def plot_model_density(self,
overlay,
sample,
xlim=[],
ylim=[],
line_color="g",
figure_size=(7, 8),
lognorm=True,
filetype="png",
resolution=300,
filename=None):
"""
Plots the resulting regression model of the data
"""
# Plot data
plot_agency_magnitude_density(self.data, True, sample, xlim, ylim,
figure_size, lognorm)
# Plot Model
model_x, model_y, self.standard_deviation = self.retrieve_model()
title_string = self.model_type.get_string(self.keys[2], self.keys[0])
plt.plot(model_x,
model_y,
line_color,
linewidth=2.0,
label=title_string)
#plt.title(r"{:s}".format(title_string), fontsize=14)
plt.legend(loc=2, frameon=False)
if filename:
utils._save_image(filename, filetype, resolution)
if not overlay:
plt.show()
def plot_agency_magnitude_pair(data, overlay=False, xlim=[], ylim=[],
marker="o", figure_size=(7, 8), filetype="png", resolution=300,
filename=None):
"""
Plots the agency magnitude pair
:param dict data:
Query result for a particular joint agency-magnitude pair combination
:param bool overlay:
Allows another layer to be rendered on top (True) or closes the figure
for plotting (False)
:param list xlim:
Lower and upper bounds for x-axis
:param list ylim:
Lower and upper bounds for y-axis
"""
if not data:
print "No pairs found - abandoning plot!"
return
fig = plt.figure(figsize=figure_size)
keys = data.keys()
plt.errorbar(data[keys[0]], data[keys[2]],
xerr=data[keys[1]], yerr=data[keys[3]],
marker=marker, mfc="b", mec="k", ls="None",
ecolor="r")
plt.xlabel(utils._to_latex(keys[0]), fontsize=16)
plt.ylabel(utils._to_latex(keys[2]), fontsize=16)
plt.grid(True)
if len(xlim) == 2:
lowx = xlim[0]
highx = xlim[1]
else:
lowx = np.floor(np.min(data[keys[0]]))
highx = np.ceil(np.max(data[keys[0]]))
if len(ylim) == 2:
lowy = ylim[0]
highy = ylim[1]
else:
lowy = np.floor(np.min(data[keys[2]]))
highy = np.ceil(np.max(data[keys[2]]))
if lowy < lowx:
lowx = lowy
if highy > highx:
highx = highy
plt.ylim(lowx, highx)
plt.xlim(lowx, highx)
# Overlay 1:1 line
plt.plot(np.array([lowx, highx]), np.array([lowx, highx]), ls="--",
color=[0.5, 0.5, 0.5], zorder=1)
plt.tight_layout()
if filename:
utils._save_image(filename, filetype, resolution)
if not overlay:
plt.show()
return data
def plot_magnitude_conversion_model(self, model, overlay, line_color="g",
filetype="png", resolution=300, filename=None):
"""
Plots a specific magnitude conversion model (to overlay on top of
a current figure)
"""
model_x = np.arange(0.9 * np.min(self.data[self.keys[0]]),
1.1 * np.max(self.data[self.keys[0]]),
0.01)
model_y, _ = model.convert_value(model_x, 0.0)
plt.plot(model_x, model_y, line_color,
linewidth=2.0,
label=model.model_name)
plt.legend(loc=2, frameon=False)
if filename:
utils._save_image(filename, filetype, resolution)
if not overlay:
plt.show()
def plot_model_density(self, overlay, sample, xlim=[], ylim=[],
line_color="g", figure_size=(7, 8), lognorm=True, filetype="png",
resolution=300, filename=None):
"""
Plots the resulting regression model of the data
"""
# Plot data
plot_agency_magnitude_density(self.data, True, sample, xlim, ylim,
figure_size, lognorm)
# Plot Model
model_x, model_y, self.standard_deviation = self.retrieve_model()
title_string = self.model_type.get_string(self.keys[2], self.keys[0])
plt.plot(model_x, model_y, line_color,
linewidth=2.0,
label=title_string)
#plt.title(r"{:s}".format(title_string), fontsize=14)
plt.legend(loc=2, frameon=False)
if filename:
utils._save_image(filename, filetype, resolution)
if not overlay:
plt.show()
def plot_magnitude_conversion_model(self,
model,
overlay,
line_color="g",
filetype="png",
resolution=300,
filename=None):
"""
Plots a specific magnitude conversion model (to overlay on top of
a current figure)
"""
model_x = np.arange(0.9 * np.min(self.data[self.keys[0]]),
1.1 * np.max(self.data[self.keys[0]]), 0.01)
model_y, _ = model.convert_value(model_x, 0.0)
plt.plot(model_x,
model_y,
line_color,
linewidth=2.0,
label=model.model_name)
plt.legend(loc=2, frameon=False)
if filename:
utils._save_image(filename, filetype, resolution)
if not overlay:
plt.show()
def plot_agency_magnitude_density(data, overlay=False, number_samples=0,
xlim=[], ylim=[], figure_size=(7, 8), lognorm=True,
filetype="png", resolution=300, filename=None):
"""
"""
keys = data.keys()
if not data:
print "No pairs found - abandoning plot!"
return
if len(xlim) == 2:
lowx = xlim[0]
highx = xlim[1]
else:
lowx = np.floor(np.min(data[keys[0]]))
highx = np.ceil(np.max(data[keys[0]]))
if len(ylim) == 2:
lowy = ylim[0]
highy = ylim[1]
else:
lowy = np.floor(np.min(data[keys[2]]))
highy = np.ceil(np.max(data[keys[2]]))
if lowy < lowx:
lowx = lowy
if highy > highx:
highx = highy
xbins = np.linspace(lowx - 0.05, highx + 0.05,
((highx + 0.05 - lowx - 0.05) / 0.1) + 2.0)
ybins = np.linspace(lowx - 0.05, highx + 0.05,
((highx + 0.05 - lowx - 0.05) / 0.1) + 2.0)
density = sample_agency_magnitude_pairs(data, xbins, ybins, number_samples)
fig = plt.figure(figsize=figure_size)
if lognorm:
cmap = deepcopy(matplotlib.cm.get_cmap("jet"))
data_norm = LogNorm(vmin=0.1, vmax=np.max(density))
else:
cmap = deepcopy(matplotlib.cm.get_cmap("jet"))
cmap.set_under("w")
data_norm = Normalize(vmin=0.1, vmax=np.max(density))
#density[density < 1E-15] == np.nan
plt.pcolormesh(xbins[:-1] + 0.05, ybins[:-1] + 0.05, density.T,
norm=data_norm, cmap=cmap)
cbar = plt.colorbar()
cbar.set_label("Number Events", fontsize=16)
plt.xlabel(utils._to_latex(keys[0]), fontsize=16)
plt.ylabel(utils._to_latex(keys[2]), fontsize=16)
plt.grid(True)
plt.ylim(lowx, highx)
plt.xlim(lowx, highx)
# Overlay 1:1 line
plt.plot(np.array([lowx, highx]), np.array([lowx, highx]), ls="--",
color=[0.5, 0.5, 0.5], zorder=1)
plt.tight_layout()
if filename:
utils._save_image(filename, filetype, resolution)
if not overlay:
plt.show()
return data
def plot_agency_magnitude_density(data,
overlay=False,
number_samples=0,
xlim=[],
ylim=[],
figure_size=(7, 8),
lognorm=True,
filetype="png",
resolution=300,
filename=None):
"""
"""
keys = data.keys()
if not data:
print "No pairs found - abandoning plot!"
return
if len(xlim) == 2:
lowx = xlim[0]
highx = xlim[1]
else:
lowx = np.floor(np.min(data[keys[0]]))
highx = np.ceil(np.max(data[keys[0]]))
if len(ylim) == 2:
lowy = ylim[0]
highy = ylim[1]
else:
lowy = np.floor(np.min(data[keys[2]]))
highy = np.ceil(np.max(data[keys[2]]))
if lowy < lowx:
lowx = lowy
if highy > highx:
highx = highy
xbins = np.linspace(lowx - 0.05, highx + 0.05,
((highx + 0.05 - lowx - 0.05) / 0.1) + 2.0)
ybins = np.linspace(lowx - 0.05, highx + 0.05,
((highx + 0.05 - lowx - 0.05) / 0.1) + 2.0)
density = sample_agency_magnitude_pairs(data, xbins, ybins, number_samples)
fig = plt.figure(figsize=figure_size)
if lognorm:
cmap = deepcopy(matplotlib.cm.get_cmap("jet"))
data_norm = LogNorm(vmin=0.1, vmax=np.max(density))
else:
cmap = deepcopy(matplotlib.cm.get_cmap("jet"))
cmap.set_under("w")
data_norm = Normalize(vmin=0.1, vmax=np.max(density))
#density[density < 1E-15] == np.nan
plt.pcolormesh(xbins[:-1] + 0.05,
ybins[:-1] + 0.05,
density.T,
norm=data_norm,
cmap=cmap)
cbar = plt.colorbar()
cbar.set_label("Number Events", fontsize=16)
plt.xlabel(utils._to_latex(keys[0]), fontsize=16)
plt.ylabel(utils._to_latex(keys[2]), fontsize=16)
plt.grid(True)
plt.ylim(lowx, highx)
plt.xlim(lowx, highx)
# Overlay 1:1 line
plt.plot(np.array([lowx, highx]),
np.array([lowx, highx]),
ls="--",
color=[0.5, 0.5, 0.5],
zorder=1)
plt.tight_layout()
if filename:
utils._save_image(filename, filetype, resolution)
if not overlay:
plt.show()
return data
def plot_agency_magnitude_pair(data,
overlay=False,
xlim=[],
ylim=[],
marker="o",
figure_size=(7, 8),
filetype="png",
resolution=300,
filename=None):
"""
Plots the agency magnitude pair
:param dict data:
Query result for a particular joint agency-magnitude pair combination
:param bool overlay:
Allows another layer to be rendered on top (True) or closes the figure
for plotting (False)
:param list xlim:
Lower and upper bounds for x-axis
:param list ylim:
Lower and upper bounds for y-axis
"""
if not data:
print "No pairs found - abandoning plot!"
return
fig = plt.figure(figsize=figure_size)
keys = data.keys()
plt.errorbar(data[keys[0]],
data[keys[2]],
xerr=data[keys[1]],
yerr=data[keys[3]],
marker=marker,
mfc="b",
mec="k",
ls="None",
ecolor="r")
plt.xlabel(utils._to_latex(keys[0]), fontsize=16)
plt.ylabel(utils._to_latex(keys[2]), fontsize=16)
plt.grid(True)
if len(xlim) == 2:
lowx = xlim[0]
highx = xlim[1]
else:
lowx = np.floor(np.min(data[keys[0]]))
highx = np.ceil(np.max(data[keys[0]]))
if len(ylim) == 2:
lowy = ylim[0]
highy = ylim[1]
else:
lowy = np.floor(np.min(data[keys[2]]))
highy = np.ceil(np.max(data[keys[2]]))
if lowy < lowx:
lowx = lowy
if highy > highx:
highx = highy
plt.ylim(lowx, highx)
plt.xlim(lowx, highx)
# Overlay 1:1 line
plt.plot(np.array([lowx, highx]),
np.array([lowx, highx]),
ls="--",
color=[0.5, 0.5, 0.5],
zorder=1)
plt.tight_layout()
if filename:
utils._save_image(filename, filetype, resolution)
if not overlay:
plt.show()
return data
