def url_checker(url_str):
"""
Checks if the URL is valid or not.
Parameters
-----------
url_str : `str`
URL of the website to evaluate.
Raises
----------
LSSUtils_Error : `Exception`
Program exception if input parameters are accepted
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
if not (isinstance(url_str, str)):
msg = '{0} `url_str` ({1}) is not a STRING!'.format(
file_msg, type(url_str))
raise LSSUtils_Error(msg)
##
## Checking Website
request_url = requests.get(url_str)
if (request_url.status_code != 200):
msg = '{0} `url_str` ({1}) does not exist!'.format(file_msg, url_str)
raise LSSUtils_Error(msg)
def reversed_arrays(x, y):
"""
Determines if arrays increase or decrease monotonically.
Parameters
-----------
x : `numpy.ndarray`
Array containing the 1st set of values
y : `numpy.ndarray`
Array containing the 2nd set of values.
Return
-----------
mono_opt : `bool`
If True, `x` increases monotonically with increasing `y`.
If False, `x` decreases monotonically with increasing `y`.
Raises
----------
LSSUtils_Error : Exception
Program exception if input parameters are accepted
"""
file_msg = fd.Program_Msg(__file__)
# Testing input arguments
# x-array
valid_types = (list, np.ndarray)
if not (isinstance(x, valid_types)):
msg = '{0} `x` is not a valid type!'.format(file_msg, type(x))
raise LSSUtils_Error(msg)
# y-array
valid_types = (list, np.ndarray)
if not (isinstance(y, valid_types)):
msg = '{0} `y` is not a valid type!'.format(file_msg, type(y))
raise LSSUtils_Error(msg)
# x- and y-array shapes
x = np.asarray(x)
y = np.asarray(y)
#
# Checking if arrays increase or decrease monotonically
x_diff = np.diff(x).sum()
y_diff = np.diff(y).sum()
# Monotonically increasing or decreasing
if (x_diff > 0) and (y_diff > 0):
mono_opt = True
else:
mono_opt = False
return mono_opt
def wrapped_func(*args, **kwargs):
"""
This function changes the elements in `kwargs`
"""
# Extracting main dictionary
try:
p_dict = kwargs['p_dict']
except KeyError:
try:
p_dict = args[0]
except IndexError:
msg = '{0} Could not read the input dictionary `p_dict`.'
msg += 'Please check your function!'
msg = msg.format(self.file_msg)
raise LSSUtils_Error(msg)
# Checking if `p_dict` is a dictionary
if not (isinstance(p_dict, dict)):
msg = '{0} `p_dict` must be a dictionary!'.format(
self.file_msg, type(p_dict))
raise TypeError(msg)
## Parsing elements
# `argvalues`
argvalues = np.asarray(self.argvalues)
##
## Looping over elements
for argval in argvalues:
p_dict[self.argname] = argval
func(p_dict=p_dict)
def IDL_read_file(idl_file):
"""
Reads an IDL file and converts it to a Python dictionary
Parameters
----------
idl_file : string
Path to the filename being used
Returns
----------
idl_dict : python dictionary
Dictionary with the data from `idl_file`
"""
# Checking that file exists
fd.File_Exists(idl_file)
# Converting to dictionary
try:
idl_dict = readsav(idl_file, python_dict=True)
except:
msg = '{0} `idl_file` {0} is not an IDL file'.format(
fd.Program_Msg(__file__), idl_file)
raise LSSUtils_Error(msg)
return idl_dict
def get_immediate_subdirectories(pathdir, sort=True):
"""
Immediate subdirectories to a given directory path
Parameters
----------
pathdir : str
Path to the desired directory
Returns
----------
subdir_arr : array_like or array of strings
Array of paths of subdirectories of `pathdir`
sort : `bool`, optional (default = True)
If this is set to True, the output list is sorted by name
"""
if os.path.exists(pathdir):
path_obj = Path(pathdir)
# List of subdirectories
subdir_arr = np.array([
x for x in os.listdir(pathdir)
if (os.path.isdir(str(path_obj.joinpath(x))) and ('__' not in x))
])
if sort:
subdir_arr = np.sort(subdir_arr)
else:
msg = '{0} `pathdir` {1} not found!'.format(Program_Msg(__file__),
pathdir)
raise LSSUtils_Error(msg)
return subdir_arr
def read_hdf5_file_to_pandas_DF(hdf5_file, key=None):
"""
Reads content of HDF5 file and converts it to a Pandas DataFrame
Parameters
----------
hdf5_file : str
Path to the HDF5 file. This is the file that will be converted
to a pandas DataFrame.
key : str or NoneType, optional
Key or path in `hdf5_file` for the pandas DataFrame and the normal
HDF5 file.
Returns
----------
df : `pandas.DataFrame`
DataFrame from `hdf5_file` under the `key` directory.
"""
file_msg = fd.Program_Msg(__file__)
fd.File_Exists(hdf5_file)
# Reading in Pandas DataFrame
try:
df = pd.read_hdf(hdf5_file, key=key)
except:
msg = '{0} Could not read `hdf5_file` ({1})! Please check if it exists'
msg = msg.format(file_msg, hdf5_file)
raise LSSUtils_Error(file_msg)
return df
def pandas_df_to_hdf5_file(df, hdf5_file, key=None, mode='w', complevel=8):
"""
Saves a `pandas.DataFrame` into a `pandas` HDF5 FILE.
Parameters
----------
df : `pandas.DataFrame`
DataFrame to be converted and saved into a HDF5 file.
hdf5_file : str
Path to the output HDF5 file
key : str or NoneType, optional
Key or path, under which `df` will be saved in the `hdf5_file`.
mode : {'w','a'}, optional
Mode to handle `hdf5_file`. This value is set to `w` by default,
which stand for `write`.
complevel : int, optional
Level of compression for `hdf5_file`.
The range of `complevel` is rane(0-9).
This is set to a default of 8.
"""
file_msg = fd.Program_Msg(__file__)
# Saving DataFrame to `hdf5_file`
try:
df.to_hdf(hdf5_file, key, mode=mode, complevel=complevel)
msg = '{0} HDF5 file created: {1}'.format(file_msg, hdf5_file)
print(msg)
except:
msg = '{0} Could not create HDF5 file'.format(file_msg)
raise LSSUtils_Error(msg)
def cookiecutter_paths(path='./'):
"""
Paths to main folders in the `Data Science` cookiecutter template.
This structure was taken from :
- https://drivendata.github.io/cookiecutter-data-science/
Parameters
----------
path : str, optional
Path to the file within the `.git` repository
Return
----------
param_dict : python dictionary
Dictionary with info of the project that uses the Data Science
cookiecutter template.
Raises
----------
LSSUtils_Error : exception
If `path` is not within a .git directory, it raises an error.
"""
# Base Path
base_dir = git_root_dir(path) + '/'
# Checking that directory exists
if os.path.exists(base_dir):
# Plot Directory
plot_dir = os.path.join(base_dir, 'reports', 'figures/')
# Source directory
src_dir = os.path.join(base_dir, 'src', 'data/')
# Data path
data_dir = os.path.join(base_dir, 'data/')
# External path
ext_dir = os.path.join(data_dir, 'external/')
# Processed path
proc_dir = os.path.join(data_dir, 'processed/')
# External path
int_dir = os.path.join(data_dir, 'interim/')
# External path
raw_dir = os.path.join(data_dir, 'raw/')
# Creating files
for dir_ii in [plot_dir, src_dir, data_dir]:
fd.Path_Folder(dir_ii)
# Saving to dictionary
param_dict = {}
param_dict['base_dir'] = base_dir
param_dict['plot_dir'] = plot_dir
param_dict['src_dir'] = src_dir
param_dict['data_dir'] = data_dir
param_dict['ext_dir'] = ext_dir
param_dict['proc_dir'] = proc_dir
param_dict['int_dir'] = int_dir
param_dict['raw_dir'] = raw_dir
else:
msg = '{0} `base_dir` ({1}) is not a Git directory! Exiting'.format(
fd.Program_Msg(__file__), base_dir)
raise LSSUtils_Error(msg)
return param_dict
def File_Exists(filename):
"""
Detrmines if file exists or not
Parameters
-----------
filename : str
Absolute path to the file
Raises
-----------
LSSUtils_Error : Exception
"""
if os.path.exists(os.path.abspath(filename)):
try:
assert (os.path.isfile(os.path.abspath(filename)))
except:
msg = '{0} `filename` {1} not found!'.format(
Program_Msg(__file__), filename)
raise LSSUtils_Error(msg)
else:
msg = '{0} `filename` {1} not found!'.format(Program_Msg(__file__),
filename)
raise LSSUtils_Error(msg)
def luminosity_to_absolute_mag(lum,
filter_opt,
system='SDSS_Blanton_2003_z0.1'):
"""
Calculates the absolute magnitude of object through the `filter_opt`
filter.
Parameters
-----------
lum : float, int, array_like
Luminosity of 1 or more objects. In units of `solar luminosities`.
filter_opt : {'U', 'B', 'V', 'R', 'I', 'J', 'H', 'K'} str
Magnitude filter to use.
system : {'Binney_and_Merrifield_1998', 'SDSS_Blanton_2003_z0.1'} str
Kind of filter to use.
Options:
- 'Binney_and_Merrifield_1998' : See Binney and Merrifield, 1998
- 'SDSS_Blanton_2003_z0.1' : See Blanton et al. (2003) Eqn. 14.
Returns
-----------
abs_mag : float, int, or array_like
Absolute magnitude of one or multiple objects. Same type as `lum`
Raises
----------
LSSUtils_Error : Exception
Program exception if input parameters are accepted
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
valid_types = (float, int, list, np.ndarray)
if not (isinstance(lum, valid_types)):
msg = '{0} `lum` ({1}) is not a valid type!'.format(file_msg, lum)
raise LSSUtils_Error(msg)
## Obtaining Sun's absolute magnitude
abs_mag_sun = get_sun_mag(filter_opt, system=system)
## Absolute magnitude calculation
# In units of solar luminosities
lum_sun = 1.0
# Absolute magnitude of objects
abs_mag = abs_mag_sun - 2.5 * np.log10(lum / lum_sun)
return abs_mag
def absolute_magnitude_to_luminosity(abs_mag,
filter_opt,
system='SDSS_Blanton_2003_z0.1'):
"""
Calculates the luminosity of the object through `filter_opt` filter.
Parameters
-----------
abs_mag : float, int, or array_like
Absolute magnitude of one or multiple objects.
filter_opt : {'U', 'B', 'V', 'R', 'I', 'J', 'H', 'K'} str
Magnitude filter to use.
system : {'Binney_and_Merrifield_1998', 'SDSS_Blanton_2003_z0.1'} str
Kind of filter to use.
Options:
- 'Binney_and_Merrifield_1998' : See Binney and Merrifield, 1998
- 'SDSS_Blanton_2003_z0.1' : See Blanton et al. (2003) Eqn. 14.
Returns
-----------
log_L : float or array_like
Logarithmic value of the luminosity in the `filter_opt` band.
Raises
----------
LSSUtils_Error : Exception
Program exception if input parameters are accepted
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
valid_types = (float, int, list, np.ndarray)
if not (isinstance(abs_mag, valid_types)):
msg = '{0} `abs_mag` ({1}) is not a valid type!'.format(
file_msg, abs_mag)
raise LSSUtils_Error(msg)
## Obtaining Sun's absolute magnitude
abs_mag_sun = get_sun_mag(filter_opt, system=system)
## Luminosity calculations
log_L = (abs_mag_sun - abs_mag) * 0.4
return log_L
def Bins_array_create(arr, base=10, return_tuple=False):
"""
Generates an evenly-spaced array between the minimum and maximum value
of a given array,
Parameters
----------
arr : array_like
Array of of numbers or floats
base : `int` or `float`, optional
Interval used to create the evenly-spaced array of elements
return_tuple : `bool`, optional
If `True`, the function returns a set of tuples for each bin. This
variable is set to `False` by default.
Returns
----------
bins_arr : `numpy.ndarray`
Array of elements separated in intervals of `base`
"""
file_msg = fd.Program_Msg(__file__)
# Transforming input data
base = float(base)
arr = np.asarray(arr)
# Checking array dimensions
if arr.ndim != 1:
msg = '{0} The input array is not of dimension 1, but of `{1}`'.format(
file_msg, arr.ndim)
raise LSSUtils_Error(msg)
# Creating evenly-spaced array
arr_min = myfloor(arr.min(), base=base)
arr_max = myceil(arr.max(), base=base)
bins_arr = np.arange(arr_min, arr_max + 0.5 * base, base)
# Creating tuple if necessary
if return_tuple:
bins_arr_mod = (np.array([[bins_arr[ii], bins_arr[ii + 1]]
for ii in range(len(bins_arr) - 1)]))
return_obj = bins_arr_mod
else:
return_obj = bins_arr
return return_obj
def Index(pathdir, datatype, sort=True, basename=False):
"""
Indexes the files in a directory `pathdir` with a specific data type
`datatype`.
Parameters
----------
pathdir : string
Path to the directory being analyzed
datatype : string
Type of documents to look for.
sort : `bool`, optional (default = True)
If this is set to True, the output list is sorted by name
basename : `bool`, optional
If this is set to True, the output list will contain only the
basename of the files in `pathdir`
Returns
----------
file_arr : np.ndarray
List of (sorted) files in `pathdir` with datatype `.datatype`
"""
# Checking that directory exists
if os.path.exists(pathdir):
# List of files
path_obj = Path(os.path.abspath(pathdir))
file_arr = list(path_obj.rglob('*{0}'.format(datatype)))
file_arr = np.array([x.as_posix() for x in file_arr])
# Sorting
if sort:
file_arr = np.sort(file_arr)
# Basenames
if basename:
file_arr = np.array([os.path.basename(x) for x in file_arr])
else:
msg = '{0} `pathdir` {1} not found!'.format(Program_Msg(__file__),
pathdir)
raise LSSUtils_Error(msg)
return file_arr
def reshape_arr_1d(arr):
"""
Transforms the array intoa 1-dimensional array, if necessary.
Parameters
-----------
arr : `numpy.ndarray` or array-like
Array to be converted into 1-dimensional array.
Returns
-----------
arr_new : `numpy.ndarray` or array-like
Converted array into 1-dimensional array if needed.
"""
file_msg = fd.Program_Msg(__file__)
# Checking input parameters
arr_valid_types = (list, np.ndarray)
# `arr`
if not (isinstance(arr, arr_valid_types)):
msg = '{0} `arr` ({1}) is not a valid input type!'.format(file_msg,
type(arr))
raise TypeError(msg)
# Dimensions
if (isinstance(arr, arr_valid_types)):
if not (np.asarray(arr).ndim in [1, 2]):
msg = '{0} The shape of `arr` ({1}) can only have 1 or 2 '
msg += 'dimensions'
msg = msg.format(file_msg, np.asarray(arr).ndim)
raise LSSUtils_Error(msg)
# Converting to Numpy array
arr = np.asarray(arr)
# Trying to reshape it
if (arr.ndim == 2):
if (arr.shape[1] == 1):
arr = arr.reshape(len(arr),)
return arr
def absolute_to_apparent_magnitude(abs_mag, lum_dist, unit='mpc'):
"""
Calculates the apparent magnitude using the luminosity and absolute
magnitude.
Parameters
-----------
abs_mag : float, int, or array_like
Array of absolute magnitude(s)
lum_dist : array_like
Array of luminosity distnace to object. In units of `Mpc`.
unit : {'pc', 'kpc', 'mpc'} str, optional
Unit to use for `lum_dist`. This variable is set to `mpc` by
default. When `pc`, the units are in parsecs, while `mpc` is for
distances in mega-parsecs, etc.
Returns
-----------
app_mag : array_like, or float
Array of apparent magnitude(s). `app_mag` is a float if
`abs_mag` is a float or int.
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
valid_types = (float, np.ndarray, list, int)
# Type for `abs_mag`
if not (isinstance(abs_mag, valid_types)):
msg = '{0} `abs_mag` ({1}) is not a valid type!'.format(
file_msg, type(abs_mag))
raise LSSUtils_Error(msg)
# Type for `unit`
unit_valid_arr = ['mpc', 'pc']
if not (unit in unit_valid_arr):
msg = '{0} `unit` ({1}) is not a valid input!'.format(file_msg, unit)
raise LSSUtils_Error(msg)
## Converting to array-type
# `abs_mag` object
if (isinstance(abs_mag, float) or isinstance(abs_mag, int)):
abs_mag = float(abs_mag)
if (isinstance(abs_mag, list) or isinstance(abs_mag, np.ndarray)):
abs_mag = np.asarray(abs_mag)
# `lum_dist` object
if (isinstance(lum_dist, float) or isinstance(lum_dist, int)):
lum_dist = float(lum_dist)
if (isinstance(lum_dist, list) or isinstance(lum_dist, np.ndarray)):
lum_dist = np.asarray(lum_dist)
# Units - Conveting to Mpc
# This follows the formula:
# app_mag - abs_mag = 5 * (np.log10(lum_dist) + a - 1)
# Where a = 0 when [d] = parsecs
# Where a = 3 when [d] = kiloparsecs
# Where a = 6 when [d] = megaparsecs
if unit == 'pc':
a = 0
elif unit == 'kpc':
a = 3
elif unit == 'mpc':
a = 6
##
## Calcualtions
app_mag = abs_mag + 5. * (np.log10(lum_dist) - 1 + a)
return app_mag
def url_files_download(url,
ext,
outdir,
check_exist=False,
create_dir=False,
remove_files=False,
bar_opt='tqdm'):
"""
Downloads the files from a URL to a local directory. The files that
match a specific file extension, `ext`.
Parameters
-----------
url : `str`
String of the URL
ext : `str`
File extension of the files in the URL.
outdir : `str`
Path to the output directory. This is the directory, to which
the files with extensions `ext` will be saved.
check_exist : `bool`, optional
If `True`, it checks for whether or not the file exists.
This variable is set to `False` by default.
create_dir : `bool`, optional
If `True`, it creates the directory if it does not exist.
This variable is set to `False` by default.
remove_files : `bool`, optional
If `True`, local files that are present that match the files at
the URL will be replaced by the new versions. This variable is
set to ``False`` by default.
bar_opt : {'tqdm', 'native'}
Option for which type of progress bar to use when downloading files.
This variable is set to `tqdm` by default.
Options:
- 'tqdm' : Uses a tqdm-based progress bar
- 'native': Used the wget-based native progress bar.
"""
file_msg = fd.Program_Msg(__file__)
## Checking for file type
# 'URL'
if not isinstance(url, str):
msg = '{0} `url` ({1}) is not a valid type. It must be a STRING!'
msg = msg.format(file_msg, type(url))
raise TypeError(msg)
# File extension
if not isinstance(ext, str):
msg = '{0} `ext` ({1}) is not a valid type. It must be a STRING!'
msg = msg.format(file_msg, type(ext))
raise TypeError(msg)
# Output directory
if not isinstance(outdir, str):
msg = '{0} `outdir` ({1}) is not a valid type. It must be a STRING!'
msg = msg.format(file_msg, type(outdir))
raise TypeError(msg)
# `check_exist`
if not (isinstance(check_exist, bool)):
msg = '`check_exist` ({0}) must be of `boolean` type!'.format(
type(check_exist))
raise TypeError(msg)
# `create_dir`
if not (isinstance(create_dir, bool)):
msg = '`create_dir` ({0}) must be of `boolean` type!'.format(
type(create_dir))
raise TypeError(msg)
# `bar` - Type
if not (isinstance(bar_opt, str)):
msg = '`bar_opt` ({0}) must be of `boolean` type!'.format(
type(bar_opt))
raise TypeError(msg)
# Progress bar - Value
if not (bar_opt in ['tqdm', 'native']):
msg = '{0} `bar_opt` ({1}) is not a valid option! Exiting'
msg = msg.format(file_msg, bar_opt)
raise LSSUtils_Error(msg)
##
## List of files in the URL
files_arr = url_file_list(url, ext)
# Creating directory
if create_dir:
cfutils.Path_Folder(outdir)
# Check for its existence
if check_exist:
if not (os.path.exists(outdir)):
msg = '`outdir` ({0}) was not found!'.format(outdir)
raise FileNotFoundError(msg)
##
## Downloading files to output directory
if len(files_arr) > 0:
if (bar_opt == 'tqdm'):
tqdm_desc = 'Downloading files: '
for file_ii in tqdm(files_arr, desc=tqdm_desc):
# Local file
file_ii_local = os.path.join(outdir, os.path.basename(file_ii))
# Checking if local file exists
if os.path.exists(file_ii_local):
if remove_files:
os.remove(file_ii_local)
wget_opt = True
else:
wget_opt = False
else:
wget_opt = True
##
## Only downloading if necessary
if wget_opt:
wget.download(file_ii, out=outdir, bar=None)
elif (bar_opt == 'native'):
for file_ii in files_arr:
# Local file
file_ii_local = os.path.join(outdir, os.path.basename(file_ii))
# Checking if local file exists
if os.path.exists(file_ii_local):
if remove_files:
os.remove(file_ii_local)
wget_opt = True
else:
wget_opt = False
else:
wget_opt = True
##
## Only downloading if necessary
if wget_opt:
wget.download(file_ii, out=outdir)
else:
msg = '{0} Number of files is ZERO!'.format(file_msg)
print(msg)
def abundance_matching_f(dict1,
dict2,
volume1=1.,
volume2=1.,
reverse=True,
dens1_opt=False):
"""
Abundance matching based on 2 quantities.
It assigns values from `dict2` to elements in `dict1`
Parameters
-----------
dict1 : python dictionary or `numpy.ndarray`
Dictionary or array of 1st property.
Keys :
- `var` : 1st variable to be analysed
- `dens` : Density array corresponding to `var` elements.
Only if `dens` == True.
dict2 : python dictionary
dictionary or array of the 2nd property.
Keys :
- `var` : 2nd variable to be analyzed
- `dens` : Density array corresponding to `var` elements.
Given if `dens` == True.
volume1 : float
Corresponding volume to `dict1`.
reverse : `bool`, optional
Determines the relation between `var1` and `var2`.
dens1_opt : `bool`, optional
If True, `density` must be calculated.
Options :
- `True` : Density is already provided as key for `dict1`.
- `False` : Density must be calculated.
Returns
-----------
var1_ab : `numpy.ndarray`
Array of elements matching those of `dict1`, after matching with
`dict2`.
"""
file_msg = fd.Program_Msg(__file__)
# Check types of input paramenters
valid_types = (list, dict, np.ndarray)
# `dict1`
if not (isinstance(dict1, valid_types)):
msg = '{0} `dict1` ({1}) is not a valid type!'.format(
file_msg, type(dict1))
# `dict2`
if not (isinstance(dict2, dict)):
msg = '{0} `dict2` must be a dictionary. Its type is `{1}`'.format(
file_msg, type(dict2))
raise LSSUtils_Error(msg)
# 2nd property
var2 = np.asarray(dict2['var'])
dens2 = np.asarray(dict2['dens'])
#
# `dens1_opt`
if dens1_opt:
# 1st Property
var1 = np.asarray(dict1['var'])
dens_1 = np.asarray(dict1['dens'])
else:
if (isinstance(dict1, dict)):
var1 = dict1['var']
elif (isinstance(dict1, (list, np.ndarray))):
var1 = dict1.copy()
#
# Determining relation between `var1` and `var2`
mono_opt_1 = reversed_arrays(var1, var2)
# Monotonically increasing
if mono_opt_1:
counts_1 = np.array([np.where(var1 > x)[0].size for x in var1]) + 1
else:
counts_1 = np.array([np.where(var1 < x)[0].size for x in var1]) + 1
#
# Determining density of 1st property
dens_1 = counts_1.astype(float) / volume1
#
# Interpolation for 2nd property
var2_interp = interp1d(dens2, var2, bounds_error=True, assume_sorted=False)
# Assigning values to property 1
var1_ab = np.asarray([var2_interp(xx) for xx in dens_1])
return var1_ab
def data_preprocessing(feat_arr, pre_opt='min_max', reshape=False):
"""
Preprocess the data used, in order to clean and make the data more
suitable for the machine learning algorithms
Parameters
-----------
feat_arr : `numpy.ndarray`, `list`, `pandas.DataFrame`
Array of feature values. This array is used for training a
ML algorithm.
pre_opt : {'min_max', 'standard', 'normalize', 'no'} `str`, optional
Type of preprocessing to do on `feat_arr`.
Options:
- 'min_max' : Turns `feat_arr` to values between (0,1)
- 'standard' : Uses `~sklearn.preprocessing.StandardScaler` method
- 'normalize' : Uses the `~sklearn.preprocessing.Normalizer` method
- 'no' : No preprocessing on `feat_arr`
reshape : `bool`, optional
If True, it reshapes `feat_arr` into a 1d array if its shapes is
equal to (ncols, 1), where `ncols` is the number of columns.
This variable is set to `False` by default.
Returns
-----------
feat_arr_scaled : `numpy.ndarray`
Rescaled version of `feat_arr` based on the choice of `pre_opt`.
Notes
-----------
For more information on how to pre-process your data, see
`http://scikit-learn.org/stable/modules/preprocessing.html`_.
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
# `feat_arr`
feat_arr_type_valid = (list, np.ndarray, pd.DataFrame)
if not (isinstance(feat_arr, feat_arr_type_valid)):
msg = '{0} `feat_arr` ({1}) is not a valid input type'.format(
file_msg, type(feat_arr))
raise LSSUtils_Error(msg)
# `pre_opt`
pre_opt_valid = ['min_max', 'standard', 'normalize', 'no']
if not (pre_opt in pre_opt_valid):
msg = '{0} `pre_opt` ({1}) is not a valid input'.format(
file_msg, pre_opt)
raise LSSUtils_Error(msg)
##
## Reshaping `feat_arr`
if reshape:
feat_arr = gu.reshape_arr_1d(feat_arr)
##
## Scaling `feat_arr`
if (pre_opt == 'min_max'):
# Scaler
scaler = skpre.MinMaxScaler(feature_range=(0, 1))
# Rescaling
feat_arr_scaled = scaler.fit_transform(feat_arr)
## Standardize Data
if pre_opt == 'standard':
# Scaler
scaler = skpre.StandardScaler().fit(feat_arr)
# Rescaling
feat_arr_scaled = scaler.transform(feat_arr)
## Normalize Data
if pre_opt == 'normalize':
# Scaler
scaler = skpre.Normalizer().fit(feat_arr)
# Rescaling
feat_arr_scaled = scaler.transform(feat_arr)
## No Preprocessing
if pre_opt == 'no':
feat_arr_scaled = feat_arr
return feat_arr_scaled
def extract_catls(catl_kind='data',
catl_type='mr',
sample_s='19',
datatype='.hdf5',
catl_info='members',
halotype='fof',
clf_method=3,
hod_n=0,
clf_seed=1235,
dv=1.0,
sigma_clf_c=0.1417,
perf_opt=False,
return_len=False,
print_filedir=True):
"""
Extracts a list of synthetic catalogues given input parameters
Parameters
------------
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_type : {'mr', 'mstar'} str, optional
Type of catalogue to use. It shows which abundance matching method
was used for the CLF when assigning halo masses. This variable is
set to 'mr' by default.
Options:
- `mr` : Uses r-band absolute magnitude
- `mstar` : Uses stellar masses
sample_s : {'19', '20', '21'} str, optional
Volume-limited sample to use. This variable is set to '19' by default.
Options:
- '19' : Uses the Mr19 volume-limited sample, i.e. 'Consuelo'
- '20' : Uses the Mr20 volume-limited sample, i.e. 'Esmeralda'
- '21' : Uses the Mr21 volume-limited sample, i.e. 'Carmen'
datatype : {'.hdf5'} str, optional
Data type of the files to be indexed in the folder. This variable
is set to '.hdf5' by default.
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
halotype : {'fof', 'so'} str, optional
Type of the dark matter halo of the simulation used to create the
synthetic catalogues. This variable is set to `fof` by default.
Options:
- 'fof': Friends-of-Friends halos.
- 'so' : Spherical overdensity halos.
clf_method : {1, 2, 3} int, optional
Method for assigning galaxy properties to mock galaxies.
This variable is set to `3` by default.
Options:
- `1` : Independent assigment of (g-r) color, sersic, and log(ssfr)
- `2` : (g-r) decides active/passive designation and draw values
independently.
- `3` : (g-r) decides active/passive designations, and
assigns other galaxy properties for that given galaxy.
hod_n : {0, 1} int, optional
HOD model to use. Only relevant when `catl_kind == mocks`.
clf_seed : int, optional
Seed used for the `CLF` random seed. This variable is set to `1235`
by default.
dv : float, optional
Difference between galaxy and mass velocity profiles
(v_g-v_c)/(v_m-v_c). This value is set to `1.0` by default.
sigma_clf_c : `float`, optional
Value of the scatter in log(L) for central galaxies in the CLF.
This variable is set to ``0.1417`` by default.
perf_opt : `bool`, optional
If True, it chooses to analyze the `perfect` set of synthetic
catalogues. This variable is set to `False` by default.
return_len : `bool`, optional
If True, the function returns the total number of elements in
the folder that match the criteria.
print_filedir : `bool`, optional
If True, the output directory is printed onto the screen.
Returns
------------
catl_arr : `numpy.ndarray`
Array of elements/files matching the `datatype` type in the directory.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
# Checking input parameters
catl_kind_valid = ['data', 'mocks']
catl_type_valid = ['mr', 'mstar']
sample_s_valid = ['19', '20', '21']
catl_info_valid = ['members', 'groups']
halotype_valid = ['fof', 'so']
clf_method_valid = [1, 2, 3]
hod_n_valid = np.arange(0, 20)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `catl_type`
if not (catl_type in catl_type_valid):
msg = '{0} `catl_type` ({1}) is not a valid input!'.format(
file_msg, catl_type)
raise LSSUtils_Error(msg)
# `sample_s`
if not (sample_s in sample_s_valid):
msg = '{0} `sample_s` ({1}) is not a valid input!'.format(
file_msg, sample_s)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(
file_msg, catl_info)
raise LSSUtils_Error(msg)
# `halotype`
if not (halotype in halotype_valid):
msg = '{0} `halotype` ({1}) is not a valid input!'.format(
file_msg, halotype)
raise LSSUtils_Error(msg)
# `clf_method`
if not (clf_method in clf_method_valid):
msg = '{0} `clf_method` ({1}) is not a valid input!'.format(
file_msg, clf_method)
raise LSSUtils_Error(msg)
# `hod_n`
if not (hod_n in hod_n_valid):
msg = '{0} `hod_n` ({1}) is not a valid input!'.format(file_msg, hod_n)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) is not a valid type!'.format(
file_msg, type(perf_opt))
raise LSSUtils_Error(msg)
# `print_filedir`
if not (isinstance(print_filedir, bool)):
msg = '{0} `print_filedir` ({1}) is not a valid type!'.format(
file_msg, type(print_filedir))
raise LSSUtils_Error(msg)
# `dv`
if not (dv > 0):
msg = '{0} `dv` ({1}) must be larger than 0!'.format(file_msg, dv)
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Type
if not (isinstance(sigma_clf_c, float)):
msg = '{0} `sigma_clf_c` ({1}) is not a valid input type!'
msg = msg.format(file_msg, type(sigma_clf_c))
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Value
if not (sigma_clf_c >= 0.):
msg = '{0} `sigma_clf_c` ({1}) must be larger than 0!'
msg = msg.format(file_msg, sigma_clf_c)
raise LSSUtils_Error(msg)
# `return_len`
if not (isinstance(return_len, bool)):
msg = '{0} `return_len` ({1}) is not a valid type!'.format(
file_msg, type(return_len))
raise LSSUtils_Error(msg)
# `datatype`
if not (isinstance(datatype, str)):
msg = '{0} `datatype` ({1}) is not a valid type!'.format(
file_msg, type(datatype))
raise LSSUtils_Error(msg)
#
# Extracting the path of the catalogues
filedir = catl_sdss_dir(catl_kind=catl_kind,
catl_type=catl_type,
sample_s=sample_s,
catl_info=catl_info,
halotype=halotype,
clf_method=clf_method,
hod_n=hod_n,
clf_seed=clf_seed,
dv=dv,
sigma_clf_c=sigma_clf_c,
perf_opt=perf_opt,
print_filedir=print_filedir)
#
# Converting to array
catl_arr = np.sort(fd.Index(filedir, datatype))
# Checking number of elements
if len(catl_arr) == 0:
msg = '{0} `catl_arr` contains 0 entries!'.format(file_msg)
raise LSSUtils_Error(msg)
#
# Returning elements
if return_len:
return catl_arr, len(catl_arr)
else:
return catl_arr
def concatenate_pd_df(directory, filetype='hdf5', foutput=None, outonly=True):
"""
Concatenates pandas DataFrames into a single DataFrame
Parameters
----------
directory : str
Path to the folder containing multiple pandas-HDF5 files
filetype : str, optional
File format of the file in `directory` to be read
This is set to `hdf5` by default.
foutput : str or NoneType
If not `None`, it is the basename of the output file in HDF5 format
outonly : `bool`, optional
If True, it returns the pandas DataFrame.
If False, it only saved the concatenated `pandas.DataFrame`.
Returns
----------
df_conc : `pandas.DataFrame`
DataFrame containing the combined datasets from the files in
`directory`.
Raises
----------
LSSUtils_Error : Exception
If no files are found in `directory`, it raises an error
warning about this.
"""
file_msg = fd.Program_Msg(__file__)
# Checking that `directory` exists
if not os.path.exists(directory):
msg = '{0} `directory` {1} is not a valid path! Exiting!'.format(
file_msg, directory)
raise LSSUtils_Error(msg)
# Concatenating files
files_arr = fd.index(directory, '.' + filetype, sort=True)
print('{0} Found `{1}` files'.format(file_msg, files_arr.size))
if len(files_arr) > 0:
# Initializing array that contains info
df_arr = [[] for x in range(len(files_arr))]
# Looping over HDF5 (pandas) files
for ii, file_ii in enumerate(files_arr):
df_arr[ii] = read_pandas_hdf5(file_ii)
# Concatenating arrays
df_conc = pd.concat(df_arr, ignore_index=True)
# Deciding name of resulting output file
if (foutput is not None) and (type(foutput) == str):
foutput_file = os.path.join(directory,
'{0}.{1}'.format(foutput, filetype))
# Saving resulting DataFrame
pandas_df_to_hdf5_file(df_conc, foutput_file, key='/Main')
# Checking file exists
fd.File_Exists(foutput_file)
print('{0} Output file saved in: {2}'.format(
file_msg, foutput_file))
# If only outputting concatenated DataFrame
if outonly:
return df_conc
else:
msg = '{0} No files in `{1}` with extension `{2}`'.format(
file_msg, directory, filetype)
raise LSSUtils_Error(msg)
def catl_keys_prop(catl_kind, catl_info='members', return_type='list'):
"""
Dictionary keys for the diffeent galaxy and group properties of
catalogues.
Parameters
------------
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
return_type : {'list', 'dict'} str, optional
Type of output to the be returned. This variable is set to `list`
by default.
Options:
- 'list' : Returns the values as part of a list
- 'dict' : Returns the values as part of a python dictionary
Return
------------
catl_objs : python dictionary or array_like
Dictionary/array with the proper keys for the catalogue(s).
Order : 1) `ssfr_key`, 2) `mstar_key`
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
Examples
------------
>>> catl_keys_prop('data')
['logssfr', 'logMstar_JHU']
>>> catl_keys_prop('mocks', catl_info='groups', return_type='list')
['logssfr', 'logMstar']
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
catl_kind_valid = ['data', 'mocks']
catl_info_valid = ['members', 'groups']
return_type_valid = ['list', 'dict']
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(
file_msg, catl_info)
raise LSSUtils_Error(msg)
# `return_type`
if not (return_type in return_type_valid):
msg = '{0} `return_type` ({1}) is not a valid input!'.format(
file_msg, return_type)
raise LSSUtils_Error(msg)
#
# Property keys
#
# Data
if (catl_kind == 'data'):
# Members
if catl_info == 'members':
# SSFR and Stellar mass
logssfr_key, logmstar_key = ['logssfr', 'logMstar_JHU']
# Groups
if catl_info == 'groups':
# SSFR and Stellar mass
logssfr_key, logmstar_key = ['logssfr_tot', 'logMstar_tot']
#
# Mocks
if (catl_kind == 'mocks'):
# Members
if catl_info == 'members':
# SSFR and Stellar mass
logssfr_key, logmstar_key = ['logssfr', 'logMstar']
# Groups
if catl_info == 'groups':
# SSFR and Stellar mass
logssfr_key, logmstar_key = ['logssfr', 'logMstar']
#
# Saving values
if return_type == 'dict':
catl_objs = {'logssfr_key': logssfr_key, 'logmstar_key': logmstar_key}
elif return_type == 'list':
catl_objs = [logssfr_key, logmstar_key]
return catl_objs
def catl_sdss_dir(catl_kind='data',
catl_type='mr',
sample_s='19',
catl_info='members',
halotype='fof',
clf_method=3,
hod_n=0,
clf_seed=1235,
dv=1.0,
sigma_clf_c=0.1417,
perf_opt=False,
print_filedir=True):
"""
Extracts the path to the synthetic catalogues.
Parameters
-----------
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_type : {'mr', 'mstar'} str, optional
Type of catalogue to use. It shows which abundance matching method
was used for the CLF when assigning halo masses. This variable is
set to 'mr' by default.
Options:
- `mr` : Uses r-band absolute magnitude
- `mstar` : Uses stellar masses
sample_s : {'19', '20', '21'} str, optional
Volume-limited sample to use. This variable is set to '19' by default.
Options:
- '19' : Uses the Mr19 volume-limited sample, i.e. 'Consuelo'
- '20' : Uses the Mr20 volume-limited sample, i.e. 'Esmeralda'
- '21' : Uses the Mr21 volume-limited sample, i.e. 'Carmen'
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
halotype : {'fof', 'so'} str, optional
Type of the dark matter halo of the simulation used to create the
synthetic catalogues. This variable is set to `fof` by default.
Options:
- 'fof': Friends-of-Friends halos.
- 'so' : Spherical overdensity halos.
clf_method : {1, 2, 3} int, optional
Method for assigning galaxy properties to mock galaxies.
This variable is set to `3` by default.
Options:
- `1` : Independent assigment of (g-r) color, sersic, and log(ssfr)
- `2` : (g-r) decides active/passive designation and draw values
independently.
- `3` : (g-r) decides active/passive designations, and
assigns other galaxy properties for that given galaxy.
hod_n : {0, 1} int, optional
HOD model to use. Only relevant when `catl_kind == mocks`.
clf_seed : int, optional
Seed used for the `CLF` random seed. This variable is set to `1235`
by default.
dv : float, optional
Difference between galaxy and mass velocity profiles
(v_g-v_c)/(v_m-v_c). This value is set to `1.0` by default.
sigma_clf_c : `float`, optional
Value of the scatter in log(L) for central galaxies in the CLF.
This variable is set to ``0.1417`` by default.
perf_opt : `bool`, optional
If True, it chooses to analyze the `perfect` set of synthetic
catalogues. This variable is set to `False` by default.
print_filedir : `bool`, optional
If True, the output directory is printed onto the screen.
Returns
-----------
catls_path : str
Path to the desired set of synthetic catalogues.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
catl_kind_valid = ['data', 'mocks']
catl_type_valid = ['mr', 'mstar']
sample_s_valid = ['19', '20', '21']
catl_info_valid = ['members', 'groups']
halotype_valid = ['fof', 'so']
clf_method_valid = [1, 2, 3]
hod_n_valid = np.arange(0, 20)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `catl_type`
if not (catl_type in catl_type_valid):
msg = '{0} `catl_type` ({1}) is not a valid input!'.format(
file_msg, catl_type)
raise LSSUtils_Error(msg)
# `sample_s`
if not (sample_s in sample_s_valid):
msg = '{0} `sample_s` ({1}) is not a valid input!'.format(
file_msg, sample_s)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(
file_msg, catl_info)
raise LSSUtils_Error(msg)
# `halotype`
if not (halotype in halotype_valid):
msg = '{0} `halotype` ({1}) is not a valid input!'.format(
file_msg, halotype)
raise LSSUtils_Error(msg)
# `clf_method`
if not (clf_method in clf_method_valid):
msg = '{0} `clf_method` ({1}) is not a valid input!'.format(
file_msg, clf_method)
raise LSSUtils_Error(msg)
# `hod_n`
if not (hod_n in hod_n_valid):
msg = '{0} `hod_n` ({1}) is not a valid input!'.format(file_msg, hod_n)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) is not a valid type!'.format(
file_msg, type(perf_opt))
raise LSSUtils_Error(msg)
# `print_filedir`
if not (isinstance(print_filedir, bool)):
msg = '{0} `print_filedir` ({1}) is not a valid type!'.format(
file_msg, type(print_filedir))
raise LSSUtils_Error(msg)
# `dv`
if not (dv > 0):
msg = '{0} `dv` ({1}) must be larger than 0!'.format(file_msg, dv)
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Type
if not (isinstance(sigma_clf_c, float)):
msg = '{0} `sigma_clf_c` ({1}) is not a valid input type!'
msg = msg.format(file_msg, type(sigma_clf_c))
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Value
if not (sigma_clf_c >= 0.):
msg = '{0} `sigma_clf_c` ({1}) must be larger than 0!'
msg = msg.format(file_msg, sigma_clf_c)
raise LSSUtils_Error(msg)
#
# Type of catalogue
if catl_info == 'members':
catl_info_str = 'member_galaxy_catalogues'
elif catl_info == 'groups':
catl_info_str = 'group_galaxy_catalogues'
#
# Perfect catalogue
if perf_opt:
# Data
if catl_kind == 'data':
msg = '{0} Invalid `catl_kind` ({1}) for when `perf_opt == True'
msg = msg.format(file_msg, catl_kind)
raise LSSUtils_Error(msg)
# Mocks
catl_info_perf_str = 'perfect_{0}'.format(catl_info_str)
else:
# Mocks
catl_info_perf_str = catl_info_str
#
# Extracting path of the files
# Data
if catl_kind == 'data':
# Joining paths
filedir = os.path.join(wp.get_output_path(), 'SDSS', catl_kind,
catl_type, 'Mr' + sample_s, catl_info_perf_str)
# Mocks
if catl_kind == 'mocks':
# Joining paths
filedir = os.path.join(wp.get_output_path(), 'SDSS', catl_kind,
'halos_{0}'.format(halotype),
'dv_{0}'.format(dv),
'hod_model_{0}'.format(hod_n),
'clf_seed_{0}'.format(clf_seed),
'clf_method_{0}'.format(clf_method),
'sigma_c_{0}'.format(sigma_clf_c), catl_type,
'Mr' + sample_s, catl_info_perf_str)
#
# Making sure `filedir` exists
if not (os.path.exists(filedir)):
msg = '{0} `filedir` ({1}) does NOT exist! Check input variables'
msg = msg.format(file_msg, filedir)
raise LSSUtils_Error(msg)
#
# Printing out paths
if print_filedir:
print('{0} `filedir`: {1}'.format(file_msg, filedir))
return filedir
def sdss_catl_clean_nmin(catl_pd,
catl_kind,
catl_info='members',
nmin=1,
perf_opt=False):
"""
Cleans the catalogue removing `failed` values, and only includes
galaxies that are in groups/halos above a `nmin` threshold.
Parameters
-----------
catl_pd : `pandas.DataFrame`
Dataset with the catalogue information.
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
nmin : int, optional
Minimum group richness to have in the (galaxy) group catalogue.
This variable is set to `1` by default.
perf_opt : `bool`, optional
Option for using a `perfect` mock catalogue.
Return
-----------
catl_pd_clean : `pandas.DataFrame`
Cleaned version of `catl_pd` after having removed `failed` values,
and having choosen only galaxies within groups above a group richness
threshold of `nmin`.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
# Checking input parameters
catl_kind_valid = ['data', 'mocks']
catl_info_valid = ['members', 'groups']
# `catl_pd`
if not (isinstance(catl_pd, pd.DataFrame)):
msg = '{0} `catl_pd` ({1}) is not a valid type!'.format(
file_msg, catl_pd)
raise LSSUtils_Error(msg)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(
file_msg, catl_info)
raise LSSUtils_Error(msg)
# `nmin`
if not ((nmin > 0) and (isinstance(nmin, int))):
msg = '{0} `nmin` must be an integer and have a value above `0`'
msg = msg.format(file_msg)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) is not a valid type!'.format(
file_msg, type(perf_opt))
raise LSSUtils_Error(msg)
#
# Types of galaxies
cens = int(1)
nmin = int(nmin)
#
# Getting keys for catalogue
(gm_key, id_key, galtype_key) = catl_keys(catl_kind,
return_type='list',
perf_opt=perf_opt)
# Cleaning catalogue entries
catl_pd_clean_all = sdss_catl_clean(catl_pd,
catl_kind=catl_kind,
catl_info=catl_info,
reindex=True)
# Choosing only galaxies in groups of richness >= `nmin`
# Member galaxies
if catl_info == 'members':
# Centrals
catl_pd_cens = catl_pd_clean_all.loc[(
catl_pd_clean_all[galtype_key] == cens), id_key]
catl_pd_cl = catl_pd_clean_all[(
catl_pd_clean_all[id_key].isin(catl_pd_cens))]
# Group counts
group_counts = Counter(catl_pd_cl[id_key])
group_ngals = np.array(
[xx for xx in group_counts.keys() if group_counts[xx] >= nmin])
# Cleaned version
catl_pd_clean = catl_pd_cl[catl_pd_cl[id_key].isin(group_ngals)]
catl_pd_clean.reset_index(inplace=True, drop=True)
# Group catalogue
if catl_info == 'groups':
if ('ngals' in catl_pd_clean_all.columns.tolist()):
catl_pd_clean = catl_pd_clean_all.loc[
catl_pd_clean_all['ngals'] >= nmin]
catl_pd_clean.reset_index(inplace=True, drop=True)
else:
msg = '{0} Key `ngals` not found in DataFrame ... Exiting!'
msg = msg.format(file_msg)
raise LSSUtils_Error(msg)
return catl_pd_clean
def sdss_catl_clean(catl_pd, catl_kind, catl_info='members', reindex=True):
"""
Cleans the catalogue by removing `failed` values.
Parameters
-----------
catl_pd : `pandas.DataFrame`
Dataset with the catalogue information.
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
reindex : `bool`, optional
If True, the output catalogue is re-indexed.
Return
-----------
catl_pd_clean : `pandas.DataFrame`
Cleaned version of `catl_pd`, after having removed `failed` values.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
# Checking input parameters
catl_kind_valid = ['data', 'mocks']
catl_info_valid = ['members', 'groups']
# `catl_pd`
if not (isinstance(catl_pd, pd.DataFrame)):
msg = '{0} `catl_pd` ({1}) is not a valid type!'.format(
file_msg, catl_pd)
raise LSSUtils_Error(msg)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(
file_msg, catl_info)
raise LSSUtils_Error(msg)
# `reindex
if not (isinstance(reindex, bool)):
msg = '{0} `reindex` ({1}) is not a valid type!'.format(
file_msg, type(reindex))
raise LSSUtils_Error(msg)
#
# Defining `failed` values
ssfr_fail_arr = [0, -99, -999, np.nan]
mstar_fail_arr = [-1, 0, np.nan]
#
# Getting keys for catalogues
(logssfr_key, logmstar_key) = catl_keys_prop(catl_kind=catl_kind,
catl_info=catl_info,
return_type='list')
#
# Cleaning catalogue entries
#
# Data
if catl_kind == 'data':
# Clean version
catl_pd_clean = catl_pd[~catl_pd[logssfr_key].isin(ssfr_fail_arr) & \
~catl_pd[logmstar_key].isin(mstar_fail_arr)]
# Mocks
if catl_kind == 'mocks':
# Clean version
catl_pd_clean = catl_pd[~catl_pd[logssfr_key].isin(ssfr_fail_arr)]
#
# Reindexing
if reindex:
catl_pd_clean.reset_index(inplace=True, drop=True)
return catl_pd_clean
def absolute_magnitude_lim(z, mag_lim, cosmo=None, H0=100., verbose=True):
"""
Calculates the absolute magnitude limit as function of redshift `z` for
a flux-limited survey.
Parameters
-----------
z : float, int, or array_like
Maximum redshift for a given flux-limited survey.
mag_lim : float
Apparent magnitude limit of the flux-limited survey.
cosmo : `astropy.cosmology` object
Cosmology object from Astropy.
H0 : float, optional
Hubble parameters value used to estimate distances.
This variable is set to 100 km/s/Mpc by default.
verbose : `bool`, optional
If True, a message will appear when the default cosmology is used.
Returns
-----------
abs_mag : float, int, or array_like
Absolute magnitude limit in units of `abs_mag` + 5*log10(h),
where `h` is the little Hubble parameter.
Raises
----------
LSSUtils_Error : Exception
Program exception if input parameters are accepted
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
# Redshift
z_valid_types = (float, int, list, np.ndarray)
if not (isinstance(z, z_valid_types)):
msg = '{0} `z` ({1}) is not a valid type!'.format(file_msg, type(z))
raise LSSUtils_Error(msg)
# Magnitude limit
mag_lim_valid_types = (float, int)
if not (isinstance(mag_lim, mag_lim_valid_types)):
msg = '{0} `mag_lim` ({1}) is not a valid type!'.format(
file_msg, type(mag_lim))
raise LSSUtils_Error(msg)
# Hubble parameter value
H0_valid_types = (float, int)
if not (isinstance(H0, H0_valid_types)):
msg = '{0} `H0` ({1}) is not a valid type!'.format(file_msg, type(H0))
raise LSSUtils_Error(msg)
##
## Calculations
if not cosmo:
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=H0, Om0=0.316)
if verbose:
print(">> Warning: No cosmology was specified. Using default:",
cosmo)
## Luminosity distance
lum_dist = cosmo.luminosity_distance(z).value
## Absolute magnitude
abs_mag = apparent_to_absolute_magnitude(mag_lim, lum_dist)
return abs_mag
def Moster2010_relation(log_halo_mass, return_h0=False):
"""
Returns the halo mass of a central galaxy as a function of its stellar
mass.
Parameters
-----------
log_halo_mass : `float` ,`np.ndarray`, or array-like
Value or array of values of base-10 logarithm of halo mass
in ``h = 1`` solar mass units.
return_h0 : `bool`, optional
If `True`, the function returns both stellar mass and halo mass
arrays in units of ``h = 1`` units.
Returns
-----------
mass_dict : `dict`
Dictionary containing arrays or floats containing 10-base logarithm
of halo mass and stellar mass of central galaxies in units of
either ``h = 1`` or ``h = 0.7``. The units of ``h`` depend on the
choice of ``return_h0``.
Notes
----------
The parameter values in Moster+10 were fit to data assuming ``h=0.7``.
Thus, we will transform our input stellar mass to ``h=0.7`` units,
evaluate using the Moster parameters, and then transform back to
``h=1`` units before returning the result.
"""
# file_msg = fd.Program_Msg(__file__)
file_msg = '>>>'
little_h = 0.72
## Checking input parameters
# `log_halo_mass`
mstar_valid_types = (int, float, np.ndarray, list)
if not (isinstance(log_halo_mass, mstar_valid_types)):
msg = '{0} `log_halo_mass` ({1}) is not a valid type!'.format(
file_msg, type(log_halo_mass))
raise LSSUtils_Error(msg)
##
## Moster dictionary
param_dict = _retrieve_Moster_default_dict()
# Converting halo mass from h=1 to h=0.7
log_halo_mass -= np.log10(little_h)
halo_mass = (10.**log_halo_mass)
##
## Moster function
mass_over_m1 = halo_mass / (10.**param_dict['smhm_m1'])
m_m1_beta = mass_over_m1**(-1. * param_dict['smhm_beta'])
m_m1_gamma = mass_over_m1**(param_dict['smhm_gamma'])
inverse_term = (m_m1_beta + m_m1_gamma)**(-1)
mass_over_M = 2. * param_dict['smhm_m_M_0'] * inverse_term
stellar_mass = halo_mass * mass_over_M
# Converting to log-scale
log_stellar_mass = np.log10(stellar_mass)
# Choosing which values to return
if (return_h0):
# Returns both masses in units of ``h = 1``
log_stellar_mass += np.log10(little_h**2)
log_halo_mass += np.log10(little_h)
# Dictionary of masses
return_obj = [log_halo_mass, log_stellar_mass]
mass_dict = dict(zip(['m_halo', 'm_stellar'], return_obj))
return mass_dict
def train_test_dataset(pred_arr,
feat_arr,
pre_opt='min_max',
shuffle_opt=True,
random_state=0,
test_size=0.25,
reshape=False,
return_idx=False):
"""
Function to create the training and testing datasets for a given set
of features array and predicted array.
Parameters
-----------
pred_arr : `pandas.DataFrame` `numpy.ndarray` or array-like, shape (n_samples, n_outcomes)
Array consisting of the `predicted values`. The dimensions of
`pred_arr` are `n_samples` by `n_outcomes`, where `n_samples` is the
number of observations, and `n_outcomes` the number of predicted
outcomes.
feat_arr : `numpy.ndarray`, `pandas.DataFrame` or array-like, shape (n_samples, n_features)
Array consisting of the `predicted values`. The dimensions of
`feat_arr` are `n_samples` by `n_features`, where `n_samples`
is the number of observations, and `n_features` the number of
features used.
pre_opt : {'min_max', 'standard', 'normalize', 'no'} `str`, optional
Type of preprocessing to do on `feat_arr`.
Options:
- 'min_max' : Turns `feat_arr` to values between (0,1)
- 'standard' : Uses `sklearn.preprocessing.StandardScaler` method
- 'normalize' : Uses the `sklearn.preprocessing.Normalizer` method
- 'no' : No preprocessing on `feat_arr`
shuffle_opt : `bool`, optional
If True, the data is shuffled before splitting into testing and
training datasets. This variable is set to True by default.
random_state : int, optional
Random state number used for when splitting into training and
testing datasets. If set, it will always have the same seed
`random_state`. This variable is set to `0` by default.
test_size : float, optional
Percentage of the catalogue that represents the `test` size of
the testing dataset. This variable must be between (0,1).
This variable is set to `0.25` by default.
reshape : `bool`, optional
If True, it reshapes `feat_arr` into a 1d array if its shapes is
equal to (ncols, 1), where `ncols` is the number of columns.
This variable is set to `False` by default.
return_idx : `bool`, optional
If `True`, it returns the indices of the `training` and `testing`
datasets. This variable is set to `False` by default.
Returns
-----------
train_dict : `dict`
Dictionary containing the `training` data from the catalogue.
test_dict : `dict`
Dictionary containing the `testing` data from the catalogue.
See also
-----------
data_preprocessing : Function to preprocess a dataset.
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
# `pred_arr`
pred_arr_type_valid = (list, np.ndarray, pd.DataFrame)
if not (isinstance(pred_arr, pred_arr_type_valid)):
msg = '{0} `pred_arr` ({1}) is not a valid input type'.format(
file_msg, type(pred_arr))
raise LSSUtils_Error(msg)
# `feat_arr`
feat_arr_type_valid = (list, np.ndarray, pd.DataFrame)
if not (isinstance(feat_arr, feat_arr_type_valid)):
msg = '{0} `feat_arr` ({1}) is not a valid input type'.format(
file_msg, type(feat_arr))
raise LSSUtils_Error(msg)
# `pre_opt`
pre_opt_valid = ['min_max', 'standard', 'normalize', 'no']
if not (pre_opt in pre_opt_valid):
msg = '{0} `pre_opt` ({1}) is not a valid input'.format(
file_msg, pre_opt)
raise LSSUtils_Error(msg)
# `shuffle_opt`
shuffle_opt_type_valid = (bool)
if not (isinstance(shuffle_opt, shuffle_opt_type_valid)):
msg = '{0} `shuffle_opt` ({1}) is not a valid input type'.format(
file_msg, type(shuffle_opt))
raise LSSUtils_Error(msg)
# `random_state`
random_state_type_valid = (int)
if not (isinstance(random_state, random_state_type_valid)):
msg = '{0} `random_state` ({1}) is not a valid input'.format(
file_msg, random_state)
raise LSSUtils_Error(msg)
# `test_size`
if not ((test_size > 0) and (test_size < 1.)):
msg = '{0} `test_size` ({1}) must be in range (0,1)'.format(
file_msg, test_size)
raise LSSUtils_Error(msg)
##
## Checking indices of `pred_arr` and `feat_arr`
if return_idx:
# If object is a DataFrame
if (isinstance(pred_arr, pd.DataFrame)
and isinstance(feat_arr, pd.DataFrame)):
pred_arr_idx = pred_arr.index.values
feat_arr_idx = feat_arr.index.values
else:
pred_arr_idx = np.arange(len(pred_arr))
feat_arr_idx = np.arange(len(feat_arr))
# Reshaping if necessary
if reshape:
pred_arr_idx = gu.reshape_arr_1d(pred_arr_idx)
feat_arr_idx = gu.reshape_arr_1d(feat_arr_idx)
##
## Checking dimensions of `pred_arr` and `feat_arr`
pred_arr = np.asarray(pred_arr)
feat_arr = np.asarray(feat_arr)
# Dimensions
if reshape:
pred_arr = gu.reshape_arr_1d(pred_arr)
feat_arr = gu.reshape_arr_1d(feat_arr)
# Shape
if (len(pred_arr) != len(feat_arr)):
msg = '{0} The shape of `pred_arr` ({1}) and `feat_arr` ({2}) must '
msg += 'have the same length'
msg = msg.format(file_msg, len(pred_arr), len(feat_arr))
raise LSSUtils_Error(msg)
##
## Rescaling Dataset
feat_arr_scaled = data_preprocessing(feat_arr,
pre_opt=pre_opt,
reshape=reshape)
##
## Splitting into `Training` and `Testing` datasets.
# Scaled
(X_train, X_test, Y_train,
Y_test) = skms.train_test_split(feat_arr_scaled,
pred_arr,
test_size=test_size,
shuffle=shuffle_opt,
random_state=random_state)
# Not-scaled
(X_train_ns, X_test_ns, Y_train_ns,
Y_test_ns) = skms.train_test_split(feat_arr,
pred_arr,
test_size=test_size,
shuffle=shuffle_opt,
random_state=random_state)
# Returning indices if necessary
if return_idx:
# Splitting to `training` and `testing`
(X_train_idx, X_test_idx, Y_train_idx,
Y_test_idx) = skms.train_test_split(feat_arr_idx,
pred_arr_idx,
test_size=test_size,
shuffle=shuffle_opt,
random_state=random_state)
if not (np.array_equal(X_train_idx, Y_train_idx)
and np.array_equal(X_test_idx, Y_test_idx)):
msg = '{0} Index arrays are not equal to each other!'
raise LSSUtils_Error(msg)
##
## Assigning `training` and `testing` datasets to dictionaries
# Saving indices if necessary
if return_idx:
# Adding 'indices' to dictionaries
train_dict = {
'X_train': X_train,
'Y_train': Y_train,
'X_train_ns': X_train_ns,
'Y_train_ns': Y_train_ns,
'train_idx': X_train_idx
}
test_dict = {
'X_test': X_test,
'Y_test': Y_test,
'X_test_ns': X_test_ns,
'Y_test_ns': Y_test_ns,
'test_idx': X_test_idx
}
else:
train_dict = {
'X_train': X_train,
'Y_train': Y_train,
'X_train_ns': X_train_ns,
'Y_train_ns': Y_train_ns
}
test_dict = {
'X_test': X_test,
'Y_test': Y_test,
'X_test_ns': X_test_ns,
'Y_test_ns': Y_test_ns
}
return train_dict, test_dict
def catl_keys(catl_kind, perf_opt=False, return_type='list'):
"""
Dictionary keys for the different types of catalogues
Parameters
----------
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
perf_opt : `bool`, optional
Option for using a `perfect` mock catalogue.
return_type : {'list', 'dict'} str, optional
Type of output to the be returned. This variable is set to `list`
by default.
Options:
- 'list' : Returns the values as part of a list
- 'dict' : Returns the values as part of a python dictionary
Returns
----------
catl_keys : python dictionary or array_like
Dictionary/array with the proper keys for the catalogue(s).
Order : 1) `gm_key`, 2) `id_key`, 3) `galtype_key`
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
Examples
----------
>>> catl_keys('data', perf_opt=False, return_type='list')
['M_h', 'groupid', 'galtype']
>>> catl_keys('mocks', perf_opt=True, return_type='list')
['M_h', 'haloid', 'galtype']
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
# `catl_kind`
if not (catl_kind in ['data', 'mocks']):
msg = '{0} `catl_kind` ({1}) is not a valid input parameter!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `return_type`
if not (return_type in ['list', 'dict']):
msg = '{0} `return_type` ({1}) is not a valid input parameter'.format(
file_msg, return_type)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) must be a boolean object!'.format(
file_msg, type(perf_opt))
raise LSSUtils_Error(msg)
##
## Perfect Catalogue
if catl_kind == 'data':
perf_opt = False
##
## Property keys
if catl_kind == 'data':
gm_key, id_key, galtype_key = ['M_h', 'groupid', 'galtype']
elif catl_kind == 'mocks':
if perf_opt:
gm_key, id_key, galtype_key = ['M_h', 'haloid', 'galtype']
else:
gm_key, id_key, galtype_key = ['M_group', 'groupid', 'g_galtype']
##
## Saving values
if return_type == 'dict':
catl_objs = {
'gm_key': gm_key,
'id_key': id_key,
'galtype_key': galtype_key
}
elif return_type == 'list':
catl_objs = [gm_key, id_key, galtype_key]
return catl_objs
def catl_sdss_merge(catl_pd_ii,
catl_kind='data',
catl_type='mr',
sample_s='19',
halotype='fof',
clf_method=3,
hod_n=0,
clf_seed=1235,
dv=1.0,
sigma_clf_c=0.1417,
perf_opt=False,
return_memb_group=False,
print_filedir=False):
"""
Merges the member and group catalogues for a given set of input parameters,
and returns a modified version of the galaxy group catalogues with added
info about the galaxy groups.
Parameters
------------
catl_pd_ii : `int`
Index of the catalogue to match,
from :func:`~cosmo_utils.mock_catalogues.catls_utils.extract_catls`
function.
catl_kind : {'data', 'mocks'} `str`, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_type : {'mr', 'mstar'} `str`, optional
Type of catalogue to use. It shows which abundance matching method
was used for the CLF when assigning halo masses. This variable is
set to 'mr' by default.
Options:
- `mr` : Uses r-band absolute magnitude
- `mstar` : Uses stellar masses
sample_s : {'19', '20', '21'} str, optional
Volume-limited sample to use. This variable is set to '19' by default.
Options:
- '19' : Uses the Mr19 volume-limited sample, i.e. 'Consuelo'
- '20' : Uses the Mr20 volume-limited sample, i.e. 'Esmeralda'
- '21' : Uses the Mr21 volume-limited sample, i.e. 'Carmen'
halotype : {'fof', 'so'} str, optional
Type of the dark matter halo of the simulation used to create the
synthetic catalogues. This variable is set to `fof` by default.
Options:
- 'fof': Friends-of-Friends halos.
- 'so' : Spherical overdensity halos.
clf_method : {1, 2, 3} int, optional
Method for assigning galaxy properties to mock galaxies.
This variable is set to `3` by default.
Options:
- `1` : Independent assigment of (g-r) color, sersic, and log(ssfr)
- `2` : (g-r) decides active/passive designation and draw values
independently.
- `3` : (g-r) decides active/passive designations, and
assigns other galaxy properties for that given galaxy.
hod_n : {0, 1} int, optional
HOD model to use. Only relevant when `catl_kind == mocks`.
clf_seed : int, optional
Seed used for the `CLF` random seed. This variable is set to `1235`
by default.
dv : float, optional
Difference between galaxy and mass velocity profiles
(v_g-v_c)/(v_m-v_c). This value is set to `1.0` by default.
sigma_clf_c : `float`, optional
Value of the scatter in log(L) for central galaxies in the CLF.
This variable is set to ``0.1417`` by default.
perf_opt : `bool`, optional
If True, it chooses to analyze the `perfect` set of synthetic
catalogues. This variable is set to `False` by default.
return_memb_group : `bool`, optional
If True, the function returns the member and group catalogues,
along with the merged catalogue.
It returns ``<memb_group_pd, memb_pd, group_pd>``
print_filedir : `bool`, optional
If True, the output directory is printed onto the screen.
Return
------------
memb_group_pd : `pandas.DataFrame`
Combined version of the i-th member and group catalogues.
It contains both galaxy and group information.
memb_pd : `pandas.DataFrame`
Catalogue of the member galaxies of the i-th catalogue.
This catalogue contains information of the `member galaxies`.
group_pd : `pandas.DataFrame`
Catalogue of the groups of the i-th catalogue.
This catalogue contains information of the `galaxy groups`.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
catl_pd_ii_valid = (float, int, np.int64, np.int32, np.float32, np.float64)
catl_kind_valid = ['data', 'mocks']
catl_type_valid = ['mr', 'mstar']
sample_s_valid = ['19', '20', '21']
halotype_valid = ['fof', 'so']
clf_method_valid = [1, 2, 3]
hod_n_valid = np.arange(0, 20)
# `catl_pd_ii`
if (isinstance(catl_pd_ii, catl_pd_ii_valid)):
catl_pd_ii = int(catl_pd_ii)
else:
msg = '{0} `catl_pd_ii` ({1}) is not a valid input!'.format(
file_msg, type(catl_pd_ii))
raise LSSUtils_Error(msg)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `catl_type`
if not (catl_type in catl_type_valid):
msg = '{0} `catl_type` ({1}) is not a valid input!'.format(
file_msg, catl_type)
raise LSSUtils_Error(msg)
# `sample_s`
if not (sample_s in sample_s_valid):
msg = '{0} `sample_s` ({1}) is not a valid input!'.format(
file_msg, sample_s)
raise LSSUtils_Error(msg)
# `halotype`
if not (halotype in halotype_valid):
msg = '{0} `halotype` ({1}) is not a valid input!'.format(
file_msg, halotype)
raise LSSUtils_Error(msg)
# `clf_method`
if not (clf_method in clf_method_valid):
msg = '{0} `clf_method` ({1}) is not a valid input!'.format(
file_msg, clf_method)
raise LSSUtils_Error(msg)
# `dv`
if not (dv > 0):
msg = '{0} `dv` ({1}) must be larger than 0!'.format(file_msg, dv)
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Type
if not (isinstance(sigma_clf_c, float)):
msg = '{0} `sigma_clf_c` ({1}) is not a valid input type!'
msg = msg.format(file_msg, type(sigma_clf_c))
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Value
if not (sigma_clf_c >= 0.):
msg = '{0} `sigma_clf_c` ({1}) must be larger than 0!'
msg = msg.format(file_msg, sigma_clf_c)
raise LSSUtils_Error(msg)
# `hod_n`
if not (hod_n in hod_n_valid):
msg = '{0} `hod_n` ({1}) is not a valid input!'.format(file_msg, hod_n)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) is not a valid type!'.format(
file_msg, type(perf_opt))
raise LSSUtils_Error(msg)
# `return_memb_group`
if not (isinstance(return_memb_group, bool)):
msg = '{0} `return_memb_group` ({1}) is not a valid type!'.format(
file_msg, type(return_memb_group))
raise LSSUtils_Error(msg)
# `print_filedir`
if not (isinstance(print_filedir, bool)):
msg = '{0} `print_filedir` ({1}) is not a valid type!'.format(
file_msg, type(print_filedir))
raise LSSUtils_Error(msg)
#
# Extracting catalogues given input parameters
(memb_arr, memb_len) = extract_catls(catl_kind=catl_kind,
catl_type=catl_type,
sample_s=sample_s,
halotype=halotype,
clf_method=clf_method,
hod_n=hod_n,
clf_seed=clf_seed,
dv=dv,
sigma_clf_c=sigma_clf_c,
perf_opt=perf_opt,
catl_info='members',
return_len=True,
print_filedir=print_filedir)
# Checking number of catalogues
if catl_pd_ii > (memb_len - 1):
msg = '{0} `catl_pd_ii` ({1}) is OUT of range ({2})!'.format(
file_msg, catl_pd_ii, memb_len)
raise LSSUtils_Error(msg)
#
# Extracting group catalogue
# i-th Galaxy catalogue
memb_path = memb_arr[catl_pd_ii]
# i-th Galaxy Group catalogue
group_path = catl_sdss_dir(catl_kind=catl_kind,
catl_type=catl_type,
sample_s=sample_s,
halotype=halotype,
clf_method=clf_method,
dv=dv,
sigma_clf_c=sigma_clf_c,
hod_n=hod_n,
clf_seed=clf_seed,
perf_opt=perf_opt,
catl_info='groups',
print_filedir=print_filedir)
#
# Paths to catalogue
# Mocks
if catl_kind == 'mocks':
group_path = os.path.join(
group_path,
os.path.basename(memb_path).replace('memb', 'group'))
# Data
if catl_kind == 'data':
group_path = os.path.join(
group_path,
os.path.basename(memb_path).replace('Gals', 'Group'))
# Checking that file exists
fd.File_Exists(group_path)
##
## Reading in Catalogues
memb_pd = fr.read_hdf5_file_to_pandas_DF(memb_path)
group_pd = fr.read_hdf5_file_to_pandas_DF(group_path)
## Keys for the catalogues
(gm_key, id_key, galtype_key) = catl_keys(catl_kind,
perf_opt=perf_opt,
return_type='list')
## Matching keys from Group catalogue
if len(np.unique(memb_pd[id_key])) == len(np.unique(group_pd[id_key])):
# Group column names
group_colnames = np.sort(group_pd.columns.values)
## Sorting `memb_pd` by `id_key`
# Member catalogue
memb_pd.sort_values(by=id_key, inplace=True)
memb_pd.reset_index(inplace=True, drop=True)
# Group catalogue
group_pd.sort_values(by=id_key, inplace=True)
group_pd.reset_index(inplace=True, drop=True)
## Renaming columns
g_colnames_dict = {ii: 'GG_' + ii for ii in group_colnames}
group_pd.rename(columns=g_colnames_dict, inplace=True)
group_pd.rename(columns={'GG_' + id_key: id_key}, inplace=True)
##
## Merging the 2 DataFrames
memb_group_pd = pd.merge(left=memb_pd,
right=group_pd,
how='left',
left_on=id_key,
right_on=id_key)
else:
msg = '{0} Lengths of the 2 DataFrames (`memb_pd`, `group_pd`) '
msg += 'do not match!'
msg = msg.format(file_msg)
raise LSSUtils_Error(msg)
##
## Returning DataFrames
if return_memb_group:
return_obj = (memb_group_pd, memb_pd, group_pd)
else:
return_obj = memb_group_pd
return return_obj
def scoring_methods(truth_arr,
feat_arr=None,
pred_arr=None,
model=None,
score_method='perc',
threshold=0.1,
perc=0.68):
"""
Determines the overall score for given arrays, i.e. the `predicted`
array and the `truth` array
Parameters
-----------
truth_arr : `numpy.ndarray` or array-like, shape (n_samples, n_outcomes)
Array consisting of the `true` values for the `n_samples`
observations. The dimensions of `truth_arr` are
`n_samples` by `n_outcomes`, where `n_samples` is the
number of observations, and `n_outcomes` the number of predicted
outcomes.
feat_arr : `numpy.ndarray`, array-like, or `NoneType`, shape (n_samples, n_features)
Array consisting of the `predicted values`. The dimensions of
`feat_arr` are `n_samples` by `n_features`, where `n_samples`
is the number of observations, and `n_features` the number of
features used. This variable is set to `None` by default.
pred_arr : `numpy.ndarray`, array-like, or `NoneType`, shape (n_samples, n_outcomes)
Array of predicted values from `feat_arr`. If ``model == None``,
this variable must be an array-like object. If ``model != None``,
this variable will not be used, and will be calculated using
the `model` object. This variable is set to `None` by default.
model : scikit-learn model object or `NoneType`
Model used to estimate the score if ``score_method == 'model_score'``
This variable is set to `None` by default.
score_method : {'perc', 'threshold', 'model_score', 'r2'} `str`, optional
Type of scoring to use when determining how well an algorithm
is performing.
Options:
- 'perc' : Use percentage and rank-ordering of the values
- 'threshold' : Score based on diffs of `threshold` or less from true value.
- 'model_score' : Out-of-the-box metod from `sklearn` to determine success.
- 'r2': R-squared statistic for error calcuation.
threshold : float, optional
Value to use when calculating the error within `threshold` value
from the truth. This variable is set to `None` by default.
If `None`, this variable assumes a value of `0.1`.
perc : float, optional
Value used when determining score within some `perc` percentile
value form [0,1]. This variable is set to `None` by default.
If `None`, it assumes a value of `0.68`.
Returns
-----------
method_score : float
Overall score from `pred_arr` to predict `truth_arr`.
Notes
-----------
For more information on how to pre-process your data, see
`http://scikit-learn.org/stable/modules/model_evaluation.html`_.
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
# `feat_arr`
feat_arr_type_valid = (list, np.ndarray, type(None))
if not (isinstance(feat_arr, feat_arr_type_valid)):
msg = '{0} `feat_arr` ({1}) is not a valid input type'.format(
file_msg, type(feat_arr))
raise LSSUtils_Error(msg)
# `truth_arr`
truth_arr_type_valid = (list, np.ndarray)
if not (isinstance(truth_arr, truth_arr_type_valid)):
msg = '{0} `truth_arr` ({1}) is not a valid input type'.format(
file_msg, type(truth_arr))
raise LSSUtils_Error(msg)
# `score_method` - Type
score_method_type_valid = (str)
if not (isinstance(score_method, score_method_type_valid)):
msg = '{0} `score_method` ({1}) is not a valid input type'.format(
file_msg, type(score_method))
raise LSSUtils_Error(msg)
# `score_method` - Value
score_method_valid = ['perc', 'threshold', 'model_score', 'r2']
if not (score_method in score_method_valid):
msg = '{0} `score_method` ({1}) is not a valid input!'.format(
file_msg, score_method)
raise LSSUtils_Error(score_method)
# `threshold` - Type
threshold_valid = (float, int)
if not (isinstance(threshold, threshold_valid)):
msg = '{0} `threshold` ({1}) is not a valid input type'.format(
file_msg, type(threshold))
raise LSSUtils_Error(msg)
# `threshold` - Value
if not (threshold >= 0.):
msg = '{0} `threshold` ({1}) must be larger than 0!'.format(
file_msg, threshold)
raise LSSUtils_Error(msg)
##
## Checking for `model`, `pred_arr` and `feat_arr`
# If both are none
if ((model is None) and (pred_arr is None)):
msg = '{0} `model` and `pred_arr` cannot both be `None`. '
msg += 'Only one can be `None`'
msg = msg.format(file_msg)
raise LSSUtils_Error(msg)
# If `feat_arr` and `pred_arr` are `None`
if ((feat_arr is None) and (pred_arr is None)):
msg = '{0} `feat_arr` and `pred_arr` cannot both be `None`'.format(
file_msg)
raise TypeError(msg)
# `pred_arr` - Type
# If both are `None`
pred_arr_valid = ((list, np.ndarray))
if (model is None):
if not (isinstance(pred_arr, pred_arr_valid)):
msg = '{0} `pred_arr` ({1}) is not a valid input type!'.format(
file_msg, type(pred_arr))
raise LSSUtils_Error(msg)
##
## Choosing scoring method
# Percentile method
if (score_method == 'perc'):
# Checking for `pred_arr`
if (pred_arr is None):
pred_arr = model.predict(feat_arr)
# Checking for `model`
if (model is None):
pred_arr = np.asarray(pred_arr)
# Error calcualtion
pred_err = np.abs(pred_arr - truth_arr)
method_score = scipy.stats.scoreatpercentile(pred_err, 100. * perc)
# Threshold method
if (score_method == 'threshold'):
# Checking for `pred_arr`
if (pred_arr is None):
pred_arr = model.predict(feat_arr)
# Checking for `model`
if (model is None):
pred_arr = np.asarray(pred_arr)
# Error calcualtion
pred_err = np.abs(pred_arr - truth_arr)
pred_thresh = len(pred_err[pred_err <= threshold])
method_score = pred_thresh / len(pred_arr)
# R-squared method
if (score_method == 'r2'):
# Checking for `pred_arr`
if (pred_arr is None):
pred_arr = model.predict(feat_arr)
# Checking for `model`
if (model is None):
pred_arr = np.asarray(pred_arr)
# Error calcualtion
method_score = skmetrics.r2_score(truth_arr, pred_arr)
# Model method
if (score_method == 'model_score'):
method_score = model.score(feat_arr, truth_arr)
return method_score
