import pandas as pd
from matplotlib import pylab as plt
import matplotlib.dates as mdates
def figure_plotting_space():
"""
defines the plotting space
"""
fig = plt.figure(figsize=(10,10))
bar_height = 0.04
mini_gap = 0.03
gap = 0.05
graph_height = 0.24
axH = fig.add_axes([0.1,gap+3*graph_height+2.5*mini_gap,0.87,bar_height])
axS = fig.add_axes([0.1,gap+2*graph_height+2*mini_gap,0.87,graph_height])
axV = fig.add_axes([0.1,gap+graph_height+mini_gap,0.87,graph_height])
return fig, axH, axS, axV
def plot_colorbar(ax,image,ylabel=False):
"""
display a colorbar (e.g. hue-stretch)
"""
# plot image inside of figure 'axes'
ax.imshow(image, interpolation='nearest', aspect='auto')
ax.set_xticks([])
ax.set_yticks([])
ax.set_ylabel(ylabel)
def plot_timeseries(DF, ax, name, startDate, stopDate, ylim=False):
"""
plots timeseries graphs
"""
# original time series
ax.plot(DF[name],color='#1f77b4')
ax.set_ylabel(name)
ax.set_ylim(ylim)
ax.set_xlim(pd.datetime.strptime(startDate,'%Y-%m-%d'),\
pd.datetime.strptime(stopDate,'%Y-%m-%d'))
# boxcar average
ax.plot(DF[name].rolling(180).mean(),color='red')
# make the dates exact
ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
def plotTimeSeries(DF, hue_stretch, startDate, stopDate):
fig, axH, axS, axV = figure_plotting_space()
plot_colorbar(axH,[hue_stretch], ylabel='hue')
plot_timeseries(DF, axS,'sat', startDate, stopDate, ylim=[0,1])
plot_timeseries(DF, axV,'val', startDate, stopDate)
