"""This module contains all the plotting methods for PastaStore.
Pastastore comes with a number helpful plotting methods to quickly
visualize time series or the locations of the time series contained in the
store. Plotting time series or data availability is available through the
`plots` attribute of the PastaStore object. Plotting locations of time series
or model statistics on maps is available through the `maps` attribute.
For example, if we have a :class:`pastastore.PastaStore` called `pstore`
linking to an existing database, the plot and map methods are available as
follows::
pstore.plots.oseries()
ax = pstore.maps.oseries()
pstore.maps.add_background_map(ax) # for adding a background map
"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pastas as ps
from matplotlib import patheffects
from matplotlib.collections import LineCollection
from matplotlib.colors import BoundaryNorm, LogNorm
from matplotlib.lines import Line2D
from mpl_toolkits.axes_grid1 import make_axes_locatable
[docs]
class Plots:
"""Plot class for Pastastore.
Allows plotting of time series and data availability.
"""
def __init__(self, pstore):
"""Initialize Plots class for Pastastore.
Parameters
----------
pstore : pastastore.Pastastore
Pastastore object
"""
self.pstore = pstore
[docs]
def _timeseries(
self,
libname,
names=None,
ax=None,
split=False,
figsize=(10, 5),
progressbar=True,
show_legend=True,
labelfunc=None,
legend_kwargs=None,
**kwargs,
):
"""Internal method to plot time series from pastastore.
Parameters
----------
libname : str
name of the library to obtain time series from (oseries
or stresses)
names : list of str, optional
list of time series names to plot, by default None
ax : matplotlib.Axes, optional
pass axes object to plot on existing axes, by default None,
which creates a new figure
split : bool, optional
create a separate subplot for each time series, by default False.
A maximum of 20 time series is supported when split=True.
figsize : tuple, optional
figure size, by default (10, 5)
progressbar : bool, optional
show progressbar when loading time series from store,
by default True
show_legend : bool, optional
show legend, default is True.
labelfunc : callable, optional
function to create custom labels, function should take name of time series
as input
legend_kwargs : dict, optional
additional arguments to pass to legend
Returns
-------
ax : matplotlib.Axes
axes handle
Raises
------
ValueError
split=True is only supported if there are less than 20 time series
to plot.
"""
names = self.pstore.conn._parse_names(names, libname)
if len(names) > 20 and split:
raise ValueError(
"More than 20 time series leads to too many "
"subplots, set split=False."
)
if ax is None:
if split:
_, axes = plt.subplots(len(names), 1, sharex=True, figsize=figsize)
else:
_, axes = plt.subplots(1, 1, figsize=figsize)
else:
axes = ax
tsdict = self.pstore.conn._get_series(
libname, names, progressbar=progressbar, squeeze=False
)
for i, (n, ts) in enumerate(tsdict.items()):
if split and ax is None:
iax = axes[i]
elif ax is None:
iax = axes
else:
iax = ax
if labelfunc is not None:
n = labelfunc(n)
iax.plot(ts.index, ts.squeeze(), label=n, **kwargs)
if split and show_legend:
iax.legend(loc="best", fontsize="x-small")
if not split and show_legend:
if legend_kwargs is None:
legend_kwargs = {}
ncol = legend_kwargs.pop("ncol", 7)
fontsize = legend_kwargs.pop("fontsize", "x-small")
axes.legend(loc=(0, 1), frameon=False, ncol=ncol, fontsize=fontsize)
return axes
[docs]
def oseries(
self,
names=None,
ax=None,
split=False,
figsize=(10, 5),
show_legend=True,
labelfunc=None,
legend_kwargs=None,
**kwargs,
):
"""Plot oseries.
Parameters
----------
names : list of str, optional
list of oseries names to plot, by default None, which loads
all oseries from store
ax : matplotlib.Axes, optional
pass axes object to plot oseries on existing figure,
by default None, in which case a new figure is created
split : bool, optional
create a separate subplot for each time series, by default False.
A maximum of 20 time series is supported when split=True.
figsize : tuple, optional
figure size, by default (10, 5)
show_legend : bool, optional
show legend, default is True.
labelfunc : callable, optional
function to create custom labels, function should take name of time series
as input
legend_kwargs : dict, optional
additional arguments to pass to legend
Returns
-------
ax : matplotlib.Axes
axes handle
"""
return self._timeseries(
"oseries",
names=names,
ax=ax,
split=split,
figsize=figsize,
show_legend=show_legend,
labelfunc=labelfunc,
legend_kwargs=legend_kwargs,
**kwargs,
)
[docs]
def stresses(
self,
names=None,
kind=None,
ax=None,
split=False,
figsize=(10, 5),
show_legend=True,
labelfunc=None,
legend_kwargs=None,
**kwargs,
):
"""Plot stresses.
Parameters
----------
names : list of str, optional
list of oseries names to plot, by default None, which loads
all oseries from store
kind : str, optional
only plot stresses of a certain kind, by default None, which
includes all stresses
ax : matplotlib.Axes, optional
pass axes object to plot oseries on existing figure,
by default None, in which case a new figure is created
split : bool, optional
create a separate subplot for each time series, by default False.
A maximum of 20 time series is supported when split=True.
figsize : tuple, optional
figure size, by default (10, 5)
show_legend : bool, optional
show legend, default is True.
labelfunc : callable, optional
function to create custom labels, function should take name of time series
as input
legend_kwargs : dict, optional
additional arguments to pass to legend
Returns
-------
ax : matplotlib.Axes
axes handle
"""
names = self.pstore.conn._parse_names(names, "stresses")
masknames = self.pstore.stresses.index.isin(names)
stresses = self.pstore.stresses.loc[masknames]
if kind:
mask = stresses["kind"] == kind
names = stresses.loc[mask].index.to_list()
return self._timeseries(
"stresses",
names=names,
ax=ax,
split=split,
figsize=figsize,
show_legend=show_legend,
labelfunc=labelfunc,
legend_kwargs=legend_kwargs,
**kwargs,
)
[docs]
def data_availability(
self,
libname,
names=None,
kind=None,
intervals=None,
ignore=("second", "minute", "14 days"),
ax=None,
cax=None,
normtype="log",
cmap="viridis_r",
set_yticks=False,
figsize=(10, 8),
progressbar=True,
dropna=True,
**kwargs,
):
"""Plot the data-availability for multiple time series in pastastore.
Parameters
----------
libname : str
name of library to get time series from (oseries or stresses)
names : list, optional
specify names in a list to plot data availability for certain
time series
kind : str, optional
if library is stresses, kind can be specified to obtain only
stresses of a specific kind
intervals: dict, optional
A dict with frequencies as keys and number of seconds as values
ignore : list, optional
A list with frequencies in intervals to ignore
ax: matplotlib Axes, optional
pass axes object to plot data availability on existing figure. by
default None, in which case a new figure is created
cax: matplotlib Axes, optional
pass object axes to plot the colorbar on. by default None, which
gives default Maptlotlib behavior
normtype : str, optional
Determines the type of color normalisations, default is 'log'
cmap : str, optional
A reference to a matplotlib colormap
set_yticks : bool, optional
Set the names of the series as yticks
figsize : tuple, optional
The size of the new figure in inches (h,v)
progressbar : bool
Show progressbar
dropna : bool
Do not show NaNs as available data
kwargs : dict, optional
Extra arguments are passed to matplotlib.pyplot.subplots()
Returns
-------
ax : matplotlib Axes
The axes in which the data-availability is plotted
"""
names = self.pstore.conn._parse_names(names, libname)
if libname == "stresses":
masknames = self.pstore.stresses.index.isin(names)
stresses = self.pstore.stresses.loc[masknames]
if kind:
mask = stresses["kind"] == kind
names = stresses.loc[mask].index.to_list()
series = self.pstore.conn._get_series(
libname, names, progressbar=progressbar, squeeze=False
).values()
ax = self._data_availability(
series,
names=names,
intervals=intervals,
ignore=ignore,
ax=ax,
cax=cax,
normtype=normtype,
cmap=cmap,
set_yticks=set_yticks,
figsize=figsize,
dropna=dropna,
**kwargs,
)
return ax
[docs]
@staticmethod
def _data_availability(
series,
names=None,
intervals=None,
ignore=("second", "minute", "14 days"),
ax=None,
cax=None,
normtype="log",
cmap="viridis_r",
set_yticks=False,
figsize=(10, 8),
dropna=True,
**kwargs,
):
"""Plot the data-availability for a list of time series.
Parameters
----------
libname : list of pandas.Series
list of series to plot data availability for
names : list, optional
specify names of series, default is None in which case names
will be taken from series themselves.
kind : str, optional
if library is stresses, kind can be specified to obtain only
stresses of a specific kind
intervals: dict, optional
A dict with frequencies as keys and number of seconds as values
ignore : list, optional
A list with frequencies in intervals to ignore
ax: matplotlib Axes, optional
pass axes object to plot data availability on existing figure. by
default None, in which case a new figure is created
cax: matplotlib Axes, optional
pass object axes to plot the colorbar on. by default None, which
gives default Maptlotlib behavior
normtype : str, optional
Determines the type of color normalisations, default is 'log'
cmap : str, optional
A reference to a matplotlib colormap
set_yticks : bool, optional
Set the names of the series as yticks
figsize : tuple, optional
The size of the new figure in inches (h,v)
progressbar : bool
Show progressbar
dropna : bool
Do not show NaNs as available data
kwargs : dict, optional
Extra arguments are passed to matplotlib.pyplot.subplots()
Returns
-------
ax : matplotlib Axes
The axes in which the data-availability is plotted
"""
# a good colormap is cmap='RdYlGn_r' or 'cubehelix'
if ax is None:
fig, ax = plt.subplots(figsize=figsize, **kwargs)
else:
fig = ax.get_figure()
ax.invert_yaxis()
if intervals is None:
intervals = {
"second": 1,
"minute": 60,
"hour": 60 * 60,
"day": 60 * 60 * 24,
"week": 60 * 60 * 24 * 7,
"14 days": 60 * 60 * 24 * 14,
"month": 60 * 60 * 24 * 31,
"quarter": 60 * 60 * 24 * 31 * 4,
"year": 60 * 60 * 24 * 366,
}
for i in ignore:
if i in intervals:
intervals.pop(i)
bounds = np.array([intervals[i] for i in intervals])
bounds = bounds.astype(float) * (10**9)
labels = intervals.keys()
if normtype == "log":
norm = LogNorm(vmin=bounds[0], vmax=bounds[-1])
else:
norm = BoundaryNorm(boundaries=bounds, ncolors=256)
cmap = plt.get_cmap(cmap, 256)
cmap.set_over((1.0, 1.0, 1.0))
for i, s in enumerate(series):
if not s.empty:
if dropna:
s = s.dropna()
pc = ax.pcolormesh(
s.index,
[i, i + 1],
[np.diff(s.index).astype(float)],
norm=norm,
cmap=cmap,
linewidth=0,
rasterized=True,
)
# make a colorbar in an ax on the
# right side, then set the current axes to ax again
cb = fig.colorbar(pc, ax=ax, cax=cax, extend="both")
cb.set_ticks(bounds)
cb.ax.set_yticklabels(labels)
cb.ax.minorticks_off()
if set_yticks:
ax.set_yticks(np.arange(0.5, len(series) + 0.5), minor=False)
ax.set_yticks(np.arange(0, len(series) + 1), minor=True)
if names is None:
names = [s.name for s in series]
ax.set_yticklabels(names)
for tick in ax.yaxis.get_major_ticks(): # don't show major ytick marker
tick.tick1line.set_visible(False)
ax.grid(True, which="minor", axis="y")
ax.grid(True, which="major", axis="x")
else:
ax.set_ylabel("Timeseries (-)")
ax.grid(True, which="both")
ax.grid(True, which="both")
return ax
[docs]
def cumulative_hist(
self,
statistic="rsq",
modelnames=None,
extend=False,
ax=None,
figsize=(6, 6),
label=None,
legend=True,
):
"""Plot a cumulative step histogram for a model statistic.
Parameters
----------
statistic: str
name of the statistic, e.g. "evp" or "rmse", by default "rsq"
modelnames: list of str, optional
modelnames to plot statistic for, by default None, which
uses all models in the store
extend: bool, optional
force extend the stats Series with a dummy value to move the
horizontal line outside figure bounds. If True the results
are skewed a bit, especially if number of models is low.
ax: matplotlib.Axes, optional
axes to plot histogram, by default None which creates an Axes
figsize: tuple, optional
figure size, by default (6,6)
label: str, optional
label for the legend, by default None, which shows the number
of models
legend: bool, optional
show legend, by default True
Returns
-------
ax : matplotlib Axes
The axes in which the cumulative histogram is plotted
"""
statsdf = self.pstore.get_statistics(
[statistic], modelnames=modelnames, progressbar=False
)
if ax is None:
_, ax = plt.subplots(1, 1, figsize=figsize)
ax.set_xticks(np.linspace(0, 1, 11))
ax.set_xlim(0, 1)
ax.set_ylabel(statistic)
ax.set_xlabel("Density")
ax.set_title("Cumulative Step Histogram")
if statistic == "evp":
ax.set_yticks(np.linspace(0, 100, 11))
if extend:
statsdf = statsdf.append(pd.Series(100, index=["dummy"]))
ax.set_ylim(0, 100)
else:
ax.set_ylim(0, statsdf.max())
elif statistic in ("rsq", "nse", "kge_2012"):
ax.set_yticks(np.linspace(0, 1, 11))
if extend:
statsdf = statsdf.append(pd.Series(1, index=["dummy"]))
statsdf[statsdf < 0] = 0
ax.set_ylim(0, 1)
else:
ax.set_ylim(0, statsdf.max())
elif statistic in ("aic", "bic"):
ax.set_ylim(statsdf.min(), statsdf.max())
else:
if extend:
statsdf = statsdf.append(pd.Series(0, index=["dummy"]))
ax.set_ylim(0, statsdf.max())
if label is None:
if extend:
label = f"No. Models = {len(statsdf)-1}"
else:
label = f"No. Models = {len(statsdf)}"
statsdf.hist(
ax=ax,
bins=len(statsdf),
density=True,
cumulative=True,
histtype="step",
orientation="horizontal",
label=label,
)
if legend:
ax.legend(loc=4)
return ax
[docs]
def compare_models(self, modelnames, ax=None, **kwargs):
models = self.pstore.get_models(modelnames)
names = []
onames = [iml.oseries.name for iml in models]
if len(np.unique(onames)) == 1:
for modelname in modelnames:
if onames[0] in modelname:
names.append(modelname.replace(onames[0], ""))
else:
names.append(modelname)
else:
names = modelnames
cm = ps.CompareModels(models, names=names)
cm.plot(**kwargs)
return cm
[docs]
class Maps:
"""Map Class for PastaStore.
Allows plotting locations and model statistics on maps.
Usage
-----
Example usage of the maps methods: :
>> > ax = pstore.maps.oseries() # plot oseries locations
>> > pstore.maps.add_background_map(ax) # add background map
"""
def __init__(self, pstore):
"""Initialize Plots class for Pastastore.
Parameters
----------
pstore: pastastore.Pastastore
Pastastore object
"""
self.pstore = pstore
[docs]
def stresses(
self,
names=None,
kind=None,
extent=None,
labels=True,
adjust=False,
figsize=(10, 8),
backgroundmap=False,
label_kwargs=None,
**kwargs,
):
"""Plot stresses locations on map.
Parameters
----------
names : list of str, optional
list of names to plot
kind: str, optional
if passed, only plot stresses of a specific kind, default is None
which plots all stresses.
extent : list of float, optional
plot only stresses within extent [xmin, xmax, ymin, ymax]
labels: bool, optional
label models, by default True
adjust: bool, optional
automated smart label placement using adjustText, by default False
ax : matplotlib.Axes, optional
axes handle, if not provided a new figure is created.
figsize: tuple, optional
figure size, by default(10, 8)
backgroundmap: bool, optional
if True, add background map (default CRS is EPSG:28992) with default tiles
by OpenStreetMap.Mapnik. Default option is False.
label_kwargs: dict, optional
dictionary with keyword arguments to pass to add_labels method
Returns
-------
ax: matplotlib.Axes
axes object
See also
--------
self.add_background_map
"""
names = self.pstore.conn._parse_names(names, "stresses")
if extent is not None:
names = self.pstore.within(extent, names=names, libname="stresses")
df = self.pstore.stresses.loc[names]
if kind is not None:
if isinstance(kind, str):
kind = [kind]
mask = df["kind"].isin(kind)
stresses = df[mask]
else:
stresses = df
mask0 = (stresses["x"] != 0.0) | (stresses["y"] != 0.0)
if "c" in kwargs:
c = kwargs.pop("c", None)
else:
c = stresses.loc[mask0, "kind"]
kind_to_color = {k: f"C{i}" for i, k in enumerate(c.unique())}
c = c.apply(lambda k: kind_to_color[k])
r = self._plotmap_dataframe(stresses.loc[mask0], c=c, figsize=figsize, **kwargs)
if "ax" in kwargs:
ax = kwargs["ax"]
else:
ax = r
if labels:
if label_kwargs is None:
label_kwargs = {}
self.add_labels(stresses, ax, adjust=adjust, **label_kwargs)
if backgroundmap:
self.add_background_map(ax)
return ax
[docs]
def oseries(
self,
names=None,
extent=None,
labels=True,
adjust=False,
figsize=(10, 8),
backgroundmap=False,
label_kwargs=None,
**kwargs,
):
"""Plot oseries locations on map.
Parameters
----------
names: list, optional
oseries names, by default None which plots all oseries locations
extent : list of float, optional
plot only oseries within extent [xmin, xmax, ymin, ymax]
labels: bool or str, optional
label models, by default True, if passed as "grouped", only the first
label for each x,y-location is shown.
adjust: bool, optional
automated smart label placement using adjustText, by default False
figsize: tuple, optional
figure size, by default(10, 8)
backgroundmap: bool, optional
if True, add background map (default CRS is EPSG:28992) with default tiles
by OpenStreetMap.Mapnik. Default option is False.
label_kwargs: dict, optional
dictionary with keyword arguments to pass to add_labels method
Returns
-------
ax: matplotlib.Axes
axes object
See also
--------
self.add_background_map
"""
names = self.pstore.conn._parse_names(names, "oseries")
if extent is not None:
names = self.pstore.within(extent, names=names)
oseries = self.pstore.oseries.loc[names]
mask0 = (oseries["x"] != 0.0) | (oseries["y"] != 0.0)
r = self._plotmap_dataframe(oseries.loc[mask0], figsize=figsize, **kwargs)
if "ax" in kwargs:
ax = kwargs["ax"]
else:
ax = r
if labels:
if label_kwargs is None:
label_kwargs = {}
if labels == "grouped":
gr = oseries.sort_index().reset_index().groupby(["x", "y"])
oseries = oseries.loc[gr["index"].first().tolist()]
self.add_labels(oseries, ax, adjust=adjust, **label_kwargs)
if backgroundmap:
self.add_background_map(ax)
return ax
[docs]
def models(
self, labels=True, adjust=False, figsize=(10, 8), backgroundmap=False, **kwargs
):
"""Plot model locations on map.
Parameters
----------
labels: bool, optional
label models, by default True
adjust: bool, optional
automated smart label placement using adjustText, by default False
ax : matplotlib.Axes, optional
axes handle, if not provided a new figure is created.
figsize: tuple, optional
figure size, by default(10, 8)
backgroundmap: bool, optional
if True, add background map (default CRS is EPSG:28992) with default tiles
by OpenStreetMap.Mapnik. Default option is False.
Returns
-------
ax: matplotlib.Axes
axes object
See also
--------
self.add_background_map
"""
model_oseries = [
self.pstore.get_models(m, return_dict=True)["oseries"]["name"]
for m in self.pstore.model_names
]
models = self.pstore.oseries.loc[model_oseries]
models.index = self.pstore.model_names
# mask out 0.0 coordinates
mask0 = (models["x"] != 0.0) | (models["y"] != 0.0)
r = self._plotmap_dataframe(models.loc[mask0], figsize=figsize, **kwargs)
if "ax" in kwargs:
ax = kwargs["ax"]
else:
ax = r
if labels:
self.add_labels(models, ax, adjust=adjust)
if backgroundmap:
self.add_background_map(ax)
return ax
[docs]
def modelstat(
self,
statistic,
modelnames=None,
label=True,
adjust=False,
cmap="viridis",
norm=None,
vmin=None,
vmax=None,
figsize=(10, 8),
backgroundmap=False,
**kwargs,
):
"""Plot model statistic on map.
Parameters
----------
statistic: str
name of the statistic, e.g. "evp" or "aic"
modelnames : list of str, optional
list of modelnames to include
label: bool, optional
label points, by default True
adjust: bool, optional
automated smart label placement using adjustText, by default False
cmap: str or colormap, optional
(name of) the colormap, by default "viridis"
norm: norm, optional
normalization for colorbar, by default None
vmin: float, optional
vmin for colorbar, by default None
vmax: float, optional
vmax for colorbar, by default None
ax : matplotlib.Axes, optional
axes handle, if not provided a new figure is created.
figsize: tuple, optional
figuresize, by default(10, 8)
backgroundmap: bool, optional
if True, add background map (default CRS is EPSG:28992) with default tiles
by OpenStreetMap.Mapnik. Default option is False.
Returns
-------
ax: matplotlib.Axes
axes object
See also
--------
self.add_background_map
"""
statsdf = self.pstore.get_statistics(
[statistic], modelnames=modelnames, progressbar=False
).to_frame()
statsdf["oseries"] = [
self.pstore.get_models(m, return_dict=True)["oseries"]["name"]
for m in statsdf.index
]
statsdf = statsdf.reset_index().set_index("oseries")
df = statsdf.join(self.pstore.oseries, how="left")
scatter_kwargs = {
"cmap": cmap,
"norm": norm,
"vmin": vmin,
"vmax": vmax,
"edgecolors": "w",
"linewidths": 0.7,
}
scatter_kwargs.update(kwargs)
ax = self._plotmap_dataframe(
df, column=statistic, figsize=figsize, **scatter_kwargs
)
if label:
df.set_index("index", inplace=True)
self.add_labels(df, ax, adjust=adjust)
if backgroundmap:
self.add_background_map(ax)
return ax
[docs]
@staticmethod
def _plotmap_dataframe(
df,
x="x",
y="y",
column=None,
colorbar=True,
ax=None,
figsize=(10, 8),
**kwargs,
):
"""Internal method for plotting dataframe with point locations.
Can be called directly for more control over plot characteristics.
Parameters
----------
df : pandas.DataFrame
DataFrame containing coordinates and data to plot, with
index providing names for each location
x : str, optional
name of the column with x - coordinate data, by default "x"
y : str, optional
name of the column with y - coordinate data, by default "y"
column : str, optional
name of the column containing data used for determining the
color of each point, by default None (all one color)
colorbar : bool, optional
show colorbar, only if column is provided, by default True
ax : matplotlib Axes
axes handle to plot dataframe, optional, default is None
which creates a new figure
figsize : tuple, optional
figure size, by default(10, 8)
**kwargs :
dictionary containing keyword arguments for ax.scatter,
by default None
Returns
-------
ax : matplotlib.Axes
axes object, returned if ax is None
sc : scatter handle
scatter plot handle, returned if ax is not None
"""
if ax is None:
return_scatter = False
fig, ax = plt.subplots(figsize=figsize)
else:
return_scatter = True
fig = ax.figure
# set default size and marker if not passed
if kwargs:
s = kwargs.pop("s", 70)
marker = kwargs.pop("marker", "o")
else:
s = 70
marker = "o"
kwargs = {}
# if column is passed for coloring pts
if column:
c = df[column]
if "cmap" not in kwargs:
kwargs["cmap"] = "viridis"
else:
c = kwargs.pop("c", None)
sc = ax.scatter(df[x], df[y], marker=marker, s=s, c=c, **kwargs)
# add colorbar
if column and colorbar:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.05)
cbar = fig.colorbar(sc, ax=ax, cax=cax)
cbar.set_label(column)
# set axes properties
ax.set_xlabel("x")
ax.set_ylabel("y")
for label in ax.get_yticklabels():
label.set_rotation(90)
label.set_verticalalignment("center")
fig.tight_layout()
if return_scatter:
return sc
else:
return ax
[docs]
def model(
self,
ml,
label=True,
metadata_source="model",
offset=0.0,
ax=None,
figsize=(10, 10),
backgroundmap=False,
):
"""Plot oseries and stresses from one model on a map.
Parameters
----------
ml: str or pastas.Model
pastas model or name of pastas model to plot on map
label: bool, optional, default is True
add labels to points on map
metadata_source: str, optional
whether to obtain metadata from model Timeseries or from
metadata in pastastore("store"), default is "model"
offset : float, optional
add offset to current extent of model time series, useful
for zooming out around models
ax : matplotlib.Axes, optional
axes handle, if not provided a new figure is created.
figsize: tuple, optional
figsize, default is (10, 10)
backgroundmap: bool, optional
if True, add background map (default CRS is EPSG:28992) with default tiles
by OpenStreetMap.Mapnik. Default option is False.
Returns
-------
ax: axes object
axis handle of the resulting figure
See also
--------
self.add_background_map
"""
if isinstance(ml, str):
ml = self.pstore.get_models(ml)
elif not isinstance(ml, ps.Model):
raise TypeError("Pass model name as string or pastas.Model!")
stresses = pd.DataFrame(columns=["x", "y", "stressmodel", "color"])
count = 0
for name, sm in ml.stressmodels.items():
for istress in sm.stress:
if metadata_source == "model":
xi = istress.metadata["x"]
yi = istress.metadata["y"]
elif metadata_source == "store":
imeta = self.pstore.get_metadata(
"stresses", istress.name, as_frame=False
)
xi = imeta.pop("x", np.nan)
yi = imeta.pop("y", np.nan)
else:
raise ValueError(
"metadata_source must be either " "'model' or 'store'!"
)
if np.isnan(xi) or np.isnan(yi):
print(f"No x,y-data for {istress.name}!")
continue
if xi == 0.0 or yi == 0.0:
print(f"x,y-data is 0.0 for {istress.name}, not plotting!")
continue
stresses.loc[istress.name, :] = (xi, yi, name, f"C{count%10}")
count += 1
# create figure
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=figsize)
else:
fig = ax.figure
# add oseries
osize = 50
oserieslabel = ml.oseries.name
if metadata_source == "model":
xm = float(ml.oseries.metadata["x"])
ym = float(ml.oseries.metadata["y"])
elif metadata_source == "store":
ometa = self.pstore.get_metadata("oseries", ml.oseries.name, as_frame=False)
xm = float(ometa.pop("x", np.nan))
ym = float(ometa.pop("y", np.nan))
else:
raise ValueError("metadata_source must be either " "'model' or 'store'!")
po = ax.scatter(xm, ym, s=osize, marker="o", label=oserieslabel, color="k")
legend_list = [po]
# add stresses
ax.scatter(
stresses["x"],
stresses["y"],
s=50,
c=stresses.color,
marker="o",
edgecolors="k",
linewidths=0.75,
)
# label oseries
if label:
stroke = [patheffects.withStroke(linewidth=3, foreground="w")]
txt = ax.annotate(
text=oserieslabel,
xy=(xm, ym),
fontsize=8,
textcoords="offset points",
xytext=(10, 10),
)
txt.set_path_effects(stroke)
# get legend entries for stressmodels
uniques = stresses.loc[:, ["stressmodel", "color"]].drop_duplicates(
keep="first"
)
for name, row in uniques.iterrows():
(h,) = ax.plot(
[],
[],
marker="o",
label=row.stressmodel,
ls="",
mec="k",
ms=10,
color=row.color,
)
legend_list.append(h)
# add legend
ax.legend(legend_list, [i.get_label() for i in legend_list], loc="best")
# set axes properties
ax.set_xlabel("x")
ax.set_ylabel("y")
for label in ax.get_yticklabels():
label.set_rotation(90)
label.set_verticalalignment("center")
if offset > 0.0:
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
ax.set_xlim(xmin - offset, xmax + offset)
ax.set_ylim(ymin - offset, ymax + offset)
# label stresses
if label:
for name, row in stresses.iterrows():
namestr = str(name)
namestr += f"\n({row.stressmodel})"
txt = ax.annotate(
text=namestr,
xy=(row.x, row.y),
fontsize=8,
textcoords="offset points",
xytext=(10, 10),
)
txt.set_path_effects(stroke)
if backgroundmap:
self.add_background_map(ax)
fig.tight_layout()
return ax
[docs]
def stresslinks(
self,
kinds=None,
model_names=None,
color_lines=False,
alpha=0.4,
ax=None,
figsize=(10, 8),
legend=True,
labels=False,
adjust=False,
backgroundmap=False,
):
"""Create a map linking models with their stresses.
Parameters
----------
kinds: list, optional
kinds of stresses to plot, defaults to None, which selects
all kinds.
model_names: list, optional
list of model names to plot, substrings of model names
are also accepted, defaults to None, which selects all
models.
color_lines: bool, optional
if True, connecting lines have the same colors as the stresses,
defaults to False, which uses a black line.
alpha: float, optional
alpha value for the connecting lines, defaults to 0.4.
ax : matplotlib.Axes, optional
axes handle, if not provided a new figure is created.
figsize : tuple, optional
figure size, by default (10, 8)
legend: bool, optional
create a legend for all unique kinds, defaults to True.
labels: bool, optional
add labels for stresses and oseries, defaults to False.
adjust: bool, optional
automated smart label placement using adjustText, by
default False
backgroundmap: bool, optional
if True, add background map (default CRS is EPSG:28992) with default tiles
by OpenStreetMap.Mapnik. Default option is False.
Returns
-------
ax: axes object
axis handle of the resulting figure
See also
--------
self.add_background_map
"""
if model_names:
m_idx = self.pstore.search(libname="models", s=model_names)
else:
m_idx = self.pstore.model_names
struct = self.pstore.get_model_timeseries_names(progressbar=False).loc[m_idx]
oseries = self.pstore.oseries
stresses = self.pstore.stresses
skind = stresses.kind.unique()
stused = np.array([])
if kinds is None:
kinds = skind
if ax is None:
_, ax = plt.subplots(figsize=figsize)
segments = []
segment_colors = []
scatter_colors = []
ax.scatter(
oseries.loc[struct["oseries"], "x"],
oseries.loc[struct["oseries"], "y"],
color="C0",
)
for m in struct.index:
os = oseries.loc[struct.loc[m, "oseries"]]
mstresses = struct.loc[m].drop("oseries").dropna().index
st = stresses.loc[mstresses]
for s in mstresses:
if np.isin(st.loc[s, "kind"], kinds):
(c,) = np.where(skind == st.loc[s, "kind"])
if color_lines:
color = f"C{c[0]+1}"
else:
color = "k"
segments.append(
[[os["x"], os["y"]], [st.loc[s, "x"], st.loc[s, "y"]]]
)
segment_colors.append(color)
scatter_colors.append(f"C{c[0]+1}")
stused = np.append(stused, s)
if labels:
self.add_labels(oseries.loc[struct["oseries"].unique()], ax, adjust=adjust)
self.add_labels(stresses.loc[np.unique(stused)], ax, adjust=adjust)
ax.scatter(
[x[1][0] for x in segments],
[y[1][1] for y in segments],
color=scatter_colors,
)
ax.add_collection(
LineCollection(segments, colors=segment_colors, linewidths=0.5, alpha=alpha)
)
if legend:
legend_elements = [
Line2D(
[],
[],
marker="o",
color="w",
markerfacecolor="C0",
label="oseries",
markersize=10,
)
]
for kind in kinds:
(c,) = np.where(skind == kind)
legend_elements.append(
Line2D(
[],
[],
marker="o",
color="w",
markerfacecolor=f"C{c[0]+1}",
label=kind,
markersize=10,
)
)
ax.legend(handles=legend_elements)
if backgroundmap:
self.add_background_map(ax)
return ax
[docs]
@staticmethod
def _list_contextily_providers():
"""List contextily providers.
Taken from contextily notebooks.
Returns
-------
providers : dict
dictionary containing all providers. See keys for names
that can be passed as map_provider arguments.
"""
import contextily as ctx
providers = {}
def get_providers(provider):
if "url" in provider:
providers[provider["name"]] = provider
else:
for prov in provider.values():
get_providers(prov)
get_providers(ctx.providers)
return providers
[docs]
@staticmethod
def add_background_map(
ax, proj="epsg:28992", map_provider="OpenStreetMap.Mapnik", **kwargs
):
"""Add background map to axes using contextily.
Parameters
----------
ax: matplotlib.Axes
axes to add background map to
map_provider: str, optional
name of map provider, see `contextily.providers` for options.
Default is 'OpenStreetMap.Mapnik'
proj: pyproj.Proj or str, optional
projection for background map, default is 'epsg:28992'
(RD Amersfoort, a projection for the Netherlands)
"""
import contextily as ctx
if isinstance(proj, str):
import pyproj
proj = pyproj.Proj(proj)
providers = Maps._list_contextily_providers()
ctx.add_basemap(ax, source=providers[map_provider], crs=proj.srs, **kwargs)
[docs]
@staticmethod
def add_labels(df, ax, adjust=False, objects=None, **kwargs):
"""Add labels to points on plot.
Uses dataframe index to label points.
Parameters
----------
df: pd.DataFrame
DataFrame containing x, y - data. Index is used as label
ax: matplotlib.Axes
axes object to label points on
adjust: bool
automated smart label placement using adjustText
objects : list of matplotlib objects
use to avoid labels overlapping markers
**kwargs:
keyword arguments to ax.annotate or adjusttext
"""
stroke = [patheffects.withStroke(linewidth=3, foreground="w")]
fontsize = kwargs.pop("fontsize", 10)
if adjust:
from adjustText import adjust_text
texts = []
for name, row in df.iterrows():
texts.append(
ax.text(
row["x"],
row["y"],
name,
fontsize=fontsize,
**{"path_effects": stroke},
)
)
adjust_text(
texts,
objects=objects,
force_text=(0.05, 0.10),
**kwargs,
**{
"arrowprops": {
"arrowstyle": "-",
"color": "k",
"alpha": 0.5,
}
},
)
else:
textcoords = kwargs.pop("textcoords", "offset points")
xytext = kwargs.pop("xytext", (10, 10))
for name, row in df.iterrows():
namestr = str(name)
ax.annotate(
text=namestr,
xy=(row["x"], row["y"]),
fontsize=fontsize,
textcoords=textcoords,
xytext=xytext,
**{"path_effects": stroke},
**kwargs,
)