stock-tracker-discord/table2.py

98 lines
2.7 KiB
Python

import matplotlib.pyplot as plt
import matplotlib, io
import plotly.express as px
import plotly
from datetime import datetime
def generate_table(headers, data, title="", bg="skyblue", border="steelblue"):
timestamp = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
font = {"family": "monospace", "weight": "normal", "size": 12}
matplotlib.rc("font", **font)
title_text = title
footer_text = timestamp
fig_background_color = bg
fig_border = border
column_headers = headers
if data:
cell_text = []
for row in data:
cell_text.append([x for x in row])
else:
cell_text = [["No Data"] + ["" for x in range(len(headers) - 1)]]
# Create the figure. Setting a small pad on tight_layout
# seems to better regulate white space. Sometimes experimenting
# with an explicit figsize here can produce better outcome.
dpi = 160
# image_height = (( 0.5 + ( 0.4125 * ( len(data) + 1) ) ) * dpi)
image_height = 100 + (67 * (len(data) + 1))
image_width = 6 * dpi
plt.figure(
linewidth=2,
edgecolor=fig_border,
facecolor=fig_background_color,
tight_layout={"pad": 1},
dpi=dpi,
figsize=(image_width / dpi, image_height / dpi),
)
# Add a table at the bottom of the axes
the_table = plt.table(
cellText=cell_text, colLabels=column_headers, loc="upper center"
)
# Scaling is the only influence we have over top and bottom cell padding.
# Make the rows taller (i.e., make cell y scale larger).
the_table.scale(1, 2)
# Hide axes
ax = plt.gca()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# Hide axes border
plt.box(on=None)
# Add title
plt.suptitle(title_text, x=0.5, y=((image_height - 15) / image_height))
# Add footer
plt.figtext(
((image_width - 15) / image_width),
1 - ((image_height - 15) / image_height),
footer_text,
horizontalalignment="right",
size=12,
weight="light",
)
# Force the figure to update, so backends center objects correctly within the figure.
# Without plt.draw() here, the title will center on the axes and not the figure.
plt.draw()
# Create image. plt.savefig ignores figure edge and face colors, so map them.
image_buffer = io.BytesIO()
plt.savefig(image_buffer, format="png")
image_buffer.seek(0)
return image_buffer
def generate_chart(ticker, history):
fig = px.line(
history,
x=history.index,
y="Open",
title="{} Stock Prices".format(ticker),
labels={"Open": "Price Per Share"},
)
img_bytes = plotly.io.to_image(fig, format="png")
return io.BytesIO(img_bytes)