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Pull.py
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Pull.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Feb 27 13:15:05 2023
@author: coss.31
"""
#standard imports
import requests
from zipfile import ZipFile
from io import BytesIO
from numpy import genfromtxt
import numpy as np
from os import scandir, remove
from shutil import rmtree
from datetime import datetime as dt
from datetime import date as dtd
import pandas as pd
#third party imports
from hsclient import HydroShare
#from Clara
#"https:/www.hydroshare.org/hsapi/resource/a0a51f97bd064896b91ac0e23926468e/__;!!KGKeukY!1wHawDYfAK-I7ewHZg4WfibYP8yRayvGclS54hkJz6IaPYI4PvRI4bMTxyDVGZlNGOPo3Mze3xfLzgsHWO8$"
#authenticate
UN="SteveCossSWOT"
PW="9Jn3FJNJs!!KXDj"
#RI='96f1928c95834539ba260ab65ea8db8e'
RI='38feeef698ca484b907b7b3eb84ad05b'
URLst='https://www.hydroshare.org/hsapi/resource/' + RI +'/'
DLpath='C:/Users/coss.31/OneDrive - The Ohio State University/Documents/SWOT_Mission_REPOS/HydroSharePull/resources'
DLpathL='C:/Users/coss.31/OneDrive - The Ohio State University/Documents/SWOT_Mission_REPOS/HydroSharePull/List'
#UL.request.urlretrieve("https://www.hydroshare.org/resource/96f1928c95834539ba260ab65ea8db8e/",DLpathL)
def remove_files(DLdir):
"""Remove files found in directory.
Excludes run type directories.
Parameters
----------
sos_dir: Path
Path to sos directory to delete files in
"""
with scandir(DLdir) as entries:
HS_files = [ entry for entry in entries if entry.name ]
for HS_file in HS_files:
rmtree(DLdir +"/" +HS_file.name)
""" clear previous pull"""
remove_files(DLpathL)
remove_files(DLpath)
r=requests.get(URLst)
r.headers.get('Content-Type')
z = ZipFile(BytesIO(r.content))
file = z.extractall( DLpathL)
csvpath=DLpathL+"/"+RI+"/data/contents/collection_list_"+RI+".csv"
Collection= genfromtxt(csvpath, delimiter=',', dtype='unicode',skip_header=1)
#log in
hs = HydroShare(UN,PW)
#dl all resources
Sf=[]
Rid=[]
Nid=[]
x=[]
y=[]
T=[]
Q=[]
Qu=[]
D=[]
Du=[]
V=[]
Vu=[]
for resource in Collection:
#hs.sign_in()
Tstr=resource[2]
res = hs.resource(Tstr)
NOWres=res.download(DLpath)
z = ZipFile(DLpath+'/'+Tstr+'.zip')
file =z.extractall(DLpath)
z.close()
remove(DLpath+'/'+Tstr+'.zip')
csvpath= DLpath+'/'+ Tstr + '/data/contents/'
with scandir(csvpath) as entries:
RES_files = [ entry for entry in entries if entry.name ]
for RES_file in RES_files:
if RES_file.name[-12:-4] != 'template':
if RES_file.name[0:6] != 'SCoss2':
if RES_file.name[-12:-4] != 'OT_SHCQ1':
RESlist= genfromtxt(csvpath+"/" +RES_file.name, delimiter=',',dtype ='unicode',skip_header=1)
c=1
for measurement in RESlist:
if len(measurement[0])>0:
print(c)
c=c+1
Sf.append(RES_file.name)
Rid.append(measurement[0].astype(np.int64))
Nid.append(measurement[1].astype(np.int64))
x.append(measurement[2].astype(np.float32))
y.append(measurement[3].astype(np.float32))
date=measurement[4].strip()
date=date.strip("'")
d=dt.strptime( date, '%d-%m-%Y')
T.append(d.toordinal())
Q.append(measurement[5].astype(float))
Qu.append(measurement[6].astype(float))
D.append(measurement[7].astype(float))
Du.append(measurement[8].astype(float))
V.append(measurement[9].astype(float))
Vu.append(measurement[10].astype(float))
data_id=[]
data_rid=[]
data_Nid=[]
data_x=[]
data_y=[]
data_t=[]
data_q=[]
data_qu=[]
data_d=[]
data_du=[]
data_v=[]
data_vu=[]
print('Concatinate')
Ureach=np.unique(Rid)
for reach in Ureach:
TR=np.where(Rid==reach)
#sort all reach data by time
Tnp=np.array(T)
Rtid=np.argsort(Tnp[TR],axis=None)
idx=list(TR[0])
data_id.append(np.array(Sf)[TR][0])
data_rid.append(reach)
data_Nid.append(np.array(Nid)[idx])
data_x.append(np.array(x)[idx][0])
data_y.append(np.array(y)[idx][0])
data_t.append(np.array(T)[idx])
data_q.append(np.array(Q)[idx])
data_qu.append(np.array(Qu)[idx])
data_d.append(np.array(D)[idx])
data_du.append(np.array(Du)[idx])
data_v.append(np.array(V)[idx])
data_vu.append(np.array(Vu)[idx])
# generate empty arrays for nc output
st=dtd.fromordinal(min(T))
et=dtd.fromordinal(max(T))
ALLt=pd.date_range(start=st,end=et)
EMPTY=np.nan
MONQ=np.full((len(data_rid),12),EMPTY)
Qmean=np.full((len(data_rid)),EMPTY)
Qmin=np.full((len(data_rid)),EMPTY)
Qmax=np.full((len(data_rid)),EMPTY)
FDQS=np.full((len(data_rid),20),EMPTY)
TwoYr=np.full(len(data_rid),EMPTY)
Twrite=np.full((len(data_rid),len(ALLt)),EMPTY)
Qwrite=np.full((len(data_rid),len(ALLt)),EMPTY)
Mt=list(range(1,13))
P=list(range(1,99,5))
# process recrds for dictionary
for i in range(len(data_rid)):
# pull in Q
Q=data_q[i]
if Q.size >0:
print(i)
t=data_t[i]
T=[]
for time in t:
T.append(pd.Timestamp.fromordinal(time))
T=pd.DatetimeIndex(T)
moy=T.month
yyyy=T.year
moy=moy.to_numpy()
thisT=np.zeros(len(T))
for j in range((len(T))):
thisT=np.where(ALLt==np.datetime64(T[j]))
Qwrite[i,thisT]=Q[j]
Twrite[i,thisT]=dtd.toordinal(T[j])
# with df pulled in run some stats
#basic stats
Qmean[i]=np.nanmean(Q)
Qmax[i]=np.nanmax(Q)
Qmin[i]=np.nanmin(Q)
#monthly means
Tmonn={}
for j in range(12):
Tmonn=np.where(moy==j+1)
if not np.isnan(Tmonn).all() and Tmonn:
MONQ[i,j]=np.nanmean(Q[Tmonn])
#flow duration curves (n=20)
p=np.empty(len(Q))
if len(Q)>21: #do not FDQ on fewer than 21 datum
for j in range(len(Q)):
p[j]=100* ((j+1)/(len(Q)+1))
thisQ=np.flip(np.sort(Q))
FDq=thisQ
FDp=p
FDQS[i]=np.interp(list(range(1,99,5)),FDp,FDq)
#FDPS=list(range(0,99,5))
# Two year recurrence flow
Yy=np.unique(yyyy)
Ymax=np.empty(len(Yy))
for j in range(len(Yy)):
Ymax[j]=np.nanmax(Q[np.where(yyyy==Yy[j])]);
MAQ=np.flip(np.sort(Ymax))
m = (len(Yy)+1)/2
TwoYr[i]=MAQ[int(np.ceil(m))-1]
Mt=list(range(1,13))
P=list(range(1,99,5))
HydroShare_dict = {
"data": data_id,
"reachId": data_rid,
"Qwrite": Qwrite,
"Twrite": Twrite,
"Qmean": Qmean,
"Qmax": Qmax,
"Qmin": Qmin,
"MONQ": MONQ,
"Mt": Mt,
"P": P,
"FDQS": FDQS,
"TwoYr": TwoYr,
"Agency":['HydroWebPull']*len(data_id)
}