'es_slope_asce': 0.23488581814172638,
'eto': 7.942481120179387,
'etr': 10.571560006380153,
'fcd': 0.8569860867772078,
'omega_s': 1.9298904620748385,
'q': 0.008691370735727117, # Computed from Ea from Tdew
'q_asce': 0.008692530868140688, # Computed from Ea from Tdew
'ra': 41.67610845067083,
'ra_asce': 41.64824567735701,
'rn': 15.174377350374275,
'rnl': 6.556533974825727,
'rs': 674.07 * 0.041868, # Conversion from Langleys to MJ m-2
'rso': 31.565939444861765,
'rso_simple': 32.26439287925584,
'sc': -0.05874166519510547,
'tdew': units._f2c(49.84),
'tmin': units._f2c(66.65),
'tmax': units._f2c(102.80),
# 'tmean': f2c(84.725),
'w': 17.107650595384076,
'uz': 4.80 * 0.44704, # Conversion from mph to m s-1
'u2': 1.976111757722194,
}
# Hourly test parameters for 2015-07-01 18:00 UTC (11:00 AM PDT)
h_args = {
'doy': 182,
'ea': 1.1990099614301906, # Computed from Tdew
'es': 5.09318785259078,
'es_slope': 0.2852040944519568,
'es_slope_asce': 0.28519744022482574,
'eto': 0.6065255163817055,
class HourlyData():
"""Setup hourly validation data from Fallon AgriMet station"""
val_ws = os.path.join(os.getcwd(), 'tests', 'data')
# val_ws = os.path.join(os.path.dirname(os.getcwd()), 'tests', 'data')
csv_path = os.path.join(val_ws, 'FALN_Agrimet_hourly_raw_2015.csv')
# in2_path = os.path.join(val_ws, 'FALN_Agrimet_hourly_raw_2015.in2')
out_path = os.path.join(val_ws, 'FALN_Agrimet_hourly_raw_2015.out')
# Read in the inputs CSV file using pandas
csv_df = pd.read_csv(csv_path, engine='python', na_values='NO RECORD')
csv_df.rename(
columns={'OB': 'TEMP', 'TP': 'TDEW', 'WS': 'WIND', 'SI': 'RS'},
inplace=True)
# AgriMet times are local with DST (this will drop one hour)
# DEADBEEF - Can't set timezone as variable in class?
csv_df['DATETIME'] = csv_df[['YEAR', 'MONTH', 'DAY', 'HOUR']].apply(
lambda x: pytz.timezone('US/Pacific').localize(dt.datetime(*x)), axis=1)
# To match RefET IN2 values exactly, compute DOY using localtime (not UTC)
csv_df['DOY'] = csv_df['DATETIME'].apply(lambda x: int(x.strftime('%j')))
csv_df['DATETIME'] = csv_df['DATETIME'].apply(
lambda x: x.tz_convert('UTC').strftime('%Y-%m-%d %H:00'))
csv_df.set_index('DATETIME', inplace=True, drop=True)
# Convert inputs units
csv_df['TEMP'] = units._f2c(csv_df['TEMP'])
csv_df['TDEW'] = units._f2c(csv_df['TDEW'])
csv_df['WIND'] *= 0.44704
csv_df['RS'] *= 0.041868 # Conversion from Langleys to MJ m-2 to match RefET
# csv_df['RS'] *= 0.041840 # Alternate conversion from Langleys to MJ m-2
csv_df['EA'] = 0.6108 * np.exp(17.27 * csv_df['TDEW'] / (csv_df['TDEW'] + 237.3))
# Eventually compare ancillary functions directly to IN2 values
# # Identify the row number of the IN2 data
# with open(in2_path) as in2_f:
# in2_data = in2_f.readlines()
# in2_start = [i for i, x in enumerate(in2_data)
# if x.startswith(' Mo Da Year ')][0]
# # Read in the IN2 file using pandas
# in2_df = pd.read_csv(
# in2_path, delim_whitespace=True, skiprows=in2_start, header=[0, 1, 2])
# # Flatten multi-row header
# in2_df.columns = [
# ' '.join(col).replace('-', '').strip()
# for col in in2_df.columns.values]
# in2_df.rename(
# columns={'Year': 'YEAR', 'Mo': 'MONTH', 'Da': 'DAY', 'DoY': 'DOY',
# 'HrMn': 'HOUR'},
# inplace=True)
# in2_df['HOUR'] = (in2_df['HOUR'] / 100).astype(int)
# # AgriMet times are local with DST (this will drop one hour)
# in2_df['DATETIME'] = in2_df[['YEAR', 'MONTH', 'DAY', 'HOUR']].apply(
# lambda x: pytz.timezone('US/Pacific').localize(dt.datetime(*x)),
# axis=1)
# in2_df['DATETIME'] = in2_df['DATETIME'].apply(
# lambda x: x.tz_convert('UTC').strftime('%Y-%m-%d %H:00'))
# in2_df.set_index('DATETIME', inplace=True, drop=True)
# Identify the row number of the OUT data
with open(out_path) as out_f:
out_data = out_f.readlines()
out_start = [
i for i, x in enumerate(out_data) if x.startswith(' Mo Day Yr')][0]
# Read in the OUT file using pandas (skip header and units)
out_df = pd.read_csv(
out_path, delim_whitespace=True, index_col=False,
skiprows=list(range(out_start)) + [out_start + 1])
out_df.rename(
columns={'Yr': 'YEAR', 'Mo': 'MONTH', 'Day': 'DAY', 'HrMn': 'HOUR',
'Tmax': 'TMAX', 'Tmin': 'TMIN', 'DewP': 'TDEW',
'Wind': 'WIND', 'Rs': 'RS'},
inplace=True)
out_df['HOUR'] = (out_df['HOUR'] / 100).astype(int)
# AgriMet times are local with DST (this will drop one hour)
out_df['DATETIME'] = out_df[['YEAR', 'MONTH', 'DAY', 'HOUR']].apply(
lambda x: pytz.timezone('US/Pacific').localize(dt.datetime(*x)),
axis=1)
out_df['DATETIME'] = out_df['DATETIME'].apply(
lambda x: x.tz_convert('UTC').strftime('%Y-%m-%d %H:00'))
# out_df['DATETIME'] = out_df[['YEAR', 'MONTH', 'DAY', 'HOUR']].apply(
# lambda x: dt.datetime(*x).strftime('%Y-%m-%d %H:00'), axis=1)
out_df.set_index('DATETIME', inplace=True, drop=True)
# Read the station properties from the IN2 file for now
# The values should probably be read using a regular expression
for line in out_data:
if line.strip().startswith('The anemometer height is'):
zw = float(line.split(':')[1].split()[0])
elif line.strip().startswith('The weather station elevation is'):
elev = float(line.split(':')[1].split()[0])
elif line.strip().startswith('The weather station latitude is'):
lat = float(line.split(':')[1].split()[0])
elif line.strip().startswith('The weather station longitude is'):
lon = float(line.split(':')[1].split()[0])
if 'West' in line:
lon *= -1
# Get list of date strings from the input CSV file
values, ids = [], []
for test_date in list(csv_df.index):
# Datetime that has issues with fcd calculation
if not test_date.startswith('2015-07-01'):
continue
# Only check day time values for now
if float(csv_df.loc[test_date, 'RS']) <= 1.0:
continue
test_dt = dt.datetime.strptime(test_date, '%Y-%m-%d %H:%M')
# Can the surface type be parameterized inside pytest_generate_tests?
for surface in ['ETr', 'ETo']:
date_values = csv_df \
.loc[test_date, ['TEMP', 'EA', 'RS', 'WIND', 'DOY']] \
.rename({
'DOY': 'doy', 'TEMP': 'tmean', 'EA': 'ea', 'RS': 'rs',
'WIND': 'uz'}) \
.to_dict()
date_values.update({
'surface': surface.lower(),
'expected': out_df.loc[test_date, surface],
# 'doy': int(test_dt.strftime('%j')),
'time': test_dt.hour,
'zw': zw,
'elev': elev,
'lat': lat,
'lon': lon,
'method': 'refet'
})
values.append(date_values)
ids.append('{}-{}'.format(test_date, surface))
}
# Hourly test parameters for 2015-07-01 18:00 UTC (11:00 AM PDT)
h_args = {
'doy': 182,
'ea': 1.1990099614301906,
'es': 5.09318785259078,
'eto_asce': 0.6063515410076268,
'eto_refet': 0.6068613650177561,
'etr_refet': 0.7201865213918281,
'etr_asce': 0.7196369609713682,
'q': 0.008536365803069757,
'ra': 4.30824147948541,
'rnl': 0.22897874401150786,
'rs': 61.16 * 0.041868, # Convert Rs from Langleys to MJ m-2
'tdew': units._f2c(49.36),
'time': 18.0,
'time_mid': 18.5,
'tmean': units._f2c(91.80),
'uz': 3.33 * 0.44704, # Conversion wind speed from mph to m s-1
'u2': 1.3709275319197722,
}
# Test full hourly functions with positional inputs
def test_refet_hourly_input_positions():
etr = Hourly(h_args['tmean'], h_args['ea'], h_args['rs'], h_args['uz'],
s_args['zw'], s_args['elev'], s_args['lat'], s_args['lon'],
h_args['doy'], h_args['time'], 'asce').etr()
assert float(etr) == pytest.approx(h_args['etr_asce'])
def test_f2c(f=68, c=20):
assert units._f2c(f) == c
class DailyData():
"""Setup daily validation data from Fallon AgriMet station"""
val_ws = os.path.join(os.getcwd(), 'tests', 'data')
# val_ws = os.path.join(os.path.dirname(os.getcwd()), 'tests', 'data')
csv_path = os.path.join(val_ws, 'FALN_Agrimet_daily_raw_2015.csv')
# in2_path = os.path.join(val_ws, 'FALN_Agrimet_daily_raw_2015.in2')
out_path = os.path.join(val_ws, 'FALN_Agrimet_daily_raw_2015.out')
# Read in the inputs CSV file using pandas
csv_df = pd.read_csv(csv_path, engine='python', na_values='NO RECORD')
csv_df.rename(columns={
'MN': 'TMIN',
'MX': 'TMAX',
'YM': 'TDEW',
'UA': 'WIND',
'SR': 'RS'
},
inplace=True)
csv_df['DATE'] = csv_df[['YEAR', 'MONTH', 'DAY']].apply(
lambda x: dt.datetime(*x).strftime('%Y-%m-%d'), axis=1)
csv_df.set_index('DATE', inplace=True, drop=True)
# Convert inputs units
csv_df['TMIN'] = units._f2c(csv_df['TMIN'])
csv_df['TMAX'] = units._f2c(csv_df['TMAX'])
csv_df['TDEW'] = units._f2c(csv_df['TDEW'])
csv_df['WIND'] *= 0.44704
csv_df[
'RS'] *= 0.041868 # Conversion from Langleys to MJ m-2 to match RefET
# csv_df['RS'] *= 0.041840 # Alternate conversion from Langleys to MJ m-2
csv_df['EA'] = 0.6108 * np.exp(17.27 * csv_df['TDEW'] /
(csv_df['TDEW'] + 237.3))
# Eventually compare ancillary functions directly to IN2 values
# # Identify the row number of the IN2 data
# with open(in2_path) as in2_f:
# in2_data = in2_f.readlines()
# in2_start = [i for i, x in enumerate(in2_data)
# if x.startswith(' Mo Da Year ')][0]
# # Read in the IN2 file using pandas
# in2_df = pd.read_csv(
# in2_path, delim_whitespace=True, skiprows=in2_start, header=[0, 1, 2])
# in2_df.rename(
# columns={'Year': 'YEAR', 'Mo': 'MONTH', 'Da': 'DAY', 'DoY': 'DOY'},
# inplace=True)
# in2_df['DATE'] = in2_df[['YEAR', 'MONTH', 'DAY']].apply(
# lambda x: dt.datetime(*x).strftime('%Y-%m-%d'), axis=1)
# in2_df.set_index('DATE', inplace=True, drop=True)
# Identify the row number of the OUT data
with open(out_path) as out_f:
out_data = out_f.readlines()
out_start = [
i for i, x in enumerate(out_data) if x.startswith(' Mo Day Yr')
][0]
# Read in the OUT file using pandas (skip header and units)
out_df = pd.read_csv(out_path,
delim_whitespace=True,
index_col=False,
skiprows=list(range(out_start)) + [out_start + 1])
out_df.rename(columns={
'Yr': 'YEAR',
'Mo': 'MONTH',
'Day': 'DAY',
'Tmax': 'TMAX',
'Tmin': 'TMIN',
'Wind': 'WIND',
'Rs': 'RS',
'DewP': 'TDEW'
},
inplace=True)
out_df['DATE'] = out_df[['YEAR', 'MONTH', 'DAY']].apply(
lambda x: dt.datetime(*x).strftime('%Y-%m-%d'), axis=1)
out_df.set_index('DATE', inplace=True, drop=True)
# Read the station properties from the IN2 file for now
# The values should probably be read using a regular expression
for line in out_data:
if line.strip().startswith('The anemometer height is'):
zw = float(line.split(':')[1].split()[0])
elif line.strip().startswith('The weather station elevation is'):
elev = float(line.split(':')[1].split()[0])
elif line.strip().startswith('The weather station latitude is'):
lat = float(line.split(':')[1].split()[0])
# Get list of date strings from the input CSV file
values, ids = [], []
for test_date in list(csv_df.index):
# Datetime that has issues with fcd calculation
if not test_date.startswith('2015-07'):
continue
# This day has missing data and is not being handled correctly
# if test_date.startswith('2015-04-22'):
# continue
test_dt = dt.datetime.strptime(test_date, '%Y-%m-%d')
# Can the surface type be parameterized inside pytest_generate_tests?
for surface in ['ETr', 'ETo']:
date_values = csv_df \
.loc[test_date, ['TMIN', 'TMAX', 'EA', 'RS', 'WIND']] \
.rename({
'TMIN': 'tmin', 'TMAX': 'tmax', 'EA': 'ea', 'RS': 'rs',
'WIND': 'uz'}) \
.to_dict()
date_values.update({
'surface':
surface.lower(),
'expected':
out_df.loc[test_date, surface],
'doy':
int(
dt.datetime.strptime(test_date,
'%Y-%m-%d').strftime('%j')),
'zw':
zw,
'elev':
elev,
'lat':
lat,
'rso_type':
'full',
'method':
'refet'
})
values.append(date_values)
ids.append('{}-{}'.format(test_date, surface))