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%matplotlib inline


    

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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
rd = 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(rd[1], colormap=colormap, opacity=0.7)


    

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legend.set_index("Code").style.applymap(highlight)


    

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from geonotebook.vis.ktile.utils import get_layer_vrt

print(get_layer_vrt(M.layers[0]))


    

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M.remove_layer(M.layers[0])


    

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M.add_layer(rd[1], colormap=colormap, opacity=0.7, vrt_path="vrt/nlcd.vrt")


    

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print(get_layer_vrt(M.layers[0]))


    

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l, d = M.layers.annotation.polygons[0].data.next()


    

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d.shape


    

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df = pd.DataFrame({"Code": d.data.ravel()})
df["Count"] = 1
df = df.groupby("Code").sum().join(legend.set_index("Code"))\
    .sort_values("Count", ascending=False).dropna()
df.style.applymap(highlight)


    

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from matplotlib.colors import LinearSegmentedColormap

df.set_index("Desc").T.ix['Count'].plot(kind="barh", color=list(df["Color"]))


    

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M.layers[0].name


    

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import ipykernel
ipykernel.get_connection_file()