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View Music_genre_classification.ipynb
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@parulnith
parulnith / Moving Sine Wave.py
Last active Jan 8, 2021
Using matplotlib's FuncAnimation to do a basic animation of a sine wave moving across the screen:
View Moving Sine Wave.py
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)
View evaluate.py
%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
parulnith / coil.py
Last active Jul 17, 2020
Creating a growing coil with matplotlib
View coil.py
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)
View data.py
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
View live_graph.py
#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):
View Activating.py
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, '')
View Query_MODIS_Imagery.py
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',
)
View timeseries.py
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)
View spark_dataframe.py
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
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