{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statistics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CanoPyHydro provides access to a wealth of data regarding tree canopies. \n",
"\n",
"To make these statistics more accessable, they may be calculated in bulk via the 'statistics' function. \\\n",
"The function is called below, and definitions for the statistics can be found in the Glossary. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"2024.10.14 21:18:19.499 |MainThread | INFO | CylinderCollection.py:291 - from_csv() | model - Processing <_io.TextIOWrapper name='./data/input/5_SmallTree.csv' mode='r' encoding='UTF-8'>\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:19.499 |MainThread | INFO | CylinderCollection.py:291 - from_csv() | model - Processing <_io.TextIOWrapper name='./data/input/5_SmallTree.csv' mode='r' encoding='UTF-8'>\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"2024.10.14 21:18:19.535 |MainThread | INFO | CylinderCollection.py:320 - from_csv() | model - ./data/input/5_SmallTree.csv initialized with 517 cylinders\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:19.535 |MainThread | INFO | CylinderCollection.py:320 - from_csv() | model - ./data/input/5_SmallTree.csv initialized with 517 cylinders\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"2024.10.14 21:18:19.539 |MainThread | INFO | CylinderCollection.py:335 - project_cylinders() | model - Projection into XY axis begun for file 5_SmallTree.csv\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:19.539 |MainThread | INFO | CylinderCollection.py:335 - project_cylinders() | model - Projection into XY axis begun for file 5_SmallTree.csv\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"2024.10.14 21:18:21.362 |MainThread | INFO | CylinderCollection.py:344 - project_cylinders() | model - Projection into XY axis complete for file 5_SmallTree.csv\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:21.362 |MainThread | INFO | CylinderCollection.py:344 - project_cylinders() | model - Projection into XY axis complete for file 5_SmallTree.csv\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"2024.10.14 21:18:21.915 |MainThread | INFO | CylinderCollection.py:769 - find_flow_components() | model - 5_SmallTree.csv found to have 70 drip components\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:21.915 |MainThread | INFO | CylinderCollection.py:769 - find_flow_components() | model - 5_SmallTree.csv found to have 70 drip components\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"reached_End of find flows\n"
]
},
{
"data": {
"text/html": [
"2024.10.14 21:18:22.034 |MainThread | INFO | CylinderCollection.py:996 - statistics() | model - Found hull alpha shape stats\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:22.034 |MainThread | INFO | CylinderCollection.py:996 - statistics() | model - Found hull alpha shape stats\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"2024.10.14 21:18:22.400 |MainThread | INFO | CylinderCollection.py:1016 - statistics() | model - found projected areas\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:22.400 |MainThread | INFO | CylinderCollection.py:1016 - statistics() | model - found projected areas\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"2024.10.14 21:18:23.113 |MainThread | INFO | utils.py:150 - save_file() | model - [['total_psa', 'psa_w_overlap', 'stem_psa', 'stem_psa_w_overlap', 'tot_surface_area', 'stem_surface_area', 'tot_hull_area', 'tot_hull_boundary', 'stem_hull_area', 'stem_hull_boundary', 'num_drip_points', 'max_bo', 'topQuarterTotPsa', 'topHalfTotPsa', 'topThreeQuarterTotPsa', 'TotalShade', 'top_quarter_shade', 'top_half_shade', 'top_three_quarter_shade', 'DBH', 'volume', 'X_max', 'Y_max', 'Z_max', 'X_min', 'Y_min', 'Z_min', 'Order_zero_angle_avg', 'Order_zero_angle_std', 'Order_one_angle_avg', 'Order_one_angle_std', 'Order_two_angle_avg', 'Order_two_angle_std', 'Order_three_angle_avg', 'Order_three_angle_std', 'order_gr_four_angle_avg', 'order_gr_four_angle_std', 'file_name']]\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:23.113 |MainThread | INFO | utils.py:150 - save_file() | model - [['total_psa', 'psa_w_overlap', 'stem_psa', 'stem_psa_w_overlap', 'tot_surface_area', 'stem_surface_area', 'tot_hull_area', 'tot_hull_boundary', 'stem_hull_area', 'stem_hull_boundary', 'num_drip_points', 'max_bo', 'topQuarterTotPsa', 'topHalfTotPsa', 'topThreeQuarterTotPsa', 'TotalShade', 'top_quarter_shade', 'top_half_shade', 'top_three_quarter_shade', 'DBH', 'volume', 'X_max', 'Y_max', 'Z_max', 'X_min', 'Y_min', 'Z_min', 'Order_zero_angle_avg', 'Order_zero_angle_std', 'Order_one_angle_avg', 'Order_one_angle_std', 'Order_two_angle_avg', 'Order_two_angle_std', 'Order_three_angle_avg', 'Order_three_angle_std', 'order_gr_four_angle_avg', 'order_gr_four_angle_std', 'file_name']]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"2024.10.14 21:18:23.121 |MainThread | INFO | utils.py:177 - save_file() | model - attempting to write to ./data/output//statistics/5_SmallTree__statistics.csv\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:23.121 |MainThread | INFO | utils.py:177 - save_file() | model - attempting to write to ./data/output//statistics/5_SmallTree__statistics.csv\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# This function \n",
"import os\n",
"\n",
"os.environ[\"CANOPYHYDRO_CONFIG\"] = \"./canopyhydro_config.toml\"\n",
"from canopyhydro.CylinderCollection import CylinderCollection\n",
"\n",
"# Initializing a CylinderCollection object\n",
"myCollection = CylinderCollection()\n",
"\n",
"# Converting a specified file to a CylinderCollection object\n",
"myCollection.from_csv(\"5_SmallTree.csv\")\n",
"\n",
"# Requesting an plot of the tree projected onto the XY plane (birds-eye view)\n",
"myCollection.project_cylinders(\"XY\")\n",
"\n",
"# creating the digraph model\n",
"myCollection.initialize_digraph_from()\n",
"\n",
"stat_file = myCollection.statistics()\n",
"\n",
"# Will generate a file with all of the statisics listed below \n",
"# total_psa\n",
"# psa_w_overlap\n",
"# stem_psa\n",
"# stem_psa_w_overlap\n",
"# tot_surface_area\n",
"# stem_surface_area\n",
"# tot_hull_area\n",
"# tot_hull_boundary\n",
"# stem_hull_area\n",
"\n",
"# stem_hull_boundary\n",
"# num_drip_points\n",
"# max_bo\n",
"# topQuarterTotPsa\n",
"# topHalfTotPsa\n",
"# topThreeQuarterTotPsa\n",
"# TotalShade\n",
"# top_quarter_shade\n",
"# top_half_shade\n",
"# top_three_quarter_shade\n",
"# DBH\n",
"# volume\n",
"# X_max\n",
"# Y_max\n",
"# Z_max\n",
"# X_min\n",
"# Y_min\n",
"# Z_min\n",
"# Order_zero_angle_avg\n",
"# Order_zero_angle_std\n",
"# Order_one_angle_avg\n",
"# Order_one_angle_std\n",
"# Order_two_angle_avg\n",
"# Order_two_angle_std\n",
"# Order_three_angle_avg\n",
"# Order_three_angle_std\n",
"# order_gr_four_angle_avg\n",
"# order_gr_four_angle_std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to this bulk function, some individual statistics can be found using a variety of dedicated functions. A few such funcitons are shown below."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"2024.10.14 21:18:46.444 |MainThread | INFO | CylinderCollection.py:769 - find_flow_components() | model - 5_SmallTree.csv found to have 70 drip components\n",
" \n"
],
"text/plain": [
"2024.10.14 21:18:46.444 |MainThread | INFO | CylinderCollection.py:769 - find_flow_components() | model - 5_SmallTree.csv found to have 70 drip components\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"reached_End of find flows\n",
"{25: {'sum_area': 0.03185684182767693, 'effective_area': 0.029894983741628172, 'internal_overlap': 0.0019618580860487587, 'overlap_with_previous': 0.029894983741628172}, 50: {'sum_area': 0.029130503264231784, 'effective_area': 0.027960517250591167, 'internal_overlap': 0.0011699860136406177, 'overlap_with_previous': 0.027960517250591167}, 75: {'sum_area': 0.029862028471880096, 'effective_area': 0.02854408983609465, 'internal_overlap': 0.0013179386357854463, 'overlap_with_previous': 0.028544089836094647}}\n",
"{33: {'sum_area': 0.04205788206106595, 'effective_area': 0.03938659205799218, 'internal_overlap': 0.00267129000307377, 'overlap_with_previous': 0.03938659205799218}, 66: {'sum_area': 0.038149333485791935, 'effective_area': 0.03639941352493353, 'internal_overlap': 0.0017499199608584023, 'overlap_with_previous': 0.03639941352493353}, 99: {'sum_area': 0.03757534277917172, 'effective_area': 0.03516864948497811, 'internal_overlap': 0.0024066932941936084, 'overlap_with_previous': 0.03516864948497811}}\n",
"{10: {'sum_area': 0.010467265213352325, 'effective_area': 0.009682942614845636, 'internal_overlap': 0.0007843225985066891, 'overlap_with_previous': 0.009682942614845636}, 20: {'sum_area': 0.013024295480890522, 'effective_area': 0.01197165484170726, 'internal_overlap': 0.0010526406391832624, 'overlap_with_previous': 0.011971654841707261}, 30: {'sum_area': 0.011715396253001897, 'effective_area': 0.01071389206352086, 'internal_overlap': 0.001001504189481037, 'overlap_with_previous': 0.010713892063520858}, 70: {'sum_area': 0.047188893724672075, 'effective_area': 0.04288988057954176, 'internal_overlap': 0.004299013145130315, 'overlap_with_previous': 0.04288988057954176}}\n",
"1.058276\n"
]
}
],
"source": [
"# Identifying flow information (covered later)\n",
"myCollection.find_flow_components()\n",
"myCollection.calculate_flows(plane='XY')\n",
"\n",
"# Identifying the overlap of branches at various heights/depths in the canopy\n",
"print(myCollection.find_overlap_by_percentile(plane = 'XY'))\n",
"print(myCollection.find_overlap_by_percentile(plane = 'XY',percentiles=[33,66,99]))\n",
"print(myCollection.find_overlap_by_percentile(plane = 'XZ',percentiles=[10,20,30,70]))\n",
"\n",
"# Diameter at breast height\n",
"print(myCollection.get_dbh())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Though many statistics do not have functions used to calculate them directly. Many are readily accessible. \\\n",
"Browsing through the larger 'statistics' function and the 'calculate_flows_function' can show users methods for finding a variety of summary statistics. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"xy_projected_area=16.612411399042763, xz_projected_area=9.643371487823918, total_volume=3.9067559999999997\n"
]
}
],
"source": [
"# Finding the total projected area \n",
"# from 'statistics'\n",
"import numpy as np\n",
"from canopyhydro.geometry import unary_union\n",
"twod_polys = myCollection.pSV\n",
"projection_of_all_branches = unary_union(twod_polys)\n",
"projected_area_without_overlap = projection_of_all_branches.area\n",
"\n",
"# Finding the sum of all cylinder projected areas\n",
"xy_projected_area= np.sum([cyl.projected_data['XY'][\"area\"]\n",
" for cyl in myCollection.cylinders]\n",
" )\n",
"xz_projected_area= np.sum([cyl.projected_data['XZ'][\"area\"]\n",
" for cyl in myCollection.cylinders]\n",
" )\n",
"total_volume= np.sum([cyl.volume\n",
" for cyl in myCollection.cylinders]\n",
" )\n",
"print(f'{xy_projected_area=}, {xz_projected_area=}, {total_volume=}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Flow Statistics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For additional details on finding flow attributes, see [Flow Identification]<(file_ref.here)>. \\\n",
"The main output of the calculate flows function is the 'flows' attribute, which is added to myCollection and is populated with flow statistics (See below)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Flow(num_cylinders=216.0, projected_area=16.51706021532694, surface_area=19.495074988550606, angle_sum=180.03288665020426, volume=3.9062960000000007, sa_to_vol=83646.49652248439, drip_node_id=0.0, drip_node_loc=(-0.299115, 2.537844, -0.598273)),\n",
" Flow(num_cylinders=2, projected_area=0.0005597565466076131, surface_area=0.001924052712727801, angle_sum=-0.8861661864942612, volume=2e-06, sa_to_vol=1924.052712727801, drip_node_id=148, drip_node_loc=(1.736771, 2.700067, 14.216883)),\n",
" Flow(num_cylinders=4, projected_area=0.0012679984136685896, surface_area=0.0043875954238933096, angle_sum=-1.1103646559240672, volume=4.9999999999999996e-06, sa_to_vol=3681.4517891642977, drip_node_id=150, drip_node_loc=(1.476039, 2.744678, 14.221036)),\n",
" Flow(num_cylinders=1, projected_area=0.00026175097799692134, surface_area=0.0008268043545717619, angle_sum=0.20259148846207123, volume=1e-06, sa_to_vol=826.8043545717619, drip_node_id=159, drip_node_loc=(1.298384, 2.459679, 14.389457)),\n",
" Flow(num_cylinders=3, projected_area=0.0008824774277666259, surface_area=0.0032463019367339413, angle_sum=-1.7087376025874943, volume=3e-06, sa_to_vol=3246.3019367339416, drip_node_id=164, drip_node_loc=(1.047176, 2.391643, 14.266511)),\n",
" Flow(num_cylinders=2, projected_area=0.0006600186476422296, surface_area=0.0023746199311056493, angle_sum=0.534393748750537, volume=3e-06, sa_to_vol=1719.5271769974715, drip_node_id=175, drip_node_loc=(1.730164, 2.302889, 14.604732)),\n",
" Flow(num_cylinders=2, projected_area=0.0004876054032408554, surface_area=0.001917581031861406, angle_sum=1.1425108485836808, volume=3e-06, sa_to_vol=1179.8879529087187, drip_node_id=179, drip_node_loc=(1.652252, 2.280224, 14.47647)),\n",
" Flow(num_cylinders=1, projected_area=0.00041651108754572896, surface_area=0.0014212408085207547, angle_sum=-0.4465420656788475, volume=2e-06, sa_to_vol=710.6204042603774, drip_node_id=183, drip_node_loc=(1.455036, 2.239273, 14.49977)),\n",
" Flow(num_cylinders=2, projected_area=0.0012733736410329834, surface_area=0.004064592575214475, angle_sum=-0.3665923044681696, volume=4.9999999999999996e-06, sa_to_vol=1666.127955868079, drip_node_id=187, drip_node_loc=(1.917529, 2.347151, 14.212591)),\n",
" Flow(num_cylinders=0, projected_area=0.0, surface_area=0.0, angle_sum=0.0, volume=0.0, sa_to_vol=0.0, drip_node_id=189, drip_node_loc=(1.984224, 2.47876, 14.194845)),\n",
" Flow(num_cylinders=6, projected_area=0.0022330283517565724, surface_area=0.007333246943678708, angle_sum=2.127804515876493, volume=9.999999999999999e-06, sa_to_vol=4446.06831715825, drip_node_id=193, drip_node_loc=(1.583993, 2.563848, 13.968024)),\n",
" Flow(num_cylinders=1, projected_area=0.00020343043979622444, surface_area=0.0006478435290600194, angle_sum=0.2846026246124941, volume=1e-06, sa_to_vol=647.8435290600194, drip_node_id=198, drip_node_loc=(1.284843, 2.628993, 14.019867)),\n",
" Flow(num_cylinders=3, projected_area=0.0007860084747630203, surface_area=0.003617512524682111, angle_sum=-2.5833501908977685, volume=4.9999999999999996e-06, sa_to_vol=2085.8447343876755, drip_node_id=208, drip_node_loc=(1.325472, 2.856421, 14.043133)),\n",
" Flow(num_cylinders=2, projected_area=0.0006471769977986668, surface_area=0.0020395847825635657, angle_sum=0.26090730746353386, volume=2e-06, sa_to_vol=2039.584782563566, drip_node_id=209, drip_node_loc=(1.435887, 2.797216, 14.208758)),\n",
" Flow(num_cylinders=8, projected_area=0.0017034251569991014, surface_area=0.007463890074178239, angle_sum=5.354426428330182, volume=8.999999999999999e-06, sa_to_vol=6831.314685414667, drip_node_id=214, drip_node_loc=(1.178362, 2.670965, 14.017909)),\n",
" Flow(num_cylinders=3, projected_area=0.0012809041391582444, surface_area=0.004173213141212342, angle_sum=0.3207042500076585, volume=6e-06, sa_to_vol=2086.606570606171, drip_node_id=219, drip_node_loc=(1.152153, 2.815179, 13.969726)),\n",
" Flow(num_cylinders=2, projected_area=0.0008570559793830896, surface_area=0.0027383692365015432, angle_sum=-0.10344238545184348, volume=4e-06, sa_to_vol=1369.1846182507718, drip_node_id=222, drip_node_loc=(1.607588, 2.540239, 13.949144)),\n",
" Flow(num_cylinders=0, projected_area=0.0, surface_area=0.0, angle_sum=0.0, volume=0.0, sa_to_vol=0.0, drip_node_id=227, drip_node_loc=(1.868195, 2.555224, 13.948772)),\n",
" Flow(num_cylinders=18, projected_area=0.005313162793692856, surface_area=0.021339283807471944, angle_sum=10.274852917465788, volume=2.6e-05, sa_to_vol=14370.354273860652, drip_node_id=232, drip_node_loc=(1.920238, 2.162247, 13.938462)),\n",
" Flow(num_cylinders=7, projected_area=0.0029795345832851253, surface_area=0.013586461456943049, angle_sum=4.695171005225782, volume=1.8e-05, sa_to_vol=5378.31106616918, drip_node_id=242, drip_node_loc=(2.047486, 1.966398, 14.326444))]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# printing the first 20 flows found \n",
"myCollection.flows[0:20]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"The first element of the above array is always stemflow. The other flows are all drip flows (those that contribute to through fall) and are listed in no particular order. \\\n",
"Note also that each flow has a 'drip_node_loc' (drip node location) listed for each flow. This attribute refers to the x,y and z coordinates of the point at which a flow drops off of a branch to the ground. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to defining 'myCollection.flows', the above functions set the attribute 'is_stem' for each cylinder. \\\n",
"By using 'is_stem', we can isolate the stemflow generating portion of the tree. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"2024.10.14 21:19:27.690 |MainThread | INFO | geometry.py:563 - draw_cyls() | model - Plotting cylinder collection\n",
" \n"
],
"text/plain": [
"2024.10.14 21:19:27.690 |MainThread | INFO | geometry.py:563 - draw_cyls() | model - Plotting cylinder collection\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"2024.10.14 21:19:27.904 |MainThread | INFO | geometry.py:563 - draw_cyls() | model - Plotting cylinder collection\n",
" \n"
],
"text/plain": [
"2024.10.14 21:19:27.904 |MainThread | INFO | geometry.py:563 - draw_cyls() | model - Plotting cylinder collection\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"whole_tree_hull,_ = myCollection.watershed_boundary(\n",
" plane=\"XY\",\n",
" curvature_alpha=0.15,\n",
" draw=True,\n",
")\n",
"\n",
"# plotting the boundary of the stemflow generating portion alone\n",
"stem_flow_hull,_ = myCollection.watershed_boundary(\n",
" plane=\"XY\",\n",
" curvature_alpha=0.15,\n",
" filter_lambda=lambda: is_stem ,\n",
" draw=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In doing so, we can calculate statistics for only the subset of cylinders that contribute to stemflow."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Whole Tree Coverage Area:\n",
"4.828740395823999\n",
"Stem Flow Coverage Area:\n",
"2.66555606734\n",
"\n",
"Whole Tree Coverage Bounds:\n",
"(-0.788937, 1.434109, 2.336527, 4.043691)\n",
"Stem Flow Coverage Bounds:\n",
"(-0.788937, 1.434109, 2.241708, 3.394286)\n",
"\n"
]
}
],
"source": [
"# Demonstrating how statistics can be generated from hul objects\n",
"print(type(stem_flow_hull))\n",
"\n",
"print('Whole Tree Coverage Area:')\n",
"print(whole_tree_hull.area)\n",
"\n",
"print('Stem Flow Coverage Area:')\n",
"print(stem_flow_hull.area)\n",
"\n",
"\n",
"print('')\n",
"\n",
"print('Whole Tree Coverage Bounds:')\n",
"print(whole_tree_hull.bounds)\n",
"\n",
"print('Stem Flow Coverage Bounds:')\n",
"print(stem_flow_hull.bounds)\n",
"\n",
"print('')\n",
"\n",
"# Demonstrating how is_stem might be used in other calculations \n",
"total_volume= np.sum([cyl.volume\n",
" for cyl in myCollection.cylinders\n",
" if cyl.is_stem]\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For detailed definitions of all of the statistics availible to users, see the [Glossary](../glossary.rst)"
]
}
],
"metadata": {
"jupytext": {
"formats": "ipynb,py:percent",
"main_language": "python"
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}