8000 more pep8 by wholmgren · Pull Request #256 · pvlib/pvlib-python · GitHub
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Merged
merged 2 commits into from
Oct 23, 2016
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more pep8 #256

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c3fa82f
pep8 attack
wholmgren Oct 22, 2016
f0cc0c9
clean up tmy.py
wholmgren Oct 22, 2016
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clean up tmy.py
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@wholmgren
wholmgren committed Oct 22, 2016
commit f0cc0c9b0fce6fee6fe2d37cc37f245fb30d5a64
153 changes: 79 additions & 74 deletions pvlib/tmy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,9 +2,6 @@
Import functions for TMY2 and TMY3 data files.
"""

import logging
pvl_logger = logging.getLogger('pvlib')

import re
import datetime
import dateutil
Expand All @@ -15,9 +12,6 @@
from urllib.request import urlopen

import pandas as pd
import numpy as np

from pvlib import tools


def readtmy3(filename=None, coerce_year=None, recolumn=True):
Expand Down Expand Up @@ -164,7 +158,9 @@ def readtmy3(filename=None, coerce_year=None, recolumn=True):
try:
filename = _interactive_load()
except:
raise Exception('Interactive load failed. Tkinter not supported on this system. Try installing X-Quartz and reloading')
raise Exception('Interactive load failed. Tkinter not supported ' +
'on this system. Try installing X-Quartz and ' +
'reloading')

head = ['USAF', 'Name', 'State', 'TZ', 'latitude', 'longitude', 'altitude']

Expand All @@ -184,24 +180,24 @@ def readtmy3(filename=None, coerce_year=None, recolumn=True):
meta['TZ'] = float(meta['TZ'])
meta['USAF'] = int(meta['USAF'])

TMYData = pd.read_csv(
data = pd.read_csv(
filename, header=1,
parse_dates={'datetime':['Date (MM/DD/YYYY)','Time (HH:MM)']},
parse_dates={'datetime': ['Date (MM/DD/YYYY)', 'Time (HH:MM)']},
date_parser=lambda *x: _parsedate(*x, year=coerce_year),
index_col='datetime')

if recolumn:
_recolumn(TMYData) #rename to standard column names
_recolumn(data) # rename to standard column names

TMYData = TMYData.tz_localize(int(meta['TZ']*3600))
data = data.tz_localize(int(meta['TZ']*3600))

return TMYData, meta
return data, meta


def _interactive_load():
import Tkinter
from tkFileDialog import askopenfilename
Tkinter.Tk().withdraw() #Start interactive file input
Tkinter.Tk().withdraw() # Start interactive file input
return askopenfilename()


Expand Down Expand Up @@ -233,20 +229,25 @@ def _recolumn(tmy3_dataframe, inplace=True):
"""
raw_columns = 'ETR (W/m^2),ETRN (W/m^2),GHI (W/m^2),GHI source,GHI uncert (%),DNI (W/m^2),DNI source,DNI uncert (%),DHI (W/m^2),DHI source,DHI uncert (%),GH illum (lx),GH illum source,Global illum uncert (%),DN illum (lx),DN illum source,DN illum uncert (%),DH illum (lx),DH illum source,DH illum uncert (%),Zenith lum (cd/m^2),Zenith lum source,Zenith lum uncert (%),TotCld (tenths),TotCld source,TotCld uncert (code),OpqCld (tenths),OpqCld source,OpqCld uncert (code),Dry-bulb (C),Dry-bulb source,Dry-bulb uncert (code),Dew-point (C),Dew-point source,Dew-point uncert (code),RHum (%),RHum source,RHum uncert (code),Pressure (mbar),Pressure source,Pressure uncert (code),Wdir (degrees),Wdir source,Wdir uncert (code),Wspd (m/s),Wspd source,Wspd uncert (code),Hvis (m),Hvis source,Hvis uncert (code),CeilHgt (m),CeilHgt source,CeilHgt uncert (code),Pwat (cm),Pwat source,Pwat uncert (code),AOD (unitless),AOD source,AOD uncert (code),Alb (unitless),Alb source,Alb uncert (code),Lprecip depth (mm),Lprecip quantity (hr),Lprecip source,Lprecip uncert (code),PresWth (METAR code),PresWth source,PresWth uncert (code)'

new_columns = ['ETR','ETRN','GHI','GHISource','GHIUncertainty',
'DNI','DNISource','DNIUncertainty','DHI','DHISource','DHIUncertainty',
'GHillum','GHillumSource','GHillumUncertainty','DNillum','DNillumSource',
'DNillumUncertainty','DHillum','DHillumSource','DHillumUncertainty',
'Zenithlum','ZenithlumSource','ZenithlumUncertainty','TotCld','TotCldSource',
'TotCldUnertainty','OpqCld','OpqCldSource','OpqCldUncertainty','DryBulb',
'DryBulbSource','DryBulbUncertainty','DewPoint','DewPointSource',
'DewPointUncertainty','RHum','RHumSource','RHumUncertainty','Pressure',
'PressureSource','PressureUncertainty','Wdir','WdirSource','WdirUncertainty',
'Wspd','WspdSource','WspdUncertainty','Hvis','HvisSource','HvisUncertainty',
'CeilHgt','CeilHgtSource','CeilHgtUncertainty','Pwat','PwatSource',
'PwatUncertainty','AOD','AODSource','AODUncertainty','Alb','AlbSource',
'AlbUncertainty','Lprecipdepth','Lprecipquantity','LprecipSource',
'LprecipUncertainty','PresWth','PresWthSource','PresWthUncertainty']
new_columns = [
'ETR', 'ETRN', 'GHI', 'GHISource', 'GHIUncertainty',
'DNI', 'DNISource', 'DNIUncertainty', 'DHI', 'DHISource',
'DHIUncertainty', 'GHillum', 'GHillumSource', 'GHillumUncertainty',
'DNillum', 'DNillumSource', 'DNillumUncertainty', 'DHillum',
'DHillumSource', 'DHillumUncertainty', 'Zenithlum',
'ZenithlumSource', 'ZenithlumUncertainty', 'TotCld', 'TotCldSource',
'TotCldUnertainty', 'OpqCld', 'OpqCldSource', 'OpqCldUncertainty',
'DryBulb', 'DryBulbSource', 'DryBulbUncertainty', 'DewPoint',
'DewPointSource', 'DewPointUncertainty', 'RHum', 'RHumSource',
'RHumUncertainty', 'Pressure', 'PressureSource',
'PressureUncertainty', 'Wdir', 'WdirSource', 'WdirUncertainty',
'Wspd', 'WspdSource', 'WspdUncertainty', 'Hvis', 'HvisSource',
'HvisUncertainty', 'CeilHgt', 'CeilHgtSource', 'CeilHgtUncertainty',
'Pwat', 'PwatSource', 'PwatUncertainty', 'AOD', 'AODSource',
'AODUncertainty', 'Alb', 'AlbSource', 'AlbUncertainty',
'Lprecipdepth', 'Lprecipquantity', 'LprecipSource',
'LprecipUncertainty', 'PresWth', 'PresWthSource',
'PresWthUncertainty']

mapping = dict(zip(raw_columns.split(','), new_columns))

Expand Down Expand Up @@ -395,7 +396,7 @@ def readtmy2(filename):
columns = 'year,month,day,hour,ETR,ETRN,GHI,GHISource,GHIUncertainty,DNI,DNISource,DNIUncertainty,DHI,DHISource,DHIUncertainty,GHillum,GHillumSource,GHillumUncertainty,DNillum,DNillumSource,DNillumUncertainty,DHillum,DHillumSource,DHillumUncertainty,Zenithlum,ZenithlumSource,ZenithlumUncertainty,TotCld,TotCldSource,TotCldUnertainty,OpqCld,OpqCldSource,OpqCldUncertainty,DryBulb,DryBulbSource,DryBulbUncertainty,DewPoint,DewPointSource,DewPointUncertainty,RHum,RHumSource,RHumUncertainty,Pressure,PressureSource,PressureUncertainty,Wdir,WdirSource,WdirUncertainty,Wspd,WspdSource,WspdUncertainty,Hvis,HvisSource,HvisUncertainty,CeilHgt,CeilHgtSource,CeilHgtUncertainty,PresentWeather,Pwat,PwatSource,PwatUncertainty,AOD,AODSource,AODUncertainty,SnowDepth,SnowDepthSource,SnowDepthUncertainty,LastSnowfall,LastSnowfallSource,LastSnowfallUncertaint'
hdr_columns = 'WBAN,City,State,TZ,latitude,longitude,altitude'

TMY2, TMY2_meta = _readTMY2(string, columns, hdr_columns, filename)
TMY2, TMY2_meta = _read_tmy2(string, columns, hdr_columns, filename)

return TMY2, TMY2_meta

Expand All @@ -405,7 +406,6 @@ def _parsemeta_tmy2(columns, line):

Parameters
----------

columns : string
String of column headings in the header

Expand All @@ -414,86 +414,91 @@ def _parsemeta_tmy2(columns, line):

Returns
-------

meta : Dict of metadata contained in the header string

"""
rawmeta=" ".join(line.split()).split(" ") #Remove sduplicated spaces, and read in each element
meta=rawmeta[:3] #take the first string entries
# Remove duplicated spaces, and read in each element
rawmeta = " ".join(line.split()).split(" ")
meta = rawmeta[:3] # take the first string entries
meta.append(int(rawmeta[3]))
longitude=(float(rawmeta[5])+float(rawmeta[6])/60)*(2*(rawmeta[4]=='N')-1)#Convert to decimal notation with S negative
latitude=(float(rawmeta[8])+float(rawmeta[9])/60)*(2*(rawmeta[7]=='E')-1) #Convert to decimal notation with W negative
# Convert to decimal notation with S negative
longitude = (
float(rawmeta[5]) + float(rawmeta[6])/60) * (2*(rawmeta[4] == 'N') - 1)
# Convert to decimal notation with W negative
latitude = (
float(rawmeta[8]) + float(rawmeta[9])/60) * (2*(rawmeta[7] == 'E') - 1)
meta.append(longitude)
meta.append(latitude)
meta.append(float(rawmeta[10]))

meta_dict = dict(zip(columns.split(','),meta)) #Creates a dictionary of metadata
pvl_logger.debug('meta: %s', meta_dict)

# Creates a dictionary of metadata
meta_dict = dict(zip(columns.split(','), meta))
return meta_dict


def _readTMY2(string, columns, hdr_columns, fname):
head=1
date=[]
def _read_tmy2(string, columns, hdr_columns, fname):
head = 1
date = []
with open(fname) as infile:
fline=0
fline = 0
for line in infile:
#Skip the header
if head!=0:
meta = _parsemeta_tmy2(hdr_columns,line)
head-=1
# Skip the header
if head != 0:
meta = _parsemeta_tmy2(hdr_columns, line)
head -= 1
continue
#Reset the cursor and array for each line
cursor=1
part=[]
# Reset the cursor and array for each line
cursor = 1
part = []
for marker in string.split('%'):
#Skip the first line of markers
if marker=='':
# Skip the first line of markers
if marker == '':
continue

#Read the next increment from the marker list
increment=int(re.findall('\d+',marker)[0])

#Extract the value from the line in the file
val=(line[cursor:cursor+increment])
#increment the cursor by the length of the read value
cursor=cursor+increment
# Read the next increment from the marker list
increment = int(re.findall('\d+', marker)[0])

# Extract the value from the line in the file
val = (line[cursor:cursor+increment])
# increment the cursor by the length of the read value
cursor = cursor+increment

# Determine the datatype from the marker string
if marker[-1]=='d':
if marker[-1] == 'd':
try:
val=float(val)
val = float(val)
except:
raise Exception('WARNING: In'+__name__+' Read value is not an integer" '+val+' " ')
elif marker[-1]=='s':
raise Exception('WARNING: In' + fname +
' Read value is not an integer " ' +
val + ' " ')
elif marker[-1] == 's':
try:
val=str(val)
val = str(val)
except:
raise Exception('WARNING: In'+__name__+' Read value is not a string" '+val+' " ')
raise Exception('WARNING: In' + fname +
' Read value is not a string" ' +
val + ' " ')
else:
raise Exception('WARNING: In'+__name__+'Improper column DataFrameure " %'+marker+' " ')
raise Exception('WARNING: In' + __name__ +
'Improper column DataFrame " %' +
marker + ' " ')

part.append(val)

if fline==0:
axes=[part]
year=part[0]+1900
fline=1
if fline == 0:
axes = [part]
year = part[0]+1900
fline = 1
else:
axes.append(part)

#Create datetime objects from read data
# Create datetime objects from read data
date.append(datetime.datetime(year=int(year),
month=int(part[1]),
day=int(part[2]),
hour=int(part[3])-1))

TMYData = pd.DataFrame(
data = pd.DataFrame(
axes, index=date,
columns=columns.split(',')).tz_localize(int(meta['TZ']*3600))

return TMYData, meta


return data, meta
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