Add generic heatplot method

Implement the ability to generate plots like this below into the Python Toolkit, in a manner that can be inherited from and extended by other Python_TK-interiting toolkits.


Features to include:
- Ability to pass lists of X, Y, Z values
- Ability to enable contours and contour density
- Colormap
- Title
- Axis labels
- Enable or disable legend, and set title
- Apply formats to contour and legend values

The code below was used to generate the plot above:

```
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import matplotlib.ticker as ticker

def myfmt(x, pos):
return '{0:.1f}'.format(x)

def pcfmt(x, pos):
return '{0:.0f}%'.format(x)

# Convert from pandas dataframes to numpy arrays
X0, Y0, Z0, = np.array([]), np.array([]), np.array([])
for i in range(len(X_dat)):
X0 = np.append(X0, X[i])
Y0 = np.append(Y0, Y[i])
Z0 = np.append(Z0, Z[i])

# create x-y points to be used in heatmap
xi = np.linspace(X0.min(), X0.max(), 1000)
yi = np.linspace(Y0.min(), Y0.max(), 1000)

# Interpolate for plotting
zi = griddata((X0, Y0), Z0, (xi[None,:], yi[:,None]), method='cubic')

# Create the contour plot
CS = plt.contourf(xi, yi, zi, 150, cmap="Oranges_r",
vmax=zi.max(), vmin=zi.min())

intervals = int(zi.max() - zi.min())
CS2 = plt.contour(xi, yi, zi, intervals, colors='k')
plt.clabel(CS2, inline=1, fontsize=10, colors='k', fmt=ticker.FuncFormatter(pcfmt))
plt.colorbar(CS, label="% hours comfortable", format=ticker.FuncFormatter(myfmt))
plt.xlabel("Evaporative cooling ratio")
plt.ylabel("Wind porosity ratio")
plt.title("% annual hours with UTCI between 9°C and 32°C")
plt.show()
```

cc @tg359