def plot_kres_nom_nom_scatter(self, directory, ylim=None):
plot_scatter(pjoin(directory, "k_res_nominal_scatter.pdf"),
self.get_kres_nom_nom_musamples(),
self.get_times_musamples(),
xlbl="Time",
ylbl="Nominal K Residual (" +
str(int(self.get_musamples_total())) + " Samples)",
title="",
legend=self.get_mu_labels(),
legend_loc="outside",
solid_ylines=[0],
fig_size=(12, 8),
ylim=ylim)
return 0
def plot_kres_nom_nom_scatter_monthlymean(self, directory, ylim=None):
kres_nom_nom_monthlymean, months = self.get_kres_nom_nom_monthlymean()
plot_scatter(pjoin(directory, "k_res_nominal_monthlymean.pdf"),
kres_nom_nom_monthlymean,
months,
xlbl="Time",
ylbl="Nominal K Residual (Monthly Mean)",
title="",
legend=self.get_mu_labels(),
legend_loc="outside",
solid_ylines=[0],
fig_size=(12, 8),
ylim=ylim,
line="-")
return 0
def plot_L1_harm_v_L2_harm_scatter(self, directory):
plot_scatter(pjoin(directory, "L1_vs_L2_harmonised_scatter.pdf"),
self.get_L1_harm_musamples(),
self.get_L2_harm_musamples(),
xlbl="Harmonised Sensor 2 Measurand",
ylbl="Harmonised/Reference Sensor 1 Measurand (" +
str(self.get_musamples_total()) + " Samples)",
title="",
yerr=None,
xerr=None,
legend=self.get_mu_labels(),
legend_loc="outside",
solid_ylines=[0],
fig_size=(12, 8))
return 0
def plot_compare_calibration(self,
directory,
parameter_comparison,
parameter_covariance_matrix_comparison,
verbose=False):
MatchUpSim = self.get_MatchUpSim(n_sim=20, verbose=verbose)
measurand_1 = evaluate_measurand(MatchUpSim)
MatchUpSim.a = parameter_comparison
measurand_2, u_measurand_cov_2 = evaluate_measurand(
MatchUpSim,
parameter_covariance_matrix=parameter_covariance_matrix_comparison)
measurand_diff = measurand_1 - measurand_2
# Just make one plot per sensor
sensors = MatchUpSim.idx['sensors']
plotted_sensors = []
i_mu = 0
while set(plotted_sensors) != set(sensors):
istart = MatchUpSim.idx['cNm'][i_mu]
iend = MatchUpSim.idx['cNm'][i_mu + 1]
pair = MatchUpSim.idx['Im'][i_mu]
for i_pair, n_sensor in enumerate(pair):
sensor_name = sensors[n_sensor]
measurand_1_i = measurand_1[istart:iend, i_pair]
measurand_diff_i = measurand_diff[istart:iend, i_pair]
u_measurand_cov_2_i = u_measurand_cov_2[istart:iend, i_pair]
path = pjoin(directory,
"compare_calibration." + sensor_name + ".pdf")
plot_scatter(
path,
measurand_diff_i,
measurand_1_i,
yerr=2 * u_measurand_cov_2_i,
title="Sampled Radiance Residual - " + str(sensor_name),
ylbl="Radiance Residual, $\Delta L = L_{Data} - L_{Est}$",
xlbl="Data Radiance, $L_{DATA}$",
dash_ylines=[0])
plotted_sensors.append(sensor_name)
i_mu += 1
def plot_Y_x_binned_line(self,
directory,
Y,
x,
x_name,
x_label,
title,
fname,
ylim=None,
mus=None):
"""
Output plot to given directory for the difference between data and estimated k against variable x
:type directory: str
:param directory: directory to write plots to
:type MatchUpData: eopy.matchup.matchupIO.MatchUp
:param MatchUpData: MatchUp data object to plot
:type k_est: numpy.ndarray
:param k_est: estimate of k to plot
:type x: numpy.ndarray
:param x: variable to plot against
:type x_name: str
:param x_name: name for file for x variable
:type x_label: str
:param x_label: label for plotting x variable
:type mus: list
:param mus: match-up series to plot for
"""
# 1. Preamble
# a. Requried data
# - Full range of x-variable
MatchUpData = self.MatchUpData
# 1. Initialise
matchup_labels = self.get_mu_labels()
matchup_fname_suffix = self.get_mu_fname_suffix()
xmin = min(x)
xmax = max(x)
# b. Initialise data lists
averages = []
stds = []
averages_x = []
if mus is None:
mus = list(range(1, max(MatchUpData.idx['n_mu']) + 1))
# 2. Plot by match-up series
for i, mu in enumerate(mus):
# Indices and labels
i1 = MatchUpData.idx["cNm"][mu - 1]
i2 = MatchUpData.idx["cNm"][mu]
sensor_2_name = MatchUpData.idx['sensors'][MatchUpData.idx['Im'][
mu - 1][1]]
mu_fn = matchup_fname_suffix[mu - 1]
# Bin by x for match-up series (match-up series data range)
x_i = x[i1:i2]
x_i_range = [min(x_i), max(x_i)]
averages_i, stds_i, averages_x_i = bin(Y[i1:i2],
x_i,
bins=50,
X_range=x_i_range)
# Plot match-up series data
fname_i = pjoin(
directory, fname + "." + x_name + "_" + sensor_2_name +
"_binned." + mu_fn + ".pdf")
plot_scatter(fname_i,
averages_i,
averages_x_i,
xlbl=x_label + " - " + sensor_2_name,
ylbl=title + " (" + matchup_labels[i] + ")",
title="",
yerr=stds_i,
xerr=None,
txt=None,
line='-',
legend=None,
solid_ylines=[0],
ylim=ylim)
# Bin by x for match-up series (all data range)
averages_i, stds_i, averages_x_i = bin(Y[i1:i2],
x_i,
bins=50,
X_range=[xmin, xmax])
averages.append(averages_i[~isnan(averages_i)])
stds.append(stds_i[~isnan(averages_i)])
averages_x.append(averages_x_i[~isnan(averages_i)])
# 3. Plot all match-up series together
plot_scatter(pjoin(directory,
fname + "." + x_name + "_sensor_2_binned.pdf"),
averages,
averages_x,
xlbl=x_label + " - Sensor 2",
ylbl=title,
title="",
yerr=stds,
xerr=None,
txt=None,
line='-',
legend=matchup_labels,
legend_loc="outside",
solid_ylines=[0],
fig_size=(12, 8),
ylim=ylim)
return 0
def plot_Y_X_binned_line(self,
directory,
Y,
title,
fname,
ylim=None,
mus=None):
MatchUpData = self.MatchUpData
# 1. Initialise
matchup_labels = self.get_mu_labels()
matchup_fname_suffix = self.get_mu_fname_suffix()
if mus is None:
mus = list(range(1, max(MatchUpData.idx['n_mu']) + 1))
# 2. Plot by X by match-up series
# Loop through X
for cov in range(1, max(MatchUpData.idx['sensor_ms']) + 1):
Xmin = self.get_Xmin(cov)
Xmax = self.get_Xmax(cov)
# Initialise data lists
averages = []
stds = []
Xaverages = []
# Plot per match-up series
for i, mu in enumerate(mus):
# Indices and labels
i1 = MatchUpData.idx["cNm"][mu - 1]
i2 = MatchUpData.idx["cNm"][mu]
sensor_2_name = MatchUpData.idx['sensors'][
MatchUpData.idx['Im'][mu - 1][1]]
cov_name = self.get_X_name(cov, sensor_2_name)
cov_label = "$" + self.get_X_label(
cov, sensor_2_name) + "$ (Binned)"
mu_fn = matchup_fname_suffix[mu - 1]
# Plot if sensor 2 of given match-up series has an cov-th X
sensor_2_m = MatchUpData.idx['sensor_ms'][MatchUpData.idx['Im']
[mu - 1][1]]
if cov <= sensor_2_m:
X = MatchUpData.getVariableData(cov, sensor_2_name, mu)
# Bin data by match-up series limits
averages_i, stds_i, Xaverages_i = bin(
Y[i1:i2], X, bins=50, X_range=[min(X), max(X)])
# Plot match-up series
fname_i = pjoin(
directory, fname + "." + cov_name + "_" +
sensor_2_name + "_binned." + mu_fn + ".pdf")
plot_scatter(fname_i,
averages_i,
Xaverages_i,
xlbl=cov_label + " - " + sensor_2_name,
ylbl=title + " (" + matchup_labels[i] + ")",
title="",
yerr=stds_i,
xerr=None,
txt=None,
line='-',
legend=None,
solid_ylines=[0],
ylim=ylim)
# Bin data by full limits
averages_i, stds_i, Xaverages_i = bin(Y[i1:i2],
X,
bins=50,
X_range=[Xmin, Xmax])
averages.append(averages_i[~isnan(averages_i)])
stds.append(stds_i[~isnan(averages_i)])
Xaverages.append(Xaverages_i[~isnan(averages_i)])
# Plot X for all match-up series
plot_scatter(pjoin(directory, fname + "." + cov_name +
"_sensor_2_binned.pdf"),
averages,
Xaverages,
xlbl=cov_label + " - Sensor 2",
ylbl=title,
title="",
yerr=stds,
xerr=None,
txt=None,
line='-',
legend=matchup_labels,
legend_loc="outside",
solid_ylines=[0],
fig_size=(12, 8),
ylim=ylim)