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communitiesTS.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 20 18:50:41 2019
@author: Didier
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import networkx as nx
import os, sys
from matplotlib.ticker import MultipleLocator
from itertools import chain
import graphConstruction as gc
import parallel
import codnet as coms
from mpl_toolkits.basemap import Basemap
sys.stdout.flush()
from scipy.stats import pearsonr, spearmanr
owd = os.getcwd()
class communityTS(coms.codnet):
def __init__(self):
super().__init__()
self.pattern='net-year_*'
def printLinesGlobeSmall(self, m):
lats = m.drawparallels(np.arange(-90,90,10),labels=[1,0,0,1])
lons = m.drawmeridians(np.arange(m.lonmin,m.lonmax+30,10),labels=[1,1,0,1])
# keys contain the plt.Line2D instances
lat_lines = chain(*(tup[1][0] for tup in lats.items()))
lon_lines = chain(*(tup[1][0] for tup in lons.items()))
all_lines = chain(lat_lines, lon_lines)
# cycle through these lines and set the desired style
for line in all_lines:
line.set(linestyle='-', alpha=0.3, color='w',linewidth=2)
def getValidConfNodes(self,dfPrec,dfTemp, conf):
sP = set(dfPrec.index)
sT = set(dfTemp.index)
sC = set(conf.index)
validIds = sP & sT & sC
return conf.loc[validIds]
def initNewDataFrameYear(self,conf,year):
dfComm = pd.DataFrame(conf[['Label','cLabel']])
dfComm.set_index('Label',drop=False,append=False,inplace=True)
dfComm.rename(columns={'Label':'gid'}, inplace=True)
dfComm['n'] = np.nan
dfComm['n_norm_comm'] = np.nan
dfComm['m'] = np.nan
dfComm['<k>'] = np.nan
dfComm['<s>'] = np.nan
dfComm['lo'] = np.nan
dfComm['tmp_i'] = np.nan
dfComm['pre_i'] = np.nan
dfComm['tmp_mean_community'] = np.nan
dfComm['pre_mean_community'] = np.nan
dfComm.sort_values(by='cLabel',inplace=True)
dfComm.dropna(how='all',inplace=True)
dfComm['year'] = year
return dfComm
#SAVE FOR ALL THE CELLS ['n', 'm', '<k>', 'k_i', temp_i, prec_i]
def altFunc(self, dyears, args):
dfPrec,dfTemp,conf,communities = args
dfFull = pd.DataFrame()
for dyear in dyears:
G = nx.read_gml(dyear)
year = int(dyear.split('_')[1].split('.')[0])
dfComm = self.initNewDataFrameYear(conf, year)
precYear = dfPrec.loc[(dfPrec['year'] == year),['year','value']]
tempYear = dfTemp.loc[(dfTemp['year'] == year),['year','value']]
for comm in communities.itertuples():
nodes = [x for x in conf.loc[(conf['cLabel'] == comm.cLabel),:].index]
nodesC = [str(x) for x in nodes]
H = G.subgraph(nodesC)
n = nx.number_of_nodes(H)
dfComm.loc[nodes,'n'] = n
dfComm.loc[nodes,'n_norm_comm'] = n/comm.size
dfComm.loc[nodes,'m'] = nx.number_of_edges(H)
tempNodes = tempYear.reindex(index=nodes)
tempNodes.dropna(axis=0,how='any',inplace=True)
precNodes = precYear.reindex(index=nodes)
precNodes.dropna(axis=0,how='any',inplace=True)
dfComm.loc[nodes,'tmp_mean_community'] = tempNodes['value'].mean()
dfComm.loc[nodes,'pre_mean_community'] = precNodes['value'].mean()
if n == 0:
continue;
strength = list(dict(H.degree(weight='weight')).values())
degree = list(dict(H.degree).values())
# <k>
avgK = 2*nx.number_of_edges(H)/n
dfComm.loc[nodes,'<k>'] = avgK
#lo localization or complexity
dfComm.loc[nodes,'lo'] = (self.complexity(degree) if avgK > 1.0 else 0.0)
# <s>
dfComm.loc[nodes,'<s>'] = np.mean(strength)
precNodes = precYear.reindex(index=dfComm.index)
tempNodes = tempYear.reindex(index=dfComm.index)
#tmperature and precipitation
dfComm.loc[precNodes.index,'pre_i'] = precNodes['value']
dfComm.loc[tempNodes.index,'tmp_i'] = tempNodes['value']
dfFull = dfFull.append(dfComm, ignore_index=True, verify_integrity=False, sort=None)
return dfFull.values.tolist()
def _get_precipitation_AND_temperature_data(self):
csvFile = os.path.join(gc.datafolder,'prec_dat.csv')
dfPrec = pd.read_csv(csvFile, sep=',')
dfPrec.dropna(axis=0,how='any',inplace=True)
dfPrec.drop_duplicates(subset=['gid','year'],keep='first',inplace=True)
dfPrec.set_index('gid',drop=False,append=False,inplace=True)
dfPrec.rename(columns={'prec_gpcp':'value'}, inplace=True)
csvFile = os.path.join(gc.datafolder,'gridtemp8914.csv')
dfTemp = pd.read_csv(csvFile, sep=',')
dfTemp.dropna(axis=0,how='any',inplace=True)
dfTemp.drop_duplicates(subset=['gid','year'],keep='first',inplace=True)
dfTemp.set_index('gid',drop=False,append=False,inplace=True)
dfTemp.rename(columns={'temp':'value'}, inplace=True)
return dfPrec, dfTemp
def _construct_TableTSCells(self,timeWindow='year',MAX_DISTANCE=500,neighbors=3):
opattern = gc.getFilePattern(neighbors=neighbors,timeWindow=timeWindow,MAX_DISTANCE=MAX_DISTANCE)
fileGML= gc.getFileName(begin='net-ALL-years',neighbors=neighbors,
MAX_DISTANCE=MAX_DISTANCE,
timeWindow=timeWindow, path=os.path.join(owd, 'net-All-Years'))
print(opattern,'\n', fileGML)
conf = self.getCommunityLabel(fileGML)
conf.set_index('Label',drop=False,append=False,inplace=True)
dfFull = pd.DataFrame()
communities = pd.DataFrame(conf['cLabel'])
csize= communities.groupby('cLabel').agg({'cLabel': 'count'})
communities.drop_duplicates(inplace=True)
communities.loc[:,'size'] = [csize.loc[cx,'cLabel'] for cx in communities['cLabel'].values]
communities.sort_values(by=['size'],ascending=False,inplace=True)
dfiles = self.getFolderFilesYears(self.folderData, opattern)
#removing the last 3 years
dfiles = dfiles[:-3]
dfPrec, dfTemp = self._get_precipitation_AND_temperature_data()
conf = self.getValidConfNodes(dfPrec,dfTemp, conf)
nodes = list(conf.index)
dfPrec = dfPrec.loc[nodes,:]
dfTemp = dfTemp.loc[nodes,:]
lfiles = dfiles['FileAddress'].values.tolist()
lFull = parallel.For(lfiles,self.altFunc,argsF=(dfPrec,dfTemp,conf,communities))
print('\n CoDNet network with ',len(lFull),' temporal nodes!')
headers = ['gid','cLabel','n','n_norm_comm','m', '<k>', '<s>',
'lo','tmp_i','pre_i','tmp_mean_community','pre_mean_community','year']
dfFull = pd.DataFrame(lFull, columns= headers)
return dfFull
#SAVE FOR ALL THE CELLS ['n', 'm', '<k>', 'be_i','k_i', temp_i, prec_i]
def _print_TableTSCells(self,timeWindow='year',MAX_DISTANCE=500,neighbors=3):
dfFull = self._construct_TableTSCells(timeWindow=timeWindow,MAX_DISTANCE=MAX_DISTANCE,neighbors=neighbors)
dfFull.fillna(-9999, inplace=True)
filename = gc.getFileName(begin='tableAllCells', timeWindow=timeWindow, neighbors=neighbors,
MAX_DISTANCE=MAX_DISTANCE, ext='csv',path=os.curdir)
dfFull.to_csv(filename,index=False,sep=',')
def read_TableTSCells(self,timeWindow='year',MAX_DISTANCE=500,neighbors=3):
filename = gc.getFileName(begin='tableAllCells', timeWindow=timeWindow, neighbors=neighbors,
MAX_DISTANCE=MAX_DISTANCE, ext='csv',path=os.curdir)
if os.path.exists(filename):
with open(filename) as infile:
dfFull = pd.read_csv(infile)
else:
dfFull = self._print_TableTSCells(timeWindow=timeWindow,MAX_DISTANCE=MAX_DISTANCE,neighbors=neighbors)
return dfFull
#cols = # ['n',"n_norm_comm","m","<k>","<s>","lo",'tmp_i','pre_i','tmp_mean_community','pre_mean_community']
#cols = ['n',"m"]
def drawTs(self, cols, timeWindow='year',MAX_DISTANCE=500,neighbors=3):
pltname = "TS-"
for co in cols:
pltname = pltname + co + "-"
pltname = pltname.replace('<','_').replace('>',"")
pltname = gc.getFileName(begin=pltname, timeWindow=timeWindow, neighbors=neighbors,
MAX_DISTANCE=MAX_DISTANCE, ext='png',path=os.curdir)
dfFull = self.read_TableTSCells(timeWindow=timeWindow,MAX_DISTANCE=MAX_DISTANCE,neighbors=neighbors)
lcorr = list()
dfFull.dropna(inplace=True)
dfFull = dfFull.loc[~((dfFull['lo'] == -9999) | (dfFull['tmp_i'] == -9999))]
dfComm = dfFull.groupby(by=['cLabel','year']).mean()
dfComm.reset_index(inplace=True)
dfComm = dfComm.groupby(by=['cLabel'])
rows = np.sort(dfFull['cLabel'].unique())
nrows= len(dfComm)
ncols = len(cols)+2
nplot = 1
plt.rcParams['figure.figsize'] = (7*ncols, 1.9*nrows)
figP = plt.figure()
for i in rows:
row = dfComm.get_group(i)
if len(row) < 11:
continue
xdata = row['year'].values
tempR = row['tmp_mean_community'].values
precR = row['pre_mean_community'].values
for col in cols:
plt.subplot(nrows, ncols, nplot)
nplot+=1
data = row[col].values
plt.plot(xdata, data, 'o-', label="cluster "+str(row['cLabel'].values[0])+' '+col)
plt.legend(loc="best")
ax = plt.gca()
plt.xticks(np.arange(1989, 2014, 2), fontsize=8)
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.xaxis.grid(True)
rT, rpvT = pearsonr(data, tempR) # Pearson's r
sT, spvT = spearmanr(data, tempR) # Pearson's r
sP, spvP = spearmanr(data, precR)
rP, rpvP = pearsonr(data, precR)
lcorr.append({"cLabel":row['cLabel'].values[0],"size":len(row),
"timeWindow":timeWindow,
"Distance":MAX_DISTANCE,"neighbors":neighbors,
"measure":col, "rT":rT, 'rpvT':rpvT,'sT':sT,'spvT':spvT,
'rP':rP,'rpvP':rpvP,'sP':sP,'spvP':spvP})
plt.subplot(nrows, ncols, nplot)
nplot+=1
plt.plot(xdata, tempR, 'o-', label='MeanTemp', color='red')
plt.legend(loc="best")
ax = plt.gca()
plt.xticks(np.arange(1989, 2014, 2), fontsize=8)
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.xaxis.grid(True)
plt.subplot(nrows, ncols, nplot)
nplot+=1
plt.plot(xdata, precR,'o-', label='MeanPrec', color='orange')
plt.legend(loc="best")
ax = plt.gca()
plt.xticks(np.arange(1989, 2014, 2), fontsize=8)
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.xaxis.grid(True)
plt.subplots_adjust(wspace=0.13, hspace = 0.18)
plt.savefig(pltname, format='png', bbox_inches='tight')
return pd.DataFrame(lcorr)
def draw_TSMap(self, corrDf, corr_measure="r", climate_measure='T'):
timeWindow = corrDf['timeWindow'].values[0]
MAX_DISTANCE = corrDf['Distance'].values[0]
neighbors = corrDf['neighbors'].values[0]
corr_m = corr_measure + climate_measure
corr_pv = corr_measure + 'pv' + climate_measure
fileGML= gc.getFileName(begin='net-ALL-years',neighbors=neighbors,
MAX_DISTANCE=MAX_DISTANCE,
timeWindow=timeWindow, path=os.path.join(owd, 'net-All-Years'))
Gy = nx.read_gml(fileGML)
locations = pd.DataFrame(dict(Gy.nodes(data=True))).T
del locations['month'], locations['year'], locations['event']
locations.drop_duplicates(inplace=True)
locations.index = locations.index.astype(int)
dfFull = self.read_TableTSCells(timeWindow=timeWindow,MAX_DISTANCE=MAX_DISTANCE,neighbors=neighbors)
dfFull = dfFull[['gid','cLabel']].drop_duplicates()
cols = corrDf['measure'].unique()
nrows= 1
ncols = 3
nplot = 1
plt.rcParams['figure.figsize'] = (10*ncols, 7*nrows)
figP = plt.figure()
dfComm = dfFull.groupby(by='cLabel')
corr_name = "Pearson" if corr_measure == "r" else "Spearman"
print(f"{corr_name} correlation with all-period Codnet",end=" ")
print(f"constructed with time window {timeWindow}, max.distance",end=" ")
print(f"{MAX_DISTANCE}, neighbors {neighbors}\nSignificant",end=" ")
print(f"communities (p-value < 0.05) with {climate_measure}:")
for col in cols:
nodesDf = pd.DataFrame()
corrCol = corrDf.loc[(corrDf['measure']==col) & (corrDf[corr_pv] <= 0.05)]
comms = list(corrCol['cLabel'].unique())
print(f"\t Communties: {comms} for metric {col}")
for i in comms:
nodes = dfComm.get_group(i)['gid'].values
nDf = locations.reindex(index= nodes).dropna()
nDf['corr'] = corrCol.loc[corrCol['cLabel'] == i,corr_m].values[0]
#nDf['cLabel'] = i
nodesDf= nodesDf.append(nDf,ignore_index=False)
plt.subplot(nrows, ncols, nplot)
nplot+=1
m = Basemap(projection='eck4', lat_0=0, lon_0=0)
m.drawcoastlines(linewidth=1,color='k')
m.drawcountries(linewidth=1,color='grey')
m.drawmapboundary(fill_color='white')
# draw a shaded-relief image
m.shadedrelief(scale=0.2,alpha=0.7)
self.printLinesGlobe(m)
if comms != []:
title='correlation of {} with {}'.format(col, corr_m)
m.scatter(nodesDf['lng'].values, nodesDf['lat'].values, latlon=True,
s= 10.8, c= nodesDf['corr'].values,
cmap='seismic_r', alpha=0.6, marker='o') #edgecolor='white'
else:
m.scatter([0.0], [0.0], latlon=True,
s= 10.8, c= [0.0],
cmap='seismic_r', alpha=0.6, marker='o') #edgecolor='white'
title='NO significant p-vaulue with {}'.format(col)
plt.clim(-1, 1)
plt.title(title,fontsize = 18)
plt.colorbar(label='correlation', shrink=0.37)
print("\n")
plt.subplots_adjust(wspace=-0.02, hspace = -0.4)
figname= gc.getFileName(begin='corrGraph-'+corr_m,neighbors=neighbors,
MAX_DISTANCE=MAX_DISTANCE,ext='png',
timeWindow=timeWindow,
path=os.path.join(owd, 'net-All-Years'))
plt.savefig(figname, format='png', bbox_inches='tight')
def draw_TSMapComunity(self, cols, timeWindow ='year', MAX_DISTANCE=500,neighbors=3, communities=[11,27,37,24]):
conf = gc.getConfEventData()
conf.set_index('gid',drop=False,append=False,inplace=True)
pltname = "TS_SelComm-"
for co in cols:
pltname = pltname + co + "-"
pltname = pltname.replace('<','_').replace('>',"")
pltname = gc.getFileName(begin=pltname, timeWindow=timeWindow, neighbors=neighbors,
MAX_DISTANCE=MAX_DISTANCE, ext='png',path=os.curdir)
dfFull = self.read_TableTSCells(timeWindow=timeWindow,MAX_DISTANCE=MAX_DISTANCE,neighbors=neighbors)
dfFull.dropna(inplace=True)
dfFull = dfFull.loc[~((dfFull['lo'] == -9999) | (dfFull['tmp_i'] == -9999))]
dfComm = dfFull.groupby(by=['cLabel','year']).mean()
dfComm.reset_index(inplace=True)
dfComm = dfComm.groupby(by=['cLabel'])
rows = np.sort(dfFull['cLabel'].unique())
assert set(communities) & set(rows) == set(communities) , 'wrong community names'
rows = np.array(communities)
nrows= len(rows)
ncols = len(cols) + 1
plt.rcParams['figure.figsize'] = (10*ncols, 5*nrows)
figP = plt.figure()
measureLabel = {'<k>':r' $Average$ $intensity$',
'n':r' $Dispersion$',
'm': r" $Intraconnectivity$"}
def plotLine(nplot, data, xdata, label):
plt.subplot(nrows, ncols, nplot)
plt.plot(xdata, data, 'o-', label=label)
#https://stackoverflow.com/questions/4700614/how-to-put-the-legend-outside-the-plot
plt.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower left",
mode="expand", borderaxespad=0,fontsize=20)
#plt.legend(loc="best",fontsize=15)
ax = plt.gca()
plt.xticks(np.arange(1989, 2014, 2), fontsize=13)
plt.yticks(fontsize=13)
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.xaxis.grid(True)
nplot = 0
count = 1
lit = 4
initR = 1
for i in rows:
commIds = dfFull.loc[(dfFull['cLabel'] == i),'gid'].unique()
commCount = conf.loc[commIds]
commCount.sort_values(by='year',inplace=True)
commCount['stateSum'] = commCount['state']
commCount['stateMean'] = commCount['state']
commCount = commCount.loc[commCount['year'] <= 2014]
commCountYear = commCount.groupby(by=['year']).agg({'stateSum':'sum','stateMean':'mean'})
row = dfComm.get_group(i)
xdata = row['year'].values
for col in cols:
data = row[col].values
label= r"${ \bf ("+str(count)+")}$ Community "+str(row['cLabel'].values[0])+", "+measureLabel[col]
nplot+=1
plotLine(nplot, data, xdata, label)
initR+=4
count+=1
if count == lit:
xticks = commCountYear.index
data = commCountYear['stateSum'].values#
label=r"${ \bf ("+str(count)+")}$ Community "+str(row['cLabel'].values[0]) +r", $Conflicts$ $Sum$ "
nplot+=1
plotLine(nplot, data, xticks, label)
count+=1
lit +=4
plt.subplots_adjust(wspace=0.13, hspace = 0.28)
plt.savefig(pltname, format='png', bbox_inches='tight')
if __name__ == "__main__":
teste = communityTS()
#cols = # ['n',"n_norm_comm","m","<k>","<s>","lo",'tmp_i','pre_i','tmp_mean_community','pre_mean_community']
cols = ["<k>",'n', "m"]
neighbors = [3]
MAX_DISTANCE = [500] # km[100, ]
timeWindow = ['month']
teste.draw_TSMapComunity(cols, timeWindow='month', MAX_DISTANCE=500, neighbors=3, communities=[11,27,37,24])
for k in neighbors:
for distance in MAX_DISTANCE:
for twindow in timeWindow:
teste.read_TableTSCells(timeWindow=twindow,MAX_DISTANCE=distance,neighbors=k)
corrDf = teste.drawTs(cols, timeWindow=twindow,MAX_DISTANCE=distance,neighbors=k)
teste.draw_TSMap(corrDf, corr_measure="r", climate_measure='T')
teste.draw_TSMap(corrDf, corr_measure="s", climate_measure='T')
teste.draw_TSMap(corrDf, corr_measure="r", climate_measure='P')
teste.draw_TSMap(corrDf, corr_measure="s", climate_measure='P')