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Merge pull request #310 from neurodata/develop
update for 0.3.0 release
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| from brainlit.algorithms.trace_analysis.fit_spline import * | ||
| from brainlit.algorithms.trace_analysis.spline_fxns import * |
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187 changes: 187 additions & 0 deletions
187
experiments/axon_geometry/notebooks/resample_trace.ipynb
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| Original file line number | Diff line number | Diff line change |
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| { | ||
| "metadata": { | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.10" | ||
| }, | ||
| "orig_nbformat": 4, | ||
| "kernelspec": { | ||
| "name": "python3", | ||
| "display_name": "Python 3.8.6 ('env': venv)" | ||
| }, | ||
| "interpreter": { | ||
| "hash": "c48f87b51193a18c36f6ec1b39da40df380a4c3fb2fa54b3e413dc2378ec9052" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2, | ||
| "cells": [ | ||
| { | ||
| "source": [ | ||
| "In order to run this notebook, you must have generated data with random dropout of trace vertices. This can be done by uncommenting the bottom of `generate_trace_data.ipynb`" | ||
| ], | ||
| "cell_type": "markdown", | ||
| "metadata": {} | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from pathlib import Path\n", | ||
| "import os\n", | ||
| "import pandas as pd\n", | ||
| "import seaborn as sns\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import numpy as np\n", | ||
| "import scipy\n", | ||
| "from tqdm import tqdm" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "max_id = 300\n", | ||
| "\n", | ||
| "def classify_height(row):\n", | ||
| " height = row[\"height\"]\n", | ||
| " if height <= 2:\n", | ||
| " return height\n", | ||
| " else:\n", | ||
| " return 3\n", | ||
| "\n", | ||
| "def numerical_class(row):\n", | ||
| " _class = row[\"class\"]\n", | ||
| " if _class == \"primary\":\n", | ||
| " return 0\n", | ||
| " if _class == \"collateral\":\n", | ||
| " return 1\n", | ||
| " if _class == \"terminal\":\n", | ||
| " return 2" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#specify brain1 or brain2 below\n", | ||
| "brain = \"brain1\"\n", | ||
| "\n", | ||
| "root_dir = Path(os.path.abspath('')).parents[1]\n", | ||
| "experiment_dir = os.path.join(root_dir, \"axon_geometry\")\n", | ||
| "data_dir = os.path.join(experiment_dir, \"data\", brain)\n", | ||
| "segments_swc_dir = os.path.join(data_dir, \"segments_swc\")\n", | ||
| "print(f\"Directory where swcs reside: {segments_swc_dir}\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\n", | ||
| "for i in range(20):\n", | ||
| " print(f\"****************Iter: {i}********************\")\n", | ||
| " dropout_version = \"10pct_iter\" + str(i)\n", | ||
| " trace_data_dir = os.path.join(data_dir, \"trace_data\")\n", | ||
| " trace_data_dir = os.path.join(trace_data_dir, \"1\")\n", | ||
| " trace_data_dir = os.path.join(trace_data_dir, dropout_version)\n", | ||
| " df_path = os.path.join(trace_data_dir, \"df.csv\")\n", | ||
| " if os.path.exists(df_path):\n", | ||
| " df = pd.read_csv(df_path)\n", | ||
| " else:\n", | ||
| " df = pd.DataFrame(columns=[\"seg_id\", \"class\", \"height\", \"log_seg_length\", \"measure\", \"value\", \"log_value\"])\n", | ||
| " for i in tqdm(np.arange(0, max_id)):\n", | ||
| " i = int(i)\n", | ||
| " trace_data_path = os.path.join(trace_data_dir, \"{}.npy\".format(i))\n", | ||
| " if os.path.exists(trace_data_path) is True:\n", | ||
| " trace_data = np.load(trace_data_path, allow_pickle=True)\n", | ||
| "\n", | ||
| " for node in trace_data:\n", | ||
| " seg_length = node[\"seg_length\"]\n", | ||
| " height = node[\"height\"]\n", | ||
| " _class = node[\"class\"]\n", | ||
| " mean_curvature = node[\"mean_curvature\"]\n", | ||
| " mean_torsion = node[\"mean_torsion\"]\n", | ||
| " \n", | ||
| " log_seg_length = np.log10(seg_length)\n", | ||
| "\n", | ||
| " log_mean_curvature = np.log10(mean_curvature)\n", | ||
| " df = df.append({\"seg_id\": i, \"height\": height, \"class\": _class, \"log_seg_length\": log_seg_length, \"measure\": \"curvature\", \"value\": mean_curvature, \"log_value\": log_mean_curvature}, ignore_index=True)\n", | ||
| "\n", | ||
| " log_mean_torsion = np.log10(mean_torsion)\n", | ||
| " df = df.append({\"seg_id\": i, \"height\": height, \"class\": _class, \"log_seg_length\": log_seg_length, \"measure\": \"torsion\", \"value\": mean_torsion, \"log_value\": log_mean_torsion}, ignore_index=True)\n", | ||
| " df.to_csv(df_path)\n", | ||
| " df[\"class\"] = df.apply(numerical_class, axis=1)\n", | ||
| " df[\"height_class\"] = df.apply(classify_height, axis=1)\n", | ||
| "\n", | ||
| "\n", | ||
| " measures = [\"curvature\", \"torsion\"]\n", | ||
| "\n", | ||
| " classes = {0: \"primary\", 1: \"collateral\", 2: \"terminal\"}\n", | ||
| " n = len(classes)\n", | ||
| " matrix_pairs = np.triu(np.ones((n, n)), k=1)\n", | ||
| " (coord_pairs_x, coord_pairs_y) = np.where(matrix_pairs == 1)\n", | ||
| "\n", | ||
| " for measure in measures:\n", | ||
| " for class_1, class_2 in zip(coord_pairs_x, coord_pairs_y):\n", | ||
| " Y1 = []\n", | ||
| " Y2 = []\n", | ||
| " segment_numbers = []\n", | ||
| " X_class = []\n", | ||
| " neuron_id = 0\n", | ||
| " #collect data from all neurons\n", | ||
| " for i in np.arange(0, max_id):\n", | ||
| " sample_query = df.loc[(df['seg_id'] == i) & ((df['class'] == class_1)) & (df['measure'] == measure)]\n", | ||
| " num_segments = len(sample_query.index)\n", | ||
| " if num_segments > 0:\n", | ||
| " Y1.append(np.mean(sample_query[\"value\"].to_numpy()))\n", | ||
| " sample_query = df.loc[(df['seg_id'] == i) & ((df['class'] == class_2)) & (df['measure'] == measure)]\n", | ||
| " Y2.append(np.mean(sample_query[\"value\"].to_numpy()))\n", | ||
| "\n", | ||
| " # Sign Test\n", | ||
| " dif = np.subtract(Y1, Y2)\n", | ||
| " k = np.sum(dif > 0)\n", | ||
| " n = dif.shape[0]\n", | ||
| " upper_bool = k > n // 2\n", | ||
| " if upper_bool:\n", | ||
| " p = np.sum(scipy.stats.binom.pmf(range(k,n+1), n=n, p=0.5))\n", | ||
| " else:\n", | ||
| " p = np.sum(scipy.stats.binom.pmf(range(0,k+1), n=n, p=0.5))\n", | ||
| "\n", | ||
| " if p < 0.05/12:\n", | ||
| " if upper_bool:\n", | ||
| " symb = \">\"\n", | ||
| " else:\n", | ||
| " symb = \"<\"\n", | ||
| " print(f\"{classes[class_1]} {symb} {classes[class_2]} in {measure}\")\n", | ||
| " print(f\"p-val was: {p}\")\n", | ||
| " else:\n", | ||
| " print(f\"No difference between {classes[class_1]} and {classes[class_2]} in {measure}\")\n", | ||
| " print(f\"p-val was: {p}\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ] | ||
| } |
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