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

# In[1]:


import sys
print(sys.executable)
print(sys.version)
print(sys.version_info)


# In[2]:


import pysal as ps
#import fiona
import numpy as np
import pandas as pd
import geopandas as gpd


# In[4]:


get_ipython().system('pwd')


# In[5]:


ps.examples.available()


# In[6]:


ps.examples.explain('nyc_bikes')


# In[7]:


ps.examples.explain('tokyo')


# In[8]:


ps.examples.explain('us_income')


# In[10]:


print(ps.examples)
#
print("  "*90)
#
print(ps.examples.get_path)


# In[11]:


get_ipython().system('cd /home/dhankar/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples')


# In[12]:


get_ipython().system('pwd')


# In[ ]:


"""

(gisenv) dhankar@dhankar-VPCEB44EN:~/Desktop/Geo_1$ 
(gisenv) dhankar@dhankar-VPCEB44EN:~/Desktop/Geo_1$ pwd
/home/dhankar/Desktop/Geo_1
(gisenv) dhankar@dhankar-VPCEB44EN:~/Desktop/Geo_1$ 
(gisenv) dhankar@dhankar-VPCEB44EN:~/Desktop/Geo_1$ cd /home/dhankar/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples
(gisenv) dhankar@dhankar-VPCEB44EN:~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples$ ls
10740   burkitt     columbus      georgia      londonhp  newHaven   Polygon_Holes  sids2      street_net_pts    tokyo
arcgis  calemp      desmith       __init__.py  mexico    nyc_bikes  __pycache__    snow_maps  taz               us_income
baltim  chicago     examples.txt  juvenile     nat       Point      README.txt     south      test_examples.py  virginia
book    clearwater  geodanet      Line         networks  Polygon    sacramento2    stl        tmpIvIYa5.gwt     wmat
(gisenv) dhankar@dhankar-VPCEB44EN:~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples$ 
(gisenv) dhankar@dhankar-VPCEB44EN:~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples$ cd us_income
(gisenv) dhankar@dhankar-VPCEB44EN:~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples/us_income$ ls
README.md  spi_download.csv  states48.gal  us48.dbf  us48.shp  us48.shx  usjoin.csv
(gisenv) dhankar@dhankar-VPCEB44EN:~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples/us_income$ 
(gisenv) dhankar@dhankar-VPCEB44EN:~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples/us_income$ pwd
/home/dhankar/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples/us_income
(gisenv) dhankar@dhankar-VPCEB44EN:~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples/us_income$ 
(gisenv) dhankar@dhankar-VPCEB44EN:~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/examples/us_income$ 


"""


# In[3]:


csv_path = ps.examples.get_path('usjoin.csv')
print("CSV Local File Path___ ",csv_path)


# In[4]:


f = ps.open(csv_path)
#
print("  "*10)
#
print(f.header[0]) ## Column 1 of CVS File 
#
print("  "*10)
#
print(f.header[5])
#
print("  "*10)
#
print(f.header)
#
print("  "*10)

#print(type(f)) ## <class 'pysal.core.IOHandlers.csvWrapper.csvWrapper'>


# In[5]:


year_09 = f.by_col('2009')
print(year_09)



# In[6]:


## What are .shp -SHAPE Files ? --- https://www.esri.com/library/whitepapers/pdfs/shapefile.pdf
## PySAL reading -SHAPE Files --- 
## 

path_shp_file = "/media/dhankar/Dhankar_1/a1_18/a1____GeoSpatialANAL/GIS_DATA/ShapeFiles/tl_2010_02_place10.shp"

print("path_shp_file____",path_shp_file)
#
shp_f = ps.open(path_shp_file)
#
print(type(shp_f))
#
# <class 'pysal.core.IOHandlers.pyShpIO.PurePyShpWrapper'>


# In[7]:


# Exploring SHAPE File

print(shp_f.header)
#
print("  "*10)
#
print(shp_f.header[0])  # Cant Index --PurePyShpWrapper --  like the - csvWrapper -- File Object above. 
#
print("  "*10)




# In[33]:


# The Bounding Boxes 
shp_f.bbox


# In[37]:


# Source -- http://nbviewer.jupyter.org/github/pysal/notebooks/blob/master/notebooks/PySAL_io.ipynb
#
ploygons_list =[]
#
for poly in shp_f:
#    print("Iterating over polygon: ", poly.id) 
     ## OK Prints ALL POLYGON ID's
    ploygons_list.append(poly)
print(len(ploygons_list))



# In[41]:


# Polygon - ONE of SHAPE File 
poly1 = shp_f[0]
print(type(poly1))
#
print("poly1.area_____",poly1.area)
#
print("poly1.centroid_____",poly1.centroid)
#
print("poly1.holes_____",poly1.holes)
#
print("poly1.perimeter_____",poly1.perimeter)
#


# In[43]:


# Writing Own SHAPE Files -- Creating a COPY 
#
# Create a NEW DUMMY SHAPE File on local DIR 
shp_f1 = ps.open('shp_test.shp', 'w')
#
# Copy the Headers
shp_f1.header = shp_f.header
#
# Write ONE Polygon to New Shape File 
#
shp_f1.write(poly1)
shp_f1.close()
#
print(type(shp_f1))



# In[8]:


shp_f1 = ps.open('/home/dhankar/Desktop/Geo_1/shp_test.shp', 'w')
print(shp_f1)

## WIP --- Check Below ----


# Polygon - ONE of NEW SHAPE File == shp_f1
poly_1_1 = shp_f1[0]
print(type(poly_1_1))
#
print("poly1.area_____",poly_1_1.area)
#
print("poly1.centroid_____",poly_1_1.centroid)
#
print("poly1.holes_____",poly_1_1.holes)
#
print("poly1.perimeter_____",poly1.perimeter)
#


# # DBF Files 
# ## The dBase - dbf Files Wiki --- https://en.wikipedia.org/wiki/DBase
# 
# 

# In[9]:


dbf_path = ps.examples.get_path('us48.dbf')
print(".dbf Local File Path___ ",dbf_path)


# In[59]:


# explore the .dbf file 
dbf_1 = ps.open(dbf_path)
print(type(dbf_1))
#
print(" "*10)
#
print(dbf_1.header[0])
#
print(" "*10)
#
print(dbf_1.header[1])
#
print(" "*10)
#
print(dbf_1.header[2])
#
print(" "*10)
#
print(dbf_1.header[3])
#
print(" "*10)
#
print(dbf_1.header[4:7])
#
print(" "*10)
#
print(dbf_1.field_spec)
#



# ### SOURCE -- http://pysal.readthedocs.io/en/latest/library/core/IOHandlers/pyDbfIO.html
# 
# field_spec == A list describing the data types of each field. 
# It is comprised of a list of tuples, each tuple describing a field.
# The format for the tuples is (“Type”,len,precision). 
# Valid Types are ‘C’ for characters, ‘L’ for bool, ‘D’ for data, ‘N’ or ‘F’ for number.
# 
# 

# In[58]:


# Print values from any ONE COLUMN - with COLUMN LABEL 
# We get LIST Object

state_id = dbf_1.by_col("STATE_ID")
print(state_id)
#
print("  "*40)
#
print(state_id[0:5])

#
print("  "*40)
#

print(type(state_id))


# In[18]:


# Get a NUMPY.nd.array from the dbf file columns 

ids_np_array = np.array([dbf_1.by_col(ids) for ids in dbf_1.header])
#
t_ids_np_array = np.array([dbf_1.by_col(ids) for ids in dbf_1.header]).T ### TRANSPOSED 
#
print(ids_np_array.shape)
#
print("TRANSPOSED Shape ==",t_ids_np_array.shape)

#
print("  "*90)
#
# As seen below - we have "All Columns Values" in a 2D Numpy Array - we dont have COLUMN LABELS
# Not intelligent...
print(ids_np_array)
#
print("  "*90)
#
# We TRANSPOSED the Numpy Array to show it in a DATAFRAME ROWS like format 
#
print("TRANSPOSED==",t_ids_np_array)
#



# In[17]:


# Accessing all values within a COLUMN 

states = dbf_1.by_col("STATE_NAME")
print(states)


# In[20]:


## Extracting Column Data -- with specific Value Types
## Writing a List Comprehension
## NUMERIC Only Columns 

num_cols = np.array([dbf_1.by_col(dbf_1.header[i]) for i in range(len(dbf_1.header)) if dbf_1.field_spec[i][0] == 'N']).T
print(type(num_cols))
print("  "*20)
print("SHAPE of the numpy Array ____",num_cols.shape) ## 2 D Array with SHAPE - (48, 4)
print("  "*20)
print(num_cols)


# In[23]:


str_cols = np.array([dbf_1.by_col(dbf_1.header[i]) for i in range(len(dbf_1.header)) if dbf_1.field_spec[i][0] == 'C']).T
print(type(str_cols))
print("  "*20)
print("SHAPE of the numpy Array ____",str_cols.shape) ## 2 D Array with SHAPE - (48, 4)
print("  "*20)
print(str_cols)


# In[36]:


# Creating a DICTIONARY n then a PANADAS DataFrame from the Numpy Arrays
#
dic_1 = dict([(col, np.array(dbf_1.by_col(col))) for col in dbf_1.header])
df_1 = pd.DataFrame(dic_1)

# Print out Top Five and Bottom Five Observations / Rows

print(df_1.head(5))
print("_ _"*60)
print("   "*60)
print(df_1.tail(5))



# In[37]:


# to get Original Order of Columns - pass the dbf_1.header , LIST , to the PANDAS DF as a LIST index 
#
print(type(dbf_1.header))
print("   "*60)

#df_1 = df_1[dbf_1.header]

print(df_1.head(5))
print("_ _"*60)
print("   "*60)
print(df_1.tail(5))


# In[39]:


print(df_1.describe())
print("  "*60)
print(df_1.describe().T)


# In[50]:


us_income_sh_path = ps.examples.get_path('us48.shp')
print(".dbf Local File Path___ ",us_income_sh_path)


# In[57]:


# Visualizing the POLYGONS 
#
get_ipython().run_line_magic('matplotlib', 'inline')

import matplotlib
import matplotlib.pyplot as plt
from pysal.contrib.viz import mapping as maps
#
fig = plt.figure()
#

#path_shp_file = "/media/dhankar/Dhankar_1/a1_18/a1____GeoSpatialANAL/GIS_DATA/ShapeFiles/tl_2010_02_place10.shp"
#
#print("path_shp_file____",path_shp_file)
#
shp_f = ps.open(us_income_sh_path)
#
print(type(shp_f))
#
base = maps.map_poly_shp(shp_f)
base.set_facecolor('blue')
base.set_linewidth(0.75)
base.set_edgecolor('0.3') ## 0.8 --- gives Light Greyish edges 


ax = maps.setup_ax([base],[shp_f.bbox, shp_f.bbox, shp_f.bbox])
print(type(ax))

fig.add_axes(ax)
plt.show()

#maps.plot_poly_lines(path_shp_file)


# In[60]:


london_hp = ps.examples.get_path('londonhp.shp') 
## This FUNC searches in the "examples" DIR for File Name STRING being passed.
print(".shp Local File Path___ ",london_hp)


# In[62]:


# Visualizing the POLYGONS 
#
get_ipython().run_line_magic('matplotlib', 'inline')

import matplotlib
import matplotlib.pyplot as plt
from pysal.contrib.viz import mapping as maps
#
fig1 = plt.figure()
#

shp_f1 = ps.open(london_hp)
#
print(type(shp_f1))
#
base = maps.map_poly_shp(shp_f1)
base.set_facecolor('blue')
base.set_linewidth(0.75)
base.set_edgecolor('0.3') ## 0.8 --- gives Light Greyish edges 


ax1 = maps.setup_ax([base],[shp_f1.bbox, shp_f1.bbox, shp_f1.bbox])
print(type(ax1))

fig1.add_axes(ax1)
plt.show()

#maps.plot_poly_lines(path_shp_file)


# In[63]:


natregimes_shp = ps.examples.get_path('natregimes.shp') 
## This FUNC searches in the "examples" DIR for File Name STRING being passed.
print(".shp Local File Path___ ",natregimes_shp)


# In[69]:


# Visualizing the POLYGONS 
#
get_ipython().run_line_magic('matplotlib', 'inline')

import random as rdm
import matplotlib
import matplotlib.pyplot as plt
from pysal.contrib.viz import mapping as maps
#
fig2 = plt.figure(figsize=(9, 9), dpi=100)

#

shp_f2 = ps.open(natregimes_shp)
#
print(type(shp_f2))
#
base = maps.map_poly_shp(shp_f2)
base.set_facecolor('pink')
base.set_linewidth(0.25)
base.set_edgecolor('0.3') ## 0.8 --- gives Light Greyish edges 


ax2 = maps.setup_ax([base],[shp_f2.bbox, shp_f2.bbox, shp_f2.bbox])
print(type(ax2))

# examples.get_path('natregimes.shp') 

fig2.add_axes(ax2)
plt.show()



# In[75]:


# Level TWO Visualization with PySAL 
# SOURCE --- http://darribas.org/gds_scipy16/ipynb_md/02_geovisualization.html
#
#
net_link = ps.examples.get_path('eberly_net.shp')
net_link_dbf = ps.examples.get_path('eberly_net.dbf')
print(net_link)
print(net_link_dbf)
#
net = ps.open(net_link)
values = np.array(ps.open(net_link.replace('.shp', '.dbf')).by_col('TNODE'))
# Above line of Code == stores VALUES of COLUMN TNODE - from dbase .dbf File 
#
pts_link = ps.examples.get_path('eberly_net_pts_onnetwork.shp')
print(pts_link)
pts = ps.open(pts_link)
#
fig_l2 = plt.figure(figsize=(9,9))  ### CHECK --- BOKEH or MATPLOTLIB Figure ? 
#
netm = maps.map_line_shp(net)
netc = maps.base_choropleth_unique(netm, values)
#
ptsm = maps.map_point_shp(pts)
ptsm = maps.base_choropleth_classif(ptsm, values)
ptsm.set_alpha(0.5)
ptsm.set_linewidth(0.)
#
ax = maps.setup_ax([netc, ptsm], [net.bbox, net.bbox])
fig.add_axes(ax)
show()


# In[7]:


# Online TEXAS Shape Files 
"""
blockgroup: https://www2.census.gov/geo/tiger/TIGER2010/BG/2010/tl_2010_48_bg10.zip
state: https://www2.census.gov/geo/tiger/TIGER2010/STATE/2010/tl_2010_48_state10.zip
county: https://www2.census.gov/geo/tiger/TIGER2010/COUNTY/2010/tl_2010_48_county10.zip
tract: https://www2.census.gov/geo/tiger/TIGER2010/TRACT/2010/tl_2010_48_tract10.zip
cd: https://www2.census.gov/geo/tiger/TIGER2010/CD/111/tl_2010_48_cd111.zip
block: https://www2.census.gov/geo/tiger/TIGER2010/TABBLOCK/2010/tl_2010_48_tabblock10.zip
zcta: https://www2.census.gov/geo/tiger/TIGER2010/ZCTA5/2010/tl_2010_48_zcta510.zip

"""


tex_shp = "/home/dhankar/Desktop/Geo_1/data/texas.shp"
#
shp_tex = ps.open(tex_shp)
#
print(type(shp_tex))
#
print("  "*10)
#
print(shp_tex.header)
#
print("  "*10)
#
print(shp_tex.header[0])  # Cant Index --PurePyShpWrapper --  like the - csvWrapper -- File Object above. 
#
print("  "*10)




# In[8]:


# The Bounding Boxes 
shp_tex.bbox


# In[9]:


# Source -- http://nbviewer.jupyter.org/github/pysal/notebooks/blob/master/notebooks/PySAL_io.ipynb
# We got only the EXTERNAL STATE Boundary Polygon for TEXAS ? 

ploygons_list =[]
#
for poly in shp_tex:
#    print("Iterating over polygon: ", poly.id) 
     ## OK Prints ALL POLYGON ID's
    ploygons_list.append(poly)
print(len(ploygons_list))


# In[10]:


# Polygon - ONE of SHAPE File 
poly1 = shp_tex[0]
print(type(poly1))
#
print("poly1.area_____",poly1.area)
#
print("poly1.centroid_____",poly1.centroid)
#
print("poly1.holes_____",poly1.holes)
#
print("poly1.perimeter_____",poly1.perimeter)
#


# In[21]:


get_ipython().run_line_magic('matplotlib', 'inline')
import numpy as np
import pysal as ps
import random as rdm

from pysal.contrib.viz import mapping as maps

tex_shp_path = "/home/dhankar/Desktop/Geo_1/data/texas.shp"
tex_cnts_shp_path = "/home/dhankar/Desktop/Geo_1/data/texas_counties/tl_2010_48_county10.shp"

tex_dbf_path = '/home/dhankar/Desktop/Geo_1/data/texas.dbf'
tex_cnts_dbf_path = '/home/dhankar/Desktop/Geo_1/data/texas_counties/tl_2010_48_county10.dbf'

tex_dbf = ps.open(tex_dbf_path)
tex_cnts_dbf = ps.open(tex_cnts_dbf_path)

#
# explore the .dbf file 
print(type(tex_dbf))
#
print(" "*10)
#
print(tex_dbf.header)
print(tex_cnts_dbf.header)
#
print(" "*60)
#
print(tex_dbf.header[0])
print(tex_cnts_dbf.header[0])
#
print(" "*60)
#
# Get a NUMPY.nd.array from the dbf file columns 
#
tex_np_array = np.array([tex_dbf.by_col(ids) for ids in tex_dbf.header]) ## Texas State
t_tex_np_array = np.array([tex_dbf.by_col(ids) for ids in tex_dbf.header]).T ## TRANSPOSED - Texas State
#
#
tex_cnts_np_array = np.array([tex_cnts_dbf.by_col(ids) for ids in tex_cnts_dbf.header]) ## Texas State Counties
t_tex_cnts_np_array = np.array([tex_cnts_dbf.by_col(ids) for ids in tex_cnts_dbf.header]).T ## TRANSPOSED - Texas State Counties
#

#
print(tex_np_array.shape)
print("TRANSPOSED Shape ==",t_tex_np_array.shape)
print(tex_cnts_np_array.shape)
print("TRANSPOSED Shape ==",t_tex_cnts_np_array.shape)
#
#
print(" "*60)
#
# As seen below - we have "All Columns Values" in a 2D Numpy Array - we dont have COLUMN LABELS
# Not intelligent...
print(ids_np_array)
#
print("  "*90)
#
# We TRANSPOSED the Numpy Array to show it in a DATAFRAME ROWS like format 
#
print("TRANSPOSED==",t_ids_np_array)

values = np.array(ps.open('/home/dhankar/Desktop/Geo_1/data/texas.dbf').by_col('NAME10'))

types = ['classless', 'unique_values', 'quantiles', 'equal_interval', 'fisher_jenks']

for typ in types:
    #
    maps.plot_choropleth(tex_shp,values,typ)
#


# In[ ]:





# In[ ]:





# In[ ]:


# Online TEXAS Shape Files 
"""
blockgroup: https://www2.census.gov/geo/tiger/TIGER2010/BG/2010/tl_2010_48_bg10.zip
state: https://www2.census.gov/geo/tiger/TIGER2010/STATE/2010/tl_2010_48_state10.zip
county: https://www2.census.gov/geo/tiger/TIGER2010/COUNTY/2010/tl_2010_48_county10.zip
tract: https://www2.census.gov/geo/tiger/TIGER2010/TRACT/2010/tl_2010_48_tract10.zip
cd: https://www2.census.gov/geo/tiger/TIGER2010/CD/111/tl_2010_48_cd111.zip
block: https://www2.census.gov/geo/tiger/TIGER2010/TABBLOCK/2010/tl_2010_48_tabblock10.zip
zcta: https://www2.census.gov/geo/tiger/TIGER2010/ZCTA5/2010/tl_2010_48_zcta510.zip

"""


tex_shp = "/home/dhankar/Desktop/Geo_1/data/texas.shp"

shp_tex = ps.open(tex_shp)
#
print(type(shp_tex))

"""
# Without Specifically asking for a .shx file --- the package is searching for a .shx file when .shp is passed...

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-3-7233cd10fe38> in <module>()
     14 tex_shp = "/home/dhankar/Desktop/Geo_1/data/texas.shp"
     15 
---> 16 shp_tex = ps.open(tex_shp)
     17 #
     18 print(type(shp_tex))

~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/core/IOHandlers/pyShpIO.py in __init__(self, *args, **kwargs)
     66         self.dataObj = None
     67         if self.mode == 'r' or self.mode == 'rb':
---> 68             self.__open()
     69         elif self.mode == 'w' or self.mode == 'wb':
     70             self.__create()

~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/core/IOHandlers/pyShpIO.py in __open(self)
     77 
     78     def __open(self):
---> 79         self.dataObj = shp_file(self.dataPath)
     80         self.header = self.dataObj.header
     81         self.bbox = self.dataObj.bbox

~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/core/util/shapefile.py in __init__(self, fileName, mode, shape_type)
    219 
    220         if mode == 'r':
--> 221             self._open_shp_file()
    222         elif mode == 'w':
    223             if shape_type not in self.SHAPE_TYPES:

~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/core/util/shapefile.py in _open_shp_file(self)
    249         fileName = self.fileName
    250         self.fileObj = open(fileName + '.shp', 'rb')
--> 251         self._shx = shx_file(fileName)
    252         self.header = _unpackDict(UHEADERSTRUCT, self.fileObj)
    253         self.shape = TYPE_DISPATCH[self.header['Shape Type']]

~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/core/util/shapefile.py in __init__(self, fileName, mode)
    440 
    441         if mode == 'r':
--> 442             self._open_shx_file()
    443         elif mode == 'w':
    444             self._create_shx_file()

~/anaconda2/envs/gisenv/lib/python3.4/site-packages/pysal/core/util/shapefile.py in _open_shx_file(self)
    462         
    463         self.__isreadable()
--> 464         self.fileObj = open(self.fileName + '.shx', 'rb')
    465         self._header = _unpackDict(UHEADERSTRUCT, self.fileObj)
    466         self.numRecords = numRecords = (self._header['File Length'] - 50) // 4

FileNotFoundError: [Errno 2] No such file or directory: '/home/dhankar/Desktop/Geo_1/data/texas.shx'

"""




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### GET DATA Points out of this FILE only for TEXAS ...




# Visualizing the POLYGONS of NAT.shp 
#
get_ipython().run_line_magic('matplotlib', 'inline')

import random as rdm
import matplotlib
import matplotlib.pyplot as plt
from pysal.contrib.viz import mapping as maps
#
fig3 = plt.figure(figsize=(9, 9), dpi=100)

#

shp_f3 = ps.open(nat_shp)
#
print(type(shp_f3))
#
base = maps.map_poly_shp(shp_f3)
base.set_facecolor('white')
base.set_linewidth(0.25)
base.set_edgecolor('0.2') ## 0.8 --- gives Light Greyish edges 


ax3 = maps.setup_ax([base],[shp_f3.bbox, shp_f3.bbox, shp_f3.bbox])
print(type(ax3))


fig3.add_axes(ax3)
plt.show()



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#SOURCE -- https://github.com/pysal/notebooks/blob/master/notebooks/PySAL_io.ipynb
# Hold for now 

"""
f = figure()
ax = f.add_subplot(111)
poly_collection = viz.map_poly_shp(ps.open(path_shp_file))
poly_collection.set_facecolor('none')
ax.add_collection(poly_collection)
ax.autoscale_view()
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False)
show()
"""


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## What are .shp -SHAPE Files ? --- https://www.esri.com/library/whitepapers/pdfs/shapefile.pdf
## PySAL reading -SHAPE Files --- 
## 

path_shp_file = "/media/dhankar/Dhankar_1/a1_18/a1____GeoSpatialANAL/GIS_DATA/ShapeFiles/tl_2010_02_place10.shp"

print("path_shp_file____",path_shp_file)
#
shp_f = ps.open(path_shp_file)
#
print(type(shp_f))
#
# <class 'pysal.core.IOHandlers.pyShpIO.PurePyShpWrapper'>


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# ## MARKOV based methods - significance in GEOMATICS 
# #
# 

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## MARKOV based methods - significance in GEOMATICS 
# https://brilliant.org/wiki/markov-chains/
# https://en.wikipedia.org/wiki/Markov_chain

# Cross refer the CRAN -- 
# https://cran.r-project.org/web/packages/markovchain/vignettes/an_introduction_to_markovchain_package.pdf


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