import knime.scripting.io as knio
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.cm import ScalarMappable
import matplotlib
matplotlib.use("TkAgg")

sns.set()

selected_project = knio.flow_variables['value-selection']
#selected_values = knio.flow_variables['value-filter']


data = knio.input_tables[0].to_pandas()
df_sorted=data.sort_values(by="Sum(TM Total Cases KM-Exclusive NOT DISTINCT)", ascending=True)
# Applica il filtro basato sui valori selezionati
df_sorted=data[df_sorted['PROJECT_CODE'] == selected_project]

#filtered_data = data[df_sorted['PROJECT_CODE'].isin(selected_values)]

# Plot the histogram with a custom color gradient
##fig, ax = plt.subplots()
fig, ax = plt.subplots(figsize=(10,10))
sns.set_style('white')

# Define a custom colormap from dark blue to dark red
#colors = [(0, 0, 0.5), (1, 1, 0), (0.5, 0, 0)]  # RGB values for dark blue, yellow, and dark red
colors = ['darkred','darkorange']
##colors = ['blue','red']

#custom_cmap = LinearSegmentedColormap.from_list('custom_gradient', colors, N=256)
custom_cmap = LinearSegmentedColormap.from_list('darkred_to_darkorange', colors, N=256)
#custom_cmap = LinearSegmentedColormap.from_list('blue_to_red', colors, N=256)
# Normalize the data to map it to the custom colormap
normalize = plt.Normalize(vmin=df_sorted["Sum(TM Total Cases KM-Exclusive NOT DISTINCT)"].min(),
                          vmax=df_sorted["Sum(TM Total Cases KM-Exclusive NOT DISTINCT)"].max())

bars = plt.barh(
    y=df_sorted["PROJECT_CODE"],
    width=df_sorted["Sum(TM Total Cases KM-Exclusive NOT DISTINCT)"],
    color=custom_cmap(normalize(df_sorted["Sum(TM Total Cases KM-Exclusive NOT DISTINCT)"])),
)

plt.title("Case by Project", fontsize=16, fontweight='bold', loc='left')  # Set font size, weight, and position
plt.grid(False)

plt.yticks(df_sorted["PROJECT_CODE"])

# Add each item to the legend
#legend_labels = df_sorted["PROJECT_CODE"]
#for barh, label in zip(bars, legend_labels):
#    barh.set_label(label)

# Add value annotations over the bars
for barh, value in zip(bars, df_sorted["Sum(TM Total Cases KM-Exclusive NOT DISTINCT)"]):
    #plt.text(barh.get_x() + barh.get_width() + 0.02, barh.get_y + barh.get_height()/2, f'{value}', ha='right', va='center')
    plt.text(barh.get_x() + barh.get_width() + 1.5, barh.get_y() + barh.get_height() / 2, f'{value}', ha='left', va='center')

#plt.legend(loc="best", bbox_to_anchor=(1, 0.5),  labelspacing=1.2, borderpad=-8, borderaxespad=0, frameon=False, fontsize=9)
#plt.axis('off')

# Hide x-axis labels
plt.xticks([])
# Add a color scale
cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=custom_cmap, norm=normalize), ax=ax, orientation='horizontal', pad=0.02)
#cbar.set_label('Your Colorbar Label')  # Customize the label as needed

# Print the dimensions of the figure in pixels
dpi = fig.get_dpi()
width_px, height_px = fig.get_size_inches() * dpi
print(f"Dimensioni del Grafico: {width_px} x {height_px} pixel")

ax.set_facecolor('white')

#Controllo zorder
#for obj in ax.findobj():
#    print(f"Tipo: {type(obj)}, Z-order: {obj.get_zorder()}")


# Assign the figure to the output_view variable
#knio.output_view = knio.view(fig)
knio.output_view = knio.view(plt.gcf())