#!/usr/bin/env python
# coding: utf-8

# # Check `GDS` Python stack
# 
# This notebook checks all software requirements for the course Geographic Data Science are correctly installed. 
# 
# A successful run of the notebook implies no errors returned in any cell *and* every cell beyond the first one returning a printout of `True`. This ensures a correct environment installed.

# ---

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import bokeh as bk
float(bk.__version__[:1]) >= 1


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import matplotlib as mpl
float(mpl.__version__[:3]) >= 1.5


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import mplleaflet as mpll


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import seaborn as sns
float(sns.__version__[:3]) >= 0.6


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import datashader as ds
float(ds.__version__[:3]) >= 0.6


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import palettable as pltt
float(pltt.__version__[:3]) >= 3.1


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sns.palplot(pltt.matplotlib.Viridis_10.hex_colors)


# ---

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import qgrid


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import pandas as pd
float(pd.__version__[:4]) >= 0.18


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import dask
float(dask.__version__[:1]) >= 1


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import sklearn
float(sklearn.__version__[:4]) >= 0.20


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import statsmodels.api as sm
float(sm.version.version[:3]) >= 0.9


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import pymc3 as pm
float(pm.__version__[:3]) >= 3.6 


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import xlrd


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import xlsxwriter


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import networkx as nx


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import osmnx


# ---

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import fiona
float(fiona.__version__[:3]) >= 1.8


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import geopandas as gpd
float(gpd.__version__[:3]) >= 0.4


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import pysal as ps
float(ps.__version__[:1]) >= 2


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import rasterio as rio
float(rio.__version__[:1]) >= 1


# # Test

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shp = ps.lib.examples.get_path('columbus.shp')
db = gpd.read_file(shp)
db.head()


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db[['AREA', 'PERIMETER']].to_feather('db.feather')
tst = pd.read_feather('db.feather')
get_ipython().system(' rm db.feather')


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import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
f, ax = plt.subplots(1)
db.plot(facecolor='yellow', ax=ax)
ax.set_axis_off()
plt.show()


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db.crs['init'] = 'epsg:26918'


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db_wgs84 = db.to_crs(epsg=4326)
db_wgs84.plot()
plt.show()


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from pysal.viz.splot.mapping import vba_choropleth

f, ax = vba_choropleth(db['INC'], db['HOVAL'], db)


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# Out until geopandas 0.5 comes in
#db.plot(column='INC', scheme='fisher_jenks', cmap=plt.matplotlib.cm.Blues)
#plt.show()


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city = osmnx.gdf_from_place('Berkeley, California')
osmnx.plot_shape(osmnx.project_gdf(city));


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import numpy as np
import contextily as ctx
tl = ctx.tile_providers.ST_TONER_LITE

db = gpd.read_file(ps.lib.examples.get_path('us48.shp'))
db.crs = {'init': 'epsg:4326'}
w, s, e, n = db.to_crs(epsg=3857).total_bounds
dbp = db.to_crs(epsg=3857)
# Download raster
_ = ctx.bounds2raster(w, s, e, n, 'us.tif', url=tl)
# Load up and plot
source = rio.open('us.tif', 'r')
red = source.read(1)
green = source.read(2)
blue = source.read(3)
pix = np.dstack((red, green, blue))
bounds = (source.bounds.left, source.bounds.right, \
          source.bounds.bottom, source.bounds.top)
f = plt.figure(figsize=(6, 6))
ax = plt.imshow(pix, extent=bounds)

get_ipython().system(' rm us.tif')


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ax = db.to_crs(epsg=3857).plot()
ctx.add_basemap(ax)


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from ipyleaflet import Map, basemaps, basemap_to_tiles, SplitMapControl

m = Map(center=(42.6824, 365.581), zoom=5)

right_layer = basemap_to_tiles(basemaps.NASAGIBS.ModisTerraTrueColorCR, "2017-11-11")
left_layer = basemap_to_tiles(basemaps.NASAGIBS.ModisAquaBands721CR, "2017-11-11")

control = SplitMapControl(left_layer=left_layer, right_layer=right_layer)
m.add_control(control)

m