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figures.py
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figures.py
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# -*- coding: utf-8 -*-
import math
from logging import getLogger
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg
from matplotlib.figure import Figure
from PySide2 import QtWidgets
from activity_browser.mod.bw2data import methods
from activity_browser.utils import savefilepath
from ..bwutils.commontasks import wrap_text
log = getLogger(__name__)
# todo: sizing of the figures needs to be improved and systematized...
# todo: Bokeh is a potential alternative as it allows interactive visualizations,
# but this issue needs to be resolved first: https://github.com/bokeh/bokeh/issues/8169
class Plot(QtWidgets.QWidget):
ALL_FILTER = "All Files (*.*)"
PNG_FILTER = "PNG (*.png)"
SVG_FILTER = "SVG (*.svg)"
def __init__(self, parent=None):
super().__init__(parent)
# create figure, canvas, and axis
# self.figure = Figure(tight_layout=True)
self.figure = Figure(constrained_layout=True)
self.canvas = FigureCanvasQTAgg(self.figure)
self.canvas.setMinimumHeight(0)
self.ax = self.figure.add_subplot(111) # create an axis
self.plot_name = "Figure"
# set the layout
layout = QtWidgets.QVBoxLayout()
layout.addWidget(self.canvas)
self.setLayout(layout)
self.setSizePolicy(
QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding
)
self.updateGeometry()
def plot(self, *args, **kwargs):
raise NotImplementedError
def reset_plot(self) -> None:
self.figure.clf()
self.ax = self.figure.add_subplot(111)
def get_canvas_size_in_inches(self):
# print("Canvas size:", self.canvas.get_width_height())
return tuple(x / self.figure.dpi for x in self.canvas.get_width_height())
def to_png(self):
"""Export to .png format."""
filepath = savefilepath(
default_file_name=self.plot_name, file_filter=self.PNG_FILTER
)
if filepath:
if not filepath.endswith(".png"):
filepath += ".png"
self.figure.savefig(filepath)
def to_svg(self):
"""Export to .svg format."""
filepath = savefilepath(
default_file_name=self.plot_name, file_filter=self.SVG_FILTER
)
if filepath:
if not filepath.endswith(".svg"):
filepath += ".svg"
self.figure.savefig(filepath)
class LCAResultsBarChart(Plot):
""" " Generate a bar chart comparing the absolute LCA scores of the products"""
def __init__(self, parent=None):
super().__init__(parent)
self.plot_name = "LCA scores"
def plot(self, df: pd.DataFrame, method: tuple, labels: list):
self.reset_plot()
height_inches, width_inches = self.get_canvas_size_in_inches()
self.figure.set_size_inches(height_inches, width_inches)
# https://github.com/LCA-ActivityBrowser/activity-browser/issues/489
df.index = pd.Index(labels) # Replace index of tuples
show_legend = df.shape[1] != 1 # Do not show the legend for 1 column
df.plot.barh(ax=self.ax, legend=show_legend)
self.ax.invert_yaxis()
# labels
self.ax.set_yticks(np.arange(len(labels)))
self.ax.set_xlabel(methods[method].get("unit"))
self.ax.set_title(", ".join([m for m in method]))
# self.ax.set_yticklabels(labels, minor=False)
# grid
self.ax.grid(which="major", axis="x", color="grey", linestyle="dashed")
self.ax.set_axisbelow(True) # puts gridlines behind bars
# draw
self.canvas.draw()
class LCAResultsPlot(Plot):
def __init__(self, parent=None):
super().__init__(parent)
self.plot_name = "LCA heatmap"
def plot(self, df: pd.DataFrame, invert_plot: bool = False):
"""Plot a heatmap grid of the different impact categories and reference flows."""
# need to clear the figure and add axis again
# because of the colorbar which does not get removed by the ax.clear()
self.reset_plot()
dfp = df.copy()
dfp.index = dfp["index"]
dfp.drop(
dfp.select_dtypes(["object"]), axis=1, inplace=True
) # get rid of all non-numeric columns (metadata)
if "amount" in dfp.columns:
dfp.drop(["amount"], axis=1, inplace=True) # Drop the 'amount' col
if "Total" in dfp.index:
dfp.drop("Total", inplace=True)
# avoid figures getting too large horizontally
dfp.index = [wrap_text(i, max_length=40) for i in dfp.index]
dfp.columns = [wrap_text(i, max_length=20) for i in dfp.columns]
prop = dfp.divide(dfp.abs().max(axis=0)).multiply(100)
dfp.replace(np.nan, 0, inplace=True)
if invert_plot:
dfp = dfp.T
prop = prop.T
# set different color palette depending on whether all values are positive or not
if (
dfp.min(axis=None) < 0 and dfp.max(axis=None) > 0
): # has both negative AND positive values
cmap = sns.color_palette("vlag_r", as_cmap=True)
else: # has only positive OR negative values
cmap = sns.color_palette("Blues", as_cmap=True)
sns.heatmap(
prop,
ax=self.ax,
cmap=cmap,
annot=dfp,
linewidths=0.05,
annot_kws={
"size": 11 if dfp.shape[1] <= 8 else 9,
"rotation": 0 if dfp.shape[1] <= 8 else 60,
},
cbar_kws={"format": "%.0f%%"},
)
self.ax.tick_params(labelsize=8)
if dfp.shape[1] > 5:
self.ax.set_xticklabels(self.ax.get_xticklabels(), rotation="vertical")
self.ax.set_yticklabels(self.ax.get_yticklabels(), rotation="horizontal")
# refresh canvas
size_inches = (2 + dfp.shape[0] * 0.5, 4 + dfp.shape[0] * 0.55)
self.figure.set_size_inches(self.get_canvas_size_in_inches()[0], size_inches[1])
size_pixels = self.figure.get_size_inches() * self.figure.dpi
self.setMinimumHeight(size_pixels[1])
self.canvas.draw()
class ContributionPlot(Plot):
MAX_LEGEND = 30
def __init__(self):
super().__init__()
self.plot_name = "Contributions"
def plot(self, df: pd.DataFrame, unit: str = None):
"""Plot a horizontal bar chart of the process contributions."""
dfp = df.copy()
dfp.index = dfp["index"]
dfp.drop(
dfp.select_dtypes(["object"]), axis=1, inplace=True
) # get rid of all non-numeric columns (metadata)
if "Total" in dfp.index:
dfp.drop("Total", inplace=True)
self.ax.clear()
canvas_width_inches, canvas_height_inches = self.get_canvas_size_in_inches()
optimal_height_inches = 4 + dfp.shape[1] * 0.55
# print('Optimal Contribution plot height:', optimal_height_inches)
self.figure.set_size_inches(canvas_width_inches, optimal_height_inches)
# avoid figures getting too large horizontally
dfp.index = pd.Index([wrap_text(str(i), max_length=40) for i in dfp.index])
dfp.columns = pd.Index([wrap_text(i, max_length=40) for i in dfp.columns])
# Strip invalid characters from the ends of row/column headers
dfp.index = dfp.index.str.strip("_ \n\t")
dfp.columns = dfp.columns.str.strip("_ \n\t")
dfp.T.plot.barh(
stacked=True,
cmap=plt.cm.nipy_spectral_r,
ax=self.ax,
legend=False if dfp.shape[0] >= self.MAX_LEGEND else True,
)
self.ax.tick_params(labelsize=8)
if unit:
self.ax.set_xlabel(unit)
# show legend if not too many items
if not dfp.shape[0] >= self.MAX_LEGEND:
plt.rc("legend", **{"fontsize": 8})
ncols = math.ceil(dfp.shape[0] * 0.6 / optimal_height_inches)
# print('Ncols:', ncols, dfp.shape[0] * 0.55, optimal_height_inches)
self.ax.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=ncols)
# grid
self.ax.grid(which="major", axis="x", color="grey", linestyle="dashed")
self.ax.set_axisbelow(True) # puts gridlines behind bars
# TODO review: remove or enable
# refresh canvas
# size_inches = (2 + dfp.shape[0] * 0.5, 4 + dfp.shape[1] * 0.55)
# self.figure.set_size_inches(self.get_canvas_size_in_inches()[0], size_inches[1])
size_pixels = self.figure.get_size_inches() * self.figure.dpi
self.setMinimumHeight(size_pixels[1])
self.canvas.draw()
class CorrelationPlot(Plot):
def __init__(self, parent=None):
super().__init__(parent)
sns.set(style="darkgrid")
def plot(self, df: pd.DataFrame):
"""Plot a heatmap of correlations between different reference flows."""
# need to clear the figure and add axis again
# because of the colorbar which does not get removed by the ax.clear()
self.reset_plot()
canvas_size = self.canvas.get_width_height()
# print("Canvas size:", canvas_size)
size = (4 + df.shape[1] * 0.3, 4 + df.shape[1] * 0.3)
self.figure.set_size_inches(size[0], size[1])
corr = df.corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=bool)
mask[np.triu_indices_from(mask)] = True
# Draw the heatmap with the mask and correct aspect ratio
vmax = np.abs(corr.values[~mask]).max()
# vmax = np.abs(corr).max()
sns.heatmap(
corr,
mask=mask,
cmap=plt.cm.PuOr,
vmin=-vmax,
vmax=vmax,
square=True,
linecolor="lightgray",
linewidths=1,
ax=self.ax,
)
df_lte8_cols = df.shape[1] <= 8
for i in range(len(corr)):
self.ax.text(
i + 0.5,
i + 0.5,
corr.columns[i],
ha="center",
va="center",
rotation=0 if df_lte8_cols else 45,
size=11 if df_lte8_cols else 9,
)
for j in range(i + 1, len(corr)):
s = "{:.3f}".format(corr.values[i, j])
self.ax.text(
j + 0.5,
i + 0.5,
s,
ha="center",
va="center",
rotation=0 if df_lte8_cols else 45,
size=11 if df_lte8_cols else 9,
)
self.ax.axis("off")
# refresh canvas
size_pixels = self.figure.get_size_inches() * self.figure.dpi
self.setMinimumHeight(size_pixels[1])
self.canvas.draw()
class MonteCarloPlot(Plot):
"""Monte Carlo plot."""
def __init__(self, parent=None):
super().__init__(parent)
self.plot_name = "Monte Carlo"
def plot(self, df: pd.DataFrame, method: tuple):
self.ax.clear()
for col in df.columns:
color = self.ax._get_lines.get_next_color()
df[col].hist(
ax=self.ax,
figure=self.figure,
label=col,
density=True,
color=color,
alpha=0.5,
) # , histtype="step")
# self.ax.axvline(df[col].median(), color=color)
self.ax.axvline(df[col].mean(), color=color)
self.ax.set_xlabel(methods[method]["unit"])
self.ax.set_ylabel("Probability")
self.ax.legend(
loc="upper center",
bbox_to_anchor=(0.5, -0.07),
) # ncol=2
# lconfi, upconfi =mc['statistics']['interval'][0], mc['statistics']['interval'][1]
self.canvas.draw()
class SimpleDistributionPlot(Plot):
def plot(self, data: np.ndarray, mean: float, label: str = "Value"):
self.reset_plot()
try:
sns.histplot(data.T, kde=True, stat="density", ax=self.ax, edgecolor="none")
except RuntimeError as e:
log.error("{}: Plotting without KDE.".format(e))
sns.histplot(
data.T, kde=False, stat="density", ax=self.ax, edgecolor="none"
)
self.ax.set_xlabel(label)
self.ax.set_ylabel("Probability density")
# Add vertical line at given mean of x-axis
self.ax.axvline(mean, label="Mean / amount", c="r", ymax=0.98)
self.ax.legend(loc="upper right")
_, height = self.canvas.get_width_height()
self.setMinimumHeight(height / 2)
self.canvas.draw()