"""
NCL_bar_7.py
===============
This script illustrates the following concepts:
  - Drawing filled bars
  - Filling the bars in a bar plot with different colors
  - Setting the minimum/maximum value of the Y axis in a bar plot
  - Adding text to a plot
  - Rotating text 45 degrees
  - Drawing a custom legend

See following URLs to see the reproduced NCL plot & script:
    - Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/bar_7.ncl
    - Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/bar_7_1_lg.png and https://www.ncl.ucar.edu/Applications/Images/bar_7_2_lg.png
"""

###############################################################################
# Import packages:
import numpy as np
import matplotlib.pyplot as plt

from geocat.viz import util as gvutil

###############################################################################
# Generate data:
x = [1, 2, 3, 4, 5, 6, 7, 8]
data = [154900, 56600, 40000, 30200, 29700, 24400, 21700, 13900]
labels = [
    'Lung', 'Colon/rectum', 'Breast', 'Prostate', 'Pancreas',
    'Non-Hodgkin\'s Lymphoma', 'Leukemias', 'Ovary'
]

###############################################################################
# Create the custom color list.
color_list = [
    'firebrick', 'red', 'orange', 'green', 'navy', 'blue', 'skyblue',
    'slateblue'
]

###############################################################################
# Specify some plot settings to use in both plots:

# Title settings
title = 'Estimated Cancer Deaths for 2002'
title_fontsize = 16

# Axis Settings
plot_y_max = 180_000

###############################################################################
# Plot 1 (Bar chart):

# Generate figure (set its size (width, height) in inches) and axes
plt.figure(1, figsize=(6, 5))
ax = plt.gca()

# Bar-plot the data
plt.bar(x, data, color=color_list, edgecolor='black')
plt.title(title, fontsize=title_fontsize, y=1.04)

# Add a rotated label to each bar.
for k, label in enumerate(labels):
    plt.text(x[k], data[k] + 2000, label, rotation=45)

# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax,
                                 ylim=(0, plot_y_max),
                                 xticks=[],
                                 yticks=np.linspace(0, plot_y_max, 7))

# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
                             x_minor_per_major=0,
                             y_minor_per_major=3,
                             labelsize=12)

# Draw plot on the screen.
plt.show()

###############################################################################
# Plot 2 (Bar chart with a legend):

# Generate figure (set its size (width, height) in inches) and axes
plt.figure(2, figsize=(6, 5))
ax = plt.gca()

# Bar-plot the data
bar_handle = plt.bar(x, data, color=color_list, edgecolor='black')

# Reverse the legend ordering to match NCL
bars_reversed = bar_handle[::-1]
labels_reversed = labels[::-1]

# Add the legend
plt.legend(bars_reversed, labels_reversed)

# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax,
                                 ylim=(0, plot_y_max),
                                 xticks=[],
                                 yticks=np.linspace(0, plot_y_max, 7))

# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax,
                             x_minor_per_major=0,
                             y_minor_per_major=3,
                             labelsize=12)

# Use geocat.viz.util convenience function to set titles and labels without calling several matplotlib functions
gvutil.set_titles_and_labels(ax,
                             maintitle=title,
                             maintitlefontsize=title_fontsize,
                             ylabel="Number of Deaths")

# Show the plot
plt.tight_layout()
plt.show()