sync notebooks

AllenInstitute · Dec 12, 2024 · 9a59571 · 9a59571
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diff --git a/notebooks/analyze_block_shuffling_for_decision_function.ipynb b/notebooks/analyze_block_shuffling_for_decision_function.ipynb
diff --git a/notebooks/analyze_block_shuffling_for_decoding.ipynb b/notebooks/analyze_block_shuffling_for_decoding.ipynb
diff --git a/notebooks/append_surface_channel_metrics.ipynb b/notebooks/append_surface_channel_metrics.ipynb
diff --git a/notebooks/area_boundary_metrics.ipynb b/notebooks/area_boundary_metrics.ipynb
diff --git a/notebooks/baseline_context_offset_vs_sensory_responses.ipynb b/notebooks/baseline_context_offset_vs_sensory_responses.ipynb
diff --git a/notebooks/ccf_widget.ipynb b/notebooks/ccf_widget.ipynb
diff --git a/notebooks/check_surface_channels.ipynb b/notebooks/check_surface_channels.ipynb
diff --git a/notebooks/compare_decoder_models.ipynb b/notebooks/compare_decoder_models.ipynb
diff --git a/notebooks/compare_decoder_runs.ipynb b/notebooks/compare_decoder_runs.ipynb
diff --git a/notebooks/compare_ibl_unit_metrics.ipynb b/notebooks/compare_ibl_unit_metrics.ipynb
diff --git a/notebooks/compute_metrics_for_alignment.ipynb b/notebooks/compute_metrics_for_alignment.ipynb
diff --git a/notebooks/data_access_example_egm_scratch.ipynb b/notebooks/data_access_example_egm_scratch.ipynb
diff --git a/notebooks/decode_context_from_videos.ipynb b/notebooks/decode_context_from_videos.ipynb
diff --git a/notebooks/decode_context_linear_shift_w_repeats_analysis.ipynb b/notebooks/decode_context_linear_shift_w_repeats_analysis.ipynb
diff --git a/notebooks/decode_context_linear_shift_w_repeats_trialwise_analysis.ipynb b/notebooks/decode_context_linear_shift_w_repeats_trialwise_analysis.ipynb
diff --git a/notebooks/decoder_accuracy_vs_n_units.ipynb b/notebooks/decoder_accuracy_vs_n_units.ipynb
@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import scipy.stats as stats\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from dynamic_routing_analysis import decoding_utils, plot_utils\n",
+    "import dynamic_routing_analysis as dra\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.font_manager as fm\n",
+    "\n",
+    "matplotlib.rcParams['font.size'] = 8\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42\n",
+    "fm.FontProperties().set_family('arial')\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline\n",
+    "# %matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loadpath={}\n",
+    "\n",
+    "# loadpath['no_unit_criteria']=r\"D:\\decoding_results_from_CO\\n_units_test_2024-11-06\"\n",
+    "loadpath['svc_medium_unit_criteria']=r\"D:\\decoding_results_from_CO\\n_units_test_medium_unit_criteria_2024-11-07\"\n",
+    "loadpath['lda_medium_criteria']=r\"D:\\decoding_results_from_CO\\lda_test_2024-11-11\"\n",
+    "loadpath['logreg_medium_criteria']=r\"D:\\decoding_results_from_CO\\logreg_test_2024-11-13\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_results={}\n",
+    "session_list={}\n",
+    "for ll in loadpath.keys():\n",
+    "    all_decoder_results_path=os.path.join(loadpath[ll],'decoder_results_summary.csv')\n",
+    "    decoder_results[ll]=pd.read_csv(all_decoder_results_path)\n",
+    "    session_list[ll]=decoder_results[ll]['session_id'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       '636397_2022-09-26', '636397_2022-09-27', '636766_2023-01-23',\n",
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+       "       '703880_2024-04-17', '703880_2024-04-18', '703882_2024-04-22',\n",
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+       "       '706401_2024-04-23', '706401_2024-04-24', '708016_2024-04-29',\n",
+       "       '708016_2024-04-30', '708016_2024-05-01', '708016_2024-05-02',\n",
+       "       '712815_2024-05-21', '712815_2024-05-22', '713655_2024-08-05',\n",
+       "       '713655_2024-08-06', '713655_2024-08-07', '713655_2024-08-08',\n",
+       "       '713655_2024-08-09', '714748_2024-06-24', '714748_2024-06-25',\n",
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+       "       '714753_2024-07-02', '715710_2024-07-15', '715710_2024-07-16',\n",
+       "       '715710_2024-07-17', '715710_2024-07-18', '715710_2024-07-19',\n",
+       "       '726088_2024-06-17', '726088_2024-06-18', '726088_2024-06-20',\n",
+       "       '726088_2024-06-21', '733780_2024-08-26', '733780_2024-08-29',\n",
+       "       '733780_2024-08-30', '733780_2024-09-03', '733780_2024-09-05',\n",
+       "       '733780_2024-09-06'], dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#need to match sessions\n",
+    "# shared_sessions=np.intersect1d(np.intersect1d(np.intersect1d(\n",
+    "#     session_list['no_unit_criteria'],session_list['svc_medium_unit_criteria']),\n",
+    "#     session_list['lda_medium_criteria']),session_list['logreg_medium_criteria'])\n",
+    "\n",
+    "shared_sessions=np.intersect1d(np.intersect1d(\n",
+    "    session_list['logreg_medium_criteria'],session_list['svc_medium_unit_criteria']),\n",
+    "    session_list['lda_medium_criteria'])\n",
+    "shared_sessions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "area='CP'\n",
+    "savepath=r\"C:\\Users\\ethan.mcbride\\OneDrive - Allen Institute\\quick figures\\2024-11-13-compare_decoder_models\"\n",
+    "plot_n_units=[10,20,40]\n",
+    "\n",
+    "fig,ax=plt.subplots(1,1,figsize=(4,3))\n",
+    "\n",
+    "for ll in list(loadpath.keys())[:]:\n",
+    "    all_decoder_results=decoder_results[ll].query('area==@area and session_id in @shared_sessions and project==\"DynamicRouting\"')\n",
+    "    #compute fraction significant, difference from null for DynamicRouting and Templeton projects\n",
+    "    all_frac_sig_df,all_diff_from_null_df=decoding_utils.compute_significant_decoding_by_area(all_decoder_results)\n",
+    "\n",
+    "    unique_areas=all_decoder_results['area'].unique()\n",
+    "    n_units=[]\n",
+    "    n_units_int=[]\n",
+    "    for col in all_frac_sig_df.filter(like='frac_sig_DR').columns.values:\n",
+    "        if len(col.split('_'))==4:\n",
+    "            if col.split('_')[3]=='all':\n",
+    "                continue\n",
+    "\n",
+    "            n_units.append('_'+col.split('_')[3])\n",
+    "            n_units_int.append(int(col.split('_')[3]))\n",
+    "            \n",
+    "        else:\n",
+    "            n_units.append('')\n",
+    "            n_units_int.append(None)\n",
+    "\n",
+    "    n_units_int=np.array(n_units_int)\n",
+    "\n",
+    "    # for area in unique_areas[:]:\n",
+    "\n",
+    "    area_df=all_diff_from_null_df.query('area==@area')\n",
+    "\n",
+    "    temp_accuracy=[]\n",
+    "    temp_sem=[]\n",
+    "\n",
+    "    for nu in n_units:\n",
+    "        if nu=='all':\n",
+    "            continue\n",
+    "        if len(area_df['diff_from_null_median_DR'+nu].values)==0:\n",
+    "            continue\n",
+    "        temp_accuracy.append(area_df['diff_from_null_median_DR'+nu].values[0])\n",
+    "        temp_sem.append(area_df['diff_from_null_sem_DR'+nu].values[0])\n",
+    "\n",
+    "    # if len(temp_accuracy)==0:\n",
+    "    #     continue\n",
+    "\n",
+    "    temp_accuracy=np.array(temp_accuracy)\n",
+    "    temp_sem=np.array(temp_sem)\n",
+    "\n",
+    "    n_units_incl=[]\n",
+    "    for ii,nui in enumerate(n_units_int):\n",
+    "        if nui in plot_n_units:\n",
+    "            n_units_incl.append(ii)\n",
+    "    n_units_incl=np.array(n_units_incl)\n",
+    "\n",
+    "    # ax.axhline(y=0,color='k',linestyle='--',alpha=0.5)\n",
+    "    ax.errorbar(n_units_int[n_units_incl],temp_accuracy[n_units_incl],\n",
+    "                yerr=temp_sem[n_units_incl],marker='.',linestyle='-',label=ll)\n",
+    "    ax.set_xlabel('number of units')\n",
+    "    ax.set_ylabel('decoder accuracy above null')\n",
+    "    ax.set_title(area)\n",
+    "    # ax.set_ylim([-0.05,0.25])\n",
+    "\n",
+    "ax.legend()\n",
+    "# fig.savefig(os.path.join(savepath,area+'_decoder_acc_by_n_units_comp.png'),format='png',bbox_inches='tight')\n",
+    "# plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#correlation between decoder models\n",
+    "\n",
+    "area_wise_decoder_model_comp={}\n",
+    "\n",
+    "#loop through areas\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dr_analysis",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/decoder_confidence_vs_n_units.ipynb b/notebooks/decoder_confidence_vs_n_units.ipynb