parulnith

parulnith

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
plt.style.use('seaborn-pastel')


fig = plt.figure()
ax = plt.axes(xlim=(0, 4), ylim=(-2, 2))
line, = ax.plot([], [], lw=3)

parulnith

%matplotlib inline
from sklearn.metrics import roc_curve, precision_recall_curve, auc
import matplotlib.pyplot as plt
import numpy as np



def get_auc(labels, scores):
    fpr, tpr, thresholds = roc_curve(labels, scores)
    auc_score = auc(fpr, tpr)

parulnith

import matplotlib.pyplot as plt 
import matplotlib.animation as animation 
import numpy as np 
plt.style.use('dark_background')

fig = plt.figure() 
ax = plt.axes(xlim=(-50, 50), ylim=(-50, 50)) 
line, = ax.plot([], [], lw=2) 

parulnith

df = pd.read_csv("diabetes.csv")
df.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

parulnith

#importing libraries
import matplotlib.pyplot as plt
import matplotlib.animation as animation


fig = plt.figure()
#creating a subplot 
ax1 = fig.add_subplot(1,1,1)

def animate(i):

parulnith

links = result.json()[u"analytic"]["_links"]
self_link = links["_self"]
activation_link = links["activate"]

# Request activation of the 'analytic' asset:
activate_result = \
  requests.get(
    activation_link,
    auth=HTTPBasicAuth(PLANET_API_KEY, '')

parulnith

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',
)

parulnith

time_series = df.groupBy(F.year('datetime').alias('year'),
                         F.weekofyear('datetime').alias('week')) \
                .agg(rf_agg_mean('ndvi').alias('mean_ndvi'))

ts_pd = time_series.toPandas()

#Visualizing using matplotlib
ts_pd.sort_values(['year', 'week'], inplace=True)
# Create a compact label of year and week number yyyy_ww
ts_pd['year_week'] = ts_pd.apply(lambda r:'{0:g}_{1:02g}'.format(r.year, r.week), axis=1)

parulnith

df.select('red',
          'nir',
          'datetime',
          'id',
          rf_extent('red').alias('extent'),
          rf_crs('red').alias('crs')) \
    .filter(rf_no_data_cells(rf_with_no_data('red', 0)) < 800) 
        # show tiles that have lots of valid data