#!/usr/bin/env python
# coding: utf-8

# In[17]:


# ms-python.python added
import os
try:
	os.chdir(os.path.join(os.getcwd(), 'MyGeoScripts'))
	print(os.getcwd())
except:
	pass


# 

# In[4]:


import geopandas as gpd
import pandas as pd

shapefile = '../Data/County/County.shp'

# read shape file using geopandas
county = gpd.read_file(shapefile)
county.head()



# In[5]:


county = gpd.read_file(shapefile)[['COUNTY','geometry']]
county.head()



# In[6]:


datafile = '../Data/County/kewi_water_test_and_results1.csv'
df = pd.read_csv(datafile,sep=',')
df.head()



# In[7]:


#take county and water alkalinity levels
alkaline_df1 = df[['COUNTY','ALKALINITY_Mg/L']]

#renamed second column
alkaline_df1.rename(columns={"ALKALINITY_Mg/L" : "ALKALINITY"}, inplace=True)
alkaline_df1.head()
alkaline_df1.describe()



# In[8]:


# remove rows with missing values in alkalinity
alkaline_df2= alkaline_df1.dropna()
alkaline_df2.head()



# In[9]:


alkaline_df2.dtypes



# In[10]:


# remove duplicated county names by getting the mean alkalinity of the respective duplicates and having it as one county
alkaline_df3 = alkaline_df2.groupby('COUNTY').mean().reset_index()
alkaline_df3.head()
alkaline_df1.describe()



# In[11]:


# merge county(geodata) and alkaline_df3(alkaline values) frames
# perform left merge to preserve every row on county geodata
merged = county.merge(alkaline_df3,how = 'left')

# replace NaN values with string NoData
merged.fillna('No data',inplace=True)
merged.head()



# In[12]:


import json

# read data(a dataframe) to json
merged_json = json.loads(merged.to_json())

# convert to string like object
json_data = json.dumps(merged_json)
# json_data



# In[13]:


from bokeh.io import output_notebook, show, output_file
from bokeh.plotting import figure
from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar
from bokeh.palettes import brewer

#Input GeoJSON source that contains features for plotting.
geosource = GeoJSONDataSource(geojson = json_data)

palette = brewer['YlGnBu'][8]

#Reverse color order so that dark blue is highest alkalinity
palette = palette[::-1]

#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors.
color_mapper = LinearColorMapper(palette = palette, low = 1, high = 38600)

#Define custom tick labels for color bar.
tick_labels = {'0': '0%','9650':'25%', '19300':'50%','28950':'75%', '38600': '100%'}

#Create color bar. 
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal', major_label_overrides = tick_labels)

#Create figure object.
p = figure(title = 'Alkalinity of water in Kenyan counties', plot_height = 700 , plot_width = 700, toolbar_location = None)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None

#Add patch renderer to figure. 
p.patches('xs','ys', source = geosource,fill_color = {'field' :'ALKALINITY', 'transform' : color_mapper},
          line_color = 'black', line_width = 0.25, fill_alpha = 1)

#Specify figure layout.
p.add_layout(color_bar, 'below')

#Display figure inline in Jupyter Notebook.
output_notebook()

#Display figure.
show(p)



# In[14]:


from bokeh.io import curdoc, output_notebook
from bokeh.models import Slider, HoverTool
from bokeh.layouts import widgetbox, row, column


#Input GeoJSON source that contains features for plotting.
geosource = GeoJSONDataSource(geojson = json_data)

#Define a sequential multi-hue color palette.
palette = brewer['BuPu'][9]

#Reverse color order so that dark blue is highest alkalinity.
palette = palette[::-1]

#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors. Input nan_color.
color_mapper = LinearColorMapper(palette = palette, low = 1, high = 1000, nan_color = '#d9d9d9')

#Define custom tick labels for color bar.
#tick_labels = {'0': 'No data','200':'200', '19300':'50%','28950':'75%', '38600': '100%'}

#Add hover tool
hover = HoverTool(tooltips = [ ('Country/region','@COUNTY'),('% ALKALINITY_MG/L', '@ALKALINITY')])


#Create color bar. 
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 1000, height = 20,
                     border_line_color=None,location = (0,0), orientation = 'horizontal', major_label_overrides = tick_labels)


#Create figure object.
p = figure(title = 'Alkalinity of water in Kenyan counties', plot_height = 750 , plot_width = 700, toolbar_location = None, tools = [hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None

#Add patch renderer to figure. 
p.patches('xs','ys', source = geosource,fill_color = {'field' :'ALKALINITY', 'transform' : color_mapper},
          line_color = 'black', line_width = 0.25, fill_alpha = 1)


p.add_layout(color_bar, 'below')

# # Make a column layout of widgetbox(slider) and plot, and add it to the current document
layout = column(p,widgetbox())
curdoc().add_root(layout)

#Display plot inline in Jupyter notebook
output_notebook()

#Display plot
show(layout)