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GWChemPlots.py
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GWChemPlots.py
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# -*- coding: utf-8 -*-
"""
Created on 2024-02-17
@author: solisgb
"""
import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
ions = {'Cl': (35.453, -1), 'SO4': (96.0626, -2),
'CO3': (60.0089, -2), 'HCO3': (61.0168, -1), 'NO3': (62.0049, -1),
'Ca': (40.078, 2), 'Mg': (24.305, 2), 'K': (39.0983, 1), 'Na': (22.989769, 1)}
def cbe(df:pd.DataFrame):
"""
Calcula el CBE (Charge Balance Error)
df tiene las columnas de los mayoritarios en mg/L. Se crean las siguientes columnas
* Para cada ion mayoritario, por ej Ca, (en el diccionario ions) se crea la columna rCa con la
la concentración expresada en meq/L
* Se calculan las columnas sum_anions y sum_cations
* Se calcula el CBE
"""
col_names = df.columns.tolist()
for k, v in ions.items():
if k not in col_names:
print(f'{k} is not present')
continue
new_column = 'r'+ k
df[new_column] = df[k] * np.abs(v[1]) / v[0]
df['sum_anions'] = 0
df['sum_cations'] = 0
for k, v in ions.items():
if k not in col_names:
continue
col_name = 'r'+ k
if v[1] < 0:
df['sum_anions'] += df[col_name]
else:
df['sum_cations'] += df[col_name]
df['cbe'] = 100 * (df['sum_cations'] - np.abs(df['sum_anions'])) / (df['sum_cations'] + np.abs(df['sum_anions']))
def cation_dominant(row: pd.core.series.Series) -> str:
"""
Groundwater cation dominant classification
Custodio E (1983). Hidrogeoquímica.
In Hidrología subterránea pp 1001-1095. Ed. Omga
"""
if row['rNa'] + row['rK'] > row['rMg'] > row['rCa']:
if row['rNa'] + row['rK'] >= 0.5:
return 'Na (Mg-Ca)'
else:
return 'Na-Mg-Ca'
elif row['rNa'] + row['rK'] > row['rCa'] > row['rMg']:
if row['rNa'] >= 0.5:
return'Na (Mg-Ca)'
else:
return'Na-Ca-Mg)'
elif row['rMg'] > row['rNa'] + row['rK'] > row['rCa']:
if row['rMg'] >= 0.5:
return'Mg (Na-Ca)'
else:
return'Mg-Na-Ca'
elif row['rMg'] > row['rCa'] > row['rNa'] + row['rK']:
if row['rMg'] >= 0.5:
return'Mg (Ca-Na)'
else:
return'Mg-Ca-Na'
elif row['rCa'] > row['rNa'] + row['rK'] > row['rMg']:
if row['rCa'] >= 0.5:
return'Ca (Na-Mg)'
else:
return'Ca-Na-Mg'
elif row['rCa'] > row['rMg'] > row['rNa'] + row['rK']:
if row['rCa'] >= 0.5:
return'Ca (Mg-Na)'
else:
return'Ca-Mg-Na)'
def anion_dominant(row: pd.core.series.Series, NO3=True) -> str:
"""
Groundwater anion dominant classification
Custodio E (1983). Hidrogeoquímica.
In Hidrología subterránea pp 1001-1095. Ed.
row: Must have the anion keys in dictionary ions with a 'r' as prefix indicating
concentrations in meq/L. rNO3 and rCO3 couldn't be present.
NO3:
*If NO3 is True
and rNO3 is in the columns of row, rNO3 is added to rSO4
*Otherwise, rNO3 is not considered
"""
if NO3:
try:
rno3 = row.iloc[0]['rNO3']
except:
rno3 = 0.
else:
rno3 = 0.
try:
rco3 = row.iloc[0]['rCO3']
except:
rco3 = 0.
if row['rCl'] + row['rK'] > row['rSO4'] + rno3 > row['rHCO3'] + rco3:
if row['rCl'] >= 0.5:
return 'Cl (SO4-HCO3)'
else:
return 'Cl-SO4-HCO3'
elif row['rCl'] + row['rK'] > row['rHCO3'] + rco3 > row['rSO4'] + rno3:
if row['rCl'] + row['rK'] >= 0.5:
return 'Cl (HCO3-SO4)'
else:
return 'Cl-HCO3-SO4'
elif row['rSO4'] + rno3 > row['rCl'] + row['rK'] > row['rHCO3'] + rco3 :
if row['rSO4'] + rno3 >= 0.5:
return ('SO4 (Cl-HCO3)')
else:
return ('SO4-Cl-HCO3')
elif row['rSO4'] + rno3 > row['rHCO3'] + rco3 > row['rCl'] + row['rK']:
if row['rSO4'] >= 0.5:
return ('SO4 (HCO3-Cl)')
else:
return ('SO4-HCO3-Cl')
elif row['rHCO3'] + rco3 > row['rCl'] + row['rK'] > row['rSO4'] + rno3:
if row['rHCO3'] + rco3 >= 0.5:
return ('HCO3 (Cl-SO4)')
else:
return ('HCO3-Cl-SO4)')
elif row['rHCO3'] + rco3 > row['rSO4'] + rno3 > row['rCl'] + row['rK']:
if row['rHCO3'] + rco3 >= 0.5:
return ('HCO3 (SO4-Cl)')
else:
return ('HCO3-(SO4-Cl)')
def triangle_piper(df, file_path, unit='mg/L', dpi=300) -> None:
"""
Plots the Piper diagram.
https://github.com/jyangfsu/WQChartPy/tree/main/wqchartpy
Parameters
----------
df : pandas.DataFrame. Geochemical data.
file_path : str or Path. File path where figure wil saved.
unit : str. Units used in df (mg/L or meq/L).
"""
# Basic data check
# -------------------------------------------------------------------------
# Determine if the required geochemical parameters are defined.
if not {'Ca', 'Mg', 'Na', 'K',
'HCO3', 'CO3', 'Cl', 'SO4'}.issubset(df.columns):
raise ValueError("""
Piper diagram requires:
Ca, Mg, Na, K, HCO3, CO3, Cl, and SO4""")
# Determine if the provided unit is allowed
ALLOWED_UNITS = ['mg/L', 'meq/L']
if unit not in ALLOWED_UNITS:
raise ValueError("Unit must be mg/L or meq/L")
ions_WEIGHT = {key: value[0] for key, value in ions.items()}
ions_CHARGE = {key: value[1] for key, value in ions.items()}
# Global plot settings
# -------------------------------------------------------------------------
# Change default settings for figures
plt.style.use('default')
plt.rcParams['font.size'] = 9
plt.rcParams['axes.labelsize'] = 9
plt.rcParams['axes.labelweight'] = 'bold'
plt.rcParams['axes.titlesize'] = 9
plt.rcParams['xtick.labelsize'] = 9
plt.rcParams['ytick.labelsize'] = 9
plt.rcParams['legend.fontsize'] = 9
plt.rcParams['figure.titlesize'] = 10
# Plot background settings
# -------------------------------------------------------------------------
# Define the offset between the diamond and traingle
offset = 0.10
offsety = offset * np.tan(np.pi / 3.0)
h = 0.5 * np.tan(np.pi / 3.0)
# Calculate the traingles' location
ltriangle_x = np.array([0, 0.5, 1, 0])
ltriangle_y = np.array([0, h, 0, 0])
rtriangle_x = ltriangle_x + 2 * offset + 1
rtriangle_y = ltriangle_y
# Calculate the diamond's location
diamond_x = np.array([0.5, 1, 1.5, 1, 0.5]) + offset
diamond_y = h * (np.array([1, 2, 1, 0, 1])) + (offset * np.tan(np.pi / 3))
# Plot the traingles and diamond
fig = plt.figure(figsize=(10, 10), dpi=100)
ax = fig.add_subplot(111, aspect='equal', frameon=False,
xticks=[], yticks=[])
ax.plot(ltriangle_x, ltriangle_y, '-k', lw=1.0)
ax.plot(rtriangle_x, rtriangle_y, '-k', lw=1.0)
ax.plot(diamond_x, diamond_y, '-k', lw=1.0)
# Plot the lines with interval of 20%
interval = 0.2
ticklabels = ['0', '20', '40', '60', '80', '100']
for i, x in enumerate(np.linspace(0, 1, int(1/interval+1))):
# the left traingle
ax.plot([x, x - x / 2.0],
[0, x / 2.0 * np.tan(np.pi / 3)],
'k:', lw=1.0)
## the bottom ticks
if i in [1, 2, 3, 4]:
ax.text(x, 0-0.03, ticklabels[-i-1],
ha='center', va='center')
ax.plot([x, (1-x)/2.0+x],
[0, (1-x)/2.0*np.tan(np.pi/3)],
'k:', lw=1.0)
## the right ticks
if i in [1, 2, 3, 4]:
ax.text((1-x)/2.0+x + 0.026, (1-x)/2.0*np.tan(np.pi/3) + 0.015,
ticklabels[i], ha='center', va='center', rotation=-60)
ax.plot([x/2, 1-x/2],
[x/2*np.tan(np.pi/3), x/2*np.tan(np.pi/3)],
'k:', lw=1.0)
## the left ticks
if i in [1, 2, 3, 4]:
ax.text(x/2 - 0.026, x/2*np.tan(np.pi/3) + 0.015,
ticklabels[i], ha='center', va='center', rotation=60)
# the right traingle
ax.plot([x+1+2*offset, x-x/2.0+1+2*offset],
[0, x/2.0*np.tan(np.pi/3)],
'k:', lw=1.0)
## the bottom ticks
if i in [1, 2, 3, 4]:
ax.text(x+1+2*offset, 0-0.03,
ticklabels[i], ha='center', va='center')
ax.plot([x+1+2*offset, (1-x)/2.0+x+1+2*offset],
[0, (1-x)/2.0*np.tan(np.pi/3)],
'k:', lw=1.0)
## the right ticks
if i in [1, 2, 3, 4]:
ax.text((1-x)/2.0+x+1+2*offset + 0.026, (1-x)/2.0*np.tan(np.pi/3) + 0.015,
ticklabels[-i-1], ha='center', va='center', rotation=-60)
ax.plot([x/2+1+2*offset, 1-x/2+1+2*offset],
[x/2*np.tan(np.pi/3), x/2*np.tan(np.pi/3)],
'k:', lw=1.0)
## the left ticks
if i in [1, 2, 3, 4]:
ax.text(x/2+1+2*offset - 0.026, x/2*np.tan(np.pi/3) + 0.015,
ticklabels[-i-1], ha='center', va='center', rotation=60)
# the diamond
ax.plot([0.5+offset+0.5/(1/interval)*x/interval, 1+offset+0.5/(1/interval)*x/interval],
[h+offset*np.tan(np.pi/3)+0.5/(1/interval)*x/interval*np.tan(np.pi/3),
offset*np.tan(np.pi/3)+0.5/(1/interval)*x/interval*np.tan(np.pi/3)],
'k:', lw=1.0)
## the upper left and lower right
if i in [1, 2, 3, 4]:
ax.text(0.5+offset+0.5/(1/interval)*x/interval - 0.026,
h+offset*np.tan(np.pi/3)+0.5/(1/interval)*x/interval*np.tan(np.pi/3) + 0.015, ticklabels[i],
ha='center', va='center', rotation=60)
ax.text(1+offset+0.5/(1/interval)*x/interval + 0.026,
offset*np.tan(np.pi/3)+0.5/(1/interval)*x/interval*np.tan(np.pi/3) - 0.015, ticklabels[-i-1],
ha='center', va='center', rotation=60)
ax.plot([0.5+offset+0.5/(1/interval)*x/interval, 1+offset+0.5/(1/interval)*x/interval],
[h+offset*np.tan(np.pi/3)-0.5/(1/interval)*x/interval*np.tan(np.pi/3),
2*h+offset*np.tan(np.pi/3)-0.5/(1/interval)*x/interval*np.tan(np.pi/3)],
'k:', lw=1.0)
## the lower left and upper right
if i in [1, 2, 3, 4]:
ax.text(0.5+offset+0.5/(1/interval)*x/interval- 0.026,
h+offset*np.tan(np.pi/3)-0.5/(1/interval)*x/interval*np.tan(np.pi/3) - 0.015, ticklabels[i],
ha='center', va='center', rotation=-60)
ax.text(1+offset+0.5/(1/interval)*x/interval + 0.026,
2*h+offset*np.tan(np.pi/3)-0.5/(1/interval)*x/interval*np.tan(np.pi/3) + 0.015, ticklabels[-i-1],
ha='center', va='center', rotation=-60)
# Labels and title
plt.text(0.5, -offset, '%' + '$Ca^{2+}$',
ha='center', va='center')
plt.text(1+2*offset+0.5, -offset, '%' + '$Cl^{-}$',
ha='center', va='center')
plt.text(0.25-offset*np.cos(np.pi/30), 0.25*np.tan(np.pi/3)+offset*np.sin(np.pi/30), '%' + '$Mg^{2+}$',
ha='center', va='center', rotation=60)
plt.text(1.75+2*offset+offset*np.cos(np.pi/30),
0.25*np.tan(np.pi/3)+offset*np.sin(np.pi/30), '%' + '$SO_4^{2-}$',
ha='center', va='center', rotation=-60)
plt.text(0.75+offset*np.cos(np.pi/30),
0.25*np.tan(np.pi/3)+offset*np.sin(np.pi/30), '%' + '$Na^+$' + '+%' + '$K^+$',
ha='center', va='center', rotation=-60)
plt.text(1+2*offset+0.25-offset*np.cos(np.pi/30), 0.25*np.tan(np.pi/3)+offset*np.sin(np.pi/30),
'%' + '$HCO_3^-$' + '+%' + '$CO_3^{2-}$',
ha='center', va='center', rotation=60)
plt.text(0.5+offset+0.5*offset+offset*np.cos(np.pi/30),
h+offset*np.tan(np.pi/3)+0.25*np.tan(np.pi/3)+offset*np.sin(np.pi/30), '%' + '$SO_4^{2-}$' + '+%' + '$Cl^-$',
ha='center', va='center', rotation=60)
plt.text(1.5+offset-0.25+offset*np.cos(np.pi/30),
h+offset*np.tan(np.pi/3)+0.25*np.tan(np.pi/3)+offset*np.sin(np.pi/30), '%' + '$Ca^{2+}$' + '+%' + '$Mg^{2+}$',
ha='center', va='center', rotation=-60)
# Fill the water types domain
## the left traingle
plt.fill([0.25, 0.5, 0.75, 0.25],
[h/2, 0, h/2, h/2], color = (0.8, 0.8, 0.8), zorder=0)
## the right traingle
plt.fill([1+2*offset+0.25, 1+2*offset+0.5, 1+2*offset+0.75, 1+2*offset+0.25],
[h/2, 0, h/2, h/2], color = (0.8, 0.8, 0.8), zorder=0)
## the diamond
plt.fill([0.5+offset+0.25, 0.5+offset+0.25+0.5, 0.5+offset+0.25+0.25, 0.5+offset+0.25],
[h+offset*np.tan(np.pi/3) - 0.5*np.sin(np.pi/3),
h+offset*np.tan(np.pi/3) - 0.5*np.sin(np.pi/3), h+offset*np.tan(np.pi/3),
h+offset*np.tan(np.pi/3) - 0.5*np.sin(np.pi/3)],
color = (0.8, 0.8, 0.8), zorder=0)
plt.fill([0.5+offset+0.25, 0.5+offset+0.25+0.25, 0.5+offset+0.25+0.5, 0.5+offset+0.25],
[h+offset*np.tan(np.pi/3) + 0.5*np.sin(np.pi/3), h+offset*np.tan(np.pi/3),
h+offset*np.tan(np.pi/3) + 0.5*np.sin(np.pi/3), h+offset*np.tan(np.pi/3) + 0.5*np.sin(np.pi/3)],
color = (0.8, 0.8, 0.8), zorder=0)
# Convert unit if needed
if unit == 'mg/L':
gmol = np.array([ions_WEIGHT['Ca'], ions_WEIGHT['Mg'], ions_WEIGHT['Na'], ions_WEIGHT['K'],
ions_WEIGHT['HCO3'], ions_WEIGHT['CO3'], ions_WEIGHT['Cl'], ions_WEIGHT['SO4']])
eqmol = np.array([ions_CHARGE['Ca'], ions_CHARGE['Mg'], ions_CHARGE['Na'], ions_CHARGE['K'],
ions_CHARGE['HCO3'], ions_CHARGE['CO3'], ions_CHARGE['Cl'], ions_CHARGE['SO4']])
tmpdf = df[['Ca', 'Mg', 'Na', 'K', 'HCO3', 'CO3', 'Cl', 'SO4']]
dat = tmpdf.values
meqL = (dat / abs(gmol)) * abs(eqmol)
elif unit == 'meq/L':
meqL = df[['Ca', 'Mg', 'Na', 'K', 'HCO3', 'CO3', 'Cl', 'SO4']].values
else:
raise ValueError("""
Currently only mg/L and meq/L are supported.
Convert the unit if needed.""")
# Calculate the percentages
sumcat = np.sum(meqL[:, 0:4], axis=1)
suman = np.sum(meqL[:, 4:8], axis=1)
cat = np.zeros((dat.shape[0], 3))
an = np.zeros((dat.shape[0], 3))
cat[:, 0] = meqL[:, 0] / sumcat # Ca
cat[:, 1] = meqL[:, 1] / sumcat # Mg
cat[:, 2] = (meqL[:, 2] + meqL[:, 3]) / sumcat # Na+K
an[:, 0] = (meqL[:, 4] + meqL[:, 5]) / suman # HCO3 + CO3
an[:, 2] = meqL[:, 6] / suman # Cl
an[:, 1] = meqL[:, 7] / suman # SO4
# Convert into cartesian coordinates
cat_x = 0.5 * (2 * cat[:, 2] + cat[:, 1])
cat_y = h * cat[:, 1]
an_x = 1 + 2 * offset + 0.5 * (2 * an[:, 2] + an[:, 1])
an_y = h * an[:, 1]
d_x = an_y / (4 * h) + 0.5 * an_x - cat_y / (4 * h) + 0.5 * cat_x
d_y = 0.5 * an_y + h * an_x + 0.5 * cat_y - h * cat_x
# Plot the scatters
Labels = []
for i in range(len(df)):
if (df.at[i, 'Label'] in Labels or df.at[i, 'Label'] == ''):
TmpLabel = ''
else:
TmpLabel = df.at[i, 'Label']
Labels.append(TmpLabel)
try:
if (df['Color'].dtype is np.dtype('float')) or \
(df['Color'].dtype is np.dtype('int64')):
vmin = np.min(df['Color'].values)
vmax = np.max(df['Color'].values)
cf = plt.scatter(cat_x[i], cat_y[i],
marker=df.at[i, 'Marker'],
s=df.at[i, 'Size'],
c=df.at[i, 'Color'], vmin=vmin, vmax=vmax,
alpha=df.at[i, 'Alpha'],
#label=TmpLabel,
edgecolors='black')
plt.scatter(an_x[i], an_y[i],
marker=df.at[i, 'Marker'],
s=df.at[i, 'Size'],
c=df.at[i, 'Color'], vmin=vmin, vmax=vmax,
alpha=df.at[i, 'Alpha'],
label=TmpLabel,
edgecolors='black')
plt.scatter(d_x[i], d_y[i],
marker=df.at[i, 'Marker'],
s=df.at[i, 'Size'],
c=df.at[i, 'Color'], vmin=vmin, vmax=vmax,
alpha=df.at[i, 'Alpha'],
#label=TmpLabel,
edgecolors='black')
else:
plt.scatter(cat_x[i], cat_y[i],
marker=df.at[i, 'Marker'],
s=df.at[i, 'Size'],
c=df.at[i, 'Color'],
alpha=df.at[i, 'Alpha'],
#label=TmpLabel,
edgecolors='black')
plt.scatter(an_x[i], an_y[i],
marker=df.at[i, 'Marker'],
s=df.at[i, 'Size'],
c=df.at[i, 'Color'],
alpha=df.at[i, 'Alpha'],
label=TmpLabel,
edgecolors='black')
plt.scatter(d_x[i], d_y[i],
marker=df.at[i, 'Marker'],
s=df.at[i, 'Size'],
c=df.at[i, 'Color'],
alpha=df.at[i, 'Alpha'],
#label=TmpLabel,
edgecolors='black')
except(ValueError):
pass
# Creat the legend
if (df['Color'].dtype is np.dtype('float')) or (df['Color'].dtype is np.dtype('int64')):
cb = plt.colorbar(cf, extend='both', spacing='uniform',
orientation='vertical', fraction=0.025, pad=0.05)
cb.ax.set_ylabel('$TDS$' + ' ' + '$(mg/L)$', rotation=90, labelpad=-75)
plt.legend(bbox_to_anchor=(0.15, 0.875), markerscale=1,
frameon=False,
labelspacing=0.25, handletextpad=0.25)
# Save the figure
plt.savefig(file_path, bbox_inches='tight', dpi=dpi)
print('Se ha grabado:', file_path)
plt.clf() # Clear the current figure.
plt.close() # Close the figure window.
plt.rcParams.update(plt.rcParamsDefault) # Reset the parameters to their default values.
def stiff(df: pd.DataFrame, dir_path: Path, unit='mg/L', figformat='png', dpi=200, verbose=True) -> None:
"""
Plots the Stiff diagram for each Sample in df
https://github.com/jyangfsu/WQChartPy/tree/main/wqchartpy
Parameters
----------
df : pandas.DataFrame
Geochemical data to draw Gibbs diagram.
dir_path : str or Path
Directory path where figures will be saved.
unit : str
The unit used in df (mg/L or meq/L).
figformat : str default 'png'
The file format, e.g. 'png', 'pdf', 'svg'
dpi : image figure resolution
verbose : print the Sample of each figure
"""
# Basic data check
# -------------------------------------------------------------------------
# Determine if the required geochemical parameters are defined.
if not {'Sample', 'Ca', 'Mg', 'Na', 'K', 'HCO3', 'Cl', 'SO4'}.issubset(df.columns):
raise ValueError("Dataframe must have columns: Sample, Ca, Mg, Na, K, HCO3, Cl, SO4")
# Determine if the provided unit is allowed
ALLOWED_UNITS = ['mg/L', 'meq/L']
if unit not in ALLOWED_UNITS:
raise ValueError("Unit must be mg/L or meq/L")
plt.style.use('default')
plt.rcParams['font.size'] = 10
plt.rcParams['axes.labelsize'] = 10
plt.rcParams['axes.labelweight'] = 'bold'
plt.rcParams['axes.titlesize'] = 11
plt.rcParams['xtick.labelsize'] = 10
plt.rcParams['ytick.labelsize'] = 10
plt.rcParams['legend.fontsize'] = 10
plt.rcParams['figure.titlesize'] = 11
ions_WEIGHT = {key: value[0] for key, value in ions.items()}
ions_CHARGE = {key: value[1] for key, value in ions.items()}
# Convert unit if needed
if unit == 'mg/L':
gmol = np.array([ions_WEIGHT['Ca'], ions_WEIGHT['Mg'], ions_WEIGHT['Na'], ions_WEIGHT['K'],
ions_WEIGHT['HCO3'], ions_WEIGHT['Cl'], ions_WEIGHT['SO4']])
eqmol = np.array([ions_CHARGE['Ca'], ions_CHARGE['Mg'], ions_CHARGE['Na'], ions_CHARGE['K'],
ions_CHARGE['HCO3'], ions_CHARGE['Cl'], ions_CHARGE['SO4']])
tmpdf = df[['Ca', 'Mg', 'Na', 'K', 'HCO3', 'Cl', 'SO4']]
dat = tmpdf.values
meqL = (dat / abs(gmol)) * abs(eqmol)
else:
meqL = df[['Ca', 'Mg', 'Na', 'K', 'HCO3', 'Cl', 'SO4']].values
cat_max = np.max(np.array(((meqL[:, 2] + meqL[:, 3]), meqL[:, 0], meqL[:, 1])))
an_max = np.max(meqL[:, 4:])
if isinstance(dir_path, str):
dir_path = Path(dir_path)
figformat = figformat.replace('.','')
for i, (index, row) in enumerate(df.iterrows()):
x = [-(meqL[i, 2] + meqL[i, 3]), -meqL[i, 0], -meqL[i, 1],
meqL[i, 6], meqL[i, 4], meqL[i, 5], -(meqL[i, 2] + meqL[i, 3])]
y = [3, 2, 1, 1, 2, 3, 3]
plt.figure(figsize=(3, 3))
plt.fill(x, y, facecolor='w', edgecolor='k', linewidth=1.25)
plt.plot([0, 0], [1, 3], 'k-.', linewidth=1.25)
plt.plot([-0.5, 0.5], [2, 2], 'k-')
cmax = cat_max if cat_max > an_max else an_max
plt.xlim([-cmax, cmax])
plt.text(-cmax, 2.9, 'Na$^+$' + '+' + 'K$^+$', ha= 'right')
plt.text(-cmax, 1.9, 'Ca$^{2+}$', ha= 'right')
plt.text(-cmax, 1.0, 'Mg$^{2+}$', ha= 'right')
plt.text(cmax, 2.9,'Cl$^-$', ha= 'left')
plt.text(cmax, 1.9,'HCO'+'$_{3}^-$',ha= 'left')
plt.text(cmax, 1.0,'SO'+'$_{4}^{2-}$',ha= 'left')
ax = plt.gca()
ax.spines['left'].set_color('None')
ax.spines['right'].set_color('None')
ax.spines['top'].set_color('None')
ax.spines['bottom'].set_linewidth(1.25)
ax.spines['bottom'].set_color('k')
plt.ylim(0.8, 3.2)
plt.yticks([])
plt.gca().yaxis.set_visible(False)
plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))
ticks = np.array([-cmax, -cmax/2, 0, cmax/2, cmax])
tickla = [f'{tick:1.0f}' for tick in abs(ticks)]
ax.xaxis.set_ticks(ticks)
ax.xaxis.set_ticklabels(tickla)
ax.set_xlabel('Stiff diagram (meq/L)', weight='normal')
ax.set_title(row['Sample'], weight='normal')
file_name = str(row['Sample']) + '_stiff.' + figformat
if verbose:
print(file_name)
plt.savefig(dir_path.joinpath(file_name), bbox_inches='tight', dpi=dpi)
plt.clf() # Clear the current figure.
plt.close() # Close the figure window.
plt.rcParams.update(plt.rcParamsDefault) # Reset the parameters to their default values.
def schoeller(df, file_path: Path, unit='mg/L', dpi=200) -> None:
"""
Plots Schoeller diagram.
Parameters
----------
df : pd.DataFrame
Geochemical data to draw HFE-D diagram.
file_path : str or Path.
File path where figure wil saved.
unit : str.
Units used in df (mg/L or meq/L).
dpi : int
Resolution of the digital image (dots per inch)
"""
# Determine if the required geochemical parameters are defined.
if not {'Ca', 'Mg', 'Na', 'K', 'Cl', 'SO4', 'HCO3'}.issubset(df.columns):
raise RuntimeError("Schoeller diagram needs Ca, Mg, Na, K, Cl, SO4, and HCO3 in the columns of df")
# Determine if the provided unit is allowed
ALLOWED_UNITS = ['mg/L', 'meq/L']
if unit not in ALLOWED_UNITS:
raise ValueError("unit must be mg/L or meq/L")
plt.style.use('default')
plt.rcParams['font.size'] = 9
plt.rcParams['axes.labelsize'] = 9
plt.rcParams['axes.labelweight'] = 'bold'
plt.rcParams['axes.titlesize'] = 9
plt.rcParams['xtick.labelsize'] = 9
plt.rcParams['ytick.labelsize'] = 9
plt.rcParams['legend.fontsize'] = 9
plt.rcParams['figure.titlesize'] = 10
ions_WEIGHT = {key: value[0] for key, value in ions.items()}
ions_CHARGE = {key: value[1] for key, value in ions.items()}
# Convert unit if needed
# -------------------------------------------------------------------------
if unit == 'mg/L':
gmol = np.array([ions_WEIGHT['Ca'], ions_WEIGHT['Mg'], ions_WEIGHT['Na'],
ions_WEIGHT['K'], ions_WEIGHT['Cl'], ions_WEIGHT['SO4'],
ions_WEIGHT['HCO3']])
eqmol = np.array([ions_CHARGE['Ca'], ions_CHARGE['Mg'], ions_CHARGE['Na'],
ions_CHARGE['K'], ions_CHARGE['Cl'], ions_CHARGE['SO4'],
ions_CHARGE['HCO3']])
tmpdf = df[['Ca', 'Mg', 'Na', 'K', 'Cl', 'SO4', 'HCO3']]
dat = tmpdf.values
meqL = (dat / abs(gmol)) * abs(eqmol)
else:
meqL = df[['Ca', 'Mg', 'Na', 'K', 'Cl', 'SO4', 'HCO3']].values
# -------------------------------------------------------------------------
fig = plt.figure(figsize=(6, 3))
ax = fig.add_subplot(111)
ax.semilogy()
# Plot the lines
# -------------------------------------------------------------------------
Labels = []
for i in range(len(df)):
if (df.at[i, 'Label'] in Labels or df.at[i, 'Label'] == ''):
TmpLabel = ''
else:
TmpLabel = df.at[i, 'Label']
Labels.append(TmpLabel)
try:
ax.plot([1, 2, 3, 4, 5, 6, 7], meqL[i, :],
marker=df.at[i, 'Marker'],
color=df.at[i, 'Color'],
alpha=df.at[i, 'Alpha'],
label=TmpLabel)
except(ValueError):
pass
# Background settings
ax.set_xticks([1, 2, 3, 4, 5, 6, 7])
ax.set_xticklabels(['Ca$^{2+}$', 'Mg$^{2+}$', 'Na$^+$', 'K$^+$',
'Cl$^-$', 'SO$_4^{2-}$', 'HCO$_3^-$'])
ax.set_ylabel('meq/L', fontsize=12, weight='normal')
# Set the limits
ax.set_xlim([1, 7])
ax.set_ylim([np.min(meqL) * 0.5, np.max(meqL) * 1.5])
# Plot the vertical lines
for xtick in [1, 2, 3, 4, 5, 6, 7]:
plt.axvline(xtick, linewidth=1, color='grey', linestyle='dashed')
# Creat the legend
ax.legend(loc='best', markerscale=1, frameon=False,
labelspacing=0.25, handletextpad=0.25)
# Save the figure
plt.savefig(file_path, bbox_inches='tight', dpi=dpi)
print('Se ha grabado:', file_path)
plt.clf() # Clear the current figure.
plt.close() # Close the figure window.
plt.rcParams.update(plt.rcParamsDefault) # Reset the parameters to their default values.