How to plot the loss & hyperparameters in RandomizedSearchCV - matplotlib

This is my results from RandomizedSearchCV's cv_results_.
I had 10 Kfolds here. How would you plot each alpha amount (10 total) with the loss that each of the 10 KFold's results resulted in?
I'm thinking 10 lines with the X axis being K fold split number, Y axis being Loss Amount, and each line being color coded as Alpha = n?
data =\
{'mean_fit_time': np.array([0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0]),
'std_fit_time': np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
'mean_score_time': np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
'std_score_time': np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
'param_alpha': np.ma.masked_array(data=[0.03, 10.0, 0.05, 0.0005, 0.1, 0.02, 0.0001, 100.0, 0.01, 1.0],
mask=[False, False, False, False, False, False, False, False, False, False],
fill_value='?', dtype=object),
'params': [{'alpha': 0.03}, {'alpha': 10.0}, {'alpha': 0.05}, {'alpha': 0.0005}, {'alpha': 0.1}, {'alpha': 0.02}, {'alpha': 0.0001}, {'alpha': 100.0}, {'alpha': 0.01}, {'alpha': 1.0}],
'split0_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.2, -0.4]),
'split1_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.1, -0.4]),
'split2_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.2, -0.4]),
'split3_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.2, -0.4]),
'split4_test_score': np.array([-0.2, -0.5, -0.2, -0.1, -0.2, -0.2, -0.1, -0.5, -0.2, -0.5]),
'split5_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.1, -0.4]),
'split6_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.2, -0.4]),
'split7_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.2, -0.4]),
'split8_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.1, -0.4]),
'split9_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.1, -0.4]),
'mean_test_score': np.array([-0.2, -0.4, -0.2, -0.1, -0.2, -0.2, -0.1, -0.4, -0.1, -0.4]),
'std_test_score': np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
'rank_test_score': np.array([5, 8, 6, 1, 7, 4, 2, 8, 3, 8], dtype=np.int32)}

Related

i have 2 regions , in 2 regions there are 2 cities. i have to output the count and name of cities. how can i get it

{'_id': ObjectId('619f4e58b1a936e640635e97'), 'OrderDate': '01-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Bars', 'Product': 'Carrot', 'Quantity': '33', 'UnitPrice': '1.77', 'TotalPrice': 58.0, '': 'Sum of TotalPrice'}
{'_id': ObjectId('619f4e58b1a936e640635e98'), 'OrderDate': '04-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Crackers', 'Product': 'Whole Wheat', 'Quantity': '87', 'UnitPrice': '3.49', 'TotalPrice': 303.0, '': '17988.66'}
{'_id': ObjectId('619f4e58b1a936e640635e99'), 'OrderDate': '07-01-2020', 'Region': 'West', 'City': 'Los Angeles', 'Category': 'Cookies', 'Product': 'Chocolate Chip', 'Quantity': '58', 'UnitPrice': '1.87', 'TotalPrice': 108.46, '': '15336.92'}
{'_id': ObjectId('619f4e58b1a936e640635e9a'), 'OrderDate': '10-01-2020', 'Region': 'East', 'City': 'New York', 'Category': 'Cookies', 'Product': 'Chocolate Chip', 'Quantity': '82', 'UnitPrice': '1.87', 'TotalPrice': 153.34, '': '33325.58'}
{'_id': ObjectId('619f4e58b1a936e640635e9b'), 'OrderDate': '13-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Cookies', 'Product': 'Arrowroot', 'Quantity': '38', 'UnitPrice': '2.18', 'TotalPrice': 82.84}
{'_id': ObjectId('619f4e58b1a936e640635e9c'), 'OrderDate': '16-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Bars', 'Product': 'Carrot', 'Quantity': '54', 'UnitPrice': '1.77', 'TotalPrice': 95.58}
{'_id': ObjectId('619f4e58b1a936e640635e9d'), 'OrderDate': '19-01-2020', 'Region': 'East', 'City': 'Boston', 'Category': 'Crackers', 'Product': 'Whole Wheat', 'Quantity': '149', 'UnitPrice': '3.49', 'TotalPrice': 520.01}
{'_id': ObjectId('619f4e58b1a936e640635e9e'), 'OrderDate': '22-01-2020', 'Region': 'West', 'City': 'Los Angeles', 'Category': 'Bars', 'Product': 'Carrot', 'Quantity': '51', 'UnitPrice': '1.77', 'TotalPrice': 90.27}
w=db.sales.count_documents({"Region":"West"})
e=db.sales.count_documents({"Region":"East"})
print("Total count of orders in East region and West region are",w,"and",e)
a=db.sales.aggregate([{"$project":{"Region":1,"City":1}}])
for i in a:
print(i)

Try this instead, Refer https://mongoplayground.net/p/d4L-QQXh1Id
db.collection.aggregate([
{
"$group": {
"_id": {
"Region": "$Region",
"City": "$City"
},
"Total Amount": {
"$sum": "$TotalPrice"
},
"count": {
"$sum": 1
}
},
},
{
"$project": {
"_id": 0,
"Cities": "$_id.City",
"Total Count": "$count",
"Total Amount": "$Total Amount"
}
}
])

Finding maximal regions in data

Suppose I have the following data:
import pandas pd
data = [44.5, 27.0, 22.0, 23.0, 23.0, 24.0, 23.0, 21.0, 22.0, 20.0, 22.0, 19.0, 20.0, 19.0, 20.0, 20.0, 18.0, 18.0, 18.0, 16.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.5, 16.0, 16.0, 16.0, 16.0, 21.0, 18.0, 17.0, 18.0, 16.0, 17.0, 16.0, 16.0, 16.0, 17.0, 17.0, 16.0, 16.0, 16.0, 16.0, 17.0, 16.0, 16.0, 16.0, 16.0, 17.0, 17.0, 17.0, 17.0, 19.0, 18.0, 18.0, 17.0, 18.0, 19.0, 54.0, 37.0, 31.0, 30.0, 30.0, 30.0, 28.0, 26.0, 27.5, 25.0, 26.0, 26.0, 26.0, 26.0, 25.0, 30.0, 29.0, 26.0, 28.0, 26.0, 27.0, 25.0, 26.0, 26.0, 25.0, 24.0, 23.0, 23.0, 24.0, 25.0, 36.0, 29.0, 27.0, 26.0, 26.0, 27.0, 25.0, 25.0, 25.0, 25.0, 26.0, 23.0, 25.0, 24.0, 25.0, 27.0, 25.0, 24.0, 24.0, 23.0, 24.0, 24.0, 23.5, 22.0, 22.0, 23.0, 23.0, 23.0, 22.0, 24.0, 41.0, 28.0, 26.0, 25.0, 24.0, 25.0, 24.0, 22.0, 22.0, 23.0, 23.0, 22.0, 21.0, 22.0, 23.0, 23.0, 21.0, 19.0, 20.0, 19.0, 20.0, 19.0, 20.0, 19.0, 19.0, 20.0, 19.0, 19.0, 20.0, 20.0, 25.0, 21.0, 21.0, 20.0, 20.0, 20.0, 21.0, 20.0, 21.0, 19.5, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 18.0, 18.0, 17.0, 17.0, 18.0, 19.0, 19.0, 17.0, 18.0, 19.0, 19.0, 18.0, 18.0, 18.0, 26.0, 20.0, 19.0, 18.0, 18.0, 20.0, 17.0, 19.0, 17.0, 18.0, 19.0, 17.0, 17.0, 18.0, 17.0, 20.0, 18.0, 18.0, 17.0, 17.0, 18.0, 16.0, 17.0, 17.0, 17.0, 17.0, 17.0, 16.0, 18.0, 20.0, 23.0, 18.0, 17.0, 17.0, 16.0, 16.0, 15.0, 15.0, 15.0, 14.0, 14.0, 15.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 12.0, 14.0, 13.0, 14.0, 12.0, 13.0, 13.0, 13.0, 13.0, 13.0, 12.0, 20.0, 15.0, 14.0, 14.0, 12.0, 13.0, 13.0, 13.0, 12.0, 12.0, 12.0, 12.0, 12.0, 13.0, 12.0, 14.0, 12.0, 13.0, 12.0, 12.0, 13.0, 12.0, 12.0, 11.0, 12.0, 12.0, 11.0, 12.0, 12.0, 12.0, 33.0, 27.0, 24.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 15.0, 16.0, 14.0, 14.0, 13.0, 13.0, 14.0, 13.0, 14.0, 14.0, 13.0, 14.0, 13.0, 13.0, 13.0, 12.0, 13.0, 13.0, 12.0, 13.0, 13.0, 24.0, 15.0, 16.0, 14.0, 14.0, 16.0, 15.5, 15.0, 14.0, 15.0, 15.0, 15.0, 14.0, 14.0, 14.0, 16.0, 14.0, 14.0, 14.0, 15.0, 15.0, 14.0, 14.0, 14.0, 14.0, 15.0, 15.0, 15.0, 14.0, 17.0, 26.0, 21.0, 19.0, 19.0, 19.0, 19.0, 20.0, 19.0, 19.0, 19.0, 20.0, 19.0, 19.0, 19.0, 19.0, 20.0, 20.0, 20.0, 19.0, 18.0, 19.0, 19.0, 20.0, 18.0, 19.0, 19.0, 20.0, 18.0, 19.0, 22.0, 47.0, 31.0, 30.0, 28.0, 28.0, 28.0, 27.0, 26.0, 28.0, 29.0, 31.0, 29.0, 30.0, 30.0, 32.0, 36.0, 27.0, 29.0, 29.0, 27.0, 30.0, 28.0, 27.0, 26.0, 28.0, 28.0, 28.0, 28.0, 28.0, 31.0, 38.0, 33.0, 31.0, 30.0, 29.0, 30.0, 29.0, 30.0, 29.0, 30.0, 33.0, 30.0, 30.0, 30.0, 31.0, 32.0, 32.0, 32.0, 31.0, 31.0, 33.0, 32.0, 31.0, 32.0, 32.0, 33.5, 32.0, 32.0, 34.0, 41.0, 99.0, 73.0, 76.0, 64.0, 64.0, 72.0, 72.0, 68.0, 63.0, 66.0, 70.0, 70.0, 62.0, 63.0, 63.0, 72.0, 66.0, 66.0, 65.0, 62.0, 65.0, 63.0, 59.0, 59.0, 62.0, 61.0, 62.0, 61.0, 60.0, 64.0, 82.0, 69.0, 67.0, 67.0, 62.0, 68.5, 65.0, 66.0, 64.0, 66.0, 66.0, 62.0, 65.0, 65.0, 61.0, 68.0, 64.0, 64.0, 65.0, 65.0, 69.0, 66.0, 62.0, 64.0, 64.0, 70.0, 65.0, 65.0, 67.0, 75.0, 167.0, 120.0, 111.0, 102.0, 100.0, 107.0, 105.0, 102.5, 96.0, 97.0, 101.5, 99.0, 95.0, 87.0, 89.0, 101.0, 94.0, 94.0, 93.0, 95.0, 100.0, 96.0, 96.0, 94.0, 101.0, 119.0, 134.0, 140.0, 149.0, 211.0, 219.0, 160.0, 127.0, 115.0, 112.0, 108.0, 101.0, 96.0, 90.0, 91.0, 94.0, 90.0, 86.0, 85.0, 89.0, 94.0, 91.0, 83.0, 82.0, 81.0, 86.0, 82.0, 82.0, 77.0, 79.0, 83.0, 84.0, 77.0, 76.0, 83.0, 132.5, 108.0, 104.0, 97.0, 99.0, 97.0, 94.0, 90.0, 85.0, 87.0, 92.0, 86.0, 86.0, 84.0, 83.0, 92.0, 86.0, 86.0, 86.0, 83.0, 86.0, 83.0, 78.0, 77.0, 81.0, 81.0, 76.0, 78.0, 77.0, 78.0, 98.0, 91.0, 84.0, 83.0, 81.0, 84.0, 81.0, 81.0, 78.0, 76.0, 82.0, 79.0, 78.0, 74.0, 77.0, 81.0, 78.0, 78.0, 80.0, 79.0, 85.0, 76.0, 76.5, 75.0, 76.0, 79.0, 75.0, 73.0, 70.0, 73.0, 109.0, 93.0, 91.0, 85.0, 85.0, 88.0, 82.0, 81.0, 79.0, 76.0, 82.0, 78.0, 80.0, 75.0, 77.0, 81.0, 79.0, 76.0, 78.0, 74.0, 81.0, 78.0, 74.0, 72.0, 73.0, 77.0, 74.0, 75.0, 71.0, 73.0, 95.0, 83.0, 83.0, 81.0, 79.0, 83.0, 79.0, 76.0, 75.0, 74.0, 84.0, 74.0, 75.5, 75.0, 76.0, 86.0, 79.0, 76.0, 73.0, 74.0, 80.0, 77.0, 75.5, 70.0, 73.0, 76.0, 73.0, 73.0, 70.0, 80.0, 112.0, 88.0, 89.0, 86.0, 87.0, 89.0, 87.0, 85.0, 85.0, 82.0, 90.0, 88.0, 87.0, 87.0, 86.0, 92.0, 87.0, 88.0, 86.0, 88.0, 90.0, 93.0, 89.0, 85.0, 90.0, 94.0, 91.0, 84.5, 86.0, 86.0, 110.0, 104.0, 102.0, 94.5, 101.0, 106.0, 100.0, 96.0, 89.0, 92.0, 99.0, 95.0, 95.0, 94.0, 95.0, 108.0, 104.0, 98.0, 110.0, 106.0, 117.0, 112.0, 108.5, 107.0, 105.0, 109.0, 108.0, 110.0, 103.0, 112.0, 187.0, 152.0, 138.0, 137.0, 135.5, 146.0, 136.0, 129.0, 130.0, 134.0, 141.0, 133.0, 137.0, 131.0, 137.0, 153.0, 147.0, 140.0, 142.0, 150.0, 174.0, 157.0, 149.0, 145.0, 151.0, 160.0, 147.0, 136.0, 132.0, 138.0, 254.0, 229.0, 222.0, 212.0, 207.0, 230.0, 210.0, 206.0, 201.0, 194.0, 209.0, 199.0, 201.0, 202.0, 200.0, 225.0, 220.0, 201.0, 202.0, 212.0, 225.0, 210.0, 210.0, 200.0, 210.0, 231.0, 231.0, 218.0, 225.0, 321.0, 1018.0, 588.0, 491.0, 456.0, 441.0, 477.0, 427.0, 411.0, 375.0, 377.0, 422.0, 368.0, 359.0, 342.0, 342.0, 400.0, 373.0, 355.0, 358.0, 363.0, 387.0, 357.0, 350.0, 336.0, 328.0, 348.0, 316.0, 301.0, 305.0, 313.0, 599.0, 535.0, 504.0, 498.5, 485.0, 536.0, 505.0, 468.5, 455.0, 470.0, 516.0, 464.0, 452.5, 436.0, 430.0, 519.0, 473.0, 451.0, 433.0, 436.0, 495.0, 456.0, 456.0, 431.0, 437.0, 467.0, 424.0, 400.0, 372.0, 452.0, 1067.0, 804.0, 715.0, 667.5, 632.0, 689.0, 624.0, 575.5, 569.0, 555.0, 605.0, 546.5, 522.0, 495.0, 511.0, 603.5, 532.0, 512.5, 512.0, 491.0, 543.0, 499.0, 472.0, 451.0, 463.0, 500.0, 477.0, 457.0, 435.0, 461.0, 773.0, 705.5, 680.0, 644.0, 639.0, 689.0, 668.0, 620.5, 581.0, 584.0, 667.0, 597.5, 590.5, 568.0, 559.0, 668.0, 605.0, 577.0, 571.0, 566.0, 610.0, 585.5, 575.0, 537.0, 548.0, 586.0, 532.0, 520.0, 491.0, 519.0, 778.0, 703.0, 648.0, 607.0, 586.0, 633.0, 578.0, 552.0, 534.0, 523.0, 610.0, 566.5, 534.0, 525.0, 516.0, 595.0, 552.0, 522.0, 516.5, 518.0, 560.0, 531.0, 513.0, 498.0, 530.0, 578.0, 550.0, 545.0, 602.0, 799.0, 918.0, 644.5, 585.0, 548.0, 525.0, 569.0, 523.0, 496.0, 489.0, 470.0, 526.0, 480.0, 470.0, 455.0, 469.5, 545.0, 466.0, 440.0, 424.0, 440.0, 466.0, 434.0, 415.0, 404.5, 412.0, 449.0, 430.0, 416.0, 408.0, 443.0, 586.0, 495.5, 445.0, 435.0, 434.0, 461.0, 437.0, 416.0, 395.0, 404.0, 437.0, 400.0, 387.0, 381.0, 373.5, 427.0, 404.0, 375.0, 394.0, 380.0, 418.0, 397.0, 386.0, 369.0, 384.5, 411.0, 383.0, 385.0, 420.0, 511.0, 555.0, 415.0, 373.0, 358.0, 359.5, 362.0, 348.5, 339.0, 321.0, 324.5, 348.0, 329.0, 316.0, 315.0, 312.0, 356.0, 321.0, 310.0, 301.0, 310.0, 329.0, 319.0, 305.0, 302.0, 296.0, 315.0, 293.0, 289.0, 289.0, 297.5, 407.0, 342.0, 321.0, 312.0, 304.0, 335.0, 315.0, 302.0, 297.0, 293.5, 308.0, 285.5, 290.0, 283.0, 289.0, 326.0, 300.5, 294.0, 285.0, 281.0, 310.0, 291.0, 289.0, 277.0, 281.0, 306.0, 292.0, 280.5, 279.0, 292.5, 365.0, 316.0, 303.0, 290.0, 287.0, 319.0, 298.0, 287.0, 274.0, 287.0, 303.5, 282.0, 275.0, 271.0, 273.0, 312.0, 289.0, 280.0, 276.0, 274.0, 299.0, 295.0, 281.0, 280.0, 285.0, 306.0, 299.0, 288.0, 295.0, 319.0, 439.0, 379.0, 378.0, 368.0, 353.0, 396.0, 384.0, 363.0, 356.0, 366.0, 406.0, 389.0, 388.5, 378.5, 396.0, 477.0, 460.5, 436.0, 424.0, 443.5, 524.5, 506.0, 503.0, 508.0, 571.5, 687.5, 739.5, 1058.0, 1998.0, 1973.0, 916.5, 459.5, 358.5, 310.0, 274.0, 262.0, 239.0, 225.0, 212.5, 203.0, 214.5, 191.0, 186.0, 176.0, 182.5, 185.0, 170.0, 163.0, 161.0, 162.5, 167.0, 156.0, 156.5, 151.0, 153.0, 157.0, 151.0, 150.0, 140.0, 164.0, 206.0, 164.0, 159.0, 157.5, 156.0, 156.0, 150.0, 148.0, 144.0, 150.5, 161.0, 154.0, 148.0, 152.0, 260.5, 267.0, 168.0, 161.0, 153.0, 150.0, 147.0, 147.0, 148.0, 144.0, 145.0, 148.0, 151.5, 147.0, 145.0, 150.0, 174.0, 142.0, 134.0, 120.0, 113.0, 110.0, 105.0, 101.0, 96.0, 94.0, 98.0, 93.0, 89.0, 86.0, 89.0, 100.0, 121.0, 82.0, 79.0, 78.0, 100.0, 80.0, 74.0, 72.5, 77.0, 91.0, 74.0, 80.0, 86.0, 126.0, 108.0, 63.0, 52.0, 48.0, 46.0, 48.0, 46.0, 44.0, 43.0, 43.0, 45.0, 42.0, 42.0, 42.0, 53.0, 52.0, 38.0, 37.0, 34.0, 33.0, 37.0, 35.0, 33.0, 33.0, 33.0, 42.0, 35.0, 34.0, 34.0, 39.0, 75.0, 46.0, 41.0, 39.0, 38.0, 40.0, 36.0, 36.0, 35.0, 38.0, 44.0, 35.0, 35.0, 34.0, 41.0, 63.0, 44.0, 37.0, 33.0, 40.0, 70.0, 37.0, 34.0, 34.0, 41.0, 66.0, 43.0, 40.0, 53.0, 87.0, 43.0, 27.0, 21.0, 22.0, 20.0, 22.0, 19.0, 18.0, 18.0, 19.0, 23.0, 21.0, 20.0, 23.0, 35.0, 23.0, 23.5, 21.0, 23.0, 21.0, 22.0, 20.0, 21.0, 20.0, 20.0, 29.0, 21.0, 20.0, 21.0, 27.0, 69.5, 33.0, 31.5, 30.0, 28.0, 28.0, 25.0, 26.0, 26.0, 28.0, 29.0, 23.0, 22.0, 24.0, 28.0, 23.0, 25.0, 26.0, 25.0, 29.0, 55.0, 27.0, 24.0, 25.0, 29.0, 55.0, 32.0, 27.0, 38.0, 54.0, 77.0, 28.5, 23.0, 19.0, 18.0, 17.0, 15.0, 15.0, 13.0, 13.0, 14.0, 12.0, 14.0, 13.5, 17.0, 12.0, 11.0, 11.0, 10.0, 10.0, 11.0, 11.0, 10.0, 9.5, 10.0, 10.0, 9.0, 9.0, 9.0, 9.0, 12.0, 10.0, 10.0, 10.0, 9.0, 9.0, 10.0, 10.0, 9.0, 9.0, 10.0, 9.0, 9.0, 9.0, 8.0, 8.0, 9.0, 9.0, 9.0, 8.0, 9.0, 9.0, 9.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 9.0, 30.0, 18.0, 15.0, 14.0, 14.0, 14.0, 13.0, 12.5, 12.0, 11.0, 11.0, 10.0, 11.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 8.0, 9.0, 9.0, 9.0, 9.0, 11.0, 10.0, 9.0, 9.0, 9.0, 10.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 12.0, 11.0, 10.0, 10.0, 9.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 9.0, 10.0, 10.0, 13.0]
​
which if we plot we can see (qualitatively) a maximal region between roughly 750 and 1100.
We can see this maximal region more clearly if we smooth the data:
pd.Series(data).ewm(span=100).mean().plot()
My question is, what techniques/algorithms can be used to identify the interval say (800, 1200)? I have many such datasets which have different shapes, but all include 1 or 2 maximal "regions".
Any ideas? Thanks

Here's the mountain climber solution to the problem I mentioned in my comment. I saved the data you posted to a numpy file: https://drive.google.com/file/d/192jp5LvEE0Dc8QVMVmzzuHSehl2_bBLF/view?usp=sharing
Graph after mean filtering and mountain climbing
Bounds after thresholding based on the value at the start of the rise.
import numpy as np
import matplotlib.pyplot as plt
# returns direction of gradient
# 1 if positive, -1 if negative, 0 if flat
def getDirection(one, two):
dx = two - one;
if dx == 0:
return 0;
if dx > 0:
return 1;
return -1;
# detects and returns peaks and valleys
def mountainClimber(vals, minClimb):
# init trackers
last_valley = vals[0];
last_peak = vals[0];
last_val = vals[0];
last_dir = getDirection(vals[0], vals[1]);
# get climbing
peak_valley = []; # index, height, climb (positive for peaks, negative for valleys)
for a in range(1, len(vals)):
# get current direction
sign = getDirection(last_val, vals[a]);
last_val = vals[a];
# if not equal, check gradient
if sign != 0:
if sign != last_dir:
# change in gradient, record peak or valley
# peak
if last_dir > 0:
last_peak = vals[a];
climb = last_peak - last_valley;
climb = round(climb, 2);
peak_valley.append([a, vals[a], climb]);
else:
# valley
last_valley = vals[a];
climb = last_valley - last_peak;
climb = round(climb, 2);
peak_valley.append([a, vals[a], climb]);
# change direction
last_dir = sign;
# filter out very small climbs
filtered_pv = [];
for dot in peak_valley:
if abs(dot[2]) > minClimb:
filtered_pv.append(dot);
return filtered_pv;
# run an mean filter over the graph values
def meanFilter(vals, size):
fil = [];
filtered_vals = [];
for val in vals:
fil.append(val);
# check if full
if len(fil) >= size:
# pop front
fil = fil[1:];
filtered_vals.append(sum(fil) / size);
else:
# pad to maintain index positions
filtered_vals.append(0);
return filtered_vals;
# load from file
data = np.load("data.npy");
# filter and round values
mean_filter_size = 150;
filtered_vals = meanFilter(data, mean_filter_size);
# get peaks and valleys
pv = mountainClimber(filtered_vals, 0);
# filter for the largest climb
biggest_climb = -1;
top_index = None;
for pv_index, feature in enumerate(pv):
# unpack
_, _, climb = feature;
# check climb
if climb > biggest_climb:
biggest_climb = climb;
top_index = pv_index;
# pull out the threshold
start = pv[top_index - 1][0];
threshold = pv[top_index - 1][1];
# look through and find the first spot where the graph drops below threshold
end = None;
for index in range(start + 1, len(data)):
if data[index] < threshold:
end = index;
break;
# draw the bounding lines
markers_x = [start, end];
markers_y = [data[start], data[end]];
# draw plot
x = [a for a in range(len(data))];
fig = plt.figure();
ax = plt.axes();
ax.plot(x, data);
ax.plot(markers_x, markers_y, 'or');
plt.show();

OpenLayers v6.3.1 How to instantiate several polygon sub-objects that make up a TopoJSON object inserted in my Script with the “.readFeatures” method?

My TopoJSON object contains 3 polygonal sub-objects of which the first D2P1 appears with the geometry correctly drawn on the OSM map, but the other 2 polygons D2P2 and D2P3 are drawn distorted on the map turning into projected lines instead of their! correct geometry.
Could you help me to write my code correctly so that my 3 polygons appear correctly configured?
<!DOCTYPE html>
<html lang="es">
<head>
<meta charset="utf-8" />
<script src="https://cdn.jsdelivr.net/gh/openlayers/openlayers.github.io#master/en/v6.
3.1/build/ol.js" type="text/javascript"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/openlayers/openlayers.github.io#master/en/v6
.3.1/css/ol.css" type="text/css">
<style>
/*estilo de la caja del mapa*/
#map {
width: 100%;
height: 700px;
box-shadow: 5px 5px 5px #888;
}
</style>
<style>
/*estilo de los controles del mapa*/
.ol-mouse-position {
/*estilo del control de longitud y latitud */
font-size: 12px;
font-family: Arial Black;
}
</style>
</head>
<body>
<div id="map"></div>
<script>
var osm = new ol.layer.Tile({ // capa osm (Open Street Maps)
source: new ol.source.OSM()
});
// OBJETO TopoJSON
var Y = {
"type": "Topology",
"arcs": [
[
[711, 1839],
[-37, -99],
[-3, -8],
[-2, -4],
[-3, -4],
[-2, -3],
[-1, -1],
[-2, -1],
[-3, -3],
[-2, -1],
[-4, -2],
[-1, 0],
[-3, -1],
[-24, 1],
[-12, -3],
[-10, -15],
[-1, -3],
[-5, -13],
[-2, -23],
[-1, -94],
[275, -220],
[17, -31],
[34, -52],
[117, -112],
[19, -19],
[30, -142]
],
[
[1085, 986],
[-5, -9],
[0, 0],
[-30, -40],
[-47, -48],
[-18, -16],
[-21, -16],
[-45, -61],
[-16, -16],
[-4, -4],
[-101, -125],
[-28, -6],
[-5, -7],
[-3, -2],
[-3, -1],
[-7, -16],
[-573, 431],
[-1, 1],
[-12, 5],
[-9, 3],
[-157, 6],
[16, 868],
[14, 15],
[82, 49],
[599, -158]
],
[
[711, 1839],
[315, -82],
[459, -119],
[-3, -4],
[-143, -167],
[75, -84],
[-12, -10],
[-39, -51],
[-3, -3],
[-15, -16],
[-76, -26],
[-133, -211],
[-28, -44],
[-23, -36]
],
[
[2354, 410],
[-76, -313],
[-220, 43],
[-37, -140],
[-11, 11],
[-129, 128],
[-17, 17],
[-6, 5],
[-5, 2],
[-7, 3],
[-8, 2],
[-15, 4],
[-173, 40],
[-169, 39],
[-2, 6],
[3, 33],
[-239, 651],
[-21, 32],
[-86, 93],
[133, 211],
[76, 26],
[15, 16],
[3, 3],
[39, 51],
[12, 10],
[-75, 84],
[146, 171],
[2, 1],
[16, 3],
[11, 2],
[9, 1],
[16, 2],
[20, 3],
[20, 1],
[16, 2],
[21, -2],
[14, -2],
[20, -3],
[18, -4],
[21, -8],
[24, -10],
[15, -9],
[23, -14],
[39, -28],
[32, -27],
[20, -14],
[32, -18],
[22, -10],
[23, -6],
[35, -4],
[33, -5],
[41, -1],
[74, -8],
[44, -15],
[73, -32],
[101, -41],
[169, -77],
[118, -51],
[74, -35],
[12, -5],
[-15, -66],
[-41, -179],
[-69, -252],
[-35, -26],
[-3, -9],
[-7, -15],
[-14, -48],
[-4, -57],
[-7, -16],
[-39, -41],
[-14, -22],
[-21, -44],
[-23, -3],
[-15, -1],
[-9, -2],
[-8, -6],
[-10, -16],
[-5, -11],
[0, 0]
]
],
"transform": {
"scale": [0.00001991525380089962, 0.000012987212987305721],
"translate": [-98.20093192354561, 19.06017018766488]
},
"objects": {
"D2P1": {
"type": "GeometryCollection",
"geometries": [{
"arcs": [
[0, 1]
],
"type": "Polygon",
"properties": {
"Name": "(STA. MARÍA) 1 P.MARIA AUXILIO DE LOS CRISTIANOS",
"description": null,
"timestamp": null,
"begin": null,
"end": null,
"altitudeMode": null,
"tessellate": -1,
"extrude": 0,
"visibility": -1,
"drawOrder": null,
"icon": null,
"snippet": ""
}
}]
},
"D2P2": {
"type": "GeometryCollection",
"geometries": [{
"arcs": [
[-1, 2]
],
"type": "Polygon",
"properties": {
"Name": "(STA. MARÍA) 2 P. NUESTRA SEÑORA DE LA CANDELARIA",
"description": null,
"timestamp": null,
"begin": null,
"end": null,
"altitudeMode": null,
"tessellate": -1,
"extrude": 0,
"visibility": -1,
"drawOrder": null,
"icon": null,
"snippet": ""
}
}]
},
"D2P3": {
"type": "GeometryCollection",
"geometries": [{
"arcs": [
[3]
],
"type": "Polygon",
"properties": {
"Name": "(STA. MARÍA) 3 P. SEÑOR DE LAS MARAVILLAS",
"description": null,
"timestamp": null,
"begin": null,
"end": null,
"altitudeMode": null,
"tessellate": -1,
"extrude": 0,
"visibility": -1,
"drawOrder": null,
"icon": null,
"snippet": ""
}
}]
}
}
};
/* source*/
var sourceD2P1 = new ol.source.Vector({
features: (new ol.format.TopoJSON({
layers: ['D2P1']
})).readFeatures(Y, {
dataProjection: 'EPSG:4326',
featureProjection: 'EPSG:3857'
}),
});
var sourceD2P2 = new ol.source.Vector({
features: (new ol.format.TopoJSON({
layers: ['D2P2']
})).readFeatures(Y, {
dataProjection: 'EPSG:4326',
featureProjection: 'EPSG:3857'
}),
});
var sourceD2P3 = new ol.source.Vector({
features: (new ol.format.TopoJSON({
layers: ['D2P3']
})).readFeatures(Y, {
dataProjection: 'EPSG:4326',
featureProjection: 'EPSG:3857'
}),
});
/*LAYER.VECTOR*/
var vectorD2P1 = new ol.layer.Vector({ // ----D2P1
source: sourceD2P1,
style: new ol.style.Style({
fill: new ol.style.Fill({
color: 'green',
}),
stroke: new ol.style.Stroke({
color: 'green',
width: 1
})
})
});
var vectorD2P2 = new ol.layer.Vector({ // ----D2P2
source: sourceD2P2,
style: new ol.style.Style({
fill: new ol.style.Fill({
color: 'red',
}),
stroke: new ol.style.Stroke({
color: 'red',
width: 1
})
})
});
var vectorD2P3 = new ol.layer.Vector({ // ----D2P3
source: sourceD2P3,
style: new ol.style.Style({
fill: new ol.style.Fill({
color: 'blue',
}),
stroke: new ol.style.Stroke({
color: 'blue',
width: 1
})
})
});
var controls = ol.control.defaults().extend([
new ol.control.ScaleLine(), /*control del mapita lateral*/
new ol.control.Attribution(), /*control del letrero de Open Street Maps*/
new ol.control.MousePosition({ /*control de LONGITUD Y LATITUD en donde se coloque el mause*/
coordinateFormat: ol.coordinate.createStringXY(4),
projection: 'EPSG:4326'
}),
new ol.control.OverviewMap({
layers: [
new ol.layer.Tile({
source: new ol.source.OSM()
})
]
}),
/*new ol.control.ZoomSlider(),*/
new ol.control.FullScreen() /*control de expancon u cerrado del mapa*/
]);
var Mivista = new ol.View({
center: ol.proj.fromLonLat([-98.2172, 19.03464]),
zoom: 12
}) //-98.197, 19.0433 puebla
var layers = [osm,
vectorD2P1, vectorD2P2, vectorD2P3,
];
// Creacion del mapa con las 3 capas
var map = new ol.Map({
/*target------*/
target: 'map',
/*layers------*/
layers: layers,
controls: controls,
/*view--------*/
view: Mivista,
});
Mivista.setRotation(-.4999) // Rota el plano 27 grados
</script>
</body>
</html>

I LEAVE YOU THE CODE THAT ANSWERS MY QUESTION, I HOPE IT WILL BE VERY USEFUL
<!DOCTYPE html>
<html lang="es">
<head>
<meta charset="utf-8" />
<script src="https://cdn.jsdelivr.net/gh/openlayers/openlayers.github.io#master/en/v6.
3.1/build/ol.js" type="text/javascript"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/openlayers/openlayers.github.io#master/en/v6
.3.1/css/ol.css" type="text/css">
<style>
/*estilo de la caja del mapa*/
#map {
width: 100%;
height: 1000px;
box-shadow: 5px 5px 5px #888;
}
</style>
<style>
/*estilo de los controles del mapa*/
.ol-mouse-position {
/*estilo del control de longitud y latitud */
font-size: 12px;
font-family: Arial Black;
}
</style>
</head>
<body>
<div id="map"></div>
<script>
var osm = new ol.layer.Tile({ // capa osm (Open Street Maps)
source: new ol.source.OSM()
});
// OBJECTO_1 TopoJSON
var geojsonObjectochentaPARROQUIAS = {
// OBJETO TopoJSON
"type": "Topology",
"arcs": [
[
[711, 1839],
[-37, -99],
[-3, -8],
[-2, -4],
[-3, -4],
[-2, -3],
[-1, -1],
[-2, -1],
[-3, -3],
[-2, -1],
[-4, -2],
[-1, 0],
[-3, -1],
[-24, 1],
[-12, -3],
[-10, -15],
[-1, -3],
[-5, -13],
[-2, -23],
[-1, -94],
[275, -220],
[17, -31],
[34, -52],
[117, -112],
[19, -19],
[30, -142]
],
[
[1085, 986],
[-5, -9],
[0, 0],
[-30, -40],
[-47, -48],
[-18, -16],
[-21, -16],
[-45, -61],
[-16, -16],
[-4, -4],
[-101, -125],
[-28, -6],
[-5, -7],
[-3, -2],
[-3, -1],
[-7, -16],
[-573, 431],
[-1, 1],
[-12, 5],
[-9, 3],
[-157, 6],
[16, 868],
[14, 15],
[82, 49],
[599, -158]
],
[
[711, 1839],
[315, -82],
[459, -119],
[-3, -4],
[-143, -167],
[75, -84],
[-12, -10],
[-39, -51],
[-3, -3],
[-15, -16],
[-76, -26],
[-133, -211],
[-28, -44],
[-23, -36]
],
[
[2354, 410],
[-76, -313],
[-220, 43],
[-37, -140],
[-11, 11],
[-129, 128],
[-17, 17],
[-6, 5],
[-5, 2],
[-7, 3],
[-8, 2],
[-15, 4],
[-173, 40],
[-169, 39],
[-2, 6],
[3, 33],
[-239, 651],
[-21, 32],
[-86, 93],
[133, 211],
[76, 26],
[15, 16],
[3, 3],
[39, 51],
[12, 10],
[-75, 84],
[146, 171],
[2, 1],
[16, 3],
[11, 2],
[9, 1],
[16, 2],
[20, 3],
[20, 1],
[16, 2],
[21, -2],
[14, -2],
[20, -3],
[18, -4],
[21, -8],
[24, -10],
[15, -9],
[23, -14],
[39, -28],
[32, -27],
[20, -14],
[32, -18],
[22, -10],
[23, -6],
[35, -4],
[33, -5],
[41, -1],
[74, -8],
[44, -15],
[73, -32],
[101, -41],
[169, -77],
[118, -51],
[74, -35],
[12, -5],
[-15, -66],
[-41, -179],
[-69, -252],
[-35, -26],
[-3, -9],
[-7, -15],
[-14, -48],
[-4, -57],
[-7, -16],
[-39, -41],
[-14, -22],
[-21, -44],
[-23, -3],
[-15, -1],
[-9, -2],
[-8, -6],
[-10, -16],
[-5, -11],
[0, 0]
]
],
"transform": {
"scale": [0.00001991525380089962, 0.000012987212987305721],
"translate": [-98.20093192354561, 19.06017018766488]
},
"objects": {
"D2P1": {
"type": "GeometryCollection",
"geometries": [{
"arcs": [
[0, 1]
],
"type": "Polygon",
"properties": {
"Name": "(STA. MARÍA) 1 P.MARIA AUXILIO DE LOS CRISTIANOS",
"description": null,
"timestamp": null,
"begin": null,
"end": null,
"altitudeMode": null,
"tessellate": -1,
"extrude": 0,
"visibility": -1,
"drawOrder": null,
"icon": null,
"snippet": ""
}
}]
},
"D2P2": {
"type": "GeometryCollection",
"geometries": [{
"arcs": [
[-1, 2]
],
"type": "Polygon",
"properties": {
"Name": "(STA. MARÍA) 2 P. NUESTRA SEÑORA DE LA CANDELARIA",
"description": null,
"timestamp": null,
"begin": null,
"end": null,
"altitudeMode": null,
"tessellate": -1,
"extrude": 0,
"visibility": -1,
"drawOrder": null,
"icon": null,
"snippet": ""
}
}]
},
"D2P3": {
"type": "GeometryCollection",
"geometries": [{
"arcs": [
[3]
],
"type": "Polygon",
"properties": {
"Name": "(STA. MARÍA) 3 P. SEÑOR DE LAS MARAVILLAS",
"description": null,
"timestamp": null,
"begin": null,
"end": null,
"altitudeMode": null,
"tessellate": -1,
"extrude": 0,
"visibility": -1,
"drawOrder": null,
"icon": null,
"snippet": ""
}
}]
}
}
};
// OBJECTO_2 TopoJSON
var geojsonObjectXXX = { // -------------------------------------------------ObjectXXX-----Municipio_TopoJSON
"type": "Topology",
"arcs": [
[
[3698, 68],
[-126, 23],
[-54, 8],
[-370, -99],
[-112, 35],
[-249, 34],
[-133, 44],
[-62, 66],
[-35, 73],
[29, 117],
[-38, 91],
[-95, 135],
[-58, 226],
[-38, 29],
[-24, 82],
[-102, 79],
[-64, 94],
[-91, 38],
[-98, 4],
[-104, -18],
[-77, -53],
[-95, -37],
[-37, -47],
[-139, -59],
[-55, -59],
[-105, -57],
[-62, -10],
[-93, 35],
[-536, -78],
[-105, 19],
[-76, 98],
[-39, 16],
[-160, 3],
[14, 30],
[59, 50],
[63, 87],
[112, 135],
[23, 71],
[-49, 67],
[-52, 37],
[-19, 0],
[-20, -17],
[-66, 9],
[-79, -8],
[-62, -14],
[-82, 14],
[-43, 3],
[-62, 63],
[-32, 81],
[9, 39],
[41, 52],
[30, 18],
[5, 40],
[13, 25],
[25, 3],
[46, -9],
[46, -1],
[38, 9],
[33, 28],
[-10, 36],
[-23, 53],
[-8, 30],
[22, 45],
[35, 110],
[2, 15],
[25, 173],
[1, 79],
[1, 142],
[-5, 202],
[28, 10],
[58, -2],
[54, 4],
[42, 16],
[13, 41],
[-94, 114],
[-39, 34],
[-6, 11],
[-8, 15],
[9, 43],
[-35, 77],
[5, 103],
[-4, 66],
[-39, 64],
[-28, 20],
[-66, 49],
[-58, 63],
[68, 31],
[18, 35],
[0, 61],
[3, 61],
[32, 55],
[38, 29],
[-80, 137],
[87, 24],
[35, 47],
[52, 23],
[96, 28],
[39, -76],
[44, -33],
[47, 42],
[30, 22],
[111, -8],
[44, 17],
[19, 62],
[28, 17],
[90, -2],
[55, 76],
[59, 91],
[20, 68],
[20, 69],
[34, 48],
[115, 70],
[27, 46],
[46, -1],
[78, -2],
[75, 71],
[92, -18],
[41, 21],
[2, 2],
[54, 90],
[68, 18],
[3, 4],
[38, 63],
[15, 45],
[51, 28],
[35, 62],
[5, 46],
[-68, 115],
[-66, 37],
[-54, 31],
[-78, 33],
[-23, 33],
[-12, 19],
[-19, -18],
[41, -92],
[-123, 10],
[-240, 19],
[39, 16],
[-23, 7],
[-98, 33],
[-27, 12],
[-37, 17],
[-144, -129],
[-16, 101],
[-113, 53],
[-45, 71],
[-186, 20],
[48, 138],
[53, -5],
[156, 112],
[-61, 124],
[-86, 32],
[52, 150],
[47, 145],
[12, 39],
[18, 39],
[2, 4],
[22, 77],
[-53, 28],
[-39, 24],
[110, 66],
[25, 15],
[14, 35],
[18, 139],
[62, -7],
[58, 85],
[91, -52],
[78, -12],
[106, -17],
[22, 86],
[5, 22],
[10, 42],
[57, 31],
[6, 36],
[-17, 37],
[-11, 8],
[-11, -5],
[1, 9],
[-29, 54],
[-4, 62],
[-3, 4],
[-18, 24],
[86, 51],
[27, -26],
[30, 34],
[15, 38],
[93, -57],
[27, 15],
[22, 15],
[8, 14],
[-5, 32],
[-15, 36],
[6, 28],
[24, 27],
[7, 42],
[62, 57],
[1, 16],
[-79, 55],
[1, 14],
[42, 25],
[21, 1],
[24, 20],
[5, 69],
[-11, 22],
[-24, 23],
[0, 13],
[4, 38],
[11, 8],
[6, 21],
[-12, 12],
[0, 16],
[15, 19],
[6, 19],
[4, 91],
[9, 10],
[-7, 19],
[14, 10],
[17, 2],
[17, -8],
[44, -7],
[23, 9],
[15, 11],
[12, 28],
[-12, 35],
[9, 8],
[-6, 101],
[10, -12],
[25, -36],
[31, 34],
[21, -30],
[3, -16],
[60, 2],
[-5, 12],
[40, -12],
[40, -17],
[37, -19],
[48, -8],
[15, 3],
[32, -3],
[29, -9],
[27, -2],
[48, 4],
[26, 2],
[29, 7],
[25, 4],
[23, 11],
[15, 10],
[22, 3],
[-1, -8],
[16, -15],
[28, -17],
[11, -10],
[3, -7],
[-3, -8],
[-18, -21],
[-3, -14],
[1, -12],
[12, -23],
[3, -10],
[-3, -11],
[-14, -11],
[-10, -14],
[-4, -20],
[4, -13],
[7, -14],
[6, -12],
[8, -24],
[15, -68],
[-1, -106],
[-5, -23],
[-2, -64],
[-5, -17],
[-16, -36],
[17, -9],
[25, -4],
[40, 4],
[40, 9],
[28, 16],
[20, 19],
[43, -1],
[16, 10],
[16, -6],
[23, -22],
[77, -31],
[123, -53],
[83, -43],
[22, -7],
[125, -52],
[210, -80],
[174, -74],
[45, -19],
[44, -14],
[61, 12],
[14, 5],
[12, 7],
[4, 5],
[9, 4],
[2, 3],
[10, 3],
[38, 27],
[3, 3],
[3, 5],
[2, 5],
[0, 4],
[5, 8],
[27, -18],
[18, -12],
[31, 42],
[39, 35],
[58, 34],
[129, 56],
[53, 30],
[34, 21],
[29, 21],
[63, 48],
[33, 14],
[20, 6],
[19, 1],
[24, 6],
[15, 6],
[19, 13],
[213, 202],
[161, 201],
[31, 79],
[22, 52],
[93, 97],
[86, 64],
[37, 63],
[112, 50],
[-11, 11],
[46, 16],
[22, 16],
[24, 28],
[12, 4],
[70, 27],
[15, 10],
[19, 7],
[16, 9],
[7, 15],
[2, 13],
[3, 9],
[7, 8],
[20, 16],
[13, 11],
[15, 9],
[11, 19],
[24, 10],
[13, 9],
[9, 1],
[11, 5],
[11, 1],
[18, -2],
[22, -2],
[44, 0],
[38, 32],
[6, 56],
[-7, 76],
[30, 76],
[93, 127],
[209, 2],
[33, -1],
[118, -5],
[120, 116],
[203, 130],
[178, 202],
[177, 151],
[141, 43],
[124, 102],
[137, 213],
[133, 176],
[127, 158],
[12, 217],
[51, 110],
[94, -27],
[65, -115],
[6, -171],
[3, -22],
[241, -231],
[-135, -147],
[-47, 16],
[-6, -79],
[-126, -184],
[-13, -90],
[-71, 70],
[-52, -46],
[1, -114],
[35, -117],
[-16, -246],
[3, -183],
[-1, -35],
[-50, -141],
[1, -113],
[-46, -302],
[57, -66],
[136, 198],
[86, 8],
[-24, -126],
[-65, -35],
[-13, -69],
[-13, -72],
[-34, -43],
[-34, -102],
[-87, 11],
[-37, -13],
[-81, -95],
[-61, -33],
[-89, -50],
[16, -78],
[-18, -75],
[-67, -46],
[-130, -61],
[-22, -37],
[43, -182],
[-22, -105],
[-42, -20],
[-27, -5],
[-24, -8],
[-37, -65],
[-65, -47],
[-70, -17],
[-103, -52],
[-74, -89],
[-6, -101],
[-36, -43],
[-46, -146],
[-97, -161],
[-22, -93],
[-31, -21],
[-46, -165],
[-54, -190],
[-4, -40],
[77, -92],
[28, -57],
[-1, 0],
[-315, 73],
[-74, 2],
[-138, 4],
[-162, -25],
[-132, -20],
[-157, -3],
[-65, -52],
[-24, -160],
[-42, -30],
[-7, -15],
[2, -29],
[21, 6],
[23, -32],
[-104, 0],
[-12, -2],
[-254, -4],
[1, -12],
[-29, 0],
[0, -25],
[-42, 4],
[-93, 9],
[2, -7],
[-7, 0],
[-27, -1],
[-23, -13],
[0, -16],
[48, -67],
[40, -5],
[38, -6],
[44, -6],
[40, -31],
[11, -8],
[39, -30],
[32, -26],
[93, -72],
[-26, -120],
[-159, -128],
[-12, -10],
[-23, -139],
[-24, -149],
[-118, -199],
[44, -84],
[-23, -60],
[-28, 22],
[-17, -29],
[-75, -43],
[-73, -9],
[-4, -18],
[-45, -53],
[51, -150],
[70, -150],
[19, -90],
[-65, -91],
[-21, -30],
[3, -345],
[-10, -48],
[14, -49],
[-29, -32],
[13, -18],
[34, -47],
[-2, -32],
[-3, -41],
[85, -25],
[62, -46],
[70, -52],
[80, -33],
[193, -65],
[80, -66],
[55, -138],
[-38, -73],
[23, -56],
[278, -196],
[92, -53],
[117, -37],
[118, -210],
[-120, -1],
[-185, -48],
[-77, 56],
[-285, -61],
[-28, -169],
[-205, -26],
[67, 172],
[-131, 34],
[-3, 73],
[-174, 7],
[-16, -66],
[-50, -23],
[-54, 33],
[-208, -45],
[-43, -70],
[-42, -3],
[-70, -39],
[-162, -442],
[-7, -315],
[-153, -388],
[-11, -185],
[-98, -58]
]
],
"transform": {
"scale": [3.750052683335661, 4.395371608583082],
"translate": [-10941343.283091985, 2137380.706460472]
},
"objects": {
"municipioDePuebla": {
"type": "GeometryCollection",
"geometries": [{
"arcs": [
[0]
],
"type": "Polygon",
"properties": {
"Name": "MUNICIPIO DE PUEBLA",
"description": null,
"timestamp": null,
"begin": null,
"end": null,
"altitudeMode": null,
"tessellate": -1,
"extrude": 0,
"visibility": -1,
"drawOrder": null,
"icon": null,
"snippet": ""
}
}]
}
}
}; // Municipio_TopoJSON
/* ol.format.TopoJSON------------------------------------------------------------------------------------------------------------------*/
var sourceOchentaPARROQUIAS = new ol.source.Vector({
features: (new ol.format.TopoJSON({
layers: ['D2P1', 'D2P2', 'D2P3']
})).readFeatures(geojsonObjectochentaPARROQUIAS, {
dataProjection: 'EPSG:4326',
featureProjection: 'EPSG:3857'
})
});
var sourceMunicipio = new ol.source.Vector({
features: (new ol.format.TopoJSON()).readFeatures(geojsonObjectXXX)
});
/* ol.layer.Vector-----------------------------------------------------------------------------------------------------------------------*/
function generateRandomColor(seed) {
let r = Math.floor(Math.random() * 255)
let g = Math.floor(Math.random() * 255)
let b = Math.floor(Math.random() * 255)
return `rgba(${r}, ${g}, ${b}, 0.3)`
}
let features = sourceOchentaPARROQUIAS.getFeatures()
let colors = {}
features.forEach((feature, i) => {
let geom = feature.getGeometry()
colors[geom.ol_uid] = generateRandomColor(geom.ol_uid)
})
function vectorStyleFunction(feature) {
let geom = feature.getGeometry()
let id = geom.ol_uid
let style = new ol.style.Style({
fill: new ol.style.Fill({
color: colors[id]
}),
stroke: new ol.style.Stroke({
color: /*'rgba(9, 87, 140, 1)',*/ 'blue',
width: 1
}),
text: new ol.style.Text({
font: '12px Calibri,sans-serif',
fill: new ol.style.Fill({
color: '#000',
}),
stroke: new ol.style.Stroke({
color: '#fff',
width: 3,
}),
}),
})
// style.getText().setText(geom.ol_uid);
return style
}
var vectorLayerOchentaPARROQUIAS = new ol.layer.Vector({ // ----82 POLIGONOS
source: sourceOchentaPARROQUIAS,
style: vectorStyleFunction
});
var vectorLayerMunicipioDePuebla = new ol.layer.Vector({ // ----1 POLIGONO
source: sourceMunicipio,
style: new ol.style.Style({
fill: new ol.style.Fill({
color: 'rgba(255, 255, 255, 0.00)'
}),
stroke: new ol.style.Stroke({
color: '#09a7ee',
width: 2
}),
})
});
/* CONTROLES-----------------------------------------------------------------------------------------------------------------------*/
var controls = ol.control.defaults().extend([
new ol.control.ScaleLine(), /*control del mapita lateral*/
new ol.control.Attribution(), /*control del letrero de Open Street Maps*/
new ol.control.MousePosition({ /*control de LONGITUD Y LATITUD en donde se coloque el mause*/
coordinateFormat: ol.coordinate.createStringXY(4),
projection: 'EPSG:4326'
}),
new ol.control.OverviewMap({
layers: [
new ol.layer.Tile({
source: new ol.source.OSM()
})
]
}),
/*new ol.control.ZoomSlider(),*/
new ol.control.FullScreen() /*control de expancon u cerrado del mapa*/
]);
// Creacion del mapa con las TRES capas
var Mivista = new ol.View({
center: ol.proj.fromLonLat([-98.2172, 19.0150]),
zoom: 12
}) //-98.197, 19.0433 puebla
// pedida de capas
/*LAYERS*/
var layers = [osm, vectorLayerOchentaPARROQUIAS, vectorLayerMunicipioDePuebla];
var map = new ol.Map({
/*target------*/
target: 'map',
layers: layers,
controls: controls,
/*view--------*/
view: Mivista,
});
Mivista.setRotation(-.4999) // Rota el plano 27 grados en sentido contrario de las manecillas del reloj
</script>
</body>
</html>

How to export matplotlib plots as iframes

I want to generate some plots with Matplotlib and then generate iframes to embed in a Google Sites site.
I would like that if the image change, the change be reflected on my Google Site.
I have tried:
from matplotlib.pyplot import figure
import mpld3
fig = figure()
ax = fig.gca()
ax.plot([1,5,3,4])
hola= mpld3.save_html(fig,'myfig.html',template_type='simple')
If I open myfig.html on the browser and go to View Page Source, I can see
<script type="text/javascript" src="https://mpld3.github.io/js/d3.v3.min.js"></script>
<script type="text/javascript" src="https://mpld3.github.io/js/mpld3.v0.3.js"></script>
<style>
</style>
<div id="fig_el190911208844747686822225975"></div>
<script type="text/javascript">
!function(mpld3){
mpld3.draw_figure("fig_el190911208844747686822225975", {"width": 432.0, "height": 288.0, "axes": [{"bbox": [0.125, 0.125, 0.775, 0.755], "xlim": [-0.15000000000000002, 3.15], "ylim": [0.8, 5.2], "xdomain": [-0.15000000000000002, 3.15], "ydomain": [0.8, 5.2], "xscale": "linear", "yscale": "linear", "axes": [{"position": "bottom", "nticks": 9, "tickvalues": null, "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}, {"position": "left", "nticks": 11, "tickvalues": null, "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}], "axesbg": "#FFFFFF", "axesbgalpha": null, "zoomable": true, "id": "el19091120884476504", "lines": [{"data": "data01", "xindex": 0, "yindex": 1, "coordinates": "data", "id": "el19091120885398384", "color": "#1F77B4", "linewidth": 1.5, "dasharray": "none", "alpha": 1, "zorder": 2, "drawstyle": "default"}], "paths": [], "markers": [], "texts": [], "collections": [], "images": [], "sharex": [], "sharey": []}], "data": {"data01": [[0.0, 1.0], [1.0, 5.0], [2.0, 3.0], [3.0, 4.0]]}, "id": "el19091120884474768", "plugins": [{"type": "reset"}, {"type": "zoom", "button": true, "enabled": false}, {"type": "boxzoom", "button": true, "enabled": false}]});
}(mpld3);
So If I copy this code in my Google Sites I can see the picture.
But I need that if I save a new picture in myfig.html that I can see those changes in the site.

Visual Basic state tax calculator

Im working on a visual basic project creating a tax calculator. Right now, i have two arrays, one for the states and one for the tax rates. States starting with A-L have the same tax rate of 2%, M-P 3.5%, and R-W 4%. Right now this is what the two arrays look like:
Dim states() As String = {"Alabama", "Alaska", "Arizona", "Arkansas",
"California", "Colorado", "Connecticut", "Delaware", "Florida",
"Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas",
"Kentucky", "Louisiana", "Maine", "Maryland", "Massachusetts",
"Michigan", "Minnesota", "Mississippi", "Missouri", "Montana",
"Nebraska", "Nevada", "New Hampshire", "New Jersey", "New Mexico", "New
York", "North Carolina", "North Dakota", "Ohio", "Oklahoma", "Oregon",
"Pennsylvania", "Rhode Island", "South Carolina", "South Dakota",
"Tennessee", "Texas", "Utah", "Vermont", "Virginia", "Washington",
"West Virginia", "Wisconsin", "Wyoming"}
Dim statetax() As Double = {0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.035,
0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035,
0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04}
Then, to calculate the state tax I have a bunch of If selectstate starts with "a" "b" "c" or "d" then totalstatetax = statetax(0) * income
I was wondering if there was a simplified version, or a way or creating an index that would allow the states and state tax to coincide. any help is appreciated. thanks!

Dictionary could be right tool for the job
var stateTaxes = new Dictionary<string, decimal>
{
{ "Alabama", 0.02m },
{ "Maine", 0.035m },
// other states
}
var alabamaTaxAmount = income * stateTaxes["Alabama"];