merging/cleaning

merging/cleaning

Exercises_Modulo_Download_DATA/Download_Data.py

@@ -1,19 +1,34 @@
 # -*- coding: utf-8 -*-
 """
 Created on Sun Feb  2 15:11:03 2020
+Expanded and cleaned on Tue Jan  2 17:09:08 2024
 
 @author: mendez
 """
 
-# This is a script to download the data for the exercises 3,4,5.
+# This is a script to download the data for the all exercises in this repo.
 #  Make sure you have installed request
 #  
 # The data is available at https://osf.io/5na4h/
-# You can access the data as follows
+# This collects the portion of script you should copy paste onto your file for downloading the data
+# (thus forgive me for the multiple import of the same packages)
+
+# NOTE: for each exercise, the data is stored in a folder (EX1,EX2, EX3,....)
+
+# However, the numbering differes within the brunches. For example, the original 
+# matlab version was build from exercises 1,2,3,4,5. Exercise 6 is not used for MODULO but as part of 
+# a different work on nonlinear decompositions. Exercise 7 and 8 are used in the examples of MODULO v2.
+
+# To handle the different numbering, in the examples from modulo the variable "FOLDER" is renamed accordingly.
 
 
 #%% Exercise 1
+# This exercise presents an example of the use of mPOD and POD on a 1D scalar dataset.
+# This dataset is the velocity profile of a pulsating Poiseuille Flow. 
+# More info on the test case is presented in Mendez & Buchlin, TN215, 10.13140/RG.2.2.32209.02404 '''
+import os 
 import urllib.request
+FOLDER='Ex_1'
 print('Downloading Data for Ex 1...')
 url = 'https://osf.io/zqrp5/download'
 urllib.request.urlretrieve(url, 'Ex_1_1D_Analytical.zip')
@@ -22,14 +37,22 @@
 from zipfile import ZipFile
 String='Ex_1_1D_Analytical.zip'
 zf = ZipFile(String,'r')
-zf.extractall('Ex_1_1D_Analytical')
+zf.extractall('./')
 zf.close()
-print('Done!')
+os.rename('Ex_1_1D_Analytical', FOLDER) # rename the data flolder to FOLDER
+os.remove(String) # Delete the zip file with the data 
+print('Data set unzipped and ready ! ')
 
 
 
 #%% Exercise 2
+# This exercise presents an example of the use of mPOD and POD on a 2D scalar dataset.
+# This dataset is constructed by adding three modes of similar energy content, to challenge the POD.
+# Data from Section 4 of https://arxiv.org/abs/1804.09646
+
+import os
 import urllib.request
+FOLDER='Ex_2'
 print('Downloading Data for Ex 2...')
 url = 'https://osf.io/jhfmn/download'
 urllib.request.urlretrieve(url, 'Ex_2_Synthetic.zip')
@@ -38,13 +61,23 @@
 from zipfile import ZipFile
 String='Ex_2_Synthetic.zip'
 zf = ZipFile(String,'r')
-zf.extractall('Ex_2_Synthetic')
+zf.extractall('./')
 zf.close()
-print('Done!')
+os.rename('Ex_2_Synthetic', FOLDER) # rename the data flolder to FOLDER
+os.remove(String) # Delete the zip file with the data 
+print('Data set unzipped and ready ! ')
+
 
 
 #%% Exercise 3
+
+# This exercise presents an example of the use of mPOD and POD on a 2D scalar dataset.
+# This dataset is constructed from the CFD simulation of the 2D Navier-stokes problem in the vorticity-stream function
+# formulation. Data from Section 5 of https://arxiv.org/abs/1804.09646
+
+import os
 import urllib.request
+FOLDER='Ex_3'
 print('Downloading Data for Ex 3...')
 url = 'https://osf.io/zgujk/download'
 urllib.request.urlretrieve(url, 'Ex_3_CFD_Vortices.zip')
@@ -53,12 +86,24 @@
 from zipfile import ZipFile
 String='Ex_3_CFD_Vortices.zip'
 zf = ZipFile(String,'r')
-zf.extractall('Ex_3_CFD_Vortices')
+zf.extractall(FOLDER)
 zf.close()
-print('Done!')
+os.remove(String) # Delete the zip file with the data 
+print('Data set unzipped and ready ! ')
+
 
 #%% Exercise 4
+
+# This exercise presents an example of the use of mPOD and POD on a 2D vector dataset.
+# This dataset is the TR-PIV of an impinging jet.
+# More info on the test case is presented in Sec. 6 of https://arxiv.org/abs/1804.09646
+# About the data from the Tr-PIV analysis: the sampling frequency is 2kHz.
+# Each file contains the X and Y coordinates (in mm) in the first two columns;
+#  the U and V components in m/s in the third and fourth.
+
+import os
 import urllib.request
+FOLDER='Ex_4'
 print('Downloading Data for Ex 4...')
 url = 'https://osf.io/c28de/download'
 urllib.request.urlretrieve(url, 'Ex_4_TR_PIV_Jet.zip')
@@ -67,24 +112,127 @@
 from zipfile import ZipFile
 String='Ex_4_TR_PIV_Jet.zip'
 zf = ZipFile(String,'r')
-zf.extractall('Ex_4_TR_PIV_Jet')
-zf.close()
-print('Done!')
+zf.extractall('./')
+zf.close() 
+os.rename('Ex_4_TR_PIV_Jet', FOLDER) # rename the data flolder to FOLDER
+os.remove(String) # Delete the zip file with the data 
+print('Data set unzipped and ready ! ')
+
 
 
 #%% Exercise 5
+
+#This exercise presents an example of the use of mPOD and POD with memory saving options on a 2D vector dataset.
+# This dataset is the TR-PIV of the flow past a cylinder in transient conditions.
+# More info on the test case is presented in https://arxiv.org/abs/2001.01971
+# About the data from the from the Tr-PIV analysis: the sampling frequency is 3kHz.
+# MESH.dat contains the X and Y coordinates (in mm). Each file called Res*.dat containes the U and V components in m/s .
+
+import os
 import urllib.request
+FOLDER='Ex_5'
 print('Downloading Data for Ex 5...')
 url = 'https://osf.io/47ftd/download'
 urllib.request.urlretrieve(url, 'Ex_5_TR_PIV_Cylinder.zip')
 print('Download Completed! I prepare data Folder')
 # Unzip the file 
 from zipfile import ZipFile
 String='Ex_5_TR_PIV_Cylinder.zip'
+zf = ZipFile(String,'r'); 
+zf.extractall('./DATA_CYLINDER'); zf.close()
+os.rename('DATA_CYLINDER', FOLDER) # rename the data flolder to FOLDER
+os.remove(String) # Delete the zip file with the data 
+print('Data set unzipped and ready ! ')
+
+#%% Exercise 6
+
+# This is a database of synthetic PIV images taken from https://doi.org/10.1016/j.expthermflusci.2016.08.021
+# It contains two folders: X and X_P, both containing 400 images (200 PIV pairs).
+# X contains the noisy images while X_P contains the ideal ones (noise free) linked to these.
+# In the notation of the paper, X_B=X-X_P is the background one would ideally remove.
+
+FOLDER='Ex_6'
+# First we unzip the file 
+import urllib.request
+print('Downloading Data for Ex 6...')
+url = 'https://osf.io/g7asz/download'
+urllib.request.urlretrieve(url, 'Ex_6_PIV_Images_Denoising.zip ')
+print('Download Completed! I prepare data Folder')
+# Unzip the file 
+from zipfile import ZipFile
+String='Ex_6_PIV_Images_Denoising.zip'
 zf = ZipFile(String,'r')
-zf.extractall('Ex_5_TR_PIV_Cylinder')
-zf.close()
-print('Done!')
+zf.extractall('./')
+zf.close() 
+os.rename('Ex_6_PIV_Images_Denoising', FOLDER) # rename the data flolder to FOLDER
+os.remove(String) # Delete the zip file with the data 
+print('Data set unzipped and ready ! ')
+
+
+
+#%% Exercise 7
+# This database collects the velocity field from a 2D simulations of the flow past a cylinder.
+# The dataset is organized in 4 txt files. Some key information: the cylinder has a diameter of 15mm, in an overly
+# large domain of 300 x 600 mm. The simulations were carried out in Openfoam then exported in a regulard grid.
+# This was part of the STP of Denis Dumoulin (see VKI Library for more info).
+
+# The inlet velocity is 10m/s, with a TI of 5%. The sampling frequency in the data is Fs=100 Hz.
+
+# It is important to note that the dataset contains NANs in the location of the 
+# cylinder. These can be safely ignored using numpy's nan_to_num, which 
+# replaces the Nans with zeros.
+import os 
+import urllib.request
+from zipfile import ZipFile
+
+FOLDER='Ex_7'
+
+url = 'https://osf.io/emgv2/download'
+urllib.request.urlretrieve(url, 'Ex_7_2D_CFD.zip')
+print('Download Completed! I prepare data Folder')
+# Unzip the file 
+String='Ex_7_2D_CFD.zip'
+zf = ZipFile(String,'r')
+zf.extractall('./')
+zf.close() 
+os.rename('Ex_7_2D_Cylinder_CFD', FOLDER) # rename the data flolder to FOLDER
+os.remove(String) # Delete the zip file with the data 
+print('Data set unzipped and ready ! ')
+
+
+
+#%% Exercise 8
+
+# This database collects a portion of the domain in a 3D LES simulation of a turbulent impinging jet.
+# The nozzle has a width of D=XX mm and is placed at a distance Z=XX  from the wall.
+# The mean velocity is approximately U= XXX m/s, resulting in a Reynolds number Re= U D/nu = XXXX
+# The dataset is sampled with dt = 5e-05 s.
+
+# Note: To visualize this data (VTK format) it is strongly adviced to use 
+# pyvista (# https://docs.pyvista.org/version/stable/examples/99-advanced/openfoam-tubes.html)
+
+
+
+import os 
+import urllib.request
+from zipfile import ZipFile
+
+FOLDER='Ex_8'
+
+url = 'https://osf.io/aqkc8/download'
+urllib.request.urlretrieve(url, 'Ex_8_2D_Impinging_JET_CFD.zip')
+print('Download Completed! I prepare data Folder')
+# Unzip the file 
+String='Ex_8_2D_Impinging_JET_CFD.zip'
+zf = ZipFile(String,'r')
+zf.extractall('./')
+zf.close() 
+os.rename('Ex_8_2D_Impinging_JET_CFD', FOLDER) # rename the data flolder to FOLDER
+os.remove(String) # Delete the zip file with the data 
+print('Data set unzipped and ready ! ')
+
+
+
 
 
 

Exercises_Modulo_Download_DATA/readme.txt

@@ -0,0 +1,8 @@
+
+
+running "Download_Data" you will download all the data for
+the exercises in this repo. That's about 2.8 GB of data.
+
+However, this file is not meant to be used that way.
+Instead, you are supposed to copy the portion of code related to the 
+download of the dataset of your interest and paste it into your script.

modulo_python_package/modulo_vki/docs/build/html/.buildinfo

@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 94589ef51073681e8dea51f4867f5d3b
+tags: 645f666f9bcd5a90fca523b33c5a78b7