"""welltestpy subpackage to make diagnostic plots."""
# pylint: disable=C0103
import matplotlib.pyplot as plt
import numpy as np
from ..process import processlib
from . import plotter
[docs]def diagnostic_plot_pump_test(
observation,
rate,
method="bourdet",
linthresh_time=1.0,
linthresh_head=1e-5,
fig=None,
ax=None,
plotname=None,
style="WTP",
):
"""
Plot the derivative with the original data.
Parameters
----------
observation : :class:`welltestpy.data.Observation`
The observation to calculate the derivative.
rate : :class:`float`
Pumping rate.
method : :class:`str`, optional
Method to calculate the time derivative.
Default: "bourdet"
linthresh_time : :class: 'float'
Range of time around 0 that behaves linear.
Default: 1
linthresh_head : :class: 'float'
Range of head values around 0 that behaves linear.
Default: 1e-5
fig : Figure, optional
Matplotlib figure to plot on.
Default: None.
ax : :class:`Axes`
Matplotlib axes to plot on.
Default: None.
plotname : str, optional
Plot name if the result should be saved.
Default: None.
style : str, optional
Plot style.
Default: "WTP".
Returns
-------
Diagnostic plot
"""
head, time = observation()
head = np.array(head, dtype=float).reshape(-1)
time = np.array(time, dtype=float).reshape(-1)
if rate < 0:
head = head * -1
derivative = processlib.smoothing_derivative(
head=head, time=time, method=method
)
# setting variables
dx = time[1:-1]
dy = derivative[1:-1]
# plotting
if style == "WTP":
style = "ggplot"
font_size = plt.rcParams.get("font.size", 10.0)
keep_fs = True
with plt.style.context(style):
if keep_fs:
plt.rcParams.update({"font.size": font_size})
fig, ax = plotter._get_fig_ax(fig, ax)
ax.scatter(time, head, color="C0", label="drawdown")
ax.plot(dx, dy, color="C1", label="time derivative")
ax.set_xscale("symlog", linthresh=linthresh_time)
ax.set_yscale("symlog", linthresh=linthresh_head)
ax.set_xlabel("$t$ in [s]", fontsize=16)
ax.set_ylabel("$h$ and $dh/dx$ in [m]", fontsize=16)
lgd = ax.legend(loc="upper left", facecolor="w")
min_v = min(np.min(head), np.min(dy))
max_v = max(np.max(head), np.max(dy))
max_e = int(np.ceil(np.log10(max_v)))
if min_v < linthresh_head:
min_e = -np.inf
else:
min_e = int(np.floor(np.log10(min_v)))
ax.set_ylim(10.0**min_e, 10.0**max_e)
yticks = [0 if min_v < linthresh_head else 10.0**min_e]
thresh_e = int(np.floor(np.log10(linthresh_head)))
first_e = thresh_e if min_v < linthresh_head else (min_e + 1)
yticks += list(10.0 ** np.arange(first_e, max_e + 1))
ax.set_yticks(yticks)
fig.tight_layout()
if plotname is not None:
fig.savefig(
plotname,
format="pdf",
bbox_extra_artists=(lgd,),
bbox_inches="tight",
)
return ax