#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""drill_plot for a drill hole data set"""
#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
from . set_style import set_plot_style
[docs]
@set_plot_style
def drill_plot(z, var, cat=None, categorical_dictionary=None, lw=2, line_color='k', barwidth=None, namelist=None,
legend_fontsize=10, title=None, ylabel=None, unit=None, grid=None, axis_xy=None,
reverse_y=False, xlim=None, ylim=None, figsize=(2,10), ax=None,plot_style=None,
custom_style=None, output_file=None, out_kws=None, **kwargs):
'''
A well log plot for both continuous and categorical variables. This plot handles one well log
plot at a time and the user can choose to generate subplots and pass the axes to this function
if multiple well log plots are required.
Parameters:
z (Elevation/Depth or distance along the well): Tidy (long-form) 1D data where a single
column of the variable exists with each row is an observation. A pandas dataframe/series
or numpy array can be passed.
var (Variable being plotted): Tidy (long-form) 1D data where a single column of the variable
exists with each row is an observation. A pandas dataframe/series or numpy array can be
passed.
lw (float): line width for log plot of a continuous variable
line_color (string): line color for the continuous variable
barwidth(float): width of categorical bars
categorical_dictionary (dictionary): a dictionary of colors and names for categorical codes. E.g.
{1: {'name': 'Sand', 'color': 'gold'},
2: {'name': 'SHIS','color': 'orange'},
3: {'name': 'MHIS','color': 'green'},
4: {'name': 'MUD','color': 'gray'}}
legend_fontsize(float): fontsize for the legend plot rleated to the categorical codes. set
this parameter to 0 if you do not want to have a legend
title (str): title for the variable
ylabel (str): Y-axis label, based on ``Parameters['plotting.zname']`` if None.
unit (str): Unit to place inside the y-label parentheses, based on
``Parameters['plotting.unit']`` if None.
grid (bool): Plots the major grid lines if True. Based on ``Parameters['plotting.grid']`` if
None.
axis_xy (bool): converts the axis to GSLIB-style axis visibility (only left and bottom
visible) if axis_xy is True. Based on ``Parameters['plotting.axis_xy']`` if None.
reverse_y(bool): if true, the yaxis direction is set to reverse(applies to the cases that
depth is plotted and not elevation)
aspect (str): Set a permissible aspect ratio of the image to pass to matplotlib.
xlim (float tuple): X-axis limits
ylim (float tuple): Y-axis limits
figsize (tuple): Figure size (width, height)
ax (mpl.axis): Existing matplotlib axis to plot the figure onto
out_kws (dict): Optional dictionary of permissible keyword arguments to pass to
:func:`gs.export_image() <pygeostat.plotting.export_image.export_image>`
Returns:
ax (ax): Matplotlib axis instance which contains the gridded figure
Examples:
A simple call to plot a continuous variable
.. plot::
import pygeostat as gs
dat = gs.ExampleData('point3d_ind_mv')
data = dat.data[dat.data['HoleID'] == 3]
gs.drill_plot(data['Elevation'], data['Sw'], grid = True)
|
Plot a categorical variable
.. plot::
import pygeostat as gs
dat = gs.ExampleData('point3d_ind_mv')
data = dat.data[dat.data['HoleID'] == 3]
gs.drill_plot(data['Elevation'], data['Lithofacies'])
|
Plot a categorical variable and provide a categorical dictionary
.. plot::
import pygeostat as gs
dat = gs.ExampleData('point3d_ind_mv')
data = dat.data[dat.data['HoleID'] == 3]
cat_dict = {1: {'name': 'Sand', 'color': 'gold'},
3: {'name': 'SHIS','color': 'orange'},
4: {'name': 'MHIS','color': 'green'},
5: {'name': 'MUD','color': 'gray'}}
gs.drill_plot(data['Elevation'], data['Lithofacies'], categorical_dictionary=cat_dict)
'''
from matplotlib.ticker import FormatStrFormatter
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from .export_image import export_image
import numpy as np
import pandas as pd
from .. pygeostat_parameters import Parameters
from .utils import format_plot, get_label, setup_plot, catcmapfromcontinuous, _spatial_aspect
# Sanity checks
if not isinstance(z, (np.ndarray, np.generic, pd.Series)):
raise ValueError("The parameter `z` must be a numpy array or a pandas series")
if not isinstance(var, (np.ndarray, np.generic, pd.Series)):
raise ValueError("The parameter `var` must be a numpy array or a pandas series")
if title is None:
title = get_label(var)
if unit is None:
unit = Parameters['plotting.unit']
if unit is None or unit == '':
unit = ''
else:
unit = ' ({})'.format(unit)
if ylabel:
ax.set_ylabel(ylabel)
elif ylabel is None:
ylabel = Parameters['plotting.zname'] + unit
if not out_kws:
out_kws = dict()
fig, ax, cax = setup_plot(ax, figsize=figsize, aspect = False)
# Create a new data frame
df = pd.DataFrame()
df['Z'] = np.array(z)
df['Var'] = np.array(var)
df.sort_values('Z', axis=0, ascending=True, inplace=True)
df.reset_index(inplace=True, drop=True)
if barwidth is None:
barwidth = (np.max(df['Z'].values) - np.min(df['Z'].values))/10
if cat is None:
if (len(set(df['Var'].values)) < (len(df) / 5)) :
cat = True
# Start of the plot
if not cat:
ax.plot(df['Var'], df['Z'], lw=lw, c=line_color, **kwargs)
xlim = [np.nanmin(df['Var']), np.nanmax(df['Var'])]
ylim = [np.nanmin(df['Z']), np.nanmax(df['Z'])]
ax.set_xticks(np.linspace(np.nanmin(df['Var']),
np.nanmax(df['Var']), 2))
ax.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.xaxis.set_label_position('top')
ax.xaxis.set_ticks_position('top')
else:
categories = sorted(set(df['Var'].values))
ncat = len(categories)
# make sure grid option is not triggered
grid = False
if categorical_dictionary is None:
colorlist = catcmapfromcontinuous("Spectral", ncat).colors
namelist = []
for i in range(ncat):
string = 'Category Code %i' % (i + 1)
namelist.append(string)
categorical_dictionary = {}
for i, item in enumerate(categories):
categorical_dictionary.update({item: {'name': namelist[i], 'color': colorlist[i]} })
else:
if len(categorical_dictionary)<ncat:
raise ValueError('The provided categorical_dictionary does not match the number of categorical codes')
for item in categorical_dictionary.keys():
if item not in categories:
raise ValueError('The provided categorical code, {} does not exist in the provided data'.format(item))
# First bar from bottom based on sorting the z column
category = df['Var'][0]
color = categorical_dictionary[category]['color']
offset = (df['Z'][1] - df['Z'][0])/2
z_b = df['Z'][0]
z_t = z_b + offset
ax.fill_between([0, barwidth], [z_b, z_b], [z_t,z_t], color = color)
for i in range(1,len(df)-1):
upper_offset = (df['Z'][i+1] - df['Z'][i])/2
lower_offset = (df['Z'][i-1] - df['Z'][i])/2
z_b = df['Z'][i] + lower_offset
z_t = df['Z'][i] + upper_offset
category = df['Var'][i]
color = categorical_dictionary[category]['color']
ax.fill_between([0, barwidth], [z_b, z_b], [z_t,z_t], color = color)
# Last bar from bottom based on sorting the z column
category = df['Var'][i+1]
color = categorical_dictionary[category]['color']
z_b = z_t
z_t = df['Z'][i+1]
ax.fill_between([0, barwidth], [z_b, z_b], [z_t,z_t], color = color)
Patch_list = []
for item in categorical_dictionary.values():
Patch_list.append(mpatches.Patch(color=item['color'], label=item['name']))
if legend_fontsize > 0:
ax.legend(handles=Patch_list, loc='upper left',
bbox_to_anchor=(1, 1), fontsize=legend_fontsize)
ylim = [np.nanmin(df['Z']), np.nanmax(df['Z'])]
xlim = [0, barwidth]
ax.xaxis.set_label_position('top')
ax.get_xaxis().set_visible(False)
format_plot(ax, xlabel='', ylabel = ylabel, title = title, grid = grid, axis_xy = axis_xy, xlim = xlim, ylim = ylim)
if reverse_y:
ax.invert_yaxis()
if output_file or ('pdfpages' in out_kws):
export_image(output_file, **out_kws)
return ax
