Fixes

Author: kastnerp

.gitignore

@@ -132,3 +132,5 @@ __pycache__/
 *.bak
 .vscode/settings.json
 run_act_test.bat
+.DS_Store
+*.py~

nrel_psm3_2_epw/assets.py

@@ -1,18 +1,18 @@
-import json
+import calendar
 import sys
+from datetime import datetime
 
 import numpy as np
 import pandas as pd
 import requests
+
 from . import epw
-from datetime import datetime
-import calendar
 
 
 def download_epw(lon, lat, year, location, attributes, interval, utc, your_name, api_key, reason_for_use,
                  your_affiliation, your_email, mailing_list, leap_year):
     currentYear = datetime.now().year
-    if int(year) == currentYear or int(year) == currentYear-1:
+    if int(year) == currentYear or int(year) == currentYear - 1:
         raise Exception("NREL does not provide data for the current year " + str(
             year) + ". It is also unlikely that there is data availability for " + str(int(year) - 1) + ".")
 
@@ -42,8 +42,8 @@ def download_epw(lon, lat, year, location, attributes, interval, utc, your_name,
 
     try:
         r = requests.request("GET", url, params=
-            payload, headers=headers, timeout=20)
-        
+        payload, headers=headers, timeout=20)
+
         print(r.text)
         r.raise_for_status()
 
@@ -66,7 +66,7 @@ def download_epw(lon, lat, year, location, attributes, interval, utc, your_name,
 
     hours_per_year = 8760
 
-    if calendar.isleap(int(year)) and bool(leap_year) is True and all_data.shape[0] == 8784+2:
+    if calendar.isleap(int(year)) and bool(leap_year) is True and all_data.shape[0] == 8784 + 2:
         hours_per_year = 8784
 
     datetimes = pd.date_range('01/01/' + str(year),
@@ -310,7 +310,7 @@ def download_epw(lon, lat, year, location, attributes, interval, utc, your_name,
 
     d = "_"
     file_name = str(location) + d + str(lat) + d + \
-        str(lon) + d + str(year) + '.epw'
+                str(lon) + d + str(year) + '.epw'
 
     out.write(file_name)
     print("Success: File", file_name, "written")

nrel_psm3_2_epw/epw.py

@@ -1,31 +1,31 @@
 # -*- coding: utf-8 -*-
-import pandas as pd
 import csv
 
+import pandas as pd
+
+
 class EPW():
     """A class which represents an EnergyPlus weather (epw) file
     """
-    
+
     def __init__(self):
         """
         """
-        self.headers={}
-        self.dataframe=pd.DataFrame()
-            
-    
-    def read(self,fp):
+        self.headers = {}
+        self.dataframe = pd.DataFrame()
+
+    def read(self, fp):
         """Reads an epw file 
         
         Arguments:
             - fp (str): the file path of the epw file   
         
         """
-        
-        self.headers=self._read_headers(fp)
-        self.dataframe=self._read_data(fp)
-                
-        
-    def _read_headers(self,fp):
+
+        self.headers = self._read_headers(fp)
+        self.dataframe = self._read_data(fp)
+
+    def _read_headers(self, fp):
         """Reads the headers of an epw file
         
         Arguments:
@@ -35,19 +35,18 @@ def _read_headers(self,fp):
             - d (dict): a dictionary containing the header rows 
             
         """
-        
-        d={}
+
+        d = {}
         with open(fp, newline='') as csvfile:
             csvreader = csv.reader(csvfile, delimiter=',', quotechar='"')
             for row in csvreader:
                 if row[0].isdigit():
                     break
                 else:
-                    d[row[0]]=row[1:]
+                    d[row[0]] = row[1:]
         return d
-    
-    
-    def _read_data(self,fp):
+
+    def _read_data(self, fp):
         """Reads the climate data of an epw file
         
         Arguments:
@@ -57,52 +56,51 @@ def _read_data(self,fp):
             - df (pd.DataFrame): a DataFrame comtaining the climate data
             
         """
-        
-        names=['Year',
-               'Month',
-               'Day',
-               'Hour',
-               'Minute',
-               'Data Source and Uncertainty Flags',
-               'Dry Bulb Temperature',
-               'Dew Point Temperature',
-               'Relative Humidity',
-               'Atmospheric Station Pressure',
-               'Extraterrestrial Horizontal Radiation',
-               'Extraterrestrial Direct Normal Radiation',
-               'Horizontal Infrared Radiation Intensity',
-               'Global Horizontal Radiation',
-               'Direct Normal Radiation',
-               'Diffuse Horizontal Radiation',
-               'Global Horizontal Illuminance',
-               'Direct Normal Illuminance',
-               'Diffuse Horizontal Illuminance',
-               'Zenith Luminance',
-               'Wind Direction',
-               'Wind Speed',
-               'Total Sky Cover',
-               'Opaque Sky Cover (used if Horizontal IR Intensity missing)',
-               'Visibility',
-               'Ceiling Height',
-               'Present Weather Observation',
-               'Present Weather Codes',
-               'Precipitable Water',
-               'Aerosol Optical Depth',
-               'Snow Depth',
-               'Days Since Last Snowfall',
-               'Albedo',
-               'Liquid Precipitation Depth',
-               'Liquid Precipitation Quantity']
-        
-        first_row=self._first_row_with_climate_data(fp)
-        df=pd.read_csv(fp,
-                       skiprows=first_row,
-                       header=None,
-                       names=names)
+
+        names = ['Year',
+                 'Month',
+                 'Day',
+                 'Hour',
+                 'Minute',
+                 'Data Source and Uncertainty Flags',
+                 'Dry Bulb Temperature',
+                 'Dew Point Temperature',
+                 'Relative Humidity',
+                 'Atmospheric Station Pressure',
+                 'Extraterrestrial Horizontal Radiation',
+                 'Extraterrestrial Direct Normal Radiation',
+                 'Horizontal Infrared Radiation Intensity',
+                 'Global Horizontal Radiation',
+                 'Direct Normal Radiation',
+                 'Diffuse Horizontal Radiation',
+                 'Global Horizontal Illuminance',
+                 'Direct Normal Illuminance',
+                 'Diffuse Horizontal Illuminance',
+                 'Zenith Luminance',
+                 'Wind Direction',
+                 'Wind Speed',
+                 'Total Sky Cover',
+                 'Opaque Sky Cover (used if Horizontal IR Intensity missing)',
+                 'Visibility',
+                 'Ceiling Height',
+                 'Present Weather Observation',
+                 'Present Weather Codes',
+                 'Precipitable Water',
+                 'Aerosol Optical Depth',
+                 'Snow Depth',
+                 'Days Since Last Snowfall',
+                 'Albedo',
+                 'Liquid Precipitation Depth',
+                 'Liquid Precipitation Quantity']
+
+        first_row = self._first_row_with_climate_data(fp)
+        df = pd.read_csv(fp,
+                         skiprows=first_row,
+                         header=None,
+                         names=names)
         return df
-        
-        
-    def _first_row_with_climate_data(self,fp):
+
+    def _first_row_with_climate_data(self, fp):
         """Finds the first row with the climate data of an epw file
         
         Arguments:
@@ -112,27 +110,26 @@ def _first_row_with_climate_data(self,fp):
             - i (int): the row number
             
         """
-        
+
         with open(fp, newline='') as csvfile:
             csvreader = csv.reader(csvfile, delimiter=',', quotechar='"')
-            for i,row in enumerate(csvreader):
+            for i, row in enumerate(csvreader):
                 if row[0].isdigit():
                     break
         return i
-        
-        
-    def write(self,fp):
+
+    def write(self, fp):
         """Writes an epw file 
         
         Arguments:
             - fp (str): the file path of the new epw file   
         
         """
-        
+
         with open(fp, 'w', newline='') as csvfile:
             csvwriter = csv.writer(csvfile, delimiter=',',
-                                    quotechar='"', quoting=csv.QUOTE_MINIMAL)
-            for k,v in self.headers.items():
-                csvwriter.writerow([k]+v)
-            for row in self.dataframe.itertuples(index= False):
-                csvwriter.writerow(i for i in row)
+                                   quotechar='"', quoting=csv.QUOTE_MINIMAL)
+            for k, v in self.headers.items():
+                csvwriter.writerow([k] + v)
+            for row in self.dataframe.itertuples(index=False):
+                csvwriter.writerow(i for i in row)

tests/test_download.py

@@ -1,16 +1,16 @@
-from nrel_psm3_2_epw.assets import *
-from pathlib import Path
 import os
+from pathlib import Path
+
+from nrel_psm3_2_epw.assets import *
 
 
 def test_download_epw():
-
     lat, lon = 40.755840, -73.982684
     location = "NYC"
     year = '2012'
     attributes = 'air_temperature,clearsky_dhi,clearsky_dni,clearsky_ghi,cloud_type,dew_point,dhi,dni,fill_flag,ghi,' \
-        'relative_humidity,solar_zenith_angle,surface_albedo,surface_pressure,total_precipitable_water,' \
-        'wind_direction,wind_speed,ghuv-280-400,ghuv-295-385'
+                 'relative_humidity,solar_zenith_angle,surface_albedo,surface_pressure,total_precipitable_water,' \
+                 'wind_direction,wind_speed,ghuv-280-400,ghuv-295-385'
 
     interval = '60'
     utc = 'false'
@@ -19,22 +19,26 @@ def test_download_epw():
     cwd = Path.cwd()
     api_key_file_path = cwd / Path("api_key")
 
+    api_key = None
+
     if api_key_file_path.is_file():
         api_key_file = open(api_key_file_path, 'r')
         api_key = api_key_file.readline().rstrip()
     else:
         api_key = os.environ['APIKEY']
 
+    print(api_key)
+
     reason_for_use = 'beta+testing'
     your_affiliation = 'aaa'
     your_email = "[email protected]"
     mailing_list = 'false'
     leap_year = 'true'
 
-    download_epw(lat, lon, year, location, attributes, interval, utc, your_name, api_key, reason_for_use, your_affiliation,
-                 your_email, mailing_list, leap_year)
+    download_epw(lon, lat, int(year), location, attributes, interval, utc, your_name,
+                 api_key, reason_for_use, your_affiliation, your_email, mailing_list, leap_year)
 
     data = epw.EPW()
     data.read("NYC_40.75584_-73.982684_2012.epw")
 
-    assert(data.dataframe['Year'][0] == 2012)
+    assert (data.dataframe['Year'][0] == 2012)