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10. 11H05-GAL5>UAS-ChRmine-attP5 with Red Stimulus
2026-05-26 15:14:21 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "ae9b8ff2-033e-4e48-926a-62c43535bba4",
"metadata": {},
"outputs": [],
"source": [
"import ethoscopy as etho\n",
"import pandas as pd\n",
"from scipy.stats import mannwhitneyu\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"from scipy.stats import wilcoxon, shapiro, ttest_rel"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6cbb4f52-dd5f-4888-9c9e-ba4f1f6dc429",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2.1.0'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"etho.__version__"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "dc5c910a-b8ac-4574-87f6-3df51839a50e",
"metadata": {},
"outputs": [],
"source": [
"meta = '/home/rdingjin/UASxGal4_2026-04-28/metadata_phase6_11H(1).csv' \n",
"local = '/mnt/ethoscope_results'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "78e2a925-62ab-4c2c-bd11-e27e1b6789f5",
"metadata": {},
"outputs": [],
"source": [
"metadata = etho.link_meta_index(meta, local)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "65d71cd5-21c5-4cc0-a19b-7de730b45a65",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading ROI_1 from ETHOSCOPE_302\n",
"Loading ROI_2 from ETHOSCOPE_302\n",
"Loading ROI_3 from ETHOSCOPE_302\n",
"Loading ROI_4 from ETHOSCOPE_302\n",
"Loading ROI_5 from ETHOSCOPE_302\n",
"Loading ROI_6 from ETHOSCOPE_302\n",
"Loading ROI_7 from ETHOSCOPE_302\n",
"Loading ROI_8 from ETHOSCOPE_302\n",
"Loading ROI_9 from ETHOSCOPE_302\n",
"Loading ROI_10 from ETHOSCOPE_302\n",
"Loading ROI_11 from ETHOSCOPE_302\n",
"Loading ROI_12 from ETHOSCOPE_302\n",
"Loading ROI_13 from ETHOSCOPE_302\n",
"Loading ROI_14 from ETHOSCOPE_302\n",
"Loading ROI_15 from ETHOSCOPE_302\n",
"Loading ROI_16 from ETHOSCOPE_302\n",
"Loading ROI_17 from ETHOSCOPE_302\n",
"Loading ROI_18 from ETHOSCOPE_302\n",
"Loading ROI_19 from ETHOSCOPE_302\n",
"Loading ROI_20 from ETHOSCOPE_302\n"
]
}
],
"source": [
"data = etho.load_ethoscope(metadata, reference_hour = 9.0, FUN = etho.sleep_annotation)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a2c12f1d-3ca9-469b-9447-7d117f320006",
"metadata": {},
"outputs": [],
"source": [
"df = etho.behavpy(data, metadata, check = True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e256c265-07b7-4d90-96ed-44d2aa76f14d",
"metadata": {},
"outputs": [],
"source": [
"df.to_pickle('/home/rdingjin/UASxGal4_2026-04-28/phase6.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "1ee0efda-db87-4de1-8ee0-dea02fd3348f",
"metadata": {},
"outputs": [],
"source": [
"import ethoscopy as etho\n",
"import pandas as pd\n",
"from scipy.stats import mannwhitneyu\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"from scipy.stats import wilcoxon, shapiro, ttest_rel\n",
"df = pd.read_pickle('/home/rdingjin/UASxGal4_2026-04-28/phase6.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b3073dcc-7801-45dc-a24a-3e2e91006bdf",
"metadata": {},
"outputs": [],
"source": [
"df = df.baseline(column = 'baseline')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "124a805c-1cf8-44d0-b114-71329cb0ede5",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" ==== METADATA ====\n",
"\n",
" date machine_name region_id \\\n",
"id \n",
"2026-04-28_15-28-39_30232c|01 2026-04-28 ETHOSCOPE_302 1 \n",
"2026-04-28_15-28-39_30232c|02 2026-04-28 ETHOSCOPE_302 2 \n",
"2026-04-28_15-28-39_30232c|03 2026-04-28 ETHOSCOPE_302 3 \n",
"2026-04-28_15-28-39_30232c|04 2026-04-28 ETHOSCOPE_302 4 \n",
"2026-04-28_15-28-39_30232c|05 2026-04-28 ETHOSCOPE_302 5 \n",
"2026-04-28_15-28-39_30232c|06 2026-04-28 ETHOSCOPE_302 6 \n",
"2026-04-28_15-28-39_30232c|07 2026-04-28 ETHOSCOPE_302 7 \n",
"2026-04-28_15-28-39_30232c|08 2026-04-28 ETHOSCOPE_302 8 \n",
"2026-04-28_15-28-39_30232c|09 2026-04-28 ETHOSCOPE_302 9 \n",
"2026-04-28_15-28-39_30232c|10 2026-04-28 ETHOSCOPE_302 10 \n",
"2026-04-28_15-28-39_30232c|11 2026-04-28 ETHOSCOPE_302 11 \n",
"2026-04-28_15-28-39_30232c|12 2026-04-28 ETHOSCOPE_302 12 \n",
"2026-04-28_15-28-39_30232c|13 2026-04-28 ETHOSCOPE_302 13 \n",
"2026-04-28_15-28-39_30232c|14 2026-04-28 ETHOSCOPE_302 14 \n",
"2026-04-28_15-28-39_30232c|15 2026-04-28 ETHOSCOPE_302 15 \n",
"2026-04-28_15-28-39_30232c|16 2026-04-28 ETHOSCOPE_302 16 \n",
"2026-04-28_15-28-39_30232c|17 2026-04-28 ETHOSCOPE_302 17 \n",
"2026-04-28_15-28-39_30232c|18 2026-04-28 ETHOSCOPE_302 18 \n",
"2026-04-28_15-28-39_30232c|19 2026-04-28 ETHOSCOPE_302 19 \n",
"2026-04-28_15-28-39_30232c|20 2026-04-28 ETHOSCOPE_302 20 \n",
"\n",
" sleep_deprived species \\\n",
"id \n",
"2026-04-28_15-28-39_30232c|01 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|02 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|03 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|04 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|05 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|06 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|07 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|08 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|09 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|10 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|11 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|12 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|13 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|14 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|15 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|16 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|17 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|18 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|19 False UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"2026-04-28_15-28-39_30232c|20 True UAS_ChRmine_attP5 x 11H05_Gal4 \n",
"\n",
" food baseline sex time \n",
"id \n",
"2026-04-28_15-28-39_30232c|01 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|02 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|03 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|04 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|05 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|06 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|07 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|08 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|09 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|10 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|11 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|12 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|13 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|14 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|15 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|16 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|17 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|18 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|19 normal_food 0 male 15-28-39 \n",
"2026-04-28_15-28-39_30232c|20 normal_food 0 male 15-28-39 \n",
" ====== DATA ======\n",
"\n",
" t x y w \\\n",
"id \n",
"2026-04-28_15-28-39_30232c|01 23320 193.227273 28.863636 33.681818 \n",
"2026-04-28_15-28-39_30232c|01 23330 286.868421 30.078947 33.000000 \n",
"2026-04-28_15-28-39_30232c|01 23340 310.000000 31.000000 33.000000 \n",
"2026-04-28_15-28-39_30232c|01 23350 310.000000 31.000000 33.000000 \n",
"2026-04-28_15-28-39_30232c|01 23360 310.000000 31.000000 33.000000 \n",
"... ... ... ... ... \n",
"2026-04-28_15-28-39_30232c|20 618010 313.533333 19.000000 28.866667 \n",
"2026-04-28_15-28-39_30232c|20 618020 313.392857 19.000000 28.964286 \n",
"2026-04-28_15-28-39_30232c|20 618030 313.192308 19.000000 29.076923 \n",
"2026-04-28_15-28-39_30232c|20 618040 313.161290 19.000000 29.032258 \n",
"2026-04-28_15-28-39_30232c|20 618050 313.375000 19.000000 29.000000 \n",
"\n",
" h phi max_velocity \\\n",
"id \n",
"2026-04-28_15-28-39_30232c|01 14.000000 41.181818 89.511482 \n",
"2026-04-28_15-28-39_30232c|01 13.447368 32.289474 20.600547 \n",
"2026-04-28_15-28-39_30232c|01 13.000000 0.000000 1.428495 \n",
"2026-04-28_15-28-39_30232c|01 13.000000 0.000000 1.428495 \n",
"2026-04-28_15-28-39_30232c|01 13.000000 0.000000 1.428495 \n",
"... ... ... ... \n",
"2026-04-28_15-28-39_30232c|20 11.100000 13.600000 0.774246 \n",
"2026-04-28_15-28-39_30232c|20 11.035714 15.892857 0.744524 \n",
"2026-04-28_15-28-39_30232c|20 11.000000 15.846154 0.737698 \n",
"2026-04-28_15-28-39_30232c|20 11.000000 15.935484 0.797772 \n",
"2026-04-28_15-28-39_30232c|20 11.031250 16.312500 0.803302 \n",
"\n",
" mean_velocity dist has_interacted \\\n",
"id \n",
"2026-04-28_15-28-39_30232c|01 9.254820 0.610818 0.0 \n",
"2026-04-28_15-28-39_30232c|01 3.274451 0.373287 0.0 \n",
"2026-04-28_15-28-39_30232c|01 1.428495 0.158563 0.0 \n",
"2026-04-28_15-28-39_30232c|01 1.428495 0.175705 0.0 \n",
"2026-04-28_15-28-39_30232c|01 1.428495 0.124279 0.0 \n",
"... ... ... ... \n",
"2026-04-28_15-28-39_30232c|20 0.663063 0.059676 0.0 \n",
"2026-04-28_15-28-39_30232c|20 0.667116 0.056038 0.0 \n",
"2026-04-28_15-28-39_30232c|20 0.657137 0.051257 0.0 \n",
"2026-04-28_15-28-39_30232c|20 0.670289 0.062337 0.0 \n",
"2026-04-28_15-28-39_30232c|20 0.680839 0.065361 0.0 \n",
"\n",
" beam_cross moving micro walk \\\n",
"id \n",
"2026-04-28_15-28-39_30232c|01 0.0 True False True \n",
"2026-04-28_15-28-39_30232c|01 0.0 True False True \n",
"2026-04-28_15-28-39_30232c|01 0.0 True True False \n",
"2026-04-28_15-28-39_30232c|01 0.0 True True False \n",
"2026-04-28_15-28-39_30232c|01 0.0 True True False \n",
"... ... ... ... ... \n",
"2026-04-28_15-28-39_30232c|20 0.0 False False False \n",
"2026-04-28_15-28-39_30232c|20 0.0 False False False \n",
"2026-04-28_15-28-39_30232c|20 0.0 False False False \n",
"2026-04-28_15-28-39_30232c|20 0.0 False False False \n",
"2026-04-28_15-28-39_30232c|20 0.0 False False False \n",
"\n",
" is_interpolated asleep \n",
"id \n",
"2026-04-28_15-28-39_30232c|01 False False \n",
"2026-04-28_15-28-39_30232c|01 False False \n",
"2026-04-28_15-28-39_30232c|01 False False \n",
"2026-04-28_15-28-39_30232c|01 False False \n",
"2026-04-28_15-28-39_30232c|01 False False \n",
"... ... ... \n",
"2026-04-28_15-28-39_30232c|20 False False \n",
"2026-04-28_15-28-39_30232c|20 False False \n",
"2026-04-28_15-28-39_30232c|20 False False \n",
"2026-04-28_15-28-39_30232c|20 False False \n",
"2026-04-28_15-28-39_30232c|20 False False \n",
"\n",
"[1188874 rows x 16 columns]\n"
]
}
],
"source": [
"df.display()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "de0b9beb-7861-4091-b443-1913e4aa04b8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/srv/venv/lib/python3.12/site-packages/ethoscopy/behavpy_core.py:1611: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
" tdf.groupby(\"id\", group_keys=False).apply(\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 950x200 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"start_hour = 0\n",
"end_hour = 120\n",
"fig = df.t_filter(start_time=start_hour, end_time=end_hour).heatmap(variable = 'asleep')\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "57702793-afd1-4824-b360-50f41cd2161e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Task was destroyed but it is pending!\n",
"task: <Task pending name='Task-529' coro=<_async_in_context.<locals>.run_in_context() done, defined at /srv/venv/lib/python3.12/site-packages/ipykernel/utils.py:57> wait_for=<Task pending name='Task-530' coro=<Kernel.shell_main() running at /srv/venv/lib/python3.12/site-packages/ipykernel/kernelbase.py:597> cb=[Task.task_wakeup()]> cb=[ZMQStream._run_callback.<locals>._log_error() at /srv/venv/lib/python3.12/site-packages/zmq/eventloop/zmqstream.py:563]>\n",
"/srv/venv/lib/python3.12/site-packages/matplotlib/cbook.py:260: RuntimeWarning: coroutine 'Kernel.shell_main' was never awaited\n",
" self._signals = None if signals is None else list(signals) # Copy it.\n",
"Task was destroyed but it is pending!\n",
"task: <Task pending name='Task-530' coro=<Kernel.shell_main() running at /srv/venv/lib/python3.12/site-packages/ipykernel/kernelbase.py:597> cb=[Task.task_wakeup()]>\n",
"Task was destroyed but it is pending!\n",
"task: <Task pending name='Task-547' coro=<_async_in_context.<locals>.run_in_context() done, defined at /srv/venv/lib/python3.12/site-packages/ipykernel/utils.py:57> wait_for=<Task pending name='Task-548' coro=<Kernel.shell_main() running at /srv/venv/lib/python3.12/site-packages/ipykernel/kernelbase.py:597> cb=[Task.task_wakeup()]> cb=[ZMQStream._run_callback.<locals>._log_error() at /srv/venv/lib/python3.12/site-packages/zmq/eventloop/zmqstream.py:563]>\n",
"Task was destroyed but it is pending!\n",
"task: <Task pending name='Task-548' coro=<Kernel.shell_main() running at /srv/venv/lib/python3.12/site-packages/ipykernel/kernelbase.py:597> cb=[Task.task_wakeup()]>\n",
"Task was destroyed but it is pending!\n",
"task: <Task pending name='Task-565' coro=<_async_in_context.<locals>.run_in_context() done, defined at /srv/venv/lib/python3.12/site-packages/ipykernel/utils.py:57> wait_for=<Task pending name='Task-566' coro=<Kernel.shell_main() running at /srv/venv/lib/python3.12/site-packages/ipykernel/kernelbase.py:597> cb=[Task.task_wakeup()]> cb=[ZMQStream._run_callback.<locals>._log_error() at /srv/venv/lib/python3.12/site-packages/zmq/eventloop/zmqstream.py:563]>\n",
"Task was destroyed but it is pending!\n",
"task: <Task pending name='Task-566' coro=<Kernel.shell_main() running at /srv/venv/lib/python3.12/site-packages/ipykernel/kernelbase.py:597> cb=[Task.task_wakeup()]>\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1266.67x483.333 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"start_hour = 0\n",
"end_hour = 120\n",
"\n",
"# Generate plot from Ethoscopy\n",
"fig = df.t_filter(start_time=start_hour, end_time=end_hour).plot_overtime(variable='asleep', avg_window=30, grids=True)\n",
"\n",
"# Extract the main axis from the figure\n",
"ax = fig.axes[0]\n",
"\n",
"# Add shading stimulus shading\n",
"# Draw a vertical span from ZT 48 to ZT 72\n",
"ax.axvspan(72, 96, color='#ff4c4c', alpha=0.3, zorder=1)\n",
"\n",
"# Set custom labels\n",
"ax.set_xlabel(\"Zeitgeber Time (Hours)\", fontsize=12)\n",
"ax.set_ylabel(\"Fraction of Time Asleep\", fontsize=12)\n",
"\n",
"# Show plot\n",
"fig.show()\n",
"\n",
"# Save as a high-resolution PNG\n",
"plt.savefig(\"Figure_6(11H)_1.png\", dpi=600, bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "f64dea93-8b25-462e-9e75-e47a3703f5e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Individual Fly Sleep (Baseline: 0h to 72h, Averaged per day) ---\n",
" Fly_Label Base_Sleep_24h Base_Day_12h Base_Night_12h\n",
"ETHOSCOPE_302 - Fly 01 6.119444 2.753858 3.861111\n",
"ETHOSCOPE_302 - Fly 02 8.446296 3.862627 5.278704\n",
"ETHOSCOPE_302 - Fly 03 10.862963 4.041404 7.549074\n",
"ETHOSCOPE_302 - Fly 04 5.987037 2.223184 4.163889\n",
"ETHOSCOPE_302 - Fly 05 8.436111 3.732781 5.375000\n",
"ETHOSCOPE_302 - Fly 06 8.968519 3.315017 6.250000\n",
"ETHOSCOPE_302 - Fly 07 10.404630 3.748589 7.330556\n",
"ETHOSCOPE_302 - Fly 08 5.198148 2.900640 2.819444\n",
"ETHOSCOPE_302 - Fly 09 4.959259 3.109815 2.409259\n",
"ETHOSCOPE_302 - Fly 10 8.729630 5.744825 4.018519\n",
"ETHOSCOPE_302 - Fly 11 6.402778 2.366802 4.462037\n",
"ETHOSCOPE_302 - Fly 12 11.563889 4.842680 7.592593\n",
"ETHOSCOPE_302 - Fly 13 7.686111 4.075663 4.344444\n",
"ETHOSCOPE_302 - Fly 14 9.391667 4.764540 5.485185\n",
"ETHOSCOPE_302 - Fly 15 8.819444 3.817463 5.688889\n",
"ETHOSCOPE_302 - Fly 16 7.112037 1.676703 5.737037\n",
"ETHOSCOPE_302 - Fly 17 8.734259 2.290930 6.855556\n",
"ETHOSCOPE_302 - Fly 18 5.836111 2.669678 3.647222\n",
"ETHOSCOPE_302 - Fly 19 8.003704 3.871259 4.829630\n",
"ETHOSCOPE_302 - Fly 20 11.876852 5.086871 7.706481\n",
"... (Showing first 20 rows)\n",
"\n",
"--- Grand Summary Statistics (Baseline: 0h to 72h) ---\n",
"Total Flies Analyzed: n = 20\n",
"\n",
"[ OVERALL SLEEP (Scaled to 24h Baseline Average) ]\n",
"Mean ± SEM: 8.18 ± 0.46 hours\n",
"Median: 8.44 hours\n",
"\n",
"[ DAY TIME (ZT 0-12, Averaged to 12h blocks) ]\n",
"Mean ± SEM: 3.54 ± 0.24 hours\n",
"Median: 3.74 hours\n",
"\n",
"[ NIGHT TIME (ZT 12-24, Averaged to 12h blocks) ]\n",
"Mean ± SEM: 5.27 ± 0.36 hours\n",
"Median: 5.33 hours\n"
]
}
],
"source": [
"# BASELINE\n",
"\n",
"# Define baseline time period\n",
"start_hour = 0\n",
"end_hour = 72\n",
"\n",
"# Filter the dataset to ONLY include data within specific baseline window\n",
"base_df = df.t_filter(start_time=start_hour, end_time=end_hour).copy()\n",
"\n",
"# Calculate Overall 24h Baseline Average\n",
"sleep_hours = (base_df.groupby('id')['asleep'].sum() * 10) / 3600\n",
"\n",
"# (72 - 0) / 24 evaluates to exactly 3 days. \n",
"# Dividing total sleep by 3 to average the 72 hours into a single 24-hour metric\n",
"days_tracked = (end_hour - start_hour) / 24 \n",
"base_overall = (sleep_hours / days_tracked).reset_index(name='Base_Sleep_24h')\n",
"\n",
"# Calculate Day/Night 12h Baseline Averages\n",
"base_df['zt_hour'] = (base_df['t'] / 3600) % 24\n",
"base_df['Phase'] = np.where(base_df['zt_hour'] < 12, 'Day', 'Night')\n",
"\n",
"base_phase = base_df.groupby(['id', 'Phase']).agg(\n",
" total_sleep_epochs=('asleep', 'sum'),\n",
" total_tracking_epochs=('t', 'count')\n",
").reset_index()\n",
"\n",
"# Tracking_Hours will be ~24 for Day and ~24 for Night \n",
"# Dividing sleep by tracking hours and multiplying by 12 averages it to a single 12h block.\n",
"base_phase['Sleep_Hours'] = (base_phase['total_sleep_epochs'] * 10) / 3600\n",
"base_phase['Tracking_Hours'] = (base_phase['total_tracking_epochs'] * 10) / 3600\n",
"base_phase['Base_Sleep_12h'] = (base_phase['Sleep_Hours'] / base_phase['Tracking_Hours']) * 12\n",
"\n",
"base_pivot = base_phase.pivot(index='id', columns='Phase', values='Base_Sleep_12h').reset_index()\n",
"# Rename columns\n",
"base_pivot.columns = ['id', 'Base_Day_12h', 'Base_Night_12h']\n",
"\n",
"# Merge and clean up data\n",
"merged_df = pd.merge(base_overall, base_pivot, on='id')\n",
"\n",
"merged_df['Machine'] = merged_df['id'].map(df.meta['machine_name'])\n",
"merged_df['Tube'] = merged_df['id'].apply(lambda x: str(x).split('|')[-1])\n",
"merged_df['Fly_Label'] = merged_df['Machine'] + \" - Fly \" + merged_df['Tube']\n",
"\n",
"clean_table = merged_df.dropna(subset=['Machine']).sort_values(by=['Machine', 'Tube'])\n",
"baseline_stats = clean_table[['Fly_Label', 'Base_Sleep_24h', 'Base_Day_12h', 'Base_Night_12h']]\n",
"\n",
"# PRINT RESULTS\n",
"print(f\"--- Individual Fly Sleep (Baseline: {start_hour}h to {end_hour}h, Averaged per day) ---\")\n",
"print(baseline_stats.head(20).to_string(index=False)) \n",
"print(\"... (Showing first 20 rows)\\n\")\n",
"\n",
"print(f\"--- Grand Summary Statistics (Baseline: {start_hour}h to {end_hour}h) ---\")\n",
"print(f\"Total Flies Analyzed: n = {len(baseline_stats)}\")\n",
"\n",
"# 1. OVERALL 24H STATS\n",
"print(\"\\n[ OVERALL SLEEP (Scaled to 24h Baseline Average) ]\")\n",
"print(f\"Mean ± SEM: {baseline_stats['Base_Sleep_24h'].mean():.2f} ± {baseline_stats['Base_Sleep_24h'].sem():.2f} hours\")\n",
"print(f\"Median: {baseline_stats['Base_Sleep_24h'].median():.2f} hours\")\n",
"\n",
"# 2. DAY TIME STATS\n",
"print(f\"\\n[ DAY TIME (ZT 0-12, Averaged to 12h blocks) ]\")\n",
"print(f\"Mean ± SEM: {baseline_stats['Base_Day_12h'].mean():.2f} ± {baseline_stats['Base_Day_12h'].sem():.2f} hours\")\n",
"print(f\"Median: {baseline_stats['Base_Day_12h'].median():.2f} hours\")\n",
"\n",
"# 3. NIGHT TIME STATS\n",
"print(f\"\\n[ NIGHT TIME (ZT 12-24, Averaged to 12h blocks) ]\")\n",
"print(f\"Mean ± SEM: {baseline_stats['Base_Night_12h'].mean():.2f} ± {baseline_stats['Base_Night_12h'].sem():.2f} hours\")\n",
"print(f\"Median: {baseline_stats['Base_Night_12h'].median():.2f} hours\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "a89a9da0-2db2-4006-a9f0-0addb71ecac5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Individual Fly Sleep (Stimulus: 72h to 96h) ---\n",
" Fly_Label Stim_Sleep_24h Stim_Day_12h Stim_Night_12h\n",
"ETHOSCOPE_302 - Fly 01 8.388889 1.888889 6.500000\n",
"ETHOSCOPE_302 - Fly 02 9.630556 4.277778 5.352778\n",
"ETHOSCOPE_302 - Fly 03 12.416667 2.661111 9.755556\n",
"ETHOSCOPE_302 - Fly 04 6.447222 1.694444 4.752778\n",
"ETHOSCOPE_302 - Fly 05 9.386111 3.919444 5.466667\n",
"ETHOSCOPE_302 - Fly 06 10.580556 2.830556 7.750000\n",
"ETHOSCOPE_302 - Fly 07 10.122222 3.200000 6.922222\n",
"ETHOSCOPE_302 - Fly 08 5.427778 2.750000 2.677778\n",
"ETHOSCOPE_302 - Fly 09 5.622222 2.291667 3.330556\n",
"ETHOSCOPE_302 - Fly 10 10.438889 4.280556 6.158333\n",
"ETHOSCOPE_302 - Fly 11 6.063889 1.286111 4.777778\n",
"ETHOSCOPE_302 - Fly 12 14.011111 5.405556 8.605556\n",
"ETHOSCOPE_302 - Fly 13 8.586111 2.572222 6.013889\n",
"ETHOSCOPE_302 - Fly 14 11.230556 4.672222 6.558333\n",
"ETHOSCOPE_302 - Fly 15 11.713889 4.969444 6.744444\n",
"ETHOSCOPE_302 - Fly 16 6.391667 1.363889 5.027778\n",
"ETHOSCOPE_302 - Fly 17 7.536111 1.986111 5.550000\n",
"ETHOSCOPE_302 - Fly 18 4.133333 1.169444 2.963889\n",
"ETHOSCOPE_302 - Fly 19 14.430556 5.975000 8.455556\n",
"ETHOSCOPE_302 - Fly 20 12.691667 4.383333 8.308333\n",
"... (Showing first 20 rows)\n",
"\n",
"--- Grand Summary Statistics (Stimulus: 72h to 96h) ---\n",
"Total Flies Analyzed: n = 20\n",
"\n",
"[ OVERALL SLEEP (24h Stimulus Block) ]\n",
"Mean ± SEM: 9.26 ± 0.67 hours\n",
"Median: 9.51 hours\n",
"\n",
"[ DAY TIME (ZT 0-12 during Stimulus) ]\n",
"Mean ± SEM: 3.18 ± 0.33 hours\n",
"Median: 2.79 hours\n",
"\n",
"[ NIGHT TIME (ZT 12-24 during Stimulus) ]\n",
"Mean ± SEM: 6.08 ± 0.43 hours\n",
"Median: 6.09 hours\n"
]
}
],
"source": [
"# Define stimulus time period\n",
"start_hour = 72\n",
"end_hour = 96\n",
"\n",
"# Filter the dataset to ONLY include data within specific stimulus window\n",
"stim_df = df.t_filter(start_time=start_hour, end_time=end_hour).copy()\n",
"\n",
"# Calculate Overall 24h Stimulus Average\n",
"sleep_hours = (stim_df.groupby('id')['asleep'].sum() * 10) / 3600\n",
"days_tracked = (end_hour - start_hour) / 24 \n",
"stim_overall = (sleep_hours / days_tracked).reset_index(name='Stim_Sleep_24h')\n",
"\n",
"# Calculate Day/Night 12h Stimulus Averages\n",
"stim_df['zt_hour'] = (stim_df['t'] / 3600) % 24\n",
"stim_df['Phase'] = np.where(stim_df['zt_hour'] < 12, 'Day', 'Night')\n",
"\n",
"stim_phase = stim_df.groupby(['id', 'Phase']).agg(\n",
" total_sleep_epochs=('asleep', 'sum'),\n",
" total_tracking_epochs=('t', 'count')\n",
").reset_index()\n",
"\n",
"# Tracking_Hours will now be ~12 for Day and ~12 for Night\n",
"# Dividing sleep by tracking hours and multiplying by 12 averages it to a single 12h block.\n",
"stim_phase['Sleep_Hours'] = (stim_phase['total_sleep_epochs'] * 10) / 3600\n",
"stim_phase['Tracking_Hours'] = (stim_phase['total_tracking_epochs'] * 10) / 3600\n",
"stim_phase['Stim_Sleep_12h'] = (stim_phase['Sleep_Hours'] / stim_phase['Tracking_Hours']) * 12\n",
"\n",
"stim_pivot = stim_phase.pivot(index='id', columns='Phase', values='Stim_Sleep_12h').reset_index()\n",
"# Rename columns \n",
"stim_pivot.columns = ['id', 'Stim_Day_12h', 'Stim_Night_12h']\n",
"\n",
"# Merge and clean up data\n",
"merged_df = pd.merge(stim_overall, stim_pivot, on='id')\n",
"\n",
"merged_df['Machine'] = merged_df['id'].map(df.meta['machine_name'])\n",
"merged_df['Tube'] = merged_df['id'].apply(lambda x: str(x).split('|')[-1])\n",
"merged_df['Fly_Label'] = merged_df['Machine'] + \" - Fly \" + merged_df['Tube']\n",
"\n",
"clean_table = merged_df.dropna(subset=['Machine']).sort_values(by=['Machine', 'Tube'])\n",
"stim_stats = clean_table[['Fly_Label', 'Stim_Sleep_24h', 'Stim_Day_12h', 'Stim_Night_12h']]\n",
"\n",
"# PRINT RESULTS\n",
"print(f\"--- Individual Fly Sleep (Stimulus: {start_hour}h to {end_hour}h) ---\")\n",
"print(stim_stats.head(20).to_string(index=False)) \n",
"print(\"... (Showing first 20 rows)\\n\")\n",
"\n",
"print(f\"--- Grand Summary Statistics (Stimulus: {start_hour}h to {end_hour}h) ---\")\n",
"print(f\"Total Flies Analyzed: n = {len(stim_stats)}\")\n",
"\n",
"# 1. OVERALL 24H STATS\n",
"print(\"\\n[ OVERALL SLEEP (24h Stimulus Block) ]\")\n",
"print(f\"Mean ± SEM: {stim_stats['Stim_Sleep_24h'].mean():.2f} ± {stim_stats['Stim_Sleep_24h'].sem():.2f} hours\")\n",
"print(f\"Median: {stim_stats['Stim_Sleep_24h'].median():.2f} hours\")\n",
"\n",
"# 2. DAY TIME STATS\n",
"print(f\"\\n[ DAY TIME (ZT 0-12 during Stimulus) ]\")\n",
"print(f\"Mean ± SEM: {stim_stats['Stim_Day_12h'].mean():.2f} ± {stim_stats['Stim_Day_12h'].sem():.2f} hours\")\n",
"print(f\"Median: {stim_stats['Stim_Day_12h'].median():.2f} hours\")\n",
"\n",
"# 3. NIGHT TIME STATS\n",
"print(f\"\\n[ NIGHT TIME (ZT 12-24 during Stimulus) ]\")\n",
"print(f\"Mean ± SEM: {stim_stats['Stim_Night_12h'].mean():.2f} ± {stim_stats['Stim_Night_12h'].sem():.2f} hours\")\n",
"print(f\"Median: {stim_stats['Stim_Night_12h'].median():.2f} hours\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "9be717e4-a077-468d-aafc-21479f1beac1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"--- 24h Total Statistical Analysis ---\n",
"Shapiro-Wilk P-value (Normality of differences): 0.0631\n",
"Result: Data is normal. Used Paired T-test.\n",
"P-value: 1.2030e-02\n",
"Significance: *\n",
"\n",
"--- Daytime Statistical Analysis ---\n",
"Shapiro-Wilk P-value (Normality of differences): 0.1149\n",
"Result: Data is normal. Used Paired T-test.\n",
"P-value: 9.3149e-02\n",
"Significance: ns\n",
"\n",
"--- Nighttime Statistical Analysis ---\n",
"Shapiro-Wilk P-value (Normality of differences): 0.9500\n",
"Result: Data is normal. Used Paired T-test.\n",
"P-value: 8.5019e-03\n",
"Significance: **\n"
]
}
],
"source": [
"# STATISTICS\n",
"\n",
"# Merge Baseline and Stimulus data on Fly_Label\n",
"# This ensures we are comparing the SAME fly to itself\n",
"paired_df = pd.merge(baseline_stats, stim_stats, on='Fly_Label')\n",
"\n",
"def run_paired_stats(df, base_col, stim_col, label):\n",
" # Calculate the differences for the paired test\n",
" diffs = df[stim_col] - df[base_col]\n",
" \n",
" # 2. Shapiro-Wilk Test for Normality\n",
" _, p_shapiro = shapiro(diffs)\n",
" \n",
" print(f\"\\n--- {label} Statistical Analysis ---\")\n",
" print(f\"Shapiro-Wilk P-value (Normality of differences): {p_shapiro:.4f}\")\n",
" \n",
" # 3. Determine Test: If P > 0.05, data is normal (use t-test). If P < 0.05, use Wilcoxon.\n",
" if p_shapiro > 0.05:\n",
" stat, p_val = ttest_rel(df[base_col], df[stim_col])\n",
" print(f\"Result: Data is normal. Used Paired T-test.\")\n",
" else:\n",
" stat, p_val = wilcoxon(df[base_col], df[stim_col])\n",
" print(f\"Result: Data is not normal. Used Wilcoxon Signed-Rank test.\")\n",
" \n",
" print(f\"P-value: {p_val:.4e}\")\n",
" if p_val < 0.0001: print(\"Significance: ****\")\n",
" elif p_val < 0.001: print(\"Significance: ***\")\n",
" elif p_val < 0.01: print(\"Significance: **\")\n",
" elif p_val < 0.05: print(\"Significance: *\")\n",
" else: print(\"Significance: ns\")\n",
"\n",
"# Run for all three metrics\n",
"run_paired_stats(paired_df, 'Base_Sleep_24h', 'Stim_Sleep_24h', '24h Total')\n",
"run_paired_stats(paired_df, 'Base_Day_12h', 'Stim_Day_12h', 'Daytime')\n",
"run_paired_stats(paired_df, 'Base_Night_12h', 'Stim_Night_12h', 'Nighttime')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "eeabf9c2-6a02-41ee-b756-a473a4224032",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#PLOT\n",
"\n",
"# 1. PREPARE DATA & CALCULATE STATS\n",
"combined = pd.merge(baseline_stats, stim_stats, on='Fly_Label')\n",
"\n",
"# Calculate the paired statistics for the brackets\n",
"p_values = {}\n",
"metrics = {\n",
" '24h': ('Base_Sleep_24h', 'Stim_Sleep_24h'),\n",
" 'Day': ('Base_Day_12h', 'Stim_Day_12h'),\n",
" 'Night': ('Base_Night_12h', 'Stim_Night_12h')\n",
"}\n",
"\n",
"for phase, (base_col, stim_col) in metrics.items():\n",
" diffs = combined[stim_col] - combined[base_col]\n",
" _, p_shapiro = shapiro(diffs)\n",
" \n",
" if p_shapiro > 0.05:\n",
" _, p_val = ttest_rel(combined[base_col], combined[stim_col])\n",
" else:\n",
" _, p_val = wilcoxon(combined[base_col], combined[stim_col])\n",
" p_values[phase] = p_val\n",
"\n",
"plot_df = combined.melt(id_vars=['Fly_Label'], var_name='Variable', value_name='Hours')\n",
"plot_df['Condition'] = plot_df['Variable'].apply(lambda x: '24h' if '24h' in x else ('Day' if 'Day' in x else 'Night'))\n",
"plot_df['State'] = plot_df['Variable'].apply(lambda x: 'Baseline' if 'Base' in x else 'Stimulus')\n",
"\n",
"# 2. GENERATE PLOT\n",
"plt.figure(figsize=(7, 6))\n",
"sns.set_theme(style=\"ticks\")\n",
"\n",
"order = ['24h', 'Day', 'Night']\n",
"hue_order = ['Baseline', 'Stimulus']\n",
"palette = {'Baseline': '#cccccc', 'Stimulus': '#ff4c4c'}\n",
"\n",
"# Boxplot \n",
"sns.boxplot(\n",
" data=plot_df, x='Condition', y='Hours', hue='State', \n",
" order=order, hue_order=hue_order, palette=palette,\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",
"# Stripplot\n",
"sns.stripplot(\n",
" data=plot_df, x='Condition', y='Hours', hue='State', \n",
" order=order, hue_order=hue_order, dodge=True,\n",
" palette=['black', 'black'], alpha=0.5, size=4, jitter=0.05,\n",
" legend=False\n",
")\n",
"\n",
"# 3. ADD MEDIAN LABELS\n",
"ax = plt.gca()\n",
"medians = plot_df.groupby(['Condition', 'State'], sort=False)['Hours'].median()\n",
"\n",
"# Offsets remain exactly as provided\n",
"offsets = {'Baseline': -0.4, 'Stimulus': 0.4}\n",
"\n",
"for i, cond in enumerate(order):\n",
" for state in hue_order:\n",
" val = medians[(cond, state)]\n",
" ax.text(i + offsets[state], val, f\"{val:.1f}h\", \n",
" ha='center', va='bottom', fontsize=10)\n",
"\n",
"# 4. ADD SIGNIFICANCE BRACKETS\n",
"y_max = plot_df['Hours'].max()\n",
"y_line = y_max + 1.0 \n",
"h = 0.4 \n",
"\n",
"for i, cond in enumerate(order):\n",
" p = p_values[cond]\n",
" \n",
" if p < 0.0001: star = '****'\n",
" elif p < 0.001: star = '***'\n",
" elif p < 0.01: star = '**'\n",
" elif p < 0.05: star = '*'\n",
" else: star = 'ns'\n",
" \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",
"# 5. FORMATTING\n",
"sns.despine()\n",
"plt.ylabel('Average time spent asleep (hours)', fontsize=12)\n",
"plt.xlabel('')\n",
"plt.xlim(-0.6, 2.5) \n",
"\n",
"plt.ylim(0, max(20, y_line + h + 1.5)) \n",
"\n",
"# Legend moved outside the plot to the right\n",
"plt.legend(title='', frameon=False, loc='center left', bbox_to_anchor=(1, 0.5))\n",
"plt.tight_layout()\n",
"\n",
"plt.savefig(\"Phase6(11H)_Baseline_vs_Stimulus.png\", dpi=600, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "681dc07b-e5f7-4682-aee0-c8fca43af2ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Delta Sleep (Stimulus - Baseline) per Fly ---\n",
" Fly_Label Delta_24h Delta_Day Delta_Night\n",
"ETHOSCOPE_302 - Fly 01 2.269444 -0.864969 2.638889\n",
"ETHOSCOPE_302 - Fly 02 1.184259 0.415151 0.074074\n",
"ETHOSCOPE_302 - Fly 03 1.553704 -1.380293 2.206481\n",
"ETHOSCOPE_302 - Fly 04 0.460185 -0.528740 0.588889\n",
"ETHOSCOPE_302 - Fly 05 0.950000 0.186663 0.091667\n",
"ETHOSCOPE_302 - Fly 06 1.612037 -0.484461 1.500000\n",
"ETHOSCOPE_302 - Fly 07 -0.282407 -0.548589 -0.408333\n",
"ETHOSCOPE_302 - Fly 08 0.229630 -0.150640 -0.141667\n",
"ETHOSCOPE_302 - Fly 09 0.662963 -0.818148 0.921296\n",
"ETHOSCOPE_302 - Fly 10 1.709259 -1.464269 2.139815\n",
"ETHOSCOPE_302 - Fly 11 -0.338889 -1.080690 0.315741\n",
"ETHOSCOPE_302 - Fly 12 2.447222 0.562876 1.012963\n",
"ETHOSCOPE_302 - Fly 13 0.900000 -1.503441 1.669444\n",
"ETHOSCOPE_302 - Fly 14 1.838889 -0.092318 1.073148\n",
"ETHOSCOPE_302 - Fly 15 2.894444 1.151981 1.055556\n",
"ETHOSCOPE_302 - Fly 16 -0.720370 -0.312814 -0.709259\n",
"ETHOSCOPE_302 - Fly 17 -1.198148 -0.304819 -1.305556\n",
"ETHOSCOPE_302 - Fly 18 -1.702778 -1.500234 -0.683333\n",
"ETHOSCOPE_302 - Fly 19 6.426852 2.103741 3.625926\n",
"ETHOSCOPE_302 - Fly 20 0.814815 -0.703537 0.601852\n",
"\n",
"==================================================\n",
"\n",
"--- Average Change (Delta) across all flies ---\n",
"Delta_24h: 1.09 ± 0.39 hours\n",
"Delta_Day: -0.37 ± 0.21 hours\n",
"Delta_Night: 0.81 ± 0.28 hours\n"
]
}
],
"source": [
"#DELTA\n",
"\n",
"# 1. Ensure we have the merged paired data\n",
"combined = pd.merge(baseline_stats, stim_stats, on='Fly_Label')\n",
"\n",
"# 2. Calculate the Delta (Stimulus - Baseline) for each fly\n",
"combined['Delta_24h'] = combined['Stim_Sleep_24h'] - combined['Base_Sleep_24h']\n",
"combined['Delta_Day'] = combined['Stim_Day_12h'] - combined['Base_Day_12h']\n",
"combined['Delta_Night'] = combined['Stim_Night_12h'] - combined['Base_Night_12h']\n",
"\n",
"# 3. Print the Delta for each individual fly\n",
"print(\"--- Delta Sleep (Stimulus - Baseline) per Fly ---\")\n",
"delta_table = combined[['Fly_Label', 'Delta_24h', 'Delta_Day', 'Delta_Night']]\n",
"print(delta_table.to_string(index=False))\n",
"print(\"\\n\" + \"=\"*50 + \"\\n\")\n",
"\n",
"# 4. Calculate and print the group averages\n",
"print(\"--- Average Change (Delta) across all flies ---\")\n",
"avg_delta = delta_table[['Delta_24h', 'Delta_Day', 'Delta_Night']].mean()\n",
"sem_delta = delta_table[['Delta_24h', 'Delta_Day', 'Delta_Night']].sem()\n",
"\n",
"for metric in ['Delta_24h', 'Delta_Day', 'Delta_Night']:\n",
" print(f\"{metric}: {avg_delta[metric]:.2f} ± {sem_delta[metric]:.2f} hours\")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "ff242cdd-721c-4e06-a9a6-91591589fe8a",
"metadata": {},
"outputs": [],
"source": [
"#export to csv\n",
"delta_table['Condition'] = 'Red'\n",
"delta_table.to_csv('delta_11H_phase6.csv', index=False)"
]
}
],
"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
}
Reference in a new issue Copy permalink