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%matplotlib inline
from matplotlib import pylab as plt
import os
from geonotebook.wrappers import RasterData, RasterDataCollection

Raster Data



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rd = RasterData('data/L57.Globe.month09.2010.hh09vv04.h6v1.doy247to273.NBAR.v3.0.tiff')
M.add_layer(rd[[1,2,3]], opacity=0.9, interval=(0.,0.2)).then(
  M.set_center(-120.32, 47.84, 9))



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d, n = next(M.layers.annotation.polygons[0].data)
# d, n = next(M.layers.annotation.rectangles[1].data)



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from skimage.exposure import rescale_intensity
fig, ax = plt.subplots(figsize=(16, 16))
ax.imshow(rescale_intensity(n, (0, 0.3)), interpolation='none')



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M.remove_layer(M.layers[-1].name)
M.layers.annotation.clear_annotations()

Raster Data Collection



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DATA_DIR="data/"



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def sort_NBAR(path):
  m, y = int(path.split(".")[2][-2:]), int(path.split(".")[3])
  return (y * 100) + m


PATHS = [DATA_DIR + p for p in sorted([
    f for f in os.listdir(DATA_DIR) if f.startswith('L57')], key=sort_NBAR)]



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PATHS



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rdc = RasterDataCollection(PATHS)



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import matplotlib as mpl
import numpy as np

def ndvi_colormap(numcolors=11, name='custom_div_cmap', 
                  mincol='blue', midcol='beige', maxcol='green'):
  return mpl.colors.LinearSegmentedColormap.from_list(
    name=name, colors=[mincol, midcol, maxcol], N=numcolors)

cmap = ndvi_colormap()



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M.add_layer(rdc[:,4], 'NBAR', opacity=0.8, colormap=cmap).then(
 M.set_center(-120.32, 47.84, 9))



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M.layers['NBAR'].forward()



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from ipywidgets import interact
import ipywidgets as widgets

M.layers['NBAR'].idx(0)

def render_timeseries(idx=0):
  M.layers["NBAR"].idx(idx)
  
interact(render_timeseries, idx=(0, len(M.layers["NBAR"].data) - 1))

Points



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import seaborn as sns

labels = ["{}/{}".format(n.split(".")[2][5:], n.split(".")[3]) for n in 
                         M.layers[-1].data.get_names()]

fig, ax = plt.subplots(1,1, figsize=(12, 8))
plt.title("NDVI over selected points")
plt.xticks(range(len(M.layers[-1].data))[::4], labels[::4])

for p in M.layers.annotation.points:
  layer, data = next(p.data)
  ax.plot(data, color=p.rgb, label=p.name)
  
plt.legend()



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M.layers.annotation.clear_annotations()

Polygons



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import seaborn as sns

labels = ["{}/{}".format(n.split(".")[2][5:], n.split(".")[3]) for n in 
                         M.layers[-1].data.get_names()]

fig, ax = plt.subplots(1,1, figsize=(12, 8))
plt.title("Mean NDVI over selected polygons")
plt.xticks(range(len(M.layers[-1].data))[::4], labels[::4])

for p in M.layers.annotation.polygons:
  layer, data = next(p.data)
  time, lat, lon = data.shape
  ax.plot(data.reshape(time, lat * lon).mean(axis=1),
          color=p.rgb, label=p.name)

plt.legend()



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M.layers.annotation.clear_annotations()
M.remove_layer("NBAR")