
+        entry = b.replace(' ','').split(',')
+        if entry[1] in extras['blacklist']:
+            extras['blacklist'][entry[1]] += 1
+        else:
+            extras['blacklist'][entry[1]] = 1
+        done_set[entry[0]] = True
+
+    phases = {'init':{}, 'scan': {}, 'compute': {}, 'validate': {}, 'complete': {}}
+    savecodes = []
+    for p in proj_codes:
+        if p not in done_set:
+            proj_dir = f'{args.workdir}/in_progress/{args.groupID}/{p}'
+            status_log = f'{proj_dir}/status_log.csv'
+            if os.path.isfile(status_log):
+                with open(status_log) as f:
+                    current = f.readlines()[-1].strip()
+            else:
+                extras['unknown']['no data'] += 1
+                continue
+            entry = current.split(',')
+
+
+            match_phase = (bool(args.phase) and args.phase == entry[0])
+            match_error = (bool(args.error) and args.error == entry[1])
+
+            if match_phase or match_error:
+                if args.examine:
+                    examine_log(args.workdir, p, entry[0], groupID=args.groupID, repeat_id=args.repeat_id)
+                if args.new_id:
+                    savecodes.append(p)
+
+            if entry[1] in phases[entry[0]]:
+                phases[entry[0]][entry[1]] += 1
+            else:
+                phases[entry[0]][entry[1]] = 1
+
+    num_codes  = len(proj_codes)
+    print()
+    print(f'Group: {args.groupID}')
+    print(f'  Total Codes: {num_codes}')
+
+    def summary_dict(pdict, num_codes):
+        """Display summary information for a dictionary structure of the expected format."""
+        for entry in pdict.keys():
+            pcount = len(list(pdict[entry].keys()))
+            num_types = sum([pdict[entry][pop] for pop in pdict[entry].keys()])
+            if pcount > 0:
+                print()
+                fmentry = format_str(entry,10)
+                fmnum_types = format_str(num_types,5)
+                fmcalc = format_str(f'{num_types*100/num_codes:.1f}',4)
+                print(f'   {fmentry}: {fmnum_types} [{fmcalc}%] (Variety: {int(pcount)})')
+
+                for err in pdict[entry]:
+                    num_errs = pdict[entry][err]
+                    print(f'    - {format_str(err, 25)}: {num_errs}')
+
+    summary_dict(phases, num_codes)
+    summary_dict(extras, num_codes)
+    print()
+
+    if args.new_id:
+        logger.debug(f'Preparing to write {len(savecodes)} codes to proj_codes_{args.new_id}.txt')
+        if args.write:
+            save_selection(savecodes, groupdir, args.new_id, logger, overwrite=args.overwrite)
+        else:
+            print('Skipped writing new codes - no overwrite mechanism specified')
+    
+def status_check(args, logger):
     """Check general progress of pipeline for a specific group.
     
     Lists progress up to the provided phase, options to save all project codes stuck at a specific phase to a repeat_id for later use."""
 
+    status_check(args, logger)
+
+    """
     if args.phase not in phases:
         logger.error(f'Phase not accepted here - {args.phase}')
         return None
     else:
-        logger.info(f'Discovering dataset progress within group {args.groupID}')
+        print(f'Discovering dataset progress within group {args.groupID}')
         ignore_pcodes = []
         blacklist = get_blacklist(args.groupdir)
         if blacklist:
             ignore_pcodes = [b.split(',')[0] for b in blacklist]
-            logger.info(f'blacklist: {len(blacklist)} datasets')
+            print(f'blacklist: {len(blacklist)} datasets')
 
         for index, phase in enumerate(phases[:-1]): # Ignore complete check as this happens as a byproduct
             redo_pcodes, completes = find_codes(phase, args.workdir, args.groupID, checks[index], ignore=ignore_pcodes)
-            logger.info(f'{phase}: {len(redo_pcodes)} datasets')
+            print(f'{phase}: {len(redo_pcodes)} datasets')
             if completes:
-                logger.info(f'complete: {len(completes)} datasets')
+                print(f'complete: {len(completes)} datasets')
             if phase == args.phase:
                 break
             ignore_pcodes += redo_pcodes
@@ -319,53 +327,10 @@ def progress_check(args, logger):
 
         # Written new pcodes
         print(f'Written {len(redo_pcodes)} pcodes, repeat label: {args.repeat_id}')
-
-def error_check(args, logger):
-    """Check error files and summarise results
-    
-    Extract savedcodes and total number of errors from each type given a specific path, save selection of codes for later use if required.
     """
-    job_path = f'{args.workdir}/groups/{args.groupID}/errs/{args.jobID}'
-    logger.info(f'Checking error files for {args.groupID} ID: {args.jobID}')
-
-    savedcodes, errs, total = extract_keys(f'{job_path}/*.err', logger, 
-                                           savetype=args.inspect, examine=args.examine,
-                                           phase=args.phase, groupID=args.groupID, 
-                                           repeat_id='_'.join(args.jobID.split('_')[1:]))
-    
-    # Summarise results
-    print(f'Found {total} error files:')
-    for key in errs.keys():
-        if errs[key] > 0 and key != 'Warning':
-            known_hint = 'Unknown'
-            if key in HINTS:
-                known_hint = HINTS[key]
-            print(f'{key}: {errs[key]}    - ({known_hint})')
-
-    print('')
-    print(f'Identified {errs["Warning"]} files with Warnings')
-
-    if args.inspect:
-        logger.info(f'Found {len(savedcodes)} proj_codes with matching errory type "{args.inspect}"')
-
-    if args.write:
-        if args.blacklist:
-            add_to_blacklist(savedcodes, args.groupdir, args.reason, logger)
-        elif args.repeat_id:
-            save_sel(savedcodes, args.groupdir, args.repeat_id, logger, overwrite=args.overwrite)
-        else:
-            logger.info('No repeat_id supplied, proj_codes were not saved.')
-    else:
-        if len(savedcodes) != 0:
-            logger.info(f'Skipped writing {len(savedcodes)}')
-
-def output_check(args, logger):
-    """Not implemented output log checker"""
-    job_path = f'{args.workdir}/groups/{args.groupID}/errs/{args.jobID}'
-    logger.info(f'Checking output files for {args.groupID} ID: {args.jobID}')
-    raise NotImplementedError
 
 def add_to_blacklist(savedcodes, groupdir, reason, logger):
+    """Add a set of codes to the blacklist for a given reason"""
     blackfile = f'{groupdir}/blacklist_codes.txt'
     if not os.path.isfile(blackfile):
         os.system(f'touch {blackfile}')
@@ -376,94 +341,175 @@ def add_to_blacklist(savedcodes, groupdir, reason, logger):
         blackcodes = [r.strip().split(',') for r in f.readlines()]
 
     merged = merge_old_new(blackcodes, savedcodes, index_old=0, index_new=1, reason=reason)
-    print(merged)
     blacklist = '\n'.join([f'{m}' for m in merged])
     
     with open(blackfile,'w') as f:
         f.write(blacklist)
-    logger.info(f'Blacklist now contains {len(merged)} codes.')
+    print(f'Blacklist now contains {len(merged)} codes.')
 
-def get_blacklist(groupdir):
-    blackfile = f'{groupdir}/blacklist_codes.txt'
-    if os.path.isfile(blackfile):
-        with open(blackfile) as f:
-            blackcodes = [r.strip().split(',')[0] for r in f.readlines()]
-    else:
-        return None
-    return blackcodes
-
-def blacklisted(proj_code: str, groupdir: str, logger):
-    blacklist = get_blacklist(groupdir)
-    if blacklist:
-        for code in blacklist:
-            if proj_code in code:
-                return True
-        return False
-    else:
-        logger.debug('No blacklist file preset for this group')
-        return False
+def upgrade_version(args, logger):
+    """Upgrade the version info in the kerchunk file
+    - Default - assume phase = validate - alter in kerchunk-1a.json
+                       phase = compute - just replace in detail-cfg.
+                       phase = complete - alter in complete
 
-def retro_errors(args, logger):
-    """Retrospective analysis of errors for all project codes within this group.
-     - Saved in ErrorSummary.json file"""
-    
-    proj_file = f'{args.groupdir}/proj_codes_1.txt'
-    summ_file  = f'{args.groupdir}/ErrorSummary.json'
-
-    with open(proj_file) as f:
-        proj_codes = [r.strip() for r in f.readlines()]
+    In all cases, alter in detail-cfg.
+    """
 
-    try:
-        with open(summ_file) as f:
-            summ_refs = json.load(f)
-    except FileNotFoundError:
-        logger.info("No prior summary error file detected")
-        summ_refs = {}
+    proj_codes = []
+    if not args.repeat_id:
+        logger.warning('No repeat ID specified - upgrade whole group using "-r 1"')
+        return None
     
-    if args.write:
-
-        logger.info(f"Initialising {len(proj_codes)} error entries")
-        # Initialise summary file
-        for pcode in proj_codes:
-            if pcode not in summ_refs:
-                summ_refs[pcode] = None
-
-        errs = {}
+    if not args.upgrade:
+        logger.warning('New version not specified - should be of the format "krX.X"')
+        return None
 
-        logs = glob.glob(f'{args.groupdir}/errs/*')
-        for l in logs:
-            logger.info(f'Reading error files for {l.split("/")[-1]}')
-            pcode_file = f'{l}/proj_codes.txt'
+    projfile = f'{args.groupdir}/proj_codes_{args.repeat_id}.txt'
+    if os.path.isfile(projfile):
+        with open(projfile) as f:
+            proj_codes = [r.strip() for r in f.readlines()]
+    else:
+        logger.warning(f'Repeat id {args.repeat_id} not found for {args.groupID}')
+        return None
+        
+    # Upgrade each code
+    for code in proj_codes:
+        try:
+            proj_dir   = f'{args.workdir}/in_progress/{args.groupID}/{code}'
+            print(f'Upgrading {code} to {args.upgrade}')
+
+            logger.debug(f'Updating detail-cfg for {code}')
+            detailfile = f'{proj_dir}/detail-cfg.json'
             try:
-                with open(pcode_file) as f:
-                    repeat_pcodes = [r.strip() for r in f.readlines()]
-            except FileNotFoundError:
-                logger.info(f'Skipped {l.split("/")[-1]} - old version')
-            for x, rpc in enumerate(repeat_pcodes):
-                rpc_err = f'{l}/{x}.err'
-                try:
-                    with open(rpc_err) as f:
-                        err = f.readlines()[-1].split(':')[0]
-                except FileNotFoundError:
-                    logger.debug(f'Not able to locate error file {x}')
-                summ_refs[rpc] = err
-
-    for rpc in summ_refs.keys():
-        err = summ_refs[rpc]
-        if err not in errs:
-            errs[err] = 1
-        else:
-            errs[err] += 1
+                with open(detailfile) as f:
+                    details = json.load(f)
+                details['version_no'] = args.upgrade
+                if args.reason:
+                    details['version_reason'] = args.reason
+                if args.write:
+                    with open(detailfile,'w') as f:
+                        f.write(json.dumps(details))
+            except:
+                print("Skipped detail step")
+
+            logger.debug(f'Locating kerchunk file for {code}')
+            in_filename = False
+            if args.phase == 'validate':
+                # Locate kerchunk file with latest version
+                files = glob.glob(f'{args.workdir}/in_progress/{args.groupID}/{code}/kerchunk*.json')
+            elif args.phase == 'complete':
+                # TEMPORARY DIVERSION
+                files = glob.glob(f'{args.workdir}/needs_version_updating/{code}*.json')
+                in_filename = True
+            if files:
+                kfile = files[-1]
+            else:
+                print(f'Skipping for {code} - file not found')
+                continue
+
+            logger.debug(f'Found kerchunk file {kfile}')
+            with open(kfile) as f:
+                refs = json.load(f)
+            attrs = json.loads(refs['refs']['.zattrs'])
+            # Open with json and upgrade correctly
+                
+            logger.debug(f'Adding history to {code}')
+            now = datetime.now()
+            if 'history' in attrs:
+                if type(attrs['history']) == str:
+                    hist = attrs['history'].split('\n')
+                else:
+                    hist = attrs['history']
 
-    print('Retrospective Error Summary:')
-    print(errs)
+                hist.append(f'Kerchunk file revised on {now.strftime("%D")} - {args.reason}')
+                attrs['history'] = '\n'.join(hist)
+            else:
+                attrs['history'] = f'Kerchunk file revised on {now.strftime("%D")} - {args.reason}'
+            old_version = 'kr1.0' # Hardcoded for now
+            attrs['kerchunk_revision'] = args.upgrade
+
+            logger.debug(attrs)
+            
+            refs['refs']['.zattrs'] = json.dumps(attrs)
+            if in_filename:
+                logger.debug('Replacing revision number in file')
+                kfile = kfile.replace(old_version, args.upgrade)
+            if args.write:
+                if not os.path.isfile(kfile):
+                    os.system(f'touch {kfile}')
+                with open(kfile,'w') as f:
+                    f.write(json.dumps(refs))
+                print(f'Written new attributes for {code} to {kfile}')
+            else:
+                print(f'Skipped writing new attributes for {code} to {kfile}')
+        except:
+            print('Failed for',code)
 
+def analyse_data(g, workdir):
+    """Show some statistics of netcdf and kerchunk data amounts for this particular group"""
+    ncf, ker, kus = 0, 0, 0
+    complete, scanned = 0, 0
+    projset = get_codes(g, workdir, 'proj_codes_1')
+
+    # Add individual error log checking PPC here.
+    
+    for p in projset:
+        details = None
+        try:
+            with open(f'{workdir}/in_progress/{g}/{p}/detail-cfg.json') as f:
+                details = json.load(f)
+        except:
+            pass
+        if details:
+            scanned += 1
+            if 'netcdf_data' in details:
+                ncf += mem_to_val(details['netcdf_data'])
+                ker += mem_to_val(details['kerchunk_data'])
+                if os.path.isfile(f'{workdir}/in_progress/{g}/{p}/kerchunk-1a.json.complete'):
+                    kus += mem_to_val(details['kerchunk_data'])
+                    complete += 1
+    return ncf, ker, kus, scanned, complete
+
+def summary_data(args, logger):
+    """Display summary info in terms of data representation"""
+    from pipeline.logs import FalseLogger
+    from pipeline.scan import format_float
+    if ',' in args.groupID:
+        groups = args.groupID.split(',')
+    else:
+        groups = [args.groupID]
+
+    Tncf, Tker, Tkus, Tscan, Tcomp = 0, 0, 0, 0, 0
+    for g in groups:
+        print()
+        print(g)
+        ncf, ker, kus, scanned, complete = analyse_data(g, args.workdir)
+        print(f'   Datasets          : {len(get_codes(g, args.workdir, "proj_codes_1"))}')
+        print(f'    - Unavailable    : {len(get_codes(g, args.workdir, "blacklist_codes"))}')
+        print(f'   Data:')
+        print(f'    - NetCDF         : {format_float(ncf, FalseLogger())}')
+        print(f'    - Kerchunk Estm  : {format_float(ker, FalseLogger())} ({scanned})')
+        print(f'    - Kerchunk Actual: {format_float(kus, FalseLogger())} ({complete})')
+        print()
+
+        Tncf += ncf
+        Tker += ker
+        Tkus += kus
+        Tscan += scanned
+        Tcomp += complete
+
+    if len(groups) > 1:
+        print(f'Total Across {len(groups)} groups')
+        print(f'         NetCDF: {format_float(Tncf, FalseLogger())}')
+        print(f'  Kerchunk Estm: {format_float(Tker, FalseLogger())} ({Tscan})')
+        print(f'Kerchunk Actual: {format_float(Tkus, FalseLogger())} ({Tcomp})')
+        
 operations = {
     'progress': progress_check,
-    'errors': error_check,
-    'outputs': output_check,
     'blacklist': add_to_blacklist,
-    'retro': retro_errors
+    'upgrade': upgrade_version,
+    'summarise': summary_data,
 }
 
 def assess_main(args):
@@ -473,16 +519,17 @@ def assess_main(args):
 
     args.workdir  = get_attribute('WORKDIR', args, 'workdir')
 
-    if args.groupID == 'A':
+    if args.groupID == 'A': # Just show which groups exist
         os.system(f'ls {args.workdir}/groups/')
         return None
+    
     args.groupdir = f'{args.workdir}/groups/{args.groupID}'
 
-    if ',' in args.inspect:
-        args.inspect = args.inspect.split(',')
+    if ',' in args.error:
+        args.error = args.error.split(',')
 
-    if args.show_opts:
-        show_options(args.show_opts, args.groupdir, args.operation, logger)
+    if args.option:
+        show_options(args, logger)
         return None
 
     if args.cleanup:
@@ -491,31 +538,43 @@ def assess_main(args):
 
     if args.operation in operations:
         operations[args.operation](args, logger)
+    else:
+        print(f'{args.operation} - Unknown operation, not one of {list(operations.keys())}')
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='Run a pipeline step for a single dataset')
-    parser.add_argument('groupID',type=str, help='Group identifier code')
     parser.add_argument('operation',type=str, help=f'Operation to perform - choose from {operations.keys()}.')
+    parser.add_argument('groupID',type=str, help='Group identifier code')
 
+    # Blacklist with a given reason
     parser.add_argument('-B','--blacklist', dest='blacklist', action='store_true', help='Use when saving project codes to the blacklist')
-    parser.add_argument('-R','--blacklist-reason', dest='reason', help='Provide the reason for saving project codes to the blacklist')
+    parser.add_argument('-R','--reason', dest='reason', default='No reason given',help='Provide the reason for handling project codes when saving to the blacklist or upgrading')
 
-    parser.add_argument('-j','--jobid', dest='jobID', help='Identifier of job to inspect')
-    parser.add_argument('-p','--phase', dest='phase', default='validate', help='Pipeline phase to inspect')
-    parser.add_argument('-s','--show-opts', dest='show_opts', help='Show options for jobids, labels')
+    # Special options
+    parser.add_argument('-s','--show-opts', dest='option', help='Show options for jobids, labels')
+    parser.add_argument('-c','--clean-up', dest='cleanup', default=None, help='Clean up group directory of errors/outputs/labels')
+    parser.add_argument('-U','--upgrade', dest='upgrade', default=None, help='Upgrade to new version')
+    # Note this will be replaced with upgrader tool at some point
+
+    # Select subgroups and save new repeat groups
+    parser.add_argument('-j','--jobid', dest='jobID', default=None, help='Identifier of job to inspect')
+    parser.add_argument('-p','--phase', dest='phase', default=None, help='Pipeline phase to inspect')
+    parser.add_argument('-r','--repeat_id', dest='repeat_id', default='1', help='Inspect an existing ID for errors')
+    parser.add_argument('-n','--new_id', dest='new_id', default=None, help='Create a new repeat ID, specify selection of codes by phase, error etc.')
 
-    parser.add_argument('-r','--repeat_id', dest='repeat_id', default=None, help='Save a selection of codes which failed on a given error - input a repeat id.')
-    parser.add_argument('-i','--inspect', dest='inspect', default='', help='Inspect error/output of a given type/label')
+    # Error inspection
+    parser.add_argument('-e','--error', dest='error', default='', help='Inspect error of a specific type')
     parser.add_argument('-E','--examine', dest='examine', action='store_true', help='Examine log outputs individually.')
-    parser.add_argument('-c','--clean-up', dest='cleanup', default=None, help='Clean up group directory of errors/outputs/labels')
 
+    # Output to new text files - need both to ensure no accidental writing
+    parser.add_argument('-W','--write',  dest='write',  action='count', help='Write outputs to files' )
     parser.add_argument('-O','--overwrite', dest='overwrite', action='count', help='Force overwrite of steps if previously done')
 
+    # Generic Pipeline
     parser.add_argument('-w','--workdir',   dest='workdir',      help='Working directory for pipeline')
     parser.add_argument('-g','--groupdir',  dest='groupdir',     help='Group directory for pipeline')
     parser.add_argument('-v','--verbose', dest='verbose', action='count', default=1, help='Print helpful statements while running')
     parser.add_argument('-m','--mode',        dest='mode', default=None, help='Print or record information (log or std)')
-    parser.add_argument('-W','--write',  dest='write',  action='store_true', help='Write outputs to files' )
 
     args = parser.parse_args()
 

From b9643188d48159950e5a96e955742e821331c471 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 12:31:18 +0000
Subject: [PATCH 16/45] Minor changes experimenting with streamline/dependency
 - abandoned

---
 group_run.py | 22 +++++++++++++++++++---
 1 file changed, 19 insertions(+), 3 deletions(-)

diff --git a/group_run.py b/group_run.py
index 0591c0a..9c43106 100644
--- a/group_run.py
+++ b/group_run.py
@@ -2,6 +2,7 @@
 import json
 import os
 import argparse
+import subprocess
 
 from pipeline.logs import init_logger, BypassSwitch
 
@@ -28,8 +29,15 @@ def get_attribute(env, args, var):
     else:
         print(f'Error: Missing attribute {var}')
         return None
+    
 
 def main(args):
+
+    logger = init_logger(args.verbose, 0, 'main-group')
+
+    deploy(args, logger)
+
+def deploy(args, logger, get_id=False, dependent_id=False):
     """Assemble sbatch script for parallel running jobs"""
 
     logger = init_logger(args.verbose, 0, 'main-group')
@@ -98,8 +106,6 @@ def main(args):
         f'{group}_{phase}_array',             # Job name
         time,                                 # Time
         mem,                                  # Memory
-        f'{GROUPDIR}/outs/%A_{label}/%a.out', # Outs
-        f'{GROUPDIR}/errs/%A_{label}/%a.err', # Errs
         VENV,
         WORKDIR,
         GROUPDIR,
@@ -132,7 +138,17 @@ def main(args):
         logger.info('DRYRUN: sbatch command: ')
         print(f'sbatch --array=0-{group_len-1} {group_phase_sbatch}')
     else:
-        os.system(f'sbatch --array=0-{group_len-1} {group_phase_sbatch}')
+        if get_id: # Unused section to save the ID of the process
+            result = subprocess.run(['sbatch', f'--array=0-{group_len-1}', group_phase_sbatch], stdout=subprocess.PIPE)
+            try:
+                id = result.stdout.decode('utf-8').split(' ')[3].strip() # Check!
+                assert len(id) == 8
+                return id
+            except:
+                logger.error('Slurm submission failed')
+                return None
+        else:
+            os.system(f'sbatch --array=0-{group_len-1} {group_phase_sbatch}')
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Run a pipeline step for a group of datasets')

From d8790b2e7e2c6c4338d029ac2b4e12e1144319ed Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 12:31:43 +0000
Subject: [PATCH 17/45] Removed error and output log options per job

---
 extensions/templates/phase.sbatch.template | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/extensions/templates/phase.sbatch.template b/extensions/templates/phase.sbatch.template
index f21818d..3e4a934 100644
--- a/extensions/templates/phase.sbatch.template
+++ b/extensions/templates/phase.sbatch.template
@@ -1,14 +1,10 @@
 #!/bin/bash
-#SBATCH --partition=short-serial-4hr
-#SBATCH --account=short4hr
-#SBATCH --job-name={}
+#SBATCH --partition=short-serial
+#SBATCH --job-name={}_kerchunk
 
 #SBATCH --time={}
 #SBATCH --mem={}
 
-#SBATCH -o {}
-#SBATCH -e {}
-
 module add jaspy
 source {}/bin/activate
 

From 6334ec43fa7f2b016241bb2f25720dacb00e1ea9 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 12:33:19 +0000
Subject: [PATCH 18/45] Set up a dedicated place for config files

---
 config/setup-cci.sh    | 8 ++++++++
 config/setup-cmip6.sh  | 8 ++++++++
 config/setup-metoff.sh | 3 +++
 3 files changed, 19 insertions(+)
 create mode 100755 config/setup-cci.sh
 create mode 100755 config/setup-cmip6.sh
 create mode 100644 config/setup-metoff.sh

diff --git a/config/setup-cci.sh b/config/setup-cci.sh
new file mode 100755
index 0000000..cb021db
--- /dev/null
+++ b/config/setup-cci.sh
@@ -0,0 +1,8 @@
+module load jaspy
+
+export WORKDIR=/gws/nopw/j04/esacci_portal/kerchunk_conversion/
+export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
+export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
+
+source $KVENV/bin/activate
+module load jaspy
\ No newline at end of file
diff --git a/config/setup-cmip6.sh b/config/setup-cmip6.sh
new file mode 100755
index 0000000..9d4e76e
--- /dev/null
+++ b/config/setup-cmip6.sh
@@ -0,0 +1,8 @@
+module load jaspy
+
+export WORKDIR=/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline
+export GROUPDIR=/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233
+export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
+export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
+
+source $KVENV/bin/activate
\ No newline at end of file
diff --git a/config/setup-metoff.sh b/config/setup-metoff.sh
new file mode 100644
index 0000000..ccf62dc
--- /dev/null
+++ b/config/setup-metoff.sh
@@ -0,0 +1,3 @@
+export WORKDIR=/home/users/dwest77/temp_netcdf/
+export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
+export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
\ No newline at end of file

From f5ea0dfb74a968f131073a52dea830f6a622a6b3 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 12:33:46 +0000
Subject: [PATCH 19/45] Organised multi-purpose functions to a single script
 within the pipeline

---
 pipeline/utils.py | 78 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 pipeline/utils.py

diff --git a/pipeline/utils.py b/pipeline/utils.py
new file mode 100644
index 0000000..b8ddf77
--- /dev/null
+++ b/pipeline/utils.py
@@ -0,0 +1,78 @@
+__author__    = "Daniel Westwood"
+__contact__   = "daniel.westwood@stfc.ac.uk"
+__copyright__ = "Copyright 2023 United Kingdom Research and Innovation"
+
+import os
+
+from pipeline.errors import MissingVariableError
+
+def get_attribute(env: str, args, var: str):
+    """Assemble environment variable or take from passed argument.
+    
+    Finds value of variable from Environment or ParseArgs object, or reports failure
+    """
+    if getattr(args, var):
+        return getattr(args, var)
+    elif os.getenv(env):
+        return os.getenv(env)
+    else:
+        print(var)
+        raise MissingVariableError(type=var)
+    
+def format_str(string: str, length: int):
+    """Simple function to format a string to a correct length"""
+    string = str(string)
+    while len(string) < length:
+        string += ' '
+    return string[:length]
+
+class BypassSwitch:
+    def __init__(self, switch='DBSCM'):
+        if switch.startswith('+'):
+            switch = 'DBSCM' + switch[1:]
+        self.switch = switch
+        if type(switch) == str:
+            switch = list(switch)
+        
+        self.skip_driver   = ('D' in switch)
+        self.skip_boxfail  = ('B' in switch)
+        self.skip_softfail = ('S' in switch)
+        self.skip_data_sum = ('C' in switch)
+        self.skip_xkshape  = ('X' in switch)
+        self.skip_report   = ('R' in switch)
+
+        # Removed scanfile and memory skips
+
+    def __str__(self):
+        return self.switch
+    
+    def help(self):
+        return str("""
+Bypass switch options: \n
+  "F" - * Skip individual file scanning errors.
+  "D" - * Skip driver failures - Pipeline tries different options for NetCDF (default).
+      -   Only need to turn this skip off if all drivers fail (KerchunkFatalDriverError).
+  "B" -   Skip Box compute errors.
+  "S" - * Skip Soft fails (NaN-only boxes in validation) (default).
+  "C" - * Skip calculation (data sum) errors (time array typically cannot be summed) (default).
+  "M" -   Skip memory checks (validate/compute aborts if utilisation estimate exceeds cap).
+""")
+    
+def mem_to_val(value):
+    """Convert a value in Bytes to an integer number of bytes"""
+    suffixes = {
+        'KB': 1000,
+        'MB': 1000000,
+        'GB': 1000000000,
+        'TB': 1000000000000,
+        'PB': 1000000000000000}
+    suff = suffixes[value.split(' ')[1]]
+    return float(value.split(' ')[0]) * suff
+
+def get_codes(group, workdir, filename):
+    """Returns a list of the project codes given a filename (repeat id)"""
+    try:
+        with open(f'{workdir}/groups/{group}/{filename}.txt') as f:
+            return [r.strip() for r in f.readlines()]
+    except FileNotFoundError:
+        return []
\ No newline at end of file

From 84139a83d4b94b873448a19393a25094567953d4 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 12:34:02 +0000
Subject: [PATCH 20/45] Initialised allocator script

---
 pipeline/allocator.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 pipeline/allocator.py

diff --git a/pipeline/allocator.py b/pipeline/allocator.py
new file mode 100644
index 0000000..e5be62a
--- /dev/null
+++ b/pipeline/allocator.py
@@ -0,0 +1,23 @@
+__author__    = "Daniel Westwood"
+__contact__   = "daniel.westwood@stfc.ac.uk"
+__copyright__ = "Copyright 2023 United Kingdom Research and Innovation"
+
+# Job Subset Allocation Script
+# - Calculate Expected Utilisation for each Job (500 + Kerchunk Size*5 MB)
+
+# First-Fit Bin Packing Algorithm
+# - Sort Utilisations from largest to smallest
+# - Bin Capacity is largest size rounded up to next memory cap (1, 2, 3, 4 GB)
+# - Allocate item to first bin with space remaining
+# - End with N bins (subsets) - write list of project codes for each subset to a separate file in proj_code_subsets/set_N.txt
+# - Run array with number of subsets already set.
+
+# Utilisation estimate is (total_chunks * 835) + 500 (MB)
+"""
+for proj_code in (repeat_id set):
+    open detail-cfg (for this code)
+    calculate utilisation
+    add to dict [utilisation, proj_code]
+    keep track of max/min
+get bins using binpacking (pypi)
+"""
\ No newline at end of file

From 7fc1387add71d89a970e72d5ba8015fb32591c5d Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 12:34:24 +0000
Subject: [PATCH 21/45] Moved scripts

---
 extensions/templates/setup-cci.sh    | 7 -------
 extensions/templates/setup-cmip6.sh  | 8 --------
 extensions/templates/setup-metoff.sh | 3 ---
 3 files changed, 18 deletions(-)
 delete mode 100755 extensions/templates/setup-cci.sh
 delete mode 100755 extensions/templates/setup-cmip6.sh
 delete mode 100644 extensions/templates/setup-metoff.sh

diff --git a/extensions/templates/setup-cci.sh b/extensions/templates/setup-cci.sh
deleted file mode 100755
index bceeba7..0000000
--- a/extensions/templates/setup-cci.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-module load jaspy
-
-export WORKDIR=/gws/nopw/j04/esacci_portal/kerchunk_conversion/
-export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
-export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
-
-source $KVENV/bin/activate
\ No newline at end of file
diff --git a/extensions/templates/setup-cmip6.sh b/extensions/templates/setup-cmip6.sh
deleted file mode 100755
index 9d4e76e..0000000
--- a/extensions/templates/setup-cmip6.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-module load jaspy
-
-export WORKDIR=/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline
-export GROUPDIR=/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233
-export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
-export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
-
-source $KVENV/bin/activate
\ No newline at end of file
diff --git a/extensions/templates/setup-metoff.sh b/extensions/templates/setup-metoff.sh
deleted file mode 100644
index ccf62dc..0000000
--- a/extensions/templates/setup-metoff.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-export WORKDIR=/home/users/dwest77/temp_netcdf/
-export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
-export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
\ No newline at end of file

From c4bbc233a3f0b8d387d9f33dc21d2bf60a10cd49 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 15:10:02 +0000
Subject: [PATCH 22/45] Added requirements that were misplaced

---
 requirements.txt | 58 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 58 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index e69de29..41cdd8f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,58 @@
+asciitree==0.3.3
+bigtiff==0.1.2
+ccplot==2.1.2
+certifi==2023.5.7
+cf-plot==3.2.15
+cf-python==3.15.1
+cf-view==3.2.24
+cfchecker==4.1.0
+cfdm==1.10.1.0
+cfunits==3.3.6
+click-plugins==1.1.1
+colorspacious==1.1.2
+descartes==1.1.0
+docopt==0.6.2
+ecmwf-api-client==1.6.3
+eofs==1.4.0
+f90nml==1.4.3
+GDAL==3.5.2
+google-pasta==0.2.0
+ipython-genutils==0.2.0
+kaitaistruct==0.8
+kerchunk==0.2.3
+llvmlite==0.40.1
+mo-pack==0.2.0
+munkres==1.1.4
+nappy==2.0.2
+nco==1.1.0
+netcdf-flattener==1.2.0
+nose2==0.9.2
+oggm==1.6.0
+pep8==1.7.1
+pipenv==2023.7.11
+ply==3.11
+progressbar2==4.2.0
+protobuf==4.21.12
+pyarrow==9.0.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.7
+pycairo==1.24.0
+pygeode==1.4.1
+PyGObject==3.44.1
+pypdftk==0.5
+PyQt5==5.15.7
+PyQt5-sip==12.11.0
+PySide2==5.15.8
+pyspharm==1.0.9
+python-utils==3.7.0
+pyugrid==0.3.1
+QScintilla==2.13.3
+shiboken2==5.15.8
+snuggs==1.4.7
+sphinxcontrib-devhelp==1.0.2
+sphinxcontrib-jsmath==1.0.1
+sphinxcontrib-qthelp==1.0.3
+virtualenv-clone==0.5.7
+webencodings==0.5.1
+wrf-python==1.3.4.1
+wxPython==4.2.0

From 512b26afe8a7478f3a7be128f79618b2591f5427 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 15:27:44 +0000
Subject: [PATCH 23/45] Added new features; loading and creating refs as
 required, saving refs once created, minor variable changes

---
 pipeline/compute/serial_process.py | 217 ++++++++++++++++-------------
 1 file changed, 119 insertions(+), 98 deletions(-)

diff --git a/pipeline/compute/serial_process.py b/pipeline/compute/serial_process.py
index bbea74c..cf3357d 100644
--- a/pipeline/compute/serial_process.py
+++ b/pipeline/compute/serial_process.py
@@ -23,22 +23,26 @@ def __init__(self, message="All drivers failed when performing conversion"):
 CONCAT_MSG = 'See individual files for more details'
 
 class Converter:
+    """Class for converting a single file to a Kerchunk reference object"""
+
     def __init__(self, clogger, bypass_driver=False):
         self.logger = clogger
+        self.ctype = None
         self.success = True
         self.bypass_driver = bypass_driver
 
-    def convert_to_zarr(self, nfile, ctype, **kwargs):
-        #ctype = 'hdf5'
+    def convert_to_zarr(self, nfile, **kwargs):
+        """Perform conversion to zarr with exceptions for bypassing driver errors."""
+        drivers = {
+            'ncf3': self.ncf3_to_zarr,
+            'hdf5': self.hdf5_to_zarr,
+            'tif' : self.tiff_to_zarr
+        }
         try:
-            if ctype == 'ncf3':
-                return self.ncf3_to_zarr(nfile, **kwargs)
-            elif ctype == 'hdf5':
-                return self.hdf5_to_zarr(nfile, **kwargs)
-            elif ctype == 'tif':
-                return self.tiff_to_zarr(nfile, **kwargs)
+            if self.ctype in drivers:
+                return drivers[self.ctype](nfile, **kwargs)
             else:
-                self.logger.debug(f'Extension {ctype} not valid')
+                self.logger.debug(f'Extension {self.ctype} not valid')
                 return None
         except Exception as err:
             if self.bypass_driver:
@@ -74,34 +78,35 @@ def __init__(self,
         super().__init__(init_logger(verb, mode, 'compute-serial'), bypass_driver=bypass.skip_driver)
 
         self.logger.debug('Starting variable definitions')
+
         self.bypass     = bypass
         self.limiter    = limiter
         self.workdir    = workdir
         self.proj_code  = proj_code
         self.version_no = version_no
         self.concat_msg = concat_msg
+        self.thorough   = thorough
+        self.forceful   = forceful
 
-        self.dryrun = dryrun
+        self.dryrun      = dryrun
         self.issave_meta = issave_meta
         self.updates, self.removals, self.load_refs = False, False, False
 
-        self.verb = verb
-        self.mode = mode
-        if mode != 'std':
-            self.log = ''
-        else:
-            self.log = None
-
         self.logger.debug('Loading config information')
         with open(cfg_file) as f:
             cfg = json.load(f)
 
+        self.detailfile = detail_file
         with open(detail_file) as f:
             self.detail = json.load(f)
 
         if 'virtual_concat' not in self.detail:
             self.detail['virtual_concat'] = False
 
+        if version_no != 'trial-':
+            if 'version_no' in self.detail:
+                self.version_no = self.detail['version_no']
+
         if groupID:
             self.proj_dir = f'{self.workdir}/in_progress/{groupID}/{self.proj_code}'
         else:
@@ -125,6 +130,8 @@ def __init__(self,
         else:
             self.use_json = True
 
+        self.use_json = True
+
         self.outfile     = f'{self.proj_dir}/kerchunk-{version_no}a.json'
         self.outstore    = f'{self.proj_dir}/kerchunk-{version_no}a.parq'
         self.record_size = 167 # Default
@@ -143,17 +150,20 @@ def __init__(self,
         if thorough:
             os.system(f'rm -rf {self.cache}/*')
 
-        self.thorough = thorough
-
-        self.combine_kwargs = {'concat_dims':['time']} # Always try time initially
+        self.combine_kwargs = {} # Now using concat_dims and identical dims finders.
         self.create_kwargs  = {'inline_threshold':1000}
         self.pre_kwargs     = {}
 
+        self.special_attrs = {}
+
         self.set_filelist()
         self.logger.debug('Finished all setup steps')
 
     def collect_details(self):
+        """Collect kwargs for combining and any special attributes - save to detail file."""
         self.detail['combine_kwargs'] = self.combine_kwargs
+        if self.special_attrs:
+            self.detail['special_attrs'] = list(self.special_attrs.keys())
         return self.detail
 
     def set_filelist(self):
@@ -164,17 +174,11 @@ def set_filelist(self):
             self.limiter = len(self.listfiles)
 
     def add_download_link(self, refs):
-        timecount = 0
-        total = len(list(refs.keys()))
-        t1 = datetime.now()
+        """Add the download link to the Kerchunk references"""
         for key in refs.keys():
             if len(refs[key]) == 3:
                 if refs[key][0][0] == '/':
                     refs[key][0] = 'https://dap.ceda.ac.uk' + refs[key][0]
-            if timecount == 100:
-                end_time = (datetime.now()-t1).total_seconds()*(total/6000)
-                self.logger.debug(f'Expected time remaining: {end_time} mins')
-            timecount += 1
         return refs
 
     def add_kerchunk_history(self, attrs):
@@ -204,10 +208,13 @@ def add_kerchunk_history(self, attrs):
         return attrs
     
     def find_concat_dims(self, ds_examples):
+        """Find dimensions to use when combining for concatenation
+        - Dimensions which change over the set of files must be concatenated together
+        - Dimensions which do not change (typically lat/lon) are instead identified as identical_dims"""
         concat_dims = []
         for dim in ds_examples[0].dims:
             try:
-                validate_selection(ds_examples[0][dim], ds_examples[1][dim], dim, 1, 1, self.logger, bypass=self.bypass)          
+                validate_selection(ds_examples[0][dim], ds_examples[1][dim], dim, 128, 128, self.logger, bypass=self.bypass)          
             except ValidationError:
                 self.logger.debug(f'Non-identical dimension: {dim} - if this dimension should be identical across the files, please inspect.')
                 concat_dims.append(dim)
@@ -222,6 +229,10 @@ def find_concat_dims(self, ds_examples):
         self.combine_kwargs['concat_dims'] = concat_dims
 
     def find_identical_dims(self, ds_examples):
+        """Find dimensions and variables that are identical across the set of files.
+        - Variables which do not change (typically lat/lon) are identified as identical_dims and not concatenated over the set of files.
+        - Variables which do change are concatenated as usual.
+        """
         identical_dims = []
         normal_dims = []
         for var in ds_examples[0].variables:
@@ -231,7 +242,7 @@ def find_identical_dims(self, ds_examples):
                     identical_check = False
             if identical_check:
                 try:
-                    validate_selection(ds_examples[0][var], ds_examples[1][var], var, 1, 1, self.logger, bypass=self.bypass)
+                    validate_selection(ds_examples[0][var], ds_examples[1][var], var, 128, 128, self.logger, bypass=self.bypass)
                     identical_dims.append(var)
                 except ValidationError:
                     self.logger.debug(f'Non-identical variable: {var} - if this variable should be identical across the files, please rerun.')
@@ -293,8 +304,17 @@ def combine_and_save(self, refs, zattrs):
             self.logger.info('Concatenating to Parquet format Kerchunk store')
             self.data_to_parq(refs)
 
+        if not self.dryrun:
+            self.collect_details()
+            with open(self.detailfile,'w') as f:
+                f.write(json.dumps(self.detail))
+            self.logger.info("Details updated in detail-cfg.json")
+
     def construct_virtual_dim(self, refs):
+        """Construct a Virtual dimension for stacking multiple files where no suitable concatenation dimension is present."""
+        # For now this just means creating a list of numbers 0 to N files
         vdim = 'file_number'
+
         for idx in range(len(refs)):
             ref = refs[idx]
             zarray = json.dumps({
@@ -325,7 +345,7 @@ def construct_virtual_dim(self, refs):
                 ref[f'{vdim}/0'] = values
         return refs, vdim
 
-    def data_to_parq(self, create_refs):
+    def data_to_parq(self, refs):
         """Concatenating to Parquet format Kerchunk store"""
         from kerchunk.combine import MultiZarrToZarr
         from fsspec import filesystem
@@ -360,6 +380,7 @@ def data_to_json(self, refs, zattrs):
             if self.detail['virtual_concat']:
                 refs, vdim = self.construct_virtual_dim(refs)
                 self.combine_kwargs['concat_dims'] = [vdim]
+            print(self.combine_kwargs)
             mzz = MultiZarrToZarr(list(refs), **self.combine_kwargs).translate()
             if zattrs:
                 zattrs = self.add_kerchunk_history(zattrs)
@@ -381,9 +402,10 @@ def data_to_json(self, refs, zattrs):
             self.logger.info(f'Skipped writing to JSON file - {self.outfile}')
 
     def correct_metadata(self, allzattrs):
-        # General function for correcting metadata
-        # - Combine all existing metadata in standard way
-        # - Add updates and remove removals specified by configuration
+        """General function for correcting metadata
+        - Combine all existing metadata in standard way (cleaning arrays)
+        - Add updates and remove removals specified by configuration
+        """
 
         self.logger.debug('Starting metadata corrections')
         if type(allzattrs) == list:
@@ -410,27 +432,32 @@ def correct_metadata(self, allzattrs):
         return new_zattrs
         
     def clean_attr_array(self, allzattrs):
-        # Collect attributes from all files, 
-        # determine which are always equal, which have differences
+        """Collect global attributes from all refs:
+        - Determine which differ between refs and apply changes
+        """
         base = json.loads(allzattrs[0])
 
         self.logger.debug('Correcting time attributes')
         # Sort out time metadata here
         times = {}
         all_values = {}
+
+        # Global attributes with 'time' in the name i.e start_datetime
         for k in base.keys():
             if 'time' in k:
                 times[k] = [base[k]]
             all_values[k] = []
 
         nonequal = {}
+        # Compare other attribute sets to a starting set 0
         for ref in allzattrs[1:]:
             zattrs = json.loads(ref)
             for attr in zattrs.keys():
+                # Compare each attribute.
                 if attr in all_values:
                     all_values[attr].append(zattrs[attr])
                 else:
-                    all_values[attr] = zattrs[attr]
+                    all_values[attr] = [zattrs[attr]]
                 if attr in times:
                     times[attr].append(zattrs[attr])
                 elif attr not in base:
@@ -439,6 +466,7 @@ def clean_attr_array(self, allzattrs):
                     if base[attr] != zattrs[attr]:
                         nonequal[attr] = False
 
+        # Requires something special for start and end times
         base = {**base, **self.check_time_attributes(times)}
         self.logger.debug('Comparing similar keys')
 
@@ -447,18 +475,23 @@ def clean_attr_array(self, allzattrs):
                 base[attr] = all_values[attr][0]
             else:
                 base[attr] = self.concat_msg
+                self.special_attrs[attr] = 0
 
         self.logger.debug('Finished checking similar keys')
         return base
 
     def clean_attrs(self, zattrs):
+        """Ammend any saved attributes post-combining
+        - Not currently implemented, may be unnecessary
+        """
         self.logger.warning('Attribute cleaning post-loading from temp is not implemented')
         return zattrs
 
     def check_time_attributes(self, times):
-        # Takes dict of time attributes with lists of values
-        # Sort time arrays
-        # Assume time_coverage_start, time_coverage_end, duration (2 or 3 variables)
+        """Takes dict of time attributes with lists of values
+        - Sort time arrays
+        - Assume time_coverage_start, time_coverage_end, duration (2 or 3 variables)
+        """
         combined = {}
         for k in times.keys():
             if 'start' in k:
@@ -490,44 +523,29 @@ def save_metadata(self,zattrs):
         else:
             self.logger.debug('Skipped saving global attribute cache')
 
-    def save_cache(self, refs, zattrs):
-        """Cache reference data in temporary json reference files"""
-        self.logger.info('Saving pre-concat cache')
-        if not self.dryrun:
-            for x, r in enumerate(refs):
-                cache_ref = f'{self.cache}/{x}.json'
-                with open(cache_ref,'w') as f:
-                    f.write(json.dumps(r))
-            self.logger.debug('Saved kerchunk data cache')
-        else:
-            self.logger.debug('Skipped saving metadata cache')
-        self.save_metadata(zattrs)
-        self.issave_meta = False
-        # Save is only valid prior to concatenation
-        # For a bizarre reason, the download link is applied to the whole cache on concatenation,
-        # So cache saving MUST occur BEFORE concatenation.
+    def save_individual_ref(self, ref, cache_ref):
+        if not os.path.isfile(cache_ref) or self.forceful:
+            with open(cache_ref,'w') as f:
+                f.write(json.dumps(ref))
 
     def try_all_drivers(self, nfile, **kwargs):
         """Safe creation allows for known issues and tries multiple drivers"""
 
         extension = False
-
-        if '.' in nfile:
-            ctype = f'.{nfile.split(".")[-1]}'
-        else:
-            ctype = '.nc'
-
         supported_extensions = ['ncf3','hdf5','tif']
 
-        self.logger.debug(f'Attempting conversion for 1 {ctype} extension')
+        self.logger.debug(f'Attempting conversion for 1 {self.ctype} extension')
 
-        tdict = self.convert_to_zarr(nfile, ctype, **kwargs)
+        if not self.ctype:
+            self.ctype = supported_extensions[0]
+
+        tdict = self.convert_to_zarr(nfile, **kwargs)
         ext_index = 0
         while not tdict and ext_index < len(supported_extensions)-1:
             # Try the other ones
             extension = supported_extensions[ext_index]
             self.logger.debug(f'Attempting conversion for {extension} extension')
-            if extension != ctype:
+            if extension != self.ctype:
                 tdict = self.convert_to_zarr(nfile, extension, **kwargs)
             ext_index += 1
 
@@ -536,54 +554,57 @@ def try_all_drivers(self, nfile, **kwargs):
             raise KerchunkDriverFatalError
         else:
             if extension:
-                self.logger.debug(f'Scan successful with {extension} driver')
-            else:
-                self.logger.debug(f'Scan successful with {ctype} driver')
+                self.ctype = extension
+            self.logger.debug(f'Scan successful with {self.ctype} driver')
             return tdict
     
-    def convert_to_kerchunk(self):
-        refs = []
-        allzattrs = []
-        for x, nfile in enumerate(self.listfiles[:self.limiter]):
-            self.logger.info(f'Creating refs: {x+1}/{len(self.listfiles)}')
-            zarr_content = self.try_all_drivers(nfile, **self.create_kwargs)
-            if zarr_content:
-                allzattrs.append(zarr_content['refs']['.zattrs'])
-                refs.append(zarr_content)
-        return allzattrs, refs
-    
-    def load_cache(self):
-        refs = []
-        for x, nfile in enumerate(self.listfiles[:self.limiter]):
-            self.logger.info(f'Loading refs: {x+1}/{len(self.listfiles)}')
-            cache_ref = f'{self.cache}/{x}.json'
-            with open(cache_ref) as f:
-                refs.append(json.load(f))
-
+    def load_temp_zattrs(self):
         self.logger.debug(f'Loading attributes')
         with open(f'{self.cache}/temp_zattrs.json') as f:
             zattrs = json.load(f)
         if not zattrs:
-            self.logger.error('No attributes loaded from temp store')
-            raise ValueError
-        return zattrs, refs
+            self.logger.debug('No attributes loaded from temp store')
+            return None
+        return zattrs
 
     def create_refs(self):
+        """Organise creation and loading of refs
+        - Load existing cached refs
+        - Create new refs
+        - Combine metadata and global attributes into a single set
+        - Coordinate combining and saving of data"""
         self.logger.info(f'Starting computation for components of {self.proj_code}')
-        if not self.load_refs:
-            allzattrs, refs = self.convert_to_kerchunk()
+        refs, allzattrs = []
+        zattrs = None
+        use_temp_zattrs = True
+
+        # Attempt to load existing file - create if not exists already
+        for x, nfile in enumerate(self.listfiles[:self.limiter]):
+            cache_ref = f'{self.cache}/{x}.json'
+            ref = None
+            if os.path.isfile(cache_ref) and not self.thorough:
+                self.logger.info(f'Loading refs: {x+1}/{len(self.listfiles)}')
+                ref = self.load_cached(cache_ref)
+            if not ref:
+                self.logger.info(f'Creating refs: {x+1}/{len(self.listfiles)}')
+                ref = self.try_all_drivers(nfile, **self.create_kwargs)
+                use_temp_zattrs = False
+            if ref:
+                allzattrs.append(ref['refs']['.zattrs'])
+                refs.append(ref)
+                cache_ref = f'{self.cache}/{x}.json'
+                self.save_individual_ref(ref, cache_ref)
+
+        if use_temp_zattrs:
+            zattrs = self.load_temp_zattrs()
+        if not zattrs:
             zattrs = self.correct_metadata(allzattrs)
-        else:
-            zattrs, refs = self.load_cache()
-            zattrs = self.correct_metadata(zattrs)
+
         try:
-            if self.issave_meta and (not self.load_refs):
-                self.save_cache(refs, zattrs)
             if self.success:
                 self.combine_and_save(refs, zattrs)
         except Exception as err:
-            if self.issave_meta and (not self.load_refs or self.thorough):
-                self.save_cache(refs, zattrs)
+            # Any additional parts here.
             raise err
 
 if __name__ == '__main__':

From 1d66435882779fd3b3587a185b30970fe42385d3 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 15:29:03 +0000
Subject: [PATCH 24/45] Added file handling and status logging

---
 pipeline/init.py | 10 ++++--
 pipeline/logs.py | 88 +++++++++++++++++++++++++-----------------------
 2 files changed, 52 insertions(+), 46 deletions(-)

diff --git a/pipeline/init.py b/pipeline/init.py
index d506c5e..7748415 100644
--- a/pipeline/init.py
+++ b/pipeline/init.py
@@ -53,7 +53,9 @@ def make_filelist(pattern: str, proj_dir: str, logger):
     if pattern.endswith('.txt'):
         os.system(f'cp {pattern} {proj_dir}/allfiles.txt')
     elif os.path.isdir(proj_dir):
-        os.system(f'ls {pattern} > {proj_dir}/allfiles.txt')
+        name = pattern.split('/')[-1]
+        path = '/'.join(pattern.split('/')[:-1])
+        os.system(f'find {path} -name "{name}" -type f > {proj_dir}/allfiles.txt')
     else:
         logger.error(f'Project Directory not located - {proj_dir}')
         return None
@@ -157,7 +159,7 @@ def make_dirs(args, logger):
         proj_codes = proj_codes[1:]
     
     for index, proj_code in enumerate(proj_codes):
-        cfg_values = dict(config)      # Ensure no linking
+        cfg_values = dict(config)
         ds_values  = datasets[proj_code]
         pattern    = ds_values[0]
 
@@ -205,7 +207,9 @@ def make_dirs(args, logger):
             if not args.forceful:
                 logger.warn(f'{proj_code} directory already exists')
 
-        status = make_filelist(pattern, proj_dir, logger)
+        status = True
+        if not os.path.isfile(f'{proj_dir}/allfiles.txt'):
+            status = make_filelist(pattern, proj_dir, logger)
         if not status:
             logger.error(f'Issue creating filelist for {proj_code}')
         else:
diff --git a/pipeline/logs.py b/pipeline/logs.py
index 9ea53c6..fc0d9a5 100644
--- a/pipeline/logs.py
+++ b/pipeline/logs.py
@@ -5,6 +5,7 @@
 import logging
 import os
 from pipeline.errors import MissingVariableError
+from datetime import datetime
 
 levels = [
     logging.WARN,
@@ -20,36 +21,29 @@
     'G': 1000000000
 }
 
-class BypassSwitch:
-    def __init__(self, switch='FDSC'):
-        if switch.startswith('+'):
-            switch = 'FDSC' + switch[1:]
-        self.switch = switch
-        if type(switch) == str:
-            switch = list(switch)
-        
-        self.skip_scanfile = ('F' in switch)
-        self.skip_driver   = ('D' in switch)
-        self.skip_boxfail  = ('B' in switch)
-        self.skip_softfail = ('S' in switch)
-        self.skip_data_sum = ('C' in switch)
-        self.skip_memcheck = ('M' in switch)
-        self.skip_xkshape  = ('X' in switch)
-
-    def __str__(self):
-        return self.switch
+def log_status(phase, proj_dir, status, logger, jobid='', dryrun=''):
+    """Find the status file for this project code, add a new entry for the status.
+    - Entry should be of the form:
+    - phase, status, time, jobid
+    """
+
+    # Create file if not already present
+    status_log = f'{proj_dir}/status_log.csv'
+    if not os.path.isfile(status_log):
+        logger.debug(f'Creating status file {status_log}')
+        os.system(f'touch {status_log}')
     
-    def help(self):
-        return str("""
-Bypass switch options: \n
-  "F" - * Skip individual file scanning errors.
-  "D" - * Skip driver failures - Pipeline tries different options for NetCDF (default).
-      -   Only need to turn this skip off if all drivers fail (KerchunkFatalDriverError).
-  "B" -   Skip Box compute errors.
-  "S" - * Skip Soft fails (NaN-only boxes in validation) (default).
-  "C" - * Skip calculation (data sum) errors (time array typically cannot be summed) (default).
-  "M" -   Skip memory checks (validate/compute aborts if utilisation estimate exceeds cap).
-""")
+    # Open existing file
+    with open(status_log) as f:
+        lines = [r.strip() for r in f.readlines()]
+
+    # Add new content from most recent run
+    lines.append(f'{phase},{status},{datetime.now().strftime("%H:%M %D")},{jobid},{dryrun}')
+
+    # Save content
+    with open(status_log, 'w') as f:
+        f.write('\n'.join(lines))
+    logger.info(f'Updated new status: {phase} - {status}')
 
 class FalseLogger:
     def __init__(self):
@@ -63,7 +57,23 @@ def warning(self, message):
     def error(self, message):
         pass
 
-def init_logger(verbose, mode, name):
+def reset_file_handler(logger, verbose, new_log):
+    logger.handlers.clear()
+
+    ch = logging.StreamHandler()
+    ch.setLevel(levels[verbose])
+
+    formatter = logging.Formatter('%(levelname)s [%(name)s]: %(message)s')
+    ch.setFormatter(formatter)
+    logger.addHandler(ch)
+
+    fh = logging.FileHandler(new_log)
+    fh.setLevel(levels[verbose])
+    logger.addHandler(fh)
+
+    return logger
+
+def init_logger(verbose, mode, name, fh=None):
     """Logger object init and configure with formatting"""
     verbose = min(verbose, len(levels)-1)
 
@@ -77,17 +87,9 @@ def init_logger(verbose, mode, name):
     ch.setFormatter(formatter)
     logger.addHandler(ch)
 
-    return logger
+    if fh:
+        handle = logging.FileHandler(fh)
+        handle.setLevel(levels[verbose])
+        logger.addHandler(handle)
 
-def get_attribute(env: str, args, var: str):
-    """Assemble environment variable or take from passed argument.
-    
-    Finds value of variable from Environment or ParseArgs object, or reports failure
-    """
-    if getattr(args, var):
-        return getattr(args, var)
-    elif os.getenv(env):
-        return os.getenv(env)
-    else:
-        print(var)
-        raise MissingVariableError(type=var)
\ No newline at end of file
+    return logger
\ No newline at end of file

From 2f3b15473018057e563263aebafa394cd82ebe8a Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 15:32:06 +0000
Subject: [PATCH 25/45] Updated import locations

---
 pipeline/compute/serial_process.py |  3 ++-
 pipeline/scan.py                   | 28 +++++++++++++++++-----------
 2 files changed, 19 insertions(+), 12 deletions(-)

diff --git a/pipeline/compute/serial_process.py b/pipeline/compute/serial_process.py
index cf3357d..9ddd9da 100644
--- a/pipeline/compute/serial_process.py
+++ b/pipeline/compute/serial_process.py
@@ -9,7 +9,8 @@
 import numpy as np
 import base64
 
-from pipeline.logs import init_logger, BypassSwitch, FalseLogger
+from pipeline.logs import init_logger, FalseLogger
+from pipeline.utils import BypassSwitch
 from pipeline.errors import IdenticalVariablesError, ValidationError, ConcatenationError, SoftfailBypassError
 from pipeline.validate import validate_data, open_kerchunk, validate_selection
 
diff --git a/pipeline/scan.py b/pipeline/scan.py
index 0505dac..ed411d5 100644
--- a/pipeline/scan.py
+++ b/pipeline/scan.py
@@ -17,16 +17,17 @@
 import json
 import numpy as np
 
-from pipeline.logs import *
-from pipeline.errors import ExpectTimeoutError, FilecapExceededError, ConcatFatalError
+from pipeline.logs import init_logger
+from pipeline.utils import get_attribute, BypassSwitch
+from pipeline.errors import *
 from pipeline.compute.serial_process import Converter, Indexer
 
 def format_float(value: int, logger):
     """Format byte-value with proper units"""
     logger.debug(f'Formatting value {value} in bytes')
     if value:
-        unit_index = -1
-        units = ['K','M','G','T','P']
+        unit_index = 0
+        units = ['','K','M','G','T','P']
         while value > 1000:
             value = value / 1000
             unit_index += 1
@@ -67,10 +68,10 @@ def trial_kerchunk(args, nfile: str, ctype: str, logger):
     
     if not tdict:
         logger.error('Scanning failed for all drivers, file type is not Kerchunkable')
-        return None, None, None
+        raise KerchunkDriverFatalError
     else:
         logger.info(f'Scan successful with {ctype} driver')
-        return tdict['refs'], ctype, t_len
+        return tdict, ctype, t_len
     
 def load_from_previous(args, cache_id, logger):
     cachefile = f'{args.proj_dir}/cache/{cache_id}.json'
@@ -82,9 +83,9 @@ def load_from_previous(args, cache_id, logger):
     else:
         return None
         
-def perform_scan(args, testfile: str, ctype: str, logger, savecache=False, cache_id=None):
+def perform_scan(args, testfile: str, ctype: str, logger, savecache=False, cache_id=None, thorough=False):
     """Map to kerchunk data and perform calculations on test netcdf file."""
-    if cache_id:
+    if cache_id and not thorough:
         refs = load_from_previous(args, cache_id, logger)
         ctype = None
         time = 0
@@ -93,13 +94,15 @@ def perform_scan(args, testfile: str, ctype: str, logger, savecache=False, cache
     else:
         refs, ctype, time = trial_kerchunk(args, testfile, ctype, logger)
     if not refs:
-        return None, None, None
+        return None, None, None, None, None
     logger.debug(f'Starting Analysis of references')
 
     # Perform summations, extract chunk attributes
     sizes = []
     vars = {}
     chunks = 0
+    if 'refs' in refs:
+        refs = refs['refs']
     for chunkkey in refs.keys():
         if len(refs[chunkkey]) >= 2:
             try:
@@ -238,7 +241,9 @@ def scan_dataset(args, files: list, logger):
 
         try:
             # Measure time and ensure job will not overrun if it can be prevented.
-            volume, chunks_per_file, varchunks, ctype, time = perform_scan(args, scanfile, extension, logger, savecache=True, cache_id=str(count))
+            volume, chunks_per_file, varchunks, ctype, time = perform_scan(args, scanfile, extension, logger, 
+                                                                           savecache=True, cache_id=str(count),
+                                                                           thorough=args.quality)
 
             vars = sorted(list(varchunks.keys()))
             if not std_vars:
@@ -283,7 +288,7 @@ def scan_dataset(args, files: list, logger):
      spatial_res, data_represented, num_files, 
      total_chunks, addition, estm_time) = perform_safe_calculations(std_vars, cpf, volms, files, times, logger)
     
-    c2m = 1.67e-4 # Memory for each chunk in kerchunk in MB
+    c2m = 167 # Memory for each chunk in kerchunk in B
 
     details = {
         'netcdf_data'      : format_float(data_represented, logger), 
@@ -354,6 +359,7 @@ def scan_config(args):
         with open(cfg_file) as f:
             cfg = json.load(f)
     else:
+        os.system(f'ls {args.proj_dir}')
         logger.error(f'cfg file missing or not provided - {cfg_file}')
         return None
     

From 89e7f373b8f120e7d3041e4e582573f2ff5f9423 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 15:34:16 +0000
Subject: [PATCH 26/45] Removed scanfile skip option

---
 pipeline/scan.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/pipeline/scan.py b/pipeline/scan.py
index ed411d5..c9869ae 100644
--- a/pipeline/scan.py
+++ b/pipeline/scan.py
@@ -271,12 +271,8 @@ def scan_dataset(args, files: list, logger):
             raise err
         except ConcatFatalError as err:
             raise err
-        except Exception as e:
-            if args.bypass.skip_scanfile:
-                logger.warning(f'Skipped file {count} - {e}')
-                is_skipwarn = True
-            else:
-                raise e
+        except Exception as err:
+            raise err
         count += 1
         if count >= filecap:
             escape = True

From ab9890d2312b8c4ab4982a7510c0c1894e8400fc Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 15:35:43 +0000
Subject: [PATCH 27/45] Added file handlers and status logging, also pass/fail
 counters

---
 single_run.py | 56 +++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 45 insertions(+), 11 deletions(-)

diff --git a/single_run.py b/single_run.py
index 8dbe4ab..c870ea1 100644
--- a/single_run.py
+++ b/single_run.py
@@ -8,8 +8,10 @@
 import os
 import json
 import logging
+from datetime import datetime
 
-from pipeline.logs import init_logger, get_attribute, BypassSwitch
+from pipeline.logs import init_logger, reset_file_handler, log_status
+from pipeline.utils import get_attribute, BypassSwitch
 from pipeline.errors import ProjectCodeError, MissingVariableError, BlacklistProjectCode
 
 def run_init(args, logger):
@@ -103,15 +105,23 @@ def blacklisted(proj_code: str, groupdir: str, logger):
     else:
         logger.debug('No blacklist file preset for this group')
         return False
-
+    
 def main(args):
     """Main function for single run processing"""
 
-    logger = init_logger(args.verbose, args.mode, 'main')
+    jobid = ''
+    fh    = ''
+
+    if os.getenv('SLURM_ARRAY_JOB_ID'):
+        jobid = os.getenv('SLURM_ARRAY_JOB_ID')
 
     args.workdir  = get_attribute('WORKDIR', args, 'workdir')
     if args.groupID:
         args.groupdir = f'{args.workdir}/groups/{args.groupID}'
+        if jobid != '':
+            fh = f'{args.groupdir}/errs/{jobid}_{args.phase}_{args.repeat_id}.log'
+
+    logger = init_logger(args.verbose, args.mode, 'main', fh=fh)
 
     args.bypass = BypassSwitch(switch=args.bypass)
 
@@ -129,6 +139,8 @@ def main(args):
 
     logger.debug('Passed initial writability checks')
 
+    passes, fails = 0, 0
+
     for id in range(args.subset):
         print()
         if args.subset > 1:
@@ -147,9 +159,8 @@ def main(args):
                 args.proj_code = get_proj_code(args.groupdir, proj_code, args.repeat_id, subset=args.subset, id=id)
                 args.proj_dir = f'{args.workdir}/in_progress/{args.groupID}/{args.proj_code}'
 
-                # Get ID from within a job?
-                if os.getenv('SLURM_ARRAY_JOB_ID'):
-                    jobid = os.getenv('SLURM_ARRAY_JOB_ID')
+                # Get rid of this section if necessary - redo to put code list elsewhere
+                if jobid != '':
                     errs_dir = f'{args.workdir}/groups/{args.groupID}/errs'
                     if not os.path.isdir(f'{errs_dir}/{jobid}_{args.repeat_id}'):
                         os.makedirs(f'{errs_dir}/{jobid}_{args.repeat_id}')
@@ -162,8 +173,8 @@ def main(args):
             else:
                 args.proj_dir = f'{args.workdir}/in_progress/{args.proj_code}'
 
-            if blacklisted(args.proj_code, args.groupdir, logger) and not args.backtrack:
-                raise BlacklistProjectCode
+            #if blacklisted(args.proj_code, args.groupdir, logger) and not args.backtrack:
+                #raise BlacklistProjectCode
 
             if args.phase in drivers:
                 logger.debug('Pipeline variables (reconfigured):')
@@ -172,14 +183,37 @@ def main(args):
                 logger.debug('Using attributes:')
                 logger.debug(f'proj_code: {args.proj_code}')
                 logger.debug(f'proj_dir : {args.proj_dir}')
-                drivers[args.phase](args, logger)
+
+                # Refresh log for this phase
+                proj_log = f'{args.proj_dir}/phase_logs/{args.phase}.log'
+                os.system(f'rm {proj_log}')
+                if not args.bypass.skip_report:
+                    log_status(args.phase, args.proj_dir, 'pending', logger, jobid=jobid, dryrun=args.dryrun)
+                try:
+                    if jobid != '':
+                        logger = reset_file_handler(logger, args.verbose, proj_log)
+                        drivers[args.phase](args, logger)
+                        logger = reset_file_handler(logger, args.verbose, fh)
+                    else:
+                        drivers[args.phase](args, logger)
+                    passes += 1
+                    if not args.bypass.skip_report:
+                        log_status(args.phase, args.proj_dir, 'complete', logger, jobid=jobid, dryrun=args.dryrun)
+                except Exception as err:
+                    if jobid != '':
+                        logger = reset_file_handler(logger, args.verbose, fh)
+                    fails += 1
+                    if not args.bypass.skip_report:
+                        log_status(args.phase, args.proj_dir, err.get_str(), logger, jobid=jobid, dryrun=args.dryrun)
+                    else:
+                        raise err
             else:
                 logger.error(f'"{args.phase}" not recognised, please select from {list(drivers.keys())}')
         except Exception as err:
-            # Capture all errors - any error handled here is fatal
+            # Capture all errors - any error handled here is a setup error
             raise err
     logger.info('Pipeline phase execution finished')
-    print('Success')
+    logger.info(f'Success: {passes}, Error: {fails}') 
     return True
 
 if __name__ == '__main__':

From 6355ed836e67335c44a28bb701e8c5579117f03e Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 15:36:28 +0000
Subject: [PATCH 28/45] Added test/example notebooks

---
 .../{Untitled.ipynb => EShifts.ipynb}         |    0
 showcase/notebooks/Kerchunk JSON.ipynb        | 2848 +-----------
 showcase/notebooks/TestDask.ipynb             |  649 +++
 showcase/notebooks/TestKerchunking.ipynb      | 4113 ++++++-----------
 showcase/notebooks/TestMemory.ipynb           |   99 +
 showcase/notebooks/cci.ipynb                  | 1903 ++++++++
 6 files changed, 4262 insertions(+), 5350 deletions(-)
 rename showcase/notebooks/{Untitled.ipynb => EShifts.ipynb} (100%)
 create mode 100644 showcase/notebooks/TestDask.ipynb
 create mode 100644 showcase/notebooks/TestMemory.ipynb
 create mode 100644 showcase/notebooks/cci.ipynb

diff --git a/showcase/notebooks/Untitled.ipynb b/showcase/notebooks/EShifts.ipynb
similarity index 100%
rename from showcase/notebooks/Untitled.ipynb
rename to showcase/notebooks/EShifts.ipynb
diff --git a/showcase/notebooks/Kerchunk JSON.ipynb b/showcase/notebooks/Kerchunk JSON.ipynb
index ba5399a..4c455f8 100644
--- a/showcase/notebooks/Kerchunk JSON.ipynb	
+++ b/showcase/notebooks/Kerchunk JSON.ipynb	
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "e2d976ae-58b8-4c6f-a34d-836ffebdd14c",
    "metadata": {},
    "outputs": [],
@@ -30,43 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "78a06225-11bb-4748-a44f-fe67aeb3881d",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "https://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/CMIP/AS-RCEC/TaiESM1/CMIP6_CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_Amon_clt_gn_v20200623_kr1.0.json/.zmetadata",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mClientResponseError\u001b[0m                       Traceback (most recent call last)",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/implementations/http.py:417\u001b[0m, in \u001b[0;36mHTTPFileSystem._info\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    415\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    416\u001b[0m     info\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[0;32m--> 417\u001b[0m         \u001b[38;5;28;01mawait\u001b[39;00m _file_info(\n\u001b[1;32m    418\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencode_url(url),\n\u001b[1;32m    419\u001b[0m             size_policy\u001b[38;5;241m=\u001b[39mpolicy,\n\u001b[1;32m    420\u001b[0m             session\u001b[38;5;241m=\u001b[39msession,\n\u001b[1;32m    421\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs,\n\u001b[1;32m    422\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    423\u001b[0m         )\n\u001b[1;32m    424\u001b[0m     )\n\u001b[1;32m    425\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/implementations/http.py:837\u001b[0m, in \u001b[0;36m_file_info\u001b[0;34m(url, session, size_policy, **kwargs)\u001b[0m\n\u001b[1;32m    836\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mwith\u001b[39;00m r:\n\u001b[0;32m--> 837\u001b[0m     \u001b[43mr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    839\u001b[0m     \u001b[38;5;66;03m# TODO:\u001b[39;00m\n\u001b[1;32m    840\u001b[0m     \u001b[38;5;66;03m#  recognise lack of 'Accept-Ranges',\u001b[39;00m\n\u001b[1;32m    841\u001b[0m     \u001b[38;5;66;03m#                 or 'Accept-Ranges': 'none' (not 'bytes')\u001b[39;00m\n\u001b[1;32m    842\u001b[0m     \u001b[38;5;66;03m#  to mean streaming only, no random access => return None\u001b[39;00m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/aiohttp/client_reqrep.py:1005\u001b[0m, in \u001b[0;36mClientResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m-> 1005\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ClientResponseError(\n\u001b[1;32m   1006\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequest_info,\n\u001b[1;32m   1007\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhistory,\n\u001b[1;32m   1008\u001b[0m     status\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus,\n\u001b[1;32m   1009\u001b[0m     message\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreason,\n\u001b[1;32m   1010\u001b[0m     headers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[1;32m   1011\u001b[0m )\n",
-      "\u001b[0;31mClientResponseError\u001b[0m: 404, message='Not Found', url=URL('https://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/CMIP/AS-RCEC/TaiESM1/CMIP6_CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_Amon_clt_gn_v20200623_kr1.0.json/.zmetadata')",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m kfile \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/CMIP/AS-RCEC/TaiESM1/CMIP6_CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_Amon_clt_gn_v20200623_kr1.0.json\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m mapper \u001b[38;5;241m=\u001b[39m \u001b[43mfsspec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_mapper\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreference://\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mfo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/mapping.py:237\u001b[0m, in \u001b[0;36mget_mapper\u001b[0;34m(url, check, create, missing_exceptions, alternate_root, **kwargs)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Create key-value interface for given URL and options\u001b[39;00m\n\u001b[1;32m    207\u001b[0m \n\u001b[1;32m    208\u001b[0m \u001b[38;5;124;03mThe URL will be of the form \"protocol://location\" and point to the root\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;124;03m``FSMap`` instance, the dict-like key-value store.\u001b[39;00m\n\u001b[1;32m    235\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;66;03m# Removing protocol here - could defer to each open() on the backend\u001b[39;00m\n\u001b[0;32m--> 237\u001b[0m fs, urlpath \u001b[38;5;241m=\u001b[39m \u001b[43murl_to_fs\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m root \u001b[38;5;241m=\u001b[39m alternate_root \u001b[38;5;28;01mif\u001b[39;00m alternate_root \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m urlpath\n\u001b[1;32m    239\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m FSMap(root, fs, check, create, missing_exceptions\u001b[38;5;241m=\u001b[39mmissing_exceptions)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/core.py:375\u001b[0m, in \u001b[0;36murl_to_fs\u001b[0;34m(url, **kwargs)\u001b[0m\n\u001b[1;32m    373\u001b[0m     inkwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfo\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m urls\n\u001b[1;32m    374\u001b[0m urlpath, protocol, _ \u001b[38;5;241m=\u001b[39m chain[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 375\u001b[0m fs \u001b[38;5;241m=\u001b[39m \u001b[43mfilesystem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fs, urlpath\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/registry.py:267\u001b[0m, in \u001b[0;36mfilesystem\u001b[0;34m(protocol, **storage_options)\u001b[0m\n\u001b[1;32m    260\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    261\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124marrow_hdfs\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m protocol has been deprecated and will be \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    262\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mremoved in the future. Specify it as \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhdfs\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    263\u001b[0m         \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m,\n\u001b[1;32m    264\u001b[0m     )\n\u001b[1;32m    266\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m get_filesystem_class(protocol)\n\u001b[0;32m--> 267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/spec.py:79\u001b[0m, in \u001b[0;36m_Cached.__call__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[token]\n\u001b[1;32m     78\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 79\u001b[0m     obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;66;03m# Setting _fs_token here causes some static linters to complain.\u001b[39;00m\n\u001b[1;32m     81\u001b[0m     obj\u001b[38;5;241m.\u001b[39m_fs_token_ \u001b[38;5;241m=\u001b[39m token\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/implementations/reference.py:607\u001b[0m, in \u001b[0;36mReferenceFileSystem.__init__\u001b[0;34m(self, fo, target, ref_storage_args, target_protocol, target_options, remote_protocol, remote_options, fs, template_overrides, simple_templates, max_gap, max_block, cache_size, **kwargs)\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    605\u001b[0m         \u001b[38;5;66;03m# Lazy parquet refs\u001b[39;00m\n\u001b[1;32m    606\u001b[0m         logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOpen lazy reference dict from URL \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, fo)\n\u001b[0;32m--> 607\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreferences \u001b[38;5;241m=\u001b[39m \u001b[43mLazyReferenceMapper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    608\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfo2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    609\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mref_fs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    610\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcache_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    611\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    612\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    613\u001b[0m     \u001b[38;5;66;03m# dictionaries\u001b[39;00m\n\u001b[1;32m    614\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_references(fo, template_overrides)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/implementations/reference.py:121\u001b[0m, in \u001b[0;36mLazyReferenceMapper.__init__\u001b[0;34m(self, root, fs, out_root, cache_size, categorical_threshold)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdirs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs \u001b[38;5;241m=\u001b[39m fsspec\u001b[38;5;241m.\u001b[39mfilesystem(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m fs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m fs\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.zmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_items[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.zmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m    123\u001b[0m met \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_items[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.zmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/spec.py:1241\u001b[0m, in \u001b[0;36mAbstractFileSystem.open\u001b[0;34m(self, path, mode, block_size, cache_options, compression, **kwargs)\u001b[0m\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1240\u001b[0m     ac \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mautocommit\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_intrans)\n\u001b[0;32m-> 1241\u001b[0m     f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1242\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1243\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1244\u001b[0m \u001b[43m        \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mac\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1247\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1248\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1249\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1250\u001b[0m         \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfsspec\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompression\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compr\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/implementations/http.py:356\u001b[0m, in \u001b[0;36mHTTPFileSystem._open\u001b[0;34m(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\u001b[0m\n\u001b[1;32m    354\u001b[0m kw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124masynchronous\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39masynchronous\n\u001b[1;32m    355\u001b[0m kw\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[0;32m--> 356\u001b[0m size \u001b[38;5;241m=\u001b[39m size \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    357\u001b[0m session \u001b[38;5;241m=\u001b[39m sync(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloop, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_session)\n\u001b[1;32m    358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m block_size \u001b[38;5;129;01mand\u001b[39;00m size:\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/asyn.py:121\u001b[0m, in \u001b[0;36msync_wrapper.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    119\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m obj \u001b[38;5;129;01mor\u001b[39;00m args[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/asyn.py:106\u001b[0m, in \u001b[0;36msync\u001b[0;34m(loop, func, timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m FSTimeoutError \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreturn_result\u001b[39;00m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(return_result, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[0;32m--> 106\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m return_result\n\u001b[1;32m    107\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    108\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m return_result\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/asyn.py:61\u001b[0m, in \u001b[0;36m_runner\u001b[0;34m(event, coro, result, timeout)\u001b[0m\n\u001b[1;32m     59\u001b[0m     coro \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mwait_for(coro, timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 61\u001b[0m     result[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m coro\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[1;32m     63\u001b[0m     result[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m ex\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/fsspec/implementations/http.py:430\u001b[0m, in \u001b[0;36mHTTPFileSystem._info\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    428\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m policy \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    429\u001b[0m             \u001b[38;5;66;03m# If get failed, then raise a FileNotFoundError\u001b[39;00m\n\u001b[0;32m--> 430\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(url) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m    431\u001b[0m         logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;28mstr\u001b[39m(exc))\n\u001b[1;32m    433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: url, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39minfo, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: https://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/CMIP/AS-RCEC/TaiESM1/CMIP6_CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_Amon_clt_gn_v20200623_kr1.0.json/.zmetadata"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "kfile = 'https://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/CMIP/AS-RCEC/TaiESM1/CMIP6_CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_Amon_clt_gn_v20200623_kr1.0.json'\n",
-    "mapper = fsspec.get_mapper('reference://',fo=kfile, backend_kwargs={'compression':None})"
+    "complete_path = '/gws/nopw/j04/esacci_portal/kerchunk_conversion/in_progress//'\n",
+    "kfile = f'{complete_path}/cloud_v1/ESACCI-L3C_CLOUD-CLD_PRODUCTS-MODIS_TERRA-200002-201412-fv2.0/kerchunk-1a.json'\n",
+    "\n",
+    "mapper = fsspec.get_mapper('reference://',fo=kfile, backend_kwargs={'compression':None}, remote_protocol='https')\n",
+    "ds = xr.open_zarr(mapper, consolidated=False, decode_times=True)"
    ]
   },
   {
@@ -79,18 +52,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "45aac096-a60f-4a18-9ae2-77d29ceb2a03",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = xr.open_zarr(mapper, consolidated=False)"
+    "ds = xr.open_zarr(mapper, consolidated=False, decode_times=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "3fea9e67-f1c3-40c0-8c49-318ac90cdc3d",
+   "execution_count": 5,
+   "id": "8406e2e7-6d9f-4061-906f-a142f0be3205",
    "metadata": {},
    "outputs": [
     {
@@ -459,880 +432,21 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:          (channel: 2, time: 198, lat: 18000, lon: 36000,\n",
-       "                      length_scale: 1)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;prsn&#x27; (time: 1980, lat: 192, lon: 288)&gt;\n",
+       "dask.array&lt;open_dataset-prsn, shape=(1980, 192, 288), dtype=float32, chunksize=(1, 192, 288), chunktype=numpy.ndarray&gt;\n",
        "Coordinates:\n",
-       "  * channel          (channel) float32 11.0 12.0\n",
-       "  * lat              (lat) float32 -90.0 -89.99 -89.98 ... 89.97 89.98 89.99\n",
-       "  * lon              (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
-       "  * time             (time) datetime64[ns] 2002-07-01 2002-08-01 ... 2018-12-01\n",
-       "Dimensions without coordinates: length_scale\n",
-       "Data variables: (12/14)\n",
-       "    dtime            (time, lat, lon) timedelta64[ns] dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    lcc              (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    lst              (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    lst_unc_loc_atm  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    lst_unc_loc_sfc  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    lst_unc_ran      (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    ...               ...\n",
-       "    n                (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    sataz            (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    satze            (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    solaz            (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    solze            (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "    variance         (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;\n",
-       "Attributes: (12/44)\n",
-       "    Conventions:                CF-1.8\n",
-       "    cdm_data_type:              grid\n",
-       "    comment:                    These data were produced as part of the ESA L...\n",
-       "    creator_email:              djg20@le.ac.uk\n",
-       "    creator_name:               University of Leicester Surface Temperature G...\n",
-       "    creator_url:                https://climate.esa.int/en/projects/land-surf...\n",
-       "    ...                         ...\n",
-       "    time_coverage_resolution:   P1M\n",
-       "    time_coverage_start:        20020701T000000\n",
-       "    title:                      ESA LST CCI land surface temperature data at ...\n",
-       "    kerchunk_revision:          kr1.1\n",
-       "    kerchunk_creation_date:     031023T093248\n",
-       "    tracking_id:                8efceb61-53cb-4361-a3b4-e809046b9a19</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-359855b7-138f-4469-a207-860e027745fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-359855b7-138f-4469-a207-860e027745fa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>channel</span>: 2</li><li><span class='xr-has-index'>time</span>: 198</li><li><span class='xr-has-index'>lat</span>: 18000</li><li><span class='xr-has-index'>lon</span>: 36000</li><li><span>length_scale</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6f1daca7-692b-470e-b157-d210b1c22fd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6f1daca7-692b-470e-b157-d210b1c22fd8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>11.0 12.0</div><input id='attrs-add52554-e44f-4308-a936-c3855525fa28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-add52554-e44f-4308-a936-c3855525fa28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01729825-3049-4835-a61b-18f67e64e043' class='xr-var-data-in' type='checkbox'><label for='data-01729825-3049-4835-a61b-18f67e64e043' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>channel wavelength in microns</dd><dt><span>units :</span></dt><dd>microns</dd><dt><span>valid_max :</span></dt><dd>15000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>array([10.999001, 11.999001], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -89.99 -89.98 ... 89.98 89.99</div><input id='attrs-b220725c-7fbe-44fc-8b70-ccd8cb295787' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b220725c-7fbe-44fc-8b70-ccd8cb295787' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41f98156-4073-441b-8110-2868986c7129' class='xr-var-data-in' type='checkbox'><label for='data-41f98156-4073-441b-8110-2868986c7129' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude_coordinates</dd><dt><span>reference_datum :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-89.995   , -89.985   , -89.975006, ...,  89.975   ,  89.98499 ,\n",
-       "        89.99499 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-9e785d4e-8230-49b7-89f3-95b23aa8eca7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e785d4e-8230-49b7-89f3-95b23aa8eca7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f3733af-235d-45f3-a064-a7c59de13e16' class='xr-var-data-in' type='checkbox'><label for='data-0f3733af-235d-45f3-a064-a7c59de13e16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude_coordinates</dd><dt><span>reference_datum :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-179.995  , -179.985  , -179.97499, ...,  179.975  ,  179.98499,\n",
-       "        179.995  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-07-01 ... 2018-12-01</div><input id='attrs-55c5ef2c-3abd-43b0-ab00-09ecac705443' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55c5ef2c-3abd-43b0-ab00-09ecac705443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da0481e3-beb6-4aec-8d29-40b9c1550939' class='xr-var-data-in' type='checkbox'><label for='data-da0481e3-beb6-4aec-8d29-40b9c1550939' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reference time of file</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-07-01T00:00:00.000000000&#x27;, &#x27;2002-08-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2002-09-01T00:00:00.000000000&#x27;, &#x27;2002-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2002-11-01T00:00:00.000000000&#x27;, &#x27;2002-12-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-01-01T00:00:00.000000000&#x27;, &#x27;2003-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-03-01T00:00:00.000000000&#x27;, &#x27;2003-04-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-05-01T00:00:00.000000000&#x27;, &#x27;2003-06-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-07-01T00:00:00.000000000&#x27;, &#x27;2003-08-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-09-01T00:00:00.000000000&#x27;, &#x27;2003-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-11-01T00:00:00.000000000&#x27;, &#x27;2003-12-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2004-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2004-03-01T00:00:00.000000000&#x27;, &#x27;2004-04-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2004-05-01T00:00:00.000000000&#x27;, &#x27;2004-06-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2004-07-01T00:00:00.000000000&#x27;, &#x27;2004-08-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2004-09-01T00:00:00.000000000&#x27;, &#x27;2004-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2004-11-01T00:00:00.000000000&#x27;, &#x27;2004-12-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2005-01-01T00:00:00.000000000&#x27;, &#x27;2005-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2005-03-01T00:00:00.000000000&#x27;, &#x27;2005-04-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2005-05-01T00:00:00.000000000&#x27;, &#x27;2005-06-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2005-07-01T00:00:00.000000000&#x27;, &#x27;2005-08-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2005-09-01T00:00:00.000000000&#x27;, &#x27;2005-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2005-11-01T00:00:00.000000000&#x27;, &#x27;2005-12-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2006-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-03-01T00:00:00.000000000&#x27;, &#x27;2006-04-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-05-01T00:00:00.000000000&#x27;, &#x27;2006-06-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-07-01T00:00:00.000000000&#x27;, &#x27;2006-08-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-09-01T00:00:00.000000000&#x27;, &#x27;2006-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-11-01T00:00:00.000000000&#x27;, &#x27;2006-12-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2007-01-01T00:00:00.000000000&#x27;, &#x27;2007-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2007-03-01T00:00:00.000000000&#x27;, &#x27;2007-04-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2007-05-01T00:00:00.000000000&#x27;, &#x27;2007-06-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2007-07-01T00:00:00.000000000&#x27;, &#x27;2007-08-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2007-09-01T00:00:00.000000000&#x27;, &#x27;2007-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2007-11-01T00:00:00.000000000&#x27;, &#x27;2007-12-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2008-02-01T00:00:00.000000000&#x27;,\n",
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-       "       &#x27;2008-05-01T00:00:00.000000000&#x27;, &#x27;2008-06-01T00:00:00.000000000&#x27;,\n",
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-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab5c4836-ce6d-440b-be6a-9d2b44768a66' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab5c4836-ce6d-440b-be6a-9d2b44768a66' class='xr-section-summary' >Data variables: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dtime</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-30545af0-4a3b-4e7d-b422-1c08b64b5791' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30545af0-4a3b-4e7d-b422-1c08b64b5791' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2793536f-212f-42a8-97f6-905a9e1702e5' class='xr-var-data-in' type='checkbox'><label for='data-2793536f-212f-42a8-97f6-905a9e1702e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time difference from reference time</dd><dt><span>valid_max :</span></dt><dd>2678400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
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-       "                        <td> 7.63 MiB </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-546f7d99-981f-4ab4-a40c-38ff67ea9b41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-546f7d99-981f-4ab4-a40c-38ff67ea9b41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be8e853d-bc51-4c0f-995a-7e932623f4f4' class='xr-var-data-in' type='checkbox'><label for='data-be8e853d-bc51-4c0f-995a-7e932623f4f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_meanings :</span></dt><dd>cropland_rainfed cropland_rainfed_herbaceous_cover cropland_rainfed_tree_or_shrub_cover cropland_irrigated mosaic_cropland mosaic_natural_vegetation tree_broadleaved_evergreen_closed_to_open tree_broadleaved_deciduous_closed_to_open tree_broadleaved_deciduous_closed tree_broadleaved_deciduous_open tree_needleleaved_evergreen_closed_to_open tree_needleleaved_evergreen_closed tree_needleleaved_evergreen_open tree_needleleaved_deciduous_closed_to_open tree_needleleaved_deciduous_closed tree_needleleaved_deciduous_open tree_mixed mosaic_tree_and_shrub mosaic_herbaceous shrubland shrubland_evergreen shrubland_deciduous grassland lichens_and_mosses sparse_vegetation sparse_tree sparse_shrub sparse_herbaceous tree_cover_flooded_fresh_or_brakish_water tree_cover_flooded_saline_water shrub_or_herbaceous_cover_flooded urban Bare_areas_of_soil_types_not_contained_in_biomes_21_to_25 Unconsolidated_bare_areas_of_soil_types_not_contained_in_biomes_21_to_25 Consolidated_bare_areas_of_soil_types_not_contained_in_biomes_21_to_25 Bare_areas_of_soil_type_Entisols_Orthents Bare_areas_of_soil_type_Shifting_sand Bare_areas_of_soil_type_Aridisols_Calcids Bare_areas_of_soil_type_Aridisols_Cambids Bare_areas_of_soil_type_Gelisols_Orthels water snow_and_ice Sea_ice</dd><dt><span>flag_values :</span></dt><dd>[10, 11, 12, 20, 30, 40, 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153, 160, 170, 180, 190, 200, 201, 202, 203, 204, 205, 206, 207, 210, 220, 230]</dd><dt><span>long_name :</span></dt><dd>land cover class</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>230</dd><dt><span>valid_min :</span></dt><dd>10</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                        <td> </td>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 1000, 1000) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 128304 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-bba0f263-2fb9-4cc6-b474-a7e363b21253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bba0f263-2fb9-4cc6-b474-a7e363b21253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa983ecb-4853-4340-a0f1-973cf523e5ff' class='xr-var-data-in' type='checkbox'><label for='data-fa983ecb-4853-4340-a0f1-973cf523e5ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land surface temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>7685</dd><dt><span>valid_min :</span></dt><dd>-8315</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (198, 18000, 36000) </td>\n",
-       "                        <td> (1, 1000, 1000) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 128304 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "            </table>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lst_unc_loc_atm</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-f5ebae27-4718-4ae8-9509-f5b0f52dfb94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5ebae27-4718-4ae8-9509-f5b0f52dfb94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a24c2823-4566-408e-bc0b-db5020a09069' class='xr-var-data-in' type='checkbox'><label for='data-a24c2823-4566-408e-bc0b-db5020a09069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>uncertainty from locally correlated errors on atmospheric scales</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">198</text>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lst_unc_loc_sfc</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-cacbac4d-0cf8-4118-9797-c8cf90467cd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cacbac4d-0cf8-4118-9797-c8cf90467cd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5fabdc5-e5ff-42c5-80cf-285757d03669' class='xr-var-data-in' type='checkbox'><label for='data-b5fabdc5-e5ff-42c5-80cf-285757d03669' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>uncertainty from locally correlated errors on surface scales</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
+       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
+       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
+       "Attributes:\n",
+       "    cell_measures:  area: areacella\n",
+       "    cell_methods:   area: time: mean\n",
+       "    comment:        At surface; includes precipitation of all forms of water ...\n",
+       "    history:        2020-06-08T08:46:50Z altered by CMOR: Converted type from...\n",
+       "    long_name:      Snowfall Flux\n",
+       "    original_name:  PRECSC+PRECSL\n",
+       "    standard_name:  snowfall_flux\n",
+       "    units:          kg m-2 s-1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'prsn'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-03eb5dd1-4081-4212-aa76-a6d898c3f576' class='xr-array-in' type='checkbox' checked><label for='section-03eb5dd1-4081-4212-aa76-a6d898c3f576' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1347,18 +461,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
+       "                        <td> 417.66 MiB </td>\n",
+       "                        <td> 216.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (198, 18000, 36000) </td>\n",
-       "                        <td> (1, 1000, 1000) </td>\n",
+       "                        <td> (1980, 192, 288) </td>\n",
+       "                        <td> (1, 192, 288) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 128304 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1980 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1368,1775 +482,203 @@
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lst_unc_ran</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-29c4d984-b297-49a7-9f42-c384ef98d0e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29c4d984-b297-49a7-9f42-c384ef98d0e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22af3b17-4107-4fd6-bfc1-92a6438c738f' class='xr-var-data-in' type='checkbox'><label for='data-22af3b17-4107-4fd6-bfc1-92a6438c738f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>uncertainty from uncorrelated errors</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
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-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
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-       "                        <th> Dask graph </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lst_unc_sys</span></div><div class='xr-var-dims'>(time, length_scale)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</div><input id='attrs-a66f3553-e0cd-4ddc-86ea-324e1487f16e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a66f3553-e0cd-4ddc-86ea-324e1487f16e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a7f3e4f-8f93-47f3-8800-0d3e278360dc' class='xr-var-data-in' type='checkbox'><label for='data-2a7f3e4f-8f93-47f3-8800-0d3e278360dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>uncertainty from large-scale systematic errors</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 792 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (198, 1) </td>\n",
-       "                        <td> (1, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 198 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
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-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 1000, 1000) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 128304 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "    <tr>\n",
-       "        <td>\n",
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-       "                        <th> Bytes </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sataz</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-67b5f8e0-4656-44c7-9c5d-bc5fdc61c8cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67b5f8e0-4656-44c7-9c5d-bc5fdc61c8cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44e1210e-d4b4-42eb-bdde-db319769a5b8' class='xr-var-data-in' type='checkbox'><label for='data-44e1210e-d4b4-42eb-bdde-db319769a5b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>satellite azimuth angle</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>18000</dd><dt><span>valid_min :</span></dt><dd>-18000</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
-       "                    \n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>satze</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-41e898c2-8136-4388-8229-44c96c6f2197' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41e898c2-8136-4388-8229-44c96c6f2197' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d25080e0-eeb5-4a7e-b8d6-0f970b8d102f' class='xr-var-data-in' type='checkbox'><label for='data-d25080e0-eeb5-4a7e-b8d6-0f970b8d102f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>satellite zenith angle</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>18000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 1000, 1000) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 128304 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solaz</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-4e72c1d7-b7ea-41f9-b724-74d86a8d26f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e72c1d7-b7ea-41f9-b724-74d86a8d26f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b22e400-30fe-4581-a910-54218044e2b4' class='xr-var-data-in' type='checkbox'><label for='data-6b22e400-30fe-4581-a910-54218044e2b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>solar azimuth angle</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>18000</dd><dt><span>valid_min :</span></dt><dd>-18000</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (198, 18000, 36000) </td>\n",
-       "                        <td> (1, 1000, 1000) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 128304 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solze</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-6372819a-6b9b-4e6b-a472-4f6c749a2ceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6372819a-6b9b-4e6b-a472-4f6c749a2ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26d4f642-aefb-484a-9828-26a344a11767' class='xr-var-data-in' type='checkbox'><label for='data-26d4f642-aefb-484a-9828-26a344a11767' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>solar zenith angle</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_max :</span></dt><dd>18000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                    <tr>\n",
-       "                        <td> </td>\n",
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-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 1000, 1000) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 128304 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>variance</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1000, 1000), meta=np.ndarray&gt;</div><input id='attrs-4c12124a-1263-4b8f-9663-310ad2cde048' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c12124a-1263-4b8f-9663-310ad2cde048' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-153349be-e85d-4aaf-aa68-d530113fb852' class='xr-var-data-in' type='checkbox'><label for='data-153349be-e85d-4aaf-aa68-d530113fb852' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land surface temperature variance</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 477.97 GiB </td>\n",
-       "                        <td> 3.81 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 1000, 1000) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 128304 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "  <line x1=\"138\" y1=\"14\" x2=\"138\" y2=\"74\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"74\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,74.9485979497544 24.9485979497544,74.9485979497544\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"94.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >36000</text>\n",
-       "  <text x=\"164.948598\" y=\"44.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,44.948598)\">18000</text>\n",
-       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">198</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-904fd380-50bd-4d6a-be39-e87b6329b6c9' class='xr-section-summary-in' type='checkbox'  ><label for='section-904fd380-50bd-4d6a-be39-e87b6329b6c9' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>channel</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-73fc77a4-7437-4392-80b2-222a79c8079a' class='xr-index-data-in' type='checkbox'/><label for='index-73fc77a4-7437-4392-80b2-222a79c8079a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([10.999000549316406, 11.999000549316406], dtype=&#x27;float64&#x27;, name=&#x27;channel&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba760dcd-d6d7-4612-9f68-8c4560e62c1f' class='xr-index-data-in' type='checkbox'/><label for='index-ba760dcd-d6d7-4612-9f68-8c4560e62c1f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-89.99500274658203, -89.98500061035156, -89.97500610351562,\n",
-       "              -89.96500396728516, -89.95500183105469, -89.94499969482422,\n",
-       "              -89.93500518798828, -89.92500305175781, -89.91500091552734,\n",
-       "               -89.9050064086914,\n",
-       "              ...\n",
-       "               89.90499114990234,  89.91498565673828,  89.92499542236328,\n",
-       "               89.93498992919922,  89.94499969482422,  89.95499420166016,\n",
-       "                89.9649887084961,   89.9749984741211,  89.98499298095703,\n",
-       "               89.99498748779297],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=18000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-582a426a-f192-48c2-9bdf-fccea261c0f4' class='xr-index-data-in' type='checkbox'/><label for='index-582a426a-f192-48c2-9bdf-fccea261c0f4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -179.9949951171875, -179.98500061035156, -179.97499084472656,\n",
-       "              -179.96499633789062,  -179.9550018310547,  -179.9449920654297,\n",
-       "              -179.93499755859375, -179.92498779296875,  -179.9149932861328,\n",
-       "              -179.90499877929688,\n",
-       "              ...\n",
-       "               179.90499877929688,  179.91500854492188,  179.92498779296875,\n",
-       "               179.93499755859375,  179.94500732421875,  179.95498657226562,\n",
-       "               179.96499633789062,  179.97500610351562,   179.9849853515625,\n",
-       "                179.9949951171875],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=36000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9121a1e5-9e61-4719-8df4-c49f240f9bf4' class='xr-index-data-in' type='checkbox'/><label for='index-9121a1e5-9e61-4719-8df4-c49f240f9bf4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2002-07-01&#x27;, &#x27;2002-08-01&#x27;, &#x27;2002-09-01&#x27;, &#x27;2002-10-01&#x27;,\n",
-       "               &#x27;2002-11-01&#x27;, &#x27;2002-12-01&#x27;, &#x27;2003-01-01&#x27;, &#x27;2003-02-01&#x27;,\n",
-       "               &#x27;2003-03-01&#x27;, &#x27;2003-04-01&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2018-03-01&#x27;, &#x27;2018-04-01&#x27;, &#x27;2018-05-01&#x27;, &#x27;2018-06-01&#x27;,\n",
-       "               &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;, &#x27;2018-09-01&#x27;, &#x27;2018-10-01&#x27;,\n",
-       "               &#x27;2018-11-01&#x27;, &#x27;2018-12-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=198, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f132a3c2-e6bd-49a5-b2cf-4a18b67fc9b2' class='xr-section-summary-in' type='checkbox'  ><label for='section-f132a3c2-e6bd-49a5-b2cf-4a18b67fc9b2' class='xr-section-summary' >Attributes: <span>(44)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>These data were produced as part of the ESA LST CCI project.</dd><dt><span>creator_email :</span></dt><dd>djg20@le.ac.uk</dd><dt><span>creator_name :</span></dt><dd>University of Leicester Surface Temperature Group</dd><dt><span>creator_url :</span></dt><dd>https://climate.esa.int/en/projects/land-surface-temperature</dd><dt><span>date_created :</span></dt><dd>20220404T141650Z</dd><dt><span>doi :</span></dt><dd>10.5285/fe98aa1c666d42b9a2a0d19a72bb8a36</dd><dt><span>format_version :</span></dt><dd>CCI Data Standards v2.2</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.99498748779297</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.99500274658203</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.009999999776482582</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>179.9949951171875</dd><dt><span>geospatial_lon_min :</span></dt><dd>-179.9949951171875</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.009999999776482582</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>history :</span></dt><dd>Created using software developed at University of Leicester\n",
-       "Kerchunk file created on 26/09/23\n",
-       "Kerchunk file last updated by CEDA on 03/10/23 in the context of the CCI Knowledge Exchange Project</dd><dt><span>id :</span></dt><dd>ESACCI-LST-L3C-LST-MODISA-0.01deg_1MONTHLY_DAY-200207-201812-fv3.00</dd><dt><span>institution :</span></dt><dd>University of Leicester</dd><dt><span>key_variables :</span></dt><dd>land_surface_temperature</dd><dt><span>keywords :</span></dt><dd>Earth Science; Land Surface; Land Temperature; Land Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>ESA CCI Data Policy: free and open access</dd><dt><span>naming_authority :</span></dt><dd>le.ac.uk</dd><dt><span>platform :</span></dt><dd>Aqua</dd><dt><span>product_version :</span></dt><dd>3.00</dd><dt><span>project :</span></dt><dd>Climate Change Initiative - European Space Agency</dd><dt><span>references :</span></dt><dd>https://climate.esa.int/en/projects/land-surface-temperature</dd><dt><span>sensor :</span></dt><dd>MODIS</dd><dt><span>source :</span></dt><dd>ESA LST CCI MODISA L3U V3.00</dd><dt><span>spatial_resolution :</span></dt><dd>0.01 degree</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v71</dd><dt><span>summary :</span></dt><dd>This file contains level L3C global land surface temperatures from MODIS. L3C data are derived from multiple orbits of single sensor L3U data combined onto a space and/or time grid.</dd><dt><span>time_coverage_duration :</span></dt><dd>P16Y6M</dd><dt><span>time_coverage_end :</span></dt><dd>20181231T235959</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>20020701T000000</dd><dt><span>title :</span></dt><dd>ESA LST CCI land surface temperature data at product level L3C from Moderate Resolution Imaging Spectroradiometer.</dd><dt><span>kerchunk_revision :</span></dt><dd>kr1.1</dd><dt><span>kerchunk_creation_date :</span></dt><dd>031023T093248</dd><dt><span>tracking_id :</span></dt><dd>8efceb61-53cb-4361-a3b4-e809046b9a19</dd></dl></div></li></ul></div></div>"
+       "</table></div></div></li><li class='xr-section-item'><input id='section-00ff2874-fd07-423f-b11b-16a712e7ee2e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00ff2874-fd07-423f-b11b-16a712e7ee2e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-24c67858-42f4-4d33-9d5e-98cfd91172d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24c67858-42f4-4d33-9d5e-98cfd91172d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f79825d-d7e0-4c32-a31e-62f87b120276' class='xr-var-data-in' type='checkbox'><label for='data-7f79825d-d7e0-4c32-a31e-62f87b120276' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
+       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
+       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
+       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
+       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
+       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
+       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
+       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
+       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
+       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
+       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
+       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
+       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
+       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
+       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
+       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
+       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
+       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
+       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
+       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
+       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
+       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
+       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
+       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
+       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
+       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
+       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
+       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
+       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
+       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
+       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
+       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-0faa05ed-c38b-4367-a267-197fb98c5032' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0faa05ed-c38b-4367-a267-197fb98c5032' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68dd2ff5-34b3-4529-8297-5de2a395889e' class='xr-var-data-in' type='checkbox'><label for='data-68dd2ff5-34b3-4529-8297-5de2a395889e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-950a507e-dbe0-4d8f-8eac-998293ed5a63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-950a507e-dbe0-4d8f-8eac-998293ed5a63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab217700-1eca-4207-93df-425b377b80a3' class='xr-var-data-in' type='checkbox'><label for='data-ab217700-1eca-4207-93df-425b377b80a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       ...,\n",
+       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a93fb62-fda6-4bf6-90b4-9b97d70f8841' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a93fb62-fda6-4bf6-90b4-9b97d70f8841' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10127fd6-35ad-4806-a6e5-1e7644dad83e' class='xr-index-data-in' type='checkbox'/><label for='index-10127fd6-35ad-4806-a6e5-1e7644dad83e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
+       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
+       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
+       "       -81.51832460732984,\n",
+       "       ...\n",
+       "        81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
+       "        84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
+       "        87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
+       "                     90.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4ac33788-b5ff-4494-aefc-593bda119d1e' class='xr-index-data-in' type='checkbox'/><label for='index-4ac33788-b5ff-4494-aefc-593bda119d1e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
+       "        11.25,\n",
+       "       ...\n",
+       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
+       "       358.75],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8255fd1b-92ef-49d3-8b21-82335055f67a' class='xr-index-data-in' type='checkbox'/><label for='index-8255fd1b-92ef-49d3-8b21-82335055f67a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
+       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
+       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
+       "             1850-10-16 12:00:00,\n",
+       "             ...\n",
+       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
+       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
+       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
+       "             2014-12-16 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f959d75-c725-4daa-9077-18ac1a790078' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f959d75-c725-4daa-9077-18ac1a790078' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>At surface; includes precipitation of all forms of water in the solid phase</dd><dt><span>history :</span></dt><dd>2020-06-08T08:46:50Z altered by CMOR: Converted type from &#x27;d&#x27; to &#x27;f&#x27;.</dd><dt><span>long_name :</span></dt><dd>Snowfall Flux</dd><dt><span>original_name :</span></dt><dd>PRECSC+PRECSL</dd><dt><span>standard_name :</span></dt><dd>snowfall_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:          (channel: 2, time: 198, lat: 18000, lon: 36000,\n",
-       "                      length_scale: 1)\n",
+       "<xarray.DataArray 'prsn' (time: 1980, lat: 192, lon: 288)>\n",
+       "dask.array<open_dataset-prsn, shape=(1980, 192, 288), dtype=float32, chunksize=(1, 192, 288), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
-       "  * channel          (channel) float32 11.0 12.0\n",
-       "  * lat              (lat) float32 -90.0 -89.99 -89.98 ... 89.97 89.98 89.99\n",
-       "  * lon              (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
-       "  * time             (time) datetime64[ns] 2002-07-01 2002-08-01 ... 2018-12-01\n",
-       "Dimensions without coordinates: length_scale\n",
-       "Data variables: (12/14)\n",
-       "    dtime            (time, lat, lon) timedelta64[ns] dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    lcc              (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    lst              (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    lst_unc_loc_atm  (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    lst_unc_loc_sfc  (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    lst_unc_ran      (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    ...               ...\n",
-       "    n                (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    sataz            (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    satze            (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    solaz            (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    solze            (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "    variance         (time, lat, lon) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>\n",
-       "Attributes: (12/44)\n",
-       "    Conventions:                CF-1.8\n",
-       "    cdm_data_type:              grid\n",
-       "    comment:                    These data were produced as part of the ESA L...\n",
-       "    creator_email:              djg20@le.ac.uk\n",
-       "    creator_name:               University of Leicester Surface Temperature G...\n",
-       "    creator_url:                https://climate.esa.int/en/projects/land-surf...\n",
-       "    ...                         ...\n",
-       "    time_coverage_resolution:   P1M\n",
-       "    time_coverage_start:        20020701T000000\n",
-       "    title:                      ESA LST CCI land surface temperature data at ...\n",
-       "    kerchunk_revision:          kr1.1\n",
-       "    kerchunk_creation_date:     031023T093248\n",
-       "    tracking_id:                8efceb61-53cb-4361-a3b4-e809046b9a19"
+       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
+       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
+       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
+       "Attributes:\n",
+       "    cell_measures:  area: areacella\n",
+       "    cell_methods:   area: time: mean\n",
+       "    comment:        At surface; includes precipitation of all forms of water ...\n",
+       "    history:        2020-06-08T08:46:50Z altered by CMOR: Converted type from...\n",
+       "    long_name:      Snowfall Flux\n",
+       "    original_name:  PRECSC+PRECSL\n",
+       "    standard_name:  snowfall_flux\n",
+       "    units:          kg m-2 s-1"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ds"
+    "ds.prsn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3fea9e67-f1c3-40c0-8c49-318ac90cdc3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f94fe4af1c0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds.clt.sel(time='1850-01-16').plot()"
    ]
   },
   {
diff --git a/showcase/notebooks/TestDask.ipynb b/showcase/notebooks/TestDask.ipynb
new file mode 100644
index 0000000..2b4cc62
--- /dev/null
+++ b/showcase/notebooks/TestDask.ipynb
@@ -0,0 +1,649 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7fbabb0b-4571-4ef0-8d15-01a51db9d04e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "\n",
+    "kfile = 'https://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/CMIP/AS-RCEC/TaiESM1/kr1.0/CMIP6_CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_Amon_prsn_gn_v20200623_kr1.0.json'\n",
+    "ds = xr.open_zarr(fsspec.get_mapper('reference://',fo=kfile, remote_protocol='https'), consolidated=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "218abcae-f9b7-4e4d-8b88-b3dc1931a5b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/jaspy/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 34376 instead\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-4790ce83-d25e-11ee-83eb-02e7fd06541f</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34376/status\" target=\"_blank\">http://127.0.0.1:34376/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
+       "    </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">9676bea7</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:34376/status\" target=\"_blank\">http://127.0.0.1:34376/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 1\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 4\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 7.45 GiB\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            \n",
+       "            <tr>\n",
+       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
+       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
+       "</tr>\n",
+       "\n",
+       "            \n",
+       "        </table>\n",
+       "\n",
+       "        <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\">\n",
+       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
+       "            </summary>\n",
+       "\n",
+       "            <div style=\"\">\n",
+       "    <div>\n",
+       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
+       "        <div style=\"margin-left: 48px;\">\n",
+       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-7dbd4ffe-759e-4467-87e5-fc42721332e7</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:43543\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 1\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:34376/status\" target=\"_blank\">http://127.0.0.1:34376/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 4\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Started:</strong> Just now\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total memory:</strong> 7.45 GiB\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "            </table>\n",
+       "        </div>\n",
+       "    </div>\n",
+       "\n",
+       "    <details style=\"margin-left: 48px;\">\n",
+       "        <summary style=\"margin-bottom: 20px;\">\n",
+       "            <h3 style=\"display: inline;\">Workers</h3>\n",
+       "        </summary>\n",
+       "\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:37617\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34591/status\" target=\"_blank\">http://127.0.0.1:34591/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.45 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43307\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /work/scratch-pw3/dask-scratch-space/worker-hu908owt\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "\n",
+       "    </details>\n",
+       "</div>\n",
+       "\n",
+       "        </details>\n",
+       "    </div>\n",
+       "</div>\n",
+       "            </details>\n",
+       "        \n",
+       "\n",
+       "    </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:43543' processes=1 threads=4, memory=7.45 GiB>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask.distributed import LocalCluster, Client\n",
+    "cluster = LocalCluster(n_workers=1,\n",
+    "                       threads_per_worker=4,\n",
+    "                       memory_limit='8GB',\n",
+    "                       local_directory='/work/scratch-pw3/kerchunk-dask/')\n",
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5013fbb7-4f3b-4c3d-b682-e63d955a5c63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 31 s, sys: 6.96 s, total: 37.9 s\n",
+      "Wall time: 4min 50s\n"
+     ]
+    },
+    {
+     "data": {
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+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;prsn&#x27; ()&gt;\n",
+       "array(466.979, dtype=float32)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'prsn'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-49bd424c-8331-4e25-afbe-4e387f17abb6' class='xr-array-in' type='checkbox' checked><label for='section-49bd424c-8331-4e25-afbe-4e387f17abb6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>467.0</span></div><div class='xr-array-data'><pre>array(466.979, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f3366f6f-63d6-41b7-a503-3ae257933d99' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f3366f6f-63d6-41b7-a503-3ae257933d99' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e5baa951-bfa3-4176-82c7-1e6b2bb7cfa5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5baa951-bfa3-4176-82c7-1e6b2bb7cfa5' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ba0679c9-c0fe-43e1-8d39-8be0eac3de8b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba0679c9-c0fe-43e1-8d39-8be0eac3de8b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'prsn' ()>\n",
+       "array(466.979, dtype=float32)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.prsn.sum().compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bc39415-7d81-42f8-bee2-f5b01dd9911d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "build_venv",
+   "language": "python",
+   "name": "build_venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/showcase/notebooks/TestKerchunking.ipynb b/showcase/notebooks/TestKerchunking.ipynb
index 47ba746..f0b6639 100644
--- a/showcase/notebooks/TestKerchunking.ipynb
+++ b/showcase/notebooks/TestKerchunking.ipynb
@@ -2,440 +2,38 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "id": "70c74604-24ee-4407-a788-07ac13dd6c32",
    "metadata": {},
    "outputs": [],
    "source": [
     "import fsspec\n",
     "import xarray as xr\n",
-    "from kerchunk import combine, hdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d99cbc80-7285-4119-9d40-d7fbcb276c49",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['BinaryIO',\n",
-       " 'FillStringsCodec',\n",
-       " 'SingleHdf5ToZarr',\n",
-       " 'Union',\n",
-       " '_HIDDEN_ATTRS',\n",
-       " '__builtins__',\n",
-       " '__cached__',\n",
-       " '__doc__',\n",
-       " '__file__',\n",
-       " '__loader__',\n",
-       " '__name__',\n",
-       " '__package__',\n",
-       " '__spec__',\n",
-       " '_encode_for_JSON',\n",
-       " '_is_netcdf_datetime',\n",
-       " '_is_netcdf_variable',\n",
-       " '_read_block',\n",
-       " '_simple_type',\n",
-       " 'base64',\n",
-       " 'encode_fill_value',\n",
-       " 'fsspec',\n",
-       " 'h5py',\n",
-       " 'lggr',\n",
-       " 'logging',\n",
-       " 'np',\n",
-       " 'numcodecs',\n",
-       " 'zarr']"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(hdf)"
+    "from kerchunk import combine, hdf, netCDF3\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "id": "9cba2403-8b24-4884-ac3a-6644da3bdc69",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_refs(nfile):\n",
+    "def get_refs_hdf(nfile):\n",
     "    return hdf.SingleHdf5ToZarr(nfile).translate()\n",
     "\n",
+    "def get_refs_ncf3(nfile):\n",
+    "    return netCDF3.NetCDF3ToZarr(nfile).translate()\n",
+    "\n",
     "def combine_refs(refset,**kwargs):\n",
     "    return combine.MultiZarrToZarr(refset, concat_dims=['time'], **kwargs).translate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "08f08790-c78a-4e8d-b949-5788226cbe38",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19801201-19901130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19901201-20001130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20001201-20101130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20101201-20201130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20201201-20301130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20301201-20401130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20401201-20501130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20501201-20601130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20601201-20701130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20701201-20801130.nc']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ukcp_fileset = \"\"\"/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19801201-19901130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19901201-20001130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20001201-20101130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20101201-20201130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20201201-20301130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20301201-20401130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20401201-20501130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20501201-20601130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20601201-20701130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20701201-20801130.nc\"\"\".split('\\n')\n",
-    "ukcp_fileset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "581f3323-ce3a-4c1e-9d88-e6232e2822df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ref1 = get_refs(ukcp_fileset[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "4d35c558-999d-496c-bbce-a6be60de92e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ref2 = get_refs(ukcp_fileset[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "315c6336-5b63-4c85-ad48-2636628ba7da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mzz = combine_refs([ref1,ref2], identical_dims=['ensemble_member_id', 'grid_latitude','grid_longitude'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "b5666072-5a8b-4dad-89b5-6ffa94e14f22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_zarr(fsspec.get_mapper('reference://',fo=mzz2),consolidated=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "c61faf5d-0adc-40f6-9204-46c6a63363ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((1,), (1,))"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.ensemble_member_id.shape, ds.ensemble_member.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "79d508d0-65bd-4b31-b5df-9b7d23c48cf1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'{\"_ARRAY_DIMENSIONS\":[\"ensemble_member\",\"string27\"],\"long_name\":\"ensemble_member_id\",\"units\":\"1\"}'"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ref1['refs']['ensemble_member_id/.zattrs']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "36475463-0554-4ea1-a46f-3f461d3941e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clt ['ensemble_member', 'time', 'projection_y_coordinate', 'projection_x_coordinate']\n",
-      "transverse_mercator []\n",
-      "ensemble_member ['ensemble_member']\n",
-      "time ['time']\n",
-      "time_bnds ['time', 'bnds']\n",
-      "projection_y_coordinate ['projection_y_coordinate']\n",
-      "projection_y_coordinate_bnds ['projection_y_coordinate', 'bnds']\n",
-      "projection_x_coordinate ['projection_x_coordinate']\n",
-      "projection_x_coordinate_bnds ['projection_x_coordinate', 'bnds']\n",
-      "ensemble_member_id ['ensemble_member', 'string27']\n",
-      "grid_latitude ['projection_y_coordinate', 'projection_x_coordinate']\n",
-      "grid_longitude ['projection_y_coordinate', 'projection_x_coordinate']\n",
-      "month_number ['time']\n",
-      "year ['time']\n",
-      "yyyymmdd ['time', 'string64']\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "for var in list(xarr_1.variables):\n",
-    "    attrs = json.loads(ref1['refs'][f'{var}/.zattrs'])\n",
-    "    print(var, attrs['_ARRAY_DIMENSIONS'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c29e072-1352-4c0c-991c-56b9ef356c34",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mzz['refs']['grid_latitude/.zattrs']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "d4138e90-a68b-4d4c-9891-d76e1880ccb7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xarr_1 = xr.open_mfdataset(ukcp_fileset[:2])\n",
-    "xarr_2 = xr.concat([xr.open_dataset(ukcp_fileset[0]), xr.open_dataset(ukcp_fileset[1])], dim='time', data_vars='minimal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "41f78598-9131-4042-9552-cfe2f06a0db1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clt (1, 7200, 112, 82) (1, 7200, 112, 82)\n",
-      "transverse_mercator () ()\n",
-      "ensemble_member (1,) (1,)\n",
-      "time (7200,) (7200,)\n",
-      "time_bnds (7200, 2) (7200, 2)\n",
-      "projection_y_coordinate (112,) (112,)\n",
-      "projection_y_coordinate_bnds (112, 2) (112, 2)\n",
-      "projection_x_coordinate (82,) (82,)\n",
-      "projection_x_coordinate_bnds (82, 2) (82, 2)\n",
-      "ensemble_member_id (1,) (1,)\n",
-      "grid_latitude (112, 82) (112, 82)\n",
-      "grid_longitude (112, 82) (112, 82)\n",
-      "month_number (7200,) (7200,)\n",
-      "year (7200,) (7200,)\n",
-      "yyyymmdd (7200,) (7200,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for var in list(xarr_1.variables):\n",
-    "    print(var, xarr_2[var].shape, ds[var].shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "30bde3de-dd8d-4aba-8d5d-7ee935c100c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(112, 82)"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xarr_1.grid_latitude.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "a34c0156-b950-4096-9786-3a9e4eb756c3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(7200, 112, 82)"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.grid_latitude.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "b38ffe42-a9fb-4e0e-a036-8b436b6b9f20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pipeline.validate import validate_data\n",
-    "from pipeline.logs import init_logger\n",
-    "testlog = init_logger(1,0,'test')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0533c908-828b-4c14-972d-5dee11891fb7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO [test]: ensemble_member : Starting growbox data tests for 0\n",
-      "INFO [test]: ensemble_member : Starting growbox data tests for 0\n",
-      "INFO [test]: ensemble_member_id : Starting growbox data tests for 0\n",
-      "INFO [test]: ensemble_member_id : Starting growbox data tests for 0\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time-dependent:  clt\n",
-      "Duplicate:  ensemble_member\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING [test]: Max comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Max comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Min comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Min comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Mean comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Mean comparison skipped for non-summable values in ensemble_member_id\n",
-      "INFO [test]: grid_latitude : Starting growbox data tests for 0\n",
-      "INFO [test]: grid_latitude : Starting growbox data tests for 0\n",
-      "INFO [test]: grid_longitude : Starting growbox data tests for 0\n",
-      "INFO [test]: grid_longitude : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_x_coordinate : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_x_coordinate : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_x_coordinate_bnds : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_x_coordinate_bnds : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_y_coordinate : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_y_coordinate : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_y_coordinate_bnds : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_y_coordinate_bnds : Starting growbox data tests for 0\n",
-      "INFO [test]: transverse_mercator : Starting growbox data tests for 0\n",
-      "INFO [test]: transverse_mercator : Starting growbox data tests for 0\n",
-      "WARNING [test]: Failed to find non-NaN slice (tried: 7, var: transverse_mercator)\n",
-      "WARNING [test]: Failed to find non-NaN slice (tried: 7, var: transverse_mercator)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Duplicate:  ensemble_member_id\n",
-      "Duplicate:  grid_latitude\n",
-      "Duplicate:  grid_longitude\n",
-      "Time-dependent:  month_number\n",
-      "Duplicate:  projection_x_coordinate\n",
-      "Duplicate:  projection_x_coordinate_bnds\n",
-      "Duplicate:  projection_y_coordinate\n",
-      "Duplicate:  projection_y_coordinate_bnds\n",
-      "Time-dependent:  time\n",
-      "Time-dependent:  time_bnds\n",
-      "nan\n",
-      "Duplicate:  transverse_mercator\n",
-      "Time-dependent:  year\n",
-      "Time-dependent:  yyyymmdd\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pipeline.errors import ShapeMismatchError\n",
-    "import xarray as xr\n",
-    "import fsspec\n",
-    "ds1 = xr.open_zarr(fsspec.get_mapper('reference://',fo=ref1),consolidated=False)\n",
-    "ds2 = xr.open_zarr(fsspec.get_mapper('reference://',fo=ref2),consolidated=False)\n",
-    "identicals = []\n",
-    "for var in ds1.variables:\n",
-    "    if 'time' not in ds1[var].dims:\n",
-    "        try:\n",
-    "            validate_data(ds1, ds2, var, 0, testlog, bypass=True)\n",
-    "            print('Duplicate: ',var)\n",
-    "            identicals.append(var)\n",
-    "        except ShapeMismatchError:\n",
-    "            # Non duplicate\n",
-    "            print('Non-duplicate: ',var)\n",
-    "    else:\n",
-    "        print('Time-dependent: ',var)\n",
-    "mzz2 = combine_refs([ref1,ref2], identical_dims=identicals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "0bd64b78-1ce3-49f4-8d10-2cbc8e1e6b21",
+   "execution_count": 10,
+   "id": "0a027fa1-ef28-401c-8dbb-75cdac924f70",
    "metadata": {},
    "outputs": [
     {
@@ -804,379 +402,485 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;transverse_mercator&#x27; (time: 7200)&gt;\n",
-       "array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)\n",
-       "Coordinates:\n",
-       "  * time          (time) object 1980-12-01 12:00:00 ... 2000-11-30 12:00:00\n",
-       "    month_number  (time) int32 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
-       "    year          (time) int32 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
-       "    yyyymmdd      (time) |S64 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    grid_mapping_name:                 transverse_mercator\n",
-       "    longitude_of_prime_meridian:       0.0\n",
-       "    semi_major_axis:                   6377563.396\n",
-       "    semi_minor_axis:                   6356256.909\n",
-       "    longitude_of_central_meridian:     -2.0\n",
-       "    latitude_of_projection_origin:     49.0\n",
-       "    false_easting:                     400000.0\n",
-       "    false_northing:                    -100000.0\n",
-       "    scale_factor_at_central_meridian:  0.9996012717</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'transverse_mercator'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7d6cfbd9-a222-40bb-994d-6e98e7f4e20c' class='xr-array-in' type='checkbox' checked><label for='section-7d6cfbd9-a222-40bb-994d-6e98e7f4e20c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-2147483647 -2147483647 -2147483647 ... -2147483647 -2147483647</span></div><div class='xr-array-data'><pre>array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)</pre></div></div></li><li class='xr-section-item'><input id='section-0c7d3ede-40e6-4651-90e9-47dd03331f01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c7d3ede-40e6-4651-90e9-47dd03331f01' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-12-01 12:00:00 ... 2000-11-...</div><input id='attrs-fa13be28-4dc6-4016-ad54-96952d1c33dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa13be28-4dc6-4016-ad54-96952d1c33dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-523c7203-8f55-4935-9447-b98cda2dcb5c' class='xr-var-data-in' type='checkbox'><label for='data-523c7203-8f55-4935-9447-b98cda2dcb5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(1980, 12, 1, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(1980, 12, 2, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(1980, 12, 3, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.Datetime360Day(2000, 11, 28, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(2000, 11, 29, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(2000, 11, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-70f67d4f-18f9-481f-8dbc-1c8724e00a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70f67d4f-18f9-481f-8dbc-1c8724e00a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02612eae-4ba9-454d-8acc-a0cdb1426b6d' class='xr-var-data-in' type='checkbox'><label for='data-02612eae-4ba9-454d-8acc-a0cdb1426b6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>month_number</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 28.12 kiB </td>\n",
-       "                        <td> 14.06 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (7200,) </td>\n",
-       "                        <td> (3600,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-1fc7650c-e5fd-4afe-bbc5-cb0ea16e5788' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fc7650c-e5fd-4afe-bbc5-cb0ea16e5788' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff9c012-d22b-42b2-a110-278a6351a7c6' class='xr-var-data-in' type='checkbox'><label for='data-dff9c012-d22b-42b2-a110-278a6351a7c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>year</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 28.12 kiB </td>\n",
-       "                        <td> 14.06 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (7200,) </td>\n",
-       "                        <td> (3600,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yyyymmdd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-4cf64572-db15-4397-8a96-7523eb2b2dba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cf64572-db15-4397-8a96-7523eb2b2dba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be74de0-e927-4d31-9639-f7fb24bffbeb' class='xr-var-data-in' type='checkbox'><label for='data-7be74de0-e927-4d31-9639-f7fb24bffbeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>yyyymmdd</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 450.00 kiB </td>\n",
-       "                        <td> 225.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (7200,) </td>\n",
-       "                        <td> (3600,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> |S64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-431dc5c2-896f-4efe-9486-74f11ae8736f' class='xr-section-summary-in' type='checkbox'  ><label for='section-431dc5c2-896f-4efe-9486-74f11ae8736f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b13002ad-c241-47d5-81f0-f6f7c3a36c5d' class='xr-index-data-in' type='checkbox'/><label for='index-b13002ad-c241-47d5-81f0-f6f7c3a36c5d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1980-12-01 12:00:00, 1980-12-02 12:00:00, 1980-12-03 12:00:00,\n",
-       "             1980-12-04 12:00:00, 1980-12-05 12:00:00, 1980-12-06 12:00:00,\n",
-       "             1980-12-07 12:00:00, 1980-12-08 12:00:00, 1980-12-09 12:00:00,\n",
-       "             1980-12-10 12:00:00,\n",
-       "             ...\n",
-       "             2000-11-21 12:00:00, 2000-11-22 12:00:00, 2000-11-23 12:00:00,\n",
-       "             2000-11-24 12:00:00, 2000-11-25 12:00:00, 2000-11-26 12:00:00,\n",
-       "             2000-11-27 12:00:00, 2000-11-28 12:00:00, 2000-11-29 12:00:00,\n",
-       "             2000-11-30 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=7200, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a16d8c3-004b-4cd3-805a-d386e81bbbcd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a16d8c3-004b-4cd3-805a-d386e81bbbcd' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6377563.396</dd><dt><span>semi_minor_axis :</span></dt><dd>6356256.909</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-2.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>49.0</dd><dt><span>false_easting :</span></dt><dd>400000.0</dd><dt><span>false_northing :</span></dt><dd>-100000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996012717</dd></dl></div></li></ul></div></div>"
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;AOD670_mean&#x27; ()&gt;\n",
+       "array(True)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'AOD670_mean'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-03086059-0073-4a3b-b4d1-168c8f290540' class='xr-array-in' type='checkbox' checked><label for='section-03086059-0073-4a3b-b4d1-168c8f290540' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>True</span></div><div class='xr-array-data'><pre>array(True)</pre></div></div></li><li class='xr-section-item'><input id='section-38bf50ed-c102-4ee8-8520-935b29f69ccb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-38bf50ed-c102-4ee8-8520-935b29f69ccb' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8ebd4812-33d8-4c4e-be22-4268501e3953' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8ebd4812-33d8-4c4e-be22-4268501e3953' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-3a836abc-a975-4e95-bc09-25af80a7e9da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a836abc-a975-4e95-bc09-25af80a7e9da' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'transverse_mercator' (time: 7200)>\n",
-       "array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)\n",
-       "Coordinates:\n",
-       "  * time          (time) object 1980-12-01 12:00:00 ... 2000-11-30 12:00:00\n",
-       "    month_number  (time) int32 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
-       "    year          (time) int32 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
-       "    yyyymmdd      (time) |S64 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    grid_mapping_name:                 transverse_mercator\n",
-       "    longitude_of_prime_meridian:       0.0\n",
-       "    semi_major_axis:                   6377563.396\n",
-       "    semi_minor_axis:                   6356256.909\n",
-       "    longitude_of_central_meridian:     -2.0\n",
-       "    latitude_of_projection_origin:     49.0\n",
-       "    false_easting:                     400000.0\n",
-       "    false_northing:                    -100000.0\n",
-       "    scale_factor_at_central_meridian:  0.9996012717"
+       "<xarray.DataArray 'AOD670_mean' ()>\n",
+       "array(True)"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "15a02ef3-a0fd-4d84-b6a0-aa34ac9b68d3",
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "class Args:\n",
-    "    def __init__(self):\n",
-    "        self.bypass = True"
+    "import numpy as np\n",
+    "nfile0 = '/neodc/esacci/aerosol/data/AATSR_ADV/L3/v2.31/MONTHLY/2002/200205-ESACCI-L3C_AEROSOL-AER_PRODUCTS-AATSR-ENVISAT-ADV_MOTNHLY-v2.30.nc'\n",
+    "nfile1 = '/neodc/esacci/aerosol/data/AATSR_ADV/L3/v2.31/MONTHLY/2002/200206-ESACCI-L3C_AEROSOL-AER_PRODUCTS-AATSR-ENVISAT-ADV_MOTNHLY-v2.30.nc'\n",
+    "\n",
+    "ds = xr.open_dataset(nfile0)\n",
+    "ds1 = xr.open_dataset(nfile1)\n",
+    "\n",
+    "\n",
+    "ab = ds.AOD670_mean\n",
+    "np.all(ab!=ab)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "6573a292-e489-47a5-851d-1cbe9f61227e",
+   "execution_count": 4,
+   "id": "80359973-5c7e-461f-86ec-7de5f8a84e92",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f1d11455600>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "allfile = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/allfiles.txt\"\n",
-    "with open(allfile) as f:\n",
-    "    fileset = [r.strip() for r in f.readlines()]"
+    "ds1.AOD670_mean.plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "d21cb5a9-96ab-4fb9-b330-f06f899f15ca",
+   "execution_count": null,
+   "id": "ff4fe227-6c64-4677-9aed-343c0de6c1a5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import xarray as xr\n",
-    "ds_set = []\n",
-    "for f in fileset:\n",
-    "    ds_set.append(xr.open_dataset(f))\n",
-    "ds = xr.concat(ds_set, dim='time',data_vars='minimal')"
+    "nfile_cci1 = '/neodc/esacci/aerosol/data/ATSR2_ADV/L3/v2.31/MONTHLY/1995/199506-ESACCI-L3C_AEROSOL-AER_PRODUCTS-ATSR2-ERS2-ADV_MONTHLY-v2.30.nc'\n",
+    "refs = get_refs_ncf3(nfile_cci1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "eb934cb4-bede-4534-9d08-e2fd1fbe577e",
+   "execution_count": 11,
+   "id": "08f08790-c78a-4e8d-b949-5788226cbe38",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(384, 2)"
+       "['/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19801201-19901130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19901201-20001130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20001201-20101130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20101201-20201130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20201201-20301130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20301201-20401130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20401201-20501130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20501201-20601130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20601201-20701130.nc',\n",
+       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20701201-20801130.nc']"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ds.lon_bnds.shape"
+    "ukcp_fileset = \"\"\"/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19801201-19901130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19901201-20001130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20001201-20101130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20101201-20201130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20201201-20301130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20301201-20401130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20401201-20501130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20501201-20601130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20601201-20701130.nc\n",
+    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20701201-20801130.nc\"\"\".split('\\n')\n",
+    "ukcp_fileset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "b7f2245c-0f27-4ef9-88a7-e5eec050339d",
+   "execution_count": 12,
+   "id": "581f3323-ce3a-4c1e-9d88-e6232e2822df",
    "metadata": {},
    "outputs": [],
    "source": [
-    "kfile1 = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/cache/0.json\"\n",
-    "kfile2 = kfile1.replace(\"0.json\", \"1.json\")"
+    "ref1 = get_refs(ukcp_fileset[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "55406257-4906-439c-be88-1f3d19a85e58",
+   "execution_count": 13,
+   "id": "4d35c558-999d-496c-bbce-a6be60de92e8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pipeline.validate import open_kerchunk\n",
-    "from pipeline.logs import FalseLogger\n",
-    "import fsspec\n",
-    "import xarray as xr\n",
-    "\n",
-    "kfile1 = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/kerchunk-1a.json\"\n",
-    "\n",
-    "kf1 = open_kerchunk(kfile1, FalseLogger())\n",
-    "mapper = fsspec.get_mapper('reference://',fo=kfile1, remote_protocol='https')\n",
-    "kf2 = xr.open_zarr(mapper, consolidated=False)"
+    "ref2 = get_refs(ukcp_fileset[1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "bc8791ea-4a1e-48fe-902a-1753632dde62",
+   "execution_count": 88,
+   "id": "315c6336-5b63-4c85-ad48-2636628ba7da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mzz = combine_refs([ref1,ref2], identical_dims=['ensemble_member_id', 'grid_latitude','grid_longitude'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "b5666072-5a8b-4dad-89b5-6ffa94e14f22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_zarr(fsspec.get_mapper('reference://',fo=mzz2),consolidated=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "c61faf5d-0adc-40f6-9204-46c6a63363ce",
    "metadata": {},
    "outputs": [
     {
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
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-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
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-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
+      "text/plain": [
+       "((1,), (1,))"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.ensemble_member_id.shape, ds.ensemble_member.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "79d508d0-65bd-4b31-b5df-9b7d23c48cf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'{\"_ARRAY_DIMENSIONS\":[\"ensemble_member\",\"string27\"],\"long_name\":\"ensemble_member_id\",\"units\":\"1\"}'"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ref1['refs']['ensemble_member_id/.zattrs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "36475463-0554-4ea1-a46f-3f461d3941e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "clt ['ensemble_member', 'time', 'projection_y_coordinate', 'projection_x_coordinate']\n",
+      "transverse_mercator []\n",
+      "ensemble_member ['ensemble_member']\n",
+      "time ['time']\n",
+      "time_bnds ['time', 'bnds']\n",
+      "projection_y_coordinate ['projection_y_coordinate']\n",
+      "projection_y_coordinate_bnds ['projection_y_coordinate', 'bnds']\n",
+      "projection_x_coordinate ['projection_x_coordinate']\n",
+      "projection_x_coordinate_bnds ['projection_x_coordinate', 'bnds']\n",
+      "ensemble_member_id ['ensemble_member', 'string27']\n",
+      "grid_latitude ['projection_y_coordinate', 'projection_x_coordinate']\n",
+      "grid_longitude ['projection_y_coordinate', 'projection_x_coordinate']\n",
+      "month_number ['time']\n",
+      "year ['time']\n",
+      "yyyymmdd ['time', 'string64']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "for var in list(xarr_1.variables):\n",
+    "    attrs = json.loads(ref1['refs'][f'{var}/.zattrs'])\n",
+    "    print(var, attrs['_ARRAY_DIMENSIONS'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c29e072-1352-4c0c-991c-56b9ef356c34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mzz['refs']['grid_latitude/.zattrs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "d4138e90-a68b-4d4c-9891-d76e1880ccb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xarr_1 = xr.open_mfdataset(ukcp_fileset[:2])\n",
+    "xarr_2 = xr.concat([xr.open_dataset(ukcp_fileset[0]), xr.open_dataset(ukcp_fileset[1])], dim='time', data_vars='minimal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "41f78598-9131-4042-9552-cfe2f06a0db1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "clt (1, 7200, 112, 82) (1, 7200, 112, 82)\n",
+      "transverse_mercator () ()\n",
+      "ensemble_member (1,) (1,)\n",
+      "time (7200,) (7200,)\n",
+      "time_bnds (7200, 2) (7200, 2)\n",
+      "projection_y_coordinate (112,) (112,)\n",
+      "projection_y_coordinate_bnds (112, 2) (112, 2)\n",
+      "projection_x_coordinate (82,) (82,)\n",
+      "projection_x_coordinate_bnds (82, 2) (82, 2)\n",
+      "ensemble_member_id (1,) (1,)\n",
+      "grid_latitude (112, 82) (112, 82)\n",
+      "grid_longitude (112, 82) (112, 82)\n",
+      "month_number (7200,) (7200,)\n",
+      "year (7200,) (7200,)\n",
+      "yyyymmdd (7200,) (7200,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for var in list(xarr_1.variables):\n",
+    "    print(var, xarr_2[var].shape, ds[var].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "30bde3de-dd8d-4aba-8d5d-7ee935c100c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(112, 82)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xarr_1.grid_latitude.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "a34c0156-b950-4096-9786-3a9e4eb756c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7200, 112, 82)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.grid_latitude.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b38ffe42-a9fb-4e0e-a036-8b436b6b9f20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pipeline.validate import validate_data\n",
+    "from pipeline.logs import init_logger\n",
+    "testlog = init_logger(1,0,'test')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0533c908-828b-4c14-972d-5dee11891fb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO [test]: ensemble_member : Starting growbox data tests for 0\n",
+      "INFO [test]: ensemble_member : Starting growbox data tests for 0\n",
+      "INFO [test]: ensemble_member_id : Starting growbox data tests for 0\n",
+      "INFO [test]: ensemble_member_id : Starting growbox data tests for 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time-dependent:  clt\n",
+      "Duplicate:  ensemble_member\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING [test]: Max comparison skipped for non-summable values in ensemble_member_id\n",
+      "WARNING [test]: Max comparison skipped for non-summable values in ensemble_member_id\n",
+      "WARNING [test]: Min comparison skipped for non-summable values in ensemble_member_id\n",
+      "WARNING [test]: Min comparison skipped for non-summable values in ensemble_member_id\n",
+      "WARNING [test]: Mean comparison skipped for non-summable values in ensemble_member_id\n",
+      "WARNING [test]: Mean comparison skipped for non-summable values in ensemble_member_id\n",
+      "INFO [test]: grid_latitude : Starting growbox data tests for 0\n",
+      "INFO [test]: grid_latitude : Starting growbox data tests for 0\n",
+      "INFO [test]: grid_longitude : Starting growbox data tests for 0\n",
+      "INFO [test]: grid_longitude : Starting growbox data tests for 0\n",
+      "INFO [test]: projection_x_coordinate : Starting growbox data tests for 0\n",
+      "INFO [test]: projection_x_coordinate : Starting growbox data tests for 0\n",
+      "INFO [test]: projection_x_coordinate_bnds : Starting growbox data tests for 0\n",
+      "INFO [test]: projection_x_coordinate_bnds : Starting growbox data tests for 0\n",
+      "INFO [test]: projection_y_coordinate : Starting growbox data tests for 0\n",
+      "INFO [test]: projection_y_coordinate : Starting growbox data tests for 0\n",
+      "INFO [test]: projection_y_coordinate_bnds : Starting growbox data tests for 0\n",
+      "INFO [test]: projection_y_coordinate_bnds : Starting growbox data tests for 0\n",
+      "INFO [test]: transverse_mercator : Starting growbox data tests for 0\n",
+      "INFO [test]: transverse_mercator : Starting growbox data tests for 0\n",
+      "WARNING [test]: Failed to find non-NaN slice (tried: 7, var: transverse_mercator)\n",
+      "WARNING [test]: Failed to find non-NaN slice (tried: 7, var: transverse_mercator)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Duplicate:  ensemble_member_id\n",
+      "Duplicate:  grid_latitude\n",
+      "Duplicate:  grid_longitude\n",
+      "Time-dependent:  month_number\n",
+      "Duplicate:  projection_x_coordinate\n",
+      "Duplicate:  projection_x_coordinate_bnds\n",
+      "Duplicate:  projection_y_coordinate\n",
+      "Duplicate:  projection_y_coordinate_bnds\n",
+      "Time-dependent:  time\n",
+      "Time-dependent:  time_bnds\n",
+      "nan\n",
+      "Duplicate:  transverse_mercator\n",
+      "Time-dependent:  year\n",
+      "Time-dependent:  yyyymmdd\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pipeline.errors import ShapeMismatchError\n",
+    "import xarray as xr\n",
+    "import fsspec\n",
+    "ds1 = xr.open_zarr(fsspec.get_mapper('reference://',fo=ref1),consolidated=False)\n",
+    "ds2 = xr.open_zarr(fsspec.get_mapper('reference://',fo=ref2),consolidated=False)\n",
+    "identicals = []\n",
+    "for var in ds1.variables:\n",
+    "    if 'time' not in ds1[var].dims:\n",
+    "        try:\n",
+    "            validate_data(ds1, ds2, var, 0, testlog, bypass=True)\n",
+    "            print('Duplicate: ',var)\n",
+    "            identicals.append(var)\n",
+    "        except ShapeMismatchError:\n",
+    "            # Non duplicate\n",
+    "            print('Non-duplicate: ',var)\n",
+    "    else:\n",
+    "        print('Time-dependent: ',var)\n",
+    "mzz2 = combine_refs([ref1,ref2], identical_dims=identicals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "0bd64b78-1ce3-49f4-8d10-2cbc8e1e6b21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
        ".xr-wrap {\n",
        "  display: block !important;\n",
        "  min-width: 300px;\n",
@@ -1469,71 +1173,359 @@
        "  width: auto;\n",
        "}\n",
        "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;transverse_mercator&#x27; (time: 7200)&gt;\n",
+       "array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
+       "       -2147483647, -2147483647], dtype=int32)\n",
+       "Coordinates:\n",
+       "  * time          (time) object 1980-12-01 12:00:00 ... 2000-11-30 12:00:00\n",
+       "    month_number  (time) int32 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
+       "    year          (time) int32 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
+       "    yyyymmdd      (time) |S64 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    grid_mapping_name:                 transverse_mercator\n",
+       "    longitude_of_prime_meridian:       0.0\n",
+       "    semi_major_axis:                   6377563.396\n",
+       "    semi_minor_axis:                   6356256.909\n",
+       "    longitude_of_central_meridian:     -2.0\n",
+       "    latitude_of_projection_origin:     49.0\n",
+       "    false_easting:                     400000.0\n",
+       "    false_northing:                    -100000.0\n",
+       "    scale_factor_at_central_meridian:  0.9996012717</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'transverse_mercator'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7d6cfbd9-a222-40bb-994d-6e98e7f4e20c' class='xr-array-in' type='checkbox' checked><label for='section-7d6cfbd9-a222-40bb-994d-6e98e7f4e20c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-2147483647 -2147483647 -2147483647 ... -2147483647 -2147483647</span></div><div class='xr-array-data'><pre>array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
+       "       -2147483647, -2147483647], dtype=int32)</pre></div></div></li><li class='xr-section-item'><input id='section-0c7d3ede-40e6-4651-90e9-47dd03331f01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c7d3ede-40e6-4651-90e9-47dd03331f01' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-12-01 12:00:00 ... 2000-11-...</div><input id='attrs-fa13be28-4dc6-4016-ad54-96952d1c33dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa13be28-4dc6-4016-ad54-96952d1c33dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-523c7203-8f55-4935-9447-b98cda2dcb5c' class='xr-var-data-in' type='checkbox'><label for='data-523c7203-8f55-4935-9447-b98cda2dcb5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(1980, 12, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.Datetime360Day(1980, 12, 2, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.Datetime360Day(1980, 12, 3, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       ...,\n",
+       "       cftime.Datetime360Day(2000, 11, 28, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.Datetime360Day(2000, 11, 29, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.Datetime360Day(2000, 11, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-70f67d4f-18f9-481f-8dbc-1c8724e00a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70f67d4f-18f9-481f-8dbc-1c8724e00a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02612eae-4ba9-454d-8acc-a0cdb1426b6d' class='xr-var-data-in' type='checkbox'><label for='data-02612eae-4ba9-454d-8acc-a0cdb1426b6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>month_number</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 28.12 kiB </td>\n",
+       "                        <td> 14.06 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (7200,) </td>\n",
+       "                        <td> (3600,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-1fc7650c-e5fd-4afe-bbc5-cb0ea16e5788' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fc7650c-e5fd-4afe-bbc5-cb0ea16e5788' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff9c012-d22b-42b2-a110-278a6351a7c6' class='xr-var-data-in' type='checkbox'><label for='data-dff9c012-d22b-42b2-a110-278a6351a7c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>year</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 28.12 kiB </td>\n",
+       "                        <td> 14.06 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (7200,) </td>\n",
+       "                        <td> (3600,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yyyymmdd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-4cf64572-db15-4397-8a96-7523eb2b2dba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cf64572-db15-4397-8a96-7523eb2b2dba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be74de0-e927-4d31-9639-f7fb24bffbeb' class='xr-var-data-in' type='checkbox'><label for='data-7be74de0-e927-4d31-9639-f7fb24bffbeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>yyyymmdd</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 450.00 kiB </td>\n",
+       "                        <td> 225.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (7200,) </td>\n",
+       "                        <td> (3600,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> |S64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 1)&gt;\n",
-       "array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)\n",
-       "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00\n",
-       "Attributes:\n",
-       "    axis:           T\n",
-       "    bounds:         time_bnds\n",
-       "    long_name:      time\n",
-       "    standard_name:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3b548d65-ca93-4653-9533-c02224c77528' class='xr-array-in' type='checkbox' checked><label for='section-3b548d65-ca93-4653-9533-c02224c77528' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1850-01-16T12:00:00</span></div><div class='xr-array-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-1a3d8a94-af61-4180-8c19-492bbb7d10f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a3d8a94-af61-4180-8c19-492bbb7d10f9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf78290e-9997-492c-a9c4-e90f8f1c820a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf78290e-9997-492c-a9c4-e90f8f1c820a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aa463b1-0f5a-4ad9-adcf-cfcecc3640a5' class='xr-var-data-in' type='checkbox'><label for='data-9aa463b1-0f5a-4ad9-adcf-cfcecc3640a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00</div><input id='attrs-eafe1426-c808-4848-ac4a-677425fb1a08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eafe1426-c808-4848-ac4a-677425fb1a08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e21f0f6-3ee6-41bc-b527-1874e82e0a9e' class='xr-var-data-in' type='checkbox'><label for='data-4e21f0f6-3ee6-41bc-b527-1874e82e0a9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b14eaaae-40d9-4d42-8644-4e47392ee796' class='xr-section-summary-in' type='checkbox'  ><label for='section-b14eaaae-40d9-4d42-8644-4e47392ee796' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-06e5daa7-c022-494c-a115-88ca44266cb9' class='xr-index-data-in' type='checkbox'/><label for='index-06e5daa7-c022-494c-a115-88ca44266cb9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1850-01-16 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5ab734f-eb23-437d-ace2-d113b8de93fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5ab734f-eb23-437d-ace2-d113b8de93fe' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-431dc5c2-896f-4efe-9486-74f11ae8736f' class='xr-section-summary-in' type='checkbox'  ><label for='section-431dc5c2-896f-4efe-9486-74f11ae8736f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b13002ad-c241-47d5-81f0-f6f7c3a36c5d' class='xr-index-data-in' type='checkbox'/><label for='index-b13002ad-c241-47d5-81f0-f6f7c3a36c5d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1980-12-01 12:00:00, 1980-12-02 12:00:00, 1980-12-03 12:00:00,\n",
+       "             1980-12-04 12:00:00, 1980-12-05 12:00:00, 1980-12-06 12:00:00,\n",
+       "             1980-12-07 12:00:00, 1980-12-08 12:00:00, 1980-12-09 12:00:00,\n",
+       "             1980-12-10 12:00:00,\n",
+       "             ...\n",
+       "             2000-11-21 12:00:00, 2000-11-22 12:00:00, 2000-11-23 12:00:00,\n",
+       "             2000-11-24 12:00:00, 2000-11-25 12:00:00, 2000-11-26 12:00:00,\n",
+       "             2000-11-27 12:00:00, 2000-11-28 12:00:00, 2000-11-29 12:00:00,\n",
+       "             2000-11-30 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=7200, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a16d8c3-004b-4cd3-805a-d386e81bbbcd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a16d8c3-004b-4cd3-805a-d386e81bbbcd' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6377563.396</dd><dt><span>semi_minor_axis :</span></dt><dd>6356256.909</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-2.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>49.0</dd><dt><span>false_easting :</span></dt><dd>400000.0</dd><dt><span>false_northing :</span></dt><dd>-100000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996012717</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'time' (time: 1)>\n",
-       "array(['1850-01-16T12:00:00.000000000'], dtype='datetime64[ns]')\n",
+       "<xarray.DataArray 'transverse_mercator' (time: 7200)>\n",
+       "array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
+       "       -2147483647, -2147483647], dtype=int32)\n",
        "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00\n",
+       "  * time          (time) object 1980-12-01 12:00:00 ... 2000-11-30 12:00:00\n",
+       "    month_number  (time) int32 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
+       "    year          (time) int32 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
+       "    yyyymmdd      (time) |S64 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
        "Attributes:\n",
-       "    axis:           T\n",
-       "    bounds:         time_bnds\n",
-       "    long_name:      time\n",
-       "    standard_name:  time"
+       "    grid_mapping_name:                 transverse_mercator\n",
+       "    longitude_of_prime_meridian:       0.0\n",
+       "    semi_major_axis:                   6377563.396\n",
+       "    semi_minor_axis:                   6356256.909\n",
+       "    longitude_of_central_meridian:     -2.0\n",
+       "    latitude_of_projection_origin:     49.0\n",
+       "    false_easting:                     400000.0\n",
+       "    false_northing:                    -100000.0\n",
+       "    scale_factor_at_central_meridian:  0.9996012717"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "15a02ef3-a0fd-4d84-b6a0-aa34ac9b68d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Args:\n",
+    "    def __init__(self):\n",
+    "        self.bypass = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6573a292-e489-47a5-851d-1cbe9f61227e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allfile = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/allfiles.txt\"\n",
+    "with open(allfile) as f:\n",
+    "    fileset = [r.strip() for r in f.readlines()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d21cb5a9-96ab-4fb9-b330-f06f899f15ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "ds_set = []\n",
+    "for f in fileset:\n",
+    "    ds_set.append(xr.open_dataset(f))\n",
+    "ds = xr.concat(ds_set, dim='time',data_vars='minimal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "eb934cb4-bede-4534-9d08-e2fd1fbe577e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(384, 2)"
+      ]
+     },
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kf2.time.sel(time='1850-01-16')"
+    "ds.lon_bnds.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "8980814a-d963-46be-8adb-ca1c9aba0bba",
+   "execution_count": 1,
+   "id": "b7f2245c-0f27-4ef9-88a7-e5eec050339d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfile1 = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/cache/0.json\"\n",
+    "kfile2 = kfile1.replace(\"0.json\", \"1.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "55406257-4906-439c-be88-1f3d19a85e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pipeline.validate import open_kerchunk\n",
+    "from pipeline.logs import FalseLogger\n",
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "\n",
+    "kfile1 = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/kerchunk-1a.json\"\n",
+    "\n",
+    "kf1 = open_kerchunk(kfile1, FalseLogger())\n",
+    "mapper = fsspec.get_mapper('reference://',fo=kfile1, remote_protocol='https')\n",
+    "kf2 = xr.open_zarr(mapper, consolidated=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bc8791ea-4a1e-48fe-902a-1753632dde62",
    "metadata": {},
    "outputs": [
     {
@@ -1902,251 +1894,43 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lon_bnds&#x27; (lon: 384, bnds: 2)&gt;\n",
-       "array([[ -0.46875,   0.46875],\n",
-       "       [  0.46875,   1.40625],\n",
-       "       [  1.40625,   2.34375],\n",
-       "       [  2.34375,   3.28125],\n",
-       "       [  3.28125,   4.21875],\n",
-       "       [  4.21875,   5.15625],\n",
-       "       [  5.15625,   6.09375],\n",
-       "       [  6.09375,   7.03125],\n",
-       "       [  7.03125,   7.96875],\n",
-       "       [  7.96875,   8.90625],\n",
-       "       [  8.90625,   9.84375],\n",
-       "       [  9.84375,  10.78125],\n",
-       "       [ 10.78125,  11.71875],\n",
-       "       [ 11.71875,  12.65625],\n",
-       "       [ 12.65625,  13.59375],\n",
-       "       [ 13.59375,  14.53125],\n",
-       "       [ 14.53125,  15.46875],\n",
-       "       [ 15.46875,  16.40625],\n",
-       "       [ 16.40625,  17.34375],\n",
-       "       [ 17.34375,  18.28125],\n",
-       "...\n",
-       "       [340.78125, 341.71875],\n",
-       "       [341.71875, 342.65625],\n",
-       "       [342.65625, 343.59375],\n",
-       "       [343.59375, 344.53125],\n",
-       "       [344.53125, 345.46875],\n",
-       "       [345.46875, 346.40625],\n",
-       "       [346.40625, 347.34375],\n",
-       "       [347.34375, 348.28125],\n",
-       "       [348.28125, 349.21875],\n",
-       "       [349.21875, 350.15625],\n",
-       "       [350.15625, 351.09375],\n",
-       "       [351.09375, 352.03125],\n",
-       "       [352.03125, 352.96875],\n",
-       "       [352.96875, 353.90625],\n",
-       "       [353.90625, 354.84375],\n",
-       "       [354.84375, 355.78125],\n",
-       "       [355.78125, 356.71875],\n",
-       "       [356.71875, 357.65625],\n",
-       "       [357.65625, 358.59375],\n",
-       "       [358.59375, 359.53125]])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 1)&gt;\n",
+       "array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)\n",
        "Coordinates:\n",
-       "    height   float64 10.0\n",
-       "  * lon      (lon) float64 6.946e-310 6.946e-310 ... 2.451e+198 4.572e+228\n",
-       "Dimensions without coordinates: bnds</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lon_bnds'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 384</li><li><span>bnds</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8ff5f241-a99e-4ae0-8b3d-6f3d28706048' class='xr-array-in' type='checkbox' checked><label for='section-8ff5f241-a99e-4ae0-8b3d-6f3d28706048' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.4688 0.4688 0.4688 1.406 1.406 ... 357.7 357.7 358.6 358.6 359.5</span></div><div class='xr-array-data'><pre>array([[ -0.46875,   0.46875],\n",
-       "       [  0.46875,   1.40625],\n",
-       "       [  1.40625,   2.34375],\n",
-       "       [  2.34375,   3.28125],\n",
-       "       [  3.28125,   4.21875],\n",
-       "       [  4.21875,   5.15625],\n",
-       "       [  5.15625,   6.09375],\n",
-       "       [  6.09375,   7.03125],\n",
-       "       [  7.03125,   7.96875],\n",
-       "       [  7.96875,   8.90625],\n",
-       "       [  8.90625,   9.84375],\n",
-       "       [  9.84375,  10.78125],\n",
-       "       [ 10.78125,  11.71875],\n",
-       "       [ 11.71875,  12.65625],\n",
-       "       [ 12.65625,  13.59375],\n",
-       "       [ 13.59375,  14.53125],\n",
-       "       [ 14.53125,  15.46875],\n",
-       "       [ 15.46875,  16.40625],\n",
-       "       [ 16.40625,  17.34375],\n",
-       "       [ 17.34375,  18.28125],\n",
-       "...\n",
-       "       [340.78125, 341.71875],\n",
-       "       [341.71875, 342.65625],\n",
-       "       [342.65625, 343.59375],\n",
-       "       [343.59375, 344.53125],\n",
-       "       [344.53125, 345.46875],\n",
-       "       [345.46875, 346.40625],\n",
-       "       [346.40625, 347.34375],\n",
-       "       [347.34375, 348.28125],\n",
-       "       [348.28125, 349.21875],\n",
-       "       [349.21875, 350.15625],\n",
-       "       [350.15625, 351.09375],\n",
-       "       [351.09375, 352.03125],\n",
-       "       [352.03125, 352.96875],\n",
-       "       [352.96875, 353.90625],\n",
-       "       [353.90625, 354.84375],\n",
-       "       [354.84375, 355.78125],\n",
-       "       [355.78125, 356.71875],\n",
-       "       [356.71875, 357.65625],\n",
-       "       [357.65625, 358.59375],\n",
-       "       [358.59375, 359.53125]])</pre></div></div></li><li class='xr-section-item'><input id='section-3442477a-da38-4f41-ae34-ff9a3fc8fb70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3442477a-da38-4f41-ae34-ff9a3fc8fb70' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-4234e3ed-60e9-4a30-9db5-b9873a33bf93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4234e3ed-60e9-4a30-9db5-b9873a33bf93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f9cc817-26ea-4cce-bc52-475f867e3c48' class='xr-var-data-in' type='checkbox'><label for='data-4f9cc817-26ea-4cce-bc52-475f867e3c48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(10.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.946e-310 ... 4.572e+228</div><input id='attrs-db57a267-b928-499c-a7ea-c3c40cc56c0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db57a267-b928-499c-a7ea-c3c40cc56c0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e1312b-0004-4a4d-abf0-fdf8154bcdb8' class='xr-var-data-in' type='checkbox'><label for='data-85e1312b-0004-4a4d-abf0-fdf8154bcdb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([6.946461e-310, 6.946461e-310, 4.661759e-310, ..., 4.787194e+180,\n",
-       "       2.450673e+198, 4.572376e+228])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49cccdf7-b0ce-4914-9c64-e021faf97f1f' class='xr-section-summary-in' type='checkbox'  ><label for='section-49cccdf7-b0ce-4914-9c64-e021faf97f1f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed1cc9d3-f3b0-4b9c-b5df-af69e8ffdce8' class='xr-index-data-in' type='checkbox'/><label for='index-ed1cc9d3-f3b0-4b9c-b5df-af69e8ffdce8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([  6.94646063411794e-310,   6.94646063411794e-310,\n",
-       "                 4.6617585837179e-310,    4.6617585837179e-310,\n",
-       "                               5e-324,                     0.0,\n",
-       "              3.6471404775824516e+180,  7.628377678647842e+228,\n",
-       "              1.6943165045757204e-152, 4.0719047313359087e+223,\n",
-       "              ...\n",
-       "               1.1858693676012526e+49,   3.845809172237913e-86,\n",
-       "              6.0112290352011415e+175,  9.088948731942852e+223,\n",
-       "               5.030572423582765e+223, 4.3720591034250617e-153,\n",
-       "              1.0647870195102642e+248,  4.787194240623723e+180,\n",
-       "              2.4506726540155096e+198,  4.572375645356952e+228],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=384))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1027352c-cf9b-46c2-bf63-9d40a4fb7f77' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1027352c-cf9b-46c2-bf63-9d40a4fb7f77' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "    height   float64 ...\n",
+       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00\n",
+       "Attributes:\n",
+       "    axis:           T\n",
+       "    bounds:         time_bnds\n",
+       "    long_name:      time\n",
+       "    standard_name:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3b548d65-ca93-4653-9533-c02224c77528' class='xr-array-in' type='checkbox' checked><label for='section-3b548d65-ca93-4653-9533-c02224c77528' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1850-01-16T12:00:00</span></div><div class='xr-array-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-1a3d8a94-af61-4180-8c19-492bbb7d10f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a3d8a94-af61-4180-8c19-492bbb7d10f9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf78290e-9997-492c-a9c4-e90f8f1c820a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf78290e-9997-492c-a9c4-e90f8f1c820a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aa463b1-0f5a-4ad9-adcf-cfcecc3640a5' class='xr-var-data-in' type='checkbox'><label for='data-9aa463b1-0f5a-4ad9-adcf-cfcecc3640a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00</div><input id='attrs-eafe1426-c808-4848-ac4a-677425fb1a08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eafe1426-c808-4848-ac4a-677425fb1a08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e21f0f6-3ee6-41bc-b527-1874e82e0a9e' class='xr-var-data-in' type='checkbox'><label for='data-4e21f0f6-3ee6-41bc-b527-1874e82e0a9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b14eaaae-40d9-4d42-8644-4e47392ee796' class='xr-section-summary-in' type='checkbox'  ><label for='section-b14eaaae-40d9-4d42-8644-4e47392ee796' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-06e5daa7-c022-494c-a115-88ca44266cb9' class='xr-index-data-in' type='checkbox'/><label for='index-06e5daa7-c022-494c-a115-88ca44266cb9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1850-01-16 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5ab734f-eb23-437d-ace2-d113b8de93fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5ab734f-eb23-437d-ace2-d113b8de93fe' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'lon_bnds' (lon: 384, bnds: 2)>\n",
-       "array([[ -0.46875,   0.46875],\n",
-       "       [  0.46875,   1.40625],\n",
-       "       [  1.40625,   2.34375],\n",
-       "       [  2.34375,   3.28125],\n",
-       "       [  3.28125,   4.21875],\n",
-       "       [  4.21875,   5.15625],\n",
-       "       [  5.15625,   6.09375],\n",
-       "       [  6.09375,   7.03125],\n",
-       "       [  7.03125,   7.96875],\n",
-       "       [  7.96875,   8.90625],\n",
-       "       [  8.90625,   9.84375],\n",
-       "       [  9.84375,  10.78125],\n",
-       "       [ 10.78125,  11.71875],\n",
-       "       [ 11.71875,  12.65625],\n",
-       "       [ 12.65625,  13.59375],\n",
-       "       [ 13.59375,  14.53125],\n",
-       "       [ 14.53125,  15.46875],\n",
-       "       [ 15.46875,  16.40625],\n",
-       "       [ 16.40625,  17.34375],\n",
-       "       [ 17.34375,  18.28125],\n",
-       "...\n",
-       "       [340.78125, 341.71875],\n",
-       "       [341.71875, 342.65625],\n",
-       "       [342.65625, 343.59375],\n",
-       "       [343.59375, 344.53125],\n",
-       "       [344.53125, 345.46875],\n",
-       "       [345.46875, 346.40625],\n",
-       "       [346.40625, 347.34375],\n",
-       "       [347.34375, 348.28125],\n",
-       "       [348.28125, 349.21875],\n",
-       "       [349.21875, 350.15625],\n",
-       "       [350.15625, 351.09375],\n",
-       "       [351.09375, 352.03125],\n",
-       "       [352.03125, 352.96875],\n",
-       "       [352.96875, 353.90625],\n",
-       "       [353.90625, 354.84375],\n",
-       "       [354.84375, 355.78125],\n",
-       "       [355.78125, 356.71875],\n",
-       "       [356.71875, 357.65625],\n",
-       "       [357.65625, 358.59375],\n",
-       "       [358.59375, 359.53125]])\n",
+       "<xarray.DataArray 'time' (time: 1)>\n",
+       "array(['1850-01-16T12:00:00.000000000'], dtype='datetime64[ns]')\n",
        "Coordinates:\n",
-       "    height   float64 10.0\n",
-       "  * lon      (lon) float64 6.946e-310 6.946e-310 ... 2.451e+198 4.572e+228\n",
-       "Dimensions without coordinates: bnds"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kf2.lon_bnds.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b2dc1449-999a-494f-8275-f83f1cfc3a15",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "kf1 = np.array(kf1.lon_bnds)\n",
-    "kf2 = np.array(kf2.lon_bnds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "e1fe251d-e133-44bf-b9fe-7171a51599ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
+       "    height   float64 ...\n",
+       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00\n",
+       "Attributes:\n",
+       "    axis:           T\n",
+       "    bounds:         time_bnds\n",
+       "    long_name:      time\n",
+       "    standard_name:  time"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.array_equal(kf1, kf2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "6c46e794-135e-4f63-931e-693a3ab82f2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "ingest_file = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233/proj_codes_complete_ingest.txt'\n",
-    "in_place = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233'\n",
-    "with open(ingest_file) as f:\n",
-    "    datasets = [f'{in_place}/{r.strip()}_kr1.0.json' for r in f.readlines()]\n",
-    "\n",
-    "for d in datasets[3:8]:\n",
-    "    os.system(f'cp {d} {d.replace(\"CMIP6_rel1_6233\",\"sample\")}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5b495d56-aded-40e9-bab9-58c38577e49f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for x, d in enumerate(datasets):\n",
-    "    with open(d) as f:\n",
-    "        print(x, len(f.readlines()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "e306c7b8-72d2-415b-88e2-7b13da810d2e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0.json\n"
-     ]
-    }
-   ],
-   "source": [
-    "path = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233'\n",
-    "kfile0 = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0.json'\n",
-    "kfile0a = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0a.json'\n",
-    "kfile1 = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/1.json'\n",
-    "print(kfile0)"
+    "kf2.time.sel(time='1850-01-16')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "82619c91-4e00-40de-b654-21a1f804bee1",
+   "execution_count": 11,
+   "id": "8980814a-d963-46be-8adb-ca1c9aba0bba",
    "metadata": {},
    "outputs": [
     {
@@ -2515,249 +2299,501 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lat&#x27; (lat: 192)&gt;\n",
-       "array([6.918039e-310, 4.656565e-310, 0.000000e+000, 0.000000e+000,\n",
-       "       6.013470e-154, 0.000000e+000, 6.013470e-154, 2.318609e-052,\n",
-       "       6.031042e-154, 2.318609e-052, 6.031042e-154, 1.398043e-076,\n",
-       "       6.031042e-154, 1.398043e-076, 2.440053e-154, 5.516754e-048,\n",
-       "       2.628400e+179, 3.615501e-043, 2.624599e+179, 2.990673e-067,\n",
-       "       3.113418e+179, 1.284484e-057, 3.920697e+179, 5.981491e-154,\n",
-       "       3.845686e-086, 5.205553e-090, 4.247839e-086, 5.205557e-090,\n",
-       "       3.443533e-086, 5.207482e-090, 3.845686e-086, 5.200759e-090,\n",
-       "       6.013345e-154, 1.303543e-076, 1.182951e-076, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303712e-076, 3.572812e-062, 1.577110e-052,\n",
-       "       1.269312e-076, 6.013470e-154, 4.079204e-033, 1.174786e-047,\n",
-       "       1.028977e-071, 1.688992e-052, 3.308835e-033, 1.688992e-052,\n",
-       "       1.084292e-042, 1.174786e-047, 6.013470e-154, 1.398041e-076,\n",
-       "       2.421365e-052, 1.723981e-047, 1.518178e-047, 1.656093e-047,\n",
-       "       6.750722e-067, 1.520050e-047, 1.030078e-071, 6.013470e-154,\n",
-       "       1.398043e-076, 7.578808e-096, 1.398043e-076, 7.578808e-096,\n",
-       "       1.398043e-076, 7.578808e-096, 2.318609e-052, 7.580658e-096,\n",
-       "       6.013470e-154, 5.157233e-062, 1.051480e-153, 1.588475e-047,\n",
-       "       1.051188e-153, 3.942742e-062, 1.051188e-153, 3.942742e-062,\n",
-       "       1.145834e-259, 6.013470e-154, 8.604357e-043, 6.031042e-154,\n",
-       "       1.398043e-076, 6.031042e-154, 2.318609e-052, 6.031042e-154,\n",
-       "       1.398043e-076, 2.440053e-154, 2.990638e-067, 3.602222e+179,\n",
-       "       6.963202e-077, 2.623958e+179, 1.552846e-033, 3.597770e+179,\n",
-       "       5.516817e-048, 3.436948e+179, 5.981491e-154, 3.845686e-086,\n",
-       "       5.203662e-090, 4.448916e-086, 5.206504e-090, 4.448916e-086,\n",
-       "       5.209399e-090, 4.448916e-086, 5.209399e-090, 6.013345e-154,\n",
-       "       2.909255e-033, 3.572813e-062, 2.909255e-033, 3.572813e-062,\n",
-       "       2.909255e-033, 3.572813e-062, 2.909255e-033, 3.572813e-062,\n",
-       "       6.013470e-154, 1.425167e-071, 1.085398e-071, 1.425167e-071,\n",
-       "       1.085398e-071, 1.425167e-071, 1.085398e-071, 1.425167e-071,\n",
-       "       1.085398e-071, 6.013470e-154, 7.723222e-043, 1.485413e-076,\n",
-       "       7.723222e-043, 1.485413e-076, 7.723222e-043, 1.485413e-076,\n",
-       "       7.723222e-043, 1.485413e-076, 6.013470e-154, 1.039978e-042,\n",
-       "       7.580658e-096, 1.039978e-042, 7.580658e-096, 1.039978e-042,\n",
-       "       7.580658e-096, 1.039978e-042, 7.580658e-096, 6.013470e-154,\n",
-       "       2.423821e-052, 1.051188e-153, 2.423821e-052, 1.051188e-153,\n",
-       "       2.423821e-052, 1.051188e-153, 2.423821e-052, 1.145834e-259,\n",
-       "       6.013470e-154, 4.085992e-033, 6.031042e-154, 4.085992e-033,\n",
-       "       6.031042e-154, 4.085992e-033, 6.031042e-154, 4.085992e-033,\n",
-       "       2.440053e-154, 5.516754e-048, 4.085956e+179, 5.516817e-048,\n",
-       "       4.085956e+179, 6.963202e-077, 2.623958e+179, 6.963202e-077,\n",
-       "       2.623958e+179, 5.981491e-154, 3.242457e-086, 1.210894e-099,\n",
-       "       3.242457e-086, 1.210894e-099, 3.242457e-086, 1.210894e-099,\n",
-       "       3.242457e-086, 1.210894e-099, 6.013345e-154, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303543e-076, 1.182951e-076, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303543e-076, 1.182951e-076, 6.013470e-154,\n",
-       "       1.397369e-076, 1.396426e-076, 1.397369e-076, 1.396426e-076,\n",
-       "       1.425167e-071, 1.085398e-071, 1.425167e-071, 1.085398e-071])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lon_bnds&#x27; (lon: 384, bnds: 2)&gt;\n",
+       "array([[ -0.46875,   0.46875],\n",
+       "       [  0.46875,   1.40625],\n",
+       "       [  1.40625,   2.34375],\n",
+       "       [  2.34375,   3.28125],\n",
+       "       [  3.28125,   4.21875],\n",
+       "       [  4.21875,   5.15625],\n",
+       "       [  5.15625,   6.09375],\n",
+       "       [  6.09375,   7.03125],\n",
+       "       [  7.03125,   7.96875],\n",
+       "       [  7.96875,   8.90625],\n",
+       "       [  8.90625,   9.84375],\n",
+       "       [  9.84375,  10.78125],\n",
+       "       [ 10.78125,  11.71875],\n",
+       "       [ 11.71875,  12.65625],\n",
+       "       [ 12.65625,  13.59375],\n",
+       "       [ 13.59375,  14.53125],\n",
+       "       [ 14.53125,  15.46875],\n",
+       "       [ 15.46875,  16.40625],\n",
+       "       [ 16.40625,  17.34375],\n",
+       "       [ 17.34375,  18.28125],\n",
+       "...\n",
+       "       [340.78125, 341.71875],\n",
+       "       [341.71875, 342.65625],\n",
+       "       [342.65625, 343.59375],\n",
+       "       [343.59375, 344.53125],\n",
+       "       [344.53125, 345.46875],\n",
+       "       [345.46875, 346.40625],\n",
+       "       [346.40625, 347.34375],\n",
+       "       [347.34375, 348.28125],\n",
+       "       [348.28125, 349.21875],\n",
+       "       [349.21875, 350.15625],\n",
+       "       [350.15625, 351.09375],\n",
+       "       [351.09375, 352.03125],\n",
+       "       [352.03125, 352.96875],\n",
+       "       [352.96875, 353.90625],\n",
+       "       [353.90625, 354.84375],\n",
+       "       [354.84375, 355.78125],\n",
+       "       [355.78125, 356.71875],\n",
+       "       [356.71875, 357.65625],\n",
+       "       [357.65625, 358.59375],\n",
+       "       [358.59375, 359.53125]])\n",
        "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * lat      (lat) float64 6.918e-310 4.657e-310 0.0 ... 1.425e-71 1.085e-71\n",
-       "Attributes:\n",
-       "    axis:           Y\n",
-       "    bounds:         lat_bnds\n",
-       "    long_name:      Latitude\n",
-       "    standard_name:  latitude\n",
-       "    units:          degrees_north</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lat'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-927378b1-1b57-4cfb-b50f-7974a02ce295' class='xr-array-in' type='checkbox' checked><label for='section-927378b1-1b57-4cfb-b50f-7974a02ce295' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.918e-310 4.657e-310 0.0 0.0 ... 1.085e-71 1.425e-71 1.085e-71</span></div><div class='xr-array-data'><pre>array([6.918039e-310, 4.656565e-310, 0.000000e+000, 0.000000e+000,\n",
-       "       6.013470e-154, 0.000000e+000, 6.013470e-154, 2.318609e-052,\n",
-       "       6.031042e-154, 2.318609e-052, 6.031042e-154, 1.398043e-076,\n",
-       "       6.031042e-154, 1.398043e-076, 2.440053e-154, 5.516754e-048,\n",
-       "       2.628400e+179, 3.615501e-043, 2.624599e+179, 2.990673e-067,\n",
-       "       3.113418e+179, 1.284484e-057, 3.920697e+179, 5.981491e-154,\n",
-       "       3.845686e-086, 5.205553e-090, 4.247839e-086, 5.205557e-090,\n",
-       "       3.443533e-086, 5.207482e-090, 3.845686e-086, 5.200759e-090,\n",
-       "       6.013345e-154, 1.303543e-076, 1.182951e-076, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303712e-076, 3.572812e-062, 1.577110e-052,\n",
-       "       1.269312e-076, 6.013470e-154, 4.079204e-033, 1.174786e-047,\n",
-       "       1.028977e-071, 1.688992e-052, 3.308835e-033, 1.688992e-052,\n",
-       "       1.084292e-042, 1.174786e-047, 6.013470e-154, 1.398041e-076,\n",
-       "       2.421365e-052, 1.723981e-047, 1.518178e-047, 1.656093e-047,\n",
-       "       6.750722e-067, 1.520050e-047, 1.030078e-071, 6.013470e-154,\n",
-       "       1.398043e-076, 7.578808e-096, 1.398043e-076, 7.578808e-096,\n",
-       "       1.398043e-076, 7.578808e-096, 2.318609e-052, 7.580658e-096,\n",
-       "       6.013470e-154, 5.157233e-062, 1.051480e-153, 1.588475e-047,\n",
-       "       1.051188e-153, 3.942742e-062, 1.051188e-153, 3.942742e-062,\n",
-       "       1.145834e-259, 6.013470e-154, 8.604357e-043, 6.031042e-154,\n",
-       "       1.398043e-076, 6.031042e-154, 2.318609e-052, 6.031042e-154,\n",
-       "       1.398043e-076, 2.440053e-154, 2.990638e-067, 3.602222e+179,\n",
-       "       6.963202e-077, 2.623958e+179, 1.552846e-033, 3.597770e+179,\n",
-       "       5.516817e-048, 3.436948e+179, 5.981491e-154, 3.845686e-086,\n",
-       "       5.203662e-090, 4.448916e-086, 5.206504e-090, 4.448916e-086,\n",
-       "       5.209399e-090, 4.448916e-086, 5.209399e-090, 6.013345e-154,\n",
-       "       2.909255e-033, 3.572813e-062, 2.909255e-033, 3.572813e-062,\n",
-       "       2.909255e-033, 3.572813e-062, 2.909255e-033, 3.572813e-062,\n",
-       "       6.013470e-154, 1.425167e-071, 1.085398e-071, 1.425167e-071,\n",
-       "       1.085398e-071, 1.425167e-071, 1.085398e-071, 1.425167e-071,\n",
-       "       1.085398e-071, 6.013470e-154, 7.723222e-043, 1.485413e-076,\n",
-       "       7.723222e-043, 1.485413e-076, 7.723222e-043, 1.485413e-076,\n",
-       "       7.723222e-043, 1.485413e-076, 6.013470e-154, 1.039978e-042,\n",
-       "       7.580658e-096, 1.039978e-042, 7.580658e-096, 1.039978e-042,\n",
-       "       7.580658e-096, 1.039978e-042, 7.580658e-096, 6.013470e-154,\n",
-       "       2.423821e-052, 1.051188e-153, 2.423821e-052, 1.051188e-153,\n",
-       "       2.423821e-052, 1.051188e-153, 2.423821e-052, 1.145834e-259,\n",
-       "       6.013470e-154, 4.085992e-033, 6.031042e-154, 4.085992e-033,\n",
-       "       6.031042e-154, 4.085992e-033, 6.031042e-154, 4.085992e-033,\n",
-       "       2.440053e-154, 5.516754e-048, 4.085956e+179, 5.516817e-048,\n",
-       "       4.085956e+179, 6.963202e-077, 2.623958e+179, 6.963202e-077,\n",
-       "       2.623958e+179, 5.981491e-154, 3.242457e-086, 1.210894e-099,\n",
-       "       3.242457e-086, 1.210894e-099, 3.242457e-086, 1.210894e-099,\n",
-       "       3.242457e-086, 1.210894e-099, 6.013345e-154, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303543e-076, 1.182951e-076, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303543e-076, 1.182951e-076, 6.013470e-154,\n",
-       "       1.397369e-076, 1.396426e-076, 1.397369e-076, 1.396426e-076,\n",
-       "       1.425167e-071, 1.085398e-071, 1.425167e-071, 1.085398e-071])</pre></div></div></li><li class='xr-section-item'><input id='section-74146a66-fcc1-44f1-92ca-13d1b92fff47' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74146a66-fcc1-44f1-92ca-13d1b92fff47' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93711399-afd4-4c82-bed7-231d8754cc53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93711399-afd4-4c82-bed7-231d8754cc53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c49232de-487c-476e-a77d-8107de8264e1' class='xr-var-data-in' type='checkbox'><label for='data-c49232de-487c-476e-a77d-8107de8264e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.918e-310 4.657e-310 ... 1.085e-71</div><input id='attrs-593f84cd-f958-44b2-9e97-67dda26529ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-593f84cd-f958-44b2-9e97-67dda26529ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3414009-8123-41dc-8bfc-783de55e68e3' class='xr-var-data-in' type='checkbox'><label for='data-b3414009-8123-41dc-8bfc-783de55e68e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([6.918039e-310, 4.656565e-310, 0.000000e+000, 0.000000e+000,\n",
-       "       6.013470e-154, 0.000000e+000, 6.013470e-154, 2.318609e-052,\n",
-       "       6.031042e-154, 2.318609e-052, 6.031042e-154, 1.398043e-076,\n",
-       "       6.031042e-154, 1.398043e-076, 2.440053e-154, 5.516754e-048,\n",
-       "       2.628400e+179, 3.615501e-043, 2.624599e+179, 2.990673e-067,\n",
-       "       3.113418e+179, 1.284484e-057, 3.920697e+179, 5.981491e-154,\n",
-       "       3.845686e-086, 5.205553e-090, 4.247839e-086, 5.205557e-090,\n",
-       "       3.443533e-086, 5.207482e-090, 3.845686e-086, 5.200759e-090,\n",
-       "       6.013345e-154, 1.303543e-076, 1.182951e-076, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303712e-076, 3.572812e-062, 1.577110e-052,\n",
-       "       1.269312e-076, 6.013470e-154, 4.079204e-033, 1.174786e-047,\n",
-       "       1.028977e-071, 1.688992e-052, 3.308835e-033, 1.688992e-052,\n",
-       "       1.084292e-042, 1.174786e-047, 6.013470e-154, 1.398041e-076,\n",
-       "       2.421365e-052, 1.723981e-047, 1.518178e-047, 1.656093e-047,\n",
-       "       6.750722e-067, 1.520050e-047, 1.030078e-071, 6.013470e-154,\n",
-       "       1.398043e-076, 7.578808e-096, 1.398043e-076, 7.578808e-096,\n",
-       "       1.398043e-076, 7.578808e-096, 2.318609e-052, 7.580658e-096,\n",
-       "       6.013470e-154, 5.157233e-062, 1.051480e-153, 1.588475e-047,\n",
-       "       1.051188e-153, 3.942742e-062, 1.051188e-153, 3.942742e-062,\n",
-       "       1.145834e-259, 6.013470e-154, 8.604357e-043, 6.031042e-154,\n",
-       "       1.398043e-076, 6.031042e-154, 2.318609e-052, 6.031042e-154,\n",
-       "       1.398043e-076, 2.440053e-154, 2.990638e-067, 3.602222e+179,\n",
-       "       6.963202e-077, 2.623958e+179, 1.552846e-033, 3.597770e+179,\n",
-       "       5.516817e-048, 3.436948e+179, 5.981491e-154, 3.845686e-086,\n",
-       "       5.203662e-090, 4.448916e-086, 5.206504e-090, 4.448916e-086,\n",
-       "       5.209399e-090, 4.448916e-086, 5.209399e-090, 6.013345e-154,\n",
-       "       2.909255e-033, 3.572813e-062, 2.909255e-033, 3.572813e-062,\n",
-       "       2.909255e-033, 3.572813e-062, 2.909255e-033, 3.572813e-062,\n",
-       "       6.013470e-154, 1.425167e-071, 1.085398e-071, 1.425167e-071,\n",
-       "       1.085398e-071, 1.425167e-071, 1.085398e-071, 1.425167e-071,\n",
-       "       1.085398e-071, 6.013470e-154, 7.723222e-043, 1.485413e-076,\n",
-       "       7.723222e-043, 1.485413e-076, 7.723222e-043, 1.485413e-076,\n",
-       "       7.723222e-043, 1.485413e-076, 6.013470e-154, 1.039978e-042,\n",
-       "       7.580658e-096, 1.039978e-042, 7.580658e-096, 1.039978e-042,\n",
-       "       7.580658e-096, 1.039978e-042, 7.580658e-096, 6.013470e-154,\n",
-       "       2.423821e-052, 1.051188e-153, 2.423821e-052, 1.051188e-153,\n",
-       "       2.423821e-052, 1.051188e-153, 2.423821e-052, 1.145834e-259,\n",
-       "       6.013470e-154, 4.085992e-033, 6.031042e-154, 4.085992e-033,\n",
-       "       6.031042e-154, 4.085992e-033, 6.031042e-154, 4.085992e-033,\n",
-       "       2.440053e-154, 5.516754e-048, 4.085956e+179, 5.516817e-048,\n",
-       "       4.085956e+179, 6.963202e-077, 2.623958e+179, 6.963202e-077,\n",
-       "       2.623958e+179, 5.981491e-154, 3.242457e-086, 1.210894e-099,\n",
-       "       3.242457e-086, 1.210894e-099, 3.242457e-086, 1.210894e-099,\n",
-       "       3.242457e-086, 1.210894e-099, 6.013345e-154, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303543e-076, 1.182951e-076, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303543e-076, 1.182951e-076, 6.013470e-154,\n",
-       "       1.397369e-076, 1.396426e-076, 1.397369e-076, 1.396426e-076,\n",
-       "       1.425167e-071, 1.085398e-071, 1.425167e-071, 1.085398e-071])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-335df2dc-d296-499d-b601-f7780fb1b1c3' class='xr-section-summary-in' type='checkbox'  ><label for='section-335df2dc-d296-499d-b601-f7780fb1b1c3' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e74e90a6-63c8-4b25-942c-2e1e519646d2' class='xr-index-data-in' type='checkbox'/><label for='index-e74e90a6-63c8-4b25-942c-2e1e519646d2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 6.91803882007545e-310,  4.65656529258115e-310,\n",
-       "                                 0.0,                    0.0,\n",
-       "              6.013470016999328e-154,                    0.0,\n",
-       "              6.013470016999068e-154,  2.318608601885791e-52,\n",
-       "               6.03104192680939e-154,  2.318608601885791e-52,\n",
+       "    height   float64 10.0\n",
+       "  * lon      (lon) float64 6.946e-310 6.946e-310 ... 2.451e+198 4.572e+228\n",
+       "Dimensions without coordinates: bnds</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lon_bnds'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 384</li><li><span>bnds</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8ff5f241-a99e-4ae0-8b3d-6f3d28706048' class='xr-array-in' type='checkbox' checked><label for='section-8ff5f241-a99e-4ae0-8b3d-6f3d28706048' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.4688 0.4688 0.4688 1.406 1.406 ... 357.7 357.7 358.6 358.6 359.5</span></div><div class='xr-array-data'><pre>array([[ -0.46875,   0.46875],\n",
+       "       [  0.46875,   1.40625],\n",
+       "       [  1.40625,   2.34375],\n",
+       "       [  2.34375,   3.28125],\n",
+       "       [  3.28125,   4.21875],\n",
+       "       [  4.21875,   5.15625],\n",
+       "       [  5.15625,   6.09375],\n",
+       "       [  6.09375,   7.03125],\n",
+       "       [  7.03125,   7.96875],\n",
+       "       [  7.96875,   8.90625],\n",
+       "       [  8.90625,   9.84375],\n",
+       "       [  9.84375,  10.78125],\n",
+       "       [ 10.78125,  11.71875],\n",
+       "       [ 11.71875,  12.65625],\n",
+       "       [ 12.65625,  13.59375],\n",
+       "       [ 13.59375,  14.53125],\n",
+       "       [ 14.53125,  15.46875],\n",
+       "       [ 15.46875,  16.40625],\n",
+       "       [ 16.40625,  17.34375],\n",
+       "       [ 17.34375,  18.28125],\n",
+       "...\n",
+       "       [340.78125, 341.71875],\n",
+       "       [341.71875, 342.65625],\n",
+       "       [342.65625, 343.59375],\n",
+       "       [343.59375, 344.53125],\n",
+       "       [344.53125, 345.46875],\n",
+       "       [345.46875, 346.40625],\n",
+       "       [346.40625, 347.34375],\n",
+       "       [347.34375, 348.28125],\n",
+       "       [348.28125, 349.21875],\n",
+       "       [349.21875, 350.15625],\n",
+       "       [350.15625, 351.09375],\n",
+       "       [351.09375, 352.03125],\n",
+       "       [352.03125, 352.96875],\n",
+       "       [352.96875, 353.90625],\n",
+       "       [353.90625, 354.84375],\n",
+       "       [354.84375, 355.78125],\n",
+       "       [355.78125, 356.71875],\n",
+       "       [356.71875, 357.65625],\n",
+       "       [357.65625, 358.59375],\n",
+       "       [358.59375, 359.53125]])</pre></div></div></li><li class='xr-section-item'><input id='section-3442477a-da38-4f41-ae34-ff9a3fc8fb70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3442477a-da38-4f41-ae34-ff9a3fc8fb70' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-4234e3ed-60e9-4a30-9db5-b9873a33bf93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4234e3ed-60e9-4a30-9db5-b9873a33bf93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f9cc817-26ea-4cce-bc52-475f867e3c48' class='xr-var-data-in' type='checkbox'><label for='data-4f9cc817-26ea-4cce-bc52-475f867e3c48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(10.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.946e-310 ... 4.572e+228</div><input id='attrs-db57a267-b928-499c-a7ea-c3c40cc56c0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db57a267-b928-499c-a7ea-c3c40cc56c0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e1312b-0004-4a4d-abf0-fdf8154bcdb8' class='xr-var-data-in' type='checkbox'><label for='data-85e1312b-0004-4a4d-abf0-fdf8154bcdb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([6.946461e-310, 6.946461e-310, 4.661759e-310, ..., 4.787194e+180,\n",
+       "       2.450673e+198, 4.572376e+228])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49cccdf7-b0ce-4914-9c64-e021faf97f1f' class='xr-section-summary-in' type='checkbox'  ><label for='section-49cccdf7-b0ce-4914-9c64-e021faf97f1f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed1cc9d3-f3b0-4b9c-b5df-af69e8ffdce8' class='xr-index-data-in' type='checkbox'/><label for='index-ed1cc9d3-f3b0-4b9c-b5df-af69e8ffdce8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([  6.94646063411794e-310,   6.94646063411794e-310,\n",
+       "                 4.6617585837179e-310,    4.6617585837179e-310,\n",
+       "                               5e-324,                     0.0,\n",
+       "              3.6471404775824516e+180,  7.628377678647842e+228,\n",
+       "              1.6943165045757204e-152, 4.0719047313359087e+223,\n",
        "              ...\n",
-       "              1.1829506974336984e-76,  6.01346952903951e-154,\n",
-       "              1.3973685027430167e-76,  1.396426376485746e-76,\n",
-       "               1.397368502743964e-76,  1.396426376485746e-76,\n",
-       "                1.42516655184342e-71, 1.0853981750795007e-71,\n",
-       "                1.42516655184342e-71,  1.085398175052785e-71],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-28d79502-ff56-4140-b980-223a30850352' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28d79502-ff56-4140-b980-223a30850352' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div></li></ul></div></div>"
+       "               1.1858693676012526e+49,   3.845809172237913e-86,\n",
+       "              6.0112290352011415e+175,  9.088948731942852e+223,\n",
+       "               5.030572423582765e+223, 4.3720591034250617e-153,\n",
+       "              1.0647870195102642e+248,  4.787194240623723e+180,\n",
+       "              2.4506726540155096e+198,  4.572375645356952e+228],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=384))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1027352c-cf9b-46c2-bf63-9d40a4fb7f77' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1027352c-cf9b-46c2-bf63-9d40a4fb7f77' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'lat' (lat: 192)>\n",
-       "array([6.918039e-310, 4.656565e-310, 0.000000e+000, 0.000000e+000,\n",
-       "       6.013470e-154, 0.000000e+000, 6.013470e-154, 2.318609e-052,\n",
-       "       6.031042e-154, 2.318609e-052, 6.031042e-154, 1.398043e-076,\n",
-       "       6.031042e-154, 1.398043e-076, 2.440053e-154, 5.516754e-048,\n",
-       "       2.628400e+179, 3.615501e-043, 2.624599e+179, 2.990673e-067,\n",
-       "       3.113418e+179, 1.284484e-057, 3.920697e+179, 5.981491e-154,\n",
-       "       3.845686e-086, 5.205553e-090, 4.247839e-086, 5.205557e-090,\n",
-       "       3.443533e-086, 5.207482e-090, 3.845686e-086, 5.200759e-090,\n",
-       "       6.013345e-154, 1.303543e-076, 1.182951e-076, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303712e-076, 3.572812e-062, 1.577110e-052,\n",
-       "       1.269312e-076, 6.013470e-154, 4.079204e-033, 1.174786e-047,\n",
-       "       1.028977e-071, 1.688992e-052, 3.308835e-033, 1.688992e-052,\n",
-       "       1.084292e-042, 1.174786e-047, 6.013470e-154, 1.398041e-076,\n",
-       "       2.421365e-052, 1.723981e-047, 1.518178e-047, 1.656093e-047,\n",
-       "       6.750722e-067, 1.520050e-047, 1.030078e-071, 6.013470e-154,\n",
-       "       1.398043e-076, 7.578808e-096, 1.398043e-076, 7.578808e-096,\n",
-       "       1.398043e-076, 7.578808e-096, 2.318609e-052, 7.580658e-096,\n",
-       "       6.013470e-154, 5.157233e-062, 1.051480e-153, 1.588475e-047,\n",
-       "       1.051188e-153, 3.942742e-062, 1.051188e-153, 3.942742e-062,\n",
-       "       1.145834e-259, 6.013470e-154, 8.604357e-043, 6.031042e-154,\n",
-       "       1.398043e-076, 6.031042e-154, 2.318609e-052, 6.031042e-154,\n",
-       "       1.398043e-076, 2.440053e-154, 2.990638e-067, 3.602222e+179,\n",
-       "       6.963202e-077, 2.623958e+179, 1.552846e-033, 3.597770e+179,\n",
-       "       5.516817e-048, 3.436948e+179, 5.981491e-154, 3.845686e-086,\n",
-       "       5.203662e-090, 4.448916e-086, 5.206504e-090, 4.448916e-086,\n",
-       "       5.209399e-090, 4.448916e-086, 5.209399e-090, 6.013345e-154,\n",
-       "       2.909255e-033, 3.572813e-062, 2.909255e-033, 3.572813e-062,\n",
-       "       2.909255e-033, 3.572813e-062, 2.909255e-033, 3.572813e-062,\n",
-       "       6.013470e-154, 1.425167e-071, 1.085398e-071, 1.425167e-071,\n",
-       "       1.085398e-071, 1.425167e-071, 1.085398e-071, 1.425167e-071,\n",
-       "       1.085398e-071, 6.013470e-154, 7.723222e-043, 1.485413e-076,\n",
-       "       7.723222e-043, 1.485413e-076, 7.723222e-043, 1.485413e-076,\n",
-       "       7.723222e-043, 1.485413e-076, 6.013470e-154, 1.039978e-042,\n",
-       "       7.580658e-096, 1.039978e-042, 7.580658e-096, 1.039978e-042,\n",
-       "       7.580658e-096, 1.039978e-042, 7.580658e-096, 6.013470e-154,\n",
-       "       2.423821e-052, 1.051188e-153, 2.423821e-052, 1.051188e-153,\n",
-       "       2.423821e-052, 1.051188e-153, 2.423821e-052, 1.145834e-259,\n",
-       "       6.013470e-154, 4.085992e-033, 6.031042e-154, 4.085992e-033,\n",
-       "       6.031042e-154, 4.085992e-033, 6.031042e-154, 4.085992e-033,\n",
-       "       2.440053e-154, 5.516754e-048, 4.085956e+179, 5.516817e-048,\n",
-       "       4.085956e+179, 6.963202e-077, 2.623958e+179, 6.963202e-077,\n",
-       "       2.623958e+179, 5.981491e-154, 3.242457e-086, 1.210894e-099,\n",
-       "       3.242457e-086, 1.210894e-099, 3.242457e-086, 1.210894e-099,\n",
-       "       3.242457e-086, 1.210894e-099, 6.013345e-154, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303543e-076, 1.182951e-076, 1.303543e-076,\n",
-       "       1.182951e-076, 1.303543e-076, 1.182951e-076, 6.013470e-154,\n",
-       "       1.397369e-076, 1.396426e-076, 1.397369e-076, 1.396426e-076,\n",
-       "       1.425167e-071, 1.085398e-071, 1.425167e-071, 1.085398e-071])\n",
-       "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * lat      (lat) float64 6.918e-310 4.657e-310 0.0 ... 1.425e-71 1.085e-71\n",
-       "Attributes:\n",
-       "    axis:           Y\n",
-       "    bounds:         lat_bnds\n",
-       "    long_name:      Latitude\n",
-       "    standard_name:  latitude\n",
-       "    units:          degrees_north"
+       "<xarray.DataArray 'lon_bnds' (lon: 384, bnds: 2)>\n",
+       "array([[ -0.46875,   0.46875],\n",
+       "       [  0.46875,   1.40625],\n",
+       "       [  1.40625,   2.34375],\n",
+       "       [  2.34375,   3.28125],\n",
+       "       [  3.28125,   4.21875],\n",
+       "       [  4.21875,   5.15625],\n",
+       "       [  5.15625,   6.09375],\n",
+       "       [  6.09375,   7.03125],\n",
+       "       [  7.03125,   7.96875],\n",
+       "       [  7.96875,   8.90625],\n",
+       "       [  8.90625,   9.84375],\n",
+       "       [  9.84375,  10.78125],\n",
+       "       [ 10.78125,  11.71875],\n",
+       "       [ 11.71875,  12.65625],\n",
+       "       [ 12.65625,  13.59375],\n",
+       "       [ 13.59375,  14.53125],\n",
+       "       [ 14.53125,  15.46875],\n",
+       "       [ 15.46875,  16.40625],\n",
+       "       [ 16.40625,  17.34375],\n",
+       "       [ 17.34375,  18.28125],\n",
+       "...\n",
+       "       [340.78125, 341.71875],\n",
+       "       [341.71875, 342.65625],\n",
+       "       [342.65625, 343.59375],\n",
+       "       [343.59375, 344.53125],\n",
+       "       [344.53125, 345.46875],\n",
+       "       [345.46875, 346.40625],\n",
+       "       [346.40625, 347.34375],\n",
+       "       [347.34375, 348.28125],\n",
+       "       [348.28125, 349.21875],\n",
+       "       [349.21875, 350.15625],\n",
+       "       [350.15625, 351.09375],\n",
+       "       [351.09375, 352.03125],\n",
+       "       [352.03125, 352.96875],\n",
+       "       [352.96875, 353.90625],\n",
+       "       [353.90625, 354.84375],\n",
+       "       [354.84375, 355.78125],\n",
+       "       [355.78125, 356.71875],\n",
+       "       [356.71875, 357.65625],\n",
+       "       [357.65625, 358.59375],\n",
+       "       [358.59375, 359.53125]])\n",
+       "Coordinates:\n",
+       "    height   float64 10.0\n",
+       "  * lon      (lon) float64 6.946e-310 6.946e-310 ... 2.451e+198 4.572e+228\n",
+       "Dimensions without coordinates: bnds"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kf2.lon_bnds.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b2dc1449-999a-494f-8275-f83f1cfc3a15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "kf1 = np.array(kf1.lon_bnds)\n",
+    "kf2 = np.array(kf2.lon_bnds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e1fe251d-e133-44bf-b9fe-7171a51599ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array_equal(kf1, kf2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "6c46e794-135e-4f63-931e-693a3ab82f2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "ingest_file = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233/proj_codes_complete_ingest.txt'\n",
+    "in_place = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233'\n",
+    "with open(ingest_file) as f:\n",
+    "    datasets = [f'{in_place}/{r.strip()}_kr1.0.json' for r in f.readlines()]\n",
+    "\n",
+    "for d in datasets[3:8]:\n",
+    "    os.system(f'cp {d} {d.replace(\"CMIP6_rel1_6233\",\"sample\")}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b495d56-aded-40e9-bab9-58c38577e49f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for x, d in enumerate(datasets):\n",
+    "    with open(d) as f:\n",
+    "        print(x, len(f.readlines()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "e306c7b8-72d2-415b-88e2-7b13da810d2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233'\n",
+    "kfile0 = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0.json'\n",
+    "kfile0a = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0a.json'\n",
+    "kfile1 = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/1.json'\n",
+    "print(kfile0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82619c91-4e00-40de-b654-21a1f804bee1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "\n",
+    "mapper = fsspec.get_mapper('reference://',fo=kfile0, remote_protocol='https')\n",
+    "kf = xr.open_zarr(mapper, consolidated=False)\n",
+    "kf.lata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16e324e3-3166-4ad5-b219-c15325fb12d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import xarray as xr\n",
+    "\n",
+    "mapper = fsspec.get_mapper('reference://',fo=kfile0a, remote_protocol='file')\n",
+    "kfa = xr.open_zarr(mapper, consolidated=False)\n",
+    "kfa.lat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a1d2ac6c-ba5a-4176-9b11-31a2c79f1c16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ncf1 = '/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc'\n",
+    "ds = xr.open_dataset('/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "311f2e8b-b56d-49ce-a2ea-964d09421364",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from kerchunk.hdf import SingleHdf5ToZarr\n",
+    "import xarray as xr\n",
+    "import fsspec\n",
+    "import json\n",
+    "ncf1 = '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/huss/day/v20190731/huss_rcp85_land-rcm_uk_12km_01_day_19801201-19901130.nc'\n",
+    "refs = SingleHdf5ToZarr(ncf1, inline_threshold=65).translate()\n",
+    "refs['refs']['time_bnds/.zarray']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a9d13db-0796-42cc-8581-6b0e86cf3a1e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf09a8a8-416f-4504-acd1-3ce28788f98f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['BREAKER'] = 'false'\n",
+    "ds = xr.open_zarr(fsspec.get_mapper('reference://',fo=refs), consolidated=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7199afd6-a062-493c-a6b0-97cfef5674f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['BREAKER'] = 'true'\n",
+    "ds.time_bnds.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "faebaef5-ea19-469d-8abe-dd94074c5967",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfile2 = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/kerchunk-1a.json'\n",
+    "ds2 = xr.open_zarr(fsspec.get_mapper('reference://',fo=kfile2, remote_protocol='https'), consolidated=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "adf599ed-b3b9-4747-a322-2d7ff6ceb2e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "os.system(f'rm {kfile0a}')\n",
+    "with open(kfile0a,'w') as f:\n",
+    "    f.write(json.dumps(refs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce447394-0f5c-4e1a-88b1-885cccf26543",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "e94a4ac2-bd29-442b-9ac3-c21545f736d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc',\n",
+       " 41084,\n",
+       " 1536]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import fsspec\n",
+    "refs['refs']['lat/0']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ea28e2cf-44cb-40af-a2d9-dee5202fde7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "complete = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233/*.json'\n",
+    "\n",
+    "paths = glob.glob(complete)\n",
+    "for p in paths:\n",
+    "    if not p.split('/')[-1].startswith('CMIP6_'):\n",
+    "        os.system(f\"mv {p} {p.replace('6233/','6233/CMIP6_')}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb2d1e7f-d840-4a13-81c0-9b3a89c2c094",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "incomplete = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233/proj_codes_cmip6_fail.txt'\n",
+    "with open(incomplete) as f:\n",
+    "    keys = [r.strip() for r in f.readlines()]\n",
+    "\n",
+    "for k in keys:\n",
+    "    print(k)\n",
+    "    complete_1057[k] = 'FAIL'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2e5a5c8e-7e3e-4ae3-b6bd-6a21fe2c92ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PASS, FAIL = 0, 0\n",
+    "day = 0\n",
+    "for key in complete_1057.keys():\n",
+    "    if complete_1057[key] == 'FAIL':\n",
+    "        FAIL += 1\n",
+    "        if 'day' in key:\n",
+    "            day += 1\n",
+    "    else:\n",
+    "        PASS += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3b13dc79-116c-4614-be01-fa8046706c93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(326, 731, 257)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FAIL, PASS, day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "182c4eb3-0f2f-4eb9-a24c-ce9036a9abf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2190,)\n",
+      "(3650,)\n",
+      "(3650,)\n",
+      "(3650,)\n",
+      "(3650,)\n",
+      "(3650,)\n",
+      "(3650,)\n",
+      "(3650,)\n",
+      "(3650,)\n"
+     ]
+    }
+   ],
+   "source": [
     "import xarray as xr\n",
+    "allfile = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_day_psl_gn_v20191108/allfiles.txt'\n",
     "\n",
-    "mapper = fsspec.get_mapper('reference://',fo=kfile0, remote_protocol='https')\n",
-    "kf = xr.open_zarr(mapper, consolidated=False)\n",
-    "kf.lata"
+    "with open(allfile) as f:\n",
+    "    xrobjs = [r.strip() for r in f.readlines()]\n",
+    "\n",
+    "for x in xrobjs:\n",
+    "    ds = xr.open_dataset(x)\n",
+    "    print(ds.time.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "16e324e3-3166-4ad5-b219-c15325fb12d4",
+   "execution_count": 2,
+   "id": "74558c75-f7f6-40c6-8d0c-9eff6a8a57b2",
    "metadata": {},
    "outputs": [
     {
@@ -3126,208 +3162,149 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lat&#x27; (lat: 192)&gt;\n",
-       "array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                       (ensemble_member: 1, time: 3600,\n",
+       "                                   projection_y_coordinate: 244,\n",
+       "                                   projection_x_coordinate: 180, bnds: 2)\n",
        "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
-       "Attributes:\n",
-       "    axis:           Y\n",
-       "    bounds:         lat_bnds\n",
-       "    long_name:      Latitude\n",
-       "    standard_name:  latitude\n",
-       "    units:          degrees_north</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lat'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1dd040ba-dd91-4c61-b4db-45ae1eac069d' class='xr-array-in' type='checkbox' checked><label for='section-1dd040ba-dd91-4c61-b4db-45ae1eac069d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-90.0 -89.06 -88.12 -87.17 -86.23 ... 86.23 87.17 88.12 89.06 90.0</span></div><div class='xr-array-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></div></li><li class='xr-section-item'><input id='section-ed8d4bbf-9d38-4416-9fbc-0fbda1133344' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed8d4bbf-9d38-4416-9fbc-0fbda1133344' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92f2b13f-790b-4974-b0f2-a8dfd63c3474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92f2b13f-790b-4974-b0f2-a8dfd63c3474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1751f46e-8ab0-48bb-a9af-88ca3d7f83c7' class='xr-var-data-in' type='checkbox'><label for='data-1751f46e-8ab0-48bb-a9af-88ca3d7f83c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-97e7c3f5-e8ab-48e3-8e58-9bb70db7c003' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97e7c3f5-e8ab-48e3-8e58-9bb70db7c003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c377822-c3e0-433e-816b-776c11f5bedf' class='xr-var-data-in' type='checkbox'><label for='data-1c377822-c3e0-433e-816b-776c11f5bedf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-937d484a-1aa5-44ee-9ee8-85a10855b09d' class='xr-section-summary-in' type='checkbox'  ><label for='section-937d484a-1aa5-44ee-9ee8-85a10855b09d' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3dd572f5-bf25-4658-be63-3d3d2e9b0016' class='xr-index-data-in' type='checkbox'/><label for='index-3dd572f5-bf25-4658-be63-3d3d2e9b0016' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
-       "              -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
-       "               -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
-       "              -81.51832460732984,\n",
-       "              ...\n",
-       "               81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
-       "               84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
-       "               87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
-       "                            90.0],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83b62ab4-d4f8-4d0b-8113-57b454265a70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83b62ab4-d4f8-4d0b-8113-57b454265a70' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div></li></ul></div></div>"
+       "  * ensemble_member               (ensemble_member) int32 1\n",
+       "  * time                          (time) object 2020-12-01 12:00:00 ... 2030-...\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float64 -3.25e+04...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float64 -1.975e+0...\n",
+       "    ensemble_member_id            (ensemble_member) |S27 ...\n",
+       "    latitude                      (projection_y_coordinate, projection_x_coordinate) float64 ...\n",
+       "    longitude                     (projection_y_coordinate, projection_x_coordinate) float64 ...\n",
+       "    month_number                  (time) int32 ...\n",
+       "    year                          (time) int32 ...\n",
+       "    yyyymmdd                      (time) |S64 ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    tas                           (ensemble_member, time, projection_y_coordinate, projection_x_coordinate) float32 ...\n",
+       "    transverse_mercator           int32 ...\n",
+       "    time_bnds                     (time, bnds) object ...\n",
+       "    projection_y_coordinate_bnds  (projection_y_coordinate, bnds) float64 ...\n",
+       "    projection_x_coordinate_bnds  (projection_x_coordinate, bnds) float64 ...\n",
+       "Attributes: (12/15)\n",
+       "    collection:      land-cpm\n",
+       "    contact:         ukcpproject@metoffice.gov.uk\n",
+       "    creation_date:   2021-06-09T16:58:45\n",
+       "    domain:          uk\n",
+       "    frequency:       day\n",
+       "    institution:     Met Office Hadley Centre (MOHC), FitzRoy Road, Exeter, D...\n",
+       "    ...              ...\n",
+       "    resolution:      5km\n",
+       "    scenario:        rcp85\n",
+       "    source:          UKCP18 realisation from a set of 12 convection-permittin...\n",
+       "    title:           UKCP18 land projections - Regridded 2.2km convection-per...\n",
+       "    version:         v20210615\n",
+       "    Conventions:     CF-1.7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3d446351-7b27-4bdd-8565-d40e26b65e48' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3d446351-7b27-4bdd-8565-d40e26b65e48' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ensemble_member</span>: 1</li><li><span class='xr-has-index'>time</span>: 3600</li><li><span class='xr-has-index'>projection_y_coordinate</span>: 244</li><li><span class='xr-has-index'>projection_x_coordinate</span>: 180</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cac2456d-a6c3-4b00-ae5b-951146909b3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cac2456d-a6c3-4b00-ae5b-951146909b3b' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ensemble_member</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-299a4b4f-d9d5-4d3f-a11f-c43180e3dac8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-299a4b4f-d9d5-4d3f-a11f-c43180e3dac8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-413c738b-954d-4941-9b7a-73ef6fd100e5' class='xr-var-data-in' type='checkbox'><label for='data-413c738b-954d-4941-9b7a-73ef6fd100e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ensemble_member</dd></dl></div><div class='xr-var-data'><pre>array([1], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2020-12-01 12:00:00 ... 2030-11-...</div><input id='attrs-ffff6636-4661-4a2b-b58c-b92aa5e6b648' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffff6636-4661-4a2b-b58c-b92aa5e6b648' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-756fd6d4-b095-4ef3-9a08-12ff3dfcc44d' class='xr-var-data-in' type='checkbox'><label for='data-756fd6d4-b095-4ef3-9a08-12ff3dfcc44d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2020, 12, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.Datetime360Day(2020, 12, 2, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.Datetime360Day(2020, 12, 3, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       ...,\n",
+       "       cftime.Datetime360Day(2030, 11, 28, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.Datetime360Day(2030, 11, 29, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.Datetime360Day(2030, 11, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_y_coordinate</span></div><div class='xr-var-dims'>(projection_y_coordinate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.25e+04 -2.75e+04 ... 1.182e+06</div><input id='attrs-51e05955-2d73-4a14-90a8-4bcf930bccd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51e05955-2d73-4a14-90a8-4bcf930bccd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93e37ff9-9fed-4a79-b35d-12ce78ee07a9' class='xr-var-data-in' type='checkbox'><label for='data-93e37ff9-9fed-4a79-b35d-12ce78ee07a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>projection_y_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ -32500.,  -27500.,  -22500., ..., 1172500., 1177500., 1182500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_x_coordinate</span></div><div class='xr-var-dims'>(projection_x_coordinate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.975e+05 -1.925e+05 ... 6.975e+05</div><input id='attrs-a8345d15-960d-4353-a1e3-6d7a34eccb60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8345d15-960d-4353-a1e3-6d7a34eccb60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cfa3f1a-4cd2-4a80-81cb-a53ff3e948fb' class='xr-var-data-in' type='checkbox'><label for='data-8cfa3f1a-4cd2-4a80-81cb-a53ff3e948fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>projection_x_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-197500., -192500., -187500., -182500., -177500., -172500., -167500.,\n",
+       "       -162500., -157500., -152500., -147500., -142500., -137500., -132500.,\n",
+       "       -127500., -122500., -117500., -112500., -107500., -102500.,  -97500.,\n",
+       "        -92500.,  -87500.,  -82500.,  -77500.,  -72500.,  -67500.,  -62500.,\n",
+       "        -57500.,  -52500.,  -47500.,  -42500.,  -37500.,  -32500.,  -27500.,\n",
+       "        -22500.,  -17500.,  -12500.,   -7500.,   -2500.,    2500.,    7500.,\n",
+       "         12500.,   17500.,   22500.,   27500.,   32500.,   37500.,   42500.,\n",
+       "         47500.,   52500.,   57500.,   62500.,   67500.,   72500.,   77500.,\n",
+       "         82500.,   87500.,   92500.,   97500.,  102500.,  107500.,  112500.,\n",
+       "        117500.,  122500.,  127500.,  132500.,  137500.,  142500.,  147500.,\n",
+       "        152500.,  157500.,  162500.,  167500.,  172500.,  177500.,  182500.,\n",
+       "        187500.,  192500.,  197500.,  202500.,  207500.,  212500.,  217500.,\n",
+       "        222500.,  227500.,  232500.,  237500.,  242500.,  247500.,  252500.,\n",
+       "        257500.,  262500.,  267500.,  272500.,  277500.,  282500.,  287500.,\n",
+       "        292500.,  297500.,  302500.,  307500.,  312500.,  317500.,  322500.,\n",
+       "        327500.,  332500.,  337500.,  342500.,  347500.,  352500.,  357500.,\n",
+       "        362500.,  367500.,  372500.,  377500.,  382500.,  387500.,  392500.,\n",
+       "        397500.,  402500.,  407500.,  412500.,  417500.,  422500.,  427500.,\n",
+       "        432500.,  437500.,  442500.,  447500.,  452500.,  457500.,  462500.,\n",
+       "        467500.,  472500.,  477500.,  482500.,  487500.,  492500.,  497500.,\n",
+       "        502500.,  507500.,  512500.,  517500.,  522500.,  527500.,  532500.,\n",
+       "        537500.,  542500.,  547500.,  552500.,  557500.,  562500.,  567500.,\n",
+       "        572500.,  577500.,  582500.,  587500.,  592500.,  597500.,  602500.,\n",
+       "        607500.,  612500.,  617500.,  622500.,  627500.,  632500.,  637500.,\n",
+       "        642500.,  647500.,  652500.,  657500.,  662500.,  667500.,  672500.,\n",
+       "        677500.,  682500.,  687500.,  692500.,  697500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_member_id</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>|S27</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8f7237e-f1c9-4b7f-a3c2-92a260c7a258' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8f7237e-f1c9-4b7f-a3c2-92a260c7a258' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3680afa8-306b-45e6-8ee6-240c5a99aeb2' class='xr-var-data-in' type='checkbox'><label for='data-3680afa8-306b-45e6-8ee6-240c5a99aeb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ensemble_member_id</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S27]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d11c909-9cc7-4052-868f-9c02850bf4e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d11c909-9cc7-4052-868f-9c02850bf4e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4561b26-778c-4e52-8020-d164c7460d26' class='xr-var-data-in' type='checkbox'><label for='data-e4561b26-778c-4e52-8020-d164c7460d26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[43920 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c5785b5f-e1fc-45e6-b3be-bd844ff28974' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5785b5f-e1fc-45e6-b3be-bd844ff28974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b8653f1-9527-47c6-bcc7-4b36875eed3c' class='xr-var-data-in' type='checkbox'><label for='data-4b8653f1-9527-47c6-bcc7-4b36875eed3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[43920 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e611607-0f4d-4917-a431-018964935025' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e611607-0f4d-4917-a431-018964935025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7951996-a90f-41e2-a4fc-9eeb0e6c56c0' class='xr-var-data-in' type='checkbox'><label for='data-e7951996-a90f-41e2-a4fc-9eeb0e6c56c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>month_number</dd></dl></div><div class='xr-var-data'><pre>[3600 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6bd0bec0-021c-4753-9890-72cad4e487e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bd0bec0-021c-4753-9890-72cad4e487e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2d8ee76-74c1-4286-b133-dc3535d97d4e' class='xr-var-data-in' type='checkbox'><label for='data-c2d8ee76-74c1-4286-b133-dc3535d97d4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>year</dd></dl></div><div class='xr-var-data'><pre>[3600 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yyyymmdd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad9237d9-42f1-4d54-873c-b0953616bf71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad9237d9-42f1-4d54-873c-b0953616bf71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-640abdb6-9cb7-4b68-ac24-34d632be50b5' class='xr-var-data-in' type='checkbox'><label for='data-640abdb6-9cb7-4b68-ac24-34d632be50b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>yyyymmdd</dd></dl></div><div class='xr-var-data'><pre>[3600 values with dtype=|S64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3e9703d-a95e-4342-a2f4-035995ef3921' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3e9703d-a95e-4342-a2f4-035995ef3921' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(ensemble_member, time, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8db3f4ca-a3d1-48d0-83e7-72499b52708f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8db3f4ca-a3d1-48d0-83e7-72499b52708f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da3554e3-4ba1-4ff7-ac86-ee04160a8b00' class='xr-var-data-in' type='checkbox'><label for='data-da3554e3-4ba1-4ff7-ac86-ee04160a8b00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Mean air temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>description :</span></dt><dd>Mean air temperature</dd><dt><span>label_units :</span></dt><dd>°C</dd><dt><span>plot_label :</span></dt><dd>Mean air temperature at 1.5m (°C)</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_mapping :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>[158112000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transverse_mercator</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-55551d1e-d4b7-4c19-9217-80d4aad1428b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55551d1e-d4b7-4c19-9217-80d4aad1428b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e879f500-297d-4b9b-9cf0-e8b9c402c67e' class='xr-var-data-in' type='checkbox'><label for='data-e879f500-297d-4b9b-9cf0-e8b9c402c67e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6377563.396</dd><dt><span>semi_minor_axis :</span></dt><dd>6356256.909</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-2.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>49.0</dd><dt><span>false_easting :</span></dt><dd>400000.0</dd><dt><span>false_northing :</span></dt><dd>-100000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996012717</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0fe2532a-4b02-434e-a3a7-7da0f50aa2c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fe2532a-4b02-434e-a3a7-7da0f50aa2c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84fa40a3-7ffc-474d-bd4a-64d7c0822e86' class='xr-var-data-in' type='checkbox'><label for='data-84fa40a3-7ffc-474d-bd4a-64d7c0822e86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[7200 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_y_coordinate_bnds</span></div><div class='xr-var-dims'>(projection_y_coordinate, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad32e589-6a9a-4bc3-89a2-23bc8c625d78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad32e589-6a9a-4bc3-89a2-23bc8c625d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dcc6844-6aff-4156-ac25-2b07a3c49520' class='xr-var-data-in' type='checkbox'><label for='data-8dcc6844-6aff-4156-ac25-2b07a3c49520' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[488 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_x_coordinate_bnds</span></div><div class='xr-var-dims'>(projection_x_coordinate, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d61c6556-fc00-469b-a547-acc978968452' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d61c6556-fc00-469b-a547-acc978968452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a4f7305-2803-4b0e-85de-501a2c0e4f12' class='xr-var-data-in' type='checkbox'><label for='data-3a4f7305-2803-4b0e-85de-501a2c0e4f12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[360 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c799ebd-6246-4afe-b9ce-3153f6b07ad8' class='xr-section-summary-in' type='checkbox'  ><label for='section-7c799ebd-6246-4afe-b9ce-3153f6b07ad8' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ensemble_member</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0793767-b234-4381-bcc6-4fd216c5b8a4' class='xr-index-data-in' type='checkbox'/><label for='index-a0793767-b234-4381-bcc6-4fd216c5b8a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1], dtype=&#x27;int32&#x27;, name=&#x27;ensemble_member&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a45d4920-812e-4e6a-9cd1-581307aff52d' class='xr-index-data-in' type='checkbox'/><label for='index-a45d4920-812e-4e6a-9cd1-581307aff52d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2020-12-01 12:00:00, 2020-12-02 12:00:00, 2020-12-03 12:00:00,\n",
+       "             2020-12-04 12:00:00, 2020-12-05 12:00:00, 2020-12-06 12:00:00,\n",
+       "             2020-12-07 12:00:00, 2020-12-08 12:00:00, 2020-12-09 12:00:00,\n",
+       "             2020-12-10 12:00:00,\n",
+       "             ...\n",
+       "             2030-11-21 12:00:00, 2030-11-22 12:00:00, 2030-11-23 12:00:00,\n",
+       "             2030-11-24 12:00:00, 2030-11-25 12:00:00, 2030-11-26 12:00:00,\n",
+       "             2030-11-27 12:00:00, 2030-11-28 12:00:00, 2030-11-29 12:00:00,\n",
+       "             2030-11-30 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=3600, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>projection_y_coordinate</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5400c4cd-5d21-487b-bfca-7b742706aaa0' class='xr-index-data-in' type='checkbox'/><label for='index-5400c4cd-5d21-487b-bfca-7b742706aaa0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -32500.0,  -27500.0,  -22500.0,  -17500.0,  -12500.0,   -7500.0,\n",
+       "         -2500.0,    2500.0,    7500.0,   12500.0,\n",
+       "       ...\n",
+       "       1137500.0, 1142500.0, 1147500.0, 1152500.0, 1157500.0, 1162500.0,\n",
+       "       1167500.0, 1172500.0, 1177500.0, 1182500.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;projection_y_coordinate&#x27;, length=244))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>projection_x_coordinate</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b06c8cf0-03e9-441f-8a4e-454042b13b57' class='xr-index-data-in' type='checkbox'/><label for='index-b06c8cf0-03e9-441f-8a4e-454042b13b57' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-197500.0, -192500.0, -187500.0, -182500.0, -177500.0, -172500.0,\n",
+       "       -167500.0, -162500.0, -157500.0, -152500.0,\n",
+       "       ...\n",
+       "        652500.0,  657500.0,  662500.0,  667500.0,  672500.0,  677500.0,\n",
+       "        682500.0,  687500.0,  692500.0,  697500.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;projection_x_coordinate&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-877b5799-ad6d-4d24-b0e3-b5b97d069883' class='xr-section-summary-in' type='checkbox'  ><label for='section-877b5799-ad6d-4d24-b0e3-b5b97d069883' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>collection :</span></dt><dd>land-cpm</dd><dt><span>contact :</span></dt><dd>ukcpproject@metoffice.gov.uk</dd><dt><span>creation_date :</span></dt><dd>2021-06-09T16:58:45</dd><dt><span>domain :</span></dt><dd>uk</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>institution :</span></dt><dd>Met Office Hadley Centre (MOHC), FitzRoy Road, Exeter, Devon, EX1 3PB, UK.</dd><dt><span>institution_id :</span></dt><dd>MOHC</dd><dt><span>project :</span></dt><dd>UKCP18</dd><dt><span>references :</span></dt><dd>https://ukclimateprojections.metoffice.gov.uk</dd><dt><span>resolution :</span></dt><dd>5km</dd><dt><span>scenario :</span></dt><dd>rcp85</dd><dt><span>source :</span></dt><dd>UKCP18 realisation from a set of 12 convection-permitting models (HadREM3-RA11M) driven by perturbed variants of the Met Office Unified Model Global Atmosphere GA7 model (HadREM3-GA705) at 12km resolution.  The HadREM3-GA705 models were driven by perturbed variants of the global HadGEM3-GC3.05</dd><dt><span>title :</span></dt><dd>UKCP18 land projections - Regridded 2.2km convection-permitting climate model results on 5km British National Grid from Ordnance Survey (OSGB), Mean air temperature over the UK for the RCP8.5 scenario</dd><dt><span>version :</span></dt><dd>v20210615</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'lat' (lat: 192)>\n",
-       "array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])\n",
+       "<xarray.Dataset>\n",
+       "Dimensions:                       (ensemble_member: 1, time: 3600,\n",
+       "                                   projection_y_coordinate: 244,\n",
+       "                                   projection_x_coordinate: 180, bnds: 2)\n",
        "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
-       "Attributes:\n",
-       "    axis:           Y\n",
-       "    bounds:         lat_bnds\n",
-       "    long_name:      Latitude\n",
-       "    standard_name:  latitude\n",
-       "    units:          degrees_north"
+       "  * ensemble_member               (ensemble_member) int32 1\n",
+       "  * time                          (time) object 2020-12-01 12:00:00 ... 2030-...\n",
+       "  * projection_y_coordinate       (projection_y_coordinate) float64 -3.25e+04...\n",
+       "  * projection_x_coordinate       (projection_x_coordinate) float64 -1.975e+0...\n",
+       "    ensemble_member_id            (ensemble_member) |S27 ...\n",
+       "    latitude                      (projection_y_coordinate, projection_x_coordinate) float64 ...\n",
+       "    longitude                     (projection_y_coordinate, projection_x_coordinate) float64 ...\n",
+       "    month_number                  (time) int32 ...\n",
+       "    year                          (time) int32 ...\n",
+       "    yyyymmdd                      (time) |S64 ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    tas                           (ensemble_member, time, projection_y_coordinate, projection_x_coordinate) float32 ...\n",
+       "    transverse_mercator           int32 ...\n",
+       "    time_bnds                     (time, bnds) object ...\n",
+       "    projection_y_coordinate_bnds  (projection_y_coordinate, bnds) float64 ...\n",
+       "    projection_x_coordinate_bnds  (projection_x_coordinate, bnds) float64 ...\n",
+       "Attributes: (12/15)\n",
+       "    collection:      land-cpm\n",
+       "    contact:         ukcpproject@metoffice.gov.uk\n",
+       "    creation_date:   2021-06-09T16:58:45\n",
+       "    domain:          uk\n",
+       "    frequency:       day\n",
+       "    institution:     Met Office Hadley Centre (MOHC), FitzRoy Road, Exeter, D...\n",
+       "    ...              ...\n",
+       "    resolution:      5km\n",
+       "    scenario:        rcp85\n",
+       "    source:          UKCP18 realisation from a set of 12 convection-permittin...\n",
+       "    title:           UKCP18 land projections - Regridded 2.2km convection-per...\n",
+       "    version:         v20210615\n",
+       "    Conventions:     CF-1.7"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import fsspec\n",
     "import xarray as xr\n",
-    "\n",
-    "mapper = fsspec.get_mapper('reference://',fo=kfile0a, remote_protocol='file')\n",
-    "kfa = xr.open_zarr(mapper, consolidated=False)\n",
-    "kfa.lat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a1d2ac6c-ba5a-4176-9b11-31a2c79f1c16",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ncf1 = '/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc'\n",
-    "ds = xr.open_dataset('/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "76bc6f4e-377d-4bbc-a996-727ad6317a8f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from kerchunk.hdf import SingleHdf5ToZarr\n",
-    "import json\n",
-    "ncf1 = '/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc'\n",
-    "refs = SingleHdf5ToZarr(ncf1, inline_threshold=1000).translate()\n",
-    "ds = xr.open_zarr(fsspec.get_mapper('reference://',fo=refs), consolidated=False)"
+    "nfile = '/badc/ukcp18/data/land-cpm/uk/5km/rcp85/01/tas/day/v20210615/tas_rcp85_land-cpm_uk_5km_01_day_20201201-20301130.nc'\n",
+    "ds = xr.open_dataset(nfile)\n",
+    "ds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "faebaef5-ea19-469d-8abe-dd94074c5967",
+   "execution_count": 3,
+   "id": "cf396697-ff28-4b09-ab65-ca214a837dd8",
    "metadata": {},
    "outputs": [
     {
@@ -3696,1305 +3673,47 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lat&#x27; (lat: 192)&gt;\n",
-       "array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])\n",
-       "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;transverse_mercator&#x27; ()&gt;\n",
+       "[1 values with dtype=int32]\n",
        "Attributes:\n",
-       "    axis:           Y\n",
-       "    bounds:         lat_bnds\n",
-       "    long_name:      Latitude\n",
-       "    standard_name:  latitude\n",
-       "    units:          degrees_north</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lat'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c8894f19-746d-488c-900c-c2d4417419c3' class='xr-array-in' type='checkbox' checked><label for='section-c8894f19-746d-488c-900c-c2d4417419c3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-90.0 -89.06 -88.12 -87.17 -86.23 ... 86.23 87.17 88.12 89.06 90.0</span></div><div class='xr-array-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></div></li><li class='xr-section-item'><input id='section-266a84a1-714a-4847-9da1-520435ef1f07' class='xr-section-summary-in' type='checkbox'  checked><label for='section-266a84a1-714a-4847-9da1-520435ef1f07' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3e18959-64b1-457f-b690-b18a1edcf037' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3e18959-64b1-457f-b690-b18a1edcf037' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30da4135-477b-4cf4-bb6f-d6abde495c22' class='xr-var-data-in' type='checkbox'><label for='data-30da4135-477b-4cf4-bb6f-d6abde495c22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-b555c0c3-2505-47f5-8cef-e72aa4cf8456' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b555c0c3-2505-47f5-8cef-e72aa4cf8456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f36e4ce6-8e68-4f3d-9e28-79bba67b499e' class='xr-var-data-in' type='checkbox'><label for='data-f36e4ce6-8e68-4f3d-9e28-79bba67b499e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8bae8094-cc6d-4eeb-b8a4-f85159f3d8e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-8bae8094-cc6d-4eeb-b8a4-f85159f3d8e0' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3cf9bcca-33e7-4cc6-bca1-a2999c219dcc' class='xr-index-data-in' type='checkbox'/><label for='index-3cf9bcca-33e7-4cc6-bca1-a2999c219dcc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
-       "              -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
-       "               -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
-       "              -81.51832460732984,\n",
-       "              ...\n",
-       "               81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
-       "               84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
-       "               87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
-       "                            90.0],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ade6c584-ad43-4514-af42-482e69169217' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ade6c584-ad43-4514-af42-482e69169217' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div></li></ul></div></div>"
+       "    grid_mapping_name:                 transverse_mercator\n",
+       "    longitude_of_prime_meridian:       0.0\n",
+       "    semi_major_axis:                   6377563.396\n",
+       "    semi_minor_axis:                   6356256.909\n",
+       "    longitude_of_central_meridian:     -2.0\n",
+       "    latitude_of_projection_origin:     49.0\n",
+       "    false_easting:                     400000.0\n",
+       "    false_northing:                    -100000.0\n",
+       "    scale_factor_at_central_meridian:  0.9996012717</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'transverse_mercator'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a2fd9e96-bff8-4980-94a6-d60cb9f37f47' class='xr-array-in' type='checkbox' checked><label for='section-a2fd9e96-bff8-4980-94a6-d60cb9f37f47' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1 values with dtype=int32]</pre></div></div></li><li class='xr-section-item'><input id='section-031fa41a-ed0f-4b70-a2f8-622473cd3f3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-031fa41a-ed0f-4b70-a2f8-622473cd3f3a' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b06c0b8f-78e4-49cb-807f-6b4580b65374' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b06c0b8f-78e4-49cb-807f-6b4580b65374' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-f799abed-1ee7-4fb5-993c-7c6fadfafb9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f799abed-1ee7-4fb5-993c-7c6fadfafb9b' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6377563.396</dd><dt><span>semi_minor_axis :</span></dt><dd>6356256.909</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-2.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>49.0</dd><dt><span>false_easting :</span></dt><dd>400000.0</dd><dt><span>false_northing :</span></dt><dd>-100000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996012717</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'lat' (lat: 192)>\n",
-       "array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
-       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
-       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
-       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
-       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
-       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
-       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
-       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
-       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
-       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
-       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
-       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
-       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
-       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
-       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
-       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
-       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
-       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
-       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
-       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
-       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
-       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
-       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
-       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
-       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
-       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
-       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
-       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
-       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
-       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
-       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
-       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])\n",
-       "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
+       "<xarray.DataArray 'transverse_mercator' ()>\n",
+       "[1 values with dtype=int32]\n",
        "Attributes:\n",
-       "    axis:           Y\n",
-       "    bounds:         lat_bnds\n",
-       "    long_name:      Latitude\n",
-       "    standard_name:  latitude\n",
-       "    units:          degrees_north"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.lat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "adf599ed-b3b9-4747-a322-2d7ff6ceb2e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import os\n",
-    "os.system(f'rm {kfile0a}')\n",
-    "with open(kfile0a,'w') as f:\n",
-    "    f.write(json.dumps(refs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ce447394-0f5c-4e1a-88b1-885cccf26543",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "e94a4ac2-bd29-442b-9ac3-c21545f736d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc',\n",
-       " 41084,\n",
-       " 1536]"
+       "    grid_mapping_name:                 transverse_mercator\n",
+       "    longitude_of_prime_meridian:       0.0\n",
+       "    semi_major_axis:                   6377563.396\n",
+       "    semi_minor_axis:                   6356256.909\n",
+       "    longitude_of_central_meridian:     -2.0\n",
+       "    latitude_of_projection_origin:     49.0\n",
+       "    false_easting:                     400000.0\n",
+       "    false_northing:                    -100000.0\n",
+       "    scale_factor_at_central_meridian:  0.9996012717"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "refs['refs']['lat/0']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "ea28e2cf-44cb-40af-a2d9-dee5202fde7e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_clt_gn_v20201013\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_rlus_gn_v20201013\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_rsds_gn_v20201013\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_day_clt_gn_v20200626\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_day_pr_gn_v20200626\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_day_psl_gn_v20200626\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_day_rlds_gn_v20200626\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_day_rlus_gn_v20200626\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_day_rsds_gn_v20200626\n",
-      "CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_day_rsus_gn_v20200626\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_3hr_rlus_gn_v20181218\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_clt_gn_v20200511\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_pr_gn_v20200511\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_prsn_gn_v20200511\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_psl_gn_v20181218\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_rlds_gn_v20200511\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_rlus_gn_v20200511\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_rsds_gn_v20200511\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_rsus_gn_v20200511\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511\n",
-      "CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_day_snw_gn_v20181218\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_3hr_clt_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_3hr_pr_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_3hr_prsn_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_3hr_rlds_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_3hr_rlus_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_3hr_rsds_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_3hr_rsus_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_Amon_clt_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_Amon_pr_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_Amon_prsn_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_Amon_psl_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_Amon_rlds_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_Amon_rlus_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_Amon_rsds_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_Amon_rsus_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_day_clt_gn_v20200212\n",
-      "CMIP_AWI_AWI-ESM-1-1-LR_historical_r1i1p1f1_day_snw_gn_v20200212\n",
-      "CMIP_CAS_FGOALS-f3-L_historical_r1i1p1f1_day_clt_gr_v20200605\n",
-      "CMIP_CAS_FGOALS-f3-L_historical_r1i1p1f1_day_rsds_gr_v20191019\n",
-      "CMIP_CAS_FGOALS-f3-L_historical_r1i1p1f1_day_rsus_gr_v20191019\n",
-      "CMIP_CAS_FGOALS-g3_historical_r1i1p1f1_3hr_clt_gn_v20190826\n",
-      "CMIP_CAS_FGOALS-g3_historical_r1i1p1f1_3hr_pr_gn_v20190826\n",
-      "CMIP_CAS_FGOALS-g3_historical_r1i1p1f1_3hr_prsn_gn_v20190826\n",
-      "CMIP_CAS_FGOALS-g3_historical_r1i1p1f1_3hr_rlds_gn_v20190826\n",
-      "CMIP_CAS_FGOALS-g3_historical_r1i1p1f1_3hr_rlus_gn_v20190826\n",
-      "CMIP_CAS_FGOALS-g3_historical_r1i1p1f1_3hr_rsds_gn_v20190826\n",
-      "CMIP_CAS_FGOALS-g3_historical_r1i1p1f1_3hr_rsus_gn_v20190826\n",
-      "CMIP_CAS_FGOALS-g3_historical_r1i1p1f1_Amon_clt_gn_v20190818\n",
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-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_3hr_pr_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_3hr_prsn_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_3hr_rlds_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_3hr_rlus_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_3hr_rsds_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_3hr_rsus_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_Amon_clt_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_Amon_pr_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_Amon_prsn_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_Amon_psl_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_Amon_rlds_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_Amon_rlus_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_Amon_rsds_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_Amon_rsus_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_clt_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_pr_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_prsn_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_psl_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_rlds_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_rlus_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_rsds_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_rsus_gn_v20190323\n",
-      "CMIP_SNU_SAM0-UNICON_historical_r1i1p1f1_day_snw_gn_v20190323\n",
-      "ScenarioMIP_AS-RCEC_TaiESM1_ssp126_r1i1p1f1_day_pr_gn_v20210323\n",
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-      "ScenarioMIP_AWI_AWI-CM-1-1-MR_ssp126_r1i1p1f1_3hr_pr_gn_v20190529\n",
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-      "ScenarioMIP_CAS_FGOALS-g3_ssp119_r1i1p1f1_3hr_pr_gn_v20191202\n",
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-      "ScenarioMIP_CAS_FGOALS-g3_ssp119_r1i1p1f1_Amon_clt_gn_v20200527\n",
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-      "ScenarioMIP_CAS_FGOALS-g3_ssp126_r1i1p1f1_3hr_pr_gn_v20200929\n",
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-      "ScenarioMIP_CCCR-IITM_IITM-ESM_ssp126_r1i1p1f1_3hr_clt_gn_v20201112\n",
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-      "ScenarioMIP_CMCC_CMCC-CM2-SR5_ssp126_r1i1p1f1_3hr_pr_gn_v20200717\n",
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-      "ScenarioMIP_CNRM-CERFACS_CNRM-CM6-1-HR_ssp126_r1i1p1f2_day_pr_gr_v20200127\n",
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-      "ScenarioMIP_CNRM-CERFACS_CNRM-ESM2-1_ssp119_r1i1p1f2_3hr_pr_gr_v20190328\n",
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-      "ScenarioMIP_CSIRO-ARCCSS_ACCESS-CM2_ssp126_r1i1p1f1_3hr_clt_gn_v20191108\n",
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-      "ScenarioMIP_CSIRO_ACCESS-ESM1-5_ssp126_r1i1p1f1_day_clt_gn_v20191115\n",
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-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3-Veg_ssp245_r1i1p1f1_Amon_rsds_gr_v20200225\n",
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-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3-Veg_ssp245_r1i1p1f1_day_clt_gr_v20200225\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3-Veg_ssp245_r1i1p1f1_day_pr_gr_v20200225\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3-Veg_ssp245_r1i1p1f1_day_psl_gr_v20200225\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_3hr_pr_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_3hr_prsn_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_3hr_rsds_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_Amon_clt_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_Amon_pr_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_Amon_prsn_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_Amon_psl_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_Amon_rlds_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_Amon_rlus_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_Amon_rsds_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_Amon_rsus_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_day_pr_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_day_prsn_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_day_psl_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_day_rsds_gr_v20200310\n",
-      "ScenarioMIP_EC-Earth-Consortium_EC-Earth3_ssp126_r1i1p1f1_day_snw_gr_v20210324\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_Amon_pr_gr_v20201218\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_Amon_prsn_gr_v20201218\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_Amon_psl_gr_v20201218\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_Amon_rlds_gr_v20201218\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_Amon_rlus_gr_v20201218\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_Amon_rsds_gr_v20201218\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_Amon_rsus_gr_v20201218\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_day_pr_gr_v20201218\n",
-      "ScenarioMIP_IPSL_IPSL-CM5A2-INCA_ssp126_r1i1p1f1_day_psl_gr_v20201218\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp119_r1i1p1f1_day_pr_gn_v20191016\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp119_r1i1p1f1_day_prsn_gn_v20191016\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp119_r1i1p1f1_day_psl_gn_v20191016\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp119_r1i1p1f1_day_snw_gn_v20191016\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp126_r1i1p1f1_3hr_pr_gn_v20190912\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp126_r1i1p1f1_3hr_prsn_gn_v20190912\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp126_r1i1p1f1_3hr_rsds_gn_v20190912\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp126_r1i1p1f1_day_pr_gn_v20191016\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp126_r1i1p1f1_day_prsn_gn_v20191016\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp126_r1i1p1f1_day_psl_gn_v20191016\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp126_r1i1p1f1_day_rsds_gn_v20191016\n",
-      "ScenarioMIP_MIROC_MIROC6_ssp126_r1i1p1f1_day_snw_gn_v20191016\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_Amon_clt_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_Amon_pr_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_Amon_prsn_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_Amon_psl_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_Amon_rlds_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_Amon_rlus_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_Amon_rsds_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_Amon_rsus_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_day_clt_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_day_pr_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_day_prsn_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_day_psl_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_day_rlus_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_day_rsds_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_day_rsus_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-LL_ssp126_r1i1p1f3_day_snw_gn_v20200114\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_3hr_clt_gn_v20201006\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_3hr_pr_gn_v20201006\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_3hr_prsn_gn_v20201006\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_3hr_rlds_gn_v20201006\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_3hr_rlus_gn_v20201006\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_3hr_rsds_gn_v20201006\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_3hr_rsus_gn_v20201006\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_Amon_clt_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_Amon_pr_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_Amon_prsn_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_Amon_psl_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_Amon_rlds_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_Amon_rlus_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_Amon_rsds_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_Amon_rsus_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_clt_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_pr_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_prsn_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_psl_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_rlds_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_rlus_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_rsds_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_rsus_gn_v20200515\n",
-      "ScenarioMIP_MOHC_HadGEM3-GC31-MM_ssp126_r1i1p1f3_day_snw_gn_v20200515\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_Amon_clt_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_Amon_pr_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_Amon_prsn_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_Amon_psl_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_Amon_rlds_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_Amon_rlus_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_Amon_rsds_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_Amon_rsus_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_clt_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_pr_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_prsn_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_psl_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_rlds_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_rlus_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_rsds_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_rsus_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp119_r1i1p1f2_day_snw_gn_v20190830\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_Amon_pr_gn_v20190503\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_Amon_prsn_gn_v20190503\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_Amon_psl_gn_v20190503\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_Amon_rlus_gn_v20190503\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_Amon_rsus_gn_v20190503\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_day_pr_gn_v20190708\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_day_prsn_gn_v20190708\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_day_psl_gn_v20190708\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_day_rlds_gn_v20190708\n",
-      "ScenarioMIP_MOHC_UKESM1-0-LL_ssp126_r1i1p1f2_day_rlus_gn_v20190708\n",
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-      "ScenarioMIP_MOHC_UKESM1-1-LL_ssp126_r1i1p1f2_Amon_clt_gn_v20220512\n",
-      "ScenarioMIP_MOHC_UKESM1-1-LL_ssp126_r1i1p1f2_Amon_pr_gn_v20220512\n",
-      "ScenarioMIP_MOHC_UKESM1-1-LL_ssp126_r1i1p1f2_Amon_prsn_gn_v20220512\n",
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-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_3hr_prsn_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_Amon_clt_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_Amon_pr_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_Amon_prsn_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_Amon_psl_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_Amon_rlds_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_Amon_rlus_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_Amon_rsds_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_Amon_rsus_gn_v20190710\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_CF3hr_psl_gn_v20190815\n",
-      "ScenarioMIP_MPI-M_MPI-ESM1-2-LR_ssp126_r1i1p1f1_day_pr_gn_v20190710\n",
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-      "ScenarioMIP_MRI_MRI-ESM2-0_ssp119_r1i1p1f1_day_pr_gn_v20190603\n",
-      "ScenarioMIP_MRI_MRI-ESM2-0_ssp119_r1i1p1f1_day_prsn_gn_v20190603\n",
-      "ScenarioMIP_MRI_MRI-ESM2-0_ssp119_r1i1p1f1_day_psl_gn_v20190603\n",
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-      "ScenarioMIP_NASA-GISS_GISS-E2-1-G_ssp119_r1i1p1f2_Amon_clt_gn_v20200115\n",
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-      "ScenarioMIP_NCC_NorESM2-LM_ssp126_r1i1p1f1_day_pr_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-LM_ssp126_r1i1p1f1_day_psl_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-LM_ssp126_r1i1p1f1_day_rsds_gn_v20191108\n",
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-      "ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_Amon_psl_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_Amon_rlds_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_Amon_rlus_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_Amon_rsds_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_Amon_rsus_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_day_pr_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_day_psl_gn_v20191108\n",
-      "ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_day_rsds_gn_v20191108\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp119_r1i1p1f1_3hr_pr_gr1_v20180701\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp119_r1i1p1f1_day_pr_gr1_v20180701\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp119_r1i1p1f1_day_psl_gr1_v20180701\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp119_r1i1p1f1_day_snw_gr1_v20180701\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp126_r1i1p1f1_day_pr_gr1_v20180701\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp126_r1i1p1f1_day_prsn_gr1_v20180701\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp126_r1i1p1f1_day_psl_gr1_v20180701\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp126_r1i1p1f1_day_rsds_gr1_v20180701\n",
-      "ScenarioMIP_NOAA-GFDL_GFDL-ESM4_ssp126_r1i1p1f1_day_snw_gr1_v20180701\n"
-     ]
-    }
-   ],
-   "source": [
-    "import glob\n",
-    "complete = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233/*.json'\n",
-    "\n",
-    "paths = glob.glob(complete)\n",
-    "for p in paths:\n",
-    "    allfiles = p.replace('complete','in_progress').replace('_kr1.0.json','/allfiles.txt')\n",
-    "    with open(allfiles) as f:\n",
-    "        if len(f.readlines()) > 1:\n",
-    "            print(p[73:-11])"
+    "ds.transverse_mercator"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bb2d1e7f-d840-4a13-81c0-9b3a89c2c094",
+   "id": "fb46302f-98d2-4110-a498-dabdc9e72f03",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/showcase/notebooks/TestMemory.ipynb b/showcase/notebooks/TestMemory.ipynb
new file mode 100644
index 0000000..dbd4b65
--- /dev/null
+++ b/showcase/notebooks/TestMemory.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7e0e9bb4-8ee7-4053-a3a6-1c95832dceea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seff 19433517_0 > temp.txt\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sh: line 1: seff: command not found\n"
+     ]
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 19\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtemp.txt\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m     17\u001b[0m     contents \u001b[38;5;241m=\u001b[39m [r\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m f\u001b[38;5;241m.\u001b[39mreadlines()]\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOMPLETED\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[43mcontents\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m:\n\u001b[1;32m     20\u001b[0m     memory_used \u001b[38;5;241m=\u001b[39m contents[\u001b[38;5;241m9\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtemplate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mproj_codes[x]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/detail-cfg.json\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "\n",
+    "with open('/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233/proj_codes_httpcache.txt') as f:\n",
+    "    proj_codes = [r.strip() for r in f.readlines()]\n",
+    "\n",
+    "template = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/'\n",
+    "\n",
+    "values = []\n",
+    "id = '19433517'\n",
+    "for x in range(0, 953):\n",
+    "    jobid = f'{id}_{x}'\n",
+    "    print(f'seff {jobid} > temp.txt')\n",
+    "    os.system(f'seff {jobid} > temp.txt')\n",
+    "    if os.path.isfile('temp.txt'):\n",
+    "        with open('temp.txt') as f:\n",
+    "            contents = [r.strip() for r in f.readlines()]\n",
+    "\n",
+    "        if 'COMPLETED' in contents[4]:\n",
+    "            memory_used = contents[9].split(':')[-1]\n",
+    "\n",
+    "        with open(f'{template}/{proj_codes[x]}/detail-cfg.json') as f:\n",
+    "            detail = json.load(f)\n",
+    "        values.append([\n",
+    "            memory_used,\n",
+    "            detail['estm_chunksize'],\n",
+    "            detail['total_chunks']])\n",
+    "    print(values)\n",
+    "    y = input()\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20869282-7b14-4c3d-bfef-ee91cf54f053",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "build_venv",
+   "language": "python",
+   "name": "build_venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/showcase/notebooks/cci.ipynb b/showcase/notebooks/cci.ipynb
new file mode 100644
index 0000000..ad99adc
--- /dev/null
+++ b/showcase/notebooks/cci.ipynb
@@ -0,0 +1,1903 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4f6a84f-f73e-4a9c-8e9d-4fdc7ce9cbf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## CCI Assessor\n",
+    "\n",
+    "import glob\n",
+    "\n",
+    "complete = '/gws/nopw/j04/esacci_portal/kerchunk_conversion/complete/'\n",
+    "\n",
+    "with open('/gws/nopw/j04/esacci_portal/kerchunk_conversion/groups/cci_group_v1/blacklist_codes.txt') as f:\n",
+    "    blacklist = [r.split(',')[0] for r in f.readlines()]\n",
+    "\n",
+    "revalidate = []\n",
+    "\n",
+    "types = glob.glob('./*.csv')\n",
+    "for t in types:\n",
+    "    with open(t) as f:\n",
+    "        proj_codes = [r.split(',')[0] for r in f.readlines()]\n",
+    "    sumt = 0\n",
+    "    for proj_code in proj_codes:\n",
+    "        if len(glob.glob(f'{complete}/cci_group_v1/{proj_code}*')) > 0:\n",
+    "            sumt += 1\n",
+    "        elif proj_code in blacklist:\n",
+    "            sumt += 1\n",
+    "        else:\n",
+    "            if 'cci_group' not in t:\n",
+    "                revalidate.append(proj_code)\n",
+    "        \n",
+    "    if sumt == len(proj_codes):\n",
+    "        print(f'{t} - complete')\n",
+    "    else:\n",
+    "        print(f'{t} - {sumt} vs {len(proj_codes)}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "018cb5bd-f317-478d-9272-db6b46508b91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "complete = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233'\n",
+    "in_progress = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233'\n",
+    "\n",
+    "completes = glob.glob(f'{complete}/*.json')\n",
+    "proj_codes = [c.split('kr1.')[0][:-1] for c in completes]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "87810ef2-6597-466a-a2f5-0f2d07c211b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_progress = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d32f9a68-cadf-40bc-9d9d-4b96beb77610",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "for p in proj_codes:\n",
+    "    proj_code = p.split('/')[-1].replace('CMIP6_','')\n",
+    "    newlog = f'{in_progress}/{proj_code}/status_log.csv'\n",
+    "    os.system(f\"touch {newlog}\")\n",
+    "    with open(f\"{newlog}\",'w') as f:\n",
+    "        f.write('complete,complete,12:07 03/06/24,,False')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0e57caaa-a5a0-48b9-8dbb-2bf2f1f8d2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Fix statistics issues\n",
+    "\n",
+    "workdir = '/gws/nopw/j04/esacci_portal/kerchunk_conversion/'\n",
+    "\n",
+    "groups = [\n",
+    "    'aerosol_v1',\n",
+    "    'cci_group_v1',\n",
+    "    'cloud_v1',\n",
+    "    'land_cover_v1',\n",
+    "    'reccap_v1',\n",
+    "    'salinity_v1',\n",
+    "    'sea_level_v1',\n",
+    "    'seaice_v1',\n",
+    "    'snow_v1'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "314c996e-8d29-4a8d-9d3a-b49e759d7091",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_codes(group, filename):\n",
+    "    with open(f'{workdir}/groups/{group}/{filename}.txt') as f:\n",
+    "        return [r.strip() for r in f.readlines()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ce742c7f-dc05-4297-aaec-184dd8328a39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_float(value: int, logger):\n",
+    "    \"\"\"Format byte-value with proper units\"\"\"\n",
+    "    logger.debug(f'Formatting value {value} in bytes')\n",
+    "    if value:\n",
+    "        unit_index = 0\n",
+    "        units = ['','K','M','G','T','P']\n",
+    "        while value > 1000:\n",
+    "            value = value / 1000\n",
+    "            unit_index += 1\n",
+    "        return f'{value:.2f} {units[unit_index]}B'\n",
+    "    else:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c39dc382-7fba-4e20-8aa4-d68e00cf1049",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5579"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pipeline.logs import FalseLogger\n",
+    "from pipeline.scan import format_float\n",
+    "import json\n",
+    "\n",
+    "workdir = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline'\n",
+    "\n",
+    "def fix_scan(group, proj):\n",
+    "    try:\n",
+    "        with open(f'{workdir}/in_progress/{group}/{proj}/detail-cfg.json') as f:\n",
+    "            detail = json.load(f)\n",
+    "    except FileNotFoundError:\n",
+    "        return 0\n",
+    "    except json.JSONDecodeError:\n",
+    "        return 0\n",
+    "\n",
+    "    if 'skipped' in detail:\n",
+    "        return 0\n",
+    "\n",
+    "    detail['kerchunk_data'] = format_float(float(detail['chunks_per_file']) * int(detail['num_files']) * 167, FalseLogger())\n",
+    "    if 'B' not in detail['data_represented']:\n",
+    "        detail['netcdf_data'] = format_float(float(detail['data_represented']), FalseLogger())\n",
+    "    else:\n",
+    "        detail['netcdf_data'] = detail['data_represented']\n",
+    "    \n",
+    "    with open(f'{workdir}/in_progress/{group}/{proj}/detail-cfg.json','w') as f:\n",
+    "        f.write(json.dumps(detail))\n",
+    "    return 1\n",
+    "\n",
+    "corrected = 0\n",
+    "for g in ['CMIP6_rel1_6233']:\n",
+    "    projs = get_codes(g, 'proj_codes_1')\n",
+    "    for p in projs:\n",
+    "        corrected += fix_scan(g, p)\n",
+    "corrected"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "813197e9-1928-40c8-9536-438e2cdcffce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mem_to_val(value):\n",
+    "    suffixes = {\n",
+    "        'KB': 1000,\n",
+    "        'MB': 1000000,\n",
+    "        'GB': 1000000000,\n",
+    "        'TB': 1000000000000,\n",
+    "        'PB': 1000000000000000}\n",
+    "    suff = suffixes[value.split(' ')[1]]\n",
+    "    return float(value.split(' ')[0]) * suff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "435e75ac-569c-4298-a69b-03520397a2aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import glob\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def analyse_data(g):\n",
+    "    print(g)\n",
+    "    ncf, ker = 0, 0\n",
+    "    kus = 0\n",
+    "    complete, scanned = 0, 0\n",
+    "    kerarr = []\n",
+    "    projset = glob.glob(f'/gws/nopw/j04/esacci_portal/kerchunk_conversion/in_progress/{g}/*')\n",
+    "    for p in projset:\n",
+    "        details = None\n",
+    "        try:\n",
+    "            with open(f'{p}/detail-cfg.json') as f:\n",
+    "                details = json.load(f)\n",
+    "        except:\n",
+    "            pass\n",
+    "        if details:\n",
+    "            scanned += 1\n",
+    "            if 'netcdf_data' in details:\n",
+    "                ncf += mem_to_val(details['netcdf_data'])\n",
+    "                ker += mem_to_val(details['kerchunk_data'])\n",
+    "                kerarr.append((details['netcdf_data'], details['kerchunk_data']))\n",
+    "                if os.path.isfile(f'{p}/kerchunk-1a.json.complete'):\n",
+    "                    kus += mem_to_val(details['kerchunk_data'])\n",
+    "                    complete += 1\n",
+    "    return ncf, ker, kus, scanned, complete"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4ddf59f7-01f3-4782-91d4-8ccf681eecfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aerosol_v1\n",
+      "         NetCDF: 675.60 GB\n",
+      "  Kerchunk Estm: 2.00 GB (38)\n",
+      "Kerchunk Actual: 339.48 MB (26)\n",
+      "\n",
+      "cci_group_v1\n",
+      "         NetCDF: 59.84 TB\n",
+      "  Kerchunk Estm: 1.58 TB (311)\n",
+      "Kerchunk Actual: 5.37 GB (128)\n",
+      "\n",
+      "cloud_v1\n",
+      "         NetCDF: 111.41 GB\n",
+      "  Kerchunk Estm: 49.75 MB (24)\n",
+      "Kerchunk Actual: 49.75 MB (24)\n",
+      "\n",
+      "land_cover_v1\n",
+      "         NetCDF: 112.27 GB\n",
+      "  Kerchunk Estm: 209.68 MB (3)\n",
+      "Kerchunk Actual: 209.68 MB (2)\n",
+      "\n",
+      "reccap_v1\n",
+      "         NetCDF: None\n",
+      "  Kerchunk Estm: None (1)\n",
+      "Kerchunk Actual: None (0)\n",
+      "\n",
+      "salinity_v1\n",
+      "         NetCDF: 79.37 GB\n",
+      "  Kerchunk Estm: 38.60 MB (10)\n",
+      "Kerchunk Actual: 38.60 MB (10)\n",
+      "\n",
+      "sea_level_v1\n",
+      "         NetCDF: 2.77 GB\n",
+      "  Kerchunk Estm: 5.23 MB (7)\n",
+      "Kerchunk Actual: None (0)\n",
+      "\n",
+      "seaice_v1\n",
+      "         NetCDF: 253.43 GB\n",
+      "  Kerchunk Estm: 92.47 MB (16)\n",
+      "Kerchunk Actual: 78.86 MB (12)\n",
+      "\n",
+      "snow_v1\n",
+      "         NetCDF: 767.62 GB\n",
+      "  Kerchunk Estm: 1.71 GB (10)\n",
+      "Kerchunk Actual: 1.71 GB (10)\n",
+      "\n",
+      "Total CCI Deliverable Summary\n",
+      "         NetCDF: 61.84 TB\n",
+      "  Kerchunk Estm: 1.59 TB (420)\n",
+      "Kerchunk Actual: 7.79 GB (212)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Tncf, Tker, Tkus, Tscan, Tcomp = 0, 0, 0, 0, 0\n",
+    "for g in groups:\n",
+    "    ncf, ker, kus, scanned, complete = analyse_data(g)\n",
+    "    print(f'         NetCDF: {format_float(ncf, FalseLogger())}')\n",
+    "    print(f'  Kerchunk Estm: {format_float(ker, FalseLogger())} ({scanned})')\n",
+    "    print(f'Kerchunk Actual: {format_float(kus, FalseLogger())} ({complete})')\n",
+    "    print()\n",
+    "\n",
+    "    Tncf += ncf\n",
+    "    Tker += ker\n",
+    "    Tkus += kus\n",
+    "    Tscan += scanned\n",
+    "    Tcomp += complete\n",
+    "\n",
+    "print('Total CCI Deliverable Summary')\n",
+    "print(f'         NetCDF: {format_float(Tncf, FalseLogger())}')\n",
+    "print(f'  Kerchunk Estm: {format_float(Tker, FalseLogger())} ({Tscan})')\n",
+    "print(f'Kerchunk Actual: {format_float(Tkus, FalseLogger())} ({Tcomp})')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a5522d5f-dbf6-43b7-9879-c400333d8810",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('68.01 GB', '8.34 GB'),\n",
+       " ('70.91 GB', '8.34 GB'),\n",
+       " ('262.08 GB', '774.76 MB'),\n",
+       " ('151.04 GB', '25.46 GB'),\n",
+       " ('119.88 GB', '53.88 MB'),\n",
+       " ('210.22 GB', '68.97 MB'),\n",
+       " ('214.39 GB', '68.97 MB'),\n",
+       " ('7.18 GB', '721.44 KB'),\n",
+       " ('22.31 GB', '9.51 GB'),\n",
+       " ('20.31 GB', '9.51 GB'),\n",
+       " ('318.24 GB', '917.67 MB'),\n",
+       " ('183.96 GB', '27.77 GB'),\n",
+       " ('462.00 GB', '1.40 GB'),\n",
+       " ('269.04 GB', '42.59 GB'),\n",
+       " ('107.16 GB', '14.38 GB'),\n",
+       " ('110.48 GB', '14.38 GB'),\n",
+       " ('124.91 GB', '17.63 GB'),\n",
+       " ('163.34 GB', '17.63 GB'),\n",
+       " ('624.98 GB', '1.80 GB'),\n",
+       " ('324.30 GB', '54.38 GB'),\n",
+       " ('18.39 GB', '9.44 GB'),\n",
+       " ('17.61 GB', '9.44 GB'),\n",
+       " ('466.10 GB', '2.54 GB'),\n",
+       " ('41.46 GB', '15.28 GB'),\n",
+       " ('914.40 GB', '4.91 GB'),\n",
+       " ('79.52 GB', '29.80 GB'),\n",
+       " ('80.60 GB', '12.08 GB'),\n",
+       " ('80.68 GB', '12.08 GB'),\n",
+       " ('384.54 GB', '1.13 GB'),\n",
+       " ('204.20 GB', '33.35 GB'),\n",
+       " ('43.17 GB', '16.44 GB'),\n",
+       " ('42.30 GB', '16.44 GB'),\n",
+       " ('907.12 GB', '2.58 GB'),\n",
+       " ('459.37 GB', '80.14 GB'),\n",
+       " ('128.55 GB', '18.25 GB'),\n",
+       " ('133.59 GB', '18.25 GB'),\n",
+       " ('642.46 GB', '1.69 GB'),\n",
+       " ('360.52 GB', '57.77 GB'),\n",
+       " ('47.35 GB', '23.12 GB'),\n",
+       " ('51.44 GB', '23.12 GB'),\n",
+       " ('1.25 TB', '6.95 GB'),\n",
+       " ('103.88 GB', '40.34 GB'),\n",
+       " ('70.14 GB', '8.75 GB'),\n",
+       " ('70.91 GB', '8.34 GB'),\n",
+       " ('182.70 GB', '26.43 GB'),\n",
+       " ('197.51 GB', '26.43 GB'),\n",
+       " ('323.35 GB', '866.06 MB'),\n",
+       " ('170.76 GB', '26.69 GB'),\n",
+       " ('68.12 GB', '11.16 GB'),\n",
+       " ('70.57 GB', '11.16 GB'),\n",
+       " ('272.20 GB', '1.02 GB'),\n",
+       " ('98.86 GB', '32.81 GB'),\n",
+       " ('153.59 GB', '24.62 GB'),\n",
+       " ('183.99 GB', '24.62 GB'),\n",
+       " ('809.26 GB', '2.38 GB'),\n",
+       " ('402.30 GB', '77.21 GB'),\n",
+       " ('154.32 GB', '19.17 GB'),\n",
+       " ('158.75 GB', '19.17 GB'),\n",
+       " ('676.62 GB', '1.79 GB'),\n",
+       " ('370.29 GB', '57.55 GB'),\n",
+       " ('547.75 MB', '26.05 KB'),\n",
+       " ('51.59 GB', '34.79 MB'),\n",
+       " ('3.14 MB', '10.02 KB'),\n",
+       " ('25.01 MB', '721.44 KB'),\n",
+       " ('2.75 GB', '2.56 MB'),\n",
+       " ('3.44 GB', '1.40 MB'),\n",
+       " ('12.03 GB', '42.76 MB'),\n",
+       " ('554.14 GB', '1.81 GB'),\n",
+       " ('575.72 GB', '1.81 GB'),\n",
+       " ('160.31 GB', '118.97 MB'),\n",
+       " ('148.44 GB', '118.97 MB'),\n",
+       " ('801.98 GB', '2.51 GB'),\n",
+       " ('723.36 GB', '2.51 GB'),\n",
+       " ('221.06 GB', '164.29 MB'),\n",
+       " ('203.12 GB', '164.29 MB'),\n",
+       " ('2.05 GB', '2.01 MB'),\n",
+       " ('2.05 GB', '2.01 MB'),\n",
+       " ('1.86 GB', '1.89 MB'),\n",
+       " ('519.75 MB', '523.54 KB'),\n",
+       " ('2.10 GB', '2.01 MB'),\n",
+       " ('2.13 GB', '2.01 MB'),\n",
+       " ('2.18 GB', '2.03 MB'),\n",
+       " ('2.13 GB', '2.03 MB'),\n",
+       " ('2.05 GB', '2.00 MB'),\n",
+       " ('553.53 MB', '539.41 KB'),\n",
+       " ('553.73 MB', '539.41 KB'),\n",
+       " ('526.66 MB', '507.68 KB'),\n",
+       " ('500.19 MB', '507.68 KB'),\n",
+       " ('516.59 MB', '523.54 KB'),\n",
+       " ('517.39 MB', '523.54 KB'),\n",
+       " ('504.87 MB', '507.68 KB'),\n",
+       " ('550.32 MB', '523.54 KB'),\n",
+       " ('546.85 MB', '523.54 KB'),\n",
+       " ('546.79 MB', '539.41 KB'),\n",
+       " ('549.71 MB', '539.41 KB'),\n",
+       " ('521.16 MB', '523.54 KB'),\n",
+       " ('515.37 MB', '523.54 KB'),\n",
+       " ('504.14 MB', '507.68 KB'),\n",
+       " ('496.72 MB', '523.54 KB'),\n",
+       " ('2.05 GB', '2.01 MB'),\n",
+       " ('2.05 GB', '2.01 MB'),\n",
+       " ('1.86 GB', '1.89 MB'),\n",
+       " ('519.75 MB', '523.54 KB'),\n",
+       " ('2.10 GB', '2.01 MB'),\n",
+       " ('2.13 GB', '2.01 MB'),\n",
+       " ('2.18 GB', '2.03 MB'),\n",
+       " ('2.13 GB', '2.03 MB'),\n",
+       " ('2.05 GB', '2.00 MB'),\n",
+       " ('546.79 MB', '539.41 KB'),\n",
+       " ('553.53 MB', '539.41 KB'),\n",
+       " ('553.73 MB', '539.41 KB'),\n",
+       " ('549.71 MB', '539.41 KB'),\n",
+       " ('496.72 MB', '523.54 KB'),\n",
+       " ('500.19 MB', '507.68 KB'),\n",
+       " ('517.39 MB', '523.54 KB'),\n",
+       " ('516.59 MB', '523.54 KB'),\n",
+       " ('504.14 MB', '507.68 KB'),\n",
+       " ('504.87 MB', '507.68 KB'),\n",
+       " ('526.66 MB', '507.68 KB'),\n",
+       " ('550.32 MB', '523.54 KB'),\n",
+       " ('546.85 MB', '523.54 KB'),\n",
+       " ('521.16 MB', '523.54 KB'),\n",
+       " ('515.37 MB', '523.54 KB'),\n",
+       " ('478.78 GB', '280.43 MB'),\n",
+       " ('484.45 GB', '280.43 MB'),\n",
+       " ('4.66 TB', '5.43 GB'),\n",
+       " ('3.56 TB', '5.43 GB'),\n",
+       " ('172.92 MB', '24.23 MB'),\n",
+       " ('198.19 MB', '24.23 MB'),\n",
+       " ('103.94 GB', '79.31 MB'),\n",
+       " ('103.70 GB', '79.31 MB'),\n",
+       " ('257.96 GB', '589.61 MB'),\n",
+       " ('218.08 GB', '589.61 MB'),\n",
+       " ('47.95 GB', '35.41 MB'),\n",
+       " ('43.64 GB', '35.41 MB'),\n",
+       " ('28.18 GB', '27.45 MB'),\n",
+       " ('26.56 GB', '27.45 MB'),\n",
+       " ('1.59 GB', '901.80 KB'),\n",
+       " ('1.59 GB', '901.80 KB'),\n",
+       " ('123.75 MB', '75.15 KB'),\n",
+       " ('122.97 MB', '75.15 KB'),\n",
+       " ('2.23 TB', '13.26 GB'),\n",
+       " ('2.23 TB', '13.26 GB'),\n",
+       " ('66.55 GB', '5.26 GB'),\n",
+       " ('65.86 GB', '5.26 GB'),\n",
+       " ('97.37 GB', '5.26 GB'),\n",
+       " ('66.16 GB', '5.26 GB'),\n",
+       " ('77.96 GB', '5.26 GB'),\n",
+       " ('64.17 GB', '5.26 GB'),\n",
+       " ('112.06 GB', '5.26 GB'),\n",
+       " ('70.79 GB', '5.26 GB'),\n",
+       " ('9.51 GB', '219.49 MB'),\n",
+       " ('11.93 GB', '219.49 MB'),\n",
+       " ('13.60 GB', '219.49 MB'),\n",
+       " ('11.07 GB', '219.49 MB'),\n",
+       " ('9.53 GB', '219.49 MB'),\n",
+       " ('11.95 GB', '219.49 MB'),\n",
+       " ('14.05 GB', '219.49 MB'),\n",
+       " ('11.08 GB', '219.49 MB'),\n",
+       " ('29.36 GB', '38.38 MB'),\n",
+       " ('29.53 GB', '37.27 MB'),\n",
+       " ('30.72 GB', '38.40 MB'),\n",
+       " ('29.63 GB', '38.33 MB'),\n",
+       " ('28.67 GB', '37.65 MB'),\n",
+       " ('23.37 GB', '30.27 MB'),\n",
+       " ('29.37 GB', '37.83 MB'),\n",
+       " ('28.91 GB', '37.74 MB'),\n",
+       " ('1.19 GB', '1.21 MB'),\n",
+       " ('1.20 GB', '1.21 MB'),\n",
+       " ('1.21 GB', '1.21 MB'),\n",
+       " ('1.19 GB', '1.21 MB'),\n",
+       " ('1.14 GB', '1.19 MB'),\n",
+       " ('1.02 GB', '1.14 MB'),\n",
+       " ('1.14 GB', '1.19 MB'),\n",
+       " ('1.16 GB', '1.17 MB'),\n",
+       " ('116.41 GB', '7.96 GB'),\n",
+       " ('300.50 GB', '6.03 GB'),\n",
+       " ('62.62 GB', '198.71 MB'),\n",
+       " ('6.75 GB', '22.44 MB'),\n",
+       " ('6.38 GB', '16.99 MB'),\n",
+       " ('110.75 GB', '1.60 GB'),\n",
+       " ('139.79 GB', '1.21 GB'),\n",
+       " ('100.44 GB', '1.01 GB'),\n",
+       " ('115.71 GB', '762.14 MB'),\n",
+       " ('939.70 GB', '43.45 GB'),\n",
+       " ('995.98 GB', '28.63 GB'),\n",
+       " ('605.95 GB', '943.47 MB'),\n",
+       " ('73.65 GB', '122.48 MB'),\n",
+       " ('62.66 GB', '80.69 MB'),\n",
+       " ('1.16 TB', '8.73 GB'),\n",
+       " ('1.06 TB', '5.75 GB'),\n",
+       " ('1.07 TB', '5.49 GB'),\n",
+       " ('970.16 GB', '3.62 GB'),\n",
+       " ('116.91 GB', '7.96 GB'),\n",
+       " ('300.88 GB', '6.03 GB'),\n",
+       " ('63.35 GB', '198.71 MB'),\n",
+       " ('6.87 GB', '22.44 MB'),\n",
+       " ('6.48 GB', '16.99 MB'),\n",
+       " ('111.72 GB', '1.60 GB'),\n",
+       " ('140.65 GB', '1.21 GB'),\n",
+       " ('101.41 GB', '1.01 GB'),\n",
+       " ('116.62 GB', '762.14 MB'),\n",
+       " ('1.65 TB', '73.42 GB'),\n",
+       " ('1.59 TB', '47.71 GB'),\n",
+       " ('1.07 TB', '1.57 GB'),\n",
+       " ('130.30 GB', '206.96 MB'),\n",
+       " ('110.23 GB', '134.48 MB'),\n",
+       " ('2.05 TB', '14.76 GB'),\n",
+       " ('1.85 TB', '9.59 GB'),\n",
+       " ('1.89 TB', '9.28 GB'),\n",
+       " ('1.70 TB', '6.03 GB'),\n",
+       " ('647.74 GB', '30.83 GB'),\n",
+       " ('750.13 GB', '20.59 GB'),\n",
+       " ('408.52 GB', '678.67 MB'),\n",
+       " ('49.75 GB', '86.91 MB'),\n",
+       " ('42.46 GB', '58.04 MB'),\n",
+       " ('781.82 GB', '6.20 GB'),\n",
+       " ('731.59 GB', '4.14 GB'),\n",
+       " ('719.95 GB', '3.90 GB'),\n",
+       " ('662.77 GB', '2.60 GB'),\n",
+       " ('13.25 GB', '21.65 MB'),\n",
+       " ('1.91 MB', '48.10 KB'),\n",
+       " ('530.30 MB', '264.53 KB'),\n",
+       " ('384.42 MB', '185.37 KB'),\n",
+       " ('943.42 MB', '52.10 KB'),\n",
+       " ('2.67 GB', '147.63 KB'),\n",
+       " ('9.21 GB', '408.82 KB'),\n",
+       " ('26.08 GB', '1.16 MB'),\n",
+       " ('389.03 MB', '1.01 MB'),\n",
+       " ('1.98 GB', '24.26 MB'),\n",
+       " ('1.89 GB', '19.73 MB'),\n",
+       " ('1.96 GB', '19.73 MB'),\n",
+       " ('2.02 GB', '20.40 MB'),\n",
+       " ('1.66 GB', '20.40 MB'),\n",
+       " ('1.75 GB', '26.82 MB'),\n",
+       " ('4.59 GB', '35.15 MB'),\n",
+       " ('4.88 GB', '28.29 MB'),\n",
+       " ('6.12 GB', '28.29 MB'),\n",
+       " ('6.27 GB', '28.96 MB'),\n",
+       " ('8.69 GB', '28.96 MB'),\n",
+       " ('9.17 GB', '29.63 MB'),\n",
+       " ('5.10 GB', '25.72 MB'),\n",
+       " ('8.63 GB', '26.33 MB'),\n",
+       " ('4.63 GB', '35.15 MB'),\n",
+       " ('4.54 GB', '28.29 MB'),\n",
+       " ('5.84 GB', '28.29 MB'),\n",
+       " ('5.97 GB', '28.96 MB'),\n",
+       " ('8.78 GB', '28.96 MB'),\n",
+       " ('9.46 GB', '29.63 MB'),\n",
+       " ('233.67 GB', '51.73 GB'),\n",
+       " ('12.84 GB', '5.16 MB'),\n",
+       " ('73.51 GB', '141.86 MB'),\n",
+       " ('194.40 MB', '434.87 KB'),\n",
+       " ('1.98 GB', '13.24 MB')]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kerarr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "b34e401e-3f75-4e97-99f8-2b1a8b1d1ca7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "mv: '/gws/nopw/j04/esacci_portal/kerchunk_conversion/needs_version_updating/ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0_kr1.2.json' and '/gws/nopw/j04/esacci_portal/kerchunk_conversion/needs_version_updating/ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0_kr1.2.json' are the same file\n"
+     ]
+    }
+   ],
+   "source": [
+    "import glob\n",
+    "import json\n",
+    "from datetime import datetime \n",
+    "\n",
+    "reason = 'Archived at kr1.1 already'\n",
+    "\n",
+    "for kfile in glob.glob('/gws/nopw/j04/esacci_portal/kerchunk_conversion/needs_version_updating/*'):\n",
+    "    old_version = 'kr1.1' # Hardcoded for now\n",
+    "    os.system(f\"mv {kfile} {kfile.replace(old_version, 'kr1.2')}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2b61914d-cbab-4e27-bb5a-d7c0d2d7dd64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(revalidate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a1c80b39-7bce-46e6-b61c-6b59f30d647b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2003-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2004-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2005-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2006-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2007-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2008-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2009-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2010-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2011-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2012-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2013-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2014-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2015-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2016-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2017-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2018-fv03.0.nc',\n",
+       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2019-fv03.0.nc']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fileset = \"\"\"/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2003-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2004-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2005-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2006-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2007-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2008-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2009-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2010-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2011-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2012-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2013-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2014-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2015-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2016-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2017-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2018-fv03.0.nc\n",
+    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2019-fv03.0.nc\"\"\".split('\\n')\n",
+    "fileset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b5882bc9-1559-4223-9ec5-0deecfede4c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "completes = os.walk(complete)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2cda28a5-e019-4908-9f62-4b6bee425e80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fileset = []\n",
+    "for roots, dirs, files in completes:\n",
+    "    fileset = files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "1b001119-7d3a-450a-9a24-72b0148c8b69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "byhand = \"\"\"ESACCI-OC-L3S-K_490-MERGED-8D_DAILY_4km_SIN_PML_K_490_Lee-199709-202212-fv6.1\n",
+    "ESACCI-LST-L3S-LST-IRMGP_-0.05deg_1MONTHLY-20090101150000-20201201150000-fv1.00\n",
+    "ESACCI-LST-L3S-LST-IRMGP_-0.05deg_1MONTHLY-20090101180000-20201201180000-fv1.00\n",
+    "ESACCI-LST-L3S-LST-IRMGP_-0.05deg_1MONTHLY-20090101210000-20201201210000-fv1.00\n",
+    "ESACCI-OC-L3S-RRS-MERGED-1M_MONTHLY_4km_SIN_PML_RRS-199709-202212-fv6.0\n",
+    "ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_SIN_PML_OCx_QAA-199709-202212-fv6.0\n",
+    "ESACCI-OC-L3S-CHLOR_A-MERGED-8D_DAILY_4km_GEO_PML_OCx-19970829-20221227-fv6.0\n",
+    "ESACCI-OC-L3S-K_490-MERGED-5D_DAILY_4km_SIN_PML_K_490_Lee-199709-202212-fv6.2\n",
+    "ESACCI-OC-L3S-CHLOR_A-MERGED-5D_DAILY_4km_SIN_PML_OCx-19970903-20221227-fv6.0\n",
+    "ESACCI-OC-L3S-CHLOR_A-MERGED-8D_DAILY_4km_SIN_PML_OCx-19970829-20221227-fv6.0\n",
+    "ESACCI-OC-L3S-K_490-MERGED-8D_DAILY_4km_GEO_PML_K_490_Lee-199709-202212-fv6.1\n",
+    "ESACCI-LST-L3C-LST-SLSTRA-0.01deg_1DAILY_DAY-20160501-20201231-fv3.00.nc\n",
+    "ESACCI-LST-L3C-LST-ATSR_2-0.01deg_1DAILY_DAY-19950801-20030622-fv3.00\n",
+    "ESACCI-LST-L3C-LST-ATSR_2-0.01deg_1DAILY_NIGHT-19950801-20030622-fv3.00\n",
+    "ESACCI-LST-L3C-LST-ATSR_3-0.01deg_1DAILY_DAY-20020725-20120408-fv3.00\n",
+    "ESACCI-OC-L3S-CHLOR_A-MERGED-5D_DAILY_4km_GEO_PML_OCx-19970903-20221227-fv6.0\n",
+    "ESACCI-LST-L3C-LST-SLSTRA-0.01deg_1DAILY_NIGHT-20160501-20201231-fv3.00.nc\n",
+    "ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1M_MONTHLY_4km_SIN_PML_OCx_QAA-199709-202212-fv6.0\n",
+    "ESACCI-OC-L3S-K_490-MERGED-5D_DAILY_4km_GEO_PML_K_490_Lee-199709-202212-fv6.2\n",
+    "ESACCI-OC-L3S-CHLOR_A-MERGED-1Y_YEARLY_4km_GEO_PML_OCx-1997-2022-fv6.0\n",
+    "ESACCI-OC-L3S-IOP-MERGED-1Y_YEARLY_4km_GEO_PML_OCx_QAA_Lee-199709-202212-fv6.1\"\"\".split('\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c7d87b49-b749-47bf-88df-2355a72f0fa7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ESACCI-L3C_SNOW-SCFG-AATSR_ENVISAT-20020520-20120408-fv1.0_kr1.0.json',\n",
+       " 'ESACCI-L3C_SNOW-SCFG-ATSR-2_ERS-2-19950801-20030622-fv1.0_kr1.0.json',\n",
+       " 'ESACCI-L3C_SNOW-SCFG-AVHRR_MERGED-19820101-20181230-fv2.0_kr1.0.json',\n",
+       " 'ESACCI-L3C_SNOW-SCFG-MODIS_TERRA-20000224-20201231-fv2.0_kr1.0.json',\n",
+       " 'ESACCI-L3C_SNOW-SCFV-AATSR_ENVISAT-20020520-20120408-fv1.0_kr1.0.json',\n",
+       " 'ESACCI-L3C_SNOW-SCFV-ATSR-2_ERS-2-19950801-20030622-fv1.0_kr1.0.json',\n",
+       " 'ESACCI-L3C_SNOW-SCFV-AVHRR_MERGED-19820101-20181230-fv2.0_kr1.0.json',\n",
+       " 'ESACCI-L3C_SNOW-SCFV-MODIS_TERRA-20000224-20201231-fv2.0_kr1.0.json',\n",
+       " 'ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0_kr1.2.json',\n",
+       " 'ESACCI-L3S_SNOW-SCFG-MERGED-CRYOCLIM-19820101-2019_kr1.0.json']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fileset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "bf9815e2-fb3b-49f0-9955-2fa403b2d728",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20210226-ESACCI-L4_AIS-GMB-BASIN-fv3.0 complete\n",
+      "20210226-ESACCI-L3C_AIS-GMB-GRID-fv3.0 complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Assessor 2\n",
+    "import os\n",
+    "\n",
+    "tryset = \"\"\"20210226-ESACCI-L4_AIS-GMB-BASIN-fv3.0\n",
+    "20210226-ESACCI-L3C_AIS-GMB-GRID-fv3.0\n",
+    "20170101-20200801-ESACCI-L3C_AIS-IV-S1-1M_200m-fv1.0.nc\"\"\".split('\\n')\n",
+    "\n",
+    "groups = \"\"\"aerosol_v1\n",
+    "cci_group_v1\n",
+    "cloud_v1\n",
+    "land_cover_v1\n",
+    "reccap_v1\n",
+    "salinity_v1\n",
+    "seaice_v1\n",
+    "snow_v1\"\"\".split('\\n')\n",
+    "\n",
+    "for t in tryset:\n",
+    "    if t in byhand:\n",
+    "        print(t, 'byhand')\n",
+    "    for g in groups:\n",
+    "        if len(glob.glob(f'{complete}/{g}/{t}*')) > 0:\n",
+    "            print(t, 'complete')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e1d73fbc-d358-4cf0-a2b1-d1ec7f329379",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ESACCI-OC-L3S-K_490-MERGED-1Y_YEARLY_4km_SIN_PML_K_490_Lee-199709-202212-fv6.1',\n",
+       " 'ESACCI-OC-MAPPED-CLIMATOLOGY-1M_MONTHLY_4km_PML_OCx_QAA-01-12-fv6.0.nc',\n",
+       " 'ESACCI-OZONE-L3-LP-MEGRIDOP_MERGED-20011101_20221201-fv0004',\n",
+       " 'ESACCI-OZONE-L3-LP-SAGE_CCI_OMPS_plus_MERGED-19841001_20211201-fv0007',\n",
+       " 'ESACCI-OZONE-L3-LP-MERGED-MZM-200701-200812-fv0002']"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tryset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "47b08258-8bec-40f2-b499-01571107feec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
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+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
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+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
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+       "  display: inline-block;\n",
+       "  padding: 0;\n",
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+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;polar_stereographic&#x27; (time: 17)&gt;\n",
+       "array([-2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647], dtype=int32)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2003-01-01 2004-01-01 ... 2019-01-01\n",
+       "Attributes: (12/13)\n",
+       "    grid_mapping_name:                      polar_stereographic\n",
+       "    straight_vertical_longitude_from_pole:  0.0\n",
+       "    false_easting:                          0.0\n",
+       "    false_northing:                         0.0\n",
+       "    latitude_of_projection_origin:          90.0\n",
+       "    standard_parallel:                      71.0\n",
+       "    ...                                     ...\n",
+       "    semi_major_axis:                        6378137.0\n",
+       "    inverse_flattening:                     298.257223563\n",
+       "    spatial_ref:                            GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,...\n",
+       "    crs_wkt:                                PROJCS[&quot;WGS 84 / Arctic Polar Ste...\n",
+       "    esri_wkt:                               PROJCS[&quot;WGS_84_Arctic_Polar_Stere...\n",
+       "    GeoTransform:                           -8679599.425969256 926.6254331383...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'polar_stereographic'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 17</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-193938ea-0ee4-4594-9a86-e50860c1ccd4' class='xr-array-in' type='checkbox' checked><label for='section-193938ea-0ee4-4594-9a86-e50860c1ccd4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-2147483647 -2147483647 -2147483647 ... -2147483647 -2147483647</span></div><div class='xr-array-data'><pre>array([-2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647], dtype=int32)</pre></div></div></li><li class='xr-section-item'><input id='section-d588fa39-d43a-43b5-8e30-ae18068cefe3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d588fa39-d43a-43b5-8e30-ae18068cefe3' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2003-01-01 ... 2019-01-01</div><input id='attrs-76beb3b1-a15e-41e7-8823-8fa3cc34d41e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76beb3b1-a15e-41e7-8823-8fa3cc34d41e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a604458b-79fc-40ec-82d6-8d971b8daf4f' class='xr-var-data-in' type='checkbox'><label for='data-a604458b-79fc-40ec-82d6-8d971b8daf4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2003-01-01T00:00:00.000000000&#x27;, &#x27;2004-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2005-01-01T00:00:00.000000000&#x27;, &#x27;2006-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2007-01-01T00:00:00.000000000&#x27;, &#x27;2008-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2010-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2011-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a330cad-efa2-454a-aea9-48bb4715bdeb' class='xr-section-summary-in' type='checkbox'  ><label for='section-4a330cad-efa2-454a-aea9-48bb4715bdeb' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b1691023-56ed-4faf-924b-ae8640a8dd29' class='xr-index-data-in' type='checkbox'/><label for='index-b1691023-56ed-4faf-924b-ae8640a8dd29' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2003-01-01&#x27;, &#x27;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;,\n",
+       "               &#x27;2007-01-01&#x27;, &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;,\n",
+       "               &#x27;2011-01-01&#x27;, &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;,\n",
+       "               &#x27;2015-01-01&#x27;, &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;,\n",
+       "               &#x27;2019-01-01&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a908a9d4-717b-442f-b96f-ace62cb1c53f' class='xr-section-summary-in' type='checkbox'  ><label for='section-a908a9d4-717b-442f-b96f-ace62cb1c53f' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>polar_stereographic</dd><dt><span>straight_vertical_longitude_from_pole :</span></dt><dd>0.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>90.0</dd><dt><span>standard_parallel :</span></dt><dd>71.0</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Polar_Stereographic&quot;],PARAMETER[&quot;latitude_of_origin&quot;,45],PARAMETER[&quot;central_meridian&quot;,-170],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]]</dd><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / Arctic Polar Stereographic&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Polar_Stereographic&quot;],PARAMETER[&quot;latitude_of_origin&quot;,71],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;X&quot;,EAST],AXIS[&quot;Y&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;3995&quot;]]</dd><dt><span>esri_wkt :</span></dt><dd>PROJCS[&quot;WGS_84_Arctic_Polar_Stereographic&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.017453292519943295]],PROJECTION[&quot;Stereographic_North_Pole&quot;],PARAMETER[&quot;standard_parallel_1&quot;,71],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;Meter&quot;,1]]</dd><dt><span>GeoTransform :</span></dt><dd>-8679599.425969256 926.6254331383326 0 4120958.560533902 0 -926.6254331383326 </dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'polar_stereographic' (time: 17)>\n",
+       "array([-2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
+       "       -2147483647, -2147483647], dtype=int32)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2003-01-01 2004-01-01 ... 2019-01-01\n",
+       "Attributes: (12/13)\n",
+       "    grid_mapping_name:                      polar_stereographic\n",
+       "    straight_vertical_longitude_from_pole:  0.0\n",
+       "    false_easting:                          0.0\n",
+       "    false_northing:                         0.0\n",
+       "    latitude_of_projection_origin:          90.0\n",
+       "    standard_parallel:                      71.0\n",
+       "    ...                                     ...\n",
+       "    semi_major_axis:                        6378137.0\n",
+       "    inverse_flattening:                     298.257223563\n",
+       "    spatial_ref:                            GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",...\n",
+       "    crs_wkt:                                PROJCS[\"WGS 84 / Arctic Polar Ste...\n",
+       "    esri_wkt:                               PROJCS[\"WGS_84_Arctic_Polar_Stere...\n",
+       "    GeoTransform:                           -8679599.425969256 926.6254331383..."
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.polar_stereographic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7db7e16a-15e9-4956-a50c-ed7b5fbf7d6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Dataset' object has no attribute 'time'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdsx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n",
+      "File \u001b[0;32m/opt/jaspy/lib/python3.10/site-packages/xarray/core/common.py:278\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[1;32m    277\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    279\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m )\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Dataset' object has no attribute 'time'"
+     ]
+    }
+   ],
+   "source": [
+    "dsx.isel(time=0).time.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "69248cb6-dc2d-46cb-be52-143737ff9949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ding\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "update = ['ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0']\n",
+    "group = 'snow_v1'\n",
+    "\n",
+    "for proj_code in update:\n",
+    "    detailfile = f'/gws/nopw/j04/esacci_portal/kerchunk_conversion/in_progress/{group}/{proj_code}/detail-cfg.json'\n",
+    "    with open(detailfile) as f:\n",
+    "        details = json.load(f)\n",
+    "    details['version_no'] = 'kr1.1'\n",
+    "    with open(detailfile,'w') as f:\n",
+    "        f.write(json.dumps(details))\n",
+    "print('ding')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eab0d126-c2cc-438e-9cf2-9e00e2e162eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import json\n",
+    "\n",
+    "checkfile = f'/gws/nopw/j04/esacci_portal/kerchunk_conversion/in_progress/cci_group_v1'\n",
+    "proj_codes = glob.glob(checkfile)\n",
+    "\n",
+    "with open('/gws/nopw/j04/esacci_portal/kerchunk_conversion/input_files/cci_group_v1.csv') as f:\n",
+    "    origin = [r.strip() for r in f.readlines()]\n",
+    "\n",
+    "for x, entry in enumerate(origin):\n",
+    "    row = entry.split(',')\n",
+    "    path = f'{checkfile}/{row[0]}'\n",
+    "    try:\n",
+    "        with open(f'{path}/allfiles.txt') as f:\n",
+    "            length = len(f.readlines())\n",
+    "        assert length > 0\n",
+    "        print(x, 'Success')\n",
+    "    except:\n",
+    "        print(row[1])\n",
+    "        q=input()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c13bf0ae-3e22-4695-97fb-e4e933fefaef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "107"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workdir = '/gws/nopw/j04/esacci_portal/kerchunk_conversion/'\n",
+    "with open(f'{workdir}/groups/cci_group_v1/proj_codes_continue.txt') as f:\n",
+    "    firstset = [r.strip() for r in f.readlines()]\n",
+    "\n",
+    "with open(f'{workdir}/groups/cci_group_v1/proj_codes_continue2.txt') as f:\n",
+    "    secset = [r.strip() for r in f.readlines()]\n",
+    "\n",
+    "newset = []\n",
+    "for s in secset:\n",
+    "    if s not in firstset:\n",
+    "        newset.append(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "841d0f83-2f43-4872-85ec-3737190c9b9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d047f1dd-6391-4c6e-93c9-00323c47ec17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f'{workdir}/groups/cci_group_v1/proj_codes_continue2.txt','w') as f:\n",
+    "    f.write('\\n'.join(newset))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "7a2b7e11-d439-4764-9faa-948ba7c58a95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
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+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n",
+      "(180, 360)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "fileset = glob.glob('/neodc/esacci/aerosol/data/MS_UVAI/L3/v1.7/MONTHLY/*/ESACCI-AEROSOL-L3-AAI-MSAAI-1M-*-fv1.7.nc')\n",
+    "for f in fileset:\n",
+    "    ds = xr.open_dataset(f)\n",
+    "    print(ds.number_of_observations.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4efbad91-4194-433c-9fee-21886d485009",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "build_venv",
+   "language": "python",
+   "name": "build_venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 32f7c3fa72bb58f8d8b057206ec542275f208fc8 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Wed, 6 Mar 2024 16:02:33 +0000
Subject: [PATCH 29/45] Updated with comments and rearranged sections

---
 pipeline/validate.py | 187 ++++++++++++++++++++++++++++++-------------
 1 file changed, 130 insertions(+), 57 deletions(-)

diff --git a/pipeline/validate.py b/pipeline/validate.py
index 8b3eace..03f83e9 100644
--- a/pipeline/validate.py
+++ b/pipeline/validate.py
@@ -15,7 +15,9 @@
 import math
 
 from pipeline.errors import *
-from pipeline.logs import init_logger, SUFFIXES, SUFFIX_LIST, BypassSwitch
+from pipeline.logs import init_logger, SUFFIXES, SUFFIX_LIST
+from pipeline.utils import BypassSwitch
+from ujson import JSONDecodeError
 
 ## 1. Array Selection Tools
 
@@ -40,13 +42,15 @@ def get_vslice(shape: list, dtypes: list, lengths: list, divisions: list, logger
     return vslice
 
 def get_concat_dims(xfiles, detailfile=None):
+    # Not usable with virtual dimensions
     concat_dims = {'time':0}
     if os.path.isfile(detailfile):
         with open(detailfile) as f:
             details = json.load(f)
     # Initialise concat dims
     if 'concat_dims' in details:
-        concat_dims[details['concat_dims']] = 0
+        for dim in details['concat_dims']:
+            concat_dims[dim] = 0
 
     for xf in xfiles:
         # Open netcdf in lowest memory intensive way possible.
@@ -162,7 +166,11 @@ def open_kerchunk(kfile: str, logger, isparq=False, remote_protocol='file'):
         )
     else:
         logger.debug('Opening Kerchunk JSON file')
-        mapper  = fsspec.get_mapper('reference://',fo=kfile, target_options={"compression":None}, remote_protocol=remote_protocol)
+        try:
+            mapper  = fsspec.get_mapper('reference://',fo=kfile, target_options={"compression":None}, remote_protocol=remote_protocol)
+        except JSONDecodeError as err:
+            logger.error(f"Kerchunk file {kfile} appears to be empty")
+            raise MissingKerchunkError
         # Need a safe repeat here
         ds = None
         attempts = 0
@@ -200,7 +208,7 @@ def check_memory(nfiles, indexes, mem, logger):
     if nftotal > memcap:
         raise ExpectMemoryError(required=value_to_mem(nftotal), current=mem)
 
-def open_netcdfs(args, logger, thorough=False):
+def open_netcdfs(args, logger, thorough=False, concat_dims='time'):
     """Returns a single xarray object with one timestep:
      - Select a single file and a single timestep from that file
      - Verify that a single timestep can be selected (Yes: return this xarray object, No: select all files and select a single timestep from that)
@@ -229,7 +237,7 @@ def open_netcdfs(args, logger, thorough=False):
             check_memory(xfiles, [i for i in range(len(xfiles))], args.memory, logger)
         else:
             logger.warning('Memory checks bypassed')
-        xobj = xr.concat([xr.open_dataset(fx) for fx in xfiles], dim='time', data_vars='minimal')
+        xobj = xr.concat([xr.open_dataset(fx) for fx in xfiles], dim=concat_dims, data_vars='minimal')
         return xobj, None, xfiles
 
 ## 3. Validation Testing
@@ -276,7 +284,7 @@ def compare_data(vname: str, xbox, kerchunk_box, logger, bypass=False):
         tolerance = None
 
     testpass = True
-    if not np.array_equal(xbox, kerchunk_box):
+    if not np.array_equal(xbox, kerchunk_box, equal_nan=True):
         logger.warning(f'Failed equality check for {vname}')
         raise ValidationError
     try:
@@ -370,17 +378,48 @@ def validate_shapes(xobj, kobj, step: int, nfiles: list, xv: str, logger, proj_c
             except:
                 pass
     logger.debug(f'{xv} - dimension-adjusted shapes - K: {kshape}, X: {xshape}')
-    
-    if len(xshape) != len(kshape):
-        raise ShapeMismatchError(var=xv, first=kshape, second=xshape)
-    elif xshape != kshape and bypass_shape: # Special bypass-shape testing
-        if concat_dims == {}:
+    if xshape != kshape:
+        # Incorrect dimensions on the shapes of the arrays
+        if xshape != kshape and bypass_shape: # Special bypass-shape testing
             logger.info('Attempting special bypass using tolerance feature')
             validate_shape_to_tolerance(nfiles, xv, xobj[xv].dims, xshape, kshape, logger, detailfile=detailfile)
         else:
-            raise TrueShapeValidationError
+            raise ShapeMismatchError(var=xv, first=xshape, second=kshape)
+        
+def check_for_nan(box, bypass, logger, label=None):
+    """Special function for assessing if a box selection has non-NaN values within it.
+    
+    - Needs further testing using different data types"""
+    logger.debug(f'Checking nan values for {label}')
+
+    def handle_boxissue(err):
+        if type(err) == TypeError:
+            return False
+        else:
+            if bypass.skip_boxfail:
+                logger.warning(f'{err} - Uncaught error bypassed')
+                return False
+            else:
+                raise err
+
+    if box.size == 1:
+        try:
+            isnan = np.isnan(box)
+        except Exception as err:
+            isnan = handle_boxissue(err)
     else:
-        pass
+        try:
+            kb = np.array(box)
+            isnan = np.all(kb!=kb)
+        except Exception as err:
+            isnan = handle_boxissue(err)
+        
+        if not isnan and box.size >= 1:
+            try:
+                isnan = np.all(kb == np.mean(kb))
+            except Exception as err:
+                isnan = handle_boxissue(err)
+    return isnan
 
 def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv: int, logger, bypass=BypassSwitch()):
     """Validate this data selection in xvariable/kvariable objects
@@ -395,8 +434,9 @@ def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv:
     logger.debug(f'Attempt {repeat} - {currentdiv} divs for {vname}')
 
     vslice = []
-    shape = {}
+    shape = []
     if divs > 1:
+        shape = xvariable.shape
         logger.debug(f'Detected shape {shape} for {vname}')
         dtypes  = [xvariable[xvariable.dims[x]].dtype for x in range(len(xvariable.shape))]
         lengths = [len(xvariable[xvariable.dims[x]])  for x in range(len(xvariable.shape))]
@@ -409,28 +449,24 @@ def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv:
         kbox = kvariable
 
     # Zero shape means no point running divisions - just perform full check
-    if shape == {} and vslice == []:
+    if shape == [] and vslice == []:
         logger.debug(f'Skipping to full selection (1 division) for {vname}')
         currentdiv = 1
 
-    try:
-        kb = np.array(kbox)
-        if np.all(kb!=kb):
-            isnan = True
-        elif np.all(kb == np.mean(kb)):
-            isnan = True
-        else:
-            isnan = False
-    except Exception as err:
-        if bypass.skip_boxfail:
-            logger.warning(f'{err} - check versions')
-            isnan = True
-        else:
-            raise err
+    try_multiple = 0
+    knan, xnan = False, True
+    # Attempt nan checking multiple times due to network issues.
+    while try_multiple < 3 and knan != xnan:
+        knan = check_for_nan(kbox, bypass, logger, label='Kerchunk')
+        xnan = check_for_nan(xbox, bypass, logger, label='Xarray')
+        try_multiple += 1
+
+    if knan != xnan:
+        raise ValidationError('Kerchunk/NetCDF value mismatch - expected NaN, received values')
         
-    if kbox.size >= 1 and not isnan:
+    if kbox.size >= 1 and not knan:
         # Evaluate kerchunk vs xarray and stop here
-        logger.debug(f'Found non-NaN values with box-size: {int(kbox.size)}')
+        logger.debug(f'Found comparable box-size: {int(kbox.size)} values')
         compare_data(vname, xbox, kbox, logger, bypass=bypass.skip_data_sum)
     else:
         logger.debug(f'Attempt {repeat} - slice is Null')
@@ -442,26 +478,39 @@ def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv:
             if not bypass.skip_softfail:
                 raise SoftfailBypassError
 
-def validate_data(xobj, kobj, xv: str, step: int, logger, bypass=BypassSwitch(), depth_default=128):
+def validate_data(xobj, kobj, xv: str, step: int, logger, bypass=BypassSwitch(), depth_default=128, nfiles=2):
     """Run growing selection test for specified variable from xarray and kerchunk datasets"""
-    logger.info(f'{xv} : Starting growbox data tests for {step}')
+    logger.info(f'{xv} : Starting growbox data tests for {step+1} - {depth_default}')
 
-    kvariable, xvariable = match_timestamp(xobj[xv], kobj[xv], logger)
+    if nfiles > 1: # Timestep matching not required if only one file
+        kvariable, xvariable = match_timestamp(xobj[xv], kobj[xv], logger)
+    else:
+        kvariable = kobj[xv]
+        xvariable = xobj[xv]
 
     # Attempt 128 divisions within selection - 128, 64, 32, 16, 8, 4, 2, 1
     return validate_selection(xvariable, kvariable, xv, depth_default, depth_default, logger, bypass=bypass)
 
-def validate_timestep(args, xobj, kobj, step: int, nfiles: int, logger, concat_dims={}):
+def validate_timestep(args, xobj, kobj, step: int, nfiles: int, logger, concat_dims={}, index=0):
     """Run all tests for a single file which may or may not equate to 1 timestep"""
+    # Note: step indexed from 0
 
     # Run Variable and Shape validation
+
+    if 'virtual' in concat_dims:
+        # Assume virtual dimension is first?
+        logger.info("Filtering out virtual dimension for testing")
+        virtual = {concat_dims['virtual']:index}
+        logger.debug(f'Kerchunk index: {index}')
+        kobj = kobj.isel(**virtual)
+
     xvars = set(xobj.variables)
     kvars = set(kobj.variables)
     if xvars&kvars != xvars: # Overlap of sets - all xvars should be in kvars
         missing = (xvars^kvars)&xvars 
         raise VariableMismatchError(missing=missing)
     else:
-        logger.info(f'Passed Variable tests')
+        logger.info(f'Passed Variable tests - all required variables are present')
         print()
         for xv in xvars:
             validate_shapes(xobj, kobj, step, nfiles, xv, logger, args.proj_code,
@@ -472,7 +521,7 @@ def validate_timestep(args, xobj, kobj, step: int, nfiles: int, logger, concat_d
         logger.info(f'Passed all Shape tests')
         print()
         for xv in xvars:
-            validate_data(xobj, kobj, xv, step, logger, bypass=args.bypass)
+            validate_data(xobj, kobj, xv, step, logger, bypass=args.bypass, nfiles=nfiles)
             logger.info(f'{xv} : Passed Data test')
 
 def run_successful(args, logger):
@@ -495,8 +544,18 @@ def run_successful(args, logger):
         os.makedirs(complete_dir)
 
     # Open config file to get correct version
+    version_no = 'kr1.0'
+    detailfile = f'{args.proj_dir}/detail-cfg.json'
+    if os.path.isfile(detailfile):
+        with open(detailfile) as f:
+            details = json.load(f)
+        if 'version_no' in details:
+            version_no = details['version_no']
+            logger.info(f'Found version {version_no} in detail-cfg')
+    else:
+        logger.warning('detail-cfg.json file missing or unreachable - using default version number')
 
-    newfile = f'{complete_dir}/{args.proj_code}_kr1.0.json'
+    newfile = f'{complete_dir}/{args.proj_code}_{version_no}.json'
     if args.dryrun:
         logger.info(f'DRYRUN: mv {kfile} {newfile}')
     else:
@@ -531,21 +590,16 @@ def run_backtrack(args, logger):
 
     logger.info(f'{args.proj_code} Successfully backtracked to pre-validation')
     
-def attempt_timestep(args, xobj, kobj, step, nfiles, logger, xfiles, depth=0, concat_dims={}):
+def attempt_timestep(args, xobj, kobj, step, nfiles, logger, concat_dims={}, fullset=False):
+    """Handler for attempting processing on a timestep multiple times.
+    - Handles error conditions"""
     try:
         validate_timestep(args, xobj, kobj, step, nfiles, logger, concat_dims=concat_dims)
     except ShapeMismatchError as err:
-        if depth == 2:
+        if fullset:
             raise TrueShapeValidationError
         else:
             return True
-    except XKShapeToleranceError as err:
-        if depth < 1:
-            # Try new routine to just get the key variable sizes
-            concat_dims = get_concat_dims(xfiles, detailfile=f'{args.proj_dir}/detail-cfg.json')
-            attempt_timestep(args, xobj, kobj, step+1, nfiles, logger, xfiles, depth=depth+1, concat_dims=concat_dims)
-        else:
-            return True
     except Exception as err:
         raise err
 
@@ -575,26 +629,45 @@ def validate_dataset(args):
     if indexes == None:
         args.quality = True
 
+    detailfile = f'{args.proj_dir}/detail-cfg.json'
+    with open(detailfile) as f:
+        details = json.load(f)
+
     ## Open kerchunk file
     kobj, _v = locate_kerchunk(args, logger)
     if not kobj:
         raise MissingKerchunkError
 
-    ## Set up loop variables
-    fullset = bool(args.quality)
+    virtual = False
+    if 'virtual_concat' in details:
+        virtual = details['virtual_concat']
+    
+    if virtual:
+        concat_dims = {'virtual': details['combine_kwargs']['concat_dims'][0]}
+        # Perform virtual attempt
+        logger.info(f"Attempting file subset validation: {len(indexes)}/{nfiles} (virtual dimension)")
+        for step, index in enumerate(indexes):
+            xobj = xobjs[step]
+            logger.info(f'Running tests for selected file: {index} ({step+1}/{len(indexes)})')
+            attempt_timestep(args, xobj, kobj, step, nfiles, logger, concat_dims=concat_dims, index=index)
+    else:
+        ## Set up loop variables
+        fullset     = bool(args.quality)
+        concat_dims = get_concat_dims(xfiles, detailfile=f'{args.proj_dir}/detail-cfg.json')
 
-    if not fullset:
         logger.info(f"Attempting file subset validation: {len(indexes)}/{nfiles}")
         for step, index in enumerate(indexes):
             xobj = xobjs[step]
             logger.info(f'Running tests for selected file: {index} ({step+1}/{len(indexes)})')
-            fullset = attempt_timestep(args, xobj, kobj, step+1, nfiles, logger, xfiles)
-
-    if fullset:
-        print()
-        logger.info(f"Attempting total validation")
-        xobjs, indexes, nfiles = open_netcdfs(args, logger, thorough=True)
-        fullset = attempt_timestep(args, xobjs, kobj, 0, 1, logger, xfiles)
+            fullset = attempt_timestep(args, xobj, kobj, step, nfiles, logger, concat_dims=concat_dims)
+            if fullset:
+                break
+
+        if fullset:
+            print()
+            logger.info(f"Attempting total validation")
+            xobjs, indexes, nfiles = open_netcdfs(args, logger, thorough=True)
+            attempt_timestep(args, xobjs, kobj, 0, 1, logger, xfiles, concat_dims=concat_dims, fullset=True)
     
     logger.info('All tests passed successfully')
     print()

From 834e86729ab33169550ade65ebb97f3e86d881e9 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 14:43:26 +0000
Subject: [PATCH 30/45] Removed old examples

---
 extensions/examples/UKCP_test1.csv | 3 ---
 extensions/examples/UKCP_test1.txt | 2 --
 2 files changed, 5 deletions(-)
 delete mode 100644 extensions/examples/UKCP_test1.csv
 delete mode 100644 extensions/examples/UKCP_test1.txt

diff --git a/extensions/examples/UKCP_test1.csv b/extensions/examples/UKCP_test1.csv
deleted file mode 100644
index d1ac95d..0000000
--- a/extensions/examples/UKCP_test1.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-proj_code,pattern/filename, updates, removals
-land-rcm_uk_12km_rcp85_01_tasmax_day_v20190731,/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/tasmax/day/v20190731/*.nc,,
-land-rcm_uk_12km_rcp85_01_tasmin_day_v20190731,/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/tasmin/day/v20190731/*.nc,,
\ No newline at end of file
diff --git a/extensions/examples/UKCP_test1.txt b/extensions/examples/UKCP_test1.txt
deleted file mode 100644
index 05e6195..0000000
--- a/extensions/examples/UKCP_test1.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/tasmax/day/v20190731/*.nc
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/tasmin/day/v20190731/*.nc

From b004939c74c25027e899d184f4eaaeff7383fee9 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 14:45:52 +0000
Subject: [PATCH 31/45] Removed unnecessary or outdated files

---
 extensions/archive/combine_time.py            |   14 -
 extensions/archive/combine_times.py           |   14 -
 extensions/archive/control_agg_meta.py        |   17 -
 extensions/archive/copy_refs.py               |   37 -
 extensions/archive/correct_meta.py            |  161 --
 extensions/archive/create_cfgs.py             |   14 -
 extensions/archive/create_parq.py             |   60 -
 extensions/archive/generic_test.py            |  320 ---
 extensions/archive/merge_kerchunks.py         |   69 -
 extensions/archive/specific_test.py           |  252 ---
 extensions/deployments/UKCP_12km.txt          | 1800 -----------------
 extensions/deployments/UKCP_12km_day.txt      |  180 --
 extensions/issues/showcase_issue01.py         |    9 -
 extensions/scripts/deploy_gh.sh               |    5 -
 extensions/scripts/extra_tools/add_dap.py     |   26 -
 extensions/scripts/extra_tools/corrections.py |  183 --
 .../scripts/extra_tools/metadata_viewer.py    |   79 -
 .../scripts/extra_tools/wide_corrections.py   |   95 -
 18 files changed, 3335 deletions(-)
 delete mode 100644 extensions/archive/combine_time.py
 delete mode 100644 extensions/archive/combine_times.py
 delete mode 100644 extensions/archive/control_agg_meta.py
 delete mode 100644 extensions/archive/copy_refs.py
 delete mode 100644 extensions/archive/correct_meta.py
 delete mode 100644 extensions/archive/create_cfgs.py
 delete mode 100644 extensions/archive/create_parq.py
 delete mode 100644 extensions/archive/generic_test.py
 delete mode 100644 extensions/archive/merge_kerchunks.py
 delete mode 100644 extensions/archive/specific_test.py
 delete mode 100644 extensions/deployments/UKCP_12km.txt
 delete mode 100644 extensions/deployments/UKCP_12km_day.txt
 delete mode 100644 extensions/issues/showcase_issue01.py
 delete mode 100644 extensions/scripts/deploy_gh.sh
 delete mode 100644 extensions/scripts/extra_tools/add_dap.py
 delete mode 100644 extensions/scripts/extra_tools/corrections.py
 delete mode 100644 extensions/scripts/extra_tools/metadata_viewer.py
 delete mode 100644 extensions/scripts/extra_tools/wide_corrections.py

diff --git a/extensions/archive/combine_time.py b/extensions/archive/combine_time.py
deleted file mode 100644
index a71e6ad..0000000
--- a/extensions/archive/combine_time.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import pandas as pd
-
-PATH = '/home/users/dwest77/Documents/kerchunk_dev/parquet/dev'
-raw = None
-for x in range(0,76):
-    df = pd.read_parquet(f'{PATH}/batch/esacci7_full/time/refs.{x}.parq')
-    if not raw:
-        raw = df['raw'][0]
-    else:
-        raw += df['raw'][0]
-
-df.to_parquet(f'{PATH}/batch/esacci7_full/time/refs.0.parq')
-
-#df.to_csv('time0.7.csv')
\ No newline at end of file
diff --git a/extensions/archive/combine_times.py b/extensions/archive/combine_times.py
deleted file mode 100644
index a71e6ad..0000000
--- a/extensions/archive/combine_times.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import pandas as pd
-
-PATH = '/home/users/dwest77/Documents/kerchunk_dev/parquet/dev'
-raw = None
-for x in range(0,76):
-    df = pd.read_parquet(f'{PATH}/batch/esacci7_full/time/refs.{x}.parq')
-    if not raw:
-        raw = df['raw'][0]
-    else:
-        raw += df['raw'][0]
-
-df.to_parquet(f'{PATH}/batch/esacci7_full/time/refs.0.parq')
-
-#df.to_csv('time0.7.csv')
\ No newline at end of file
diff --git a/extensions/archive/control_agg_meta.py b/extensions/archive/control_agg_meta.py
deleted file mode 100644
index 52f48e8..0000000
--- a/extensions/archive/control_agg_meta.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import os
-
-workdir = '/gws/nopw/j04/esacci_portal/kerchunk'
-
-with open(f'{workdir}/filesets.csv') as f:
-    contents = [r.replace('\n','') for r in f.readlines()]
-
-cfg = '/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/temp/lccs_cfg.txt'
-
-for line in contents:
-    proj_code = line.split(',')[1]
-
-    old = 'kr1.1'
-    new = 'kr1.1'
-    print(proj_code)
-    os.system(f'python correct_meta.py {proj_code}-{old} {old} {new} {workdir} {cfg}')
-    #x=input()
\ No newline at end of file
diff --git a/extensions/archive/copy_refs.py b/extensions/archive/copy_refs.py
deleted file mode 100644
index 7289379..0000000
--- a/extensions/archive/copy_refs.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-
-
-PATH = '/home/users/dwest77/Documents/kerchunk_dev/parquet/dev/batch'
-
-PARTS = f'{PATH}/gargant_parts'
-FULL = f'{PATH}/gargant'
-
-
-# Combine metadatae into a single zmeta directory
-#if not os.path.isdir(f'batch/{FULL}'):
-    #os.makedirs(f'batch/{FULL}')
-varnames = []
-for dirname in os.listdir(f'{PARTS}/batch0'):
-    if dirname != '.zmetadata':
-        try:
-            os.makedirs(f'{FULL}/{dirname}')
-        except:
-            pass
-        varnames.append(dirname)
-        
-specials = {'lat':1, 'lon':1}
-repeat = 2
-for varname in varnames:
-#if True:
-    #varname = 'time'
-    print(varname)
-    refid = 0
-    if varname in specials:
-        repeat = specials[varname]
-
-    for index in range(repeat):
-        directory = f'{PARTS}/batch{index}/{varname}'
-        for ref in os.listdir(directory):
-            #if not os.path.isfile(f'batch/{FULL}/{varname}/refs.{refid}.parq'):
-            os.system(f'cp {directory}/{ref} {FULL}/{varname}/refs.{refid}.parq')
-            refid += 1
diff --git a/extensions/archive/correct_meta.py b/extensions/archive/correct_meta.py
deleted file mode 100644
index 5790e5d..0000000
--- a/extensions/archive/correct_meta.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import os
-import xarray as xr
-import json
-from datetime import datetime
-import sys
-import uuid
-
-OVERWRITE=True
-
-def correct_shape(old, new, storepath):
-    old = 5
-    new = 10
-    PATH = '/home/users/dwest77/Documents/kerchunk_dev/parquet/dev/batch/gargant'
-    with open(f'{storepath}/.zmetadata') as f:
-        refs = json.load(f)
-        
-    meta = refs['metadata']
-
-    for key in meta.keys():
-        if '.zarray' in key:
-            # Correct chunks
-            if meta[key]['shape'][0] == old:
-                meta[key]['shape'][0] = new
-
-    refs['metadata'] = meta
-    with open(f'{storepath}/.zmetadata','w') as f:
-        f.write(json.dumps(refs))
-
-def direct_comparison(xattrs, kattrs):
-    corrections = []
-    for key in xattrs.keys():
-        if key not in kattrs.keys():
-            kattrs[key] = str(xattrs[key])
-            corrections.append(key)
-        else:
-            if kattrs[key] != xattrs[key]:
-                kattrs[key] = str(xattrs[key])
-                corrections.append(key)
-    return xattrs, kattrs, corrections
-
-def specific_comparison(xattrs, kattrs, cfg):
-    extras = {}
-    if cfg:
-        print('Found cfg file for extra attribute config')
-        with open(cfg) as f:
-            for r in f.readlines():
-                values = r.replace('\n','').split(',')
-                extras[values[0]] = values[1]
-
-    missing = []
-    corrections = []
-    for key in xattrs.keys():
-        if key not in kattrs.keys():
-            kattrs[key] = str(xattrs[key])
-            missing.append(key)
-        else:
-            if kattrs[key] != xattrs[key]:
-                if 'end' in key:
-                    kattrs[key] = str(xattrs[key])
-                    corrections.append(key)
-                elif 'start' in key:
-                    pass
-                elif key in extras.keys():
-                    if extras[key] == 'replace':
-                        kattrs[key] = str(xattrs[key])
-                else:
-                    missing.append(key)
-            else:
-                pass
-    if len(missing) > 0:
-        print('WARNING: Missing additional fields - check manually')
-        print(' >> ' + ','.join(missing))
-        return 0, 0, 0, None
-    else:
-        return xattrs, kattrs, corrections, True
-
-def correct_attrs(proj_code, old, revision, textref, old_revision, new_revision, cfg=None, skip=False):
-    # Default approach is to compare with xr.open_mfdataset
-    # Correct any different metadata and save kerchunk attributes
-    direct = False
-    # Get Xarray Global Attributes
-    if not skip:
-        with open(textref) as f:
-            xopens = [r.replace('\n','') for r in f.readlines()]
-        if len(xopens) >= 2:
-            xopens = [xopens[0], xopens[-1]]
-            if xopens[0] == xopens[1]:
-                skip = True
-        else:
-            skip = True
-    if not skip:
-        try:
-            xattrs = xr.open_mfdataset(xopens).attrs
-            direct = True
-        except ValueError:
-            xattrs = xr.open_dataset(xopens[-1]).attrs
-
-    # Get Kerchunk Attributes
-    with open(old_revision) as f:
-        refs = json.load(f)
-    kattrs = json.loads(refs['refs']['.zattrs'])
-
-    if not skip:
-        # Set all attributes if they are incorrect
-        if direct:
-            print('Using direct comparison')
-            xattrs, kattrs, corrections = direct_comparison(xattrs, kattrs)
-            success = True
-        else:
-            print('Using specific comparison')
-            xattrs, kattrs, corrections, success = specific_comparison(xattrs, kattrs, cfg) 
-
-        if not success:
-            return None
-        
-        print('Corrected: ',end='')
-        if not corrections:
-            print(None)
-        else:
-            print(', '.join(corrections))
-
-    # Set kerchunk specific attributes
-    now = datetime.now()
-    stamp = now.strftime("%y%m%dT%H%M%SZ")
-    ymd = now.strftime("%d/%m/%y")
-    if 'Kerchunk' in kattrs['history'].split('\n')[-1]:
-        pass
-    else:
-        kattrs['history'] = kattrs['history'] + f"\nKerchunk file last updated by CEDA on {ymd} in the context of the CCI Knowledge Exchange Project"
-    kattrs['kerchunk_revision'] = revision
-    kattrs['kerchunk_creation_date'] = str(stamp)
-    kattrs['tracking_id'] = str(uuid.uuid4())
-
-    # Export new attributes
-    refs['refs']['.zattrs'] = json.dumps(kattrs)
-    if not os.path.isfile(new_revision) or OVERWRITE:
-        with open(new_revision,'w') as f:
-            f.write(json.dumps(refs))
-        print('Written to',new_revision)
-    return None
-
-#correct_attrs(proj_code, old, revision, textref, old_revision, new_revision)
-proj_code    = sys.argv[1]
-old          = sys.argv[2]
-revision     = sys.argv[3]
-workdir      = sys.argv[4]
-
-try:
-    cfg = sys.argv[5]
-except:
-    cfg = None
-
-textref      = f'{workdir}/metaref_filelists/{proj_code}.txt'
-old_revision = f'{workdir}/kfiles_1.1/{proj_code}.json'
-new_revision = f'{workdir}/kfiles_1.1/{proj_code.replace(old, revision)}.json'
-if '.nc' in new_revision:
-    new_revision = new_revision.replace('.nc','')
-if not os.path.isfile(new_revision) or OVERWRITE:
-    correct_attrs(proj_code, old, revision, textref, old_revision, new_revision,cfg=cfg, skip=True)
-else:
-    print('skipped existing file')
\ No newline at end of file
diff --git a/extensions/archive/create_cfgs.py b/extensions/archive/create_cfgs.py
deleted file mode 100644
index 1a1787c..0000000
--- a/extensions/archive/create_cfgs.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import os
-
-workdir = '/gws/nopw/j04/esacci_portal/kerchunk'
-
-revision = input('Enter revision: ')
-
-with open(f'{workdir}/filesets.csv') as f:
-    contents = [r.replace('\n','') for r in f.readlines()]
-for x, line in enumerate(contents):
-    pattern   = line.split(',')[0]
-    proj_code = line.split(',')[1]
-
-    with open(f'{workdir}/temp/configs/{x}.txt','w') as f:
-        f.write(f'{pattern},{proj_code},{revision}')
diff --git a/extensions/archive/create_parq.py b/extensions/archive/create_parq.py
deleted file mode 100644
index 9252494..0000000
--- a/extensions/archive/create_parq.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from kerchunk import hdf, combine, df
-import fsspec.implementations.reference
-from fsspec.implementations.reference import LazyReferenceMapper
-from tempfile import TemporaryDirectory
-
-import matplotlib.pyplot as plt
-import json
-import xarray as xr
-import os, sys
-
-# Requires:
-# - Filelist: list of filepaths to use for creating parquet files
-# - Config file: contains parquet outdir, filelistdir, pqname, record_size
-
-# python create_parq.py OUTDIR FILELISTDIR pqname 
-
-# From config file
-OUTDIR = '/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/test_parqs/parqs'
-FILELISTDIR = '/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/test_parqs/filelists'
-
-def make_parquet(pqname, record_size):
-    print(pqname)
-    pq = f'{OUTDIR}/{pqname}'
-    with open(f'{FILELISTDIR}/{pqname}.txt') as f:
-        files = [r.split('\n')[0] for r in f.readlines()]
-
-    try:
-        os.makedirs(pq)
-    except:
-        pass
-
-    single_ref_sets = []
-    for url in files:
-        print('[INFO]', url)
-        single_ref_sets.append(hdf.SingleHdf5ToZarr(url, inline_threshold=-1).translate())
-        
-    out = LazyReferenceMapper.create(record_size, pq, fs = fsspec.filesystem("file"))
-
-    out_dict = combine.MultiZarrToZarr(
-        single_ref_sets,
-        remote_protocol="file",
-        concat_dims=["time"],
-        out=out
-    ).translate()
-    
-    out.flush()
-
-    print('Written refs to df', pq)
-
-# From config file
-pqname = sys.argv[-1]
-record_size = 100
-#try:
-if True:
-    if not os.path.isfile(f'{OUTDIR}/{pqname}/.zmetadata'):
-        make_parquet(pqname, record_size)
-    else:
-        print('Zmetadata file already exists')
-#except:
-    #print('Error recorded for', pqname)
\ No newline at end of file
diff --git a/extensions/archive/generic_test.py b/extensions/archive/generic_test.py
deleted file mode 100644
index 6412508..0000000
--- a/extensions/archive/generic_test.py
+++ /dev/null
@@ -1,320 +0,0 @@
-import os
-import xarray as xr
-import json
-from datetime import datetime
-import sys
-import fsspec
-import random
-import numpy as np
-import glob
-
-# Pick a specific variable
-# Find number of dimensions
-# Start in the middle
-# Get bigger until we have a box that has non nan values
-
-def vprint(msg,verb=False):
-    if verb:
-        print(msg)
-
-def compare_xk(xbox, kbox, faillog, vname, step='', verb=False):
-    vprint('Starting xk comparison',verb=verb)
-    rtol = np.abs(np.nanmean(kbox))/20
-    # Tolerance 0.1% of mean value for xarray set
-    failed = False
-    closeness = np.isclose(xbox, kbox, rtol=rtol, equal_nan=True)
-    if closeness[closeness == False].size > 0:
-        vprint(' > Failed elementwise comparison XXX',verb=verb)
-        faillog.append(f'ElementwiseFail: {vname} - {step}')
-        failed = True
-    if np.abs(np.nanmax(kbox) - np.nanmax(xbox)) > rtol:
-        vprint(' > Failed maximum comparison XXX',verb=verb)
-        faillog.append(f'MaxFail: {vname} - {step}')
-        failed = True
-    if np.abs(np.nanmin(kbox) - np.nanmin(xbox)) > rtol:
-        vprint(' > Failed minimum comparison XXX',verb=verb)
-        faillog.append(f'MinFail: {vname} - {step}')
-        failed = True
-    if np.abs(np.nanmean(kbox) - np.nanmean(xbox)) > rtol:
-        vprint(' > Failed mean comparison XXX',verb=verb)
-        faillog.append(f'MeanFail: {vname} - {step}')
-        failed = True
-    if not failed:
-        vprint('Passed all growbox tests',verb=verb)
-    return (not failed), faillog 
-
-def find_dimensions(dimlen, depth):
-    # Round down then add 1
-    slicemax = int(dimlen/depth)+1
-    return slicemax
-
-def squeeze_dims(variable):
-    # Correct 1-size dimensions:
-    concat_axes = []
-    for x, d in enumerate(variable.shape):
-        if d == 1:
-            concat_axes.append(x)
-    for dim in concat_axes:
-        variable = variable.squeeze(axis=dim)
-    return variable
-
-def get_vslice(shape, dtypes, lengths, depth):
-    vslice = []
-    for x, dim in enumerate(shape):
-        if np.issubdtype(dtypes[x], np.datetime64):
-            vslice.append(slice(0,find_dimensions(lengths[x],depth)))
-        elif dim == 1:
-            vslice.append(slice(0,1))
-        else:
-            vslice.append(slice(0,find_dimensions(dim,depth)))
-    return vslice
-
-def test_growbox(xvariable, kvariable, vname, depth, faillog, verb=False):
-    # Run growing-box test for specified variable from xarray and kerchunk datasets
-    #if depth % 10 == 0:
-        #vprint(depth, verb=verb)
-    if xvariable.size > 1 and kvariable.size > 1:
-        xvariable = squeeze_dims(xvariable)
-        kvariable = squeeze_dims(kvariable)
-
-    vslice = []
-    if depth != 2:
-        shape = xvariable.shape
-        dtypes  = [xvariable[xvariable.dims[x]].dtype for x in range(len(xvariable.shape))]
-        lengths = [len(xvariable[xvariable.dims[x]]) for x in range(len(xvariable.shape))]
-        vslice = get_vslice(shape, dtypes, lengths, depth)
-
-        xbox = xvariable[tuple(vslice)]
-        kbox = kvariable[tuple(vslice)]
-    else:
-        xbox = xvariable
-        kbox = kvariable
-
-    try:
-        kb = np.array(kbox)
-        isnan = np.all(kb!=kb)
-    except TypeError as err:
-        faillog.append(f'TypeError:{err} - nan conversion')
-        isnan = True
-    except KeyError as err:
-        faillog.append(f'KeyError:{err} - check versions')
-        isnan = True
-    if kbox.size > 1 and not isnan:
-        # Evaluate kerchunk vs xarray and stop here
-        vprint(f' > Found non-nan values with box-size: {int(kbox.size)}',verb=verb)
-        return compare_xk(xbox, kbox, faillog, vname, verb=verb)
-    else:
-        if depth > 2:
-            return test_growbox(xvariable, kvariable, vname, int(depth/2), faillog, verb=verb)
-        else:
-            print(f' > Failed to find non-NaN slice (depth: {depth}, var: {vname})')
-            return 'soft-fail', faillog
-
-def test_single_time(xobj, kobj, faillog, step='', verb=False):
-    # Test all variables
-    vars = []
-    for v in list(xobj.variables):
-        if 'time' not in str(v) and str(xobj[v].dtype) in ['float32','int32','int64','float64']:
-            vars.append(v)
-
-    # Determine number of attempts to make - now testing all variables
-    attempts = len(vars)
-
-    totalpass = True
-    softfail = True
-    sizes = []
-    print()
-    print(' . Starting generic metadata tests .')
-    for v in vars:
-        if v not in list(kobj.variables):
-            vprint(f'Test Failed: Variable missing from kerchunk dataset: {v}',verb=verb)
-            faillog.append(f'VariableMissing: {v} - {step}')
-            totalpass = False
-        else:
-            xshape = xobj[v].shape
-            kshape = kobj[v].shape
-            k1shape = kshape[1:]
-            if xshape != kshape and xshape != k1shape:
-                vprint(f'Test Failed: Kerchunk/Xarray shape mismatch for: {v} - ({xobj[v].shape}, {kobj[v].shape})',verb=verb)
-                faillog.append(f'ShapeMismatch: {v} - ({xobj[v].shape}, {kobj[v].shape}) - {step}')
-                totalpass = False
-        sizes.append(xobj[v].size)
-    if totalpass:
-        print('All generic tests have passed, proceeding\n')
-    else:
-        print('One or more generic tests failed - aborting\n')
-        return None, None, faillog
-    
-    # Make depth such that initial box has ~1000 items
-    defaults = np.array(sizes)/10000
-    defaults[defaults < 100] = 100
-    print(f' . Starting growbox method ({attempts} variables).')
-    tried_vars = []
-    for index in range(attempts):
-        if int(index/attempts) % 10:
-            print(f' > {int((index/attempts)/10)}%')
-        var =  vars[index]
-        tried_times = 0
-        while var in tried_vars and tried_times < 100 and len(tried_vars) < len(vars):
-            var =  vars[random.randint(0,len(vars))]
-            tried_times += 1
-        tried_vars.append(var)
-
-        vprint(f'\nTesting grow-box method for {var}',verb=verb)
-        status, faillog = test_growbox(xobj[var],kobj[var], var, int(defaults[index]), faillog, verb=verb) 
-        if status == 'soft-fail':
-            vprint(f' > Grow-box failed softly for {var}',verb=verb)
-            softfail = True
-        elif status:
-            vprint(f' > Grow-box method passed for {var}',verb=verb)
-        else:
-            vprint(f' > Grow-box method failed for {var} XXX',verb=verb)
-            totalpass = False
-    if totalpass:
-        print('Growbox tests have passed, proceeding\n')
-    else:
-        print('One or more growbox tests failed - aborting\n')
-    return totalpass, softfail, faillog
-
-def get_select_files(testpattern):
-    xfiles = glob.glob(testpattern)
-    # Open 3 random files in turn
-    numfiles = int(len(xfiles)/1000)
-    if numfiles < 3:
-        numfiles = 3
-    if numfiles > len(xfiles):
-        numfiles = len(xfiles)
-
-    return xfiles, numfiles
-
-def open_kerchunk(kfile, isparq=False):
-    if isparq:
-        kobj = None
-    else:
-        mapper  = fsspec.get_mapper('reference://',fo=kfile, target_options={"compression":None})
-        kobj    = xr.open_zarr(mapper, consolidated=False)
-    return kobj
-
-def pick_index(nfiles, indexes):
-    index = random.randint(0,nfiles)
-    count = 0
-    while index in indexes and count < 100:
-        index = random.randint(0,nfiles)
-        count += 1
-    indexes.append(index)
-    return indexes
-
-def run_tests(testpattern, proj_code, workdir,verb=False, mem_override=False):
-    kfile   = f'{workdir}/{proj_code}.json'
-    if not os.path.isfile(kfile):
-        print('no such file',kfile)
-        return False
-
-    print('Starting tests for',proj_code)
-    print()
-    if type(testpattern) == list:
-        xfiles = testpattern
-        numfiles = len(xfiles)
-    else:
-        xfiles, numfiles = get_select_files(testpattern)
-    indexes = []
-
-    selection = int(len(xfiles)/100)
-    if selection < 10:
-        selection = 10
-
-    vprint(f'Opening kerchunk dataset',verb=verb)
-    kobj = open_kerchunk(kfile)
-
-    totalpass = True
-    faillog = []
-
-    for step in range(selection):
-        print(f'Running tests for file {step+1}/{selection}')
-        indexes = pick_index(len(xfiles)-1, indexes)
-        index   = indexes[-1]
-        xfile = xfiles[index]
-
-        vprint(f'Opening xarray object {step+1}/{selection}',verb=verb)
-
-        # Memory Size Check
-        if os.path.getsize(xfile) > 4e9 and not mem_override: # 3GB file
-            print('Memory Exception warning - ensure you have 12GB or more dedicated to this task')
-            faillog.append(f'MemException: {os.path.getsize(xfile)}, file {index}')
-            return False, False, faillog
-
-        # Temporal Aquisition Check
-        xobj = xr.open_dataset(xfile)
-        try:
-            ksel = kobj.sel(time=xobj.time)
-        except:
-            try:
-                ksel = kobj.isel(time=index)
-            except:
-                vprint('Temporal Selection Error: Unable to select time')
-                faillog.append(f'TemporalError: file {index}')
-                return False, False, faillog
-
-        # Proceed with testing
-        testpass, softfail, faillog = test_single_time(xobj, ksel, faillog, step=index, verb=verb)
-        if not testpass:
-            vprint(f'Testing failed for {step+1}', verb=verb)
-        totalpass = totalpass and testpass
-    
-    if totalpass:
-        print()
-        print('All tests passed for',proj_code)
-    return totalpass, softfail, faillog
-
-mem_override = ('-f' in sys.argv)
-verb         = ('-v' in sys.argv)
-nosave       = ('-n' in sys.argv)
-
-if ('-s' in sys.argv):
-    
-    code     = sys.argv[1]
-    vprint('Opening filelist to extract files')
-    with open(sys.argv[2]) as f:
-        filelist = [r.replace('\n','') for r in f.readlines()]
-
-    workdir  = sys.argv[3]
-
-    pf, soft, log = run_tests(filelist, code, workdir, verb=verb, mem_override=mem_override)
-
-    if not pf:
-        fname = f'{code}_FAIL.txt'
-    else:
-        if soft:
-            fname = f'{code}_SPASS.txt'
-        else:
-            fname = f'{code}_PASS.txt'
-    if not nosave:
-        with open(f'{workdir}/{fname}','w') as f:
-            f.write('\n'.join(log))
-
-else:
-    id = sys.argv[1]
-    workdir = '/gws/nopw/j04/esacci_portal/kerchunk/kfiles_1.1'
-    tempdir = '/gws/nopw/j04/esacci_portal/kerchunk/temp'
-
-    with open(f'{tempdir}/configs/{id}.txt') as f:
-        content = [r.replace('\n','') for r in f.readlines()]
-    pattern  = content[0].split(',')[0]
-    code     = f'{content[0].split(",")[1]}-{content[0].split(",")[2]}'
-
-    if not os.path.isfile(f'{tempdir}/flags/{code}_PASS.txt') and not os.path.isfile(f'{tempdir}/flags/{code}_SPASS.txt'):
-        if os.path.isfile(f'{tempdir}/flags/{code}_FAIL.txt'):
-            os.remove(f'{tempdir}/flags/{code}_FAIL.txt')
-        pf, soft, log = run_tests(pattern, code, workdir, verb=verb, mem_override=mem_override)
-        if not pf:
-            fname = f'{code}_FAIL.txt'
-        else:
-            if soft:
-                fname = f'{code}_SPASS.txt'
-            else:
-                fname = f'{code}_PASS.txt'
-        if not nosave:
-            with open(f'{tempdir}/flags/{fname}','w') as f:
-                f.write('\n'.join(log))
-    else:
-        print('Pass/Softpass detected - skipping')
diff --git a/extensions/archive/merge_kerchunks.py b/extensions/archive/merge_kerchunks.py
deleted file mode 100644
index 08934e7..0000000
--- a/extensions/archive/merge_kerchunks.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Merging Kerchunks
-
-import json
-import os
-
-#  - Take two existing kerchunk files, one being the primary and a secondary full of replacements
-#  - Map all secondary changes into the primary file
-#  - Save as a .mg.json to then be renamed later on.
-
-def replace_kerchunk():
-
-    # Open Primary file
-
-    # Open secondary file
-
-    # How to match up files? 
-    #  - Match by key of chunkdict
-
-    #  - Determine number of files that came before, with the index of the latest file
-    #  - Ensure ordering by sorting filenames by integer value
-    return None
-
-def eat_kerchunk(primary, secondary, last, prim_files, sec_files):
-
-    # Open Primary file
-    # Open Secondary file
-
-    # Take as input the last file before new patch needs to be added
-    # Determine chunk index of that file
-    # Determine number of new chunks. (temporal)
-
-    new_chunks = len(sec_files)
-    last_index = 0
-    for x, p in enumerate(sorted(prim_files)):
-        if p == last:
-            last_index = x
-
-    newsize = len(prim_files) + new_chunks
-
-    with open(primary) as f:
-        refs = json.load(f)
-
-    with open(secondary) as f:
-        new_refs = json.load(f)
-
-    for key in refs['refs'].keys():
-        if '.z' in key:
-            # Handle changing sizes/shapes to correct version
-            pass
-        else:
-            try:
-                var, coords = key.split('/')
-                time_index = int(coords.split('.')[0])
-                if time_index > last_index:
-                    # Set the new position
-                    new_index = time_index + new_chunks
-                    new_pos_key = f'{var}/{new_index}.' + '.'.join(coords.split('.')[1:])
-                    refs['refs'][new_pos_key] = refs['refs'][key]
-
-                    # Add the new chunk data to the original spot
-                    new_chunk = time_index - last_index - 1
-                    rep_pos_key = f'{var}/{new_chunk}.' + '.'.join(coords.split('.')[1:])
-                    refs['refs'][key] = new_refs['refs'][rep_pos_key]
-            except KeyError as err:
-                print(err)
-    # Update old chunks correctly with offset
-    # Insert new chunks into chunkdict with correct offset
-    # Save new chunkdict
-    return None
\ No newline at end of file
diff --git a/extensions/archive/specific_test.py b/extensions/archive/specific_test.py
deleted file mode 100644
index 5fe12ee..0000000
--- a/extensions/archive/specific_test.py
+++ /dev/null
@@ -1,252 +0,0 @@
-import os
-import xarray as xr
-import json
-from datetime import datetime
-import sys
-import fsspec
-import random
-import numpy as np
-import glob
-
-# Pick a specific variable
-# Find number of dimensions
-# Start in the middle
-# Get bigger until we have a box that has non nan values
-
-def vprint(msg,verb=False):
-    if verb:
-        print(msg)
-
-def compare_xk(xbox, kbox, faillog, vname, step='', verb=False):
-    vprint('Starting xk comparison',verb=verb)
-    rtol = np.abs(np.nanmean(kbox))/20
-    # Tolerance 0.1% of mean value for xarray set
-    failed = False
-    closeness = np.isclose(xbox, kbox, rtol=rtol, equal_nan=True)
-    if closeness[closeness == False].size > 0:
-        vprint(' > Failed elementwise comparison XXX',verb=verb)
-        faillog.append(f'ElementwiseFail: {vname} - {step}')
-        failed = True
-    if np.abs(np.nanmax(kbox) - np.nanmax(xbox)) > rtol:
-        vprint(' > Failed maximum comparison XXX',verb=verb)
-        faillog.append(f'MaxFail: {vname} - {step}')
-        failed = True
-    if np.abs(np.nanmin(kbox) - np.nanmin(xbox)) > rtol:
-        vprint(' > Failed minimum comparison XXX',verb=verb)
-        faillog.append(f'MinFail: {vname} - {step}')
-        failed = True
-    if np.abs(np.nanmean(kbox) - np.nanmean(xbox)) > rtol:
-        vprint(' > Failed mean comparison XXX',verb=verb)
-        faillog.append(f'MeanFail: {vname} - {step}')
-        failed = True
-    if not failed:
-        vprint('Passed all growbox tests',verb=verb)
-    return (not failed), faillog 
-
-def find_dimensions(dimlen, depth):
-    # Round down then add 1
-    slicemax = int(dimlen/depth)+1
-    return slicemax
-
-def test_growbox(xvariable, kvariable, vname, depth, faillog, verb=False):
-    # Run growing-box test for specified variable from xarray and kerchunk datasets
-    #if depth % 10 == 0:
-        #vprint(depth, verb=verb)
-
-    vslice = []
-    if depth != 2:
-        for x, dim in enumerate(xvariable.shape):
-            dname = xvariable.dims[x]
-            if np.issubdtype(xvariable[dname].dtype, np.datetime64):
-                #vslice.append([0,find_dimensions(len(xvariable[dname]),depth)])
-                vslice.append(slice(0,find_dimensions(len(xvariable[dname]),depth)))
-            elif dim == 1:
-                #vslice.append([0,1])
-                vslice.append(slice(0,1))
-            else:
-                #vslice.append([0,find_dimensions(dim,depth)])
-                vslice.append(slice(0,find_dimensions(dim,depth)))
-
-        xbox = xvariable[tuple(vslice)]
-        kbox = kvariable[tuple(vslice)]
-    else:
-        xbox = xvariable
-        kbox = kvariable
-    nmkbox = None
-    try:
-        nmkbox = np.nanmean(kbox)
-        checknan = np.abs(nmkbox)     
-    except TypeError as err:
-        print(' > nanmean type error:',err)
-        return None
-    if kbox.size > 1 and checknan >= 0:
-        # Evaluate kerchunk vs xarray and stop here
-        vprint(f' > Found non-nan values with box-size: {int(kbox.size)}',verb=verb)
-        return compare_xk(xbox, kbox, faillog, vname, verb=verb)
-    else:
-        if depth > 2:
-            return test_growbox(xvariable, kvariable, vname, int(depth/2), faillog, verb=verb)
-        else:
-            print(f' > Failed to find non-NaN slice (depth: {depth}, var: {vname})')
-            return 'soft-fail', faillog
-
-workdir     = '/gws/nopw/j04/esacci_portal/kerchunk'
-patterns = []
-codes = []
-with open(f'{workdir}/filesets.csv') as f:
-    lines = [r.replace('\n','') for r in f.readlines()]
-    patterns = [l.split(',')[0] for l in lines]
-    codes    = [l.split(',')[1] for l in lines]
-
-#testpattern = '/neodc/esacci/biomass/data/agb/maps/v4.0/netcdf/ESACCI-BIOMASS-L4-AGB-*MERGED*nc'
-#proj_code   = 'ESACCI-BIOMASS-L4-AGB-MERGED-100m-2010-2020-fv4.0-kr1.0'
-workdir     = '/gws/nopw/j04/esacci_portal/kerchunk/kfiles_1'
-
-def test_single_time(xobj, kobj, faillog, step='', verb=False):
-    # Test all variables
-    vars = []
-    for v in list(xobj.variables):
-        if 'time' not in str(v) and str(xobj[v].dtype) in ['float32','int32','int64','float64']:
-            vars.append(v)
-
-    vars = ['Rrs_490']
-    # Determine number of attempts to make - now testing all variables
-    attempts = len(vars)
-
-    totalpass = True
-    softfail = True
-    sizes = []
-    print()
-    print(' . Starting generic metadata tests .')
-    for v in vars:
-        if v not in list(kobj.variables):
-            vprint(f'Test Failed: Variable missing from kerchunk dataset: {v}',verb=verb)
-            faillog.append(f'VariableMissing: {v} - {step}')
-            totalpass = False
-        else:
-            xshape = xobj[v].shape
-            kshape = kobj[v].shape
-            k1shape = kshape[1:]
-            if xshape != kshape and xshape != k1shape:
-                vprint(f'Test Failed: Kerchunk/Xarray shape mismatch for: {v} - ({xobj[v].shape}, {kobj[v].shape})',verb=verb)
-                faillog.append(f'ShapeMismatch: {v} - ({xobj[v].shape}, {kobj[v].shape}) - {step}')
-                totalpass = False
-        sizes.append(xobj[v].size)
-    if totalpass:
-        print('All generic tests have passed, proceeding\n')
-    else:
-        print('One or more generic tests failed - aborting\n')
-        return None, None, faillog
-    
-    # Make depth such that initial box has ~1000 items
-    defaults = np.array(sizes)/10000
-    defaults[defaults < 100] = 100
-    print(f' . Starting growbox method ({attempts} variables).')
-    tried_vars = []
-    for index in range(attempts):
-        if int(index/attempts) % 10:
-            print(f' > {int((index/attempts)/10)}%')
-        var =  vars[index]
-        tried_times = 0
-        while var in tried_vars and tried_times < 100 and len(tried_vars) < len(vars):
-            var =  vars[random.randint(0,len(vars))]
-            tried_times += 1
-        tried_vars.append(var)
-
-        vprint(f'\nTesting grow-box method for {var}',verb=verb)
-        status, faillog = test_growbox(xobj[var],kobj[var], var, int(defaults[index]), faillog, verb=verb) 
-        if status == 'soft-fail':
-            vprint(f' > Grow-box failed softly for {var}',verb=verb)
-            softfail = True
-        elif status:
-            vprint(f' > Grow-box method passed for {var}',verb=verb)
-        else:
-            vprint(f' > Grow-box method failed for {var} XXX',verb=verb)
-            totalpass = False
-    if totalpass:
-        print('Growbox tests have passed, proceeding\n')
-    else:
-        print('One or more growbox tests failed - aborting\n')
-        return totalpass, softfail, faillog
-    return totalpass, softfail, faillog
-
-def run_tests(testpattern, proj_code, workdir,verb=False, mem_override=False):
-    kfile   = f'{workdir}/{proj_code}.json'
-    if not os.path.isfile(kfile):
-        print('no such file',kfile)
-        return False
-
-    print('Starting tests for',proj_code)
-    print()
-    xfiles = glob.glob(testpattern)
-    # Open 3 random files in turn
-    times = int(len(xfiles)/2)#/1000)
-    if times < 3:
-        times = 3
-    indexes = []
-
-    vprint(f'Opening kerchunk dataset',verb=verb)
-    mapper  = fsspec.get_mapper('reference://',fo=kfile, target_options={"compression":None})
-    kobj    = xr.open_zarr(mapper, consolidated=False)
-
-    faillog = []
-
-    for step in range(53,55):
-        print(f'Running tests for file {step+1}/{times}')
-        index = step #random.randint(0,len(xfiles)-1)
-        #count = 0
-        #while index in indexes or count > 100:
-            #index = random.randint(0,len(xfiles)-1)
-            #count += 1
-        indexes.append(index)
-
-        vprint(f'Opening xarray object {step+1}/{times}',verb=verb)
-        if os.path.getsize(xfiles[index]) > 4e9 and not mem_override: # 3GB file
-            print('Memory Exception warning - ensure you have 12GB or more dedicated to this task')
-            return False, False, [f'MemException: {os.path.getsize(xfiles[index])}']
-        xobj    = xr.open_dataset(xfiles[index])
-
-        ksel    = kobj.sel(time=xobj.time)
-
-        #print(f'{ksel.time} matched to {xobj.time}')
-
-        totalpass, softfail, faillog = test_single_time(xobj, ksel, faillog, step=index, verb=verb)
-        if not totalpass:
-            vprint(f'Testing failed for {step+1}', verb=verb)
-            return None, None, faillog
-    
-    if totalpass:
-        print()
-        print('All tests passed for',proj_code)
-    return totalpass, softfail, faillog
-
-id = sys.argv[1]
-
-mem_override = ('-f' in sys.argv)
-
-workdir = '/gws/nopw/j04/esacci_portal/kerchunk/kfiles_1'
-tempdir = '/gws/nopw/j04/esacci_portal/kerchunk/temp'
-
-with open(f'{tempdir}/configs/{id}.txt') as f:
-    content = [r.replace('\n','') for r in f.readlines()]
-pattern = content[0].split(',')[0]
-code = content[0].split(',')[1]
-
-verb    = True
-
-if True:#not os.path.isfile(f'{tempdir}/flags/{code}_PASS.txt') and not os.path.isfile(f'{tempdir}/flags/{code}_SPASS.txt'):
-    if os.path.isfile(f'{tempdir}/flags/{code}_FAIL.txt'):
-        os.remove(f'{tempdir}/flags/{code}_FAIL.txt')
-    pf, soft, log = run_tests(pattern, code, workdir, verb=verb, mem_override=mem_override)
-    print('Run: ',pf)
-    if not pf:
-        fname = f'{code}_FAIL.txt'
-    else:
-        if soft:
-            fname = f'{code}_SPASS.txt'
-        else:
-            fname = f'{code}_PASS.txt'
-    with open(f'{tempdir}/flags/{fname}','w') as f:
-        f.write('\n'.join(log))
-else:
-    print('Pass/Softpass detected - skipping')
diff --git a/extensions/deployments/UKCP_12km.txt b/extensions/deployments/UKCP_12km.txt
deleted file mode 100644
index 28d9973..0000000
--- a/extensions/deployments/UKCP_12km.txt
+++ /dev/null
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diff --git a/extensions/deployments/UKCP_12km_day.txt b/extensions/deployments/UKCP_12km_day.txt
deleted file mode 100644
index 50eb7c9..0000000
--- a/extensions/deployments/UKCP_12km_day.txt
+++ /dev/null
@@ -1,180 +0,0 @@
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-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/10/tas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/10/tasmax/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/10/tasmin/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/10/uas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/10/vas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/clt/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/hurs/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/huss/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/pr/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/prsn/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/psl/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/rls/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/rss/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/sfcWind/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/snw/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/tas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/tasmax/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/tasmin/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/uas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/11/vas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/clt/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/hurs/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/huss/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/pr/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/prsn/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/psl/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/rls/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/rss/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/sfcWind/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/snw/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/tas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/tasmax/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/tasmin/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/uas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/12/vas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/clt/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/hurs/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/huss/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/pr/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/prsn/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/psl/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/rls/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/rss/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/sfcWind/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/snw/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/tas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/tasmax/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/tasmin/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/uas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/13/vas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/clt/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/hurs/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/huss/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/pr/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/prsn/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/psl/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/rls/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/rss/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/sfcWind/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/snw/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/tas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/tasmax/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/tasmin/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/uas/day/latest
-/badc/ukcp18/data/land-rcm/uk/12km/rcp85/15/vas/day/latest
diff --git a/extensions/issues/showcase_issue01.py b/extensions/issues/showcase_issue01.py
deleted file mode 100644
index 0f0a5ec..0000000
--- a/extensions/issues/showcase_issue01.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import fsspec
-import xarray as xr
-kfile = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_BCC_BCC-CSM2-MR_historical_r1i1p1f1_Amon_huss_gn_v20181126/kerchunk-1c.json'
-kfile2 = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_clt_gn_v20201013-kr1.0.json'
-mapper  = fsspec.get_mapper('reference://',fo=kfile, target_options={"compression":None})
-# Need a safe repeat here
-ds = xr.open_zarr(mapper, consolidated=False, decode_times=True)
-print(ds.time)
-print('Pass')
\ No newline at end of file
diff --git a/extensions/scripts/deploy_gh.sh b/extensions/scripts/deploy_gh.sh
deleted file mode 100644
index e3060d4..0000000
--- a/extensions/scripts/deploy_gh.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-git checkout gh-pages
-
-git merge main
-
-git push origin gh-pages
\ No newline at end of file
diff --git a/extensions/scripts/extra_tools/add_dap.py b/extensions/scripts/extra_tools/add_dap.py
deleted file mode 100644
index 10e99fb..0000000
--- a/extensions/scripts/extra_tools/add_dap.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import pandas as pd
-import os, sys
-
-def update_pd(ref, old, new):
-    df = pd.read_parquet(ref)
-    df['path'] = df['path'].str.replace(old, new)
-    df.to_parquet(ref)
-    print(ref)
-
-
-def recursive_locate_refs(pq, old, new):
-    files = os.listdir(pq)
-    for f in files:
-        if f.endswith('.parq'):
-            update_pd(os.path.join(pq,f), old, new)
-        elif not os.path.isfile(os.path.join(pq,f)):
-            recursive_locate_refs(os.path.join(pq,f), old, new)
-        else:
-            pass
-
-if __name__ == '__main__':
-    pq = sys.argv[-3]
-    old = sys.argv[-2]
-    new = sys.argv[-1]
-
-    recursive_locate_refs(pq, old, new)
\ No newline at end of file
diff --git a/extensions/scripts/extra_tools/corrections.py b/extensions/scripts/extra_tools/corrections.py
deleted file mode 100644
index c7d2169..0000000
--- a/extensions/scripts/extra_tools/corrections.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import xarray as xr
-import json
-from datetime import datetime
-import sys
-import uuid
-
-OVERWRITE=True
-
-def correct_shape(old, new, storepath):
-    old = 5
-    new = 10
-    PATH = '/home/users/dwest77/Documents/kerchunk_dev/parquet/dev/batch/gargant'
-    with open(f'{storepath}/.zmetadata') as f:
-        refs = json.load(f)
-        
-    meta = refs['metadata']
-
-    for key in meta.keys():
-        if '.zarray' in key:
-            # Correct chunks
-            if meta[key]['shape'][0] == old:
-                meta[key]['shape'][0] = new
-
-    refs['metadata'] = meta
-    with open(f'{storepath}/.zmetadata','w') as f:
-        f.write(json.dumps(refs))
-
-def direct_comparison(xattrs, kattrs):
-    corrections = []
-    for key in xattrs.keys():
-        if key not in kattrs.keys():
-            kattrs[key] = str(xattrs[key])
-            corrections.append(key)
-        else:
-            if kattrs[key] != xattrs[key]:
-                kattrs[key] = str(xattrs[key])
-                corrections.append(key)
-    return xattrs, kattrs, corrections
-
-def specific_comparison(xattrs, kattrs, cfg):
-    extras = {}
-    if cfg:
-        print('Found cfg file for extra attribute config')
-        with open(cfg) as f:
-            for r in f.readlines():
-                values = r.replace('\n','').split(',')
-                extras[values[0]] = values[1]
-
-    missing = []
-    corrections = []
-    for key in xattrs.keys():
-        if key not in kattrs.keys():
-            kattrs[key] = str(xattrs[key])
-            missing.append(key)
-        else:
-            if kattrs[key] != xattrs[key]:
-                if 'end' in key:
-                    kattrs[key] = str(xattrs[key])
-                    corrections.append(key)
-                elif 'start' in key:
-                    pass
-                elif key in extras.keys():
-                    if extras[key] == 'replace':
-                        kattrs[key] = str(xattrs[key])
-                else:
-                    missing.append(key)
-            else:
-                pass
-    if len(missing) > 0:
-        print('WARNING: Missing additional fields - check manually')
-        print(' >> ' + ','.join(missing))
-        return 0, 0, 0, None
-    else:
-        return xattrs, kattrs, corrections, True
-
-def correct_attrs(proj_code, revision, old_file, new_file, fl, cfg=None, skip=False):
-    # Default approach is to compare with xr.open_mfdataset
-    # Correct any different metadata and save kerchunk attributes
-    direct = False
-    # Get Xarray Global Attributes
-    if len(fl) == 1:
-        skip = True
-    if not skip:
-        print('Opening Xarray Datasets')
-        xattr0 = xr.open_dataset(fl[0]).attrs
-        xattr1 = xr.open_dataset(fl[1]).attrs
-        skip=True
-
-    # Get Kerchunk Attributes
-    with open(old_file) as f:
-        refs = json.load(f)
-    kattrs = json.loads(refs['refs']['.zattrs'])
-
-    kattrs['time_coverage_end'] = xattr1['time_coverage_end']
-    
-    if not skip:
-        # Set all attributes if they are incorrect
-        if direct:
-            print('Using direct comparison')
-            xattrs, kattrs, corrections = direct_comparison(xattrs, kattrs)
-            success = True
-        else:
-            print('Using specific comparison')
-            xattrs, kattrs, corrections, success = specific_comparison(xattrs, kattrs, cfg) 
-
-        if not success:
-            return None
-        
-        print('Corrected: ',end='')
-        if not corrections:
-            print(None)
-        else:
-            print(', '.join(corrections))
-
-    # Set kerchunk specific attributes
-    now = datetime.now()
-    stamp = now.strftime("%d%m%yT%H%M%S")
-    ymd = now.strftime("%d/%m/%y")
-    kattrs['history'] = kattrs['history'] + f"\nKerchunk file last updated by CEDA on {ymd} in the context of the CCI Knowledge Exchange Project"
-    kattrs['kerchunk_revision'] = revision + 'b'
-    kattrs['kerchunk_creation_date'] = str(stamp)
-    kattrs['tracking_id'] = str(uuid.uuid4())
-    print('Added Kerchunk Attributes')
-
-    # Export new attributes
-    refs['refs']['.zattrs'] = json.dumps(kattrs)
-    if not os.path.isfile(new_file) or OVERWRITE:
-        with open(new_file,'w') as f:
-            f.write(json.dumps(refs))
-        print('Written to',new_file)
-    return None
-
-def find_firstlast(workdir, proj_code, getall=False):
-    filelist = f'{workdir}/{proj_code}/allfiles.txt'
-    if os.path.isfile(filelist):
-        with open(filelist) as f:
-            content = [r.replace('\n','') for r in f.readlines()]
-        if getall:
-            return content
-        if content[0] != content[-1]:
-            return [content[0], content[-1]]
-        else:
-            return [content[0]]
-    else:
-        print('File not found - ',filelist)
-
-#correct_attrs(proj_code, old, revision, textref, old_file, new_file)
-config_file = sys.argv[1]
-
-OVERWRITE = ('-f' in sys.argv)
-
-if True:#os.path.isfile(config_file):
-    #with open(config_file) as f:
-        #cfg_attrs = json.load(f)
-
-## Revisions
-# Initially generated       1.0a       - uncorrected version
-# Post metadata corrections 1.0b - corrected, untested version
-# Post testing version      1.0
-
-    cfg_attrs = {
-        'proj_code':'ESACCI-L4_FIRE-BA-MODIS-20010101-20200120-fv5.1',
-        'revision':'kr1.2',
-        'workdir': '/gws/nopw/j04/esacci_portal/kerchunk/pipeline/in_progress/'
-    }
-
-    proj_code    = cfg_attrs['proj_code']
-    revision     = cfg_attrs['revision']
-    workdir      = cfg_attrs['workdir']
-
-    # Assume no metaref
-    fl = find_firstlast(workdir, proj_code)
-
-    old_file = f'{workdir}/{proj_code}/kerchunk-{revision}a.json'
-    new_file = f'{workdir}/{proj_code}/kerchunk-{revision}b.json'
-
-    if not os.path.isfile(new_file) or OVERWRITE:
-        correct_attrs(proj_code, revision, old_file, new_file, fl, cfg=cfg_attrs, skip=False)
-    else:
-        print('skipped existing file')
-else:
-    print(f'Config file not found - {config_file}')
\ No newline at end of file
diff --git a/extensions/scripts/extra_tools/metadata_viewer.py b/extensions/scripts/extra_tools/metadata_viewer.py
deleted file mode 100644
index 96e80bd..0000000
--- a/extensions/scripts/extra_tools/metadata_viewer.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Script for viewing and editing current metadata
-# Global attributes (.zattrs)
-# .zgroup
-# List all variables that contain a .zattrs/.zarray (inspectable)
-
-import sys
-import json
-
-def format(text, size):
-    newtext = str(text)
-    for x in range(size - len(text)):
-        newtext += ' '
-    return newtext
-
-def rundecode(cfgs):
-    """
-    cfgs - list of command inputs depending on user input to this program
-    """
-    flags = {
-        '-f':'filename'
-    }
-    kwargs = {}
-    for x in range(0,int(len(cfgs)),2):
-        flag = flags[cfgs[x]]
-        kwargs[flag] = cfgs[x+1]
-
-    return kwargs
-
-class Editor:
-
-    def __init__(self, filename=None):
-        self.filename=filename
-        if not self.filename:
-            self.filename = input('Kerchunk file: ')
-
-        with open(self.filename) as f:
-            self.refs = json.load(f)
-
-        self.glob_attrs = json.loads(self.refs['refs']['.zattrs'])
-        self.zgroup     = json.loads(self.refs['refs']['.zgroup'])
-
-        self.variables = {}
-        for key in self.refs['refs']:
-            if '/.z' in key:
-                var, type = key.split('/')
-                if var not in self.variables:
-                    self.variables[var] = {}
-                self.variables[var][type] = self.refs['refs'][key]
-
-    def display(self):
-        print()
-        print(self.filename)
-        buffer = len(self.filename) - 35
-        print('_'.join(['' for x in range(len(self.filename)+1)]))
-
-        print('Global Attributes:')
-        for k in self.zgroup.keys():
-            print(f'    {format(k,30)}: {self.zgroup[k]}')
-        for key in self.glob_attrs.keys():
-            print(f'   {format(key,30)}: ',end='')
-            lvalue = len(self.glob_attrs[key])
-            cs = lvalue // 4
-            if cs < 1:
-                print(self.glob_attrs[key])
-            else:
-                raw = self.glob_attrs[key].replace('\n','. ')
-                value = [raw[i:i+buffer] for i in range(0, lvalue, buffer)]
-                print(value[0])
-                for v in value[1:]:
-                    print(format('',35) + v)
-            
-
-        print('Variables:')
-        for key in self.variables.keys():
-            print(f'    {key}')
-
-if __name__ == '__main__':
-    cfgs   = sys.argv[1:]
-    Editor(**rundecode(cfgs)).display()
\ No newline at end of file
diff --git a/extensions/scripts/extra_tools/wide_corrections.py b/extensions/scripts/extra_tools/wide_corrections.py
deleted file mode 100644
index b999bcd..0000000
--- a/extensions/scripts/extra_tools/wide_corrections.py
+++ /dev/null
@@ -1,95 +0,0 @@
-
-import json
-import glob
-import os
-
-with open('corrections.json') as f:
-    refs = json.load(f)
-
-workdir = '/gws/nopw/j04/esacci_portal/kerchunk/DELIVERY_09_10_23'
-
-for proj in refs.keys():
-    kfile = glob.glob(f'{workdir}/{proj}*.json')[0]
-    newfile = kfile.replace('DELIVERY_09_10_23','DELIVERY_10_10_23')
-    if not os.path.isfile(newfile):
-    
-        with open(kfile) as f:
-            kdata = json.load(f)
-        zattrs = json.loads(kdata['refs']['.zattrs'])
-        new_zattrs = {}
-        for attr in zattrs:
-            if attr in refs[proj].keys():
-                if refs[proj][attr] == 'remove':
-                    pass 
-                else:
-                    new_zattrs[attr] = refs[proj][attr]
-            else:
-                new_zattrs[attr] = zattrs[attr]]
-                
-
-        kdata['refs']['.zattrs'] = json.dumps(new_zattrs)
-        with open(newfile,'w') as f:
-            f.write(json.dumps(kdata))
-        print('Written corrections to ', newfile)
-    else:
-        print('File exists - ',newfile)
-
-print('End')
-
-
-
-"""
-with open('corrections.csv') as f:
-    content = f.readlines()
-
-def get_portion(raw, proj_id):
-    raw = raw.replace('\n','').replace('\t','')
-    if raw.replace(' ','') == 'id':
-        return {'id':proj_id}
-    else:
-        if '=' in raw:
-            attr = raw.split('=')[0].replace(' ','')
-            value = raw.split('=')[1]
-        else:
-            attr = 'id'
-            value = proj_id
-        if '"' in value:
-            # Loop and cut everything not inside ""
-            x = 0
-            inside = False
-            reached = False
-            fval = ''
-            while not reached:
-                if value[x] == '"' and inside:
-                    inside = False
-                    reached = True
-                elif value[x] == '"':
-                    inside = True
-                else:
-                    pass
-                if inside and value[x] != '"':
-                    fval += value[x]
-                x += 1
-            value = fval
-        else:
-            value = value.replace(' ','')
-        return {attr: value}
-
-projects = {}
-for x, line in enumerate(content):
-    print(x, len(content))
-    if line.startswith('ESACCI'):
-        proj_id = ''
-        x=0
-        while line[x:x+3] != '-kr':
-            proj_id += line[x]
-            x += 1
-        projects[proj_id] = {**get_portion(line[x:], proj_id)}
-    else:
-        projects[proj_id] = {**projects[proj_id], **get_portion(line, proj_id)}
-        
-with open('corrections.json','w') as f:
-    f.write(json.dumps(projects))
-
-
-"""
\ No newline at end of file

From 2a9669bbe5446162899e4508c862b5a2ee09817c Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 14:49:21 +0000
Subject: [PATCH 32/45] Updated for move to cedaproc

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 9864b20..5d76e5d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 .ipynb_checkpoints
 build_venv/
+kvenv/
 temp/
 testing/
 *__pycache__*

From 905291e18d19756a17cfedbb34d09166de73fe0d Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 14:49:55 +0000
Subject: [PATCH 33/45] Added template for cedaproc use

---
 config/setup-cci.sh              | 3 +--
 config/setup-project.sh.template | 7 +++++++
 2 files changed, 8 insertions(+), 2 deletions(-)
 create mode 100755 config/setup-project.sh.template

diff --git a/config/setup-cci.sh b/config/setup-cci.sh
index cb021db..bceeba7 100755
--- a/config/setup-cci.sh
+++ b/config/setup-cci.sh
@@ -4,5 +4,4 @@ export WORKDIR=/gws/nopw/j04/esacci_portal/kerchunk_conversion/
 export SRCDIR=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
 export KVENV=/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv
 
-source $KVENV/bin/activate
-module load jaspy
\ No newline at end of file
+source $KVENV/bin/activate
\ No newline at end of file
diff --git a/config/setup-project.sh.template b/config/setup-project.sh.template
new file mode 100755
index 0000000..5cf03ac
--- /dev/null
+++ b/config/setup-project.sh.template
@@ -0,0 +1,7 @@
+module load jaspy
+
+export WORKDIR=/path/to/kerchunk-pipeline
+export SRCDIR=/gws/nopw/j04/cedaproc/kerchunk_builder/kerchunk-builder
+export KVENV=$SRCDIR/kvenv
+
+source $KVENV/bin/activate
\ No newline at end of file

From 775a640f05f3f44ba3db8956c4bb033418fc0dc6 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 14:51:08 +0000
Subject: [PATCH 34/45] Removed testing notebooks and updated showcase tools

---
 showcase/notebooks/EShifts.ipynb         |  632 ----
 showcase/notebooks/TestDask.ipynb        |  649 ----
 showcase/notebooks/TestKerchunking.ipynb | 3743 ----------------------
 showcase/notebooks/TestMemory.ipynb      |   99 -
 showcase/notebooks/cci.ipynb             | 1903 -----------
 5 files changed, 7026 deletions(-)
 delete mode 100644 showcase/notebooks/EShifts.ipynb
 delete mode 100644 showcase/notebooks/TestDask.ipynb
 delete mode 100644 showcase/notebooks/TestKerchunking.ipynb
 delete mode 100644 showcase/notebooks/TestMemory.ipynb
 delete mode 100644 showcase/notebooks/cci.ipynb

diff --git a/showcase/notebooks/EShifts.ipynb b/showcase/notebooks/EShifts.ipynb
deleted file mode 100644
index f01ddd2..0000000
--- a/showcase/notebooks/EShifts.ipynb
+++ /dev/null
@@ -1,632 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fb676b02-d648-42b9-9853-77e3ba9d8b36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dates = \"\"\"11/01/2024\n",
-    "12/01/2024\n",
-    "13/01/2024\n",
-    "14/01/2024\n",
-    "15/01/2024\n",
-    "16/01/2024\n",
-    "17/01/2024\n",
-    "18/01/2024\n",
-    "19/01/2024\n",
-    "20/01/2024\n",
-    "21/01/2024\n",
-    "22/01/2024\n",
-    "23/01/2024\n",
-    "24/01/2024\n",
-    "25/01/2024\n",
-    "26/01/2024\n",
-    "27/01/2024\n",
-    "28/01/2024\n",
-    "29/01/2024\n",
-    "30/01/2024\n",
-    "31/01/2024\n",
-    "01/02/2024\n",
-    "02/02/2024\n",
-    "03/02/2024\n",
-    "04/02/2024\n",
-    "05/02/2024\n",
-    "06/02/2024\n",
-    "07/02/2024\n",
-    "08/02/2024\n",
-    "09/02/2024\n",
-    "10/02/2024\n",
-    "11/02/2024\n",
-    "12/02/2024\n",
-    "13/02/2024\n",
-    "14/02/2024\n",
-    "15/02/2024\n",
-    "16/02/2024\n",
-    "17/02/2024\n",
-    "18/02/2024\n",
-    "19/02/2024\n",
-    "20/02/2024\n",
-    "21/02/2024\n",
-    "22/02/2024\n",
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-    "26/02/2024\n",
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-    "29/02/2024\n",
-    "01/03/2024\n",
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-    "03/03/2024\n",
-    "04/03/2024\n",
-    "05/03/2024\n",
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-    "07/03/2024\n",
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-    "11/03/2024\n",
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-    "28/12/2024\n",
-    "29/12/2024\n",
-    "30/12/2024\n",
-    "31/12/2024\"\"\".split('\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "45036c15-1108-4057-b95b-b296829940f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pattern = ['Evening','Day','Day','Evening','Night']\n",
-    "starts = ['15:00','7:00','7:00','15:00','23:00']\n",
-    "ends = ['23:00','15:00','15:00','23:00','7:00']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a8465327-2c8f-426d-9342-708b6b1cb8ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Subject,Start Date,Start Time,End Date,End Time\n",
-      "Ellie Evening Shift, 11/01/2024, 15:00, 11/01/2024, 23:00\n",
-      "Ellie Evening Shift, 12/01/2024, 15:00, 12/01/2024, 23:00\n",
-      "Ellie Evening Shift, 13/01/2024, 15:00, 13/01/2024, 23:00\n",
-      "Ellie Evening Shift, 14/01/2024, 15:00, 14/01/2024, 23:00\n",
-      "Ellie Day Shift, 19/01/2024, 7:00, 19/01/2024, 15:00\n",
-      "Ellie Day Shift, 20/01/2024, 7:00, 20/01/2024, 15:00\n",
-      "Ellie Day Shift, 21/01/2024, 7:00, 21/01/2024, 15:00\n",
-      "Ellie Day Shift, 22/01/2024, 7:00, 22/01/2024, 15:00\n",
-      "Ellie Day Shift, 27/01/2024, 7:00, 27/01/2024, 15:00\n",
-      "Ellie Day Shift, 28/01/2024, 7:00, 28/01/2024, 15:00\n",
-      "Ellie Day Shift, 29/01/2024, 7:00, 29/01/2024, 15:00\n",
-      "Ellie Day Shift, 30/01/2024, 7:00, 30/01/2024, 15:00\n",
-      "Ellie Evening Shift, 04/02/2024, 15:00, 04/02/2024, 23:00\n",
-      "Ellie Evening Shift, 05/02/2024, 15:00, 05/02/2024, 23:00\n",
-      "Ellie Evening Shift, 06/02/2024, 15:00, 06/02/2024, 23:00\n",
-      "Ellie Evening Shift, 07/02/2024, 15:00, 07/02/2024, 23:00\n",
-      "Ellie Night Shift, 12/02/2024, 23:00, 13/02/2024, 7:00\n",
-      "Ellie Night Shift, 13/02/2024, 23:00, 14/02/2024, 7:00\n",
-      "Ellie Night Shift, 14/02/2024, 23:00, 15/02/2024, 7:00\n",
-      "Ellie Night Shift, 15/02/2024, 23:00, 16/02/2024, 7:00\n",
-      "Ellie Evening Shift, 20/02/2024, 15:00, 20/02/2024, 23:00\n",
-      "Ellie Evening Shift, 21/02/2024, 15:00, 21/02/2024, 23:00\n",
-      "Ellie Evening Shift, 22/02/2024, 15:00, 22/02/2024, 23:00\n",
-      "Ellie Evening Shift, 23/02/2024, 15:00, 23/02/2024, 23:00\n",
-      "Ellie Day Shift, 28/02/2024, 7:00, 28/02/2024, 15:00\n",
-      "Ellie Day Shift, 29/02/2024, 7:00, 29/02/2024, 15:00\n",
-      "Ellie Day Shift, 01/03/2024, 7:00, 01/03/2024, 15:00\n",
-      "Ellie Day Shift, 02/03/2024, 7:00, 02/03/2024, 15:00\n",
-      "Ellie Day Shift, 07/03/2024, 7:00, 07/03/2024, 15:00\n",
-      "Ellie Day Shift, 08/03/2024, 7:00, 08/03/2024, 15:00\n",
-      "Ellie Day Shift, 09/03/2024, 7:00, 09/03/2024, 15:00\n",
-      "Ellie Day Shift, 10/03/2024, 7:00, 10/03/2024, 15:00\n",
-      "Ellie Evening Shift, 15/03/2024, 15:00, 15/03/2024, 23:00\n",
-      "Ellie Evening Shift, 16/03/2024, 15:00, 16/03/2024, 23:00\n",
-      "Ellie Evening Shift, 17/03/2024, 15:00, 17/03/2024, 23:00\n",
-      "Ellie Evening Shift, 18/03/2024, 15:00, 18/03/2024, 23:00\n",
-      "Ellie Night Shift, 23/03/2024, 23:00, 24/03/2024, 7:00\n",
-      "Ellie Night Shift, 24/03/2024, 23:00, 25/03/2024, 7:00\n",
-      "Ellie Night Shift, 25/03/2024, 23:00, 26/03/2024, 7:00\n",
-      "Ellie Night Shift, 26/03/2024, 23:00, 27/03/2024, 7:00\n",
-      "Ellie Evening Shift, 31/03/2024, 15:00, 31/03/2024, 23:00\n",
-      "Ellie Evening Shift, 01/04/2024, 15:00, 01/04/2024, 23:00\n",
-      "Ellie Evening Shift, 02/04/2024, 15:00, 02/04/2024, 23:00\n",
-      "Ellie Evening Shift, 03/04/2024, 15:00, 03/04/2024, 23:00\n",
-      "Ellie Day Shift, 08/04/2024, 7:00, 08/04/2024, 15:00\n",
-      "Ellie Day Shift, 09/04/2024, 7:00, 09/04/2024, 15:00\n",
-      "Ellie Day Shift, 10/04/2024, 7:00, 10/04/2024, 15:00\n",
-      "Ellie Day Shift, 11/04/2024, 7:00, 11/04/2024, 15:00\n",
-      "Ellie Day Shift, 16/04/2024, 7:00, 16/04/2024, 15:00\n",
-      "Ellie Day Shift, 17/04/2024, 7:00, 17/04/2024, 15:00\n",
-      "Ellie Day Shift, 18/04/2024, 7:00, 18/04/2024, 15:00\n",
-      "Ellie Day Shift, 19/04/2024, 7:00, 19/04/2024, 15:00\n",
-      "Ellie Evening Shift, 24/04/2024, 15:00, 24/04/2024, 23:00\n",
-      "Ellie Evening Shift, 25/04/2024, 15:00, 25/04/2024, 23:00\n",
-      "Ellie Evening Shift, 26/04/2024, 15:00, 26/04/2024, 23:00\n",
-      "Ellie Evening Shift, 27/04/2024, 15:00, 27/04/2024, 23:00\n",
-      "Ellie Night Shift, 02/05/2024, 23:00, 03/05/2024, 7:00\n",
-      "Ellie Night Shift, 03/05/2024, 23:00, 04/05/2024, 7:00\n",
-      "Ellie Night Shift, 04/05/2024, 23:00, 05/05/2024, 7:00\n",
-      "Ellie Night Shift, 05/05/2024, 23:00, 06/05/2024, 7:00\n",
-      "Ellie Evening Shift, 10/05/2024, 15:00, 10/05/2024, 23:00\n",
-      "Ellie Evening Shift, 11/05/2024, 15:00, 11/05/2024, 23:00\n",
-      "Ellie Evening Shift, 12/05/2024, 15:00, 12/05/2024, 23:00\n",
-      "Ellie Evening Shift, 13/05/2024, 15:00, 13/05/2024, 23:00\n",
-      "Ellie Day Shift, 18/05/2024, 7:00, 18/05/2024, 15:00\n",
-      "Ellie Day Shift, 19/05/2024, 7:00, 19/05/2024, 15:00\n",
-      "Ellie Day Shift, 20/05/2024, 7:00, 20/05/2024, 15:00\n",
-      "Ellie Day Shift, 21/05/2024, 7:00, 21/05/2024, 15:00\n",
-      "Ellie Day Shift, 26/05/2024, 7:00, 26/05/2024, 15:00\n",
-      "Ellie Day Shift, 27/05/2024, 7:00, 27/05/2024, 15:00\n",
-      "Ellie Day Shift, 28/05/2024, 7:00, 28/05/2024, 15:00\n",
-      "Ellie Day Shift, 29/05/2024, 7:00, 29/05/2024, 15:00\n",
-      "Ellie Evening Shift, 03/06/2024, 15:00, 03/06/2024, 23:00\n",
-      "Ellie Evening Shift, 04/06/2024, 15:00, 04/06/2024, 23:00\n",
-      "Ellie Evening Shift, 05/06/2024, 15:00, 05/06/2024, 23:00\n",
-      "Ellie Evening Shift, 06/06/2024, 15:00, 06/06/2024, 23:00\n",
-      "Ellie Night Shift, 11/06/2024, 23:00, 12/06/2024, 7:00\n",
-      "Ellie Night Shift, 12/06/2024, 23:00, 13/06/2024, 7:00\n",
-      "Ellie Night Shift, 13/06/2024, 23:00, 14/06/2024, 7:00\n",
-      "Ellie Night Shift, 14/06/2024, 23:00, 15/06/2024, 7:00\n",
-      "Ellie Evening Shift, 19/06/2024, 15:00, 19/06/2024, 23:00\n",
-      "Ellie Evening Shift, 20/06/2024, 15:00, 20/06/2024, 23:00\n",
-      "Ellie Evening Shift, 21/06/2024, 15:00, 21/06/2024, 23:00\n",
-      "Ellie Evening Shift, 22/06/2024, 15:00, 22/06/2024, 23:00\n",
-      "Ellie Day Shift, 27/06/2024, 7:00, 27/06/2024, 15:00\n",
-      "Ellie Day Shift, 28/06/2024, 7:00, 28/06/2024, 15:00\n",
-      "Ellie Day Shift, 29/06/2024, 7:00, 29/06/2024, 15:00\n",
-      "Ellie Day Shift, 30/06/2024, 7:00, 30/06/2024, 15:00\n",
-      "Ellie Day Shift, 05/07/2024, 7:00, 05/07/2024, 15:00\n",
-      "Ellie Day Shift, 06/07/2024, 7:00, 06/07/2024, 15:00\n",
-      "Ellie Day Shift, 07/07/2024, 7:00, 07/07/2024, 15:00\n",
-      "Ellie Day Shift, 08/07/2024, 7:00, 08/07/2024, 15:00\n",
-      "Ellie Evening Shift, 13/07/2024, 15:00, 13/07/2024, 23:00\n",
-      "Ellie Evening Shift, 14/07/2024, 15:00, 14/07/2024, 23:00\n",
-      "Ellie Evening Shift, 15/07/2024, 15:00, 15/07/2024, 23:00\n",
-      "Ellie Evening Shift, 16/07/2024, 15:00, 16/07/2024, 23:00\n",
-      "Ellie Night Shift, 21/07/2024, 23:00, 22/07/2024, 7:00\n",
-      "Ellie Night Shift, 22/07/2024, 23:00, 23/07/2024, 7:00\n",
-      "Ellie Night Shift, 23/07/2024, 23:00, 24/07/2024, 7:00\n",
-      "Ellie Night Shift, 24/07/2024, 23:00, 25/07/2024, 7:00\n",
-      "Ellie Evening Shift, 29/07/2024, 15:00, 29/07/2024, 23:00\n",
-      "Ellie Evening Shift, 30/07/2024, 15:00, 30/07/2024, 23:00\n",
-      "Ellie Evening Shift, 31/07/2024, 15:00, 31/07/2024, 23:00\n",
-      "Ellie Evening Shift, 01/08/2024, 15:00, 01/08/2024, 23:00\n",
-      "Ellie Day Shift, 06/08/2024, 7:00, 06/08/2024, 15:00\n",
-      "Ellie Day Shift, 07/08/2024, 7:00, 07/08/2024, 15:00\n",
-      "Ellie Day Shift, 08/08/2024, 7:00, 08/08/2024, 15:00\n",
-      "Ellie Day Shift, 09/08/2024, 7:00, 09/08/2024, 15:00\n",
-      "Ellie Day Shift, 14/08/2024, 7:00, 14/08/2024, 15:00\n",
-      "Ellie Day Shift, 15/08/2024, 7:00, 15/08/2024, 15:00\n",
-      "Ellie Day Shift, 16/08/2024, 7:00, 16/08/2024, 15:00\n",
-      "Ellie Day Shift, 17/08/2024, 7:00, 17/08/2024, 15:00\n",
-      "Ellie Evening Shift, 22/08/2024, 15:00, 22/08/2024, 23:00\n",
-      "Ellie Evening Shift, 23/08/2024, 15:00, 23/08/2024, 23:00\n",
-      "Ellie Evening Shift, 24/08/2024, 15:00, 24/08/2024, 23:00\n",
-      "Ellie Evening Shift, 25/08/2024, 15:00, 25/08/2024, 23:00\n",
-      "Ellie Night Shift, 30/08/2024, 23:00, 31/08/2024, 7:00\n",
-      "Ellie Night Shift, 31/08/2024, 23:00, 01/09/2024, 7:00\n",
-      "Ellie Night Shift, 01/09/2024, 23:00, 02/09/2024, 7:00\n",
-      "Ellie Night Shift, 02/09/2024, 23:00, 03/09/2024, 7:00\n",
-      "Ellie Evening Shift, 07/09/2024, 15:00, 07/09/2024, 23:00\n",
-      "Ellie Evening Shift, 08/09/2024, 15:00, 08/09/2024, 23:00\n",
-      "Ellie Evening Shift, 09/09/2024, 15:00, 09/09/2024, 23:00\n",
-      "Ellie Evening Shift, 10/09/2024, 15:00, 10/09/2024, 23:00\n",
-      "Ellie Day Shift, 15/09/2024, 7:00, 15/09/2024, 15:00\n",
-      "Ellie Day Shift, 16/09/2024, 7:00, 16/09/2024, 15:00\n",
-      "Ellie Day Shift, 17/09/2024, 7:00, 17/09/2024, 15:00\n",
-      "Ellie Day Shift, 18/09/2024, 7:00, 18/09/2024, 15:00\n",
-      "Ellie Day Shift, 23/09/2024, 7:00, 23/09/2024, 15:00\n",
-      "Ellie Day Shift, 24/09/2024, 7:00, 24/09/2024, 15:00\n",
-      "Ellie Day Shift, 25/09/2024, 7:00, 25/09/2024, 15:00\n",
-      "Ellie Day Shift, 26/09/2024, 7:00, 26/09/2024, 15:00\n",
-      "Ellie Evening Shift, 01/10/2024, 15:00, 01/10/2024, 23:00\n",
-      "Ellie Evening Shift, 02/10/2024, 15:00, 02/10/2024, 23:00\n",
-      "Ellie Evening Shift, 03/10/2024, 15:00, 03/10/2024, 23:00\n",
-      "Ellie Evening Shift, 04/10/2024, 15:00, 04/10/2024, 23:00\n",
-      "Ellie Night Shift, 09/10/2024, 23:00, 10/10/2024, 7:00\n",
-      "Ellie Night Shift, 10/10/2024, 23:00, 11/10/2024, 7:00\n",
-      "Ellie Night Shift, 11/10/2024, 23:00, 12/10/2024, 7:00\n",
-      "Ellie Night Shift, 12/10/2024, 23:00, 13/10/2024, 7:00\n",
-      "Ellie Evening Shift, 17/10/2024, 15:00, 17/10/2024, 23:00\n",
-      "Ellie Evening Shift, 18/10/2024, 15:00, 18/10/2024, 23:00\n",
-      "Ellie Evening Shift, 19/10/2024, 15:00, 19/10/2024, 23:00\n",
-      "Ellie Evening Shift, 20/10/2024, 15:00, 20/10/2024, 23:00\n",
-      "Ellie Day Shift, 25/10/2024, 7:00, 25/10/2024, 15:00\n",
-      "Ellie Day Shift, 26/10/2024, 7:00, 26/10/2024, 15:00\n",
-      "Ellie Day Shift, 27/10/2024, 7:00, 27/10/2024, 15:00\n",
-      "Ellie Day Shift, 28/10/2024, 7:00, 28/10/2024, 15:00\n",
-      "Ellie Day Shift, 02/11/2024, 7:00, 02/11/2024, 15:00\n",
-      "Ellie Day Shift, 03/11/2024, 7:00, 03/11/2024, 15:00\n",
-      "Ellie Day Shift, 04/11/2024, 7:00, 04/11/2024, 15:00\n",
-      "Ellie Day Shift, 05/11/2024, 7:00, 05/11/2024, 15:00\n",
-      "Ellie Evening Shift, 10/11/2024, 15:00, 10/11/2024, 23:00\n",
-      "Ellie Evening Shift, 11/11/2024, 15:00, 11/11/2024, 23:00\n",
-      "Ellie Evening Shift, 12/11/2024, 15:00, 12/11/2024, 23:00\n",
-      "Ellie Evening Shift, 13/11/2024, 15:00, 13/11/2024, 23:00\n",
-      "Ellie Night Shift, 18/11/2024, 23:00, 19/11/2024, 7:00\n",
-      "Ellie Night Shift, 19/11/2024, 23:00, 20/11/2024, 7:00\n",
-      "Ellie Night Shift, 20/11/2024, 23:00, 21/11/2024, 7:00\n",
-      "Ellie Night Shift, 21/11/2024, 23:00, 22/11/2024, 7:00\n",
-      "Ellie Evening Shift, 26/11/2024, 15:00, 26/11/2024, 23:00\n",
-      "Ellie Evening Shift, 27/11/2024, 15:00, 27/11/2024, 23:00\n",
-      "Ellie Evening Shift, 28/11/2024, 15:00, 28/11/2024, 23:00\n",
-      "Ellie Evening Shift, 29/11/2024, 15:00, 29/11/2024, 23:00\n",
-      "Ellie Day Shift, 04/12/2024, 7:00, 04/12/2024, 15:00\n",
-      "Ellie Day Shift, 05/12/2024, 7:00, 05/12/2024, 15:00\n",
-      "Ellie Day Shift, 06/12/2024, 7:00, 06/12/2024, 15:00\n",
-      "Ellie Day Shift, 07/12/2024, 7:00, 07/12/2024, 15:00\n",
-      "Ellie Day Shift, 12/12/2024, 7:00, 12/12/2024, 15:00\n",
-      "Ellie Day Shift, 13/12/2024, 7:00, 13/12/2024, 15:00\n",
-      "Ellie Day Shift, 14/12/2024, 7:00, 14/12/2024, 15:00\n",
-      "Ellie Day Shift, 15/12/2024, 7:00, 15/12/2024, 15:00\n",
-      "Ellie Evening Shift, 20/12/2024, 15:00, 20/12/2024, 23:00\n",
-      "Ellie Evening Shift, 21/12/2024, 15:00, 21/12/2024, 23:00\n",
-      "Ellie Evening Shift, 22/12/2024, 15:00, 22/12/2024, 23:00\n",
-      "Ellie Evening Shift, 23/12/2024, 15:00, 23/12/2024, 23:00\n",
-      "Ellie Night Shift, 28/12/2024, 23:00, 29/12/2024, 7:00\n",
-      "Ellie Night Shift, 29/12/2024, 23:00, 30/12/2024, 7:00\n",
-      "Ellie Night Shift, 30/12/2024, 23:00, 31/12/2024, 7:00\n",
-      "Ellie Night Shift, 31/12/2024, 23:00, 31/12/2024, 7:00\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "pid = 0\n",
-    "count = 0\n",
-    "word = 'Subject,Start Date,Start Time,End Date,End Time\\n'\n",
-    "for id, date in enumerate(dates):\n",
-    "    try:\n",
-    "        if pid == 4:\n",
-    "            ndate = dates[id+1]\n",
-    "        else:\n",
-    "            ndate = date\n",
-    "    except:\n",
-    "        ndate = date\n",
-    "    if count < 4:\n",
-    "        word += f'Ellie {pattern[pid]} Shift, {date}, {starts[pid]}, {ndate}, {ends[pid]}\\n'\n",
-    "    else:\n",
-    "        pass\n",
-    "    if count == 7:\n",
-    "        count = 0\n",
-    "        pid += 1\n",
-    "    else:\n",
-    "        count += 1\n",
-    "    if pid == 5:\n",
-    "        pid = 0\n",
-    "\n",
-    "print(word)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ddcf553-1b1e-4129-895b-453d057e81d5",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 + Jaspy",
-   "language": "python",
-   "name": "jaspy"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/showcase/notebooks/TestDask.ipynb b/showcase/notebooks/TestDask.ipynb
deleted file mode 100644
index 2b4cc62..0000000
--- a/showcase/notebooks/TestDask.ipynb
+++ /dev/null
@@ -1,649 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7fbabb0b-4571-4ef0-8d15-01a51db9d04e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import fsspec\n",
-    "import xarray as xr\n",
-    "\n",
-    "kfile = 'https://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/CMIP/AS-RCEC/TaiESM1/kr1.0/CMIP6_CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_Amon_prsn_gn_v20200623_kr1.0.json'\n",
-    "ds = xr.open_zarr(fsspec.get_mapper('reference://',fo=kfile, remote_protocol='https'), consolidated=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "218abcae-f9b7-4e4d-8b88-b3dc1931a5b7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/jaspy/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
-      "Perhaps you already have a cluster running?\n",
-      "Hosting the HTTP server on port 34376 instead\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-4790ce83-d25e-11ee-83eb-02e7fd06541f</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "\n",
-       "        <tr>\n",
-       "        \n",
-       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
-       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
-       "        \n",
-       "        </tr>\n",
-       "\n",
-       "        \n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34376/status\" target=\"_blank\">http://127.0.0.1:34376/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\"></td>\n",
-       "            </tr>\n",
-       "        \n",
-       "\n",
-       "        </table>\n",
-       "\n",
-       "        \n",
-       "\n",
-       "        \n",
-       "            <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
-       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">9676bea7</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:34376/status\" target=\"_blank\">http://127.0.0.1:34376/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Workers:</strong> 1\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total threads:</strong> 4\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total memory:</strong> 7.45 GiB\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            \n",
-       "            <tr>\n",
-       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
-       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
-       "</tr>\n",
-       "\n",
-       "            \n",
-       "        </table>\n",
-       "\n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
-       "            </summary>\n",
-       "\n",
-       "            <div style=\"\">\n",
-       "    <div>\n",
-       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
-       "        <div style=\"margin-left: 48px;\">\n",
-       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-7dbd4ffe-759e-4467-87e5-fc42721332e7</p>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:43543\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 1\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:34376/status\" target=\"_blank\">http://127.0.0.1:34376/status</a>\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total threads:</strong> 4\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Started:</strong> Just now\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total memory:</strong> 7.45 GiB\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "            </table>\n",
-       "        </div>\n",
-       "    </div>\n",
-       "\n",
-       "    <details style=\"margin-left: 48px;\">\n",
-       "        <summary style=\"margin-bottom: 20px;\">\n",
-       "            <h3 style=\"display: inline;\">Workers</h3>\n",
-       "        </summary>\n",
-       "\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:37617\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 4\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34591/status\" target=\"_blank\">http://127.0.0.1:34591/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 7.45 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43307\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /work/scratch-pw3/dask-scratch-space/worker-hu908owt\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "\n",
-       "    </details>\n",
-       "</div>\n",
-       "\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
-       "            </details>\n",
-       "        \n",
-       "\n",
-       "    </div>\n",
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-      "text/plain": [
-       "<Client: 'tcp://127.0.0.1:43543' processes=1 threads=4, memory=7.45 GiB>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from dask.distributed import LocalCluster, Client\n",
-    "cluster = LocalCluster(n_workers=1,\n",
-    "                       threads_per_worker=4,\n",
-    "                       memory_limit='8GB',\n",
-    "                       local_directory='/work/scratch-pw3/kerchunk-dask/')\n",
-    "client = Client(cluster)\n",
-    "client"
-   ]
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-   "execution_count": 6,
-   "id": "5013fbb7-4f3b-4c3d-b682-e63d955a5c63",
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-   "outputs": [
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-     "name": "stdout",
-     "output_type": "stream",
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-       "array(466.979, dtype=float32)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'prsn'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-49bd424c-8331-4e25-afbe-4e387f17abb6' class='xr-array-in' type='checkbox' checked><label for='section-49bd424c-8331-4e25-afbe-4e387f17abb6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>467.0</span></div><div class='xr-array-data'><pre>array(466.979, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f3366f6f-63d6-41b7-a503-3ae257933d99' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f3366f6f-63d6-41b7-a503-3ae257933d99' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e5baa951-bfa3-4176-82c7-1e6b2bb7cfa5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5baa951-bfa3-4176-82c7-1e6b2bb7cfa5' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ba0679c9-c0fe-43e1-8d39-8be0eac3de8b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba0679c9-c0fe-43e1-8d39-8be0eac3de8b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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-       "<xarray.DataArray 'prsn' ()>\n",
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-     },
-     "execution_count": 6,
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-    "%%time\n",
-    "ds.prsn.sum().compute()"
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diff --git a/showcase/notebooks/TestKerchunking.ipynb b/showcase/notebooks/TestKerchunking.ipynb
deleted file mode 100644
index f0b6639..0000000
--- a/showcase/notebooks/TestKerchunking.ipynb
+++ /dev/null
@@ -1,3743 +0,0 @@
-{
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-   "execution_count": 1,
-   "id": "70c74604-24ee-4407-a788-07ac13dd6c32",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import fsspec\n",
-    "import xarray as xr\n",
-    "from kerchunk import combine, hdf, netCDF3\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9cba2403-8b24-4884-ac3a-6644da3bdc69",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_refs_hdf(nfile):\n",
-    "    return hdf.SingleHdf5ToZarr(nfile).translate()\n",
-    "\n",
-    "def get_refs_ncf3(nfile):\n",
-    "    return netCDF3.NetCDF3ToZarr(nfile).translate()\n",
-    "\n",
-    "def combine_refs(refset,**kwargs):\n",
-    "    return combine.MultiZarrToZarr(refset, concat_dims=['time'], **kwargs).translate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0a027fa1-ef28-401c-8dbb-75cdac924f70",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;AOD670_mean&#x27; ()&gt;\n",
-       "array(True)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'AOD670_mean'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-03086059-0073-4a3b-b4d1-168c8f290540' class='xr-array-in' type='checkbox' checked><label for='section-03086059-0073-4a3b-b4d1-168c8f290540' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>True</span></div><div class='xr-array-data'><pre>array(True)</pre></div></div></li><li class='xr-section-item'><input id='section-38bf50ed-c102-4ee8-8520-935b29f69ccb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-38bf50ed-c102-4ee8-8520-935b29f69ccb' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8ebd4812-33d8-4c4e-be22-4268501e3953' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8ebd4812-33d8-4c4e-be22-4268501e3953' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-3a836abc-a975-4e95-bc09-25af80a7e9da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a836abc-a975-4e95-bc09-25af80a7e9da' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'AOD670_mean' ()>\n",
-       "array(True)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "nfile0 = '/neodc/esacci/aerosol/data/AATSR_ADV/L3/v2.31/MONTHLY/2002/200205-ESACCI-L3C_AEROSOL-AER_PRODUCTS-AATSR-ENVISAT-ADV_MOTNHLY-v2.30.nc'\n",
-    "nfile1 = '/neodc/esacci/aerosol/data/AATSR_ADV/L3/v2.31/MONTHLY/2002/200206-ESACCI-L3C_AEROSOL-AER_PRODUCTS-AATSR-ENVISAT-ADV_MOTNHLY-v2.30.nc'\n",
-    "\n",
-    "ds = xr.open_dataset(nfile0)\n",
-    "ds1 = xr.open_dataset(nfile1)\n",
-    "\n",
-    "\n",
-    "ab = ds.AOD670_mean\n",
-    "np.all(ab!=ab)"
-   ]
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-   "execution_count": 4,
-   "id": "80359973-5c7e-461f-86ec-7de5f8a84e92",
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-   "outputs": [
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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds1.AOD670_mean.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ff4fe227-6c64-4677-9aed-343c0de6c1a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nfile_cci1 = '/neodc/esacci/aerosol/data/ATSR2_ADV/L3/v2.31/MONTHLY/1995/199506-ESACCI-L3C_AEROSOL-AER_PRODUCTS-ATSR2-ERS2-ADV_MONTHLY-v2.30.nc'\n",
-    "refs = get_refs_ncf3(nfile_cci1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "08f08790-c78a-4e8d-b949-5788226cbe38",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19801201-19901130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19901201-20001130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20001201-20101130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20101201-20201130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20201201-20301130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20301201-20401130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20401201-20501130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20501201-20601130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20601201-20701130.nc',\n",
-       " '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20701201-20801130.nc']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ukcp_fileset = \"\"\"/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19801201-19901130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_19901201-20001130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20001201-20101130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20101201-20201130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20201201-20301130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20301201-20401130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20401201-20501130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20501201-20601130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20601201-20701130.nc\n",
-    "/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/clt_rcp85_land-rcm_uk_12km_01_day_20701201-20801130.nc\"\"\".split('\\n')\n",
-    "ukcp_fileset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "581f3323-ce3a-4c1e-9d88-e6232e2822df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ref1 = get_refs(ukcp_fileset[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "4d35c558-999d-496c-bbce-a6be60de92e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ref2 = get_refs(ukcp_fileset[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "315c6336-5b63-4c85-ad48-2636628ba7da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mzz = combine_refs([ref1,ref2], identical_dims=['ensemble_member_id', 'grid_latitude','grid_longitude'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "b5666072-5a8b-4dad-89b5-6ffa94e14f22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_zarr(fsspec.get_mapper('reference://',fo=mzz2),consolidated=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "c61faf5d-0adc-40f6-9204-46c6a63363ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((1,), (1,))"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.ensemble_member_id.shape, ds.ensemble_member.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "79d508d0-65bd-4b31-b5df-9b7d23c48cf1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'{\"_ARRAY_DIMENSIONS\":[\"ensemble_member\",\"string27\"],\"long_name\":\"ensemble_member_id\",\"units\":\"1\"}'"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ref1['refs']['ensemble_member_id/.zattrs']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "36475463-0554-4ea1-a46f-3f461d3941e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clt ['ensemble_member', 'time', 'projection_y_coordinate', 'projection_x_coordinate']\n",
-      "transverse_mercator []\n",
-      "ensemble_member ['ensemble_member']\n",
-      "time ['time']\n",
-      "time_bnds ['time', 'bnds']\n",
-      "projection_y_coordinate ['projection_y_coordinate']\n",
-      "projection_y_coordinate_bnds ['projection_y_coordinate', 'bnds']\n",
-      "projection_x_coordinate ['projection_x_coordinate']\n",
-      "projection_x_coordinate_bnds ['projection_x_coordinate', 'bnds']\n",
-      "ensemble_member_id ['ensemble_member', 'string27']\n",
-      "grid_latitude ['projection_y_coordinate', 'projection_x_coordinate']\n",
-      "grid_longitude ['projection_y_coordinate', 'projection_x_coordinate']\n",
-      "month_number ['time']\n",
-      "year ['time']\n",
-      "yyyymmdd ['time', 'string64']\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "for var in list(xarr_1.variables):\n",
-    "    attrs = json.loads(ref1['refs'][f'{var}/.zattrs'])\n",
-    "    print(var, attrs['_ARRAY_DIMENSIONS'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c29e072-1352-4c0c-991c-56b9ef356c34",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mzz['refs']['grid_latitude/.zattrs']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "d4138e90-a68b-4d4c-9891-d76e1880ccb7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xarr_1 = xr.open_mfdataset(ukcp_fileset[:2])\n",
-    "xarr_2 = xr.concat([xr.open_dataset(ukcp_fileset[0]), xr.open_dataset(ukcp_fileset[1])], dim='time', data_vars='minimal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "41f78598-9131-4042-9552-cfe2f06a0db1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clt (1, 7200, 112, 82) (1, 7200, 112, 82)\n",
-      "transverse_mercator () ()\n",
-      "ensemble_member (1,) (1,)\n",
-      "time (7200,) (7200,)\n",
-      "time_bnds (7200, 2) (7200, 2)\n",
-      "projection_y_coordinate (112,) (112,)\n",
-      "projection_y_coordinate_bnds (112, 2) (112, 2)\n",
-      "projection_x_coordinate (82,) (82,)\n",
-      "projection_x_coordinate_bnds (82, 2) (82, 2)\n",
-      "ensemble_member_id (1,) (1,)\n",
-      "grid_latitude (112, 82) (112, 82)\n",
-      "grid_longitude (112, 82) (112, 82)\n",
-      "month_number (7200,) (7200,)\n",
-      "year (7200,) (7200,)\n",
-      "yyyymmdd (7200,) (7200,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for var in list(xarr_1.variables):\n",
-    "    print(var, xarr_2[var].shape, ds[var].shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "30bde3de-dd8d-4aba-8d5d-7ee935c100c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(112, 82)"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xarr_1.grid_latitude.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "a34c0156-b950-4096-9786-3a9e4eb756c3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(7200, 112, 82)"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.grid_latitude.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "b38ffe42-a9fb-4e0e-a036-8b436b6b9f20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pipeline.validate import validate_data\n",
-    "from pipeline.logs import init_logger\n",
-    "testlog = init_logger(1,0,'test')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0533c908-828b-4c14-972d-5dee11891fb7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO [test]: ensemble_member : Starting growbox data tests for 0\n",
-      "INFO [test]: ensemble_member : Starting growbox data tests for 0\n",
-      "INFO [test]: ensemble_member_id : Starting growbox data tests for 0\n",
-      "INFO [test]: ensemble_member_id : Starting growbox data tests for 0\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time-dependent:  clt\n",
-      "Duplicate:  ensemble_member\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING [test]: Max comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Max comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Min comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Min comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Mean comparison skipped for non-summable values in ensemble_member_id\n",
-      "WARNING [test]: Mean comparison skipped for non-summable values in ensemble_member_id\n",
-      "INFO [test]: grid_latitude : Starting growbox data tests for 0\n",
-      "INFO [test]: grid_latitude : Starting growbox data tests for 0\n",
-      "INFO [test]: grid_longitude : Starting growbox data tests for 0\n",
-      "INFO [test]: grid_longitude : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_x_coordinate : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_x_coordinate : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_x_coordinate_bnds : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_x_coordinate_bnds : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_y_coordinate : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_y_coordinate : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_y_coordinate_bnds : Starting growbox data tests for 0\n",
-      "INFO [test]: projection_y_coordinate_bnds : Starting growbox data tests for 0\n",
-      "INFO [test]: transverse_mercator : Starting growbox data tests for 0\n",
-      "INFO [test]: transverse_mercator : Starting growbox data tests for 0\n",
-      "WARNING [test]: Failed to find non-NaN slice (tried: 7, var: transverse_mercator)\n",
-      "WARNING [test]: Failed to find non-NaN slice (tried: 7, var: transverse_mercator)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Duplicate:  ensemble_member_id\n",
-      "Duplicate:  grid_latitude\n",
-      "Duplicate:  grid_longitude\n",
-      "Time-dependent:  month_number\n",
-      "Duplicate:  projection_x_coordinate\n",
-      "Duplicate:  projection_x_coordinate_bnds\n",
-      "Duplicate:  projection_y_coordinate\n",
-      "Duplicate:  projection_y_coordinate_bnds\n",
-      "Time-dependent:  time\n",
-      "Time-dependent:  time_bnds\n",
-      "nan\n",
-      "Duplicate:  transverse_mercator\n",
-      "Time-dependent:  year\n",
-      "Time-dependent:  yyyymmdd\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pipeline.errors import ShapeMismatchError\n",
-    "import xarray as xr\n",
-    "import fsspec\n",
-    "ds1 = xr.open_zarr(fsspec.get_mapper('reference://',fo=ref1),consolidated=False)\n",
-    "ds2 = xr.open_zarr(fsspec.get_mapper('reference://',fo=ref2),consolidated=False)\n",
-    "identicals = []\n",
-    "for var in ds1.variables:\n",
-    "    if 'time' not in ds1[var].dims:\n",
-    "        try:\n",
-    "            validate_data(ds1, ds2, var, 0, testlog, bypass=True)\n",
-    "            print('Duplicate: ',var)\n",
-    "            identicals.append(var)\n",
-    "        except ShapeMismatchError:\n",
-    "            # Non duplicate\n",
-    "            print('Non-duplicate: ',var)\n",
-    "    else:\n",
-    "        print('Time-dependent: ',var)\n",
-    "mzz2 = combine_refs([ref1,ref2], identical_dims=identicals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "0bd64b78-1ce3-49f4-8d10-2cbc8e1e6b21",
-   "metadata": {},
-   "outputs": [
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-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
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-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;transverse_mercator&#x27; (time: 7200)&gt;\n",
-       "array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)\n",
-       "Coordinates:\n",
-       "  * time          (time) object 1980-12-01 12:00:00 ... 2000-11-30 12:00:00\n",
-       "    month_number  (time) int32 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
-       "    year          (time) int32 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
-       "    yyyymmdd      (time) |S64 dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    grid_mapping_name:                 transverse_mercator\n",
-       "    longitude_of_prime_meridian:       0.0\n",
-       "    semi_major_axis:                   6377563.396\n",
-       "    semi_minor_axis:                   6356256.909\n",
-       "    longitude_of_central_meridian:     -2.0\n",
-       "    latitude_of_projection_origin:     49.0\n",
-       "    false_easting:                     400000.0\n",
-       "    false_northing:                    -100000.0\n",
-       "    scale_factor_at_central_meridian:  0.9996012717</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'transverse_mercator'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7d6cfbd9-a222-40bb-994d-6e98e7f4e20c' class='xr-array-in' type='checkbox' checked><label for='section-7d6cfbd9-a222-40bb-994d-6e98e7f4e20c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-2147483647 -2147483647 -2147483647 ... -2147483647 -2147483647</span></div><div class='xr-array-data'><pre>array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)</pre></div></div></li><li class='xr-section-item'><input id='section-0c7d3ede-40e6-4651-90e9-47dd03331f01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c7d3ede-40e6-4651-90e9-47dd03331f01' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-12-01 12:00:00 ... 2000-11-...</div><input id='attrs-fa13be28-4dc6-4016-ad54-96952d1c33dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa13be28-4dc6-4016-ad54-96952d1c33dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-523c7203-8f55-4935-9447-b98cda2dcb5c' class='xr-var-data-in' type='checkbox'><label for='data-523c7203-8f55-4935-9447-b98cda2dcb5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(1980, 12, 1, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(1980, 12, 2, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(1980, 12, 3, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.Datetime360Day(2000, 11, 28, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(2000, 11, 29, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(2000, 11, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-70f67d4f-18f9-481f-8dbc-1c8724e00a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70f67d4f-18f9-481f-8dbc-1c8724e00a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02612eae-4ba9-454d-8acc-a0cdb1426b6d' class='xr-var-data-in' type='checkbox'><label for='data-02612eae-4ba9-454d-8acc-a0cdb1426b6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>month_number</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 28.12 kiB </td>\n",
-       "                        <td> 14.06 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (7200,) </td>\n",
-       "                        <td> (3600,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-1fc7650c-e5fd-4afe-bbc5-cb0ea16e5788' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fc7650c-e5fd-4afe-bbc5-cb0ea16e5788' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff9c012-d22b-42b2-a110-278a6351a7c6' class='xr-var-data-in' type='checkbox'><label for='data-dff9c012-d22b-42b2-a110-278a6351a7c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>year</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 28.12 kiB </td>\n",
-       "                        <td> 14.06 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (7200,) </td>\n",
-       "                        <td> (3600,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yyyymmdd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3600,), meta=np.ndarray&gt;</div><input id='attrs-4cf64572-db15-4397-8a96-7523eb2b2dba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cf64572-db15-4397-8a96-7523eb2b2dba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be74de0-e927-4d31-9639-f7fb24bffbeb' class='xr-var-data-in' type='checkbox'><label for='data-7be74de0-e927-4d31-9639-f7fb24bffbeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>yyyymmdd</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 450.00 kiB </td>\n",
-       "                        <td> 225.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (7200,) </td>\n",
-       "                        <td> (3600,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> |S64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7200</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-431dc5c2-896f-4efe-9486-74f11ae8736f' class='xr-section-summary-in' type='checkbox'  ><label for='section-431dc5c2-896f-4efe-9486-74f11ae8736f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b13002ad-c241-47d5-81f0-f6f7c3a36c5d' class='xr-index-data-in' type='checkbox'/><label for='index-b13002ad-c241-47d5-81f0-f6f7c3a36c5d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1980-12-01 12:00:00, 1980-12-02 12:00:00, 1980-12-03 12:00:00,\n",
-       "             1980-12-04 12:00:00, 1980-12-05 12:00:00, 1980-12-06 12:00:00,\n",
-       "             1980-12-07 12:00:00, 1980-12-08 12:00:00, 1980-12-09 12:00:00,\n",
-       "             1980-12-10 12:00:00,\n",
-       "             ...\n",
-       "             2000-11-21 12:00:00, 2000-11-22 12:00:00, 2000-11-23 12:00:00,\n",
-       "             2000-11-24 12:00:00, 2000-11-25 12:00:00, 2000-11-26 12:00:00,\n",
-       "             2000-11-27 12:00:00, 2000-11-28 12:00:00, 2000-11-29 12:00:00,\n",
-       "             2000-11-30 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=7200, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a16d8c3-004b-4cd3-805a-d386e81bbbcd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a16d8c3-004b-4cd3-805a-d386e81bbbcd' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6377563.396</dd><dt><span>semi_minor_axis :</span></dt><dd>6356256.909</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-2.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>49.0</dd><dt><span>false_easting :</span></dt><dd>400000.0</dd><dt><span>false_northing :</span></dt><dd>-100000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996012717</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'transverse_mercator' (time: 7200)>\n",
-       "array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)\n",
-       "Coordinates:\n",
-       "  * time          (time) object 1980-12-01 12:00:00 ... 2000-11-30 12:00:00\n",
-       "    month_number  (time) int32 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
-       "    year          (time) int32 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
-       "    yyyymmdd      (time) |S64 dask.array<chunksize=(3600,), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    grid_mapping_name:                 transverse_mercator\n",
-       "    longitude_of_prime_meridian:       0.0\n",
-       "    semi_major_axis:                   6377563.396\n",
-       "    semi_minor_axis:                   6356256.909\n",
-       "    longitude_of_central_meridian:     -2.0\n",
-       "    latitude_of_projection_origin:     49.0\n",
-       "    false_easting:                     400000.0\n",
-       "    false_northing:                    -100000.0\n",
-       "    scale_factor_at_central_meridian:  0.9996012717"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "15a02ef3-a0fd-4d84-b6a0-aa34ac9b68d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Args:\n",
-    "    def __init__(self):\n",
-    "        self.bypass = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "6573a292-e489-47a5-851d-1cbe9f61227e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allfile = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/allfiles.txt\"\n",
-    "with open(allfile) as f:\n",
-    "    fileset = [r.strip() for r in f.readlines()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "d21cb5a9-96ab-4fb9-b330-f06f899f15ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "ds_set = []\n",
-    "for f in fileset:\n",
-    "    ds_set.append(xr.open_dataset(f))\n",
-    "ds = xr.concat(ds_set, dim='time',data_vars='minimal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "eb934cb4-bede-4534-9d08-e2fd1fbe577e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(384, 2)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.lon_bnds.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b7f2245c-0f27-4ef9-88a7-e5eec050339d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kfile1 = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/cache/0.json\"\n",
-    "kfile2 = kfile1.replace(\"0.json\", \"1.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "55406257-4906-439c-be88-1f3d19a85e58",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pipeline.validate import open_kerchunk\n",
-    "from pipeline.logs import FalseLogger\n",
-    "import fsspec\n",
-    "import xarray as xr\n",
-    "\n",
-    "kfile1 = \"/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AWI_AWI-CM-1-1-MR_historical_r1i1p1f1_Amon_uas_gn_v20200511/kerchunk-1a.json\"\n",
-    "\n",
-    "kf1 = open_kerchunk(kfile1, FalseLogger())\n",
-    "mapper = fsspec.get_mapper('reference://',fo=kfile1, remote_protocol='https')\n",
-    "kf2 = xr.open_zarr(mapper, consolidated=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "bc8791ea-4a1e-48fe-902a-1753632dde62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
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-       "\n",
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-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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-       "  margin-bottom: 4px;\n",
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-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
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-       ".xr-array-name {\n",
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-       "\n",
-       ".xr-obj-type {\n",
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-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
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-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
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-       "\n",
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-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
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-       "\n",
-       ".xr-section-summary {\n",
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-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
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-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
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-       "  content: '▼';\n",
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-       ".xr-section-summary-in:checked + label > span {\n",
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-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
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-       "  display: grid;\n",
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-       ".xr-preview {\n",
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-       ".xr-array-data {\n",
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-       "\n",
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-       "  display: none;\n",
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-       "\n",
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-       "\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 1)&gt;\n",
-       "array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)\n",
-       "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00\n",
-       "Attributes:\n",
-       "    axis:           T\n",
-       "    bounds:         time_bnds\n",
-       "    long_name:      time\n",
-       "    standard_name:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3b548d65-ca93-4653-9533-c02224c77528' class='xr-array-in' type='checkbox' checked><label for='section-3b548d65-ca93-4653-9533-c02224c77528' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1850-01-16T12:00:00</span></div><div class='xr-array-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-1a3d8a94-af61-4180-8c19-492bbb7d10f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a3d8a94-af61-4180-8c19-492bbb7d10f9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf78290e-9997-492c-a9c4-e90f8f1c820a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf78290e-9997-492c-a9c4-e90f8f1c820a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aa463b1-0f5a-4ad9-adcf-cfcecc3640a5' class='xr-var-data-in' type='checkbox'><label for='data-9aa463b1-0f5a-4ad9-adcf-cfcecc3640a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00</div><input id='attrs-eafe1426-c808-4848-ac4a-677425fb1a08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eafe1426-c808-4848-ac4a-677425fb1a08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e21f0f6-3ee6-41bc-b527-1874e82e0a9e' class='xr-var-data-in' type='checkbox'><label for='data-4e21f0f6-3ee6-41bc-b527-1874e82e0a9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b14eaaae-40d9-4d42-8644-4e47392ee796' class='xr-section-summary-in' type='checkbox'  ><label for='section-b14eaaae-40d9-4d42-8644-4e47392ee796' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-06e5daa7-c022-494c-a115-88ca44266cb9' class='xr-index-data-in' type='checkbox'/><label for='index-06e5daa7-c022-494c-a115-88ca44266cb9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1850-01-16 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5ab734f-eb23-437d-ace2-d113b8de93fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5ab734f-eb23-437d-ace2-d113b8de93fe' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
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-       "<xarray.DataArray 'time' (time: 1)>\n",
-       "array(['1850-01-16T12:00:00.000000000'], dtype='datetime64[ns]')\n",
-       "Coordinates:\n",
-       "    height   float64 ...\n",
-       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00\n",
-       "Attributes:\n",
-       "    axis:           T\n",
-       "    bounds:         time_bnds\n",
-       "    long_name:      time\n",
-       "    standard_name:  time"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "kf2.time.sel(time='1850-01-16')"
-   ]
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-   "execution_count": 11,
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-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lon_bnds&#x27; (lon: 384, bnds: 2)&gt;\n",
-       "array([[ -0.46875,   0.46875],\n",
-       "       [  0.46875,   1.40625],\n",
-       "       [  1.40625,   2.34375],\n",
-       "       [  2.34375,   3.28125],\n",
-       "       [  3.28125,   4.21875],\n",
-       "       [  4.21875,   5.15625],\n",
-       "       [  5.15625,   6.09375],\n",
-       "       [  6.09375,   7.03125],\n",
-       "       [  7.03125,   7.96875],\n",
-       "       [  7.96875,   8.90625],\n",
-       "       [  8.90625,   9.84375],\n",
-       "       [  9.84375,  10.78125],\n",
-       "       [ 10.78125,  11.71875],\n",
-       "       [ 11.71875,  12.65625],\n",
-       "       [ 12.65625,  13.59375],\n",
-       "       [ 13.59375,  14.53125],\n",
-       "       [ 14.53125,  15.46875],\n",
-       "       [ 15.46875,  16.40625],\n",
-       "       [ 16.40625,  17.34375],\n",
-       "       [ 17.34375,  18.28125],\n",
-       "...\n",
-       "       [340.78125, 341.71875],\n",
-       "       [341.71875, 342.65625],\n",
-       "       [342.65625, 343.59375],\n",
-       "       [343.59375, 344.53125],\n",
-       "       [344.53125, 345.46875],\n",
-       "       [345.46875, 346.40625],\n",
-       "       [346.40625, 347.34375],\n",
-       "       [347.34375, 348.28125],\n",
-       "       [348.28125, 349.21875],\n",
-       "       [349.21875, 350.15625],\n",
-       "       [350.15625, 351.09375],\n",
-       "       [351.09375, 352.03125],\n",
-       "       [352.03125, 352.96875],\n",
-       "       [352.96875, 353.90625],\n",
-       "       [353.90625, 354.84375],\n",
-       "       [354.84375, 355.78125],\n",
-       "       [355.78125, 356.71875],\n",
-       "       [356.71875, 357.65625],\n",
-       "       [357.65625, 358.59375],\n",
-       "       [358.59375, 359.53125]])\n",
-       "Coordinates:\n",
-       "    height   float64 10.0\n",
-       "  * lon      (lon) float64 6.946e-310 6.946e-310 ... 2.451e+198 4.572e+228\n",
-       "Dimensions without coordinates: bnds</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lon_bnds'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 384</li><li><span>bnds</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8ff5f241-a99e-4ae0-8b3d-6f3d28706048' class='xr-array-in' type='checkbox' checked><label for='section-8ff5f241-a99e-4ae0-8b3d-6f3d28706048' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.4688 0.4688 0.4688 1.406 1.406 ... 357.7 357.7 358.6 358.6 359.5</span></div><div class='xr-array-data'><pre>array([[ -0.46875,   0.46875],\n",
-       "       [  0.46875,   1.40625],\n",
-       "       [  1.40625,   2.34375],\n",
-       "       [  2.34375,   3.28125],\n",
-       "       [  3.28125,   4.21875],\n",
-       "       [  4.21875,   5.15625],\n",
-       "       [  5.15625,   6.09375],\n",
-       "       [  6.09375,   7.03125],\n",
-       "       [  7.03125,   7.96875],\n",
-       "       [  7.96875,   8.90625],\n",
-       "       [  8.90625,   9.84375],\n",
-       "       [  9.84375,  10.78125],\n",
-       "       [ 10.78125,  11.71875],\n",
-       "       [ 11.71875,  12.65625],\n",
-       "       [ 12.65625,  13.59375],\n",
-       "       [ 13.59375,  14.53125],\n",
-       "       [ 14.53125,  15.46875],\n",
-       "       [ 15.46875,  16.40625],\n",
-       "       [ 16.40625,  17.34375],\n",
-       "       [ 17.34375,  18.28125],\n",
-       "...\n",
-       "       [340.78125, 341.71875],\n",
-       "       [341.71875, 342.65625],\n",
-       "       [342.65625, 343.59375],\n",
-       "       [343.59375, 344.53125],\n",
-       "       [344.53125, 345.46875],\n",
-       "       [345.46875, 346.40625],\n",
-       "       [346.40625, 347.34375],\n",
-       "       [347.34375, 348.28125],\n",
-       "       [348.28125, 349.21875],\n",
-       "       [349.21875, 350.15625],\n",
-       "       [350.15625, 351.09375],\n",
-       "       [351.09375, 352.03125],\n",
-       "       [352.03125, 352.96875],\n",
-       "       [352.96875, 353.90625],\n",
-       "       [353.90625, 354.84375],\n",
-       "       [354.84375, 355.78125],\n",
-       "       [355.78125, 356.71875],\n",
-       "       [356.71875, 357.65625],\n",
-       "       [357.65625, 358.59375],\n",
-       "       [358.59375, 359.53125]])</pre></div></div></li><li class='xr-section-item'><input id='section-3442477a-da38-4f41-ae34-ff9a3fc8fb70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3442477a-da38-4f41-ae34-ff9a3fc8fb70' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-4234e3ed-60e9-4a30-9db5-b9873a33bf93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4234e3ed-60e9-4a30-9db5-b9873a33bf93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f9cc817-26ea-4cce-bc52-475f867e3c48' class='xr-var-data-in' type='checkbox'><label for='data-4f9cc817-26ea-4cce-bc52-475f867e3c48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(10.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.946e-310 ... 4.572e+228</div><input id='attrs-db57a267-b928-499c-a7ea-c3c40cc56c0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db57a267-b928-499c-a7ea-c3c40cc56c0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e1312b-0004-4a4d-abf0-fdf8154bcdb8' class='xr-var-data-in' type='checkbox'><label for='data-85e1312b-0004-4a4d-abf0-fdf8154bcdb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([6.946461e-310, 6.946461e-310, 4.661759e-310, ..., 4.787194e+180,\n",
-       "       2.450673e+198, 4.572376e+228])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49cccdf7-b0ce-4914-9c64-e021faf97f1f' class='xr-section-summary-in' type='checkbox'  ><label for='section-49cccdf7-b0ce-4914-9c64-e021faf97f1f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed1cc9d3-f3b0-4b9c-b5df-af69e8ffdce8' class='xr-index-data-in' type='checkbox'/><label for='index-ed1cc9d3-f3b0-4b9c-b5df-af69e8ffdce8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([  6.94646063411794e-310,   6.94646063411794e-310,\n",
-       "                 4.6617585837179e-310,    4.6617585837179e-310,\n",
-       "                               5e-324,                     0.0,\n",
-       "              3.6471404775824516e+180,  7.628377678647842e+228,\n",
-       "              1.6943165045757204e-152, 4.0719047313359087e+223,\n",
-       "              ...\n",
-       "               1.1858693676012526e+49,   3.845809172237913e-86,\n",
-       "              6.0112290352011415e+175,  9.088948731942852e+223,\n",
-       "               5.030572423582765e+223, 4.3720591034250617e-153,\n",
-       "              1.0647870195102642e+248,  4.787194240623723e+180,\n",
-       "              2.4506726540155096e+198,  4.572375645356952e+228],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=384))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1027352c-cf9b-46c2-bf63-9d40a4fb7f77' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1027352c-cf9b-46c2-bf63-9d40a4fb7f77' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'lon_bnds' (lon: 384, bnds: 2)>\n",
-       "array([[ -0.46875,   0.46875],\n",
-       "       [  0.46875,   1.40625],\n",
-       "       [  1.40625,   2.34375],\n",
-       "       [  2.34375,   3.28125],\n",
-       "       [  3.28125,   4.21875],\n",
-       "       [  4.21875,   5.15625],\n",
-       "       [  5.15625,   6.09375],\n",
-       "       [  6.09375,   7.03125],\n",
-       "       [  7.03125,   7.96875],\n",
-       "       [  7.96875,   8.90625],\n",
-       "       [  8.90625,   9.84375],\n",
-       "       [  9.84375,  10.78125],\n",
-       "       [ 10.78125,  11.71875],\n",
-       "       [ 11.71875,  12.65625],\n",
-       "       [ 12.65625,  13.59375],\n",
-       "       [ 13.59375,  14.53125],\n",
-       "       [ 14.53125,  15.46875],\n",
-       "       [ 15.46875,  16.40625],\n",
-       "       [ 16.40625,  17.34375],\n",
-       "       [ 17.34375,  18.28125],\n",
-       "...\n",
-       "       [340.78125, 341.71875],\n",
-       "       [341.71875, 342.65625],\n",
-       "       [342.65625, 343.59375],\n",
-       "       [343.59375, 344.53125],\n",
-       "       [344.53125, 345.46875],\n",
-       "       [345.46875, 346.40625],\n",
-       "       [346.40625, 347.34375],\n",
-       "       [347.34375, 348.28125],\n",
-       "       [348.28125, 349.21875],\n",
-       "       [349.21875, 350.15625],\n",
-       "       [350.15625, 351.09375],\n",
-       "       [351.09375, 352.03125],\n",
-       "       [352.03125, 352.96875],\n",
-       "       [352.96875, 353.90625],\n",
-       "       [353.90625, 354.84375],\n",
-       "       [354.84375, 355.78125],\n",
-       "       [355.78125, 356.71875],\n",
-       "       [356.71875, 357.65625],\n",
-       "       [357.65625, 358.59375],\n",
-       "       [358.59375, 359.53125]])\n",
-       "Coordinates:\n",
-       "    height   float64 10.0\n",
-       "  * lon      (lon) float64 6.946e-310 6.946e-310 ... 2.451e+198 4.572e+228\n",
-       "Dimensions without coordinates: bnds"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kf2.lon_bnds.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b2dc1449-999a-494f-8275-f83f1cfc3a15",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "kf1 = np.array(kf1.lon_bnds)\n",
-    "kf2 = np.array(kf2.lon_bnds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "e1fe251d-e133-44bf-b9fe-7171a51599ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.array_equal(kf1, kf2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "6c46e794-135e-4f63-931e-693a3ab82f2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "ingest_file = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233/proj_codes_complete_ingest.txt'\n",
-    "in_place = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233'\n",
-    "with open(ingest_file) as f:\n",
-    "    datasets = [f'{in_place}/{r.strip()}_kr1.0.json' for r in f.readlines()]\n",
-    "\n",
-    "for d in datasets[3:8]:\n",
-    "    os.system(f'cp {d} {d.replace(\"CMIP6_rel1_6233\",\"sample\")}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5b495d56-aded-40e9-bab9-58c38577e49f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for x, d in enumerate(datasets):\n",
-    "    with open(d) as f:\n",
-    "        print(x, len(f.readlines()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "e306c7b8-72d2-415b-88e2-7b13da810d2e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0.json\n"
-     ]
-    }
-   ],
-   "source": [
-    "path = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233'\n",
-    "kfile0 = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0.json'\n",
-    "kfile0a = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/0a.json'\n",
-    "kfile1 = f'{path}/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/cache/1.json'\n",
-    "print(kfile0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "82619c91-4e00-40de-b654-21a1f804bee1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import fsspec\n",
-    "import xarray as xr\n",
-    "\n",
-    "mapper = fsspec.get_mapper('reference://',fo=kfile0, remote_protocol='https')\n",
-    "kf = xr.open_zarr(mapper, consolidated=False)\n",
-    "kf.lata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "16e324e3-3166-4ad5-b219-c15325fb12d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import fsspec\n",
-    "import xarray as xr\n",
-    "\n",
-    "mapper = fsspec.get_mapper('reference://',fo=kfile0a, remote_protocol='file')\n",
-    "kfa = xr.open_zarr(mapper, consolidated=False)\n",
-    "kfa.lat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a1d2ac6c-ba5a-4176-9b11-31a2c79f1c16",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ncf1 = '/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc'\n",
-    "ds = xr.open_dataset('/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "311f2e8b-b56d-49ce-a2ea-964d09421364",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from kerchunk.hdf import SingleHdf5ToZarr\n",
-    "import xarray as xr\n",
-    "import fsspec\n",
-    "import json\n",
-    "ncf1 = '/badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/huss/day/v20190731/huss_rcp85_land-rcm_uk_12km_01_day_19801201-19901130.nc'\n",
-    "refs = SingleHdf5ToZarr(ncf1, inline_threshold=65).translate()\n",
-    "refs['refs']['time_bnds/.zarray']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3a9d13db-0796-42cc-8581-6b0e86cf3a1e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf09a8a8-416f-4504-acd1-3ce28788f98f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ['BREAKER'] = 'false'\n",
-    "ds = xr.open_zarr(fsspec.get_mapper('reference://',fo=refs), consolidated=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7199afd6-a062-493c-a6b0-97cfef5674f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.environ['BREAKER'] = 'true'\n",
-    "ds.time_bnds.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "faebaef5-ea19-469d-8abe-dd94074c5967",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kfile2 = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/CMIP_AS-RCEC_TaiESM1_historical_r1i1p1f1_3hr_huss_gn_v20201013/kerchunk-1a.json'\n",
-    "ds2 = xr.open_zarr(fsspec.get_mapper('reference://',fo=kfile2, remote_protocol='https'), consolidated=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "adf599ed-b3b9-4747-a322-2d7ff6ceb2e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import os\n",
-    "os.system(f'rm {kfile0a}')\n",
-    "with open(kfile0a,'w') as f:\n",
-    "    f.write(json.dumps(refs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ce447394-0f5c-4e1a-88b1-885cccf26543",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "e94a4ac2-bd29-442b-9ac3-c21545f736d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/badc/cmip6/data/CMIP6/CMIP/AS-RCEC/TaiESM1/historical/r1i1p1f1/3hr/huss/gn/v20201013/huss_3hr_TaiESM1_historical_r1i1p1f1_gn_185001010000-185912312230.nc',\n",
-       " 41084,\n",
-       " 1536]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "refs['refs']['lat/0']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "ea28e2cf-44cb-40af-a2d9-dee5202fde7e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import os\n",
-    "complete = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233/*.json'\n",
-    "\n",
-    "paths = glob.glob(complete)\n",
-    "for p in paths:\n",
-    "    if not p.split('/')[-1].startswith('CMIP6_'):\n",
-    "        os.system(f\"mv {p} {p.replace('6233/','6233/CMIP6_')}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb2d1e7f-d840-4a13-81c0-9b3a89c2c094",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "incomplete = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233/proj_codes_cmip6_fail.txt'\n",
-    "with open(incomplete) as f:\n",
-    "    keys = [r.strip() for r in f.readlines()]\n",
-    "\n",
-    "for k in keys:\n",
-    "    print(k)\n",
-    "    complete_1057[k] = 'FAIL'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "2e5a5c8e-7e3e-4ae3-b6bd-6a21fe2c92ce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PASS, FAIL = 0, 0\n",
-    "day = 0\n",
-    "for key in complete_1057.keys():\n",
-    "    if complete_1057[key] == 'FAIL':\n",
-    "        FAIL += 1\n",
-    "        if 'day' in key:\n",
-    "            day += 1\n",
-    "    else:\n",
-    "        PASS += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "3b13dc79-116c-4614-be01-fa8046706c93",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(326, 731, 257)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "FAIL, PASS, day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "182c4eb3-0f2f-4eb9-a24c-ce9036a9abf3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2190,)\n",
-      "(3650,)\n",
-      "(3650,)\n",
-      "(3650,)\n",
-      "(3650,)\n",
-      "(3650,)\n",
-      "(3650,)\n",
-      "(3650,)\n",
-      "(3650,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import xarray as xr\n",
-    "allfile = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/ScenarioMIP_NCC_NorESM2-MM_ssp126_r1i1p1f1_day_psl_gn_v20191108/allfiles.txt'\n",
-    "\n",
-    "with open(allfile) as f:\n",
-    "    xrobjs = [r.strip() for r in f.readlines()]\n",
-    "\n",
-    "for x in xrobjs:\n",
-    "    ds = xr.open_dataset(x)\n",
-    "    print(ds.time.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "74558c75-f7f6-40c6-8d0c-9eff6a8a57b2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:                       (ensemble_member: 1, time: 3600,\n",
-       "                                   projection_y_coordinate: 244,\n",
-       "                                   projection_x_coordinate: 180, bnds: 2)\n",
-       "Coordinates:\n",
-       "  * ensemble_member               (ensemble_member) int32 1\n",
-       "  * time                          (time) object 2020-12-01 12:00:00 ... 2030-...\n",
-       "  * projection_y_coordinate       (projection_y_coordinate) float64 -3.25e+04...\n",
-       "  * projection_x_coordinate       (projection_x_coordinate) float64 -1.975e+0...\n",
-       "    ensemble_member_id            (ensemble_member) |S27 ...\n",
-       "    latitude                      (projection_y_coordinate, projection_x_coordinate) float64 ...\n",
-       "    longitude                     (projection_y_coordinate, projection_x_coordinate) float64 ...\n",
-       "    month_number                  (time) int32 ...\n",
-       "    year                          (time) int32 ...\n",
-       "    yyyymmdd                      (time) |S64 ...\n",
-       "Dimensions without coordinates: bnds\n",
-       "Data variables:\n",
-       "    tas                           (ensemble_member, time, projection_y_coordinate, projection_x_coordinate) float32 ...\n",
-       "    transverse_mercator           int32 ...\n",
-       "    time_bnds                     (time, bnds) object ...\n",
-       "    projection_y_coordinate_bnds  (projection_y_coordinate, bnds) float64 ...\n",
-       "    projection_x_coordinate_bnds  (projection_x_coordinate, bnds) float64 ...\n",
-       "Attributes: (12/15)\n",
-       "    collection:      land-cpm\n",
-       "    contact:         ukcpproject@metoffice.gov.uk\n",
-       "    creation_date:   2021-06-09T16:58:45\n",
-       "    domain:          uk\n",
-       "    frequency:       day\n",
-       "    institution:     Met Office Hadley Centre (MOHC), FitzRoy Road, Exeter, D...\n",
-       "    ...              ...\n",
-       "    resolution:      5km\n",
-       "    scenario:        rcp85\n",
-       "    source:          UKCP18 realisation from a set of 12 convection-permittin...\n",
-       "    title:           UKCP18 land projections - Regridded 2.2km convection-per...\n",
-       "    version:         v20210615\n",
-       "    Conventions:     CF-1.7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3d446351-7b27-4bdd-8565-d40e26b65e48' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3d446351-7b27-4bdd-8565-d40e26b65e48' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ensemble_member</span>: 1</li><li><span class='xr-has-index'>time</span>: 3600</li><li><span class='xr-has-index'>projection_y_coordinate</span>: 244</li><li><span class='xr-has-index'>projection_x_coordinate</span>: 180</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cac2456d-a6c3-4b00-ae5b-951146909b3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cac2456d-a6c3-4b00-ae5b-951146909b3b' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ensemble_member</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-299a4b4f-d9d5-4d3f-a11f-c43180e3dac8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-299a4b4f-d9d5-4d3f-a11f-c43180e3dac8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-413c738b-954d-4941-9b7a-73ef6fd100e5' class='xr-var-data-in' type='checkbox'><label for='data-413c738b-954d-4941-9b7a-73ef6fd100e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ensemble_member</dd></dl></div><div class='xr-var-data'><pre>array([1], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2020-12-01 12:00:00 ... 2030-11-...</div><input id='attrs-ffff6636-4661-4a2b-b58c-b92aa5e6b648' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffff6636-4661-4a2b-b58c-b92aa5e6b648' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-756fd6d4-b095-4ef3-9a08-12ff3dfcc44d' class='xr-var-data-in' type='checkbox'><label for='data-756fd6d4-b095-4ef3-9a08-12ff3dfcc44d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(2020, 12, 1, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(2020, 12, 2, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(2020, 12, 3, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.Datetime360Day(2030, 11, 28, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(2030, 11, 29, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.Datetime360Day(2030, 11, 30, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_y_coordinate</span></div><div class='xr-var-dims'>(projection_y_coordinate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.25e+04 -2.75e+04 ... 1.182e+06</div><input id='attrs-51e05955-2d73-4a14-90a8-4bcf930bccd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51e05955-2d73-4a14-90a8-4bcf930bccd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93e37ff9-9fed-4a79-b35d-12ce78ee07a9' class='xr-var-data-in' type='checkbox'><label for='data-93e37ff9-9fed-4a79-b35d-12ce78ee07a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>projection_y_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ -32500.,  -27500.,  -22500., ..., 1172500., 1177500., 1182500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>projection_x_coordinate</span></div><div class='xr-var-dims'>(projection_x_coordinate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.975e+05 -1.925e+05 ... 6.975e+05</div><input id='attrs-a8345d15-960d-4353-a1e3-6d7a34eccb60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8345d15-960d-4353-a1e3-6d7a34eccb60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cfa3f1a-4cd2-4a80-81cb-a53ff3e948fb' class='xr-var-data-in' type='checkbox'><label for='data-8cfa3f1a-4cd2-4a80-81cb-a53ff3e948fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>projection_x_coordinate_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-197500., -192500., -187500., -182500., -177500., -172500., -167500.,\n",
-       "       -162500., -157500., -152500., -147500., -142500., -137500., -132500.,\n",
-       "       -127500., -122500., -117500., -112500., -107500., -102500.,  -97500.,\n",
-       "        -92500.,  -87500.,  -82500.,  -77500.,  -72500.,  -67500.,  -62500.,\n",
-       "        -57500.,  -52500.,  -47500.,  -42500.,  -37500.,  -32500.,  -27500.,\n",
-       "        -22500.,  -17500.,  -12500.,   -7500.,   -2500.,    2500.,    7500.,\n",
-       "         12500.,   17500.,   22500.,   27500.,   32500.,   37500.,   42500.,\n",
-       "         47500.,   52500.,   57500.,   62500.,   67500.,   72500.,   77500.,\n",
-       "         82500.,   87500.,   92500.,   97500.,  102500.,  107500.,  112500.,\n",
-       "        117500.,  122500.,  127500.,  132500.,  137500.,  142500.,  147500.,\n",
-       "        152500.,  157500.,  162500.,  167500.,  172500.,  177500.,  182500.,\n",
-       "        187500.,  192500.,  197500.,  202500.,  207500.,  212500.,  217500.,\n",
-       "        222500.,  227500.,  232500.,  237500.,  242500.,  247500.,  252500.,\n",
-       "        257500.,  262500.,  267500.,  272500.,  277500.,  282500.,  287500.,\n",
-       "        292500.,  297500.,  302500.,  307500.,  312500.,  317500.,  322500.,\n",
-       "        327500.,  332500.,  337500.,  342500.,  347500.,  352500.,  357500.,\n",
-       "        362500.,  367500.,  372500.,  377500.,  382500.,  387500.,  392500.,\n",
-       "        397500.,  402500.,  407500.,  412500.,  417500.,  422500.,  427500.,\n",
-       "        432500.,  437500.,  442500.,  447500.,  452500.,  457500.,  462500.,\n",
-       "        467500.,  472500.,  477500.,  482500.,  487500.,  492500.,  497500.,\n",
-       "        502500.,  507500.,  512500.,  517500.,  522500.,  527500.,  532500.,\n",
-       "        537500.,  542500.,  547500.,  552500.,  557500.,  562500.,  567500.,\n",
-       "        572500.,  577500.,  582500.,  587500.,  592500.,  597500.,  602500.,\n",
-       "        607500.,  612500.,  617500.,  622500.,  627500.,  632500.,  637500.,\n",
-       "        642500.,  647500.,  652500.,  657500.,  662500.,  667500.,  672500.,\n",
-       "        677500.,  682500.,  687500.,  692500.,  697500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_member_id</span></div><div class='xr-var-dims'>(ensemble_member)</div><div class='xr-var-dtype'>|S27</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8f7237e-f1c9-4b7f-a3c2-92a260c7a258' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8f7237e-f1c9-4b7f-a3c2-92a260c7a258' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3680afa8-306b-45e6-8ee6-240c5a99aeb2' class='xr-var-data-in' type='checkbox'><label for='data-3680afa8-306b-45e6-8ee6-240c5a99aeb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>ensemble_member_id</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S27]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d11c909-9cc7-4052-868f-9c02850bf4e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d11c909-9cc7-4052-868f-9c02850bf4e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4561b26-778c-4e52-8020-d164c7460d26' class='xr-var-data-in' type='checkbox'><label for='data-e4561b26-778c-4e52-8020-d164c7460d26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[43920 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c5785b5f-e1fc-45e6-b3be-bd844ff28974' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5785b5f-e1fc-45e6-b3be-bd844ff28974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b8653f1-9527-47c6-bcc7-4b36875eed3c' class='xr-var-data-in' type='checkbox'><label for='data-4b8653f1-9527-47c6-bcc7-4b36875eed3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[43920 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e611607-0f4d-4917-a431-018964935025' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e611607-0f4d-4917-a431-018964935025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7951996-a90f-41e2-a4fc-9eeb0e6c56c0' class='xr-var-data-in' type='checkbox'><label for='data-e7951996-a90f-41e2-a4fc-9eeb0e6c56c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>month_number</dd></dl></div><div class='xr-var-data'><pre>[3600 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6bd0bec0-021c-4753-9890-72cad4e487e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bd0bec0-021c-4753-9890-72cad4e487e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2d8ee76-74c1-4286-b133-dc3535d97d4e' class='xr-var-data-in' type='checkbox'><label for='data-c2d8ee76-74c1-4286-b133-dc3535d97d4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>year</dd></dl></div><div class='xr-var-data'><pre>[3600 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yyyymmdd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad9237d9-42f1-4d54-873c-b0953616bf71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad9237d9-42f1-4d54-873c-b0953616bf71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-640abdb6-9cb7-4b68-ac24-34d632be50b5' class='xr-var-data-in' type='checkbox'><label for='data-640abdb6-9cb7-4b68-ac24-34d632be50b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>yyyymmdd</dd></dl></div><div class='xr-var-data'><pre>[3600 values with dtype=|S64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3e9703d-a95e-4342-a2f4-035995ef3921' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3e9703d-a95e-4342-a2f4-035995ef3921' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(ensemble_member, time, projection_y_coordinate, projection_x_coordinate)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8db3f4ca-a3d1-48d0-83e7-72499b52708f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8db3f4ca-a3d1-48d0-83e7-72499b52708f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da3554e3-4ba1-4ff7-ac86-ee04160a8b00' class='xr-var-data-in' type='checkbox'><label for='data-da3554e3-4ba1-4ff7-ac86-ee04160a8b00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Mean air temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>description :</span></dt><dd>Mean air temperature</dd><dt><span>label_units :</span></dt><dd>°C</dd><dt><span>plot_label :</span></dt><dd>Mean air temperature at 1.5m (°C)</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_mapping :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>[158112000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transverse_mercator</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-55551d1e-d4b7-4c19-9217-80d4aad1428b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55551d1e-d4b7-4c19-9217-80d4aad1428b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e879f500-297d-4b9b-9cf0-e8b9c402c67e' class='xr-var-data-in' type='checkbox'><label for='data-e879f500-297d-4b9b-9cf0-e8b9c402c67e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6377563.396</dd><dt><span>semi_minor_axis :</span></dt><dd>6356256.909</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-2.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>49.0</dd><dt><span>false_easting :</span></dt><dd>400000.0</dd><dt><span>false_northing :</span></dt><dd>-100000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996012717</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0fe2532a-4b02-434e-a3a7-7da0f50aa2c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fe2532a-4b02-434e-a3a7-7da0f50aa2c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84fa40a3-7ffc-474d-bd4a-64d7c0822e86' class='xr-var-data-in' type='checkbox'><label for='data-84fa40a3-7ffc-474d-bd4a-64d7c0822e86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[7200 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_y_coordinate_bnds</span></div><div class='xr-var-dims'>(projection_y_coordinate, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad32e589-6a9a-4bc3-89a2-23bc8c625d78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad32e589-6a9a-4bc3-89a2-23bc8c625d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dcc6844-6aff-4156-ac25-2b07a3c49520' class='xr-var-data-in' type='checkbox'><label for='data-8dcc6844-6aff-4156-ac25-2b07a3c49520' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[488 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection_x_coordinate_bnds</span></div><div class='xr-var-dims'>(projection_x_coordinate, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d61c6556-fc00-469b-a547-acc978968452' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d61c6556-fc00-469b-a547-acc978968452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a4f7305-2803-4b0e-85de-501a2c0e4f12' class='xr-var-data-in' type='checkbox'><label for='data-3a4f7305-2803-4b0e-85de-501a2c0e4f12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[360 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c799ebd-6246-4afe-b9ce-3153f6b07ad8' class='xr-section-summary-in' type='checkbox'  ><label for='section-7c799ebd-6246-4afe-b9ce-3153f6b07ad8' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ensemble_member</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0793767-b234-4381-bcc6-4fd216c5b8a4' class='xr-index-data-in' type='checkbox'/><label for='index-a0793767-b234-4381-bcc6-4fd216c5b8a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1], dtype=&#x27;int32&#x27;, name=&#x27;ensemble_member&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a45d4920-812e-4e6a-9cd1-581307aff52d' class='xr-index-data-in' type='checkbox'/><label for='index-a45d4920-812e-4e6a-9cd1-581307aff52d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2020-12-01 12:00:00, 2020-12-02 12:00:00, 2020-12-03 12:00:00,\n",
-       "             2020-12-04 12:00:00, 2020-12-05 12:00:00, 2020-12-06 12:00:00,\n",
-       "             2020-12-07 12:00:00, 2020-12-08 12:00:00, 2020-12-09 12:00:00,\n",
-       "             2020-12-10 12:00:00,\n",
-       "             ...\n",
-       "             2030-11-21 12:00:00, 2030-11-22 12:00:00, 2030-11-23 12:00:00,\n",
-       "             2030-11-24 12:00:00, 2030-11-25 12:00:00, 2030-11-26 12:00:00,\n",
-       "             2030-11-27 12:00:00, 2030-11-28 12:00:00, 2030-11-29 12:00:00,\n",
-       "             2030-11-30 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;, length=3600, calendar=&#x27;360_day&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>projection_y_coordinate</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5400c4cd-5d21-487b-bfca-7b742706aaa0' class='xr-index-data-in' type='checkbox'/><label for='index-5400c4cd-5d21-487b-bfca-7b742706aaa0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -32500.0,  -27500.0,  -22500.0,  -17500.0,  -12500.0,   -7500.0,\n",
-       "         -2500.0,    2500.0,    7500.0,   12500.0,\n",
-       "       ...\n",
-       "       1137500.0, 1142500.0, 1147500.0, 1152500.0, 1157500.0, 1162500.0,\n",
-       "       1167500.0, 1172500.0, 1177500.0, 1182500.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;projection_y_coordinate&#x27;, length=244))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>projection_x_coordinate</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b06c8cf0-03e9-441f-8a4e-454042b13b57' class='xr-index-data-in' type='checkbox'/><label for='index-b06c8cf0-03e9-441f-8a4e-454042b13b57' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-197500.0, -192500.0, -187500.0, -182500.0, -177500.0, -172500.0,\n",
-       "       -167500.0, -162500.0, -157500.0, -152500.0,\n",
-       "       ...\n",
-       "        652500.0,  657500.0,  662500.0,  667500.0,  672500.0,  677500.0,\n",
-       "        682500.0,  687500.0,  692500.0,  697500.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;projection_x_coordinate&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-877b5799-ad6d-4d24-b0e3-b5b97d069883' class='xr-section-summary-in' type='checkbox'  ><label for='section-877b5799-ad6d-4d24-b0e3-b5b97d069883' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>collection :</span></dt><dd>land-cpm</dd><dt><span>contact :</span></dt><dd>ukcpproject@metoffice.gov.uk</dd><dt><span>creation_date :</span></dt><dd>2021-06-09T16:58:45</dd><dt><span>domain :</span></dt><dd>uk</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>institution :</span></dt><dd>Met Office Hadley Centre (MOHC), FitzRoy Road, Exeter, Devon, EX1 3PB, UK.</dd><dt><span>institution_id :</span></dt><dd>MOHC</dd><dt><span>project :</span></dt><dd>UKCP18</dd><dt><span>references :</span></dt><dd>https://ukclimateprojections.metoffice.gov.uk</dd><dt><span>resolution :</span></dt><dd>5km</dd><dt><span>scenario :</span></dt><dd>rcp85</dd><dt><span>source :</span></dt><dd>UKCP18 realisation from a set of 12 convection-permitting models (HadREM3-RA11M) driven by perturbed variants of the Met Office Unified Model Global Atmosphere GA7 model (HadREM3-GA705) at 12km resolution.  The HadREM3-GA705 models were driven by perturbed variants of the global HadGEM3-GC3.05</dd><dt><span>title :</span></dt><dd>UKCP18 land projections - Regridded 2.2km convection-permitting climate model results on 5km British National Grid from Ordnance Survey (OSGB), Mean air temperature over the UK for the RCP8.5 scenario</dd><dt><span>version :</span></dt><dd>v20210615</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:                       (ensemble_member: 1, time: 3600,\n",
-       "                                   projection_y_coordinate: 244,\n",
-       "                                   projection_x_coordinate: 180, bnds: 2)\n",
-       "Coordinates:\n",
-       "  * ensemble_member               (ensemble_member) int32 1\n",
-       "  * time                          (time) object 2020-12-01 12:00:00 ... 2030-...\n",
-       "  * projection_y_coordinate       (projection_y_coordinate) float64 -3.25e+04...\n",
-       "  * projection_x_coordinate       (projection_x_coordinate) float64 -1.975e+0...\n",
-       "    ensemble_member_id            (ensemble_member) |S27 ...\n",
-       "    latitude                      (projection_y_coordinate, projection_x_coordinate) float64 ...\n",
-       "    longitude                     (projection_y_coordinate, projection_x_coordinate) float64 ...\n",
-       "    month_number                  (time) int32 ...\n",
-       "    year                          (time) int32 ...\n",
-       "    yyyymmdd                      (time) |S64 ...\n",
-       "Dimensions without coordinates: bnds\n",
-       "Data variables:\n",
-       "    tas                           (ensemble_member, time, projection_y_coordinate, projection_x_coordinate) float32 ...\n",
-       "    transverse_mercator           int32 ...\n",
-       "    time_bnds                     (time, bnds) object ...\n",
-       "    projection_y_coordinate_bnds  (projection_y_coordinate, bnds) float64 ...\n",
-       "    projection_x_coordinate_bnds  (projection_x_coordinate, bnds) float64 ...\n",
-       "Attributes: (12/15)\n",
-       "    collection:      land-cpm\n",
-       "    contact:         ukcpproject@metoffice.gov.uk\n",
-       "    creation_date:   2021-06-09T16:58:45\n",
-       "    domain:          uk\n",
-       "    frequency:       day\n",
-       "    institution:     Met Office Hadley Centre (MOHC), FitzRoy Road, Exeter, D...\n",
-       "    ...              ...\n",
-       "    resolution:      5km\n",
-       "    scenario:        rcp85\n",
-       "    source:          UKCP18 realisation from a set of 12 convection-permittin...\n",
-       "    title:           UKCP18 land projections - Regridded 2.2km convection-per...\n",
-       "    version:         v20210615\n",
-       "    Conventions:     CF-1.7"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import xarray as xr\n",
-    "nfile = '/badc/ukcp18/data/land-cpm/uk/5km/rcp85/01/tas/day/v20210615/tas_rcp85_land-cpm_uk_5km_01_day_20201201-20301130.nc'\n",
-    "ds = xr.open_dataset(nfile)\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cf396697-ff28-4b09-ab65-ca214a837dd8",
-   "metadata": {},
-   "outputs": [
-    {
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-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;transverse_mercator&#x27; ()&gt;\n",
-       "[1 values with dtype=int32]\n",
-       "Attributes:\n",
-       "    grid_mapping_name:                 transverse_mercator\n",
-       "    longitude_of_prime_meridian:       0.0\n",
-       "    semi_major_axis:                   6377563.396\n",
-       "    semi_minor_axis:                   6356256.909\n",
-       "    longitude_of_central_meridian:     -2.0\n",
-       "    latitude_of_projection_origin:     49.0\n",
-       "    false_easting:                     400000.0\n",
-       "    false_northing:                    -100000.0\n",
-       "    scale_factor_at_central_meridian:  0.9996012717</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'transverse_mercator'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a2fd9e96-bff8-4980-94a6-d60cb9f37f47' class='xr-array-in' type='checkbox' checked><label for='section-a2fd9e96-bff8-4980-94a6-d60cb9f37f47' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1 values with dtype=int32]</pre></div></div></li><li class='xr-section-item'><input id='section-031fa41a-ed0f-4b70-a2f8-622473cd3f3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-031fa41a-ed0f-4b70-a2f8-622473cd3f3a' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b06c0b8f-78e4-49cb-807f-6b4580b65374' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b06c0b8f-78e4-49cb-807f-6b4580b65374' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-f799abed-1ee7-4fb5-993c-7c6fadfafb9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f799abed-1ee7-4fb5-993c-7c6fadfafb9b' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6377563.396</dd><dt><span>semi_minor_axis :</span></dt><dd>6356256.909</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-2.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>49.0</dd><dt><span>false_easting :</span></dt><dd>400000.0</dd><dt><span>false_northing :</span></dt><dd>-100000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996012717</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'transverse_mercator' ()>\n",
-       "[1 values with dtype=int32]\n",
-       "Attributes:\n",
-       "    grid_mapping_name:                 transverse_mercator\n",
-       "    longitude_of_prime_meridian:       0.0\n",
-       "    semi_major_axis:                   6377563.396\n",
-       "    semi_minor_axis:                   6356256.909\n",
-       "    longitude_of_central_meridian:     -2.0\n",
-       "    latitude_of_projection_origin:     49.0\n",
-       "    false_easting:                     400000.0\n",
-       "    false_northing:                    -100000.0\n",
-       "    scale_factor_at_central_meridian:  0.9996012717"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.transverse_mercator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb46302f-98d2-4110-a498-dabdc9e72f03",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "build_venv",
-   "language": "python",
-   "name": "build_venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/showcase/notebooks/TestMemory.ipynb b/showcase/notebooks/TestMemory.ipynb
deleted file mode 100644
index dbd4b65..0000000
--- a/showcase/notebooks/TestMemory.ipynb
+++ /dev/null
@@ -1,99 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7e0e9bb4-8ee7-4053-a3a6-1c95832dceea",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seff 19433517_0 > temp.txt\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "sh: line 1: seff: command not found\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 19\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtemp.txt\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m     17\u001b[0m     contents \u001b[38;5;241m=\u001b[39m [r\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m f\u001b[38;5;241m.\u001b[39mreadlines()]\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOMPLETED\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[43mcontents\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m:\n\u001b[1;32m     20\u001b[0m     memory_used \u001b[38;5;241m=\u001b[39m contents[\u001b[38;5;241m9\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtemplate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mproj_codes[x]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/detail-cfg.json\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import json\n",
-    "\n",
-    "with open('/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233/proj_codes_httpcache.txt') as f:\n",
-    "    proj_codes = [r.strip() for r in f.readlines()]\n",
-    "\n",
-    "template = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233/'\n",
-    "\n",
-    "values = []\n",
-    "id = '19433517'\n",
-    "for x in range(0, 953):\n",
-    "    jobid = f'{id}_{x}'\n",
-    "    print(f'seff {jobid} > temp.txt')\n",
-    "    os.system(f'seff {jobid} > temp.txt')\n",
-    "    if os.path.isfile('temp.txt'):\n",
-    "        with open('temp.txt') as f:\n",
-    "            contents = [r.strip() for r in f.readlines()]\n",
-    "\n",
-    "        if 'COMPLETED' in contents[4]:\n",
-    "            memory_used = contents[9].split(':')[-1]\n",
-    "\n",
-    "        with open(f'{template}/{proj_codes[x]}/detail-cfg.json') as f:\n",
-    "            detail = json.load(f)\n",
-    "        values.append([\n",
-    "            memory_used,\n",
-    "            detail['estm_chunksize'],\n",
-    "            detail['total_chunks']])\n",
-    "    print(values)\n",
-    "    y = input()\n",
-    "\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "20869282-7b14-4c3d-bfef-ee91cf54f053",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "build_venv",
-   "language": "python",
-   "name": "build_venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/showcase/notebooks/cci.ipynb b/showcase/notebooks/cci.ipynb
deleted file mode 100644
index ad99adc..0000000
--- a/showcase/notebooks/cci.ipynb
+++ /dev/null
@@ -1,1903 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d4f6a84f-f73e-4a9c-8e9d-4fdc7ce9cbf7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## CCI Assessor\n",
-    "\n",
-    "import glob\n",
-    "\n",
-    "complete = '/gws/nopw/j04/esacci_portal/kerchunk_conversion/complete/'\n",
-    "\n",
-    "with open('/gws/nopw/j04/esacci_portal/kerchunk_conversion/groups/cci_group_v1/blacklist_codes.txt') as f:\n",
-    "    blacklist = [r.split(',')[0] for r in f.readlines()]\n",
-    "\n",
-    "revalidate = []\n",
-    "\n",
-    "types = glob.glob('./*.csv')\n",
-    "for t in types:\n",
-    "    with open(t) as f:\n",
-    "        proj_codes = [r.split(',')[0] for r in f.readlines()]\n",
-    "    sumt = 0\n",
-    "    for proj_code in proj_codes:\n",
-    "        if len(glob.glob(f'{complete}/cci_group_v1/{proj_code}*')) > 0:\n",
-    "            sumt += 1\n",
-    "        elif proj_code in blacklist:\n",
-    "            sumt += 1\n",
-    "        else:\n",
-    "            if 'cci_group' not in t:\n",
-    "                revalidate.append(proj_code)\n",
-    "        \n",
-    "    if sumt == len(proj_codes):\n",
-    "        print(f'{t} - complete')\n",
-    "    else:\n",
-    "        print(f'{t} - {sumt} vs {len(proj_codes)}')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "018cb5bd-f317-478d-9272-db6b46508b91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "complete = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/complete/CMIP6_rel1_6233'\n",
-    "in_progress = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233'\n",
-    "\n",
-    "completes = glob.glob(f'{complete}/*.json')\n",
-    "proj_codes = [c.split('kr1.')[0][:-1] for c in completes]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "87810ef2-6597-466a-a2f5-0f2d07c211b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "in_progress = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/in_progress/CMIP6_rel1_6233'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "d32f9a68-cadf-40bc-9d9d-4b96beb77610",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "for p in proj_codes:\n",
-    "    proj_code = p.split('/')[-1].replace('CMIP6_','')\n",
-    "    newlog = f'{in_progress}/{proj_code}/status_log.csv'\n",
-    "    os.system(f\"touch {newlog}\")\n",
-    "    with open(f\"{newlog}\",'w') as f:\n",
-    "        f.write('complete,complete,12:07 03/06/24,,False')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0e57caaa-a5a0-48b9-8dbb-2bf2f1f8d2e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Fix statistics issues\n",
-    "\n",
-    "workdir = '/gws/nopw/j04/esacci_portal/kerchunk_conversion/'\n",
-    "\n",
-    "groups = [\n",
-    "    'aerosol_v1',\n",
-    "    'cci_group_v1',\n",
-    "    'cloud_v1',\n",
-    "    'land_cover_v1',\n",
-    "    'reccap_v1',\n",
-    "    'salinity_v1',\n",
-    "    'sea_level_v1',\n",
-    "    'seaice_v1',\n",
-    "    'snow_v1'\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "314c996e-8d29-4a8d-9d3a-b49e759d7091",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_codes(group, filename):\n",
-    "    with open(f'{workdir}/groups/{group}/{filename}.txt') as f:\n",
-    "        return [r.strip() for r in f.readlines()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ce742c7f-dc05-4297-aaec-184dd8328a39",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def format_float(value: int, logger):\n",
-    "    \"\"\"Format byte-value with proper units\"\"\"\n",
-    "    logger.debug(f'Formatting value {value} in bytes')\n",
-    "    if value:\n",
-    "        unit_index = 0\n",
-    "        units = ['','K','M','G','T','P']\n",
-    "        while value > 1000:\n",
-    "            value = value / 1000\n",
-    "            unit_index += 1\n",
-    "        return f'{value:.2f} {units[unit_index]}B'\n",
-    "    else:\n",
-    "        return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c39dc382-7fba-4e20-8aa4-d68e00cf1049",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5579"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pipeline.logs import FalseLogger\n",
-    "from pipeline.scan import format_float\n",
-    "import json\n",
-    "\n",
-    "workdir = '/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline'\n",
-    "\n",
-    "def fix_scan(group, proj):\n",
-    "    try:\n",
-    "        with open(f'{workdir}/in_progress/{group}/{proj}/detail-cfg.json') as f:\n",
-    "            detail = json.load(f)\n",
-    "    except FileNotFoundError:\n",
-    "        return 0\n",
-    "    except json.JSONDecodeError:\n",
-    "        return 0\n",
-    "\n",
-    "    if 'skipped' in detail:\n",
-    "        return 0\n",
-    "\n",
-    "    detail['kerchunk_data'] = format_float(float(detail['chunks_per_file']) * int(detail['num_files']) * 167, FalseLogger())\n",
-    "    if 'B' not in detail['data_represented']:\n",
-    "        detail['netcdf_data'] = format_float(float(detail['data_represented']), FalseLogger())\n",
-    "    else:\n",
-    "        detail['netcdf_data'] = detail['data_represented']\n",
-    "    \n",
-    "    with open(f'{workdir}/in_progress/{group}/{proj}/detail-cfg.json','w') as f:\n",
-    "        f.write(json.dumps(detail))\n",
-    "    return 1\n",
-    "\n",
-    "corrected = 0\n",
-    "for g in ['CMIP6_rel1_6233']:\n",
-    "    projs = get_codes(g, 'proj_codes_1')\n",
-    "    for p in projs:\n",
-    "        corrected += fix_scan(g, p)\n",
-    "corrected"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "813197e9-1928-40c8-9536-438e2cdcffce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mem_to_val(value):\n",
-    "    suffixes = {\n",
-    "        'KB': 1000,\n",
-    "        'MB': 1000000,\n",
-    "        'GB': 1000000000,\n",
-    "        'TB': 1000000000000,\n",
-    "        'PB': 1000000000000000}\n",
-    "    suff = suffixes[value.split(' ')[1]]\n",
-    "    return float(value.split(' ')[0]) * suff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "435e75ac-569c-4298-a69b-03520397a2aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import glob\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def analyse_data(g):\n",
-    "    print(g)\n",
-    "    ncf, ker = 0, 0\n",
-    "    kus = 0\n",
-    "    complete, scanned = 0, 0\n",
-    "    kerarr = []\n",
-    "    projset = glob.glob(f'/gws/nopw/j04/esacci_portal/kerchunk_conversion/in_progress/{g}/*')\n",
-    "    for p in projset:\n",
-    "        details = None\n",
-    "        try:\n",
-    "            with open(f'{p}/detail-cfg.json') as f:\n",
-    "                details = json.load(f)\n",
-    "        except:\n",
-    "            pass\n",
-    "        if details:\n",
-    "            scanned += 1\n",
-    "            if 'netcdf_data' in details:\n",
-    "                ncf += mem_to_val(details['netcdf_data'])\n",
-    "                ker += mem_to_val(details['kerchunk_data'])\n",
-    "                kerarr.append((details['netcdf_data'], details['kerchunk_data']))\n",
-    "                if os.path.isfile(f'{p}/kerchunk-1a.json.complete'):\n",
-    "                    kus += mem_to_val(details['kerchunk_data'])\n",
-    "                    complete += 1\n",
-    "    return ncf, ker, kus, scanned, complete"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "4ddf59f7-01f3-4782-91d4-8ccf681eecfd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aerosol_v1\n",
-      "         NetCDF: 675.60 GB\n",
-      "  Kerchunk Estm: 2.00 GB (38)\n",
-      "Kerchunk Actual: 339.48 MB (26)\n",
-      "\n",
-      "cci_group_v1\n",
-      "         NetCDF: 59.84 TB\n",
-      "  Kerchunk Estm: 1.58 TB (311)\n",
-      "Kerchunk Actual: 5.37 GB (128)\n",
-      "\n",
-      "cloud_v1\n",
-      "         NetCDF: 111.41 GB\n",
-      "  Kerchunk Estm: 49.75 MB (24)\n",
-      "Kerchunk Actual: 49.75 MB (24)\n",
-      "\n",
-      "land_cover_v1\n",
-      "         NetCDF: 112.27 GB\n",
-      "  Kerchunk Estm: 209.68 MB (3)\n",
-      "Kerchunk Actual: 209.68 MB (2)\n",
-      "\n",
-      "reccap_v1\n",
-      "         NetCDF: None\n",
-      "  Kerchunk Estm: None (1)\n",
-      "Kerchunk Actual: None (0)\n",
-      "\n",
-      "salinity_v1\n",
-      "         NetCDF: 79.37 GB\n",
-      "  Kerchunk Estm: 38.60 MB (10)\n",
-      "Kerchunk Actual: 38.60 MB (10)\n",
-      "\n",
-      "sea_level_v1\n",
-      "         NetCDF: 2.77 GB\n",
-      "  Kerchunk Estm: 5.23 MB (7)\n",
-      "Kerchunk Actual: None (0)\n",
-      "\n",
-      "seaice_v1\n",
-      "         NetCDF: 253.43 GB\n",
-      "  Kerchunk Estm: 92.47 MB (16)\n",
-      "Kerchunk Actual: 78.86 MB (12)\n",
-      "\n",
-      "snow_v1\n",
-      "         NetCDF: 767.62 GB\n",
-      "  Kerchunk Estm: 1.71 GB (10)\n",
-      "Kerchunk Actual: 1.71 GB (10)\n",
-      "\n",
-      "Total CCI Deliverable Summary\n",
-      "         NetCDF: 61.84 TB\n",
-      "  Kerchunk Estm: 1.59 TB (420)\n",
-      "Kerchunk Actual: 7.79 GB (212)\n"
-     ]
-    }
-   ],
-   "source": [
-    "Tncf, Tker, Tkus, Tscan, Tcomp = 0, 0, 0, 0, 0\n",
-    "for g in groups:\n",
-    "    ncf, ker, kus, scanned, complete = analyse_data(g)\n",
-    "    print(f'         NetCDF: {format_float(ncf, FalseLogger())}')\n",
-    "    print(f'  Kerchunk Estm: {format_float(ker, FalseLogger())} ({scanned})')\n",
-    "    print(f'Kerchunk Actual: {format_float(kus, FalseLogger())} ({complete})')\n",
-    "    print()\n",
-    "\n",
-    "    Tncf += ncf\n",
-    "    Tker += ker\n",
-    "    Tkus += kus\n",
-    "    Tscan += scanned\n",
-    "    Tcomp += complete\n",
-    "\n",
-    "print('Total CCI Deliverable Summary')\n",
-    "print(f'         NetCDF: {format_float(Tncf, FalseLogger())}')\n",
-    "print(f'  Kerchunk Estm: {format_float(Tker, FalseLogger())} ({Tscan})')\n",
-    "print(f'Kerchunk Actual: {format_float(Tkus, FalseLogger())} ({Tcomp})')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "a5522d5f-dbf6-43b7-9879-c400333d8810",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('68.01 GB', '8.34 GB'),\n",
-       " ('70.91 GB', '8.34 GB'),\n",
-       " ('262.08 GB', '774.76 MB'),\n",
-       " ('151.04 GB', '25.46 GB'),\n",
-       " ('119.88 GB', '53.88 MB'),\n",
-       " ('210.22 GB', '68.97 MB'),\n",
-       " ('214.39 GB', '68.97 MB'),\n",
-       " ('7.18 GB', '721.44 KB'),\n",
-       " ('22.31 GB', '9.51 GB'),\n",
-       " ('20.31 GB', '9.51 GB'),\n",
-       " ('318.24 GB', '917.67 MB'),\n",
-       " ('183.96 GB', '27.77 GB'),\n",
-       " ('462.00 GB', '1.40 GB'),\n",
-       " ('269.04 GB', '42.59 GB'),\n",
-       " ('107.16 GB', '14.38 GB'),\n",
-       " ('110.48 GB', '14.38 GB'),\n",
-       " ('124.91 GB', '17.63 GB'),\n",
-       " ('163.34 GB', '17.63 GB'),\n",
-       " ('624.98 GB', '1.80 GB'),\n",
-       " ('324.30 GB', '54.38 GB'),\n",
-       " ('18.39 GB', '9.44 GB'),\n",
-       " ('17.61 GB', '9.44 GB'),\n",
-       " ('466.10 GB', '2.54 GB'),\n",
-       " ('41.46 GB', '15.28 GB'),\n",
-       " ('914.40 GB', '4.91 GB'),\n",
-       " ('79.52 GB', '29.80 GB'),\n",
-       " ('80.60 GB', '12.08 GB'),\n",
-       " ('80.68 GB', '12.08 GB'),\n",
-       " ('384.54 GB', '1.13 GB'),\n",
-       " ('204.20 GB', '33.35 GB'),\n",
-       " ('43.17 GB', '16.44 GB'),\n",
-       " ('42.30 GB', '16.44 GB'),\n",
-       " ('907.12 GB', '2.58 GB'),\n",
-       " ('459.37 GB', '80.14 GB'),\n",
-       " ('128.55 GB', '18.25 GB'),\n",
-       " ('133.59 GB', '18.25 GB'),\n",
-       " ('642.46 GB', '1.69 GB'),\n",
-       " ('360.52 GB', '57.77 GB'),\n",
-       " ('47.35 GB', '23.12 GB'),\n",
-       " ('51.44 GB', '23.12 GB'),\n",
-       " ('1.25 TB', '6.95 GB'),\n",
-       " ('103.88 GB', '40.34 GB'),\n",
-       " ('70.14 GB', '8.75 GB'),\n",
-       " ('70.91 GB', '8.34 GB'),\n",
-       " ('182.70 GB', '26.43 GB'),\n",
-       " ('197.51 GB', '26.43 GB'),\n",
-       " ('323.35 GB', '866.06 MB'),\n",
-       " ('170.76 GB', '26.69 GB'),\n",
-       " ('68.12 GB', '11.16 GB'),\n",
-       " ('70.57 GB', '11.16 GB'),\n",
-       " ('272.20 GB', '1.02 GB'),\n",
-       " ('98.86 GB', '32.81 GB'),\n",
-       " ('153.59 GB', '24.62 GB'),\n",
-       " ('183.99 GB', '24.62 GB'),\n",
-       " ('809.26 GB', '2.38 GB'),\n",
-       " ('402.30 GB', '77.21 GB'),\n",
-       " ('154.32 GB', '19.17 GB'),\n",
-       " ('158.75 GB', '19.17 GB'),\n",
-       " ('676.62 GB', '1.79 GB'),\n",
-       " ('370.29 GB', '57.55 GB'),\n",
-       " ('547.75 MB', '26.05 KB'),\n",
-       " ('51.59 GB', '34.79 MB'),\n",
-       " ('3.14 MB', '10.02 KB'),\n",
-       " ('25.01 MB', '721.44 KB'),\n",
-       " ('2.75 GB', '2.56 MB'),\n",
-       " ('3.44 GB', '1.40 MB'),\n",
-       " ('12.03 GB', '42.76 MB'),\n",
-       " ('554.14 GB', '1.81 GB'),\n",
-       " ('575.72 GB', '1.81 GB'),\n",
-       " ('160.31 GB', '118.97 MB'),\n",
-       " ('148.44 GB', '118.97 MB'),\n",
-       " ('801.98 GB', '2.51 GB'),\n",
-       " ('723.36 GB', '2.51 GB'),\n",
-       " ('221.06 GB', '164.29 MB'),\n",
-       " ('203.12 GB', '164.29 MB'),\n",
-       " ('2.05 GB', '2.01 MB'),\n",
-       " ('2.05 GB', '2.01 MB'),\n",
-       " ('1.86 GB', '1.89 MB'),\n",
-       " ('519.75 MB', '523.54 KB'),\n",
-       " ('2.10 GB', '2.01 MB'),\n",
-       " ('2.13 GB', '2.01 MB'),\n",
-       " ('2.18 GB', '2.03 MB'),\n",
-       " ('2.13 GB', '2.03 MB'),\n",
-       " ('2.05 GB', '2.00 MB'),\n",
-       " ('553.53 MB', '539.41 KB'),\n",
-       " ('553.73 MB', '539.41 KB'),\n",
-       " ('526.66 MB', '507.68 KB'),\n",
-       " ('500.19 MB', '507.68 KB'),\n",
-       " ('516.59 MB', '523.54 KB'),\n",
-       " ('517.39 MB', '523.54 KB'),\n",
-       " ('504.87 MB', '507.68 KB'),\n",
-       " ('550.32 MB', '523.54 KB'),\n",
-       " ('546.85 MB', '523.54 KB'),\n",
-       " ('546.79 MB', '539.41 KB'),\n",
-       " ('549.71 MB', '539.41 KB'),\n",
-       " ('521.16 MB', '523.54 KB'),\n",
-       " ('515.37 MB', '523.54 KB'),\n",
-       " ('504.14 MB', '507.68 KB'),\n",
-       " ('496.72 MB', '523.54 KB'),\n",
-       " ('2.05 GB', '2.01 MB'),\n",
-       " ('2.05 GB', '2.01 MB'),\n",
-       " ('1.86 GB', '1.89 MB'),\n",
-       " ('519.75 MB', '523.54 KB'),\n",
-       " ('2.10 GB', '2.01 MB'),\n",
-       " ('2.13 GB', '2.01 MB'),\n",
-       " ('2.18 GB', '2.03 MB'),\n",
-       " ('2.13 GB', '2.03 MB'),\n",
-       " ('2.05 GB', '2.00 MB'),\n",
-       " ('546.79 MB', '539.41 KB'),\n",
-       " ('553.53 MB', '539.41 KB'),\n",
-       " ('553.73 MB', '539.41 KB'),\n",
-       " ('549.71 MB', '539.41 KB'),\n",
-       " ('496.72 MB', '523.54 KB'),\n",
-       " ('500.19 MB', '507.68 KB'),\n",
-       " ('517.39 MB', '523.54 KB'),\n",
-       " ('516.59 MB', '523.54 KB'),\n",
-       " ('504.14 MB', '507.68 KB'),\n",
-       " ('504.87 MB', '507.68 KB'),\n",
-       " ('526.66 MB', '507.68 KB'),\n",
-       " ('550.32 MB', '523.54 KB'),\n",
-       " ('546.85 MB', '523.54 KB'),\n",
-       " ('521.16 MB', '523.54 KB'),\n",
-       " ('515.37 MB', '523.54 KB'),\n",
-       " ('478.78 GB', '280.43 MB'),\n",
-       " ('484.45 GB', '280.43 MB'),\n",
-       " ('4.66 TB', '5.43 GB'),\n",
-       " ('3.56 TB', '5.43 GB'),\n",
-       " ('172.92 MB', '24.23 MB'),\n",
-       " ('198.19 MB', '24.23 MB'),\n",
-       " ('103.94 GB', '79.31 MB'),\n",
-       " ('103.70 GB', '79.31 MB'),\n",
-       " ('257.96 GB', '589.61 MB'),\n",
-       " ('218.08 GB', '589.61 MB'),\n",
-       " ('47.95 GB', '35.41 MB'),\n",
-       " ('43.64 GB', '35.41 MB'),\n",
-       " ('28.18 GB', '27.45 MB'),\n",
-       " ('26.56 GB', '27.45 MB'),\n",
-       " ('1.59 GB', '901.80 KB'),\n",
-       " ('1.59 GB', '901.80 KB'),\n",
-       " ('123.75 MB', '75.15 KB'),\n",
-       " ('122.97 MB', '75.15 KB'),\n",
-       " ('2.23 TB', '13.26 GB'),\n",
-       " ('2.23 TB', '13.26 GB'),\n",
-       " ('66.55 GB', '5.26 GB'),\n",
-       " ('65.86 GB', '5.26 GB'),\n",
-       " ('97.37 GB', '5.26 GB'),\n",
-       " ('66.16 GB', '5.26 GB'),\n",
-       " ('77.96 GB', '5.26 GB'),\n",
-       " ('64.17 GB', '5.26 GB'),\n",
-       " ('112.06 GB', '5.26 GB'),\n",
-       " ('70.79 GB', '5.26 GB'),\n",
-       " ('9.51 GB', '219.49 MB'),\n",
-       " ('11.93 GB', '219.49 MB'),\n",
-       " ('13.60 GB', '219.49 MB'),\n",
-       " ('11.07 GB', '219.49 MB'),\n",
-       " ('9.53 GB', '219.49 MB'),\n",
-       " ('11.95 GB', '219.49 MB'),\n",
-       " ('14.05 GB', '219.49 MB'),\n",
-       " ('11.08 GB', '219.49 MB'),\n",
-       " ('29.36 GB', '38.38 MB'),\n",
-       " ('29.53 GB', '37.27 MB'),\n",
-       " ('30.72 GB', '38.40 MB'),\n",
-       " ('29.63 GB', '38.33 MB'),\n",
-       " ('28.67 GB', '37.65 MB'),\n",
-       " ('23.37 GB', '30.27 MB'),\n",
-       " ('29.37 GB', '37.83 MB'),\n",
-       " ('28.91 GB', '37.74 MB'),\n",
-       " ('1.19 GB', '1.21 MB'),\n",
-       " ('1.20 GB', '1.21 MB'),\n",
-       " ('1.21 GB', '1.21 MB'),\n",
-       " ('1.19 GB', '1.21 MB'),\n",
-       " ('1.14 GB', '1.19 MB'),\n",
-       " ('1.02 GB', '1.14 MB'),\n",
-       " ('1.14 GB', '1.19 MB'),\n",
-       " ('1.16 GB', '1.17 MB'),\n",
-       " ('116.41 GB', '7.96 GB'),\n",
-       " ('300.50 GB', '6.03 GB'),\n",
-       " ('62.62 GB', '198.71 MB'),\n",
-       " ('6.75 GB', '22.44 MB'),\n",
-       " ('6.38 GB', '16.99 MB'),\n",
-       " ('110.75 GB', '1.60 GB'),\n",
-       " ('139.79 GB', '1.21 GB'),\n",
-       " ('100.44 GB', '1.01 GB'),\n",
-       " ('115.71 GB', '762.14 MB'),\n",
-       " ('939.70 GB', '43.45 GB'),\n",
-       " ('995.98 GB', '28.63 GB'),\n",
-       " ('605.95 GB', '943.47 MB'),\n",
-       " ('73.65 GB', '122.48 MB'),\n",
-       " ('62.66 GB', '80.69 MB'),\n",
-       " ('1.16 TB', '8.73 GB'),\n",
-       " ('1.06 TB', '5.75 GB'),\n",
-       " ('1.07 TB', '5.49 GB'),\n",
-       " ('970.16 GB', '3.62 GB'),\n",
-       " ('116.91 GB', '7.96 GB'),\n",
-       " ('300.88 GB', '6.03 GB'),\n",
-       " ('63.35 GB', '198.71 MB'),\n",
-       " ('6.87 GB', '22.44 MB'),\n",
-       " ('6.48 GB', '16.99 MB'),\n",
-       " ('111.72 GB', '1.60 GB'),\n",
-       " ('140.65 GB', '1.21 GB'),\n",
-       " ('101.41 GB', '1.01 GB'),\n",
-       " ('116.62 GB', '762.14 MB'),\n",
-       " ('1.65 TB', '73.42 GB'),\n",
-       " ('1.59 TB', '47.71 GB'),\n",
-       " ('1.07 TB', '1.57 GB'),\n",
-       " ('130.30 GB', '206.96 MB'),\n",
-       " ('110.23 GB', '134.48 MB'),\n",
-       " ('2.05 TB', '14.76 GB'),\n",
-       " ('1.85 TB', '9.59 GB'),\n",
-       " ('1.89 TB', '9.28 GB'),\n",
-       " ('1.70 TB', '6.03 GB'),\n",
-       " ('647.74 GB', '30.83 GB'),\n",
-       " ('750.13 GB', '20.59 GB'),\n",
-       " ('408.52 GB', '678.67 MB'),\n",
-       " ('49.75 GB', '86.91 MB'),\n",
-       " ('42.46 GB', '58.04 MB'),\n",
-       " ('781.82 GB', '6.20 GB'),\n",
-       " ('731.59 GB', '4.14 GB'),\n",
-       " ('719.95 GB', '3.90 GB'),\n",
-       " ('662.77 GB', '2.60 GB'),\n",
-       " ('13.25 GB', '21.65 MB'),\n",
-       " ('1.91 MB', '48.10 KB'),\n",
-       " ('530.30 MB', '264.53 KB'),\n",
-       " ('384.42 MB', '185.37 KB'),\n",
-       " ('943.42 MB', '52.10 KB'),\n",
-       " ('2.67 GB', '147.63 KB'),\n",
-       " ('9.21 GB', '408.82 KB'),\n",
-       " ('26.08 GB', '1.16 MB'),\n",
-       " ('389.03 MB', '1.01 MB'),\n",
-       " ('1.98 GB', '24.26 MB'),\n",
-       " ('1.89 GB', '19.73 MB'),\n",
-       " ('1.96 GB', '19.73 MB'),\n",
-       " ('2.02 GB', '20.40 MB'),\n",
-       " ('1.66 GB', '20.40 MB'),\n",
-       " ('1.75 GB', '26.82 MB'),\n",
-       " ('4.59 GB', '35.15 MB'),\n",
-       " ('4.88 GB', '28.29 MB'),\n",
-       " ('6.12 GB', '28.29 MB'),\n",
-       " ('6.27 GB', '28.96 MB'),\n",
-       " ('8.69 GB', '28.96 MB'),\n",
-       " ('9.17 GB', '29.63 MB'),\n",
-       " ('5.10 GB', '25.72 MB'),\n",
-       " ('8.63 GB', '26.33 MB'),\n",
-       " ('4.63 GB', '35.15 MB'),\n",
-       " ('4.54 GB', '28.29 MB'),\n",
-       " ('5.84 GB', '28.29 MB'),\n",
-       " ('5.97 GB', '28.96 MB'),\n",
-       " ('8.78 GB', '28.96 MB'),\n",
-       " ('9.46 GB', '29.63 MB'),\n",
-       " ('233.67 GB', '51.73 GB'),\n",
-       " ('12.84 GB', '5.16 MB'),\n",
-       " ('73.51 GB', '141.86 MB'),\n",
-       " ('194.40 MB', '434.87 KB'),\n",
-       " ('1.98 GB', '13.24 MB')]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kerarr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "b34e401e-3f75-4e97-99f8-2b1a8b1d1ca7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "mv: '/gws/nopw/j04/esacci_portal/kerchunk_conversion/needs_version_updating/ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0_kr1.2.json' and '/gws/nopw/j04/esacci_portal/kerchunk_conversion/needs_version_updating/ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0_kr1.2.json' are the same file\n"
-     ]
-    }
-   ],
-   "source": [
-    "import glob\n",
-    "import json\n",
-    "from datetime import datetime \n",
-    "\n",
-    "reason = 'Archived at kr1.1 already'\n",
-    "\n",
-    "for kfile in glob.glob('/gws/nopw/j04/esacci_portal/kerchunk_conversion/needs_version_updating/*'):\n",
-    "    old_version = 'kr1.1' # Hardcoded for now\n",
-    "    os.system(f\"mv {kfile} {kfile.replace(old_version, 'kr1.2')}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2b61914d-cbab-4e27-bb5a-d7c0d2d7dd64",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "25"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(revalidate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a1c80b39-7bce-46e6-b61c-6b59f30d647b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2003-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2004-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2005-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2006-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2007-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2008-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2009-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2010-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2011-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2012-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2013-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2014-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2015-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2016-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2017-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2018-fv03.0.nc',\n",
-       " '/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2019-fv03.0.nc']"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fileset = \"\"\"/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2003-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2004-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2005-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2006-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2007-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2008-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2009-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2010-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2011-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2012-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2013-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2014-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2015-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2016-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2017-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2018-fv03.0.nc\n",
-    "/neodc/esacci/permafrost/data/permafrost_extent/L4/area4/pp/v03.0/ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-2019-fv03.0.nc\"\"\".split('\\n')\n",
-    "fileset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b5882bc9-1559-4223-9ec5-0deecfede4c3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "completes = os.walk(complete)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "2cda28a5-e019-4908-9f62-4b6bee425e80",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fileset = []\n",
-    "for roots, dirs, files in completes:\n",
-    "    fileset = files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "1b001119-7d3a-450a-9a24-72b0148c8b69",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "byhand = \"\"\"ESACCI-OC-L3S-K_490-MERGED-8D_DAILY_4km_SIN_PML_K_490_Lee-199709-202212-fv6.1\n",
-    "ESACCI-LST-L3S-LST-IRMGP_-0.05deg_1MONTHLY-20090101150000-20201201150000-fv1.00\n",
-    "ESACCI-LST-L3S-LST-IRMGP_-0.05deg_1MONTHLY-20090101180000-20201201180000-fv1.00\n",
-    "ESACCI-LST-L3S-LST-IRMGP_-0.05deg_1MONTHLY-20090101210000-20201201210000-fv1.00\n",
-    "ESACCI-OC-L3S-RRS-MERGED-1M_MONTHLY_4km_SIN_PML_RRS-199709-202212-fv6.0\n",
-    "ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_SIN_PML_OCx_QAA-199709-202212-fv6.0\n",
-    "ESACCI-OC-L3S-CHLOR_A-MERGED-8D_DAILY_4km_GEO_PML_OCx-19970829-20221227-fv6.0\n",
-    "ESACCI-OC-L3S-K_490-MERGED-5D_DAILY_4km_SIN_PML_K_490_Lee-199709-202212-fv6.2\n",
-    "ESACCI-OC-L3S-CHLOR_A-MERGED-5D_DAILY_4km_SIN_PML_OCx-19970903-20221227-fv6.0\n",
-    "ESACCI-OC-L3S-CHLOR_A-MERGED-8D_DAILY_4km_SIN_PML_OCx-19970829-20221227-fv6.0\n",
-    "ESACCI-OC-L3S-K_490-MERGED-8D_DAILY_4km_GEO_PML_K_490_Lee-199709-202212-fv6.1\n",
-    "ESACCI-LST-L3C-LST-SLSTRA-0.01deg_1DAILY_DAY-20160501-20201231-fv3.00.nc\n",
-    "ESACCI-LST-L3C-LST-ATSR_2-0.01deg_1DAILY_DAY-19950801-20030622-fv3.00\n",
-    "ESACCI-LST-L3C-LST-ATSR_2-0.01deg_1DAILY_NIGHT-19950801-20030622-fv3.00\n",
-    "ESACCI-LST-L3C-LST-ATSR_3-0.01deg_1DAILY_DAY-20020725-20120408-fv3.00\n",
-    "ESACCI-OC-L3S-CHLOR_A-MERGED-5D_DAILY_4km_GEO_PML_OCx-19970903-20221227-fv6.0\n",
-    "ESACCI-LST-L3C-LST-SLSTRA-0.01deg_1DAILY_NIGHT-20160501-20201231-fv3.00.nc\n",
-    "ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1M_MONTHLY_4km_SIN_PML_OCx_QAA-199709-202212-fv6.0\n",
-    "ESACCI-OC-L3S-K_490-MERGED-5D_DAILY_4km_GEO_PML_K_490_Lee-199709-202212-fv6.2\n",
-    "ESACCI-OC-L3S-CHLOR_A-MERGED-1Y_YEARLY_4km_GEO_PML_OCx-1997-2022-fv6.0\n",
-    "ESACCI-OC-L3S-IOP-MERGED-1Y_YEARLY_4km_GEO_PML_OCx_QAA_Lee-199709-202212-fv6.1\"\"\".split('\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "c7d87b49-b749-47bf-88df-2355a72f0fa7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['ESACCI-L3C_SNOW-SCFG-AATSR_ENVISAT-20020520-20120408-fv1.0_kr1.0.json',\n",
-       " 'ESACCI-L3C_SNOW-SCFG-ATSR-2_ERS-2-19950801-20030622-fv1.0_kr1.0.json',\n",
-       " 'ESACCI-L3C_SNOW-SCFG-AVHRR_MERGED-19820101-20181230-fv2.0_kr1.0.json',\n",
-       " 'ESACCI-L3C_SNOW-SCFG-MODIS_TERRA-20000224-20201231-fv2.0_kr1.0.json',\n",
-       " 'ESACCI-L3C_SNOW-SCFV-AATSR_ENVISAT-20020520-20120408-fv1.0_kr1.0.json',\n",
-       " 'ESACCI-L3C_SNOW-SCFV-ATSR-2_ERS-2-19950801-20030622-fv1.0_kr1.0.json',\n",
-       " 'ESACCI-L3C_SNOW-SCFV-AVHRR_MERGED-19820101-20181230-fv2.0_kr1.0.json',\n",
-       " 'ESACCI-L3C_SNOW-SCFV-MODIS_TERRA-20000224-20201231-fv2.0_kr1.0.json',\n",
-       " 'ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0_kr1.2.json',\n",
-       " 'ESACCI-L3S_SNOW-SCFG-MERGED-CRYOCLIM-19820101-2019_kr1.0.json']"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fileset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "bf9815e2-fb3b-49f0-9955-2fa403b2d728",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20210226-ESACCI-L4_AIS-GMB-BASIN-fv3.0 complete\n",
-      "20210226-ESACCI-L3C_AIS-GMB-GRID-fv3.0 complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "## Assessor 2\n",
-    "import os\n",
-    "\n",
-    "tryset = \"\"\"20210226-ESACCI-L4_AIS-GMB-BASIN-fv3.0\n",
-    "20210226-ESACCI-L3C_AIS-GMB-GRID-fv3.0\n",
-    "20170101-20200801-ESACCI-L3C_AIS-IV-S1-1M_200m-fv1.0.nc\"\"\".split('\\n')\n",
-    "\n",
-    "groups = \"\"\"aerosol_v1\n",
-    "cci_group_v1\n",
-    "cloud_v1\n",
-    "land_cover_v1\n",
-    "reccap_v1\n",
-    "salinity_v1\n",
-    "seaice_v1\n",
-    "snow_v1\"\"\".split('\\n')\n",
-    "\n",
-    "for t in tryset:\n",
-    "    if t in byhand:\n",
-    "        print(t, 'byhand')\n",
-    "    for g in groups:\n",
-    "        if len(glob.glob(f'{complete}/{g}/{t}*')) > 0:\n",
-    "            print(t, 'complete')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "e1d73fbc-d358-4cf0-a2b1-d1ec7f329379",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['ESACCI-OC-L3S-K_490-MERGED-1Y_YEARLY_4km_SIN_PML_K_490_Lee-199709-202212-fv6.1',\n",
-       " 'ESACCI-OC-MAPPED-CLIMATOLOGY-1M_MONTHLY_4km_PML_OCx_QAA-01-12-fv6.0.nc',\n",
-       " 'ESACCI-OZONE-L3-LP-MEGRIDOP_MERGED-20011101_20221201-fv0004',\n",
-       " 'ESACCI-OZONE-L3-LP-SAGE_CCI_OMPS_plus_MERGED-19841001_20211201-fv0007',\n",
-       " 'ESACCI-OZONE-L3-LP-MERGED-MZM-200701-200812-fv0002']"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tryset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "47b08258-8bec-40f2-b499-01571107feec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;polar_stereographic&#x27; (time: 17)&gt;\n",
-       "array([-2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2003-01-01 2004-01-01 ... 2019-01-01\n",
-       "Attributes: (12/13)\n",
-       "    grid_mapping_name:                      polar_stereographic\n",
-       "    straight_vertical_longitude_from_pole:  0.0\n",
-       "    false_easting:                          0.0\n",
-       "    false_northing:                         0.0\n",
-       "    latitude_of_projection_origin:          90.0\n",
-       "    standard_parallel:                      71.0\n",
-       "    ...                                     ...\n",
-       "    semi_major_axis:                        6378137.0\n",
-       "    inverse_flattening:                     298.257223563\n",
-       "    spatial_ref:                            GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,...\n",
-       "    crs_wkt:                                PROJCS[&quot;WGS 84 / Arctic Polar Ste...\n",
-       "    esri_wkt:                               PROJCS[&quot;WGS_84_Arctic_Polar_Stere...\n",
-       "    GeoTransform:                           -8679599.425969256 926.6254331383...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'polar_stereographic'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 17</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-193938ea-0ee4-4594-9a86-e50860c1ccd4' class='xr-array-in' type='checkbox' checked><label for='section-193938ea-0ee4-4594-9a86-e50860c1ccd4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-2147483647 -2147483647 -2147483647 ... -2147483647 -2147483647</span></div><div class='xr-array-data'><pre>array([-2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)</pre></div></div></li><li class='xr-section-item'><input id='section-d588fa39-d43a-43b5-8e30-ae18068cefe3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d588fa39-d43a-43b5-8e30-ae18068cefe3' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2003-01-01 ... 2019-01-01</div><input id='attrs-76beb3b1-a15e-41e7-8823-8fa3cc34d41e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76beb3b1-a15e-41e7-8823-8fa3cc34d41e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a604458b-79fc-40ec-82d6-8d971b8daf4f' class='xr-var-data-in' type='checkbox'><label for='data-a604458b-79fc-40ec-82d6-8d971b8daf4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2003-01-01T00:00:00.000000000&#x27;, &#x27;2004-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2005-01-01T00:00:00.000000000&#x27;, &#x27;2006-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2007-01-01T00:00:00.000000000&#x27;, &#x27;2008-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2010-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2011-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2019-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a330cad-efa2-454a-aea9-48bb4715bdeb' class='xr-section-summary-in' type='checkbox'  ><label for='section-4a330cad-efa2-454a-aea9-48bb4715bdeb' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b1691023-56ed-4faf-924b-ae8640a8dd29' class='xr-index-data-in' type='checkbox'/><label for='index-b1691023-56ed-4faf-924b-ae8640a8dd29' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2003-01-01&#x27;, &#x27;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;,\n",
-       "               &#x27;2007-01-01&#x27;, &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;,\n",
-       "               &#x27;2011-01-01&#x27;, &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;,\n",
-       "               &#x27;2015-01-01&#x27;, &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;,\n",
-       "               &#x27;2019-01-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a908a9d4-717b-442f-b96f-ace62cb1c53f' class='xr-section-summary-in' type='checkbox'  ><label for='section-a908a9d4-717b-442f-b96f-ace62cb1c53f' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>polar_stereographic</dd><dt><span>straight_vertical_longitude_from_pole :</span></dt><dd>0.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>90.0</dd><dt><span>standard_parallel :</span></dt><dd>71.0</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Polar_Stereographic&quot;],PARAMETER[&quot;latitude_of_origin&quot;,45],PARAMETER[&quot;central_meridian&quot;,-170],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]]</dd><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / Arctic Polar Stereographic&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Polar_Stereographic&quot;],PARAMETER[&quot;latitude_of_origin&quot;,71],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;X&quot;,EAST],AXIS[&quot;Y&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;3995&quot;]]</dd><dt><span>esri_wkt :</span></dt><dd>PROJCS[&quot;WGS_84_Arctic_Polar_Stereographic&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.017453292519943295]],PROJECTION[&quot;Stereographic_North_Pole&quot;],PARAMETER[&quot;standard_parallel_1&quot;,71],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;Meter&quot;,1]]</dd><dt><span>GeoTransform :</span></dt><dd>-8679599.425969256 926.6254331383326 0 4120958.560533902 0 -926.6254331383326 </dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'polar_stereographic' (time: 17)>\n",
-       "array([-2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647, -2147483647, -2147483647, -2147483647,\n",
-       "       -2147483647, -2147483647], dtype=int32)\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2003-01-01 2004-01-01 ... 2019-01-01\n",
-       "Attributes: (12/13)\n",
-       "    grid_mapping_name:                      polar_stereographic\n",
-       "    straight_vertical_longitude_from_pole:  0.0\n",
-       "    false_easting:                          0.0\n",
-       "    false_northing:                         0.0\n",
-       "    latitude_of_projection_origin:          90.0\n",
-       "    standard_parallel:                      71.0\n",
-       "    ...                                     ...\n",
-       "    semi_major_axis:                        6378137.0\n",
-       "    inverse_flattening:                     298.257223563\n",
-       "    spatial_ref:                            GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",...\n",
-       "    crs_wkt:                                PROJCS[\"WGS 84 / Arctic Polar Ste...\n",
-       "    esri_wkt:                               PROJCS[\"WGS_84_Arctic_Polar_Stere...\n",
-       "    GeoTransform:                           -8679599.425969256 926.6254331383..."
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.polar_stereographic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "7db7e16a-15e9-4956-a50c-ed7b5fbf7d6c",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Dataset' object has no attribute 'time'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdsx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n",
-      "File \u001b[0;32m/opt/jaspy/lib/python3.10/site-packages/xarray/core/common.py:278\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[1;32m    277\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    279\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m )\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'Dataset' object has no attribute 'time'"
-     ]
-    }
-   ],
-   "source": [
-    "dsx.isel(time=0).time.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "69248cb6-dc2d-46cb-be52-143737ff9949",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ding\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "update = ['ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0']\n",
-    "group = 'snow_v1'\n",
-    "\n",
-    "for proj_code in update:\n",
-    "    detailfile = f'/gws/nopw/j04/esacci_portal/kerchunk_conversion/in_progress/{group}/{proj_code}/detail-cfg.json'\n",
-    "    with open(detailfile) as f:\n",
-    "        details = json.load(f)\n",
-    "    details['version_no'] = 'kr1.1'\n",
-    "    with open(detailfile,'w') as f:\n",
-    "        f.write(json.dumps(details))\n",
-    "print('ding')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eab0d126-c2cc-438e-9cf2-9e00e2e162eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import json\n",
-    "\n",
-    "checkfile = f'/gws/nopw/j04/esacci_portal/kerchunk_conversion/in_progress/cci_group_v1'\n",
-    "proj_codes = glob.glob(checkfile)\n",
-    "\n",
-    "with open('/gws/nopw/j04/esacci_portal/kerchunk_conversion/input_files/cci_group_v1.csv') as f:\n",
-    "    origin = [r.strip() for r in f.readlines()]\n",
-    "\n",
-    "for x, entry in enumerate(origin):\n",
-    "    row = entry.split(',')\n",
-    "    path = f'{checkfile}/{row[0]}'\n",
-    "    try:\n",
-    "        with open(f'{path}/allfiles.txt') as f:\n",
-    "            length = len(f.readlines())\n",
-    "        assert length > 0\n",
-    "        print(x, 'Success')\n",
-    "    except:\n",
-    "        print(row[1])\n",
-    "        q=input()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c13bf0ae-3e22-4695-97fb-e4e933fefaef",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "107"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "workdir = '/gws/nopw/j04/esacci_portal/kerchunk_conversion/'\n",
-    "with open(f'{workdir}/groups/cci_group_v1/proj_codes_continue.txt') as f:\n",
-    "    firstset = [r.strip() for r in f.readlines()]\n",
-    "\n",
-    "with open(f'{workdir}/groups/cci_group_v1/proj_codes_continue2.txt') as f:\n",
-    "    secset = [r.strip() for r in f.readlines()]\n",
-    "\n",
-    "newset = []\n",
-    "for s in secset:\n",
-    "    if s not in firstset:\n",
-    "        newset.append(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "841d0f83-2f43-4872-85ec-3737190c9b9b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d047f1dd-6391-4c6e-93c9-00323c47ec17",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(f'{workdir}/groups/cci_group_v1/proj_codes_continue2.txt','w') as f:\n",
-    "    f.write('\\n'.join(newset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "7a2b7e11-d439-4764-9faa-948ba7c58a95",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n",
-      "(180, 360)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import xarray as xr\n",
-    "fileset = glob.glob('/neodc/esacci/aerosol/data/MS_UVAI/L3/v1.7/MONTHLY/*/ESACCI-AEROSOL-L3-AAI-MSAAI-1M-*-fv1.7.nc')\n",
-    "for f in fileset:\n",
-    "    ds = xr.open_dataset(f)\n",
-    "    print(ds.number_of_observations.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4efbad91-4194-433c-9fee-21886d485009",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "build_venv",
-   "language": "python",
-   "name": "build_venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 5aefe8ee3540b956f59b732ca4863731b546ba8d Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 14:51:27 +0000
Subject: [PATCH 35/45] Updated assessor documentation

---
 docs/source/assess-overview.rst | 66 +++++++++++++++++++++++++++------
 1 file changed, 55 insertions(+), 11 deletions(-)

diff --git a/docs/source/assess-overview.rst b/docs/source/assess-overview.rst
index 140dbde..fb1d786 100644
--- a/docs/source/assess-overview.rst
+++ b/docs/source/assess-overview.rst
@@ -4,6 +4,16 @@ Assessor Tool
 The assessor script ```assess.py``` is an all-purpose pipeline checking tool which can be used to assess:
  - The current status of all datasets within a given group in the pipeline (which phase each dataset currently sits in)
  - The errors/outputs associated with previous job runs.
+ - Specific logs from datasets which are presenting a specific type of error.
+
+An example command to run the assessor tool can be found below:
+::
+    python assess.py <operation> <group>
+
+Where the operation can be one of the below options:
+ - Progress: Get a general overview of the pipeline; how many datasets have completed or are stuck on each phase.
+ - Summarise: Get an assessment of the data processed for this group
+ - Display: Display a specific type of information about the pipeline (blacklisted codes, datasets with virtual dimensions or using parquet)
 
 1. Overall Progress of the Pipeline
 -----------------------------------
@@ -11,38 +21,72 @@ The assessor script ```assess.py``` is an all-purpose pipeline checking tool whi
 To see the general status of the pipeline for a given group:
 ::
 
-    python assess.py <group> progress
+    python assess.py progress <group>
+
+An example output from this command can be seen below:
+::
+
+    Group: cci_group_v1
+    Total Codes: 361
+
+    scan      : 1     [0.3 %] (Variety: 1)
+        - Complete                 : 1
+
+    complete  : 185   [51.2%] (Variety: 1)
+        - complete                 : 185
+
+    unknown   : 21    [5.8 %] (Variety: 1)
+        - no data                  : 21
+
+    blacklist : 162   [44.9%] (Variety: 7)
+        - NonKerchunkable          : 50
+        - PartialDriver            : 3
+        - PartialDriverFail        : 5
+        - ExhaustedMemoryLimit     : 64
+        - ExhaustedTimeLimit       : 18
+        - ExhaustedTimeLimit*      : 1
+        - ValidationMemoryLimit    : 21
+
+In this case there are 185 datasets that have completed the pipeline with 1 left to be scanned. The 21 unknowns have no log file so there is no information on these. This will be resolved in later versions where a `seek` function will automatically run when checking the progress, to fix gaps in the logs for missing datasets.
 
 
 An example use case is to write out all datasets that require scanning to a new label (repeat_label):
 ::
 
-    python assess.py <group> progress -p scan -r <label_for_scan_subgroup> -W
+    python assess.py progress <group> -p scan -r <label_for_scan_subgroup> -W
 
 
 The last flag ```-W``` is required when writing an output file from this program, otherwise the program will dryrun and produce no files.
 
 2. Checking errors
 ------------------
-Check what repeat labels are available already using
+Check what repeat labels are available already using:
 ::
 
-    python assess.py <group> errors -s labels
-
+    python assess.py display <group> -s labels
 
-Show what jobs have previously run
+For listing the status of all datasets from a previous repeat idL
 ::
 
-    python assess.py <group> errors -s jobids
+    python assess.py progress <group> -r <repeat_id>
 
 
-For showing all errors from a previous job run
+For selecting a specific type of error (-e) and examine the full log for each example (-E)
 ::
 
-    python assess.py <group> errors -j <jobid>
+    python assess.py progress <group> -r <old_id> -e "type_of_error" -p scan -E
+
+Following from this, you may want to rerun the pipeline for just one type of error previously found:
+::
+
+    python assess.py progress <group> -r <old_repeat_id> -e "type_of_error" -p scan -n <new_repeat_id>
+
+Note that if you are looking at a specific repeat ID, you can forego the phase (-p) flag, since it is expected this set would appear in the same phase anyway.
 
+3. Special Display options
+--------------------------
 
-For selecting a specific type of error to investigate (-i) and examine the full log for each example (-E)
+Check how many of the datasets in a group have virtual dimensions
 ::
 
-    python assess.py test errors -j <jobid> -i "type_of_error" -E
+    python assess.py display <group> -s virtuals
\ No newline at end of file

From 42335f2b879ca5b66d350154d860573a0a4d6bcc Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 14:52:03 +0000
Subject: [PATCH 36/45] updated gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 5d76e5d..783b48e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@ kvenv/
 temp/
 testing/
 *__pycache__*
+.vscode/
 docs/build/
 build/
 pipeline.egg-info

From 2721234983f05c3286a3dd7424bc7204189cd6b1 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 14:54:24 +0000
Subject: [PATCH 37/45] Added example input file for tutorial and startup:

---
 extensions/example_water_vapour/water_vapour_v1.csv | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 extensions/example_water_vapour/water_vapour_v1.csv

diff --git a/extensions/example_water_vapour/water_vapour_v1.csv b/extensions/example_water_vapour/water_vapour_v1.csv
new file mode 100644
index 0000000..24d88db
--- /dev/null
+++ b/extensions/example_water_vapour/water_vapour_v1.csv
@@ -0,0 +1,4 @@
+ESACCI-WATERVAPOUR-L3C-TCWV-meris-05deg-20020701-20171231-fv3.2,/neodc/esacci/water_vapour/data/TCWV-land/L3/v3.2/0.5deg/daily/*/*/*nc,,
+ESACCI-WATERVAPOUR-L3C-TCWV-meris-005deg-20020701-20171231-fv3.2,/neodc/esacci/water_vapour/data/TCWV-land/L3/v3.2/0.05deg/daily/*/*/*nc,,
+ESACCI-WATERVAPOUR-L3C-TCWV-meris-05deg-200207-201712-fv3.2,/neodc/esacci/water_vapour/data/TCWV-land/L3/v3.2/0.5deg/monthly/*/*nc,,
+ESACCI-WATERVAPOUR-L3C-TCWV-meris-005deg-200207-201712-fv3.2,/neodc/esacci/water_vapour/data/TCWV-land/L3/v3.2/0.05deg/monthly/*/*nc,,

From 8d4fc67eefbd2bd67d11e2cf89129913f7d6628f Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 15:04:50 +0000
Subject: [PATCH 38/45] Added introductory powerpoints

---
 docs/source/examples.rst                    |  67 --------------------
 showcase/powerpoints/kerchunk_intro.pptx    | Bin 0 -> 1401539 bytes
 showcase/powerpoints/kerchunk_pipeline.pptx | Bin 0 -> 596686 bytes
 3 files changed, 67 deletions(-)
 delete mode 100644 docs/source/examples.rst
 create mode 100644 showcase/powerpoints/kerchunk_intro.pptx
 create mode 100755 showcase/powerpoints/kerchunk_pipeline.pptx

diff --git a/docs/source/examples.rst b/docs/source/examples.rst
deleted file mode 100644
index 1317aca..0000000
--- a/docs/source/examples.rst
+++ /dev/null
@@ -1,67 +0,0 @@
-Worked Examples
-===============
-
-=================================
-2. Example Group of UKCP Datasets 
-=================================
-
-Consider a set of 180 UKCP datasets for which we want to create some Kerchunk files.
-
-This set of datasets all have the following properties:
- - sub_collection: land-rcm
- - domain: uk
- - resolution: 12km
- - scenario: rcp85
-
-Which is reflected in the path to the NetCDF files:
-`/badc/ukcp18/data/land-rcm/uk/12km/rcp85/`
-
-Below this directory we have directories for __ensemble_member__, __variable_id__, __frequency__ and __version__. For this example we will be using:
- - frequency: day
- - version: v20190731
- - ensemble_member(s) \[x12]: 01, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13, 15
- - variable_id(s) \[x15]: clt, hurs, huss, pr, prsn, psl, rls, rss, sfcWind, snw, tas, tasmax, tasmin, uas, vas
-
-Hence there will be 12x15 = 180 Kerchunk datasets at the end of this process.
-
-1. Initialising the Pipeline
-----------------------------
-The easiest way to set up the pipeline for running this group is to create a CSV-type file with the details below for each dataset:
-::
-    
-    project_code, pattern, updates*, removals*
-
-Updates and Removals can be ignored (left blank) unless a specific metadata change is known and required. 
-If this is the case, these two can be set as paths to different JSON files which contain the relevant information in the proper format:
-::
-
-    { # my_updates.json
-        'id': '<new_id_value>',
-        'history': '<new_history_value>
-    }
-    { # my_removals.json
-        'version_no': True # Value is irrelevant, only matters that this attribute is present.
-    }
-
-E.g
-
-::
-    
-    UKCP18_land-rcm_12km_rcp85_01_clt_day_v20190731, /badc/ukcp18/data/land-rcm/uk/12km/rcp85/01/clt/day/v20190731/*, path/to/updates.json, path/to/removals.json
-
-For all 180 datasets in this group. A method of pattern matching and extracting the key information from the path to add to the project code should be used if possible.
-
-To initialise the pipeline with this input CSV file:
-
-::
-    python group_run.py init UKCP_12km_day -i /path/to/csvfile.csv
-
-
-2. Scan the datasets
---------------------
-
-::
-    python group_run.py scan UKCP_12km_day
-
-Parameters that may be necessary at this point:
- - time flag: If scan jobs fail for ``slurmstepd`` errors, use the time flag to change the job time allotment: (```-t 30:00``` for 30 mins)
\ No newline at end of file
diff --git a/showcase/powerpoints/kerchunk_intro.pptx b/showcase/powerpoints/kerchunk_intro.pptx
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diff --git a/showcase/powerpoints/kerchunk_pipeline.pptx b/showcase/powerpoints/kerchunk_pipeline.pptx
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From e4e7dacd225604496cfd22dea178e121029b489a Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 15:46:44 +0000
Subject: [PATCH 39/45] Updated docs with example

---
 docs/source/cci_water.rst | 138 ++++++++++++++++++++++++++++++++++++++
 docs/source/index.rst     |   2 +-
 docs/source/start.rst     |  18 +++--
 3 files changed, 147 insertions(+), 11 deletions(-)
 create mode 100644 docs/source/cci_water.rst

diff --git a/docs/source/cci_water.rst b/docs/source/cci_water.rst
new file mode 100644
index 0000000..f097227
--- /dev/null
+++ b/docs/source/cci_water.rst
@@ -0,0 +1,138 @@
+CCI Water Vapour Example
+========================
+
+The CCI water vapour input CSV file can be found in ``extensions/example_water_vapour/`` within this repository. This guide will take you through running the pipeline for this example set of 4 datasets.
+Assuming you have already gone through the setup instructions in *Getting Started*, you can now proceed with creating a group for this test dataset.
+
+A new *group* is created within the pipeline using the ``init`` operation as follows:
+
+::
+    python group_run.py init <my_new_group> -i extensions/example_water_vapour/water_vapour.csv -v
+
+.. note::
+    Multiple flag options are available throughout the pipeline for more specific operations and methods. In the above case we have used the (-v) *verbose* flag to indicate we want to see the ``[INFO]`` messages put out by the pipeline. Adding a second (v) would also show ``[DEBUG]`` messages.
+    Also the ``init`` phase is always run as a serial process since it just involves creating the directories and config files required by the pipeline.
+
+The output of the above command should look something like this:
+
+.. code-block:: console
+
+    INFO [main-group]: Running init steps as serial process
+    INFO [init]: Starting initialisation
+    INFO [init]: Copying input file from relative path - resolved to /home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder
+    INFO [init]: Creating project directories
+    INFO [init]: Creating directories/filelists for 1/4
+    INFO [init]: Updated new status: init - complete
+    INFO [init]: Creating directories/filelists for 2/4
+    INFO [init]: Updated new status: init - complete
+    INFO [init]: Creating directories/filelists for 3/4
+    INFO [init]: Updated new status: init - complete
+    INFO [init]: Creating directories/filelists for 4/4
+    INFO [init]: Updated new status: init - complete
+    INFO [init]: Created 24 files, 8 directories in group my_new_group
+    INFO [init]: Written as group ID: my_new_group
+
+Ok great, we've initialised the pipeline for our new group! Here's a summary diagram of what directories and files were just created:
+
+::
+
+    WORKDIR
+      - groups
+         -  my_new_group
+             -  proj_codes
+                 -  main.txt
+             -  blacklist_codes.txt
+             -  datasets.csv # (a copy of the input file)
+
+      - in_progress
+         -  my_new_group
+             -  code_1 # (codes 1 to 4 in this example)
+                 -  allfiles.txt
+                 -  base-cfg.json
+                 -  phase_logs
+                     -  scan.log
+                     -  compute.log
+                     -  validate.log
+                 -  status_log.csv
+
+For peace of mind and to check you understand the pipeline assessor tool we would suggest running this command next:
+
+::
+
+    python assess.py progress my_new_group
+
+Upon which your output should look something like this:
+
+.. code-block:: console
+
+    Group: my_new_group
+    Total Codes: 4
+
+    Pipeline Current:
+
+    init      : 4     [100.%] (Variety: 1)
+        - complete : 4
+
+    Pipeline Complete:
+
+    complete  : 0     [0.0 %]
+
+All 4 of our datasets were initialised successfully, no datasets are complete through the pipeline yet.
+
+The next steps are to ``scan``, ``compute``, and ``validate`` the datasets which would complete the pipeline.
+
+.. note::
+    For each of the above phases, jobs will be submitted to SLURM when using the ``group_run`` script. Please make sure to wait until all jobs are complete for one phase *before* running the next job.
+    After each job, check the progress of the pipeline with the same command as before to check all the datasets ``complete`` as expected. See below on what to do if datasets encounter errors.
+
+.. code-block:: console
+
+    python group_run.py scan my_new_group
+    python group_run.py compute my_new_group
+    python group_run.py validate my_new_group
+
+An more complex example of what you might see while running the pipeline in terms of errors encountered can be found below:
+
+.. code-block:: console
+
+    Group: cci_group_v1
+    Total Codes: 361
+
+    Pipeline Current:
+
+    compute   : 21    [5.8 %] (Variety: 2)
+        - complete                 : 20
+        - KeyError 'refs'          : 1
+
+    Pipeline Complete:
+
+    complete  : 185   [51.2%]
+
+    blacklist : 155   [42.9%] (Variety: 8)
+        - NonKerchunkable          : 50
+        - PartialDriver            : 3
+        - PartialDriverFail        : 5
+        - ExhaustedMemoryLimit     : 56
+        - ExhaustedTimeLimit       : 18
+        - ExhaustedTimeLimit*      : 1
+        - ValidationMemoryLimit    : 21
+        - ScipyDimIssue            : 1
+
+In this example ``cci_group_v1`` group, 185 of the datasets have completed the pipeline, while 155 have been excluded (See blacklisting in the Assessor Tool section). 
+Of the remaining 21 datasets, 20 of them have completed the ``compute`` phase and now need to be run through ``validate``, but one encountered a KeyError which needs to be inspected. To view the log for this dataset we can use the command below:
+
+.. code-block:: console
+
+    python assess.py progress cci_group_v1 -e "KeyError 'refs'" -p compute -E
+
+This will match with our ``compute``-phase error with that message, and the (-E) flag will give us the whole error log from that run. This may be enough to assess and fix the issue but otherwise, to rerun just this dataset a rerun command will be suggested by the assessor:
+
+.. code-block:: console
+
+    Project Code: 201601-201612-ESACCI-L4_FIRE-BA-MSI-fv1.1 - <class 'KeyError'>'refs'
+    Rerun suggested command:    python single_run.py compute 218 -G cci_group_v1 -vv -d
+
+This rerun command has several flags included, the most importand here is the (-G) group flag, since we need to use the ``single_run`` script so now need to specify the group. The (-d) dryrun flag will simply mean we are not producing any output files since we may need to test and rerun several times.
+
+
+
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 2e94d8d..4947bf5 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -19,7 +19,7 @@ The pipeline consists of four central phases, with an additional phase for inges
 
    Introduction <pipeline-overview>
    Getting Started <start>
-   Worked Examples <examples>
+   Example CCI Water Vapour <cci_water>
    Pipeline Flags/Options <execution>
    Assessor Tool Overview <assess-overview>
    Error Codes <errors>
diff --git a/docs/source/start.rst b/docs/source/start.rst
index 1c1d5b1..68ba1c6 100644
--- a/docs/source/start.rst
+++ b/docs/source/start.rst
@@ -3,11 +3,11 @@ Getting Started
 
 .. note::
 
-    Ensure you have local modules enabled such that you have python 3.x installed in your local environment.
+    Ensure you have local modules enabled such that you have python 3.x installed in your local environment. A version of the pipeline source code exists at ``/gws/nopw/j04/cedaproc/kerchunk_builder`` so please see if this can be used before cloning the repository elsewhere.
 
 Step 0: Git clone the repository
 --------------------------------
-The Kerchunk builder will soon be updated to version 1.0.1, which you can clone using:
+If you need to clone the repository, either simply clone the main branch of the repository (no branch specified) or check the latest version of the repository at github.com/cedadev/kerchunk-builder, which you can clone using:
 ::
 
     git clone git@github.com:cedadev/kerchunk-builder.git --branch v1.0.1
@@ -15,7 +15,7 @@ The Kerchunk builder will soon be updated to version 1.0.1, which you can clone
 Step 1: Set up Virtual Environment
 ----------------------------------
 
-Step 1 is to create a virtual environment and install the necessary packages with pip. This can be done inside the local repo you've cloned as ```local``` or ```build_venv``` which will be ignored by the repository, or you can create a venv elsewhere in your home directory i.e ```~/venvs/build_venv```
+Step 1 is to create a virtual environment and install the necessary packages with pip. This can be done inside the local repo you've cloned as ``local`` or ``kvenv`` which will be ignored by the repository, or you can create a venv elsewhere in your home directory i.e ``~/venvs/build_venv``. If you are using the pipeline version in ``cedaproc`` there should already be a virtual environment set up.
 
 .. code-block:: console
 
@@ -26,14 +26,13 @@ Step 1 is to create a virtual environment and install the necessary packages wit
 
 Step 2: Environment configuration
 ---------------------------------
-Create a config file to set necessary environment variables. (Suggested to place these in a local `templates/` folder as this will be ignored by git). Eg:
+Create a config file to set necessary environment variables. (Suggested to place these in the local `config/` folder as this will be ignored by git). Eg:
 
 .. code-block:: console
 
-    export WORKDIR  =/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline;
-    export GROUPDIR =/gws/nopw/j04/cmip6_prep_vol1/kerchunk-pipeline/groups/CMIP6_rel1_6233;
-    export SRCDIR   =/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder;
-    export KVENV    =/home/users/dwest77/Documents/kerchunk_dev/kerchunk-builder/build_venv;
+    export WORKDIR = /path/to/kerchunk-pipeline
+    export SRCDIR  = /gws/nopw/j04/cedaproc/kerchunk_builder/kerchunk-builder
+    export KVENV   = $SRCDIR/kvenv
 
 
 Now you should be set up to run the pipeline properly. For any of the pipeline scripts, running ```python <script>.py -h # or --help``` will bring up a list of options to use for that script as well as the required parameters.
@@ -49,7 +48,6 @@ In order to successfully run the pipeline you need the following input files:
 
 It is also helpful to create a setup/config bash script to set all your environment variables which include:
  - WORKDIR: The working directory for the pipeline (where to store all the cache files)
- - GROUPDIR: Subdirectory under the working directory for the particular group you are running. (This is not required but could make things easier)
  - SRCDIR: Path to the kerchunk-builder repo where it has been cloned.
  - KVENV: Path to a virtual environment for the pipeline.
 
@@ -69,7 +67,7 @@ Some useful option/flags to add:
     -Q # Quality
        #  - thorough run - use to ignore cache files and perform checks on all netcdf files
     -r # repeat_id
-       #  - default uses main (1), if you have created repeat_ids manually or with assess.py, specify here [omit "proj_codes_"]
+       #  - default uses main, if you have created repeat_ids manually or with assess.py, specify here.
     -d # dryrun
        #  - Skip creating any new files in this phase
 

From ff9d0681223265b84fe80aba956571a1c7ca34df Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 15:50:07 +0000
Subject: [PATCH 40/45] Added template setup file

---
 assess.py                        | 257 ++++++++++++++++++++-----------
 config/setup-project.sh.template |   6 +-
 2 files changed, 168 insertions(+), 95 deletions(-)

diff --git a/assess.py b/assess.py
index 8545452..2baf882 100644
--- a/assess.py
+++ b/assess.py
@@ -5,7 +5,7 @@
 import sys
 from datetime import datetime
 
-from pipeline.logs import init_logger
+from pipeline.logs import init_logger, log_status, FalseLogger
 from pipeline.utils import get_attribute, format_str, mem_to_val, get_codes
 import pipeline.errors as errs
 
@@ -25,11 +25,11 @@
 
 def get_rerun_command(phase: str, ecode: str, groupID: str, repeat_id: str):
     """Print a rerun command for inspecting a single dataset using single_run.py"""
-    print(f'python single_run.py {phase} {ecode} -G {groupID} -r {repeat_id} -vvv -d')
+    print(f'python single_run.py {phase} {ecode} -G {groupID} -r {repeat_id} -vv -d')
 
 def get_index_of_code(workdir, groupID, repeat_id, code):
     """Get the index of a project code within some repeat set of codes"""
-    proj_codes = get_codes(groupID, workdir, f'proj_codes_{repeat_id}')
+    proj_codes = get_codes(groupID, workdir, f'proj_codes/{repeat_id}')
     pindex = 0
     pcode = proj_codes[pindex]
     while pcode != code:
@@ -37,7 +37,7 @@ def get_index_of_code(workdir, groupID, repeat_id, code):
         pcode = proj_codes[pindex]
     return pindex
 
-def examine_log(workdir: str, proj_code: str,  phase: str, groupID=None, repeat_id=None):
+def examine_log(workdir: str, proj_code: str, phase: str, groupID=None, repeat_id=None, error=None):
     """Open and examine a log file from a previous run of a given phase
     - Show full error log
     - Suggest rerun/examination command
@@ -51,14 +51,14 @@ def examine_log(workdir: str, proj_code: str,  phase: str, groupID=None, repeat_
             log = [r.strip() for r in f.readlines()]
     else:
         print(f'Phase log file not found: {phase_log}')
-        return None
+        log = []
 
     print()
     print('\n'.join(log))
-    print(f'Project Code: {proj_code}')
+    print(f'Project Code: {proj_code} - {error}')
     print('Rerun suggested command:    ',end='')
 
-    ecode = get_index_of_code(groupID, repeat_id, proj_code)
+    ecode = get_index_of_code(workdir, groupID, repeat_id, proj_code)
     get_rerun_command(phase, ecode, groupID, repeat_id)
 
     paused=input('Type "E" to exit assessment: ')
@@ -93,32 +93,35 @@ def merge_old_new(old_codes, new_codes, index_old='', index_new='', reason=None)
             merged.append(f'{mkey}')
     return merged
 
-def save_selection(codes: list, groupdir: str, label: str, logger, overwrite=0):
+def save_selection(codes: list, groupdir: str, label: str, logger, overwrite=0, index=False):
     """Save selection of codes to a file with a given repeat label. 
     
     Requires a groupdir (directory belonging to a group), list of codes and a label for the new file.
     """
     if len(codes) > 0:
-        codeset = '\n'.join([code[1].strip() for code in codes])
-        if os.path.isfile(f'{groupdir}/proj_codes_{label}.txt'):
+        if index:
+            codeset = '\n'.join([code[index].strip() for code in codes])
+        else:
+            codeset = '\n'.join(codes)
+        if os.path.isfile(f'{groupdir}/proj_codes/{label}.txt'):
             if overwrite == 0:
-                print(f'Skipped writing {len(codes)} to proj_codes_{label} - file exists and overwrite not set')
+                print(f'Skipped writing {len(codes)} to proj_codes/{label} - file exists and overwrite not set')
             elif overwrite == 1:
-                print(f'Adding {len(codes)} to existing proj_codes_{label}')
-                with open(f'{groupdir}/proj_codes_{label}.txt') as f:
+                print(f'Adding {len(codes)} to existing proj_codes/{label}')
+                with open(f'{groupdir}/proj_codes/{label}.txt') as f:
                     old_codes = [r.strip() for r in f.readlines()]
                 merged = merge_old_new(old_codes, codes)
                 # Need check for duplicates here
-                with open(f'{groupdir}/proj_codes_{label}.txt','w') as f:
+                with open(f'{groupdir}/proj_codes/{label}.txt','w') as f:
                     f.write('\n'.join(merged))
             elif overwrite == 2:
-                print(f'Overwriting with {len(codes)} in existing proj_codes_{label} file')
-                with open(f'{groupdir}/proj_codes_{label}.txt','w') as f:
+                print(f'Overwriting with {len(codes)} in existing proj_codes/{label} file')
+                with open(f'{groupdir}/proj_codes/{label}.txt','w') as f:
                     f.write(codeset)
         else:
-            with open(f'{groupdir}/proj_codes_{label}.txt','w') as f:
+            with open(f'{groupdir}/proj_codes/{label}.txt','w') as f:
                 f.write(codeset)
-            print(f'Written {len(codes)} to proj_codes_{label}')
+            print(f'Written {len(codes)} to proj_codes/{label}')
     else:
         print('No codes identified, no files written')
 
@@ -128,24 +131,28 @@ def show_options(args, logger):
     List output or error directories (one per job id), or list all proj_codes text files."""
 
     # Load the blacklist here 
-    if args.option in ['blacklist', 'virtuals']:
+    if args.option in ['blacklist', 'virtuals'] or 'variable' in args.option:
         blackset = get_codes(args.groupID, args.workdir, 'blacklist_codes')
         if blackset:
             blackcodes = {b.split(',')[0]: b.split(',')[1] for b in blackset}
         else:
             blackcodes = {}
 
-    if args.option == 'jobids':
+    if args.option == 'groups':
+        os.system(f'ls {args.workdir}/groups/')
+
+    elif args.option == 'jobids':
         print('Detecting IDs from previous runs:')
         if args.operation == 'outputs':
             os.system(f'ls {args.groupdir}/outs/')
         else:
             os.system(f'ls {args.groupdir}/errs/')
+
     elif args.option == 'labels':
         print('Detecting labels from previous runs:')
-        labels = glob.glob(f'{args.groupdir}/proj_codes*')
+        labels = glob.glob(f'{args.groupdir}/proj_codes/*')
         for l in labels:
-            pcode = l.split('/')[-1].replace("proj_codes_","").replace(".txt","")
+            pcode = l.split('/')[-1].replace(".txt","")
             if pcode == '1':
                 pcode = 'main'
             print(f'{format_str(pcode,20)} - {l}')
@@ -154,46 +161,80 @@ def show_options(args, logger):
         for b in blackcodes.keys():
             print(f'{b} - {blackcodes[b]}')
     elif args.option == 'virtuals':
+
         print('Finding datasets with a virtual stacking dimension')
-        codefile = f'{args.groupdir}/proj_codes_1.txt'
+        codefile = f'{args.groupdir}/proj_codes/main.txt'
         if os.path.isfile(codefile):
             with open(codefile) as f:
                 proj_codes = [r.strip() for r in f.readlines()]
-            for p in proj_codes:
-                virtual = False
-                if p in blackcodes:
-                    continue
-                detailfile = f'{args.workdir}/in_progress/{args.groupID}/{p}/detail-cfg.json'
-                if os.path.isfile(detailfile):
-                    with open(detailfile) as f:
-                        details = json.load(f)
-                    if 'virtual_concat' in details:
-                        if details['virtual_concat']:
-                            virtual = True
-                else:
-                    print(f'Missing detailfile for {p}')
-                            
-                if virtual:
-                    print(f'Project code: {p} - Virtual present')
-                else:
-                    print(f'Project code: {p}')
         else:
-            logger.warning(f'No proj_codes file found for {args.groupID}')
+            logger.error(f'No proj_codes file found for {args.groupID}')
+            raise FileNotFoundError(file=codefile)
+        non_virtual = 0
+        for p in proj_codes:
+            virtual = False
+            if p in blackcodes:
+                continue
+            detailfile = f'{args.workdir}/in_progress/{args.groupID}/{p}/detail-cfg.json'
+            if os.path.isfile(detailfile):
+                with open(detailfile) as f:
+                    details = json.load(f)
+                if 'virtual_concat' in details:
+                    if details['virtual_concat']:
+                        virtual = True
+            else:
+                print(f'Missing detailfile for {p}')
+                        
+            if virtual:
+                print(f'Project code: {p} - Virtual present')
+            else:
+                non_virtual += 1
+        print(f'Non-virtual datasets : {non_virtual}')
+        print(f'Total datasets       : {len(proj_codes) - len(blackcodes)}')
+    elif 'variable' in args.option:
+        raise NotImplementedError
+        var = args.option.split(':')[-1]
+        print('Finding datasets with a virtual stacking dimension')
+        codefile = f'{args.groupdir}/proj_codes/main.txt'
+        if os.path.isfile(codefile):
+            with open(codefile) as f:
+                proj_codes = [r.strip() for r in f.readlines()]
+        else:
+            logger.error(f'No proj_codes file found for {args.groupID}')
+            raise FileNotFoundError(file=codefile)
+
     else:
         print(f'{args.option} not accepted - use "jobids" or "labels"')
 
 def cleanup(cleantype: str, groupdir: str, logger):
     """Remove older versions of project code files, error or output logs. Clear directories."""
     if cleantype == 'labels' or cleantype == 'all':
-        projset = glob.glob(f'{groupdir}/proj_codes_*')
+        projset = glob.glob(f'{groupdir}/proj_codes/*')
         for p in projset:
-            if 'proj_codes_1' not in p:
+            if 'main' not in p:
                 os.system(f'rm {p}')
     if cleantype == 'errors' or cleantype == 'all':
         os.system(f'rm -rf {groupdir}/errs/*')
     if cleantype == 'outputs' or cleantype == 'all':
         os.system(f'rm -rf {groupdir}/outs/*')
         
+def seek_unknown(proj_dir):
+    phase = None
+    status = 'complete'
+    if len(glob.glob(f'{proj_dir}/*complete*')) > 0:
+        phase = 'complete'
+    elif len(glob.glob(f'{proj_dir}/kerchunk-*.*')) > 0:
+        phase = 'compute'
+    elif len(glob.glob(f'{proj_dir}/detail-cfg.json')) > 0:
+        phase = 'scan'
+    elif not os.path.isdir(proj_dir):
+        phase = 'init'
+        status = 'incomplete'
+    else:
+        phase = 'init'
+    
+    log_status(phase, proj_dir, status, FalseLogger())
+
 def progress_check(args, logger):
     """Give a general overview of progress within the pipeline
     - How many datasets currently at each stage of the pipeline
@@ -202,12 +243,13 @@ def progress_check(args, logger):
     - Allows saving codes matching an error type into a new repeat group
     """
     blacklist  = get_codes(args.groupID, args.workdir, 'blacklist_codes')
-    proj_codes = get_codes(args.groupID, args.workdir, f'proj_codes_{args.repeat_id}')
+    proj_codes = get_codes(args.groupID, args.workdir, f'proj_codes/{args.repeat_id}')
 
     groupdir = f'{args.workdir}/groups/{args.groupID}'
 
     done_set = {}
-    extras = {'unknown': {'no data': 0}, 'blacklist': {}}
+    extras = {'blacklist': {}}
+    complete = 0
 
     for b in blacklist:
         entry = b.replace(' ','').split(',')
@@ -217,8 +259,9 @@ def progress_check(args, logger):
             extras['blacklist'][entry[1]] = 1
         done_set[entry[0]] = True
 
-    phases = {'init':{}, 'scan': {}, 'compute': {}, 'validate': {}, 'complete': {}}
+    phases = {'init':{}, 'scan': {}, 'compute': {}, 'validate': {}}
     savecodes = []
+    longest_err = 0
     for p in proj_codes:
         if p not in done_set:
             proj_dir = f'{args.workdir}/in_progress/{args.groupID}/{p}'
@@ -227,31 +270,46 @@ def progress_check(args, logger):
                 with open(status_log) as f:
                     current = f.readlines()[-1].strip()
             else:
-                extras['unknown']['no data'] += 1
+                seek_unknown(proj_dir)
+                if 'unknown' in extras:
+                    extras['unknown']['no data'] += 1
+                else:
+                    extras['unknown'] = {'no data': 1}
                 continue
             entry = current.split(',')
-
+            if len(entry[1]) > longest_err:
+                longest_err = len(entry[1])
 
             match_phase = (bool(args.phase) and args.phase == entry[0])
             match_error = (bool(args.error) and args.error == entry[1])
 
-            if match_phase or match_error:
+            if bool(args.phase) != (args.phase == entry[0]):
+                total_match = False
+            elif bool(args.error) != (args.error == entry[1]):
+                total_match = False
+            else:
+                total_match = match_phase or match_error
+
+            if total_match:
                 if args.examine:
-                    examine_log(args.workdir, p, entry[0], groupID=args.groupID, repeat_id=args.repeat_id)
-                if args.new_id:
+                    examine_log(args.workdir, p, entry[0], groupID=args.groupID, repeat_id=args.repeat_id, error=entry[1])
+                if args.new_id or args.blacklist:
                     savecodes.append(p)
 
-            if entry[1] in phases[entry[0]]:
-                phases[entry[0]][entry[1]] += 1
+            if entry[0] == 'complete':
+                complete += 1
             else:
-                phases[entry[0]][entry[1]] = 1
+                if entry[1] in phases[entry[0]]:
+                    phases[entry[0]][entry[1]] += 1
+                else:
+                    phases[entry[0]][entry[1]] = 1
 
     num_codes  = len(proj_codes)
     print()
     print(f'Group: {args.groupID}')
     print(f'  Total Codes: {num_codes}')
 
-    def summary_dict(pdict, num_codes):
+    def summary_dict(pdict, num_codes, status_len=5):
         """Display summary information for a dictionary structure of the expected format."""
         for entry in pdict.keys():
             pcount = len(list(pdict[entry].keys()))
@@ -265,18 +323,34 @@ def summary_dict(pdict, num_codes):
 
                 for err in pdict[entry]:
                     num_errs = pdict[entry][err]
-                    print(f'    - {format_str(err, 25)}: {num_errs}')
+                    print(f'    - {format_str(err, status_len+1)}: {num_errs}')
 
-    summary_dict(phases, num_codes)
-    summary_dict(extras, num_codes)
-    print()
+    if not args.write:
+        print()
+        print('Pipeline Current:')
+        summary_dict(phases, num_codes, status_len=longest_err)
+        print()
+        print('Pipeline Complete:')
+        print()
+        complete_percent = format_str(f'{complete*100/num_codes:.1f}',4)
+        print(f'   complete  : {format_str(complete,5)} [{complete_percent}%]')
+        summary_dict(extras, num_codes, status_len=longest_err)
+        print()
 
     if args.new_id:
-        logger.debug(f'Preparing to write {len(savecodes)} codes to proj_codes_{args.new_id}.txt')
+        logger.debug(f'Preparing to write {len(savecodes)} codes to proj_codes/{args.new_id}.txt')
         if args.write:
             save_selection(savecodes, groupdir, args.new_id, logger, overwrite=args.overwrite)
         else:
-            print('Skipped writing new codes - no overwrite mechanism specified')
+            print('Skipped writing new codes - Write flag not present')
+
+    if args.blacklist:
+        logger.debug(f'Preparing to add {len(savecodes)} codes to the blacklist')
+        if args.write:
+            add_to_blacklist(savecodes, args.groupdir, args.reason, logger)
+        else:
+            print('Skipped blacklisting codes - Write flag not present')
+        
     
 def status_check(args, logger):
     """Check general progress of pipeline for a specific group.
@@ -312,14 +386,14 @@ def status_check(args, logger):
     # Write pcodes
     if not args.repeat_id:
         id = 1
-        new_projcode_file = f'{args.workdir}/groups/{args.groupID}/proj_codes_{args.phase}_{id}.txt'
+        new_projcode_file = f'{args.workdir}/groups/{args.groupID}/proj_codes/{args.phase}_{id}.txt'
         while os.path.isfile(new_projcode_file):
             id += 1
-            new_projcode_file = f'{args.workdir}/groups/{args.groupID}/proj_codes_{args.phase}_{id}.txt'
+            new_projcode_file = f'{args.workdir}/groups/{args.groupID}/proj_codes/{args.phase}_{id}.txt'
 
         args.repeat_id = f'{args.phase}_{id}'
 
-    new_projcode_file = f'{args.workdir}/groups/{args.groupID}/proj_codes_{args.repeat_id}.txt'
+    new_projcode_file = f'{args.workdir}/groups/{args.groupID}/proj_codes/{args.repeat_id}.txt'
 
     if args.write:
         with open(new_projcode_file,'w') as f:
@@ -339,22 +413,15 @@ def add_to_blacklist(savedcodes, groupdir, reason, logger):
     merged = ''
     with open(blackfile) as f:
         blackcodes = [r.strip().split(',') for r in f.readlines()]
-
-    merged = merge_old_new(blackcodes, savedcodes, index_old=0, index_new=1, reason=reason)
+    merged = merge_old_new(blackcodes, savedcodes, index_old=0, reason=reason)
     blacklist = '\n'.join([f'{m}' for m in merged])
-    
+
     with open(blackfile,'w') as f:
         f.write(blacklist)
-    print(f'Blacklist now contains {len(merged)} codes.')
+    print(f'Added {len(merged) - len(blackcodes)} new codes to blacklist')
 
 def upgrade_version(args, logger):
-    """Upgrade the version info in the kerchunk file
-    - Default - assume phase = validate - alter in kerchunk-1a.json
-                       phase = compute - just replace in detail-cfg.
-                       phase = complete - alter in complete
-
-    In all cases, alter in detail-cfg.
-    """
+    """Upgrade the version info in the kerchunk file"""
 
     proj_codes = []
     if not args.repeat_id:
@@ -365,7 +432,7 @@ def upgrade_version(args, logger):
         logger.warning('New version not specified - should be of the format "krX.X"')
         return None
 
-    projfile = f'{args.groupdir}/proj_codes_{args.repeat_id}.txt'
+    projfile = f'{args.groupdir}/proj_codes/{args.repeat_id}.txt'
     if os.path.isfile(projfile):
         with open(projfile) as f:
             proj_codes = [r.strip() for r in f.readlines()]
@@ -450,7 +517,7 @@ def analyse_data(g, workdir):
     """Show some statistics of netcdf and kerchunk data amounts for this particular group"""
     ncf, ker, kus = 0, 0, 0
     complete, scanned = 0, 0
-    projset = get_codes(g, workdir, 'proj_codes_1')
+    projset = get_codes(g, workdir, 'proj_codes/main')
 
     # Add individual error log checking PPC here.
     
@@ -485,7 +552,7 @@ def summary_data(args, logger):
         print()
         print(g)
         ncf, ker, kus, scanned, complete = analyse_data(g, args.workdir)
-        print(f'   Datasets          : {len(get_codes(g, args.workdir, "proj_codes_1"))}')
+        print(f'   Datasets          : {len(get_codes(g, args.workdir, "proj_codes/main"))}')
         print(f'    - Unavailable    : {len(get_codes(g, args.workdir, "blacklist_codes"))}')
         print(f'   Data:')
         print(f'    - NetCDF         : {format_float(ncf, FalseLogger())}')
@@ -504,12 +571,13 @@ def summary_data(args, logger):
         print(f'         NetCDF: {format_float(Tncf, FalseLogger())}')
         print(f'  Kerchunk Estm: {format_float(Tker, FalseLogger())} ({Tscan})')
         print(f'Kerchunk Actual: {format_float(Tkus, FalseLogger())} ({Tcomp})')
-        
+
 operations = {
     'progress': progress_check,
     'blacklist': add_to_blacklist,
     'upgrade': upgrade_version,
     'summarise': summary_data,
+    'display': show_options,
 }
 
 def assess_main(args):
@@ -519,25 +587,30 @@ def assess_main(args):
 
     args.workdir  = get_attribute('WORKDIR', args, 'workdir')
 
-    if args.groupID == 'A': # Just show which groups exist
-        os.system(f'ls {args.workdir}/groups/')
-        return None
-    
-    args.groupdir = f'{args.workdir}/groups/{args.groupID}'
-
     if ',' in args.error:
         args.error = args.error.split(',')
 
-    if args.option:
-        show_options(args, logger)
-        return None
-
+    """ Removed for now
     if args.cleanup:
         cleanup(args.cleanup, args.groupdir, logger)
         return None
+    """
 
+    if args.groupID == 'A':
+        groups = []
+        for d in glob.glob(f'{args.workdir}/groups/'):
+            if os.path.isdir(d):
+                groups.append(d.split('/')[-1])
+    elif ',' in args.groupID:
+        groups = args.groupID.split(',')
+    else:
+        groups = [args.groupID]
+    
     if args.operation in operations:
-        operations[args.operation](args, logger)
+        for groupID in groups:
+            args.groupID = groupID
+            args.groupdir = f'{args.workdir}/groups/{args.groupID}'
+            operations[args.operation](args, logger)
     else:
         print(f'{args.operation} - Unknown operation, not one of {list(operations.keys())}')
 
@@ -559,7 +632,7 @@ def assess_main(args):
     # Select subgroups and save new repeat groups
     parser.add_argument('-j','--jobid', dest='jobID', default=None, help='Identifier of job to inspect')
     parser.add_argument('-p','--phase', dest='phase', default=None, help='Pipeline phase to inspect')
-    parser.add_argument('-r','--repeat_id', dest='repeat_id', default='1', help='Inspect an existing ID for errors')
+    parser.add_argument('-r','--repeat_id', dest='repeat_id', default='main', help='Inspect an existing ID for errors')
     parser.add_argument('-n','--new_id', dest='new_id', default=None, help='Create a new repeat ID, specify selection of codes by phase, error etc.')
 
     # Error inspection
diff --git a/config/setup-project.sh.template b/config/setup-project.sh.template
index 5cf03ac..f9e32b1 100755
--- a/config/setup-project.sh.template
+++ b/config/setup-project.sh.template
@@ -1,7 +1,7 @@
 module load jaspy
 
-export WORKDIR=/path/to/kerchunk-pipeline
-export SRCDIR=/gws/nopw/j04/cedaproc/kerchunk_builder/kerchunk-builder
-export KVENV=$SRCDIR/kvenv
+export WORKDIR =/path/to/kerchunk-pipeline
+export SRCDIR  =/gws/nopw/j04/cedaproc/kerchunk_builder/kerchunk-builder
+export KVENV   =$SRCDIR/kvenv
 
 source $KVENV/bin/activate
\ No newline at end of file

From 43181e186b468bd068cc30b4441fd7a0f4230aa5 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 15:50:20 +0000
Subject: [PATCH 41/45] Fixed syntax issue

---
 docs/source/cci_water.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/cci_water.rst b/docs/source/cci_water.rst
index f097227..357f279 100644
--- a/docs/source/cci_water.rst
+++ b/docs/source/cci_water.rst
@@ -7,6 +7,7 @@ Assuming you have already gone through the setup instructions in *Getting Starte
 A new *group* is created within the pipeline using the ``init`` operation as follows:
 
 ::
+
     python group_run.py init <my_new_group> -i extensions/example_water_vapour/water_vapour.csv -v
 
 .. note::

From 8e9f3ec61b78da3aab47868f445f85154f51c787 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 15:50:34 +0000
Subject: [PATCH 42/45] Renamed file

---
 .../{water_vapour_v1.csv => water_vapour.csv}                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename extensions/example_water_vapour/{water_vapour_v1.csv => water_vapour.csv} (100%)

diff --git a/extensions/example_water_vapour/water_vapour_v1.csv b/extensions/example_water_vapour/water_vapour.csv
similarity index 100%
rename from extensions/example_water_vapour/water_vapour_v1.csv
rename to extensions/example_water_vapour/water_vapour.csv

From 85ea009d07d6dfb770ec53f57a24e5df2e398a98 Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 15:50:57 +0000
Subject: [PATCH 43/45] Removed deploy function for dependent jobs

---
 group_run.py | 18 ++++++------------
 1 file changed, 6 insertions(+), 12 deletions(-)

diff --git a/group_run.py b/group_run.py
index 9c43106..0520606 100644
--- a/group_run.py
+++ b/group_run.py
@@ -4,11 +4,12 @@
 import argparse
 import subprocess
 
-from pipeline.logs import init_logger, BypassSwitch
+from pipeline.logs import init_logger
+from pipeline.utils import BypassSwitch
 
 def get_group_len(workdir, group, repeat_id=1):
     """Implement parallel reads from single 'group' file"""
-    with open(f'{workdir}/groups/{group}/proj_codes_{repeat_id}.txt') as f:
+    with open(f'{workdir}/groups/{group}/proj_codes/{repeat_id}.txt') as f:
         group_len = len(list(f.readlines()))
     return group_len
 
@@ -29,15 +30,8 @@ def get_attribute(env, args, var):
     else:
         print(f'Error: Missing attribute {var}')
         return None
-    
 
-def main(args):
-
-    logger = init_logger(args.verbose, 0, 'main-group')
-
-    deploy(args, logger)
-
-def deploy(args, logger, get_id=False, dependent_id=False):
+def main(args,get_id=False, dependent_id=False):
     """Assemble sbatch script for parallel running jobs"""
 
     logger = init_logger(args.verbose, 0, 'main-group')
@@ -165,7 +159,7 @@ def deploy(args, logger, get_id=False, dependent_id=False):
     parser.add_argument('-v','--verbose', dest='verbose', action='count', default=0, help='Print helpful statements while running')
     parser.add_argument('-d','--dryrun',  dest='dryrun',  action='store_true', help='Perform dry-run (i.e no new files/dirs created)' )
     parser.add_argument('-Q','--quality', dest='quality', action='store_true', help='Quality assured checks - thorough run')
-    parser.add_argument('-b','--bypass-errs', dest='bypass', default='FDSC', help=BypassSwitch().help())
+    parser.add_argument('-b','--bypass-errs', dest='bypass', default='DBSCM', help=BypassSwitch().help())
     parser.add_argument('-B','--backtrack', dest='backtrack', action='store_true', help='Backtrack to previous position, remove files that would be created in this job.')
 
     # Environment variables
@@ -178,7 +172,7 @@ def deploy(args, logger, get_id=False, dependent_id=False):
     parser.add_argument('-t','--time-allowed',dest='time_allowed',  help='Time limit for this job')
     parser.add_argument('-M','--memory', dest='memory', default='2G', help='Memory allocation for this job (i.e "2G" for 2GB)')
     parser.add_argument('-s','--subset',    dest='subset',    default=1,   type=int, help='Size of subset within group')
-    parser.add_argument('-r','--repeat_id', dest='repeat_id', default='1', help='Repeat id (1 if first time running, <phase>_<repeat> otherwise)')
+    parser.add_argument('-r','--repeat_id', dest='repeat_id', default='main', help='Repeat id (main if first time running, <phase>_<repeat> otherwise)')
 
     # Specialised
     parser.add_argument('-n','--new_version', dest='new_version',   help='If present, create a new version')

From 363d953f9fc6415df2d4b9851f9515d8dad8287b Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 15:51:34 +0000
Subject: [PATCH 44/45] Updated utils, added filehandler continuation support
 for all pipeline scripts

---
 pipeline/compute/serial_process.py | 39 ++++++++++++++------
 pipeline/errors.py                 | 10 ++++++
 pipeline/init.py                   | 43 ++++++++++++++++------
 pipeline/logs.py                   |  8 ++++-
 pipeline/scan.py                   | 57 ++++++++++++++++--------------
 pipeline/utils.py                  | 11 +++---
 pipeline/validate.py               | 16 +++++++--
 7 files changed, 124 insertions(+), 60 deletions(-)

diff --git a/pipeline/compute/serial_process.py b/pipeline/compute/serial_process.py
index 9ddd9da..7f69384 100644
--- a/pipeline/compute/serial_process.py
+++ b/pipeline/compute/serial_process.py
@@ -11,7 +11,7 @@
 
 from pipeline.logs import init_logger, FalseLogger
 from pipeline.utils import BypassSwitch
-from pipeline.errors import IdenticalVariablesError, ValidationError, ConcatenationError, SoftfailBypassError
+from pipeline.errors import *
 from pipeline.validate import validate_data, open_kerchunk, validate_selection
 
 class KerchunkDriverFatalError(Exception):
@@ -26,19 +26,21 @@ def __init__(self, message="All drivers failed when performing conversion"):
 class Converter:
     """Class for converting a single file to a Kerchunk reference object"""
 
-    def __init__(self, clogger, bypass_driver=False):
+    def __init__(self, clogger, bypass_driver=False, ctype=None):
         self.logger = clogger
-        self.ctype = None
+        self.ctype = ctype
         self.success = True
         self.bypass_driver = bypass_driver
 
-    def convert_to_zarr(self, nfile, **kwargs):
+    def convert_to_zarr(self, nfile, extension=False, **kwargs):
         """Perform conversion to zarr with exceptions for bypassing driver errors."""
         drivers = {
             'ncf3': self.ncf3_to_zarr,
             'hdf5': self.hdf5_to_zarr,
             'tif' : self.tiff_to_zarr
         }
+        if extension:
+            self.ctype=extension
         try:
             if self.ctype in drivers:
                 return drivers[self.ctype](nfile, **kwargs)
@@ -74,9 +76,9 @@ def __init__(self,
                  issave_meta=False, thorough=False, forceful=False, 
                  verb=0, mode=None, version_no='trial-',
                  concat_msg=CONCAT_MSG, bypass=BypassSwitch(), 
-                 groupID=None, limiter=None, dryrun=True):
+                 groupID=None, limiter=None, dryrun=True, ctype=None, fh=None, logid=None, **kwargs):
         """Initialise indexer for this dataset, set all variables and prepare for computation"""
-        super().__init__(init_logger(verb, mode, 'compute-serial'), bypass_driver=bypass.skip_driver)
+        super().__init__(init_logger(verb, mode, 'compute-serial', fh=fh, logid=logid), bypass_driver=bypass.skip_driver, ctype=ctype)
 
         self.logger.debug('Starting variable definitions')
 
@@ -561,8 +563,11 @@ def try_all_drivers(self, nfile, **kwargs):
     
     def load_temp_zattrs(self):
         self.logger.debug(f'Loading attributes')
-        with open(f'{self.cache}/temp_zattrs.json') as f:
-            zattrs = json.load(f)
+        try:
+            with open(f'{self.cache}/temp_zattrs.json') as f:
+                zattrs = json.load(f)
+        except FileNotFoundError:
+            zattrs = None
         if not zattrs:
             self.logger.debug('No attributes loaded from temp store')
             return None
@@ -575,7 +580,8 @@ def create_refs(self):
         - Combine metadata and global attributes into a single set
         - Coordinate combining and saving of data"""
         self.logger.info(f'Starting computation for components of {self.proj_code}')
-        refs, allzattrs = []
+        refs, allzattrs = [], []
+        partials = []
         zattrs = None
         use_temp_zattrs = True
 
@@ -585,10 +591,18 @@ def create_refs(self):
             ref = None
             if os.path.isfile(cache_ref) and not self.thorough:
                 self.logger.info(f'Loading refs: {x+1}/{len(self.listfiles)}')
-                ref = self.load_cached(cache_ref)
+                if os.path.isfile(cache_ref):
+                    with open(cache_ref) as f:
+                        ref = json.load(f)
             if not ref:
                 self.logger.info(f'Creating refs: {x+1}/{len(self.listfiles)}')
-                ref = self.try_all_drivers(nfile, **self.create_kwargs)
+                try:
+                    ref = self.try_all_drivers(nfile, **self.create_kwargs)
+                except KerchunkDriverFatalError as err:
+                    if len(refs) == 0:
+                        raise err
+                    else:
+                        partials.append(x)
                 use_temp_zattrs = False
             if ref:
                 allzattrs.append(ref['refs']['.zattrs'])
@@ -596,6 +610,9 @@ def create_refs(self):
                 cache_ref = f'{self.cache}/{x}.json'
                 self.save_individual_ref(ref, cache_ref)
 
+        if len(partials) > 0:
+            raise PartialDriverError(filenums=partials)
+
         if use_temp_zattrs:
             zattrs = self.load_temp_zattrs()
         if not zattrs:
diff --git a/pipeline/errors.py b/pipeline/errors.py
index a7d7908..403075c 100644
--- a/pipeline/errors.py
+++ b/pipeline/errors.py
@@ -24,6 +24,16 @@ def __init__(self, proj_code, groupdir):
     def save(self):
         upload_err(self.proj_code, self.groupdir, self.get_str())
 
+class PartialDriverError(KerchunkException):
+    """All drivers failed (NetCDF3/Hdf5/Tiff) for one or more files within the list"""
+    def __init__(self,filenums=None, verbose=0, proj_code=None, groupdir=None):
+        self.message = f"All drivers failed when performing conversion for files {filenums}"
+        super().__init__(proj_code, groupdir)
+        if verbose < 1:
+            self.__class__.__module__ = 'builtins'
+    def get_str(self):
+        return 'PartialDriverError'
+
 class KerchunkDriverFatalError(KerchunkException):
     """All drivers failed (NetCDF3/Hdf5/Tiff) - run without driver bypass to assess the issue with each driver type."""
     def __init__(self,verbose=0, proj_code=None, groupdir=None):
diff --git a/pipeline/init.py b/pipeline/init.py
index 7748415..df53f44 100644
--- a/pipeline/init.py
+++ b/pipeline/init.py
@@ -8,7 +8,7 @@
 import logging
 import glob
 
-from pipeline.logs import init_logger
+from pipeline.logs import init_logger, log_status
 
 config = {
     'proj_code': None,
@@ -143,7 +143,7 @@ def text_file_to_csv(args, logger, prefix=None):
     
     # Output completed csv setup part
 
-def make_dirs(args, logger):
+def make_directories(args, logger):
     """Set up directory structure for working directory"""
     logger.info('Creating project directories')
 
@@ -224,22 +224,43 @@ def make_dirs(args, logger):
             else:
                 logger.warn(f'{base_file} already exists - skipping')
 
-    logger.info(f'Exporting {len(proj_codes)} dataset config files')
+        # New directories and files required
+        if not os.path.isfile(f'{proj_dir}/status_log.csv'):
+            os.system(f'touch {proj_dir}/status_log.csv')
+        log_status('init', proj_dir, 'complete',logger)
+        
+        if not os.path.isdir(f'{proj_dir}/phase_logs'):
+            os.makedirs(f'{proj_dir}/phase_logs')
+        for phase in ['scan','compute','validate']:
+            if not os.path.isfile(f'{proj_dir}/phase_logs/{phase}.log'):
+                os.system(f'touch {proj_dir}/phase_logs/{phase}.log')
 
-    if args.dryrun:
-        logger.debug(f'DRYRUN: Skip writing {len(proj_codes)} project codes list {args.groupdir}/proj_codes_1.txt')
-    else:
-        with open(f'{args.groupdir}/proj_codes_1.txt','w') as f:
-            f.write('\n'.join(proj_codes))
+    logger.info(f'Created {len(proj_codes)*6} files, {len(proj_codes)*2} directories in group {args.groupID}')
+
+    if not os.path.isdir(args.groupdir):
+        os.makedirs(args.groupdir)
+
+    if not os.path.isdir(f'{args.groupdir}/proj_codes/'):
+        os.makedirs(f'{args.groupdir}/proj_codes/')
+
+    with open(f'{args.groupdir}/proj_codes/main.txt','w') as f:
+        f.write('\n'.join(proj_codes))
+    if not os.path.isfile(f'{args.groupdir}/blacklist_codes.txt'):
+        os.system(f'touch {args.groupdir}/blacklist_codes.txt')
 
     logger.info(f'Written as group ID: {args.groupID}')
 
-def init_config(args):
+def init_config(args, fh=None, logid=None, **kwargs):
     """Main configuration script, load configurations from input sources"""
 
-    logger = init_logger(args.verbose, args.mode, 'init')
+    logger = init_logger(args.verbose, args.mode, 'init',fh=fh, logid=logid)
     logger.info('Starting initialisation')
 
+    if not args.input.startswith('/'):
+        pwd = os.getcwd()
+        logger.info(f'Copying input file from relative path - resolved to {pwd}')
+        args.input = os.path.join(pwd, args.input)
+
     groupID = None
     if hasattr(args, 'groupID'):
         groupID = getattr(args,'groupID')
@@ -264,7 +285,7 @@ def init_config(args):
                 os.makedirs(args.groupdir)
 
             os.system(f'cp {args.input} {new_csv}')
-        make_dirs(args, logger)
+        make_directories(args, logger)
 
     else:
         logger.debug('Starting single project initialisation')
diff --git a/pipeline/logs.py b/pipeline/logs.py
index fc0d9a5..de253c8 100644
--- a/pipeline/logs.py
+++ b/pipeline/logs.py
@@ -59,6 +59,7 @@ def error(self, message):
 
 def reset_file_handler(logger, verbose, new_log):
     logger.handlers.clear()
+    verbose = min(verbose, len(levels)-1)
 
     ch = logging.StreamHandler()
     ch.setLevel(levels[verbose])
@@ -69,13 +70,16 @@ def reset_file_handler(logger, verbose, new_log):
 
     fh = logging.FileHandler(new_log)
     fh.setLevel(levels[verbose])
+    fh.setFormatter(formatter)
     logger.addHandler(fh)
 
     return logger
 
-def init_logger(verbose, mode, name, fh=None):
+def init_logger(verbose, mode, name, fh=None, logid=None):
     """Logger object init and configure with formatting"""
     verbose = min(verbose, len(levels)-1)
+    if logid != None:
+        name = f'{name}_{logid}'
 
     logger = logging.getLogger(name)
     logger.setLevel(levels[verbose])
@@ -90,6 +94,8 @@ def init_logger(verbose, mode, name, fh=None):
     if fh:
         handle = logging.FileHandler(fh)
         handle.setLevel(levels[verbose])
+        handle.setFormatter(formatter)
         logger.addHandler(handle)
+        print(fh)
 
     return logger
\ No newline at end of file
diff --git a/pipeline/scan.py b/pipeline/scan.py
index c9869ae..016ccda 100644
--- a/pipeline/scan.py
+++ b/pipeline/scan.py
@@ -51,6 +51,8 @@ def trial_kerchunk(args, nfile: str, ctype: str, logger):
     kwargs = {}
     supported_extensions = ['ncf3','hdf5','tif']
 
+    usetype = ctype
+
     logger.debug(f'Attempting conversion for 1 {ctype} extension')
     t1 = datetime.now()
     tdict = quickConvert.convert_to_zarr(nfile, ctype, **kwargs)
@@ -60,18 +62,20 @@ def trial_kerchunk(args, nfile: str, ctype: str, logger):
         # Try the other ones
         extension = supported_extensions[ext_index]
         logger.debug(f'Attempting conversion for {extension} extension')
+
         if extension != ctype:
             t1 = datetime.now()
             tdict = quickConvert.convert_to_zarr(nfile, extension, **kwargs)
             t_len = (datetime.now()-t1).total_seconds()
+            usetype = extension
         ext_index += 1
     
     if not tdict:
         logger.error('Scanning failed for all drivers, file type is not Kerchunkable')
         raise KerchunkDriverFatalError
     else:
-        logger.info(f'Scan successful with {ctype} driver')
-        return tdict, ctype, t_len
+        logger.info(f'Scan successful with {usetype} driver')
+        return tdict, usetype, t_len
     
 def load_from_previous(args, cache_id, logger):
     cachefile = f'{args.proj_dir}/cache/{cache_id}.json'
@@ -83,11 +87,10 @@ def load_from_previous(args, cache_id, logger):
     else:
         return None
         
-def perform_scan(args, testfile: str, ctype: str, logger, savecache=False, cache_id=None, thorough=False):
+def perform_scan(args, testfile: str, ctype: str, logger, savecache=True, cache_id=None, thorough=False):
     """Map to kerchunk data and perform calculations on test netcdf file."""
     if cache_id and not thorough:
         refs = load_from_previous(args, cache_id, logger)
-        ctype = None
         time = 0
         if not refs:
             refs, ctype, time = trial_kerchunk(args, testfile, ctype, logger)
@@ -95,18 +98,18 @@ def perform_scan(args, testfile: str, ctype: str, logger, savecache=False, cache
         refs, ctype, time = trial_kerchunk(args, testfile, ctype, logger)
     if not refs:
         return None, None, None, None, None
-    logger.debug(f'Starting Analysis of references')
+
+    logger.debug('Starting Analysis of references')
 
     # Perform summations, extract chunk attributes
     sizes = []
     vars = {}
     chunks = 0
-    if 'refs' in refs:
-        refs = refs['refs']
-    for chunkkey in refs.keys():
-        if len(refs[chunkkey]) >= 2:
+    kdict = refs['refs']
+    for chunkkey in kdict.keys():
+        if len(kdict[chunkkey]) >= 2:
             try:
-                sizes.append(int(refs[chunkkey][2]))
+                sizes.append(int(kdict[chunkkey][2]))
                 chunks += 1
             except ValueError:
                 pass
@@ -114,10 +117,10 @@ def perform_scan(args, testfile: str, ctype: str, logger, savecache=False, cache
             var = chunkkey.split('/')[0]
             chunksize = 0
             if var not in vars:
-                if type(refs[chunkkey]) == str:
-                    chunksize = json.loads(refs[chunkkey])['chunks']
+                if type(kdict[chunkkey]) == str:
+                    chunksize = json.loads(kdict[chunkkey])['chunks']
                 else:
-                    chunksize = dict(refs[chunkkey])['chunks']
+                    chunksize = dict(kdict[chunkkey])['chunks']
                 vars[var] = chunksize
 
     # Save refs individually within cache.
@@ -228,23 +231,23 @@ def scan_dataset(args, files: list, logger):
     std_vars   = None
     std_chunks = None
     ctypes   = []
+
+    scanfile = files[0]
+    if '.' in scanfile:
+        ctype = f'.{scanfile.split(".")[-1]}'
+    else:
+        ctype = 'ncf3'
+
     filecap = min(100,len(files))
     while not escape and len(cpf) < trial_files:
         logger.info(f'Attempting scan for file {count+1} (min 5, max 100)')
         # Add random file selector here
-
         scanfile = files[count]
-        if '.' in scanfile:
-            extension = f'.{scanfile.split(".")[-1]}'
-        else:
-            extension = 'ncf3'
-
         try:
             # Measure time and ensure job will not overrun if it can be prevented.
-            volume, chunks_per_file, varchunks, ctype, time = perform_scan(args, scanfile, extension, logger, 
+            volume, chunks_per_file, varchunks, ctype, time = perform_scan(args, scanfile, ctype, logger, 
                                                                            savecache=True, cache_id=str(count),
                                                                            thorough=args.quality)
-
             vars = sorted(list(varchunks.keys()))
             if not std_vars:
                 std_vars = vars
@@ -323,20 +326,20 @@ def scan_dataset(args, files: list, logger):
         logger.info(f'Written output file {proj_code}/detail-cfg.json')
     logger.info('Performing concatenation attempt with minimal files')
     try:
-        assemble_trial_concatenation(args, logger)
+        assemble_trial_concatenation(args, ctype, logger)
     except Exception as err:
         logger.error('Error in concatenating files')
         raise err
 
-def assemble_trial_concatenation(args, logger):
+def assemble_trial_concatenation(args, ctype, logger):
 
     cfg_file    = f'{args.proj_dir}/base-cfg.json'
     detail_file = f'{args.proj_dir}/detail-cfg.json'
 
     idx_trial = Indexer(args.proj_code, cfg_file=cfg_file, detail_file=detail_file, 
-                    workdir=args.workdir, issave_meta=True, thorough=args.quality, forceful=args.forceful,
+                    workdir=args.workdir, issave_meta=True, thorough=False, forceful=args.forceful,
                     verb=args.verbose, mode=args.mode,
-                    bypass=args.bypass, groupID=args.groupID, limiter=2)
+                    bypass=args.bypass, groupID=args.groupID, limiter=2, ctype=ctype)
     
     idx_trial.create_refs()
     with open(detail_file,'w') as f:
@@ -344,10 +347,10 @@ def assemble_trial_concatenation(args, logger):
     logger.debug('Collected new details into detail-cfg.json')
 
 
-def scan_config(args):
+def scan_config(args, fh=None, logid=None, **kwargs):
     """Configure scanning and access main section"""
 
-    logger = init_logger(args.verbose, args.mode, 'scan')
+    logger = init_logger(args.verbose, args.mode, 'scan',fh=fh, logid=logid)
     logger.debug(f'Setting up scanning process')
 
     cfg_file = f'{args.proj_dir}/base-cfg.json'
diff --git a/pipeline/utils.py b/pipeline/utils.py
index b8ddf77..33a8ba3 100644
--- a/pipeline/utils.py
+++ b/pipeline/utils.py
@@ -27,9 +27,9 @@ def format_str(string: str, length: int):
     return string[:length]
 
 class BypassSwitch:
-    def __init__(self, switch='DBSCM'):
+    def __init__(self, switch='DBSCMR'):
         if switch.startswith('+'):
-            switch = 'DBSCM' + switch[1:]
+            switch = 'DBSCMR' + switch[1:]
         self.switch = switch
         if type(switch) == str:
             switch = list(switch)
@@ -71,8 +71,5 @@ def mem_to_val(value):
 
 def get_codes(group, workdir, filename):
     """Returns a list of the project codes given a filename (repeat id)"""
-    try:
-        with open(f'{workdir}/groups/{group}/{filename}.txt') as f:
-            return [r.strip() for r in f.readlines()]
-    except FileNotFoundError:
-        return []
\ No newline at end of file
+    with open(f'{workdir}/groups/{group}/{filename}.txt') as f:
+        return [r.strip() for r in f.readlines()]
\ No newline at end of file
diff --git a/pipeline/validate.py b/pipeline/validate.py
index 03f83e9..a29b369 100644
--- a/pipeline/validate.py
+++ b/pipeline/validate.py
@@ -179,7 +179,9 @@ def open_kerchunk(kfile: str, logger, isparq=False, remote_protocol='file'):
             try:
                 ds = xr.open_zarr(mapper, consolidated=False, decode_times=True)
             except OverflowError:
-                ds = None  
+                ds = None
+            except Exception as err:
+                raise MissingKerchunkError(message=f'Failed to open kerchunk file {kfile}')
         if not ds:
             raise ChunkDataError
         logger.debug('Successfully opened Kerchunk with virtual xarray ds')
@@ -453,6 +455,14 @@ def validate_selection(xvariable, kvariable, vname: str, divs: int, currentdiv:
         logger.debug(f'Skipping to full selection (1 division) for {vname}')
         currentdiv = 1
 
+    # Need to check datatype and do something special here for strings
+    if xbox.dtype == 'object':
+        # Skip all other comparison steps for data types
+        if not np.array_equal(xbox, kbox):
+            raise ValidationError
+        else:
+            return None
+
     try_multiple = 0
     knan, xnan = False, True
     # Attempt nan checking multiple times due to network issues.
@@ -603,12 +613,12 @@ def attempt_timestep(args, xobj, kobj, step, nfiles, logger, concat_dims={}, ful
     except Exception as err:
         raise err
 
-def validate_dataset(args):
+def validate_dataset(args, fh=None, logid=None, **kwargs):
     """Perform validation steps for specific dataset defined here
      - Determine the number of NetCDF files in total
      - Run validation for a minimum subset of those files
     """
-    logger = init_logger(args.verbose, args.mode,'validate')
+    logger = init_logger(args.verbose, args.mode,'validate', fh=fh, logid=logid)
     logger.info(f'Starting tests for {args.proj_code}')
 
     if hasattr(args, 'backtrack'):

From c03aeb3629b96067841f50365e8dbad1a01c00df Mon Sep 17 00:00:00 2001
From: dwest77 <daniel.westwood@stfc.ac.uk>
Date: Fri, 8 Mar 2024 15:52:08 +0000
Subject: [PATCH 45/45] Added job log system support and traceback within error
 logs

---
 single_run.py | 60 ++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 40 insertions(+), 20 deletions(-)

diff --git a/single_run.py b/single_run.py
index c870ea1..53c7ec3 100644
--- a/single_run.py
+++ b/single_run.py
@@ -9,24 +9,25 @@
 import json
 import logging
 from datetime import datetime
+import traceback
 
 from pipeline.logs import init_logger, reset_file_handler, log_status
-from pipeline.utils import get_attribute, BypassSwitch
+from pipeline.utils import get_attribute, BypassSwitch, get_codes
 from pipeline.errors import ProjectCodeError, MissingVariableError, BlacklistProjectCode
 
-def run_init(args, logger):
+def run_init(args, logger, fh=None, **kwargs):
     """Start initialisation for single dataset"""
     from pipeline.init import init_config
     logger.info('Starting init process')
-    return init_config(args)
+    return init_config(args, fh=fh, **kwargs)
 
-def run_scan(args, logger):
+def run_scan(args, logger, fh=None,**kwargs):
     """Start scanning process for individual dataset"""
     from pipeline.scan import scan_config
     logger.info('Starting scan process')
-    return scan_config(args)
+    return scan_config(args,fh=fh, **kwargs)
 
-def run_compute(args, logger):
+def run_compute(args, logger, fh=None, logid=None, **kwargs):
     """Setup computation parameters for individual dataset"""
     from pipeline.compute.serial_process import Indexer
 
@@ -66,16 +67,17 @@ def run_compute(args, logger):
         return Indexer(args.proj_code, cfg_file=cfg_file, detail_file=detail_file, 
                 workdir=args.workdir, issave_meta=True, thorough=args.quality, forceful=args.forceful,
                 verb=args.verbose, mode=args.mode,
-                version_no=version_no, concat_msg=concat_msg, bypass=args.bypass, groupID=args.groupID, dryrun=args.dryrun).create_refs()
+                version_no=version_no, concat_msg=concat_msg, bypass=args.bypass, groupID=args.groupID, 
+                dryrun=args.dryrun, fh=fh, logid=logid).create_refs()
     else:
         logger.error('Output file already exists and there is no plan to overwrite')
         return None
 
-def run_validation(args, logger):
+def run_validation(args, logger, fh=None, **kwargs):
     """Start validation of single dataset"""
     from pipeline.validate import validate_dataset
     logger.info('Starting validation process')
-    return validate_dataset(args)
+    return validate_dataset(args, fh=fh, **kwargs)
 
 drivers = {
     'init':run_init,
@@ -84,11 +86,11 @@ def run_validation(args, logger):
     'validate': run_validation
 }
 
-def get_proj_code(groupdir: str, pid, repeat_id, subset=0, id=0):
+def get_proj_code(workdir: str, group: str, pid, repeat_id, subset=0, id=0):
     """Get the correct code given a slurm id from a group of project codes"""
     try:
-        with open(f'{groupdir}/proj_codes_{repeat_id}.txt') as f:
-            proj_code = f.readlines()[int(id)*subset + pid].strip()
+        proj_codes = get_codes(group, workdir, f'proj_codes/{repeat_id}')
+        proj_code = proj_codes[int(id)*subset + pid]
     except:
         raise ProjectCodeError
     return proj_code
@@ -114,12 +116,13 @@ def main(args):
 
     if os.getenv('SLURM_ARRAY_JOB_ID'):
         jobid = os.getenv('SLURM_ARRAY_JOB_ID')
+        taskid = os.getenv('SLURM_ARRAY_TASK_ID')
 
     args.workdir  = get_attribute('WORKDIR', args, 'workdir')
     if args.groupID:
         args.groupdir = f'{args.workdir}/groups/{args.groupID}'
         if jobid != '':
-            fh = f'{args.groupdir}/errs/{jobid}_{args.phase}_{args.repeat_id}.log'
+            fh = f'{args.groupdir}/errs/{jobid}_{taskid}_{args.phase}_{args.repeat_id}.log'
 
     logger = init_logger(args.verbose, args.mode, 'main', fh=fh)
 
@@ -156,7 +159,7 @@ def main(args):
 
                 proj_code = int(args.proj_code)
 
-                args.proj_code = get_proj_code(args.groupdir, proj_code, args.repeat_id, subset=args.subset, id=id)
+                args.proj_code = get_proj_code(args.workdir, args.groupID, proj_code, args.repeat_id, subset=args.subset, id=id)
                 args.proj_dir = f'{args.workdir}/in_progress/{args.groupID}/{args.proj_code}'
 
                 # Get rid of this section if necessary - redo to put code list elsewhere
@@ -165,7 +168,7 @@ def main(args):
                     if not os.path.isdir(f'{errs_dir}/{jobid}_{args.repeat_id}'):
                         os.makedirs(f'{errs_dir}/{jobid}_{args.repeat_id}')
 
-                    proj_code_file = f'{args.workdir}/groups/{args.groupID}/proj_codes_{args.repeat_id}.txt'
+                    proj_code_file = f'{args.workdir}/groups/{args.groupID}/proj_codes/{args.repeat_id}.txt'
 
                     if not os.path.isfile(f'{errs_dir}/{jobid}_{args.repeat_id}/proj_codes.txt'):
                         os.system(f'cp {proj_code_file} {errs_dir}/{jobid}_{args.repeat_id}/proj_codes.txt')
@@ -186,13 +189,19 @@ def main(args):
 
                 # Refresh log for this phase
                 proj_log = f'{args.proj_dir}/phase_logs/{args.phase}.log'
-                os.system(f'rm {proj_log}')
+                if not os.path.isdir(f'{args.proj_dir}/phase_logs'):
+                    os.makedirs(f'{args.proj_dir}/phase_logs')
+                if jobid != '':
+                    if os.path.isfile(proj_log):
+                        os.system(f'rm {proj_log}')
+                    if os.path.isfile(fh):
+                        os.system(f'rm {fh}')
                 if not args.bypass.skip_report:
                     log_status(args.phase, args.proj_dir, 'pending', logger, jobid=jobid, dryrun=args.dryrun)
                 try:
                     if jobid != '':
                         logger = reset_file_handler(logger, args.verbose, proj_log)
-                        drivers[args.phase](args, logger)
+                        drivers[args.phase](args, logger, fh=proj_log, logid=id)
                         logger = reset_file_handler(logger, args.verbose, fh)
                     else:
                         drivers[args.phase](args, logger)
@@ -200,11 +209,22 @@ def main(args):
                     if not args.bypass.skip_report:
                         log_status(args.phase, args.proj_dir, 'complete', logger, jobid=jobid, dryrun=args.dryrun)
                 except Exception as err:
+                    # Add error traceback
+                    tb = traceback.format_exc()
+                    logger.error(tb)
+
                     if jobid != '':
                         logger = reset_file_handler(logger, args.verbose, fh)
                     fails += 1
                     if not args.bypass.skip_report:
-                        log_status(args.phase, args.proj_dir, err.get_str(), logger, jobid=jobid, dryrun=args.dryrun)
+                        try:
+                            status = err.get_str()
+                        except AttributeError:
+                            status = type(err).__name__ + ' ' + str(err)
+                            
+                        # Messes up the csv if there are commas
+                        status = status.replace(',','-')
+                        log_status(args.phase, args.proj_dir, status, logger, jobid=jobid, dryrun=args.dryrun)
                     else:
                         raise err
             else:
@@ -226,7 +246,7 @@ def main(args):
     parser.add_argument('-v','--verbose', dest='verbose', action='count', default=0, help='Print helpful statements while running')
     parser.add_argument('-d','--dryrun',  dest='dryrun',  action='store_true', help='Perform dry-run (i.e no new files/dirs created)' )
     parser.add_argument('-Q','--quality', dest='quality', action='store_true', help='Quality assured checks - thorough run')
-    parser.add_argument('-b','--bypass-errs', dest='bypass', default='FDSC', help=BypassSwitch().help())
+    parser.add_argument('-b','--bypass-errs', dest='bypass', default='DBSCMR', help=BypassSwitch().help())
     parser.add_argument('-B','--backtrack', dest='backtrack', action='store_true', help='Backtrack to previous position, remove files that would be created in this job.')
 
     # Environment variables
@@ -239,7 +259,7 @@ def main(args):
     parser.add_argument('-t','--time-allowed',dest='time_allowed',  help='Time limit for this job')
     parser.add_argument('-M','--memory', dest='memory', default='2G', help='Memory allocation for this job (i.e "2G" for 2GB)')
     parser.add_argument('-s','--subset',    dest='subset',    default=1,   type=int, help='Size of subset within group')
-    parser.add_argument('-r','--repeat_id', dest='repeat_id', default='1', help='Repeat id (1 if first time running, <phase>_<repeat> otherwise)')
+    parser.add_argument('-r','--repeat_id', dest='repeat_id', default='main', help='Repeat id (1 if first time running, <phase>_<repeat> otherwise)')
 
     # Specialised
     parser.add_argument('-n','--new_version', dest='new_version',   help='If present, create a new version')