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df = spark.read.raster(catalog, 
                       catalog_col_names=['B01', 'B02'],
                      ) \
        .withColumnRenamed('B01', 'red') \
        .withColumnRenamed('B02', 'nir')
 

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start = '2018-07-01'
end   = '2019-08-31'

catalog = earth_ondemand.read_catalog(
    geo="POINT(88.92 21.88)", # Coordinates of Sunderban National Park
    start_datetime=start,
    end_datetime=end,
    collections='mcd43a4',
)

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Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

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S.nO	Name	Age
1	Andy	56

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,CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT
0,0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98
1,0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14
2,0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03
3,0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94
4,0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33

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fixed acidity,volatile acidity,citric acid,residual,sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
1	-0.0226973	0.289181	0.0890207	0.0230856	-0.0493959	0.0910698	0.265331	-0.425858	-0.017143	-0.120881	-0.113663
-0.0226973	1	-0.149472	0.0642861	0.0705116	-0.0970119	0.0892605	0.0271138	-0.0319154	-0.0357281	0.0677179	-0.194723
0.289181	-0.149472	1	0.0942116	0.114364	0.0940772	0.121131	0.149503	-0.163748	0.0623309	-0.0757287	-0.00920909
0.0890207	0.0642861	0.0942116	1	0.0886845	0.299098	0.401439	0.838966	-0.194133	-0.0266644	-0.450631	-0.0975768
0.0230856	0.0705116	0.114364	0.0886845	1	0.101392	0.19891	0.257211	-0.0904395	0.0167629	-0.360189	-0.209934
-0.0493959	-0.0970119	0.0940772	0.299098	0.101392	1	0.615501	0.29421	-0.000617796	0.0592172	-0.250104	0.00815807
0.0910698	0.0892605	0.121131	0.401439	0.19891	0.615501	1	0.529881	0.00232097	0.134562	-0.448892	-0.174737
0.265331	0.0271138	0.149503	0.838966	0.257211	0.29421	0.529881	1	-0.0935915	0.0744931	-0.780138	-0.30

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import matplotlib
from matplotlib import pyplot as plt
from celluloid import Camera

fig = plt.figure()
camera = Camera(fig)
for i in range(20):
    t = plt.plot(range(i, i + 5))
    plt.legend(t, [f'line {i}'])
    camera.snap()

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import numpy as np
from matplotlib import pyplot as plt
from celluloid import Camera

fig, axes = plt.subplots(2)
camera = Camera(fig)
t = np.linspace(0, 2 * np.pi, 128, endpoint=False)
for i in t:
    axes[0].plot(t, np.sin(t + i), color='blue')
    axes[1].plot(t, np.sin(t - i), color='blue')

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from matplotlib import pyplot as plt
from celluloid import Camera

fig = plt.figure()
camera = Camera(fig)
for i in range(10):
    plt.plot([i] * 10)
    camera.snap()
animation = camera.animate()
animation.save('celluloid_minimal.gif', writer = 'imagemagick')