def test_timecon_get_T():
"""verify we get correct time frame"""
# given
con_tc = TimeConstraint(np.atleast_2d([0, 0, 0, 119, 0, 1]).T,
index=['k', 'x', 'y', 'z', 0, 1],
columns=['u_p0'])
theo_T = 2 # pulls out last two rows
# when
T = con_tc.get_T()
# then
np.testing.assert_array_equal(theo_T, T)
def test_timecon_get_spat():
"""verify we get correct spatial frame"""
# given
con_tc = TimeConstraint(np.atleast_2d([0, 0, 0, 119, 0, 1]).T,
index=['k', 'x', 'y', 'z', 0, 1],
columns=['u_p0'])
theo_time = con_tc.loc[['k', 'x', 'y', 'z']] # pulls out last two rows
# when
time_vec = con_tc.get_spat()
# then
np.testing.assert_array_equal(theo_time, time_vec)
def test_data_spectrum_nolabels():
"""test 1)can interpolate with no/incorrect column labels 2) float comparisons"""
# given
dt, z, T = 1, 65, 4
con_tc = TimeConstraint([[0, 0], [0, 0], [0, 0], [60, 70], [9, 17],
[11, 20], [10, 18], [9, 16]],
index=['k', 'x', 'y', 'z', 0, dt, 2 * dt, 3 * dt],
columns=['dog', 'cat'])
mags_theo = np.abs(np.fft.rfft(con_tc.get_time(), axis=0) / (T / dt))
spec_theo = np.mean(2 * mags_theo**2 * T, axis=1).reshape(-1, 1)
f = np.arange(mags_theo.shape[0]) / T + 1e-12
# when
spec = data_spectrum(f, 0, 0, z, con_tc=con_tc)
# then
np.testing.assert_array_almost_equal(spec, spec_theo)
def test_gen_turb_con():
"""mean & std of iec turbulence, con'd turb is regen'd, correct columns
"""
# given -- constraining points
con_spat_df = pd.DataFrame([[0, 0, 0, 70]], columns=_spat_rownames).T
kwargs = {'u_ref': 10, 'turb_class': 'B', 'l_c': 340.2, 'z_ref': 70, 'T': 300,
'dt': 0.5, 'seed': 1337}
coh_model = 'iec'
con_turb_df = gen_turb(con_spat_df, coh_model=coh_model, **kwargs)
con_tc = TimeConstraint().from_con_data(con_spat_df=con_spat_df.T,
con_turb_df=con_turb_df) # old spat_df was T
# given -- simulated, constrainted turbulence
y, z = 0, [70, 72]
spat_df = gen_spat_grid(y, z)
wsp_func, sig_func, spec_func = power_profile, iec_sig, kaimal_spectrum
sig_theo = np.tile([1.834, 1.4672, 0.917], 2) # sig_u, sig_v, sig_w
u_theo = np.array([10, 0, 0, 10.05650077210035, 0, 0]) # U1, ... U2, ...
theo_cols = [f'{"uvw"[ic]}_p{ip}' for ip in range(2)for ic in range(3)]
# when
sim_turb_df = gen_turb(spat_df, con_tc=con_tc, wsp_func=wsp_func, sig_func=sig_func,
spec_func=spec_func, coh_model=coh_model, **kwargs)
# then (std dev, mean, and regen'd time series should be close; right colnames)
pd.testing.assert_index_equal(sim_turb_df.columns, pd.Index(theo_cols))
np.testing.assert_allclose(sig_theo, sim_turb_df.std(axis=0), atol=0.01, rtol=0.50)
np.testing.assert_allclose(u_theo, sim_turb_df.mean(axis=0), atol=0.01)
np.testing.assert_allclose(con_turb_df.u_p0, sim_turb_df.u_p0, atol=0.01)
def test_timecon_from_condata():
"""verify correct converstion from old con_data format"""
# given
con_spat_df = pd.DataFrame([[0, 0, 0, 0, 119]],
columns=['k', 'p_id', 'x', 'y', 'z'])
con_turb_df = pd.DataFrame([[0], [1]], index=[0, 1], columns=['u_p0'])
con_data = {'con_spat_df': con_spat_df, 'con_turb_df': con_turb_df}
theo_tc = TimeConstraint(np.atleast_2d([0, 0, 0, 119, 0, 1]).T,
index=['k', 'x', 'y', 'z', 0, 1],
columns=['u_p0'])
# when
con_tc1 = TimeConstraint().from_con_data(con_data)
con_tc2 = TimeConstraint().from_con_data(con_spat_df=con_spat_df,
con_turb_df=con_turb_df)
# then
pd.testing.assert_frame_equal(theo_tc, con_tc1, check_dtype=False)
pd.testing.assert_frame_equal(theo_tc, con_tc2, check_dtype=False)
def test_combine_spat_con_nonunique():
"""should raise an error when we try to combine badly named columns"""
# given
spat_df = pd.DataFrame([[0], [0], [0], [60]], index=_spat_rownames, columns=['u_p0_con'])
con_tc = TimeConstraint([[0], [0], [0], [70]], index=_spat_rownames, columns=['u_p0'])
# when and then
with pytest.raises(ValueError):
utils.combine_spat_con(spat_df, con_tc)
def test_data_spectrum():
"""verify 1) data interpolator implement in data_spectrum works, 2) dtype"""
# given
f, T, dt = [0, 0.5], 2, 1
k, y, z = np.repeat(range(3),
3), np.zeros(9, dtype=int), np.tile([40, 70, 100], 3)
con_tc = TimeConstraint(
[[0, 0, 1, 1, 2, 2], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
[50, 90, 50, 90, 50, 90], [0, 0, 0, 0, 0, 0], [1, 2, 3, 4, 5, 6]],
index=['k', 'x', 'y', 'z', 0.0, 1.0])
spec_cols = 2 * np.abs(np.fft.rfft(con_tc.get_time(), axis=0) /
(T / dt))**2 * T
spec_theo = np.tile(
np.interp([0, 0.5, 1, 2, 2.5, 3, 4, 4.5, 5], np.arange(6),
spec_cols[0]), (2, 1))
# when
spec_arr = data_spectrum(f, k, y, z, con_tc=con_tc)
# then
np.testing.assert_array_equal(spec_theo, spec_arr)
def test_gen_turb_bad_con_tc():
"""verify the errors are thrown when con_tc dt doesn't match sim dt"""
y, z = 0, [70, 80]
spat_df = gen_spat_grid(y, z)
kwargs = {'u_ref': 10, 'turb_class': 'B', 'l_c': 340.2, 'z_ref': 75, 'T': 1,
'dt': 0.5}
con_spat_df = pd.DataFrame([[0, 0, 0, 0, 75]],
columns=['k', 'p_id', 'x', 'y', 'z'])
con_turb_df = pd.DataFrame([[9], [11]], index=[0, 5], columns=['u_p0'])
con_tc = TimeConstraint().from_con_data(con_spat_df=con_spat_df, con_turb_df=con_turb_df)
# when and then
with pytest.raises(ValueError): # no con_tc given
gen_turb(spat_df, con_tc=con_tc, **kwargs)
def test_combine_spat_con_tcinspat():
"""some columns in TimeConstraint are in spat_df and float precision"""
# given
spat_df = pd.DataFrame([[0, 0], [0, 0], [0, 0], [50, 60.]],
index=_spat_rownames, columns=['u_p0', 'u_p1'])
con_tc = TimeConstraint([[0, 0], [0, 0], [0, 0], [60 + 1e-13, 70]],
index=_spat_rownames, columns=['u_p0', 'u_p1'])
theo_df = pd.DataFrame([[0, 0, 0], [0, 0, 0], [0, 0, 0], [60, 70, 50]],
index=_spat_rownames, columns=['u_p0_con', 'u_p1_con', 'u_p0'])
# when
comb_df = utils.combine_spat_con(spat_df, con_tc)
# then
pd.testing.assert_frame_equal(theo_df, comb_df, check_dtype=False)
def test_gen_turb_sims_collocated():
"""Should return None if all sim pts collocated with constraints"""
# given -- constraining points
con_spat_df = pd.DataFrame([[0, 0, 0, 70]], columns=_spat_rownames).T
kwargs = {'u_ref': 10, 'turb_class': 'B', 'l_c': 340.2, 'z_ref': 70, 'T': 300,
'dt': 0.5, 'seed': 1337}
coh_model = 'iec'
con_turb_df = gen_turb(con_spat_df, coh_model=coh_model, **kwargs)
con_tc = TimeConstraint().from_con_data(con_spat_df=con_spat_df.T, con_turb_df=con_turb_df)
# given -- simulated, constrainted turbulence
spat_df = con_spat_df # simulate same points as constraint
# when
sim_turb_df = gen_turb(spat_df, con_tc=con_tc, coh_model=coh_model, **kwargs)
# then (std dev, mean, and regen'd time series should be close; right colnames)
assert sim_turb_df is None
def test_check_sims_collocated():
"""verify function works when no unique simulation points"""
# given -- constraining points
con_arr = np.array([[0, 0, 0, 70, 1], [1, 0, 0, 70, 1], [2, 0, 0, 70, 1]]).T
kwargs = {'u_ref': 10, 'turb_class': 'B', 'l_c': 340.2, 'z_ref': 70, 'T': 300,
'dt': 0.5, 'seed': 1337}
inp_out = [(0, 1, False), (0, 2, True)]
# given -- points to simulate
spat_df = pd.DataFrame([[0, 0, 0, 70],
[1, 0, 0, 70]], columns=_spat_rownames).T
for (start, stop, res_theo) in inp_out:
# given -- constraint
con_tc = TimeConstraint(con_arr[:, start:stop], index=_spat_rownames + [0])
# when
res = utils.check_sims_collocated(spat_df, con_tc)
# then
assert res == res_theo
def test_gen_turb_bad_interp():
"""verify the errors are thrown for bad interp_data options"""
# given
y, z = 0, [70, 80]
spat_df = gen_spat_grid(y, z)
kwargs = {'u_ref': 10, 'turb_class': 'B', 'l_c': 340.2, 'z_ref': 75, 'T': 2,
'dt': 1}
con_spat_df = pd.DataFrame([[0, 0, 0, 0, 75]],
columns=['k', 'p_id', 'x', 'y', 'z'])
con_turb_df = pd.DataFrame([[0], [1]], index=[9, 11], columns=['u_p0'])
con_tc = TimeConstraint().from_con_data(con_spat_df=con_spat_df, con_turb_df=con_turb_df)
# when and then
with pytest.raises(ValueError): # no con_tc given
gen_turb(spat_df, interp_data='all', **kwargs)
with pytest.raises(ValueError): # bad string
gen_turb(spat_df, interp_data='dog', con_tc=con_tc, **kwargs)
with pytest.raises(ValueError): # bad string in list
gen_turb(spat_df, interp_data=['dog'], con_tc=con_tc, **kwargs)
def test_combine_spat_con_empty():
"""verify we can combine empty/nonempty spat_df and con_tc"""
# given
empt_df = pd.DataFrame(index=_spat_rownames)
df_sp = pd.DataFrame([[0], [0], [0], [60]], index=_spat_rownames, columns=['u_p0'])
df_cn = pd.DataFrame([[0], [0], [0], [70]], index=_spat_rownames, columns=['u_p0'])
theo_dfs = [empt_df, # empty+empt=empt
df_sp, # empt con, full spat=spat
df_cn.rename(index=str, columns={'u_p0': 'u_p0_con'}), # emp spt, ful cn
pd.concat((df_cn.rename(index=str, columns={'u_p0': 'u_p0_con'}), df_sp),
axis=1)] # full con, full spat=con/spat
for i in range(4): # 4 diff tests
spat_df = [empt_df, df_sp][i % 2] # spat_df empty on 0, 2
con_tc = TimeConstraint([empt_df, df_cn][i // 2]) # con_tc empty on 0, 1
# when
comb_df = utils.combine_spat_con(spat_df, con_tc)
# then
pd.testing.assert_frame_equal(comb_df, theo_dfs[i])
Input = pd.read_csv(
r'../InputsForConstrainedTurb/PyconturbInput/PythonWrapperInput.csv',
index_col=None)
#%%
for i in range(0, Input.shape[0]):
# Reading CSV File from Matlab an running PyConTurb
Variables = pd.read_csv(Input.variables[i], index_col=None)
GridY = pd.read_csv(Input.gridy[i], index_col=None)
GridZ = pd.read_csv(Input.gridz[i], index_col=None)
con_df = pd.read_csv(Input.contc[i], index_col=0)
savename = Input.savename[i]
print('running file ' + savename)
# import pdb; pdb.set_trace()
con_tc = TimeConstraint(con_df) # Constraining in time
con_tc.index = con_tc.index.map(lambda x: float(x)
if (x not in 'kxyz') else x)
# Inputs to constrained Turbulence:
spat_df = gen_spat_grid(GridY.iloc[0, :], GridZ.iloc[
0, :]) # create our spatial pandas dataframe with our grid data.
kwargs = {
'u_ref': Variables.iloc[0, 2],
'z_ref': Variables.iloc[0, 4],
'z_hub':
Variables.iloc[0, 4], # necessary keyword arguments for IEC turbulence
'T': con_tc.get_T(),
'dt': con_tc.get_time().index[1],
'turb_class': Variables.iloc[0, -1],
'alpha': Variables.iloc[0, -2],
Plot=False
# Plot=False
GenerateBox=True
# GenerateBox=False
# --- Constants and derived params
pkl_file = '{}{}.pkl'.format(Case,Suffix)
pkl_space = '{}{}_space.pkl'.format(Case,Suffix)
box_file = '{}{}{}.bts'.format(OUT_Folder,Case,Suffix)
con_file = '35Hz_data/{}_pyConTurb_tc.csv'.format(Case)
# --- Loading files
print('Loading files...')
sim_ts = pickle.load(open(pkl_file,'rb'))
sim_sp = pickle.load(open(pkl_space,'rb'))
con_tc = TimeConstraint(pd.read_csv(con_file, index_col=0)) # load data from csv directly into tc
con_tc.index = con_tc.index.map(lambda x: float(x) if (x not in 'kxyz') else x) # index cleaning
con_ts = con_tc.get_time()
con_sp = con_tc.loc[['k','x','y','z']]
RefPoints = con_sp.filter(regex='u_', axis=1).loc[['x','y','z']]
print(RefPoints)
print(sim_ts.columns)
print(sim_sp.columns)
if GenerateBox:
print('Generating turbulence file:',box_file)
ts = TurbSimFile()
ts['y'] = np.unique(np.around(sim_sp.loc['y'].values,7))
ts['z'] = np.unique(np.around(sim_sp.loc['z'].values,7))
ts['t'] = np.around(sim_ts.index,10)
nz = 160
Suffix=Suffix+'_'+str(ny)'_'+str(nz)
print('>>> Case: {}'.format(Case))
print('>>> Suffix: {}'.format(Suffix))
print('>>> Write: {} {}'.format(write_freq_data,combine_freq_data))
print('>>> y: {} {} {}'.format(ymin,ymax,ny))
print('>>> z: {} {} {}'.format(zmin,zmax,nz))
h_hub=57;
pkl_file = '{}{}.pkl'.format(Case,Suffix)
pkl_space = '{}{}_space.pkl'.format(Case,Suffix)
con_file = '35Hz_data/{}_pyConTurb_tc.csv'.format(Case)
# --- Constraint time series
con_tc = TimeConstraint(pd.read_csv(con_file, index_col=0)) # load data from csv directly into tc
con_tc.index = con_tc.index.map(lambda x: float(x) if (x not in 'kxyz') else x) # index cleaning
time_df = con_tc.get_time()
dt = con_tc.get_time().index[1]
T = con_tc.get_T()-dt # TODO
print('>>> Pkl : {}Mb'.format(0.07*ny*nz))
print('>>> T, dt:',T,dt)
# --- Fitting power law and sigma
# alpha,u_ref, my_wsp_func,veer_func = get_wsp_func(con_tc, h_hub, plot = False)
# my_sig_func = get_sigma_func(con_tc, plot = False)
if Case=='A1':
def my_sig_func(k, y, z, **kwargs):
sig = np.zeros(y.shape)
sig[k==0] = 0.46577620264919134*np.exp(-0.009459702869284256*z[k==0])+0.5242096170226613
sig[k==1] = 0.32501065767141885*np.exp(-0.009059732365499604*z[k==1])+0.4664357892808409
