def test_colormap():
viridis = plt.get_cmap('viridis')
cmap = ColorPalette.fromColorMap('viridis', np.arange(0, 10),
np.arange(1, 11), viridis)
zero_value = np.array(
[0.26666666666666666, 0.00392156862745098, 0.32941176470588235, 1.0])
ten_value = np.array(
[0.9921568627450981, 0.9058823529411765, 0.1450980392156863, 1.0])
np.testing.assert_almost_equal(cmap.getDataColor(0), zero_value)
np.testing.assert_almost_equal(cmap.getDataColor(10), ten_value)
viridis = plt.get_cmap('viridis')
cmap = ColorPalette.fromColorMap('viridis',
np.arange(-4, 5),
np.arange(-3, 6),
viridis,
is_log=True)
dcolor = cmap.getDataColor(np.exp(-4.0))
tcolor = np.array(
(0.26666666666666666, 0.00392156862745098, 0.32941176470588235, 1.0))
np.testing.assert_almost_equal(dcolor, tcolor)
def test_cpt():
rm = 100 # number of lines to remove on black end of magma_r
# how much at the zero end should be *just* white before transitioning to
# meet colors
ad = 50
magma_cpt = cm.get_cmap('magma_r', 512) # start with magma_r
white_bit = np.array([255 / 256, 250 / 256, 250 / 256,
1]) # create array of white
slip_cpt = magma_cpt(np.linspace(0, 1, 512)) # initialize slip_cpt
# move beginning up to remove black end
slip_cpt[rm:, :] = slip_cpt[0:-rm, :]
# gradient from white to beginning of new magma
r_s = np.linspace(white_bit[0], slip_cpt[rm][0], rm - ad)
g_s = np.linspace(white_bit[1], slip_cpt[rm][1], rm - ad)
b_s = np.linspace(white_bit[2], slip_cpt[rm][2], rm - ad)
slip_cpt[ad:rm, :][:, 0] = r_s
slip_cpt[ad:rm, :][:, 1] = g_s
slip_cpt[ad:rm, :][:, 2] = b_s
slip_cpt[:ad, :] = white_bit
slipmap = ListedColormap(slip_cpt)
z0 = np.arange(0, 300, 1)
z1 = np.arange(1, 301, 1)
ncolors = 64
resolution = (z1.max() - z0.min()) / ncolors
name = 'test'
cpt = ColorPalette.fromColorMap(name,
z0,
z1,
slipmap,
resolution=resolution)
try:
tdir = tempfile.mkdtemp()
tfile = os.path.join(tdir, 'test.cpt')
cpt.write(tfile)
cpt2 = ColorPalette.fromFile(tfile)
np.testing.assert_almost_equal(
cpt.getDataColor(150)[0],
cpt2.getDataColor(150)[0])
except:
pass
finally:
if os.path.isdir(tdir):
shutil.rmtree(tdir)
def _create_palette(imtype, levels):
"""Create a ColorPalette object from given levels and IMT type.
Args:
imtype (str): One of 'PGV','PGA','SA(0.3)',etc.
levels (sequence): Sequence of contour levels.
Returns:
ColorPalette: ColorPalette using range of input data and IMT_CMAP.
"""
# this method assumes that levels are in logspace
if len(levels) > 1:
if len(levels) % 2:
levels.append(levels[-1])
nsteps = 256
z0 = np.linspace(np.log(levels[0]), np.log(levels[-2]), nsteps)
z1 = np.linspace(np.log(levels[1]), np.log(levels[-1]), nsteps)
else:
z0 = np.array([levels[0], levels[0]*10])
z1 = np.array([levels[0], levels[0]*10])
cmap = plt.get_cmap(IMT_CMAP)
palette = ColorPalette.fromColorMap(imtype, z0, z1, cmap, is_log=True)
return palette
def plot_regression(event_table,
imc,
imc_table,
imt,
filename,
distance_metric='EpicentralDistance',
colormap='viridis'):
"""Make summary "regression" plot.
TODO:
* Add GMPE curve and compute mean/sd for all the observations
and then also report the standardized residuals.
* Better definitions of column names and units.
"""
fig = plt.figure(figsize=(10, 5))
# ax = plt.subplot(1, 1, 1)
ax = fig.add_axes([BOTTOM, AX1_LEFT, AX1_WIDTH, AX1_HEIGHT])
if distance_metric not in imc_table.columns:
raise KeyError('Distance metric "%s" not found in table' %
distance_metric)
imt = imt.upper()
# Stupid hack to get units for now. Need a better, more systematic
# approach
if imt.startswith("SA") | (imt == "PGA"):
units = "%g"
elif imt.startswith('FAS') or imt in ['ARIAS', 'PGV']:
units = "cm/s"
else:
units = 'Unknown units for IMT %s' % imt
if imt not in imc_table.columns:
raise KeyError('IMT "%s" not found in table' % imt)
# get the event information
# group imt data by event id
# plot imts by event using colors banded by magnitude
eventids = event_table['id']
# set up the color bands
minmag = event_table['magnitude'].min()
min_mag = min(np.floor(minmag / DELTA_MAG) * DELTA_MAG, MIN_MAG)
maxmag = event_table['magnitude'].max()
max_mag = max(np.ceil(maxmag / DELTA_MAG) * DELTA_MAG, MAX_MAG)
z0 = np.arange(min_mag, max_mag, 0.5)
z1 = np.arange(min_mag + DELTA_MAG, max_mag + DELTA_MAG, DELTA_MAG)
cmap = plt.get_cmap(colormap)
palette = ColorPalette.fromColorMap('mag', z0, z1, cmap)
colors = []
for zval in np.arange(min_mag, max_mag + 0.5, 0.5):
tcolor = palette.getDataColor(zval, 'hex')
colors.append(tcolor)
cmap2 = mpl.colors.ListedColormap(colors)
for eventid in eventids:
emag = event_table[event_table['id'] ==
eventid].magnitude.to_numpy()[0]
norm_mag = (emag - min_mag) / (max_mag - min_mag)
color = cmap2(norm_mag)
erows = imc_table[imc_table['EarthquakeId'] == eventid]
distance = erows[distance_metric]
imtdata = erows[imt]
ax.loglog(distance,
imtdata,
mfc=color,
mec='k',
marker='o',
linestyle='None')
ax.set_xlabel('%s (km)' % distance_metric)
ax.set_ylabel('%s (%s)' % (imt, units))
bounds = np.arange(min_mag, max_mag + 1.0, 0.5)
norm = mpl.colors.BoundaryNorm(bounds, cmap2.N)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.05)
mpl.colorbar.ColorbarBase(
cax,
cmap=cmap2,
norm=norm,
ticks=bounds, # optional
spacing='proportional',
orientation='vertical')
plt.sca(ax)
plt.suptitle('%s vs %s (#eqks=%i)' % (distance_metric, imt, len(eventids)))
plt.title('for component %s' % (imc))
plt.savefig(filename)
def plot_regression(
event_table,
imc,
imc_table,
imt,
filename,
distance_metric="EpicentralDistance",
colormap="viridis",
):
"""Make summary "regression" plot.
TODO:
* Add GMPE curve and compute mean/sd for all the observations
and then also report the standardized residuals.
* Better definitions of column names and units.
"""
fig = plt.figure(figsize=(10, 5))
ax = fig.add_axes([BOTTOM, AX1_LEFT, AX1_WIDTH, AX1_HEIGHT])
if distance_metric not in imc_table.columns:
raise KeyError(
f'Distance metric "{distance_metric}" not found in table')
imt = imt.upper()
# Stupid hack to get units for now. Need a better, more systematic
# approach
if imt.startswith("SA") | (imt == "PGA"):
units = "%g"
elif imt.startswith("FAS") or imt in ["ARIAS", "PGV"]:
units = "cm/s"
else:
units = f"Unknown units for IMT {imt}"
if imt not in imc_table.columns:
raise KeyError(f'IMT "{imt}" not found in table')
# get the event information
# group imt data by event id
# plot imts by event using colors banded by magnitude
eventids = event_table["id"]
# set up the color bands
minmag = event_table["magnitude"].min()
min_mag = min(np.floor(minmag / DELTA_MAG) * DELTA_MAG, MIN_MAG)
maxmag = event_table["magnitude"].max()
max_mag = max(np.ceil(maxmag / DELTA_MAG) * DELTA_MAG, MAX_MAG)
z0 = np.arange(min_mag, max_mag, 0.5)
z1 = np.arange(min_mag + DELTA_MAG, max_mag + DELTA_MAG, DELTA_MAG)
cmap = plt.get_cmap(colormap)
palette = ColorPalette.fromColorMap("mag", z0, z1, cmap)
colors = []
for zval in np.arange(min_mag, max_mag + 0.5, 0.5):
tcolor = palette.getDataColor(zval, "hex")
colors.append(tcolor)
cmap2 = mpl.colors.ListedColormap(colors)
for eventid in eventids:
emag = event_table[event_table["id"] ==
eventid].magnitude.to_numpy()[0]
norm_mag = (emag - min_mag) / (max_mag - min_mag)
color = cmap2(norm_mag)
erows = imc_table[imc_table["EarthquakeId"] == eventid]
distance = erows[distance_metric]
imtdata = erows[imt]
ax.loglog(distance,
imtdata,
mfc=color,
mec="k",
marker="o",
linestyle="None")
ax.set_xlabel(f"{distance_metric} (km)")
ax.set_ylabel(f"{imt} ({units})")
bounds = np.arange(min_mag, max_mag + 1.0, 0.5)
norm = mpl.colors.BoundaryNorm(bounds, cmap2.N)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.05)
mpl.colorbar.ColorbarBase(
cax,
cmap=cmap2,
norm=norm,
ticks=bounds, # optional
spacing="proportional",
orientation="vertical",
)
plt.sca(ax)
plt.suptitle("%s vs %s (#eqks=%i)" % (distance_metric, imt, len(eventids)))
plt.title(f"for component {imc}")
plt.savefig(filename)
