def test_read_crn():
columns = [
'WBANNO', 'UTC_DATE', 'UTC_TIME', 'LST_DATE', 'LST_TIME', 'CRX_VN',
'longitude', 'latitude', 'temp_air', 'PRECIPITATION', 'ghi',
'ghi_flag',
'SURFACE_TEMPERATURE', 'ST_TYPE', 'ST_FLAG', 'relative_humidity',
'relative_humidity_flag', 'SOIL_MOISTURE_5', 'SOIL_TEMPERATURE_5',
'WETNESS', 'WET_FLAG', 'wind_speed', 'wind_speed_flag']
index = pd.DatetimeIndex(['2019-01-01 16:10:00',
'2019-01-01 16:15:00',
'2019-01-01 16:20:00',
'2019-01-01 16:25:00'],
freq=None).tz_localize('UTC')
values = np.array([
[53131, 20190101, 1610, 20190101, 910, 3, -111.17, 32.24, nan,
0.0, 296.0, 0, 4.4, 'C', 0, 90.0, 0, nan, nan, 24, 0, 0.78, 0],
[53131, 20190101, 1615, 20190101, 915, 3, -111.17, 32.24, 3.3,
0.0, 183.0, 0, 4.0, 'C', 0, 87.0, 0, nan, nan, 1182, 0, 0.36, 0],
[53131, 20190101, 1620, 20190101, 920, 3, -111.17, 32.24, 3.5,
0.0, 340.0, 0, 4.3, 'C', 0, 83.0, 0, nan, nan, 1183, 0, 0.53, 0],
[53131, 20190101, 1625, 20190101, 925, 3, -111.17, 32.24, 4.0,
0.0, 393.0, 0, 4.8, 'C', 0, 81.0, 0, nan, nan, 1223, 0, 0.64, 0]])
dtypes = [
dtype('int64'), dtype('int64'), dtype('int64'), dtype('int64'),
dtype('int64'), dtype('int64'), dtype('float64'), dtype('float64'),
dtype('float64'), dtype('float64'), dtype('float64'),
dtype('int64'), dtype('float64'), dtype('O'), dtype('int64'),
dtype('float64'), dtype('int64'), dtype('float64'),
dtype('float64'), dtype('int64'), dtype('int64'), dtype('float64'),
dtype('int64')]
expected = pd.DataFrame(values, columns=columns, index=index)
for (col, _dtype) in zip(expected.columns, dtypes):
expected[col] = expected[col].astype(_dtype)
out = crn.read_crn(testfile)
assert_frame_equal(out, expected)
def test_read_crn(testfile, columns, dtypes):
index = pd.DatetimeIndex([
'2019-01-01 16:10:00', '2019-01-01 16:15:00', '2019-01-01 16:20:00',
'2019-01-01 16:25:00'
],
freq=None).tz_localize('UTC')
values = np.array([[
53131, 20190101, 1610, 20190101, 910, 3, -111.17, 32.24, nan, 0.0,
296.0, 0, 4.4, 'C', 0, 90.0, 0, nan, nan, 24, 0, 0.78, 0
],
[
53131, 20190101, 1615, 20190101, 915, 3, -111.17,
32.24, 3.3, 0.0, 183.0, 0, 4.0, 'C', 0, 87.0, 0,
nan, nan, 1182, 0, 0.36, 0
],
[
53131, 20190101, 1620, 20190101, 920, 3, -111.17,
32.24, 3.5, 0.0, 340.0, 0, 4.3, 'C', 0, 83.0, 0,
nan, nan, 1183, 0, 0.53, 0
],
[
53131, 20190101, 1625, 20190101, 925, 3, -111.17,
32.24, 4.0, 0.0, 393.0, 0, 4.8, 'C', 0, 81.0, 0,
nan, nan, 1223, 0, 0.64, 0
]])
expected = pd.DataFrame(values, columns=columns, index=index)
for (col, _dtype) in zip(expected.columns, dtypes):
expected[col] = expected[col].astype(_dtype)
out = crn.read_crn(testfile)
assert_frame_equal(out, expected)
def test_read_crn_problems(testfile_problems, columns_mapped, dtypes):
# GH1025
index = pd.DatetimeIndex(['2020-07-06 12:00:00',
'2020-07-06 13:10:00'],
freq=None).tz_localize('UTC')
values = np.array([
[92821, 20200706, 1200, 20200706, 700, '3', -80.69, 28.62, 24.9,
0.0, np.nan, 0, 25.5, 'C', 0, 93.0, 0, nan, nan, 990, 0, 1.57, 0],
[92821, 20200706, 1310, 20200706, 810, '2.623', -80.69, 28.62,
26.9, 0.0, 430.0, 0, 30.2, 'C', 0, 87.0, 0, nan, nan, 989, 0,
1.64, 0]])
expected = pd.DataFrame(values, columns=columns_mapped, index=index)
for (col, _dtype) in zip(expected.columns, dtypes):
expected[col] = expected[col].astype(_dtype)
out = crn.read_crn(testfile_problems)
assert_frame_equal(out, expected)
def test_read_crn():
columns = [
'WBANNO', 'UTC_DATE', 'UTC_TIME', 'LST_DATE', 'LST_TIME', 'CRX_VN',
'longitude', 'latitude', 'temp_air', 'PRECIPITATION', 'ghi',
'ghi_flag', 'SURFACE_TEMPERATURE', 'ST_TYPE', 'ST_FLAG',
'relative_humidity', 'relative_humidity_flag', 'SOIL_MOISTURE_5',
'SOIL_TEMPERATURE_5', 'WETNESS', 'WET_FLAG', 'wind_speed',
'wind_speed_flag'
]
index = pd.DatetimeIndex([
'2019-01-01 16:10:00', '2019-01-01 16:15:00', '2019-01-01 16:20:00',
'2019-01-01 16:25:00'
],
freq=None).tz_localize('UTC')
values = np.array([[
53131, 20190101, 1610, 20190101, 910, 3, -111.17, 32.24, nan, 0.0,
296.0, 0, 4.4, 'C', 0, 90.0, 0, nan, nan, 24, 0, 0.78, 0
],
[
53131, 20190101, 1615, 20190101, 915, 3, -111.17,
32.24, 3.3, 0.0, 183.0, 0, 4.0, 'C', 0, 87.0, 0,
nan, nan, 1182, 0, 0.36, 0
],
[
53131, 20190101, 1620, 20190101, 920, 3, -111.17,
32.24, 3.5, 0.0, 340.0, 0, 4.3, 'C', 0, 83.0, 0,
nan, nan, 1183, 0, 0.53, 0
],
[
53131, 20190101, 1625, 20190101, 925, 3, -111.17,
32.24, 4.0, 0.0, 393.0, 0, 4.8, 'C', 0, 81.0, 0,
nan, nan, 1223, 0, 0.64, 0
]])
dtypes = [
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('int64'),
dtype('float64'),
dtype('O'),
dtype('int64'),
dtype('float64'),
dtype('int64'),
dtype('float64'),
dtype('float64'),
dtype('int64'),
dtype('int64'),
dtype('float64'),
dtype('int64')
]
expected = pd.DataFrame(values, columns=columns, index=index)
for (col, _dtype) in zip(expected.columns, dtypes):
expected[col] = expected[col].astype(_dtype)
out = crn.read_crn(testfile)
assert_frame_equal(out, expected)
def test_read_crn_map_variables(testfile, columns_unmapped, dtypes):
out = crn.read_crn(testfile, map_variables=False)
assert_index_equal(out.columns, pd.Index(columns_unmapped))
