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%matplotlib inline
import matplotlib.pylab as plt
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from geonotebook.wrappers import RasterData
rd = RasterData('/data/kotfic/srtm/srtm_all.vrt')
# rd = RasterData('/data/doruk/srtm/srtm_all.vrt')
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colormap=[{"quantity": 0, "color": '#C2FAB3'},
{"quantity": 300, "color": '#D4EA86'},
{"quantity": 600, "color": '#169B28'},
{"quantity": 900, "color": '#639231'},
{"quantity": 1200, "color": '#E9B50D'},
{"quantity": 1500, "color": '#BE4200'},
{"quantity": 1800, "color": '#770701'},
{"quantity": 2100, "color": '#6D2308'},
{"quantity": 2400, "color": '#7B4928'},
{"quantity": 2700, "color": '#A49288'},
{"quantity": 3000, "color": '#CCCCCC'}]
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# %matplotlib inline
# from matplotlib import pylab as plt
# colormap = plt.get_cmap('hot_r', 50)
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M.add_layer(rd[1], opacity=0.7,
colormap=colormap,
interval=(0, 3000))
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M.remove_layer(M.layers[0])
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M.layers
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M.remove_layer(M.layers[0])
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from matplotlib.pylab import plt
import numpy as np
import pandas as pd
legend = pd.DataFrame([
(11, "Open Water", "#476BA0"),
(12, "Perennial Ice/Snow", "#D1DDF9"),
(21, "Developed, Open Space","#DDC9C9"),
(22, "Developed, Low Intensity", "#D89382"),
(23, "Developed, Medium Intensity", "#ED0000"),
(24, "Developed High Intensity", "#AA0000"),
(31, "Barren Land (Rock/Sand/Clay)", "#B2ADA3"),
(41, "Deciduous Forest", "#68AA63"),
(42, "Evergreen Forest", "#1C6330"),
(43, "Mixed Forest", "#B5C98E"),
(51, "Dwarf Scrub", "#A58C30"),
(52, "Shrub/Scrub", "#CCBA7C"),
(71, "Grassland/Herbaceous", "#E2E2C1"),
(72, "Sedge/Herbaceous", "#C9C977"),
(73, "Lichens", "#99C147"),
(74, "Moss", "#77AD93"),
(81, "Pasture/Hay", "#DBD83D"),
(82, "Cultivated Crops", "#AA7028"),
(90, "Woody Wetlands", "#BAD8EA"),
(95, "Emergent Herbaceous Wetlands","#70A3BA")],
columns=["Code", "Desc", "Color"])
def highlight(e):
return 'background-color: {}'.format(e)
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from geonotebook.wrappers import RasterData
rd2 = RasterData("../data/nlcd_2011_landcover_2011_edition_2014_10_10.tif")
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colormap = legend[["Code", "Color"]].rename(columns={
"Code": "quantity", "Color": "color"}).to_dict("records")
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M.add_layer(rd2[1], colormap=colormap, opacity=5.0)
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a = M.layers.annotation.rectangles[0].data
_, srtm = next(a)
_, nlcd = next(a)
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from skimage.transform import resize
srtm_r = resize(srtm, nlcd.shape, order=0, preserve_range=True)
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df = pd.DataFrame({"SRTM": srtm_r.ravel(), "NLCD": nlcd.ravel()})\
.merge(legend, left_on='NLCD', right_on='Code').drop("Code", axis=1)
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df.groupby("Desc")['SRTM'].count().sort_values(ascending=False)
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import seaborn as sns
plt.figure(figsize=(12., 12.))
palette = {d['Desc']: d['Color'] for d in legend[['Desc', 'Color']].to_dict('record')}
kinds = [
"Developed High Intensity",
"Developed, Low Intensity",
"Developed, Medium Intensity",
"Open Water",
"Woody Wetlands"
]
img = sns.violinplot(
x="Desc", y="SRTM", palette=palette,
data=df[df['Desc'].isin(kinds)])
for i in img.get_xticklabels():
i.set_rotation(90)
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M.layers.annotation.clear_annotations()
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M.remove_layer(M.layers[-1])