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Pull Request Pull Request #287: Plotting api

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/orbitize/results.py

import numpy as np
import h5py
import os

import astropy.table as table

import orbitize.system
import orbitize.basis
import orbitize.plot
import orbitize.gaia, orbitize.hipparcos


class Results(object):
    """
    A class to store accepted orbital configurations from the sampler

    Args:
        system (orbitize.system.System): System object used to do the fit.
        sampler_name (string): name of sampler class that generated these results 
            (default: None).
        post (np.array of float): MxN array of orbital parameters
            (posterior output from orbit-fitting process), where M is the
            number of orbits generated, and N is the number of varying orbital
            parameters in the fit (default: None).
        lnlike (np.array of float): M array of log-likelihoods corresponding to
            the orbits described in ``post`` (default: None).
        version_number (str): version of orbitize that produced these results. 
        data (astropy.table.Table): output from ``orbitize.read_input.read_file()``
        curr_pos (np.array of float): for MCMC only. A multi-D array of the 
            current walker positions that is used for restarting a MCMC sampler. 

    Written: Henry Ngo, Sarah Blunt, 2018
    API Update: Sarah Blunt, 2021
    """

    def __init__(
        self, system=None, sampler_name=None, post=None, lnlike=None,
        version_number=None, curr_pos=None
    ):

        self.system = system
        self.sampler_name = sampler_name
        self.post = post
        self.lnlike = lnlike
        self.curr_pos = curr_pos
        self.version_number = version_number

        if self.system is not None:
            self.tau_ref_epoch = self.system.tau_ref_epoch
            self.labels = self.system.labels
            self.data = self.system.data_table
            self.num_secondary_bodies = self.system.num_secondary_bodies
            self.fitting_basis = self.system.fitting_basis
            self.basis = self.system.basis
            self.param_idx = self.system.param_idx

    def add_samples(self, orbital_params, lnlikes, curr_pos=None): 
        """
        Add accepted orbits, their likelihoods, and the orbitize version number 
            to the results

        Args:
            orbital_params (np.array): add sets of orbital params (could be multiple) 
                to results
            lnlike (np.array): add corresponding lnlike values to results
            curr_pos (np.array of float): for MCMC only. A multi-D array of the 
                current walker positions

        Written: Henry Ngo, 2018
        API Update: Sarah Blunt, 2021
        """
        
        # Adding the orbitize version number to the results
        if self.version_number is None:
            self.version_number = orbitize.__version__

        # If no exisiting results then it is easy
        if self.post is None:
            self.post = orbital_params
            self.lnlike = lnlikes

        # Otherwise, need to append properly
        else:
            self.post = np.vstack((self.post, orbital_params))
            self.lnlike = np.append(self.lnlike, lnlikes)

        if curr_pos is not None:
            self.curr_pos = curr_pos

    def save_results(self, filename):
        """
        Save results.Results object to an hdf5 file

        Args:
            filename (string): filepath to save to

        Save attributes from the ``results.Results`` object.

        ``sampler_name``, ``tau_ref_epcoh``, ``version_number`` are attributes of the root group.
        ``post``, ``lnlike``, and ``parameter_labels`` are datasets
        that are members of the root group.

        Written: Henry Ngo, 2018
        API Update: Sarah Blunt, 2021
        """

        hf = h5py.File(filename, 'w')  # Creates h5py file object
        # Add sampler_name as attribute of the root group

        hf.attrs['sampler_name'] = self.sampler_name
        hf.attrs['version_number'] = self.version_number

        # Now add post and lnlike from the results object as datasets
        hf.create_dataset('post', data=self.post)
        # hf.create_dataset('data', data=self.data)
        if self.lnlike is not None:
            hf.create_dataset('lnlike', data=self.lnlike)

        if self.curr_pos is not None:
            hf.create_dataset("curr_pos", data=self.curr_pos)

        self.system.save(hf)

        hf.close()  # Closes file object, which writes file to disk

    def load_results(self, filename, append=False):
        """
        Populate the ``results.Results`` object with data from a datafile

        Args:
            filename (string): filepath where data is saved
            append (boolean): if True, then new data is added to existing object.
                If False (default), new data overwrites existing object

        See the ``save_results()`` method in this module for information on how the
        data is structured.

        Written: Henry Ngo, 2018
        API Update: Sarah Blunt, 2021
        """

        hf = h5py.File(filename, 'r')  # Opens file for reading
        # Load up each dataset from hdf5 file
        sampler_name = np.str(hf.attrs['sampler_name'])
        try:
            version_number = np.str(hf.attrs['version_number'])
        except KeyError:
            version_number = "<= 1.13"
        post = np.array(hf.get('post'))
        lnlike = np.array(hf.get('lnlike'))


        try:
            num_secondary_bodies = int(hf.attrs['num_secondary_bodies'])
        except KeyError:
            # old, has to be single planet fit
            num_secondary_bodies = 1  

        try:
            data_table = table.Table(np.array(hf.get('data')))
        except ValueError: # old version of results; add a dummy table
            data_table = table.Table(
                names = (
                    'epoch', 'object', 'quant1', 'quant1_err', 'quant2', 
                    'quant2_err', 'quant12_corr', 'quant_type', 'instrument'
                ),
                dtype=('<f8', '<i8', '<f8', '<f8', '<f8', '<f8', '<f8', 'S5', 'S5')
            )

        try: # these are all saved keywords introduced in v2
            restrict_angle_ranges = bool(hf.attrs['restrict_angle_ranges'])
            stellar_mass = float(hf.attrs['stellar_mass'])
            mass_err = float(hf.attrs['mass_err'])
            plx_err = float(hf.attrs['plx_err'])
            plx = float(hf.attrs['plx'])
            fit_secondary_mass = bool(hf.attrs['fit_secondary_mass'])
            use_rebound = bool(hf.attrs['use_rebound'])
        except KeyError:
            restrict_angle_ranges = False
            stellar_mass = np.nan
            plx = np.nan
            plx_err = 0
            mass_err = 0
            fit_secondary_mass = False
            use_rebound = False
        try:
            tau_ref_epoch = float(hf.attrs['tau_ref_epoch'])
        except KeyError:
            # probably an old results file when reference epoch was fixed at MJD = 0
            tau_ref_epoch = 0

        iad_data = hf.get("IAD_datafile")
        if iad_data is not None:
            
            tmpfile = 'thisisprettyhackysorrylmao'
            np.array(iad_data).tofile(tmpfile)

            hip_num = str(hf.attrs['hip_num'])
            alphadec0_epoch = float(hf.attrs['alphadec0_epoch'])
            renormalize_errors = bool(hf.attrs['renormalize_errors'])

            hipparcos_IAD = orbitize.hipparcos.HipparcosLogProb(
                tmpfile, hip_num, alphadec0_epoch, renormalize_errors
            )

            os.system('rm {}'.format(tmpfile))
            try:
                gaia_num = int(hf.attrs['gaia_num'])
                dr = str(hf.attrs['dr'])
                gaia = orbitize.gaia.GaiaLogProb(gaia_num, hipparcos_IAD, dr)
            except KeyError:
                gaia = None
        else:
            hipparcos_IAD = None
            gaia = None

        try:
            fitting_basis = np.str(hf.attrs['fitting_basis'])
        except KeyError:
            # if key does not exist, then it was fit in the standard basis
            fitting_basis = 'Standard'

        self.system = orbitize.system.System(
            num_secondary_bodies, data_table, stellar_mass,
            plx, mass_err, plx_err, restrict_angle_ranges,
            tau_ref_epoch, fit_secondary_mass,
            hipparcos_IAD, gaia, fitting_basis, use_rebound
        )

        self.tau_ref_epoch = self.system.tau_ref_epoch
        self.labels = self.system.labels
        self.data = self.system.data_table
        self.num_secondary_bodies = self.system.num_secondary_bodies
        self.fitting_basis = self.system.fitting_basis
        self.basis = self.system.basis
        self.param_idx = self.system.param_idx

        try:
            curr_pos = np.array(hf.get('curr_pos'))
        except KeyError:
            curr_pos = None

        hf.close()  # Closes file object

        # doesn't matter if append or not. Overwrite curr_pos if not None
        if curr_pos is not None:
            self.curr_pos = curr_pos

        # Adds loaded data to object as per append keyword
        if append:
            # if no sampler_name set, use the input file's value
            if self.sampler_name is None:
                self.sampler_name = sampler_name
            # otherwise only proceed if the sampler_names match
            elif self.sampler_name != sampler_name:
                raise Exception(
                    'Unable to append file {} to Results object. sampler_name of object and file do not match'.format(filename))
            # if no version_number set, use the input file's value
            if self.version_number is None:
                self.version_number = version_number
            # otherwise only proceed if the version_numbers match
            elif self.version_number != version_number:
                raise Exception(
                    'Unable to append file {} to Results object. version_number of object and file do not match'.format(filename))

            # Now append post and lnlike
            self.add_samples(post, lnlike)#, self.labels)
        else:

            # Only proceed if object is completely empty
            if self.sampler_name is None and self.post is None and self.lnlike is None and self.version_number is None:# and self.tau_ref_epoch is None :
                self.sampler_name = sampler_name
                self.version_number = version_number
                self.add_samples(post, lnlike)#, self.labels)

            else:
                raise Exception(
                    'Unable to load file {} to Results object. append is set to False but object is not empty'.format(filename))

    def print_results(self):
        """
        Prints median and 68% credible intervals alongside fitting labels
        """

        print('\nparam: med [68% CI]')
        print('-------------------\n')
        for i, l in enumerate(self.system.labels):
            print(
                '{}: {:.3f} [{:.3f} - {:.3f}]'.format(
                    l, 
                    np.median(self.post[:,i]),
                    np.quantile(self.post[:,i], 0.16),
                    np.quantile(self.post[:,i], 0.84)
                )
            )
        print('-------------------\n')
    
    def plot_corner(self, param_list=None, **corner_kwargs):
        """
        Wrapper for orbitize.plot.plot_corner
        """
        return orbitize.plot.plot_corner(self, param_list, **corner_kwargs)

    def plot_orbits(self, object_to_plot=1, start_mjd=51544.,
        num_orbits_to_plot=100, num_epochs_to_plot=100,
        square_plot=True, show_colorbar=True, cmap=orbitize.plot.cmap,
        sep_pa_color='lightgrey', sep_pa_end_year=2025.0,
        cbar_param='Epoch [year]', mod180=False, rv_time_series=False, 
        plot_astrometry=True,
        plot_astrometry_insts=False, fig=None
    ):
        """
        Wrapper for orbitize.plot.plot_orbits
        """

        return orbitize.plot.plot_orbits(
            self, object_to_plot=object_to_plot, start_mjd=start_mjd,
            num_orbits_to_plot=num_orbits_to_plot, 
            num_epochs_to_plot=num_epochs_to_plot,
            square_plot=square_plot, show_colorbar=show_colorbar, cmap=cmap,
            sep_pa_color=sep_pa_color, sep_pa_end_year=sep_pa_end_year,
            cbar_param=cbar_param, mod180=mod180, rv_time_series=rv_time_series, 
            plot_astrometry=plot_astrometry,
            plot_astrometry_insts=plot_astrometry_insts, fig=fig
        )




1	import numpy as np	2✔
2	import h5py	2✔
3	import os	2✔
4
5	import astropy.table as table	2✔
6
7	import orbitize.system	2✔
8	import orbitize.basis	2✔
9	import orbitize.plot	2✔
10	import orbitize.gaia, orbitize.hipparcos	2✔
11
12
13	class Results(object):	2✔
14	"""
15	A class to store accepted orbital configurations from the sampler
16
17	Args:
18	system (orbitize.system.System): System object used to do the fit.
19	sampler_name (string): name of sampler class that generated these results
20	(default: None).
21	post (np.array of float): MxN array of orbital parameters
22	(posterior output from orbit-fitting process), where M is the
23	number of orbits generated, and N is the number of varying orbital
24	parameters in the fit (default: None).
25	lnlike (np.array of float): M array of log-likelihoods corresponding to
26	the orbits described in ``post`` (default: None).
27	version_number (str): version of orbitize that produced these results.
28	data (astropy.table.Table): output from ``orbitize.read_input.read_file()``
29	curr_pos (np.array of float): for MCMC only. A multi-D array of the
30	current walker positions that is used for restarting a MCMC sampler.
31
32	Written: Henry Ngo, Sarah Blunt, 2018
33	API Update: Sarah Blunt, 2021
34	"""
35
36	def __init__(	2✔
37	self, system=None, sampler_name=None, post=None, lnlike=None,
38	version_number=None, curr_pos=None
39	):
40
41	self.system = system	2✔
42	self.sampler_name = sampler_name	2✔
43	self.post = post	2✔
44	self.lnlike = lnlike	2✔
45	self.curr_pos = curr_pos	2✔
46	self.version_number = version_number	2✔
47
48	if self.system is not None:	2✔
49	self.tau_ref_epoch = self.system.tau_ref_epoch	2✔
50	self.labels = self.system.labels	2✔
51	self.data = self.system.data_table	2✔
52	self.num_secondary_bodies = self.system.num_secondary_bodies	2✔
53	self.fitting_basis = self.system.fitting_basis	2✔
54	self.basis = self.system.basis	2✔
55	self.param_idx = self.system.param_idx	2✔
56
57	def add_samples(self, orbital_params, lnlikes, curr_pos=None):	2✔
58	"""
59	Add accepted orbits, their likelihoods, and the orbitize version number
60	to the results
61
62	Args:
63	orbital_params (np.array): add sets of orbital params (could be multiple)
64	to results
65	lnlike (np.array): add corresponding lnlike values to results
66	curr_pos (np.array of float): for MCMC only. A multi-D array of the
67	current walker positions
68
69	Written: Henry Ngo, 2018
70	API Update: Sarah Blunt, 2021
71	"""
72
73	# Adding the orbitize version number to the results
74	if self.version_number is None:	2✔
75	self.version_number = orbitize.__version__	2✔
76
77	# If no exisiting results then it is easy
78	if self.post is None:	2✔
79	self.post = orbital_params	2✔
80	self.lnlike = lnlikes	2✔
81
82	# Otherwise, need to append properly
83	else:
84	self.post = np.vstack((self.post, orbital_params))	2✔
85	self.lnlike = np.append(self.lnlike, lnlikes)	2✔
86
87	if curr_pos is not None:	2✔
88	self.curr_pos = curr_pos	2✔
89
90	def save_results(self, filename):	2✔
91	"""
92	Save results.Results object to an hdf5 file
93
94	Args:
95	filename (string): filepath to save to
96
97	Save attributes from the ``results.Results`` object.
98
99	``sampler_name``, ``tau_ref_epcoh``, ``version_number`` are attributes of the root group.
100	``post``, ``lnlike``, and ``parameter_labels`` are datasets
101	that are members of the root group.
102
103	Written: Henry Ngo, 2018
104	API Update: Sarah Blunt, 2021
105	"""
106
107	hf = h5py.File(filename, 'w') # Creates h5py file object	2✔
108	# Add sampler_name as attribute of the root group
109
110	hf.attrs['sampler_name'] = self.sampler_name	2✔
111	hf.attrs['version_number'] = self.version_number	2✔
112
113	# Now add post and lnlike from the results object as datasets
114	hf.create_dataset('post', data=self.post)	2✔
115	# hf.create_dataset('data', data=self.data)
116	if self.lnlike is not None:	2✔
117	hf.create_dataset('lnlike', data=self.lnlike)	2✔
118
119	if self.curr_pos is not None:	2✔
120	hf.create_dataset("curr_pos", data=self.curr_pos)	2✔
121
122	self.system.save(hf)	2✔
123
124	hf.close() # Closes file object, which writes file to disk	2✔
125
126	def load_results(self, filename, append=False):	2✔
127	"""
128	Populate the ``results.Results`` object with data from a datafile
129
130	Args:
131	filename (string): filepath where data is saved
132	append (boolean): if True, then new data is added to existing object.
133	If False (default), new data overwrites existing object
134
135	See the ``save_results()`` method in this module for information on how the
136	data is structured.
137
138	Written: Henry Ngo, 2018
139	API Update: Sarah Blunt, 2021
140	"""
141
142	hf = h5py.File(filename, 'r') # Opens file for reading	2✔
143	# Load up each dataset from hdf5 file
144	sampler_name = np.str(hf.attrs['sampler_name'])	2✔
145	try:	2✔
146	version_number = np.str(hf.attrs['version_number'])	2✔
147	except KeyError:	2✔
148	version_number = "<= 1.13"	2✔
149	post = np.array(hf.get('post'))	2✔
150	lnlike = np.array(hf.get('lnlike'))	2✔
151
152
153	try:	2✔
154	num_secondary_bodies = int(hf.attrs['num_secondary_bodies'])	2✔
155	except KeyError:	2✔
156	# old, has to be single planet fit
157	num_secondary_bodies = 1	2✔
158
159	try:	2✔
160	data_table = table.Table(np.array(hf.get('data')))	2✔
161	except ValueError: # old version of results; add a dummy table	2✔
162	data_table = table.Table(	2✔
163	names = (
164	'epoch', 'object', 'quant1', 'quant1_err', 'quant2',
165	'quant2_err', 'quant12_corr', 'quant_type', 'instrument'
166	),
167	dtype=('<f8', '<i8', '<f8', '<f8', '<f8', '<f8', '<f8', 'S5', 'S5')
168	)
169
170	try: # these are all saved keywords introduced in v2	2✔
171	restrict_angle_ranges = bool(hf.attrs['restrict_angle_ranges'])	2✔
172	stellar_mass = float(hf.attrs['stellar_mass'])	2✔
173	mass_err = float(hf.attrs['mass_err'])	2✔
174	plx_err = float(hf.attrs['plx_err'])	2✔
175	plx = float(hf.attrs['plx'])	2✔
176	fit_secondary_mass = bool(hf.attrs['fit_secondary_mass'])	2✔
177	use_rebound = bool(hf.attrs['use_rebound'])	2✔
178	except KeyError:	2✔
179	restrict_angle_ranges = False	2✔
180	stellar_mass = np.nan	2✔
181	plx = np.nan	2✔
182	plx_err = 0	2✔
183	mass_err = 0	2✔
184	fit_secondary_mass = False	2✔
185	use_rebound = False	2✔
186	try:	2✔
187	tau_ref_epoch = float(hf.attrs['tau_ref_epoch'])	2✔
188	except KeyError:	2✔
189	# probably an old results file when reference epoch was fixed at MJD = 0
190	tau_ref_epoch = 0	2✔
191
192	iad_data = hf.get("IAD_datafile")	2✔
193	if iad_data is not None:	2✔
194
195	tmpfile = 'thisisprettyhackysorrylmao'	2✔
196	np.array(iad_data).tofile(tmpfile)	2✔
197
198	hip_num = str(hf.attrs['hip_num'])	2✔
199	alphadec0_epoch = float(hf.attrs['alphadec0_epoch'])	2✔
200	renormalize_errors = bool(hf.attrs['renormalize_errors'])	2✔
201
202	hipparcos_IAD = orbitize.hipparcos.HipparcosLogProb(	2✔
203	tmpfile, hip_num, alphadec0_epoch, renormalize_errors
204	)
205
206	os.system('rm {}'.format(tmpfile))	2✔
207	try:	2✔
208	gaia_num = int(hf.attrs['gaia_num'])	2✔
209	dr = str(hf.attrs['dr'])	2✔
210	gaia = orbitize.gaia.GaiaLogProb(gaia_num, hipparcos_IAD, dr)	2✔
211	except KeyError:	2✔
212	gaia = None	2✔
213	else:
214	hipparcos_IAD = None	2✔
215	gaia = None	2✔
216
217	try:	2✔
218	fitting_basis = np.str(hf.attrs['fitting_basis'])	2✔
219	except KeyError:	2✔
220	# if key does not exist, then it was fit in the standard basis
221	fitting_basis = 'Standard'	2✔
222
223	self.system = orbitize.system.System(	2✔
224	num_secondary_bodies, data_table, stellar_mass,
225	plx, mass_err, plx_err, restrict_angle_ranges,
226	tau_ref_epoch, fit_secondary_mass,
227	hipparcos_IAD, gaia, fitting_basis, use_rebound
228	)
229
230	self.tau_ref_epoch = self.system.tau_ref_epoch	2✔
231	self.labels = self.system.labels	2✔
232	self.data = self.system.data_table	2✔
233	self.num_secondary_bodies = self.system.num_secondary_bodies	2✔
234	self.fitting_basis = self.system.fitting_basis	2✔
235	self.basis = self.system.basis	2✔
236	self.param_idx = self.system.param_idx	2✔
237
238	try:	2✔
239	curr_pos = np.array(hf.get('curr_pos'))	2✔
240	except KeyError:	×
241	curr_pos = None	×
242
243	hf.close() # Closes file object	2✔
244
245	# doesn't matter if append or not. Overwrite curr_pos if not None
246	if curr_pos is not None:	2✔
247	self.curr_pos = curr_pos	2✔
248
249	# Adds loaded data to object as per append keyword
250	if append:	2✔
251	# if no sampler_name set, use the input file's value
252	if self.sampler_name is None:	2✔
253	self.sampler_name = sampler_name	×
254	# otherwise only proceed if the sampler_names match
255	elif self.sampler_name != sampler_name:	2✔
256	raise Exception(	×
257	'Unable to append file {} to Results object. sampler_name of object and file do not match'.format(filename))
258	# if no version_number set, use the input file's value
259	if self.version_number is None:	2✔
260	self.version_number = version_number	2✔
261	# otherwise only proceed if the version_numbers match
262	elif self.version_number != version_number:	2✔
263	raise Exception(	×
264	'Unable to append file {} to Results object. version_number of object and file do not match'.format(filename))
265
266	# Now append post and lnlike
267	self.add_samples(post, lnlike)#, self.labels)	2✔
268	else:
269
270	# Only proceed if object is completely empty
271	if self.sampler_name is None and self.post is None and self.lnlike is None and self.version_number is None:# and self.tau_ref_epoch is None :	2✔
272	self.sampler_name = sampler_name	2✔
273	self.version_number = version_number	2✔
274	self.add_samples(post, lnlike)#, self.labels)	2✔
275
276	else:
277	raise Exception(	×
278	'Unable to load file {} to Results object. append is set to False but object is not empty'.format(filename))
279
280	def print_results(self):	2✔
281	"""
282	Prints median and 68% credible intervals alongside fitting labels
283	"""
284
285	print('\nparam: med [68% CI]')	2✔
286	print('-------------------\n')	2✔
287	for i, l in enumerate(self.system.labels):	2✔
288	print(	2✔
289	'{}: {:.3f} [{:.3f} - {:.3f}]'.format(
290	l,
291	np.median(self.post[:,i]),
292	np.quantile(self.post[:,i], 0.16),
293	np.quantile(self.post[:,i], 0.84)
294	)
295	)
296	print('-------------------\n')	2✔
297
298	def plot_corner(self, param_list=None, **corner_kwargs):	2✔
299	"""
300	Wrapper for orbitize.plot.plot_corner
301	"""
302	return orbitize.plot.plot_corner(self, param_list, **corner_kwargs)	2✔
303
304	def plot_orbits(self, object_to_plot=1, start_mjd=51544.,	2✔
305	num_orbits_to_plot=100, num_epochs_to_plot=100,
306	square_plot=True, show_colorbar=True, cmap=orbitize.plot.cmap,
307	sep_pa_color='lightgrey', sep_pa_end_year=2025.0,
308	cbar_param='Epoch [year]', mod180=False, rv_time_series=False,
309	plot_astrometry=True,
310	plot_astrometry_insts=False, fig=None
311	):
312	"""
313	Wrapper for orbitize.plot.plot_orbits
314	"""
315
316	return orbitize.plot.plot_orbits(	2✔
317	self, object_to_plot=object_to_plot, start_mjd=start_mjd,
318	num_orbits_to_plot=num_orbits_to_plot,
319	num_epochs_to_plot=num_epochs_to_plot,
320	square_plot=square_plot, show_colorbar=show_colorbar, cmap=cmap,
321	sep_pa_color=sep_pa_color, sep_pa_end_year=sep_pa_end_year,
322	cbar_param=cbar_param, mod180=mod180, rv_time_series=rv_time_series,
323	plot_astrometry=plot_astrometry,
324	plot_astrometry_insts=plot_astrometry_insts, fig=fig
325	)
326
327
328

sblunt / orbitize / 1635

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