def test_unpickle():
cmd = load.unpickle(cmd_path)
assert cmd is not None, 'Unpickle returned None'
def test_plot_pickle(self):
cmd = unpickle(cmd_path)
myplot = cmd.plot(log_age=7)
myplot.set_xlabel('bla')
del myplot
del cmd
import hoki.age_utils as au
import hoki.load as load
import pkg_resources
import numpy as np
import pandas as pd
import pytest
from hoki.utils.exceptions import HokiFatalError, HokiUserWarning, HokiFormatError
# Loading Data
data_path = pkg_resources.resource_filename('hoki', 'data')
hr_file = data_path + '/hrs-sin-imf_chab100.zem4.dat'
cmd_file = data_path + '/cmd_bv_z002_bin_imf135_300'
myhrd = load.model_output(hr_file, hr_type='TL')
mycmd = load.unpickle(cmd_file)
# Creating Test Inputs
fake_hrd_input = pd.DataFrame.from_dict({
'name': ['star1', 'star2', 'star3'],
'logT': np.array([4.58, 4.48, 4.14]),
'logL': np.array([4.83, 5.07, 5.40])
})
bad_hrd_input = pd.DataFrame.from_dict({
'logT': np.array(['bla']),
'logL': np.array([4.83])
})
no_name_input = pd.DataFrame.from_dict({
'logT': np.array([4.58, 4.48, 4.14]),
'logL': np.array([4.83, 5.07, 5.40])
def __init__(self, obs_df, model):
"""
Initialisation of the AgeWizard object
Parameters
----------
obs_df: pandas.DataFrame
Observational data. MUST contain a logT and logL column (for HRD comparison) or a col and mag column
(for CMD comparison)
model: str or hoki.hrdiagrams.HRDiagrams() hoki.cmd.CMD()
Location of the modeled HRD or CMD. This can be an already instanciated HRDiagram or CMD() object, or a
path to an HR Diagram file or a pickled CMD.
"""
# Making sure the osbervational properties are given in a format we can use.
if not isinstance(obs_df, pd.DataFrame):
raise HokiFormatError(
"Observations should be stored in a Data Frame")
if 'name' not in obs_df.columns:
warnings.warn(
"We expect the name of sources to be given in the 'name' column. "
"If I can't find names I'll make my own ;)", HokiFormatWarning)
# Checking what format they giving for the model:
if isinstance(model, hoki.hrdiagrams.HRDiagram):
self.model = model
elif isinstance(model, hoki.cmd.CMD):
self.model = model
elif isinstance(model, str) and 'hrs' in model:
self.model = load.model_output(model, hr_type='TL')
elif isinstance(model, str):
try:
self.model = load.unpickle(path=model)
except AssertionError:
print('-----------------')
print(
'HOKI DEBUGGER:\nThe model param should be a path to \na BPASS HRDiagram output file or pickled CMD,'
'or\na hoki.hrdiagrams.HRDiagram or a hoki.cmd.CMD')
print('-----------------')
raise HokiFatalError('model is ' + str(type(model)))
else:
print('-----------------')
print(
'HOKI DEBUGGER:\nThe model param should be a path to \na BPASS HRDiagram output file or pickled CMD,'
'or\na hoki.hrdiagrams.HRDiagram or a hoki.cmd.CMD')
print('-----------------')
raise HokiFatalError('model is ' + str(type(model)))
self.obs_df = obs_df
self.coordinates = find_coordinates(self.obs_df, self.model)
# This line is obsolete but might need revival if we ever want to add the not normalised distributions again
# self._distributions = calculate_distributions_normalised(self.obs_df, self.model)
self.pdfs = calculate_individual_pdfs(self.obs_df,
self.model).fillna(0)
self.sources = self.pdfs.columns.to_list()
self.sample_pdf = None
self._most_likely_age = None
def __init__(self, obs_df, model, nsamples=100):
"""
Initialisation of the AgeWizard object
Parameters
----------
obs_df: pandas.DataFrame
Observational data. MUST contain a logT and logL column (for HRD comparison) or a col and mag column
(for CMD comparison)
model: str or hoki.hrdiagrams.HRDiagrams() hoki.cmd.CMD()
Location of the modeled HRD or CMD. This can be an already instanciated HRDiagram or CMD() object, or a
path to an HR Diagram file or a pickled CMD.
nsamples: int, optional
Number of times each data point should be sampled from its error distribution. Default is 100.
This only matters if you are taking errors into account.
"""
print(f"{Dialogue.info()} AgeWizard Starting")
print(f"{Dialogue.running()} Initial Checks")
# Making sure the osbervational properties are given in a format we can use.
if not isinstance(obs_df, pd.DataFrame):
raise HokiFormatError(
"Observations should be stored in a Data Frame")
if 'name' not in obs_df.columns:
warnings.warn(
"We expect the name of sources to be given in the 'name' column. "
"If I can't find names I'll make my own ;)", HokiFormatWarning)
# Checking what format they giving for the model:
if isinstance(model, hoki.hrdiagrams.HRDiagram):
self.model = model
elif isinstance(model, hoki.cmd.CMD):
self.model = model
elif isinstance(model, str) and 'hrs' in model:
self.model = load.model_output(model, hr_type='TL')
elif isinstance(model, str):
try:
self.model = load.unpickle(path=model)
except AssertionError:
print(f'{Dialogue.ORANGE}-----------------{Dialogue.ENDC}')
print(
f'{Dialogue.debugger()}\nThe model param should be a path to \na BPASS HRDiagram output file or pickled CMD,'
'or\na hoki.hrdiagrams.HRDiagram or a hoki.cmd.CMD')
print(f'{Dialogue.ORANGE}-----------------{Dialogue.ENDC}')
raise HokiFatalError('model is ' + str(type(model)))
else:
print(f'{Dialogue.ORANGE}-----------------{Dialogue.ENDC}')
print(
f'{Dialogue.debugger()}\nThe model param should be a path to \na BPASS HRDiagram output file or pickled CMD,'
'or\na hoki.hrdiagrams.HRDiagram or a hoki.cmd.CMD')
print(f'{Dialogue.ORANGE}-----------------{Dialogue.ENDC}')
raise HokiFatalError('model is ' + str(type(model)))
print(f"{Dialogue.complete()} Initial Checks")
self.obs_df = obs_df.copy()
# not needed?
# self.coordinates = find_coordinates(self.obs_df, self.model)
# This line is obsolete but might need revival if we ever want to add the not normalised distributions again
# self._distributions = calculate_distributions_normalised(self.obs_df, self.model)
self.pdfs = au.calculate_individual_pdfs(self.obs_df,
self.model,
nsamples=nsamples).fillna(0)
self.sources = self.pdfs.columns.to_list()
self.sample_pdf = None
self._most_likely_age = None