Source code for

"""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:`` 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 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