<filename>geostatsmodels/geoplot.py
#!/usr/bin/env python

from pylab import *
import numpy as np
import matplotlib
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.patches as mpatches
from geostatsmodels import variograms, utilities


def hscattergram(data, pwdist, lag, tol):
    '''
    Input:  (data)    NumPy array with three columns, the first two
                      columns should be the x and y coordinates, and
                      third should be the measurements of the variable
                      of interest
            (lag)     the lagged distance of interest
            (tol)     the allowable tolerance about (lag)
            (pwdist)  a square pairwise distance matrix
    Output:           h-scattergram figure showing the distribution of
                      measurements taken at a certain lag and tolerance
    '''
    # calculate the pairwise distances
    indices = variograms.lagindices(pwdist, lag, tol)
    # collect the head and tail measurements
    head = data[indices[:, 0], 2]
    tail = data[indices[:, 1], 2]
    # create a scatterplot with equal axes
    fig, ax = subplots()
    ax.scatter(head, tail, marker="o", facecolor="none", edgecolor="k", alpha=0.5)
    ax.set_aspect("equal")
    # set the labels and the title
    ax.set_ylabel("$z(u+h)$")
    ax.set_xlabel("$z(u)$")
    ax.set_title("Lags Between " + str(lag - tol) + " and " + str(lag + tol))
    # grab the limits of the axes
    xmin, xmax = ax.get_xlim()
    ymin, ymax = ax.get_ylim()
    # calculate the covariance and annotate
    cv = variograms.covariance(data, indices)
    ax.text(xmin * 1.25, ymin * 1.050, 'Covariance = {:3.2f}'.format(cv))
    # calculate the semivariance and annotate
    sv = variograms.semivariance(data, indices)
    ax.text(xmin * 1.25, ymin * 1.025, 'Semivariance = {:3.2f}'.format(sv))
    show()


def laghistogram(data, pwdist, lags, tol):
    '''
    Input:  (data)    NumPy array with three columns, the first two
                      columns should be the x and y coordinates, and
                      third should be the measurements of the variable
                      of interest
            (pwdist)  the pairwise distances
            (lags)    the lagged distance of interest
            (tol)     the allowable tolerance about (lag)
    Output:           lag histogram figure showing the number of
                      distances at each lag
    '''
    # collect the distances at each lag
    indices = [variograms.lagindices(pwdist, lag, tol) for lag in lags]
    # record the number of indices at each lag
    indices = [len(i) for i in indices]
    # create a bar plot
    fig, ax = subplots()
    ax.bar(lags + tol, indices)
    ax.set_ylabel('Number of Lags')
    ax.set_xlabel('Lag Distance')
    ax.set_title('Lag Histogram')
    show()


def semivariogram(data, lags, tol, model=None):
    '''
    Input:  (data)    NumPy array with three columns, the first two
                      columns should be the x and y coordinates, and
                      third should be the measurements of the variable
                      of interest
            (lags)    the lagged distance of interest
            (tol)     the allowable tolerance about (lag)
            (model)   model function taking a distance and returning
                      an approximation of the semivariance
    Output:           empirical semivariogram
    '''
    # h, sv = variograms.semivariogram(data, lags, tol)
    vdata = variograms.semivariogram(data, lags, tol)
    h, sv = vdata[0], vdata[1]
    sill = np.var(data[:, 2])
    fig, ax = subplots()
    if model:
        ax.plot(h, model(h), 'r')
    ax.plot(h, sv, 'ko-')
    ax.set_ylabel('Semivariance')
    ax.set_xlabel('Lag Distance')
    ax.set_title('Semivariogram')
    ax.text(tol * 3, sill * 1.025, str(np.round(sill, decimals=3)))
    ax.axhline(sill, ls='--', color='k')
    show()


def anisotropiclags(data, pwdist, lag, tol, angle, atol):
    '''
    SPatial ANIsotropy PLOT
    '''
    index = variograms.lagindices(pwdist, lag, tol)
    anindex = variograms.anilagindices(data, pwdist, lag, tol, angle, atol)

    fig, ax = subplots()

    # plot the lagged distances
    for pair in index:
        head, tail = data[pair]
        hx, hy, hz = head
        tx, ty, tz = tail
        x = [hx, tx]
        y = [hy, ty]
        ax.plot(x, y, 'k-', lw=2, alpha=0.25)

    # plot the lagged distances within
    # the anisotropy angle and tolerance
    for pair in anindex:
        head, tail = data[pair]
        hx, hy, hz = head
        tx, ty, tz = tail
        x = [hx, tx]
        y = [hy, ty]
        ax.plot(x, y, 'r-', lw=1)

    ax.set_xlabel('X')
    ax.set_ylabel('Y')


def polaranisotropy(data, pwdist, lags, tol, nsectors):
    angle = 360.0 / nsectors
    atol = angle / 2.0
    sectors = [atol + i * angle for i in range(nsectors)]

    fig, ax = subplots()
    cnorm = colors.Normalize(vmin=0, vmax=1)
    scalarmap = cm.ScalarMappable(norm=cnorm, cmap=cm.jet)

    for sector in sectors:
        for lag in lags:

            anisodata = (data, pwdist, lag, tol, sector, atol)
            indices = variograms.anilagindices(*anisodata)
            sv = variograms.semivariance(data, indices)
            fc = scalarmap.to_rgba(sv)

            center, r, width = (0, 0), lag, lags[0] * 2
            theta1 = utilities.degree_to_bearing(sector + atol)
            theta2 = utilities.degree_to_bearing(sector - atol)
            wedge = mpatches.Wedge(center, r, theta1, theta2, width, color=fc)
            ax.add_patch(wedge)

    ax.set_xlim(-lags[-1], lags[-1])
    ax.set_ylim(-lags[-1], lags[-1])
    ax.set_aspect('equal')


# this is a colormap that ranges from yellow to purple to black
cdict = {'red':   ((0.0, 1.0, 1.0),
                   (0.5, 225/255., 225/255. ),
                   (0.75, 0.141, 0.141 ),
                   (1.0, 0.0, 0.0)),
         'green': ((0.0, 1.0, 1.0),
                   (0.5, 57/255., 57/255. ),
                   (0.75, 0.0, 0.0 ),
                   (1.0, 0.0, 0.0)),
         'blue':  ((0.0, 0.376, 0.376),
                   (0.5, 198/255., 198/255. ),
                   (0.75, 1.0, 1.0 ),
                   (1.0, 0.0, 0.0)) }
                   
YPcmap = matplotlib.colors.LinearSegmentedColormap('my_colormap', cdict, 256)

<filename>geostatsmodels/geoplot.py
#!/usr/bin/env python

from pylab import *
import numpy as np
import matplotlib
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.patches as mpatches
from geostatsmodels import variograms, utilities


def hscattergram(data, pwdist, lag, tol):
    '''
    Input:  (data)    NumPy array with three columns, the first two
                      columns should be the x and y coordinates, and
                      third should be the measurements of the variable
                      of interest
            (lag)     the lagged distance of interest
            (tol)     the allowable tolerance about (lag)
            (pwdist)  a square pairwise distance matrix
    Output:           h-scattergram figure showing the distribution of
                      measurements taken at a certain lag and tolerance
    '''
    # calculate the pairwise distances
    indices = variograms.lagindices(pwdist, lag, tol)
    # collect the head and tail measurements
    head = data[indices[:, 0], 2]
    tail = data[indices[:, 1], 2]
    # create a scatterplot with equal axes
    fig, ax = subplots()
    ax.scatter(head, tail, marker="o", facecolor="none", edgecolor="k", alpha=0.5)
    ax.set_aspect("equal")
    # set the labels and the title
    ax.set_ylabel("$z(u+h)$")
    ax.set_xlabel("$z(u)$")
    ax.set_title("Lags Between " + str(lag - tol) + " and " + str(lag + tol))
    # grab the limits of the axes
    xmin, xmax = ax.get_xlim()
    ymin, ymax = ax.get_ylim()
    # calculate the covariance and annotate
    cv = variograms.covariance(data, indices)
    ax.text(xmin * 1.25, ymin * 1.050, 'Covariance = {:3.2f}'.format(cv))
    # calculate the semivariance and annotate
    sv = variograms.semivariance(data, indices)
    ax.text(xmin * 1.25, ymin * 1.025, 'Semivariance = {:3.2f}'.format(sv))
    show()


def laghistogram(data, pwdist, lags, tol):
    '''
    Input:  (data)    NumPy array with three columns, the first two
                      columns should be the x and y coordinates, and
                      third should be the measurements of the variable
                      of interest
            (pwdist)  the pairwise distances
            (lags)    the lagged distance of interest
            (tol)     the allowable tolerance about (lag)
    Output:           lag histogram figure showing the number of
                      distances at each lag
    '''
    # collect the distances at each lag
    indices = [variograms.lagindices(pwdist, lag, tol) for lag in lags]
    # record the number of indices at each lag
    indices = [len(i) for i in indices]
    # create a bar plot
    fig, ax = subplots()
    ax.bar(lags + tol, indices)
    ax.set_ylabel('Number of Lags')
    ax.set_xlabel('Lag Distance')
    ax.set_title('Lag Histogram')
    show()


def semivariogram(data, lags, tol, model=None):
    '''
    Input:  (data)    NumPy array with three columns, the first two
                      columns should be the x and y coordinates, and
                      third should be the measurements of the variable
                      of interest
            (lags)    the lagged distance of interest
            (tol)     the allowable tolerance about (lag)
            (model)   model function taking a distance and returning
                      an approximation of the semivariance
    Output:           empirical semivariogram
    '''
    # h, sv = variograms.semivariogram(data, lags, tol)
    vdata = variograms.semivariogram(data, lags, tol)
    h, sv = vdata[0], vdata[1]
    sill = np.var(data[:, 2])
    fig, ax = subplots()
    if model:
        ax.plot(h, model(h), 'r')
    ax.plot(h, sv, 'ko-')
    ax.set_ylabel('Semivariance')
    ax.set_xlabel('Lag Distance')
    ax.set_title('Semivariogram')
    ax.text(tol * 3, sill * 1.025, str(np.round(sill, decimals=3)))
    ax.axhline(sill, ls='--', color='k')
    show()


def anisotropiclags(data, pwdist, lag, tol, angle, atol):
    '''
    SPatial ANIsotropy PLOT
    '''
    index = variograms.lagindices(pwdist, lag, tol)
    anindex = variograms.anilagindices(data, pwdist, lag, tol, angle, atol)

    fig, ax = subplots()

    # plot the lagged distances
    for pair in index:
        head, tail = data[pair]
        hx, hy, hz = head
        tx, ty, tz = tail
        x = [hx, tx]
        y = [hy, ty]
        ax.plot(x, y, 'k-', lw=2, alpha=0.25)

    # plot the lagged distances within
    # the anisotropy angle and tolerance
    for pair in anindex:
        head, tail = data[pair]
        hx, hy, hz = head
        tx, ty, tz = tail
        x = [hx, tx]
        y = [hy, ty]
        ax.plot(x, y, 'r-', lw=1)

    ax.set_xlabel('X')
    ax.set_ylabel('Y')


def polaranisotropy(data, pwdist, lags, tol, nsectors):
    angle = 360.0 / nsectors
    atol = angle / 2.0
    sectors = [atol + i * angle for i in range(nsectors)]

    fig, ax = subplots()
    cnorm = colors.Normalize(vmin=0, vmax=1)
    scalarmap = cm.ScalarMappable(norm=cnorm, cmap=cm.jet)

    for sector in sectors:
        for lag in lags:

            anisodata = (data, pwdist, lag, tol, sector, atol)
            indices = variograms.anilagindices(*anisodata)
            sv = variograms.semivariance(data, indices)
            fc = scalarmap.to_rgba(sv)

            center, r, width = (0, 0), lag, lags[0] * 2
            theta1 = utilities.degree_to_bearing(sector + atol)
            theta2 = utilities.degree_to_bearing(sector - atol)
            wedge = mpatches.Wedge(center, r, theta1, theta2, width, color=fc)
            ax.add_patch(wedge)

    ax.set_xlim(-lags[-1], lags[-1])
    ax.set_ylim(-lags[-1], lags[-1])
    ax.set_aspect('equal')


# this is a colormap that ranges from yellow to purple to black
cdict = {'red':   ((0.0, 1.0, 1.0),
                   (0.5, 225/255., 225/255. ),
                   (0.75, 0.141, 0.141 ),
                   (1.0, 0.0, 0.0)),
         'green': ((0.0, 1.0, 1.0),
                   (0.5, 57/255., 57/255. ),
                   (0.75, 0.0, 0.0 ),
                   (1.0, 0.0, 0.0)),
         'blue':  ((0.0, 0.376, 0.376),
                   (0.5, 198/255., 198/255. ),
                   (0.75, 1.0, 1.0 ),
                   (1.0, 0.0, 0.0)) }
                   
YPcmap = matplotlib.colors.LinearSegmentedColormap('my_colormap', cdict, 256)

#!/usr/bin/env python Input:  (data)    NumPy array with three columns, the first two                       columns should be the x and y coordinates, and                       third should be the measurements of the variable                       of interest             (lag)     the lagged distance of interest             (tol)     the allowable tolerance about (lag)             (pwdist)  a square pairwise distance matrix     Output:           h-scattergram figure showing the distribution of                       measurements taken at a certain lag and tolerance # calculate the pairwise distances # collect the head and tail measurements # create a scatterplot with equal axes # set the labels and the title # grab the limits of the axes # calculate the covariance and annotate # calculate the semivariance and annotate Input:  (data)    NumPy array with three columns, the first two                       columns should be the x and y coordinates, and                       third should be the measurements of the variable                       of interest             (pwdist)  the pairwise distances             (lags)    the lagged distance of interest             (tol)     the allowable tolerance about (lag)     Output:           lag histogram figure showing the number of                       distances at each lag # collect the distances at each lag # record the number of indices at each lag # create a bar plot Input:  (data)    NumPy array with three columns, the first two                       columns should be the x and y coordinates, and                       third should be the measurements of the variable                       of interest             (lags)    the lagged distance of interest             (tol)     the allowable tolerance about (lag)             (model)   model function taking a distance and returning                       an approximation of the semivariance     Output:           empirical semivariogram # h, sv = variograms.semivariogram(data, lags, tol) SPatial ANIsotropy PLOT # plot the lagged distances # plot the lagged distances within # the anisotropy angle and tolerance # this is a colormap that ranges from yellow to purple to black