def test_compare_ascii_bin(self):
res_ascii = windIO.LoadResults(self.respath, self.fascii)
res_bin = windIO.LoadResults(self.respath, self.fbin)
for k in range(res_ascii.sig.shape[1]):
np.testing.assert_allclose(res_ascii.sig[:, k],
res_bin.sig[:, k],
rtol=1e-02,
atol=0.001)
def test_unified_chan_names_extensive(self):
# ---------------------------------------------------------------------
res = windIO.LoadResults(self.respath, self.f1_chant, readdata=False)
self.assertFalse(hasattr(res, 'sig'))
np.testing.assert_array_equal(res.ch_df.index.values,
np.arange(0, 432))
self.assertEqual(res.ch_df.unique_ch_name.values[0], 'Time')
df = res.ch_df
self.assertEqual(2, len(df[df['bearing_name'] == 'shaft_rot']))
self.assertEqual(18, len(df[df['sensortype'] == 'State pos']))
self.assertEqual(11, len(df[df['blade_nr'] == 1]))
exp = [
[38, 'global-blade2-elem-019-zrel-1.00-State pos-z', 'm'],
[200, 'blade2-blade2-node-017-momentvec-z', 'kNm'],
[296, 'blade1-blade1-node-008-forcevec-z', 'kN'],
[415, 'Cl-1-54.82', 'deg'],
[421, 'qwerty-is-azerty', 'is'],
[422, 'wind_wake-wake_pos_x_1', 'm'],
[423, 'wind_wake-wake_pos_y_2', 'm'],
[424, 'wind_wake-wake_pos_z_5', 'm'],
[425, 'statevec_new-blade1-c2def-blade1-absolute-014.00-Dx', 'm'],
[429, 'statevec_new-blade1-c2def-blade1-elastic-014.00-Ry', 'deg'],
]
for k in exp:
self.assertEqual(df.loc[k[0], 'unique_ch_name'], k[1])
self.assertEqual(df.loc[k[0], 'units'], k[2])
self.assertEqual(res.ch_dict[k[1]]['chi'], k[0])
self.assertEqual(res.ch_dict[k[1]]['units'], k[2])
# also check we have the tag from a very long description because
# we truncate after 150 characters
self.assertEqual(df.loc[426, 'sensortag'], 'this is a tag')
# ---------------------------------------------------------------------
res = windIO.LoadResults(self.respath, self.f2_chant, readdata=False)
self.assertFalse(hasattr(res, 'sig'))
np.testing.assert_array_equal(res.ch_df.index.values,
np.arange(0, 217))
df = res.ch_df
self.assertEqual(4, len(df[df['sensortype'] == 'wsp-global']))
self.assertEqual(2, len(df[df['sensortype'] == 'harmonic']))
self.assertEqual(2, len(df[df['blade_nr'] == 3]))
# ---------------------------------------------------------------------
res = windIO.LoadResults(self.respath, self.f3_chant, readdata=False)
self.assertFalse(hasattr(res, 'sig'))
np.testing.assert_array_equal(res.ch_df.index.values,
np.arange(0, 294))
df1 = res.ch_df
self.assertEqual(8, len(df1[df1['sensortype'] == 'CT']))
self.assertEqual(8, len(df1[df1['sensortype'] == 'CQ']))
self.assertEqual(8, len(df1[df1['sensortype'] == 'a_grid']))
self.assertEqual(84, len(df1[df1['blade_nr'] == 1]))
def test_unified_chan_names(self):
res = windIO.LoadResults(self.respath, self.fascii, readdata=False)
self.assertFalse(hasattr(res, 'sig'))
np.testing.assert_array_equal(res.ch_df.index.values, np.arange(0, 28))
self.assertEqual(res.ch_df.unique_ch_name.values[0], 'Time')
self.assertEqual(res.ch_df.unique_ch_name.values[27],
'windspeed-global-Vy--2.50-1.00--52.50')
def test_unified_chan_names_extensive2(self):
res = windIO.LoadResults(self.respath, self.f3_chant, readdata=False)
df1 = res.ch_df
fname = os.path.join(self.respath,
self.f3_chant.replace('.sel', '.ch_df.csv'))
# when changing the tests, update the reference, check, and commit
# but keep the same column ordering to not make the diffs to big
# cols = ['azimuth', 'bearing_name', 'blade_nr', 'bodyname',
# 'component', 'coord', 'direction', 'dll', 'flap_nr', 'io',
# 'io_nr', 'output_type', 'pos', 'radius', 'sensortag',
# 'sensortype', 'unique_ch_name', 'units', ]
# df1[cols].to_csv(fname)
# df1.to_excel(fname.replace('.csv', '.xlsx'))
# FIXME: read_csv for older pandas versions fails on reading the
# mixed str/tuple column. Ignore the pos column for now
colref = [
'azimuth', 'bearing_name', 'blade_nr', 'bodyname', 'component',
'coord', 'direction', 'dll', 'flap_nr', 'io', 'io_nr',
'output_type', 'radius', 'sensortag', 'sensortype',
'unique_ch_name', 'units'
] # 'pos',
# keep_default_na: leave empyt strings as empty strings and not nan's
# you can't have nice things: usecols in combination with index_col
# doesn't work
df2 = pd.read_csv(fname,
usecols=['chi'] + colref,
keep_default_na=False)
df2.index = df2['chi']
df2.drop(labels='chi', inplace=True, axis=1)
# for the comparison we need to have the columns with empty/number
# mixed data types in a consistent data type
for col in [
'azimuth', 'radius', 'blade_nr', 'io_nr', 'flap_nr', 'dll'
]:
df1.loc[df1[col] == '', col] = np.nan
df1[col] = df1[col].astype(np.float32)
df2.loc[df2[col] == '', col] = np.nan
df2[col] = df2[col].astype(np.float32)
# print(df1.pos[14], df2.pos[14])
# the pos columns contains also tuples, read from csv doesn't get that
# df1['pos'] = df1['pos'].astype(np.str)
# df1['pos'] = df1['pos'].str.replace("'", "")
# print(df1.pos[14], df2.pos[14])
# sort columns in the same way so we can assert the df are equal
# df1 = df1[colref].copy()
# df2 = df2[colref].copy()
# FIXME: when pandas is more recent we can use assert_frame_equal
# pd.testing.assert_frame_equal(df1, df2)
# ...but there is no testing.assert_frame_equal in pandas 0.14
for col in colref:
np.testing.assert_array_equal(df1[col].values, df2[col].values)
def loadresfile(self, resfile):
res = windIO.LoadResults(self.respath, resfile)
self.assertTrue(hasattr(res, 'sig'))
self.assertEqual(res.Freq, 40.0)
self.assertEqual(res.N, 800)
self.assertEqual(res.Nch, 28)
self.assertEqual(res.Time, 20.0)
self.assertEqual(res.sig.shape, (800, 28))
return res
def calc(fpath,
no_bins=46,
m=[3, 4, 6, 8, 10, 12],
neq=None,
i0=0,
i1=None,
ftype=False,
fsave=False):
"""
Should we load m, statchans, delchans from another file? This function
will be called from a PBS script.
"""
if fpath[-4:] == '.sel' or fpath[-4:] == '.dat':
fpath = fpath[-4:]
fdir = os.path.dirname(fpath)
fname = os.path.basename(fpath)
res = windIO.LoadResults(fdir,
fname,
debug=False,
usecols=None,
readdata=True)
statsdel = res.statsdel_df(i0=i0,
i1=i1,
statchans='all',
neq=neq,
no_bins=no_bins,
m=m,
delchans='all')
if fsave:
if fsave[-4:] == '.csv':
statsdel.to_csv(fsave)
elif fsave[-3:] == '.h5':
statsdel.to_hdf(fsave, 'table', complib='zlib', complevel=9)
elif ftype == '.csv':
statsdel.to_csv(fpath + ftype)
elif ftype == '.h5':
statsdel.to_hdf(fpath + ftype, 'table', complib='zlib', complevel=9)
return res, statsdel
def test_unified_chan_names_extensive(self):
# ---------------------------------------------------------------------
res = windIO.LoadResults(self.respath, self.f1_chant, readdata=False)
self.assertFalse(hasattr(res, 'sig'))
np.testing.assert_array_equal(res.ch_df.index.values,
np.arange(0, 425))
self.assertEqual(res.ch_df.unique_ch_name.values[0], 'Time')
df = res.ch_df
self.assertEqual(2, len(df[df['bearing_name'] == 'shaft_rot']))
self.assertEqual(18, len(df[df['sensortype'] == 'State pos']))
self.assertEqual(11, len(df[df['blade_nr'] == 1]))
exp = [
[38, 'global-blade2-elem-019-zrel-1.00-State pos-z', 'm'],
[200, 'blade2-blade2-node-017-momentvec-z', 'kNm'],
[296, 'blade1-blade1-node-008-forcevec-z', 'kN'],
[415, 'Cl-1-54.82', 'deg'],
[421, 'qwerty-is-azerty', 'is'],
[422, 'wind_wake-wake_pos_x_1', 'm'],
[423, 'wind_wake-wake_pos_y_2', 'm'],
[424, 'wind_wake-wake_pos_z_5', 'm'],
]
for k in exp:
self.assertEqual(df.loc[k[0], 'unique_ch_name'], k[1])
self.assertEqual(df.loc[k[0], 'units'], k[2])
self.assertEqual(res.ch_dict[k[1]]['chi'], k[0])
self.assertEqual(res.ch_dict[k[1]]['units'], k[2])
# ---------------------------------------------------------------------
res = windIO.LoadResults(self.respath, self.f2_chant, readdata=False)
self.assertFalse(hasattr(res, 'sig'))
np.testing.assert_array_equal(res.ch_df.index.values,
np.arange(0, 217))
df = res.ch_df
self.assertEqual(4, len(df[df['sensortype'] == 'wsp-global']))
self.assertEqual(2, len(df[df['sensortype'] == 'harmonic']))
self.assertEqual(2, len(df[df['blade_nr'] == 3]))
# ---------------------------------------------------------------------
res = windIO.LoadResults(self.respath, self.f3_chant, readdata=False)
self.assertFalse(hasattr(res, 'sig'))
np.testing.assert_array_equal(res.ch_df.index.values,
np.arange(0, 294))
df1 = res.ch_df
self.assertEqual(8, len(df1[df1['sensortype'] == 'CT']))
self.assertEqual(8, len(df1[df1['sensortype'] == 'CQ']))
self.assertEqual(8, len(df1[df1['sensortype'] == 'a_grid']))
self.assertEqual(84, len(df1[df1['blade_nr'] == 1]))
fname = os.path.join(self.respath,
self.f3_chant.replace('.sel', '.ch_df.csv'))
# FIXME: read_csv for older pandas versions fails on reading the
# mixed str/tuple column. Ignore the pos column for now
colref = [
'azimuth', 'bearing_name', 'blade_nr', 'bodyname', 'component',
'coord', 'direction', 'dll', 'flap_nr', 'io', 'io_nr',
'output_type', 'radius', 'sensortag', 'sensortype',
'unique_ch_name', 'units'
] # 'pos',
# keep_default_na: leave empyt strings as empty strings and not nan's
# you can't have nice things: usecols in combination with index_col
# doesn't work
df2 = pd.read_csv(fname,
usecols=['chi'] + colref,
keep_default_na=False)
df2.index = df2['chi']
df2.drop(labels='chi', inplace=True, axis=1)
# for the comparison we need to have the columns with empty/number
# mixed data types in a consistent data type
for col in [
'azimuth', 'radius', 'blade_nr', 'io_nr', 'flap_nr', 'dll'
]:
df1.loc[df1[col] == '', col] = np.nan
df1[col] = df1[col].astype(np.float32)
df2.loc[df2[col] == '', col] = np.nan
df2[col] = df2[col].astype(np.float32)
# print(df1.pos[14], df2.pos[14])
# the pos columns contains also tuples, read from csv doesn't get that
# df1['pos'] = df1['pos'].astype(np.str)
# df1['pos'] = df1['pos'].str.replace("'", "")
# print(df1.pos[14], df2.pos[14])
# sort columns in the same way so we can assert the df are equal
# df1 = df1[colref].copy()
# df2 = df2[colref].copy()
# FIXME: when pandas is more recent we can use assert_frame_equal
# pd.testing.assert_frame_equal(df1, df2)
# ...but there is no testing.assert_frame_equal in pandas 0.14
for col in colref:
np.testing.assert_array_equal(df1[col].values, df2[col].values)
def test_df_stats(self):
"""
"""
res = windIO.LoadResults(self.respath, self.fascii)
df_stats = res.statsdel_df()
from wetb.prepost import hawcstab2
if __name__ == '__main__':
# =============================================================================
# READ HAWC2 RESULT FILE
# =============================================================================
# METHOD A
fname = '../wetb/hawc2/tests/test_files/hawc2io/Hawc2ascii.sel'
res = Hawc2io.ReadHawc2(fname)
sig = res.ReadAll()
# METHOD B
path, file = os.path.dirname(fname), os.path.basename(fname)
res = windIO.LoadResults(path, file)
sig = res.sig
sel = res.ch_details
# result in dataframe with unique channel names (instead of indices)
sig_df = res.sig2df()
ch_df = res.ch_df
# =============================================================================
# READ HAWCStab2 files
# =============================================================================
res = hawcstab2.results()
fname = '../wetb/prepost/tests/data/campbell_diagram.cmb'
df_cmb = res.load_cmb_df(fname)
