def test_make_colormap(self):
make_colormap([([0, 0, 1], 10), ([1, 1, 1], 10), ([1, 0, 0], 10)],
name='french_flag', interpolate=False)
show_colormaps('french_flag')
cmap = make_colormap([('dred', 5), ('blue', 2.0), ('orange', 0)],
name='my_cmap')
assert_almost_equal(cmap["red"][1],
np.array([0.00392157, 0.62400345, 0.62400345]))
assert_almost_equal(cmap["blue"][2],
np.array([0.00784314, 0.01098901, 0.01098901]))
assert_almost_equal(cmap["green"][3], np.array([0.01176471, 0.0, 0.0]))
def test_make_colormap(self):
make_colormap(
[([0, 0, 1], 10), ([1, 1, 1], 10), ([1, 0, 0], 10)],
name="french_flag",
interpolate=False,
)
show_colormaps("french_flag")
cmap = make_colormap([("dred", 5), ("blue", 2.0), ("orange", 0)],
name="my_cmap")
assert_almost_equal(cmap["red"][1],
np.array([0.00392157, 0.62400345, 0.62400345]))
assert_almost_equal(cmap["blue"][2],
np.array([0.00784314, 0.01098901, 0.01098901]))
assert_almost_equal(cmap["green"][3], np.array([0.01176471, 0.0, 0.0]))
def RecoveryPlot(resultarray):
#Define our bin arrays
minbin = 14
maxbin = 22
bs = .2
magbins = np.arange(minbin, maxbin + bs, bs)
magbins = np.array([round(b, 1) for b in magbins])
mfbins = np.arange(0, 2, .1)
mfbins = np.array([round(b, 1) for b in mfbins])
#Gridspec plot
fig, ax0 = plt.subplots(1, 1, figsize=(12, 5))
#Subplot for array
ax0 = plt.gca()
#Define custom colormap
mycm = yt.make_colormap([(np.array([88, 88, 88]) / 255, 5),
(np.array([51, 8, 94]) / 255, 5), ('dpurple', 5),
('purple', 5), ('dblue', 5), ('blue', 5),
(np.array([0, 100, 20]) / 255, 5), ('dgreen', 5),
('green', 5), (np.array([173, 255, 47]) / 255, 5),
('yellow', 5),
(np.array([255, 200, 50]) / 255, 5),
('orange', 5), (np.array([255, 69, 0]) / 255, 5),
(np.array([168, 0, 0]) / 255, 5),
(np.array([84, 0, 0]) / 255, 5)],
name='mycm',
interpolate=False)
#Use np.flip and T to set up axes correctly
im = ax0.imshow(np.flip(resultarray, 1).T,
cmap='mycm',
extent=(14, 22, 0, 2),
aspect='auto')
ax0.set_xlabel(r'$GALEX$' + ' NUV (mag)', fontsize=25)
ax0.set_ylabel('Scale Factor', fontsize=25)
ax0.set_xticks([x for x in np.arange(14, 22, 1)])
ax0 = WDutils.plotparams(ax0)
ax0.xaxis.get_major_ticks()[0].set_visible(False)
ax0.set_xlim(xmin=15, xmax=21)
ax0.set_yticks([.5, 1, 1.5, 2])
ax0.yaxis.set_minor_locator(MultipleLocator(.1))
divider = make_axes_locatable(ax0)
cax = divider.append_axes("right", size="5%", pad=.1)
cb = plt.colorbar(im, cax=cax)
cb.ax.set_ylabel('Recovery Rate (%)', fontsize=25)
plt.subplots_adjust(bottom=.175, top=.98)
fig.savefig('RecoveryPlot.svg')
def colortest(fname,
name,
cmap=palettable.cmocean.diverging.Balance_20.mpl_colors,
mhead=True,
grids=False,
show=False,
fields=['density'],
linear=False,
mins=[0.1],
maxs=[4e7]):
"""Plots the list of fields specified in yt.
Args:
fname (str): filename to plot.
cmap (list of tuple): specify mpl colorlist(yt interpolates by default).
mhead (bool): mark the position of the matchhead.
grids (bool): overplot the grid structure.
show (bool): return figure (true) or save to file (false)
fields (list of str): list of named fields to plot.
linear (bool): set linear or log scale(false).
mins (list of float): minima of scale for each field.
maxs (list of float): maxima of scale for each field.
"""
ds = yt.load(fname)
size = len(fields)
fig = plt.figure(figsize=(5 * size, 10))
grid = AxesGrid(fig, (0.075, 0.075, 0.85, 0.85),
nrows_ncols=(1, size),
axes_pad=1.2,
label_mode="L",
share_all=True,
cbar_location="right",
cbar_mode="each",
cbar_size="10%",
cbar_pad="0%")
p = yt.SlicePlot(ds, 'z', list(fields))
p.set_width((1.2e9, 2 * 1.2e9))
p.set_center((1.2e9 * 0.5, 0.0))
#plr = 6e8
#p.set_width((plr, plr))
#p.set_center((plr*0.5, plr*4.1))
p.set_origin(("center", "left", "domain"))
p.set_axes_unit('cm')
header = '{} {:10.3f} s'
if mhead:
x_match = ds.parameters['x_match']
y_match = ds.parameters['y_match']
p.annotate_marker((x_match, y_match),
coord_system='plot',
plot_args={
'color': 'black',
's': 30
})
if grids:
p.annotate_grids()
_ytcmap = cmap
cols = [tuple([list(x), 1]) for x in _ytcmap]
cols.append(([0.0, 0.0, 0.0], 0))
setcmap = yt.make_colormap(cols, name='custom')
pvars = zip(fields, mins, maxs)
for i, (f, mi, mx) in enumerate(pvars):
if not i:
p.annotate_title(header.format(name, float(ds.current_time)))
p.set_cmap(f, 'custom')
p.set_zlim(f, mi, mx)
if linear:
p.set_log(field, False)
plot = p.plots[f]
# plot.figure = fig
plot.axes = grid[i].axes
plot.cax = grid.cbar_axes[i]
p._setup_plots()
if show:
return fig
else:
num = ds.parameter_filename[-5:]
otpf, _ = os.path.split(ds.fullpath)
tag = 'colors'
savn = '{}{}.png'.format(tag, name)
savf = os.path.join(otpf, "png")
savp = os.path.join(otpf, "png", tag, savn)
# build filetree and show or save the figure
if not os.path.exists(savf):
os.mkdir(savf)
os.mkdir(os.path.join(savf, tag))
elif not os.path.exists(os.path.join(savf, tag)):
os.mkdir(os.path.join(savf, tag))
plt.savefig(savp)
plt.close(fig)
print("Wrote: {}".format(savp))
#_cScheme = ('GnBu', 'sequential', 9)
# import cmocean fails for ipyparallel. so hack it through palettable
cmap = palettable.cmocean.sequential.Tempo_20.mpl_colormap
cmap = palettable.cmocean.sequential.Amp_20.mpl_colormap
# import cmocean fails for ipyparallel.
# so hack it through palettable
_ytcmap = palettable.cmocean.sequential.Gray_20.mpl_colors
# _ytcmap = palettable.cmocean.diverging.Curl_19_r.mpl_colors
# _ytcmap = palettable.cmocean.diverging.Curl_19_r.mpl_colors
# _ytcmap = palettable.cmocean.diverging.Delta_20_r.mpl_colors
_ytcmap = palettable.cmocean.diverging.Balance_20.mpl_colors
_ytcmap = palettable.cmocean.sequential.Amp_20.mpl_colors
cols = [tuple([list(x), 1]) for x in _ytcmap]
cols.append(([0.0, 0.0, 0.0], 0)) # black initial color
# cols.append(([1.0, 1.0, 1.0], 0)) # white initial color
setcmap = yt.make_colormap(cols, name='custom')
# yt plotting functions
def planeSlice(fname,
field,
lims,
zcut=0.0,
linear=False,
show=False,
width=1.1e9):
"""Makes a slice at zcut"""
ds = yt.load(fname)
p = yt.SlicePlot(ds, 'z', field, center=[0, 0, zcut])
p.set_width((width, width))
p.set_cmap(field, 'custom')