def plot(
self,
classification=None,
water_level_NAP=None,
water_level_wrt_depth=None,
min_thickness=None,
p_a=0.1,
new=True,
show=False,
figsize=(11, 8),
df_group=None,
grid_step_x=None,
dpi=100,
colors=None,
z_NAP=False,
):
"""
Plot the cpt file and return matplotlib.pyplot.figure .
Parameters
----------
classification: str, only for cpt type
If classification ("robertson", "been_jefferies") is specified a subplot is added with the classification
for each cpt row.
water_level_NAP: float, only for cpt type, necessary for the classification: give this or water_level_wrt_depth
Water level with respect to NAP
water_level_wrt_depth: float, only for cpt type, necessary for the classification: give this or water_level_NAP
Water level with respect to the ground_level [0], it should be a negative value.
min_thickness: float, only for cpt type, optional for the classification [m]
If specified together with the do_grouping set to True, a group classification is added to the plot.
The grouping is a simple algorithm that merge all the layers < min_thickness with the last above one >
min_thickness.
In order to not make a big error do not use a value bigger then 0.2 m
p_a: float, only for cpt type, optional for the classification
Atmospheric pressure. Default: 0.1 MPa.
new: bool, only for cpt type, optional for the classification default:True
If True and the classification is robertson, the new(2016) implementation of robertson is used.
show: bool
If True the plot is showed, else the matplotlib.pytplot.figure is returned
figsize: tuple
Figsize of the plot, default (11, 8).
df_group: polars.DataFrame, only for cpt type, optional for the classification
Use this argument to plot a defined soil layering next to the other subplots.
It should contain the columns:
- layer
Name of layer, should be either BeenJefferies of Robertson soil type,
if it is different then also the argument colors should be passed.
- z_centr_NAP
Z value of the middle of the layer
- thickness
Thickness of the layer
grid_step_x: float, only for cpt type, default: None
Grid step for qc and Fr subplots.
dpi: int
Dpi figure
colors: dict
Dictionary containing the colors associated to each soil type, if specified
z_NAP: bool
If True the Z-axis is with respect to NAP.
Returns
-------
matplotlib.pyplot.figure
"""
# todo: refactor arguments, the arguments connected to each other should
# be given as a dict or tuple, check order
if classification is None:
df = self.df
else:
df = self.classify(
classification=classification,
water_level_NAP=water_level_NAP,
water_level_wrt_depth=water_level_wrt_depth,
p_a=p_a,
new=new,
)
return plot.plot_cpt(
df,
df_group,
classification,
show=show,
figsize=figsize,
grid_step_x=grid_step_x,
colors=colors,
dpi=dpi,
z_NAP=z_NAP,
)
def plot(
self,
classification=None,
water_level_NAP=None,
water_level_wrt_depth=None,
min_thickness=None,
p_a=0.1,
new=True,
show=False,
figsize=(11, 8),
df_group=None,
do_grouping=False,
grid_step_x=None,
dpi=100,
colors=None,
z_NAP=False,
):
"""
Plot cpt and return matplotlib figure.
:param classification: (str) Specify this to classify the cpt, possible choice : "robertson", "been_jefferies".
:param water_level_NAP: (float)
:param water_level_wrt_depth: (float)
:param min_thickness: (float) Minimum accepted thickness for grouping.
:param p_a: (float) Atmospheric pressure at ground level in MPa.
:param new: (bool) Old(1990) or New(2016) implementation of Robertson.
:param show: (bool) Set to True to show the plot.
:param figsize: (tpl) Figure size (x, y).
:param df_group: (DataFrame) Specify your own DataFrame if you don't agree with the automatic one.
:param do_grouping: (bool) Plot the grouping if True.
:param grid_step_x: (int) Grid step of x-axes qc.
:param dpi: (int) Matplotlib dpi settings.
:param colors: (dict) Dictionary with soil type and related color, use this to plot your own classification.
:param z_NAP: (bool) True to plot the z-axis with respect to NAP. Default: False.
:return: matplotlib Figure.
"""
if self.type == "cpt":
if classification is None:
df = self.df
else:
df = self.classify(
classification=classification,
water_level_NAP=water_level_NAP,
water_level_wrt_depth=water_level_wrt_depth,
p_a=p_a,
new=new,
)
if df_group is None and do_grouping is True:
df_group = self.classify(
classification=classification,
water_level_NAP=water_level_NAP,
water_level_wrt_depth=water_level_wrt_depth,
p_a=p_a,
new=new,
do_grouping=True,
min_thickness=min_thickness,
)
return plot.plot_cpt(
df,
df_group,
classification,
show=show,
figsize=figsize,
grid_step_x=grid_step_x,
colors=colors,
dpi=dpi,
z_NAP=z_NAP,
)
elif self.type == "bore":
return plot.plot_bore(self.df, figsize=figsize, show=show, dpi=dpi)
else:
raise ValueError(
"The selected gef file is not a cpt nor a borehole. "
"Check the REPORTCODE or the PROCEDURECODE.")