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#!/usr/bin/env python3

""" plotting.py at https://github.com/wilsonmar/python-samples/blob/main/plotting.py

This is starter sample code to create common visualizations using matplotlib.

STATUS: Working on macOS M2 14.5 (23F79) using Python 3.12.7.

"v003 + density histogram :plotting.py"

Before running this program:

brew install miniconda

conda create -n py312

conda install -c conda-forge python=3.12 matplotlib numpy scienceplots

conda activate py312

chmod +x plotting.py

./plotting.py

See https://matplotlib.org/stable/users/explain/quick_start.html

TODO: Matplotlib advanced features:

* Twin axes

* Logarithmic scales

* Customizable colormaps

* 2D Contour Plots

* 3D plotting

* Animations https://www.youtube.com/watch?v=bNbN9yoEOdU

TODO: Add nanosecond resolution timer like perf-ns.py

TODO: Use https://seaborn.pydata.org/installing.html

See https://www.youtube.com/watch?v=ooqXQ37XHMM for Seaborn

"""

import matplotlib.pyplot as plt

# FIXME: print(matplotlib.__version__)

import numpy as np

import scienceplots # https://github.com/garrettj403/SciencePlots

# Globals:

SAVE_PNG = False # FIXME: png files created with blanks.

# TODO: Define the list of charts to generate in the order specified.

# STAGE: Define sample data:

#x = [1, 2, 3, 4]

#y = [1, 4, 9, 16]

x = np.array([1, 2, 3, 4])

y = np.array([1, 4, 9,16])

# STAGE: Define enviornment:

plt.style.use(['science','ieee','no-latex','grid']) # https://github.com/garrettj403/SciencePlots/wiki/Gallery

plt.rcParams.update({'figure.dpi': '100'}) # dots per inch for IEEE publications

# STAGE: Create a density histogram: https://www.youtube.com/watch?v=cTJBJH8hacc&t=9m10s

# https://github.com/lukepolson/youtube_channel/blob/main/Python%20Tutorial%20Series/matplotlib_essentials.ipynb

# DEFINITION: A density plot normalizes the area of the histogram sums to 1.

res = np.random.randn(1000)*0.2 + 0.4

# TUTORIAL: https://www.youtube.com/watch?v=cTJBJH8hacc&t=12m

plt.suptitle('Sample Density Histogram')

plt.ylabel('Frequency')

plt.xlabel('X-axis')

#plt.figure(figsize=(8,3))

plt.hist(res, bins=5, density=True, histtype='step')

plt.show()

if SAVE_PNG:

plt.savefig('my_histogram.png')

exit()

# STAGE: Create a scatter dots plot:

# From https://matplotlib.org/stable/users/explain/quick_start.html#types-of-inputs-to-plotting-functions

np.random.seed(19680801) # seed the random number generator.

data = {'a': np.arange(50),

'c': np.random.randint(0, 50, 50),

'd': np.random.randn(50)}

data['b'] = data['a'] + 10 * np.random.randn(50)

data['d'] = np.abs(data['d']) * 100

fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')

ax.set_title('Sample Scatter Dots Plot')

ax.scatter('a', 'b', c='c', s='d', data=data)

ax.set_xlabel('entry a')

ax.set_ylabel('entry b')

# See https://matplotlib.org/stable/users/explain/text/annotations.html#basic-annotation

ax.annotate('(0,0)\nzero', xy=(0, 0), xytext=(10, 40),

arrowprops=dict(facecolor='grey', shrink=0.05))

plt.show()

if SAVE_PNG:

plt.savefig('my_scatterdots.png')

# STAGE: Create a scatter plot: https://www.youtube.com/watch?v=cTJBJH8hacc

plt.suptitle('Sample Scatter Plot')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.scatter(x, y)

plt.show()

if SAVE_PNG:

plt.savefig('my_scatterplot.png')

# TODO: Add regression line.

# STAGE: Create a line chart:

fig, ax = plt.subplots()

ax.set_title('Sample Line Chart')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

ax.plot(x, y, color='red', linestyle='--', marker='o')

plt.show()

if SAVE_PNG:

plt.savefig('my_line_chart.png')

# STAGE: Create a bar chart:

plt.suptitle('Sample Bar Chart')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.bar(x, y)

plt.show()

if SAVE_PNG:

plt.savefig('my_bar_chart.png')

# TODO: Stacked Barchart

# TODO: Close pop-up window programmatically vs. manually with control+W.

# A figure with one Axes on the left, and two on the right:

#fig, axs = plt.subplot_mosaic([['left', 'right_top'],

# ['left', 'right_bottom']])

# TODO: Add ChatGPT generation of code.

# https://apps.cognitiveclass.ai/learning/course/course-v1:IBMSkillsNetwork+GPXX0TJREN+v1/block-v1:IBMSkillsNetwork+GPXX0TJREN+v1+type@sequential+block@guided_project/block-v1:IBMSkillsNetwork+GPXX0TJREN+v1+type@vertical+block@guided_project_lets_do_it

# https://cf-courses-data.static.labs.skills.network/jupyterlite/2.5.5/lab/index.html?mode=learn&env_type=jupyterlite&notebook_url=https%3A%2F%2Fcf-courses-data.static.labs.skills.network%2FIBMSkillsNetwork-GPXX0TJREN%2Flabs%2FPlottingByChatGPT.jupyterlite.ipynb&file_path=%2Flabs%2FIBMSkillsNetwork-GPXX0TJREN%2Flabs%2FPlottingByChatGPT.jupyterlite.ipynb