Input_template.py

# ---
# jupyter:
#   jupytext:
#     formats: ipynb,py:percent
#     text_representation:
#       extension: .py
#       format_name: percent
#       format_version: '1.3'
#       jupytext_version: 1.14.0
#   kernelspec:
#     display_name: Python 3 (ipykernel)
#     language: python
#     name: python3
# ---

# %%
import geopandas as gpd
import pandas as pd
import numpy as np
from osgeo import gdal


# %%
def get_rooted(stem):
    return "D:\\LiLa_Nagapattinam\\" + stem
def read_df_UT(stem):
    return gpd.read_file(get_rooted(stem)).to_crs(epsg = 4326)


# %% [markdown]
# ### define func for creating Area_acres and area_class

# %%
def area_acres(df):
    crs_utm = 32644 
    df = df.to_crs(crs_utm)
    df["area_acres"] = (((df.geometry.area)/10**6)*247.105)
    a = df["area_acres"].max()
    def area_class(df):
        if 5<= df["area_acres"] < 20:
            return "A"
        elif 20<= df["area_acres"] < 100:
            return "B"
        elif 100<= df["area_acres"] <= a:
            return "C"  
        else:
            return "D"
    df["area_class"] =df.apply(area_class, axis=1)
    df = df.to_crs(4326)
    print("Total area: ",df.area_acres.sum(),"length : ",len(df))
    print(df.groupby(["area_class"])["area_acres"].agg(["sum","count"]))
    return (df)


# %% [markdown]
# ### define func for creating Area_hect and area_class

# %%
def area_hect(df):
    crs_utm = 32644 
    df = df.to_crs(crs_utm)
    df["area_hect"] = ((df.geometry.area)/10**4)
    a = df["area_hect"].max()
    def area_class(df):
        if 5 <= df["area_hect"] < 20:
            return "A"
        elif 20<= df["area_hect"] < 100:
            return "B"
        elif 100<= df["area_hect"] <= a:
            return "C"  
        else:
            return "D"
    df["area_class"] =df.apply(area_class, axis=1)
    df = df.to_crs(4326)
    print("Total area: ",df.area_hect.sum(),"length : ",len(df))
    print(df.groupby(["area_class"])["area_hect"].agg(["sum","count"]))
    return (df)


# %% [markdown]
# ### def fun for Calculating overlap area

# %%
def find_overlap_area(df,tag,fdf2):
    crs_utm = 32644    
    df = df.to_crs(crs_utm)
    df1 = pd.DataFrame(columns = ['olap%'+tag,'olaparea'+tag])
    df1['olap%'+tag]=df1['olap%'+tag].astype('object')
    df1['olaparea'+tag]=df1['olaparea'+tag].astype('object')
    
    fdf2=fdf2.to_crs(crs_utm)
    #set spatial index for data for faster processing
    sindex = fdf2.sindex
    for i in range(len(df)):
        geometry = df.iloc[i]['geometry']
        fids = list(sindex.intersection(geometry.bounds))
        if fids:
            olaparea = ((fdf2.iloc[fids]['geometry'].intersection(geometry)).area).sum()
            count = (fdf2.iloc[fids]['geometry'].intersection(geometry)).count()
            olap_perc = olaparea*100/geometry.area
            olaparea = (olaparea/10**6)*247.1               
        else:
            olaparea = 0
            olap_perc = 0
        df1.at[i,'olap%'+tag] =  olap_perc      
        df1.at[i,'olaparea'+tag] = olaparea
        df1.at[i,'count'+tag] = count
    df = df.to_crs(4326)
    return pd.concat([df,df1], axis= 1)


# %% [markdown]
# ### def fun for top 15 land

# %%
def top15(df,df1):
    a = df.sort_values(by=["area_acres"],ascending = False)
    b = df1.sort_values(by=["area_acres"],ascending = False)
    c = gpd.pd.concat([a,b])
    c = c[:15]
    c = c.reset_index()
    return (c)


# %% [markdown]
# ### def fun for water-runoff criteria 

# %%
def water_runoff(df):
    crs_utm = 32644 
    df = df.to_crs(crs_utm)
    df["area_acres"] = (((df.geometry.area)/10**6)*247.105)
    def water_runoff_class(df):
        if 0<= df["Run_Tot"] < 70:
            return "A"
        elif 70<= df["Run_Tot"] < 200:
            return "B"
        elif 200<= df["Run_Tot"]:
            return "C"  
        else:
            return "D"
    df["water_runoff_class"] =df.apply(water_runoff_class, axis=1)
    df = df.to_crs(4326)
    print("Total Area : ", df.area_acres.sum(),"Length :",len(df))
    print("Total Runoff : ", df.Run_Tot.sum())
    print(df.groupby(["water_runoff_class"])["area_acres"].agg(["sum","count"]))
    return (df)