# -*- coding: utf-8 -*-
import inspect
import matplotlib
import numpy as np
import pandas as pd
[docs]
def create_report(file="myreport.html", signals=None, info={"sampling_rate": 1000}, fig=None):
"""**Reports**
Create report containing description and figures of processing.
This function is meant to be used via the :func:`.rsp_process` or :func:`.ppg_process`
functions.
Parameters
----------
file : str
Name of the file to save the report to. Can also be ``"text"`` to simply print the text in
the console.
signals : pd.DataFrame
A DataFrame of signals. Usually obtained from :func:`.rsp_process`, :func:`.ppg_process`, or
:func:`.emg_process`.
info : dict
A dictionary containing the information of peaks and the signals' sampling rate. Usually
obtained from :func:`.rsp_process` or :func:`.ppg_process`.
fig : matplotlib.figure.Figure or plotly.graph_objects.Figure
A figure containing the processed signals. Usually obtained from :func:`.rsp_plot`,
:func:`.ppg_plot`, or :func:`.emg_plot`.
Returns
-------
str
The report as a string.
See Also
--------
rsp_process, ppg_process, emg_process
Examples
--------
.. ipython:: python
import neurokit2 as nk
rsp = nk.rsp_simulate(duration=30, sampling_rate=200, random_state=0)
signals, info = nk.rsp_process(rsp, sampling_rate=200, report="text")
"""
description, ref = text_combine(info)
table_html, table_md = summarize_table(signals)
# Print text in the console
for key in [k for k in info.keys() if "text_" in k]:
print(info[key] + "\n")
print(table_md)
print("\nReferences")
for s in info["references"]:
print("- " + s)
# Save report
if ".html" in file:
# Make figures
fig_html = '<h2 style="background-color: #FB661C">Visualization</h1>'
fig_html += fig_to_html(fig)
print(f"The report has been saved to {file}")
contents = [description, table_html, fig_html, ref]
html_save(contents=contents, file=file)
def summarize_table(signals):
"""Create table to summarize statistics of a signal."""
# TODO: add more features
summary = {}
rate_cols = [col for col in signals.columns if "Rate" in col]
if len(rate_cols) > 0:
rate_col = rate_cols[0]
summary[rate_col + "_Mean"] = np.mean(signals[rate_col])
summary[rate_col + "_SD"] = np.std(signals[rate_col])
summary_table = pd.DataFrame(summary, index=[0])
# Make HTML and Markdown versions
html = '<h2 style="background-color: #D60574">Summary table</h1>' + summary_table.to_html(
index=None
)
try:
md = summary_table.to_markdown(index=None)
except ImportError:
md = summary_table # in case printing markdown export fails
return html, md
else:
return "", ""
def text_combine(info):
"""Reformat dictionary describing processing methods as strings to be inserted into HTML file."""
preprocessing = '<h2 style="background-color: #FB1CF0">Preprocessing</h1>'
for key in ["text_cleaning", "text_peaks"]:
if key in info.keys():
preprocessing += info[key] + "<br>"
ref = '<h2 style="background-color: #FBB41C">References</h1>'
if "references" in info.keys():
ref += "\n <ul> \n"
for reference in info["references"]:
ref += "<li>" + reference + "</li>" + "\n"
ref += "\n </ul> \n"
return preprocessing, ref
def fig_to_html(fig):
"""Convert a figure to HTML."""
if isinstance(fig, str):
return fig
elif isinstance(fig, matplotlib.pyplot.Figure):
# https://stackoverflow.com/questions/48717794/matplotlib-embed-figures-in-auto-generated-html
import base64
from io import BytesIO
temp_file = BytesIO()
fig.savefig(temp_file, format="png")
encoded = base64.b64encode(temp_file.getvalue()).decode("utf-8")
return "<img src='data:image/png;base64,{}'>".format(encoded)
else:
try:
import plotly
if isinstance(fig, plotly.graph_objs._figure.Figure):
# https://stackoverflow.com/questions/59868987/plotly-saving-multiple-plots-into-a-single-html
return fig.to_html().split("<body>")[1].split("</body>")[0]
else:
return ""
except ImportError:
return ""
def html_save(contents=[], file="myreport.html"):
"""Combine figures and text in a single HTML document."""
# https://stackoverflow.com/questions/59868987/plotly-saving-multiple-plots-into-a-single-html
with open(file, "w") as page:
page.write(
r"""<html>
<head>
<style>
h1 {
text-align: center;
font-family: Arial, Helvetica, sans-serif;
}
h2 {
text-align: center;
font-family: Arial, Helvetica, sans-serif;
}
p {
text-align: left;
font-family: Arial, Helvetica, sans-serif;
}
div {
text-align: center;
font-family: Arial, Helvetica, sans-serif;
}
ul {
text-align: left;
list-style-position: inside;
font-family: Arial, Helvetica, sans-serif;
}
</style>
</head>
<body>
<h1>NeuroKit Processing Report</h1>"""
)
for content in contents:
if isinstance(content, str):
inner_html = content
else:
# assume the content is an interactive plotly figure and export to HTML
inner_html = content.to_html().split("<body>")[1].split("</body>")[0]
page.write(inner_html)
page.write("<br>")
page.write("</body></html>" + "\n")
def get_default_args(func):
"""Get the default values of a function's arguments."""
# https://stackoverflow.com/questions/12627118/get-a-function-arguments-default-value
signature = inspect.signature(func)
return {
k: v.default
for k, v in signature.parameters.items()
if v.default is not inspect.Parameter.empty
}
def get_kwargs(report_info, func):
"""Get keyword arguments from report_info and update report_info if defaults."""
defaults = get_default_args(func)
kwargs = {}
for key in defaults:
if key not in ["sampling_rate", "method"]:
# if arguments have not been specified by user,
# set them to the defaults
if key not in report_info:
report_info[key] = defaults[key]
elif report_info[key] != defaults[key]:
kwargs[key] = report_info[key]
return kwargs, report_info
