def main_qualify():
out_path = load_config()[0]['QUALI_OUT']
web_path = load_config()[0]['WEB_OUT']
files = []
path_to_qualify = None
f3 = {p.resolve() for p in Path(load_config()[0]['QUALI_IN']).rglob("**/*" ) if p.suffix in [EXTENSTION]}
for f4 in f3:
# Only meteorological
if '_MD_' in str(f4):
files.append(Path(f4))
process_files(files,path_to_qualify,out_path,web_path)
def read_dict(type_of_data):
# Ler dicitionario
dicionario = pd.read_excel(open(load_config()[0]['DICTIONARY'], 'rb'),sheet_name='Tabela-estacao')
colunas_dicionario = pd.read_excel(open(load_config()[0]['DICTIONARY'], 'rb'),sheet_name='Cabeçalhos SONDA',header=None)
if type_of_data == '_MD_':
header_1 = colunas_dicionario.iloc[29:30]
header_1 = header_1.iloc[0].dropna().values[1:]
header_2 = colunas_dicionario.iloc[30:31]
header_2 = header_2.iloc[0].dropna().values[1:]
else:
header_1 = None
header_2 = None
print('Implementação em andamento')
exit()
return dicionario,header_1,header_2
def menu_qualify():
top_header('Qualify SONDA Data->')
path_to_qualify = load_stations_03(load_config()[0]['QUALI_IN'])
out_path = load_config()[0]['QUALI_OUT']
web_path = load_config()[0]['WEB_OUT']
if type(path_to_qualify) == str:
files = {p.resolve() for p in Path(path_to_qualify).rglob("**/*" ) if p.suffix in [EXTENSTION]}
else:
files = []
for f2 in path_to_qualify:
f3 = {p.resolve() for p in Path(f2).rglob("**/*" ) if p.suffix in [EXTENSTION]}
for f4 in f3:
files.append(Path(f4))
process_files(files,path_to_qualify,out_path,web_path)
def open_file(select_file, station, year, file):
met_header = load_config()[0]['MET_INPUT']
solar_header = load_config()[0]['SOLAR_INPUT']
##OUTPUT HEADER
met_out_header = load_config()[0]['MET_HEADER']
sol_out_header = load_config()[0]['SOLAR_HEADER']
top_header('Main > Preprocessing > Translate Historical > ' +
str(station) + ' > ' + str(year) + ' > ' + str(file))
print('\t\tPlease select one file to translate file: ')
if 'MD' in file:
header_in = met_header
header_out = met_out_header
if 'SD' in file:
header_in = solar_header
header_out = sol_out_header
if 'TD' in file:
header_in = None
header_out = None
# print(header_in[1:])
# print(header_out)
df = pd.read_csv(select_file, sep=",")
## SELECT ONLY COLUMNS INPUT
df = df[header_in[1:]]
## IGNORE MULTINDEX INTO HISTORICAL DATA
df = df.iloc[1:]
print(df)
print('aqui')
print(load_config()[0]['FORMATED_OUT'] + str(station) + '/' + str(year))
def download_stations():
top_header('Main Menu > Preprocessing Mode > Download Data')
stations, ftp_con = connection()
### Station
count = -1
for f in stations:
count = count + 1
print("\t\t [%s]" % count + f)
while True:
try:
ans_file = int(input("\t\t Select Station: "))
except:
print("\t\t Wrong selection")
continue
if ans_file > count:
print("\t\t Wrong selection.")
continue
files_dir = load_config()[0]['FTP_DIR'] + stations[ans_file] + '/data/'
download_files(files_dir, ftp_con, stations[ans_file])
break
def process_files(files,path_to_qualify,out_path,web_path):
debug_dir = load_config()[0]['DEBUG_DIR']
debug_di = str(debug_dir) + 'qualify_erros.txt'
logging.basicConfig(filename=debug_di, filemode='a', format='%(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')
# logger.setLevel(logging.INFO)
# file = '/home/hneto/SONDA/SDT/output/dados_formatados/SBR/Meteorologicos/2016/SBR_2016_06_MD_formatado.csv'
# file = '/home/hneto/SONDA/SDT/output/dados_formatados/SLZ/Meteorologicos/2017/SLZ_2017_01_MD_formatado.csv'
count_files = 0
size_files = len(files)
for file in files:
try:
### READ DIRECTIONARY
print('Processing-> ',file.stem[:-6]+ ' -> '+str(count_files)+'/'+str(size_files))
print('Reading dictionary....!')
## DETECT TYPE OF DATA
## DETECT HEADER TYPE
if '_MD_' in str(file):
diction,header1,header2 = read_dict('_MD_')
if '_SD_' in str(file):
diction,header1,header2 = read_dict('_SD_')
print('Loading file ->',file.stem)
df = loadFile(file)
print('Processing level 01!!')
dqc,df = level_01(df,str(file),diction)
print('Processing level 02!!')
dqc = level_02(df,dqc,str(file))
print('Processing level 03!!')
dqc = level_03(df,dqc,str(file))
print('Qualify done!...')
print('Generating percentual file!')
cols = []
### SELECT COLUMNS
for c in dqc.columns:
if not 'std' in c[0]:
cols.append(c)
dqc = dqc[cols]
percent_columns = dqc.iloc[:,5:].columns
percent_cols = [cc[0] for cc in percent_columns]
percent_cols.insert(0, "Dados")
percentual_df = pd.DataFrame(columns=percent_cols)
percentual_df['Dados'] = ['Suspeitos nível 1','Suspeitos nível 2','Suspeitos nível 3','Suspeitos nível 4','Válidos','Ausentes']
percentual_df = percentual_df.fillna(0)
#Perc Válido (2)
VALID_STRING = "9999|0999|0099|0009"
## AUSENTES
NODATA = "3333"
NOSENSOR = "-5555"
## SUPECT LEVELS
SUSPECT_LVL1 = "5552|0552|0052|0002"
SUSPECT_LVL2 = "5529|0529|0029"
SUSPECT_LVL3 = "5299|0299"
SUSPECT_LVL4 = "2999"
for pc in percent_columns:
nosensor = df[pc[0]].astype(str).str.count(NOSENSOR).sum()
if nosensor > 0:
percentual_df[pc[0]] = ['N/S','N/S','N/S','N/S','N/S','N/S']
else:
valids = dqc[pc].str.count(VALID_STRING).sum()
susplvl1 = dqc[pc].str.count(SUSPECT_LVL1).sum()
susplvl2 = dqc[pc].str.count(SUSPECT_LVL2).sum()
susplvl3 = dqc[pc].str.count(SUSPECT_LVL3).sum()
susplvl4 = dqc[pc].str.count(SUSPECT_LVL4).sum()
nodata = df[pc[0]].astype(str).str.count(NODATA).sum()
total_ = valids + susplvl1 + susplvl2 + susplvl3 + susplvl4 + nodata
## ADD IN PERCENTUAL FRAME
percentual_df[pc[0]] = [susplvl1/total_,
susplvl2/total_,
susplvl3/total_,
susplvl4/total_,
valids/total_,
nodata/total_]
## WEB FILE
web_df = pd.DataFrame()
station = df.acronym.unique()[0]
stationID = diction.loc[diction['Sigla'] == station]
siglaNAME = stationID['Sigla'].values[0]
nomeNAME = stationID['Nome'].values[0]
redeNAME = stationID['Rede'].values[0]
latNAME = stationID['Latitude'].values[0]
lonNAME = stationID['Longitude'].values[0]
altNAME = stationID['Altitude'].values[0]
first_row_header = [siglaNAME,nomeNAME,'lat:'+str(latNAME),'lon:'+str(lonNAME),'alt:'+str(altNAME)+'m',redeNAME+' Network','http://sonda.ccst.inpe.br','*****@*****.**']
### INCREMENT columns into dqc frame
dqc['ws10_std','dqc_v1'] = '0000'
dqc['wd10_std','dqc_v1'] = '0000'
dqc_cols = []
dqc_mult_column = []
dqc_columns = dqc.columns.values
dqc_columns = [dqc_cols.append(c[0]) for c in dqc_columns]
for cc in range(len(dqc_cols)):
if cc > 4:
dqc_cols[cc] = dqc_cols[cc]+'_dqc'
dqc_mult_column.append((dqc_cols[cc],'dqc_v1'))
else:
dqc_mult_column.append((dqc_cols[cc],''))
mux = pd.MultiIndex.from_tuples(dqc_mult_column)
dqc.columns = dqc_cols
dqc_for_concat = dqc[dqc_cols[5:]]
dqc.columns = mux
# ### WEB FILE
web_df = pd.concat([df,dqc_for_concat],axis=1)
web_df = web_df[header1.tolist()]
## MOUNT NEW MULTINDEX
header2 = header2.tolist()
header2.insert(0,'')
header2.insert(0,'')
header2.insert(0,'')
header2.insert(0,'')
header2.insert(0,'')
new_mux = []
for cc in range(len(web_df.columns)):
if cc < len(first_row_header):
new_mux.append((first_row_header[cc],web_df.columns[cc],header2[cc]))
else:
new_mux.append(('',web_df.columns[cc],header2[cc]))
mux = pd.MultiIndex.from_tuples(new_mux)
# ## FINALIZE WEB DF
web_df.columns = mux
### CONVERT VALUES TO STRING
all_columns = list(web_df) # Creates list of all column headers
web_df[all_columns] = web_df[all_columns].astype(str)
### - Alterar 3333 = N/A antes de salvar o arquivo
### - Alterar -5555 = N/S antes de salvar o arquivo (linha 170 arquivo dqc.py)
web_df = web_df.replace('3333.0', 'N/A')
web_df = web_df.replace('-5555', 'N/S')
print(web_df)
# input()
print('Saving file...')
### SAVE FILES
if type(path_to_qualify) == str:
mount_out = Path(path_to_qualify).parts[2:]
else:
mount_out = Path(file).parts[-4:][:-1]
mount_out = '/'.join([str(elem) for elem in mount_out])
### CREATE PAATH IF NOT EXIST
Path(out_path+mount_out+'/').mkdir(parents=True, exist_ok=True)
output_file = out_path+mount_out+'/'+file.stem[:-9]+'DQC'+file.suffix
## SALVANDO
dqc.to_csv(output_file,index=False)
### WEB FILES
web_csv = web_path+mount_out
Path(web_path+mount_out+'/').mkdir(parents=True, exist_ok=True)
output_web = web_path+mount_out+'/'+file.stem[:-10]+file.suffix
percentual_out = web_path+mount_out+'/'+file.stem[:-9]+'percentuais'+file.suffix
### SAVING
web_df.to_csv(output_web,index=False)
percentual_df.to_csv(percentual_out,index=False)
print('Files has been saved into->',output_file,'\nWEB->',output_web)
count_files += 1
except:
print('Error to qualify file: '+str(file))
logging.warning('Error to qualify file: '+str(file)+'')
count_files += 1
def historic_generate():
operation_dir = load_config()[0]['OPERATIONAL_IN']
## SET DEBUG DIR
logging.basicConfig(filename=load_config()[0]['DEBUG_DIR'] +
'historical_debug.txt',
filemode='a',
format='\nProcess Date %(asctime)s \n %(message)s\n',
datefmt='%d-%b-%y %H:%M:%S',
level=os.environ.get("LOGLEVEL", "INFO"))
top_header('Main > Preprocessing > Generate Historical')
print('\t\tPlease select one stations to generate historical data: ')
operational_stations = [
fn for fn in listdir(operation_dir) if not fn.startswith('.')
]
if len(operational_stations) == 0:
print('There is no data to be formatted')
input('Press Enter to return')
# pre_processing_menu()
# return None
count = -1
for f in operational_stations:
count = count + 1
print("\t\t [%s]" % count + f)
while True:
try:
ans_file = int(input("\t\t Select Station: "))
except:
print("\t\t Wrong selection")
continue
if ans_file > count:
print("\t\t Wrong selection.")
continue
selected_st = operation_dir + operational_stations[ans_file] + '/'
break
## SELECT TYPE OF DATA
top_header('Main > Preprocessing > Generate Historical > ' +
str(operational_stations[ans_file]).upper())
print('\t\tPlease select type of data to generate historical data: ')
dataTypes = ['MD', 'SD', 'TD', '50', '25', '10']
countT = -1
for f in dataTypes:
countT = countT + 1
print("\t\t [%s]" % countT + f)
while True:
try:
ans_type = int(input("\t\t Select Station: "))
except:
print("\t\t Wrong selection")
continue
if ans_type > countT:
print("\t\t Wrong selection.")
continue
selected_file = operational_stations[ans_file].upper() + '_' + str(
dataTypes[ans_type]) + '.DAT'
break
### DATA TYPES
if operational_stations[ans_file] == 'sms':
## OPEN DATA
df = pd.read_csv(selected_st + selected_file,
sep=",",
header=None,
skiprows=4,
skipinitialspace=False)
df1 = df.copy()
head0 = pd.read_csv(selected_st + selected_file,
sep=",",
header=None,
nrows=1)
head1 = pd.read_csv(selected_st + selected_file,
sep=",",
header=None,
skiprows=1,
nrows=1)
head2 = pd.read_csv(selected_st + selected_file,
sep=",",
header=None,
skiprows=3,
nrows=1)
head0 = head0.iloc[0].values
head1 = head1.iloc[0].values
head2 = head2.iloc[0].values
df1[0] = pd.to_datetime(df1[0], format='%Y-%m-%d %H:%M:%S')
## SELECT TIMESTAMP TO PROCESSE
top_header('Main > Preprocessing > Generate Historical > ' +
str(operational_stations[ans_file]).upper() + ' > ' +
str(dataTypes[ans_type]))
print('\t\tPlease select type of data to generate historical data: ')
## AVAIBLE YEARS
years = df1[0].dt.year.unique()
countY = -1
for f in years:
countY = countY + 1
print("\t\t [%s]" % countY + str(f))
while True:
try:
ans_year = int(input("\t\t Select Year: "))
except:
print("\t\t Wrong selection")
continue
if ans_year > countY:
print("\t\t Wrong selection.")
continue
selected_year = years[ans_year]
break
df1 = df1.set_index(0)
months = df1.loc[str(selected_year)]
months = months.reset_index()
months = months[0].dt.strftime('%m').unique()
top_header('Main > Preprocessing > Generate Historical > ' +
str(operational_stations[ans_file]).upper() + ' > ' +
str(dataTypes[ans_type]) + ' > ' + str(selected_year))
print('\t\tPlease select type of data to generate historical data: ')
countM = -1
for f in months:
countM = countM + 1
print("\t\t [%s]" % countM + str(f))
while True:
try:
ans_month = int(input("\t\t Select Month: "))
except:
print("\t\t Wrong selection")
continue
if ans_month > countM:
print("\t\t Wrong selection.")
continue
selected_month = months[ans_month]
break
top_header('Main > Preprocessing > Generate Historical > ' +
str(operational_stations[ans_file]).upper() + ' > ' +
str(dataTypes[ans_type]) + ' > ' + str(selected_year) +
' > ' + str(selected_month))
#SELECTED TO GENERATE
df1 = df1.loc[str(selected_year) + '-' + str(selected_month)]
## TIME INTERVAL VERIFICATION
df1 = df1.sort_index(ascending=True)
# GET TIMES STRING
max_time = df1.index.max()
min_time = df1.index.min()
## FIND INDEX INTO ORIGINAL DATAFRAME
idx_min = df.loc[df[0] == str(min_time)].index.values[0]
idx_max = df.loc[df[0] == str(max_time)].index.values[0]
## LOC BETWEEN IDX
locked_df_chk = df.loc[idx_min:idx_max]
## FINAL DF TO COMPARE
final_df = locked_df_chk.copy()
## MOUNT INDEX OF DATES ACORDING TYPE OF DATA
if dataTypes[ans_type] == 'MD' or dataTypes[
ans_type] == '10' or dataTypes[ans_type] == '25' or dataTypes[
ans_type] == '50':
freqc = '10min'
if dataTypes[ans_type] == 'SD' or dataTypes[ans_type] == 'TD':
freqc = '1min'
## PASS COLUMN TO DATETIME
locked_df_chk[0] = pd.to_datetime(locked_df_chk[0],
format='%Y-%m-%d %H:%M:%S')
## MULTIINDEX
mux = []
for i in range(len(head1)):
mux.append([str(head1[i]).lower(), str(head2[i]).lower()])
mux = pd.MultiIndex.from_tuples(mux)
## GET ID OF STATION
id_st = locked_df_chk[2].values[0]
## DETECT non-existent SENSOR
non_sens_col = []
object_type_c = locked_df_chk.select_dtypes(include=['object'])
for c in object_type_c:
detc_mean = object_type_c[c].astype(float).mean()
if str(detc_mean) == 'nan':
non_sens_col.append(c)
## DESAPROVE WITH TIME INTERVAL
## DETECT TYPE OF FILE
if dataTypes[ans_type] == 'MD' or dataTypes[
ans_type] == '10' or dataTypes[ans_type] == '25' or dataTypes[
ans_type] == '50':
t_delta = pd.Timedelta(minutes=10)
if dataTypes[ans_type] == 'SD' or dataTypes[ans_type] == 'TD':
t_delta = pd.Timedelta(minutes=1)
## Generate all month days to compare
year_month = locked_df_chk[0].dt.strftime('%Y-%m').values[0]
# print(pd.Timestamp(year_month) + pd.offsets.MonthEnd(1) + pd.Timedelta(hours=24) - t_delta)
month_generated = pd.date_range(
start=pd.Timestamp(year_month),
end=pd.Timestamp(year_month) + pd.offsets.MonthEnd(1) +
pd.Timedelta(hours=24) - t_delta, # <-- 2018-08-31 with MonthEnd
freq=freqc)
## CHECK DUPLICAT IN TIMESTAMP COLUMN
times_dup = locked_df_chk[locked_df_chk.duplicated([0], keep=False)]
group_tdup = times_dup.groupby(0)
## RESOLVE CHOKE TIME STAMP
idx_first = []
for g, gdftum in group_tdup:
# print(gdftum)
idx_groups = gdftum.index
for idxgg in range(len(idx_groups)):
pass_idx = idx_groups[idxgg] - 1
if dataTypes[ans_type] == 'MD' or dataTypes[
ans_type] == '10' or dataTypes[
ans_type] == '25' or dataTypes[ans_type] == '50':
t_delta = pd.Timedelta(minutes=10)
locked_df_chk.loc[
idx_groups[idxgg],
0] = locked_df_chk.loc[pass_idx][0] + t_delta
if dataTypes[ans_type] == 'SD' or dataTypes[ans_type] == 'TD':
t_delta = pd.Timedelta(minutes=1)
locked_df_chk.loc[
idx_groups[idxgg],
0] = locked_df_chk.loc[pass_idx][0] + t_delta
## CHECK DUPLICAT IN TIMESTAMP COLUMN
locked_df_chk = locked_df_chk.drop_duplicates(subset=0)
## SET INDEX COLUMN OF TIME STAMP
locked_df_chk = locked_df_chk.set_index(0)
## CHEACK DUPLICATED IN ALL COLUMNS
locked_df_chk = locked_df_chk.drop_duplicates(keep='first')
# FILL
locked_df_chk = locked_df_chk.reindex(month_generated, fill_value=0)
## DESAPROVE WITH TIME INTERVAL
check_t_interval = locked_df_chk
check_t_interval = check_t_interval[min_time:max_time]
# ## LIMITE TIME DETAL
lim_delta = pd.Timedelta(minutes=50)
totalDelta = t_delta
for i, row in check_t_interval.iterrows():
if np.all(row[6:-1].values == 0):
totalDelta = totalDelta + t_delta
else:
last_ro = i
totalDelta = pd.Timedelta(minutes=0)
if totalDelta >= lim_delta:
print(
'Failed to generate file due to a longer time sequence of failures greater than ',
lim_delta, '\n')
print('')
fail = check_t_interval[last_ro:i + t_delta]
# fail[6:-1] = 3333
fail = fail.reset_index()
fail.columns = mux
print(fail)
return None
## ADD NON-EXISTENT VALUES
locked_df_chk[non_sens_col] = 5555
idx_values = locked_df_chk.loc[locked_df_chk[2] == 0].index
## ADD YEAR
locked_df_chk.loc[idx_values,
3] = locked_df_chk.loc[idx_values].index.strftime(
'%Y').values
## ADD JULIAN DAY
locked_df_chk.loc[idx_values,
4] = locked_df_chk.loc[idx_values].index.strftime(
'%j').values
## ADD MINUTE
for hm in idx_values:
s1 = hm.strftime('%d/%m/%Y 00:00')
s2 = hm.strftime('%d/%m/%Y %H:%M')
s1 = datetime.strptime(s1, '%d/%m/%Y %H:%M')
s2 = datetime.strptime(s2, '%d/%m/%Y %H:%M')
differenc = s2 - s1
minutes = divmod(differenc.seconds, 60)
locked_df_chk.loc[hm, 5] = minutes[0]
locked_df_chk[2] = id_st
## DATETIME COLUMNS + NON_SENSOR
dt_non_se = []
dt_non_se.append(2)
dt_non_se.append(3)
dt_non_se.append(4)
dt_non_se = dt_non_se + non_sens_col
### ADD VALUES 3333,
diff_columns = np.setdiff1d(locked_df_chk.columns.values, dt_non_se)
locked_df_chk.loc[idx_values, diff_columns] = 3333
# print(locked_df_chk['2020-09-09 13:30':'2020-09-09 17:20'].head(50))
## SAVE
if len(locked_df_chk) > 0:
## SAVE PROCESS
output_dir = load_config()[0]['HISTORICAL_OUT'] + str(
operational_stations[ans_file]).upper() + '/' + str(
selected_year) + '/'
output_file_name = str(operational_stations[ans_file]).upper(
) + '_' + str(selected_year) + '_' + str(
locked_df_chk.index[0].strftime('%j')) + '_a_' + str(
locked_df_chk.index[-1].strftime('%j')) + '_' + str(
dataTypes[ans_type]) + '.dat'
## COUNT VALUES
lost_counter = 0
nosen_counter = 0
for i, row in locked_df_chk.iterrows():
## COUNTER FOR LOST FILES
if 3333 in row.values[:]:
lost_counter = lost_counter
lost_counter += 1
## COUNTER FOR LOST FILES
if 5555 in row.values[:]:
nosen_counter = nosen_counter
nosen_counter += 1
## RESET INDEX
locked_df_chk = locked_df_chk.reset_index()
# ADD MULTIINDEX
locked_df_chk.columns = mux
# CREATE DIR
pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)
## CREATE VERSION DIRS
pathlib.Path(output_dir + '/versions').mkdir(parents=True,
exist_ok=True)
file__ = output_dir + output_file_name
# ## CHEACK IF FILES EXISTS
if os.path.isfile(file__):
warningmsg = ('\nSTATION-> ' + str(output_file_name[:-4]) +
' \nLOST 3333 ROWS:-> ' + str(lost_counter) +
'\nNO SENSOR 5555 ROWS:\n' + str(nosen_counter) +
'\n')
logging.warning(warningmsg)
# print(warningmsg)
# print(locked_df_chk.head())
## CHECK LAST VERSION
if len(os.listdir(output_dir + '/versions')) == 0:
shutil.move(
file__,
output_dir + '/versions/' + output_file_name + '.v01')
## CREATE FILE
locked_df_chk.to_csv(file__, index=False)
warningmsg = ('\nSTATION-> ' + str(output_file_name[:-4]) +
' File version(0) \nLOST 3333 ROWS:-> ' +
str(lost_counter) +
'\nNO SENSOR 5555 ROWS:\n' +
str(nosen_counter) + '\n')
logging.warning(warningmsg)
else:
versions = [
fn for fn in listdir(output_dir + '/versions/')
if not fn.startswith('.')
]
shutil.move(
file__, output_dir + '/versions/' + output_file_name +
'v0' + str((int(versions[-1][-2:]) + 1)))
warningmsg = ('\nSTATION-> ' + str(output_file_name[:-4]) +
' File version(' +
str(int(versions[-1][-2:]) + 1) +
') \nLOST 3333 ROWS:-> ' +
str(lost_counter) +
'\nNO SENSOR 5555 ROWS:\n' +
str(nosen_counter) + '\n')
logging.warning(warningmsg)
print(warningmsg)
## CREATE FILE
locked_df_chk.to_csv(file__, index=False)
print(locked_df_chk)
else:
warningmsg = ('\nSTATION-> ' + str(output_file_name[:-4]) +
' File version(0) \nLOST 3333 ROWS:-> ' +
str(lost_counter) + '\nNO SENSOR 5555 ROWS:\n' +
str(nosen_counter) + '\n')
logging.warning(warningmsg)
## CREATE FILE
print(warningmsg)
print(locked_df_chk)
locked_df_chk.to_csv(file__, index=False)
## UPLOAD RESULTS
ver_file_names = [
fn for fn in listdir(output_dir + str('/versions'))
if not fn.startswith('.')
]
if len(ver_file_names) > 0:
last_file_version = sorted(ver_file_names)[-1]
else:
last_file_version = None
file_to_upload = file__
print('\t\tUpload files to FTP: ')
choice = input("""
(Y) - Yes
(N) - No
Please enter your choice: """)
if choice == "Y" or choice == "y":
connection(file_to_upload, operational_stations[ans_file],
selected_year, output_file_name, last_file_version,
operational_stations[ans_file])
elif choice == "N" or choice == "n":
sys.exit
elif choice == "Q" or choice == "q":
sys.exit
else:
print("You must only select one option")
print("Please try again")
mainMenu()
# -*- coding: utf-8 -*-
from modules.top_header import top_header
from modules.load_config import load_config
from datetime import datetime
import calendar
from dependecies import *
config_file = load_config()
def historic_generate():
operation_dir = load_config()[0]['OPERATIONAL_IN']
## SET DEBUG DIR
logging.basicConfig(filename=load_config()[0]['DEBUG_DIR'] +
'historical_debug.txt',
filemode='a',
format='\nProcess Date %(asctime)s \n %(message)s\n',
datefmt='%d-%b-%y %H:%M:%S',
level=os.environ.get("LOGLEVEL", "INFO"))
top_header('Main > Preprocessing > Generate Historical')
print('\t\tPlease select one stations to generate historical data: ')
operational_stations = [
fn for fn in listdir(operation_dir) if not fn.startswith('.')
]
if len(operational_stations) == 0:
print('There is no data to be formatted')
input('Press Enter to return')
def translate_historical():
operation_dir = load_config()[0]['HISTORICAL_OUT']
top_header('Main > Preprocessing > Translate Historical')
print('\t\tPlease select one stations to translate historical data: ')
historical_pats = [
fn for fn in listdir(operation_dir) if not fn.startswith('.')
]
if len(historical_pats) == 0:
print('There is no data to be formatted')
input('Press Enter to return')
## SELECT STATION
count = -1
for f in historical_pats:
count = count + 1
print("\t\t [%s]" % count + f)
while True:
try:
ans_file = int(input("\t\t Select Station: "))
except:
print("\t\t Wrong selection")
continue
if ans_file > count:
print("\t\t Wrong selection.")
continue
selected_st = operation_dir + historical_pats[ans_file] + '/'
top_header('Main > Preprocessing > Translate Historical > ' +
str(historical_pats[ans_file]))
print('\t\tPlease select one year file: ')
select_year = [
fn for fn in listdir(selected_st) if not fn.startswith('.')
]
## SELECT FILE
count = -1
for f in select_year:
count = count + 1
print("\t\t [%s]" % count + f)
while True:
try:
ans_year = int(input("\t\t Select Station: "))
except:
print("\t\t Wrong selection")
continue
if ans_year > count:
print("\t\t Wrong selection.")
continue
selected_year = selected_st + select_year[ans_year] + '/'
top_header('Main > Preprocessing > Translate Historical > ' +
str(historical_pats[ans_file]) + ' > ' +
select_year[ans_year])
print('\t\tPlease select one file to translate file: ')
select_files = [
fn for fn in listdir(selected_year)
if not fn.startswith('.') and '.dat' in fn
]
## FOR FILE
count = -1
for f in select_files:
count = count + 1
print("\t\t [%s]" % count + f)
while True:
try:
ans_file_ = int(input("\t\t Select Station: "))
except:
print("\t\t Wrong selection")
continue
if ans_file_ > count:
print("\t\t Wrong selection.")
continue
selected_file = selected_year + select_files[ans_file_]
open_file(selected_file, historical_pats[ans_file],
select_year[ans_year], select_files[ans_file_])
break
break
break
# -*- coding: utf-8 -*-
from modules.top_header import top_header
from modules.load_config import load_config
from pathlib import Path
import numpy as np
import pandas as pd
import sys
config = load_config()
path_ = config[0]['AUTO_DATA']
def load_data_type():
top_header('Main Menu > Automatic Detection')
print('\t\tPlease select an option: ')
print('\t\tPath dir: ', path_)
choice = input("""
1: Detect Solarimetric Data
2: Detect Meteorological Data
3: Detect Anemometric Data
4: Detect Sky Camera Data
5: Set Data PATH
Q: Quit
Please enter your choice: """)
if choice == "Detect Solarimetric Data" or choice == "1":
detec_solar()
elif choice == "Detect Meteorological Data" or choice == "2":
detect_met()
elif choice == "Detect Anemometric Data" or choice == "3":
detect_ane()
def process_meteo(meteo, file):
config = load_config()
meteo['timestamp'] = pd.to_datetime(
meteo.year, format='%Y') + pd.to_timedelta(
meteo.day - 1, unit='d') + pd.to_timedelta(meteo['min'], unit='m')
meteo = meteo.set_index('timestamp')
##for calc ws10_std
meteo['ws10_std'] = meteo['ws10_avg']
## Rename columns
meteo.rename(columns={
'day': 'jday',
'temp_sfc': 'tp_sfc',
'prec': 'rain'
},
inplace=True)
# Converstions
conversion = {
'id': 'first',
'year': 'first',
'jday': 'first',
'min': 'first',
'tp_sfc': 'first',
'humid': 'first',
'press': 'first',
'rain': 'sum',
'ws10_avg': 'mean',
'ws10_std': 'std',
'wd10_avg': lambda x: arctan(x.values),
'wd10_std': lambda x: yamartino(x.values)
}
#Mask to not resample incorrect values
Maska = meteo[(meteo != 3333.0) & (meteo != -5555) & (meteo != np.nan)]
#Apply ressample based conversion
Maska = Maska.resample('10min').agg(conversion)
# frame.resample('1H').agg({'radiation': np.sum, 'tamb': np.mean})
## Unmask values
Unmask = meteo[(meteo == 3333.0)].resample('10min').first()
Unmask2 = meteo[(meteo == -5555)].resample('10min').first()
## Combine values
meteorological = Unmask.combine_first(Maska)
meteorological = Unmask2.combine_first(meteorological)
## Reset index
meteorological = meteorological.reset_index()
# Realocate order of columns
meteorological = meteorological.reindex(columns=[
'id', 'timestamp', 'year', 'jday', 'min', 'tp_sfc', 'humid', 'press',
'rain', 'ws10_avg', 'ws10_std', 'wd10_avg', 'wd10_std'
])
## Change type of columns
meteorological[['id', 'year', 'jday',
'min']] = meteorological[['id', 'year', 'jday',
'min']].astype(int)
## Header check
year_ = file.parent.name
## Extract month string
month = (meteorological['timestamp'][0].strftime('%m'))
stat_ = file.parent.parent.name
output = config[0][
'FORMATED_OUT'] + stat_ + '/Meteorologicos/' + year_ + '/' + stat_ + '_' + year_ + '_' + month + '_MD_formatado.csv'
### Create dir of output if not exist
if not os.path.exists(os.path.dirname(output)):
try:
os.makedirs(os.path.dirname(output))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
## Update Global MET_HEADER
global MET_HEADER
# For key in MET_UPDATE Check
met1 = []
met2 = []
##Change ID by name of station
meteorological['id'] = stat_
# Create Multindex based in columns from header_log
mux = pd.MultiIndex.from_tuples(MET_HEADER)
# Fix multindex on dataframe
meteorological.columns = mux
#Aux out
out_met = []
if MET_UPDATE != None:
for k in MET_UPDATE:
## Check if Updat is the station
if (k[0][0] == stat_):
if len(meteorological.loc[
meteorological['timestamp'] >= k[1]]) > 0:
for kk in k:
# Update Global variable
for idx, item in enumerate(MET_HEADER):
if kk[0] in item[0]:
MET_HEADER[idx] = kk
# Separete files
met1 = meteorological.loc[
meteorological['timestamp'] >= k[1]]
# Create Multindex based in columns from header_log
mux1 = pd.MultiIndex.from_tuples(MET_HEADER)
# Fix multindex on dataframe
met1.columns = mux1
## Second file
met2 = meteorological.loc[
meteorological['timestamp'] < k[1]]
mux2 = pd.MultiIndex.from_tuples(aux)
met2.columns = mux2
if len(met1) > len(met2):
# Rename
met2.columns = mux1
# Concat
out_met = [met1, met2]
out_met = pd.concat(out_met)
# Sort
out_met = out_met.sort_values(by=['timestamp'])
else:
#Rename
met2.columns = mux2
# Concat
out_met = [met2, met1]
out_met = pd.concat(out_met)
# Sort
out_met = out_met.sort_values(by=['timestamp'])
# If equals
if (meteorological.equals(out_met)):
# Clean screan and print first 20
print('Processing File -> ', file)
print('\nSplit weather data!: ')
## Drop second line of multindex
meteorological.columns = meteorological.columns.droplevel(1)
print(meteorological)
meteorological.to_csv(output, index=False)
# If diference
elif (len(out_met)) > 0:
# Clean screan and print first 20
## Save files
print('\nSplit weather data!: ', output)
## Drop second line of multindex
out_met.columns = out_met.columns.droplevel(1)
print(out_met, '\n')
out_met.to_csv(output, index=False)
else:
# Clean screan and print first 20
print('Processing File -> ', file)
print('\nSplit weather data!: ', output)
## Drop second line of multindex
meteorological.columns = meteorological.columns.droplevel(1)
print(meteorological)
meteorological.to_csv(output, index=False)
def process_solar(solar, file):
config = load_config()
## Create Timestamp
solar['timestamp'] = pd.to_datetime(
solar.year, format='%Y') + pd.to_timedelta(
solar.day - 1, unit='d') + pd.to_timedelta(solar['min'], unit='m')
# Change position of timestamp
cols = list(solar)
cols.insert(1, cols.pop(cols.index('timestamp')))
solar = solar.loc[:, cols]
## Header check
year_ = file.parent.name
month = (solar['timestamp'][0].strftime('%m'))
stat_ = file.parent.parent.name
output = config[0][
'FORMATED_OUT'] + stat_ + '/Solarimetricos/' + year_ + '/' + stat_ + '_' + year_ + '_' + month + '_SD_formatado.csv'
### Create dir of output if not exist
if not os.path.exists(os.path.dirname(output)):
try:
os.makedirs(os.path.dirname(output))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
## Update Global SOLAR_HEADER
global SOL_UPDATE
# For key in SOLAR_UPDATE Check
sol1 = []
sol2 = []
##Change ID by name of station
solar['id'] = stat_
# Create Multindex based in columns from header_log
mux = pd.MultiIndex.from_tuples(SOLAR_HEADER)
# Fix multindex on dataframe
solar.columns = mux
# Aux
out_sol = []
if SOL_UPDATE != None:
for k in SOL_UPDATE:
## Check if Updat is the station
if (k[0][0] == stat_):
if len(solar.loc[solar['timestamp'] >= k[1]]) > 0:
for kk in k:
# Update Global variable
for idx, item in enumerate(SOLAR_HEADER):
if kk[0] in item[0]:
SOLAR_HEADER[idx] = kk
# Separete files
sol1 = solar.loc[solar['timestamp'] >= k[1]]
# Create Multindex based in columns from header_log
mux1 = pd.MultiIndex.from_tuples(SOLAR_HEADER)
# Fix multindex on dataframe
sol1.columns = mux1
## Second file
sol2 = solar.loc[solar['timestamp'] < k[1]]
mux2 = pd.MultiIndex.from_tuples(aux2)
sol2.columns = mux2
if len(sol1) > len(sol2):
# Rename
sol2.columns = mux1
# Concat
out_sol = [sol1, sol2]
out_sol = pd.concat(out_sol)
# Sort
out_sol = out_sol.sort_values(by=['timestamp'])
else:
#Rename
sol2.columns = mux2
# Concat
out_sol = [sol2, sol1]
out_sol = pd.concat(out_sol)
# Sort
out_sol = out_sol.sort_values(by=['timestamp'])
if (solar.equals(out_sol)):
# Clean screan and print first 20
print('Processing File -> ', file)
print('\nSplit weather data!: ')
# Drop second level of multindex
solar.columns = solar.columns.droplevel(1)
print(solar)
solar.to_csv(output, index=False)
elif (len(out_sol)) > 0:
# Clean screan and print first 20
## Save files
print('\nSplit weather data!: ', output)
# Drop second level of multindex
out_sol.columns = out_sol.columns.droplevel(1)
print(out_sol)
out_sol.to_csv(output, index=False)
else:
# Clean screan and print first 20
print('Processing File -> ', file)
print('\nSplit weather data!: ', output)
# Drop second level of multindex
solar.columns = solar.columns.droplevel(1)
print(solar)
solar.to_csv(output, index=False)