def plot_waterxml_timeseries_comparison(waterxml_tree1,
waterxml_tree2,
is_visible=False,
save_path=None):
"""
Compare each timeseries for 2 WATER \*.xml files.
Parameters
----------
waterxml_data1 : dictionary
A dictionary containing data found in WATER \*.xml data file.
waterxml_data2 : dictionary
A dictionary containing data found in WATER \*.xml data file.
is_visible : bool
Boolean value to show plots
save_path : string
String path to save plot(s)
"""
project1, study1, simulation1 = waterxml.get_xml_data(
waterxml_tree=waterxml_tree1)
project2, study2, simulation2 = waterxml.get_xml_data(
waterxml_tree=waterxml_tree2)
assert len(simulation1["SimulID"]) == len(
simulation2["SimulID"]
), "The lengths of the number of SimulID's between the 2 xml files are not equal."
for i in range(len(simulation1["SimulID"])):
for timeseries_str in [
"StudyUnitDischargeSeries", "ClimaticPrecipitationSeries",
"ClimaticTemperatureSeries"
]:
region_type1 = simulation1["RegionType"][i]
sim_id1 = simulation1["SimulID"][i]
region_type2 = simulation2["RegionType"][i]
sim_id2 = simulation2["SimulID"][i]
# get the dates, values, and units - these are lists each of which contain arrays corresponding to each SimulID
dates1, values1, units1 = waterxml.get_timeseries_data(
simulation_dict=simulation1, timeseries_key=timeseries_str)
dates2, values2, units2 = waterxml.get_timeseries_data(
simulation_dict=simulation2, timeseries_key=timeseries_str)
assert region_type1 == region_type2, "Region Types {} and {} are not equal".format(
region_type1, region_type2)
assert sim_id1 == sim_id2, "Simulation Id's {} and {} are not equal".format(
sim_id1, sim_id2)
assert units1[i] == units2[
i], "Units {} and {} are not equal".format(
units1[i], units2[i])
assert len(dates1[i]) == len(
dates2[i]), "Length of dates {} and {} are not equal".format(
dates1[i], dates2[i])
assert len(values1[i]) == len(
values2[i]), "Length of values {} and {} are not equal".format(
values1[i], values2[i])
fig = plt.figure(figsize=(12, 10))
ax1 = fig.add_subplot(211)
ax1.grid(True)
if timeseries_str == "StudyUnitDischargeSeries":
ylabel = "Discharge ({})".format(units1[i])
color_str1 = "b"
color_str2 = "r"
elif timeseries_str == "ClimaticPrecipitationSeries":
ylabel = "Precipitation ({})".format(units1[i])
color_str1 = "SkyBlue"
color_str2 = "r"
elif timeseries_str == "ClimaticTemperatureSeries":
ylabel = "Temperature ({})".format(units1[i])
color_str1 = "orange"
color_str2 = "r"
else:
ylabel = "Some other parameter"
color_str1 = "k"
color_str2 = "r"
ax1.set_title("{}\nRegion Type: {}\nSimulation ID: {}".format(
timeseries_str, region_type1, sim_id1))
ax1.set_xlabel("Date")
ax1.set_ylabel(ylabel)
ax1.plot(dates1[i],
values1[i],
color=color_str1,
label=ylabel + " " + project1["ProjName"],
linewidth=2)
ax1.hold(True)
ax1.plot(dates2[i],
values2[i],
color=color_str2,
label=ylabel + " " + project2["ProjName"],
linewidth=2,
alpha=0.6)
# increase y axis to have text and legend show up better
curr_ylim = ax1.get_ylim()
ax1.set_ylim((curr_ylim[0], curr_ylim[1] * 1.5))
# rotate and align the tick labels so they look better
fig.autofmt_xdate()
# use a more precise date string for the x axis locations in the
# toolbar
ax1.fmt_xdata = mdates.DateFormatter("%Y-%m-%d")
# legend; make it transparent
handles1, labels1 = ax1.get_legend_handles_labels()
legend1 = ax1.legend(handles1, labels1, fancybox=True)
legend1.get_frame().set_alpha(0.5)
legend1.draggable(state=True)
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text1 = "mean = %.2f\nmax = %.2f\nmin = %.2f\n---\nmean = %.2f\nmax = %.2f\nmin = %.2f" % (
np.nanmean(values1[i]), np.nanmax(
values1[i]), np.nanmin(values1[i]), np.nanmean(values2[i]),
np.nanmax(values2[i]), np.nanmin(values2[i]))
patch_properties = {
"boxstyle": "round",
"facecolor": "wheat",
"alpha": 0.5
}
ax1.text(0.05,
0.95,
text1,
transform=ax1.transAxes,
fontsize=14,
verticalalignment="top",
horizontalalignment="left",
bbox=patch_properties)
# plot difference = values_b - values_a
ax2 = fig.add_subplot(212, sharex=ax1)
ax2.grid(True)
ax2.set_title('Difference: ' + timeseries_str)
ax2.set_xlabel('Date')
ax2.set_ylabel('Difference' + ' (' + units1[i] + ')')
diff = values2[i] - values1[i]
ax2.plot(dates1[i], diff, color='k', linewidth=2)
# increase y axis to have text and legend show up better
curr_ylim2 = ax2.get_ylim()
ax2.set_ylim((curr_ylim2[0], curr_ylim2[1] * 1.5))
# use a more precise date string for the x axis locations in the toolbar
ax2.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text2 = "mean = %.2f\nmax = %.2f\nmin = %.2f\n" % (
np.nanmean(diff), np.nanmax(diff), np.nanmin(diff))
ax2.text(0.05,
0.95,
text2,
transform=ax2.transAxes,
fontsize=14,
verticalalignment="top",
horizontalalignment="left",
bbox=patch_properties)
# rotate and align the tick labels so they look better; note that ax2 will
# have the dates, but ax1 will not. do not need to rotate each individual axis
# because this method does it
fig.autofmt_xdate()
# save plots
if save_path:
# set the size of the figure to be saved
curr_fig = plt.gcf()
curr_fig.set_size_inches(12, 10)
# split the parameter name to not include units because some units contain / character which Python interprets as an escape character
filename = "-".join([
project1["ProjName"], "vs", project2["ProjName"],
timeseries_str, region_type1, sim_id1
]) + ".png"
filepath = os.path.join(save_path, filename)
plt.savefig(filepath, dpi=100)
# show plots
if is_visible:
plt.show()
else:
plt.close()
def plot_waterxml_timeseries_comparison(waterxml_tree1, waterxml_tree2, is_visible = False, save_path = None):
"""
Compare each timeseries for 2 WATER \*.xml files.
Parameters
----------
waterxml_data1 : dictionary
A dictionary containing data found in WATER \*.xml data file.
waterxml_data2 : dictionary
A dictionary containing data found in WATER \*.xml data file.
is_visible : bool
Boolean value to show plots
save_path : string
String path to save plot(s)
"""
project1, study1, simulation1 = waterxml.get_xml_data(waterxml_tree = waterxml_tree1)
project2, study2, simulation2 = waterxml.get_xml_data(waterxml_tree = waterxml_tree2)
assert len(simulation1["SimulID"]) == len(simulation2["SimulID"]), "The lengths of the number of SimulID's between the 2 xml files are not equal."
for i in range(len(simulation1["SimulID"])):
for timeseries_str in ["StudyUnitDischargeSeries", "ClimaticPrecipitationSeries", "ClimaticTemperatureSeries"]:
region_type1 = simulation1["RegionType"][i]
sim_id1 = simulation1["SimulID"][i]
region_type2 = simulation2["RegionType"][i]
sim_id2 = simulation2["SimulID"][i]
# get the dates, values, and units - these are lists each of which contain arrays corresponding to each SimulID
dates1, values1, units1 = waterxml.get_timeseries_data(simulation_dict = simulation1, timeseries_key = timeseries_str)
dates2, values2, units2 = waterxml.get_timeseries_data(simulation_dict = simulation2, timeseries_key = timeseries_str)
assert region_type1 == region_type2, "Region Types {} and {} are not equal".format(region_type1, region_type2)
assert sim_id1 == sim_id2, "Simulation Id's {} and {} are not equal".format(sim_id1, sim_id2)
assert units1[i] == units2[i], "Units {} and {} are not equal".format(units1[i], units2[i])
assert len(dates1[i]) == len(dates2[i]), "Length of dates {} and {} are not equal".format(dates1[i], dates2[i])
assert len(values1[i]) == len(values2[i]), "Length of values {} and {} are not equal".format(values1[i], values2[i])
fig = plt.figure(figsize=(12,10))
ax1 = fig.add_subplot(211)
ax1.grid(True)
if timeseries_str == "StudyUnitDischargeSeries":
ylabel = "Discharge ({})".format(units1[i])
color_str1 = "b"
color_str2 = "r"
elif timeseries_str == "ClimaticPrecipitationSeries":
ylabel = "Precipitation ({})".format(units1[i])
color_str1 = "SkyBlue"
color_str2 = "r"
elif timeseries_str == "ClimaticTemperatureSeries":
ylabel = "Temperature ({})".format(units1[i])
color_str1 = "orange"
color_str2 = "r"
else:
ylabel = "Some other parameter"
color_str1 = "k"
color_str2 = "r"
ax1.set_title("{}\nRegion Type: {}\nSimulation ID: {}".format(timeseries_str, region_type1, sim_id1))
ax1.set_xlabel("Date")
ax1.set_ylabel(ylabel)
ax1.plot(dates1[i], values1[i], color = color_str1, label = ylabel + " " + project1["ProjName"], linewidth = 2)
ax1.hold(True)
ax1.plot(dates2[i], values2[i], color = color_str2, label = ylabel + " " + project2["ProjName"], linewidth = 2, alpha = 0.6)
# increase y axis to have text and legend show up better
curr_ylim = ax1.get_ylim()
ax1.set_ylim((curr_ylim[0], curr_ylim[1] * 1.5))
# rotate and align the tick labels so they look better
fig.autofmt_xdate()
# use a more precise date string for the x axis locations in the
# toolbar
ax1.fmt_xdata = mdates.DateFormatter("%Y-%m-%d")
# legend; make it transparent
handles1, labels1 = ax1.get_legend_handles_labels()
legend1 = ax1.legend(handles1, labels1, fancybox = True)
legend1.get_frame().set_alpha(0.5)
legend1.draggable(state=True)
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text1 = "mean = %.2f\nmax = %.2f\nmin = %.2f\n---\nmean = %.2f\nmax = %.2f\nmin = %.2f" % (np.nanmean(values1[i]), np.nanmax(values1[i]), np.nanmin(values1[i]), np.nanmean(values2[i]), np.nanmax(values2[i]), np.nanmin(values2[i]))
patch_properties = {"boxstyle": "round", "facecolor": "wheat", "alpha": 0.5}
ax1.text(0.05, 0.95, text1, transform = ax1.transAxes, fontsize = 14,
verticalalignment = "top", horizontalalignment = "left", bbox = patch_properties)
# plot difference = values_b - values_a
ax2 = fig.add_subplot(212, sharex = ax1)
ax2.grid(True)
ax2.set_title('Difference: ' + timeseries_str)
ax2.set_xlabel('Date')
ax2.set_ylabel('Difference' + ' (' + units1[i] + ')')
diff = values2[i] - values1[i]
ax2.plot(dates1[i], diff, color = 'k', linewidth = 2)
# increase y axis to have text and legend show up better
curr_ylim2 = ax2.get_ylim()
ax2.set_ylim((curr_ylim2[0], curr_ylim2[1] * 1.5))
# use a more precise date string for the x axis locations in the toolbar
ax2.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text2 = "mean = %.2f\nmax = %.2f\nmin = %.2f\n" % (np.nanmean(diff), np.nanmax(diff), np.nanmin(diff))
ax2.text(0.05, 0.95, text2, transform = ax2.transAxes, fontsize = 14,
verticalalignment = "top", horizontalalignment = "left", bbox = patch_properties)
# rotate and align the tick labels so they look better; note that ax2 will
# have the dates, but ax1 will not. do not need to rotate each individual axis
# because this method does it
fig.autofmt_xdate()
# save plots
if save_path:
# set the size of the figure to be saved
curr_fig = plt.gcf()
curr_fig.set_size_inches(12, 10)
# split the parameter name to not include units because some units contain / character which Python interprets as an escape character
filename = "-".join([project1["ProjName"], "vs", project2["ProjName"], timeseries_str, region_type1, sim_id1]) + ".png"
filepath = os.path.join(save_path, filename)
plt.savefig(filepath, dpi = 100)
# show plots
if is_visible:
plt.show()
else:
plt.close()
def plot_waterxml_timeseries_data(waterxml_tree,
is_visible=False,
save_path=None):
"""
Plot timeseries data from the WATER \*.xml file. The timeseries data are contained
in the study simulation dictionary. The following timeseries data are plotted:
discharge - from xml element called "StudyUnitDischargeSeries",
precipitation - from xml element called "ClimaticPrecipitationSeries",
temperature = from xml element called "ClimaticTemperatureSeries"
Parameters
----------
waterxml_data : dictionary
A dictionary containing data found in WATER \*.xml data file.
is_visible : bool
Boolean value to show plots
save_path : string
String path to save plot(s)
"""
project, study, simulation = waterxml.get_xml_data(
waterxml_tree=waterxml_tree)
for i in range(len(simulation["SimulID"])):
for timeseries_str in [
"StudyUnitDischargeSeries", "ClimaticPrecipitationSeries",
"ClimaticTemperatureSeries"
]:
region_type = simulation["RegionType"][i]
sim_id = simulation["SimulID"][i]
# get the dates, values, and units - these are lists each of which contain arrays corresponding to each SimulID
dates, values, units = waterxml.get_timeseries_data(
simulation_dict=simulation, timeseries_key=timeseries_str)
fig = plt.figure(figsize=(12, 10))
ax = fig.add_subplot(111)
ax.grid(True)
if timeseries_str == "StudyUnitDischargeSeries":
ylabel = "Discharge ({})".format(units[i])
color_str = "b"
elif timeseries_str == "ClimaticPrecipitationSeries":
ylabel = "Precipitation ({})".format(units[i])
color_str = "SkyBlue"
elif timeseries_str == "ClimaticTemperatureSeries":
ylabel = "Temperature ({})".format(units[i])
color_str = "orange"
else:
ylabel = "Some other parameter"
color_str = "k"
ax.set_title("{}\nRegion Type: {}\nSimulation ID: {}".format(
timeseries_str, region_type, sim_id))
ax.set_xlabel("Date")
ax.set_ylabel(ylabel)
plt.plot(dates[i], values[i], color=color_str, label=ylabel)
# rotate and align the tick labels so they look better
fig.autofmt_xdate()
# use a more precise date string for the x axis locations in the
# toolbar
ax.fmt_xdata = mdates.DateFormatter("%Y-%m-%d")
# legend; make it transparent
handles, labels = ax.get_legend_handles_labels()
legend = ax.legend(handles, labels, fancybox=True)
legend.get_frame().set_alpha(0.5)
legend.draggable(state=True)
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text = "mean = %.2f\nmax = %.2f\nmin = %.2f" % (np.nanmean(
values[i]), np.nanmax(values[i]), min(values[i]))
patch_properties = {
"boxstyle": "round",
"facecolor": "wheat",
"alpha": 0.5
}
ax.text(0.05,
0.95,
text,
transform=ax.transAxes,
fontsize=14,
verticalalignment="top",
horizontalalignment="left",
bbox=patch_properties)
# save plots
if save_path:
# set the size of the figure to be saved
curr_fig = plt.gcf()
curr_fig.set_size_inches(12, 10)
# split the parameter name to not include units because some units contain / character which Python interprets as an escape character
filename = "-".join([
project["UserName"], project["ProjName"], timeseries_str,
region_type, sim_id
]) + ".png"
filepath = os.path.join(save_path, filename)
plt.savefig(filepath, dpi=100)
# show plots
if is_visible:
plt.show()
else:
plt.close()
def plot_waterxml_timeseries_data(waterxml_tree, is_visible = False, save_path = None):
"""
Plot timeseries data from the WATER \*.xml file. The timeseries data are contained
in the study simulation dictionary. The following timeseries data are plotted:
discharge - from xml element called "StudyUnitDischargeSeries",
precipitation - from xml element called "ClimaticPrecipitationSeries",
temperature = from xml element called "ClimaticTemperatureSeries"
Parameters
----------
waterxml_data : dictionary
A dictionary containing data found in WATER \*.xml data file.
is_visible : bool
Boolean value to show plots
save_path : string
String path to save plot(s)
"""
project, study, simulation = waterxml.get_xml_data(waterxml_tree = waterxml_tree)
for i in range(len(simulation["SimulID"])):
for timeseries_str in ["StudyUnitDischargeSeries", "ClimaticPrecipitationSeries", "ClimaticTemperatureSeries"]:
region_type = simulation["RegionType"][i]
sim_id = simulation["SimulID"][i]
# get the dates, values, and units - these are lists each of which contain arrays corresponding to each SimulID
dates, values, units = waterxml.get_timeseries_data(simulation_dict = simulation, timeseries_key = timeseries_str)
fig = plt.figure(figsize=(12,10))
ax = fig.add_subplot(111)
ax.grid(True)
if timeseries_str == "StudyUnitDischargeSeries":
ylabel = "Discharge ({})".format(units[i])
color_str = "b"
elif timeseries_str == "ClimaticPrecipitationSeries":
ylabel = "Precipitation ({})".format(units[i])
color_str = "SkyBlue"
elif timeseries_str == "ClimaticTemperatureSeries":
ylabel = "Temperature ({})".format(units[i])
color_str = "orange"
else:
ylabel = "Some other parameter"
color_str = "k"
ax.set_title("{}\nRegion Type: {}\nSimulation ID: {}".format(timeseries_str, region_type, sim_id))
ax.set_xlabel("Date")
ax.set_ylabel(ylabel)
plt.plot(dates[i], values[i], color = color_str, label = ylabel)
# rotate and align the tick labels so they look better
fig.autofmt_xdate()
# use a more precise date string for the x axis locations in the
# toolbar
ax.fmt_xdata = mdates.DateFormatter("%Y-%m-%d")
# legend; make it transparent
handles, labels = ax.get_legend_handles_labels()
legend = ax.legend(handles, labels, fancybox = True)
legend.get_frame().set_alpha(0.5)
legend.draggable(state=True)
# show text of mean, max, min values on graph; use matplotlib.patch.Patch properies and bbox
text = "mean = %.2f\nmax = %.2f\nmin = %.2f" % (np.nanmean(values[i]), np.nanmax(values[i]), min(values[i]))
patch_properties = {"boxstyle": "round", "facecolor": "wheat", "alpha": 0.5}
ax.text(0.05, 0.95, text, transform = ax.transAxes, fontsize = 14,
verticalalignment = "top", horizontalalignment = "left", bbox = patch_properties)
# save plots
if save_path:
# set the size of the figure to be saved
curr_fig = plt.gcf()
curr_fig.set_size_inches(12, 10)
# split the parameter name to not include units because some units contain / character which Python interprets as an escape character
filename = "-".join([project["UserName"], project["ProjName"], timeseries_str, region_type, sim_id]) + ".png"
filepath = os.path.join(save_path, filename)
plt.savefig(filepath, dpi = 100)
# show plots
if is_visible:
plt.show()
else:
plt.close()