Beispiel #1
0
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')
Beispiel #2
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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')
Beispiel #3
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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)
        # a sanity check
        # mx, my = np.mean( x ), np.mean( y )
        # br = utilities.bearing( ( hx, hy ), ( tx, ty ) )
        # ax.text( mx, my, '{0:3.2f}'.format( br ) )

    # 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')
Beispiel #4
0
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')
Beispiel #5
0
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')