Score plotting tools

Created on 24 April 2024

@author: radanovics

based on code from Ange Haddjeri thesis.

class plots.scores.perfdiag.PerfDiag(figsize=(9, 8), csi_cmap='Purples', title='Performance diagram')

Bases: Mplfigure

Performance diagram Plot 1-FAR on x-axis, POD on y-axis and iso-lines for frequency bias and CSI

Example:

import matplotlib.pyplot as plt
from snowtools.plots.scores.perfdiag import PerfDiag

diag = PerfDiag()
diag.plot_scores([0.2, 0.5, 0.6, 0.9], [0.1, 0.5, 0.7, 0.4])
diag.add_legend()
diag.addlogo()
plt.show()
diag.close()

Parameters:

• figsize – figure size

• csi_cmap – colormap for CSI shading. Will be truncated

• title – Figure title

__init__(figsize=(9, 8), csi_cmap='Purples', title='Performance diagram')

Parameters:

• figsize – figure size

• csi_cmap – colormap for CSI shading. Will be truncated

• title – Figure title

add_datapoint(pod, far, marker='*', color='#6ACC64', label=None, linestyle='None', markersize=10)

Parameters:

• pod – Probability of detection

• far – False Alarm Ratio

• marker – matplotlib marker type

• color – matplotlib compatible color specification

• label – experiment label for the legend

• linestyle – matplotlib linestyle

• markersize – marker size

Returns:

add_labels()

add axis labels and tick markers. Annotate the background lines with “Frequency Bias” and “CSI score”. :return:

add_legend()

add a legend.

default_colors = <matplotlib.colors.ListedColormap object>

default colors for the different experiments

diagram_background()

create diagram background with CSI iso-lines and shading and frequency bias iso-lines

plot_scores(pod, far, list_colors=None, list_markers=None, list_labels=None, list_markersizes=None)

Parameters:

• pod – list or 1d array of Probability of detection values

• far – list or 1d array of False Alarm Ratio

• list_colors (list) – colors for the different points. List of length 1 (all points will be plotted with the same color) or same length as pod and far. If None, default colours self.default_colors will be used.

• list_markers (list) – Markers for the different points. List of length 1 (all points will be plotted with the same marker) or same length as pod and far. If None, all points will be plotted with the ‘*’ marker

• list_labels (list) – List of the same length as pod and far with labels to put on the legend. If None, the labels will be “experiment 1”, “experiment 2 etc.

• list_markersizes (list) – Marker sizes for the different points. List of length 1 (all points will be plotted with the same marker size) or same length as pod and far. If None, all points will be plotted with a marker size of 10.

Returns:

Created on 23 July 2024

@author: radanovics

based on code from Ange Haddjeri thesis. (chapter 5 notebook)

class plots.scores.moran_scatter.MoranScatter(variable_name, figsize=(9, 9), title='Moran Scatterplot')

Bases: Mplfigure

Moran Scatter plot. Scatter plot between a variable and a spatially lagged variable. https://dces.wisc.edu/wp-content/uploads/sites/128/2013/08/W4_Anselin1996.pdf

Example:

import numpy as np
import matplotlib.pyplot as plt
from snowtools.plots.scores.moran_scatter import MoranScatter

diag = MoranScatter('snow height')
diag.plot_var(np.array([0.2, 0.5, 0.6, 0.9, 0.1, 0.4, 1.3, 0.3, 0.15, 0.25]),
 np.array([0.1, 0.5, 0.7, 1.1, 0.4, 0.5, 0.8, 0.9, 0.2, 0.15]))
diag.addlogo()
plt.show()
diag.close()

Parameters:

• variable_name (str) – variable name used for axis labels

• figsize (tuple) – optional figure size. Default: (9, 9)

• title (str) – figure title. Default: “Moran Scatterplot”

plot_var(variable, lagged_variable, color='firebrick', marker='.', slopecolor='r')

plot data. :param variable: data variable :type variable: np.array :param lagged_variable: lagged data variable :type lagged_variable: np.array :param color: color of points Default: ‘firebrick’ :param marker: marker type. Default: ‘.’ :param slopecolor: color of regression line. Default: ‘r’ (red)

class plots.scores.moran_scatter.MoranScatterColored(variable_name, figsize=(9, 9), title='Moran Scatterplot')

Bases: MoranScatter

class for colored Moran Scatter Plot. The points are colored according to the quandrant and non-significant local Moran values are plotted in grey.

Parameters:

• variable_name (str) – variable name used for axis labels

• figsize (tuple) – optional figure size. Default: (9, 9)

• title (str) – figure title. Default: “Moran Scatterplot”

add_legend()

add a legend to the moran scatter plot

property legend_params

standard legend parameters for Moran Scatter plot :return: legend parameters :rtype: dict

property palette

Moran scatter specific colormap :return: colormap instance :rtype: matplotlib.colors.LinearSegmentedColormap

plot_var(variable, lagged_variable, color=None, marker='.', slopecolor='r')

plot data. :param variable: data variable :type variable: np.array :param lagged_variable: lagged data variable :type lagged_variable: np.array :param color: array of quadrant numbers to use for coloring :type color: np.array :param marker: marker type. Default: ‘.’ :param slopecolor: color of regression line. Default: ‘r’ (red)

plots.scores.moran_scatter.get_moran_palette()

get colors for the local moran Is plots. The color map is derived from the ‘Paired’ colormap, but only dark and light red and dark and light blue are selected. The under color is set to light grey. :return: a color palette :rtype: matplotlib.colors.LinearSegmentedColormap