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02_exp_vario.py
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# -*- coding: utf-8 -*-
import PySimpleGUI as sg
import os
from multiprocessing import Pool
from multiprocessing import cpu_count
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
import lva_vario_funcs as lva
if __name__ == '__main__':
nb_procs = cpu_count()-1
if (nb_procs<1): nb_procs=1
sg.theme('DarkGrey3')
sg.SetOptions( button_color=('black','#ff7636') )
dmdir = 'C:'
tab_DATA = [
[sg.Text(' ')],
[sg.Text('Dataset:', size=(20, 1)), sg.InputText(dmdir+'/dataset_angs.csv',justification='right',key='DATA'),sg.FileBrowse(initial_folder=dmdir,file_types=(("CSV Files", "*.csv"),))],
[sg.Text('Coordinate columns:', size=(20, 1)),sg.InputText('X',key='X',size=(10, 1)),sg.InputText('Y',key='Y',size=(10, 1)),sg.InputText('Z',key='Z',size=(10, 1))],
[sg.Text('Local rotation columns:', size=(20, 1)),sg.InputText('TRDIPDIR',key='AZ',size=(10, 1)),sg.InputText('TRDIP',key='DP',size=(10, 1)),sg.InputText('RAKE',key='RK',size=(10, 1))],
[sg.Text('Variable:', size=(20, 1)), sg.InputText('VAR',key='VVAR')],
[sg.Text('Output exp. vario:', size=(20, 1)), sg.InputText('exp_variography',key='OUT')],
[sg.Text(' ')],
[sg.Checkbox('Use domain:', size=(17,1),default=False,enable_events=True,key='USEDOM'), sg.InputText('BODY',key='DOM',disabled=True)],
[sg.Text(' ')]
]
tab_MAX = [
[sg.Text(' ')],
[sg.Text('Lag distance:', size=(20, 1)), sg.InputText('30.0',key='LAG1')],
[sg.Text('Number of lags:', size=(20, 1)), sg.InputText('5',key='NLAGS1')],
[sg.Text('Horizontal bandwidth:', size=(20, 1)), sg.InputText('150.0',key='BAND1')],
[sg.Text('Hor. Angular tolerance:', size=(20, 1)), sg.InputText('90.0',key='ANGTOL1')],
[sg.Text('Vertical bandwidth:', size=(20, 1)), sg.InputText('5.0',key='VBAND1')],
[sg.Text('Vert. Angular tolerance:', size=(20, 1)), sg.InputText('45.0',key='VANGTOL1')],
[sg.Text(' ')]
]
tab_MED = [
[sg.Text(' ')],
[sg.Text('Lag distance:', size=(20, 1)), sg.InputText('30.0',key='LAG2')],
[sg.Text('Number of lags:', size=(20, 1)), sg.InputText('5',key='NLAGS2')],
[sg.Text('Horizontal bandwidth:', size=(20, 1)), sg.InputText('150.0',key='BAND2')],
[sg.Text('Hor. Angular tolerance:', size=(20, 1)), sg.InputText('90.0',key='ANGTOL2')],
[sg.Text('Vertical bandwidth:', size=(20, 1)), sg.InputText('5.0',key='VBAND2')],
[sg.Text('Vert. Angular tolerance:', size=(20, 1)), sg.InputText('45.0',key='VANGTOL2')],
[sg.Text(' ')]
]
tab_MIN = [
[sg.Text(' ')],
[sg.Text('Lag distance:', size=(20, 1)), sg.InputText('1.0',key='LAG3')],
[sg.Text('Number of lags:', size=(20, 1)), sg.InputText('50',key='NLAGS3')],
[sg.Text('Bandwidth:', size=(20, 1)), sg.InputText('5.0',key='BAND3')],
[sg.Text('Angular tolerance:', size=(20, 1)), sg.InputText('45.0',key='ANGTOL3')],
[sg.Text(' ')]
]
layout = [
[sg.TabGroup([[sg.Tab('Data',tab_DATA),sg.Tab('Max',tab_MAX),sg.Tab('Med',tab_MED),sg.Tab('Min',tab_MIN)]])],
[sg.Button('Run'), sg.Button('Close')]
]
window = sg.Window('Variography with local angles', layout)
pool = Pool(processes=nb_procs)
while True:
event, vals = window.Read()
if event is None: break
if event=='Close':
window.Close()
break
if event=='USEDOM':
window.Element('DOM').Update(disabled=(not vals['USEDOM']))
if event=='Run':
window.Hide()
print('Start running...')
print(' ')
pars = {}
for x,ax in enumerate(['MAX','MED','MIN']):
pars[ax] = {}
for p in ['LAG','NLAGS','BAND','ANGTOL','VBAND','VANGTOL']:
if (ax=="MIN" and (p=='VBAND' or p=='VANGTOL')): continue
if(p=='NLAGS'): pars[ax][p]=int(vals[p+str(x+1)])
else: pars[ax][p]=float(vals[p+str(x+1)])
pars['MIN']['VBAND']=pars['MIN']['BAND']
pars['MIN']['VANGTOL']=pars['MIN']['ANGTOL']
domvar = None
if(vals['USEDOM']==True): domvar = vals['DOM']
dataset,coordx,coordy,coordz,azm_code,dip_code,rak_code,vvar,outname,dom_code = tuple((vals['DATA'],vals['X'],vals['Y'],vals['Z'],vals['AZ'],vals['DP'],vals['RK'],vals['VVAR'],vals['OUT'],domvar))
# Reading Dataset
df_data=pd.read_csv(dataset,na_values='-')
list_vars = [coordx,coordy,coordz,azm_code,dip_code,rak_code,vvar]
if dom_code==None:
df_data['DOM__'] = [1 for i in df_data.index.values]
dom_code = 'DOM__'
list_vars.append(dom_code)
df_data.dropna(subset=list_vars,inplace=True)
df_data = df_data[list_vars].reset_index(drop=True)
df_data['IDX'] = range(len(df_data))
#df_data.to_csv('idx.csv',index=False)
out_df = pd.DataFrame(columns=['AXIS','BIN','NPAIRS','DIST','CORR'])
idx_pair={}
hlimit_d={}
vlimit_d={}
for ax in ['MAX','MED','MIN']:
idx_pair[ax]=[]
hlimit_d[ax] = pars[ax]['BAND']/np.tan(np.deg2rad(pars[ax]['ANGTOL']))
vlimit_d[ax] = pars[ax]['VBAND']/np.tan(np.deg2rad(pars[ax]['VANGTOL']))
for dom in df_data[dom_code].unique():
df_full = df_data[df_data[dom_code]==dom].reset_index()
if(len(df_data[dom_code].unique())>1): print('Domain:',dom)
print(" - Nb of samples:",len(df_full))
if(len(df_full)<10):
print(" - Skipping: not enough information")
continue
chunk_size = int(np.ceil(df_full.shape[0]/nb_procs))
if(chunk_size==0): chunk_size=1
chunks = [df_full.iloc[i:i+chunk_size] for i in range(0, df_full.shape[0], chunk_size)]
id_to_print = dict(zip([int(np.percentile(df_full.index.values, x)) for x in range(0,105,20)],range(0,105,20)))
print('Progress: [', end =" ", flush=True)
results = [pool.apply_async(func=lva.exp_vario_pairs, args=(chunk,df_full,id_to_print,pars,hlimit_d,vlimit_d,coordx,coordy,coordz,azm_code,dip_code,rak_code)) for chunk in chunks]
output = [p.get() for p in results]
print(']', flush=True)
for x in output:
for ax in ['MAX','MED','MIN']:
idx_pair[ax]+=x[ax]
# Organizing pairs and corresponding values and distances
pairs = pd.Series(idx_pair['MAX']+idx_pair['MED']+idx_pair['MIN'])
pairs.drop_duplicates(inplace=True)
chunk_size = int(np.ceil(pairs.shape[0]/nb_procs))
chunks = [pairs.iloc[i:i+chunk_size] for i in range(0, pairs.shape[0], chunk_size)]
results = [pool.apply_async(func=lva.pairs_df, args=(chunk,df_data,coordx,coordy,coordz,vvar)) for chunk in chunks]
output = [p.get() for p in results]
df = pd.concat(output,ignore_index=True,sort=True)
for ax in ['MAX','MED','MIN']:
# ORGANIZING BINS
bins = np.array([(i*pars[ax]['LAG']+pars[ax]['LAG']/2.0) for i in range(pars[ax]['NLAGS']+1)])
df['BIN'] = np.digitize(df.D, bins, right=True)
## to consider mirror pairs twice
a = pd.Series(idx_pair[ax],dtype='object')
b = a.drop_duplicates(keep=False)
c = a[~a.isin(b)].unique().tolist()
d = df[df['P12'].isin(c)]
df2 = pd.concat([df,d],ignore_index=True,sort=True)
df2 = df2[df2['BIN']<=pars[ax]['NLAGS']].reset_index()
# Getting CORRELOGRAM for each lag
for lg in df2['BIN'].unique():
df_AX = df2[(df2['P12'].isin(idx_pair[ax])) & (df2['BIN']==lg)]
if len(df_AX)==0: continue
cov_matrix = np.cov(df_AX['H'],df_AX['T'],bias=True)
corr_AX = cov_matrix[0][1] / np.sqrt(cov_matrix[0][0]*cov_matrix[1][1])
dist_AX = np.average(df_AX['D'])
npairs_AX = len(df_AX)
out_df = out_df.append({'AXIS':ax, 'BIN':lg, 'NPAIRS':npairs_AX, 'DIST':dist_AX, 'CORR':corr_AX}, ignore_index=True)
out_df.sort_values(['AXIS','BIN'],inplace=True)
out_df.to_csv(outname+'.csv',index=False)
window.UnHide()
pool.close()
pool.join()