def test_pcolorimage_setdata():
ax = plt.gca()
im = PcolorImage(ax)
x = np.arange(3, dtype=np.float64)
y = np.arange(4, dtype=np.float64)
z = np.arange(6, dtype=np.float64).reshape((3, 2))
im.set_data(x, y, z)
x[0] = y[0] = z[0, 0] = 9.9
assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
def test_pcolorimage_setdata():
ax = plt.gca()
im = PcolorImage(ax)
x = np.arange(3, dtype=float)
y = np.arange(4, dtype=float)
z = np.arange(6, dtype=float).reshape((3, 2))
im.set_data(x, y, z)
x[0] = y[0] = z[0, 0] = 9.9
assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
def pcolorimage(ax, x=None, y=None, A=None, **kwargs):
img = PcolorImage(ax, x, y, A, **kwargs)
img.set_extent([x[0], x[-1], y[0],
y[-1]]) # Have to add this or the fig won't save...
ax.images.append(img)
ax.set_xlim(left=x[0], right=x[-1])
ax.set_ylim(bottom=y[0], top=y[-1])
ax.autoscale_view(tight=True)
return img, ax
def pcolorimage(ax, x=None, y=None, A=None, **kwargs):
img = PcolorImage(ax, x, y, A, **kwargs)
ax.images.append(img)
ax.set_xlim(left=x[0], right=x[-1])
ax.set_ylim(bottom=y[0], top=y[-1])
ax.autoscale_view(tight=True)
return img
def pcolorimage(ax, x, y, Z):
"""
Make a PcolorImage based on Z = (x, y, 4) [byte] array
This is to fix an omission ('bug') in the current (as of this
writing) version of MatPlotLib. I may become superfluous in the
future.
"""
img = PcolorImage(ax, x, y, Z)
ax.images.append(img)
xl, xr, yb, yt = x[0], x[-1], y[0], y[-1]
ax.update_datalim(np.array([[xl, yb], [xr, yt]]))
ax.autoscale_view(tight=True)
return img
def test(n = None):
N = 1000
x = np.exp(np.linspace(-2.0, 3.0, N))
y = np.exp(np.linspace(-2.0, 2.0, N))
x = (np.linspace(-2.0, 3.0, N))
y = (np.linspace(-2.0, 2.0, N))
X, Y = np.meshgrid(x, y)
X1 = 0.5*(X[:-1,:-1] + X[1:,1:])
Y1 = 0.5*(Y[:-1,:-1] + Y[1:,1:])
Z1 = bivariate_normal(X1, Y1, 0.1, 0.2, 1.27, 1.11) + 100.*bivariate_normal(X1, Y1, 1.0, 1.0, 0.23, 0.72)
Z1[Z1>0.9*np.max(Z1)] = +np.inf
ZR = bivariate_normal(X1, Y1, 0.1, 0.2, 1.27, 1.11) + 100.*bivariate_normal(X1, Y1, 1.0, 1.0, 0.23, 0.72)
ZG = bivariate_normal(X1, Y1, 0.1, 0.2, 2.27, 0.11) + 100.*bivariate_normal(X1, Y1, 1.0, 1.0, 0.43, 0.52)
ZB = bivariate_normal(X1, Y1, 0.1, 0.2, 0.27, 2.11) + 100.*bivariate_normal(X1, Y1, 1.0, 1.0, 0.53, 0.92)
ZA = bivariate_normal(X1, Y1, 0.1, 0.2, 3.27,-1.11) + 100.*bivariate_normal(X1, Y1, 1.0, 1.0, 0.23, 0.82)
Z = np.ndarray(ZR.shape + (4,))
Z[...,0] = ZR /ZR.max()
Z[...,1] = ZG /ZG.max()
Z[...,2] = ZB /ZB.max()
Z[...,3] = np.exp(-ZA /ZA.max())
Z = (Z*255).astype(np.uint8)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
#col = plt.get_cmap('terrain_r')
#col = get_cfunc('RGBWaves200') # need to define first, use colormap instaed
#col = plt.get_cmap('gnuplot2')
#col = colormap('cubehelix')
#col = colormap('autumn')
#col = colormap('jet')
#col = colormap('RGBWaves', 100)
#col = ColorBWC(mode=0)
#col = colormap('viridis')
col = colormap('cividis')
#col = ColorBlindRainbow()
#col = ColorGray()
#col = ColorGray(gamma=(3,1,2))
#col = IsoColorDivergeBR()
#col = ColorMapList(['Orange', 'Gold', 'Salmon', 'Tomato', 255], gamma = 2)
#col = ColorSolid(.5)
#col = ColorJoin((ColorGray(.5), BlendInFilter(ColorBlindRainbow(gamma = .5), frac=0.25)), .3)
#col = ColorJoin((ColorGray(-.5), BlendInFilter(ColorBlindRainbow(gamma = .5), frac=-0.25), IsoColorDivergeBR()), blend=(.5,.1))
#col = ColorJoin((ColorGray(.5), ColorBlindRainbow(gamma = .5), 'green'), blend = 0.1, method = 'linear')
#col = ColorBlindRainbow(gamma = .5)
#col = FilterColorBlindRed(IsoColors('BlindRainbow', 9))
#col = IsoColorDivergeBWR(n)
#col = IsoColorRainbow15()
#col = FilterColorGray(IsoColorRainbow21())
#col = IsoColorLights('BlindRainbow')
#col = IsoColorRainbow(6)
#col = ColorBlend((BlendInFilter(ColorBlindRainbow(gamma = .5), frac=0.25), ColorGray(.5)))
#col = ColorBlendBspline((ColorGray(.5), WaveFilter(ColorBlindRainbow(), nwaves = 20)),)
#col = ColorBlend((ColorMapList(['Green']), ColorMapList(['Red'])))
#col = ColorBlendBspline(('red', 'green', 'yellow', 'blue'), k=2)
#col = ColorDiverging(('#D00000','#808080','#0000D0'))
#col = ColorCircle(func = lambda x: x**10)
#col = ColorCircle(model = 'HCL', init = [0, 1, .5])
#col = ColorCircle(model = 'LChuv', init = [100, 100, .5])
#col = get_cmap('autumn')
#col = ColorBlend((ColorGray(reverse=False), HueRotateFilter(ColorBlendWave('Gold', 'Purple', nwaves = 20), angle = 180)))
#col = ShadeColor(gamma=1/2.2)
#col = ShadeColor(gamma=1, model='RGB')
#input filter
#col = ColorScale(col, lambda x : (np.log((x)+1)/(np.exp(-3)-1))
#col = ColorScaleGamma(col, -1/2.2)
#col = ColorScaleExp(col, 3)
#col = ColorScaleLog(col, 3)
# levels
#col = IsoColors(col, 12)
# COLOR FILTERS
#col = FilterColorGray(col)
#col = WaveFilter(col, nwaves = 20, property = 'H')
#col = WaveFilter(col, nwaves = 20, property = 'I', model = 'YIQ')
#col = WaveFilter(col, nwaves = 20, index = 2, method = 2, model = 'LChuv')
#col = WaveFilter(col, nwaves = 20, index = 0, method = 0, model = 'LChuv')
#col = WaveFilter(col, nwaves = 20, property = 'V', model = 'HSV')
#col = WaveFilter(col, nwaves = 20, property = 'S', model = 'HSV')
#col = WaveFilter(col, nwaves = 20, property = 'C', model = 'CMY')
#col = WaveFilter(col, nwaves = 20)
#col = BlendInFilter(col, frac=-0.5)
#col = FilterColorModel(col, model = 'sRGB')
#col = Reverse(col) # why does it invert neg inf when used with WaveFilter?
#col = FilterColorInvert(col)
#col = FilterColorHueRotate(col, model = 'HCL', angle = 60)
#col = FilterColorGamma(col, gamma = (1,2,3,4))
#col = AlphaFilter(col, func = lambda x: 0.5*(1+np.sin(100*x)))
#col = AlphaFilter(col, func = lambda x: x**2)
#col = FuncBackgroundFilter(col, func = lambda x: x**2)
#col = FuncBackgroundFilter(col, func = lambda x: 1 -2*np.abs(x-0.5))
#col = ColorFilterColor(col, color = 'Pink')
#col = HueRotateFilter(col, func = lambda x: 0.5 * np.sin(40*np.pi*x))
#col = ColorFilterColor(col, color = 'Pink', reverse = True, method = 'min', clip = False)
#col = ColorXFilterColor(col, color = 'Pink', reverse = False, method = 'vec')
#col = ColorBlendBspline(('red','green','Gold','blue', ColorScale(col, lambda x: x*5-4)), k=2)
#col = ComponentFilter(col, func = lambda x : 1-x**2)
#col = FilterColorTransparent(col, transparent = ('#00007f', '#7f7fff'))
#col = SetBackgroundFilter(col, background = '#FF0000')
#col = FilterColorGraysRGB(col)
# YFILTERS
#Z2 = (x[1:] + x[-1])[None, :] * (y[1:] + y[-1])[:, None]
#Z2 /= np.max(Z2)
#col = FuncAlphaYFilter(col, data=Z2)
# TEST INVERSE
# model = 'Luv'
# col = FilterColorModel(col, model = color_model(model).inverse())
# col = FilterColorModel(col, model = color_model(model))
# VISION IMPARED COLOR FILTERS
#col = FilterColorBlindRed(col)
#col = FilterVisCheck(col, 'tritanope')
#col = FilterColorBlindness(col, 'achroma')
#col = FilterColorBlindness(col, 'protan')
#col = FilterColorBlindness(col, 'tritan')
#col = FilterColorBlindness(col, 'deutan')
#i = ax.pcolorfast(x, y, Z1, cmap = col, alpha=1.)
#i = ax.pcolorfast(x, y, Z1, cmap = col)
#Z1 = col(Z1, bytes = True)
#i = pcolorimage(ax, x, y, Z1)
# from matplotlib.image import PcolorImage
i = PcolorImage(ax, x, y, Z)
ax.images.append(i)
# xl, xr, yb, yt = x[0], x[-1], y[0], y[-1]
# ax.update_datalim(np.array([[xl, yb], [xr, yt]]))
# ax.autoscale_view(tight=True)
plt.colorbar(i)
plt.show()
return i