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)
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],
'seed': Variables.iloc[0, 9]
} # simulation length (s) and time step (s)
wsp_funcin = Variables.iloc[0, 6]
sig_funcin = Variables.iloc[0, 7]
spec_funcin = Variables.iloc[0, 8]
interp_data2 = Variables.iloc[0, 10]
nf_chunkin = Variables.iloc[0, 3]
coh_model_in = Variables.iloc[0, 5]
if interp_data2 == "Take_list":
# here we have to define all the cases in wsp_func, spec_func and sig_func that we want to use as inputs for "gen_turb":
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
sig[k==2] = 0.25265083664918636*np.exp(-0.00816719396912162 *z[k==2])+0.27864558900332614
return sig
alpha=0.09501
u_ref=8.86407