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%matplotlib inline
import matplotlib.pyplot as plt
plt.plot([1,2,3,4])
plt.show()
# to save
# plt.savefig('test_nb.png')


    

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import pandas as pd
import numpy as np
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
df.cumsum().plot()


    

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# new figure
plt.figure()
df.diff().hist(color='k', alpha=0.5, bins=50)


    

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# Joint distributions
import seaborn as sns
sns.set(style="ticks")

rs = np.random.RandomState(11)
x = rs.gamma(2, size=1000)
y = -.5 * x + rs.normal(size=1000)

sns.jointplot(x, y, kind="hex", color="#4CB391")


    

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# Multiple linear regression
sns.set()

# Load the iris dataset
iris = sns.load_dataset("iris")

# Plot sepal with as a function of sepal_length across days
g = sns.lmplot(x="sepal_length", y="sepal_width", hue="species",
               truncate=True, height=5, data=iris)

# Use more informative axis labels than are provided by default
g.set_axis_labels("Sepal length (mm)", "Sepal width (mm)")


    

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import cartopy.crs as ccrs
from cartopy.examples.arrows import sample_data

fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax.set_extent([-90, 75, 10, 85], crs=ccrs.PlateCarree())
ax.coastlines()

x, y, u, v, vector_crs = sample_data(shape=(80, 100))
magnitude = (u ** 2 + v ** 2) ** 0.5
ax.streamplot(x, y, u, v, transform=vector_crs,
              linewidth=2, density=2, color=magnitude)
plt.show()


    

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import matplotlib.patches as mpatches
import shapely.geometry as sgeom
import cartopy.io.shapereader as shpreader

fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal())
ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic())

shapename = 'admin_1_states_provinces_lakes_shp'
states_shp = shpreader.natural_earth(resolution='110m',
                                     category='cultural', name=shapename)

# Hurricane Katrina lons and lats
lons = [-75.1, -75.7, -76.2, -76.5, -76.9, -77.7, -78.4, -79.0,
        -79.6, -80.1, -80.3, -81.3, -82.0, -82.6, -83.3, -84.0,
        -84.7, -85.3, -85.9, -86.7, -87.7, -88.6, -89.2, -89.6,
        -89.6, -89.6, -89.6, -89.6, -89.1, -88.6, -88.0, -87.0,
        -85.3, -82.9]
lats = [23.1, 23.4, 23.8, 24.5, 25.4, 26.0, 26.1, 26.2, 26.2, 26.0,
        25.9, 25.4, 25.1, 24.9, 24.6, 24.4, 24.4, 24.5, 24.8, 25.2,
        25.7, 26.3, 27.2, 28.2, 29.3, 29.5, 30.2, 31.1, 32.6, 34.1,
        35.6, 37.0, 38.6, 40.1]

# to get the effect of having just the states without a map "background" turn off the outline and background patches
ax.background_patch.set_visible(False)
ax.outline_patch.set_visible(False)
ax.set_title('US States which intersect the track of '
             'Hurricane Katrina (2005)')

# turn the lons and lats into a shapely LineString
track = sgeom.LineString(zip(lons, lats))

# buffer the linestring by two degrees (note: this is a non-physical
# distance)
track_buffer = track.buffer(2)

for state in shpreader.Reader(states_shp).geometries():
    # pick a default color for the land with a black outline,
    # this will change if the storm intersects with our track
    facecolor = [0.9375, 0.9375, 0.859375]
    edgecolor = 'black'

    if state.intersects(track):
        facecolor = 'red'
    elif state.intersects(track_buffer):
        facecolor = '#FF7E00'

    ax.add_geometries([state], ccrs.PlateCarree(),
                      facecolor=facecolor, edgecolor=edgecolor)

ax.add_geometries([track_buffer], ccrs.PlateCarree(),
                  facecolor='#C8A2C8', alpha=0.5)
ax.add_geometries([track], ccrs.PlateCarree(),
                  facecolor='none', edgecolor='k')

# make two proxy artists to add to a legend
direct_hit = mpatches.Rectangle((0, 0), 1, 1, facecolor="red")
within_2_deg = mpatches.Rectangle((0, 0), 1, 1, facecolor="#FF7E00")
labels = ['State directly intersects\nwith track',
          'State is within \n2 degrees of track']
ax.legend([direct_hit, within_2_deg], labels,
          loc='lower left', bbox_to_anchor=(0.025, -0.1), fancybox=True)

plt.show()


    

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import xarray as xr
airtemps = xr.tutorial.load_dataset('air_temperature')
airtemps


    

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# Convert to celsius
air = airtemps.air - 273.15
# copy attributes to get nice figure labels and change Kelvin to Celsius
air.attrs = airtemps.air.attrs
air.attrs['units'] = 'deg C'


    

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air.sel(lat=50, lon=225).plot()


    

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fig, axes = plt.subplots(ncols=2)
air.sel(lat=50, lon=225).plot(ax=axes[0])
air.sel(lat=50, lon=225).plot.hist(ax=axes[1])
plt.tight_layout()
plt.show()


    

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air.sel(time='2013-09-03T00:00:00').plot()


    

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# Faceting
# Plot evey 250th point
air.isel(time=slice(0, 365 * 4, 250)).plot(x='lon', y='lat', col='time', col_wrap=3)


    

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# Overlay data on cartopy map
ax = plt.axes(projection=ccrs.Orthographic(-80, 35))
air.isel(time=0).plot.contourf(ax=ax, transform=ccrs.PlateCarree());
ax.set_global(); ax.coastlines();


    

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