def plot_trunc_gr_model(
aval, bval, min_mag, max_mag, dmag,
catalogue=None, completeness=None, filename=None,
figure_size=(8, 6), filetype='png', dpi=300, ax=None):
"""
Plots a Gutenberg-Richter model
"""
input_model = TruncatedGRMFD(min_mag, max_mag, dmag, aval, bval)
if not catalogue:
# Plot only the modelled recurrence
annual_rates, cumulative_rates = _get_recurrence_model(input_model)
if ax is None:
fig, ax = plt.subplots(figsize=figure_size)
else:
fig = ax.get_figure()
ax.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
ax.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
ax.xlabel('Magnitude')
ax.set_ylabel('Annual Rate')
ax.set_legend(['Incremental Rate', 'Cumulative Rate'])
_save_image(fig, filename, filetype, dpi)
else:
completeness = _check_completeness_table(completeness, catalogue)
plot_recurrence_model(
input_model, catalogue, completeness, dmag, filename=filename,
figure_size=figure_size, filetype=filetype, dpi=dpi, ax=ax)
def plot_trunc_gr_model(aval,
bval,
min_mag,
max_mag,
dmag,
catalogue=None,
completeness=None,
figure_size=None,
filename=None,
filetype='png',
dpi=300):
"""
Plots a Gutenberg-Richter model
"""
input_model = TruncatedGRMFD(min_mag, max_mag, dmag, aval, bval)
if not catalogue:
# Plot only the modelled recurrence
annual_rates, cumulative_rates = _get_recurrence_model(input_model)
plt.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
plt.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
plt.xlabel('Magnitude', fontsize='large')
plt.ylabel('Annual Rate', fontsize='large')
plt.legend(['Incremental Rate', 'Cumulative Rate'])
_save_image(filename, filetype, dpi)
else:
completeness = _check_completeness_table(completeness, catalogue)
plot_recurrence_model(input_model, catalogue, completeness,
input_model.bin_width, figure_size, filename,
filetype, dpi)
def plot_recurrence_models(
configs, area, slip, msr, rake,
shear_modulus=30.0, disp_length_ratio=1.25E-5, msr_sigma=0.,
filename=None, filetype='png', dpi=300):
"""
Plots a set of recurrence models
:param list configs:
List of configuration dictionaries
"""
plt.figure(figsize=DEFAULT_SIZE)
for config in configs:
model = RecurrenceBranch(area, slip, msr, rake, shear_modulus,
disp_length_ratio, msr_sigma, weight=1.0)
model.get_recurrence(config)
occurrence = model.recurrence.occur_rates
cumulative = np.array([np.sum(occurrence[iloc:])
for iloc in range(0, len(occurrence))])
if 'AndersonLuco' in config['Model_Name']:
flt_label = config['Model_Name'] + ' - ' + config['Model_Type'] +\
' Type'
else:
flt_label = config['Model_Name']
flt_color = np.random.uniform(0.1, 1.0, 3)
plt.semilogy(model.magnitudes, cumulative, '-', label=flt_label,
color=flt_color, linewidth=2.)
plt.semilogy(model.magnitudes, model.recurrence.occur_rates, '--',
color=flt_color, linewidth=2.)
plt.xlabel('Magnitude', fontsize=14)
plt.ylabel('Annual Rate', fontsize=14)
plt.legend(bbox_to_anchor=(1.1, 1.0))
_save_image(filename, filetype, dpi)
def plot_trunc_gr_model(aval, bval, min_mag, max_mag, dmag, catalogue=None,
completeness=None, figure_size=None, filename=None, filetype='png',
dpi=300):
"""
Plots a Gutenberg-Richter model
"""
input_model = TruncatedGRMFD(min_mag, max_mag, dmag, aval, bval)
if not catalogue:
# Plot only the modelled recurrence
annual_rates, cumulative_rates = _get_recurrence_model(input_model)
plt.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
plt.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
plt.xlabel('Magnitude', fontsize='large')
plt.ylabel('Annual Rate', fontsize='large')
plt.legend(['Incremental Rate', 'Cumulative Rate'])
_save_image(filename, filetype, dpi)
else:
completeness = _check_completeness_table(completeness, catalogue)
plot_recurrence_model(input_model,
catalogue,
completeness,
input_model.bin_width,
figure_size,
filename,
filetype,
dpi)
def plot_recurrence_model(input_model, catalogue, completeness, dmag,
figure_size=(10, 8), filename=None, filetype='png', dpi=300):
"""
Plot a calculated recurrence model over an observed catalogue, adjusted for
time-varying completeness
"""
if figure_size is None:
figure_size=(10, 8)
if dmag is None:
dmag = 0.1
annual_rates, cumulative_rates = _get_recurrence_model(input_model)
# Get observed annual recurrence
if not catalogue.end_year:
catalogue.update_end_year()
cent_mag, t_per, n_obs = get_completeness_counts(catalogue,
completeness,
dmag)
obs_rates = n_obs / t_per
cum_obs_rates = np.array([np.sum(obs_rates[i:])
for i in range(len(obs_rates))])
# Create plot
plt.figure(figsize=figure_size)
plt.semilogy(cent_mag, obs_rates, 'bo')
plt.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
plt.semilogy(cent_mag, cum_obs_rates, 'rs')
plt.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
plt.grid(which='both')
plt.xlabel('Magnitude', fontsize='16')
plt.ylabel('Annual Rate', fontsize='16')
plt.legend(['Observed Incremental Rate',
'Model Incremental Rate',
'Observed Cumulative Rate',
'Model Cumulative Rate'], fontsize=14)
plt.tick_params(labelsize=12)
_save_image(filename, filetype, dpi)
def plot_trunc_gr_model(
aval, bval, min_mag, max_mag, dmag,
catalogue=None, completeness=None, filename=None,
figure_size=(8, 6), filetype='png', dpi=300, ax=None):
"""
Plots a Gutenberg-Richter model
"""
input_model = TruncatedGRMFD(min_mag, max_mag, dmag, aval, bval)
if not catalogue:
# Plot only the modelled recurrence
annual_rates, cumulative_rates = _get_recurrence_model(input_model)
if ax is None:
fig, ax = plt.subplots(figsize=figure_size)
else:
fig = ax.get_figure()
ax.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
ax.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
ax.xlabel('Magnitude')
ax.set_ylabel('Annual Rate')
ax.set_legend(['Incremental Rate', 'Cumulative Rate'])
_save_image(fig, filename, filetype, dpi)
else:
completeness = _check_completeness_table(completeness, catalogue)
plot_recurrence_model(
input_model, catalogue, completeness, dmag, filename=filename,
figure_size=figure_size, filetype=filetype, dpi=dpi, ax=ax)
def plot_recurrence_model(input_model, catalogue, completeness, dmag,
figure_size=(10, 8), filename=None, filetype='png', dpi=300):
"""
Plot a calculated recurrence model over an observed catalogue, adjusted for
time-varying completeness
"""
if figure_size is None:
figure_size = (10, 8)
if dmag is None:
dmag = 0.1
annual_rates, cumulative_rates = _get_recurrence_model(input_model)
# Get observed annual recurrence
if not catalogue.end_year:
catalogue.update_end_year()
cent_mag, t_per, n_obs = get_completeness_counts(catalogue,
completeness,
dmag)
obs_rates = n_obs / t_per
cum_obs_rates = np.array([np.sum(obs_rates[i:])
for i in range(len(obs_rates))])
# Create plot
plt.figure(figsize=figure_size)
plt.semilogy(cent_mag, obs_rates, 'bo')
plt.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
plt.semilogy(cent_mag, cum_obs_rates, 'rs')
plt.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
plt.grid(which='both')
plt.xlabel('Magnitude', fontsize='16')
plt.ylabel('Annual Rate', fontsize='16')
plt.legend(['Observed Incremental Rate',
'Model Incremental Rate',
'Observed Cumulative Rate',
'Model Cumulative Rate'], fontsize=14)
plt.tick_params(labelsize=12)
_save_image(filename, filetype, dpi)
def plot_recurrence_models(configs,
area,
slip,
msr,
rake,
shear_modulus=30.0,
disp_length_ratio=1.25E-5,
msr_sigma=0.,
figure_size=(8, 6),
filename=None,
filetype='png',
dpi=300,
ax=None):
"""
Plots a set of recurrence models
:param list configs:
List of configuration dictionaries
"""
if ax is None:
fig, ax = plt.subplots(figsize=figure_size)
else:
fig = ax.get_figure()
for config in configs:
model = RecurrenceBranch(area,
slip,
msr,
rake,
shear_modulus,
disp_length_ratio,
msr_sigma,
weight=1.0)
model.get_recurrence(config)
occurrence = model.recurrence.occur_rates
cumulative = np.array(
[np.sum(occurrence[iloc:]) for iloc in range(0, len(occurrence))])
if 'AndersonLuco' in config['Model_Name']:
flt_label = config['Model_Name'] + ' - ' + config['Model_Type'] +\
' Type'
else:
flt_label = config['Model_Name']
flt_color = np.random.uniform(0.1, 1.0, 3)
ax.semilogy(model.magnitudes,
cumulative,
'-',
label=flt_label,
color=flt_color,
linewidth=2.)
ax.semilogy(model.magnitudes,
model.recurrence.occur_rates,
'--',
color=flt_color,
linewidth=2.)
ax.set_xlabel('Magnitude')
ax.set_ylabel('Annual Rate')
ax.legend(bbox_to_anchor=(1.1, 1.0))
_save_image(fig, filename, filetype, dpi)
def plot_cumulative_moment(year,
mag,
figure_size=(8, 6),
filename=None,
filetype='png',
dpi=300,
ax=None):
'''Calculation of Mmax using aCumulative Moment approach, adapted from
the cumulative strain energy method of Makropoulos & Burton (1983)
:param year: Year of Earthquake
:type year: numpy.ndarray
:param mag: Magnitude of Earthquake
:type mag: numpy.ndarray
:keyword iplot: Include cumulative moment plot
:type iplot: Boolean
:return mmax: Returns Maximum Magnitude
:rtype mmax: Float
'''
# Calculate seismic moment
m_o = 10.**(9.05 + 1.5 * mag)
year_range = np.arange(np.min(year), np.max(year) + 1, 1)
nyr = np.int(np.shape(year_range)[0])
morate = np.zeros(nyr, dtype=float)
# Get moment release per year
for loc, tyr in enumerate(year_range):
idx = np.abs(year - tyr) < 1E-5
if np.sum(idx) > 0:
# Some moment release in that year
morate[loc] = np.sum(m_o[idx])
ave_morate = np.sum(morate) / float(nyr)
# Average moment rate vector
exp_morate = np.cumsum(ave_morate * np.ones(nyr))
if ax is None:
fig, ax = plt.subplots(figsize=figure_size)
else:
fig = ax.get_figure()
ax.step(year_range, np.cumsum(morate), 'b-', linewidth=2)
ax.plot(year_range, exp_morate, 'r-', linewidth=2)
# Get offsets
upper_morate = exp_morate + (np.max(np.cumsum(morate) - exp_morate))
lower_morate = exp_morate + (np.min(np.cumsum(morate) - exp_morate))
ax.plot(year_range, upper_morate, 'r--', linewidth=1)
ax.plot(year_range, lower_morate, 'r--', linewidth=1)
ax.axis([np.min(year), np.max(year), 0.0, np.sum(morate)])
_save_image(fig, filename, filetype, dpi)
def plot_cumulative_moment(year, mag, figure_size=(8, 6),
filename=None, filetype='png', dpi=300, ax=None):
'''Calculation of Mmax using aCumulative Moment approach, adapted from
the cumulative strain energy method of Makropoulos & Burton (1983)
:param year: Year of Earthquake
:type year: numpy.ndarray
:param mag: Magnitude of Earthquake
:type mag: numpy.ndarray
:keyword iplot: Include cumulative moment plot
:type iplot: Boolean
:return mmax: Returns Maximum Magnitude
:rtype mmax: Float
'''
# Calculate seismic moment
m_o = 10. ** (9.05 + 1.5 * mag)
year_range = np.arange(np.min(year), np.max(year) + 1, 1)
nyr = np.int(np.shape(year_range)[0])
morate = np.zeros(nyr, dtype=float)
# Get moment release per year
for loc, tyr in enumerate(year_range):
idx = np.abs(year - tyr) < 1E-5
if np.sum(idx) > 0:
# Some moment release in that year
morate[loc] = np.sum(m_o[idx])
ave_morate = np.sum(morate) / float(nyr)
# Average moment rate vector
exp_morate = np.cumsum(ave_morate * np.ones(nyr))
if ax is None:
fig, ax = plt.subplots(figsize=figure_size)
else:
fig = ax.get_figure()
ax.step(year_range, np.cumsum(morate), 'b-', linewidth=2)
ax.plot(year_range, exp_morate, 'r-', linewidth=2)
# Get offsets
upper_morate = exp_morate + (np.max(np.cumsum(morate) - exp_morate))
lower_morate = exp_morate + (np.min(np.cumsum(morate) - exp_morate))
ax.plot(year_range, upper_morate, 'r--', linewidth=1)
ax.plot(year_range, lower_morate, 'r--', linewidth=1)
ax.axis([np.min(year), np.max(year), 0.0, np.sum(morate)])
_save_image(fig, filename, filetype, dpi)
