From b44ffeefc2c7b6d26638f0b78205a088df9e850a Mon Sep 17 00:00:00 2001 From: nacloos Date: Fri, 8 Mar 2024 14:01:27 -0500 Subject: [PATCH] update --- demo.html | 936 ++----------------------------- example_neurogym_keras.html | 476 +++------------- example_neurogym_keras.ipynb | 3 +- example_neurogym_pytorch.html | 483 +++------------- example_neurogym_pytorch.ipynb | 5 +- example_neurogym_rl.html | 489 +++------------- tags.html | 312 ++++------- understanding_neurogym_task.html | 518 +++-------------- wrappers.html | 655 +++++++++------------ 9 files changed, 727 insertions(+), 3150 deletions(-) diff --git a/demo.html b/demo.html index e6a8715..bf42ca8 100644 --- a/demo.html +++ b/demo.html @@ -1,966 +1,128 @@ - - - - + - - - - - Exploring NeuroGym tasks — neurogym documentation - - - - - - - - - - - - - - - - - - - + + + + <no title> — neurogym documentation + + + + + + + + + + + + + + + + - - - - - - - - - - + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/example_neurogym_keras.html b/example_neurogym_keras.html index 53bbce3..36752ae 100644 --- a/example_neurogym_keras.html +++ b/example_neurogym_keras.html @@ -1,99 +1,62 @@ - - - - + - - - - - Keras example of supervised learning a NeuroGym task — neurogym documentation - + - - - - + + Keras example of supervised learning a NeuroGym task — neurogym documentation + + + + + - + - - - - - - - - - + + + + + + + + - - - - - - - - - - + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/example_neurogym_keras.ipynb b/example_neurogym_keras.ipynb index 450e5c7..2a1abfd 100644 --- a/example_neurogym_keras.ipynb +++ b/example_neurogym_keras.ipynb @@ -247,7 +247,6 @@ } ], "source": [ - "# TODO: Make this into a function in neurogym\n", "perf = 0\n", "num_trial = 200\n", "for i in range(num_trial):\n", @@ -278,7 +277,7 @@ "outputs": [ { "data": { - "image/png": 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t+/NJLr6I8edVf4ymJpaAjTGmjo4ca0PXmPsZ2TXgdpd1UqcELCJpItL3jGo0xph6LvuHHfTu0JSYmDMbeKrGBCwin4pIExFpDqwGXhCRP5xRrcYYU0+dKCzgw0PXsV9eOeN11WYPuKmqHgOux7mJ5hDg0jOu2Rhj6qHFm1agUszADn3OeF21ScBx7t2KJwB/P+MaG6h9+/bxk5/8hK5duzJgwACGDRvG22+/HfE4MjIyyMvLC5j+u9/9Lqj1LViwoMJQmb4DBxlzNvpwizN+zI/Ou+iM11WbBDwT+ADYqqorRaQrELphxBoAVeXaa69l5MiRbN++nVWrVvHaa6+Rm5sbsGzZJcSRVlUCVlVKS6u+l5V/AjbmbJe9O5tYUhmecebnudeYgFX1DVXtq6p3ua+3q+oNZ1xzA7J06VISEhKYOnVq+bTOnTvzi1/8AnDG/x0/fjyjR49mzJgxqCrTp0+nd+/e9OnTh3nznDuqfvrpp+UD7gBMmzaNOXPmAM6e7YMPPkj//v3p06dP+aDvBw8e5PLLL6dXr17ceeedVHZhzYwZMzh16hT9+vVj0qRJ5OTk0KNHD2699VZ69+7Nzp07SU1NLV9+/vz5TJ48mS+++IJ33nmH6dOn069fP7Zt2wY4Q2AOHjyY7t27849//CO0G9OYKPfPY+tondiTmJgzP4msxmvoRKQV8G9Ahu/yqnrHGdceJhfPuThg2oReE7hr0F3kF+Vz1atXBcyf3G8yk/tNJi8/jxtfv7HCvE8nf1ptfevXr6d///7VLrN69WrWrVtH8+bNefPNN1mzZg1r164lLy+PQYMG1Wos35YtW7J69WqefvppHn/8cWbPns1DDz3EiBEjeOCBB1i0aBEvvvhiQLlZs2bx5JNPsmbNGgBycnLYsmULc+fOZejQoVXWN3z4cMaPH8+4ceO48cZ/bZPi4mK+/vprFi9ezEMPPcTHH39cY+zGNAQFRSUkFVzHFb0yQrK+2qTwhUBT4GNgkc/DVOHuu+8mMzOTQYP+dZXMZZddVj4c5WeffcbNN99MbGwsbdq0YdSoUaxcubLG9V5//fVAxeEoly9fzk9/+lMArr76atLS0moVY+fOnatNvnWNw5izwaa9x2lUPJIf97k2JOurzSgSyap6X82LRY/q9liT45Ornd8yuWWNe7z+evXqxZtvvln++qmnniIvL6/CgOspKSk1ricuLq5Cf6z/nYUTExOBisNRBss/Ht8badZ0R+NQxmFMffL+ppWclm307nBJSNZXmz3gv4tI4G92U2706NEUFBTwzDPPlE8rG4KyMhdddBHz5s2jpKSEAwcOsHz5cgYPHkznzp3ZsGEDhYWFHDlyhCVLltRY98iRI/nb3/4GwHvvvcfhw4crXS4+Pp6ioqIq19OmTRs2btxIaWlphbM3qho605iz0asbnuJA0gO0b5oUkvXVJgHfg5OEC0TkuPuo/T1/zgIiwoIFC1i2bBldunRh8ODB3HbbbTzyyCOVLn/dddfRt29fMjMzGT16NI8++iht27alY8eOTJgwgd69ezNhwgQuuOCCGut+8MEHWb58Ob169eKtt96iU6dOlS43ZcoU+vbty6RJkyqdP2vWLMaNG8fw4cNp165d+fSJEyfy2GOPccEFF5QfhDPmbJVz7FvaJIXmABzYcJQmTOwzMQ3NwZPHaflYMy5Ln8qHdz5Vp7JnNByliIwXkcfdx7iaS5SXGysi34vIVhGZUcn8ySJyQETWuI87a7tuY4yJpIUbPgcpZUTGkJCtszanoc0CBgGvupPuEZELVfXXNZSLBZ4CLgNygZUi8o6q+p/VP09Vp9U9dGOMiZyPtzpXwF3Tc0TI1lmbsyCuAvqpaimAiMwFvgGqTcDAYJyr57a75V4DrgHssipjTL3TTC+lR0wqme27hmydte1JbubzvLb3YO4A+N4hMted5u8GEVknIvNFpGMt1x2gvvVlN2T2WZiGaPPeUkZ0ujik66xNAv5f4BsRmePu/a4CHg5R/e8CGaraF/gIqPTmSiIyRUSyRST7wIEDAfOTkpI4ePCgffGjgKpy8OBBkpJCc5qOMdFgz7HDrDo0m7bNj4R0vTV2Qahqloh8itMPDHCfqu6txbp3Ab57tOnuNN91H/R5ORt4tIoYngeeB+csCP/56enp5ObmUllyNpGXlJREenq612EYEzIL13/Gkfi/kpJyZUjXW2UCFpHzVHWTiJQNclA2tFd7EWmvqqtrWPdKoJuIdMFJvBOBn/jV0U5V97gvxwMb6/wOcC4y6NKlSzBFjTGmRku3fwXA+BAegIPq94D/A5gC/L6SeQqMrm7FqlosItNwhrKMBV5S1fUiMhPIVtV3gH8XkfFAMXAImFz3t2CMMeG1dt9qEmhFr7aVX+gUrCoTsKpOcZ9eqaoVBgcQkVp18KnqYmCx37QHfJ7/mprPpjDGGE/tPPEd7ZN7hXy9tTkI90UtpxljTIOz++gRCkoP0Kd19UPOBqO6PuC2OKeNNRKRC4Cy4bKaAMkhj8QYY6JQzoFiOha8zt2DQn9D+Or6gK/A6ZNNx+kHLkvAx4D/CnkkxhgThdbtOooQy+DO7UO+7ur6gOcCc0XkBlV9s6rljDGmIZu99n/RxiWkpVwd8nXXpg94gIiUXwknImki8j8hj8QYY6LQd0cWE58UeIPdUKhNAr5SVcsv/1DVwzjjQxhjTIO2LW8PhbqHvmE4AAe1S8CxIpJY9kJEGgGJ1SxvjDENwoL1zl2/L+kS3P0Ta1Kb0dBeBZaIyMs4B+ImU8WYDcYY05Asy1kBwPheob0CrkxtxoJ4RETWApfiXAH3AdA5LNEYY0wU2XfsNM1i+tE5rVVY1l/b4Sj34STfm3AuQQ5qzAZjjKlPYk9cyy3nvhS29Vd3IUZ34Gb3kQfMw7mHXGjux2yMMVFs/7ECdh8toG96bYdAr7vq9oA34eztjlPVEar6Z6AkbJEYY0wUeTH7LXITf0Zq6r6w1VFdAr4e2AN8IiIviMgY/nU1nDHGNGjLc1ZQIvsZdc55YaujygSsqgtUdSJwHvAJcC/QWkSeEZHLwxaRMcZEgQ1535Ac24l2TdLCVkeNB+FU9aSq/k1Vf4QzLsQ3wH1hi8gYY6LAnlPr6ZzaO6x11PYsCMC5Ck5Vn1fVMeEKyBhjvLZ293aKOES/tgPCWk+dErAxxpwNvtt9mNTiK/nReeHd17QEbIwxfvYdSqVV8d1c2+vCsNZjCdgYY/x8uWMz3Von0yghNqz1WAI2xhgfpaWlLNh9C3tjngx7XZaAjTHGR3buVoo5Sr+2/cJelyVgY4zx8e5GZwjKy7oND3tdloCNMcbH5z98DRrLuPPDMwawL0vAxhjjY9PBNTSJ60qzRilhr6s2A7IbY8xZQVVpVHgdwzsnR6S+sO4Bi8hYEfleRLaKyIxK5ieKyDx3/goRyTiT+p74aHPEykWyrmDL1YcYgy1nMXpbrqHGmHv4FCWn+nD9+dfWuWwwwpaARSQWeAq4EugJ3CwiPf0W+xlwWFXPBZ4AHjmTOv+0ZEvEykWyrmDL1YcYgy1nMXpbrqHGuGhjNgUx6+jZPvzdDxDeLojBwFZV3Q4gIq8B1wAbfJa5Bvhv9/l84EkREVXVula2dNMuTsZ8zv3v76kwvXOT7nRs3I2C4nyy930SUK5rU+d/wvzV3/PN/n8EzO/WrC9tUjpyrPAQ6/K+LJ9+MmY797+/h/PSLqBlcnsOFxxg/cGvA8r3bDGI5kmtyTu1h02HVpeXK9O35TCaJDZnX34uWw6vDSh/QeuLSIlvQpHs5v73ZwfMH9jmEpLiktl5fAs7jlX8j38yZjvvrOlFfGwiOUc3kXtiW0D5Ye3HEiuxbDvyHXtO7qjw3gThwg7ODbA3H17L/vyKt+aOkziGtr8CgE2HVnEy5osK7y0xthGD2o4GYH3eCg4X5lUonxyXSv82owCYtXQ+x08fqTC/cUIzMls5VyKt3reM/OITFd7bY58U0bvlEABW7l1KYcmpCuVbJLXh/BYDAVix50OKSosqbP/WjTrQvblzqtEXu96jlNIK5duldOacZr0p1dJK21Z66jlkND2PotLTrNjzUcC2LZJi3vt2T7Vtr31qF04WHavQ9spirKrtlfFve/5ty7/t+Stre8Wyv9K2Vdb2dp/4J9uPbqgw72TMdt7+5rwq2x7AkLaXBrQ93xgra3tl/Nue//b3b3t5p/ZWKJ8Y2whoxnvf7qmx7a098Hl525v33WvsS1jGua1/GfB+wkGCyHW1W7HIjcBYVb3TfX0LMERVp/ks8527TK77epu7TJ7fuqYAUwA6deo0YMeOf31YT3y0mT8t2UIJx8ltdHNAHM2KbqVp8QSKZT+7ku4ImJ92egpNSsZzWnLYkzQtYH6L0/eQWnIZhTEb2Zs4PWB+y8IZpJSO4FTMavYnPhAwv3XhQzQqHUB+zBccSPxdwPw2hY+RVHo+J2KXcDDhiYD57Qr+TIJ24VjsuxxOeC5gfvuC2cRrW47Gvc6R+L8EzE8/9SqxNOVw3FyOxb8RML/TqbcR4jkU/xzH496tOFPj6FywAIC8+D9yMu7jCrNjNJWOBa8BcCDhd+THflFhfmxpK9ILXwZgX8JvKYj9psL8+NJOtC98GoC9Cf9JYeymCvMTSnvQrvD3AOxOnEZRTE6F+UklF9Dm9P8DIDfxZ5TEVBw4u1HJMFqfvh+AnUk/oVSOVZifUjyalkX/AcCOpGtBiivMb1x8Nc2Lfo5SxA+NrsNfk6IbSSuebG2vgbW9xJJetD3t/Bi/Z0w3fnlZ94DY60pEVqnqwIDp9SEB+xo4cKBmZ2cHTN+y/yijnvgrz/60f4XpLZJb06JRK06XnCbnSOBPklbJbbn5uQ0snDaIHUcD9xBbp7SjWVJz8otOknssp3z61L+u5tmf9qddajqNE5ty8vRxdh3/IaB8h8adSElozPHCo+w5kVterkx6kwyS41M4WniYfSd2B5Tv1LQrSXGNuOyPi3jqloyA+RnNupEQm8DBUwc4mL+/wrypf13Nh7+4mbiYOA7k7+PwqcDNem7z84mRGPaf3MORgkMV3hsidG/u/ELYe2IXxwor7qHGxMRybpozWPWu4z9wy0vLKry3uJh4uqY5jTf3WA75RScrlI+PTaRLs3MZ+8d/8PztHSgorrgHmxTXiE5NuwKQc3Qbp4sLKry3v9x+EelNnG2y/fBmikuLKpRPSWhMh8adANh6eBOlpSUVtn/jxKa0S00HYPOhDeD3XWialEablPaUaimX/t+rAW0rrVFLWiW3obi0mO2Hvw/Ytne/sp2P7v1RtW0vrVELCosLKrS9shirantl/Nuef9vyb3v+ytreZX98j6du6RQwv6ztHS44xIGTFff+p/51Ne9P+3GVbQ+ga1qPgLbnG2Nlba+cX9v7yYtLK7w3/7Z38vTxCsXjYuK5a+4+3r/3omrbHsAPR7dXaHvTXsnlh1k3BbyfM1FVAkZVw/IAhgEf+Lz+NfBrv2U+AIa5z+Nw7j0n1a13wIABWpXO9/29ynnVCaZcJOsKtlx9iDHYchajt+UsxroBsrWSfBbOsyBWAt1EpIuIJAATgXf8lnkHuM19fiOw1A02KPeM6RaxcpGsK9hy9SHGYMtZjN6WsxhDI2xdEAAichXwRyAWeElVHxaRmTj/Dd4RkSTgFeAC4BAwUd2DdtWs8wCwo4rZLXH2or1mcQSKllgsjkDREktDjqOzqrbynxjWBBxpIpKtlfWzWByei5ZYLI5A0RLL2RiHXYpsjDEesQRsjDEeaWgJ+HmvA3BZHIGiJRaLI1C0xHLWxdGg+oCNMaY+aWh7wMYYU29YAjbGGI/UywQc6WEuq4iho4h8IiIbRGS9iNxTyTIXi8hREVnjPgIv2A9NLDki8q1bR8B12uL4P3d7rBOR/pWtJwRx9PB5r2tE5JiI3Ou3TFi2iYi8JCL73cvby6Y1F5GPRGSL+zetirK3uctsEZHbKlvmDON4TEQ2udv+bRFpVkXZaj/HEMXy3yKyy2f7X1VF2Wq/YyGIY55PDDkisqaKsiHbJlV9Z71oJ+Uquzwumh84F3VsA7oCCcBaoKffMncBz7rPJwLzwhBHO6C/+7wxsLmSOK4HMr4AAAWMSURBVC4G/h6BbZIDtKxm/lXAe4AAQ4EVEfqc9uKcgB72bQKMBPoD3/lMexSY4T6fATxSSbnmwHb3b5r7PC3EcVwOxLnPH6ksjtp8jiGK5b+B/6zFZ1ftd+xM4/Cb/3vggXBvk6q+s160k7JHfdwDLh/mUlVPA2XDXPq6BpjrPp8PjBERCWUQqrpHVVe7z48DG4EOoawjhK4B/qKOr4BmItIuzHWOAbapalVXLYaUqi7HuZrSl287mAtUNsr2FcBHqnpIVQ8DHwFjQxmHqn6oqmVDrX0FpAe7/jONpZZq8x0LSRzu93ICkBXs+usQR1Xf2Yi3kzL1MQF3AHb6vM4lMPGVL+M2/KNAi3AF5HZxXACsqGT2MBFZKyLviUivMIWgwIciskqcoTv91WabhdpEqv5SRWKbALRR1bJhvPYCbSpZJtLb5g6cXyOVqelzDJVpbnfIS1X83I7kNrkI2KeqVY2eHpZt4ved9ayd1McEHFVEJBV4E7hXVY/5zV6N8xM8E/gzsCBMYYxQ1f44dx+5W0RGhqmeWhFn8KXxQOAgsJHbJhWo8zvS03MuReR+oBh4tYpFIvE5PgOcA/QD9uD8/PfSzVS/9xvybVLddzbS7aQ+JuBdQEef1+nutEqXEZE4oClwMNSBiEg8zgf5qqq+5T9fVY+p6gn3+WIgXkRahjoOVd3l/t0PvI3zE9JXbbZZKF0JrFbVff4zIrVNXPvKulrcv4GD1kZo24jIZGAcMMn9kgeoxed4xlR1n6qWqGop8EIVdURqm8QB1wPzqlom1Nukiu+sZ+2kPibgiA9zWRm37+pFYKOq/qGKZdqW9T2LyGCc7R3SfwQikiIijcue4xzw+c5vsXeAW8UxFDjq85MrHKrcq4nENvHh2w5uAxZWsswHwOUikub+HL/cnRYyIjIW+BUwXlXzq1imNp9jKGLx7fu/roo6avMdC4VLgU3q3pDBX6i3STXfWe/aSSiOLkb6gXNUfzPOkdr73WkzcRo4QBLOz9+twNdA1zDEMALnp8o6YI37uAqYCkx1l5kGrMc5ivwVMDwMcXR117/Wratse/jGITg3SN0GfAsMDONnk4KTUJv6TAv7NsFJ+HuAIpz+uZ/h9PsvAbYAHwPN3WUHArN9yt7htpWtwO1hiGMrTv9hWTspO0OnPbC4us8xDLG84raBdTiJp51/LFV9x0IZhzt9Tlm78Fk2bNukmu9sxNtJ2cMuRTbGGI/Uxy4IY4xpECwBG2OMRywBG2OMRywBG2OMRywBG2OMRywBmwZLRO53R71a546mNURE7hWRZK9jMwbsjhimgRKRYcAfgItVtdC92i4B+ALnPOhouP25OcvZHrBpqNoBeapaCOAm3BtxTvT/REQ+ARCRy0XkSxFZLSJvuOMElI1D+6g7Fu3XInKuO/0mEfnOHUxouTdvzTQUtgdsGiQ3kX4GJONc3TRPVZeJSA7uHrC7V/wWcKWqnhSR+4BEVZ3pLveCqj4sIrcCE1R1nIh8C4xV1V0i0kxVj3jyBk2DYHvApkFSZ8CfAcAU4AAwzx0Qx9dQnAG5Pxfnjgy3AZ195mf5/B3mPv8cmCMi/4YzcLkxQYvzOgBjwkVVS4BPgU/dPVf/28gIziDbN1e1Cv/nqjpVRIYAVwOrRGSAqoZrMCHTwNkesGmQxLk/XTefSf2AHcBxnNvRgDMY0IU+/bspItLdp8yPff5+6S5zjqquUNUHcPasfYcoNKZObA/YNFSpwJ/FuQFmMc4IVlNwhsp8X0R2q+olbrdElogkuuV+gzMKGECaiKwDCt1yAI+5iV1wRtBaG5F3YxokOwhnTCV8D9Z5HYtpuKwLwhhjPGJ7wMYY4xHbAzbGGI9YAjbGGI9YAjbGGI9YAjbGGI9YAjbGGI/8f3kkhjfgm2xdAAAAAElFTkSuQmCC\n", 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", "text/plain": [ "
" ] diff --git a/example_neurogym_pytorch.html b/example_neurogym_pytorch.html index b7100de..47db907 100644 --- a/example_neurogym_pytorch.html +++ b/example_neurogym_pytorch.html @@ -1,99 +1,62 @@ - - - - + - - - - - Pytorch supervised learning of perceptual decision making task — neurogym documentation - + - - - - + + Pytorch supervised learning of perceptual decision making task — neurogym documentation + + + + + - + - - - - - - - - - + + + + + + + + - - - - - - - - - - + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/example_neurogym_pytorch.ipynb b/example_neurogym_pytorch.ipynb index 8ee11c9..fe4fecc 100644 --- a/example_neurogym_pytorch.ipynb +++ b/example_neurogym_pytorch.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +158,6 @@ } ], "source": [ - "# TODO: Make this into a function in neurogym\n", "perf = 0\n", "num_trial = 200\n", "for i in range(num_trial):\n", @@ -201,7 +200,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.9.8" } }, "nbformat": 4, diff --git a/example_neurogym_rl.html b/example_neurogym_rl.html index ae39ede..ccf444f 100644 --- a/example_neurogym_rl.html +++ b/example_neurogym_rl.html @@ -1,99 +1,62 @@ - - - - + - - - - - Reinforcement learning example with stable-baselines — neurogym documentation - + - - - - + + Reinforcement learning example with stable-baselines — neurogym documentation + + + + + - + - - - - - - - - - + + + + + + + + - - - - - - - - - - + - - - +
- -
- - -
- - - - - - - - - - - - - + \ No newline at end of file diff --git a/tags.html b/tags.html index 49e9e6e..dd00a97 100644 --- a/tags.html +++ b/tags.html @@ -1,105 +1,67 @@ - - - - + - - - - - Tags — neurogym documentation - + - - - - + + Tags — neurogym documentation + + + + - + - - - - - - - - - + + + + + + + + - - - - - - - - - - + - - - +
- -
- - -
-
-

continuous action space

+ +
+

continuous action space

-
-
-

delayed response

+ +
+

delayed response

-
-
-

go-no-go

+ +
+

go-no-go

-
- -
-

multidimensional action space

+ +
+

multidimensional action space

-
-
-

n-alternative

+ +
+

n-alternative

-
- -
-

reaction time

-
-
-

steps action space

+ +
+

reaction time

+
+
+

steps action space

-
- - -
-

two-alternative

+ +
+

two-alternative

-
-
-

value-based

+ +
+

value-based

-
-
-

working memory

+ +
+

working memory

-
- + + - - - - - - - - - - - - - + \ No newline at end of file diff --git a/understanding_neurogym_task.html b/understanding_neurogym_task.html index 5516fe5..1bcc223 100644 --- a/understanding_neurogym_task.html +++ b/understanding_neurogym_task.html @@ -1,111 +1,75 @@ - - - - + - - - - - Understanding Neurogym Task — neurogym documentation - + - - - - + + Understanding Neurogym Task — neurogym documentation + + + + + - + - - - - - - - - - + + + + + + + + - - - - - - - - - - + - + - - - +
- -
- - -
-
-class MyDecisionEnv(TrialEnv):
+
class MyDecisionEnv(TrialEnv):
     def __init__(self, dt=100, timing=None):
         super().__init__(dt=dt)  # dt is passed to base task
 
@@ -775,8 +450,7 @@ 
Including time, period, and observation in trial-based tasks
[10]:
 

 

-
-# Logging
+
# Logging
 log = {'ob': [], 'action': [], 'reward': []}
 
 env = MyDecisionEnv(dt=100)
@@ -806,9 +480,9 @@ 
Including time, period, and observation in trial-based tasks
 

 

-

-

-
Setting observation and ground-truth at the beginning of each trial

+
+

+
Setting observation and ground-truth at the beginning of each trial

 
In many tasks, the observation and ground-truth are pre-determined for each trial, and can be set in self._new_trial(). The generated observation and ground-truth can then be used as inputs and targets for supervised learning.

 
Observation and ground_truth can be set in self._new_trial() with the self.add_ob() and self.set_groundtruth methods. Users can specify the period and location of the observation using their names. For example, self.add_ob(1, period='stimulus', where='fixation').

 
This allows the users to access the observation and groundtruth of the entire trial with self.ob and self.gt, and access their values with self.ob_now and self.gt_now.

@@ -816,8 +490,7 @@ 
Setting observation and ground-truth at the beginning of each trial
[11]:
 

 

-
-class MyDecisionEnv(TrialEnv):
+
class MyDecisionEnv(TrialEnv):
     def __init__(self, dt=100, timing=None):
         super().__init__(dt=dt)  # dt is passed to base task
 
@@ -877,8 +550,7 @@ 
Setting observation and ground-truth at the beginning of each trial
[12]:
 

 

-
-env = MyDecisionEnv()
+
env = MyDecisionEnv()
 _ = env.reset()
 
 trial = env.new_trial()
@@ -905,8 +577,7 @@ 
Setting observation and ground-truth at the beginning of each trial
[13]:
 

 

-
-# Run the environment for 2 trials using a random agent.
+
# Run the environment for 2 trials using a random agent.
 fig = ngym.utils.plot_env(env, num_trials=2)
 

 

@@ -918,17 +589,16 @@ 
Setting observation and ground-truth at the beginning of each trial
 

 

-

-

-
An example perceptual decision-making task

+
+

+
An example perceptual decision-making task

 
Using the above style, we can define a simple perceptual decision-making task (the PerceptualDecisionMaking task from neurogym).

 

 
[14]:
 

 

-
-class PerceptualDecisionMaking(ngym.TrialEnv):
-    """Two-alternative forced choice task in which the subject has to
+
class PerceptualDecisionMaking(ngym.TrialEnv):
+    """Two-alternative forced choice task in which the subject has to
     integrate two stimuli to decide which one is higher on average.
 
     Args:
@@ -1027,8 +697,7 @@ 
An example perceptual decision-making task
[15]:
 

 

-
-env = PerceptualDecisionMaking(dt=20)
+
env = PerceptualDecisionMaking(dt=20)
 fig = ngym.utils.plot_env(env, num_trials=2)
 

 

@@ -1040,54 +709,37 @@ 
An example perceptual decision-making task
 

 

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-
 
-
-  
+
 
-  
-  
-  
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-  Wrappers — neurogym  documentation
-  
+  
 
-  
-  
-  
-  
+  
+  Wrappers — neurogym  documentation
+      
+      
+      
+      
 
   
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-
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+ 
   

-    
     
 
-    

-
-      
-      

-

-
PassReward-v0

-

-

-class neurogym.wrappers.pass_reward.PassReward(env)[source]

-

-

-reset(step_fn=None)[source]

-
Resets the state of the environment and returns an initial observation.

-

-
Returns

-
the initial observation.

-

-
Return type

-
observation (object)

-

-

+
+

+
PassReward-v0

+

+

+class neurogym.wrappers.pass_reward.PassReward(env)[source]

+

+

+reset(step_fn=None)[source]

+
Resets the environment with kwargs.

 

 
-

-

-step(action)[source]

-
Run one timestep of the environment’s dynamics. When end of
-episode is reached, you are responsible for calling reset()
-to reset this environment’s state.

-
Accepts an action and returns a tuple (observation, reward, done, info).

-

-
Parameters

-
action (object) – an action provided by the agent

-

-
Returns

-
agent’s observation of the current environment
-reward (float) : amount of reward returned after previous action
-done (bool): whether the episode has ended, in which case further step() calls will return undefined results
-info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

-

-
Return type

-
observation (object)

-

-

+

+

+step(action)[source]

+
Steps through the environment with action.

 

 
 

 
-

-

-
PassAction-v0

-

-

-class neurogym.wrappers.pass_action.PassAction(env)[source]

+
+

+
PassAction-v0

+

+

+class neurogym.wrappers.pass_action.PassAction(env)[source]

 
Modifies observation by adding the previous action.

-

-

-reset(step_fn=None)[source]

-
Resets the state of the environment and returns an initial observation.

-

-
Returns

-
the initial observation.

-

-
Return type

-
observation (object)

-

-

+

+

+reset(step_fn=None)[source]

+
Resets the environment with kwargs.

 

 
-

-

-step(action)[source]

-
Run one timestep of the environment’s dynamics. When end of
-episode is reached, you are responsible for calling reset()
-to reset this environment’s state.

-
Accepts an action and returns a tuple (observation, reward, done, info).

-

-
Parameters

-
action (object) – an action provided by the agent

-

-
Returns

-
agent’s observation of the current environment
-reward (float) : amount of reward returned after previous action
-done (bool): whether the episode has ended, in which case further step() calls will return undefined results
-info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

-

-
Return type

-
observation (object)

-

-

+

+

+step(action)[source]

+
Steps through the environment with action.

 

 
 

 
-

-

-
ReactionTime-v0

-

-

-class neurogym.wrappers.reaction_time.ReactionTime(env, urgency=0.0)[source]

+
+

+
ReactionTime-v0

+

+

+class neurogym.wrappers.reaction_time.ReactionTime(env, urgency=0.0)[source]

 
Allow reaction time response.

 
Modifies a given environment by allowing the network to act at
 any time after the fixation period.

-

-

-reset(step_fn=None)[source]

-
Resets the state of the environment and returns an initial observation.

+

+

+reset(step_fn=None, **kwargs)[source]

+
Resets the environment with kwargs.

+

+
+

+

+step(action)[source]

+
Steps through the environment with action.

+

+
+

+
+

+

+
SideBias-v0

+

+

+class neurogym.wrappers.side_bias.SideBias(env, probs=None, block_dur=200)[source]

+
Changes the probability of ground truth.

 

-
Returns

-
the initial observation.

-

-
Return type

-
observation (object)

+
Parameters:

+
    
+
  • prob – Specifies probabilities for each choice. Within each block,the
+probability should sum up to 1. (def: None, numpy array (n_block,
+n_choices))
    • 
+
  • block_dur – Number of trials per block. (def: 200, int)
    • 
+

 

 

 

 
-

-

-step(action)[source]

-
Run one timestep of the environment’s dynamics. When end of
-episode is reached, you are responsible for calling reset()
-to reset this environment’s state.

-
Accepts an action and returns a tuple (observation, reward, done, info).

+

+

+
RandomGroundTruth-v0

+

+

+class neurogym.wrappers.block.RandomGroundTruth(env, p=None)[source]

+

+
+

+

+
ScheduleAttr-v0

+

+

+class neurogym.wrappers.block.ScheduleAttr(env, schedule, attr_list)[source]

+
Schedule attributes.

 

-
Parameters

-
action (object) – an action provided by the agent

-

-
Returns

-
agent’s observation of the current environment
-reward (float) : amount of reward returned after previous action
-done (bool): whether the episode has ended, in which case further step() calls will return undefined results
-info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

-

-
Return type

-
observation (object)

+
Parameters:

+
    
+
  • env – TrialEnv object
    • 
+
  • schedule – 
    • 
+

 

 

+

+

+seed(seed=None)[source]

+
Seeds the environment.

 

 
 

 
-

-

-
SideBias-v0

-

-

-class neurogym.wrappers.side_bias.SideBias(env, probs=None, block_dur=200)[source]

-
Changes the probability of ground truth.

+
+

+
ScheduleEnvs-v0

+

+

+class neurogym.wrappers.block.ScheduleEnvs(envs, schedule, env_input=False)[source]

+
Schedule environments.

 

-
Parameters

+
Parameters:

 
    
-
  • prob – Specifies probabilities for each choice. Within each block,the
-probability should sum up to 1. (def: None, numpy array (n_block,
-n_choices))
    • 
-
  • block_dur – Number of trials per block. (def: 200, int)
    • 
+
  • envs – list of env object
    • 
+
  • schedule – utils.scheduler.BaseSchedule object
    • 
+
  • env_input – bool, if True, add scalar inputs indicating current
+environment. default False.
    • 
 

 

 

+

+

+reset(**kwargs)[source]

+
Reset each environment in self.envs and use the scheduler to select the environment returning
+the initial observation. This environment is also used to set the current environment self.env.

 

 
-

-

+

+

+seed(seed=None)[source]

+
Seeds the environment.

+

+
+

+

+set_i(i)[source]

+
Set the current environment to the i-th environment in the list envs.

+

+
+
+
+
+

+
TrialHistoryV2-v0

+

+

+class neurogym.wrappers.block.TrialHistoryV2(env, probs=None)[source]

+
Change ground truth probability based on previous outcome.

+

+
Parameters:

+
probs – matrix of probabilities of the current choice conditioned
+on the previous. Shape, num-choices x num-choices

+

+

+

+
+

+
 
 
            

-           
           

-
- - - - - - - - - - - + \ No newline at end of file