295 lines
69 KiB
Text
295 lines
69 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52cafe51-05b0-4287-bf31-418090f929de",
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"metadata": {},
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"outputs": [],
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"source": [
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"# COMPARE NO-ATR VS ATR (11H05 crosses)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "76a37581-2e43-4fec-9886-0f0849b86602",
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"metadata": {},
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"outputs": [],
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"source": [
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"import ethoscopy as etho\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from scipy.stats import ttest_ind"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "5450b41f-4a94-48f1-99c9-9a8a2862165e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing No-ATR Data...\n",
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"Processing ATR Data...\n"
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]
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}
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],
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"source": [
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"# 1. LOAD SAVED PICKLE FILES\n",
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"df_no_atr_raw = pd.read_pickle('/home/rdingjin/UASxGal4_2026-04-28/phase6.pkl')\n",
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"df_atr_raw = pd.read_pickle('/home/rdingjin/UASxGal4_2026-05-05/phase7(11H)1.pkl')\n",
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"\n",
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"# 2. HELPER FUNCTION\n",
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"def calculate_delta_sleep(df, base_start, base_end, stim_start, stim_end):\n",
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" \n",
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" # --- BASELINE ---\n",
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" base_df = df.t_filter(start_time=base_start, end_time=base_end).copy()\n",
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" sleep_hours_base = (base_df.groupby('id')['asleep'].sum() * 10) / 3600\n",
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" base_overall = (sleep_hours_base / ((base_end - base_start) / 24)).reset_index(name='Base_Sleep_24h')\n",
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" \n",
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" base_df['zt_hour'] = (base_df['t'] / 3600) % 24\n",
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" base_df['Phase'] = np.where(base_df['zt_hour'] < 12, 'Day', 'Night')\n",
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" base_phase = base_df.groupby(['id', 'Phase']).agg(\n",
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" total_sleep_epochs=('asleep', 'sum'), total_tracking_epochs=('t', 'count')\n",
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" ).reset_index()\n",
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" \n",
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" base_phase['Sleep_Hours'] = (base_phase['total_sleep_epochs'] * 10) / 3600\n",
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" base_phase['Tracking_Hours'] = (base_phase['total_tracking_epochs'] * 10) / 3600\n",
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" base_phase['Base_Sleep_12h'] = (base_phase['Sleep_Hours'] / base_phase['Tracking_Hours']) * 12\n",
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" base_pivot = base_phase.pivot(index='id', columns='Phase', values='Base_Sleep_12h').reset_index()\n",
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" base_pivot.columns = ['id', 'Base_Day_12h', 'Base_Night_12h']\n",
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" baseline_stats = pd.merge(base_overall, base_pivot, on='id')\n",
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" \n",
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" # --- STIMULUS ---\n",
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" stim_df = df.t_filter(start_time=stim_start, end_time=stim_end).copy()\n",
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" sleep_hours_stim = (stim_df.groupby('id')['asleep'].sum() * 10) / 3600\n",
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" stim_overall = (sleep_hours_stim / ((stim_end - stim_start) / 24)).reset_index(name='Stim_Sleep_24h')\n",
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" \n",
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" stim_df['zt_hour'] = (stim_df['t'] / 3600) % 24\n",
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" stim_df['Phase'] = np.where(stim_df['zt_hour'] < 12, 'Day', 'Night')\n",
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" stim_phase = stim_df.groupby(['id', 'Phase']).agg(\n",
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" total_sleep_epochs=('asleep', 'sum'), total_tracking_epochs=('t', 'count')\n",
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" ).reset_index()\n",
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" \n",
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" stim_phase['Sleep_Hours'] = (stim_phase['total_sleep_epochs'] * 10) / 3600\n",
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" stim_phase['Tracking_Hours'] = (stim_phase['total_tracking_epochs'] * 10) / 3600\n",
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" stim_phase['Stim_Sleep_12h'] = (stim_phase['Sleep_Hours'] / stim_phase['Tracking_Hours']) * 12\n",
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" stim_pivot = stim_phase.pivot(index='id', columns='Phase', values='Stim_Sleep_12h').reset_index()\n",
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" stim_pivot.columns = ['id', 'Stim_Day_12h', 'Stim_Night_12h']\n",
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" stim_stats = pd.merge(stim_overall, stim_pivot, on='id')\n",
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" \n",
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" # --- MERGE & CALCULATE DELTA ---\n",
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" combined = pd.merge(baseline_stats, stim_stats, on='id')\n",
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" unique_meta = df.meta['machine_name'][~df.meta.index.duplicated(keep='first')]\n",
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" combined['Machine'] = combined['id'].map(unique_meta)\n",
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" combined['Tube'] = combined['id'].apply(lambda x: str(x).split('|')[-1])\n",
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" combined['Fly_Label'] = combined['Machine'] + \" - Fly \" + combined['Tube']\n",
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" combined['Delta_24h'] = combined['Stim_Sleep_24h'] - combined['Base_Sleep_24h']\n",
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" combined['Delta_Day'] = combined['Stim_Day_12h'] - combined['Base_Day_12h']\n",
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" combined['Delta_Night'] = combined['Stim_Night_12h'] - combined['Base_Night_12h']\n",
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" \n",
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" return combined[['Fly_Label', 'Delta_24h', 'Delta_Day', 'Delta_Night']].dropna()\n",
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"\n",
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"# 3. PROCESS GROUPS\n",
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"print(\"Processing No-ATR Data...\")\n",
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"delta_no_atr = calculate_delta_sleep(df_no_atr_raw, base_start=0, base_end=48, stim_start=48, stim_end=72)\n",
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"delta_no_atr['Condition'] = 'No-ATR'\n",
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"\n",
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"print(\"Processing ATR Data...\")\n",
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"delta_atr = calculate_delta_sleep(df_atr_raw, base_start=0, base_end=48, stim_start=48, stim_end=72)\n",
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"delta_atr['Condition'] = 'ATR'\n",
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"\n",
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"master_delta = pd.concat([delta_no_atr, delta_atr], ignore_index=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "642d6bbc-3674-4c1e-9a41-88928ee407f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 4. MELT DATA FOR PLOTTING AND STATS\n",
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"melted_delta = master_delta.melt(\n",
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" id_vars=['Fly_Label', 'Condition'],\n",
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" value_vars=['Delta_24h', 'Delta_Day', 'Delta_Night'],\n",
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" var_name='Phase', \n",
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" value_name='Sleep_Change'\n",
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")\n",
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"# Clean up phase names\n",
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"melted_delta['Phase'] = melted_delta['Phase'].map({'Delta_24h': '24h Total', 'Delta_Day': 'Day', 'Delta_Night': 'Night'})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "fb18dd18-a4f4-4f06-a088-e2cf6b5a677c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"==================================================\n",
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"--- SCIPY T-TESTS (No-ATR vs ATR Delta) ---\n",
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"==================================================\n",
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"24h Total Phase Comparison:\n",
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" No-ATR Δ Mean: 1.24 hours\n",
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" ATR Δ Mean: -5.42 hours\n",
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" P-value: 1.3586e-13\n",
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"\n",
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"Day Phase Comparison:\n",
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" No-ATR Δ Mean: 0.07 hours\n",
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" ATR Δ Mean: -2.21 hours\n",
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" P-value: 2.5060e-07\n",
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"\n",
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"Night Phase Comparison:\n",
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" No-ATR Δ Mean: 0.22 hours\n",
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" ATR Δ Mean: -3.86 hours\n",
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" P-value: 9.3420e-12\n",
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"\n"
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]
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}
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],
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"source": [
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"# 5. STATISTICAL COMPARISON \n",
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"print(\"\\n\" + \"=\"*50)\n",
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"print(\"--- SCIPY T-TESTS (No-ATR vs ATR Delta) ---\")\n",
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"print(\"=\"*50)\n",
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"\n",
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"p_values = {}\n",
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"phases = ['24h Total', 'Day', 'Night']\n",
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"\n",
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"for phase in phases:\n",
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" group_no_atr = melted_delta[(melted_delta['Phase'] == phase) & (melted_delta['Condition'] == 'No-ATR')]['Sleep_Change']\n",
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" group_atr = melted_delta[(melted_delta['Phase'] == phase) & (melted_delta['Condition'] == 'ATR')]['Sleep_Change']\n",
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" \n",
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" # Run Independent T-Test\n",
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" t_stat, p_val = ttest_ind(group_no_atr, group_atr)\n",
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" p_values[phase] = p_val\n",
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" \n",
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" print(f\"{phase} Phase Comparison:\")\n",
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" print(f\" No-ATR Δ Mean: {group_no_atr.mean():.2f} hours\")\n",
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" print(f\" ATR Δ Mean: {group_atr.mean():.2f} hours\")\n",
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" print(f\" P-value: {p_val:.4e}\\n\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "c11c05b3-a8b5-4e86-86bf-bae60ec24103",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_50430/3429110790.py:14: FutureWarning: \n",
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"\n",
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"Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:black'` for the same effect.\n",
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"\n",
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" sns.stripplot(\n"
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]
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 800x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# 6. FINAL COMPARISON PLOT\n",
|
||
|
|
"plt.figure(figsize=(8, 6))\n",
|
||
|
|
"sns.set_theme(style=\"ticks\")\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot Boxplot\n",
|
||
|
|
"ax = sns.boxplot(\n",
|
||
|
|
" data=melted_delta, x='Phase', y='Sleep_Change', hue='Condition', \n",
|
||
|
|
" palette={'No-ATR': '#cccccc', 'ATR': '#f1c232'}, order=phases,\n",
|
||
|
|
" width=0.6, gap=0.2, boxprops={'edgecolor': 'black', 'alpha': 0.7},\n",
|
||
|
|
" medianprops={'color': 'black', 'linewidth': 1.5}, showfliers=False\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot internal points\n",
|
||
|
|
"sns.stripplot(\n",
|
||
|
|
" data=melted_delta, x='Phase', y='Sleep_Change', hue='Condition', \n",
|
||
|
|
" order=phases, dodge=True,\n",
|
||
|
|
" color='black', alpha=0.5, size=4, jitter=0.05, legend=False\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"# Add Median Labels\n",
|
||
|
|
"medians = melted_delta.groupby(['Phase', 'Condition'], sort=False)['Sleep_Change'].median()\n",
|
||
|
|
"offsets = {'No-ATR': -0.4, 'ATR': 0.4}\n",
|
||
|
|
"\n",
|
||
|
|
"for i, phase in enumerate(phases):\n",
|
||
|
|
" for condition in ['No-ATR', 'ATR']:\n",
|
||
|
|
" val = medians[(phase, condition)]\n",
|
||
|
|
" ax.text(i + offsets[condition], val, f\"{val:.1f}h\", \n",
|
||
|
|
" ha='center', va='bottom', fontsize=9)\n",
|
||
|
|
"\n",
|
||
|
|
"# Add Significance Brackets\n",
|
||
|
|
"y_max = melted_delta['Sleep_Change'].max()\n",
|
||
|
|
"y_min = melted_delta['Sleep_Change'].min()\n",
|
||
|
|
"y_range = y_max - y_min\n",
|
||
|
|
"y_line = y_max + (y_range * 0.05)\n",
|
||
|
|
"h = y_range * 0.02\n",
|
||
|
|
"\n",
|
||
|
|
"for i, phase in enumerate(phases):\n",
|
||
|
|
" p = p_values[phase]\n",
|
||
|
|
" if p < 0.05:\n",
|
||
|
|
" star = '****' if p < 0.0001 else ('***' if p < 0.001 else ('**' if p < 0.01 else '*'))\n",
|
||
|
|
" x1, x2 = i - 0.2, i + 0.2\n",
|
||
|
|
" plt.plot([x1, x1, x2, x2], [y_line, y_line+h, y_line+h, y_line], lw=1.5, c='k')\n",
|
||
|
|
" plt.text((x1+x2)*.5, y_line+h, star, ha='center', va='bottom', fontsize=12)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.axhline(0, color='black', linestyle='--', linewidth=1.5, alpha=0.7)\n",
|
||
|
|
"sns.despine()\n",
|
||
|
|
"plt.ylabel('Change in average time spent asleep (Δ hours)', fontsize=12)\n",
|
||
|
|
"plt.xlabel('')\n",
|
||
|
|
"plt.xlim(-0.5, 2.5) \n",
|
||
|
|
"\n",
|
||
|
|
"plt.legend(title='', frameon=False, loc='center left', bbox_to_anchor=(1, 0.5))\n",
|
||
|
|
"plt.ylim(y_min - (y_range * 0.1), y_max + (y_range * 0.25))\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.savefig(\"ATR_vs_NoATR_Delta.png\", dpi=600, bbox_inches='tight')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"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": "3.12.3"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|