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How to sum all the pixels of a PIL image? - python

i'm doing a project that involves getting every pixel from an image than averaging it out, i'm having some problems
here's the code
for i in range(0, 3):
for j in range(0, 3):
img = Image.open("Row " + str(i + 1) + " Col " + str(j + 1) + ".png", "r")
width, height = img.size
pixel_values = list(img.getdata())
pixel_values = np.array(pixel_values).reshape((width, height, 3))
total = list(sum(pixel_values) / len(pixel_values))
print("TOTAL: " + str(total))
and the output
TOTAL: [array([150.73267327, 147.4950495 , 135.68316832]), array([157.04950495, 153.71287129, 137.23762376]), array([162.94059406, 160.89108911, 142.02970297]), array([138.05940594, 137.72277228, 129.43564356]), array([124.86138614, 126.15841584, 124.0990099 ]), array([138.1980198 , 139.33663366, 138.45544554]), array([119.24752475, 120.17821782, 119.96039604]), array([65.2970297 , 61.94059406, 61. ]), array([42.84158416, 33.3960396 , 31.3960396 ]), array([55.82178218, 38.18811881, 33.5049505 ]), array([93.87128713, 58.76237624, 45.26732673]), array([152.16831683, 90.93069307, 58.16831683]), array([187.72277228, 100.9009901 , 45.12871287]), array([191.84158416, 89.82178218, 22.6039604 ]), array([194.38613861, 87.99009901, 19.77227723]), array([196.00990099, 87.94059406, 19.2970297 ]), array([198.16831683, 88.9009901 , 19.36633663]), array([199.15841584, 88.64356436, 18.6039604 ]), array([198.68316832, 87.83168317, 17.48514851]), array([199.31683168, 87.86138614, 17.32673267]), array([199.44554455, 87.54455446, 17.21782178]), array([199.67326733, 87.93069307, 17.34653465]), array([199.78217822, 88.26732673, 17.26732673]), array([199.99009901, 88.14851485, 16.94059406]), array([199.74257426, 88.10891089, 17.05940594]), array([200.30693069, 88.66336634, 18.17821782]), array([200.37623762, 88.47524752, 17.74257426]), array([201.2970297 , 88.92079208, 17.79207921]), array([200.33663366, 87.71287129, 16.41584158]), array([201.11881188, 88.2970297 , 16.9009901 ]), array([201.4950495 , 88.72277228, 17. ]), array([200.65346535, 88.12871287, 16.45544554]), array([200.11881188, 87.73267327, 16.33663366]), array([200.10891089, 87.79207921, 16.28712871]), array([200.36633663, 87.91089109, 16.16831683]), array([200.58415842, 88.10891089, 16.30693069]), array([199.65346535, 87.17821782, 15.42574257]), array([200.14851485, 87.42574257, 16.12871287]), array([200.2970297 , 87.16831683, 15.88118812]), array([201.00990099, 88.0990099 , 16.85148515]), array([200.11881188, 87.55445545, 16.7029703 ]), array([200.37623762, 87.87128713, 16.21782178]), array([200.55445545, 87.89108911, 16.30693069]), array([200.48514851, 87.7029703 , 15.93069307]), array([200.77227723, 87.79207921, 16.33663366]), array([200.1980198 , 87.59405941, 16.94059406]), array([201.27722772, 88.76237624, 17.65346535]), array([200.51485149, 88.28712871, 16.61386139]), array([201.43564356, 88.33663366, 16.69306931]), array([200.52475248, 87.2970297 , 15.38613861]), array([200.6039604 , 87.75247525, 15.93069307]), array([200.67326733, 87.95049505, 16.04950495]), array([201.16831683, 88.75247525, 16.6039604 ]), array([200.43564356, 88.18811881, 16.17821782]), array([201.00990099, 88.14851485, 16.35643564]), array([200.83168317, 87.85148515, 15.98019802]), array([200.66336634, 87.73267327, 15.52475248]), array([201.13861386, 88.24752475, 16. ]), array([200.56435644, 87.63366337, 15.56435644]), array([200.89108911, 87.48514851, 15.79207921]), array([201.12871287, 87.66336634, 16.32673267]), array([201.64356436, 87.95049505, 16.78217822]), array([200.93069307, 87.33663366, 15.4950495 ]), array([201.12871287, 87.83168317, 15.97029703]), array([201.23762376, 88.01980198, 15.98019802]), array([200.55445545, 87.68316832, 15.92079208]), array([201.0990099 , 87.92079208, 15.97029703]), array([201.10891089, 87.91089109, 15.82178218]), array([201.40594059, 87.6039604 , 15.13861386]), array([201.44554455, 87.63366337, 15.55445545]), array([201.6039604 , 87.88118812, 16.18811881]), array([201.97029703, 87.65346535, 15.93069307]), array([200.94059406, 86.33663366, 14.52475248]), array([201.03960396, 86.63366337, 13.75247525]), array([200.74257426, 86.97029703, 13.8019802 ]), array([200.10891089, 86.5049505 , 13.52475248]), array([198.92079208, 85.92079208, 12.77227723]), array([197.98019802, 85.63366337, 13.38613861]), array([198.4950495 , 85.79207921, 13.55445545]), array([197.54455446, 85.46534653, 13.30693069]), array([197.69306931, 85.95049505, 13.67326733]), array([197.51485149, 85.92079208, 13.47524752]), array([197.56435644, 86.12871287, 13.81188119]), array([197. , 86.06930693, 14.08910891]), array([196.88118812, 85.97029703, 13.5049505 ]), array([196.35643564, 85.11881188, 12.71287129]), array([195.86138614, 85.0990099 , 12.48514851]), array([195.15841584, 84.81188119, 12.78217822]), array([194. , 84.83168317, 13.35643564]), array([190.63366337, 83.66336634, 13.41584158]), array([187.96039604, 82.63366337, 13.78217822]), array([180.31683168, 80.66336634, 15.93069307]), array([166.03960396, 77.46534653, 25.25742574]), array([88.46534653, 45.98019802, 27. ]), array([42.02970297, 31.15841584, 29.57425743]), array([33.92079208, 28.25742574, 27.22772277]), array([30.18811881, 25.94059406, 25.51485149]), array([29.85148515, 25.75247525, 25.22772277]), array([30.10891089, 25.84158416, 25.44554455]), array([30.15841584, 26. , 25.95049505]), array([27.71287129, 23.79207921, 23.67326733])]
TOTAL: [array([22.31683168, 19.92079208, 20.45544554]), array([23.45544554, 20.98019802, 21.76237624]), array([22.24752475, 19.87128713, 20.61386139]), array([31.42574257, 30.44554455, 31.05940594]), array([104.45544554, 104.33663366, 105.20792079]), array([32.20792079, 28.98019802, 29.34653465]), array([33.30693069, 28.3960396 , 28.79207921])...74257426]), array([ 18.63366337, 87.43564356,
TOTAL: [array([20.5049505 , 18.83168317, 19.06930693]), array([17.16831683, 16.03960396, 16.71287129]), array([20.77227723, 20.27722772, 21.65346535]), array([69.94059406, 69.6039604 , 69.75247525]), array([57.92079208, 56.02970297, 55.43564356]), array([36.75247525, 33.00990099, 30.84158416]), array([34.95049505, 30.06930693, 27.92079208]), array([32.78217822, 28.04950495, 25.94059406]), array([33.76237624, 28.66336634, 26.42574257]), array([35.14851485, 30.08910891, 26.65346535]), array([37.86138614, 32.0990099 , 25.94059406]), array([53.77227723, 48.00990099, 31.6039604 ]), array([155.0990099 , 148.82178218, 68.36633663]), array([155.71287129, 146.25742574, 20.42574257]), , 17.66336634]), array([159.64356436, 150.41584158, 16.73267327]), array([159.03960396, 149.76237624, 17.07920792]), array([159.0990099 , 149.61386139, 17.82178218]), array([158.32673267, 148.7029703 , 18.94059406]), array([156.68316832, 146.3960396 , 19.59405941]), array([154.52475248, 143.94059406, 19.91089109]), array([152.69306931, 142.43564356, 22.25742574]), array([148.95049505, 138.34653465, 23.66336634]), array([142.87128713, 132.27722772, 33.37623762]), array([117.61386139, 107.61386139, 39.94059406]), array([69.86138614, 61.52475248, 31.41584158]), array([33.42574257, 28.27722772, 21.08910891]), array([29.16831683, 26.42574257, 23.12871287]), array([29.93069307, 27.23762376, 23.78217822]), array([32.76237624, 30.36633663, 26.8019802 ]), array([38.38613861, 35.74257426, 32.25742574]), array([62.72277228, 58.68316832, 55.11881188])]
TOTAL: [array([148.99009901, 148.12871287, 140.7029703 ]), array([154.83168317, 158.13861386, 155.98019802]), array([163.48514851, 167.21782178, 167.51485149]), array([149.57425743, 151.25742574, 151.6039604 ]), array([80.68316832, 79.28712871, 80.31683168]), array([34.94059406, 27.83168317, 27.55445545]), array([32.51485149, 21.89108911, 20.56435644]), array([39.78217822, 23.83168317, 20.47524752]), array([63.4950495 , 36.03960396, 27.16831683]), array([120.8019802 , 64.12871287, 38.28712871]), array([170.3960396 , 82.43564356, 30.26732673]), array([180.21782178, 73.99009901, 13.34653465]), array([182.35643564, 70.14851485, 9.53465347]), array([184.56435644, 70.37623762, 9.03960396]), array([186.47524752, 71.05940594, 8.67326733]), array([186.48514851, 70.51485149, 8.56435644]), array([187.76237624, 71.68316832, 9.28712871]), array([188.55445545, 72.38613861, 9.0990099 ]), array([189.63366337, 73.26732673, 10.33663366]), array([189.06930693, 72.35643564, 10. ]), array([190.1980198 , 72.52475248, 9.23762376]), array([190.23762376, 72.07920792, 8.74257426]), array([189.91089109, 71.68316832, 8.30693069]), array([190.20792079, 71.94059406, 8.26732673]), array([190.00990099, 71.9009901 , 8.27722772]), array([190.69306931, 72.20792079, 8.25742574]), array([191.82178218, 72.5049505 , 7.87128713]), array([193.12871287, 72.78217822, 7.66336634]), array([193.41584158, 72.63366337, 7.40594059]), array([193.66336634, 72.75247525, 7.72277228]), array([193.95049505, 73. , 8.08910891]), array([192.89108911, 71.86138614, 6.61386139]), array([194.11881188, 72.52475248, 7.0990099 ]), array([194.5049505 , 72.37623762, 7.44554455]), array([194.73267327, 72.10891089, 7.51485149]), array([194.36633663, 71.99009901, 7.14851485]), array([194.32673267, 72. , 7.30693069]), array([193.76237624, 71.62376238, 6.75247525]), array([194.41584158, 72.14851485, 7.24752475]), array([195.05940594, 72.88118812, 7.73267327]), array([194.30693069, 72.16831683, 7.34653465]), array([194.8019802 , 72.67326733, 7.38613861]), array([194.31683168, 72.27722772, 6.99009901]), array([194.17821782, 72.2970297 , 6.76237624]), array([195.23762376, 72.97029703, 7.06930693]), array([195.34653465, 72.79207921, 7.25742574]), array([194.46534653, 72.27722772, 7.02970297]), array([195.38613861, 72.83168317, 7.65346535]), array([195.10891089, 72.69306931, 7.55445545]), array([195.23762376, 72.79207921, 6.82178218]), array([195.12871287, 72.87128713, 7.18811881]), array([195.12871287, 72.64356436, 7.18811881]), array([195.56435644, 72.71287129, 7.22772277]), array([195.45544554, 72.56435644, 7.22772277]), array([196.41584158, 73.76237624, 8.1980198 ]), array([195.59405941, 73. , 7.82178218]), array([195.77227723, 72.8019802 , 7.79207921]), array([196.48514851, 73.22772277,
TOTAL: [array([12.8019802 , 7.52475248, 7.75247525]), array([12.17821782, 6.92079208, 7.22772277]), array([12.38613861, 7.08910891, 7.34653465]), array([15.82178218, 10.1980198 , 10.43564356]), 129, 9.89108911, 13.16831683]), array([183.18811881, 10.23762376, 13.47524752]), array([184.86138614, 9.55445545, 12.99009901]), array([186.4950495 , 8.62376238, 12.28712871]), array([189.04950495, 9.26732673, 12.66336634]), array([190.5049505 , 8.68316832, 11.96039604]), array([191.40594059, 7.86138614, 11.26732673]), array([193.27722772, 7.78217822, 11.05940594]), array([193.8019802 , 7.20792079, 10.36633663]), array([194.83168317, 6.91089109, 10.24752475]), array([195.91089109, 6.8019802 , 10.46534653]), array([196.57425743, 6.77227723, 10.71287129]), array([197.5049505 , 6.84158416, 10.97029703]), array([197.44554455, 6.44554455, 10.65346535]), array([197.58415842, 6.46534653, 10.38613861]), array([197.2970297 , 5.7029703 , 9.33663366]), array([197.67326733, 5.51485149, 9.16831683]), array([198.08910891, 5.17821782, 9.04950495]), array([197.73267327, 4.78217822, 8.62376238]), array([198.51485149, 5.41584158, 9.24752475]), array([198.55445545, 5.2970297 , 8.99009901]), array([198.35643564, 5. , 8.76237624]), array([198.58415842, 5.03960396, 9.0990099 ]), array([198.18811881, 4.74257426, 8.69306931]), array([197.89108911, 4.89108911, 8.89108911]), array([197.40594059, 4.72277228, 8.62376238]), array([196.2970297 ,
TOTAL: [array([20.8019802 , 15.78217822, 15.25742574]), array([19.00990099, 14.95049505, 14.59405941]), array([18.24752475, 14.4950495 , 14.27722772]), array([16.6039604 , 12.84158416, 12.94059406]), array([10.55445545, 6.87128713, 7.68316832]), array([11.15841584, 7.43564356, 8.13861386]), array([11.69306931, 7.75247525, 8.47524752]), array([14.51485149, 10.54455446, 10.68316832]), array([23.2970297 , 18.97029703, 19.53465347]), array([29.82178218, 25.06930693, 26.03960396]), array([29.98019802, 24.91089109, 25.78217822]), array([32.20792079, 26.9009901 , 27.1980198 ]), array([35.47524752, 29.57425743, 29.79207921]), array([39.61386139, 31.83168317, 31.25742574]), array([42.83168317, 32.54455446, 30.44554455]), array([83.9009901 , 32.84158416, 32.33663366]), array([105.07920792, 25.48514851, 27.12871287]), array([120.76237624, 19.66336634, 22.62376238]), array([133.52475248, 17.25742574, 21.4950495 ]), array([144.63366337, 15.64356436, 21.24752475]), array([157.11881188, 15.71287129, 22.59405941]), array([163.94059406, 13.07920792, 20.86138614]), array([170.74257426, 13.66336634, 20.8019802 ]), array([176.43564356, 12.62376238, 20.15841584]), array([181.28712871, 11.5049505 , 19.5049505 ]), array([184.12871287, 10.20792079, 18.43564356]), array([187.16831683, 9.63366337, 18.22772277]), array([188.2970297 , 8.02970297, 16.79207921]), array([188.81188119, 7.63366337, 16.28712871]), array([189.22772277, 7.63366337, 16.18811881]), array([189.43564356, 7.41584158, 16.2970297 ]), array([189.98019802, 7.82178218, 16.9009901 ]), array([189.27722772, 7. , 16.00990099]), array([189.51485149, 7.25742574, 16.31683168]), array([189.15841584, 7.22772277, 16.28712871]), array([189.06930693, 7.13861386, 16.44554455]), array([189.27722772, 7.27722772, 16.4950495 ]), array([189.34653465, 7.28712871, 16.25742574]), array([189.64356436, 7.73267327, 16.64356436]), array([189.05940594, 7.20792079, 16.47524752]), array([189.59405941, 7.61386139, 16.83168317]), array([189.41584158, 7.47524752, 16.71287129]), array([189.45544554, 7.47524752, 16.78217822]), array([188.85148515, 7.17821782, 16.79207921]), array([189.06930693, 7.26732673, 16.75247525]), array([188.9009901 , 7.2970297 , 16.56435644]), array([189.46534653, 7.81188119, 17.12871287]), array([189.26732673, 7.68316832, 17.11881188]), array([188.69306931, 7.12871287, 16.20792079]), array([188.6039604 , 7.04950495, 16.22772277]), array([189.12871287, 7.44554455, 16.7029703 ]), array([188.73267327, 7.52475248, 16.68316832]), array([188.81188119, 7.57425743, 16.79207921]), array([188.89108911, 7.4950495 , 16.5049505 ]), array([188.88118812, 7.38613861, 16.26732673]), array([189.01980198, 7.62376238, 16.54455446]), array([188.86138614, 7.47524752, 16.71287129]), array([188.58415842, 7.38613861, 16.51485149]), array([188.51485149, 7.27722772, 16.51485149]), array([188.35643564, 7.22772277, 16.36633663]), array([188.61386139, 7.66336634, 16.72277228]), array([187.95049505, 7.31683168, 16.28712871]), array([188.53465347, 7.86138614, 16.81188119]), array([188.24752475, 7.58415842, 16.7029703 ]), array([187.95049505, 7.18811881, 16.3960396 ]), array([188.23762376, 7.92079208, 16.9009901 ]), array([188.55445545, 8.18811881, 17.34653465]), array([188.17821782, 7.75247525, 16.84158416]), array([188.01980198, 8.15841584, 17.22772277]), array([187.64356436, 7.82178218, 17.20792079]), 38614, 101.67326733, 149.53465347]), array([ 8.52475248, 101.11881188, 149.21782178]), array([ 8.45544554, 101.13861386, 149.27722772]), array([ 8.17821782, 100.71287129, 148.55445545]), array([ 8.89108911, 101.56435644, 149.43564356]), array([ 8.08910891, 101.05940594, 149.07920792]), array([ 8.32673267, 101.57425743, 149.20792079]), array([ 7.85148515, 100.93069307, 148.62376238]), array([ 8.43564356, 100.61386139, 148.34653465]), array([ 7.99009901, 100.55445545, 148.13861386]), array([ 8.01980198, 100.20792079, 147.93069307]), array([ 8.63366337, 99.72277228, 146.8019802 ]), array([ 8.25742574, 98.2970297 , 145.32673267]), array([ 9.5049505 , 98.53465347, 144.51485149]), array([ 8.98019802, 96.21782178, 141.11881188]), array([ 10.01980198, 94.2970297 , 137.24752475]), array([ 13.14851485, 88.84158416, 125.86138614]), array([19.30693069, 62.61386139, 87.16831683]), array([24.57425743, 35.27722772, 42.64356436])]
i wanted the sum of all of the numbers, but i'm not getting that... what am i doing wrong here?

You can use PIL (Python Imaging Library) to load the image, convert it to an array with numpy.asarray() then use numpy.sum():
from PIL import Image
import numpy as np
image_path = '/Users/xxx/Desktop/test.png'
img = Image.open(image_path)
img.load()
data = np.asarray(img, dtype="int32")
data.sum()
Output:
1715779623
References:
numpy.asarray
numpy.sum

Related

I am using haversine_distance function to calculate distance between coordinates in a dataset to a specific coordinate. [start_lat, start_lon = 40.6976637, -74.1197643]
def haversine_distance(lat1, lon1, lat2, lon2):
r = 6371
phi1 = np.radians(lat1)
phi2 = np.radians(lat2)
delta_phi = np.radians(lat2-lat1)
delta_lambda = np.radians(lon2-lon1)
a = np.sin(delta_phi / 2)**2 + np.cos(phi1) * np.cos(phi2) * np.sin(delta_lambda / 2)**2
res = r * (2 * np.arctan2(np.sqrt(a), np.sqrt(1-a)))
return np.round(res, 2)
start_lat, start_lon = 40.6976637, -74.1197643
distances_km = []
for row in pandas_df.itertuples(index=False):
distances_km.append(
haversine_distance(start_lat, start_lon, row.lat, row.lon)
)
pandas_df['Distance'] = distances_km
pandas_df
This successfully creates a column in my data set measuring the distance from given point like this:
Now I want to modify this code so that instead of using [start_lat, start_lon = 40.6976637, -74.1197643] I want to use another dataset containing cities.
How can I modify this existing code such that I create column for every city using its coordinates instead.
So desired output shows different columns with each city name and distance as calculated above.
Any Help is appreciated, new to python!
Cities array as requested in comments
[['Nanaimo' -123.9364 49.1642]
['Prince Rupert' -130.3271 54.3122]
['Vancouver' -123.1386 49.2636]
['Victoria' -123.3673 48.4275]
['Edmonton' -113.4909 53.5445]
['Winnipeg' -97.1392 49.8994]
['Sarnia' -82.4065 42.9746]
['Sarnia' -82.4065 42.9746]
['North York' -79.4112 43.7598]
['Kingston' -76.4812 44.2305]
['St. Catharines' -79.2333 43.1833]
['Thunder Bay' -89.2461 48.3822]
['Gaspé' -64.4833 48.8333]
['Cap-aux-Meules' -61.8607 47.3801]
['Kangiqsujuaq' -71.9667 61.6]
['Montreal' -73.5534 45.5091]
['Quebec City' -71.2074 46.8142]
['Rimouski' -68.524 48.4489]
['Sept-Îles' -66.3833 50.2167]
['Bathurst' -65.6497 47.6186]
['Charlottetown' -63.1399 46.24]
['Corner Brook' -57.9711 48.9411]
['Dartmouth' -63.5714 44.6715]
['Lewisporte' -55.0667 49.2333]
['Port Hawkesbury' -61.3642 45.6153]
['Saint John' -66.0628 45.2796]
["St. John's" -52.7072 47.5675]
['Sydney' -60.1947 46.1381]
['Yarmouth' -66.1175 43.8361]]

The beauty of Python is that you can use the same code to do different things.
To consider different [start_lat, start_lon] values for every column in your data, you can use the same code that you have now. All you need to do is to define start_lat and start_lon as arrays:
# --------------------- Array Initialization ---------------------
import pandas as pd
import numpy as np
np.random.seed(0)
pandas_df = pd.DataFrame(data = {'lat': np.random.rand(100),
'lon': np.random.rand(100)})
start_cities = pd.DataFrame([['Nanaimo' , -123.9364 , 49.1642], ['Prince Rupert' , -130.3271 , 54.3122],
['Vancouver' , -123.1386 , 49.2636], ['Victoria' , -123.3673 , 48.4275],
['Edmonton' , -113.4909 , 53.5445], ['Winnipeg' , -97.1392 , 49.8994],
['Sarnia' , -82.4065 , 42.9746], ['Sarnia' , -82.4065 , 42.9746],
['North York' , -79.4112 , 43.7598], ['Kingston' , -76.4812 , 44.2305],
['St. Catharines' , -79.2333 , 43.1833], ['Thunder Bay' , -89.2461 , 48.3822],
['Gaspé' , -64.4833 , 48.8333], ['Cap-aux-Meules' , -61.8607 , 47.3801],
['Kangiqsujuaq' , -71.9667 , 61.6 ], ['Montreal' , -73.5534 , 45.5091],
['Quebec City' , -71.2074 , 46.8142], ['Rimouski' , -68.524 , 48.4489],
['Sept-Îles' , -66.3833 , 50.2167], ['Bathurst' , -65.6497 , 47.6186],
['Charlottetown' , -63.1399 , 46.24 ], ['Corner Brook' , -57.9711 , 48.9411],
['Dartmouth' , -63.5714 , 44.6715], ['Lewisporte' , -55.0667 , 49.2333],
['Port Hawkesbury' , -61.3642 , 45.6153], ['Saint John' , -66.0628 , 45.2796],
["St. John's" , -52.7072 , 47.5675], ['Sydney' , -60.1947 , 46.1381],
['Yarmouth' , -66.1175 , 43.8361]])
start_cities.columns = 'names', 'start_lat', 'start_lon'
start_lat = start_cities.start_lat
start_lon = start_cities.start_lon
# --------------------- Same code as before (as promised) ---------------------
def haversine_distance(lat1, lon1, lat2, lon2):
r = 6371
phi1 = np.radians(lat1)
phi2 = np.radians(lat2)
delta_phi = np.radians(lat2-lat1)
delta_lambda = np.radians(lon2-lon1)
a = np.sin(delta_phi / 2)**2 + np.cos(phi1) * np.cos(phi2) * np.sin(delta_lambda / 2)**2
res = r * (2 * np.arctan2(np.sqrt(a), np.sqrt(1-a)))
return np.round(res, 2)
distances_km = []
for row in pandas_df.itertuples(index=False):
distances_km.append(
haversine_distance(start_lat, start_lon, row.lat, row.lon))
# --------------------- Store data ---------------------
distances_km = np.array(distances_km)
for ind, name in enumerate(start_cities.names):
pandas_df['distance_km_' + name] = distances_km[:,ind]
# print(pandas_df.keys())
# ["lat" , "lon" ,
# "distance_km_Nanaimo" , "distance_km_Prince Rupert" ,
# "distance_km_Vancouver" , "distance_km_Victoria" ,
# "distance_km_Edmonton" , "distance_km_Winnipeg" ,
# "distance_km_Sarnia" , "distance_km_North York" ,
# "distance_km_Kingston" , "distance_km_St. Catharines" ,
# "distance_km_Thunder Bay" , "distance_km_Gaspé" ,
# "distance_km_Cap-aux-Meules" , "distance_km_Kangiqsujuaq" ,
# "distance_km_Montreal" , "distance_km_Quebec City" ,
# "distance_km_Rimouski" , "distance_km_Sept-Îles" ,
# "distance_km_Bathurst" , "distance_km_Charlottetown" ,
# "distance_km_Corner Brook" , "distance_km_Dartmouth" ,
# "distance_km_Lewisporte" , "distance_km_Port Hawkesbury",
# "distance_km_Saint John" , "distance_km_St. John's" ,
# "distance_km_Sydney" , "distance_km_Yarmouth" ]

i have a dictionary , and i want to sum for every array in every key dictionary
example :
{'Ancolmekar': array([1]),
'Cidurian': array([1]),
'dayeuhkolot': array([1]),
'Hantap': array([1]),
'Kertasari': array([1]),
'Meteolembang': array([1]),
'Sapan': array([1]),
}
the value 1 is example not a real calculated. what i supposed to do?
this is my dictionary it name DP_1
{'Ancolmekar': array([-0.07603596, -0.09520354, -0.09629883, -0.08370299, -0.13408635,
-0.14558689, -0.14421778, -0.14367014, -0.14175338, -0.14038426,
-0.15161099, -0.12340727, -0.0995847 , 0.03568364, 0.28075484,
0.46180632, 0.41755659, 0.32171869, 0.19066721, 0.09647225,
-0.02620026, -0.0073065 , -0.10122764, -0.09438207]),
'Cidurian': array([-0.15732019, -0.17139467, -0.17468054, -0.18557868, -0.17933552,
-0.19193136, -0.17851406, -0.19494341, -0.19795546, -0.19220518,
-0.17851406, -0.16098941, -0.03667397, 0.13879153, 0.38254838,
0.55637094, 0.51726908, 0.355659 , 0.25346842, 0.20335889,
0.01294268, 0.01370938, -0.10425338, -0.1298284 ]),
'Dayeuhkolot': array([-0.04123083, -0.05256709, -0.06341046, -0.0514718 , -0.0684488 ,
-0.06625821, -0.06543675, -0.06571057, -0.07419907, -0.07764923,
-0.07858023, -0.0599603 , -0.05081462, 0.06271221, 0.11780531,
0.22519852, 0.18954682, 0.14661145, 0.07941539, 0.06336939,
0.01616238, -0.00661966, -0.0226109 , -0.05585296]),
'Hantap': array([-0.04986537, -0.07067589, -0.06793766, -0.05808005, -0.0575324 ,
-0.06755431, -0.07067589, -0.072045 , -0.06684237, -0.07752145,
-0.072045 , -0.07368793, -0.04384127, 0.01064942, 0.13195281,
0.17822882, 0.17450484, 0.14898457, 0.15506344, 0.12236902,
0.0085136 , -0.02270217, -0.02303076, -0.03622901]),
'Kertasari': array([-0.13810469, -0.1709634 , -0.1794519 , -0.17863043, -0.17999954,
-0.1794519 , -0.18848804, -0.19150009, -0.1988933 , -0.1819163 ,
-0.16630842, -0.06143437, 0.05932138, 0.16967187, 0.48949662,
0.54239914, 0.44842324, 0.29699936, 0.23095336, 0.13653934,
0.02213627, -0.10716274, -0.12978049, -0.14385497]),
'Meteolembang': array([-7.71175612e-02, -9.71066083e-02, -1.07785688e-01, -9.73804308e-02,
-9.05348667e-02, -1.06142753e-01, -9.98448339e-02, -1.10250091e-01,
-1.09428624e-01, -1.13262139e-01, -1.06142753e-01, -1.73420957e-04,
1.07165024e-01, 1.21129974e-01, 2.79508945e-01, 1.29289887e-01,
2.62970062e-01, 2.07384082e-01, 1.42214312e-01, 2.77564805e-02,
-2.23530486e-02, -3.57703541e-02, -3.44012413e-02, -6.97243520e-02]),
'Sapan': array([-0.04793492, -0.05461619, -0.06419998, -0.06639056, -0.06365234,
-0.06995026, -0.0559853 , -0.0680335 , -0.07405759, -0.07323613,
-0.07843876, -0.0729623 , -0.05242561, -0.00587578, 0.13240462,
0.16663244, 0.22769487, 0.16586574, 0.13229509, 0.06477045,
0.07002784, -0.02285277, -0.02783635, -0.0612427 ])}

You can use reduce function as below:
from functools import reduce
dict2 = {}
for k, v in dict.items():
dict2[k] = reduce(lambda a, b: a+b, v)
print(dict2)

I have file test.txt (given below)
When I tried to print I get this error.
>>> print(content['mass'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: string indices must be integers
But when
>>> print(content)
works ok, and show whole content (test.txt)
I need to print each list from the dict (like mass, temperature, etc.) and plot any 2 of them.
The content of the file is as follows:
{'mass': <Quantity [0.01066337, 0.05677534, 0.01377424, 0.06982872, 0.02665362] solar_mass>, 'temperature': <[values ... ] unit>}
full content file:
{'mass': <Quantity [0.01066337, 0.05677534, 0.01377424, 0.06982872, 0.02665362,
0.05278109, 0.01705602, 0.01321568, 0.08957726, 0.01409967,
0.02680093, 0.04329349, 0.04019967, 0.01633035, 0.09329267,
0.09963128, 0.04351358, 0.07735598, 0.02380135, 0.02242677,
0.03210654, 0.08636837, 0.02273623, 0.08528297, 0.07963256,
0.01457712, 0.07067075, 0.03672347, 0.08830624, 0.0152433 ,
0.02692899, 0.06610553, 0.09237312, 0.01470838, 0.06389854,
0.0367586 , 0.01463151, 0.05156612, 0.09061456, 0.02097923,
0.09724939, 0.01442857, 0.04258596, 0.09389821, 0.04595303,
0.01288873, 0.09977905, 0.08107244, 0.07191315, 0.01770304,
0.080393 , 0.02020766, 0.07458504, 0.01695185, 0.07393091,
0.03053152, 0.06589851, 0.02035323, 0.07618899, 0.03820138,
0.02377403, 0.07995724, 0.04570385, 0.02277475, 0.01927913,
0.02415907, 0.01820019, 0.04897082, 0.05023911, 0.02736103,
0.05118501, 0.05433449, 0.05497384, 0.0278658 , 0.0109119 ,
0.04103063, 0.05278137, 0.08110729, 0.02117512, 0.02299667,
0.02712591, 0.09289156, 0.07933846, 0.07723336, 0.07550681,
0.09140791, 0.05044324, 0.04048182, 0.0540212 , 0.06348042,
0.09851312, 0.06866901, 0.04383336, 0.06000141, 0.06026228,
0.09245798, 0.06652555, 0.08638328, 0.02669351, 0.03291363,
0.09498578, 0.07123481, 0.01734788, 0.06394011, 0.09080883,
0.0100093 , 0.05614301, 0.08463198, 0.08348169, 0.0695272 ,
0.012756 , 0.08004025, 0.042433 , 0.04228984, 0.05852984,
0.09786057, 0.09222483, 0.05554798, 0.02833528, 0.05775946,
0.04866883, 0.08642554, 0.05625195, 0.04145988, 0.03118205,
0.06150846, 0.03043129, 0.08140125, 0.0371213 , 0.08422178,
0.04447278, 0.05010288, 0.03013005, 0.07293837, 0.04287462,
0.07925233, 0.03597007, 0.06064241, 0.01506324, 0.01661757,
0.09065556, 0.01877721, 0.06181287, 0.01797611, 0.08751541,
0.06245063, 0.05643477, 0.09667603, 0.02816646, 0.05963242,
0.09114687, 0.08555436, 0.03389574, 0.03619514, 0.09107117,
0.08513269, 0.02986811, 0.06354284, 0.03119169, 0.02294978,
0.08086929, 0.07718417, 0.03123856, 0.03509633, 0.05837078,
0.02374362, 0.06490901, 0.05722849, 0.05868046, 0.01302157,
0.01879107, 0.07831512, 0.04077195, 0.02944109, 0.06633705,
0.09150163, 0.03014493, 0.07650354, 0.05166767, 0.0630273 ,
0.04772543, 0.0354305 , 0.02018854, 0.09507716, 0.02150221,
0.01615494, 0.06707219, 0.06575294, 0.05319418, 0.06806339,
0.084564 , 0.06717039, 0.07972018, 0.06372022, 0.03449891,
0.01062518, 0.0362224 , 0.06631852, 0.06794747, 0.07164968] solMass>,
'age': <Quantity [2.1892995 , 0.17371445, 6.37350567, 3.65552583, 0.26690645,
9.01274312, 9.5712514 , 4.12433204, 1.46197905, 6.69672667,
2.60662951, 0.07088385, 5.68504611, 2.14170039, 8.25031197,
1.05353222, 6.38848764, 4.91127832, 4.83363973, 1.06507071,
8.91887495, 9.04766237, 6.77889849, 9.2907544 , 9.85479148,
2.02304717, 3.2270554 , 9.36069897, 8.1881205 , 8.26326698,
4.24937976, 5.43265177, 1.23439319, 0.66178519, 7.30850369,
8.53579481, 0.58917658, 9.52785688, 8.78128074, 7.49955993,
6.07008997, 2.43334746, 9.15192905, 5.34271923, 1.03902797,
7.85382611, 9.07929136, 2.34516092, 4.0336831 , 6.9754011 ,
1.65513417, 5.78941649, 6.28545569, 7.14168201, 4.5552691 ,
6.21132475, 6.81369173, 1.74626285, 9.87199907, 3.68096459,
5.70725541, 8.50758903, 4.5958371 , 9.26271041, 9.21219332,
1.12404491, 8.46718892, 2.41106556, 9.5383536 , 2.845751 ,
5.48186263, 3.75960932, 9.46408245, 3.070541 , 5.14349759,
6.72742338, 2.056287 , 8.26982877, 3.23218309, 4.17451665,
5.77088167, 0.44298759, 9.65296989, 2.61752721, 9.13504304,
7.98373538, 5.71220366, 1.56916865, 1.97688569, 1.91507324,
9.17263502, 1.77701376, 4.55412645, 3.2627102 , 7.35668915,
2.50479129, 2.61109961, 6.37928278, 4.95950159, 3.05026315,
4.05514457, 0.08672433, 2.86609243, 1.35325359, 3.42583719,
4.26484596, 5.1162449 , 4.85971091, 4.44955176, 2.23475404,
3.31668938, 8.43803181, 0.72360653, 4.15507274, 0.87411426,
7.75213716, 4.6793922 , 7.48000041, 5.72125433, 1.31397429,
5.95206492, 8.58636847, 5.89774647, 3.26018283, 8.27834433,
4.00586172, 5.53028026, 4.79525327, 3.29217389, 4.87280459,
3.7335077 , 3.30432489, 3.60355157, 9.34549498, 6.49931121,
0.02732216, 6.94381144, 5.90679801, 3.16357294, 7.82961274,
0.0888311 , 7.9029332 , 4.33901607, 5.51927395, 6.53654137,
7.64875188, 3.09088845, 6.17206639, 4.5635939 , 1.12640628,
6.55772591, 0.8635738 , 8.86321633, 6.58032887, 0.57264661,
9.99113685, 0.83339148, 6.32905049, 9.21722392, 1.65153675,
4.29805491, 4.29669901, 5.58498365, 9.08169993, 6.42132207,
2.0412622 , 4.7042499 , 8.45610063, 5.53653754, 4.27634556,
0.98283569, 4.70564712, 4.85030912, 4.43987977, 2.20253426,
5.61759252, 7.5305766 , 9.28187195, 0.06004595, 0.8131256 ,
0.9932211 , 1.47332778, 1.44624897, 9.23842621, 5.34899458,
8.43612797, 9.4825792 , 5.94245213, 6.74007551, 6.20404917,
9.53766677, 4.60019672, 1.59700463, 5.52334977, 0.48772997,
0.59965089, 1.75955369, 9.56138032, 1.04756037, 3.91171088] Gyr>, 'temperature': <Quantity [ 416.26187575, 2386.00147856, 356.96775964, 1611.48635911,
1386.90459789, 843.45377056, 359.56621029, 397.98698621,
2615.60719944, 356.35902942, 696.73157636, 2452.07321025,
738.22537858, 532.80565595, 2677.89432543, 2778.16848831,
769.31944774, 2151.4339527 , 533.16952309, 812.50564763,
548.1938236 , 2513.85620097, 471.13610308, 2481.15025828,
2297.11805108, 508.33419459, 1725.63923115, 602.54069179,
2572.14755174, 351.02698579, 599.78662322, 1332.30425907,
2664.27485445, 715.27835447, 1157.7390599 , 617.72498514,
742.04731718, 811.52591929, 2633.86501557, 434.9693978 ,
2741.4568123 , 480.34396454, 686.744989 , 2686.61464287,
1416.43680536, 325.36847056, 2782.35910147, 2382.15547751,
1761.66710638, 401.58797383, 2375.15628236, 456.84485622,
1960.34206079, 389.02690439, 1909.10788514, 582.38959148,
1253.47061676, 655.9560554 , 2071.71727096, 807.75308367,
508.31655569, 2318.63547143, 880.95939104, 431.89009998,
389.83994036, 840.95773606, 386.29226905, 1148.60436432,
789.50516397, 686.72298902, 930.60237286, 1107.69977761,
869.80628035, 678.32474609, 337.22647863, 719.64135682,
1299.92452458, 2354.81635642, 560.35816002, 546.26491365,
550.48207955, 2728.27478396, 2277.28716021, 2166.3338627 ,
2028.72650099, 2646.76330972, 907.07025528, 1112.10342809,
1347.40455825, 1567.69512142, 2762.24673578, 1792.33516425,
850.53825842, 1270.41170997, 1025.53432709, 2663.39115409,
1561.35583681, 2513.28960355, 568.35332647, 764.20122094,
2704.05935833, 2789.71414994, 508.66726781, 1748.71166657,
2636.13556182, 335.90296155, 1040.7441679 , 2460.87911061,
2429.70209659, 1757.52438765, 416.53092797, 2322.72218894,
1472.63533413, 847.29923108, 1827.91530672, 2751.58852168,
2658.86923054, 932.96214473, 567.10026795, 1608.78268184,
867.7687031 , 2515.42004061, 1004.56537506, 898.84784151,
546.83257922, 1228.64851226, 599.80749478, 2363.87785858,
818.03791184, 2448.3783556 , 917.2780149 , 1062.48713029,
678.01980883, 1699.94999111, 755.65919285, 2918.00441238,
640.80652735, 1097.67818411, 455.0697186 , 374.90100478,
2919.23401673, 400.97259608, 1221.76007185, 432.21978007,
2547.58809003, 1092.25936545, 1224.11062078, 2732.25980845,
603.32307638, 1729.78873919, 2641.86202827, 2529.80624052,
573.36519488, 653.1220142 , 2687.00147158, 2476.83545148,
1067.09489353, 1186.50146867, 531.10784434, 723.71093433,
2352.11261182, 2145.45144949, 610.27363595, 585.76505168,
1021.59377921, 695.08881158, 1326.15404201, 933.61651678,
1066.31164386, 391.34211341, 737.96295138, 2213.58596372,
780.74166508, 625.66321926, 1604.59456116, 2647.29238788,
546.95277285, 2091.59457771, 2636.78384296, 1950.52489595,
1484.64961828, 1018.81913319, 690.23845654, 2707.05845721,
485.87384115, 361.21491645, 1197.21167289, 1289.88457879,
916.51388128, 1367.72546396, 2459.51962535, 1426.2466385 ,
2345.90602429, 1233.03426045, 1420.10028775, 603.3961445 ,
979.86162211, 1167.48046769, 1996.95643785, 1759.6332142 ] K>, 'gravity': <Dex [4.43710182, 4.965187 , 4.5960181 , 5.43935524, 4.77599392,
5.36645319, 4.71685808, 4.5631087 , 5.28746289, 4.60912155,
4.92292931, 4.73522324, 5.1859695 , 4.65413841, 5.27461287,
5.24749696, 5.23839782, 5.38287012, 4.88123687, 4.7978376 ,
5.06587143, 5.31138048, 4.86481419, 5.31825235, 5.35742248,
4.59283847, 5.42541489, 5.14719842, 5.29932345, 4.65409788,
4.94470474, 5.45260548, 5.27588952, 4.558575 , 5.45716882,
5.14497198, 4.55072716, 5.35471333, 5.28628722, 4.8221192 ,
5.25810337, 4.59295229, 5.23605709, 5.27209735, 5.17053945,
4.56689609, 5.24704209, 5.33165214, 5.43725938, 4.72922773,
5.32972958, 4.79516682, 5.41802867, 4.70686194, 5.42131465,
5.02688942, 5.46371964, 4.76247288, 5.39988976, 5.13962788,
4.8853597 , 5.35351361, 5.25370809, 4.87296808, 4.78071025,
4.8384147 , 4.7483346 , 5.25428576, 5.33904289, 4.93784239,
5.32980831, 5.34280863, 5.39371378, 4.95096254, 4.47277291,
5.20372326, 5.28786486, 5.34553068, 4.80229852, 4.85641286,
4.95883889, 5.21125042, 5.36097707, 5.37256552, 5.40842553,
5.28281229, 5.32288574, 5.12740498, 5.29872025, 5.35763516,
5.25223846, 5.37453344, 5.22803649, 5.3843215 , 5.43732776,
5.27730831, 5.39646086, 5.31128667, 4.94587991, 5.04578314,
5.26737666, 4.83639934, 4.69458324, 5.33220018, 5.28474856,
4.42491336, 5.38056442, 5.32188551, 5.32767791, 5.39734114,
4.53849927, 5.35272672, 5.0955432 , 5.20069748, 5.2635995 ,
5.25519359, 5.27922436, 5.3892347 , 4.98281909, 5.29584768,
5.30308228, 5.31102091, 5.38680781, 5.17573485, 5.04666149,
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Try this:
import json
json_data = json.loads(content)
print(json_data["mass"])

I am trying to use differential_evolution from SciPy. I have three matrices: x, y and P - all of size (14,6). I have to use the following formula:
z= np.log10(g)+ np.log10(c)*np.log10(P)
to find the value of c (real number from 0 to 2) which minimize:
numpy.median(z**2)
this expression. What I try is this (I provide the data for the convenience):
import numpy as np
from scipy.optimize import differential_evolution
def func(c, args):
z = args[0] + np.log10(c)*np.log10(args[1])
return np.median(z**2)
if __name__ == '__main__':
bounds = [(0, 2)]
x = np.array([[126581.94951205, 97601.85624482, 59659.00330833,
27646.48551627, 9202.50377458, 4840.25789068],
[213571.84886437, 148750.52154776, 85979.81139937,
38757.37831212, 11775.99906427, 4619.32027948],
[195684.50299021, 131818.78542437, 74376.55189913,
32793.21715377, 10288.70838873, 4042.58093119],
[177598.13865746, 120942.50439911, 68866.09898276,
30819.5354775 , 10588.08746517, 5011.71808947],
[126433.18311483, 85863.57788065, 48923.64502157,
21828.60950911, 7907.37639781, 4410.61819399],
[103431.88029629, 67452.94418262, 37608.36861047,
16456.97701443, 6027.98704858, 3550.06927169],
[100689.06813945, 64380.21348052, 34764.02910376,
14849.85472635, 5607.19256065, 3605.5709208 ],
[ 96509.22946744, 63832.74512518, 36041.69174706,
15802.87650901, 6473.33232805, 4664.07058733],
[113078.63455882, 73227.02362359, 40861.09037499,
17385.89127848, 7074.98444924, 5136.84232454],
[121241.93118924, 78537.13681709, 44257.97654994,
18584.94999742, 7733.39219718, 5869.49536788],
[115948.06368262, 73995.07204278, 41536.21315507,
16851.59724901, 6736.25125909, 4851.5738275 ],
[115024.20359423, 72108.15245783, 40341.98473413,
15900.55422399, 6243.63777265, 4411.24859372],
[108754.83802899, 66210.25952459, 36485.42905112,
14577.73925124, 5553.23702141, 3736.5217322 ],
[ 95340.59125024, 58458.97552915, 32364.19705748,
13236.30114676, 4929.04023171, 3202.21731277]])
y = y=np.array([[118166.08 , 95784.692 , 68134.878 , 37119.959 , 17924.157 ,
7445.3083],
[ 99265.027 , 70679.135 , 43297.559 , 19822.017 , 8527.8497,
3404.7113],
[ 80227.797 , 50972.879 , 26648.604 , 11190.488 , 4836.6514,
2249.9172],
[ 68510.582 , 39288.19 , 19938.938 , 9312.6881, 4907.6661,
2681.2709],
[ 65193.15 , 36610.107 , 18612.181 , 9211.144 , 5416.1685,
3372.1282],
[ 67188.918 , 37227.699 , 20132.92 , 11663.275 , 7315.3472,
4648.1669],
[ 64802.06 , 38885.622 , 22008.537 , 13100.638 , 8043.0185,
5049.2097],
[ 68104.867 , 41212.89 , 23247.898 , 14134.707 , 8805.2547,
5526.1014],
[ 74180.595 , 41268.904 , 22868.016 , 13841.437 , 8660.1413,
5401.245 ],
[ 78920.685 , 42743.389 , 23932.305 , 13910.089 , 8439.3342,
5141.7051],
[ 91329.012 , 45733.772 , 25430.818 , 14144.185 , 8273.7953,
5016.5839],
[ 92217.594 , 44984.3 , 23353.596 , 13467.631 , 8099.728 ,
4948.26 ],
[ 94508.441 , 48114.879 , 24735.311 , 13358.097 , 7821.8587,
4806.7923],
[108211.73 , 53987.095 , 25872.772 , 13189.61 , 7552.7164,
4497.2611]])
P=10000*np.array([[0.6011,0.6011,0.6011,0.6011,0.6011,0.6011],
[0.9007,0.9007,0.9007,0.9007,0.9007,0.9007],
[1.1968,1.1968,1.1968,1.1968,1.1968,1.1968],
[1.4178,1.4178,1.4178,1.4178,1.4178,1.4178],
[1.5015,1.5015,1.5015,1.5015,1.5015,1.5015],
[1.439,1.439,1.439,1.439,1.439,1.439],
[1.2721,1.2721,1.2721,1.2721,1.2721,1.2721],
[1.0616,1.0616,1.0616,1.0616,1.0616,1.0616],
[0.8543,0.8543,0.8543,0.8543,0.8543,0.8543],
[0.6723,0.6723,0.6723,0.6723,0.6723,0.6723],
[0.5204,0.5204,0.5204,0.5204,0.5204,0.5204],
[0.3963,0.3963,0.3963,0.3963,0.3963,0.3963],
[0.2990,0.2990,0.2990,0.2990,0.2990,0.2990],
[0.2211,0.2211,0.2211,0.2211,0.2211,0.2211]])
g=np.log10(y) - np.log10(x)
args = (g,P)
result = differential_evolution(func, bounds, args=args)
print(func(bounds, args))
I get this error: TypeError: func() takes exactly 2 arguments (3 given) . Is there any way to fix this?

def func(c, g, P):
z = g + np.log10(c)*np.log10(P)
return np.median(z**2)
if __name__ == '__main__':
# Your arrays go here
g = np.log10(y) - np.log10(x)
args = (g, P)
result = differential_evolution(func, bounds, args=(g, P))
# will print the value of c and value of the optimized function
print (result.x, result.fun)

I am using scipy's BVP solver:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_bvp.html
The problem I am running into is that you can only have as many boundary conditions as you have equations. I only have one equation but I have two boundary conditions. How can this be fixed?
MWE
>>> import numpy as np
>>> from scipy.integrate import solve_bvp
>>>
>>> x = np.linspace(0, 1, 100)
>>> dydx = lambda x,y: y*np.sin(x)
>>>
>>> result = solve_bvp(dydx,
... lambda ya,yb: np.array([ (ya[0]-1)**2 + (yb[0]-1)**2 ]),
... x, [np.ones(len(x))], max_nodes=100000, tol=1e-9)
>>>
>>> result
message: 'The algorithm converged to the desired accuracy.'
niter: 2
p: None
rms_residuals: array([ 3.48054730e-10, 3.47134800e-10, 3.46220750e-10,
3.45304147e-10, 3.44446495e-10, 3.43708535e-10,
3.42834209e-10, 3.41730399e-10, 3.40902853e-10,
3.40116511e-10, 3.39286663e-10, 3.38873550e-10,
3.37853506e-10, 3.36632825e-10, 3.35880059e-10,
3.35385717e-10, 3.35453551e-10, 3.34784891e-10,
3.32401725e-10, 3.34486867e-10, 3.35674629e-10,
3.37743169e-10, 3.34329677e-10, 3.29311236e-10,
3.27606354e-10, 3.28578369e-10, 3.27772742e-10,
3.26447666e-10, 3.24908674e-10, 3.24192402e-10,
3.25862692e-10, 3.28872815e-10, 3.22757465e-10,
3.21914926e-10, 3.20227078e-10, 3.23579897e-10,
3.28140843e-10, 3.18151515e-10, 3.21177949e-10,
3.16611117e-10, 3.45372059e-10, 3.18345626e-10,
3.24069081e-10, 3.32570305e-10, 3.19141250e-10,
3.14376144e-10, 3.18278959e-10, 3.11802424e-10,
3.15597596e-10, 3.22818017e-10, 3.15384028e-10,
3.17673241e-10, 3.08099021e-10, 3.11743210e-10,
3.28763320e-10, 3.24475197e-10, 3.28343741e-10,
3.25892534e-10, 3.12411478e-10, 3.37194926e-10,
3.20060651e-10, 3.03517565e-10, 3.00795182e-10,
3.06846379e-10, 3.00064770e-10, 3.05765788e-10,
2.99543196e-10, 2.98157661e-10, 2.97863071e-10,
2.96467397e-10, 3.74567928e-10, 3.24304178e-10,
3.16165056e-10, 3.02449962e-10, 2.93348900e-10,
3.08601600e-10, 2.93492038e-10, 3.11756310e-10,
2.97438508e-10, 3.17903029e-10, 3.05491804e-10,
3.02623385e-10, 3.06340149e-10, 2.94595579e-10,
2.87571373e-10, 3.03866639e-10, 3.42985927e-10,
3.21829601e-10, 3.70164964e-10, 3.53563487e-10,
3.00178404e-10, 2.83888849e-10, 2.82310753e-10,
2.85661232e-10, 3.11405296e-10, 2.80954237e-10,
2.79523163e-10, 2.80819968e-10, 2.94406497e-10,
3.19548071e-10, 2.95355340e-10, 2.77522541e-10,
2.76703591e-10, 2.88121141e-10, 2.75290617e-10,
2.84220379e-10, 2.89876300e-10, 3.14510031e-10,
3.11057911e-10, 2.72303350e-10, 2.79168046e-10,
2.90700062e-10, 2.78438999e-10, 2.68897634e-10,
2.69286657e-10, 2.90472537e-10, 2.78378707e-10,
2.97980086e-10, 2.97008148e-10, 2.65028623e-10,
2.64744165e-10, 2.69437313e-10, 2.63909411e-10,
2.62339786e-10, 2.71045386e-10, 2.65850861e-10,
2.78162780e-10, 2.61231989e-10, 2.70109868e-10,
2.61595375e-10, 2.59299272e-10, 2.65106316e-10,
2.74283076e-10, 2.86861196e-10, 3.03175803e-10,
2.58290170e-10, 3.61324845e-10, 3.39239278e-10,
2.91296094e-10, 2.83918017e-10, 4.52002829e-10,
2.52915179e-10, 3.13709791e-10, 3.72555078e-10,
2.48903834e-10, 2.58089690e-10, 2.86634265e-10,
2.60879823e-10, 2.64643448e-10, 3.03583577e-10,
5.12385186e-10, 2.42415186e-10, 3.47677749e-10,
2.41037177e-10, 2.91624837e-10, 2.88486833e-10,
2.97731066e-10, 3.46537042e-10, 2.44416103e-10,
4.29099468e-10, 4.71320607e-10, 2.97672164e-10,
3.26787171e-10, 2.34920240e-10, 2.64792458e-10,
2.91952218e-10, 2.47064463e-10, 2.34000456e-10,
4.10948830e-10, 2.36520479e-10, 3.42444147e-10,
2.76749245e-10, 2.51379106e-10, 2.40093828e-10,
2.72602754e-10, 3.94004751e-10, 2.84796018e-10,
3.72431030e-10, 2.23313796e-10, 3.32252341e-10,
3.34369044e-10, 2.63230702e-10, 2.17694780e-10,
3.25346854e-10, 2.64869219e-10, 3.51158895e-10,
3.60872478e-10, 3.09047143e-10, 2.22308395e-10,
2.43344334e-10, 2.16527726e-10, 2.98642975e-10,
2.77152047e-10, 2.66161092e-10, 2.91230604e-10,
2.37973344e-10, 2.95802884e-10, 2.78890213e-10,
2.19485810e-10, 3.53536609e-10, 2.16716319e-10,
2.51682560e-10, 2.04749227e-10, 4.31531575e-10,
3.47595602e-10, 2.38112586e-10, 1.92156254e-10,
2.46451083e-10, 2.99903096e-10, 1.90926751e-10,
2.05652721e-10, 2.33415220e-10, 2.43209873e-10,
1.85670073e-10, 2.02780645e-10, 1.89290313e-10,
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1.65019969e-10, 7.52309342e-11, 3.59188285e-10,
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sol: <scipy.interpolate.interpolate.PPoly object at 0x2ad860930d58>
status: 0
success: True
x: array([ 0. , 0.003367 , 0.00673401, 0.01010101, 0.01346801,
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0.06734007, 0.07070707, 0.07407407, 0.07744108, 0.08080808,
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0.82491582, 0.82828283, 0.83164983, 0.83501684, 0.83838384,
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0.79568534, 0.79587013, 0.79606397, 0.79626686, 0.7964788 ,
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0.79940925, 0.79973028, 0.80006043, 0.80039973, 0.80074817,
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0.80518718, 0.80564606, 0.80611421, 0.80659164, 0.80707835,
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yp: array([[ 0. , 0.00267302, 0.0053461 , 0.0080193 , 0.01069269,
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0.0804631 , 0.08316431, 0.08586741, 0.08857244, 0.09127948,
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0.12120642, 0.12394223, 0.12668083, 0.12942228, 0.13216665,
0.134914 , 0.13766438, 0.14041786, 0.1431745 , 0.14593436,
0.1486975 , 0.15146398, 0.15423387, 0.15700722, 0.1597841 ,
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0.20474348, 0.20758965, 0.21044044, 0.21329593, 0.21615617,
0.21902122, 0.22189114, 0.22476599, 0.22764585, 0.23053076,
0.23342079, 0.236316 , 0.23921645, 0.2421222 , 0.24503332,
0.24794987, 0.2508719 , 0.25379948, 0.25673268, 0.25967155,
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0.27742732, 0.28040762, 0.28339409, 0.28638678, 0.28938576,
0.29239109, 0.29540283, 0.29842105, 0.3014458 , 0.30447715,
0.30751515, 0.31055988, 0.31361139, 0.31666974, 0.31973499,
0.32280722, 0.32588647, 0.32897281, 0.3320663 , 0.33516701,
0.33827498, 0.3413903 , 0.34451301, 0.34764319, 0.35078088,
0.35392616, 0.35707908, 0.3602397 , 0.3634081 , 0.36658432,
0.36976843, 0.37296049, 0.37616057, 0.37936872, 0.382585 ,
0.38580948, 0.38904223, 0.39228329, 0.39553273, 0.39879061,
0.402057 , 0.40533195, 0.40861553, 0.4119078 , 0.41520881,
0.41851863, 0.42183733, 0.42516495, 0.42850157, 0.43184723,
0.43520202, 0.43856597, 0.44193917, 0.44532166, 0.4487135 ,
0.45211476, 0.45552551, 0.45894578, 0.46237566, 0.4658152 ,
0.46926446, 0.47272349, 0.47619237, 0.47967114, 0.48315988,
0.48665863, 0.49016747, 0.49368644, 0.49721562, 0.50075505,
0.5043048 , 0.50786493, 0.5114355 , 0.51501656, 0.51860818,
0.52221041, 0.52582331, 0.52944695, 0.53308138, 0.53672666,
0.54038285, 0.54405001, 0.54772819, 0.55141745, 0.55511786,
0.55882946, 0.56255232, 0.5662865 , 0.57003205, 0.57378903,
0.5775575 , 0.58133751, 0.58512912, 0.58893239, 0.59274738,
0.59657414, 0.60041272, 0.60426319, 0.6081256 , 0.61200001,
0.61588646, 0.61978503, 0.62369576, 0.6276187 , 0.63155392,
0.63550147, 0.6394614 , 0.64343376, 0.64741862, 0.65141602,
0.65542602, 0.65944867, 0.66348403, 0.66753215, 0.67159308,
0.67566687, 0.67975358, 0.68385327, 0.68796597, 0.69209174,
0.69623064, 0.70038272, 0.70454802, 0.7087266 , 0.7129185 ,
0.71712379, 0.7213425 , 0.72557469, 0.72982041, 0.7340797 ,
0.73835262, 0.74263921, 0.74693953, 0.75125361, 0.75558151,
0.75992327, 0.76427895, 0.76864858, 0.77303222, 0.7774299 ,
0.78184168, 0.7862676 , 0.79070771, 0.79516204, 0.79963065,
0.80411358, 0.80861086, 0.81312256, 0.81764869, 0.82218932,
0.82674447, 0.8313142 , 0.83589854, 0.84049753, 0.84511122,
0.84973964, 0.85438283, 0.85904083, 0.86371368, 0.86840142,
0.87310408, 0.8778217 , 0.88255432, 0.88730198, 0.89206471,
0.89684254, 0.90163551, 0.90644365, 0.911267 , 0.91610559,
0.92095945, 0.92582862, 0.93071312, 0.93561298, 0.94052825,
0.94545894, 0.95040508, 0.95536671, 0.96034386, 0.96533654,
0.97034479, 0.97536863, 0.98040809, 0.98546319, 0.99053396,
0.99562042, 1.0007226 , 1.00584051, 1.01097418, 1.01612363,
1.02128888, 1.02646995, 1.03166686, 1.03687962, 1.04210827,
1.0473528 , 1.05261324, 1.0578896 ]])
As you can see, y is very far from the boundary conditions of y(x=0) = y(x=1) = 1.

If you specify two boundary conditions y(0)=1 and y(1)=1 for a first order ODE, then in general the problem is overdetermined and there is no solution. If you specify just the initial condition y(0)=y0, you have a first order initial value problem. In fact, in this case, you can derive the exact solution: y(x) = y0*exp(-cos(x)).