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Modified visualization code to wread metadata

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This commit is contained in:
Andreas Tsouchlos 2022-11-21 13:28:00 +01:00
parent d0ed9ffbaa
commit 9beda2231d
2 changed files with 45 additions and 44 deletions
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48
sw/plot_results.py
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@ -3,41 +3,39 @@ import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from utility import visualization
from utility import visualization, simulation
def plot_results():
graph_names = {"96.3.965": "n=96, k=48 - 965",
"204.3.486": "n=204, k=102 - 486",
"204.55.187": "n=204, k=102 - 187",
"408.33.844": "n=408, k=204 - 844",
"816.1A4.845": "n=816, k=272 - 843",
"999.111.3.5543": "n=999, k=888 - 5543",
"999.111.3.5565": "n=999, k=888 - 5565",
"PEGReg252x504": "n=504, k=252 - PEGReg"}
line_labels = {"BER_ML": "ML",
"BER_prox_0_15": "$\gamma = 0.15$",
"BER_prox_0_05": "$\gamma = 0.05$",
"BER_prox_0_01": "$\gamma = 0.01$"}
sim_names = [
"96.3965",
"204.3.486",
"204.55.187",
"408.33.844",
"816.1A4.845",
"999.111.3.5543",
"999.111.3.5565",
"PEGReg252x504"
]
# Read data from files
results_dir = "sim_results"
deserializer = simulation.SimulationDeSerializer(save_dir="sim_saves", results_dir="sim_results")
data = {}
for file in os.listdir(results_dir):
if file.endswith(".csv"):
filename = os.path.splitext(file)[0]
df = pd.read_csv(os.path.join(results_dir, file))
data = []
for sim_name in sim_names:
df, metadata = deserializer.read_results(sim_name)
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
data[graph_names[filename]] = df
# Create and show graphs
graph_title = sim_name
line_labels = metadata["labels"]
graph_tuple = (graph_title, df, line_labels)
data.append(graph_tuple)
sns.set_theme()
fig = visualization.show_BER_curves("Bit-Error-Rates of proximal decoder for different codes",
data, num_cols=4, line_labels=line_labels)
fig = visualization.plot_BERs(title="Bit-Error-Rates of proximal decoder for different codes",
data=data, num_cols=4)
plt.show()

29
sw/utility/visualization.py
View File

@ -18,20 +18,22 @@ def _get_num_rows(num_graphs: int, num_cols: int) -> int:
# TODO: Handle number of graphs not nicely fitting into rows and columns
def show_BER_curves(title: str,
data: typing.Dict[str, pd.DataFrame],
line_labels: typing.Dict[str, str],
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 BER curves.
"""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: Dictionary where each key corresponds to the name of a new graph and the value is a pandas Dataframe
containing the data to be plotted. Each dataframe is assumed to contain a column named "SNR" which is used
as the x-axis
:param line_labels: Dictionary mapping column names to proper labels
: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)
@ -47,14 +49,15 @@ def show_BER_curves(title: str,
axes = list(chain.from_iterable(axes))[:num_graphs] # Flatten the 2d axes array
for axis, name_data_pair in zip(axes, sorted(data.items())):
graph_name, df = name_data_pair
# 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 in column_names:
sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=line_labels[column])
for column, label in zip(column_names, labels):
sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=label)
axis.set_title(graph_name)
axis.set_title(graph_title)
axis.set(yscale="log")
axis.set_xlabel("SNR")
axis.set_ylabel("BER")