---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In [49], line 1
----> 1 uvp = ds.pspec([ANTPAIR], [ANTPAIR], dsets=(0, 1),
2 pols=[('pI', 'pI'), ('pQ', 'pQ')],
3 spw_ranges=[(bs.start, bs.stop) for bs in band_slices],
4 taper="bh",
5 store_cov=True,
6 cov_model='autos',
7 )
File /lustre/aoc/projects/hera/jsdillon/mambaforge/envs/python3/lib/python3.9/site-packages/hera_pspec/pspecdata.py:3345, in PSpecData.pspec(self, bls1, bls2, dsets, pols, n_dlys, input_data_weight, norm, taper, sampling, little_h, spw_ranges, symmetric_taper, baseline_tol, store_cov, store_cov_diag, return_q, store_window, exact_windows, ftbeam, verbose, filter_extensions, exact_norm, history, r_params, cov_model, known_cov, allow_fft)
3342 if store_cov or store_cov_diag:
3343 if nper_chunk == 1: logger.info(" Building q_hat covariance...")
3344 cov_q_real, cov_q_imag, cov_real, cov_imag \
-> 3345 = self.get_analytic_covariance(key1, key2, Mv,
3346 exact_norm=exact_norm,
3347 pol=pol,
3348 model=cov_model,
3349 known_cov=known_cov, )
3351 if self.primary_beam != None:
3352 cov_real = cov_real * (scalar)**2.
File /lustre/aoc/projects/hera/jsdillon/mambaforge/envs/python3/lib/python3.9/site-packages/hera_pspec/pspecdata.py:1897, in PSpecData.get_analytic_covariance(self, key1, key2, M, exact_norm, pol, model, known_cov)
1893 check_uniform_input = False
1894 if model != 'foreground_dependent':
1895 # When model is 'foreground_dependent', since we are processing the outer products of visibilities from different times,
1896 # we are expected to have time-dependent inputs, thus check_uniform_input is always set to be False here.
-> 1897 C11_first = self.C_model(key1, model=model, known_cov=known_cov, time_index=0)
1898 C11_last = self.C_model(key1, model=model, known_cov=known_cov, time_index=self.dsets[0].Ntimes-1)
1899 if np.isclose(C11_first, C11_last).all():
File /lustre/aoc/projects/hera/jsdillon/mambaforge/envs/python3/lib/python3.9/site-packages/hera_pspec/pspecdata.py:673, in PSpecData.C_model(self, key, model, time_index, known_cov, include_extension)
671 elif model == 'autos':
672 spw_range = self.get_spw(include_extension=include_extension)
--> 673 self.set_C({Ckey: np.diag(utils.variance_from_auto_correlations(self.dsets[dset], bl, spw_range, time_index))})
674 else:
675 raise ValueError("didn't recognize Ckey {}".format(Ckey))
File /lustre/aoc/projects/hera/jsdillon/mambaforge/envs/python3/lib/python3.9/site-packages/hera_pspec/utils.py:117, in variance_from_auto_correlations(uvd, bl, spw_range, time_index)
115 spw = slice(spw_range[0], spw_range[1])
116 x_bl1 = uvd.get_data(bl1)[time_index, spw]
--> 117 x_bl2 = uvd.get_data(bl2)[time_index, spw]
118 nsample_bl = uvd.get_nsamples(bl)[time_index, spw]
119 nsample_bl = np.where(nsample_bl>0, nsample_bl, np.median(uvd.nsample_array[:, spw, :]))
File /lustre/aoc/projects/hera/jsdillon/mambaforge/envs/python3/lib/python3.9/site-packages/pyuvdata/uvdata/uvdata.py:4148, in UVData.get_data(self, key1, key2, key3, squeeze, force_copy)
4146 if len(key) > 3:
4147 raise ValueError("no more than 3 key values can be passed")
-> 4148 ind1, ind2, indp = self._key2inds(key)
4149 out = self._smart_slicing(
4150 self.data_array, ind1, ind2, indp, squeeze=squeeze, force_copy=force_copy
4151 )
4152 return out
File /lustre/aoc/projects/hera/jsdillon/mambaforge/envs/python3/lib/python3.9/site-packages/pyuvdata/uvdata/uvdata.py:3796, in UVData._key2inds(self, key)
3794 blt_ind2 = self.antpair2ind(key[1], key[0])
3795 if len(blt_ind1) + len(blt_ind2) == 0:
-> 3796 raise KeyError(
3797 "Antenna pair {pair} not found in "
3798 "data".format(pair=(key[0], key[1]))
3799 )
3800 if type(key[2]) is str:
3801 # pol is str
3802 if len(blt_ind1) > 0:
KeyError: 'Antenna pair (4, 4) not found in data'
I also get the same error when I use hp.utils.uvp_noise_error().
Basically, what's happening is that my data has (0, 0) in it as the only autocorrelation, but the baseline I'm interested in (0, 4). If the data is redundantly averaged, there's usually only one autocorrelation per polarization, and there's no guarantee that either antenna in the key for a given baseline is the antenna used to key the autocorrelation. I'd like some elegant way to handle this where it knows to go look for the single auto that's in the data when the data is redundantly averaged.
I'm trying to predict thermal noise from autocorrelations using
PSpecData.pspec().I'm doing the following:
This gives the following error traceback:
I also get the same error when I use
hp.utils.uvp_noise_error().Basically, what's happening is that my data has (0, 0) in it as the only autocorrelation, but the baseline I'm interested in (0, 4). If the data is redundantly averaged, there's usually only one autocorrelation per polarization, and there's no guarantee that either antenna in the key for a given baseline is the antenna used to key the autocorrelation. I'd like some elegant way to handle this where it knows to go look for the single auto that's in the data when the data is redundantly averaged.