"""This module contains plotting methods for Pastas."""
import logging
from typing import Dict, List, Optional, Tuple, Union
import matplotlib.patheffects as path_effects
import matplotlib.pyplot as plt
import numpy as np
from pandas import DataFrame, Series, Timestamp, concat
from scipy.stats import gaussian_kde, norm, pearsonr, probplot
from pastas.plotting.modelcompare import CompareModels
from pastas.plotting.plotutil import share_xaxes, share_yaxes
from pastas.stats.core import acf as get_acf
from pastas.stats.metrics import evp, rmse
from pastas.typing import ArrayLike, Axes, Figure, Model, TimestampType
logger = logging.getLogger(__name__)
__all__ = ["compare", "series", "acf", "diagnostics", "cum_frequency", "TrackSolve"]
[docs]def compare(
models: List[Model],
names: Optional[List[str]] = None,
adjust_height: bool = True,
**kwargs,
) -> Dict:
"""Plot multiple Pastas models in one figure to visually compare models.
Notes
-----
The models must have the same stressmodel names, otherwise the contributions will
not be plotted, and parameters table will not display nicely.
Parameters
----------
models: list
List of pastas Models, works for N models, but certain things might not
display nicely if the list gets too long.
names : list of str
override model names by passing a list of names
adjust_height: bool, optional
Adjust the height of the graphs, so that the vertical scale of all the
subplots on the left is equal. Default is False, in which case the axes are
not rescaled to include all data, so certain data might not be visible. Set
False to ensure you can see all data.
**kwargs
The kwargs are passed to the CompareModels.plot() function.
Returns
-------
matplotlib.axes
"""
mc = CompareModels(models, names=names)
mc.plot(adjust_height=adjust_height, **kwargs)
return mc.axes
[docs]def series(
head: Optional[Series] = None,
stresses: Optional[List[Series]] = None,
hist: bool = True,
kde: bool = False,
table: bool = False,
titles: bool = True,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
labels: Optional[List[str]] = None,
figsize: tuple = (10, 5),
) -> Axes:
"""Plot all the input time Series in a single plot.
Parameters
----------
head: pd.Series
Pandas time series with DatetimeIndex.
stresses: List of pd.Series
List with Pandas time series with DatetimeIndex.
hist: bool
Histogram for the series. The number of bins is determined with Sturges rule.
kde: bool
Kernel density estimate for the series. The kde is obtained from
scipy.gaussian_kde using scott to calculate the estimator bandwidth. Returns
the number of observations, mean, skew and kurtosis.
table: bool
Show table with some basic statistics such as the number of
observations, mean, skew and kurtosis.
titles: bool
Set the titles or not. Taken from the name attribute of the series.
tmin: str or pd.Timestamp
tmax: str or pd.Timestamp
labels: List of str
List with the labels for each subplot.
figsize: tuple
Set the size of the figure.
Returns
-------
matplotlib.Axes
"""
rows = 0
if head is not None:
rows += 1
if tmin is None:
tmin = head.index[0]
if tmax is None:
tmax = head.index[-1]
if stresses is not None:
rows += len(stresses)
sharex = True
gridspec_kw = {}
cols = 1
if table and not hist and kde:
hist = True
if hist or kde:
sharex = False
gridspec_kw["width_ratios"] = [3, 1]
cols += 1
if table:
cols += 1
gridspec_kw["width_ratios"].append(1)
fig, axes = plt.subplots(
rows,
cols,
figsize=figsize,
sharex=sharex,
sharey="row",
gridspec_kw=gridspec_kw,
)
if rows == 1 and cols == 1:
axes = np.array([[axes]])
elif rows == 1:
axes = axes[np.newaxis]
elif cols == 1:
axes = axes[:, np.newaxis]
if hist:
axes[-1, 1].set_xlabel("Frequency [%]")
if kde:
axes[-1, 1].set_xlabel("Density [-]")
if head is not None:
head = head[tmin:tmax].dropna()
head.plot(ax=axes[0, 0], marker=".", linestyle=" ", color="k")
if titles:
axes[0, 0].set_title(head.name)
if labels is not None:
axes[0, 0].set_ylabel(labels[0])
if hist and kde is False:
head.hist(
ax=axes[0, 1],
orientation="horizontal",
color="k",
weights=np.ones(len(head)) / len(head) * 100,
bins=int(np.ceil(1 + np.log2(len(head)))),
grid=False,
)
if kde and hist:
head.hist(
ax=axes[0, 1],
orientation="horizontal",
color="k",
bins=int(np.ceil(1 + np.log2(len(head)))),
grid=False,
density=True,
)
if kde:
gkde = gaussian_kde(head, bw_method="scott")
sample_range = np.max(head) - np.min(head)
ind = np.linspace(
np.min(head) - 0.1 * sample_range,
np.max(head) + 0.1 * sample_range,
1000,
)
if hist:
colour = "C1"
else:
colour = "k"
axes[0, 1].plot(gkde.evaluate(ind), ind, color=colour)
if table:
# stats table
head_stats = [
["Count", f"{head.count():0.0f}"],
["Mean", f"{head.mean():0.2f}"],
["Max", f"{head.max():0.2f}"],
["Min", f"{head.min():0.2f}"],
["Skew", f"{head.skew():0.2f}"],
["Kurtosis", f"{head.kurtosis():0.2f}"],
]
axes[0, 2].table(
bbox=(0.0, 0.0, 1, 1), colWidths=(1.5, 1), cellText=head_stats
)
axes[0, 2].axis("off")
if stresses is not None:
for i, stress in enumerate(stresses, start=rows - len(stresses)):
stress = stress[tmin:tmax].dropna()
stress.plot(ax=axes[i, 0], color="k")
if titles:
axes[i, 0].set_title(stress.name)
if labels is not None:
axes[i, 0].set_ylabel(labels[i])
if hist:
# histogram
stress.hist(
ax=axes[i, 1],
orientation="horizontal",
color="k",
weights=np.ones(len(stress)) / len(stress) * 100,
bins=int(np.ceil(1 + np.log2(len(stress)))),
grid=False,
)
if kde and hist:
stress.hist(
ax=axes[i, 1],
orientation="horizontal",
color="k",
bins=int(np.ceil(1 + np.log2(len(stress)))),
grid=False,
density=True,
)
if kde:
gkde = gaussian_kde(stress, bw_method="scott")
sample_range = np.max(stress) - np.min(stress)
ind = np.linspace(
np.min(stress) - 0.1 * sample_range,
np.min(stress) + 0.1 * sample_range,
1000,
)
if hist:
colour = "C1"
else:
colour = "k"
axes[i, 1].plot(gkde.evaluate(ind), ind, color=colour)
if table:
if i > 0:
axes[i, 0].sharex(axes[0, 0])
# stats table
stress_stats = [
["Count", f"{stress.count():0.0f}"],
["Mean", f"{stress.mean():0.2f}"],
["Skew", f"{stress.skew():0.2f}"],
["Kurtosis", f"{stress.kurtosis():0.2f}"],
]
axes[i, 2].table(
bbox=(0, 0, 1, 1), colWidths=(1.5, 1), cellText=stress_stats
)
axes[i, 2].axis("off")
# temporary fix, as set_xlim currently does not work with strings mpl=3.6.1
if tmin is not None:
tmin = Timestamp(tmin)
if tmax is not None:
tmax = Timestamp(tmax)
axes[0, 0].set_xlim([tmin, tmax])
axes[0, 0].minorticks_off()
fig.tight_layout()
return axes
[docs]def acf(
series: Series,
alpha: float = 0.05,
lags: int = 365,
acf_options: Optional[dict] = None,
smooth_conf: bool = True,
color: str = "k",
ax: Optional[Axes] = None,
figsize: tuple = (5, 2),
) -> Axes:
"""Plot of the autocorrelation function of a time series.
Parameters
----------
series: pandas.Series
Residual series to plot the autocorrelation function for.
alpha: float, optional
Significance level to calculate the (1-alpha)-confidence intervals. For 95%
confidence intervals, alpha should be 0.05.
lags: int, optional
Maximum number of lags (in days) to compute the autocorrelation for.
acf_options: dict, optional
Dictionary with keyword arguments passed on to pastas.stats.acf.
smooth_conf: bool, optional
For irregular time series the confidence interval may be.
color: str, optional
Color of the vertical autocorrelation lines.
ax: matplotlib.axes.Axes, optional
Matplotlib Axes instance to plot the ACF on. A new Figure and Axes is created
when no value for ax is provided.
figsize: Tuple, optional
2-D Tuple to determine the size of the figure created. Ignored if ax is also
provided.
Returns
-------
ax: matplotlib.axes.Axes
Examples
--------
>>> res = pd.Series(index=pd.date_range(start=0, periods=1000, freq="D"),
>>> data=np.random.rand(1000))
>>> ps.plots.acf(res)
"""
if ax is None:
_, ax = plt.subplots(1, 1, figsize=figsize)
# Plot the autocorrelation
if acf_options is None:
acf_options = {}
r = get_acf(series, full_output=True, alpha=alpha, lags=lags, **acf_options)
if r.empty:
raise ValueError(
"The computed autocorrelation function has no values. Changing the input "
"arguments ('acf_options') for calculating ACF may help."
)
if smooth_conf:
conf = r.conf.rolling(10, min_periods=1).mean().values
else:
conf = r.conf.values
ax.fill_between(r.index.days, conf, -conf, alpha=0.3)
ax.vlines(r.index.days, [0], r.loc[:, "acf"].values, color=color)
ax.set_xlabel("Lag [Days]")
ax.set_xlim(0, r.index.days.max())
ax.set_ylabel("Autocorrelation [-]")
ax.set_title("Autocorrelation plot")
ax.grid(True)
return ax
[docs]def diagnostics(
series: Series,
sim: Optional[Series] = None,
alpha: float = 0.05,
bins: int = 50,
acf_options: Optional[dict] = None,
figsize: tuple = (10, 5),
fig: Optional[Figure] = None,
heteroscedasicity: bool = True,
**kwargs,
) -> Axes:
"""Plot that helps in diagnosing basic model assumptions.
Parameters
----------
series: pandas.Series
Pandas Series with the residual time series to diagnose.
sim: pandas.Series, optional
Pandas series with the simulated time series. Used to diagnose on
heteroscedasticity. Ignored if heteroscedasticity is set to False.
alpha: float, optional
Significance level to calculate the (1-alpha)-confidence intervals.
bins: int optional
Number of bins used for the histogram. 50 is default.
acf_options: dict, optional
Dictionary with keyword arguments passed on to pastas.stats.acf.
figsize: tuple, optional
Tuple with the height and width of the figure in inches.
fig: Matplotib.Figure instance, optional
Optionally provide a Matplotib.Figure instance to plot onto.
heteroscedasicity: bool, optional
Create two additional subplots to check for heteroscedasticity. If true,
a simulated time series has to be provided with the sim argument.
**kwargs: dict, optional
Optional keyword arguments, passed on to plt.figure.
Returns
-------
axes: matplotlib.axes.Axes
Examples
--------
>>> res = pd.Series(index=pd.date_range(start=0, periods=1000, freq="D"),
>>> data=np.random.normal(0, 1, 1000))
>>> ps.stats.plot_diagnostics(res)
Notes
-----
The two right-hand side plots assume that the noise or residuals follow a Normal
distribution.
See Also
--------
pastas.stats.acf
Method that computes the autocorrelation.
scipy.stats.probplot
Method use to plot the probability plot.
"""
# Create the figure and axes
if fig is None:
fig = plt.figure(figsize=figsize, constrained_layout=True, **kwargs)
if heteroscedasicity:
if sim is None:
msg = (
"A simulated time series has to be provided to make plots to "
"diagnose heteroscedasticity. Provide 'sim' argument."
)
logger.error(msg=msg)
raise KeyError(msg)
gs = fig.add_gridspec(ncols=3, nrows=2, width_ratios=[3, 1, 1])
ax4 = fig.add_subplot(gs[0, 2])
ax5 = fig.add_subplot(gs[1, 2])
else:
gs = fig.add_gridspec(ncols=2, nrows=2, width_ratios=[3, 1])
ax = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax1 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[1, 1])
# Plot the residuals or noise series
ax.axhline(0, c="k")
series.plot(ax=ax)
ax.set_ylabel(series.name)
ax.set_xlim(series.index.min(), series.index.max())
ax.set_title(
f"{series.name} (n={series.size :.0f}, $\\mu$" f"={series.mean() :.2f})"
)
ax.grid()
ax.tick_params(axis="x", labelrotation=0)
for label in ax.get_xticklabels():
label.set_horizontalalignment("center")
# Plot the autocorrelation
acf(series, alpha=alpha, acf_options=acf_options, ax=ax1)
ax1.set_title(None)
# Plot the histogram for normality and add a 'best fit' line
_, bins, _ = ax2.hist(series.values, bins=bins, density=True)
y = norm.pdf(bins, series.mean(), series.std())
ax2.plot(bins, y, "k--")
ax2.set_ylabel("Probability density")
ax2.set_title("Histogram")
# Plot the probability plot
_, (_, _, r) = probplot(series, plot=ax3, dist="norm", rvalue=False)
c = ax.get_lines()[1].get_color()
ax3.get_lines()[0].set_color(c)
ax3.get_lines()[1].set_color("k")
# Plot R2 here because probplot has suboptimal positioning
ax3.text(0.5, 0.1, "$R^2={:.2f}$".format(r**2), transform=ax3.transAxes)
if heteroscedasicity and sim is not None:
# Plot residuals vs. simulation
# interpolate simulation to times of observations
sim = sim.loc[series.index]
ax4.plot(sim, series, marker=".", linestyle=" ", color=c, alpha=0.7)
ax4.grid()
ax4.set_xlabel("Simulated values")
ax4.set_ylabel("Residuals")
# Plot residuals vs. simulation
ax5.plot(
sim, np.sqrt(series.abs()), marker=".", linestyle=" ", color=c, alpha=0.7
)
ax5.set_xlabel("Simulated values")
ax5.set_ylabel("$\\sqrt{|Residuals|}$")
ax5.grid()
return fig.axes
[docs]def cum_frequency(
obs: Series,
sim: Optional[Series] = None,
ax: Optional[Axes] = None,
figsize: tuple = (5, 2),
) -> Axes:
"""Plot of the cumulative frequency of a time Series.
Parameters
----------
sim: pandas.Series
Series with the simulated values.
obs: pandas.Series
The pandas Series with the observed values.
ax: matplotlib.axes.Axes, optional
Matplotlib Axes instance to create the plot on. A new Figure and Axes is
created when no value for ax is provided.
figsize: Tuple, optional
2-D Tuple to determine the size of the figure created. Ignored if ax is also
provided.
Returns
-------
ax: matplotlib.axes.Axes
Examples
--------
>>> obs = pd.Series(index=pd.date_range(start=0, periods=1000, freq="D"),
>>> data=np.random.normal(0, 1, 1000))
>>> ps.stats.plot_cum_frequency(obs)
"""
if ax is None:
_, ax = plt.subplots(1, 1, figsize=figsize)
ax.plot(
obs.sort_values(),
np.arange(0, obs.size) / obs.size * 100,
color="k",
marker=".",
linestyle=" ",
)
if sim is not None:
ax.plot(sim.sort_values(), np.arange(0, sim.size) / sim.size * 100)
ax.legend(["Observations", "Simulation"])
ax.set_xlabel("Head")
ax.set_ylabel("Cum. Frequency [%]")
ax.grid()
plt.tight_layout()
return ax
[docs]class TrackSolve:
"""Track and/or visualize optimization progress for Pastas models.
Parameters
----------
ml : pastas.model.Model
pastas Model to track
tmin : str or pandas.Timestamp, optional
start time for simulation, by default None which defaults to first index in
ml.oseries.series
tmax : str or pandas.Timestamp, optional
end time for simulation, by default None which defaults to last index in
ml.oseries.series
update_iter : int, optional
if visualizing optimization progress, update plot every update_iter
iterations, by default nparam
Notes
-----
Interactive plotting of optimization progress requires a matplotlib backend that
supports interactive plotting, e.g. `mpl.use("TkAgg")` and `mpl.interactive(
True)`. Some possible speedups on the matplotlib side include:
- mpl.style.use("fast")
- mpl.rcParams['path.simplify_threshold'] = 1.0
Examples
--------
Set matplotlib backend and interactive mode (put this at the top of your script)::
import matplotlib as mpl
mpl.use("TkAgg")
import matplotlib.pyplot as plt
plt.ion()
Create a TrackSolve object for your model::
track = TrackSolve(ml)
Solve model and store intermediate optimization results::
ml.solve(callback=track.track_solve)
Calculated parameters per iteration are stored in a pandas.DataFrame::
track.parameters
Other stored statistics include `track.evp` (explained variance percentage),
`track.rmse_res` (root-mean-squared error of the residuals), `track.rmse_noise` (
root mean squared error of the noise, only if noise=True).
To interactively plot model optimization progress while solving pass
`track.plot_track_solve` as callback function::
ml.solve(callback=track.plot_track_solve)
Access the resulting figure through `track.fig`.
"""
[docs] def __init__(
self,
ml: Model,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
update_iter: Optional[int] = None,
) -> None:
logger.warning(
"TrackSolve feature under development. If you find any bugs please post "
"an issue on GitHub: https://github.com/pastas/pastas/issues"
)
self.ml = ml
self.viewlim = 75 # no of iterations on axes by default
if update_iter is None:
self.update_iter = len(
self.ml.parameters.loc[self.ml.parameters.vary].index
)
else:
self.update_iter = update_iter # update plot every update_iter
# get tmin/tmax
if tmin is None:
self.tmin = self.ml.oseries.series.index[0]
else:
self.tmin = Timestamp(tmin)
if tmax is None:
self.tmax = self.ml.oseries.series.index[-1]
else:
self.tmax = Timestamp(tmax)
# parameters
self.parameters = DataFrame(columns=self.ml.parameters.index)
self.parameters.loc[0] = self.ml.parameters.initial.values
# iteration counter
self.itercount = 0
# calculate RMSE residuals
res = self._residuals(self.ml.parameters.initial.values)
self.rmse_res = np.array([rmse(res=res)])
# calculate RMSE noise
if self.ml.settings["noise"] and self.ml.noisemodel is not None:
noise = self._noise(self.ml.parameters.initial.values)
self.rmse_noise = np.array([rmse(res=noise)])
# get observations
self.obs = self.ml.observations(tmin=self.tmin, tmax=self.tmax)
# calculate EVP
self.evp = np.array([evp(obs=self.obs, res=res)])
[docs] def track_solve(self, params: ArrayLike) -> None:
"""Append parameters to self.parameters DataFrame and update itercount,
rmse values and evp.
Parameters
----------
params : array_like
array containing parameters.
"""
# update tmin/tmax and freq once after starting solve
if self.itercount == 0:
self._update_settings()
# update itercount
self.itercount += 1
# add parameters to DataFrame
self.parameters.loc[self.itercount, self.ml.parameters.index] = params.copy()
# calculate new RMSE values
r_res = self._residuals(params)
self.rmse_res = np.r_[self.rmse_res, rmse(res=r_res)]
if self.ml.settings["noise"] and self.ml.noisemodel is not None:
n_res = self._noise(params)
self.rmse_noise = np.r_[self.rmse_noise, rmse(res=n_res)]
# recalculate EVP
self.evp = np.r_[self.evp, evp(obs=self.obs, res=r_res)]
def _update_axes(self) -> None:
"""extend xlim if number of iterations exceeds current window."""
for iax in self.axes[1:]:
iax.set_xlim(right=self.viewlim)
self.fig.canvas.draw()
def _update_settings(self) -> None:
self.tmin = self.ml.settings["tmin"]
self.tmax = self.ml.settings["tmax"]
self.freq = self.ml.settings["freq"]
def _noise(self, params: ArrayLike) -> ArrayLike:
"""get noise.
Parameters
----------
params: array_like
array containing parameters.
Returns
-------
noise: array_like
array containing noise.
"""
noise = self.ml.noise(p=params, tmin=self.tmin, tmax=self.tmax)
return noise
def _residuals(self, params: ArrayLike) -> ArrayLike:
"""calculate residuals.
Parameters
----------
params: np.array
array containing parameters.
Returns
-------
res: array_like
array containing residuals.
"""
res = self.ml.residuals(p=params, tmin=self.tmin, tmax=self.tmax)
return res
def _simulate(self) -> Series:
"""simulate model with last entry in self.parameters.
Returns
-------
sim: pd.Series
Series containing model evaluation.
"""
sim = self.ml.simulate(
p=self.parameters.iloc[-1, :].values,
tmin=self.tmin,
tmax=self.tmax,
freq=self.ml.settings["freq"],
)
return sim
[docs] def plot_track_solve(self, params: ArrayLike) -> None:
"""Method to plot model simulation while model is being solved.
Parameters
----------
params : array_like
array containing parameters
Examples
--------
Pass
this method to ml.solve(), e.g.:
>>> track = TrackSolve(ml)
>>> ml.solve(callback=track.plot_track_solve)
"""
if not hasattr(self, "fig"):
self.initialize_figure()
# update parameters
self.track_solve(params)
# check if figure should be updated
if self.itercount % self.update_iter != 0:
return
# update view limits if needed
if self.itercount >= self.viewlim:
self.viewlim += 50
self._update_axes()
# update simulation
sim = self._simulate()
self.simplot.set_data(sim.index, sim.values)
# update rmse residuals
self.r_rmse_plot_line.set_data(
range(self.itercount + 1), np.array(self.rmse_res)
)
self.r_rmse_plot_dot.set_data(
np.array([self.itercount]), np.array([self.rmse_res[-1]])
)
if self.ml.settings["noise"] and self.ml.noisemodel is not None:
# update rmse noise
self.n_rmse_plot_line.set_data(
range(self.itercount + 1), np.array(self.rmse_noise)
)
self.n_rmse_plot_dot.set_data(
np.array([self.itercount]), np.array([self.rmse_noise[-1]])
)
# update parameter plots
for j, (p1, p2) in enumerate(self.param_plot_handles):
p1.set_data(
np.array([self.itercount]), np.abs([self.parameters.iloc[-1, j]])
)
p2.set_data(
range(self.itercount + 1), self.parameters.iloc[:, j].abs().values
)
# update title
self.ax0.set_title(
"Iteration: {0} (EVP: {1:.2f}%)".format(self.itercount, self.evp[-1])
)
plt.pause(1e-10)
self.fig.canvas.draw()
[docs] def plot_track_solve_history(self, fig: Optional[Figure] = None) -> List[Axes]:
"""Plot optimization history.
Parameters
----------
fig : matplotlib.pyplot.Figure, optional
figure handle, by default None, which constructs a new figure with
`self.initialize_figure()`.
Returns
-------
axes : list of matplotlib.pyplot.Axes
list of axes handles in figure.
"""
if fig is None:
fig = self.initialize_figure()
self.plot_track_solve(self.ml.parameters.optimal.values)
self.fig.axes[1].autoscale(tight=False, axis="both")
self.fig.axes[2].autoscale(tight=False, axis="both")
self.fig.axes[1].set_xlim(left=0)
# because of bug with autoscaling log axis?
self.fig.axes[1].set_ylim(top=1.05 * self.rmse_res.max())
return fig.axes
[docs]def pairplot(
data: Union[DataFrame, List[Series]],
bins: Optional[int] = None,
) -> Dict[str, Axes]:
"""Plot correlation between time series on of values on the same time steps.
Based on seaborn pairplot method.
Parameters
----------
data : Union[DataFrame, List[Series]]
List of Series or Dataframe with DateTime index
bins : Optional[int], optional
Number of bins in the histogram, by default None which uses Sturge's
Rule to determine the number bins
Returns
-------
Dict[str, Axes]
"""
if isinstance(data, list):
data = concat(data, axis=1)
df = data.dropna(how="any")
columns = df.columns
mosaic = []
for i, column in enumerate(columns):
cols = [f"scatter_{x}-{column}" for x in columns]
cols[i] = f"hist_{column}"
mosaic.append(cols)
mosaic = np.array(mosaic)
f, axd = plt.subplot_mosaic(mosaic, figsize=(6.5, 6))
for i, (column, mos) in enumerate(zip(columns, mosaic)):
# plot histogram
if bins is None:
bins = int(np.ceil(1 + np.log2(len(df.loc[:, column].values))))
counts, bins = np.histogram(df.loc[:, column].values, bins=bins)
scaled_counts = (
df.loc[:, column].max()
* (counts - np.min(counts))
/ (np.max(counts) - np.min(counts))
)
axd[f"hist_{column}"].hist(x=bins[:-1], bins=bins, weights=scaled_counts)
# plot scatter
other_cols = [x for x in columns if x is not column]
for col in other_cols:
axd[f"scatter_{column}-{col}"].scatter(
df.loc[:, column].values,
df.loc[:, col].values,
alpha=0.6,
s=20,
edgecolor="white",
linewidth=0.3,
)
r, _ = pearsonr(df.loc[:, column].values, df.loc[:, col].values)
axd[f"scatter_{column}-{col}"].annotate(
f"r = {r:.2f}",
xy=(0.5, 0.95),
horizontalalignment="center",
verticalalignment="top",
xycoords="axes fraction",
color="k",
path_effects=[
path_effects.withStroke(linewidth=2, foreground="white"),
path_effects.Normal(),
],
)
# set labels
axd[mos[0]].set_ylabel(column)
if (mos == mosaic[-1]).all():
_ = [axd[j].set_xlabel(x) for x, j in zip(columns, mos)]
# share x and y axis per row and columns
share_yaxes([axd[j] for j in mos])
_ = [share_xaxes([axd[x] for x in mosaic[:, j]]) for j in range(len(columns))]
f.tight_layout()
return axd
