pycharmers.matplotlib.plot2d module

pycharmers.matplotlib.plot2d.plot_hist(data, bins=None, ax=None, roffset=0.01, rwidth=0.95, hist_color=None, anno_color='green')

Plot Histogram

Parameters

• data (array) – Array-like sample data.

• bins (int) – Defines the number of equal-width bins in the range.

• ax (AxesSubplot) – The Axes instance.

• roffset (float) – Offset from histogram to annotation text.

• rwidth (float) – The relative width of the bars as a fraction of the bin width. If None, automatically compute the width.

• hist_color (str) – The histogram color.

• anno_color (str) – The annotation text color.

Example

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pycharmers.matplotlib import plot_hist
>>> fig, ax = plt.subplots()
>>> data = np.random.RandomState(123).normal(size=1000)
>>> ax = plot_hist(data, ax=ax, rwidth=0.95)
>>> fig.savefig("matplotlib.plot2d.plot_hist.jpg")

Results

pycharmers.matplotlib.plot2d.plot_cumulative_ratio(data, labels=None, bins=10, width=0.8, reverse=False, ax=None, bar=False, cmap=None)

Plot Cumulative Ration (bar graph / line graph)

Parameters

• data (array) – Array-like sample data.

• labels (array) – Array-like labels.

• bins (int) – Defines the number of equal-width bins in the range.

• reverse (bool) – Whether plot Reverse cumulative distribution curve or not.

• ax (AxesSubplot) – The Axes instance.

• bar (bool) – Whether plot as bar or graph.

• cmap (str) – The name of a color map known to matplotlib

Example

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pycharmers.matplotlib import plot_cumulative_ratio
>>> ndata = 1000
>>> rnd = np.random.RandomState(123)
>>> labels = rnd.randint(low=0, high=4, size=ndata)
>>> data   = rnd.normal(size=ndata) + labels*0.25
>>> fig, ax = plt.subplots()
>>> plot_cumulative_ratio(data, ax=ax, labels=labels, bar=True)
>>> ax.legend()
>>> fig.savefig("matplotlib.plot2d.plot_cumulative_ratio.jpg")

Results

pycharmers.matplotlib.plot2d.plot_classification_performance(cm=None, y_true=None, y_pred=None, cmap='RdBu', answer_label='answer', predict_label='predict', ax=None)

Plot model”s classification performance.

Parameters

• cm (array) – Confusion matrix whose i-th row and j-th column entry indicates the number of samples with true label being i-th class and prediced label being j-th class.

• y_true (array) – Ground truth (correct) target values.

• y_pred (array) – Estimated targets as returned by a classifier.

• cmap (str) – The name of a color map known to matplotlib

• ax (AxesSubplot) – The Axes instance.

• answer_label (str) – The label name on the correct answer side.

• predict_label (str) – The label name on the prediction side.

Returns

An array of Axes objects if more than one subplot was created.

Return type

axes (Axes)

Examples

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pycharmers.matplotlib import plot_classification_performance
>>> fig, ax = plt.subplots(figsize=(5,5))
>>> rnd = np.random.RandomState(123)
>>> y_true = rnd.randint(low=0, high=4, size=100)
>>> y_pred = rnd.randint(low=0, high=4, size=100)
>>> plot_classification_performance(y_true=y_true, y_pred=y_pred, ax=ax)
>>> fig.savefig("matplotlib.plot2d.plot_classification_performance.jpg")

Results

pycharmers.matplotlib.plot2d.plot_lines(data, ax=None, transpose=False, margin=0.5, label=None, linewidth=None, linestyle=None, color=None, marker=None, **kwargs)

Plot lines.

Parameters

• data (array) – Array-like sample data.

• ax (AxesSubplot) – The Axes instance.

• transpose (bool) – Whether lines are horizontal or vertical.

• margin (float) – Whether plot as bar or graph.

• label (str) – The label for line”s” data.

• kwargs (dict) – The key word arguments for matplotlib.lines.Line2D, linewidth , linestyle, color (str), marker

Examples

>>> import matplotlib.pyplot as plt
>>> from pycharmers.matplotlib import set_ax_info, plot_lines
>>> #=== Data ===
>>> names = ["A", "B", "C", "D", "E", "F", "G"]
>>> dates = ["early-Jan.","mid-Jan.","late-Jan.","early-Feb.","mid-Feb.","late-Feb.","early-Mar.","mid-Mar.","late-Mar."]
>>> month_colors = ["#e30013", "#4b73b6", "#f09eb0"]
>>> schedule_hope = [None, None, 4, 6, 2, 0, 3]
>>> schedule_inconvenient = [[], [2], [1, 2, 3], [0, 1, 2, 5, 7, 8], [1, 3, 4, 5], [1, 2, 5], [0, 5, 6]]
>>> num_names = len(names)
>>> num_dates = len(dates)
>>> #=== Plot ===
>>> fig, ax = plt.subplots(figsize=(12,8), dpi=80, facecolor="white")
>>> ax = plot_lines(data=schedule_inconvenient, ax=ax, transpose=True, color="black", label="Inconvenient")
>>> ax.scatter(x=schedule_hope, y=[i for i in range(len(schedule_hope))], color="red", s=100, marker="*", label="Hope")
>>> for i,color in enumerate(month_colors):
...     ax.fill((i*3-0.5,i*3-0.5,(i+1)*3-0.5,(i+1)*3-0.5), (num_names,0,0,num_names), color=color, alpha=0.1, label=dates[i*3].split("-")[-1])
>>> #=== Decoration ===
>>> ax = set_ax_info(ax, **{
...     "xticks":      {"ticks" : [i for i in range(num_dates)]},
...     "xticklabels": {"labels": dates, "fontsize":16},
...     "yticks":      {"ticks" : [i for i in range(num_names)]},
...     "yticklabels": {"labels": names, "fontsize":16},
...     "title":       {"label": "Results of Schedule Adjustment", "fontsize":20},
>>> })
>>> ax.legend()
>>> plt.tight_layout()
>>> fig.savefig("matplotlib.plot2d.plot_lines.jpg")

Results

pycharmers.matplotlib.plot2d.plot_radar_charts(data, varlabels=[], colors=[], datalabels=[], plottitles=[], frame='Circle', radii=[0.0, 0.2, 0.4, 0.6, 0.8, 1.0], title='', cmap='jet', ncols=1, fig=None, ax=None, figsize=4, 4)

Plot radar charts.

Parameters

• data (np.ndarray) – Array-like sample data. shape=( nplots, ndata, nvars )

• varlabels (list) – Array-like labels of varnames. ( len(varlabels)==nvars )

• colors (list) – Colors for each variables. len(colors)==ndata

• datalabels (list) – Array-like labels of data. ( len(datalabels)==ndata )

• plottitles (list) – Array-like labels of plot titles. ( len(plottitles)==nplots )

• frame (str) – Shape of frame surrounding axes. ( "Circle", or "polygon" )

• radii (list) – The radii for the radial gridlines. (default= [0.2,0.4,0.6,0.8] )

• title (str) – Figure title.

• cmap (str / matplotlib.colors) – Color map.

• ncols (int) – The number of columns.

• fig (Figure) – The Figure instance.

• ax (Axes) – The Axes instance.

• figsize (tuple) – The figure size for 1 plot.

Examples

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pycharmers.matplotlib import plot_radar_charts, mpljapanize
>>> data = np.random.RandomState(123).uniform(low=0.3, high=1.0, size=(2,3,6))
>>> varlabels  = ["Hit Points", "Attack", "Defense", "Special Attack", "Special Defense", "Speed"]
>>> datalabels = ["Evo.1", "Evo.2", "Evo.3"]
>>> plottitles = ["Ruby", "Sapphire"]
>>> fig = plt.figure(figsize=(12,6))
>>> fig.text(x=0.5, y=0.965, s="Stats", horizontalalignment="center", color="black", weight="bold", size="large")
>>> axes = plot_radar_charts(data=data, varlabels=varlabels, datalabels=datalabels, plottitles=plottitles, cmap="jet", fig=fig, ncols=2)
>>> fig.tight_layout()
>>> fig.savefig("matplotlib.plot2d.plot_radar_charts.jpg")

Results
frame	figure
Circle
polygon