ba-thesis/sw/utility/visualization.py

68 lines
2.4 KiB
Python

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import typing
from itertools import chain
import math
def _get_num_rows(num_graphs: int, num_cols: int) -> int:
"""Get the minimum number of rows needed to show a certain number of graphs,
given a certain number of columns.
:param num_graphs: Number of graphs
:param num_cols: Number of columns
:return: Number of rows
"""
return math.ceil(num_graphs / num_cols)
# TODO: Handle number of graphs not nicely fitting into rows and columns
def plot_BERs(title: str,
data: typing.Sequence[typing.Tuple[str, pd.DataFrame, typing.Sequence[str]]],
num_cols: int = 3) -> plt.figure:
"""This function creates a matplotlib figure containing a number of plots.
The plots created are logarithmic and the scaling is adjusted to be sensible for BER plots.
:param title: Title of the figure
:param data: Sequence of tuples. Each tuple corresponds to a new plot and
is of the following form: [graph_title, pd.Dataframe, [line_label_1, line_label2, ...]].
Each dataframe is assumed to have an "SNR" column that is used as the x axis.
:param num_cols: Number of columns in which the graphs should be arranged in the resulting figure
:return: Matplotlib figure
"""
# Determine layout and create figure
num_graphs = len(data)
num_rows = _get_num_rows(num_graphs, num_cols)
fig, axes = plt.subplots(num_rows, num_cols, figsize=(num_cols*4, num_rows*4), squeeze=False)
fig.suptitle(title)
fig.subplots_adjust(left=0.1,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.3,
hspace=0.4)
axes = list(chain.from_iterable(axes))[:num_graphs] # Flatten the 2d axes array
# Populate axes
for axis, (graph_title, df, labels) in zip(axes, data):
column_names = [column for column in df.columns.values.tolist() if not column == "SNR"]
for column, label in zip(column_names, labels):
sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=label)
axis.set_title(graph_title)
axis.set(yscale="log")
axis.set_xlabel("SNR")
axis.set_ylabel("BER")
axis.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
axis.legend()
return fig