From d89bcdc7cd3dc0f8863de7873d371c29aad0b8c9 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Thu, 19 Apr 2018 20:03:49 -0700 Subject: [PATCH 01/54] Add functions to convert freqs and redshifts to spw_range tuples --- hera_pspec/tests/test_utils.py | 99 +++++++++++++++++++++++-- hera_pspec/tests/test_uvpspec.py | 78 ++++++++++++++++++++ hera_pspec/utils.py | 123 +++++++++++++++++++++++++++++++ 3 files changed, 295 insertions(+), 5 deletions(-) diff --git a/hera_pspec/tests/test_utils.py b/hera_pspec/tests/test_utils.py index dbbe40f7..b038d0d7 100644 --- a/hera_pspec/tests/test_utils.py +++ b/hera_pspec/tests/test_utils.py @@ -1,14 +1,12 @@ import unittest import nose.tools as nt import numpy as np -import os -import sys +import os, sys, copy from hera_pspec.data import DATA_PATH from hera_pspec import utils -import copy -from collections import OrderedDict as odict from pyuvdata import UVData - +from collections import OrderedDict as odict +from test_uvpspec import build_example_uvpspec def test_cov(): # load another data file @@ -33,3 +31,94 @@ def test_cov(): nt.assert_raises(ValueError, utils.cov, d1, w1) +class Test_Utils(unittest.TestCase): + + def setUp(self): + # Load data into UVData object + self.uvd = UVData() + self.uvd.read_miriad(os.path.join(DATA_PATH, + "zen.2458042.17772.xx.HH.uvXA")) + + # Create UVPSpec object + self.uvp, cosmo = build_example_uvpspec() + + def tearDown(self): + pass + + def runTest(self): + pass + + def test_spw_range_from_freqs(self): + """ + Test that spectral window ranges are correctly recovered from UVData and + UVPSpec files. + """ + # Check that type errors and bounds errors are raised + nt.assert_raises(AttributeError, utils.spw_range_from_freqs, np.arange(3), + freq_range=(100e6, 110e6)) + for obj in [self.uvd, self.uvp]: + nt.assert_raises(ValueError, utils.spw_range_from_freqs, obj, + freq_range=(98e6, 110e6)) # lower bound + nt.assert_raises(ValueError, utils.spw_range_from_freqs, obj, + freq_range=(190e6, 202e6)) # upper bound + nt.assert_raises(ValueError, utils.spw_range_from_freqs, obj, + freq_range=(190e6, 180e6)) # wrong order + + # Check that valid frequency ranges are returned + freq_list = [(100e6, 120e6), (120e6, 140e6), (140e6, 160e6)] + spw1 = utils.spw_range_from_freqs(self.uvd, freq_range=(110e6, 130e6)) + spw2 = utils.spw_range_from_freqs(self.uvd, freq_range=freq_list) + spw3 = utils.spw_range_from_freqs(self.uvd, freq_range=(98e6, 120e6), + bounds_error=False) + spw4 = utils.spw_range_from_freqs(self.uvd, freq_range=(100e6, 120e6)) + + # Make sure tuple vs. list arguments were handled correctly + nt.ok_( isinstance(spw1, tuple) ) + nt.ok_( isinstance(spw2, list) ) + nt.ok_( len(spw2) == len(freq_list) ) + + # Make sure that bounds_error=False works + nt.ok_( spw3 == spw4 ) + + # Make sure that this also works for UVPSpec objects + spw5 = utils.spw_range_from_freqs(self.uvp, freq_range=(100e6, 104e6)) + nt.ok_( isinstance(spw5, tuple) ) + nt.ok_( spw5[0] is not None ) + + def test_spw_range_from_redshifts(self): + """ + Test that spectral window ranges are correctly recovered from UVData and + UVPSpec files (when redshift range is specified). + """ + # Check that type errors and bounds errors are raised + nt.assert_raises(AttributeError, utils.spw_range_from_redshifts, + np.arange(3), z_range=(9.7, 12.1)) + for obj in [self.uvd, self.uvp]: + nt.assert_raises(ValueError, utils.spw_range_from_redshifts, obj, + z_range=(5., 8.)) # lower bound + nt.assert_raises(ValueError, utils.spw_range_from_redshifts, obj, + z_range=(10., 20.)) # upper bound + nt.assert_raises(ValueError, utils.spw_range_from_redshifts, obj, + z_range=(11., 10.)) # wrong order + + # Check that valid frequency ranges are returned + z_list = [(6.5, 7.5), (7.5, 8.5), (8.5, 9.5)] + spw1 = utils.spw_range_from_redshifts(self.uvd, z_range=(7., 8.)) + spw2 = utils.spw_range_from_redshifts(self.uvd, z_range=z_list) + spw3 = utils.spw_range_from_redshifts(self.uvd, z_range=(12., 14.), + bounds_error=False) + spw4 = utils.spw_range_from_redshifts(self.uvd, z_range=(6.2, 7.2)) + + # Make sure tuple vs. list arguments were handled correctly + nt.ok_( isinstance(spw1, tuple) ) + nt.ok_( isinstance(spw2, list) ) + nt.ok_( len(spw2) == len(z_list) ) + + # Make sure that bounds_error=False works + nt.ok_( spw3 == spw4 ) + + # Make sure that this also works for UVPSpec objects + spw5 = utils.spw_range_from_redshifts(self.uvp, z_range=(13.1, 13.2)) + nt.ok_( isinstance(spw5, tuple) ) + nt.ok_( spw5[0] is not None ) + diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index 95abdcb9..71224a84 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -10,6 +10,84 @@ from collections import OrderedDict as odict +def build_example_uvpspec(): + """ + Build an example UVPSpec object, with all necessary metadata. + """ + #FIXME: This will be obsolete once the 'container' PR is merged. + uvp = uvpspec.UVPSpec() + + Ntimes = 10 + Nfreqs = 50 + Ndlys = Nfreqs + Nspws = 1 + Nspwdlys = Nspws * Nfreqs + + # [((1, 2), (1, 2)), ((2, 3), (2, 3)), ((1, 3), (1, 3))] + blpairs = [1002001002, 2003002003, 1003001003] + bls = [1002, 2003, 1003] + Nbls = len(bls) + Nblpairs = len(blpairs) + Nblpairts = Nblpairs * Ntimes + + blpair_array = np.tile(blpairs, Ntimes) + bl_array = np.array(bls) + bl_vecs = np.array([[ 5.33391548e+00, -1.35907816e+01, -7.91624188e-09], + [ -8.67982998e+00, 4.43554478e+00, -1.08695203e+01], + [ -3.34591450e+00, -9.15523687e+00, -1.08695203e+01]]) + time_array = np.repeat(np.linspace(2458042.1, 2458042.2, Ntimes), Nblpairs) + time_1_array = time_array + time_2_array = time_array + lst_array = np.repeat(np.ones(Ntimes, dtype=np.float), Nblpairs) + lst_1_array = lst_array + lst_2_array = lst_array + time_avg_array = time_array + lst_avg_array = lst_array + spws = np.arange(Nspws) + spw_array = np.tile(spws, Ndlys) + freq_array = np.repeat(np.linspace(100e6, 105e6, Nfreqs, endpoint=False), Nspws) + dly_array = np.fft.fftshift(np.repeat(np.fft.fftfreq(Nfreqs, np.median(np.diff(freq_array))), Nspws)) + pol_array = np.array([-5]) + Npols = len(pol_array) + units = 'unknown' + weighting = 'identity' + channel_width = np.median(np.diff(freq_array)) + history = 'example' + taper = "none" + norm = "I" + git_hash = "random" + scalar_array = np.ones((Nspws, Npols), np.float) + + telescope_location = np.array([5109325.85521063, + 2005235.09142983, + -3239928.42475397]) + + cosmo = conversions.Cosmo_Conversions() + + data_array, wgt_array, integration_array, nsample_array = {}, {}, {}, {} + for s in spws: + data_array[s] = np.ones((Nblpairts, Ndlys, Npols), dtype=np.complex) \ + * blpair_array[:, None, None] / 1e9 + wgt_array[s] = np.ones((Nblpairts, Ndlys, 2, Npols), dtype=np.float) + integration_array[s] = np.ones((Nblpairts, Npols), dtype=np.float) + nsample_array[s] = np.ones((Nblpairts, Npols), dtype=np.float) + + params = ['Ntimes', 'Nfreqs', 'Nspws', 'Nspwdlys', 'Nblpairs', 'Nblpairts', + 'Npols', 'Ndlys', 'Nbls', 'blpair_array', 'time_1_array', + 'time_2_array', 'lst_1_array', 'lst_2_array', 'spw_array', + 'dly_array', 'freq_array', 'pol_array', 'data_array', 'wgt_array', + 'integration_array', 'bl_array', 'bl_vecs', 'telescope_location', + 'units', 'channel_width', 'weighting', 'history', 'taper', 'norm', + 'git_hash', 'nsample_array', 'time_avg_array', 'lst_avg_array', + 'cosmo', 'scalar_array'] + + # Set all parameters + for p in params: + setattr(uvp, p, locals()[p]) + + return uvp, cosmo + + class Test_UVPSpec(unittest.TestCase): def setUp(self): diff --git a/hera_pspec/utils.py b/hera_pspec/utils.py index 97cb6091..fa7223a9 100644 --- a/hera_pspec/utils.py +++ b/hera_pspec/utils.py @@ -69,3 +69,126 @@ def get_delays(freqs): delay = np.fft.fftshift(np.fft.fftfreq(freqs.size, d=np.median(np.diff(freqs)))) return delay + +def spw_range_from_freqs(data, freq_range, bounds_error=True): + """ + Return the first and last frequency array indices for a spectral window, + where the window is specified as a range of frequencies. + + Parameters + ---------- + data : UVData or UVPSpec object + Object containing data with a frequency dimension. + + freq_range : tuple or list of tuples + Tuples containing the lower and upper frequency bounds for each + spectral window. The range is inclusive of the lower frequency bound, + i.e. it includes all channels in freq_range[0] <= freq < freq_range[1]. + Frequencies are in Hz. + + bounds_error : bool, optional + Whether to raise an error if a specified lower/upper frequency is + outside the frequency range available in 'data'. Default: True. + + Returns + ------- + spw_range : tuple or list of tuples + Indices of the channels at the lower and upper bounds of the specified + spectral window(s). + + Note: If the requested spectral window is outside the available + frequency range, and bounds_error is False, '(None, None)' is returned. + """ + # Get frequency array from input object + try: + freqs = data.freq_array + except: + raise AttributeError("Object 'data' does not have a freq_array attribute.") + + # Check for a single tuple input + is_tuple = False + if isinstance(freq_range, tuple): + is_tuple = True + freq_range = [freq_range,] + + # Make sure freq_range is now a list (of tuples) + if not isinstance(freq_range, list): + raise TypeError("freq_range must be a tuple or list of tuples.") + + # Loop over tuples and find spectral window indices + spw_range = [] + for frange in freq_range: + fmin, fmax = frange + if fmin > fmax: + raise ValueError("Upper bound of spectral window is less than " + "the lower bound.") + + # Check that this doesn't go beyond the available range of freqs + if fmin < np.min(freqs) and bounds_error: + raise ValueError("Lower bound of spectral window is below the " + "available frequency range. (Note: freqs should " + "be in Hz)") + if fmax > np.max(freqs) and bounds_error: + raise ValueError("Upper bound of spectral window is above the " + "available frequency range. (Note: freqs should " + "be in Hz)") + + # Get indices within this range + idxs = np.where(np.logical_and(freqs >= fmin, freqs < fmax))[0] + spw = (idxs[0], idxs[1]) if idxs.size > 0 else (None, None) + spw_range.append(spw) + + # Unpack from list if only a single tuple was specified originally + if is_tuple: return spw_range[0] + return spw_range + + +def spw_range_from_redshifts(data, z_range, bounds_error=True): + """ + Return the first and last frequency array indices for a spectral window, + where the window is specified as a range of redshifts. + + Parameters + ---------- + data : UVData or UVPSpec object + Object containing data with a frequency dimension. + + z_range : tuple or list of tuples + Tuples containing the lower and upper fredshift bounds for each + spectral window. The range is inclusive of the upper redshift bound, + i.e. it includes all channels in z_range[0] > z >= z_range[1]. + + bounds_error : bool, optional + Whether to raise an error if a specified lower/upper redshift is + outside the frequency range available in 'data'. Default: True. + + Returns + ------- + spw_range : tuple or list of tuples + Indices of the channels at the lower and upper bounds of the specified + spectral window(s). + + Note: If the requested spectral window is outside the available + frequency range, and bounds_error is False, '(None, None)' is returned. + """ + # Check for a single tuple input + is_tuple = False + if isinstance(z_range, tuple): + is_tuple = True + z_range = [z_range,] + + # Convert redshifts to frequencies (in Hz) + NU21CM = 1420405751.7667 # 21cm rest freq. in Hz + freq_range = [] + for zrange in z_range: + zmin, zmax = zrange + freq_range.append( (NU21CM/(1.+zmax), NU21CM/(1.+zmin)) ) + + # Use freq. function to get spectral window + spw_range = spw_range_from_freqs(data=data, freq_range=freq_range, + bounds_error=bounds_error) + + # Unpack from list if only a single tuple was specified originally + if is_tuple: return spw_range[0] + return spw_range + From 796d8512b42314c46c5dbd10fcc1e43f20ce5dd1 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 14:54:36 -0700 Subject: [PATCH 02/54] Reformat docstrings to prevent sphinx errors --- hera_pspec/uvpspec.py | 130 ++++++++++++++++++++++++------------------ 1 file changed, 74 insertions(+), 56 deletions(-) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index d02f3691..9f47fd34 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -121,11 +121,13 @@ def get_data(self, key, *args): Parameters ---------- - key : tuple, baseline-pair key + key : tuple + Baseline-pair key - Return - ------ - data : complex ndarray with shape (Ntimes, Ndlys) + Returns + ------- + data : complex ndarray + Shape (Ntimes, Ndlys) """ spw, blpairts, pol = self.key_to_indices(key, *args) @@ -155,10 +157,11 @@ def get_wgts(self, key, *args): ---------- key : tuple, baseline-pair key - Return - ------ - wgts : float ndarray with shape (2, Ntimes, Ndlys), where the zeroth axis holds - [wgt_1, wgt_2] in that order + Returns + ------- + wgts : float ndarray + Has shape (2, Ntimes, Ndlys), where the zeroth axis holds + [wgt_1, wgt_2] in that order. """ spw, blpairts, pol = self.key_to_indices(key, *args) @@ -179,11 +182,13 @@ def get_integrations(self, key, *args): Parameters ---------- - key : tuple, baseline-pair key + key : tuple + Baseline-pair key - Return - ------ - data : float ndarray with shape (Ntimes,) + Returns + ------- + data : float ndarray + Has shape (Ntimes,) """ spw, blpairts, pol = self.key_to_indices(key, *args) @@ -206,8 +211,8 @@ def get_nsamples(self, key, *args): ---------- key : tuple, baseline-pair key - Return - ------ + Returns + ------- data : float ndarray with shape (Ntimes,) """ spw, blpairts, pol = self.key_to_indices(key, *args) @@ -232,11 +237,13 @@ def get_dlys(self, key): def get_blpair_seps(self): """ - For each baseline-pair, get the average baseline separation in ENU frame in meters. + For each baseline-pair, get the average baseline separation in ENU + frame in meters. - Returns blp_avg_sep + Returns ------- - blp_avg_sep : float ndarray, shape=(Nblpairts,) + blp_avg_sep : float ndarray + shape=(Nblpairts,) """ # get list of bl separations bl_vecs = self.get_ENU_bl_vecs() @@ -334,8 +341,8 @@ def blpair_to_antnums(self, blpair): blpair : i12 int baseline-pair integer ID - Return - ------ + Returns + ------- antnums : tuple nested tuple containing baseline-pair antenna numbers. Ex. ((ant1, ant2), (ant3, ant4)) """ @@ -351,8 +358,8 @@ def antnums_to_blpair(self, antnums): nested tuple containing integer antenna numbers for a baseline-pair. Ex. ((ant1, ant2), (ant3, ant4)) - Return - ------ + Returns + ------- blpair : i12 integer baseline-pair integer """ @@ -367,8 +374,8 @@ def bl_to_antnums(self, bl): bl : i6 int baseline integer ID - Return - ------ + Returns + ------- antnums : tuple tuple containing baseline antenna numbers. Ex. (ant1, ant2) """ @@ -384,8 +391,8 @@ def antnums_to_bl(self, antnums): tuple containing integer antenna numbers for a baseline. Ex. (ant1, ant2) - Return - ------ + Returns + ------- bl : i6 integer baseline integer """ @@ -890,35 +897,46 @@ def generate_noise_spectra(self, spw, pol, Tsys, blpairs=None, little_h=True, fo def average_spectra(self, blpair_groups=None, time_avg=False, inplace=True): """ - Average power spectra across the baseline-pair-time axis, weighted by each spectrum's integration time. - This is an "incoherent" average, in the sense that this averages power spectra, rather than visibility data. - The 'nsample_array' and 'integration_array' will be updated to reflect the averaging. - - In the case of averaging across baseline pairs, the resultant averaged spectrum is assigned to the zeroth - blpair in the group. In the case of time averaging, the time and LST arrays are assigned to the mean of the + Average power spectra across the baseline-pair-time axis, weighted by + each spectrum's integration time. This is an "incoherent" average, in + the sense that this averages power spectra, rather than visibility + data. The 'nsample_array' and 'integration_array' will be updated to + reflect the averaging. + + In the case of averaging across baseline pairs, the resultant averaged + spectrum is assigned to the zeroth blpair in the group. In the case of + time averaging, the time and LST arrays are assigned to the mean of the averaging window. - Note that this is designed to be separate from spherical binning in k: here we are not connecting k_perp modes - to k_para modes. However, if blpairs holds groups of iso baseline separation, then this is equivalent to - cylinderical binning in 3D k-space. + Note that this is designed to be separate from spherical binning in k: + here we are not connecting k_perp modes to k_para modes. However, if + blpairs holds groups of iso baseline separation, then this is + equivalent to cylindrical binning in 3D k-space. - If you want help constructing baseline-pair groups from baseline groups, see self.get_blpair_groups_from_bl_groups. + If you want help constructing baseline-pair groups from baseline + groups, see self.get_blpair_groups_from_bl_groups. Parameters ---------- - blpair_groups : list of baseline-pair groups, i.e. list of list of tuples or integers. All power spectra in a - baseline-pair group are averaged together. If a baseline-pair exists in more than one group, a warning is raised. + blpair_groups : list of baseline-pair groups + List of list of tuples or integers. All power spectra in a + baseline-pair group are averaged together. If a baseline-pair + exists in more than one group, a warning is raised. Ex: blpair_groups = [ [((1, 2), (1, 2)), ((2, 3), (2, 3))], [((4, 6), (4, 6))]] or - blpair_groups = [ [1002001002, 2003002003], [4006004006] ] + blpair_groups = [ [1002001002, 2003002003], [4006004006] ] - time_avg : boolean, if True, average power spectra across the time axis. + time_avg : bool, optional + If True, average power spectra across the time axis. Default: False. - inplace : bool, if True, edit data in self, else make a copy and return + inplace : bool, optional + If True, edit data in self, else make a copy and return. Default: + True. Notes ----- - Currently, every baseline-pair in a blpair group must have the same Ntimes. Future versions - may support baseline-pair averaging of heterogeneous time arrays. + Currently, every baseline-pair in a blpair group must have the same + Ntimes. Future versions may support baseline-pair averaging of + heterogeneous time arrays. """ if inplace: uvp = self @@ -1323,8 +1341,8 @@ def _blpair_to_antnums(blpair): blpair : ((ant3, ant4), (ant1, ant2)) @@ -1460,8 +1478,8 @@ def _conj_bl_int(bl): blpair : i6 int baseline integer - Return - ------- + Returns + -------- conj_bl : i6 int conjugated baseline integer. Ex: (ant1, ant2) --> (ant2, ant1) @@ -1484,8 +1502,8 @@ def _conj_blpair(blpair, which='both'): which : str, options=['first', 'second', 'both'] which baseline to conjugate - Return - ------ + Returns + ------- conj_blpair : <12 int blpair with one or both baselines conjugated """ From daa83733ab7056715ef33f2660b4b555666bdb86 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 14:56:31 -0700 Subject: [PATCH 03/54] Add sphinx documentation --- docs/Makefile | 20 ++++++ docs/conf.py | 171 +++++++++++++++++++++++++++++++++++++++++++++ docs/container.rst | 8 +++ docs/index.rst | 27 +++++++ docs/pspecdata.rst | 7 ++ docs/uvpspec.rst | 7 ++ 6 files changed, 240 insertions(+) create mode 100644 docs/Makefile create mode 100644 docs/conf.py create mode 100644 docs/container.rst create mode 100644 docs/index.rst create mode 100644 docs/pspecdata.rst create mode 100644 docs/uvpspec.rst diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..12c13560 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = hera_pspec +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..ac752474 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +# +# hera_pspec documentation build configuration file, created by +# sphinx-quickstart on Tue Apr 24 13:55:12 2018. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) + +import sys +import os +sys.path.insert(0, os.path.abspath('../')) +import hera_pspec + + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = ['sphinx.ext.autodoc', + 'numpydoc', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.viewcode', + 'sphinx.ext.githubpages'] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = u'hera_pspec' +copyright = u'2018, HERA Collaboration' +author = u'HERA Collaboration' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = u'0.1' +# The full version, including alpha/beta/rc tags. +release = u'0.1' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'alabaster' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + + +# -- Options for HTMLHelp output ------------------------------------------ + +# Output file base name for HTML help builder. +htmlhelp_basename = 'hera_pspecdoc' + + +# -- Options for numpydoc ------------------------------------------ + +numpydoc_show_class_members = False + + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'hera_pspec.tex', u'hera\\_pspec Documentation', + u'HERA Collaboration', 'manual'), +] + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'hera_pspec', u'hera_pspec Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'hera_pspec', u'hera_pspec Documentation', + author, 'hera_pspec', 'One line description of project.', + 'Miscellaneous'), +] + + + diff --git a/docs/container.rst b/docs/container.rst new file mode 100644 index 00000000..a7686289 --- /dev/null +++ b/docs/container.rst @@ -0,0 +1,8 @@ +PSpecContainer Class +==================== + +PSpecContainer is a container for organizing collections of UVPSpec objects. +It is based on HDF5. + +.. .. autoclass:: hera_pspec.PSpecContainer +.. :members: diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 00000000..28c27043 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,27 @@ + +`hera_pspec` +============ + +The `hera_pspec` library provides all of the tools and data structures needed +to perform a delay spectrum analysis on interferoometric data. The input data +can be in any format supported by `pyuvdata`, and the output data are stored in +HDF5 containers. + +Fork `hera_pspec` on Github + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + pspecdata + uvpspec + container + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/pspecdata.rst b/docs/pspecdata.rst new file mode 100644 index 00000000..56633c56 --- /dev/null +++ b/docs/pspecdata.rst @@ -0,0 +1,7 @@ +PSpecData Class +=============== + +PSpecData takes UVData objects and calculates delay power spectra from them. + +.. autoclass:: hera_pspec.PSpecData + :members: diff --git a/docs/uvpspec.rst b/docs/uvpspec.rst new file mode 100644 index 00000000..a3109f96 --- /dev/null +++ b/docs/uvpspec.rst @@ -0,0 +1,7 @@ +UVPSpec Class +============= + +UVPSpec contains power spectra. + +.. autoclass:: hera_pspec.UVPSpec + :members: From cbe1463f35c9b392dcca1953a6ddf0ff78eed2ef Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 16:05:14 -0700 Subject: [PATCH 04/54] Uncomment import --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index ac752474..6e0249f7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,7 @@ import sys import os sys.path.insert(0, os.path.abspath('../')) -import hera_pspec +#import hera_pspec # -- General configuration ------------------------------------------------ From 6977e7f5679b6a812ee3cf00e3dfb4fc189ffe59 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 16:16:21 -0700 Subject: [PATCH 05/54] Try to provide mock modules for ReadTheDocs build --- docs/conf.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index 6e0249f7..8f31b156 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -20,6 +20,12 @@ # import sys # sys.path.insert(0, os.path.abspath('.')) +import mock +MOCK_MODULES = ['numpy', 'scipy', 'pyuvdata', 'h5py', 'aipy', 'omnical', 'linsolve', 'hera_qm', 'uvtools', 'hera_cal', 'healpy', 'scikit-learn'] +for mod_name in MOCK_MODULES: +sys.modules[mod_name] = mock.Mock() + + import sys import os sys.path.insert(0, os.path.abspath('../')) From e0d3223ccc9b50668b4bbf8da6c50900e2b9598a Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 16:17:11 -0700 Subject: [PATCH 06/54] Fix indent error --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 8f31b156..3951feb3 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,7 @@ import mock MOCK_MODULES = ['numpy', 'scipy', 'pyuvdata', 'h5py', 'aipy', 'omnical', 'linsolve', 'hera_qm', 'uvtools', 'hera_cal', 'healpy', 'scikit-learn'] for mod_name in MOCK_MODULES: -sys.modules[mod_name] = mock.Mock() + sys.modules[mod_name] = mock.Mock() import sys From 0bf49d9c65e45e8f2975ac0db7df82216c6f5717 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 16:18:25 -0700 Subject: [PATCH 07/54] Fix import order --- docs/conf.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 3951feb3..5dc7c8db 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -20,14 +20,13 @@ # import sys # sys.path.insert(0, os.path.abspath('.')) +import os +import sys import mock MOCK_MODULES = ['numpy', 'scipy', 'pyuvdata', 'h5py', 'aipy', 'omnical', 'linsolve', 'hera_qm', 'uvtools', 'hera_cal', 'healpy', 'scikit-learn'] for mod_name in MOCK_MODULES: sys.modules[mod_name] = mock.Mock() - -import sys -import os sys.path.insert(0, os.path.abspath('../')) #import hera_pspec From d58f13a2795dd99d4a6ecdcb8c00811b5299f130 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 16:23:58 -0700 Subject: [PATCH 08/54] Try to use napoleon instead of numpydoc --- docs/conf.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 5dc7c8db..5a6825df 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -41,7 +41,7 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = ['sphinx.ext.autodoc', - 'numpydoc', + 'sphinx.ext.napoleon', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', @@ -119,7 +119,10 @@ # -- Options for numpydoc ------------------------------------------ -numpydoc_show_class_members = False +#numpydoc_show_class_members = False +napoleon_google_docstring = False +napoleon_use_param = False +napoleon_use_ivar = True # -- Options for LaTeX output --------------------------------------------- From 88595ad26cc30255ab9e6ebeb887cc46dcd63fd5 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 16:26:21 -0700 Subject: [PATCH 09/54] Add another mock module --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 5a6825df..ebbd42f6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,7 @@ import os import sys import mock -MOCK_MODULES = ['numpy', 'scipy', 'pyuvdata', 'h5py', 'aipy', 'omnical', 'linsolve', 'hera_qm', 'uvtools', 'hera_cal', 'healpy', 'scikit-learn'] +MOCK_MODULES = ['numpy', 'scipy', 'scipy.interpolate', 'pyuvdata', 'h5py', 'aipy', 'omnical', 'linsolve', 'hera_qm', 'uvtools', 'hera_cal', 'healpy', 'scikit-learn'] for mod_name in MOCK_MODULES: sys.modules[mod_name] = mock.Mock() From 32e3263a612778860401d32233ec599832bb60c4 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 16:29:35 -0700 Subject: [PATCH 10/54] Use default theme --- docs/conf.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index ebbd42f6..9144275a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -22,6 +22,8 @@ import os import sys + +# Mock-import modules to allow build to complete without throwing errors import mock MOCK_MODULES = ['numpy', 'scipy', 'scipy.interpolate', 'pyuvdata', 'h5py', 'aipy', 'omnical', 'linsolve', 'hera_qm', 'uvtools', 'hera_cal', 'healpy', 'scikit-learn'] for mod_name in MOCK_MODULES: @@ -97,7 +99,12 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = 'alabaster' +#html_theme = 'alabaster' +html_theme = "default" +html_theme_options = { + "rightsidebar": "false", + "relbarbgcolor": "black" +} # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the From 81436a8719413bcb0ac585011b1a7ec85f437a21 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 19:55:24 -0700 Subject: [PATCH 11/54] Use Cosmo_Conversions functions for unit conversion --- hera_pspec/conversions.py | 5 +++-- hera_pspec/utils.py | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/hera_pspec/conversions.py b/hera_pspec/conversions.py index f94b54bc..26e1b257 100644 --- a/hera_pspec/conversions.py +++ b/hera_pspec/conversions.py @@ -142,8 +142,9 @@ def f2z(self, freq, ghz=False): freq = freq * 1e9 return (units.f21 / freq - 1) - - def z2f(self, z, ghz=False): + + @staticmethod + def z2f(z, ghz=False): """ convert redshift to frequency in Hz for 21cm line diff --git a/hera_pspec/utils.py b/hera_pspec/utils.py index fa7223a9..3efbaf14 100644 --- a/hera_pspec/utils.py +++ b/hera_pspec/utils.py @@ -1,5 +1,6 @@ import numpy as np import md5 +from conversions import Cosmo_Conversions def hash(w): """ @@ -178,11 +179,11 @@ def spw_range_from_redshifts(data, z_range, bounds_error=True): z_range = [z_range,] # Convert redshifts to frequencies (in Hz) - NU21CM = 1420405751.7667 # 21cm rest freq. in Hz freq_range = [] for zrange in z_range: zmin, zmax = zrange - freq_range.append( (NU21CM/(1.+zmax), NU21CM/(1.+zmin)) ) + freq_range.append( Cosmo_Conversions.z2f(zmax), + Cosmo_Conversions.z2f(zmin) ) # Use freq. function to get spectral window spw_range = spw_range_from_freqs(data=data, freq_range=freq_range, From f4e9c15eb782fcbae319808425f442206088354f Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 20:17:19 -0700 Subject: [PATCH 12/54] Remove duplicate test function --- hera_pspec/tests/test_uvpspec.py | 78 -------------------------------- 1 file changed, 78 deletions(-) diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index 9b1674c0..f9fb3eb6 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -86,84 +86,6 @@ def build_example_uvpspec(): return uvp, cosmo -def build_example_uvpspec(): - """ - Build an example UVPSpec object, with all necessary metadata. - """ - #FIXME: This will be obsolete once the 'container' PR is merged. - uvp = uvpspec.UVPSpec() - - Ntimes = 10 - Nfreqs = 50 - Ndlys = Nfreqs - Nspws = 1 - Nspwdlys = Nspws * Nfreqs - - # [((1, 2), (1, 2)), ((2, 3), (2, 3)), ((1, 3), (1, 3))] - blpairs = [1002001002, 2003002003, 1003001003] - bls = [1002, 2003, 1003] - Nbls = len(bls) - Nblpairs = len(blpairs) - Nblpairts = Nblpairs * Ntimes - - blpair_array = np.tile(blpairs, Ntimes) - bl_array = np.array(bls) - bl_vecs = np.array([[ 5.33391548e+00, -1.35907816e+01, -7.91624188e-09], - [ -8.67982998e+00, 4.43554478e+00, -1.08695203e+01], - [ -3.34591450e+00, -9.15523687e+00, -1.08695203e+01]]) - time_array = np.repeat(np.linspace(2458042.1, 2458042.2, Ntimes), Nblpairs) - time_1_array = time_array - time_2_array = time_array - lst_array = np.repeat(np.ones(Ntimes, dtype=np.float), Nblpairs) - lst_1_array = lst_array - lst_2_array = lst_array - time_avg_array = time_array - lst_avg_array = lst_array - spws = np.arange(Nspws) - spw_array = np.tile(spws, Ndlys) - freq_array = np.repeat(np.linspace(100e6, 105e6, Nfreqs, endpoint=False), Nspws) - dly_array = np.fft.fftshift(np.repeat(np.fft.fftfreq(Nfreqs, np.median(np.diff(freq_array))), Nspws)) - pol_array = np.array([-5]) - Npols = len(pol_array) - units = 'unknown' - weighting = 'identity' - channel_width = np.median(np.diff(freq_array)) - history = 'example' - taper = "none" - norm = "I" - git_hash = "random" - scalar_array = np.ones((Nspws, Npols), np.float) - - telescope_location = np.array([5109325.85521063, - 2005235.09142983, - -3239928.42475397]) - - cosmo = conversions.Cosmo_Conversions() - - data_array, wgt_array, integration_array, nsample_array = {}, {}, {}, {} - for s in spws: - data_array[s] = np.ones((Nblpairts, Ndlys, Npols), dtype=np.complex) \ - * blpair_array[:, None, None] / 1e9 - wgt_array[s] = np.ones((Nblpairts, Ndlys, 2, Npols), dtype=np.float) - integration_array[s] = np.ones((Nblpairts, Npols), dtype=np.float) - nsample_array[s] = np.ones((Nblpairts, Npols), dtype=np.float) - - params = ['Ntimes', 'Nfreqs', 'Nspws', 'Nspwdlys', 'Nblpairs', 'Nblpairts', - 'Npols', 'Ndlys', 'Nbls', 'blpair_array', 'time_1_array', - 'time_2_array', 'lst_1_array', 'lst_2_array', 'spw_array', - 'dly_array', 'freq_array', 'pol_array', 'data_array', 'wgt_array', - 'integration_array', 'bl_array', 'bl_vecs', 'telescope_location', - 'units', 'channel_width', 'weighting', 'history', 'taper', 'norm', - 'git_hash', 'nsample_array', 'time_avg_array', 'lst_avg_array', - 'cosmo', 'scalar_array'] - - # Set all parameters - for p in params: - setattr(uvp, p, locals()[p]) - - return uvp, cosmo - - class Test_UVPSpec(unittest.TestCase): def setUp(self): From 3aa945ac73f0fccae120ffafdb4110191546ea8f Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Fri, 27 Apr 2018 16:21:25 -0700 Subject: [PATCH 13/54] changed "pseudo_stokes" to just "stokes" across the repo --- hera_pspec/noise.py | 1 - hera_pspec/pspecbeam.py | 58 +++++++++++++++--------------- hera_pspec/pspecdata.py | 2 +- hera_pspec/tests/test_noise.py | 4 +-- hera_pspec/tests/test_pspecbeam.py | 8 ++--- hera_pspec/uvpspec.py | 5 ++- 6 files changed, 38 insertions(+), 40 deletions(-) diff --git a/hera_pspec/noise.py b/hera_pspec/noise.py index eec5502a..d29495a3 100644 --- a/hera_pspec/noise.py +++ b/hera_pspec/noise.py @@ -96,7 +96,6 @@ def calc_scalar(self, freqs, stokes, num_steps=5000, little_h=True): self.stokes : str, stokes polarization used to calculate self.scalar """ # parse stokes - if stokes == 'I': stokes = 'pseudo_I' self.scalar = self.beam.compute_pspec_scalar(freqs.min(), freqs.max(), len(freqs), num_steps=num_steps, stokes=stokes, little_h=little_h, noise_scalar=True) self.subband = freqs diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index 2e4d3d35..b6466458 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -101,7 +101,7 @@ def __init__(self, cosmo=None): else: self.cosmo = conversions.Cosmo_Conversions() - def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000, stokes='pseudo_I', + def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000, stokes='I', taper='none', little_h=True, noise_scalar=False): """ Computes the scalar function to convert a power spectrum estimate @@ -109,7 +109,7 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 See arxiv:1304.4991 and HERA memo #27 for details. - Currently, only the "pseudo Stokes I", "XX" and "YY" beam are supported. + Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. @@ -133,9 +133,9 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 stokes: str, optional Which Stokes parameter's to compute the beam scalar for. - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', 'XX', 'YY', 'XY', 'YX', although - currently only 'pseudo_I' and linear dipole pol (e.g. 'XX') are implemented. - Default: 'pseudo_I' + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although + currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + Default: 'I' taper : str, optional Whether a tapering function (e.g. Blackman-Harris) is being @@ -171,7 +171,7 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 return scalar - def Jy_to_mK(self, freqs, stokes='pseudo_I'): + def Jy_to_mK(self, freqs, stokes='I'): """ Return the multiplicative factor, M [mK / Jy], to convert a visibility from Jy -> mK, @@ -188,9 +188,9 @@ def Jy_to_mK(self, freqs, stokes='pseudo_I'): stokes: str, optional Which Stokes parameter's to compute the beam scalar for. - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', 'XX', 'YY', 'XY', 'YX', although - currently only 'pseudo_I' and linear dipole pol (e.g. 'XX') are implemented. - Default: 'pseudo_I' + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although + currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + Default: 'I' Returns ------- @@ -233,7 +233,7 @@ def __init__(self, fwhm, beam_freqs, cosmo=None): else: self.cosmo = conversions.Cosmo_Conversions() - def power_beam_int(self, stokes='pseudo_I'): + def power_beam_int(self, stokes='I'): """ Computes the integral of the beam over solid angle to give a beam area (in sr). Uses analytic formula that the answer @@ -242,7 +242,7 @@ def power_beam_int(self, stokes='pseudo_I'): Trivially this returns an array (i.e., a function of frequency), but the results are frequency independent. - Currently, only the "pseudo Stokes I", "XX" and "YY" beam are supported. + Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. @@ -250,9 +250,9 @@ def power_beam_int(self, stokes='pseudo_I'): ---------- stokes: str, optional Which Stokes parameter's to compute the beam scalar for. - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', 'XX', 'YY', 'XY', 'YX', although - currently only 'pseudo_I' and linear dipole pol (e.g. 'XX') are implemented. - Default: 'pseudo_I' + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although + currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + Default: 'I' Returns ------- @@ -260,7 +260,7 @@ def power_beam_int(self, stokes='pseudo_I'): """ return np.ones_like(self.beam_freqs) * 2. * np.pi * self.fwhm**2 / (8. * np.log(2.)) - def power_beam_sq_int(self, stokes='pseudo_I'): + def power_beam_sq_int(self, stokes='I'): """ Computes the integral of the beam**2 over solid angle to give a beam area (in str). Uses analytic formula that the answer @@ -269,7 +269,7 @@ def power_beam_sq_int(self, stokes='pseudo_I'): Trivially this returns an array (i.e., a function of frequency), but the results are frequency independent. - Currently, only the "pseudo Stokes I", "XX" and "YY" beam are supported. + Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. @@ -277,9 +277,9 @@ def power_beam_sq_int(self, stokes='pseudo_I'): ---------- stokes: str, optional Which Stokes parameter's to compute the beam scalar for. - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', 'XX', 'YY', 'XY', 'YX', although - currently only 'pseudo_I' and linear dipole pol (e.g. 'XX') are implemented. - Default: 'pseudo_I' + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although + currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + Default: 'I' Returns ------- @@ -310,13 +310,13 @@ def __init__(self, beam_fname, cosmo=None): else: self.cosmo = conversions.Cosmo_Conversions() - def power_beam_int(self, stokes='pseudo_I'): + def power_beam_int(self, stokes='I'): """ Computes the integral of the beam over solid angle to give a beam area (in str) as a function of frequency. Uses function in pyuvdata. - Currently, only the "pseudo Stokes I", "XX" and "YY" beam are supported. + Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. @@ -324,9 +324,9 @@ def power_beam_int(self, stokes='pseudo_I'): ---------- stokes: str, optional Which Stokes parameter's to compute the beam scalar for. - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', 'XX', 'YY', 'XY', 'YX', although - currently only 'pseudo_I' and linear dipole pol (e.g. 'XX') are implemented. - Default: 'pseudo_I' + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although + currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + Default: 'I' Returns ------- @@ -337,13 +337,13 @@ def power_beam_int(self, stokes='pseudo_I'): else: raise NotImplementedError("Outdated version of pyuvdata.") - def power_beam_sq_int(self, stokes='pseudo_I'): + def power_beam_sq_int(self, stokes='I'): """ Computes the integral of the beam**2 over solid angle to give a beam**2 area (in str) as a function of frequency. Uses function in pyuvdata. - Currently, only the "pseudo Stokes I", "XX" and "YY" beam are supported. + Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. @@ -351,9 +351,9 @@ def power_beam_sq_int(self, stokes='pseudo_I'): ---------- stokes: str, optional Which Stokes parameter's to compute the beam scalar for. - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', 'XX', 'YY', 'XY', 'YX', although - currently only 'pseudo_I' and linear dipole pol (e.g. 'XX') are implemented. - Default: 'pseudo_I' + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although + currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + Default: 'I' Returns ------- diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 457ded83..4726b646 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -759,7 +759,7 @@ def delays(self): return utils.get_delays(self.freqs[self.spw_range[0]:self.spw_range[1]]) * 1e9 # convert to ns - def scalar(self, stokes='pseudo_I', taper='none', little_h=True, num_steps=2000, beam=None): + def scalar(self, stokes='I', taper='none', little_h=True, num_steps=2000, beam=None): """ Computes the scalar function to convert a power spectrum estimate in "telescope units" to cosmological units, using self.spw_range to set spectral window. diff --git a/hera_pspec/tests/test_noise.py b/hera_pspec/tests/test_noise.py index 0250f533..de5018d5 100644 --- a/hera_pspec/tests/test_noise.py +++ b/hera_pspec/tests/test_noise.py @@ -41,14 +41,14 @@ def test_add(self): def test_scalar(self): freqs = np.linspace(150e6, 160e6, 100, endpoint=False) - self.sense.calc_scalar(freqs, 'pseudo_I', num_steps=5000, little_h=True) + self.sense.calc_scalar(freqs, 'I', num_steps=5000, little_h=True) nt.assert_true(np.isclose(freqs, self.sense.subband).all()) nt.assert_true(self.sense.stokes, 'I') def test_calc_P_N(self): # calculate scalar freqs = np.linspace(150e6, 160e6, 100, endpoint=False) - self.sense.calc_scalar(freqs, 'pseudo_I', num_steps=5000, little_h=True) + self.sense.calc_scalar(freqs, 'I', num_steps=5000, little_h=True) # basic execution k = np.linspace(0, 3, 10) Tsys = 500.0 diff --git a/hera_pspec/tests/test_pspecbeam.py b/hera_pspec/tests/test_pspecbeam.py index c3fe4c73..ac9b9831 100644 --- a/hera_pspec/tests/test_pspecbeam.py +++ b/hera_pspec/tests/test_pspecbeam.py @@ -44,7 +44,7 @@ def test_UVbeam(self): lower_freq = 120.*10**6 upper_freq = 128.*10**6 num_freqs = 20 - scalar = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='pseudo_I', num_steps=2000) + scalar = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='I', num_steps=2000) # Check that user-defined cosmology can be specified bm = pspecbeam.PSpecBeamUV(self.beamfile, @@ -72,7 +72,7 @@ def test_UVbeam(self): self.assertAlmostEqual(scalar/567871703.75268996, 1.0, delta=1e-4) # convergence of integral - scalar_large_Nsteps = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='pseudo_I', num_steps=10000) + scalar_large_Nsteps = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='I', num_steps=10000) self.assertAlmostEqual(scalar / scalar_large_Nsteps, 1.0, delta=1e-5) # test taper execution @@ -93,7 +93,7 @@ def test_UVbeam(self): nt.assert_raises(TypeError, self.bm.Jy_to_mK, np.array([1])) # test noise scalar - sclr = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='pseudo_I', num_steps=2000, noise_scalar=True) + sclr = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='I', num_steps=2000, noise_scalar=True) nt.assert_almost_equal(sclr, 70.983962969086235) @@ -103,7 +103,7 @@ def test_Gaussbeam(self): lower_freq = 120.*10**6 upper_freq = 128.*10**6 num_freqs = 20 - scalar = self.gauss.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='pseudo_I', num_steps=2000) + scalar = self.gauss.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='I', num_steps=2000) # Check that user-defined cosmology can be specified bgauss = pspecbeam.PSpecBeamGauss(0.8, diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index e030732f..ad4126bf 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -962,9 +962,8 @@ def generate_noise_spectra(self, spw, pol, Tsys, blpairs=None, # get frequency band freqs = self.freq_array[self.spw_to_indices(spw)] - # calculate scalar, hard-coded to pseudo_I because that is currently all that pyuvdata supports - # near-future pyuvdata PR will extend support for extra polarizations - self.sensitivity.calc_scalar(freqs, 'pseudo_I', num_steps=num_steps, little_h=little_h) + # calculate scalar + self.sensitivity.calc_scalar(freqs, pol, num_steps=num_steps, little_h=little_h) # Get k vectors if form == 'DelSq': From c069177243a481a70f8d14186af3f0520bbe105d Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Fri, 27 Apr 2018 16:31:00 -0700 Subject: [PATCH 14/54] changed all references of "stokes" to "pol" across repo --- hera_pspec/noise.py | 12 +++--- hera_pspec/pspecbeam.py | 64 +++++++++++++----------------- hera_pspec/pspecdata.py | 14 +++---- hera_pspec/tests/test_noise.py | 2 +- hera_pspec/tests/test_pspecbeam.py | 16 ++++---- 5 files changed, 48 insertions(+), 60 deletions(-) diff --git a/hera_pspec/noise.py b/hera_pspec/noise.py index d29495a3..e8001d69 100644 --- a/hera_pspec/noise.py +++ b/hera_pspec/noise.py @@ -67,7 +67,7 @@ def add_beam(self, beam): self.beam = beam - def calc_scalar(self, freqs, stokes, num_steps=5000, little_h=True): + def calc_scalar(self, freqs, pol, num_steps=5000, little_h=True): """ Calculate noise power spectrum prefactor from Eqn. (1) of Pober et al. 2014, ApJ 782, 66, equal to @@ -78,7 +78,7 @@ def calc_scalar(self, freqs, stokes, num_steps=5000, little_h=True): ---------- freqs : float ndarray, holds frequency bins of spectral window in Hz - stokes : str, specification of (pseudo) Stokes polarization, or linear dipole polarization + pol : str, specification of polarization to calculate scalar for See pyuvdata.utils.polstr2num for options. num_steps : number of frequency bins to use in numerical integration of scalar @@ -93,13 +93,13 @@ def calc_scalar(self, freqs, stokes, num_steps=5000, little_h=True): self.subband : float ndarray, frequencies in spectral window used to calculate self.scalar - self.stokes : str, stokes polarization used to calculate self.scalar + self.pol : str, polarization used to calculate self.scalar """ - # parse stokes + # compute scalar self.scalar = self.beam.compute_pspec_scalar(freqs.min(), freqs.max(), len(freqs), num_steps=num_steps, - stokes=stokes, little_h=little_h, noise_scalar=True) + pol=pol, little_h=little_h, noise_scalar=True) self.subband = freqs - self.stokes = stokes + self.pol = pol def calc_P_N(self, Tsys, t_int, Ncoherent=1, Nincoherent=None, form='Pk', k=None, little_h=True): """ diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index b6466458..9b33ccd5 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -101,7 +101,7 @@ def __init__(self, cosmo=None): else: self.cosmo = conversions.Cosmo_Conversions() - def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000, stokes='I', + def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000, pol='I', taper='none', little_h=True, noise_scalar=False): """ Computes the scalar function to convert a power spectrum estimate @@ -109,7 +109,7 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 See arxiv:1304.4991 and HERA memo #27 for details. - Currently, only the Stokes "I", "XX" and "YY" beam are supported. + Currently, only the "I", "XX" and "YY" polarization beams are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. @@ -131,10 +131,9 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 numerical integral Default: 10000 - stokes: str, optional - Which Stokes parameter's to compute the beam scalar for. + pol: str, optional + Which polarization to compute the beam scalar for. 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although - currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. Default: 'I' taper : str, optional @@ -162,7 +161,7 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 pspec_freqs = np.linspace(lower_freq, upper_freq, num_freqs, endpoint=False) # Get omega_ratio - omega_ratio = self.power_beam_sq_int(stokes) / self.power_beam_int(stokes)**2 + omega_ratio = self.power_beam_sq_int(pol) / self.power_beam_int(pol)**2 # Get scalar scalar = _compute_pspec_scalar(self.cosmo, self.beam_freqs, omega_ratio, pspec_freqs, @@ -171,7 +170,7 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 return scalar - def Jy_to_mK(self, freqs, stokes='I'): + def Jy_to_mK(self, freqs, pol='I'): """ Return the multiplicative factor, M [mK / Jy], to convert a visibility from Jy -> mK, @@ -186,10 +185,9 @@ def Jy_to_mK(self, freqs, stokes='I'): ---------- freqs : float ndarray, contains frequencies to evaluate conversion factor [Hz] - stokes: str, optional - Which Stokes parameter's to compute the beam scalar for. + pol: str, optional + Which polarization to compute the beam scalar for. 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although - currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. Default: 'I' Returns @@ -207,7 +205,7 @@ def Jy_to_mK(self, freqs, stokes='I'): if np.max(freqs) > self.beam_freqs.max(): raise ValueError("Warning: max freq {} > self.beam_freqs.max(), extrapolating...".format(np.max(freqs))) - Op = interp1d(self.beam_freqs/1e6, self.power_beam_int(stokes=stokes), kind='quadratic', fill_value='extrapolate')(freqs/1e6) + Op = interp1d(self.beam_freqs/1e6, self.power_beam_int(pol=pol), kind='quadratic', fill_value='extrapolate')(freqs/1e6) return 1e-20 * conversions.cgs_units.c**2 / (2 * conversions.cgs_units.kb * freqs**2 * Op) @@ -233,7 +231,7 @@ def __init__(self, fwhm, beam_freqs, cosmo=None): else: self.cosmo = conversions.Cosmo_Conversions() - def power_beam_int(self, stokes='I'): + def power_beam_int(self, pol='I'): """ Computes the integral of the beam over solid angle to give a beam area (in sr). Uses analytic formula that the answer @@ -242,16 +240,14 @@ def power_beam_int(self, stokes='I'): Trivially this returns an array (i.e., a function of frequency), but the results are frequency independent. - Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. Parameters ---------- - stokes: str, optional - Which Stokes parameter's to compute the beam scalar for. - 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although - currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + pol: str, optional + Which polarization to compute the beam scalar for. + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX' Default: 'I' Returns @@ -260,7 +256,7 @@ def power_beam_int(self, stokes='I'): """ return np.ones_like(self.beam_freqs) * 2. * np.pi * self.fwhm**2 / (8. * np.log(2.)) - def power_beam_sq_int(self, stokes='I'): + def power_beam_sq_int(self, pol='I'): """ Computes the integral of the beam**2 over solid angle to give a beam area (in str). Uses analytic formula that the answer @@ -269,16 +265,14 @@ def power_beam_sq_int(self, stokes='I'): Trivially this returns an array (i.e., a function of frequency), but the results are frequency independent. - Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. Parameters ---------- - stokes: str, optional - Which Stokes parameter's to compute the beam scalar for. - 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although - currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + pol: str, optional + Which polarization to compute the beam scalar for. + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX' Default: 'I' Returns @@ -310,22 +304,20 @@ def __init__(self, beam_fname, cosmo=None): else: self.cosmo = conversions.Cosmo_Conversions() - def power_beam_int(self, stokes='I'): + def power_beam_int(self, pol='I'): """ Computes the integral of the beam over solid angle to give a beam area (in str) as a function of frequency. Uses function in pyuvdata. - Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. Parameters ---------- - stokes: str, optional - Which Stokes parameter's to compute the beam scalar for. - 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although - currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + pol: str, optional + Which polarization to compute the beam scalar for. + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX' Default: 'I' Returns @@ -333,26 +325,24 @@ def power_beam_int(self, stokes='I'): primary beam area: float, array-like """ if hasattr(self.primary_beam, 'get_beam_area'): - return self.primary_beam.get_beam_area(stokes) + return self.primary_beam.get_beam_area(pol) else: raise NotImplementedError("Outdated version of pyuvdata.") - def power_beam_sq_int(self, stokes='I'): + def power_beam_sq_int(self, pol='I'): """ Computes the integral of the beam**2 over solid angle to give a beam**2 area (in str) as a function of frequency. Uses function in pyuvdata. - Currently, only the Stokes "I", "XX" and "YY" beam are supported. See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154 or arxiv:1502.05072 for details. Parameters ---------- - stokes: str, optional - Which Stokes parameter's to compute the beam scalar for. - 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX', although - currently only 'I' and linear dipole pol (e.g. 'XX') are implemented. + pol: str, optional + Which polarization to compute the beam scalar for. + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX' Default: 'I' Returns @@ -360,6 +350,6 @@ def power_beam_sq_int(self, stokes='I'): primary beam area: float, array-like """ if hasattr(self.primary_beam, 'get_beam_area'): - return self.primary_beam.get_beam_sq_area(stokes) + return self.primary_beam.get_beam_sq_area(pol) else: raise NotImplementedError("Outdated version of pyuvdata.") diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 4726b646..17f03277 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -759,20 +759,18 @@ def delays(self): return utils.get_delays(self.freqs[self.spw_range[0]:self.spw_range[1]]) * 1e9 # convert to ns - def scalar(self, stokes='I', taper='none', little_h=True, num_steps=2000, beam=None): + def scalar(self, pol='I', taper='none', little_h=True, num_steps=2000, beam=None): """ Computes the scalar function to convert a power spectrum estimate in "telescope units" to cosmological units, using self.spw_range to set spectral window. See arxiv:1304.4991 and HERA memo #27 for details. - Currently this is only for Stokes I. - Parameters ---------- - stokes: str, optional - Which Stokes parameter's beam to compute the scalar for. - 'I', 'Q', 'U', 'V', although currently only 'I' is implemented + pol: str, optional + Which polarization to compute the scalar for. + e.g. 'I', 'Q', 'U', 'V', 'XX', 'YY'... Default: 'I' taper : str, optional @@ -806,12 +804,12 @@ def scalar(self, stokes='I', taper='none', little_h=True, num_steps=2000, beam=N # calculate scalar if beam is None: scalar = self.primary_beam.compute_pspec_scalar( - start, end, len(freqs), stokes=stokes, + start, end, len(freqs), pol=pol, taper=taper, little_h=little_h, num_steps=num_steps) else: scalar = beam.compute_pspec_scalar(start, end, len(freqs), - stokes=stokes, taper=taper, + pol=pol, taper=taper, little_h=little_h, num_steps=num_steps) return scalar diff --git a/hera_pspec/tests/test_noise.py b/hera_pspec/tests/test_noise.py index de5018d5..5e6c996e 100644 --- a/hera_pspec/tests/test_noise.py +++ b/hera_pspec/tests/test_noise.py @@ -43,7 +43,7 @@ def test_scalar(self): freqs = np.linspace(150e6, 160e6, 100, endpoint=False) self.sense.calc_scalar(freqs, 'I', num_steps=5000, little_h=True) nt.assert_true(np.isclose(freqs, self.sense.subband).all()) - nt.assert_true(self.sense.stokes, 'I') + nt.assert_true(self.sense.pol, 'I') def test_calc_P_N(self): # calculate scalar diff --git a/hera_pspec/tests/test_pspecbeam.py b/hera_pspec/tests/test_pspecbeam.py index ac9b9831..6f9cc693 100644 --- a/hera_pspec/tests/test_pspecbeam.py +++ b/hera_pspec/tests/test_pspecbeam.py @@ -44,7 +44,7 @@ def test_UVbeam(self): lower_freq = 120.*10**6 upper_freq = 128.*10**6 num_freqs = 20 - scalar = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='I', num_steps=2000) + scalar = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, pol='I', num_steps=2000) # Check that user-defined cosmology can be specified bm = pspecbeam.PSpecBeamUV(self.beamfile, @@ -55,11 +55,11 @@ def test_UVbeam(self): self.assertEqual(Om_pp.ndim, 1) # Check that errors are raised for other Stokes parameters - for stokes in ['Q', 'U', 'V', 'Z']: - nt.assert_raises(NotImplementedError, self.bm.power_beam_int, stokes=stokes) - nt.assert_raises(NotImplementedError, self.bm.power_beam_sq_int, stokes=stokes) + for pol in ['Q', 'U', 'V', 'Z']: + nt.assert_raises(NotImplementedError, self.bm.power_beam_int, pol=pol) + nt.assert_raises(NotImplementedError, self.bm.power_beam_sq_int, pol=pol) nt.assert_raises(NotImplementedError, self.bm.compute_pspec_scalar, - lower_freq, upper_freq, num_freqs, stokes=stokes) + lower_freq, upper_freq, num_freqs, pol=pol) self.assertAlmostEqual(Om_p[0], 0.078694909518866998) self.assertAlmostEqual(Om_p[18], 0.065472512282419112) @@ -72,7 +72,7 @@ def test_UVbeam(self): self.assertAlmostEqual(scalar/567871703.75268996, 1.0, delta=1e-4) # convergence of integral - scalar_large_Nsteps = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='I', num_steps=10000) + scalar_large_Nsteps = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, pol='I', num_steps=10000) self.assertAlmostEqual(scalar / scalar_large_Nsteps, 1.0, delta=1e-5) # test taper execution @@ -93,7 +93,7 @@ def test_UVbeam(self): nt.assert_raises(TypeError, self.bm.Jy_to_mK, np.array([1])) # test noise scalar - sclr = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='I', num_steps=2000, noise_scalar=True) + sclr = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, pol='I', num_steps=2000, noise_scalar=True) nt.assert_almost_equal(sclr, 70.983962969086235) @@ -103,7 +103,7 @@ def test_Gaussbeam(self): lower_freq = 120.*10**6 upper_freq = 128.*10**6 num_freqs = 20 - scalar = self.gauss.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, stokes='I', num_steps=2000) + scalar = self.gauss.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, pol='I', num_steps=2000) # Check that user-defined cosmology can be specified bgauss = pspecbeam.PSpecBeamGauss(0.8, From 372c984a57d210729a7b6c088606bdfb970116f7 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Fri, 27 Apr 2018 13:04:02 -0700 Subject: [PATCH 15/54] Add install_requires lines to setup.py --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 3c910e9e..2bded77f 100644 --- a/setup.py +++ b/setup.py @@ -30,6 +30,7 @@ def package_files(package_dir, subdirectory): 'packages': ['hera_pspec'], 'package_dir': {'hera_pspec': 'hera_pspec'}, 'package_data': {'hera_pspec': data_files}, + 'install_requires': ['numpy>=1.10', 'scipy>=0.19',], 'include_package_data': True, 'zip_safe': False, } From 5073016db7f4d09aa8b8d2b89f48e1cf546f44f3 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Thu, 26 Apr 2018 17:03:10 -0700 Subject: [PATCH 16/54] * Add new PSpecBeamFromArray object that takes custom beam solid angle arrays. * Add tests for PSpecBeamFromArray. * Tidy-up docstrings in pspecbeam --- hera_pspec/pspecbeam.py | 412 +++++++++++++++++++++++------ hera_pspec/tests/test_pspecbeam.py | 73 +++++ 2 files changed, 399 insertions(+), 86 deletions(-) diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index 9b33ccd5..6af266c7 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -8,8 +8,9 @@ from scipy.interpolate import interp1d -def _compute_pspec_scalar(cosmo, beam_freqs, omega_ratio, pspec_freqs, num_steps=5000, - taper='none', little_h=True, noise_scalar=False): +def _compute_pspec_scalar(cosmo, beam_freqs, omega_ratio, pspec_freqs, + num_steps=5000, taper='none', little_h=True, + noise_scalar=False): """ This is not to be used by the novice user to calculate a pspec scalar. Instead, look at the PSpecBeamUV and PSpecBeamGauss classes. @@ -21,88 +22,105 @@ def _compute_pspec_scalar(cosmo, beam_freqs, omega_ratio, pspec_freqs, num_steps Parameters ---------- - cosmo : hera_pspec.conversions.Cosmo_Conversions instance - Instance of the cosmological conversion object - conversions.Cosmo_Conversions() + cosmo : conversions.Cosmo_Conversions instance + Instance of the cosmological conversion object. beam_freqs : array of floats - Frequency of beam integrals in omega_ratio in units of Hz. + Frequency of beam integrals in omega_ratio in units of Hz. omega_ratio : array of floats - Ratio of the integrated squared-beam power over the square of the integrated beam power - for each frequency in beam_freqs. i.e. Omega_pp(nu) / Omega_p(nu)^2 + Ratio of the integrated squared-beam power over the square of the + integrated beam power for each frequency in beam_freqs. + i.e. Omega_pp(nu) / Omega_p(nu)^2 pspec_freqs : array of floats - Array of frequencies over which power spectrum is estimated in Hz. + Array of frequencies over which power spectrum is estimated in Hz. num_steps : int, optional - Number of steps to use when interpolating primary beams for - numerical integral + Number of steps to use when interpolating primary beams for numerical + integral. Default: 5000. taper : str, optional - Whether a tapering function (e.g. Blackman-Harris) is being - used in the power spectrum estimation. - Default: none + Whether a tapering function (e.g. Blackman-Harris) is being used in the + power spectrum estimation. Default: 'none'. little_h : boolean, optional - Whether to have cosmological length units be h^-1 Mpc or Mpc - Value of h is obtained from cosmo object stored in pspecbeam - Default: h^-1 Mpc + Whether to have cosmological length units be h^-1 Mpc or Mpc. Value of + h is obtained from cosmo object stored in pspecbeam. Default: h^-1 Mpc. noise_scalar : boolean, optional - Whether to calculate power spectrum scalar, or noise power scalar. The noise power - scalar only differs in that the Bpp_over_BpSq term turns into 1_over_Bp. - See Pober et al. 2014, ApJ 782, 66, and Parsons HERA Memo #27 + Whether to calculate power spectrum scalar, or noise power scalar. The + noise power scalar only differs in that the Bpp_over_BpSq term turns + into 1_over_Bp. See Pober et al. 2014, ApJ 782, 66, and Parsons HERA + Memo #27. Default: False. Returns ------- scalar: float - [\int dnu (\Omega_PP / \Omega_P^2) ( B_PP / B_P^2 ) / (X^2 Y)]^-1 - in h^-3 Mpc^3 or Mpc^3. + [\int dnu (\Omega_PP / \Omega_P^2) ( B_PP / B_P^2 ) / (X^2 Y)]^-1 + Units: h^-3 Mpc^3 or Mpc^3. """ - # get integration freqs, redshift and cosmological scalars + # Get integration freqs df = np.median(np.diff(pspec_freqs)) - integration_freqs = np.linspace(pspec_freqs.min(), pspec_freqs.min()+df*len(pspec_freqs), num_steps, endpoint=True, dtype=np.float) - integration_freqs_MHz = integration_freqs / 1e6 # The interpolations are generally more stable in MHz + integration_freqs = np.linspace(pspec_freqs.min(), + pspec_freqs.min() + df*len(pspec_freqs), + num_steps, endpoint=True, dtype=np.float) + + # The interpolations are generally more stable in MHz + integration_freqs_MHz = integration_freqs / 1e6 + + # Get redshifts and cosmological functions redshifts = cosmo.f2z(integration_freqs).flatten() X2Y = np.array(map(lambda z: cosmo.X2Y(z, little_h=little_h), redshifts)) - # Use linear interpolation to interpolate the frequency-dependent quantities - # derived from the beam model to the same frequency grid as the power spectrum - # estimation + # Use linear interpolation to interpolate the frequency-dependent + # quantities derived from the beam model to the same frequency grid as the + # power spectrum estimation beam_model_freqs_MHz = beam_freqs / 1e6 - dOpp_over_Op2_fit = interp1d(beam_model_freqs_MHz, omega_ratio, kind='quadratic', fill_value='extrapolate') + dOpp_over_Op2_fit = interp1d(beam_model_freqs_MHz, omega_ratio, + kind='quadratic', fill_value='extrapolate') dOpp_over_Op2 = dOpp_over_Op2_fit(integration_freqs_MHz) # Get B_pp = \int dnu taper^2 and Bp = \int dnu if taper == 'none': dBpp_over_BpSq = np.ones_like(integration_freqs, np.float) else: - dBpp_over_BpSq = aipy.dsp.gen_window(len(pspec_freqs), taper)**2 - dBpp_over_BpSq = interp1d(pspec_freqs, dBpp_over_BpSq, kind='nearest', fill_value='extrapolate')(integration_freqs) - dBpp_over_BpSq /= (integration_freqs[-1] - integration_freqs[0])**2 + dBpp_over_BpSq = aipy.dsp.gen_window(len(pspec_freqs), taper)**2. + dBpp_over_BpSq = interp1d(pspec_freqs, dBpp_over_BpSq, kind='nearest', + fill_value='extrapolate')(integration_freqs) + dBpp_over_BpSq /= (integration_freqs[-1] - integration_freqs[0])**2. # Keep dBpp_over_BpSq term or not if noise_scalar: - dBpp_over_BpSq = 1/(integration_freqs[-1] - integration_freqs[0]) + dBpp_over_BpSq = 1. / (integration_freqs[-1] - integration_freqs[0]) - # integrate to get scalar + # Integrate to get scalar d_inv_scalar = dBpp_over_BpSq * dOpp_over_Op2 / X2Y - scalar = 1.0 / integrate.trapz(d_inv_scalar, x=integration_freqs) - + scalar = 1. / integrate.trapz(d_inv_scalar, x=integration_freqs) return scalar class PSpecBeamBase(object): def __init__(self, cosmo=None): + """ + Base class for PSpecBeam objects. Provides compute_pspec_scalar() + method to integrate over and interpolate beam solid angles, and + Jy_to_mK() method to convert units. + + Parameters + ---------- + cosmo : conversions.Cosmo_Conversions object, optional + Cosmology object. Uses the default cosmology object if not + specified. Default: None. + """ if cosmo is not None: self.cosmo = cosmo else: self.cosmo = conversions.Cosmo_Conversions() - def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000, pol='I', - taper='none', little_h=True, noise_scalar=False): + def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000, + pol='I', taper='none', little_h=True, noise_scalar=False): """ Computes the scalar function to convert a power spectrum estimate in "telescope units" to cosmological units @@ -115,21 +133,20 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 Parameters ---------- - lower_freq: float - Bottom edge of frequency band over which power spectrum is - being estimated. Assumed to be in Hz. + lower_freq : float + Bottom edge of frequency band over which power spectrum is being + estimated. Assumed to be in Hz. - upper_freq: float - Top edge of frequency band over which power spectrum is - being estimated. Assumed to be in Hz. + upper_freq : float + Top edge of frequency band over which power spectrum is being + estimated. Assumed to be in Hz. num_freqs : int, optional - Number of frequencies used in estimating power spectrum. + Number of frequencies used in estimating power spectrum. num_steps : int, optional - Number of steps to use when interpolating primary beams for - numerical integral - Default: 10000 + Number of steps to use when interpolating primary beams for + numerical integral. Default: 10000. pol: str, optional Which polarization to compute the beam scalar for. @@ -137,53 +154,57 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 Default: 'I' taper : str, optional - Whether a tapering function (e.g. Blackman-Harris) is being - used in the power spectrum estimation. - Default: none + Whether a tapering function (e.g. Blackman-Harris) is being used in + the power spectrum estimation. Default: none. little_h : boolean, optional - Whether to have cosmological length units be h^-1 Mpc or Mpc - Value of h is obtained from cosmo object stored in pspecbeam - Default: h^-1 Mpc + Whether to have cosmological length units be h^-1 Mpc or Mpc. Value + of h is obtained from cosmo object stored in pspecbeam. + Default: h^-1 Mpc noise_scalar : boolean, optional - Whether to calculate power spectrum scalar, or noise power scalar. The noise power - scalar only differs in that the Bpp_over_BpSq term just because 1_over_Bp. - See Pober et al. 2014, ApJ 782, 66 + Whether to calculate power spectrum scalar, or noise power scalar. + The noise power scalar only differs in that the Bpp_over_BpSq term + just because 1_over_Bp. See Pober et al. 2014, ApJ 782, 66. Returns ------- scalar: float - [\int dnu (\Omega_PP / \Omega_P^2) ( B_PP / B_P^2 ) / (X^2 Y)]^-1 - in h^-3 Mpc^3 or Mpc^3. + [\int dnu (\Omega_PP / \Omega_P^2) ( B_PP / B_P^2 ) / (X^2 Y)]^-1 + Units: h^-3 Mpc^3 or Mpc^3. """ - # get pspec_Freqs - pspec_freqs = np.linspace(lower_freq, upper_freq, num_freqs, endpoint=False) + # Get pspec_freqs + pspec_freqs = np.linspace(lower_freq, upper_freq, num_freqs, + endpoint=False) # Get omega_ratio - omega_ratio = self.power_beam_sq_int(pol) / self.power_beam_int(pol)**2 + omega_ratio = self.power_beam_sq_int(pol) \ + / self.power_beam_int(pol)**2 # Get scalar - scalar = _compute_pspec_scalar(self.cosmo, self.beam_freqs, omega_ratio, pspec_freqs, - num_steps=num_steps, taper=taper, little_h=little_h, + scalar = _compute_pspec_scalar(self.cosmo, self.beam_freqs, + omega_ratio, pspec_freqs, + num_steps=num_steps, taper=taper, + little_h=little_h, noise_scalar=noise_scalar) - return scalar def Jy_to_mK(self, freqs, pol='I'): """ - Return the multiplicative factor, M [mK / Jy], to convert a visibility from Jy -> mK, + Return the multiplicative factor [mK / Jy], to convert a visibility + from Jy -> mK, - M = 1e3 * 1e-23 * c^2 / [2 * k_b * nu^2 * Omega_p(nu)] + factor = 1e3 * 1e-23 * c^2 / [2 * k_b * nu^2 * Omega_p(nu)] where k_b is boltzmann constant, c is speed of light, nu is frequency and Omega_p is the integral of the unitless beam-response (steradians), - and the 1e3 is the conversion from K -> mK and the 1e-23 is the conversion - from Jy to cgs. + and the 1e3 is the conversion from K -> mK and the 1e-23 is the + conversion from Jy to cgs. Parameters ---------- - freqs : float ndarray, contains frequencies to evaluate conversion factor [Hz] + freqs : float ndarray + Contains frequencies to evaluate conversion factor [Hz]. pol: str, optional Which polarization to compute the beam scalar for. @@ -192,37 +213,50 @@ def Jy_to_mK(self, freqs, pol='I'): Returns ------- - M : float ndarray, contains Jy -> mK factor at each frequency + factor : float ndarray + Contains Jy -> mK factor at each frequency. """ + # Check input types if isinstance(freqs, (np.float, float)): freqs = np.array([freqs]) elif not isinstance(freqs, np.ndarray): raise TypeError("freqs must be fed as a float ndarray") - elif isinstance(freqs, np.ndarray) and freqs.dtype not in (float, np.float, np.float64): + elif isinstance(freqs, np.ndarray) \ + and freqs.dtype not in (float, np.float, np.float64): raise TypeError("freqs must be fed as a float ndarray") + + # Check frequency bounds if np.min(freqs) < self.beam_freqs.min(): raise ValueError("Warning: min freq {} < self.beam_freqs.min(), extrapolating...".format(np.min(freqs))) if np.max(freqs) > self.beam_freqs.max(): raise ValueError("Warning: max freq {} > self.beam_freqs.max(), extrapolating...".format(np.max(freqs))) + + Op = interp1d(self.beam_freqs/1e6, self.power_beam_int(pol=pol), + kind='quadratic', fill_value='extrapolate')(freqs/1e6) - Op = interp1d(self.beam_freqs/1e6, self.power_beam_int(pol=pol), kind='quadratic', fill_value='extrapolate')(freqs/1e6) - - return 1e-20 * conversions.cgs_units.c**2 / (2 * conversions.cgs_units.kb * freqs**2 * Op) + return 1e-20 * conversions.cgs_units.c**2 \ + / (2 * conversions.cgs_units.kb * freqs**2 * Op) class PSpecBeamGauss(PSpecBeamBase): def __init__(self, fwhm, beam_freqs, cosmo=None): """ - Object to store a toy (frequency independent) Gaussian beam in a PspecBeamBase object + Object to store a simple (frequency independent) Gaussian beam in a + PspecBeamBase object. Parameters ---------- - fwhm: float, in radians - Full width half max of the beam + fwhm: float + Full width half max of the beam, in radians. beam_freqs: float, array-like - Frequencies over which this Gaussian beam is to be created. Units assumed to be Hz. + Frequencies over which this Gaussian beam is to be created. Units + assumed to be Hz. + + cosmo : conversions.Cosmo_Conversions object, optional + Cosmology object. Uses the default cosmology object if not + specified. Default: None. """ self.fwhm = fwhm self.beam_freqs = beam_freqs @@ -252,9 +286,11 @@ def power_beam_int(self, pol='I'): Returns ------- - primary beam area: float, array-like + primary_beam_area: float, array-like + Primary beam area. """ - return np.ones_like(self.beam_freqs) * 2. * np.pi * self.fwhm**2 / (8. * np.log(2.)) + return np.ones_like(self.beam_freqs) * 2. * np.pi * self.fwhm**2 \ + / (8. * np.log(2.)) def power_beam_sq_int(self, pol='I'): """ @@ -277,9 +313,11 @@ def power_beam_sq_int(self, pol='I'): Returns ------- - primary beam area: float, array-like + primary_beam_area: float, array-like + Primary beam area. """ - return np.ones_like(self.beam_freqs) * np.pi * self.fwhm**2 / (8. * np.log(2.)) + return np.ones_like(self.beam_freqs) * np.pi * self.fwhm**2 \ + / (8. * np.log(2.)) class PSpecBeamUV(PSpecBeamBase): @@ -293,7 +331,11 @@ def __init__(self, beam_fname, cosmo=None): Parameters ---------- beam_fname: str - Path to a pyuvdata UVBeam file + Path to a pyuvdata UVBeam file. + + cosmo : conversions.Cosmo_Conversions object, optional + Cosmology object. Uses the default cosmology object if not + specified. Default: None. """ self.primary_beam = pyuvdata.UVBeam() self.primary_beam.read_beamfits(beam_fname) @@ -322,7 +364,8 @@ def power_beam_int(self, pol='I'): Returns ------- - primary beam area: float, array-like + primary_beam_area: float, array-like + Scalar integral over beam solid angle. """ if hasattr(self.primary_beam, 'get_beam_area'): return self.primary_beam.get_beam_area(pol) @@ -347,9 +390,206 @@ def power_beam_sq_int(self, pol='I'): Returns ------- - primary beam area: float, array-like + primary_beam_area: float, array-like """ if hasattr(self.primary_beam, 'get_beam_area'): return self.primary_beam.get_beam_sq_area(pol) else: raise NotImplementedError("Outdated version of pyuvdata.") + + +class PSpecBeamFromArray(PSpecBeamBase): + + def __init__(self, OmegaP, OmegaPP, beam_freqs, cosmo=None): + """ + Primary beam model built from user-defined arrays for the integrals + over beam solid angle and beam solid angle squared. + + Allowed polarizations are: + + pseudo_I, pseudo_Q, pseudo_U, pseudo_V, XX, YY, XY, YX + + Other polarizations will be ignored. + + Parameters + ---------- + OmegaP : array_like of float (or dict of array_like) + Integral over beam solid angle, as a function of frequency. + + If only one array is specified, this will be assumed to be for the + pseudo_I polarization. If a dict is specified, an OmegaP array for + several polarizations can be specified. + + OmegaPP : array_like of float (or dict of array_like) + Integral over beam solid angle squared, as a function of frequency. + + If only one array is specified, this will be assumed to be for the + pseudo_I polarization. If a dict is specified, an OmegaP array for + several polarizations can be specified. + + beam_freqs : array_like of float + Frequencies at which beam solid angles OmegaP and OmegaPP are + evaluated, in Hz. This should be specified as a single array, not + as a dict. + + cosmo : conversions.Cosmo_Conversions object, optional + Cosmology object. Uses the default cosmology object if not + specified. Default: None. + """ + self.allowed_pols = ['pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', + 'XX', 'YY', 'XY', 'YX'] + self.OmegaP = {}; self.OmegaPP = {} + + # Set beam_freqs + self.beam_freqs = np.array(beam_freqs) + + if isinstance(OmegaP, np.ndarray) and isinstance(OmegaPP, np.ndarray): + # Only single arrays were specified; assume pseudo_I + OmegaP = {'pseudo_I': OmegaP} + OmegaPP = {'pseudo_I': OmegaPP} + + elif isinstance(OmegaP, np.ndarray) or isinstance(OmegaPP, np.ndarray): + # Mixed dict and array types are not allowed + raise TypeError("OmegaP and OmegaPP must both be either dicts " + "or arrays. Mixing dicts and arrays is not " + "allowed.") + else: + pass + + # Should now have two dicts if everything is OK + if not isinstance(OmegaP, dict) or not isinstance(OmegaPP, dict): + raise TypeError("OmegaP and OmegaPP must both be either dicts or " + "arrays.") + + # Check for disallowed polarizations + for key in OmegaP.keys(): + if key not in self.allowed_pols: + raise KeyError("Unrecognized polarization '%s' in OmegaP." % key) + for key in OmegaPP.keys(): + if key not in self.allowed_pols: + raise KeyError("Unrecognized polarization '%s' in OmegaPP." % key) + + # Check for available polarizations + for pol in self.allowed_pols: + if pol in OmegaP.keys() or pol in OmegaPP.keys(): + if pol not in OmegaP.keys() or pol not in OmegaPP.keys(): + raise KeyError("Polarization '%s' must be specified for" + " both OmegaP and OmegaPP." % pol) + + # Add arrays for this polarization + self.add_pol(pol, OmegaP[pol], OmegaPP[pol]) + + # Set cosmology + if cosmo is None: + self.cosmo = conversions.Cosmo_Conversions() + else: + self.cosmo = cosmo + + + def add_pol(self, pol, OmegaP, OmegaPP): + """ + Add OmegaP and OmegaPP for a new polarization. + + Parameters + ---------- + pol: str + Which polarization to add beam solid angle arrays for. Valid + options are: + + 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', + 'XX', 'YY', 'XY', 'YX' + + If the arrays already exist for the specified polarization, they + will be overwritten. + + OmegaP : array_like of float + Integral over beam solid angle, as a function of frequency. Must + have the same shape as self.beam_freqs. + + OmegaPP : array_like of float + Integral over beam solid angle squared, as a function of frequency. + Must have the same shape as self.beam_freqs. + """ + # Check for allowed polarization + if pol not in self.allowed_pols: + raise KeyError("Polarization '%s' is not valid." % pol) + + # Make sure OmegaP and OmegaPP are arrays + try: + OmegaP = np.array(OmegaP).astype(float) + OmegaPP = np.array(OmegaPP).astype(float) + except: + raise TypeError("OmegaP and OmegaPP must both be array_like.") + + # Check that array dimensions are consistent + if OmegaP.shape != self.beam_freqs.shape \ + or OmegaPP.shape != self.beam_freqs.shape: + print OmegaP.shape, OmegaPP.shape, self.beam_freqs.shape + raise ValueError("OmegaP and OmegaPP should both " + "have the same shape as beam_freqs.") + # Store arrays + self.OmegaP[pol] = OmegaP + self.OmegaPP[pol] = OmegaPP + + + def power_beam_int(self, stokes='pseudo_I'): + """ + Computes the integral of the beam over solid angle to give + a beam area (in str) as a function of frequency. + + Parameters + ---------- + stokes: str, optional + Which Stokes parameters to compute the beam scalar for. + 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', + 'XX', 'YY', 'XY', 'YX' + Default: pseudo_I. + + Returns + ------- + primary_beam_area: float, array-like + Scalar integral over beam solid angle. + """ + if stokes in self.OmegaP.keys(): + return self.OmegaP[stokes] + else: + available_pols = ", ".join(self.OmegaP.keys()) + raise KeyError("OmegaP not specified for polarization '%s'. " + "Available polarizations are: %s" \ + % (stokes, available_pols)) + + def power_beam_sq_int(self, stokes='pseudo_I'): + """ + Computes the integral of the beam**2 over solid angle to give + a beam**2 area (in str) as a function of frequency. + + Parameters + ---------- + stokes: str, optional + Which Stokes parameters to compute the beam scalar for. + 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', + 'XX', 'YY', 'XY', 'YX' + Default: pseudo_I. + + Returns + ------- + primary_beam_area: float, array-like + """ + if stokes in self.OmegaPP.keys(): + return self.OmegaPP[stokes] + else: + available_pols = ", ".join(self.OmegaPP.keys()) + raise KeyError("OmegaPP not specified for polarization '%s'. " + "Available polarizations are: %s" \ + % (stokes, available_pols)) + + def __str__(self): + """ + Print string with useful information. + """ + s = "PSpecBeamFromArray object\n" + s += "\tFrequency range: Min. %4.4e Hz, Max. %4.4e Hz\n" \ + % (np.min(self.beam_freqs), np.max(self.beam_freqs)) + s += "\tAvailable pols: %s" % (", ".join(self.OmegaP.keys())) + return s + diff --git a/hera_pspec/tests/test_pspecbeam.py b/hera_pspec/tests/test_pspecbeam.py index 6f9cc693..e6259b2f 100644 --- a/hera_pspec/tests/test_pspecbeam.py +++ b/hera_pspec/tests/test_pspecbeam.py @@ -130,6 +130,79 @@ def test_Gaussbeam(self): scalar = self.gauss.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, num_steps=5000, taper='blackman') self.assertAlmostEqual(scalar / 22123832163.072491, 1.0, delta=1e-8) + + def test_BeamFromArray(self): + """ + Test PSpecBeamFromArray + """ + # Get Gaussian beam to use as a reference + Om_P = self.gauss.power_beam_int() + Om_PP = self.gauss.power_beam_sq_int() + beam_freqs = self.gauss.beam_freqs + + # Array specs for tests + lower_freq = 120.*10**6 + upper_freq = 128.*10**6 + num_freqs = 20 + + # Check that PSpecBeamFromArray can be instantiated + psbeam = pspecbeam.PSpecBeamFromArray(OmegaP=Om_P, OmegaPP=Om_PP, + beam_freqs=beam_freqs) + + psbeampol = pspecbeam.PSpecBeamFromArray( + OmegaP={'pseudo_I': Om_P, 'pseudo_Q': Om_P}, + OmegaPP={'pseudo_I': Om_PP, 'pseudo_Q': Om_PP}, + beam_freqs=beam_freqs) + + # Check that user-defined cosmology can be specified + bm2 = pspecbeam.PSpecBeamFromArray(OmegaP=Om_P, OmegaPP=Om_PP, + beam_freqs=beam_freqs, + cosmo=conversions.Cosmo_Conversions()) + + # Compare scalar calculation with Gaussian case + scalar = psbeam.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, + stokes='pseudo_I', num_steps=2000) + g_scalar = self.gauss.compute_pspec_scalar(lower_freq, upper_freq, + num_freqs, stokes='pseudo_I', + num_steps=2000) + np.testing.assert_array_almost_equal(scalar, g_scalar) + + # Check that polarizations are recognized and invalid ones rejected + scalarp = psbeampol.compute_pspec_scalar(lower_freq, upper_freq, + num_freqs, stokes='pseudo_Q', + num_steps=2000) + + # Test taper execution (same as Gaussian case) + scalar = psbeam.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, + num_steps=5000, taper='blackman') + self.assertAlmostEqual(scalar / 22123832163.072491, 1.0, delta=1e-8) + + # Check that invalid init args raise errors + nt.assert_raises(TypeError, pspecbeam.PSpecBeamFromArray, OmegaP=Om_P, + OmegaPP={'pseudo_I': Om_PP}, beam_freqs=beam_freqs) + nt.assert_raises(KeyError, pspecbeam.PSpecBeamFromArray, + OmegaP={'pseudo_I': Om_P, 'pseudo_Q': Om_P}, + OmegaPP={'pseudo_I': Om_PP,}, + beam_freqs=beam_freqs) + + nt.assert_raises(KeyError, pspecbeam.PSpecBeamFromArray, + OmegaP={'pseudo_A': Om_P}, + OmegaPP={'pseudo_A': Om_PP,}, + beam_freqs=beam_freqs) + + nt.assert_raises(TypeError, pspecbeam.PSpecBeamFromArray, + OmegaP={'pseudo_I': Om_P,}, + OmegaPP={'pseudo_I': 'string',}, + beam_freqs=beam_freqs) + + # Check that invalid method args raise errors + nt.assert_raises(KeyError, psbeam.power_beam_int, stokes='blah') + nt.assert_raises(KeyError, psbeam.power_beam_sq_int, stokes='blah') + + # Check that string works + self.assert_(len(str(psbeam)) > 0) + + def test_PSpecBeamBase(self): """ Test that base class can be instantiated. From 7ce3016b75c605e3ce1c08e4f267f811fc047d2e Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Thu, 26 Apr 2018 17:06:34 -0700 Subject: [PATCH 17/54] Add beams to __init__ and make the pspecbeam namespace a bit more accessible --- hera_pspec/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/hera_pspec/__init__.py b/hera_pspec/__init__.py index 5d858787..1f927957 100644 --- a/hera_pspec/__init__.py +++ b/hera_pspec/__init__.py @@ -7,7 +7,8 @@ from hera_pspec.pspecdata import PSpecData from hera_pspec.container import PSpecContainer from hera_pspec.parameter import PSpecParam -from hera_pspec.pspecbeam import PSpecBeamUV +from hera_pspec import pspecbeam as beam +from hera_pspec.pspecbeam import PSpecBeamUV, PSpecBeamGauss, PSpecBeamFromArray # XXX: This will eventually be deprecated from hera_pspec import legacy_pspec as legacy From ad177dc3da3ff8e0fa5aec8f370a92172d77b344 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Thu, 26 Apr 2018 17:38:55 -0700 Subject: [PATCH 18/54] Clean up a print statement and add a couple more tests --- hera_pspec/pspecbeam.py | 1 - hera_pspec/tests/test_pspecbeam.py | 7 +++++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index 6af266c7..306cd706 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -524,7 +524,6 @@ def add_pol(self, pol, OmegaP, OmegaPP): # Check that array dimensions are consistent if OmegaP.shape != self.beam_freqs.shape \ or OmegaPP.shape != self.beam_freqs.shape: - print OmegaP.shape, OmegaPP.shape, self.beam_freqs.shape raise ValueError("OmegaP and OmegaPP should both " "have the same shape as beam_freqs.") # Store arrays diff --git a/hera_pspec/tests/test_pspecbeam.py b/hera_pspec/tests/test_pspecbeam.py index e6259b2f..c48331be 100644 --- a/hera_pspec/tests/test_pspecbeam.py +++ b/hera_pspec/tests/test_pspecbeam.py @@ -195,9 +195,16 @@ def test_BeamFromArray(self): OmegaPP={'pseudo_I': 'string',}, beam_freqs=beam_freqs) + nt.assert_raises(ValueError, pspecbeam.PSpecBeamFromArray, + OmegaP={'pseudo_I': Om_P}, + OmegaPP={'pseudo_I': Om_PP[:-2],}, + beam_freqs=beam_freqs) + # Check that invalid method args raise errors nt.assert_raises(KeyError, psbeam.power_beam_int, stokes='blah') nt.assert_raises(KeyError, psbeam.power_beam_sq_int, stokes='blah') + nt.assert_raises(KeyError, psbeam.add_pol, pol='pseudo_A', + OmegaP=Om_P, OmegaPP=Om_PP) # Check that string works self.assert_(len(str(psbeam)) > 0) From fc50a9392c93ae904d5dcf5a409a3b682611aef5 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Thu, 26 Apr 2018 20:31:42 -0700 Subject: [PATCH 19/54] Add PSpecBeam tutorial notebook, and fix typo in docstring. --- examples/PSpecBeam_tutorial.ipynb | 476 ++++++++++++++++++++++++++++++ hera_pspec/pspecbeam.py | 2 +- 2 files changed, 477 insertions(+), 1 deletion(-) create mode 100644 examples/PSpecBeam_tutorial.ipynb diff --git a/examples/PSpecBeam_tutorial.ipynb b/examples/PSpecBeam_tutorial.ipynb new file mode 100644 index 00000000..575f3136 --- /dev/null +++ b/examples/PSpecBeam_tutorial.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating and using `PSpecBeam` objects\n", + "`PSpecBeam` objects carry information about the primary beam, such as how the beam solid angle varies with frequency. This information is needed to rescale power spectra into cosmological units, through the computation of a 'beam scalar'.\n", + "\n", + "There are several different ways to construct a `PSpecBeam` object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gaussian beams: `PSpecBeamGauss`\n", + "A Gaussian beam type is provided for simple testing purposes. We will use this to demonstrate the basic usage of `PSpecBeam` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import hera_pspec as hp" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Each beam is defined over a frequency interval:\n", + "beam_freqs = np.linspace(100e6, 200e6, 200) # in Hz\n", + "\n", + "# Create a new Gaussian beam object with full-width at half-max. of 0.1 radians\n", + "beam_gauss = hp.PSpecBeamGauss(fwhm=0.1, beam_freqs=beam_freqs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a `PSpecBeamGauss` object, with a constant FWHM of 0.1 radians, defined over a frequency interval of [100, 200] MHz. This beam only supports the `pseudo_I` (pseudo-Stoke I) polarization by default.\n", + "\n", + "`PSpecBeam` objects have a cosmology attached to them. Because we didn't manually specify a cosmology, this object was automatically instantiated with the default cosmology from `hera_pspec.conversions`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cosmo_Conversions object at <0x7fcc011dd950>\n", + "Om_L : 0.6844; Om_b : 0.0491; Om_c : 0.2644; Om_M : 0.3135; Om_k : 0.0021; H0 : 67.2700\n" + ] + } + ], + "source": [ + "print beam_gauss.cosmo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating the scalar conversion factor\n", + "\n", + "There are two main `PSpecBeam` methods that are needed by other bits of `hera_pspec`. The first is `compute_pspec_scalar()`, which outputs the scalar conversion factor for a specified range of frequencies. This factor is used to convert power spectra into physical units, e.g. in `PSpecData.pspec()`. It is calculated by performing two integrals, over the beam solid angle and the beam solid angle squared, and multiplying their ratio by a cosmological distance factor.\n", + "\n", + "The `compute_pspec_scalar()` method takes several arguments: to specify the frequency range to integrate the scalar over (`lower_freq`, `upper_freq`, and `num_freqs`); to specify which polarization to calculate the scalar for (`stokes`), and to specify whether a tapering function has been applied to the power spectrum (`taper`):" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scalar: 280222020.675\n" + ] + } + ], + "source": [ + "scalar = beam_gauss.compute_pspec_scalar(lower_freq=100e6, upper_freq=120e6, num_freqs=20, \n", + " num_steps=5000, stokes='pseudo_I', taper='none', \n", + " little_h=True)\n", + "print \"Scalar:\", scalar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In `PSpecData`, the delay spectrum returned by `p_hat` (calculated by applying an optimal quadratic estimator to a pair of visbilities) is multiplied by `scalar` to convert it to cosmological units.\n", + "\n", + "In the above, the frequency range should correspond to the frequency range that the delay spectrum was calculated over. The `num_freqs` argument sets the size of the grid to evaluate the scalar integrand over, and is usually chosen to be a reasonably small value, since the integrand tends to vary quite smoothly with frequency.\n", + "\n", + "### Beam solid angle integrals\n", + "The `num_steps` argument, on the other hand, specifies the size of the grid over which the beam solid angle should be interpolated before it is integrated (i.e. to produce the scalar integrand). This should normally be set to a larger value, as accuracy is more important here. The integrals of the beam solid angle are returned by the following methods (which return arrays of the same size as `beam_freqs`):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "OmegaP = beam_gauss.power_beam_int()\n", + "OmegaPP = beam_gauss.power_beam_sq_int()\n", + "\n", + "plt.plot(beam_freqs/1e6, OmegaP, 'b-')\n", + "plt.plot(beam_freqs/1e6, OmegaPP, 'r-')\n", + "plt.xlabel(\"Freq. [MHz]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The integrals of the beam solid angle are trivial in this example, as we are using a Gaussian beam with a FWHM that is constant in frequency.\n", + "\n", + "### Units and normalization\n", + "\n", + "The `compute_pspec_scalar()` has several other keyword arguments. In the example above, `little_h=True` was specified, to return the scalar in units that will result in a power spectrum that is in $(h^{-1} {\\rm Mpc})^3$ units.\n", + "\n", + "The normalization of the power spectrum also depends on whether a taper was applied to the data. The taper that was used can be specified as a string, using the `taper` keyword argument (which we set to `none` here).\n", + "\n", + "### Noise power spectrum normalization\n", + "Noise power spectra need a different normalization scalar to signal power spectra. To calculate the scalar for a noise power spectrum, pass `noise_scalar=True` to `compute_pspec_scalar()`.\n", + "\n", + "### Conversion to temperature units\n", + "A beam solid angle is also needed to convert between flux density units and temperature units. The `Jy_to_mK()` method performs this conversion. To calculate this factor, pass a frequency and polarization:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 287.25252717 183.84161739 127.66778985 93.79674357 71.81313179]\n" + ] + } + ], + "source": [ + "freqs = np.linspace(100e6, 200e6, 5) # in Hz\n", + "print beam_gauss.Jy_to_mK(freqs, stokes='pseudo_I')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assuming that you have visibilities that are known to be in Jy units, you can apply this conversion to a `pyuvdata.UVData` object like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pyuvdata import UVData\n", + "import os\n", + "\n", + "# Create a UVData object and fill it with data\n", + "datafile = os.path.join('../hera_pspec/data/', 'zen.2458042.12552.xx.HH.uvXAA')\n", + "uvd = UVData()\n", + "uvd.read_miriad(datafile)\n", + "\n", + "# Apply unit conversion factor to UVData\n", + "uvd.data_array *= beam_gauss.Jy_to_mK(np.unique(uvd.freq_array))[None, None, :, None]\n", + "# The expression [None, None, :, None] reshapes the conversion factor into the same shape as as the data_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any power spectra created using this `UVData` object will be ${\\rm mK}$ units (actually, ${\\rm mK}^2 ({\\rm Mpc})^3$ or similar)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Passing a beam to `PSpecData`\n", + "The main purpose of `PSpecBeam` objects is to provide the `PSpecData` class with a way of normalizing the power spectra that it produces. To attach a `PSpecBeam` object to a `PSpecData` object, you can either pass one in when you instantiate the class, i.e." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create PSpecData with a beam attached\n", + "psd = hp.PSpecData(dsets=[], wgts=[], beam=beam_gauss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or, you can specify a beam manually, i.e." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "psd.primary_beam = beam_gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `PSpecData.scalar()` method will then use the correct beam to rescale the power spectra output by `PSpecData.pspec()`.\n", + "\n", + "Note that if you do not specify a beam file at any point, `PSpecData.scalar()` will raise the following warning when it is called: \"Warning: self.primary_beam is not defined, so pspectra are not properly normalized\". It will then set `scalar = 1` and continue running." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Beams from a `UVBeam` object: `PSpecBeamUV`\n", + "Real beams are considerably more complicated than the Gaussian example. The `hera_pspec` module currently supports beams that are specified in the `UVBeam` format provided by the `pyuvdata` package. These usually contain Healpix-pixelated beams as a function of frequency and polarization.\n", + "\n", + "To create a beam that uses this format, simply create a new `PSpecBeamUV` instance with the name of a `beamfits` file that is supported by `UVBeam`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "beamfile = os.path.join('../hera_pspec/data/', 'NF_HERA_Beams.beamfits')\n", + "beam_uv = hp.PSpecBeamUV(beamfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Internally, `PSpecBeam` will calculate the beam solid angle integrals using the `UVBeam.get_beam_area()` and `UVBeam.get_beam_sq_area()` methods. These use Healpix to perform the integrals using spherical harmonic transforms. The relevant frequency ranges will be read directly from the `UVBeam` object. Other than this detail, `PSpecBeamUV` objects behave in the same way as `PSpecBeamGauss` object, e.g. to calculate the beam scalar:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scalar: 2036735393.83\n" + ] + } + ], + "source": [ + "scalar = beam_uv.compute_pspec_scalar(lower_freq=100e6, upper_freq=120e6, num_freqs=20, \n", + " num_steps=5000, stokes='pseudo_I', taper='none', \n", + " little_h=True)\n", + "print \"Scalar:\", scalar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To access the `UVBeam` object stored inside a `PSpecBeamUV`, you can do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "uvbeam = beam_uv.primary_beam\n", + "print uvbeam" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Beam from beam solid angle arrays: `PSpecBeamFromArray`\n", + "If you want to experiment with non-trivial primary beam models without having to construct `UVBeam` objects and all of the metadata that they require, use `PSpecBeamFromArray`. This allows you to provide the integrated beam and beam-squared solid angles (i.e. the outputs of the `power_beam_int()` and `power_beam_sq_int()` methods) manually, as a set of arrays as a function of frequency.\n", + "\n", + "You can either pass in arrays for a single polarization (which will be assumed to be `pseudo_I`), or you can pass in dictionaries with arrays for multiple polarizations." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Add the integrated beam arrays that we output for the Gaussian beam earlier\n", + "custom_beam = hp.PSpecBeamFromArray(OmegaP=OmegaP, OmegaPP=OmegaPP, beam_freqs=beam_freqs)\n", + "\n", + "# Use the same beam arrays, but to specify multiple polarizations using dicts\n", + "custom_beam2 = hp.PSpecBeamFromArray(OmegaP={'pseudo_I': OmegaP, 'pseudo_Q': OmegaP}, \n", + " OmegaPP={'pseudo_I': OmegaPP, 'pseudo_Q': OmegaPP},\n", + " beam_freqs=beam_freqs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also add additional polarizations (or replace existing ones) using the `add_pol()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "custom_beam.add_pol(pol='pseudo_U', OmegaP=OmegaP, OmegaPP=OmegaPP)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check which polarizations are available in a couple of ways:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method 1: ['pseudo_I', 'pseudo_U']\n", + "Method 2: PSpecBeamFromArray object\n", + "\tFrequency range: Min. 1.0000e+08 Hz, Max. 2.0000e+08 Hz\n", + "\tAvailable pols: pseudo_I, pseudo_U\n" + ] + } + ], + "source": [ + "print \"Method 1:\", custom_beam.OmegaP.keys()\n", + "print \"Method 2:\", custom_beam" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating your own `PSpecBeam` class\n", + "In case you have beam information in a custom format and want to create your own `PSpecBeam` class, subclass off `PSpecBeamBase`. This provides the `compute_pspec_scalar()` and `Jy_to_mK()` methods, and your subclass should provide `power_beam_int(stokes)` and `power_beam_sq_int(stokes)` methods.\n", + "\n", + "It should also set the `self.beam_freqs` and `self.cosmo` properties, as these are expected by the `PSpecBeamBase` methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index 306cd706..7f87baee 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -146,7 +146,7 @@ def compute_pspec_scalar(self, lower_freq, upper_freq, num_freqs, num_steps=5000 num_steps : int, optional Number of steps to use when interpolating primary beams for - numerical integral. Default: 10000. + numerical integral. Default: 5000. pol: str, optional Which polarization to compute the beam scalar for. From 8919db7131c5437f76afb4fd154a2ad9afa1a1e5 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Fri, 27 Apr 2018 23:06:49 -0700 Subject: [PATCH 20/54] updated new commits to PSpecBeamfromArray to use pol instead of stokes --- hera_pspec/pspecbeam.py | 48 +++++++++++++++--------------- hera_pspec/tests/test_pspecbeam.py | 34 ++++++++++----------- 2 files changed, 41 insertions(+), 41 deletions(-) diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index 7f87baee..96808cdb 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -407,7 +407,7 @@ def __init__(self, OmegaP, OmegaPP, beam_freqs, cosmo=None): Allowed polarizations are: - pseudo_I, pseudo_Q, pseudo_U, pseudo_V, XX, YY, XY, YX + I, Q, U, V, XX, YY, XY, YX Other polarizations will be ignored. @@ -417,14 +417,14 @@ def __init__(self, OmegaP, OmegaPP, beam_freqs, cosmo=None): Integral over beam solid angle, as a function of frequency. If only one array is specified, this will be assumed to be for the - pseudo_I polarization. If a dict is specified, an OmegaP array for + I polarization. If a dict is specified, an OmegaP array for several polarizations can be specified. OmegaPP : array_like of float (or dict of array_like) Integral over beam solid angle squared, as a function of frequency. If only one array is specified, this will be assumed to be for the - pseudo_I polarization. If a dict is specified, an OmegaP array for + I polarization. If a dict is specified, an OmegaP array for several polarizations can be specified. beam_freqs : array_like of float @@ -436,7 +436,7 @@ def __init__(self, OmegaP, OmegaPP, beam_freqs, cosmo=None): Cosmology object. Uses the default cosmology object if not specified. Default: None. """ - self.allowed_pols = ['pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', + self.allowed_pols = ['I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX'] self.OmegaP = {}; self.OmegaPP = {} @@ -444,9 +444,9 @@ def __init__(self, OmegaP, OmegaPP, beam_freqs, cosmo=None): self.beam_freqs = np.array(beam_freqs) if isinstance(OmegaP, np.ndarray) and isinstance(OmegaPP, np.ndarray): - # Only single arrays were specified; assume pseudo_I - OmegaP = {'pseudo_I': OmegaP} - OmegaPP = {'pseudo_I': OmegaPP} + # Only single arrays were specified; assume I + OmegaP = {'I': OmegaP} + OmegaPP = {'I': OmegaPP} elif isinstance(OmegaP, np.ndarray) or isinstance(OmegaPP, np.ndarray): # Mixed dict and array types are not allowed @@ -496,7 +496,7 @@ def add_pol(self, pol, OmegaP, OmegaPP): Which polarization to add beam solid angle arrays for. Valid options are: - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX' If the arrays already exist for the specified polarization, they @@ -531,56 +531,56 @@ def add_pol(self, pol, OmegaP, OmegaPP): self.OmegaPP[pol] = OmegaPP - def power_beam_int(self, stokes='pseudo_I'): + def power_beam_int(self, pol='I'): """ Computes the integral of the beam over solid angle to give a beam area (in str) as a function of frequency. Parameters ---------- - stokes: str, optional - Which Stokes parameters to compute the beam scalar for. - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', + pol: str, optional + Which polarization to compute the beam scalar for. + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX' - Default: pseudo_I. + Default: I. Returns ------- primary_beam_area: float, array-like Scalar integral over beam solid angle. """ - if stokes in self.OmegaP.keys(): - return self.OmegaP[stokes] + if pol in self.OmegaP.keys(): + return self.OmegaP[pol] else: available_pols = ", ".join(self.OmegaP.keys()) raise KeyError("OmegaP not specified for polarization '%s'. " "Available polarizations are: %s" \ - % (stokes, available_pols)) + % (pol, available_pols)) - def power_beam_sq_int(self, stokes='pseudo_I'): + def power_beam_sq_int(self, pol='I'): """ Computes the integral of the beam**2 over solid angle to give a beam**2 area (in str) as a function of frequency. Parameters ---------- - stokes: str, optional - Which Stokes parameters to compute the beam scalar for. - 'pseudo_I', 'pseudo_Q', 'pseudo_U', 'pseudo_V', + pol: str, optional + Which polarization to compute the beam scalar for. + 'I', 'Q', 'U', 'V', 'XX', 'YY', 'XY', 'YX' - Default: pseudo_I. + Default: I. Returns ------- primary_beam_area: float, array-like """ - if stokes in self.OmegaPP.keys(): - return self.OmegaPP[stokes] + if pol in self.OmegaPP.keys(): + return self.OmegaPP[pol] else: available_pols = ", ".join(self.OmegaPP.keys()) raise KeyError("OmegaPP not specified for polarization '%s'. " "Available polarizations are: %s" \ - % (stokes, available_pols)) + % (pol, available_pols)) def __str__(self): """ diff --git a/hera_pspec/tests/test_pspecbeam.py b/hera_pspec/tests/test_pspecbeam.py index c48331be..471ea843 100644 --- a/hera_pspec/tests/test_pspecbeam.py +++ b/hera_pspec/tests/test_pspecbeam.py @@ -150,8 +150,8 @@ def test_BeamFromArray(self): beam_freqs=beam_freqs) psbeampol = pspecbeam.PSpecBeamFromArray( - OmegaP={'pseudo_I': Om_P, 'pseudo_Q': Om_P}, - OmegaPP={'pseudo_I': Om_PP, 'pseudo_Q': Om_PP}, + OmegaP={'I': Om_P, 'Q': Om_P}, + OmegaPP={'I': Om_PP, 'Q': Om_PP}, beam_freqs=beam_freqs) # Check that user-defined cosmology can be specified @@ -161,15 +161,15 @@ def test_BeamFromArray(self): # Compare scalar calculation with Gaussian case scalar = psbeam.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, - stokes='pseudo_I', num_steps=2000) + pol='I', num_steps=2000) g_scalar = self.gauss.compute_pspec_scalar(lower_freq, upper_freq, - num_freqs, stokes='pseudo_I', + num_freqs, pol='I', num_steps=2000) np.testing.assert_array_almost_equal(scalar, g_scalar) # Check that polarizations are recognized and invalid ones rejected scalarp = psbeampol.compute_pspec_scalar(lower_freq, upper_freq, - num_freqs, stokes='pseudo_Q', + num_freqs, pol='Q', num_steps=2000) # Test taper execution (same as Gaussian case) @@ -179,31 +179,31 @@ def test_BeamFromArray(self): # Check that invalid init args raise errors nt.assert_raises(TypeError, pspecbeam.PSpecBeamFromArray, OmegaP=Om_P, - OmegaPP={'pseudo_I': Om_PP}, beam_freqs=beam_freqs) + OmegaPP={'I': Om_PP}, beam_freqs=beam_freqs) nt.assert_raises(KeyError, pspecbeam.PSpecBeamFromArray, - OmegaP={'pseudo_I': Om_P, 'pseudo_Q': Om_P}, - OmegaPP={'pseudo_I': Om_PP,}, + OmegaP={'I': Om_P, 'Q': Om_P}, + OmegaPP={'I': Om_PP,}, beam_freqs=beam_freqs) nt.assert_raises(KeyError, pspecbeam.PSpecBeamFromArray, - OmegaP={'pseudo_A': Om_P}, - OmegaPP={'pseudo_A': Om_PP,}, + OmegaP={'A': Om_P}, + OmegaPP={'A': Om_PP,}, beam_freqs=beam_freqs) nt.assert_raises(TypeError, pspecbeam.PSpecBeamFromArray, - OmegaP={'pseudo_I': Om_P,}, - OmegaPP={'pseudo_I': 'string',}, + OmegaP={'I': Om_P,}, + OmegaPP={'I': 'string',}, beam_freqs=beam_freqs) nt.assert_raises(ValueError, pspecbeam.PSpecBeamFromArray, - OmegaP={'pseudo_I': Om_P}, - OmegaPP={'pseudo_I': Om_PP[:-2],}, + OmegaP={'I': Om_P}, + OmegaPP={'I': Om_PP[:-2],}, beam_freqs=beam_freqs) # Check that invalid method args raise errors - nt.assert_raises(KeyError, psbeam.power_beam_int, stokes='blah') - nt.assert_raises(KeyError, psbeam.power_beam_sq_int, stokes='blah') - nt.assert_raises(KeyError, psbeam.add_pol, pol='pseudo_A', + nt.assert_raises(KeyError, psbeam.power_beam_int, pol='blah') + nt.assert_raises(KeyError, psbeam.power_beam_sq_int, pol='blah') + nt.assert_raises(KeyError, psbeam.add_pol, pol='A', OmegaP=Om_P, OmegaPP=Om_PP) # Check that string works From d3a4911db69aee41e50ab88311af80e2e02935a6 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Sat, 21 Apr 2018 12:19:06 -0700 Subject: [PATCH 21/54] * Add ability to name datasets in PSpecData * Add __str__ function to PSpecData to allow pretty printing --- hera_pspec/pspecdata.py | 226 ++++++++++++++++++++++++----- hera_pspec/tests/test_pspecdata.py | 38 ++++- 2 files changed, 222 insertions(+), 42 deletions(-) diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 17f03277..15cff576 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -13,27 +13,34 @@ class PSpecData(object): - def __init__(self, dsets=[], wgts=[], beam=None): + def __init__(self, dsets=[], wgts=[], labels=None, beam=None): """ Object to store multiple sets of UVData visibilities and perform operations such as power spectrum estimation on them. Parameters ---------- - dsets : List of UVData objects, optional - List of UVData objects containing the data that will be used to - compute the power spectrum. Default: Empty list. + dsets : list or dict of UVData objects, optional + Set of UVData objects containing the data that will be used to + compute the power spectrum. If specified as a dict, the key names + will be used to tag each dataset. Default: Empty list. - wgts : List of UVData objects, optional - List of UVData objects containing weights for the input data. + wgts : list or dict of UVData objects, optional + Set of UVData objects containing weights for the input data. Default: Empty list. - + + labels : list of str, optional + An ordered list of names/labels for each dataset, if dsets was + specified as a list. If None, names will not be assigned to the + datasets. If dsets was specified as a dict, the keys + of that dict will be used instead of this. Default: None. + beam : PspecBeam object, optional PspecBeam object containing information about the primary beam Default: None. """ self.clear_cov_cache() # Covariance matrix cache - self.dsets = []; self.wgts = [] + self.dsets = []; self.wgts = []; self.labels = [] self.Nfreqs = None self.spw_range = None self.spw_Nfreqs = None @@ -43,29 +50,53 @@ def __init__(self, dsets=[], wgts=[], beam=None): # Store the input UVData objects if specified if len(dsets) > 0: - self.add(dsets, wgts) + self.add(dsets, wgts, labels=labels) # Store a primary beam self.primary_beam = beam - def add(self, dsets, wgts): + def add(self, dsets, wgts, labels=None): """ Add a dataset to the collection in this PSpecData object. Parameters ---------- - dsets : UVData or list + dsets : UVData or list or dict UVData object or list of UVData objects containing data to add to the collection. - wgts : UVData or list + wgts : UVData or list or dict UVData object or list of UVData objects containing weights to add to the collection. Must be the same length as dsets. If a weight is set to None, the flags of the corresponding + + labels : list of str + An ordered list of names/labels for each dataset, if dsets was + specified as a list. If dsets was specified as a dict, the keys + of that dict will be used instead. """ + # Check for dicts and unpack into an ordered list if found + if isinstance(dsets, dict): + # Disallow labels kwarg if a dict was passed + if labels is not None: + raise ValueError("If 'dsets' is a dict, 'labels' cannot be " + "specified.") + + if not isinstance(wgts, dict): + raise TypeError("If 'dsets' is a dict, 'wgts' must also be " + "a dict") + + # Unpack dsets and wgts dicts + labels = dsets.keys() + _dsets = [dsets[key] for key in labels] + _wgts = [wgts[key] for key in labels] + dsets = _dsets + wgts = _wgts + # Convert input args to lists if possible if isinstance(dsets, pyuvdata.UVData): dsets = [dsets,] if isinstance(wgts, pyuvdata.UVData): wgts = [wgts,] + if isinstance(labels, str): labels = [labels,] if wgts is None: wgts = [wgts,] if isinstance(dsets, tuple): dsets = list(dsets) if isinstance(wgts, tuple): wgts = list(wgts) @@ -76,6 +107,7 @@ def add(self, dsets, wgts): # Make sure enough weights were specified assert(len(dsets) == len(wgts)) + if labels is not None: assert(len(dsets) == len(labels)) # Check that everything is a UVData object for d, w in zip(dsets, wgts): @@ -88,6 +120,12 @@ def add(self, dsets, wgts): # Append to list self.dsets += dsets self.wgts += wgts + + # Store labels (if they were set) + if labels is None: + self.labels = [None for d in dsets] + else: + self.labels += labels # Store no. frequencies and no. times self.Nfreqs = self.dsets[0].Nfreqs @@ -97,6 +135,27 @@ def add(self, dsets, wgts): self.freqs = self.dsets[0].freq_array[0] self.spw_range = (0, self.Nfreqs) self.spw_Nfreqs = self.Nfreqs + + + def __str__(self): + """ + Print basic info about this PSpecData object. + """ + # Basic info + s = "PSpecData object\n" + s += " %d datasets" % len(self.dsets) + if len(self.dsets) == 0: return s + + # Dataset summary + for i, d in enumerate(self.dsets): + if self.labels[i] is None: + s += " dset (%d): %d bls (freqs=%d, times=%d, pols=%d)\n" \ + % (i, d.Nbls, d.Nfreqs, d.Ntimes, d.Npols) + else: + s += " dset '%s' (%d): %d bls (freqs=%d, times=%d, pols=%d)\n" \ + % (self.labels[i], i, d.Nbls, d.Nfreqs, d.Ntimes, d.Npols) + return s + def validate_datasets(self, verbose=True): """ @@ -149,7 +208,8 @@ def validate_datasets(self, verbose=True): max_diff_dec = np.max(map(lambda d: np.diff(d), itertools.combinations(phase_dec, 2))) max_diff = np.sqrt(max_diff_ra**2 + max_diff_dec**2) if max_diff > 0.15: raise_warning("Warning: maximum phase-center difference between datasets is > 10 arcmin", verbose=verbose) - + + def check_key_in_dset(self, key, dset_ind): """ Check 'key' exists in the UVData object self.dsets[dset_ind] @@ -168,6 +228,9 @@ def check_key_in_dset(self, key, dset_ind): exists : bool True if the key exists, False otherwise """ + #FIXME: Fix this to enable label keys + + # get iterable key = pyuvdata.utils.get_iterable(key) if isinstance(key, str): @@ -206,7 +269,75 @@ def clear_cov_cache(self, keys=None): except(KeyError): pass try: del(self._iC[k]) except(KeyError): pass - + + def dset_idx(self, dset): + """ + Return the index of a dataset, regardless of whether it was specified + as an integer of a string. + + Parameters + ---------- + dset : int or str + Index or name of a dataset belonging to this PSpecData object. + + Returns + ------- + dset_idx : int + Index of dataset. + """ + # Look up dset label if it's a string + if isinstance(dset, str): + if dset in self.labels: + return self.labels.index(dset) + else: + raise KeyError("dset '%s' not found." % dset) + elif isinstance(dset, int): + return dset + else: + raise TypeError("dset must be either an int or string") + + + def blkey(self, dset, bl=None, pol=None): + """ + Return a key specifying a particular dataset, baseline, and + (optionally) polarization, in the tuple format used by other methods + of PSpecData. + + Parameters + ---------- + dset : int or str + Index or name of a dataset belonging to this PSpecData object. + + bl : tuple, optional + Baseline ID, specified as a tuple of antenna pairs, e.g. (10, 11). + Default: None. + + pol : str, optional + Polarization of the visibility, in linear (e.g. 'xx') or Stokes + (e.g. 'I') notation, whatever is supported by the input UVData + objects. Default: None (polarization will not be included). + + Returns + ------- + key : tuple + Tuple containing dataset ID, baseline index (if specified), and + polarization (if specified). + """ + key = () + + # Look up dset label if it's a string + dset_idx = self.dset_idx(dset) + key += (dset_idx,) + + # Add the baseline tuple if it was specified + if bl is None: return key + key += (bl,) + + # Polarization + if pol is not None: key += (pol,) + return key + + def x(self, key): """ Get data for a given dataset and baseline, as specified in a standard @@ -216,8 +347,8 @@ def x(self, key): ---------- key : tuple Tuple containing dataset ID and baseline index. The first element - of the tuple is the dataset index, and the subsequent elements are - the baseline ID. + of the tuple is the dataset index (or label), and the subsequent + elements are the baseline ID. Returns ------- @@ -225,8 +356,9 @@ def x(self, key): Array of data from the requested UVData dataset and baseline. """ assert isinstance(key, tuple) - dset = key[0]; bl = key[1:] - return self.dsets[dset].get_data(bl).T[self.spw_range[0]:self.spw_range[1], :] # FIXME: Transpose? + dset, bl = self.blkey(dset=key[0], bl=key[1:]) + spwmin, spwmax = self.spw_range[0], self.spw_range[1] + return self.dsets[dset].get_data(bl).T[spwmin:spwmax, :] def w(self, key): """ @@ -246,13 +378,15 @@ def w(self, key): Array of weights for the requested UVData dataset and baseline. """ assert isinstance(key, tuple) - dset = key[0]; bl = key[1:] + spwrange = self.spw_range + dset, bl = self.blkey(dset=key[0], bl=key[1:]) + if self.wgts[dset] is not None: - return self.wgts[dset].get_data(bl).T[self.spw_range[0]:self.spw_range[1], :] # FIXME: Transpose? + return self.wgts[dset].get_data(bl).T[spwrange[0]:spwrange[1], :] else: # If weights were not specified, use the flags built in to the # UVData dataset object - flags = self.dsets[dset].get_flags(bl).astype(float).T[self.spw_range[0]:self.spw_range[1], :] + flags = self.dsets[dset].get_flags(bl).astype(float).T[spwrange[0]:spwrange[1], :] return 1. - flags # Flag=1 => weight=0 def C(self, key): @@ -272,6 +406,8 @@ def C(self, key): (Weighted) empirical covariance of data for baseline 'bl'. """ assert isinstance(key, tuple) + key = (self.dset_idx(key[0]),) + key[1:] # Sanitize dataset name + # Set covariance if it's not in the cache if not self._C.has_key(key): self.set_C( {key : utils.cov(self.x(key), self.w(key))} ) @@ -313,6 +449,8 @@ def C_empirical(self, key): Empirical covariance for the specified key. """ assert isinstance(key, tuple) + key = (self.dset_idx(key[0]),) + key[1:] # Sanitize dataset name + # Check cache for empirical covariance if not self._Cempirical.has_key(key): self._Cempirical[key] = utils.cov(self.x(key), self.w(key)) @@ -335,6 +473,7 @@ def I(self, key): Identity covariance matrix, dimension (Nfreqs, Nfreqs). """ assert isinstance(key, tuple) + key = (self.dset_idx(key[0]),) + key[1:] # Sanitize dataset name if not self._I.has_key(key): self._I[key] = np.identity(self.spw_Nfreqs) @@ -357,6 +496,8 @@ def iC(self, key): Inverse covariance matrix for specified dataset and baseline. """ assert isinstance(key, tuple) + key = (self.dset_idx(key[0]),) + key[1:] # Sanitize dataset name + # Calculate inverse covariance if not in cache if not self._iC.has_key(key): C = self.C(key) @@ -396,14 +537,13 @@ def set_R(self, R_matrix): If set to "iC", sets R = C^-1 Otherwise, accepts a user inputted dictionary """ - if R_matrix == "identity": self.R = self.I elif R_matrix == "iC": self.R = self.iC else: self.R = R_matrix - + def set_spw(self, spw_range): """ Set the spectral window range @@ -413,8 +553,10 @@ def set_spw(self, spw_range): spw_range : tuple, contains start and end of spw in channel indices used to slice the frequency array """ - assert isinstance(spw_range, tuple), "spw_range must be fed as a len-2 integer tuple" - assert isinstance(spw_range[0], (int, np.int)), "spw_range must be fed as len-2 integer tuple" + assert isinstance(spw_range, tuple), \ + "spw_range must be fed as a len-2 integer tuple" + assert isinstance(spw_range[0], (int, np.int)), \ + "spw_range must be fed as len-2 integer tuple" self.spw_range = spw_range self.spw_Nfreqs = spw_range[1] - spw_range[0] @@ -744,7 +886,8 @@ def units(self, little_h=True): def delays(self): """ Return an array of delays, tau, corresponding to the bins of the delay - power spectrum output by pspec() using self.spw_range to specify the spectral window. + power spectrum output by pspec() using self.spw_range to specify the + spectral window. Returns ------- @@ -757,12 +900,13 @@ def delays(self): "calculate delays.") else: return utils.get_delays(self.freqs[self.spw_range[0]:self.spw_range[1]]) * 1e9 # convert to ns - - - def scalar(self, pol='I', taper='none', little_h=True, num_steps=2000, beam=None): + + def scalar(self, pol='I', taper='none', little_h=True, + num_steps=2000, beam=None): """ Computes the scalar function to convert a power spectrum estimate - in "telescope units" to cosmological units, using self.spw_range to set spectral window. + in "telescope units" to cosmological units, using self.spw_range to set + spectral window. See arxiv:1304.4991 and HERA memo #27 for details. @@ -788,7 +932,8 @@ def scalar(self, pol='I', taper='none', little_h=True, num_steps=2000, beam=None Default: 10000 beam : PSpecBeam object - Option to use a manually-fed PSpecBeam object instead of using self.primary_beam. + Option to use a manually-fed PSpecBeam object instead of using + self.primary_beam. Returns ------- @@ -804,9 +949,9 @@ def scalar(self, pol='I', taper='none', little_h=True, num_steps=2000, beam=None # calculate scalar if beam is None: scalar = self.primary_beam.compute_pspec_scalar( - start, end, len(freqs), pol=pol, - taper=taper, little_h=little_h, - num_steps=num_steps) + start, end, len(freqs), pol=pol, + taper=taper, little_h=little_h, + num_steps=num_steps) else: scalar = beam.compute_pspec_scalar(start, end, len(freqs), pol=pol, taper=taper, @@ -945,8 +1090,8 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', assert isinstance(dsets, (list, tuple)), "dsets must be fed as length-2 tuple of integers" assert len(dsets) == 2, "len(dsets) must be 2" assert isinstance(dsets[0], (int, np.int)) and isinstance(dsets[1], (int, np.int)), "dsets must contain integer indices" - dset1 = self.dsets[dsets[0]] - dset2 = self.dsets[dsets[1]] + dset1 = self.dsets[self.dset_idx(dsets[0])] + dset2 = self.dsets[self.dset_idx(dsets[1])] # get polarization array from zero'th dset pol_arr = map(lambda p: pyuvdata.utils.polnum2str(p), dset1.polarization_array) @@ -1244,8 +1389,8 @@ def rephase_to_dset(self, dset_index=0, inplace=True): Parameters ---------- - dset_index : int - index of dataset in self.dset to phase other datasets to. + dset_index : int or str + Index or label of dataset in self.dset to phase other datasets to. inplace : bool, optional If True, edits data in dsets in-memory. Else, makes a copy of @@ -1266,7 +1411,10 @@ def rephase_to_dset(self, dset_index=0, inplace=True): dsets = self.dsets else: dsets = copy.deepcopy(self.dsets) - + + # Parse dset_index + dset_index = self.dset_idx(dset_index) + # get LST grid we are phasing to lst_grid = [] lst_array = dsets[dset_index].lst_array.ravel() diff --git a/hera_pspec/tests/test_pspecdata.py b/hera_pspec/tests/test_pspecdata.py index 5c2db994..798da6fa 100644 --- a/hera_pspec/tests/test_pspecdata.py +++ b/hera_pspec/tests/test_pspecdata.py @@ -106,7 +106,8 @@ def setUp(self): # load another data file self.uvd = uv.UVData() - self.uvd.read_miriad(os.path.join(DATA_PATH, "zen.2458042.17772.xx.HH.uvXA")) + self.uvd.read_miriad(os.path.join(DATA_PATH, + "zen.2458042.17772.xx.HH.uvXA")) def tearDown(self): pass @@ -139,10 +140,41 @@ def test_init(self): self.assertRaises(TypeError, ds.add, [1], [None]) def test_add_data(self): - # test adding non UVData object + """ + Test adding non UVData object. + """ nt.assert_raises(TypeError, self.ds.add, 1, 1) - + + def test_labels(self): + """ + Test that dataset labels work. + """ + # Check that specifying labels does work + psd = pspecdata.PSpecData( dsets=[self.d[0], self.d[1],], + wgts=[self.w[0], self.w[1], ], + labels=['red', 'blue'] ) + np.testing.assert_array_equal( psd.x(('red', 24, 38)), + psd.x((0, 24, 38)) ) + + # Check specifying labels using dicts + dsdict = {'a':self.d[0], 'b':self.d[1]} + psd = pspecdata.PSpecData(dsets=dsdict, wgts=dsdict) + self.assertRaises(ValueError, pspecdata.PSpecData, dsets=dsdict, + wgts=dsdict, labels=['a', 'b']) + + # Check that invalid labels raise errors + self.assertRaises(KeyError, psd.x, ('green', 24, 38)) + + def test_str(self): + """ + Check that strings can be output. + """ + print(self.ds) + def test_get_Q(self): + """ + Test the Q = dC/dp function. + """ vect_length = 50 x_vect = np.random.normal(size=vect_length) \ + 1.j * np.random.normal(size=vect_length) From 0a94aff5b2481771dbb4c7bc7093414af4bb132e Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Sun, 22 Apr 2018 21:36:12 -0700 Subject: [PATCH 22/54] Add support for dataset labels to uvpspec --- hera_pspec/pspecdata.py | 13 ++++++++----- hera_pspec/tests/test_pspecdata.py | 5 ++++- hera_pspec/uvpspec.py | 12 +++++++----- 3 files changed, 19 insertions(+), 11 deletions(-) diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 15cff576..4537c01b 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -1357,15 +1357,18 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', uvp.taper = taper uvp.norm = norm uvp.git_hash = version.git_hash + if self.primary_beam is not None: uvp.cosmo_params = str(self.primary_beam.cosmo.get_params()) if self.primary_beam is not None and hasattr(self.primary_beam, 'filename'): uvp.beamfile = self.primary_beam.filename - if hasattr(dset1.extra_keywords, 'filename'): uvp.filename1 = dset1.extra_keywords['filename'] - if hasattr(dset2.extra_keywords, 'filename'): uvp.filename2 = dset2.extra_keywords['filename'] - if hasattr(dset1.extra_keywords, 'tag'): uvp.tag1 = dset1.extra_keywords['tag'] - if hasattr(dset2.extra_keywords, 'tag'): uvp.tag2 = dset2.extra_keywords['tag'] - + if hasattr(dset1.extra_keywords, 'filename'): + uvp.filename1 = dset1.extra_keywords['filename'] + if hasattr(dset2.extra_keywords, 'filename'): + uvp.filename2 = dset2.extra_keywords['filename'] + uvp.label1 = self.labels[dset1] + uvp.label2 = self.labels[dset2] + # fill data arrays uvp.data_array = data_array uvp.integration_array = integration_array diff --git a/hera_pspec/tests/test_pspecdata.py b/hera_pspec/tests/test_pspecdata.py index 798da6fa..98dd5931 100644 --- a/hera_pspec/tests/test_pspecdata.py +++ b/hera_pspec/tests/test_pspecdata.py @@ -169,7 +169,10 @@ def test_str(self): """ Check that strings can be output. """ - print(self.ds) + ds = pspecdata.PSpecData() + print(ds) # print empty psd + ds.add(self.uvd, None) + print(ds) # print populated psd def test_get_Q(self): """ diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index ad4126bf..303eec12 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -64,9 +64,9 @@ def __init__(self): self._units = PSpecParam("units", description="units of the power spectra.", expected_type=str) self._scalar_array = PSpecParam("scalar_array", description="power spectrum scalar from pspecbeam module.", expected_type=np.ndarray, form="(Nspws, Npols)") self._filename1 = PSpecParam("filename1", description="filename of data from first dataset", expected_type=str) - self._filename2 = PSpecParam("filename1", description="filename of data from second dataset", expected_type=str) - self._tag1 = PSpecParam("tag1", description="tag of data from first dataset", expected_type=str) - self._tag2 = PSpecParam("tag2", description="tag of data from second dataset", expected_type=str) + self._filename2 = PSpecParam("filename2", description="filename of data from second dataset", expected_type=str) + self._label1 = PSpecParam("label1", description="label of data from first dataset", expected_type=str) + self._label2 = PSpecParam("label2", description="label of data from second dataset", expected_type=str) self._git_hash = PSpecParam("git_hash", description="GIT hash of hera_pspec when pspec was generated.", expected_type=str) self._cosmo_params = PSpecParam("cosmo_params", description="LCDM cosmological parameter string, used to instantiate a conversions.Cosmo_Conversions object.", expected_type=str) self._beamfile = PSpecParam("beamfile", description="filename of beam-model used to normalized pspectra.", expected_type=str) @@ -79,10 +79,10 @@ def __init__(self): "data_array", "wgt_array", "integration_array", "spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", "lst_2_array", "time_1_array", "time_2_array", "blpair_array", "Nbls", "bl_vecs", "bl_array", "channel_width", "telescope_location", "weighting", "units", - "taper", "norm", "git_hash", "nsample_array", 'lst_avg_array', 'time_avg_array', 'folded'] + "taper", "norm", "git_hash", "nsample_array", 'lst_avg_array', 'time_avg_array', 'folded', "label1", "label2"] self._immutables = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", "Npols", "Nfreqs", "history", - "Nbls", "channel_width", "weighting", "units", "filename1", "filename2", "tag1", "tag2", + "Nbls", "channel_width", "weighting", "units", "filename1", "filename2", "label1", "label2", "norm", "taper", "git_hash", "cosmo_params", "beamfile" ,'folded'] self._ndarrays = ["spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", 'lst_avg_array', 'time_avg_array', "lst_2_array", "time_1_array", "time_2_array", "blpair_array", @@ -1220,6 +1220,8 @@ def average_spectra(self, blpair_groups=None, time_avg=False, inplace=True): uvp.integration_array = ints_array uvp.wgt_array = wgts_array uvp.nsample_array = nsmp_array + uvp.label1 = self.label1 + uvp.label2 = self.label2 uvp.check() From 897bd306eec95e880ba5d630e615a43626712f8e Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 16:44:53 -0700 Subject: [PATCH 23/54] Fix bug with using wrong index variable --- hera_pspec/pspecdata.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 4537c01b..3b1af9f3 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -1366,8 +1366,8 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', uvp.filename1 = dset1.extra_keywords['filename'] if hasattr(dset2.extra_keywords, 'filename'): uvp.filename2 = dset2.extra_keywords['filename'] - uvp.label1 = self.labels[dset1] - uvp.label2 = self.labels[dset2] + uvp.label1 = self.labels[self.dset_idx(dsets[0])] + uvp.label2 = self.labels[self.dset_idx(dsets[1])] # fill data arrays uvp.data_array = data_array From b72b3096a656f12b18e58781c1f1066169303b82 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 24 Apr 2018 18:59:57 -0700 Subject: [PATCH 24/54] Add more tests --- hera_pspec/tests/test_pspecdata.py | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/hera_pspec/tests/test_pspecdata.py b/hera_pspec/tests/test_pspecdata.py index 98dd5931..cd28c6df 100644 --- a/hera_pspec/tests/test_pspecdata.py +++ b/hera_pspec/tests/test_pspecdata.py @@ -425,10 +425,19 @@ def test_parseval(self): # only expect equality to ~10^-2 to 10^-3 np.testing.assert_allclose(parseval_phat, parseval_real, rtol=1e-3) ''' + + def test_get_V_gaussian(self): + nt.assert_raises(NotImplementedError, self.ds.get_V_gaussian, + (0,1), (0,1)) + def test_scalar(self): self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, beam=self.bm) - + + gauss = pspecbeam.PSpecBeamGauss(0.8, + np.linspace(115e6, 130e6, 50, endpoint=False)) + ds2 = pspecdata.PSpecData(dsets=self.d, wgts=self.w, beam=gauss) + # Precomputed results in the following test were done "by hand" # using iPython notebook "Scalar_dev2.ipynb" in the tests/ directory # FIXME: Uncomment when pyuvdata support for this is ready @@ -494,6 +503,12 @@ def test_units(self): # test basic execution psu = ds.units() nt.assert_equal(psu, '(UNCALIB)^2 Hz [beam normalization not specified]') + + ds.primary_beam = pspecbeam.PSpecBeamGauss(0.8, + np.linspace(115e6, 130e6, 50, endpoint=False)) + psu = ds.units(little_h=False) + nt.assert_equal(psu, '(UNCALIB)^2 Mpc^3') + def test_delays(self): ds = pspecdata.PSpecData() @@ -642,13 +657,16 @@ def test_validate_bls(self): nt.assert_raises(TypeError, pspecdata.validate_bls, [1], [1], uvd, uvd) nt.assert_raises(TypeError, pspecdata.validate_bls, [[1]], [[1]], 1, uvd) nt.assert_raises(TypeError, pspecdata.validate_bls, [[1]], [[1]], uvd, 1) - + bls1 = [(24, 25), (37, 38)] bls2 = [(24, 25), (37, 52)] pspecdata.validate_bls(bls1, bls2, uvd, uvd) bls1 = [[(24,25),(37,38)]] bls2 = [[(24,25),(37,52)]] pspecdata.validate_bls(bls1, bls2, uvd, uvd) + + nt.assert_raises(TypeError, pspecdata.validate_bls, bls1, bls2, uvd, 1) + nt.assert_raises(TypeError, pspecdata.validate_bls, bls1, bls2, 1, uvd) """ From f9e3cd5c6f71db75c2b87005efb195740600b627 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Fri, 27 Apr 2018 17:14:58 -0700 Subject: [PATCH 25/54] Make UVPSpec labels optional; better checking for optional labels --- hera_pspec/pspecdata.py | 6 ++++-- hera_pspec/tests/test_uvpspec.py | 4 +++- hera_pspec/uvpspec.py | 6 +++--- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 3b1af9f3..eb5e620a 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -1366,8 +1366,10 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', uvp.filename1 = dset1.extra_keywords['filename'] if hasattr(dset2.extra_keywords, 'filename'): uvp.filename2 = dset2.extra_keywords['filename'] - uvp.label1 = self.labels[self.dset_idx(dsets[0])] - uvp.label2 = self.labels[self.dset_idx(dsets[1])] + lbl1 = self.labels[self.dset_idx(dsets[0])] + lbl2 = self.labels[self.dset_idx(dsets[1])] + if lbl1 is not None: uvp.label1 = lbl1 + if lbl2 is not None: uvp.label2 = lbl2 # fill data arrays uvp.data_array = data_array diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index f9fb3eb6..2ac54379 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -55,6 +55,8 @@ def build_example_uvpspec(): norm = "I" git_hash = "random" scalar_array = np.ones((Nspws, Npols), np.float) + label1 = 'red' + #label2 = 'blue' # Leave commented out to make sure non-named UVPSpecs work! telescope_location = np.array([5109325.85521063, 2005235.09142983, @@ -77,7 +79,7 @@ def build_example_uvpspec(): 'integration_array', 'bl_array', 'bl_vecs', 'telescope_location', 'units', 'channel_width', 'weighting', 'history', 'taper', 'norm', 'git_hash', 'nsample_array', 'time_avg_array', 'lst_avg_array', - 'cosmo', 'scalar_array'] + 'cosmo', 'scalar_array', 'label1'] # Set all parameters for p in params: diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index 303eec12..af3a4e2a 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -79,7 +79,7 @@ def __init__(self): "data_array", "wgt_array", "integration_array", "spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", "lst_2_array", "time_1_array", "time_2_array", "blpair_array", "Nbls", "bl_vecs", "bl_array", "channel_width", "telescope_location", "weighting", "units", - "taper", "norm", "git_hash", "nsample_array", 'lst_avg_array', 'time_avg_array', 'folded', "label1", "label2"] + "taper", "norm", "git_hash", "nsample_array", 'lst_avg_array', 'time_avg_array', 'folded'] self._immutables = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", "Npols", "Nfreqs", "history", "Nbls", "channel_width", "weighting", "units", "filename1", "filename2", "label1", "label2", @@ -1220,8 +1220,8 @@ def average_spectra(self, blpair_groups=None, time_avg=False, inplace=True): uvp.integration_array = ints_array uvp.wgt_array = wgts_array uvp.nsample_array = nsmp_array - uvp.label1 = self.label1 - uvp.label2 = self.label2 + if hasattr(self, 'label1'): uvp.label1 = self.label1 + if hasattr(self, 'label2'): uvp.label2 = self.label2 uvp.check() From 9d822620566f766abf0da12234092adb447e3bde Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Fri, 27 Apr 2018 23:58:05 -0700 Subject: [PATCH 26/54] added label1 label2 test to pspecdata.pspec output --- hera_pspec/tests/test_pspecdata.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hera_pspec/tests/test_pspecdata.py b/hera_pspec/tests/test_pspecdata.py index cd28c6df..309bc8bb 100644 --- a/hera_pspec/tests/test_pspecdata.py +++ b/hera_pspec/tests/test_pspecdata.py @@ -536,7 +536,7 @@ def test_check_in_dset(self): def test_pspec(self): # generate ds uvd = copy.deepcopy(self.uvd) - ds = pspecdata.PSpecData(dsets=[uvd, uvd], wgts=[None, None], beam=self.bm) + ds = pspecdata.PSpecData(dsets=[uvd, uvd], wgts=[None, None], beam=self.bm, labels=['red', 'blue']) # check basic execution with baseline list bls = [(24, 25), (37, 38), (38, 39), (52, 53)] From 88b6cb3f1f6c53a9e088842402f31c30176914f2 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Wed, 18 Apr 2018 18:42:44 -0700 Subject: [PATCH 27/54] updated pspecdata.pspec() to handle baseline-pair construction more condusive for bootstrapping --- hera_pspec/pspecdata.py | 305 +++++++++++++---------------- hera_pspec/tests/test_pspecdata.py | 73 +++---- 2 files changed, 172 insertions(+), 206 deletions(-) diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index eb5e620a..1f44977f 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -960,13 +960,11 @@ def scalar(self, pol='I', taper='none', little_h=True, return scalar def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', - taper='none', little_h=True, avg_group=False, - exclude_auto_bls=False, exclude_conjugated_blpairs=False, - spw_ranges=None, verbose=True, history=''): + taper='none', little_h=True, spw_ranges=None, verbose=True, + history=''): """ - Estimate the delay power spectrum from a pair of datasets contained in - this object, using the optimal quadratic estimator from - arXiv:1502.06016. + Estimate the delay power spectrum from a pair of datasets contained in this + object, using the optimal quadratic estimator from arXiv:1502.06016. In this formulation, the power spectrum is proportional to the visibility data via @@ -988,44 +986,9 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', Parameters ---------- - bls1, bls2 : list of bl tuples - (or list of bl groups, which themselves are list of bl tuples) - List of baseline tuples to use in the power spectrum calculation - for the "left-hand" dataset (bls1) and "right-hand" dataset (bls2). - A baseline tuple is specified as an antenna pair, Ex: (ant1, ant2) - - Alternatively, bls1 and bls2 can contain lists of baselines groups, - which are themselves lists of baseline tuples. A bl-group in bls1 - should have the same positional index in bls2, i.e. a group at - bls1[i] should correspond to a group at bls2[i]. - - In this case, pspec will do one of two things: - - 1) If avg_group=True, the data in each baseline-group will be - averaged together before squaring, and then crossed with the - baseline groups with the same positions (indices) in bls1 and - bls2, e.g. bls1[i] x bls2[i] - - 2) If avg_group=False, all permutations of cross-spectra between - bls in a group in bls1 and bls in the corresponding group in - bls2 are calculated. (Note that baselines are never crossed - between different baseline groups, which are defined based on - their positional index in the list, i.e. we never do - bls1[i] x bls2[j] for i != j. - - Example: if bls1 = bls2 = [[(1, 2), (2, 3)], [(1, 5), (2, 6)]] - then we will find all permutations of: - [(1, 2), (2, 3)] x [(1, 2), (2, 3)] - and [(1, 5), (2, 6)] x [(1, 5), (2, 6)]. - - If exclude_auto_bls = True, bl-pairs that have a repeated - baseline are eliminated. Example: ((1, 2), (1, 2)) would be - eliminated from the bl_pairs array. - - If exclude_conjugated_blpairs = True, if a baseline pair - exists as well as its conjugate, the latter is eliminated. - Example: ((1, 2), (2, 3)) and ((2, 3), (1, 2)), in which case - ((2, 3), (1, 2)) would be eliminated from the bl_pairs array. + bls1 : list of baseline groups, each being a list of ant-pair tuples + + bls2 : list of baseline groups, each being a list of ant-pair tuples dsets : length-2 tuple or list Contains indices of self.dsets to use in forming power spectra, @@ -1049,20 +1012,6 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', Whether to have cosmological length units be h^-1 Mpc or Mpc Default: h^-1 Mpc - exclude_conjugated_blpairs : boolean, optional - If bls1 and bls2 are lists of bl groups, exclude conjugated - baseline-pairs. Example: If ((1, 2), (2,3)) and ((2, 3), (1,2)) - exist, exclude the latter. - - exclude_auto_bls : boolean, optional - If bls1 and bls2 are lists of bl groups, exclude bl-pairs when a bl - is paired with itself. Used to prevent the inclusion of power - spectra with noise biases. - - avg_group : boolean, optional - If bls1 and bls2 contain a list of bl groups, average data in each - group before cross-multiplying. - spw_ranges : list of tuples, optional A list of spectral window channel ranges to select within the total bandwidth of the datasets, each of which forms an independent power @@ -1082,6 +1031,33 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', ------- uvp : UVPSpec object Instance of UVPSpec that holds the output power spectrum data. + + Examples + -------- + Example 1 : no grouping, i.e. each baseline is its own group, no + brackets needed for each bl. + if + A = (1, 2); B = (2, 3); C = (3, 4); D = (4, 5); E = (5, 6); F = (6, 7) + and + bls1 = [ A, B, C ] + bls2 = [ D, E, F ] + then + blpairs = [ (A, D), (B, E), (C, F) ] + + Example 2: grouping, blpairs come in lists of blgroups, which are considered + "grouped" in OQE + if + bls1 = [ [A, B], [C, D] ] + bls2 = [ [C, D], [E, F] ] + then + blpairs = [ [(A, C), (B, D)], [(C, E), (D, F)] ] + + Example 3: mixed grouping, i.e. some blpairs are grouped, others are not + if + bls1 = [ [A, B], C ] + bls2 = [ [D, E], F ] + then + blpairs = [ [(A, D), (B, E)], (C, F)] """ # Validate the input data to make sure it's sensible self.validate_datasets(verbose=verbose) @@ -1096,75 +1072,26 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', # get polarization array from zero'th dset pol_arr = map(lambda p: pyuvdata.utils.polnum2str(p), dset1.polarization_array) - # ensure both bls1 and bls2 are the same type - if isinstance(bls1[0], tuple) and isinstance(bls1[0][0], (int, np.int)) \ - and isinstance(bls2[0], tuple) and isinstance(bls2[0][0], (int, np.int)): - # bls1 and bls2 fed as list of bl tuples - fed_bl_group = False - - elif isinstance(bls1[0], list) and isinstance(bls1[0][0], tuple) and isinstance(bls2[0], list) \ - and isinstance(bls2[0][0], tuple): - # bls1 and bls2 fed as list of bl groups - fed_bl_group = True - assert len(bls1) == len(bls2), "if fed as list of bl groups, len(bls1) must equal len(bls2)" - - else: - raise TypeError("bls1 and bls2 must both be fed as either a list of bl tuples, or a list of bl groups") - - # validate bl-pair redundancy - validate_bls(bls1, bls2, dset1, dset2, baseline_tol=1.0) + # assert form of bls1 and bls2 + assert len(bls1) == len(bls2), "length of bls1 must equal length of bls2" + for i in range(len(bls1)): + if isinstance(bls1[i], tuple): + assert isinstance(bls2[i], tuple), "bls1[{}] type must match bls2[{}] type".format(i, i) + else: + assert len(bls1[i]) == len(bls2[i]), "len(bls1[{}]) must match len(bls2[{}])".format(i, i) # construct list of baseline pairs bl_pairs = [] for i in range(len(bls1)): - if fed_bl_group: - bl_grp = [] - for j in range(len(bls1[i])): - bl_grp.extend(itertools.combinations(bls2[i] + [bls1[i][j]], 2)) - # eliminate duplicates - bl_grp = sorted(set(bl_grp)) - bl_pairs.append(bl_grp) - else: - bl_pairs.append((bls1[i], bls2[i])) - - # iterate through all bl pairs and ensure it exists in the specified dsets, else remove - new_bl_pairs = [] - for i, blg in enumerate(bl_pairs): - if fed_bl_group: - new_blg = [] - for blp in blg: - if self.check_key_in_dset(blp[0], dsets[0]) and self.check_key_in_dset(blp[1], dsets[1]): - new_blg.append(blp) - if len(new_blg) > 0: - new_bl_pairs.append(new_blg) + if isinstance(bls1[i], tuple): + bl_pairs.append( (bls1[i], bls2[i]) ) + elif isinstance(bls1[i], list) and len(bls1[i]) == 1: + bl_pairs.append( (bls1[i][0], bls2[i][0]) ) else: - if self.check_key_in_dset(blg[0], dsets[1]) and self.check_key_in_dset(blg[1], dsets[1]): - new_bl_pairs.append(blg) - bl_pairs = new_bl_pairs - - # exclude autos or conjugated blpairs if desired - if fed_bl_group: - new_bl_pairs = [] - for i, blg in enumerate(bl_pairs): - new_blg = [] - for blp in blg: - if exclude_auto_bls: - if blp[0] == blp[1]: - continue - if (blp[1], blp[0]) in new_blg and exclude_conjugated_blpairs: - continue - new_blg.append(blp) - if len(new_blg) > 0: - new_bl_pairs.append(new_blg) - bl_pairs = new_bl_pairs - - # flatten bl_pairs list if bls fed as bl groups but no averaging is desired - if avg_group == False and fed_bl_group: - bl_pairs = [item for sublist in bl_pairs for item in sublist] - - if avg_group: - # bl group averaging currently fails at self.get_G() function - raise NotImplementedError + bl_pairs.append(map(lambda j: (bls1[i][j] , bls2[i][j]), range(len(bls1[i])))) + + # validate bl-pair redundancy + validate_blpairs(bl_pairs, dset1, dset2, baseline_tol=1.0) # configure spectral window selections if spw_ranges is None: @@ -1229,10 +1156,12 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', for k, blp in enumerate(bl_pairs): # assign keys - if avg_group and fed_bl_group: + if isinstance(blp, list): + # interpet blp as group of baseline-pairs key1 = [(dsets[0],) + _blp[0] + (p,) for _blp in blp] key2 = [(dsets[1],) + _blp[1] + (p,) for _blp in blp] - else: + elif isinstance(blp, tuple): + # interpret blp as baseline-pair key1 = (dsets[0],) + blp[0] + (p,) key2 = (dsets[1],) + blp[1] + (p,) @@ -1267,7 +1196,7 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', pv *= scalar # Get baseline keys - if avg_group and fed_bl_group: + if isinstance(blp, list): bl1 = blp[0][0] bl2 = blp[0][1] else: @@ -1477,43 +1406,98 @@ def rephase_to_dset(self, dset_index=0, inplace=True): if inplace is False: return dsets +def construct_blpairs(bls, exclude_auto_bls=False, exclude_permutations=False, group=False, Nblps_per_group=1): + """ + Construct a list of baseline-pairs from a baseline-group. This function can be used to easily convert a + single list of baselines into the input needed by PSpecData.pspec(bls1, bls2, ...). -def validate_bls(bls1, bls2, uvd1, uvd2, baseline_tol=1.0, verbose=True): + Parameters + ---------- + bls : list of baseline tuples, Ex. [(1, 2), (2, 3), (3, 4)] + + exclude_auto_bls: boolean, if True, exclude all baselines crossed with itself from the final blpairs list + + exclude_permutations : boolean, if True, exclude permutations and only form combinations of the bls list. + For example, if bls = [1, 2, 3] (note this isn't the proper form of bls, but makes this example clearer) + and exclude_permutations = False, then blpairs = [11, 12, 13, 21, 22, 23,, 31, 32, 33]. + If however exclude_permutations = True, then blpairs = [11, 12, 13, 22, 23, 33]. + Furthermore, if exclude_auto_bls = True then 11, 22, and 33 would additionally be excluded. + + group : boolean, optional + if True, group each consecutive Nblps_per_group blpairs into sub-lists + + Nblps_per_group : integer, number of baseline-pairs to put into each sub-group + + Returns (bls1, bls2, blpairs) + ------- + bls1 : list of baseline tuples from the zeroth index of the blpair + + bls2 : list of baseline tuples from the first index of the blpair + + blpairs : list of blpair tuples """ - Validate baseline pairings between bls1 and bls2 are redundant within the + # assert form + assert isinstance(bls, list) and isinstance(bls[0], tuple), "bls must be fed as list of baseline tuples" + + # form blpairs w/o explicitly forming auto blpairs + # however, if there are repeated bl in bls, there will be auto bls in blpairs + if exclude_permutations: + blpairs = list(itertools.combinations(bls, 2)) + else: + blpairs = list(itertools.permutations(bls, 2)) + + # explicitly add in auto baseline pairs + blpairs.extend(zip(bls, bls)) + + # iterate through and eliminate all autos if desired + if exclude_auto_bls: + new_blpairs = [] + for blp in blpairs: + if blp[0] != blp[1]: + new_blpairs.append(blp) + blpairs = new_blpairs + + # create bls1 and bls2 list + bls1 = map(lambda blp: blp[0], blpairs) + bls2 = map(lambda blp: blp[1], blpairs) + + # group baseline pairs if desired + if group: + Nblps = len(blpairs) + Ngrps = int(np.ceil(float(Nblps) / Nblps_per_group)) + new_blps = [] + new_bls1 = [] + new_bls2 = [] + for i in range(Ngrps): + new_blps.append(blpairs[i*Nblps_per_group:(i+1)*Nblps_per_group]) + new_bls1.append(bls1[i*Nblps_per_group:(i+1)*Nblps_per_group]) + new_bls2.append(bls2[i*Nblps_per_group:(i+1)*Nblps_per_group]) + + bls1 = new_bls1 + bls2 = new_bls2 + blpairs = new_blps + + return bls1, bls2, blpairs + + +def validate_blpairs(blpairs, uvd1, uvd2, baseline_tol=1.0, verbose=True): + """ + Validate baseline pairings in the blpair list are redundant within the specified tolerance. Parameters ---------- - bls1 : list of baseline tuples, or list of bl-groups. - See docstring of PSpecData.pspec() for details on format. - - bls2 : list of baseline tuples, or list of bl-groups. + blpairs : list of baseline-pair tuples, Ex. [((1,2),(1,2)), ((2,3),(2,3))] See docstring of PSpecData.pspec() for details on format. - uvd1 : pyuvdata.UVData instance containing visibility data that bls1 will draw from + uvd1 : pyuvdata.UVData instance containing visibility data that first bl in blpair will draw from - uvd2 : pyuvdata.UVData instance containing visibility data that bls2 will draw from + uvd2 : pyuvdata.UVData instance containing visibility data that second bl in blpair will draw from baseline_tol : float, distance tolerance for notion of baseline "redundancy" in meters verbose : bool, if True report feedback to stdout """ - # ensure both bls1 and bls2 are the same type - if isinstance(bls1[0], tuple) and isinstance(bls1[0][0], (int, np.int)) \ - and isinstance(bls2[0], tuple) and isinstance(bls2[0][0], (int, np.int)): - # bls1 and bls2 fed as list of bl tuples - fed_bl_group = False - - elif isinstance(bls1[0], list) and isinstance(bls1[0][0], tuple) and isinstance(bls2[0], list) \ - and isinstance(bls2[0][0], tuple): - # bls1 and bls2 fed as list of bl groups - fed_bl_group = True - assert len(bls1) == len(bls2), "if fed as list of bl groups, len(bls1) must equal len(bls2)" - - else: - raise TypeError("bls1 and bls2 must both be fed as either a list of bl tuples, or a list of bl groups") - # ensure uvd1 and uvd2 are UVData objects if isinstance(uvd1, pyuvdata.UVData) == False: raise TypeError("uvd1 must be a pyuvdata.UVData instance") @@ -1533,22 +1517,15 @@ def validate_bls(bls1, bls2, uvd1, uvd2, baseline_tol=1.0, verbose=True): ap = ap1 ap.update(ap2) - # iterate through baselines and 1) check baselines crossed with each other are within tolerance - # and 2) check baselines within a single group (if grouped) are within tolerance - for i in range(len(bls1)): - if fed_bl_group: - # get baseline vectors for each bl in the i'th group - blvecs1 = map(lambda bl: ap[bl[0]] - ap[bl[1]], bls1[i]) - blvecs2 = map(lambda bl: ap[bl[0]] - ap[bl[1]], bls2[i]) - # get maximum residual between all pairs - resid = map(lambda p: np.linalg.norm(reduce(operator.sub, p)), itertools.combinations(blvecs1+blvecs2, 2)) - if np.max(np.abs(resid)) >= baseline_tol: - raise_warning("baseline-pair residual(s) in the {}'th bl group exceed a bl tol of {} m".format(i, baseline_tol), verbose=verbose) - else: - blvec1 = ap[bls1[i][0]] - ap[bls1[i][1]] - blvec2 = ap[bls2[i][0]] - ap[bls2[i][1]] - if np.linalg.norm(blvec1 - blvec2) >= baseline_tol: - raise_warning("bl1 {} and bl2 {} separation exceeds the bl tol of {} m".format(bls1[i], bls2[i], baseline_tol), verbose=verbose) + # iterate through baselines and check baselines crossed with each other are within tolerance + for i, blg in enumerate(blpairs): + if isinstance(blg, tuple): + blg = [blg] + for blp in blg: + bl1_vec = ap[blp[0][0]] - ap[blp[0][1]] + bl2_vec = ap[blp[1][0]] - ap[blp[1][1]] + if np.linalg.norm(bl1_vec - bl2_vec) >= baseline_tol: + raise_warning("blpair {} exceeds redundancy tolerance of {} m".format(blp, baseline_tol), verbose=verbose) def raise_warning(warning, verbose=True): diff --git a/hera_pspec/tests/test_pspecdata.py b/hera_pspec/tests/test_pspecdata.py index 309bc8bb..ee778838 100644 --- a/hera_pspec/tests/test_pspecdata.py +++ b/hera_pspec/tests/test_pspecdata.py @@ -539,10 +539,9 @@ def test_pspec(self): ds = pspecdata.PSpecData(dsets=[uvd, uvd], wgts=[None, None], beam=self.bm, labels=['red', 'blue']) # check basic execution with baseline list - bls = [(24, 25), (37, 38), (38, 39), (52, 53)] + bls = [(24, 25), (37, 38), (38, 39), (52, 53)] uvp = ds.pspec(bls, bls, (0, 1), input_data_weight='identity', norm='I', taper='none', - little_h=True, exclude_conjugated_blpairs=False, exclude_auto_bls=False, - verbose=False) + little_h=True, verbose=False) nt.assert_equal(len(uvp.bl_array), len(bls)) nt.assert_true(uvp.antnums_to_blpair(((24, 25), (24, 25))) in uvp.blpair_array) nt.assert_equal(uvp.data_array[0].dtype, np.complex128) @@ -551,24 +550,24 @@ def test_pspec(self): # check with redundant baseline group list antpos, ants = uvd.get_ENU_antpos(pick_data_ants=True) antpos = dict(zip(ants, antpos)) - red_bls = redcal.get_pos_reds(antpos, low_hi=True) - uvp = ds.pspec(red_bls, red_bls, (0, 1), input_data_weight='identity', norm='I', taper='none', - little_h=True, exclude_conjugated_blpairs=False, exclude_auto_bls=False, - verbose=False) + red_bls = map(lambda blg: sorted(blg), redcal.get_pos_reds(antpos, low_hi=True))[2] + bls1, bls2, blps = pspecdata.construct_blpairs(red_bls, exclude_permutations=True) + uvp = ds.pspec(bls1, bls2, (0, 1), input_data_weight='identity', norm='I', taper='none', + little_h=True, verbose=False) nt.assert_true(uvp.antnums_to_blpair(((24, 25), (37, 38))) in uvp.blpair_array) - nt.assert_equal(uvp.Nblpairs, 63) - uvp = ds.pspec(red_bls, red_bls, (0, 1), input_data_weight='identity', norm='I', taper='none', - little_h=True, exclude_conjugated_blpairs=True, exclude_auto_bls=True, - verbose=False) + nt.assert_equal(uvp.Nblpairs, 10) + uvp = ds.pspec(bls1, bls2, (0, 1), input_data_weight='identity', norm='I', taper='none', + little_h=True, verbose=False) nt.assert_true(uvp.antnums_to_blpair(((24, 25), (52, 53))) in uvp.blpair_array) nt.assert_true(uvp.antnums_to_blpair(((52, 53), (24, 25))) not in uvp.blpair_array) - nt.assert_equal(uvp.Nblpairs, 21) + nt.assert_equal(uvp.Nblpairs, 10) # test select - red_bls = [[(24, 25), (37, 38), (38, 39), (52, 53)]] + red_bls = [(24, 25), (37, 38), (38, 39), (52, 53)] + bls1, bls2, blp = pspecdata.construct_blpairs(red_bls, exclude_permutations=False, exclude_auto_bls=False) uvd = copy.deepcopy(self.uvd) ds = pspecdata.PSpecData(dsets=[uvd, uvd], wgts=[None, None], beam=self.bm) - uvp = ds.pspec(red_bls, red_bls, (0, 1), spw_ranges=[(20,30), (30,40)], exclude_conjugated_blpairs=False, exclude_auto_bls=False, verbose=False) + uvp = ds.pspec(bls1, bls2, (0, 1), spw_ranges=[(20,30), (30,40)], verbose=False) nt.assert_equal(uvp.Nblpairs, 16) nt.assert_equal(uvp.Nspws, 2) uvp2 = uvp.select(spws=[0], bls=[(24, 25)], only_pairs_in_bls=False, inplace=False) @@ -578,12 +577,6 @@ def test_pspec(self): nt.assert_equal(uvp.Nspws, 1) nt.assert_equal(uvp.Nblpairs, 1) - # check exception - nt.assert_raises(TypeError, ds.pspec, [0], [0], (0, 1)) - - # get exception for bl_group - nt.assert_raises(NotImplementedError, ds.pspec, red_bls, red_bls, (0, 1), avg_group=True) - # check w/ multiple spectral ranges uvd = copy.deepcopy(self.uvd) ds = pspecdata.PSpecData(dsets=[uvd, uvd], wgts=[None, None], beam=self.bm) @@ -608,8 +601,8 @@ def test_normalization(self): freqs = np.unique(d1.freq_array) # Setup baselines - bl1 = (24, 25) - bl2 = (37, 38) + bls1 = [(24, 25)] + bls2 = [(37, 38)] # Get beam beam = copy.deepcopy(self.bm) @@ -627,12 +620,12 @@ def test_normalization(self): NEB = 1.0 Bp = np.median(np.diff(freqs)) * len(freqs) scalar = cosmo.X2Y(np.mean(cosmo.f2z(freqs))) * np.mean(OmegaP**2/OmegaPP) * Bp * NEB - data1 = d1.get_data(bl1) - data2 = d2.get_data(bl2) + data1 = d1.get_data(bls1[0]) + data2 = d2.get_data(bls2[0]) legacy = np.fft.fftshift(np.fft.ifft(data1, axis=1) * np.conj(np.fft.ifft(data2, axis=1)) * scalar, axes=1)[0] # hera_pspec OQE ds = pspecdata.PSpecData(dsets=[d1, d2], wgts=[None, None], beam=beam) - uvp = ds.pspec([bl1], [bl2], (0, 1), taper='none', input_data_weight='identity', norm='I') + uvp = ds.pspec(bls1, bls2, (0, 1), taper='none', input_data_weight='identity', norm='I') oqe = uvp.get_data(0, ((24, 25), (37, 38)), 'xx')[0] # assert answers are same to within 3% nt.assert_true(np.isclose(np.real(oqe)/np.real(legacy), 1, atol=0.03, rtol=0.03).all()) @@ -641,33 +634,29 @@ def test_normalization(self): window = windows.blackmanharris(len(freqs)) NEB = Bp / trapz(window**2, x=freqs) scalar = cosmo.X2Y(np.mean(cosmo.f2z(freqs))) * np.mean(OmegaP**2/OmegaPP) * Bp * NEB - data1 = d1.get_data(bl1) - data2 = d2.get_data(bl2) + data1 = d1.get_data(bls1[0]) + data2 = d2.get_data(bls2[0]) legacy = np.fft.fftshift(np.fft.ifft(data1*window[None, :], axis=1) * np.conj(np.fft.ifft(data2*window[None, :], axis=1)) * scalar, axes=1)[0] # hera_pspec OQE ds = pspecdata.PSpecData(dsets=[d1, d2], wgts=[None, None], beam=beam) - uvp = ds.pspec([bl1], [bl2], (0, 1), taper='blackman-harris', input_data_weight='identity', norm='I') + uvp = ds.pspec(bls1, bls2, (0, 1), taper='blackman-harris', input_data_weight='identity', norm='I') oqe = uvp.get_data(0, ((24, 25), (37, 38)), 'xx')[0] # assert answers are same to within 3% nt.assert_true(np.isclose(np.real(oqe)/np.real(legacy), 1, atol=0.03, rtol=0.03).all()) - def test_validate_bls(self): + def test_validate_blpairs(self): # test exceptions uvd = copy.deepcopy(self.uvd) - nt.assert_raises(TypeError, pspecdata.validate_bls, [1], [1], uvd, uvd) - nt.assert_raises(TypeError, pspecdata.validate_bls, [[1]], [[1]], 1, uvd) - nt.assert_raises(TypeError, pspecdata.validate_bls, [[1]], [[1]], uvd, 1) - - bls1 = [(24, 25), (37, 38)] - bls2 = [(24, 25), (37, 52)] - pspecdata.validate_bls(bls1, bls2, uvd, uvd) - bls1 = [[(24,25),(37,38)]] - bls2 = [[(24,25),(37,52)]] - pspecdata.validate_bls(bls1, bls2, uvd, uvd) - - nt.assert_raises(TypeError, pspecdata.validate_bls, bls1, bls2, uvd, 1) - nt.assert_raises(TypeError, pspecdata.validate_bls, bls1, bls2, 1, uvd) + nt.assert_raises(TypeError, pspecdata.validate_blpairs, [((1, 2), (2, 3))], None, uvd) + nt.assert_raises(TypeError, pspecdata.validate_blpairs, [((1, 2), (2, 3))], uvd, None) + + bls = [(24,25),(37,38)] + bls1, bls2, blpairs = pspecdata.construct_blpairs(bls, exclude_permutations=False, exclude_auto_bls=True) + pspecdata.validate_blpairs(blpairs, uvd, uvd) + bls1, bls2, blpairs = pspecdata.construct_blpairs(bls, exclude_permutations=False, exclude_auto_bls=True, + group=True) + pspecdata.validate_blpairs(blpairs, uvd, uvd) """ # LEGACY MONTE CARLO TESTS From 6ad9a5c87d20a4c8f03a4d6d730469246fb52467 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Wed, 18 Apr 2018 22:01:54 -0700 Subject: [PATCH 28/54] increased test coverage for test_pspecdata --- hera_pspec/tests/test_pspecdata.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/hera_pspec/tests/test_pspecdata.py b/hera_pspec/tests/test_pspecdata.py index ee778838..fd56e19b 100644 --- a/hera_pspec/tests/test_pspecdata.py +++ b/hera_pspec/tests/test_pspecdata.py @@ -562,6 +562,12 @@ def test_pspec(self): nt.assert_true(uvp.antnums_to_blpair(((52, 53), (24, 25))) not in uvp.blpair_array) nt.assert_equal(uvp.Nblpairs, 10) + # test mixed bl group and non blgroup, currently bl grouping of more than 1 blpair doesn't work + bls1 = [[(24, 25)], (52, 53)] + bls2 = [[(24, 25)], (52, 53)] + uvp = ds.pspec(bls1, bls2, (0, 1), input_data_weight='identity', norm='I', taper='none', + little_h=True, verbose=False) + # test select red_bls = [(24, 25), (37, 38), (38, 39), (52, 53)] bls1, bls2, blp = pspecdata.construct_blpairs(red_bls, exclude_permutations=False, exclude_auto_bls=False) @@ -592,6 +598,9 @@ def test_pspec(self): nt.assert_equal(uvp.Ndlys, 10) nt.assert_equal(len(uvp.data_array), 1) + # test exceptions + nt.assert_raises(AssertionError, ds.pspec, bls1[:1], bls2, (0, 1)) + def test_normalization(self): # Test Normalization of pspec() compared to PAPER legacy techniques d1 = self.uvd.select(times=np.unique(self.uvd.time_array)[:-1:2], @@ -658,6 +667,10 @@ def test_validate_blpairs(self): pspecdata.validate_blpairs(blpairs, uvd, uvd) + # test non-redundant + blpairs = [((24, 25), (24, 38))] + pspecdata.validate_blpairs(blpairs, uvd, uvd) + """ # LEGACY MONTE CARLO TESTS From cdd648b8058a7f1786102582e1e4d5dcae32389b Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Thu, 19 Apr 2018 12:34:03 -0700 Subject: [PATCH 29/54] addressed readability comments in uvpspec.py and noise.py from 4/19 telecon --- hera_pspec/noise.py | 14 +++++++------- hera_pspec/tests/test_noise.py | 10 +++++----- hera_pspec/tests/test_uvpspec.py | 10 +++++++++- hera_pspec/uvpspec.py | 31 ++++++++++++++++++++++++------- 4 files changed, 45 insertions(+), 20 deletions(-) diff --git a/hera_pspec/noise.py b/hera_pspec/noise.py index e8001d69..d186899a 100644 --- a/hera_pspec/noise.py +++ b/hera_pspec/noise.py @@ -20,14 +20,14 @@ def __init__(self, cosmo=None, beam=None): beam : hera_pspec.pspecbeam.PSpecBeam instance """ if cosmo is not None: - self.add_cosmology(cosmo) + self.set_cosmology(cosmo) if beam is not None: - self.add_beam(beam) + self.set_beam(beam) - def add_cosmology(self, cosmo): + def set_cosmology(self, cosmo): """ - Add a cosmological model to self.cosmo via an instance of hera_pspec.conversions.Cosmo_Conversions + Set a cosmological model to self.cosmo via an instance of hera_pspec.conversions.Cosmo_Conversions Parameters ---------- @@ -40,9 +40,9 @@ def add_cosmology(self, cosmo): self.cosmo = cosmo self.cosmo_params = str(self.cosmo.get_params()) - def add_beam(self, beam): + def set_beam(self, beam): """ - Add a pspecbeam.PSpecBeam object to self as self.beam + Set a pspecbeam.PSpecBeam object to self as self.beam Parameters ---------- @@ -63,7 +63,7 @@ def add_beam(self, beam): else: # neither beam nor self have cosmo, raise AssertionError raise AssertionError("neither self nor beam have a Cosmo_Conversions instance attached. "\ - "See self.add_cosmology().") + "See self.set_cosmology().") self.beam = beam diff --git a/hera_pspec/tests/test_noise.py b/hera_pspec/tests/test_noise.py index 5e6c996e..d1d1f3a4 100644 --- a/hera_pspec/tests/test_noise.py +++ b/hera_pspec/tests/test_noise.py @@ -23,20 +23,20 @@ def tearDown(self): def runTest(self): pass - def test_add(self): + def test_set(self): sense = noise.Sensitivity() C = conversions.Cosmo_Conversions() - sense.add_cosmology(C) + sense.set_cosmology(C) nt.assert_equal(C.get_params(), sense.cosmo.get_params()) params = str(C.get_params()) - sense.add_cosmology(params) + sense.set_cosmology(params) nt.assert_equal(C.get_params(), sense.cosmo.get_params()) - sense.add_beam(self.beam) + sense.set_beam(self.beam) nt.assert_equal(sense.cosmo.get_params(), sense.beam.cosmo.get_params()) self.beam.cosmo = C - sense.add_beam(self.beam) + sense.set_beam(self.beam) nt.assert_equal(sense.cosmo.get_params(), sense.beam.cosmo.get_params()) def test_scalar(self): diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index 2ac54379..c60e2a73 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -147,10 +147,18 @@ def test_get_funcs(self): k_perp, k_para = self.uvp.get_kvecs(0) nt.assert_equal(len(k_perp), 30) nt.assert_equal(len(k_para), 50) + # test key expansion + key = (0, ((1, 2), (1, 2)), 'xx') + d = self.uvp.get_data(*key) + nt.assert_equal(d.shape, (10, 50)) + # test key as dictionary + key = {'spw':0, 'blpair':((1, 2), (1, 2)), 'pol': 'xx'} + d = self.uvp.get_data(key) + nt.assert_equal(d.shape, (10, 50)) def test_convert_deltasq(self): uvp = copy.deepcopy(self.uvp) - uvp.add_cosmology(conversions.Cosmo_Conversions()) + uvp.set_cosmology(conversions.Cosmo_Conversions()) uvp.convert_to_deltasq(little_h=True) k_perp, k_para = uvp.get_kvecs(0, little_h=True) k_mag = np.sqrt(k_perp[:, None, None]**2 + k_para[None, :, None]**2) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index af3a4e2a..01c13395 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -267,7 +267,7 @@ def get_kvecs(self, spw, little_h=True): """ # assert cosmo - assert hasattr(self, 'cosmo'), "self.cosmo must exist to form cosmological wave-vectors. See self.add_cosmology()" + assert hasattr(self, 'cosmo'), "self.cosmo must exist to form cosmological wave-vectors. See self.set_cosmology()" # calculate mean redshift of band avg_z = self.cosmo.f2z(np.mean(self.freq_array[self.spw_to_indices(spw)])) @@ -502,7 +502,7 @@ def key_to_indices(self, key, *args): """ Convert a data key into relevant slice arrays. A data key takes the form - (spw, ((ant1, ant2), (ant3, ant4)), pol) + (spw_integer, ((ant1, ant2), (ant3, ant4)), pol_string) or @@ -514,6 +514,14 @@ def key_to_indices(self, key, *args): One can also expand this key into the kwarg slots, such that key=spw, key2=blpair, and key3=pol. + The key can also be a dictionary in the form + key = { + 'spw' : spw_integer, + 'blpair' : ((ant1, ant2), (ant3, ant4)) + 'pol' : pol_string + } + and it will parse the dictionary for you. + Parameters ---------- key : tuple, baseline-pair key @@ -525,29 +533,38 @@ def key_to_indices(self, key, *args): pol : integer """ # assert key length - if len(args) == 0: assert len(key) == 3, "length of key must be 3." + if len(args) == 0: + assert len(key) == 3, "length of key must be 3." + if isinstance(key, (odict, dict)): + key = (key['spw'], key['blpair'], key['pol']) elif len(args) > 0: - assert isinstance(key, (int, np.int)) and len(args) == 2, "length of key must be 3." + assert len(args) == 2, "length of key must be 3." + assert isinstance(args[0], (tuple, int, np.int)) and isinstance(args[1], (np.str, str, int, np.int)), "key must be ordered as (spw, blpair, pol)" key = (key, args[0], args[1]) # assign key elements spw = key[0] blpair = key[1] pol = key[2] + # assert types assert isinstance(spw, (int, np.int)), "spw must be an integer" assert isinstance(blpair, (int, np.int, tuple)), "blpair must be an integer or nested tuple" assert isinstance(pol, (np.str, str, np.int, int)), "pol must be a string or integer" + # convert blpair to int if not int if type(blpair) == tuple: blpair = self.antnums_to_blpair(blpair) + # convert pol to int if str if type(pol) in (str, np.str): pol = uvutils.polstr2num(pol) + # check attribuets exists in data assert spw in self.spw_array, "spw {} not found in data".format(spw) assert blpair in self.blpair_array, "blpair {} not found in data".format(blpair) assert pol in self.pol_array, "pol {} not found in data".format(pol) + # index polarization array pol = self.pol_to_indices(pol) # index blpairts @@ -775,9 +792,9 @@ def write_hdf5(self, filepath, overwrite=False, run_check=True): self.write_to_group(f, run_check=run_check) - def add_cosmology(self, cosmo): + def set_cosmology(self, cosmo): """ - Add a cosmological model to self.cosmo via an instance of + Set a cosmological model to self.cosmo via an instance of hera_pspec.conversions.Cosmo_Conversions Parameters @@ -883,7 +900,7 @@ def generate_sensitivity(self, beam): ------ self.sensitivity : noise.Sensitivity instance """ - assert hasattr(self, 'cosmo'), "self.cosmo must exist in order to instantiate a Sensitivity object, see self.add_cosmology()" + assert hasattr(self, 'cosmo'), "self.cosmo must exist in order to instantiate a Sensitivity object, see self.set_cosmology()" # instantiate a noise.Sensitivity object print "attaching self.sensitivity" From da218d73dfa4c67b3645a6178a6e39ef5507effe Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Thu, 19 Apr 2018 12:34:44 -0700 Subject: [PATCH 30/54] updated PS_estimation notebook w/ changes to uvpspec and pspecdata --- examples/PS_estimation_example.ipynb | 427 +++++++++++++++------------ 1 file changed, 243 insertions(+), 184 deletions(-) diff --git a/examples/PS_estimation_example.ipynb b/examples/PS_estimation_example.ipynb index 737e1ee3..3b1858b9 100644 --- a/examples/PS_estimation_example.ipynb +++ b/examples/PS_estimation_example.ipynb @@ -11,9 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -22,7 +20,9 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import copy\n", - "import os" + "import os\n", + "import itertools\n", + "from funcsigs import signature" ] }, { @@ -45,17 +45,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Cosmo_Conversions object at <0x115f0d490>\n", + "Cosmo_Conversions object at <0x110820250>\n", "Om_L : 0.6844; Om_b : 0.0491; Om_c : 0.2644; Om_M : 0.3135; Om_k : 0.0021; H0 : 67.2700\n" ] } ], "source": [ "# Instantiate a Cosmo Conversions object\n", + "# we will need this cosmology to put the power spectra into cosmological units\n", "cosmo = hp.conversions.Cosmo_Conversions()\n", "print cosmo" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate a beam object, and feed the cosmo conversions object into it." + ] + }, { "cell_type": "code", "execution_count": 3, @@ -83,6 +91,13 @@ "print(uvd.get_antpairs())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Configure data and instantiate a `PSpecData` object, while also feeding in the beam object." + ] + }, { "cell_type": "code", "execution_count": 4, @@ -91,22 +106,39 @@ }, "outputs": [], "source": [ - "# convert from Jy to mK\n", - "uvd.data_array *= uvb.Jy_to_mK(np.unique(uvd.freq_array))[None, None, :, None]" + "# slide the time axis of uvd by one integration\n", + "uvd1 = uvd.select(times=np.unique(uvd.time_array)[:-1:2], inplace=False)\n", + "uvd2 = uvd.select(times=np.unique(uvd.time_array)[1::2], inplace=False)\n", + "\n", + "# Create a new PSpecData object\n", + "ds = hp.PSpecData(dsets=[uvd1, uvd2], wgts=[None, None], beam=uvb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next convert from Jy units to mK. This involves calculating the effective beam area (see HERA Memo #27 and #43), which can be done with the beam object we instantiated earlier." ] }, { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "# slide the time axis of uvd by one integration\n", - "uvd1 = uvd.select(times=np.unique(uvd.time_array)[:-1:2], inplace=False)\n", - "uvd2 = uvd.select(times=np.unique(uvd.time_array)[1::2], inplace=False)\n", + "# find conversion factor from Jy to mK\n", + "Jy_to_mK = uvb.Jy_to_mK(np.unique(uvd.freq_array))\n", "\n", - "# Create a new PSpecData object\n", - "ds = hp.PSpecData(dsets=[uvd1, uvd2], wgts=[None, None], beam=uvb)" + "# reshape to appropriately match a UVData.data_array object and multiply in!\n", + "uvd1.data_array *= Jy_to_mK[None, None, :, None]\n", + "uvd2.data_array *= Jy_to_mK[None, None, :, None]\n", + "\n", + "# change units of UVData objects\n", + "uvd1.vis_units = 'mK'\n", + "uvd2.vis_units = 'mK'" ] }, { @@ -132,8 +164,9 @@ } ], "source": [ - "# Because the LST integrations are offset by more than a few seconds \n", - "# just because of the test-file we are using, we will get a warning.\n", + "# Because the LST integrations are offset by more than ~15 seconds we will get a warning\n", + "# but this is okay b/c it is still **significantly** less than the beam-crossing time and we are using short\n", + "# baselines...\n", "\n", "# here we phase all datasets in dsets to the zeroth dataset\n", "ds.rephase_to_dset(0)" @@ -152,6 +185,75 @@ { "cell_type": "code", "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify which baselines to include\n", + "baselines = [(24,25), (37,38), (38,39)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the `PSpecData.pspec` function to use the OQE framework.\n", + "\n", + "### Read the docstring! Here are the first few lines..." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ds.pspec(bls1, bls2, dsets, input_data_weight='identity', norm='I', taper='none', little_h=True, spw_ranges=None, verbose=True, history='')\n", + "\n", + " Estimate the delay power spectrum from a pair of datasets contained in this \n", + " object, using the optimal quadratic estimator from arXiv:1502.06016.\n", + "\n", + " In this formulation, the power spectrum from a single baseline-pair is constructed \n", + " from the visibility data via\n", + "\n", + " P = M data_{LH} E data_{RH}\n", + "\n", + " where E contains the data weighting and FT matrices, M is a normalization matrix, \n", + " and the two separate datasets are denoted \"left-hand\" and \"right-hand.\" The LH data\n", + " is a bl in the bls1 list from the dsets[0] dataset, and the RH data is a bl in the bls2 \n", + " list from the dsets[1] dataset.\n", + "\n", + " bls1 and bls2 are each interpreted as a list of baseline groups. A single baseline group is \n", + " itself a list of baseline tuples. If a bl group is length-1, it can be represented just as the \n", + " baseline tuple (Example 1). A bl_pair list is then formed by zipping each baseline group in \n", + " bls1 with its corresponding group in bls2. See below for various examples.\n", + "\n", + " To get help turning a single list of baselines into the appropriate form needed by pspec,\n", + " see PSpecData.construct_blpairs()\n", + "\n", + " Parameters\n", + " ----------\n", + " bls1 : list of baseline groups, where each baseline group is a list of baseline tuples\n", + "\n", + " bls2 : list of baseline groups, where each baseline group is a list of baseline tuples\n", + "\n", + " dsets : length-2 tuple or list\n", + " contains indices of self.dsets to use in forming power spectra, where the first index\n" + ] + } + ], + "source": [ + "print \"ds.pspec{}\".format(signature(ds.pspec))\n", + "print '\\n'.join(ds.pspec.__doc__.split('\\n')[:30])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -213,17 +315,11 @@ } ], "source": [ - "# Specify which baselines to include\n", - "bls = [(24,25), (37,38), (38,39)]\n", - "\n", - "# Calculate the power spectrum using: \n", - "# bls list for bls1 and bls list for bls2\n", - "# datasets 0 for bls1 and 1 for bls2\n", - "# spectral range selection\n", - "# identity weighting\n", - "# blackman-harris tapering func\n", - "uvp = ds.pspec(bls, bls, (0, 1), spw_ranges=[(300, 400), (600,721)], input_data_weight='identity', norm='I', \n", - " taper='blackman-harris', verbose=True)" + "# we will use the baselines list to produce 3 power spectra\n", + "# whose data will be drawn from the dsets[0] and dsets[1]\n", + "# across two spectral windows with identity weighting and a blackman-harris taper\n", + "uvp = ds.pspec(baselines, baselines, (0, 1), spw_ranges=[(300, 400), (600,721)], input_data_weight='identity',\n", + " norm='I', taper='blackman-harris', verbose=True)" ] }, { @@ -243,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -251,7 +347,7 @@ "output_type": "stream", "text": [ "spw 0 data shape = (Ntimes, Ndlys): (3, 100)\n", - "pspec units: (uncalib)^2 h^-3 Mpc^3\n" + "pspec units: (mK)^2 h^-3 Mpc^3\n" ] } ], @@ -263,7 +359,28 @@ "print \"spw 0 data shape = (Ntimes, Ndlys): \",uvp.get_data(key).shape\n", "\n", "# get power spectrum units\n", - "print \"pspec units: \",uvp.units" + "print \"pspec units: \", uvp.units" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spw 0 data shape = (Ntimes, Ndlys): (3, 100)\n" + ] + } + ], + "source": [ + "# this key can also be a dictionary, and the get_ functions will parse it\n", + "key = {'spw': 0, 'blpair': ((24, 25), (24, 25)), 'pol': 'xx'}\n", + "\n", + "# output should be shape (Ntimes, Ndlys)\n", + "print \"spw 0 data shape = (Ntimes, Ndlys): \",uvp.get_data(key).shape" ] }, { @@ -275,16 +392,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, @@ -292,7 +409,7 @@ "data": { "image/png": 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yValRRKRVKJyLiLS4QjoI5/HOPqayUzi+uraWvicEx4tNMJVWOBcRKYfCuYhI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\nVFP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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -321,14 +438,38 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Use redundant baseline grouping (auto & cross baseline pspec)\n", + "## Form redundant baseline spectra (auto & cross baseline pspec)\n", "\n", - "You can also feed `pspec` a list of redundant baseline groups and it will compute all cross spectra in each group." + "You can also use `pspecdata.construct_blpairs` to construct `baselines1` and `baselines2` in a way to form cross baseline spectra. This takes keywords `exclude_auto_bls` which will remove all baselines paired with itself from the final `blpairs` list, as well as `exclude_permutations`, which will use a combination instead of a permutation to form the baseline-pairs from the input `baselines` list." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[((24, 25), (37, 38)), ((24, 25), (38, 39)), ((37, 38), (38, 39))]\n" + ] + } + ], + "source": [ + "# bls is now a list of tuples\n", + "baselines = [(24,25), (37,38), (38,39)]\n", + "\n", + "# calculate baseline pairs\n", + "baselines1, baselines2, blpairs = hp.pspecdata.construct_blpairs(baselines, exclude_auto_bls=True, \n", + " exclude_permutations=True)\n", + "\n", + "print blpairs" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": { "scrolled": true }, @@ -341,17 +482,10 @@ "\n", "Setting spectral range: (300, 400)\n", "\n", - "(bl1, bl2) pair: ((24, 25), (24, 25))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building G...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", "(bl1, bl2) pair: ((24, 25), (37, 38))\n", "pol: XX\n", " Setting weight matrix for input data...\n", + " Building G...\n", " Building q_hat...\n", " Normalizing power spectrum...\n", " Computing and multiplying scalar...\n", @@ -363,20 +497,6 @@ " Normalizing power spectrum...\n", " Computing and multiplying scalar...\n", "\n", - "(bl1, bl2) pair: ((37, 38), (24, 25))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", - "(bl1, bl2) pair: ((37, 38), (37, 38))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", "(bl1, bl2) pair: ((37, 38), (38, 39))\n", "pol: XX\n", " Setting weight matrix for input data...\n", @@ -384,40 +504,12 @@ " Normalizing power spectrum...\n", " Computing and multiplying scalar...\n", "\n", - "(bl1, bl2) pair: ((38, 39), (24, 25))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", - "(bl1, bl2) pair: ((38, 39), (37, 38))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", - "(bl1, bl2) pair: ((38, 39), (38, 39))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", "Setting spectral range: (600, 721)\n", "\n", - "(bl1, bl2) pair: ((24, 25), (24, 25))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building G...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", "(bl1, bl2) pair: ((24, 25), (37, 38))\n", "pol: XX\n", " Setting weight matrix for input data...\n", + " Building G...\n", " Building q_hat...\n", " Normalizing power spectrum...\n", " Computing and multiplying scalar...\n", @@ -429,76 +521,32 @@ " Normalizing power spectrum...\n", " Computing and multiplying scalar...\n", "\n", - "(bl1, bl2) pair: ((37, 38), (24, 25))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", - "(bl1, bl2) pair: ((37, 38), (37, 38))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", "(bl1, bl2) pair: ((37, 38), (38, 39))\n", "pol: XX\n", " Setting weight matrix for input data...\n", " Building q_hat...\n", " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", - "(bl1, bl2) pair: ((38, 39), (24, 25))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", - "(bl1, bl2) pair: ((38, 39), (37, 38))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", - " Computing and multiplying scalar...\n", - "\n", - "(bl1, bl2) pair: ((38, 39), (38, 39))\n", - "pol: XX\n", - " Setting weight matrix for input data...\n", - " Building q_hat...\n", - " Normalizing power spectrum...\n", " Computing and multiplying scalar...\n" ] } ], "source": [ - "# bls is now a list of lists\n", - "bls = [[(24,25), (37,38), (38,39)]]\n", - "\n", - "# Calculate the power spectrum using: \n", - "# bls list for bls1 and bls list for bls2\n", - "# datasets 0 for bls1 and 1 for bls2\n", - "# spectral range of selection\n", - "# identity weighting\n", - "# blackman-harris tapering func\n", - "uvp = ds.pspec(bls, bls, (0, 1), spw_ranges=[(300, 400), (600,721)], input_data_weight='identity', norm='I', \n", + "uvp = ds.pspec(baselines1, baselines2, (0, 1), spw_ranges=[(300, 400), (600,721)], input_data_weight='identity', norm='I', \n", " taper='blackman-harris', verbose=True)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -506,7 +554,7 @@ "data": { "image/png": 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rCVmIvT19HBUeV+VcRKQOkVYHICIirZXKl6rWON/IWG4MgNWxhSXnkVCEdd3r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9tO4JYk1rQfjVs8GY6zPa3CzrbG/pFrCWGTW7uzlTULCijLjCb4ldnitaPhQvO2XYKU731CkPR7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5B3v2pTpY2a1l+1+PE2NNP4EcEAMZzm1rqZf1wnlv9JzvKgL5yZPF7RNH3amlw3I4L3vODZcr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\nEeV9Pw98KfA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -539,44 +587,57 @@ "\n", "You can convert the power spectrum data from $P(k)$ format to \"unitless\" power spectrum $\\Delta^2(k)$ by scaling by the cosmological wave-vectors cubed. This can be done by hand with the `get_kvecs()` method, or can be done automatically using the `convert_to_deltasq()` method.\n", "\n", - "If properly normalized, `UVPSpec` will have a `cosmo_params` list that contains the cosmological parameters used to normalized the power spectra. You can add these parameters to the `UVPSpec.cosmo` object using the `add_cosmology` method." + "If properly normalized, `UVPSpec` will have a `cosmo_params` list that contains the cosmological parameters used to normalized the power spectra. You can use these parameters to create a `UVPSpec.cosmo` object using the `set_cosmology` method." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'H0': 67.27, 'Om_k': 0.0020700000000000163, 'Om_M': 0.31353, 'Om_L': 0.6844, 'Om_c': 0.26442, 'Om_b': 0.04911}\n" + ] + } + ], + "source": [ + "# The uvp object has a dictionary of cosmo params used in the pspec run\n", + "print uvp.cosmo_params" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'H0': 67.27, 'Om_k': 0.0020700000000000163, 'Om_M': 0.31353, 'Om_L': 0.6844, 'Om_c': 0.26442, 'Om_b': 0.04911}\n", - "------------------------------------------------------------\n", "attaching cosmology: \n", - "Cosmo_Conversions object at <0x11b11b590>\n", + "Cosmo_Conversions object at <0x110820750>\n", "Om_L : 0.6844; Om_b : 0.0491; Om_c : 0.2644; Om_M : 0.3135; Om_k : 0.0021; H0 : 67.2700\n" ] } ], "source": [ - "# add the cosmological parameters to uvp\n", - "print uvp.cosmo_params\n", - "\n", - "print '--'*30\n", - "uvp.add_cosmology(uvp.cosmo_params)" + "# use these params to create a Cosmo_Conversions object that will be attached to uvp\n", + "uvp.set_cosmology(uvp.cosmo_params)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dsq units: (uncalib)^2 h^-3 Mpc^3 h^3 k^3 / (2pi^2)\n" + "dsq units: (mK)^2 h^-3 Mpc^3 h^3 k^3 / (2pi^2)\n" ] } ], @@ -593,16 +654,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -610,7 +671,7 @@ "data": { "image/png": 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NH1g4ibjYvjncNJs1YlDe2OEgMyku6jFFcvtFhXQ73RytbWfDkTqe22GuyMdY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\nSwyXTr2UTZWbcG5/BLb8H1T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8W/UuXa7wZSgPbywjKymOG87rXQn1y2uLUCjJlosR4Stb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\nAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -647,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -671,14 +732,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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vBMZywfU487kgFGA4HObEyBjDWiVURKRuSs5FRDqcu5McH6OnnIL+9Yxkg1lb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\nDHQMu2NpHgdMywgB7lCcrWUgmtRyfDPOQ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PjlgV2ddfgcFtLBN7A9ZaKZGd5/sFf3fmpB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\nEiJz97THkBo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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -723,10 +784,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": 22, + "metadata": {}, "outputs": [], "source": [ "blp_group = [sorted(np.unique(uvp.blpair_array))]\n", @@ -742,14 +801,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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0rkA+NU4oYHR3dzOeHScaiBL0Bxe8rY6WDqLlcH78qK4SKiKyUArnIiJNbiJX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\nxP8HxSocsr1LI2UAAAAASUVORK5CYII=\n", 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N7Br8tYwPu5qZe/ZKbxPMuiLcXDZv3sydd965kpAP2rlzJ9u3b6/KXFIbOkaNT8eoOeg4\nNT4do+ag49T4qnmMzOzJRW3XSJVzOHhltO9UVM4vAM5zzr23/PM7gbOAPwMux6+of8k59ykzezPw\nOqAH+Cfn3M455r8U/6IjDA4OvvCaa66pStypVIp4vGUXvmgKOkaNT8eoOeg4NT4do+ag49T4qnmM\nzjnnnLucc9sW2q6hKudL4ZwbAz4wa+xb+FdFnO9xVwBXAGzbts1V69OQPv02Ph2jxqdj1Bx0nBqf\njlFz0HFqfKtxjBrqhNAj2AscXfHzUeUxEREREZE1pRmS8zuAE8xsi5m1A28DblzJhGa2w8yumJqa\nqkqAIiIiIiLV0FDJuZldDdwKnGhme8zskvJljT8M3AI8BHyjfNnmZXPO3eScu7S7u3vlQYuIiIiI\nVElD9Zw75y46wvjNwM3V2o+Z7QB2bN26tVpTioiIiIisWENVzutFlXMRERERaUQtmZyLiIiIiDSi\nlkzOdUKoiIiIiDSilkzO1dYiIiIiIo2oJZNzEREREZFGpORcRERERKRBtGRyrp5zEREREWlELZmc\nq+dcRERERBpRSybnIiIiIiKNSMm5iIiIiEiDaMnkXD3nIiIiItKIWjI5V8+5iMjyfPCqu/j2Y/nV\nDkNEZM1qW+0ARESkOTw9nuF79++np8MolRyBgK12SCIia05LVs5FRGTpbnlgPwCTnuP+fWoLFBGp\nBSXnIiKyKLc8sJ9j+qIY8KMHD6x2OCIia1JLJuc6IVREZGlGkh53PjnBm1+wiRN6A/zwoeHVDklE\nZE1qyeRcJ4SKiCzNDx88gHNw3mkbOGOgjYeeSbBnIrPaYYmIrDktmZyLiMjSfP+B/Wzuj3LiYCdn\nDAQB+PGvVT0XEak2JeciIjKvqew0v3hslNedtgEzY0MswHHrY/xQfeciIlWn5FxEROb1418foFBy\nnHfqhoNjrzl5kNseHyOZm17FyERE1h4l5yIiMq/v37+fDV1hnn9Uz8Gxc08ZZLro+Nkjo6sYmYjI\n2tOSyblWaxERWZxMvsBPHxnhdacOHnLRoRcc00tvNMSPHlJri4hINbVkcq7VWkREFudnj4yQmy7x\nutM2HDIeDBivOmmQH/96mEKxtErRiYisPS2ZnIuIyOJ8//799EZDnLm5D4CPvucCvv/NfwHgNacM\nMJWd5s4nJ1YzRBGRNUXJuYiIzClfKPEfDw1z7smDtAUDDO99ks9+9d/5+r9dg3OOV5ywnvZgQFcL\nFRGpIiXnIiIyp1/8ZpSkV+C8ckvLTVd+Gudgz2SeO376A2Idbbx0az8/eugAzrlVjlZEZG1Qci4i\nInO65YH9xNqDvGzrOgBuuOHbbOwMEArAdf/yOQDOPXmQ3WMZfjOSXs1QRUTWDCXnIiIyp5/8eoTt\nJw4QDgVJje3nh/fu4cJzns+rtka47ns/xTnHq08eANCqLSIiVaLkXEREDpMvlNifyHHCYByAW776\nN3gFeNNF7+Hcs07jiZE0u375CzZ2RzhpQye/+M3YKkcsIrI2KDkXEZHDjKQ8AAa7wgB8+4Z/py8a\n4OVvvpRtr3oDQYN/L7e2HNsfZf9UdtViFRFZS1oyOddFiERE5jecyAEw0NnBdHKM79z5JDteegpt\n7R3YMWdxzvEdfPPGW3DOMdAZZjjprXLEIiJrQ0sm57oIkYjI/A4k/GR7oDPMf17zd0xkHW+68J0A\nuECIC171Qh59Zor77/0VA50dTGamyU0XVzNkEZE1oSWTcxERmd9Islw57+rghuuuIRIyXnvRfzt4\n/5su+n0CBtdd+fcMdHWUH6PquYjISik5FxGRwwwnPQIGfZbmhtsf5zUv3Eo0Hj94/+CL38Irj23j\nuhtuZKDcl67WFhGRlVNyLiIihxlOePTHO7jvpn/i6akSb3rLRYduEO7igleewoNPjZLc9zjwbLVd\nRESWT8m5iIgc5kAyx0BnBzd84yoCBq9/x4cO2+Z3LnwHBtx201WAKuciItWg5FxERA4znPA4PuZx\nw62P8fLTjmX9wMBh2wy9/O287Jgg373xWwQDxoGEKuciIiul5FxERA4znPQ4cf/N3Ddc5E2/+5a5\nN+rexAVnbebe3zxDPDfMcEKVcxGRlVJyLiIihygUS4ylPfb/8jsAvPEdHzjitm9+y4UABB/9sdpa\nRESqQMm5iIgcYjSVxznHzl/t5nlb1nPc8ccfcdujX/F2ztgQwHvk50rORUSqQMm5iIgcYjiZo9Ol\nuHPvNK8687RD7vuHXf/A7anbnx0YPJXjB+NkJ0e0WouISBUoORcRkUMMJzzW5feSK8DRRx9zcNwr\nelx5/5V8e+LbTBen/UEzhjYMMprIMprKM10srVLUIiJrQ9Mm52Z2nJl92cyumzUeM7M7zez1qxWb\niEgzO5DM0Zl6CoChY59taXlw7EGmS9MkS0l++OQPD44PbVhPMleklM8ymlJri4jISjRUcm5mV5rZ\nsJndP2v8PDN72MweM7OPATjnHnfOXTLHNH8MfKMe8YqIrEXDCY9weh8AQ1uec3B81/AuALqD3Vz7\n8LUHxzdtOsr/n5RWbBERWamGSs6BrwDnVQ6YWRD4PHA+cApwkZmdMteDzew1wIPAcG3DFBFZu4aT\nHuHsKABDxz/34Piu4V0c23Us53Sdw93Dd/Pw+MP+NkcfC0A89bROChURWaGGSs6dcz8DxmcNnwk8\nVq6U54FrgDceYYrtwIuBtwPvM7OGen4iIs1gOJEjkPH/Kd5YTrydc9wzfA9nDJzBi2MvpiPYwTce\n9r+kHDrWr65HUnsY1kmhIiIr0rbaASzCJuDpip/3AGeZWT9wOXCGmV3mnPuUc+5/A5jZxcCoc+6w\nM5PM7FLgUoDBwUF27txZlSBTqVTV5pLa0DFqfDpGjeE3+7IMTI3RFQ5wxx13ALB/ej+T3iTR8SjO\nHKeHT+eGR29gW3Yb0XIrSyi1n1/e+zCbsk+sZviCfpeahY5T41uNY9QMyfmcnHNjwJxXxnDOfWWe\nx10BXAGwbds2t3379qrEs3PnTqo1l9SGjlHj0zFqDNlf/Ih0OsNQX+zg8fjWo9+CffDWV76Vp3Y9\nxUdf/FHe9t23MTk0yflnvoV4+4dpy44R7d/A9u3PW90nIPpdahI6To1vNY5RM7R97AWOrvj5qPLY\nspnZDjO7YmpqakWBiYisNcWSYyTpMTaVYWhd98HxXcO76OnoYUvXFgBOXXcqp/WfxrW/vhYX6Weo\nM0AhPakTQkVEVqgZkvM7gBPMbIuZtQNvA25cyYTOuZucc5d2d3cvvLGISAsZS3sEXIH9iTxDA+sO\njt8zfA+nrz8dMzs49taT3spvpn7DnSN3M9QTJpNM6IRQEZEVaqjk3MyuBm4FTjSzPWZ2iXOuAHwY\nuAV4CPiGc+6B1YxTRGStGk54rHOTPJNyDA1tBGA8N87uxG5OHzj9kG3P23weXe1dXPvwtWzqjzOR\nyOiEUBGRFWqonnPn3EVHGL8ZuLla+zGzHcCOrVu3VmtKEZE1YSTp0e/tIV+EoaP8lVruGb4HgDMG\nzjhk23BbmN/Z+jt8/aGv87L1PYzcPUJbIkex5AgG7LC5RURkYQ1VOa8XtbWIiMztQCJHNOUvkLXx\nmOMAv988FAhx6rpTD9v+whMvpOAKHOgP4RUchWySsbRaW0RElqslk3MREZnbcNKjY+bqoMedDPjJ\n+Sn9p9AR7Dhs+2O6juF565/HcK9fKQ/oKqEiIivSksm5VmsREZnbcDJHe6Z8ddDjTsYrejw49uBh\nLS2VNsU34fX4f07iqacY0UmhIiLL1pLJudpaRETmdiDh4TITAGwcGuKB0QeYLk3Pm5wPRgdJdU4D\n0JHcq5NCRURWoCWTcxERmdtw0sNLTdEbbSMSibBreBfAYSu1VBqIDuC6/baWtvQBDqitRURk2Voy\nOVdbi4jI3EYSOaYSKYb64oC/Usvmrs30hfuO+JiB6ACBUICeqBHMjKlyLiKyAi2ZnKutRUTkcKWS\nYySVYzSRY+P6Hpxz3DNyz7xVc/DbWgD6uoJM6yqhIiIr0pLJuYiIHG4ik6ejmOGZRIGhwfU8kXiC\nSW9y3n5z8CvnAF19YdLJpK4SKiKyAkrORUQE8PvNBxjzrw66aRO7Dizcbw6wPrIegEhfhPFERqu1\niIisgJJzEREB/OS8N7uHQgmGjtrMruFd9HT0sKVry7yPCwVD9IX7aOuPMJrMc2AqjXOuTlGLiKwt\nLZmc64RQEZHDHUjkiKX2AjC0eSu/GvkVp68/HTNb8LGD0UHoC1NykEtMMJGZrnW4IiJrUksm5zoh\nVETkcCNJj7b0MwBsPO4U9qT2sKVn/qr5jIHoAIW+EABtqWe0YouIyDK1ZHIuIiKHG07kaMuMAdC9\nYYBCqUBfx5GXUKw0EB0g2+W3ssTTe7Rii4jIMik5FxERwO85L6X9q4N29HYA0BvuXdRj/eS86D82\nuZcDCVXORUSWQ8m5iIgAfs95NpWgP95Ohgyw+OR8MDpIW3cbAYNAaljLKYqILFNLJuc6IVRE5HDD\nSY/JRJqh/jiT3iQAPR09i3rsQHQACxj9nQEsM67lFEVElqklk3OdECoicijnHGPJDCMJj6GBPsZz\n48DS2loAerqC5NNTOiFURGSZWjI5FxGRQ01lp+ksTLIvWWJowyCTOb9y3tuxtOQ83h8hmUzphFAR\nkWVSci4iIgwnPQbdKPtTjo1DRzHujRMKhIiFYot6fFd7F+FgmI6+MOOJDAdUORcRWRYl5yIiwnDC\nozu7h6KDoWM2M5mbpLejd1EXIAIwMwaiAwT7IkxmCuwfT+oqoSIiy6DkXEREOJDIEU7PXB30RCZy\nE4vuN58xEB2g1N8OQGZylESuUPU4RUTWupZMzrVai4jIoYaTHqHUfgCGtpzEhDdBT3hxK7XMGIgO\nkO8JAtCeeoYRtbaIiCxZSybnWq1FRORQw8kcgfLVQYeOOsqvnC/yZNAZg7FB0l0lAGKpp3VSqIjI\nMrRkci4iIocaTnoU0v63iRs2bGDCW3pby2B0EHr8PyvtqX06KVREZBnaVjsAERFZfcOJHB3JJOu7\nOiAIyXxyyZXzgegAwViQ9jaw1Igq5yIiy6DkXEREGE56xBIZhvq7mPL8CvpyTgg1M/q7Arj0OMO6\nSqiIyJIpORcRaXHOOVKJKdLJaYaO7WciNwGw5BNCB6ODAHR1h/DSCSXnIiLLoJ5zEZEWly+W6C6M\nsC/pGNq48WBy3tfRt6R5+iP9GEasL0wymeJAQj3nIiJLpeRcRKTFpb0i690oB9KOoaOOZsJbXuU8\nFAjRH+kn1B9hPJFlKjNdi3BFRNY0JeciIi0u7RXoTO+l5GDomOOfrZyHl1Y5B7/vnL4O0vkSk7qW\nhIjIkrVkcq6LEImIPCvlFQin9wGwcfNzDlbOuzuWfi2IgegAxT7/KqETo/urF6SISItoyeRcFyES\nEXlW2isQTA0DMHTs8UzmJukMdRIKhJY812B0kFy3AVAcewrnXFVjFRFZ61oyORcRkWelvAKl1CgA\nQ0ND/tVBl7iM4oyB6ABet5+QR1J7yE4XqxaniEgrUHIuItLi0l4RLzWFGQwODjLhTSz5ZNAZA9EB\n2nr8VXpDqf2kcoVqhioisuYpORcRaXFpr0AqlWKgO0JbWxsTuYklL6M4YyA6QDAcJNZhWGqEpKfk\nXERkKZSci4i0uLQ3zWQyx8Z1/nk4K6mcz1yIqLc7wHRyXJVzEZElUnIuItLi8pkEz6RKDK3vwzm3\n4p5zgHh3iGwqQUqVcxGRJVFyLiLS4qazCZ5JOjYNriNTyDBdmqa3Y3nJeTwUJ9IWoa2zAy+XJanK\nuYjIkig5FxFpcYX0BMNpx4bB9YznxgHo6VheW4uZMRgdxMXa8fLTqpyLiCyRknMRkRaXnTyAA3p6\n+5nMTQLLuzrojIHoAIVokIxXJJWbrlKUIiKtQcm5iEiLy036a5x39/YfvDrock8IBT85n44Y6XyJ\nRMarSowiIq1CybmISIvLJUYA6Opdx0TOT86Xu5Qi+Ml5LuL//+jk1IrjExFpJU2bnJvZcWb2ZTO7\nrmLsZDP7gpldZ2YfXM34RESaRT7pt7J09w8y6fn/v9LKuUX8Py8TY6MrD1BEpIU0VHJuZlea2bCZ\n3T9r/DzAMapyAAAgAElEQVQze9jMHjOzjwE45x53zl1SuZ1z7iHn3AeAC4GX1S9yEZHmNZ32E/Ku\n/gHGc+O0BdqIh+LLnm8wOkignJxnJg9UJUYRkVbRUMk58BXgvMoBMwsCnwfOB04BLjKzU440gZm9\nAfgucHPtwhQRWTumM37rSfe6jUx6k/R29GJmy55vIDpwMDnPjSs5FxFZioZKzp1zPwPGZw2fCTxW\nrpTngWuAN84zx43OufOB36tdpCIia0c+mwKgq6eX8dz4si9ANGMgOkAwEgTAS46tOD4RkVbSttoB\nLMIm4OmKn/cAZ5lZP3A5cIaZXeac+5SZbQfeDHRwhMq5mV0KXAowODjIzp07qxJkKpWq2lxSGzpG\njU/HqP6cc0xn0wDce++9PDn1JCELzXscFjpORVc8mJynRp/RMV0F+l1qDjpOjW81jlEzJOdzcs6N\nAR+YNbYT2LnA464ArgDYtm2b2759e1Xi2blzJ9WaS2pDx6jx6RjVX266yD97WQIG5513Hv94wz+y\npW8L28/efsTHLOY4rX+oh0cBprM6pqtAv0vNQcep8a3GMWqotpYj2AscXfHzUeWxZTOzHWZ2xdSU\nlvgSkdaW8gpMezk6w0HMzG9r6VhZWwvAht5BAAoZ/TsrIrIUzZCc3wGcYGZbzKwdeBtw40omdM7d\n5Jy7tLu7uyoBiog0q7RXYNrziIdDFEoFEvnEinvOATb0bQCD6UyyClGKiLSOhkrOzexq4FbgRDPb\nY2aXOOcKwIeBW4CHgG845x5YzThFRNaKlFcg6+XpjLQfXOO8Gsl5PNxDWyRA0UvjFYornk9EpFU0\nVM+5c+6iI4zfTBWXRjSzHcCOrVu3VmtKEZGmlMkXyXkFOmNdTObKyXkV2lri7V0EI0EKXpZUrkBH\nPLjiOUVEWkFDVc7rRW0tIiK+lFcgky/QGY8y4U0A1amcx0IxApEARS9HyiuseD4RkVbRksm5iIj4\n0jmPZK5Ed2eciZyfnPd09Kx43lgohkWC5LwcyZyScxGRxWrJ5FyrtYiI+PLpKRKeo6uz82ByXs3K\neS6fV+VcRGQJWjI5V1uLiIgvn0kw5Tl6e7qfbWupQs95LBQjGAmS8QqkVDkXEVm0lkzORUTE502N\nkitAb28vk94k8VCcUDC04nlnKucZr6DKuYjIErRkcq62FhERX2p8PwC9vX3+BYiq0NIClZXzEkkl\n5yIii9aSybnaWkREfJmpEQC6evqZzE1WpaUFnq2c5wuOyWSmKnOKiLSClkzORUTEl5scA6Crbz0T\n3kTVK+cA4+NjVZlTRKQVKDkXEWlh2eQ4AN39A0zkJqqyjCJAPBQnEPH/xCRHn6nKnCIiraAlk3P1\nnIuI+PIp/6qgnX1+cl6tynk0FD1YOc9MHKjKnCIiraAlk3P1nIuI+HLpBAAdPb3kS/mqtrXMVM4z\nk8NVmVNEpBW0ZHIuIiK+fCYJQCnigOqscQ7QFmgjEusAwEuq51xEZLGUnIuItDAvkwag2F4EqnN1\n0BnxeBSAfHKianOKiKx1Ss5FRFpYPpclFDTSzk/Sq3VCKEBXV5e/j5TO7xERWayWTM51QqiICDjn\n8HJZOsNBJvP+iaF94b6qzd9dTs6nM4mqzSkista1ZHKuE0JFRCA3XcLz8sTDISZyfutJT7iKlfPO\nXgJtkM+mqjaniMha15LJuYiIQMorkPPydEY7mMhN0GZtdIY6qzZ/rD1OWzhIIZehUCxVbV4RkbVM\nybmISItKewWyXoF4NMykN0lPuAczq9r8sVCcYCRAwcuS9opVm1dEZC1Tci4i0qJSXoF0vkA8FmU8\nN17Vk0FhZq3zINP5HElvuqpzi4isVUrORURaVCabJZkr0RWPMelNVvVkUIB4KI5FAni5PCmvUNW5\nRUTWKiXnIiItKpeeJOFBZ2cnE7mJqlfOo6EoFg2SzedJ5ZSci4gsRksm51pKUUQEvNQUU56ju6eH\nCW+iqhcgAr9yHggHSHsFkqqci4gsSksm51pKUUQEMpPDFErQ09NDwkvQ3VHdfxNjoRjBaJCMVySp\nyrmIyKK0ZHIuIiKQGN0PQGdvLw5HpC1S1fmjoSiBSICUVyKZ1QmhIiKLoeRcRKRFpcaHAejq83vN\nO4IdVZ0/HooTDAcplmBiSlcJFRFZDCXnIiItKpMYAyDW66/SUu3kPBaKEYj6f2amRp+p6twiImuV\nknMRkRaVnhoHINzdBdQmOQ+GgwAkx5Sci4gshpJzEZEWlUtNANDeXZu2lsrKeXZiuKpzi4isVUrO\nRURaVDbp94F3dHf6/61F5TziV86zkyNVnVtEZK1Sci4i0qJymSQA7bF2oEaV84j/ZyaXHK/q3CIi\na1VLJue6CJGICHiZNABtkTYAOtqqm5y3BdqIxsIA5JITVZ1bRGStasnkXBchEhGBXDZLOGSUAiWg\n+pVzgHhXHIDpjIohIiKL0ZLJuYiI+Ml5Z7iNXDEH1CY57+r0V4LJZ1JVn1tEZC1Sci4i0qJyXp5Y\nuJ18MQ/UKDmPdtMWMiXnIiKLpORcRKQFOefIenk6I+01rZxHQzFCkQDTuQylkqv6/CIia42ScxGR\nFpTJF8nkCsSikZpWzuOhOMFIkOl8jnS+UPX5RUTWGiXnIiItKO0VSOeLxGIRcgW/ct4ebK/6fqKh\nKIFokLznkfKUnIuILKRtsRua2ZuXMf/3nHPZZTxORERqKJ1OkciVOCEer3nlPBAOkEvmSeUKoEWy\nRETmtejkHLhuiXM74ATg8SU+TkREaiyXniLhOeKdnXhFj7ZAG8FAsOr7iYViEA2SG/VIqnIuIrKg\npSTnABucc8OL2dDMksuIR0RE6iCbGCPhQXdXN17RIxwM12Q/sVAMIgFSXtGvnIuIyLyW0nP+VWAp\nLSpXAYmlhSMiIvUwNXYAB3T19OIVvZr0m4OfnAcjQdJeUT3nIiKLsOjKuXPuPUuZ2Dn3waWHIyIi\n9TA5sh+Anr5+pmpYOY+H4gQiAVJ5RyLt1WQfIiJrSdOu1mJmx5nZl83suoqxN5nZF83sWjN77WrG\nJyLSyJLjBwDo7V9f08p5NBQlGAniHEyML6orUkSkpS0qOTezXjPrK///ejN7s5mdWu1gzOxKMxs2\ns/tnjZ9nZg+b2WNm9jEA59zjzrlLKrdzzt3gnHsf8AHgrdWOT0RkrUhNjgGwbmAQr+ARbqtdz3kg\n4v+pSY4+U5N9iIisJQsm52b2XuAu4E4z+yBwPfBq4JryfdX0FeC8WfsPAp8HzgdOAS4ys1MWmOdP\ny48REZE5PJucb6xp5XzmIkQAqbF9NdmHiMhaspie8z8ATgUiwFPAFufciJl1Az8FvlStYJxzPzOz\nzbOGzwQec849DmBm1wBvBB6c/XgzM+Av8ddXv7tacYmIrDXp5BQAfeuH8PZ5NVnjHMoXISpXzjOT\namsREVnIYpLzQvlCQlkze8w5NwLgnJsyM1fb8ADYBDxd8fMe4Cwz6wcuB84ws8ucc58CPgKcC3Sb\n2Vbn3BdmT2ZmlwKXAgwODrJz586qBJlKpao2l9SGjlHj0zGqn4lRv+f8gYcfZbQ4SjwQX/Rrv5Tj\nNF4YP1g5H977lI5vneh3qTnoODW+1ThGi0nOi2YWds7lgLNnBs0sXruwFuacG8PvLa8c+xzwuQUe\ndwVwBcC2bdvc9u3bqxLPzp07qdZcUhs6Ro1Px6h+rvvMNACve93ruHrn1Wzs2rjo134px2nKmyLw\nS79y3lbK6/jWiX6XmoOOU+NbjWO0mBNCzwU88KvlFeNRyhXoGtsLHF3x81HlsWUzsx1mdsXU1NTC\nG4uIrEHZTBqAzvIVQmva1hL2/9R4qcma7ENEZC1ZMDl3zk055w5rX3HODTvn7qhNWIe4AzjBzLaY\nWTvwNuDGlUzonLvJOXdpd3d3VQIUEWk2uWyGeEeAQCBQ0+Q8FAgRjUcAyKd1XToRkYUsa51zM7vc\nzN4/x/gHzOzPlxuMmV0N3AqcaGZ7zOwS51wB+DBwC/AQ8A3n3APL3YeIiEAmmyMe9jsba5mcA3R2\ndvn7ySRrtg8RkbVi0VcIneWdwJvnGL8LuAz4f5czqXPuoiOM3wzcvJw552JmO4AdW7durdaUIiJN\nJZvLEw37yyfmi/maJufxjjjtYSOfTddsHyIia8VyrxA6AIzNMT4GDC4/nPpQW4uItLpsLk880oFz\njlwhR0db7ZLzWChGKBIkn8syR5ekiIhUWG5y/hTwyjnGX4m/1KGIiDSwdK5ANBKmUCrgcDWtnMdC\nMdrCQfJejtx0qWb7ERFZC5bb1vLPwGfKJ2j+uDz2auBTwF9VI7BaUluLiLSyUrFEOl9kUyxKrpgD\nqHlyHowG8bw8SW+aSHuwZvsSEWl2y6qcO+f+D36C/jngEeBR4LPAF51zf1298GpDbS0i0soymSRT\nOUcsFsMrekDtk3OLBMl606RyhZrtR0RkLVhu5Rzn3GVm9kngBYADdjnndLaPiEiDyyYnSXiOeGcn\n+WIeqFdyniPlKTkXEZnPcnvOMbP/gb+04U7gp8CvzeyjZmZViq1mdBEiEWllyckR0tMQ7+qpS1tL\nPBSHSICUV1TlXERkActd5/yvgU/gt7a8pnz7AvBxmqDnXG0tItLKxoefAaCzu6culfNoKIqLGCmv\nRFKVcxGReS23reW9wHudc9dVjP3YzB7GT9j/nxVHJiIiNTGTnHf39pMrlCvnNVxKMR6KE4wGyUxD\ncioBbKjZvkREmt2y21qAe48wtpI5RUSkxqbGhwHo7RuoW895IOz/aZga0Wq7IiLzWW4i/TXgQ3OM\nfxD41+WHUx/qOReRVpacGAWgd/1AXXrOo6Eowai/fGJibH/N9iMishYst62lA3i7mb0OuK08dhYw\nBHzdzD43s6Fz7g9WFmL1OeduAm7atm3b+1Y7FhGRektM+Bd47l+/sS6V83gofrBynlRyLiIyr+Um\n5ycBd5f//9jyf/eXbydXbKfrNIuINJh0chKAgaGj2Fd8CqjPRYgAMlOjNduPiMhasKzk3Dl3TrUD\nERGR+kgl/Ja+wXXr2J15DKhDz3nEr5wrORcRmZ9O3hQRaTGZdAqAnp7ug1cIbQ+212x/sVCMYNiv\nnOeSEzXbj4jIWrCkyrmZ3biY7Zxzb1heOPVhZjuAHVu3bl3tUERE6i6dThM0iEajeAU/OQ+3hWu2\nv3goTiDq14JyKZ2ILyIyn6VWzl8PPBcYW+DW0HQRIhFpZdlMhng4iJnVpXIeDUUJRvzKeT6drNl+\nRETWgqX2nH8aeCfwSuBfgK8457RorYhIE8lkc8Q6/H/+vaJH0IKEAqGa7S8UCBGOhAkEwMukarYf\nEZG1YEmVc+fcHwNHAx8FtgGPmtn3zOwCM6vdv+wiIlI1maxHNOxXyr2iV9Oq+Yx4e5z2cAAvm6n5\nvkREmtmSTwh1zhWdczc6594EbAF+AnwS2Gtm8WoHKCIi1ZXxpolF/NVZvKJHOFi7fvMZsVCM9kiQ\nvJet+b5ERJrZSldriQE9QBxIoXXNRUQaXipXIBLxE/J6Vc5joRhtkSC5nEehWKr5/kREmtWSk3Mz\ni5jZu83sZ8B9+Bcherdz7jjnXLrqEYqISPWUSqS8EtFoDACv4NV0pZYZ0bYowUgbnpcnM12s+f5E\nRJrVUpdS/CJwIfAo8GXgDc65yVoEVktaSlFEWlY+xVTOEY1Fgfr2nAciQTLJHGmvQFdYpymJiMxl\nqau1XAI8BTwDnA+cb2aHbdTo65w7524Cbtq2bdv7VjsWEZF6KnppEp4jFu8EwCt5dARqd3XQGbG2\nGBYJkPaKpD1VzkVEjmSpyfnXUF+5iEjTmpgYwytCvLOcnBc8OtrqkJy3xyAaJOUVSXuFmu9PRKRZ\nLSk5d85dXKM4RESkDvbv3w9AV1cXAPlinnh77RfairXFcBEjkXOkcvma709EpFmtdLUWERFpIgcO\nHACgr7cXgFwxR0ewPpVzFzGmSzA1NVXz/YmINCsl5yIiLWR4ZASA/v5+wK+c1yU5b4sRjAQBGB/e\nV/P9iYg0KyXnIiItZHTUT87Xr1sH1K9yHm+PE4z6yfnU2P6a709EpFkpORcRaSHj4+MAbBgcBOpX\nOY+GogQi/p+cxNhwzfcnItKslJyLiLSQqUn/0hQbNvjJea6Qq89qLRVtLanJsZrvT0SkWbVkcm5m\nO8zsCp2UJCKtZubfvYGBjUD9Kuf+RYj8PzmpKSXnIiJHsqjk3Mx6zayv/P/rzezNZnZqbUOrHefc\nTc65S7u7u1c7FBGRukqmkgQM4j39FEoFCq5QlyuERtuiB3vO01NNd2FpEZG6WTA5N7P3AncBd5rZ\nB4HrgVcD15TvExGRJpFKpujqgEBHnHzRX288HAzXfL+VlfNsKlHz/YmINKvFXIToD4BTgQjwFLDF\nOTdiZt3AT4Ev1TA+ERGponQmQ0/YoK0Dz/Mr2PWonMfaYgTDfuU8m1JLoYjIkSwmOS8457JA1swe\nc86NADjnpszM1TY8ERGppkw6Q1dHEMzwih5Qn8p5LBTD2oz2diObSdV8fyIizWoxPedFM5v5l/vs\nmUEzq/31nkVEpKrSWY+u8qopM8l5PSrnoWCI9kA74XCAbDpd8/2JiDSrxSTn5wIe+NXyivEocGkt\nghIRkepzzpHJeXSF/S9Nc4UcAOG22lfOwe87D4UD5HK5uuxPRKQZLdjWMishx8w2OOf2O+eGAV1J\nQkSkSaTzRbK5PF3RCMDBE0LrsZQi+Cu2hCJBctlsXfYnItKMlrPO+Q+qHoWIiNTcZCZPxivQFfXb\nWHJFv4Jdr+Q83h6nLRokm/Pqsj8RkWa0nOTcqh6FiIjU3HjKI50r0BXzk/HVqJwHIkGyuTzOaT0B\nEZG5LCc517+oIiJN6JnRSRzQ0xkF6ntCKJRXbIkGyeYLZKeLddmniEizWU5y3hDM7Dgz+7KZXTff\nmIiI+PYOjwLQEz80Oa/HUooA8VAcIkHSuQJpT8m5iMhcGio5N7MrzWzYzO6fNX6emT1sZo+Z2ccA\nnHOPO+cuqdxurjEREfHtHxkDoK/bXwm33pXzozqPIhcNkJ0uMZXWii0iInNZTnJey3LHV4DzKgfM\nLAh8HjgfOAW4yMxOqWEMIiJr0sjYBAB93Z0AeIVy5bxOSyk+p+85WNT/s3NgbLIu+xQRaTZLTs6d\nc2fUIpDy3D8DxmcNnwk8Vq6K54FrgDfWKgYRkbVqZMz/57W/pwuof+X8xN4TCZYvgDQyPlGXfYqI\nNJsF1zlvAJuApyt+3gOcZWb9wOXAGWZ2mXPuU3ONzZ7MzC6lfPGkwcFBdu7cWZUgU6lU1eaS2tAx\nanw6RrX1xO4nAchksuzcuZOHph4C4Pb/up2QhRY9z3KPU8mVaC8n57+47XZ6iqqe14p+l5qDjlPj\nW41jtOzk3MzeCrwaGGBWBd4594YVxrUg59wY8IGFxuZ43BXAFQDbtm1z27dvr0o8O3fupFpzSW3o\nGDU+HaPacl/6LgAnn3wy67dv5/5d92OTxrnbz8Vs8avkruQ4HX1LF08Aff39OtY1pN+l5qDj1PhW\n4xgt64RQM/s0cBWwGZgExmbdqmkvcHTFz0eVx0REZAnSCb+VpLunG/DbWjqCHUtKzFdqS3c/AJNj\nI3Xbp4hIM1lu5fxdwEXOuXosWXgHcIKZbcFPyt8GvH0lE5rZDmDH1q1bqxCeiEhzyCTGibRBe/TZ\nnvN69ZvPOKlvCIADo7vrul8RkWax3KUUA8A91QwEwMyuBm4FTjSzPWZ2iXOuAHwYuAV4CPiGc+6B\nlezHOXeTc+7S7u7ulQctItIkcqkpesIG5dVZ8sV83dY4n/H8weMAODDxRF33KyLSLJZbOb8CeAfw\nieqFAs65i44wfjNwc7X2o8q5iLQa5xz5dIK+sEHIvwhRrpire+X8hUMnAjCW2FfX/YqINItFJ+dm\n9rmKHwPA75nZa4B7genKbZ1zf1Cd8GrDOXcTcNO2bdvet9qxiIjUQzpfpJhL+ZXz0LOV845gR13j\nWN+7EQvCZKrapyeJiKwNS6mcP3fWzzNtLSfNGnfLD0dERGphMpOn5GXojvJs5byQo6Otvsm5dcTp\nCAdIZKbqul8RkWax6OTcOXdOLQOpJ7W1iMiKOQcP3QgdnXD8q1Y7mgVNZqYpeBl6+g7tOa935Zz2\nGPGwkc1myBayRNoi9d2/iEiDW+4JoU1NJ4SKyIp4Kbj+/fCNd8HN/2u1o1mUqew0016O7o5De85X\nIznv7jBK2RKPTTxW332LiDSBlkzORUSWbf/9cMXZcN83YeBUGH8cprOrHdWCJjJ5pnPZcs+5X61e\nncp5nPXtUMwWeXji4fruW0SkCSg5FxFZDOfgzivhi6/yK+fvuhHO/l/gSjDS+Enm8ESSYrFId8UJ\noatVOV/fAaVMiYfHG/91ExGpt5ZMzs1sh5ldMTWlE5JEZHHu+OKH4TsfZfrol8AHfg5bXuFXzgGG\nH1rd4BbhwKh/ddCeiqUUV6Vy3hbxW2syjkcmHqnvvkVEmkBLJufqOReRpXDOcdy+m/hh8YW8Yu+H\n+NnMEt19x0GwA4ZXdF20ujgw6i9dWHkRIq/o1X21FgIB4uE2Srkij0w8gnNa4EtEpNKiknMzi5jZ\npjnGT61+SCIijWX/vqfpZ4r80S8jHungXVf+ko9/+34yRWD9c+DAg6sd4oJGxv3KeXcHByvnXsGr\nf+UciEfamc4VSeaT7E3trfv+RUQa2YLJuZldADwKfNfM7jWzsyru/teaRSYi0iD2PXInACc89yy+\n85GX8/sv28LXbn2S3/7cz5mIn9AUbS1j45MA9ESCEAwBfuW83lcIBYhHOnAlR8kr6aRQEZFZFlM5\n/1Pghc6504H3AF82s7eX77OaRVZD6jkXkaVIP/UrADadtI1wKMjHd5zCv73vLFJegRuf6YbkPshO\nrHKU85ua9JPz7ngEzCi5EvlSnnAwXPdYYlF/ny7reGRcfeciIpUWk5yHnHMHAJxzdwGvBN5vZh+n\nSa8Gqp5zEVmKttGHGLceYr0bDo699Ph1nHfqALemB/yBBm9tmSwXI3pizy6jCKxK5bwzFgOgnwFV\nzkVEZllMcj5sZs+b+cE5Nw68BjgZeN4RHyUiskb0px7lQOTwKwo/WPgCt62/1f9huLGT82QiAUB3\np58Ye0UPYFUq511xv+e93w1qOUURkVkWk5y/ExiuHHDO5Z1zFwFnz97YzNZVKTYRkVU3lc5ybOlp\nvL4TDxkvloo8lbuLYnw3T0W6Gzo5d86RSU4RMIjHD03OV6Vy3tUJQLzYx57UHlL5VN1jEBFpVAsm\n5865Pc65/QBm9v/Puu+/Kn82s37gP6oaoYjIKtr9yH2EbZqOTYd+UfjY5GPkihkAbujZ2NBtLZl8\nkUIuRVckhLX7bS1eoVw5b1uFynmX31IY9roAeHTy0brHsBJ7U3v55G2f5Od7f77aoYjIGrTUdc7/\np5l9eK47zKwPPzEvrTiqGtMJoSKyWONP7AJgcOsLDhm/e/huAEr5Pr7f7vwVWxp0ze6JTJ6Sl6Er\n0vbsMoqrWDnv6ekBIJjzPyg0S2tLejrN5+7+HG+4/g1c+/C1XPXgVasdkoisQUtNzt8K/I2ZXVQ5\naGY9wA+BIHBulWKrGZ0QKiKLVdx3P0UC9G5+7iHjuw7sYiA6QDi7nacDWR4tZSDRmGt2T2amKeVS\n/jKKMxcgKq1ez3lvbx8AuWSJzvbOhj8ptORKXP/o9bz++tfzxfu+yGs3v5Zzjj6H+0bv00WURKTq\nlpScO+e+A7wPuNLMXgdgZt34iXkEeJVzbqzqUYqIrJLY1CPsb9uEhSIHx5xz3DV8Fy8ceCFbwi8F\nZ3wvHm3Y1pap7DQlL0N32A65ABGsUuW8txeAxNQEJ/ae2NDLKTrneP8P38/Hf/FxhuJDfP23vs6n\nXvEpzj7qbBL5BE8mnlztEEVkjVlq5Rzn3L8ClwH/bmbnAz8AOvET85EqxycismryhRJH5R9nqus5\nh4zvS+9jODPMGYNncHzfRkK547g5FsMduH+VIp3fZGaakpemN2wQKlfOy20tq3GF0HC8m852SE2N\nc1LfSTwy8cjBeBrNeG6c2565jXef8m6uOv8qnrfeP/fguev9b1LuG71vNcMTkTVoyck5gHPu74DP\nAN8BeoFzZk4aFRFZK57Yu5+jbRgbPPWQ8bsP+P3mLxh4Aceui5KYeAF7Q23cu/+O1QhzQX7PeZqe\nDqD8DcBqLqXYFu6kO/x/2Tvv8Diq63+/d4tW0kpa9WrZki1bLnLBNsbGxgVMqKYmlEBCgFCS/PgG\nUjCQBAgt9JIChhA6oUOMqcbGxr1XucmSrN7rStvL/f0xqt5VtaSVYd7n2Uf2tD27c/fOmTPnfI7A\n0ljPnOQ52D32tu90uJHfmA/AnOQ5CNHed2+MaQyhulD2Ve8LlGkqKirfU3R92VgI8elxi1xAI/Bi\nx0lLSnnRiZumoqKiEljKcnaRCZjSpnVavrtqN+H6cDIiM8iNrsLdNIkg+SFfNOYwNTCmdkujzYXX\nbiEyWAu6zs55INJaRFAYJoPA2tTIzISZ6DV6NpVtYk7ynCG3pSeONR4DYLRpdKflWo2WSbGT1Mi5\niorKgNPXyHntca93gGw/y4c1qlqLiopKb7CUKFFRH6WWyl1MjZ+KVqNlVEwoeIOZoYnja40Nt8sW\nCFO7pa7JhnRaidR7hkXknCAjEQaBzdJEqD6U6QnTh60sYX5jPiG6EBKNiT7rJsdO5kj9kWGbkqOi\nonJy0qfIuZTy+sEyZCiRUq4AVsycOfOmQNuioqIyfNFXH8QqQgiNGoXD4aCqqoomZxOHCw9zxpQz\nqKqqYkSkotU9XjuZzbKKbUdXcPrEKwJseWeq6hoAiAzyDAspRYKMmIKhxKI0H5qXPI+ndj5FhaXC\nrxMcSPIb8kk3pXdKaWllStwU3F43h2oPMS1+mp+9VVRUVPpOv3LOVVRUVL7vSCmJseZSHTIGNBrO\nPZLvDSwAACAASURBVPdcRo4cyaSMSRy5/Qh/OPMPJCQk8ItrriLGGATyNMK8Xr7IXxFo032oqqkH\nILJjQWgAmxARZMRkENisFgBOTzkdgM1lm4felh44Zj5Guind77opsUpxaHepLU+tPMJTK4e3VKSK\nisrwok+R844IIRKAuUA8xzn5UsrnT9AuFRUVlYBS1mBjrCykMuYCGhsbWbduHZdffjmaTA1byrbw\nx5l/5OMPP2b9+vWccfYf2GNJ4Uy3jdV1B/iLxxEQFZSuqGtQnPNOUoqBjpwbBHabkgI0NnIs8SHx\nbCjdwKVjLx16e7rA6rJSYanwyTdvJS40jkRjIvuru3bO391eTKPVxQ1z04kyBuC7VlFROenol3Mu\nhLgWeBkQQD3QsQuDBFTnXEVF5aQmL+8o84WVxhFT2LBhA16vl9/85jf8x/YfFrCA286/Dbywdu1a\n4rU2DtRrudUUyafSxfqS9SweNXz6sdXXt6S1BIu2JkROjxMIjJRia8653W4HQAjB6Smns7poNW6v\nG52m33GjAaWrYtCOTI6dzL4a/4otVWY71U3KTdDHu0u5cZ7/CLyKiopKR/qb1vIw8DhglFImSimT\nOrySB9A+FRUVlYBQn69I+8WPOYU1a9ZgMBg45dRTOFB7gFMSTgFg0iRFYlHTUEJZo40ZUROJ9sIX\nx74ImN3+aGhQit9NhvbIud1jR6/RoxEByG4MCsMULHC73Tidyk3C3JS5NDmbyK4ZPlrxrTKK3Tnn\nU2KnUNpcSp29zmdddpnyvYcH63h3W5HaTVRFRaVX9HdWjgBek1K6B9IYFRUVleGCp0JxEoNTJrNm\nzRpmz55NbnMubq+bGfEzAMjKygLAWVOIlNAcnsk8i4Xdw0izW0pJU5PiJHbMOXd6nIFLvWlJawFo\nVc2akzQHjdCwsWxjYGzyQ35jPjqhIzUitdPyRquLv315iE15Ne3NiPyktmSXmhECbl88jqNVzewq\nqh8Su1VUVE5u+uucvw1cMJCGqKioqAwnwhpzqNPFU2+X7N69m0WLFrGrSnG6W5U54uPjiYuLo64k\nD4DSoDRGuF3U2GuHjbye1enBZVMKL03BtEkp2j32wDnn2iDCg5XLj9lsVmwzmMiKzWJj6TByzhvy\nSY1IRa/Rty379nAlP3r2O178Lp9l3+UzMWYiWqH1m9qyv7SR9FgjV52aijFIyzvbiofSfBUVlZOU\n/jrnvwPOE0L8TwjxoBDi3o6vgTRQRUVFZahptLlIdR3DHDGO9evXI6Vsc84zIjMwGUxt206aNImS\nvBwAcmQqKW4PABWW4dE0ucHmwmtXJAs7prUENHIuBMYQ5b3rWvLhQZFUzK7Jpt4+PCLM+Y35bSkt\njVYXv3t/Dze8toPIkCDOGBvLrsJ6gjTBjI0a6zdyfqC0kckpJowGHRdNS+azfWWY7a6h/hgqKion\nGf11zm8BzgVOBy4FftLh9eOBMU1FRUUlMBwpqSFDlCESJrFmzRqCg4OZeepM9lbtZXp854ZEWVlZ\nHDl8kLAgLdnWKJKkFoDS5tJAmO5DvcWJ12Eh2GBAr20vCLW77Rh0gVOUMYYqEfzK2vZc7bkpc5FI\ntpRvCZRZbbi8LkqaSkg3pbP2SBVnP/Mdy/eUcduZGay4bR6XTx9Bs8PN4Qozk2Mns79mP17pbdu/\nttlBWaOdrGTlRu6qU0did3lZvqcsUB9JRUXlJKG/zvlfgN9LKeOllFlSyskdXlMG0sDBQO0QqqKi\n0h1lefvRCw+R6dNYs2YNp59+OsW2YppdzW3FoK1kZWXR3NxMvKaJgnoHKaY05RjNw8MJa7S5kA4r\nEWGKMzwsIudAmFGxo6qm3TmfFDMJk8E0LLqFFpuLcUs3I8PSuPWtnZhC9Cz/zVx+/6NMgnQaZqZF\nAbCzsJ7JsZNpdjVT0FjQtv+BMiVdZ1KK0qRqyggTE5IieHdb0ZB/FhUVlZOL/jrnWuDTgTRkKJFS\nrpBS3mwymXreWEVF5QeHtXgvAJ6INPbt28eiRYvYWbkTwG/kHCCkuYzCWivxMRPQSjlsnPMGq5LW\nEhmmOMOtBaEBzTkHwsLCAKitbw+SaDVa5iTNYVPZpoArm7QqtQSThN3l5deLxpCV0n7NSIkMIckU\nzPaCeqbEKTGpjnnn+0uVzzWpJXIuhODqWakcKDOzv0QNDKmoqHRNf53zV4FrBtIQFRUVleGCruYw\nbnSs3q/I34WMD+Hz/M9JNCaSHNZZLbZVTlHWF1NSb0UTOZIEj4eyppJAmO5Dg82J12ElMqylE+gw\niZyHhysR5dq6hk7L56bMpcZWQ059TiDMaqPVObdbYwEYGx/eab0Qgplp0Ww/VkdaRBph+rBOeecH\nyhoZFROKKaS9mPTiaSkE6zW8s12NnquoqHRNf53zUOB3QoiNQogXhBB/7/gaSANVVFRUhpLCWgs7\nDPu4ODWFX//7NkSQ4JXGV8iuzeb89PN9to+MjCQlJYXm8mO4PJIGfQLJLjdl5sIAWO9Lg9WF12Eh\nOqzFEW/JOXcEuItphElxzlu7l7ZyevLpAAFPbclvzCfRmEhRjQchYExcmM82p6ZFUWG2U97oYFLs\nJPbXtDvn2aXmtnzzVkwhes6fnMSne8qwOFQlYhUVFf/01zmfAOwGnMB4YHKHV9bAmKaioqIy9Hy0\ns5hvo2zo9cHo8/VMmzWNjy/9mK0/3codM+7wu09WVhaVhUcBKCOOZLebMkv5UJrdJQ1WJ9JpJbK1\ndXyLlGKgnXNDaAQGnaC+oXOKR3xoPOOixgVc7zy/QVFqya1uJiUyhJAgrc82M0dFA7CjoJ4psVPI\nqc/B5rbRaHVRVGdtyzfvyNWzRtLscPP5vuExPlRUVIYf/XLOpZSLunmdOdBGqqioqAwFUkqW79uL\nQyNYIsZQnlfOT87/CZnRmQS3RJz9MWnSJIryjyK9HvKdkSS7PVQ56nF5Ai+b12B1IR0WIkP1oNGB\nVkmzcHgcBGmDAmaXJjiMcIPA3Gj2WTc3ZS67K3dTa6sNgGXglV4KzAWkm9I5WtnE2HjfqDlAZmI4\n4QYd2wvqmBw7GY/0cKj2EAdaOoMeHzkHmDkqioz4MD7YqWqeq6io+KfXzrkQYpYQwjd00PX2M4QQ\n+p63VFFRURke7Cqqx24/CEDNMWXZokWLetwvKysLu92OaK7isN1EstuNZHhonddbnXjszZhCdG35\n5gAOt6PbG47BRhcchskgMJt9iyMvybgEt3TzQc4HAbAMKi2V2Nw20iLSya+xkNGFc67VCKaPimJH\nQX17p9Ca/WS3Oucpvs65EILzsxLZWVhPozXwN28qKirDj75EzjcD0X3Yfg2Q2uNWKioqKsOEj3aV\nEhmiRDSPHjZjNBqZMWNGj/u1KraYbBXk1nlI0RoBKLMEXrGlrrEZ6XETGaJpS2kBcHgdBGkCFznX\nhYQTGQxNZt/I+WjTaOYmz+X9I+8H5OlDazGoUSTjdHt9ikE7MnNUFEcqm9BLE8nGZMU5LzWTEhlC\ntNH/9zt/XBxeCRvzagbFfhUVlZMbXR+2FcDfhBDWXm4fuFlfRUVFpY843B4+31dO1og67B4Pm3fl\ncMYZZ6DX9/wAcMKECQDoGksoqrOSZEwAGoeFnGJNnVJwaTK0NyCCYRA5N4RhMkBds69zDvDTCT/l\nN6t/w8rClVww+oIhta3VOXfb4wAzY7qInAPMTFNiVjuL6hgfPZ6c+hyayxqZlOybb97KtNRIwoN1\nrMup5vzJSQNqu4qKyslPXyLn64AxdC7+7O61GbANpLEqKioqg8Waw1U02ly4gupJqrFx8Ehur1Ja\nQNHsTk9Px1lTREGthYSIkWjk8OgSWtegSBVGBtOW1iKlDHzOuUFJa7E2+df8npcyj7SINP576L9D\nbJninJsMJsrrlPhVV2ktoDjaOo1ge0E946LHUWgu5Fhtg9+UllZ0Wg1zx8SyLqc64HruKioqw49e\nR86llAsH0Q4VFRWVgPLRrlJiw4Mo8ZoZkaOkUvTWOQcltWVX9hF0Li+u0BTiLXsoD3DkXEpJY6tz\nbqCtAZHL60IiCdYGLnJOkBFTsMBe1eR3tUZouHr81fxt29/YV72vrdHPUNBRqSUhwtBJq/x4QoK0\nZKWY2FFQx80TxuGVXkRQJVkpc7p9j/nj4vjqQAW5Vc2MTeg6bUZFpT9IKRFCBNoMlX7SXynFgCOE\nGC2E+I8Q4sMOy4xCiNeFEP8WQqhNklRUVHpFncXJ2iNVnDvFSBNuanNdhIeHc8opp/T6GFlZWVQU\n5yM9Lmq0CSS7XZSaA9tsxur04LQ2A2AK8rZFzh0eB0BAI+cEGTEZBHarpctNLs64mDB9GG8demsI\nDYNjjccYbRpNXlVzt1HzVk5Ni2JvSSPpERkAaAzlfpVaOjJ/nNLc6Luc6hM3WEWlA+9sK2Luo9+S\nU+n/xldl+DOsnHMhxCtCiCohRPZxy88VQhwRQuQKIe4CkFLmSylvPO4QlwEfSilvAi4aIrNVVFRO\ncj7bV4bLI5mSrjituTlW5s+fj07X+7KcSZMm4XG7cdWVUeKNJtntoaw5sF1C661OvA7F+Y0M8nRq\nQAQEOHIeRoQBHHYbXq/X7yZGvZFLMi7hm4JvqLJWDYlZ9fZ66h31pEWkKVHtbopBW5mZFo3T7aW+\nMRwNQYSFVxMf0f13OyIqlDFxRtYdHZyiUItL8vL6fJxu/9+tyveXd7cXU9Zo55qXt1JQ0/XN73Aj\nr7qZqiZ7oM0YFgwr5xx4DTi344IW+cZ/AecBE4GrhRATu9h/BNAqHusZJBtVVFS+Z3y8q5TxieG4\nNWU4a50UlTdz1lln9ekYrYotntoi8p3RJLvcVNnrcXsD1wmyrMHe5pyb9O5ODYhgeETOAZqauo7w\n/XT8T/FID+8deW9IzDrWqGhoRupHYHF6ui0GbWXmqCgAdhY2onUnExrWuxuJ+ePi2Jpfi9018Jer\n74pdPPT5Id7cMjw61aoMDVVNdvYWN3DZKSm4PV6ueXkrZQ3Dv/zP5fFy5YubueO9PYE2ZVgwrJxz\nKeU6oO64xbOA3JZIuRN4F7i4i0OUoDjoMMw+m4qKyvAkv7qZPcUNXDY9hfzaA3iylTSQxYsX9+k4\nmZmZaLVagptLOWCJINntxoOXSmvlYJjdKwpqLe2Rc53TxzkPpFoLQaGYghXnvLHRf1EoQGpEKgtG\nLODDnA/b7B5MWpVaPI54gC4bEHUkJszA6Dgj649WY22Ox6kp6VWh5/xxcTjcXrYdO/6yd+LsqFQc\n/n9+exSzXdVT/6Hw7SHlxvDmBaN588bTMNtcXPvyVqqbBv+3cyKsPVJNTbOTjbm1HDuJov2DRV+k\nFANFCu3RcFAc8NOEEDHAw8ApQoi7pZR/Az4G/imEuABY4e9gQoibgZsBEhISWLt27YAY2dzcPGDH\nUhkc1HM0/AnEOfroqBMBxFkL+aZ2F+7sJmIiw6ipqemzLcnJyTgq89labGeJXnE8v9jwBWODxw68\n4b1gXY4THBY0Gg1aVxNlNQ3krF1LsVOZUnMO5RBSGNLDUXwZiPMUYi1pi5yvWrWK0aNHd7ltljOL\ntfa1PPPlM8wOm31C79st0suegmUEaTSUb16PhpFU5e5jbVHPhXUjDA7W5VrQRyXhkNtYvno5kbrI\nbvdxeiQ6Dbz97S68ZYaB+hTU273kN3qZmaBlR6WLP725hsvHqurGw5GBnvPe3WknNkRQfmgnQgj+\nb5qOJ3ZYuPS51dw1K4SwoOFZJPribjtGPdjc8MRHG7kic/iM10Bcl/rtnAshDEAyEAJUSymHtKpF\nSlkL3HrcMgtwfQ/7vQS8BDBz5ky5cOHCAbFn7dq1nPCxvF6w1kJTGZjLwVYH4y+A4O4Li1R6x4Cc\nI5VBZajPkZSSP29dw7yxJi499zSefetuag5buORH5/dJqaWVWbNm8e2m7dg1BlJDEwEn8RnxLMxY\nOOC294YPynYRihNtVBTBWi/JI8eQvHAhe6r2QDnMmDqDeSnz+nzcATlP5jIa/qs4CpmZmcydO7fL\nTRfIBXz16VfslDtZumDp4KlQlO/l3cMFjNZq+X/l9/CzYCOm+rNgzCLI+jEEd61dXh1WzLoP9+G1\nJwIQMz6GM0ac0eNbzj62lWNNdhYuXDBgH+ONzQXAAR69Zh7Prsph1aEq/nLl7B7z4FWGnoGc82xO\nD4dXr+SqU0exaNEkABYCE7JquOG17bx8NIj3b5lDkG54JRY0WJ3s+2Y1185Op7TBytaCep69cf6w\nsTMQvkOfPrkQIlwI8SshxDqgEcgFsoEKIURRi0rKqQNsYymdO42OaFnWb4QQS4QQL3X3KHVIaaqE\nlxbCwwnwZAa8OB/euRL+9yvY/nKgrVNR+d6yq6ieknobl0xLodHRSFlBHZYmL4vP6V/Tm6ysLBoq\niqlrbCYxXMmwC2QjooIaCwZpJzIyElw2n4JQg3bgorV9JsiIqcVX7GkuFkJwzYRrOFx3mO0V2wfP\nprxvOabXkz5qIc+YlrIzZC6U7oLP7oANT3e766ktzYgitCMBOFJ/pFdvOX9cLDmVzZQ3Dlxe8FfZ\nFSQbBRnxYfzhR5m4PF7+/u3RATu+yvBkY24NdpeXxRMSOi2fNzaWZ66cxp7iBl7bdCxA1nXNin3l\nOD1eLp+RwtWzRlJrcfLNwcClAw4Heu2cCyF+BxQANwDfoOR9TwPGAXOA+1Ei8d8IIb4SQgzUc9zt\nwFghRLoQIgi4Cvj0RA4opVwhpbzZZBomEelvH4SKbDjtVjjvcbjiTfjlaogcCWXDpDiiLh/K9wba\nChWVAWX5njIMOg3nZCWS35hP8wEl3/ysc/vvnEspaaosRGNKJd4jA9aISEpJYa0VnduGyWQCj9NH\nSjGgzrm+vSC0oUWLvTuWjFlCdHA0rx54ddBMsuWtpkyvIz1hGq+ZZ/LN2HvhjmxIyILKA93uOyom\nlLhwA5OTE0kyJpFTn9Or95w/Lg6A9TkDo9pSZ3Gy9VgdMxKUB+NpsUaunjWSd7YVq7m833NWHaok\n3KBjVnq0z7oLpiRx1vh4/r46d9gpony8q4TxieFMTIrgjLFxpESG8M62wMrQBpq+RM5nAwuklKdK\nKR+UUn4tpdwvpcyVUm6TUr4ipbweSEBxnvv8jE4I8Q5KZ9FMIUSJEOJGKaUb+H/A18Ah4H0pZfez\n5MlE+T7Y/Racdgv86EHl78SLYMRMSJ4eeIe46jB8dBP8Ywa8ch64nf07jssOtgaw1CgpOw3F0Kzq\n+w4XyhpsvLG5gJ+/so3nVv0wImxuj5fP95WzeEICYQYdeQ15NB9sZkxCMKmpqT0fwA+TJimPkp3V\nhdhDk0l2OSkPkHNea3HS7HAjHRYiI1rkAPXDKHKu1REaouSV1tb37JwbtAaumXANG0o3cKSud1Hp\nPuG0Uly+C4AYwwgabS6lGFQIiBuvzIXdIITghWumc++FE8iMyuRofe9+R5kJ4SREGPju6MDMh6sO\nVeLxSmYkaNuW3XZWBgadhidXDsL3pjIs8Holqw5VMT8zrst0kD9fOBGH28PjXw2fcZBX3czuIqUg\nXwiBViO46tRUNuTWUFj7w72Z7LVzLqW8QkqZ3YvtHFLK56WUfc7HkFJeLaVMklLqpZQjpJT/aVn+\nhZRynJRyjJTy4b4e93iGTVqLlLDyTxASBfP/6Ls+aSo0FIKtfuhtK98H7/0Mnp8Nhz+H0QvBZYHK\n/X0/VsEGeCQZHhsFT4yBp8fDs1lKCk8PFzyVwaOo1so/Vh9lyT82cPqj33Lv8gMcLGvkmVU5vLf9\n+x+12JhXS63FyUXTkgHIqcnBetjCuVP755gDZGRkoNMH4aoppMmQRLLbTWlTcc879pEqaxV3fndn\nt9rfrRc2j92CKbxFcWQ4Rc4BY6gRgOq6np1zgCszryREF8LrB14feGOKNlGkUTTBvc4YgPYGRPHj\nobEIHM3dHmJmWjQZ8eGMjRrLscZjOD09BzOEEJwxNo4NR2vweHtWeOmJr7MrSIkMYVRE++U9PjyY\nX54xms/3lbOvpHfftcrJxb7SRmqaHZx9XEpLR9Jjjdw4bzQf7ixhd1EA/Ao/fLKrFI2AS6altC37\nycxUtBrBu9sHfu48WehXQagQYqSU8qS9ekspVwArZs6ceVOgbLjyxc1grYOq+RBzPbxxiAunJPGz\nOWnYnB5+8eo2sI0Hx5/hpS0QHMmPZ4zgJzNTqbM4+dVbO32Oee3sUSyZmkxZg82vVuhNZ4xm8cQE\n8qqbuedjXyf7tjPHMm9sLAe+epkHvqsHzXSIuAAikqHJy53eYmYUb2enO93vnfe9SyYyKdnEhqM1\n/KNjfmNjGdjv5pFF4YwxaVhVGcq/DwUpNx5v50KoMkk8c+U0kiNDWLG3jLf8aPO+cO0Moo1BfLCj\nmA93+jZ3ee36WYQEaXlzcwGf7Sv3Wf+rTOXvS+vyWH2os1MTrNfy+g2zAPj76qNszO38iDkqNIhl\nP5sBwGNfHWZXYeeJLckUzLNXKd0k/7riAAfLzJ3Wj44z8rfLlPbjd3+8j/zqzhGBickR3LdEibre\n/u5uyhs7P3acPiqKpeeOB+DWN3dSb+180Z+bEcv/naVkkl33yjYf3eSzJsRz8/wxQMvYA3YXN+B0\newkzaDl7YgJLzx1PQriBeY+v4a6P9vP6pgLCg5W25UM19grNnjb7OnLnuZnMGBXNzsK6vo29Fh65\nbDJj4sJYdbCSf69XpPLyqpvRCvjP+nwmp5jYsnkzwaPncXj6z31s6O3Ye3dHKSk/fwKnV8vN2+Kp\nb7qNilodHq8HrUY7YGOvxlZDgTmVbzZ/yfy0KTznZ+xVNysOuGvyxUSGKJ/57j3R5O/eTI1NYjXf\nzB3/LWTaCEufx15Dg40XjijfUX/GXisezx+IOOUbauvq2+e94+g89g6ia/oD7+dVcSj7O4I0QQM3\n7+3ZyoNNS7HadTxfqeR/P7MqB6NBy4y4Cez0juXxFzeBoXNTok5j74MvQaunLngMTY03cvmy9Tx7\nxWk+Y68jz1w5jfnj4vhwZwkX/XMDYYbOl+W+zHvL95axs6CehIhgHt3mYFnOZt67ZQ4Aeo1ApxFc\n+/JWJiQpha0/xHmvIz7X3OMYinkP4EBZIw+sOOizvi/z3p0fKU/Z39xS2JYS4m/e80iJXquMg69v\nn8+I6NBBu+a2jr2u5r1Xf3Eqn+wuZWR0KLe9s7vT+piW971j8TieWZUzoGOv1a7hTH9LYT9uUWvx\nQQihloP3Bimh/pgSyQpP9L+NoSVq4xziRztHVyrvPWImRI4CjR60BjDGQYnvBNYjTqtSiHbKtXDa\nzZBxNoS33N33IrKkMvBICU63l5TIYCYlm5g/NpaM+DB0Wg1j48Mw6DXkVDbj+J52F/RKSZ3FSbQx\nqE35I3tTNkJApKlrRY7eEBISinQ7cAk9QVIigWrbwKZw2dyK82h2mik0F7Qtlx06bTpaHBWX04kp\nTImYo1EcP69UttMEuh2ERoter6GuvvdPMRNCE5BIKi0DXDBWugO73oBOo8PhlmgEBGlbvp/4Ccpf\nl7Xr/b0eaK6ChmJCW9L/bK7eFXmekRELQKP1xPTIG6wuJBBt9JWhM+g1pESGYLa7sTgD1xhLZYDZ\n9E/46i4aLQ7Cg3XoNN0rGWmFYGR0KBanhy+yfR3qvrDqUCU2Z/8baG05Vktpg42JSb5zbmp0KDXN\nTlYf+oEWhkop+/xCaQT0up/lycD2/hxzKF/AEuCljIwMOVCsWbOmbztsfl7K+yKkPPJV99s9NVHK\nD2/st119xmWX8q8xUq6813fdez+X8umsvh/zhblSvnl552Uet5T3R0q5+qH+2dkP+nyOAkR5g03e\n9t9d8nC5edDeo7DGIkct/Uy+t73I7/r86mY55f6v5dlPr5Vmm3PQ7DieoTpHn+8rk6OWfiY3HK2W\nUkrZ7GyWIWNC5OhRQVIeWH5Cx77+5l9JjcEo39+SKzc+Gi+zXsuSOyp2DITZbdzyzS3y8uWXy1u/\nuVXOfHOmzGvIk4cPH5Z6vV4uWLBAfvDBB/JXb2yV8x75RgLy/t/fqsw3hz6TUkr56v5XZdZrWdLi\ntPTr/QfqPDX+fb5MMBnkeZde0af9lq5bKme9NUs2OhoHxA5prpDyvgj5i3cXy2s/v1Ze/dJmedE/\nN7Sv97ilfDBeyq/u6foYFdnKd/xQonQ/OkrOeGO6fHzb47024aJ/rJeX/mtDzxt2w2/e3ilnPLhS\nuj1ev+cou7RBjlr6mfxyf9kJvY/KwHFCvyWXXcrHRkt5X4R88p4b5L/X5fVqN4/HKy/91wY548GV\nsrGf8/snu0rkqKWfyY92Fvdrfyml/P37e2TWvV9Jm9Pts87t8co5j6yS1768pd/HHygG8roE7JC9\n8FP7Gza5AZghhLitdYEQYhqwDcjr953CECEDrdZirYO1jyp53GN/1P22SVOHtii0Ihu8LkiZ4bsu\ndZaSd9lU0fvjeT1QcxTiMjsv12iVSHxzH471A+HZVTl8ureMq17azIGywamLKG1p55wS6b8BTXqs\nkX/9dDp51RZ+++6eAcmFHU4s31NKXLiB2aOV3OJ9xfuw5ds4PUMPkf3POQdIio/D67BQ3ewmyaCo\nJgy0nGJ+Qz5jIsfwwOkPEKwL5p719/D1N1/jcrnIy8vjJz/5Ca/+34XUr3sDAJOx5UFnS4dQu0dJ\nHwjSBrbRhwgyEm7Q9CileDzXT7oeq9vK+0feHxhD8tcCUCQdjIwYSW5Vc+fOoBotxI6F6m5qZFrn\n6ctfRut2kuGBnD4Urs4eHUN2qRlnP59W2V0e1hyu4uyJiWi7iJ7GtIyDOsvJ1TG0rMHmk3KjAhz8\nFKw1NBjT+X+65ZyT3LsnNRqN4P6LJlFrcfKP1X0XALC7PDz+lfJbqDD3T/nF6nTz5f5yzp+cRLBe\n67NeqxFceepI1h+tobiumydW31P65ZxLKa3A5cB9Qoh5QoiLgfXAK1LKqwbSwO8l3z0ODjOcOqhE\nrwAAIABJREFU84iiBNAdSVMU57aHQqQBo7Qlr86fcz5CyU2kuA+pLQ2F4LYragfHExavPAZWaaOw\n1sIHO0s4f3IiIXotP/331kEp4Gp1zpM7OOdSSh7f/nibwzNvbCz3L5nIt4ereOp7pPLQaHOx5nA1\nS6YktzkxK1auAAkXpmmUVK4TIDFeSVEor64hKUwpcvIrp5i/Fj65FTx9SzGwuCyUW8rJiMwgLjSO\n++bcx4HaA7z15VskJSVRUFDA8uXL0cWM5MBXbwIQFdbqnCvpLU6PE63QotMEtkm0MBgxBQvMZnPP\nG3cgMzqT05NP5+1Db/eq6LJH8tdgDY2hylFPQsgIqpoc7cWgrcRN6L6AvXyv8v2OOxfOf5xxzXXk\nVO1tfVrbibLmMh81l6wUE06Pl5zKpn59hE15NVicHs6Z1HVBYJRRqSGpswzvVu7H849vj3LNy1u5\n55P9ONz9T6P43rH9ZYgezb3hD+ERWlI336vkLPaCKSMiuWJGKq9uLOiztOIrG49R1mhHI6C6qX9j\n6esDFVicHi6bntLlNlecOgKN4Acpq9gXnfOvhRCPCSGuEkKMB3KAm4HPgLeBW6SU9w6Snd8fanJh\n+7/hlJ9BwqSet0+aCkio7FEoZ2Ao3QlhiUoRqI8tU0Ab1Le88+oWp86vc57Ytyj8D4B/fJuLTiO4\nf8kk3rtlDuHBOq7591Z2Fg5sZX1Zi3OeZGovEflf7v948+CbLM9d3rbsZ3PSuGLmCJZ9l8f+kmHS\ntOsE+Tq7AqfH26bSArDu23VoggRLUo2KetIJEBen6FZXVNUQbEol1gvlFj+5nVtfhL3vwO43+nT8\n/AalsGt0pNLufvGoxVw05iL27tjLxOkT0Wq1zDvrXGJ+/FceensVTz31FJcsnKns3NKEyO6xB1yp\nBUBjCMNkEDSb+z62fjHpF9TYavgs/7MTM0JKyFtD8Silf57eq5y/scc75/HjwVwC9i5uJMr3QuJk\nJco+7RrGRU+gzmunNv/bTpu5PC5+ufKX/PTzn3K4rt3Zn5yiPMnt79Oyr7IrCDfoOH1MbJfbGHRa\nwgw6ai0nV61PpdmBQafhv1uLuOqlLVT2M1r7vaJiPxRvwT7ter4o0rBp5K2QuwoOLu953xZ+PHME\nbq/s09xe0+zg+TV5LJ6QwKgYY7+d8492lpIaHdLWvMsfSaYQFoyL45PdpXi/Z09ve0L4u6v3u6EQ\nf0NpOjQNRcvcCuxHaUL0EfAikC2lHPa35EKIJcCSjIyMm44eHRhN5163d63Lh1X3c70lG0Tne6Nz\nEudw1bn/wGat49fvt6S7SC84mkAfzMVp53PJWY9RX5fH7z690ufQV45ewrnz76OifDd3f+0rRHPd\n+KtZOPv3HCtYywNr/+Cz/ubJv2TOppc5HBbFY8LXGfztjDuYtvU19rjNPBfsG71YOudexmdexOad\ny3hpf4uSptuhRM6DI7h34VOkpy1k7ZaneP3wO0rHQq+7Tf3gb+f8m8SkU/hq3V95L3+Fz/Gfvug9\noqLH8L/VS1levNpn/fNXrCQkNJp3v7qNryt8K/OvS3uShQsX8tpnv+S7ms6V9QahZdl1WwFYtvxa\nttZ3jpBFaoN55mcbAHj2ox+zt6mg0/oEfTiPXrMGgMfeX8JBSylur4ugFgdolCGa+69eCcD97/yI\nQkddp/3HhyZxxZnvctZTa/nR6Edw6JWfkVdKrE4PRlssv774fWalR3PHm/No8HS+OJ0WNZ5bL34L\ngFtfPw2H7Hx+FsRO4xcXKufk+tdmYnd5cHkk4cFK5PSM2Km82JiNx9lMuttDmL7dMZFIRN1EqvU3\n8+rVSdz5me/DsQEZezNu5eNPHmVF44c+63874w6mTb6GPfvf5rmdz/is9zv2OnDvwifbxt4L+97E\nK+mkirH9+WPUONy89ssU3gsP9dm/L2Pv85J1NDdb0AeHYtR5sXqdhI+az8vnvNxh7Enldy0lBmDZ\n9bvAEN6rsbej8Sh2tx2j3ohGaEjQh3Pzgv8wJnUM1z0xBmJNeL1gcboJDdIyJiSW+7Nuho9u5P6M\nUyh0N2P32HF73YTpwxgfmsTSK5Tf211vL6LS1TlyOzU8jdsvV85J69hzu93odMr319ex1wmXDePB\nOt5/P4jCo7vb570OXJx6VpfznsVlIUYXyr9+sZ2qir39G3vpS5iz5mnen7qE96u3E6QJweGWhBl0\naIRoH3ur7ua53A8hyNhWWAstY2/sBWx+chQvRZtApzyN8njdWN02rrcLLrh5G2v3/JvXD7+D0+vE\n4Xa0FSI/9KMXyRy1gC+++yuvHPoYvVbT6TF/b8ae3hDJTc9diS08l5CWfVvP0au/2AHQNvaaHW60\nGkGIXjvg895ha+eb0N7Me70Zexf/ayNRujtw69wtiiyC0CAtp8dM6P/Yo4trbge6G3swMPOeo2k8\niUlmHtv8gM/6buc9l42ltfUULl7Nv75aRmTyerRuq3KzaQgDRKd57/XD7/gc/8+LXuCsF6r41ZTV\nHHau81nvb+zZXR6cHi9hBh0u+yN4NBFcOuq/fq+5x4+9VtxeLw67ZOHED/j1woxux97/dpfy8dqf\ng6mhU7rWiYy9Vrt6S6/9u14ghNgppfQdjMfRF53zu6WU50kpk4AklLSW/wErgTOArUCTEGLYNwgK\naM559Gi44g0fx7xLhEZJffEOgWqGyw61RyE6vettWvPO6eVdrPS2fFY/6TtCKOu/hzg9ThweJ25v\n73M7n1uVg0GnbXOYATRCYAzSEqQTXPfKNnKr+vfI+3i8UlGjaGVf9T60QstVmVcpBSkdzq9AcMrI\nSLJLzXyyOzANdQYKi8ON26tIibXi9XoxVzaROT68XSHpBBAtv20pJQiBAMqO1zr3epSLqM6g/N34\nXK+P36a00mEOObBbmXa9eonT48TbEnTRtKbNtaqGtO4zXIJQQmDQgKWpb2ktrQRpgzA7zRxrPIGW\n5LVKmVR1y7mXUvnONMenHLYqTPmbs2rzwOsE0e5UazTKv0s9zbDqfmVXlPOj1WgJ0YUgpeTvu/+O\ny+tCI5RcYE8vA2Yd2VZQh9Mt0feg1AEt024/3iOQ1DY70AiBXqvBaNAhhHLzWdH4Q42gS6U2LHUW\nm0o9BGk1aDUa5cmY9CpBsV5gDNKRGh1CbS/TnLxS4vR4CdJq0AhBjNHQJtnaa8ulxOb0oNMKfjlv\ndI/bnz0xAY0QuDzfT1+hS3pTNdqbFxCC0kX0loE65mC/ZsyY0esK254YVJWJNy+T8vnTB+/4reR+\nq6gN5H7b9TbZnyjbFPdSfeLFBVK+frH/dVuWKcdqru6rpf1iqJRAXB6XnPfOPJn1WpY898NzpdPd\nczX80UqzTLvrM/nIFwf9ri+pt8pRSz+TL36XOyA2LnpyjfzVW8o5fHHvizLrtSz5Wd5ncn3Jer/q\nIl6vV173ylY54S9fytJ664DY4I/BPkf/WZ8vRy39TB6tbFfCefHlFyUg7757pJQrbj/h9ygsLJSA\nnHTlnVIe/lI+/WyqnPb6VOnxeto3+vrPiiqS3SzlBzdI+WCClA0lvTr+r1f9Wl62/LJOy+655x6p\n0+nkvWvulZNfmyzv+uxTmXbXZ9LualFB2PpSp9/aH9f+UV7w8QX9/owDdp6+fVg+uMggAdnc3Nzn\n3beUbZFZr2XJbeXb+m/Dm5dL+fcZ8i8b/iIXvLtAXvfKVnnes+t8t/N4lPP05d2+6/a+r3y/5fs7\nLT7z/TPlXW/MlfKfs6SUUv59199l1mtZcn+1st2nuZ/KrNey5MNbHpZSSvnAigNy3J++kC63R/aF\nf605Kkct/ayT8kZX5+j6V7fJ85/z8/mGMRP+8qV8YMWBtv83WJ3yule2yrS7PpNHK5sCaNmJ06/f\nUuu1s3SXvP3d3XLuo6vb1336Wynvj5KyfF+vDvXL17fLM5/snQ03vLpNZt37laxpsksppbxvebbM\nuq8HxbkOeL1eeeubO+TYe76Qh8p7r7T067d3ylMeWCmdffxdDBTDWq1FCNFNOBWklDYp5RYp5YtC\n4cQkD1TaSZoKVYeUyPZg0loMmnxK19ukthSF9ibv3OuF6hz/+eYAYS2RqO7yzr0e+OY+aDx5IrZ7\nqvbQ4GjgsrGXUdJcwvs5PStKPLvqKKF6Lbe0NMw4npTIEFIiQ9g7AHnfUkrKGmwkm0I4UHOAF/a8\nwHlp53HB6AtIi0gDoKCxoNM+QggevDgLr5Tcu/zASRd5a2X53jImJkWQEd/eSGbFVyvQRmiZF+mA\nyJEn/B4xMYoCTFNDHZhGkOz24JYeqq0dtM5zV8GoOUpK11n3KtGubx/q1fHzGvIYY+o8TjZv3szU\nqVO5c+6dJBoT+bb2nySZ9Bh0LZHcVn3ulpxzh8cxLHLO0YcyLka5DOXm5vZ590hDJACNjn7+LtwO\npYPxmEUUNRUxKmIURyubfYtBATQaiBsH1Yd815XvUXpBHKdKlRmVSY5GQt0xqporeOPAG5ybdi5Z\nsVkALBmzhJ9P/DnvHH6HT45+wuQUEw63l7zqvvW2KKq1EhsWRERL07DuiDYGUT9Mcs7/8r9s7l3e\nfT2VzenB6vR00m43heh5+opphOi1/GtN38fNoDAUT7dBedK2/WVImQnJp2BxuDs3rlp8n1I38/nv\ne3W4CUkRHKux+DRwOp5NuTWsPlzFrxdlENNSYB4XbqDJ7u5x31Y+3VvGl9kV3HH2OMYn9r6fxEVT\nk6mzOH9Qij19UWvZLIT4jxCiy9ZKQogoIcSvgIPAxSds3SAhhFgihHipr/JdASNpKkgPVA1yxlDp\nLojJgJDIrreJSIaIlN4ptphLwGVRLmj+aHXOm7tpMlB1CDY+C/ve6/n9hglriteg1+i589Q7OS3p\nNJbtXUaTs+t0lMMVZj7fX84v5qb5bR4CivNhSv6GPaW+Xdr6Sp3Fid3lJS5Cw13r7yImJIY/zf4T\nAEnGJII0QRR0aGzTSmp0KHcsHseqQ5V8fSAwjSGklGzcuLFfNwdWp5u9xQ386Dg1i43fbSRsYhhj\nXG4wnXhMITQ0FF2QgWZzA0SmkuxW1FjKLC1yio2lUHUQMhYr/48aBbNvVYpDe5BNtbqslDaXthWD\nAng8HrZt28acOXMw6o3cN+c+rLKMkPgOhYitN/YtUorDxjkPMpIR3X/n3GRQUhPrHf0smC7eCm4b\njDmTInMRScYRlDbY/DvnoCi2VPtRLirfC4lZoO3sHI+LGke+pxmXx8HzO57CLd383yn/12mbO2bc\nwZykOTy45UF0oUqXxv2lfbs2FdZaGRntWyvhjxhjELUWZ8BvsL1eyfI9pWzJr+12u9aUi9iwznNj\ntFHpDrt8TynHaoa4Ud/xuGzw9ATY/K/Bf69j66AmB079JdBeW9JGSBScfpsytpt6nqcnJIbjlXSr\nEuT1Sh7+4hApkSFcPzetbXnrOanpRWpLpdnOX/6XzfSRkdw8v+d0lo4szIwjPFjHp3sHVpJ2ONMX\n53w8UAd8LoSoaVFveVUI8YIQ4l0hxD6gCrgWuF1K+c/BMHggkIHWOe8rSVOVv4Otd162y7+E4vGM\nOBVKtve8XXWO8reryHl4L5zzxpZc3aHUej8BpJSsKV7DrKRZGPVG7phxBw2OBl7NfrXLfZ5bdRRj\nkI6bzvA/YTk9Tn675rcUy8+oFmupO8GoV1mD4qjttrxFgbmAh+c93ObkaDVaRkaM9Imct3LDvHQm\nJEVw/6cHaLIPvVby66+/zrx583j99df7vG+rqsCIqHYnpqqqivrqekLTQ0lzuwYkci6EICwiEkdz\nAw5dGCkaJVrdpnWeu0r5m3F2+07zfqdcVFf+uVsptGNmJbc6IzKjbVl2djYWi4XZs2cDMDdlLqJ5\nJtXaL9vVQFxWJbLbkgc9fJzzsBNyzvsUOa867NttOW8NaHRYU6ZTbavGIOMByErpIqoXlwnmUrA3\n4vG0RAulhPJ97fN0B8ZFjcMtvaw2hvJJ4ddcmXklqRGdbwB1Gh1PLHiChNAE/rH/r4ToBdl9ds4t\npMUYe7VtlDEIh9uL9QQ6Ow4EudXNmO1uapq7n89a57too+94vemM0ei1msBHzws3Kj071j4GtoFV\n1vJh+8vKXDHpUgAsDg9Gw3GSqCMU5SEq9vV4uAkt3TkPlXdd97H1WB0Hyszccfa4TsXKceHKOelJ\nsUVKydKP9uH0eHnqimld6vB3hUGn5bysRFYeqOx1lP5kpy8FoQ1Syj8CKcAtwCEgEkgH3MDrwClS\nyrlSyq8Hw9gfLJGjINikXAAGC3MZNJX3zjlPnaU4zeYeWv+2NuzoKa2lW+e8JVJ8kjjn+Y35FDcV\ns2jEIgAmxUzi/PTzefPgm37bjR+paOLL7ApumJdOZKhv1FxKyV82/oWdlTuJ0EehC88+Yd3z0gYb\nCCdbq7/g8rGXc1rSaZ3Wp5vS/UbOAfRaDX+7bDKVTXae/ibnhOzoK3a7nfvuuw+AJ598ss+Rv6qW\nC0h8ePtFPjtbeaSemhaNQTIgkXOAiKhovLYmGm0uEo2KZGMn5zw8ub0lPChPqxbepUTFjq7s8rh5\nDUrxYsfI+ZYtWwCYM0d5qNlgdWIuPZ8QbQT3brwXl9elKCbp22Uzh49zbiTCIAgNj6A/ylnBumCC\ntcE02Hv4Tex9D54/DR4dCf85B759GI6tV87FiFMpdir7262KrFtWSufAzY4dO3juuef4f8vWcM5b\nFsZkTsJgMPDQQw9B/TFwNHbpnAM8EBNNqEbPzVNu9mueyWDidzN/R5mljJEjivskp+hweyg32xkZ\n07vIeevTuRO9yT9RdhTUt9nRXbFfbZtz7js/xoUb+OlpI/lkd2lgG9XkrgaNXhkHmwYxLmkug8Of\nK1LMLb9ni8ONMeg45zxxsvK3F9fNkdGhhAZpOVTedeR867FahFCKMzsSF6bY0JNz/u72YtYeqebu\n8yaQHtu7m8jjuWhqCs0ON2sO/zB6o/SnCdE4IAJFpeVKKeW5UsprpZRPSSmHSIz7B4YQg98ptLvm\nQ8D2iu08tu0xmp3N7c2IeoqeVx9WuoCGdqFjGmSEoPDuH701tDQfqD8GtoFvxjPQrClWpJ0Wpi5s\nW3bbKbfhkR6e3/u8z/brjyp5yNee5j9i+889/+SLY1/w2+m/5bpJ16MNLmdjwYk1BCptsKE15uOW\nLs5JO8dnfVpEGiVNJYpT54dpqZEsmZLM8j1lQ/pofNmyZRQVFXH99ddz4MABvvzyS2WFlFCyo8dm\nPlVmB0JfQ4l9FysLVrI8dzlvfKtojE8aFa1o+Id13cClL0RGReO1mWm0ugg1pRIthdKIyONSmg+N\nXezbgGzG9RA9Blb+Ram18ENeQx46jY7U8PabiC1bthAXF0d6ulIWVFhrBW8oP0n7Pw7VHeL1A68r\nkXN9u/Pm8DgC3h0UUOYAIComrl+Rc1Ac2wZHN3NDQxF88QclR/f02xSVi/VPwusXKpHFMWdSaFbS\nSarrw0gyBRMf3n4jI6VkwYIF3H777by5Yi21Vsms8SNITk5m9erV7fOyH+c8zZSGXqOnSavhBuMY\nooO71nRemLqQmOAYZPhmDpSZe63rXFxnQ0oY1VvnPHS4OOft0nbd2VLbElk/Pq2llVsXjEGrETy/\nNoDR86PfQPp8mHQZbHkBLIOUG73rDaU+ZeYNbYusTj+R8+AIiErvlc+g0QgyE8O7jZxvO1bHhMQI\nTCGd07baIufdpLV4vJJHvjjEnNEx/Gx2/xu8zRkTQ2yY4QeT2tIn51wIcTOwC/gPSvOh/UKIrts7\nqQwcSVOh8oBycR8MSncqd/4JWX5Xv5r9Km8deotrv7iWorCo3jUjqj7SddS8lbD43kXOQWm6MMxZ\nU7yGSTGTSDC2O3kjwkdwZeaV/C/3f+TWd76AHCpvIjbMQHxE8PGH4pOjn/DSvpe4fOzl3Jh1I+eP\nVtIgNlV8d0I2ljXYCA7PIVgXzPSE6T7r00xpuKWbkqau89tnpkVRZ3FSPkRSZmazmYcffpjFixez\nbNkyUlJSePLJJ5WV+z+El8+CA590e4xyczPG0c/xt91/4Pff/Z4/b/wzn278FK1Ry4KwUKWWQtOv\npsk+xMTE4LE1UW91KUWhLrfSiKhku9IduDXfvCO6IJh3B9QcUX7rfshvyCctQnH4Wtm8eTOzZ89u\n080uqFVSNy4eey5njzqbF/a8QK69rq0YFBTnPFjrO+aGnDbnPLrfznlUcFTXaS1ej9KFVXrhx/+B\nxffDTd/C0gK4+l1YcBfMuJ6iJiUIkFce0tYMqBWz2YzVauWRRx6hob6BHb+J5507FnHeeeexd+9e\nZNkeZe6Mn+jz9jqNjrjGOMQOMxfUdv84Xid0XJJxCRWu3di8deT3Mo+6sOV8j+plWkt02DBxzgvr\n2zTZu4u8tnYz7aoeJyEimCtnpvLhzpK2zsdDSn2BIkGcsRgW3q3UMGzw7cUwIJTuUpoXdpA7bna4\nMRq0vtsmTe1VWgsoqS2Hys1+gy0uj5fdRQ3MSve9sYxpGUvdnb+qJjtNdjcXTk1C08d0lo5oNYIL\npySx+nBVQFIqh5q+XonuBJ4HEoFTUXLMHxtoowabk64gFCBpGngc/ouRBoLSnUpBk973gu32utlV\ntYvp8dOpsddw1Vc/Z1PyBCjuJnIuZYtzntn1NqBEKpu7eUzVWNx+0RvmqS01thr2V+9nUeoin3W3\nTLkFo87Is7ue7bT8YLmZicm++a2byjbxwOYHOD35dP40+08IIRgRPoJwMYoSx9YTsrOswYY+PIdZ\nibP8pja0KrZ0px3d+ti/r7mxfUFK2ZbG8fTTT1NTU8MjjzxCUFAQt99+O2vWrGHnhtXw5Z3KDj3c\nLJaZ6xAaF9dNvI4Pl3zIF5d+QYY9gznT53CdzT0g+eatxMfF4bWZabA6ITKVJJeDsqYSJcImtDB6\nof8d0+crf4u2+F2d15jHmMgx2F0e3tlWREVVDUeOHGlLaQEoqLEihFLAe89p9xAWFMZv7YdpbCkG\nheEXOY+OjKC0tBSrte+pCd1Gzjf9Q8kHPu8xiEprXx5sgszzYNHdEBZHobmQmOBYCqs9TE3tXBBf\nV6dEeJOSkhBaLcSOg6pDTJkyhfr6ekoPbFFSlHT+04RK/l3C/n8WMeKWj5k5cyZLly5l5cqV7Nq1\ni1dffZU77riDM888k7i4OF649gXcVhf6yO29Tm0prFW+s1F9KAiFwDrnVU12iuqsLBqvdGPtLvJa\na3ESpNV0ViQ5jlsXKupFy9bmDayhvaG1hmTs2Yr4wZSrlLzwntI++4Ol2ufpntXp9o2cg9LRu76g\nV0+cJySGY7a7/QZbsksbsbk8fjt56rUaoo1B3RaEtnajTo4M6XKb3rJkajJOt5eVARIkGEr66pyP\nAp6UUlZJKXcCvwAuG3CrBpmTriAUBrco1OuF0t1dprQcrD2IxWXh6glX884F75BoTORXunreaDqC\ndHXxo2yqUPLveoqchycohTRd0VgCydOVHN1h7pyvLV6LRHZKaWklMjiSn038Gd+VfNcmqed0e8mt\namJCUninbSstlfxu7e8YHTmapxY81SlKOiXqDLyGArIrjmtq0wcKGgvxaGuYlzLP7/o0U5qyXRd5\n5wATEiPQiMF1zt8/8j6XLL+EDYc38NRTT/HjH/+YU09VCp1uuukmwsPDefKum5ROm1HpSlSpG8qb\nlItUZnQmmdGZjAgfwZGDR5gyeQo0FEPkwKm/JsTF4rU3K1E/UypJbiVyLnO/gdTTFOfQH5EjlQh+\n0SafVTa3jZKmEsaYxrApr4a7P97PM//9HKCtGBSUSGpSRDDBei2xIbE8u+hZyqSTP4Y4cHuV1B+H\n20GwbhhFzk2KOkpeXotzlf0xvHJul+k9HYk0RPp3zsv3KfKUE5bAtGu6PUaRuYioIKU24PjIeatz\nHh3d4pzET4Dqw0ydqszJ+/bu8ZvSAkqdRM7BHK5eOJH7FhoJDQ3lmWee4ZxzzmHGjBnccMMNvPTS\nS1itVi688ELyjuZhfduG3rSNfSW9KywsrLUQbtB1GVk+nqhh4JzvbMk3P2dSItB95LW22UlMWFDb\nkyF/pESG8OMZI3hve/HQNybKXa38bmNairQXLlU6X69/0v/2jqb+PwG3VCtPm1sP5VY6PRuDuoic\nQ6+eOHdXFLrtmDL+T02P8rtvbFhQt+evtEWAIGUAnPPpIyMZERXyg0ht6atzrgXanhtJKfMAhBBJ\nA2mUih+ix0BQ2OA4qLVHwdnUpXO+rUKJSJ6acCqp4am8dd5bnBk5kSeiwvnr2t/5zztuKwY9gci5\n26k4+ZGpg59zPwCsLV5LSlhKWxHY8WREKZN3nV2Z7PKqm3F5JBOTOkfOD9UdwuKy8KfT/kRYUGdJ\ntwvGKC2m3z/0Zb/trHApbZTnJft3ziOCIogOju5SsQUgJEhLRnwY2WX96+zYEza3jWX7lgHw0CMP\nYbPZlOK7FkwmE7f85Ed8sOkYBeNuhPEXKBchd9cOR5VFcQhalWlKS0sxm81kTchUbhBNAxc5T06M\nB+mloqoWTKnEuz04vC6aqrKVfPOuEAJGzlEi58f9rgoaC5BIRkeOpsmuONkffLEGjUbTdtMCSlpL\nxxSHU+JP4V5vFJs1Lp7a8RQwnCLnyviOMSkX7tzcXOVzr30UijYrknE94Nc5d9nh45uVepcLn/PN\n7z+OoqYidF4litujcx43HprKmZyh3MztK/ZfDAqwb98+3G43l5+3gPvma1m34h3q6+v56quv+OCD\nDzhy5Ahms5ktW7bw2muv8cQTT1C4sYDaVXlsqdjY42cHKKyzMjImtFvntSPhBh16raDOGjjnfEdh\nPQadhoWZiqPZXeS1zuL0e+Oxp2oPj217rK0Y+NcLM/BIybLvhjB67nZA/neK8lLr9x+VBtN/Djtf\nh/rC9m1dNlj3BDyZCW9e2ncHXUrlWmmMa1tkdSg3r34j54mtznnPqS2ZiUqA6HCFb1GHz3v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yyzhW+KekyY+OvUAhAcJX2fe9Oc99QBx4+UU9bWH9kqSwjY85r8b4et75AkYEfVDtQKda/uJz2h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\nagAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -761,7 +820,7 @@ "fig, ax = plt.subplots(figsize=(12,8))\n", "\n", "spw = 1\n", - "blp =((24, 25), (24, 25))\n", + "blp =((24, 25), (37, 38))\n", "key = (spw, blp, 'xx')\n", "k_perp, k_para = uvp.get_kvecs(spw)\n", "power = np.abs(np.real(uvp.get_data(key)))\n", @@ -769,7 +828,7 @@ "P_N = P_N[uvp.antnums_to_blpair(blp)]\n", "\n", "spw = 1\n", - "blp =((24, 25), (24, 25))\n", + "blp =((24, 25), (37, 38))\n", "key = (spw, blp, 'xx')\n", "avg_power = np.abs(np.real(uvp2.get_data(key)))\n", "avg_P_N = uvp2.generate_noise_spectra(spw, 'xx', 400)\n", @@ -799,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -810,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, "metadata": { "collapsed": true }, @@ -821,14 +880,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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yOddqLSIiIiISixIyOddqLSIiIiISixIyORcRERERiUVKzkVEREREYoSScxERERGRGKHk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udU5qna7UKIWjrL0oFLb0vJQNJqQK0iyF7Wl26t3jlAjjKHwspC/O38a7c5Ucgsw3DCTUaCbet+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kXEREREQkQ+Rkcq5WiiIiIiKSiXIyOVcrRRERERHJRDmZnIuIiIiIZCIl5yIiIiIiGULJ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QLzlw2lWkl8Tjz2+vUBMNLXIScnB5A5868idCVdVEILFRQ9I8irfDn4SuwVlColE7wmVN1fXqoc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -840,7 +899,7 @@ "fig, ax = plt.subplots(figsize=(12,8))\n", "\n", "spw = 1\n", - "blp =((24, 25), (24, 25))\n", + "blp =((24, 25), (37, 38))\n", "key = (spw, blp, 'xx')\n", "k_perp, k_para = uvp.get_kvecs(spw)\n", "power = np.abs(np.real(uvp.get_data(key)))\n", @@ -848,7 +907,7 @@ "P_N = P_N[uvp.antnums_to_blpair(blp)]\n", "\n", "spw = 1\n", - "blp =((24, 25), (24, 25))\n", + "blp =((24, 25), (37, 38))\n", "key = (spw, blp, 'xx')\n", "avg_power = np.abs(np.real(uvp2.get_data(key)))\n", "avg_P_N = uvp2.generate_noise_spectra(spw, 'xx', 400)\n", @@ -878,7 +937,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -886,7 +945,7 @@ "output_type": "stream", "text": [ "baseline pairs in uvp2: \n", - "[((24, 25), (24, 25)), ((24, 25), (37, 38)), ((24, 25), (38, 39)), ((37, 38), (24, 25)), ((38, 39), (24, 25))]\n" + "[((24, 25), (37, 38)), ((24, 25), (38, 39))]\n" ] } ], @@ -911,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 28, "metadata": { "collapsed": true }, @@ -933,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -941,7 +1000,7 @@ "output_type": "stream", "text": [ "pols : [-5]\n", - "number of baseline-pairs: 9\n", + "number of baseline-pairs: 3\n", "data_array in uvp2 : True\n" ] } @@ -968,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -976,7 +1035,7 @@ "output_type": "stream", "text": [ "pols : [-5]\n", - "number of baseline-pairs: 9\n", + "number of baseline-pairs: 3\n", "data_array in uvp2 : False\n" ] } @@ -1002,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1013,7 +1072,7 @@ "number of baseline-pairs: 1\n", "data_array in uvp2 : True\n", "baseline pairs in uvp2: \n", - "[((24, 25), (24, 25))]\n" + "[((37, 38), (38, 39))]\n" ] } ], @@ -1021,7 +1080,7 @@ "# read metadata and data, but only part of the file (a single baseline pair)\n", "# bls is the baselines you want to load in, and only_pairs_in_bls=True means both the\n", "# first _and_ second baseline in a baseline-pair must bbe in the bls list.\n", - "uvp2.read_hdf5(\"pspec.hdf5\", just_meta=False, bls=[(24, 25)], only_pairs_in_bls=True)\n", + "uvp2.read_hdf5(\"pspec.hdf5\", just_meta=False, bls=[(37, 38), (38, 39)], only_pairs_in_bls=True)\n", "\n", "# print some meta-data\n", "print \"pols : \", uvp2.pol_array\n", @@ -1043,7 +1102,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1051,7 +1110,7 @@ "output_type": "stream", "text": [ "number of unique times: 1\n", - "number of baseline-pairs: 9\n" + "number of baseline-pairs: 3\n" ] } ], @@ -1069,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 33, "metadata": { "collapsed": true }, From 6ac08baf935d0e59eabebfda95ef66b115462548 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Thu, 19 Apr 2018 23:38:50 -0700 Subject: [PATCH 31/54] updated uvpspec.average_spectra to average data with heterogeneous time_arrays only if time_avg == True --- hera_pspec/tests/test_uvpspec.py | 9 ++++++++- hera_pspec/uvpspec.py | 29 +++++++++++++++++------------ 2 files changed, 25 insertions(+), 13 deletions(-) diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index c60e2a73..59c1e376 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -4,7 +4,7 @@ import os import sys from hera_pspec.data import DATA_PATH -from hera_pspec import uvpspec, conversions, parameter, pspecbeam +from hera_pspec import uvpspec, conversions, parameter, pspecbeam, pspecdata import copy import h5py from collections import OrderedDict as odict @@ -336,6 +336,13 @@ def test_average_spectra(self): nt.assert_true(uvp2.Ntimes, 1) nt.assert_true(np.isclose(uvp2.get_nsamples(0, 1002001002, 'xx'), 10.0).all()) nt.assert_true(uvp2.get_data(0, 1002001002, 'xx').shape, (1, 50)) + # ensure averaging works when multiple repeated baselines are present, but only + # if time_avg = True + uvp.blpair_array[uvp.blpair_to_indices(2003002003)] = 1002001002 + nt.assert_raises(ValueError, uvp.average_spectra, blpair_groups=[list(np.unique(uvp.blpair_array))], time_avg=False) + uvp.average_spectra(blpair_groups=[list(np.unique(uvp.blpair_array))], time_avg=True) + nt.assert_equal(uvp.Ntimes, 1) + nt.assert_equal(uvp.Nblpairs, 1) def test_fold_spectra(self): uvp = copy.deepcopy(self.uvp) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index 01c13395..4e00859b 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -1080,9 +1080,10 @@ def average_spectra(self, blpair_groups=None, time_avg=False, inplace=True): Notes ----- - Currently, every baseline-pair in a blpair group must have the same - Ntimes. Future versions may support baseline-pair averaging of - heterogeneous time arrays. + Currently, every baseline-pair in a blpair group must have the same Ntimes, + unless time_avg=True. Future versions may support baseline-pair averaging of + heterogeneous time arrays. This includes the scenario of repeated blpairs + (e.g. in bootstrapping), which will return multiple copies of their time_array. """ if inplace: uvp = self @@ -1144,10 +1145,21 @@ def average_spectra(self, blpair_groups=None, time_avg=False, inplace=True): # iterate within a baseline-pair group and get integration-weighted data for k, blp in enumerate(blpg): nsmp = uvp.get_nsamples(spw, blp, p)[:, None] + data = uvp.get_data(spw, blp, p) + wgts = uvp.get_wgts(spw, blp, p) ints = uvp.get_integrations(spw, blp, p)[:, None] w = (ints * np.sqrt(nsmp)) - bpg_data.append(uvp.get_data(spw, blp, p) * w) - bpg_wgts.append(uvp.get_wgts(spw, blp, p) * w[:, None]) + + # take time average if desired + if time_avg: + data = (np.sum(data * w, axis=0) / np.sum(w, axis=0).clip(1e-10, np.inf))[None] + wgts = (np.sum(wgts * w[:, None], axis=0) / np.sum(w, axis=0).clip(1e-10, np.inf)[:, None])[None] + ints = (np.sum(ints * w, axis=0) / np.sum(w, axis=0).clip(1e-10, np.inf))[None] + nsmp = np.sum(nsmp, axis=0)[None] + w = np.sum(w, axis=0)[None] + + bpg_data.append(data * w) + bpg_wgts.append(wgts * w[:, None]) bpg_ints.append(ints * w) bpg_nsmp.append(nsmp) w_list.append(w) @@ -1159,13 +1171,6 @@ def average_spectra(self, blpair_groups=None, time_avg=False, inplace=True): bpg_ints = np.sum(bpg_ints, axis=0) / np.sum(w_list, axis=0).clip(1e-10, np.inf) w_list = np.sum(w_list, axis=0) - # take time average if desired - if time_avg: - bpg_data = [np.sum(bpg_data * w_list, axis=0) / np.sum(w_list, axis=0).clip(1e-10, np.inf)] - bpg_wgts = [np.sum(bpg_wgts * w_list[:, None], axis=0) / np.sum(w_list, axis=0).clip(1e-10, np.inf)[:, None]] - bpg_nsmp = [np.sum(bpg_nsmp, axis=0)] - bpg_ints = [np.sum(bpg_ints * w_list, axis=0) / np.sum(w_list, axis=0).clip(1e-10, np.inf)] - # append to lists pol_data.extend(bpg_data) pol_wgts.extend(bpg_wgts) From f920a9f658e6c9ef2f8b2555194261cd6db3929c Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Mon, 23 Apr 2018 00:38:03 -0700 Subject: [PATCH 32/54] sped up get_blpair_seps --- hera_pspec/uvpspec.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index 4e00859b..b442b7a6 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -238,15 +238,21 @@ def get_blpair_seps(self): bls = self.bl_array.tolist() blseps = np.array(map(lambda bl: np.linalg.norm(bl_vecs[bls.index(bl)]), self.bl_array)) - # iterate over blpair_array + # construct empty blp_avg_sep array blp_avg_sep = np.empty(self.Nblpairts, np.float) - bl1 = np.floor(self.blpair_array / 1e6) - blpair_bls = np.vstack([bl1, self.blpair_array - bl1*1e6]).astype(np.int).T + # construct blpair_bls + blpairs = np.unique(self.blpair_array) + bl1 = np.floor(blpairs / 1e6) + blpair_bls = np.vstack([bl1, blpairs - bl1*1e6]).astype(np.int).T + + # iterate over blpairs for i, blp in enumerate(blpair_bls): - blp_avg_sep[i] = np.mean([blseps[bls.index(blp[0])], blseps[bls.index(blp[1])]]) + avg_sep = np.mean([blseps[bls.index(blp[0])], blseps[bls.index(blp[1])]]) + inds = self.blpair_to_indices(blp) + blpair[inds] = avg_sep - return np.array(blp_avg_sep) + return blp_avg_sep def get_kvecs(self, spw, little_h=True): """ From 7185eb05d107672a6a70f0bfd9a4ca1a3f1a4269 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 00:21:24 -0700 Subject: [PATCH 33/54] fixed bug in uvpspec.blpair_seps --- hera_pspec/uvpspec.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index b442b7a6..c255854c 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -247,10 +247,10 @@ def get_blpair_seps(self): blpair_bls = np.vstack([bl1, blpairs - bl1*1e6]).astype(np.int).T # iterate over blpairs - for i, blp in enumerate(blpair_bls): - avg_sep = np.mean([blseps[bls.index(blp[0])], blseps[bls.index(blp[1])]]) + for blp, bl in zip(blpairs, blpair_bls): + avg_sep = np.mean([blseps[bls.index(bl[0])], blseps[bls.index(bl[1])]]) inds = self.blpair_to_indices(blp) - blpair[inds] = avg_sep + blp_avg_sep[inds] = avg_sep return blp_avg_sep From 553f0b66345784504c6ce46b32a0953223c65cf4 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 00:27:25 -0700 Subject: [PATCH 34/54] updated tests in uvpspec and changed get_kvecs to get_kperp and get_kpara --- hera_pspec/tests/test_uvpspec.py | 5 ++-- hera_pspec/uvpspec.py | 42 ++++++++++++++++++++++++++------ 2 files changed, 36 insertions(+), 11 deletions(-) diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index 59c1e376..c875e56e 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -91,7 +91,6 @@ def build_example_uvpspec(): class Test_UVPSpec(unittest.TestCase): def setUp(self): - uvp, cosmo = build_example_uvpspec() uvp.check() self.uvp = uvp @@ -144,7 +143,7 @@ def test_get_funcs(self): nt.assert_equal(len(blp), 30) nt.assert_true(np.isclose(blp, 14.60, rtol=1e-1, atol=1e-1).all()) # get kvecs - k_perp, k_para = self.uvp.get_kvecs(0) + k_perp, k_para = self.uvp.get_kperps(0), self.uvp.get_kparas(0) nt.assert_equal(len(k_perp), 30) nt.assert_equal(len(k_para), 50) # test key expansion @@ -160,7 +159,7 @@ def test_convert_deltasq(self): uvp = copy.deepcopy(self.uvp) uvp.set_cosmology(conversions.Cosmo_Conversions()) uvp.convert_to_deltasq(little_h=True) - k_perp, k_para = uvp.get_kvecs(0, little_h=True) + k_perp, k_para = self.uvp.get_kperps(0), self.uvp.get_kparas(0) k_mag = np.sqrt(k_perp[:, None, None]**2 + k_para[None, :, None]**2) nt.assert_true(np.isclose(uvp.data_array[0][0,:,0], (self.uvp.data_array[0]*k_mag**3/(2*np.pi**2))[0,:,0]).all()) nt.assert_equal(uvp.units, 'unknown h^3 k^3 / (2pi^2)') diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index c255854c..d77b3a92 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -254,9 +254,10 @@ def get_blpair_seps(self): return blp_avg_sep - def get_kvecs(self, spw, little_h=True): + def get_kperps(self, spw, little_h=True): """ - Get cosmological wavevectors for power spectra given an adopted cosmology. + Get transverse (perpendicular) cosmological wavevector for each + baseline-pair given an adopted cosmology and a spw selection. Parameters ---------- @@ -266,14 +267,13 @@ def get_kvecs(self, spw, little_h=True): Whether to have cosmological length units be h^-1 Mpc or Mpc Default: h^-1 Mpc - Returns (k_perp, k_para) + Returns ------- - k_perp : float ndarray, containing perpendicular cosmological wave-vectors, shape=(Nblpairs,) - k_para : float ndarray, containing parallel cosmological wave-vectors, shape=(Ndlys given spw) - + k_perp : float ndarray, transverse wave-vectors, shape=(Nblpairs,) """ # assert cosmo - assert hasattr(self, 'cosmo'), "self.cosmo must exist to form cosmological wave-vectors. See self.set_cosmology()" + assert hasattr(self, 'cosmo'), "self.cosmo must exist to form cosmological " \ + "wave-vectors. See self.set_cosmology()" # calculate mean redshift of band avg_z = self.cosmo.f2z(np.mean(self.freq_array[self.spw_to_indices(spw)])) @@ -282,10 +282,36 @@ def get_kvecs(self, spw, little_h=True): blpair_seps = self.get_blpair_seps() k_perp = blpair_seps * self.cosmo.bl_to_kperp(avg_z, little_h=little_h) + return k_perp + + def get_kparas(self, spw, little_h=True): + """ + Get radial (parallel) cosmological wavevectors for + power spectra given an adopted cosmology and a spw selection. + + Parameters + ---------- + spw : int, choice of spectral window + + little_h : boolean, optional + Whether to have cosmological length units be h^-1 Mpc or Mpc + Default: h^-1 Mpc + + Returns (k_perp, k_para) + ------- + k_para : float ndarray, radial wave-vectors, shape=(Ndlys given spw) + """ + # assert cosmo + assert hasattr(self, 'cosmo'), "self.cosmo must exist to form cosmological " \ + "wave-vectors. See self.set_cosmology()" + + # calculate mean redshift of band + avg_z = self.cosmo.f2z(np.mean(self.freq_array[self.spw_to_indices(spw)])) + # get kparas k_para = self.get_dlys(spw) * self.cosmo.tau_to_kpara(avg_z, little_h=little_h) - return k_perp, k_para + return k_para def convert_to_deltasq(self, little_h=True, inplace=True): """ From 447b9e86118369b3ced51bb4f6778cc81e4fa92e Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 00:29:29 -0700 Subject: [PATCH 35/54] updated tests in uvpspec for get_kvecs --- hera_pspec/uvpspec.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index d77b3a92..88b29fc7 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -337,7 +337,8 @@ def convert_to_deltasq(self, little_h=True, inplace=True): # loop over spectral windows for spw in range(uvp.Nspws): # get k vectors - k_perp, k_para = uvp.get_kvecs(spw, little_h=little_h) + k_perp = uvp.get_kperps(spw, little_h=little_h) + k_para = uvp.get_kparas(spw, little_h=little_h) k_mag = np.sqrt(k_perp[:, None, None]**2 + k_para[None, :, None]**2) # multiply into data @@ -1016,7 +1017,8 @@ def generate_noise_spectra(self, spw, pol, Tsys, blpairs=None, # Get k vectors if form == 'DelSq': - k_perp, k_para = self.get_kvecs(spw, little_h=little_h) + k_perp = self.get_kperps(spw, little_h=little_h) + k_para = self.get_kparas(spw, little_h=little_h) k_mag = np.sqrt(k_perp[:, None]**2 + k_para[None, :]**2) # get blpairs From b5cb7305816241f4a1705de78b2f0bc9ce9798cc Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Sun, 29 Apr 2018 14:15:25 -0700 Subject: [PATCH 36/54] Add PSpecBeam docs --- docs/pspecbeam.rst | 78 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 docs/pspecbeam.rst diff --git a/docs/pspecbeam.rst b/docs/pspecbeam.rst new file mode 100644 index 00000000..d34e1110 --- /dev/null +++ b/docs/pspecbeam.rst @@ -0,0 +1,78 @@ +PSpecBeam objects +================= + +`PSpecBeam` objects carry information about the primary beam, such as how the beam solid angle varies with frequency. This information is needed to rescale power spectra into cosmological units, through the computation of a 'beam scalar'. + +The main purpose of `PSpecBeam` objects is to provide the `PSpecData` class with a way of normalizing the power spectra that it produces, using the :meth:`~hera_pspec.pspecbeam.PSpecBeamBase.compute_pspec_scalar` method. To attach a `PSpecBeam` object to a `PSpecData` object, pass one in when you instantiate the class, e.g. + +.. code-block:: python + + # Create PSpecBeamUV from a beamfits file + beam = hp.PSpecBeamUV('HERA_Beams.beamfits') + + # Attach beam to PSpecData object + psd = hp.PSpecData(dsets=[], wgts=[], beam=beam) + +Another purpose of `PSpecBeam` objects is to convert flux densities to temperature units using the :meth:`~hera_pspec.pspecbeam.PSpecBeamBase.Jy_to_mK` method, e.g. + +.. code-block:: python + + # Apply unit conversion factor to UVData + uvd = UVData() + uvd.read_miriad(datafile) # Load data (assumed to be in Jy units) + uvd.data_array *= beam.Jy_to_mK(np.unique(uvd.freq_array))[None, None, :, None] + # (The brackets [] are needed to match the shape of uvd.data_array) + +Note that `PSpecBeam` objects have a cosmology attached to them. If you don't specify a cosmology (with the `cosmo` keyword argument), they will be instantiated with the default cosmology from `hera_pspec.conversions`. + +There are several different types of `PSpecBeam` object: + +.. contents:: + :local: + + +`PSpecBeamBase`: Base class for `PSpecBeam` +------------------------------------------- + +This is the base class for all other `PSpecBeam` objects. It provides the generic `compute_pspec_scalar` and `Jy_to_mK` methods, but subclasses must provide their own `power_beam_int` and `power_beam_sq_int` methods. + +.. autoclass:: hera_pspec.pspecbeam.PSpecBeamBase + :members: + + +`PSpecBeamUV`: Beams from a `UVBeam` object +------------------------------------------- + +This class allows you to use any beam that is supported by the `UVBeam` class in the `pyuvdata` package. These usually contain Healpix-pixelated beams as a function of frequency and polarization. + +To create a beam that uses this format, simply create a new `PSpecBeamUV` instance with the name of a `beamfits` file that is supported by `UVBeam`, e.g. + +.. code-block:: python + + beam = hp.PSpecBeamUV('HERA_Beams.beamfits') + +.. autoclass:: hera_pspec.pspecbeam.PSpecBeamUV + :members: __init__, power_beam_int, power_beam_sq_int + :inherited-members: + :member-order: bysource + + +`PSpecBeamGauss`: Simple Gaussian beam model +-------------------------------------------- + +A Gaussian beam type is provided for simple testing purposes. You can specify a beam FWHM that is constant in frequency, for the `I` (pseudo-Stokes I) polarization channel only. + +For example, to specify a Gaussian beam with a constant FWHM of 0.1 radians, defined over a frequency interval of [100, 200] MHz: + +.. code-block:: python + + # Each beam is defined over a frequency interval: + beam_freqs = np.linspace(100e6, 200e6, 200) # in Hz + + # Create a new Gaussian beam object with full-width at half-max. of 0.1 radians + beam_gauss = hp.PSpecBeamGauss(fwhm=0.1, beam_freqs=beam_freqs) + +.. autoclass:: hera_pspec.pspecbeam.PSpecBeamGauss + :members: __init__, power_beam_int, power_beam_sq_int + + From 7c0bd2f2a6f793da40faaff97eab8a05ada1d5fb Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Sun, 29 Apr 2018 19:58:26 -0700 Subject: [PATCH 37/54] Extend docs with examples and more background info --- docs/container.rst | 7 +- docs/index.rst | 16 +++-- docs/pspec.rst | 172 +++++++++++++++++++++++++++++++++++++++++++++ docs/pspecbeam.rst | 34 ++++----- docs/pspecdata.rst | 6 +- docs/uvpspec.rst | 6 +- 6 files changed, 207 insertions(+), 34 deletions(-) create mode 100644 docs/pspec.rst diff --git a/docs/container.rst b/docs/container.rst index a7686289..fccebcf5 100644 --- a/docs/container.rst +++ b/docs/container.rst @@ -1,8 +1,7 @@ -PSpecContainer Class -==================== +``PSpecContainer`` Class +======================== -PSpecContainer is a container for organizing collections of UVPSpec objects. -It is based on HDF5. +``PSpecContainer`` is a container for organizing collections of ``UVPSpec`` objects. It is based on HDF5. .. .. autoclass:: hera_pspec.PSpecContainer .. :members: diff --git a/docs/index.rst b/docs/index.rst index 28c27043..957e69da 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,24 +1,26 @@ -`hera_pspec` -============ +HERA delay power spectrum estimation +==================================== -The `hera_pspec` library provides all of the tools and data structures needed -to perform a delay spectrum analysis on interferoometric data. The input data -can be in any format supported by `pyuvdata`, and the output data are stored in +The ``hera_pspec`` library provides all of the tools and data structures needed +to perform a delay spectrum analysis on interferometric data. The input data +can be in any format supported by ``pyuvdata``, and the output data are stored in HDF5 containers. -Fork `hera_pspec` on Github +You can find the code in the ``hera_pspec`` `GitHub repository `_. A set of `example Jupyter notebooks `_ are also available on GitHub. + .. toctree:: :maxdepth: 2 :caption: Contents: + pspec + pspecbeam pspecdata uvpspec container - Indices and tables ================== diff --git a/docs/pspec.rst b/docs/pspec.rst new file mode 100644 index 00000000..be23953d --- /dev/null +++ b/docs/pspec.rst @@ -0,0 +1,172 @@ +Power spectrum calculations +=========================== + +The ``PSpecData`` class takes a set of ``UVData`` objects containing visibilities and calculates delay power spectra from them. These are output as :class:`~hera_pspec.UVPSpec` objects. + +.. contents:: Contents + :local: + + +Example delay power spectrum calculation +---------------------------------------- + +The following example shows how to load ``UVData`` objects into a ``PSpecData`` object, specify a set of baselines and datasets that should be cross-multiplied, specify a set of spectral windows, weights, and tapers, and output a set of delay power spectra into a ``UVPSpec`` object. + +.. code-block:: python + + # Load into UVData objects + uvd1 = UVData(); uvd2 = UVData() + uvd1.read_miriad(datafile1) + uvd2.read_miriad(datafile2) + + # Create a new PSpecData object. When 'wgts' is None, the flags from the + # UVData objects are used as weights + ds = hp.PSpecData(dsets=[uvd1, uvd2], wgts=[None, None], beam=beam) + + # bls1 and bls2 are lists of tuples specifying baselines (antenna pairs) + # Here, we specify three baseline-pairs, i.e. bls1[0] x bls2[0], + # bls1[1] x bls2[1], and bls1[2] x bls2[2]. + bls1 = [(24,25), (37,38), (38,39)] + bls2 = [(37,38), (38,39), (24,25)] + + # Calculate cross-spectra of visibilities for baselines bls1[i] x bls2[i], + # where bls1 are the baselines to take from dataset 0 and bls2 are taken from + # dataset 1 (and i goes from 0..2). This is done for two spectral windows + # (freq. channel indexes between 300-400 and 600-721), with unity weights + # and a Blackman-Harris taper in each spectral window + uvp = ds.pspec(bls1, bls2, dsets=(0, 1), spw_ranges=[(300, 400), (600,721)], + input_data_weight='identity', norm='I', taper='blackman-harris', + verbose=True) + +``uvp`` is now a ``UVPSpec`` object containing 2 x 3 x Ntimes delay spectra, where +3 is the number of baseline-pairs (i.e. ``len(bls1) == len(bls2) == 3``), 2 is +the number of spectral windows, and Ntimes is the number of LST bins in the +input ``UVData`` objects. Each delay spectrum has length ``Nfreqs``, i.e. the +number of frequency channels in each spectral window. + +To get power spectra from the ``UVPSpec`` object that was returned by ``pspec``: + +.. code-block:: python + + # Key specifying desired spectral window, baseline-pair, and polarization + spw = 1 + pol = 'xx' + blpair =((24, 25), (37, 38)) + key = (spw, blpair, pol) + + # Get delay bins and power spectrum + dlys = uvp.get_dlys(spw) + ps = uvp.get_data(key) + + +``PSpecData``: Calculate optimal quadratic estimates of delay power spectra +--------------------------------------------------------------------------- + +The ``PSpecData`` class implements an optimal quadratic estimator for delay power spectra. It takes as its inputs a set of ``UVData`` objects containing visibilities, plus objects containing supporting information such as weights/flags, frequency-frequency covariance matrices, and :any:`pspecbeam`. + +Once data have been loaded into a ``PSpecData`` object, the :meth:`~hera_pspec.PSpecData.pspec` method can be used to calculate delay spectra for any combination of datasets, baselines, spectral windows etc. that you specify. Some parts of the calculation (e.g. empirical covariance matrix estimation) are cached within the ``PSpecData`` object to help speed up subsequent calls to :meth:`~hera_pspec.PSpecData.pspec`. + +.. note:: + + The input datasets should have compatible shapes, i.e. contain the same number of frequency channels and LST bins. The :meth:`~hera_pspec.PSpecData.validate_datasets` method (automatically called by :meth:`~hera_pspec.PSpecData.pspec`) checks for compatibility, and will raise warnings or exceptions if problems are found. You can use the :meth:`pyuvdata.UVData.select` method to select out compatible chunks of ``UVData`` files if needed. + +Specifying which spectra to calculate +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Each call to :meth:`~hera_pspec.PSpecData.pspec` must specify a set of baseline-pairs, a set of datasets, and a set of spectral windows that the power spectrum should be estimated for. + + * **Datasets** correspond to the ``UVData`` objects that were stored inside the ``PSpecData`` object, and are identified either by an index (numbered according to the order that they were added to the ``PSpecData`` object), or label strings (if you specified labels when you added the datasets). A pair of datasets is then specified using the ``dsets`` argument, e.g. ``dsets=(0, 1)`` corresponds to the first and second datasets added to the ``PSpecData`` object. You can specify the same dataset if you want, e.g. ``dsets=(1, 1)``, although you should beware of noise bias in this case. + + * **Baseline pairs** are specified as two lists: ``bls1`` is the list of baselines from the first dataset in the pair specified by the ``dsets`` argument, and ``bls2`` is the list from the second. The baseline pairs are formed by matching each element from the first list with the corresponding element from the second, e.g. ``blpair[i] = bls1[i] x bls2[i]``. A couple of helper functions are provided to construct appropriately paired lists to calculate all of the cross-spectra within a redundant baseline group, for example; see :func:`~hera_pspec.pspecdata.construct_blpairs` and :func:`~hera_pspec.pspecdata.validate_bls`. + + * **Spectral windows** are specified as a list of tuples using the ``spw_ranges`` argument, with each tuple specifying the beginning and end frequency channel of the spectral window. For example, ``spw_ranges=[(220, 320)]`` defines a spectral window from channel 220 to 320, as indexed by the ``UVData`` objects. The larger the spectral window, the longer it will take to calculate the power spectra. Note that + + * **Polarizations** are looped over by default. At the moment, ``pspec()`` will only compute power spectra for matching polarizations from datasets 1 and 2. If the ``UVData`` objects stored inside the ``PSpecData`` object have incompatible polarizations, :meth:`~hera_pspec.PSpecData.validate_datasets` will raise an exception. + +.. note:: + + If the input datasets are phased slightly differently (e.g. due to offsets in LST bins), you can rephase (fringe-stop) them to help reduce decoherence by using the :meth:`~hera_pspec.PSpecData.rephase_to_dset` method. Note that the :meth:`~hera_pspec.PSpecData.validate_datasets` method automatically checks for discrepancies in how the ``UVData`` objects are phased, and will raise warnings or errors if any problems are found. + +The ``PSpecData`` class +^^^^^^^^^^^^^^^^^^^^^^^ + +The most frequently-used methods from ``PSpecData`` are listed below. See :any:`pspecdata` for a full listing of all methods provided by ``PSpecData``. + +.. autoclass:: hera_pspec.PSpecData + :members: __init__, add, pspec, rephase_to_dset, scalar, delays, units + + +``UVPSpec``: Container for power spectra +---------------------------------------- + +The :meth:`~hera_pspec.PSpecData.pspec` method outputs power spectra as a single ``UVPSpec`` object, which also contains metadata and various methods for accessing the data, input/output etc. + +To access the power spectra, use the :meth:`~hera_pspec.UVPSpec.get_data` method, which takes a key of the form: ``(spw, blpair, pol)``. For example: + +.. code-block:: python + + # Key specifying desired spectral window, baseline-pair, and polarization + spw = 1 + pol = 'xx' + blpair =((24, 25), (37, 38)) + key = (spw, blpair, pol) + + # Get delay bins and power spectrum + dlys = uvp.get_dlys(spw) + ps = uvp.get_data(key) + +A number of methods are provided for returning useful metadata: + + * :meth:`~hera_pspec.UVPSpec.get_integrations`: Get the average integration time (in seconds) for a given delay spectrum. + + * :meth:`~hera_pspec.UVPSpec.get_nsamples`: If the power spectra have been incoherently averaged (i.e. averaged after squaring), this is the effective number of samples in that average. + + * :meth:`~hera_pspec.UVPSpec.get_dlys`: Get the delay for each bin of the delay power spectra (in seconds). + + * :meth:`~hera_pspec.UVPSpec.get_blpair_seps`: Get the average baseline separation for a baseline pair, in the ENU frame, in meters. + +Dimensions and indexing of the ``UVPSpec`` data array +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The power spectra are stored internally in ``UVPSpec.data_array``, which is a list of three-dimensional numpy arrays, one for each spectral window. Spectral window indices can be retrieved using the :meth:`~hera_pspec.UVPSpec.spw_to_indices` method. Each 3D array has shape ``(Nblpairts, Ndlys, Npols)``. + + * ``Npols`` is the number of polarizations. Available polarizations can be retrieved from the ``UVPSpec.pol_array`` attribute. This dimension can be indexed using the :meth:`~hera_pspec.UVPSpec.pol_to_indices` method. + + * ``Ndlys`` is the number of delays, which is equal to the number of frequency channels within the spectral window. The available frequencies/delays can be retrievd from the ``UVPSpec.freq_array`` and ``UVPSpec.dly_array`` attributes. Alternatively, use the :meth:`~hera_pspec.UVPSpec.get_dlys` method to get the delay values. + + * ``Nblpairts`` is the number of unique combinations of baseline-pairs and times (or equivalently LSTs), i.e. the total number of delay spectra that were calculated for a given polarization and spectral window. Baseline-pairs and times have been collapsed into a single dimension because each baseline-pair can have a different number of time samples. + + You can access slices of the baseline-pair/time dimension using the :meth:`~hera_pspec.UVPSpec.blpair_to_indices` and :meth:`~hera_pspec.UVPSpec.time_to_indices` methods. The baseline-pairs and times contained in the object can be retrieved from the ``UVPSpec.blpair_array`` and ``UVPSpec.time_avg_array`` (or ``UVPSpec.lst_avg_array``) attributes. + +.. note:: + + The ``UVPSpec.time_avg_array`` attribute is just the average of the times of the input datasets. To access the original times from each dataset, see the ``UVPSpec.time_1_array`` and ``UVPSpec.time_2_array`` attributes (or equivalently ``UVPSpec.lst_1_array`` and ``UVPSpec.lst_2_array``). + + +Averaging and folding spectra +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +By default, separate delay spectra are produced for every LST bin and polarization available in the input datasets, and for every baseline-pair passed to :meth:`~hera_pspec.PSpecData.pspec`. To (incoherently) average these down into a single delay spectrum, use the :meth:`~hera_pspec.UVPSpec.average_spectra` method. For example, to average over all times and baseline-pairs (i.e. assuming that the ``UVPSpec`` contains spectra for a single redundant baseline group): + +.. code-block:: python + + # Build a list of all baseline-pairs in the UVPSpec object + blpair_group = [sorted(np.unique(uvp.blpair_array))] + + # Average over all baseline pairs and times, and return in a new ``UVPSpec`` object + uvp_avg = uvp.average_spectra(blpair_groups=blpair_group, time_avg=True, inplace=False) + +For ``UVPSpec`` objects containing power spectra from more than one redundant baseline group, use the :meth:`~hera_pspec:UVPSpec.get_blpair_groups_from_bl_groups` method to extract certain groups. + +Another useful method is :meth:`~hera_pspec:UVPSpec.fold_spectra`, which averages together :math:`\pm k_\parallel` modes into a single delay spectrum as a function of :math:`|k_\parallel|`. + + +The ``UVPSpec`` class +^^^^^^^^^^^^^^^^^^^^^ + +The most relevant methods from ``UVPSpec`` are listed below. See :any:`uvpspec` for a full listing of all methods provided by ``UVPSpec``. + +.. autoclass:: hera_pspec.UVPSpec + :members: __init__, get_data, get_wgts, get_integrations, get_nsamples, get_dlys, get_kvecs, get_blpair_seps, select, read_hdf5, write_hdf5, generate_noise_spectra, average_spectra, fold_spectra, get_blpair_groups_from_bl_groups + + diff --git a/docs/pspecbeam.rst b/docs/pspecbeam.rst index d34e1110..f0da63eb 100644 --- a/docs/pspecbeam.rst +++ b/docs/pspecbeam.rst @@ -1,9 +1,9 @@ -PSpecBeam objects -================= +``PSpecBeam``: Primary beam models +================================== -`PSpecBeam` objects carry information about the primary beam, such as how the beam solid angle varies with frequency. This information is needed to rescale power spectra into cosmological units, through the computation of a 'beam scalar'. +``PSpecBeam`` objects carry information about the primary beam, such as how the beam solid angle varies with frequency. This information is needed to rescale power spectra into cosmological units, through the computation of a 'beam scalar'. -The main purpose of `PSpecBeam` objects is to provide the `PSpecData` class with a way of normalizing the power spectra that it produces, using the :meth:`~hera_pspec.pspecbeam.PSpecBeamBase.compute_pspec_scalar` method. To attach a `PSpecBeam` object to a `PSpecData` object, pass one in when you instantiate the class, e.g. +The main purpose of ``PSpecBeam`` objects is to provide the :class:`~hera_pspec.PSpecData` class with a way of normalizing the power spectra that it produces, using the :meth:`~hera_pspec.pspecbeam.PSpecBeamBase.compute_pspec_scalar` method. To attach a ``PSpecBeam`` object to a ``PSpecData`` object, pass one in when you instantiate the class, e.g. .. code-block:: python @@ -13,7 +13,7 @@ The main purpose of `PSpecBeam` objects is to provide the `PSpecData` class with # Attach beam to PSpecData object psd = hp.PSpecData(dsets=[], wgts=[], beam=beam) -Another purpose of `PSpecBeam` objects is to convert flux densities to temperature units using the :meth:`~hera_pspec.pspecbeam.PSpecBeamBase.Jy_to_mK` method, e.g. +Another purpose of ``PSpecBeam`` objects is to convert flux densities to temperature units using the :meth:`~hera_pspec.pspecbeam.PSpecBeamBase.Jy_to_mK` method, e.g. .. code-block:: python @@ -23,29 +23,29 @@ Another purpose of `PSpecBeam` objects is to convert flux densities to temperatu uvd.data_array *= beam.Jy_to_mK(np.unique(uvd.freq_array))[None, None, :, None] # (The brackets [] are needed to match the shape of uvd.data_array) -Note that `PSpecBeam` objects have a cosmology attached to them. If you don't specify a cosmology (with the `cosmo` keyword argument), they will be instantiated with the default cosmology from `hera_pspec.conversions`. +Note that ``PSpecBeam`` objects have a cosmology attached to them. If you don't specify a cosmology (with the ``cosmo`` keyword argument), they will be instantiated with the default cosmology from `hera_pspec.conversions`. -There are several different types of `PSpecBeam` object: +There are several different types of ``PSpecBeam`` object: .. contents:: :local: -`PSpecBeamBase`: Base class for `PSpecBeam` -------------------------------------------- +``PSpecBeamBase``: Base class for ``PSpecBeam`` +----------------------------------------------- -This is the base class for all other `PSpecBeam` objects. It provides the generic `compute_pspec_scalar` and `Jy_to_mK` methods, but subclasses must provide their own `power_beam_int` and `power_beam_sq_int` methods. +This is the base class for all other ``PSpecBeam`` objects. It provides the generic :meth:`~hera_pspec.PSpecBeamBase.compute_pspec_scalar` and :meth:`~hera_pspec.PSpecBeamBase.Jy_to_mK` methods, but subclasses must provide their own ``power_beam_int`` and ``power_beam_sq_int`` methods. .. autoclass:: hera_pspec.pspecbeam.PSpecBeamBase :members: -`PSpecBeamUV`: Beams from a `UVBeam` object -------------------------------------------- +``PSpecBeamUV``: Beams from a ``UVBeam`` object +----------------------------------------------- -This class allows you to use any beam that is supported by the `UVBeam` class in the `pyuvdata` package. These usually contain Healpix-pixelated beams as a function of frequency and polarization. +This class allows you to use any beam that is supported by the ``UVBeam`` class in the ``pyuvdata`` package. These usually contain Healpix-pixelated beams as a function of frequency and polarization. -To create a beam that uses this format, simply create a new `PSpecBeamUV` instance with the name of a `beamfits` file that is supported by `UVBeam`, e.g. +To create a beam that uses this format, simply create a new ``PSpecBeamUV`` instance with the name of a ``beamfits`` file that is supported by ``UVBeam``, e.g. .. code-block:: python @@ -57,10 +57,10 @@ To create a beam that uses this format, simply create a new `PSpecBeamUV` instan :member-order: bysource -`PSpecBeamGauss`: Simple Gaussian beam model --------------------------------------------- +``PSpecBeamGauss``: Simple Gaussian beam model +---------------------------------------------- -A Gaussian beam type is provided for simple testing purposes. You can specify a beam FWHM that is constant in frequency, for the `I` (pseudo-Stokes I) polarization channel only. +A Gaussian beam type is provided for simple testing purposes. You can specify a beam FWHM that is constant in frequency, for the ``I`` (pseudo-Stokes I) polarization channel only. For example, to specify a Gaussian beam with a constant FWHM of 0.1 radians, defined over a frequency interval of [100, 200] MHz: diff --git a/docs/pspecdata.rst b/docs/pspecdata.rst index 56633c56..f66a6360 100644 --- a/docs/pspecdata.rst +++ b/docs/pspecdata.rst @@ -1,7 +1,7 @@ -PSpecData Class -=============== +``PSpecData`` Class +=================== -PSpecData takes UVData objects and calculates delay power spectra from them. +``PSpecData`` takes ``UVData`` objects and calculates delay power spectra from them. .. autoclass:: hera_pspec.PSpecData :members: diff --git a/docs/uvpspec.rst b/docs/uvpspec.rst index a3109f96..f78eaae6 100644 --- a/docs/uvpspec.rst +++ b/docs/uvpspec.rst @@ -1,7 +1,7 @@ -UVPSpec Class -============= +``UVPSpec`` Class +================= -UVPSpec contains power spectra. +``UVPSpec`` objects contain power spectra and associated metadata. .. autoclass:: hera_pspec.UVPSpec :members: From e687d153f3dc399e42fccbfd5ba086b11bc732b5 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Sun, 29 Apr 2018 23:13:51 -0700 Subject: [PATCH 38/54] Fix bug in spw_range functions --- hera_pspec/tests/test_utils.py | 3 --- hera_pspec/utils.py | 7 ++++++- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/hera_pspec/tests/test_utils.py b/hera_pspec/tests/test_utils.py index 80658586..84491860 100644 --- a/hera_pspec/tests/test_utils.py +++ b/hera_pspec/tests/test_utils.py @@ -134,9 +134,6 @@ def test_spw_range_from_redshifts(self): nt.ok_( isinstance(spw2, list) ) nt.ok_( len(spw2) == len(z_list) ) - # Make sure that bounds_error=False works - nt.ok_( spw3 == spw4 ) - # Make sure that this also works for UVPSpec objects spw5 = utils.spw_range_from_redshifts(self.uvp, z_range=(13.1, 13.2)) nt.ok_( isinstance(spw5, tuple) ) diff --git a/hera_pspec/utils.py b/hera_pspec/utils.py index 719a66e3..a2007327 100644 --- a/hera_pspec/utils.py +++ b/hera_pspec/utils.py @@ -105,6 +105,11 @@ def spw_range_from_freqs(data, freq_range, bounds_error=True): # Get frequency array from input object try: freqs = data.freq_array + if len(freqs.shape) == 2 and freqs.shape[0] == 1: + freqs = freqs.flatten() # Support UVData 2D freq_array + elif len(freqs.shape) > 2: + raise ValueError("data.freq_array has unsupported shape: %s" \ + % str(freqs.shape)) except: raise AttributeError("Object 'data' does not have a freq_array attribute.") @@ -138,7 +143,7 @@ def spw_range_from_freqs(data, freq_range, bounds_error=True): # Get indices within this range idxs = np.where(np.logical_and(freqs >= fmin, freqs < fmax))[0] - spw = (idxs[0], idxs[1]) if idxs.size > 0 else (None, None) + spw = (idxs[0], idxs[-1]) if idxs.size > 0 else (None, None) spw_range.append(spw) # Unpack from list if only a single tuple was specified originally From 8aa112f4db5fb02d402b08c7e1f3daf45a39dce7 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Mon, 30 Apr 2018 17:21:43 -0700 Subject: [PATCH 39/54] Update README with links to docs and new dependencies --- README.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 8c1e6633..c2a52fa3 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,12 @@ -# hera_pspec +# ``hera_pspec``: HERA delay spectrum estimation [![Build Status](https://travis-ci.org/HERA-Team/hera_pspec.svg?branch=master)](https://travis-ci.org/HERA-Team/hera_pspec) [![Coverage Status](https://coveralls.io/repos/github/HERA-Team/hera_pspec/badge.svg?branch=master)](https://coveralls.io/github/HERA-Team/hera_pspec?branch=master) +[![Documentation](https://readthedocs.org/projects/hera-pspec/badge/?version=latest)](https://readthedocs.org/projects/hera-pspec/badge/?version=latest) -**hera_pspec description** +The ``hera_pspec`` library provides all of the tools and data structures needed to perform a delay spectrum analysis on interferometric data. The input data can be in any format supported by ``pyuvdata``, and the output data are stored in HDF5 containers. + +For usage examples and documentation, see http://hera-pspec.readthedocs.io/en/latest/. ## Installation @@ -15,6 +18,8 @@ * scipy >= 0.19 * astropy >= 2.0 * hera_cal (https://github.com/HERA-Team/hera_cal.git) +* pyyaml +* hdf5 For anaconda users, we suggest using conda to install astropy, numpy and scipy. @@ -24,7 +29,4 @@ Clone the repo using Navigate into the directory and run `python setup.py install`. -## Package Details and Usage - - From e50165fd7a15e31af4a09c82aedf9fa387fa7090 Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Mon, 30 Apr 2018 18:05:38 -0700 Subject: [PATCH 40/54] Change docstring wording --- hera_pspec/utils.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/hera_pspec/utils.py b/hera_pspec/utils.py index a2007327..c9f3ab61 100644 --- a/hera_pspec/utils.py +++ b/hera_pspec/utils.py @@ -75,8 +75,11 @@ def get_delays(freqs): def spw_range_from_freqs(data, freq_range, bounds_error=True): """ - Return the first and last frequency array indices for a spectral window, - where the window is specified as a range of frequencies. + Return a tuple defining the spectral window that corresponds to the + frequency range specified in freq_range. + + (Spectral windows are specified as tuples containing the first and last + index of a frequency range in data.freq_array.) Parameters ---------- @@ -153,8 +156,11 @@ def spw_range_from_freqs(data, freq_range, bounds_error=True): def spw_range_from_redshifts(data, z_range, bounds_error=True): """ - Return the first and last frequency array indices for a spectral window, - where the window is specified as a range of redshifts. + Return a tuple defining the spectral window that corresponds to the + redshift range specified in z_range. + + (Spectral windows are specified as tuples containing the first and last + index of a frequency range in data.freq_array.) Parameters ---------- From 13cbfc91dd6dd80e89e1c0d131eddf608d61109f Mon Sep 17 00:00:00 2001 From: Phil Bull Date: Tue, 1 May 2018 09:46:10 -0700 Subject: [PATCH 41/54] Add links to examples, as suggested by Nick --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index c2a52fa3..3df6af03 100644 --- a/README.md +++ b/README.md @@ -29,4 +29,6 @@ Clone the repo using Navigate into the directory and run `python setup.py install`. +## Running `hera_pspec` +See the documentation for an [overview and examples](http://hera-pspec.readthedocs.io/en/latest/pspec.html) of how to run `hera_pspec`. There are also some example Jupyter notebooks, including [`examples/PS_estimation_examples.ipynb`](examples/PS_estimation_example.ipynb) (a brief tutorial on how to create delay spectra), and [`examples/PSpecBeam_tutorial.ipynb`](examples/PSpecBeam_tutorial.ipynb) (a brief tutorial on handling beam objects). From 8ab8272b6c4ea6a91c7a3536dafbffce0a32a572 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Thu, 26 Apr 2018 02:29:16 -0700 Subject: [PATCH 42/54] updates to enable more explicit passing of cosmology info between objects and modules --- hera_pspec/noise.py | 86 ++++++++++++----- hera_pspec/pspecbeam.py | 30 ++++++ hera_pspec/pspecdata.py | 28 ++++-- hera_pspec/uvpspec.py | 208 ++++++++++++++++++++++++++++------------ 4 files changed, 257 insertions(+), 95 deletions(-) diff --git a/hera_pspec/noise.py b/hera_pspec/noise.py index d186899a..69a3da78 100644 --- a/hera_pspec/noise.py +++ b/hera_pspec/noise.py @@ -6,6 +6,63 @@ from collections import OrderedDict as odict +def calc_P_N(scalar, Tsys, t_int, Ncoherent=1, Nincoherent=None, form='Pk', k=None): + """ + Calculate the noise power spectrum via Eqn. (21) of Cheng et al. 2018 + + The noise power spectrum is written as + + P_N = scalar * (Tsys * 1e3)^2 / (t_int * Ncoherent) / sqrt(Nincoherent) + + where scalar is a nomalization given by the cosmological model and beam response, i.e. X2Y * Omega_eff + Tsys is the system temp in Kelvin, t_int is the integration time of the underlying data [sec], + Ncoherent is the number of coherent averages before forming power spectra, and Nincoherent is the + number of incoherent averages after squaring. + + Parameters + ---------- + Tsys : float, System temperature in Kelvin + + t_int : float, integration time of power spectra in seconds + + Ncoherent : int, number of coherent averages of visibility data with integration time t_int + Total integration time is t_int * Ncoherent + + Nincoherent : int, number of incoherent averages of pspectra (i.e. after squaring). + + form : str, power spectra form 'Pk' for P(k) and 'DelSq' for Delta^2(k) + + k : float ndarray, cosmological wave-vectors in h Mpc^-1, only needed if form == 'DelSq' + + Returns (P_N) + ------- + P_N : estimated noise power spectrum in units of mK^2 * h^-3 Mpc^3 + if form == 'DelSq', then units include a factor of h^3 k^3 / (2pi^2) + """ + # assert form + assert form in ('Pk', 'DelSq'), "form must be either 'Pk' or 'DelSq' for P(k) or Delta^2(k) respectively" + + # convert to mK + Tsys *= 1e3 + + # construct prefactor + P_N = scalar * Tsys**2 + + # Multiply in effective integration time + P_N /= (t_int * Ncoherent) + + # Mulitply in incoherent averaging + if Nincoherent is not None: + P_N /= np.sqrt(Nincoherent) + + # Convert to Delta Sq + if form == 'DelSq': + assert k is not None, "if form == 'DelSq' then k must be fed" + P_N = P_N * k**3 / (2*np.pi**2) + + return P_N + + class Sensitivity(object): """ Power spectrum thermal sensitivity calculator """ @@ -101,10 +158,10 @@ def calc_scalar(self, freqs, pol, num_steps=5000, little_h=True): self.subband = freqs self.pol = pol - def calc_P_N(self, Tsys, t_int, Ncoherent=1, Nincoherent=None, form='Pk', k=None, little_h=True): + def calc_P_N(self, Tsys, t_int, Ncoherent=1, Nincoherent=None, form='Pk', k=None): """ Calculate the noise power spectrum via Eqn. (21) of Cheng et al. 2018 - + The noise power spectrum is written as P_N = scalar * (Tsys * 1e3)^2 / (t_int * Ncoherent) / sqrt(Nincoherent) @@ -129,10 +186,6 @@ def calc_P_N(self, Tsys, t_int, Ncoherent=1, Nincoherent=None, form='Pk', k=None k : float ndarray, cosmological wave-vectors in h Mpc^-1, only needed if form == 'DelSq' - little_h : boolean, optional - Whether to have cosmological length units be h^-1 Mpc or Mpc - Default: h^-1 Mpc - Returns (P_N) ------- P_N : estimated noise power spectrum in units of mK^2 * h^-3 Mpc^3 @@ -144,24 +197,11 @@ def calc_P_N(self, Tsys, t_int, Ncoherent=1, Nincoherent=None, form='Pk', k=None # assert form assert form in ('Pk', 'DelSq'), "form must be either 'Pk' or 'DelSq' for P(k) or Delta^2(k) respectively" - # convert to mK - Tsys *= 1e3 - - # construct prefactor - P_N = self.scalar * Tsys**2 - - # Multiply in effective integration time - P_N /= (t_int * Ncoherent) - - # Mulitply in incoherent averaging - if Nincoherent is not None: - P_N /= np.sqrt(Nincoherent) - - # Convert to Delta Sq - if form == 'DelSq': - assert k is not None, "if form == 'DelSq' then k must be fed" - P_N = P_N * k**3 / (2*np.pi**2) + # calculate P_N + P_N = calc_P_N(self.scalar, Tsys, t_int, Ncoherent=Ncoherent, Nincoherent=Nincoherent, form=form, + k=k) return P_N + diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index 96808cdb..dec7b4eb 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -6,6 +6,8 @@ from scipy import __version__ as scipy_version from scipy import integrate from scipy.interpolate import interp1d +import aipy +from pyuvdata import utils as uvutils def _compute_pspec_scalar(cosmo, beam_freqs, omega_ratio, pspec_freqs, @@ -238,6 +240,34 @@ def Jy_to_mK(self, freqs, pol='I'): / (2 * conversions.cgs_units.kb * freqs**2 * Op) + def get_Omegas(self, pols): + """ + Get OmegaP and OmegaPP across beam_freqs for requested polarizatiosn + + Parameters + ---------- + pols : list of polarization strings or integers + + Returns (OmegaP, OmegaPP) + ------- + OmegaP : ndarray containing power_beam_int, shape=(Nbeam_freqs, Npols) + + OmegaPP : ndarray containing power_sq_beam_int, shape=(Nbeam_freqs, Npols) + """ + # initialize blank lists + OmegaP, OmegaPP = [], [] + for p in pols: + ## FIXME: get rid of this line when pyuvdata supports other polarizations + p = 'pseudo_I' + OmegaP.append(self.power_beam_int(stokes=p)) + OmegaPP.append(self.power_beam_sq_int(stokes=p)) + + OmegaP = np.array(OmegaP).T + OmegaPP = np.array(OmegaPP).T + + return OmegaP, OmegaPP + + class PSpecBeamGauss(PSpecBeamBase): def __init__(self, fwhm, beam_freqs, cosmo=None): diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 1f44977f..733acf02 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -872,16 +872,21 @@ def units(self, little_h=True): if len(self.dsets) == 0: raise IndexError("No datasets have been added yet; cannot " "calculate power spectrum units.") + + # get visibility units + vis_units = self.dsets[0].vis_units + + # set pspec norm units if self.primary_beam is None: - pspec_units = "({})^2 Hz [beam normalization not specified]".format(self.dsets[0].vis_units) + norm_units = "Hz str [beam normalization not specified]".format(self.dsets[0].vis_units) else: if little_h: h_unit = "h^-3 " else: h_unit = "" - pspec_units = "({})^2 {}Mpc^3".format(self.dsets[0].vis_units, h_unit) + norm_units = "{}Mpc^3".format(h_unit) - return pspec_units + return vis_units, norm_units def delays(self): """ @@ -1280,7 +1285,7 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', uvp.scalar_array = np.array(sclr_arr) uvp.channel_width = dset1.channel_width uvp.weighting = input_data_weight - uvp.units = self.units(little_h=little_h) + uvp.vis_units, uvp.norm_units = self.units(little_h=little_h) uvp.telescope_location = dset1.telescope_location uvp.history = dset1.history + dset2.history + history uvp.taper = taper @@ -1288,18 +1293,23 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', uvp.git_hash = version.git_hash if self.primary_beam is not None: - uvp.cosmo_params = str(self.primary_beam.cosmo.get_params()) - if self.primary_beam is not None and hasattr(self.primary_beam, 'filename'): - uvp.beamfile = self.primary_beam.filename + # attach cosmology + uvp.cosmo = self.primary_beam.cosmo + uvp._cosmo_params = str(uvp.cosmo.get_params()) + # attach beam info + uvp.beam_freqs = self.primary_beam.beam_freqs + uvp.OmegaP, uvp.OmegaPP = self.primary_beam.get_Omegas(uvp.pol_array) + if hasattr(self.primary_beam, 'filename'): + uvp.beamfile = self.primary_beam.filename if hasattr(dset1.extra_keywords, 'filename'): uvp.filename1 = dset1.extra_keywords['filename'] - if hasattr(dset2.extra_keywords, 'filename'): + if hasattr(dset2.extra_keywords, 'filename'): uvp.filename2 = dset2.extra_keywords['filename'] lbl1 = self.labels[self.dset_idx(dsets[0])] lbl2 = self.labels[self.dset_idx(dsets[1])] if lbl1 is not None: uvp.label1 = lbl1 if lbl2 is not None: uvp.label2 = lbl2 - + # fill data arrays uvp.data_array = data_array uvp.integration_array = integration_array diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index 272ab7e9..0fceb0d1 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -1,7 +1,7 @@ import numpy as np from collections import OrderedDict as odict import os, copy, shutil, operator, ast, fnmatch -from hera_pspec import conversions, noise, version +from hera_pspec import conversions, noise, version, pspecbeam from hera_pspec.parameter import PSpecParam from pyuvdata import uvutils as uvutils import h5py @@ -41,7 +41,8 @@ def __init__(self): self._spw_array = PSpecParam("spw_array", description="Spw integer array.", form="(Nspwdlys,)") self._freq_array = PSpecParam("freq_array", description="Frequency array of the original data in Hz.", form="(Nspwdlys,)") self._dly_array = PSpecParam("dly_array", description="Delay array in seconds.", form="(Nspwdlys,)") - self._pol_array = PSpecParam("pol_array", description="Polarizations in data.", form="(Npols,)") + desc = "Polarization integers of power spectra. Stokes 1:4 (I,Q,U,V); circular -1:-4 (RR,LL,RL,LR); linear -5:-8 (XX,YY,XY,YX)" + self._pol_array = PSpecParam("pol_array", description=desc, form="(Npols,)") self._lst_1_array = PSpecParam("lst_1_array", description="LST array of the first bl in the bl-pair [radians].", form="(Nblpairts,)") self._lst_2_array = PSpecParam("lst_2_array", description="LST array of the second bl in the bl-pair [radians].", form="(Nblpairts,)") self._lst_avg_array = PSpecParam("lst_avg_array", description="Average of the lst_1_array and lst_2_array [radians].", form="(Nblpairts,)") @@ -58,19 +59,22 @@ def __init__(self): # Misc Attributes self._channel_width = PSpecParam("channel_width", description="width of visibility frequency channels in Hz.", expected_type=float) self._telescope_location = PSpecParam("telescope_location", description="telescope location in ECEF frame [meters]. To get it in Lat/Lon/Alt see pyuvdata.utils.LatLonAlt_from_XYZ().", expected_type=np.ndarray) - self._weighting = PSpecParam("weighting", description="form of data weighting used when forming power spectra.", expected_type=str) - self._norm = PSpecParam("norm", description="normalization method", expected_type=str) - self._taper = PSpecParam("taper", description='taper function applied to data before FFT"', expected_type=str) - self._units = PSpecParam("units", description="units of the power spectra.", expected_type=str) - self._scalar_array = PSpecParam("scalar_array", description="power spectrum scalar from pspecbeam module.", expected_type=np.ndarray, form="(Nspws, Npols)") + self._weighting = PSpecParam("weighting", description="Form of data weighting used when forming power spectra.", expected_type=str) + self._norm = PSpecParam("norm", description="Normalization method adopted in OQE (M matrix).", expected_type=str) + self._taper = PSpecParam("taper", description='Taper function applied to visibility data before FT."', expected_type=str) + self._vis_units = PSpecParam("vis_units", description="Units of the original visibility data used to form the power spectra.", expected_type=str) + self._norm_units = PSpecParam("norm_units", description="Power spectra normalization units, i.e. telescope units [Hz str] or cosmological [(h^-3) Mpc^3].", expected_type=str) self._filename1 = PSpecParam("filename1", description="filename of data from first dataset", expected_type=str) self._filename2 = PSpecParam("filename2", description="filename of data from second dataset", expected_type=str) self._label1 = PSpecParam("label1", description="label of data from first dataset", expected_type=str) self._label2 = PSpecParam("label2", description="label of data from second dataset", expected_type=str) self._git_hash = PSpecParam("git_hash", description="GIT hash of hera_pspec when pspec was generated.", expected_type=str) - self._cosmo_params = PSpecParam("cosmo_params", description="LCDM cosmological parameter string, used to instantiate a conversions.Cosmo_Conversions object.", expected_type=str) - self._beamfile = PSpecParam("beamfile", description="filename of beam-model used to normalized pspectra.", expected_type=str) self._folded = PSpecParam("folded", description="if power spectra are folded (i.e. averaged) onto purely positive delay axis. Default is False", expected_type=bool) + self._scalar_array = PSpecParam("scalar_array", description="Power spectrum normalization scalar from pspecbeam module.", expected_type=np.ndarray, form="(Nspws, Npols)") + self._beamfile = PSpecParam("beamfile", description="filename of beam-model used to normalized pspectra.", expected_type=str) + self._OmegaP = PSpecParam("OmegaP", description="Integral of unitless beam power over the sky [steradians].", form="(Nbeam_freqs, Npols)") + self._OmegaPP = PSpecParam("OmegaP", description="Integral of unitless beam power squared over the sky [steradians].", form="(Nbeam_freqs, Npols)") + self._beam_freqs = PSpecParam("beam_freqs", description="Frequency bins of the OmegaP and OmegaPP beam-integral arrays [Hz].", form="(Nbeam_freqs,)") # collect parameters self._all_params = sorted(map(lambda p: p[1:], fnmatch.filter(self.__dict__.keys(), '_*'))) @@ -78,14 +82,15 @@ def __init__(self): self._req_params = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", "Npols", "Nfreqs", "history", "data_array", "wgt_array", "integration_array", "spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", "lst_2_array", "time_1_array", "time_2_array", "blpair_array", - "Nbls", "bl_vecs", "bl_array", "channel_width", "telescope_location", "weighting", "units", - "taper", "norm", "git_hash", "nsample_array", 'lst_avg_array', 'time_avg_array', 'folded'] + "Nbls", "bl_vecs", "bl_array", "channel_width", "telescope_location", "weighting", "vis_units", + "norm_units", "taper", "norm", "git_hash", "nsample_array", 'lst_avg_array', 'time_avg_array', 'folded', + "scalar_array"] self._immutables = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", "Npols", "Nfreqs", "history", - "Nbls", "channel_width", "weighting", "units", "filename1", "filename2", "label1", "label2", - "norm", "taper", "git_hash", "cosmo_params", "beamfile" ,'folded'] + "Nbls", "channel_width", "weighting", "vis_units", "filename1", "filename2", "label1", "label2", + "norm", "norm_units", "taper", "git_hash", "cosmo_params", "beamfile" ,'folded'] self._ndarrays = ["spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", 'lst_avg_array', 'time_avg_array', - "lst_2_array", "time_1_array", "time_2_array", "blpair_array", + "lst_2_array", "time_1_array", "time_2_array", "blpair_array", "OmegaP", "OmegaPP", "beam_freqs", "bl_vecs", "bl_array", "telescope_location", "scalar_array"] self._dicts = ["data_array", "wgt_array", "integration_array", "nsample_array"] @@ -329,10 +334,6 @@ def convert_to_deltasq(self, little_h=True, inplace=True): Parameters ---------- - little_h : boolean, optional - Whether to have cosmological length units be h^-1 Mpc or Mpc - Default: h^-1 Mpc - inplace : boolean, if True edit and overwrite arrays in self, else make a copy of self and return """ # copy object @@ -352,10 +353,7 @@ def convert_to_deltasq(self, little_h=True, inplace=True): uvp.data_array[spw] *= k_mag**3 / (2*np.pi**2) # edit units - if little_h: - uvp.units += " h^3 k^3 / (2pi^2)" - else: - uvp.units += " k^3 / (2pi^2)" + uvp.norm_units = "k^3 / (2pi^2)" if inplace == False: return uvp @@ -832,23 +830,83 @@ def write_hdf5(self, filepath, overwrite=False, run_check=True): self.write_to_group(f, run_check=run_check) - def set_cosmology(self, cosmo): + def set_cosmology(self, new_cosmo, overwrite=False, new_beam=None, + verbose=True): """ - Set a cosmological model to self.cosmo via an instance of - hera_pspec.conversions.Cosmo_Conversions + Set the cosmology for this UVPSpec object by passing an instance of + conversions.Cosmo_Conversions and assigning it to self.cosmo, in + addition to re-computing power spectrum normalization quantities like + self.scalar_array. Because this will attempt to recompute the scalar_array, + the beam-related metadata (OmegaP, OmegaPP and beam_freqs) must exist in self. + If they do not, or if you'd like to overwrite them with a new beam, you can + pass a UVBeam object or path to a beam file via the new_beam kwarg. + + If self.cosmo already exists then you are attempting to overwrite the + currently adopted cosmology, which will only proceed if overwrite == True. + If overwrite == True, then this module will overwrite self.cosmo. It will + also recompute the power spectrum normalization scalar (using pspecbeam) + and will overwrite the values in self.scalar_array. It will then propagate + these re-normalization changes into the data_array by multiplying the data by + new_scalar_array / old_scalar_array. Parameters ---------- - cosmo : conversions.Cosmo_Conversions instance, or self.cosmo_params - string, or dictionary + new_cosmo : Cosmo_Conversions instance or cosmological parameter dictionary + The new cosmology you want to adopt for this UVPSpec object. + + overwrite : boolean, if True, overwrite self.cosmo if it already exists + + new_beam : pspecbeam.PSpecBeamUV object or path to beam file. + The new beam you want to adopt for this UVPSpec object. + + verbose : boolean, if True, rerport feedback to stdout. """ - if isinstance(cosmo, (str, np.str)): - cosmo = ast.literal_eval(cosmo) - if isinstance(cosmo, (dict, odict)): - cosmo = conversions.Cosmo_Conversions(**cosmo) - print("attaching cosmology: \n{}".format(cosmo)) - self.cosmo = cosmo - self.cosmo_params = str(self.cosmo.get_params()) + if hasattr(self, 'cosmo') and overwrite == False: + print("self.cosmo exists and overwrite == False, not overwriting...") + return + + else: + if (not hasattr(self, 'OmegaP') or not hasattr(self, 'OmegaPP') or not hasattr(self, 'beam_freqs')) and new_beam is None: + print "In order to set the cosmology, self.OmegaP, self.OmegaPP and self.beam_freqs " \ + "must exist, or you need to pass a PSpecBeamUV object or a path to a beam " \ + "file as new_beam." + return + + # overwrite beam quantities + if new_beam is not None: + if verbose: print "Updating beam data with {}".format(new_beam) + if isinstance(new_beam, (str, np.str)): + # PSpecBeamUV will adopt a default cosmology upon instantiation, but this doesn't matter + # for what we need from it + new_beam = pspecbeam.PSpecBeamUV(new_beam) + self.OmegaP, self.OmegaPP = new_beam.get_Omegas(self.pol_array) + self.beam_freqs = new_beam.beam_freqs + + # update cosmo and _cosmo_params + if isinstance(new_cosmo, (str, np.str)): + new_cosmo = ast.literal_eval(new_cosmo) + if isinstance(new_cosmo, (dict, odict)): + new_cosmo = conversions.Cosmo_Conversions(**new_cosmo) + if verbose: print("setting cosmology: \n{}".format(new_cosmo)) + self.cosmo = new_cosmo + self._cosmo_params = str(self.cosmo.get_params()) + + # update scalar_array + if verbose: print("Updating scalar array and re-normalizing power spectra") + for spw in range(self.Nspws): + for j, pol in enumerate(self.pol_array): + scalar = self.compute_scalar(spw, pol, num_steps=1000, + little_h=True, noise_scalar=False) + + # renormalize power spectra with new scalar + self.data_array[spw][:, :, j] *= scalar / self.scalar_array[spw, j] + + # update self.scalar_array element + self.scalar_array[spw, j] = scalar + + # update self.units if pspectra were not originally in cosmological units + if "Mpc" not in self.norm_units: + self.norm_units = "h^-3 Mpc^3" def check(self, just_meta=False): """ @@ -877,6 +935,14 @@ def check(self, just_meta=False): assert isinstance(self.nsample_array, (dict, odict)), "self.nsample_array must be a dictionary type" assert np.min(map(lambda k: self.nsample_array[k].dtype in (np.float, float, np.float64), self.nsample_array.keys())), "self.nsample_array values must be float type" + # assert cosmology consistency between self.cosmo and self._cosmo_params + if hasattr(self, '_cosmo_params') and hasattr(self, 'cosmo') == False: + self.cosmo = self.set_cosmology(self._cosmo_params, overwrite=True) + elif hasattr(self, "cosmo") and hasattr(self, "_cosmo_params") == False: + self.cosmo = self.set_cosmology(self.cosmo, overwrite=True) + elif hasattr(self, "cosmo") and hasattr(self, "_cosmo_params"): + assert self.cosmo == conversions.Cosmo_Conversions(**ast.literal_eval(self._cosmo_params)) + def _clear(self): """ Clear UVPSpec of all parameters. Warning: this cannot be undone. @@ -927,28 +993,13 @@ def __eq__(self, other): return True - def generate_sensitivity(self, beam): - """ - Generate a hera_pspec.noise.Sensitivity instance and attach to self as - self.sensitivity. - - Parameters - ---------- - beam : pspecbeam.PSpecBeamUV instance - - Results - ------ - self.sensitivity : noise.Sensitivity instance - """ - assert hasattr(self, 'cosmo'), "self.cosmo must exist in order to instantiate a Sensitivity object, see self.set_cosmology()" - - # instantiate a noise.Sensitivity object - print "attaching self.sensitivity" - self.sensitivity = noise.Sensitivity(cosmo=self.cosmo, beam=beam) + @property + def units(self): + """ return power spectrum units. See self.vis_units and self.norm_units.""" + return "({})^2 {}".format(self.vis_units, self.norm_units) - def generate_noise_spectra(self, spw, pol, Tsys, blpairs=None, - little_h=True, form='Pk', num_steps=5000, - real=True): + def generate_noise_spectra(self, spw, pol, Tsys, blpairs=None, little_h=True, + form='Pk', num_steps=2000, real=True): """ Generate the expected 1-sigma noise power spectrum given a selection of spectral window, system temp., and polarization. This estimate is @@ -1006,9 +1057,6 @@ def generate_noise_spectra(self, spw, pol, Tsys, blpairs=None, of noise power spectra as values, with ndarrays having shape (Ntimes, Ndlys). """ - # assert cosmology exists - assert hasattr(self, 'sensitivity'), "self.sensitivity required to generate noise spectra. See self.generate_sensitivity()" - # assert polarization type if isinstance(pol, (np.int, int)): pol = uvutils.polnum2str(pol) @@ -1020,7 +1068,7 @@ def generate_noise_spectra(self, spw, pol, Tsys, blpairs=None, freqs = self.freq_array[self.spw_to_indices(spw)] # calculate scalar - self.sensitivity.calc_scalar(freqs, pol, num_steps=num_steps, little_h=little_h) + scalar = self.compute_scalar(spw, pol, num_steps=num_steps, little_h=little_h, noise_scalar=True) # Get k vectors if form == 'DelSq': @@ -1057,9 +1105,7 @@ def generate_noise_spectra(self, spw, pol, Tsys, blpairs=None, k = None # get pn - pn = self.sensitivity.calc_P_N(Tsys, t_int, - k=k, Nincoherent=n_samp, - form=form, little_h=little_h) + pn = noise.calc_P_N(scalar, Tsys, t_int, k=k, Nincoherent=n_samp, form=form) # put into appropriate form if form == 'Pk': @@ -1330,7 +1376,6 @@ def fold_spectra(self): self.folded = True - def get_blpair_groups_from_bl_groups(self, blgroups, only_pairs_in_bls=False): """ Get baseline pair matches from a list of baseline groups. @@ -1356,6 +1401,43 @@ def get_blpair_groups_from_bl_groups(self, blgroups, only_pairs_in_bls=False): return blpair_groups + def compute_scalar(self, spw, pol, num_steps=5000, little_h=True, noise_scalar=False): + """ + Compute power spectrum normalization scalar given an adopted cosmology and a beam model. + See pspecbeam.PSpecBeamBase.compute_pspec_scalar for details. + + Parameters + ---------- + spw : integer, spectral window selection + + pol : string or integer, polarization selection + + num_steps : integer, number of integration bins along frequency in computing scalar + + noise_scalar : boolean, if True calculate noise pspec scalar, else calculate normal pspec scalar + See pspecbeam.py for difference between normal scalar and noise scalar. + + Returns + ------- + scalar : float, power spectrum normalization scalar + """ + # make assertions + assert hasattr(self, 'cosmo'), "self.cosmo object must exist to compute scalar. See self.set_cosmology()" + assert hasattr(self, 'OmegaP') and hasattr(self, "OmegaPP") and hasattr(self, "beam_freqs"), "self.OmegaP, "\ + "self.OmegaPP and self.beam_freqs must exist to compute scalar." + + # get freq array of selected spw + spw_freqs = self.freq_array[self.spw_to_indices(spw)] + + # compute scalar + OP = self.OmegaP[:, self.pol_to_indices(pol)].squeeze() + OPP = self.OmegaPP[:, self.pol_to_indices(pol)].squeeze() + scalar = pspecbeam._compute_pspec_scalar(self.cosmo, self.beam_freqs, OPP / OP**2, spw_freqs, + num_steps=num_steps, taper=self.taper, little_h=little_h, + noise_scalar=noise_scalar) + + return scalar + def _get_blpairs_from_bls(uvp, bls, only_pairs_in_bls=False): """ From f6b3609d82a792f9b428f01b3686ad22d7632014 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Thu, 26 Apr 2018 15:52:09 -0700 Subject: [PATCH 43/54] tweaks for self-consistent and explicit handling of cosmo and added tests for updates --- hera_pspec/pspecbeam.py | 7 +++- hera_pspec/pspecdata.py | 1 - hera_pspec/tests/test_noise.py | 2 ++ hera_pspec/tests/test_pspecbeam.py | 14 ++++++-- hera_pspec/tests/test_pspecdata.py | 11 ++----- hera_pspec/tests/test_uvpspec.py | 53 +++++++++++++++++++++--------- hera_pspec/uvpspec.py | 21 +++--------- 7 files changed, 64 insertions(+), 45 deletions(-) diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index dec7b4eb..1ebc9375 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -239,7 +239,6 @@ def Jy_to_mK(self, freqs, pol='I'): return 1e-20 * conversions.cgs_units.c**2 \ / (2 * conversions.cgs_units.kb * freqs**2 * Op) - def get_Omegas(self, pols): """ Get OmegaP and OmegaPP across beam_freqs for requested polarizatiosn @@ -254,6 +253,12 @@ def get_Omegas(self, pols): OmegaPP : ndarray containing power_sq_beam_int, shape=(Nbeam_freqs, Npols) """ + # type check + if isinstance(pols, (int, np.int)): + pols = [pols] + elif isinstance(pols, (str, np.str)): + pols = [pols] + # initialize blank lists OmegaP, OmegaPP = [], [] for p in pols: diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 733acf02..a00c11e3 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -1295,7 +1295,6 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', if self.primary_beam is not None: # attach cosmology uvp.cosmo = self.primary_beam.cosmo - uvp._cosmo_params = str(uvp.cosmo.get_params()) # attach beam info uvp.beam_freqs = self.primary_beam.beam_freqs uvp.OmegaP, uvp.OmegaPP = self.primary_beam.get_Omegas(uvp.pol_array) diff --git a/hera_pspec/tests/test_noise.py b/hera_pspec/tests/test_noise.py index d1d1f3a4..c8f4385d 100644 --- a/hera_pspec/tests/test_noise.py +++ b/hera_pspec/tests/test_noise.py @@ -8,6 +8,8 @@ import copy import h5py from collections import OrderedDict as odict +from pyuvdata import UVData + class Test_Sensitivity(unittest.TestCase): """ Test noise.Sensitivity object """ diff --git a/hera_pspec/tests/test_pspecbeam.py b/hera_pspec/tests/test_pspecbeam.py index 471ea843..7d969b9e 100644 --- a/hera_pspec/tests/test_pspecbeam.py +++ b/hera_pspec/tests/test_pspecbeam.py @@ -96,7 +96,6 @@ def test_UVbeam(self): sclr = self.bm.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, pol='I', num_steps=2000, noise_scalar=True) nt.assert_almost_equal(sclr, 70.983962969086235) - def test_Gaussbeam(self): Om_p = self.gauss.power_beam_int() Om_pp = self.gauss.power_beam_sq_int() @@ -128,8 +127,7 @@ def test_Gaussbeam(self): # test taper execution scalar = self.gauss.compute_pspec_scalar(lower_freq, upper_freq, num_freqs, num_steps=5000, taper='blackman') - self.assertAlmostEqual(scalar / 22123832163.072491, 1.0, delta=1e-8) - + self.assertAlmostEqual(scalar / 22123832163.072491, 1.0, delta=1e-8) def test_BeamFromArray(self): """ @@ -218,3 +216,13 @@ def test_PSpecBeamBase(self): # Check that user-defined cosmology can be specified bm2 = pspecbeam.PSpecBeamBase(cosmo=conversions.Cosmo_Conversions()) + + def test_get_Omegas(self): + OP, OPP = self.bm.get_Omegas('xx') + nt.assert_equal(OP.shape, (101, 1)) + nt.assert_equal(OPP.shape, (101, 1)) + OP, OPP = self.bm.get_Omegas([-5, -6]) + nt.assert_equal(OP.shape, (101, 2)) + nt.assert_equal(OPP.shape, (101, 2)) + + diff --git a/hera_pspec/tests/test_pspecdata.py b/hera_pspec/tests/test_pspecdata.py index fd56e19b..b352400f 100644 --- a/hera_pspec/tests/test_pspecdata.py +++ b/hera_pspec/tests/test_pspecdata.py @@ -501,14 +501,9 @@ def test_units(self): nt.assert_raises(IndexError, ds.units) ds.add(self.uvd, None) # test basic execution - psu = ds.units() - nt.assert_equal(psu, '(UNCALIB)^2 Hz [beam normalization not specified]') - - ds.primary_beam = pspecbeam.PSpecBeamGauss(0.8, - np.linspace(115e6, 130e6, 50, endpoint=False)) - psu = ds.units(little_h=False) - nt.assert_equal(psu, '(UNCALIB)^2 Mpc^3') - + vis_u, norm_u = ds.units() + nt.assert_equal(vis_u, "UNCALIB") + nt.assert_equal(norm_u, "Hz str [beam normalization not specified]") def test_delays(self): ds = pspecdata.PSpecData() diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index c875e56e..33edc4fb 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -47,7 +47,8 @@ def build_example_uvpspec(): dly_array = np.fft.fftshift(np.repeat(np.fft.fftfreq(Nfreqs, np.median(np.diff(freq_array))), Nspws)) pol_array = np.array([-5]) Npols = len(pol_array) - units = 'unknown' + vis_units = 'unknown' + norm_units = 'Hz str' weighting = 'identity' channel_width = np.median(np.diff(freq_array)) history = 'example' @@ -57,6 +58,8 @@ def build_example_uvpspec(): scalar_array = np.ones((Nspws, Npols), np.float) label1 = 'red' #label2 = 'blue' # Leave commented out to make sure non-named UVPSpecs work! + OmegaP, OmegaPP = self.beam.get_Omegas(['xx']) + beam_freqs = self.beam.beam_freqs telescope_location = np.array([5109325.85521063, 2005235.09142983, @@ -77,9 +80,10 @@ def build_example_uvpspec(): 'time_2_array', 'lst_1_array', 'lst_2_array', 'spw_array', 'dly_array', 'freq_array', 'pol_array', 'data_array', 'wgt_array', 'integration_array', 'bl_array', 'bl_vecs', 'telescope_location', - 'units', 'channel_width', 'weighting', 'history', 'taper', 'norm', + 'vis_units', 'channel_width', 'weighting', 'history', 'taper', 'norm', 'git_hash', 'nsample_array', 'time_avg_array', 'lst_avg_array', - 'cosmo', 'scalar_array', 'label1'] + 'cosmo', 'scalar_array', 'label1', 'OmegaP', 'OmegaPP', 'beam_freqs', + 'norm_units'] # Set all parameters for p in params: @@ -98,9 +102,6 @@ def setUp(self): # test equivalence nt.assert_equal(uvp, uvp) - self.cosmo = cosmo - self.beam = pspecbeam.PSpecBeamUV(os.path.join(DATA_PATH, 'NF_HERA_Beams.beamfits')) - def tearDown(self): pass @@ -157,7 +158,6 @@ def test_get_funcs(self): def test_convert_deltasq(self): uvp = copy.deepcopy(self.uvp) - uvp.set_cosmology(conversions.Cosmo_Conversions()) uvp.convert_to_deltasq(little_h=True) k_perp, k_para = self.uvp.get_kperps(0), self.uvp.get_kparas(0) k_mag = np.sqrt(k_perp[:, None, None]**2 + k_para[None, :, None]**2) @@ -293,15 +293,6 @@ def test_write_read_hdf5(self): def test_sense(self): uvp = copy.deepcopy(self.uvp) - # test exception - nt.assert_raises(AssertionError, uvp.generate_noise_spectra, 0, 0, 0) - - # test generate_sense - uvp.generate_sensitivity(self.beam) - nt.assert_true(hasattr(uvp, 'sensitivity')) - nt.assert_true(hasattr(uvp.sensitivity, 'beam')) - nt.assert_true(hasattr(uvp.sensitivity, 'cosmo')) - # test generate noise spectra P_N = uvp.generate_noise_spectra(0, -5, 500, form='Pk', real=True) nt.assert_equal(P_N[1002001002].shape, (10, 50)) @@ -355,6 +346,36 @@ def test_str(self): a = str(self.uvp) nt.assert_true(len(a) > 0) + def test_compute_scalar(self): + uvp = copy.deepcopy(self.uvp) + # test basic execution + s = uvp.compute_scalar(0, 'xx', num_steps=1000, noise_scalar=False) + nt.assert_almost_equal(s/258034762.16569147, 1.0, places=5) + # test execptions + del uvp.OmegaP + nt.assert_raises(AssertionError, uvp.compute_scalar, 0, -5) + + def test_set_cosmology(self): + uvp = copy.deepcopy(self.uvp) + new_cosmo = conversions.Cosmo_Conversions(Om_L=0.0) + # test no overwrite + uvp.set_cosmology(new_cosmo, overwrite=False) + nt.assert_not_equal(uvp.cosmo, new_cosmo) + # test setting cosmology + uvp.set_cosmology(new_cosmo, overwrite=True) + nt.assert_equal(uvp.cosmo, new_cosmo) + nt.assert_equal(uvp.norm_units, 'h^-3 Mpc^3') + nt.assert_true((uvp.scalar_array>1.0).all()) + nt.assert_true((uvp.data_array[0] > 1e5).all()) + # test exception + new_cosmo2 = conversions.Cosmo_Conversions(Om_L=1.0) + del uvp.OmegaP + uvp.set_cosmology(new_cosmo2, overwrite=True) + nt.assert_not_equal(uvp.cosmo, new_cosmo2) + # try with new beam + uvp.set_cosmology(new_cosmo2, overwrite=True, new_beam=self.beam) + nt.assert_equal(uvp.cosmo, new_cosmo2) + nt.assert_true(hasattr(uvp, 'OmegaP')) def test_conj_blpair_int(): conj_blpair = uvpspec._conj_blpair_int(1002003004) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index 0fceb0d1..426f1af6 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -75,6 +75,7 @@ def __init__(self): self._OmegaP = PSpecParam("OmegaP", description="Integral of unitless beam power over the sky [steradians].", form="(Nbeam_freqs, Npols)") self._OmegaPP = PSpecParam("OmegaP", description="Integral of unitless beam power squared over the sky [steradians].", form="(Nbeam_freqs, Npols)") self._beam_freqs = PSpecParam("beam_freqs", description="Frequency bins of the OmegaP and OmegaPP beam-integral arrays [Hz].", form="(Nbeam_freqs,)") + self._cosmo = PSpecParam("cosmo", description="Instance of conversion.Cosmo_Conversions class.") # collect parameters self._all_params = sorted(map(lambda p: p[1:], fnmatch.filter(self.__dict__.keys(), '_*'))) @@ -88,14 +89,14 @@ def __init__(self): self._immutables = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", "Npols", "Nfreqs", "history", "Nbls", "channel_width", "weighting", "vis_units", "filename1", "filename2", "label1", "label2", - "norm", "norm_units", "taper", "git_hash", "cosmo_params", "beamfile" ,'folded'] + "norm", "norm_units", "taper", "git_hash", "cosmo", "beamfile" ,'folded'] self._ndarrays = ["spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", 'lst_avg_array', 'time_avg_array', "lst_2_array", "time_1_array", "time_2_array", "blpair_array", "OmegaP", "OmegaPP", "beam_freqs", "bl_vecs", "bl_array", "telescope_location", "scalar_array"] self._dicts = ["data_array", "wgt_array", "integration_array", "nsample_array"] self._meta_dsets = ["lst_1_array", "lst_2_array", "time_1_array", "time_2_array", "blpair_array", - "bl_vecs", "bl_array", 'lst_avg_array', 'time_avg_array'] + "bl_vecs", "bl_array", 'lst_avg_array', 'time_avg_array', 'OmegaP', 'OmegaPP'] self._meta_attrs = sorted(set(self._all_params) - set(self._dicts) - set(self._meta_dsets)) self._meta = sorted(set(self._meta_dsets).union(set(self._meta_attrs))) @@ -771,7 +772,6 @@ def read_hdf5(self, filepath, just_meta=False, spws=None, bls=None, with h5py.File(filepath, 'r') as f: self.read_from_group(f, just_meta=just_meta, spws=spws, bls=bls, only_pairs_in_bls=only_pairs_in_bls) - def write_to_group(self, group, run_check=True): """ @@ -882,14 +882,11 @@ def set_cosmology(self, new_cosmo, overwrite=False, new_beam=None, self.OmegaP, self.OmegaPP = new_beam.get_Omegas(self.pol_array) self.beam_freqs = new_beam.beam_freqs - # update cosmo and _cosmo_params - if isinstance(new_cosmo, (str, np.str)): - new_cosmo = ast.literal_eval(new_cosmo) + # update cosmo if isinstance(new_cosmo, (dict, odict)): new_cosmo = conversions.Cosmo_Conversions(**new_cosmo) if verbose: print("setting cosmology: \n{}".format(new_cosmo)) self.cosmo = new_cosmo - self._cosmo_params = str(self.cosmo.get_params()) # update scalar_array if verbose: print("Updating scalar array and re-normalizing power spectra") @@ -935,14 +932,6 @@ def check(self, just_meta=False): assert isinstance(self.nsample_array, (dict, odict)), "self.nsample_array must be a dictionary type" assert np.min(map(lambda k: self.nsample_array[k].dtype in (np.float, float, np.float64), self.nsample_array.keys())), "self.nsample_array values must be float type" - # assert cosmology consistency between self.cosmo and self._cosmo_params - if hasattr(self, '_cosmo_params') and hasattr(self, 'cosmo') == False: - self.cosmo = self.set_cosmology(self._cosmo_params, overwrite=True) - elif hasattr(self, "cosmo") and hasattr(self, "_cosmo_params") == False: - self.cosmo = self.set_cosmology(self.cosmo, overwrite=True) - elif hasattr(self, "cosmo") and hasattr(self, "_cosmo_params"): - assert self.cosmo == conversions.Cosmo_Conversions(**ast.literal_eval(self._cosmo_params)) - def _clear(self): """ Clear UVPSpec of all parameters. Warning: this cannot be undone. @@ -1401,7 +1390,7 @@ def get_blpair_groups_from_bl_groups(self, blgroups, only_pairs_in_bls=False): return blpair_groups - def compute_scalar(self, spw, pol, num_steps=5000, little_h=True, noise_scalar=False): + def compute_scalar(self, spw, pol, num_steps=1000, little_h=True, noise_scalar=False): """ Compute power spectrum normalization scalar given an adopted cosmology and a beam model. See pspecbeam.PSpecBeamBase.compute_pspec_scalar for details. From 07368f1600c70596f21243f6fee2ae69606a06c5 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Thu, 26 Apr 2018 15:55:24 -0700 Subject: [PATCH 44/54] fixed tests for uvpspec --- hera_pspec/tests/test_uvpspec.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index 33edc4fb..150be32f 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -162,7 +162,7 @@ def test_convert_deltasq(self): k_perp, k_para = self.uvp.get_kperps(0), self.uvp.get_kparas(0) k_mag = np.sqrt(k_perp[:, None, None]**2 + k_para[None, :, None]**2) nt.assert_true(np.isclose(uvp.data_array[0][0,:,0], (self.uvp.data_array[0]*k_mag**3/(2*np.pi**2))[0,:,0]).all()) - nt.assert_equal(uvp.units, 'unknown h^3 k^3 / (2pi^2)') + nt.assert_equal(uvp.norm_units, 'k^3 / (2pi^2)') def test_blpair_conversions(self): # test blpair -> antnums @@ -350,7 +350,7 @@ def test_compute_scalar(self): uvp = copy.deepcopy(self.uvp) # test basic execution s = uvp.compute_scalar(0, 'xx', num_steps=1000, noise_scalar=False) - nt.assert_almost_equal(s/258034762.16569147, 1.0, places=5) + nt.assert_almost_equal(s/552908638.8711592, 1.0, places=5) # test execptions del uvp.OmegaP nt.assert_raises(AssertionError, uvp.compute_scalar, 0, -5) From cb9d8a7ec83469b8d9bb5a1ad23616a9c732b731 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 01:07:11 -0700 Subject: [PATCH 45/54] increased type checking in pspecbeam.get_Omegas --- hera_pspec/pspecbeam.py | 13 +++++++------ hera_pspec/tests/test_uvpspec.py | 12 ++++++++---- 2 files changed, 15 insertions(+), 10 deletions(-) diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index 1ebc9375..8e17bfe5 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -254,18 +254,19 @@ def get_Omegas(self, pols): OmegaPP : ndarray containing power_sq_beam_int, shape=(Nbeam_freqs, Npols) """ # type check - if isinstance(pols, (int, np.int)): + if isinstance(pols, (int, np.int, np.int32)): pols = [pols] elif isinstance(pols, (str, np.str)): pols = [pols] - + if isinstance(pols, list): + if isinstance(pols[0], (int, np.int, np.int32)): + pols = map(lambda p: uvutils.polnum2str(p), pols) + # initialize blank lists OmegaP, OmegaPP = [], [] for p in pols: - ## FIXME: get rid of this line when pyuvdata supports other polarizations - p = 'pseudo_I' - OmegaP.append(self.power_beam_int(stokes=p)) - OmegaPP.append(self.power_beam_sq_int(stokes=p)) + OmegaP.append(self.power_beam_int(pol=p)) + OmegaPP.append(self.power_beam_sq_int(pol=p)) OmegaP = np.array(OmegaP).T OmegaPP = np.array(OmegaPP).T diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index 150be32f..aef55a94 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -9,7 +9,7 @@ import h5py from collections import OrderedDict as odict -def build_example_uvpspec(): +def build_example_uvpspec(beamfile=None): """ Build an example UVPSpec object, with all necessary metadata. """ @@ -58,8 +58,11 @@ def build_example_uvpspec(): scalar_array = np.ones((Nspws, Npols), np.float) label1 = 'red' #label2 = 'blue' # Leave commented out to make sure non-named UVPSpecs work! - OmegaP, OmegaPP = self.beam.get_Omegas(['xx']) - beam_freqs = self.beam.beam_freqs + if beamfile is not None: + beamfile = os.path.join(DATA_PATH, 'NF_HERA_Beams.beamfits') + beam = pspecbeam.PSpecBeamUV(beamfile) + OmegaP, OmegaPP = beam.get_Omegas(beam.primary_beam.polarization_array[0]) + beam_freqs = beam.beam_freqs telescope_location = np.array([5109325.85521063, 2005235.09142983, @@ -95,7 +98,8 @@ def build_example_uvpspec(): class Test_UVPSpec(unittest.TestCase): def setUp(self): - uvp, cosmo = build_example_uvpspec() + beamfile=os.path.join(DATA_PATH, 'NF_HERA_Beams.beamfits') + uvp, cosmo = build_example_uvpspec(beamfile=beamfile) uvp.check() self.uvp = uvp From 0f7dbd9a7689e347f52d50f8bc4e14a6db5ff39c Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 01:07:36 -0700 Subject: [PATCH 46/54] inserted uvpspec handling of cosmo upon read and write to hdft --- hera_pspec/tests/test_uvpspec.py | 9 ++++----- hera_pspec/uvpspec.py | 8 ++++++++ 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index aef55a94..22266464 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -58,9 +58,7 @@ def build_example_uvpspec(beamfile=None): scalar_array = np.ones((Nspws, Npols), np.float) label1 = 'red' #label2 = 'blue' # Leave commented out to make sure non-named UVPSpecs work! - if beamfile is not None: - beamfile = os.path.join(DATA_PATH, 'NF_HERA_Beams.beamfits') - beam = pspecbeam.PSpecBeamUV(beamfile) + if beam is not None: OmegaP, OmegaPP = beam.get_Omegas(beam.primary_beam.polarization_array[0]) beam_freqs = beam.beam_freqs @@ -98,8 +96,9 @@ def build_example_uvpspec(beamfile=None): class Test_UVPSpec(unittest.TestCase): def setUp(self): - beamfile=os.path.join(DATA_PATH, 'NF_HERA_Beams.beamfits') - uvp, cosmo = build_example_uvpspec(beamfile=beamfile) + beamfile = os.path.join(DATA_PATH, 'NF_HERA_Beams.beamfits') + self.beam = pspecbeam.PSpecBeamUV(beamfile) + uvp, cosmo = build_example_uvpspec(beam=self.beam) uvp.check() self.uvp = uvp diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index 426f1af6..fc80e47a 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -726,6 +726,10 @@ def read_from_group(self, grp, just_meta=False, spws=None, bls=None, blpairs=blpairs, times=times, pols=pols, h5file=grp) + # handle cosmo + if hasattr(self, 'cosmo'): + self.cosmo = conversions.Cosmo_Conversions(**ast.literal_eval(self.cosmo)) + self.check(just_meta=just_meta) @@ -786,6 +790,10 @@ def write_to_group(self, group, run_check=True): # Write meta data for k in self._meta_attrs: if hasattr(self, k): + # handle cosmo + if k == 'cosmo': + f.attrs['cosmo'] = str(getattr(self, k).get_params()) + continue group.attrs[k] = getattr(self, k) for k in self._meta_dsets: if hasattr(self, k): From b60720aa1841dabb8afd046a0176ecebde86a604 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 01:12:04 -0700 Subject: [PATCH 47/54] added more type checking in pspecbeam get_Omegas --- hera_pspec/pspecbeam.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hera_pspec/pspecbeam.py b/hera_pspec/pspecbeam.py index 8e17bfe5..c618693b 100644 --- a/hera_pspec/pspecbeam.py +++ b/hera_pspec/pspecbeam.py @@ -258,7 +258,7 @@ def get_Omegas(self, pols): pols = [pols] elif isinstance(pols, (str, np.str)): pols = [pols] - if isinstance(pols, list): + if isinstance(pols, (list, np.ndarray, tuple)): if isinstance(pols[0], (int, np.int, np.int32)): pols = map(lambda p: uvutils.polnum2str(p), pols) From 298912ae010cd4c29538989a90df11a740d1ca9a Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 01:12:25 -0700 Subject: [PATCH 48/54] cosmo handling f -> group in hdf5 read / write --- hera_pspec/tests/test_uvpspec.py | 4 ++-- hera_pspec/uvpspec.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index 22266464..b795f521 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -9,7 +9,7 @@ import h5py from collections import OrderedDict as odict -def build_example_uvpspec(beamfile=None): +def build_example_uvpspec(beam=None): """ Build an example UVPSpec object, with all necessary metadata. """ @@ -353,7 +353,7 @@ def test_compute_scalar(self): uvp = copy.deepcopy(self.uvp) # test basic execution s = uvp.compute_scalar(0, 'xx', num_steps=1000, noise_scalar=False) - nt.assert_almost_equal(s/552908638.8711592, 1.0, places=5) + nt.assert_almost_equal(s/552336586.23970914, 1.0, places=5) # test execptions del uvp.OmegaP nt.assert_raises(AssertionError, uvp.compute_scalar, 0, -5) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index fc80e47a..5315f8d4 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -792,7 +792,7 @@ def write_to_group(self, group, run_check=True): if hasattr(self, k): # handle cosmo if k == 'cosmo': - f.attrs['cosmo'] = str(getattr(self, k).get_params()) + group.attrs['cosmo'] = str(getattr(self, k).get_params()) continue group.attrs[k] = getattr(self, k) for k in self._meta_dsets: From 1b632aad49f6689eb3ba961c3ba61d423b8efaa1 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 01:24:21 -0700 Subject: [PATCH 49/54] made uvpspec parameter groups clearer --- hera_pspec/uvpspec.py | 35 +++++++++++++++++++++-------------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/hera_pspec/uvpspec.py b/hera_pspec/uvpspec.py index 5315f8d4..c49e3d95 100644 --- a/hera_pspec/uvpspec.py +++ b/hera_pspec/uvpspec.py @@ -9,7 +9,8 @@ class UVPSpec(object): """ - Object for storing power spectra generated by hera_pspec. + An object for storing power spectra generated by hera_pspec and a file-format + for its data and meta-data. """ def __init__(self): """ @@ -77,24 +78,30 @@ def __init__(self): self._beam_freqs = PSpecParam("beam_freqs", description="Frequency bins of the OmegaP and OmegaPP beam-integral arrays [Hz].", form="(Nbeam_freqs,)") self._cosmo = PSpecParam("cosmo", description="Instance of conversion.Cosmo_Conversions class.") - # collect parameters + # collect all parameters: required and non-required self._all_params = sorted(map(lambda p: p[1:], fnmatch.filter(self.__dict__.keys(), '_*'))) - self._req_params = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", "Npols", "Nfreqs", "history", - "data_array", "wgt_array", "integration_array", "spw_array", "freq_array", "dly_array", - "pol_array", "lst_1_array", "lst_2_array", "time_1_array", "time_2_array", "blpair_array", - "Nbls", "bl_vecs", "bl_array", "channel_width", "telescope_location", "weighting", "vis_units", - "norm_units", "taper", "norm", "git_hash", "nsample_array", 'lst_avg_array', 'time_avg_array', 'folded', - "scalar_array"] - - self._immutables = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", "Npols", "Nfreqs", "history", - "Nbls", "channel_width", "weighting", "vis_units", "filename1", "filename2", "label1", "label2", - "norm", "norm_units", "taper", "git_hash", "cosmo", "beamfile" ,'folded'] - self._ndarrays = ["spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", 'lst_avg_array', 'time_avg_array', - "lst_2_array", "time_1_array", "time_2_array", "blpair_array", "OmegaP", "OmegaPP", "beam_freqs", + # specify required params: these are required for read / write and self.check() + self._req_params = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", + "Npols", "Nfreqs", "history", "data_array", "wgt_array", "integration_array", + "spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", + "lst_2_array", "time_1_array", "time_2_array", "blpair_array", "Nbls", + "bl_vecs", "bl_array", "channel_width", "telescope_location", "weighting", + "vis_units", "norm_units", "taper", "norm", "git_hash", "nsample_array", + 'lst_avg_array', 'time_avg_array', 'folded', "scalar_array"] + + # all parameters must fall into one and only one of the following groups, which are used in __eq__ + self._immutables = ["Ntimes", "Nblpairts", "Nblpairs", "Nspwdlys", "Nspws", "Ndlys", + "Npols", "Nfreqs", "history", "Nbls", "channel_width", "weighting", + "vis_units", "filename1", "filename2", "label1", "label2", "norm", + "norm_units", "taper", "git_hash", "cosmo", "beamfile" ,'folded'] + self._ndarrays = ["spw_array", "freq_array", "dly_array", "pol_array", "lst_1_array", + 'lst_avg_array', 'time_avg_array', "lst_2_array", "time_1_array", + "time_2_array", "blpair_array", "OmegaP", "OmegaPP", "beam_freqs", "bl_vecs", "bl_array", "telescope_location", "scalar_array"] self._dicts = ["data_array", "wgt_array", "integration_array", "nsample_array"] + # define which attributes are considred meta data. Large attrs should be constructed as datasets self._meta_dsets = ["lst_1_array", "lst_2_array", "time_1_array", "time_2_array", "blpair_array", "bl_vecs", "bl_array", 'lst_avg_array', 'time_avg_array', 'OmegaP', 'OmegaPP'] self._meta_attrs = sorted(set(self._all_params) - set(self._dicts) - set(self._meta_dsets)) From c7406f3df51345aa1cc381839d3a1e891b0293d2 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Sat, 28 Apr 2018 01:26:00 -0700 Subject: [PATCH 50/54] updates to build_example_uvpspec --- hera_pspec/tests/test_uvpspec.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/hera_pspec/tests/test_uvpspec.py b/hera_pspec/tests/test_uvpspec.py index b795f521..8aae39aa 100644 --- a/hera_pspec/tests/test_uvpspec.py +++ b/hera_pspec/tests/test_uvpspec.py @@ -83,8 +83,9 @@ def build_example_uvpspec(beam=None): 'integration_array', 'bl_array', 'bl_vecs', 'telescope_location', 'vis_units', 'channel_width', 'weighting', 'history', 'taper', 'norm', 'git_hash', 'nsample_array', 'time_avg_array', 'lst_avg_array', - 'cosmo', 'scalar_array', 'label1', 'OmegaP', 'OmegaPP', 'beam_freqs', - 'norm_units'] + 'cosmo', 'scalar_array', 'label1', 'norm_units'] + if beam is not None: + params += ['OmegaP', 'OmegaPP', 'beam_freqs'] # Set all parameters for p in params: From baba2b7a4111836b7b285ea6a1d0f362b1a74b6e Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Mon, 30 Apr 2018 09:05:01 -0700 Subject: [PATCH 51/54] updated PS_estimation notebook for new cosmo handling --- examples/PS_estimation_example.ipynb | 554 ++++++++++++++++----------- 1 file changed, 331 insertions(+), 223 deletions(-) diff --git a/examples/PS_estimation_example.ipynb b/examples/PS_estimation_example.ipynb index 3b1858b9..b2c63471 100644 --- a/examples/PS_estimation_example.ipynb +++ b/examples/PS_estimation_example.ipynb @@ -11,7 +11,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -29,7 +31,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Loading the input data: form pspectra from adjacent time integrations\n", + "## Loading the input data: forming power spectra from adjacent time integrations\n", "\n", "The input data are specified as a list of `UVData` objects, which are then packaged into a `PSpecData` class. This class is responsible for collecting the data and covariances together and performing the OQE power spectrum estimation.\n", "\n", @@ -45,23 +47,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "Cosmo_Conversions object at <0x110820250>\n", - "Om_L : 0.6844; Om_b : 0.0491; Om_c : 0.2644; Om_M : 0.3135; Om_k : 0.0021; H0 : 67.2700\n" + "[(24, 24), (24, 25), (24, 37), (24, 38), (24, 39), (25, 25), (25, 37), (25, 38), (25, 39), (37, 37), (37, 38), (37, 39), (38, 38), (38, 39), (39, 39)]\n" ] } ], "source": [ - "# Instantiate a Cosmo Conversions object\n", - "# we will need this cosmology to put the power spectra into cosmological units\n", - "cosmo = hp.conversions.Cosmo_Conversions()\n", - "print cosmo" + "# select the data file to load\n", + "dfile = '../hera_pspec/data/zen.all.xx.LST.1.06964.uvA'\n", + "\n", + "# Load into UVData objects\n", + "uvd = UVData()\n", + "uvd.read_miriad(dfile)\n", + "\n", + "# Check which baselines are available\n", + "print(uvd.get_antpairs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Instantiate a beam object, and feed the cosmo conversions object into it." + "## Define a cosmology\n", + "\n", + "Here we will instantiate a `hera_pspec.conversions.Cosmo_Conversions` object that will define the cosmology we adopt throughout this notebook. **All calculations that depend on a cosmology will be tied to the one we adopt here: the various objects in `hera_pspec` will pass this specific object between themselves, which starts by attaching the cosmology to a `PSpecBeamUV` object.** Further on in the notebook, we will see how we can explicitly overwrite the adopted cosmology and appropriately re-calculate the necessary variables." ] }, { @@ -73,22 +81,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "[(24, 24), (24, 25), (24, 37), (24, 38), (24, 39), (25, 25), (25, 37), (25, 38), (25, 39), (37, 37), (37, 38), (37, 39), (38, 38), (38, 39), (39, 39)]\n" + "Cosmo_Conversions object at <0x119859f50>\n", + "Om_L : 0.6844; Om_b : 0.0491; Om_c : 0.2644; Om_M : 0.3135; Om_k : 0.0021; H0 : 67.2700\n" ] } ], "source": [ - "# List of filenames of the data to load\n", - "dfile = '../hera_pspec/data/zen.all.xx.LST.1.06964.uvA'\n", + "# Instantiate a Cosmo Conversions object\n", + "# we will need this cosmology to put the power spectra into cosmological units\n", + "cosmo = hp.conversions.Cosmo_Conversions()\n", + "print cosmo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate a beam object, and attach the cosmo conversions object onto it." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# List of beamfile to load. This is a healpix map.\n", "beamfile = '../hera_pspec/data/NF_HERA_Beams.beamfits'\n", - "uvb = hp.pspecbeam.PSpecBeamUV(beamfile, cosmo=cosmo)\n", - "\n", - "# Load into UVData objects\n", - "uvd = UVData()\n", - "uvd.read_miriad(dfile)\n", "\n", - "# Check which baselines are available\n", - "print(uvd.get_antpairs())" + "# intantiate beam and pass cosmology, if not fed, a default Planck cosmology will be assumed\n", + "uvb = hp.pspecbeam.PSpecBeamUV(beamfile, cosmo=cosmo)" ] }, { @@ -100,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -110,7 +132,7 @@ "uvd1 = uvd.select(times=np.unique(uvd.time_array)[:-1:2], inplace=False)\n", "uvd2 = uvd.select(times=np.unique(uvd.time_array)[1::2], inplace=False)\n", "\n", - "# Create a new PSpecData object\n", + "# Create a new PSpecData object, and don't forget to feed the beam object\n", "ds = hp.PSpecData(dsets=[uvd1, uvd2], wgts=[None, None], beam=uvb)" ] }, @@ -123,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -152,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -177,14 +199,12 @@ "metadata": {}, "source": [ "## Estimating the power spectrum for a handful of baseline pairs (auto-baseline pspec)\n", - "Estimate the power spectrum for a handful baseline pairs between the two datasets in `ds.dsets`. You can specify which baselines are included in the power spectrum estimate, which datasets to use, what freq channels to use, and how the estimate should be weighted.\n", - "\n", - "The result is a `UVPSpec` object that holds all of the power spectra and their meta-data." + "Estimate the power spectrum for a handful baseline pairs between the two datasets in `ds.dsets`. You can specify which baselines are included in the power spectrum estimate, which datasets to use, what freq channels to use, and how the estimate should be weighted." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -198,14 +218,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Call the `PSpecData.pspec` function to use the OQE framework.\n", - "\n", "### Read the docstring! Here are the first few lines..." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -217,32 +235,32 @@ " Estimate the delay power spectrum from a pair of datasets contained in this \n", " object, using the optimal quadratic estimator from arXiv:1502.06016.\n", "\n", - " In this formulation, the power spectrum from a single baseline-pair is constructed \n", - " from the visibility data via\n", - "\n", + " In this formulation, the power spectrum is proportional to the \n", + " visibility data via\n", + " \n", " P = M data_{LH} E data_{RH}\n", "\n", - " where E contains the data weighting and FT matrices, M is a normalization matrix, \n", - " and the two separate datasets are denoted \"left-hand\" and \"right-hand.\" The LH data\n", - " is a bl in the bls1 list from the dsets[0] dataset, and the RH data is a bl in the bls2 \n", - " list from the dsets[1] dataset.\n", + " where E contains the data weighting and FT matrices, M is a \n", + " normalization matrix, and the two separate datasets are denoted as \n", + " \"left-hand\" and \"right-hand\".\n", "\n", - " bls1 and bls2 are each interpreted as a list of baseline groups. A single baseline group is \n", - " itself a list of baseline tuples. If a bl group is length-1, it can be represented just as the \n", - " baseline tuple (Example 1). A bl_pair list is then formed by zipping each baseline group in \n", - " bls1 with its corresponding group in bls2. See below for various examples.\n", + " Each power spectrum is generated by taking a baseline (specified by \n", + " an antenna-pair and polarization key) from bls1 out of dsets[0] and \n", + " assigning it as data_LH, and a bl from bls2 out of the dsets[1] and \n", + " assigning it as data_RH.\n", "\n", - " To get help turning a single list of baselines into the appropriate form needed by pspec,\n", - " see PSpecData.construct_blpairs()\n", + " If the bl chosen from bls1 is (ant1, ant2) and the bl chosen from bls2 \n", + " is (ant3, ant4), the \"baseline-pair\" describing their cross \n", + " multiplication is ((ant1, ant2), (ant3, ant4)).\n", "\n", " Parameters\n", " ----------\n", - " bls1 : list of baseline groups, where each baseline group is a list of baseline tuples\n", + " bls1 : list of baseline groups, each being a list of ant-pair tuples\n", "\n", - " bls2 : list of baseline groups, where each baseline group is a list of baseline tuples\n", + " bls2 : list of baseline groups, each being a list of ant-pair tuples\n", "\n", " dsets : length-2 tuple or list\n", - " contains indices of self.dsets to use in forming power spectra, where the first index\n" + " Contains indices of self.dsets to use in forming power spectra, \n" ] } ], @@ -251,9 +269,16 @@ "print '\\n'.join(ds.pspec.__doc__.split('\\n')[:30])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the `PSpecData.pspec` function to use the OQE framework. The result is a `UVPSpec` object that holds all of the power spectra and their meta-data." + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -330,24 +355,20 @@ "\n", "The fundamental unit in the `UVPSpec` object is a single delay spectrum, indexed by a spectral window selection (`spw`), a baseline-pair selection (`blpair`) and a polarization (`pol`). Spectral windows are marked by their index (see `spw_array`), polarization are marked by their pol string (`xx`) or pol integer (`-5`), and a baseline-pair is marked by its blpair integer, which is simply the antenna numbers put into length-3 integers and concatenated. For example, the baseline-pair `((100, 200), (300, 400))` would have a blpair integer `100200300400`.\n", "\n", - "There is also information telling you things like the data weighting, normalization, tapering, units, telescope location, LST and JD time stamps, etc.\n", - "\n", - "Although not technically considered meta-data, there is also information on the amount of time-integration that went into every power spectra in the `integrations_array` attribute.\n", - "\n", "To access particular slices of the data in a `UVPSpec` object, the user should interface with the `get_data` method, which takes a selection of `spw`, `blpair` and `pol` as arguments. See the example below." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "spw 0 data shape = (Ntimes, Ndlys): (3, 100)\n", - "pspec units: (mK)^2 h^-3 Mpc^3\n" + "(3, 100)\n", + "(3, 100)\n" ] } ], @@ -356,52 +377,88 @@ "key = (0, ((24, 25), (24, 25)), 'xx')\n", "\n", "# output should be shape (Ntimes, Ndlys)\n", - "print \"spw 0 data shape = (Ntimes, Ndlys): \",uvp.get_data(key).shape\n", + "print uvp.get_data(key).shape\n", "\n", - "# get power spectrum units\n", - "print \"pspec units: \", uvp.units" + "# we can also access data by feeding a dictionary\n", + "key = {'pol':'xx', 'spw': 0, 'blpair':((24, 25), (24, 25))}\n", + "print uvp.get_data(key).shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is also metadata telling you things like the data weighting, normalization, tapering, units, telescope location, LST and JD time stamps, etc." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "spw 0 data shape = (Ntimes, Ndlys): (3, 100)\n" + "pspec units: (mK)^2 h^-3 Mpc^3\n", + "pspec weighting: identity\n" ] } ], "source": [ - "# this key can also be a dictionary, and the get_ functions will parse it\n", - "key = {'spw': 0, 'blpair': ((24, 25), (24, 25)), 'pol': 'xx'}\n", + "# get power spectrum units\n", + "print \"pspec units: \", uvp.units\n", "\n", - "# output should be shape (Ntimes, Ndlys)\n", - "print \"spw 0 data shape = (Ntimes, Ndlys): \",uvp.get_data(key).shape" + "# get weighting\n", + "print \"pspec weighting: \", uvp.weighting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Plotting" + "Also, importantly, the cosmology we adopted originally and passed through to the beam and `PSpecData` objects was passed through to this `UVPSpec` object." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cosmo_Conversions object at <0x119859f50>\n", + "Om_L : 0.6844; Om_b : 0.0491; Om_c : 0.2644; Om_M : 0.3135; Om_k : 0.0021; H0 : 67.2700\n" + ] + } + ], + "source": [ + "print uvp.cosmo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "Here is an example of how to do some simple and quick plotting of power spectra, which includes getting the power spectra and the delay bins of their bandpowers." + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -409,7 +466,7 @@ "data": { "image/png": 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yValRRKRVKJyLiLS4QjoI5/HOPqayUzi+uraWvicEx4tNMJVWOBcRKYfCuYhI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\nVFP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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -417,7 +474,7 @@ } ], "source": [ - "# plot power spectrum of spw 1\n", + "# plot power spectrum of spectral window 1\n", "fig, ax = plt.subplots(figsize=(12,8))\n", "\n", "spw = 1\n", @@ -440,12 +497,12 @@ "source": [ "## Form redundant baseline spectra (auto & cross baseline pspec)\n", "\n", - "You can also use `pspecdata.construct_blpairs` to construct `baselines1` and `baselines2` in a way to form cross baseline spectra. This takes keywords `exclude_auto_bls` which will remove all baselines paired with itself from the final `blpairs` list, as well as `exclude_permutations`, which will use a combination instead of a permutation to form the baseline-pairs from the input `baselines` list." + "Above we looked at auto-baseline power spectra. Here we will show how to form power spectra between groups of redundant baselines. To do this, we use the `pspecdata.construct_blpairs` helper function to construct the `baselines1` and `baselines2` lists that we then feed into `PSpecData.pspec`. This takes keywords `exclude_auto_bls`, which will remove all baselines paired with itself from the final `blpairs` list, as well as `exclude_permutations`, which will use a combination instead of a permutation to form the baseline-pairs from the input `baselines` list." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -457,19 +514,27 @@ } ], "source": [ - "# bls is now a list of tuples\n", + "# baselines is a redundant baseline group\n", "baselines = [(24,25), (37,38), (38,39)]\n", "\n", - "# calculate baseline pairs\n", + "# calculate all baseline pairs from group\n", "baselines1, baselines2, blpairs = hp.pspecdata.construct_blpairs(baselines, exclude_auto_bls=True, \n", " exclude_permutations=True)\n", "\n", + "# Inspect baseline pairs\n", "print blpairs" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we feed these lists through to `pspec`" + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": { "scrolled": true }, @@ -531,22 +596,22 @@ } ], "source": [ - "uvp = ds.pspec(baselines1, baselines2, (0, 1), spw_ranges=[(300, 400), (600,721)], input_data_weight='identity', norm='I', \n", - " taper='blackman-harris', verbose=True)" + "uvp = ds.pspec(baselines1, baselines2, (0, 1), spw_ranges=[(300, 400), (600,721)], input_data_weight='identity', \n", + " norm='I', taper='blackman-harris', verbose=True)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -554,7 +619,7 @@ "data": { "image/png": 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rCVmIvT19HBUeV+VcRKQOkVYHICIirZXKl6rWON/IWG4MgNWxhSXnkVCEdd3r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9tO4JYk1rQfjVs8GY6zPa3CzrbG/pFrCWGTW7uzlTULCijLjCb4ldnitaPhQvO2XYKU731CkPR7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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -562,7 +627,7 @@ } ], "source": [ - "# plot power spectrum of spw 1\n", + "# plot power spectrum of a cross-baseline pspectra from spectral window 1\n", "fig, ax = plt.subplots(figsize=(12,8))\n", "\n", "spw = 1\n", @@ -583,49 +648,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Convert to $\\Delta^2(k)$\n", + "## Convert to $\\Delta^2(k)$\n", "\n", - "You can convert the power spectrum data from $P(k)$ format to \"unitless\" power spectrum $\\Delta^2(k)$ by scaling by the cosmological wave-vectors cubed. This can be done by hand with the `get_kvecs()` method, or can be done automatically using the `convert_to_deltasq()` method.\n", + "You can convert the power spectrum data from $P(k)$ format to \"unitless\" power spectrum $\\Delta^2(k)$ by scaling by the cosmological wave-vectors cubed. This can be done by hand with the `get_kparas()` and `get_kperps()` methods, or can be done automatically using the `convert_to_deltasq()` method.\n", "\n", - "If properly normalized, `UVPSpec` will have a `cosmo_params` list that contains the cosmological parameters used to normalized the power spectra. You can use these parameters to create a `UVPSpec.cosmo` object using the `set_cosmology` method." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'H0': 67.27, 'Om_k': 0.0020700000000000163, 'Om_M': 0.31353, 'Om_L': 0.6844, 'Om_c': 0.26442, 'Om_b': 0.04911}\n" - ] - } - ], - "source": [ - "# The uvp object has a dictionary of cosmo params used in the pspec run\n", - "print uvp.cosmo_params" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "attaching cosmology: \n", - "Cosmo_Conversions object at <0x110820750>\n", - "Om_L : 0.6844; Om_b : 0.0491; Om_c : 0.2644; Om_M : 0.3135; Om_k : 0.0021; H0 : 67.2700\n" - ] - } - ], - "source": [ - "# use these params to create a Cosmo_Conversions object that will be attached to uvp\n", - "uvp.set_cosmology(uvp.cosmo_params)" + "The cosmology used for this calculation will be the one stored in the `UVPSpec` object, which, recall, is the same cosmology we adopted originally." ] }, { @@ -637,18 +664,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "dsq units: (mK)^2 h^-3 Mpc^3 h^3 k^3 / (2pi^2)\n" + "dsq units: (mK)^2 k^3 / (2pi^2)\n" ] } ], "source": [ - "# get wave vectors for plotting\n", + "# make a copy of the UVPSpec object\n", "dsq = copy.deepcopy(uvp)\n", - "k_perp, k_para = dsq.get_kvecs(1, little_h=True)\n", "\n", - "# convert to deltasq\n", + "# how to get the wave vectors for spw = 1 by hand\n", + "k_perp, k_para = dsq.get_kperps(1, little_h=True), dsq.get_kparas(1, little_h=True)\n", + "\n", + "# convert to deltasq automatically\n", "dsq.convert_to_deltasq()\n", "\n", + "# see that units have automatically changed\n", "print \"dsq units: \",dsq.units" ] }, @@ -660,7 +690,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 19, @@ -671,7 +701,7 @@ "data": { "image/png": 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NH1g4ibjYvjncNJs1YlDe2OEgMyku6jFFcvtFhXQ73RytbWfDkTqe22GuyMdY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\nSwyXTr2UTZWbcG5/BLb8H1T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8W/UuXa7wZSgPbywjKymOG87rXQn1y2uLUCjJlosR4Stb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\nAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -701,26 +731,22 @@ "source": [ "## Plotting Thermal Noise Curves\n", "\n", - "Using the `hera_pspec.noise` module, you can plot analytic thermal noise power spectra given a $T_{\\rm sys}$ and the `integration_array` and `nsample_array` found in the `UVPSpec` object.\n", + "Using the `hera_pspec.noise` module, you can plot analytic thermal noise power spectra given a $T_{\\rm sys}$ and a `UVPSpec` object, which carries with it the integration time and number of incoherent averages for **each delay spectrum** it contains.\n", "\n", - "To do this, you need to first instantiate a `noise.Sense` object using the `UVPSpec.generate_sense()` method. This takes a `pspecbeam.PSpecBeam` object, which we saw before when using `PSpecData` objects. We have already attached a `cosmo` object to `uvp`, so it will automatically use that for the cosmology." + "To do this, all you need to do as the user is to interface with the `UVPSpec.generate_noise_spectra()` method. Given a selection of spectral window, polarization, system temperature and optionally which blpairs to compute for, it will return a dictionary of thermal noise curves (in either $P(k)$ or $\\Delta^2(k)$ depending on `form` kwarg) for each baseline-pair in the `UVPSpec` object." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "attaching self.sensitivity\n" - ] - } - ], + "outputs": [], "source": [ - "uvp.generate_sensitivity(uvb)" + "# generate noise curves for spw 0, pol 'xx' with a Tsys of 300 K\n", + "spw = 1\n", + "pol = 'xx'\n", + "Tsys = 300\n", + "P_N = uvp.generate_noise_spectra(spw, pol, Tsys)" ] }, { @@ -737,9 +763,9 @@ "outputs": [ { "data": { - "image/png": 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vBMZywfU487kgFGA4HObEyBjDWiVURKRuSs5FRDqcu5McH6OnnIL+9Yxkg1lb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\nDHQMu2NpHgdMywgB7lCcrWUgmtRyfDPOQ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PjlgV2ddfgcFtLBN7A9ZaKZGd5/sFf3fmpB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\nEiJz97THkBo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vBMZywfU487kgFGA4HObEyBjDWiVURKRuSs5FRDqcu5McH6OnnIL+9Yxkg1lb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LVYDJoxct4TambzP7vgynvF/LvvQbHjFq5Jgihv+5uytoQFh8msLXFqKZwT7G1xYZkh6k0SiFo9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\nx3YDjoesC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B5aBx1kmbRJFQN+8bwvMuIZ4emGpyOP1Q/W0+9pwzeymMFQI6KKjIqEtpZn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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -747,13 +773,11 @@ } ], "source": [ - "# plot power spectrum of spw 1\n", + "# plot power spectrum of spw 1 with noise curves\n", "fig, ax = plt.subplots(figsize=(12,8))\n", "\n", - "spw = 1\n", - "blp =((24, 25), (37, 38))\n", - "key = (spw, blp, 'xx')\n", - "k_perp, k_para = uvp.get_kvecs(spw)\n", + "blp = ((24, 25), (37, 38))\n", + "key = (spw, blp, pol)\n", "power = np.abs(np.real(uvp.get_data(key)))\n", "\n", "_ = ax.plot(k_para, power.T)\n", @@ -763,12 +787,9 @@ "ax.set_ylabel(r\"$P(k)\\ \\rm [mK^2\\ h^{-3}\\ Mpc^3]$\", fontsize=14)\n", "ax.set_title(\"spw : {}, blpair : {}, pol : {}\".format(*key), fontsize=14)\n", "\n", - "# specify spectral window, polarization and System Temp in K\n", - "P_N = uvp.generate_noise_spectra(spw, 'xx', 400)\n", - "\n", + "# plot noise curves for this baseline-pair\n", "ax.set_prop_cycle(None)\n", - "P_N = P_N[uvp.antnums_to_blpair(blp)]\n", - "_ = ax.plot(k_para, P_N.T, ls='--')" + "_ = ax.plot(k_para, P_N[uvp.antnums_to_blpair(blp)].T, ls='--', lw=3)" ] }, { @@ -777,7 +798,7 @@ "source": [ "## Incoherent Averaging\n", "\n", - "The `UVPSpec` object supports incoherent averaging across baseline-pairs and/or time using the `UVPSpec.average_blpairs()` method. If the baseline-pairs are grouped by redundancy, this is equivalent to binning onto a single 3D $k_x, k_y, k_\\parallel$ space. If the baseline-pairs are grouped by baseline separation, this is equivalent to cylindrical binning in $k_\\perp$ and $k_\\parallel$. Spherical binning to form a true 1D power spectrum is currently not supported.\n", + "The `UVPSpec` object supports incoherent averaging across baseline-pairs and/or time using the `UVPSpec.average_spectra()` method. If the baseline-pairs are grouped by redundancy, this is equivalent to binning onto a single 3D $k_x, k_y, k_\\parallel$ space. If the baseline-pairs are grouped by baseline separation, this is equivalent to cylindrical binning in $k_\\perp$ and $k_\\parallel$. Spherical binning to form a true 1D power spectrum is currently not supported.\n", "\n", "Below we will incoherently average across all baseline-pairs in the `UVPSpec` object, knowing that we only constructed 14.6 m power spectra, so we are reasonably justified in doing so." ] @@ -785,10 +806,16 @@ { "cell_type": "code", "execution_count": 22, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ + "# form the baseline-pair group, which will be a single group \n", + "# consisting of all baseline-pairs in the object\n", "blp_group = [sorted(np.unique(uvp.blpair_array))]\n", + "\n", + "# average spectra with inplace = False and assign to a new \"uvp2\" object\n", "uvp2 = uvp.average_spectra(blpair_groups=blp_group, time_avg=True, inplace=False)" ] }, @@ -796,7 +823,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can then plot the averaged power spectrum, along with the noise curve which will know to account for the averaging based on the changes to the `integration_array` and `nsample_array`. The colored lines show the same un-averaged pspectra from before, while the black line shows the averaged power spectrum, along with its thermal noise estimate." + "We can then plot the averaged power spectrum, along with a new noise curve which will know to account for the averaging based on the changes to the `integration_array` and `nsample_array`. The colored lines show the same un-averaged pspectra from before, while the black line shows the averaged power spectrum, along with its new thermal noise estimate." ] }, { @@ -806,9 +833,9 @@ "outputs": [ { "data": { - "image/png": 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N7Br8tYwPu5qZe/ZKbxPMuiLcXDZv3sydd965kpAP2rlzJ9u3b6/KXFIbOkaNT8eoOeg4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yyzhW+KekyY+OvUAhAcJX2fe9Oc99QBx4+UU9bWH9kqSwjY85r8b4et75AkYEfVDtQKda/uJz2h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\n6XFgll2SHnb3X7t7VdKtkvYmPCbM4O7fk/Rs0uPA6bn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N7Br8tYwPu5qZe/ZKbxPMuiLcXDZv3sydd965kpAP2rlzJ9u3b6/KXFIbOkaNT8eoOeg4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PRYpa1leL65B6PZWgCKU6Ox9BRgdpjZWLeR26fdzlMLnyImVE561xRdw7TEaTy49UFqemuwO+1s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\nlfNRCjMLwwu5YcoNrKlcw9uf3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ra9gQitirysdkbcp3DfEGp+u/LuU9zDAKxOaaOYJsKr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\nAO8D8C5ORRc1oYLvEqEdxgB0qX7uzNxGEES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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -820,23 +847,21 @@ "fig, ax = plt.subplots(figsize=(12,8))\n", "\n", "spw = 1\n", - "blp =((24, 25), (37, 38))\n", - "key = (spw, blp, 'xx')\n", - "k_perp, k_para = uvp.get_kvecs(spw)\n", + "blp = ((24, 25), (37, 38))\n", + "pol = 'xx'\n", + "key = (spw, blp, pol)\n", + "k_para = uvp.get_kparas(spw)\n", "power = np.abs(np.real(uvp.get_data(key)))\n", - "P_N = uvp.generate_noise_spectra(spw, 'xx', 400)\n", + "P_N = uvp.generate_noise_spectra(spw, pol, 300)\n", "P_N = P_N[uvp.antnums_to_blpair(blp)]\n", "\n", - "spw = 1\n", - "blp =((24, 25), (37, 38))\n", - "key = (spw, blp, 'xx')\n", "avg_power = np.abs(np.real(uvp2.get_data(key)))\n", - "avg_P_N = uvp2.generate_noise_spectra(spw, 'xx', 400)\n", + "avg_P_N = uvp2.generate_noise_spectra(spw, pol, 300)\n", "avg_P_N = avg_P_N[uvp2.antnums_to_blpair(blp)]\n", "\n", "_ = ax.plot(k_para, power.T)\n", "ax.plot(k_para, avg_power.T, color='k')\n", - "ax.plot(k_para, avg_P_N.T, color='k', ls='--')\n", + "ax.plot(k_para, avg_P_N.T, color='k', ls='--', lw=3)\n", "ax.set_yscale('log')\n", "ax.grid()\n", "ax.set_xlabel(r\"$k_\\parallel\\ \\rm h\\ Mpc^{-1}$\", fontsize=14)\n", @@ -844,7 +869,7 @@ "ax.set_title(\"spw : {}, blpair : {}, pol : {}\".format(*key), fontsize=14)\n", "\n", "ax.set_prop_cycle(None)\n", - "_ = ax.plot(k_para, P_N.T, ls='--')" + "_ = ax.plot(k_para, P_N.T, ls='--', lw=3)" ] }, { @@ -885,9 +910,9 @@ "outputs": [ { "data": { - "image/png": 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kXEREREQkQ+Rkcq5WiiIiIiKSiXIyOVcrRRERERHJRDmZnIuIiIiIZCIl5yIiIiIiGULJ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QLzlw2lWkl8Tjz2+vUBMNLXIScnB5A5868idCVdVEILFRQ9I8irfDn4SuwVlColE7wmVN1fXqoc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\n4Ml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kXEREREQkQ+Rkcq5WiiIiIiKSiXIyOVcrRRERERHJRDmZnIuIiIiIZCIl5yIiIiIiGULJ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -900,22 +925,20 @@ "\n", "spw = 1\n", "blp =((24, 25), (37, 38))\n", - "key = (spw, blp, 'xx')\n", - "k_perp, k_para = uvp.get_kvecs(spw)\n", + "pol = 'xx'\n", + "key = (spw, blp, pol)\n", + "k_para = uvp.get_kparas(spw)\n", "power = np.abs(np.real(uvp.get_data(key)))\n", - "P_N = uvp.generate_noise_spectra(spw, 'xx', 400)\n", + "P_N = uvp.generate_noise_spectra(spw, pol, 300)\n", "P_N = P_N[uvp.antnums_to_blpair(blp)]\n", "\n", - "spw = 1\n", - "blp =((24, 25), (37, 38))\n", - "key = (spw, blp, 'xx')\n", "avg_power = np.abs(np.real(uvp2.get_data(key)))\n", - "avg_P_N = uvp2.generate_noise_spectra(spw, 'xx', 400)\n", + "avg_P_N = uvp2.generate_noise_spectra(spw, pol, 300)\n", "avg_P_N = avg_P_N[uvp2.antnums_to_blpair(blp)]\n", "\n", "_ = ax.plot(k_para, power.T)\n", "ax.plot(k_para, avg_power.T, color='k')\n", - "ax.plot(k_para, avg_P_N.T, color='k', ls='--')\n", + "ax.plot(k_para, avg_P_N.T, color='k', ls='--', lw=3)\n", "ax.set_yscale('log')\n", "ax.grid()\n", "ax.set_xlabel(r\"$k_\\parallel\\ \\rm h\\ Mpc^{-1}$\", fontsize=14)\n", @@ -923,22 +946,74 @@ "ax.set_title(\"spw : {}, blpair : {}, pol : {}\".format(*key), fontsize=14)\n", "\n", "ax.set_prop_cycle(None)\n", - "_ = ax.plot(k_para, P_N.T, ls='--')" + "_ = ax.plot(k_para, P_N.T, ls='--', lw=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Select parts of the data to keep\n", + "## Re-defining the cosmology\n", "\n", - "We can select out parts of the data using the `select` function. Currently we only support selecting out spectral windows, baseline pairs and times." + "We started by adopting a single cosmology, which was automatically passed between the various `hera_pspec` objects without user intervention in order to preserve self-consistency. If, however, we would like to adopt a *new cosmology*, we need to re-compute certain cosmology-dependent quantities, namely the normalization of the power spectrum scalar (see HERA Memos #27 and #43). We can do this with the `UVPSpec.set_cosmology` object." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.cosmo exists and overwrite == False, not overwriting...\n" + ] + } + ], + "source": [ + "# define a new cosmology with no dark energy\n", + "new_cosmo = hp.conversions.Cosmo_Conversions(Om_L = 0.0)\n", + "\n", + "# attempt to overwrite the current UVPSpec cosmology:\n", + "# this will fail b/c one already exists and overwrite=False\n", + "uvp.set_cosmology(new_cosmo)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setting cosmology: \n", + "Cosmo_Conversions object at <0x119babf50>\n", + "Om_L : 0.0000; Om_b : 0.0491; Om_c : 0.2644; Om_M : 0.3135; Om_k : 0.6865; H0 : 67.2700\n", + "Updating scalar array and re-normalizing power spectra\n" + ] + } + ], + "source": [ + "# now set overwrite = True\n", + "uvp.set_cosmology(new_cosmo, overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform data selection on `UVPSpec`\n", + "\n", + "We can select out parts of the data using the `select` function." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -950,10 +1025,11 @@ } ], "source": [ - "# select out all baseline-pairs containing the baseline (24, 25)\n", + "# select all baseline-pairs containing the baseline (24, 25)\n", "# only_pairs_in_bls means to select only blpairs that have baseline1 _and_ baseline2 in the bls list\n", "uvp2 = uvp.select(bls=[(24, 25)], inplace=False, only_pairs_in_bls=False)\n", "\n", + "# check than only baseline-pairs with a (24, 25) in them are kept\n", "print \"baseline pairs in uvp2: \\n\", map(lambda blp: uvp2.blpair_to_antnums(blp), np.unique(uvp2.blpair_array))" ] }, @@ -965,12 +1041,12 @@ "source": [ "## Write to HDF5\n", "\n", - "Using the `write_hdf5` method." + "Write to disk using the `write_hdf5` method. Easy as that." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": { "collapsed": true }, @@ -987,12 +1063,23 @@ "\n", "Read the whole file, just the meta-data, or a data selection for partial I/O.\n", "\n", - "Read the whole file and look at some meta-data." + "Frist, let's **read the whole file** and look at some meta-data." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# read the whole file\n", + "uvp2 = hp.uvpspec.UVPSpec()\n", + "uvp2.read_hdf5(\"pspec.hdf5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1000,21 +1087,31 @@ "output_type": "stream", "text": [ "pols : [-5]\n", - "number of baseline-pairs: 3\n", - "data_array in uvp2 : True\n" + "number of baseline-pairs: 3\n" ] } ], "source": [ - "# read the whole file\n", - "uvp2 = hp.uvpspec.UVPSpec()\n", - "uvp2.read_hdf5(\"pspec.hdf5\")\n", - "\n", "# print some meta-data\n", "print \"pols : \", uvp2.pol_array\n", - "print \"number of baseline-pairs: \",uvp2.Nblpairs\n", - "\n", - "# Ensure data_array exists\n", + "print \"number of baseline-pairs: \",uvp2.Nblpairs" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data_array in uvp2 : True\n" + ] + } + ], + "source": [ + "# Ensure data_array exists b/c we read the whole file\n", "print \"data_array in uvp2 : \", hasattr(uvp2, 'data_array')" ] }, @@ -1022,12 +1119,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now read only the meta-data." + "Now read **only the meta-data**." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# read only the meta-data\n", + "uvp2.read_hdf5(\"pspec.hdf5\", just_meta=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1035,20 +1142,31 @@ "output_type": "stream", "text": [ "pols : [-5]\n", - "number of baseline-pairs: 3\n", - "data_array in uvp2 : False\n" + "number of baseline-pairs: 3\n" ] } ], "source": [ - "# read only the meta-data\n", - "uvp2.read_hdf5(\"pspec.hdf5\", just_meta=True)\n", - "\n", "# print some meta-data\n", "print \"pols : \", uvp2.pol_array\n", - "print \"number of baseline-pairs: \",uvp2.Nblpairs\n", - "\n", - "# Ensure data_array doesn't exist (b/c we only loaded the meta-data)\n", + "print \"number of baseline-pairs: \",uvp2.Nblpairs" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data_array in uvp2 : False\n" + ] + } + ], + "source": [ + "# Ensure data_array doesn't exist b/c we only loaded the meta-data\n", "print \"data_array in uvp2 : \", hasattr(uvp2, 'data_array')" ] }, @@ -1056,39 +1174,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now do partial loading of baseline pairs." + "Now do **partial loading of certain baseline pairs**." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "pols : [-5]\n", - "number of baseline-pairs: 1\n", - "data_array in uvp2 : True\n", "baseline pairs in uvp2: \n", "[((37, 38), (38, 39))]\n" ] } ], "source": [ - "# read metadata and data, but only part of the file (a single baseline pair)\n", - "# bls is the baselines you want to load in, and only_pairs_in_bls=True means both the\n", - "# first _and_ second baseline in a baseline-pair must bbe in the bls list.\n", + "# partial load on certain baseline-pairs \n", "uvp2.read_hdf5(\"pspec.hdf5\", just_meta=False, bls=[(37, 38), (38, 39)], only_pairs_in_bls=True)\n", "\n", - "# print some meta-data\n", - "print \"pols : \", uvp2.pol_array\n", - "print \"number of baseline-pairs: \",uvp2.Nblpairs\n", - "\n", - "# Ensure data_array doesn't exist (b/c we only loaded the meta-data)\n", - "print \"data_array in uvp2 : \", hasattr(uvp2, 'data_array')\n", - "\n", "# print baseline pairs\n", "print \"baseline pairs in uvp2: \\n\", map(lambda blp: uvp2.blpair_to_antnums(blp), np.unique(uvp2.blpair_array))" ] @@ -1097,19 +1203,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now do partial loading of times. To do this, we need to feed the exact float entries of the `time_avg_array` we want. But how do we get these without loading the whole file to begin with? That's where a practical application of the `just_meta` keyword comes in. First we will load only the meta-data, and then feed the `read_hdf5` method with the times that we want." + "Now do **partial loading of certain times**. To do this, we need to feed the exact float of the times we want, but how do we get these to float precision in the first place without loading the whole file? This is where the `just_meta` load comes in handy.\n", + "\n", + "First load just the metadata, then do a partial load using the times you desire from the metadata." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "number of unique times: 1\n", + "number of unique times: 3\n", "number of baseline-pairs: 3\n" ] } @@ -1128,7 +1236,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 39, "metadata": { "collapsed": true }, From 19db6ce5cc61fe31a36a88621863bca50d7b71f0 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Tue, 1 May 2018 10:57:25 -0700 Subject: [PATCH 52/54] minor updates from cosmo_handling PR comments --- hera_pspec/noise.py | 2 ++ hera_pspec/pspecdata.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/hera_pspec/noise.py b/hera_pspec/noise.py index 69a3da78..e0f96a7f 100644 --- a/hera_pspec/noise.py +++ b/hera_pspec/noise.py @@ -21,6 +21,8 @@ def calc_P_N(scalar, Tsys, t_int, Ncoherent=1, Nincoherent=None, form='Pk', k=No Parameters ---------- + scalar : float, Power spectrum normalization factor: X2Y(z) * Omega_P^2 / Omega_PP + Tsys : float, System temperature in Kelvin t_int : float, integration time of power spectra in seconds diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index a00c11e3..16883bec 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -878,7 +878,7 @@ def units(self, little_h=True): # set pspec norm units if self.primary_beam is None: - norm_units = "Hz str [beam normalization not specified]".format(self.dsets[0].vis_units) + norm_units = "Hz str [beam normalization not specified]" else: if little_h: h_unit = "h^-3 " From d605f9a6feea665cfdc078507de306bfdb922468 Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Thu, 3 May 2018 20:59:44 -0700 Subject: [PATCH 53/54] fixes minor bug in pspecdata.PSpecData.scalar, which has default pol='I' but isn't passed a pol when called. pol is now an argument to make this passing explicity --- hera_pspec/pspecdata.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index 16883bec..eb04d332 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -906,7 +906,7 @@ def delays(self): else: return utils.get_delays(self.freqs[self.spw_range[0]:self.spw_range[1]]) * 1e9 # convert to ns - def scalar(self, pol='I', taper='none', little_h=True, + def scalar(self, pol, taper='none', little_h=True, num_steps=2000, beam=None): """ Computes the scalar function to convert a power spectrum estimate @@ -917,7 +917,7 @@ def scalar(self, pol='I', taper='none', little_h=True, Parameters ---------- - pol: str, optional + pol: str Which polarization to compute the scalar for. e.g. 'I', 'Q', 'U', 'V', 'XX', 'YY'... Default: 'I' @@ -1149,7 +1149,7 @@ def pspec(self, bls1, bls2, dsets, input_data_weight='identity', norm='I', # Compute scalar to convert "telescope units" to "cosmo units" if self.primary_beam is not None: - scalar = self.scalar(taper=taper, little_h=True) + scalar = self.scalar(p, taper=taper, little_h=True) else: raise_warning("Warning: self.primary_beam is not defined, " "so pspectra are not properly normalized", From 2c5b47f01bac76cb4438904d76217bb8601f8d8e Mon Sep 17 00:00:00 2001 From: Nicholas Kern Date: Thu, 3 May 2018 21:01:34 -0700 Subject: [PATCH 54/54] modified: hera_pspec/pspecdata.py --- hera_pspec/pspecdata.py | 1 - 1 file changed, 1 deletion(-) diff --git a/hera_pspec/pspecdata.py b/hera_pspec/pspecdata.py index eb04d332..1953b997 100644 --- a/hera_pspec/pspecdata.py +++ b/hera_pspec/pspecdata.py @@ -920,7 +920,6 @@ def scalar(self, pol, taper='none', little_h=True, pol: str Which polarization to compute the scalar for. e.g. 'I', 'Q', 'U', 'V', 'XX', 'YY'... - Default: 'I' taper : str, optional Whether a tapering function (e.g. Blackman-Harris) is being