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treeParsing.py
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treeParsing.py
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# library import
import pandas as pd
import geopandas as gpd
import math
#!/usr/bin/env python
# Simpson Diversity Index
# http://en.wikipedia.org/wiki/Diversity_index
# modified from Shannon Diversity Index implementation by audy
# https://gist.github.com/audy/783125
# https://gist.github.com/audy
def simpson_di(keys, values):
data = dict(zip(keys, values))
""" Given a hash { 'species': count } , returns the Simpson Diversity Index
>>> simpson_di({'a': 10, 'b': 20, 'c': 30,})
0.3888888888888889
"""
def p(n, N):
""" Relative abundance """
if n is 0:
return 0
else:
return float(n)/N
N = sum(data.values())
return 1-sum(p(n, N)**2 for n in data.values() if n is not 0)
def SimpsonEntropy(list):
denom = sum(n*(n-1) for n in list if n != 0)
numer = sum(list)*(sum(list)-1)
return denom/numer
def ShannonEntropy(list):
ent = 0.0
for freq in list:
try:
ent = ent + freq * math.log(freq)
except:
pass
ent = -ent
return ent
def ShannonEntropyBlock(list):
ListSum = sum(list)
ent = 0.0
for val in list:
try:
ent = ent + val/ListSum * math.log(val/ListSum)
except:
pass
ent = -ent
return ent
def speciesParse(cleanTrees, grid):
treeMakeupNum = pd.DataFrame(cleanTrees[cleanTrees.within(grid.iloc[0].geometry)].groupby("Scientific").size()).T
treeNum = treeMakeupNum.sum(axis=1)
treeMakeup = pd.DataFrame(cleanTrees[cleanTrees.within(grid.iloc[0].geometry)].groupby("Scientific").sum()).T
treeSum = treeMakeup.sum(axis=1)
basalSimpson = simpson_di(list(treeMakeup.T.index), list(treeMakeup.T["BasalArea"]))
stemSimpson = simpson_di(list(treeMakeupNum.T.index), list(treeMakeupNum.T[0]))
for idx, column in enumerate(list(treeMakeup.columns)):
treeMakeup[f"{column} sumPCT"] = treeMakeup.iloc[0][column]/treeSum
treeMakeup[f"{column} numPCT"] = treeMakeupNum.iloc[0][column]/treeNum[0]
treeMakeup['sum'] = treeSum
treeMakeup['num'] = treeNum[0]
treeMakeup['shannonDiversitySpeciesBasal'] = ShannonEntropy(list(treeMakeup[treeMakeup.filter(like='sumPCT').columns].values[0]))
treeMakeup['simpsonDiversitySpeciesBasal'] = basalSimpson
treeMakeup['shannonDiversitySpeciesStem'] = ShannonEntropy(list(treeMakeup[treeMakeup.filter(like='numPCT').columns].values[0]))
treeMakeup['simpsonDiversitySpeciesStem'] = stemSimpson
try:
treeMakeup['mostAbundantPcSpeciesBasal'] = treeMakeup[treeMakeup.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
treeMakeup['mostAbundantNameSpeciesBasal'] = treeMakeup[treeMakeup.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["Scientific"][:-7]
treeMakeup['mostAbundantPcSpeciesStem'] = treeMakeup[treeMakeup.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
treeMakeup['mostAbundantNameSpeciesStem'] = treeMakeup[treeMakeup.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["Scientific"][:-7]
except:
print('error')
treeMakeup.reset_index()
tempDf = pd.DataFrame(cleanTrees[~cleanTrees.within(grid.iloc[0].geometry)].groupby("Scientific").sum()).T
tempNum = pd.DataFrame(cleanTrees[~cleanTrees.within(grid.iloc[0].geometry)].groupby("Scientific").size()).T
treeNum = tempNum.sum(axis=1)
treeSum = tempDf.sum(axis=1)
basalSimpson = simpson_di(list(tempDf.T.index), list(tempDf.T["BasalArea"]))
stemSimpson = simpson_di(list(tempNum.T.index), list(tempNum.T[0]))
for idx, column in enumerate(list(tempDf.columns)):
tempDf[f"{column} sumPCT"] = tempDf.iloc[0][column]/treeSum
tempDf[f"{column} numPCT"] = tempNum.iloc[0][column]/treeNum[0]
tempDf['sum'] = treeSum
tempDf['num'] = treeNum[0]
tempDf['shannonDiversitySpeciesBasal'] = ShannonEntropy(list(tempDf[tempDf.filter(like='sumPCT').columns].values[0]))
tempDf['simpsonDiversitySpeciesBasal'] = basalSimpson
tempDf['shannonDiversitySpeciesStem'] = ShannonEntropy(list(tempDf[tempDf.filter(like='numPCT').columns].values[0]))
tempDf['simpsonDiversitySpeciesStem'] = stemSimpson
try:
tempDf['mostAbundantPcSpeciesBasal'] = tempDf[tempDf.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
tempDf['mostAbundantNameSpeciesBasal'] = tempDf[tempDf.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["Scientific"][:-7]
tempDf['mostAbundantPcSpeciesStem'] = tempDf[tempDf.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
tempDf['mostAbundantNameSpeciesStem'] = tempDf[tempDf.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["Scientific"][:-7]
except:
print('error')
treeMakeup = treeMakeup.append(tempDf)
treeMakeup = treeMakeup.fillna(0).reset_index()
return treeMakeup
def genusParse(cleanTrees, grid):
treeMakeupNum = pd.DataFrame(cleanTrees[cleanTrees.within(grid.iloc[0].geometry)].groupby("CleanGenus").size()).T
treeNum = treeMakeupNum.sum(axis=1)
treeMakeup = pd.DataFrame(cleanTrees[cleanTrees.within(grid.iloc[0].geometry)].groupby("CleanGenus").sum()).T
treeSum = treeMakeup.sum(axis=1)
basalSimpson = simpson_di(list(treeMakeup.T.index), list(treeMakeup.T["BasalArea"]))
stemSimpson = simpson_di(list(treeMakeupNum.T.index), list(treeMakeupNum.T[0]))
for idx, column in enumerate(list(treeMakeup.columns)):
treeMakeup[f"{column} sumPCT"] = treeMakeup.iloc[0][column]/treeSum
treeMakeup[f"{column} numPCT"] = treeMakeupNum.iloc[0][column]/treeNum[0]
treeMakeup['sum'] = treeSum
treeMakeup['num'] = treeNum[0]
treeMakeup['shannonDiversityGenusBasal'] = ShannonEntropy(list(treeMakeup[treeMakeup.filter(like='sumPCT').columns].values[0]))
treeMakeup['simpsonDiversityGenusBasal'] = basalSimpson
treeMakeup['shannonDiversityGenusStem'] = ShannonEntropy(list(treeMakeup[treeMakeup.filter(like='numPCT').columns].values[0]))
treeMakeup['simpsonDiversityGenusStem'] = stemSimpson
try:
treeMakeup['mostAbundantPcGenusBasal'] = treeMakeup[treeMakeup.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
treeMakeup['mostAbundantNameGenusBasal'] = treeMakeup[treeMakeup.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["CleanGenus"][:-7]
treeMakeup['mostAbundantPcGenusStem'] = treeMakeup[treeMakeup.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
treeMakeup['mostAbundantNameGenusStem'] = treeMakeup[treeMakeup.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["CleanGenus"][:-7]
except:
print('error')
treeMakeup.reset_index()
tempDf = pd.DataFrame(cleanTrees[~cleanTrees.within(grid.iloc[0].geometry)].groupby("CleanGenus").sum()).T
tempNum = pd.DataFrame(cleanTrees[~cleanTrees.within(grid.iloc[0].geometry)].groupby("CleanGenus").size()).T
treeNum = tempNum.sum(axis=1)
treeSum = tempDf.sum(axis=1)
basalSimpson = simpson_di(list(tempDf.T.index), list(tempDf.T["BasalArea"]))
stemSimpson = simpson_di(list(tempNum.T.index), list(tempNum.T[0]))
for idx, column in enumerate(list(tempDf.columns)):
tempDf[f"{column} sumPCT"] = tempDf.iloc[0][column]/treeSum
tempDf[f"{column} numPCT"] = tempNum.iloc[0][column]/treeNum[0]
tempDf['sum'] = treeSum
tempDf['num'] = treeNum[0]
tempDf['shannonDiversityGenusBasal'] = ShannonEntropy(list(tempDf[tempDf.filter(like='sumPCT').columns].values[0]))
tempDf['simpsonDiversityGenusBasal'] = basalSimpson
tempDf['shannonDiversityGenusStem'] = ShannonEntropy(list(tempDf[tempDf.filter(like='numPCT').columns].values[0]))
tempDf['simpsonDiversityGenusStem'] = stemSimpson
try:
tempDf['mostAbundantPcGenusBasal'] = tempDf[tempDf.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
tempDf['mostAbundantNameGenusBasal'] = tempDf[tempDf.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["CleanGenus"][:-7]
tempDf['mostAbundantPcGenusStem'] = tempDf[tempDf.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["CleanGenus"]
tempDf['mostAbundantNameGenusStem'] = tempDf[tempDf.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["CleanGenus"][:-7]
except:
print('error')
treeMakeup = treeMakeup.append(tempDf)
treeMakeup = treeMakeup.fillna(0).reset_index()
return treeMakeup
def familyParse(cleanTrees, grid):
treeMakeupNum = pd.DataFrame(cleanTrees[cleanTrees.within(grid.iloc[0].geometry)].groupby("family").size()).T
treeNum = treeMakeupNum.sum(axis=1)
treeMakeup = pd.DataFrame(cleanTrees[cleanTrees.within(grid.iloc[0].geometry)].groupby("family").sum()).T
treeSum = treeMakeup.sum(axis=1)
basalSimpson = simpson_di(list(treeMakeup.T.index), list(treeMakeup.T["BasalArea"]))
stemSimpson = simpson_di(list(treeMakeupNum.T.index), list(treeMakeupNum.T[0]))
for idx, column in enumerate(list(treeMakeup.columns)):
treeMakeup[f"{column} sumPCT"] = treeMakeup.iloc[0][column]/treeSum
treeMakeup[f"{column} numPCT"] = treeMakeupNum.iloc[0][column]/treeNum[0]
treeMakeup['sum'] = treeSum
treeMakeup['num'] = treeNum[0]
treeMakeup['shannonDiversityFamilyBasal'] = ShannonEntropy(list(treeMakeup[treeMakeup.filter(like='sumPCT').columns].values[0]))
treeMakeup['simpsonDiversityFamilyBasal'] = basalSimpson
treeMakeup['shannonDiversityFamilyStem'] = ShannonEntropy(list(treeMakeup[treeMakeup.filter(like='numPCT').columns].values[0]))
treeMakeup['simpsonDiversityFamilyStem'] = stemSimpson
try:
treeMakeup['mostAbundantPcFamilyBasal'] = treeMakeup[treeMakeup.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
treeMakeup['mostAbundantNameFamilyBasal'] = treeMakeup[treeMakeup.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["family"][:-7]
treeMakeup['mostAbundantPcFamilyStem'] = treeMakeup[treeMakeup.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
treeMakeup['mostAbundantNameFamilyStem'] = treeMakeup[treeMakeup.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["family"][:-7]
except:
print('error')
treeMakeup.reset_index()
tempDf = pd.DataFrame(cleanTrees[~cleanTrees.within(grid.iloc[0].geometry)].groupby("family").sum()).T
tempNum = pd.DataFrame(cleanTrees[~cleanTrees.within(grid.iloc[0].geometry)].groupby("family").size()).T
treeNum = tempNum.sum(axis=1)
treeSum = tempDf.sum(axis=1)
basalSimpson = simpson_di(list(tempDf.T.index), list(tempDf.T["BasalArea"]))
stemSimpson = simpson_di(list(tempNum.T.index), list(tempNum.T[0]))
for idx, column in enumerate(list(tempDf.columns)):
tempDf[f"{column} sumPCT"] = tempDf.iloc[0][column]/treeSum
tempDf[f"{column} numPCT"] = tempNum.iloc[0][column]/treeNum[0]
tempDf['sum'] = treeSum
tempDf['num'] = treeNum[0]
tempDf['shannonDiversityFamilyBasal'] = ShannonEntropy(list(tempDf[tempDf.filter(like='sumPCT').columns].values[0]))
tempDf['simpsonDiversityFamilyBasal'] = basalSimpson
tempDf['shannonDiversityFamilyStem'] = ShannonEntropy(list(tempDf[tempDf.filter(like='numPCT').columns].values[0]))
tempDf['simpsonDiversityFamilyStem'] = stemSimpson
try:
tempDf['mostAbundantPcFamilyBasal'] = tempDf[tempDf.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
tempDf['mostAbundantNameFamilyBasal'] = tempDf[tempDf.filter(like='sumPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["family"][:-7]
tempDf['mostAbundantPcFamilyStem'] = tempDf[tempDf.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["BasalArea"]
tempDf['mostAbundantNameFamilyStem'] = tempDf[tempDf.filter(like='numPCT').columns].T.sort_values("BasalArea", ascending=False).reset_index().iloc[0]["family"][:-7]
except:
print('error')
treeMakeup = treeMakeup.append(tempDf)
treeMakeup = treeMakeup.fillna(0).reset_index()
return treeMakeup