from PyAstronomy.pyaC import ImportCheck ic = ImportCheck(["numpy", "scipy"], required=["numpy"]) from .XTran import palTrans from .XTran import forTrans from .XTran import LimBrightTrans, RmcL, RmcL_Hirano from .radVel import SinRadVel from .planetBrightnessPhases import GeomPlanetBrightPhase from .keplerEllipseModel import KeplerEllipseModel, KeplerRVModel #from atanProfile import AtanProfile, AtanProfileDamped from .lineListGaussModel import * from .voigtAstro import VoigtAstroP from .LyAProfile import LyaTransmission from .rotBroadProfile import RotBroadProfile
def generalizedESD(x, maxOLs, alpha=0.05, fullOutput=False):
"""
Carry out a Generalized ESD Test for Outliers.
The Generalized Extreme Studentized Deviate (ESD) test for
outliers can be used to search for outliers in a univariate
data set, which approximately follows a normal distribution.
A description of the algorithm is, e.g., given at
`Nist <http://www.itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm>`_
or [Rosner1983]_.
Parameters
----------
maxOLs : int
Maximum number of outliers in the data set.
alpha : float, optional
Significance (default is 0.05).
fullOutput : boolean, optional
Determines whether additional return values
are provided. Default is False.
Returns
-------
Number of outliers : int
The number of data points characterized as
outliers by the test.
Indices : list of ints
The indices of the data points found to
be outliers.
R : list of floats, optional
The values of the "R statistics". Only provided
if `fullOutput` is set to True.
L : list of floats, optional
The lambda values needed to test whether a point
should be regarded an outlier. Only provided
if `fullOutput` is set to True.
"""
ImportCheck(["scipy"], required=["scipy"])
from scipy.stats import t
if maxOLs < 1:
raise(PE.PyAValError("Maximum number of outliers, `maxOLs`, must be > 1.", \
solution="Specify, e.g., maxOLs = 2"))
import numpy.ma as ma
xm = ma.array(x)
n = len(xm)
# Compute R-values
R = []
L = []
minds = []
for i in smo.range(maxOLs + 1):
# Compute mean and std of x
xmean = xm.mean()
xstd = xm.std()
# Find maximum deviation
rr = np.abs((xm - xmean) / xstd)
minds.append(np.argmax(rr))
R.append(rr[minds[-1]])
if i >= 1:
p = 1.0 - alpha / (2.0 * (n - i + 1))
perPoint = t.ppf(p, n - i - 1)
L.append((n - i) * perPoint / np.sqrt(
(n - i - 1 + perPoint**2) * (n - i + 1)))
# Mask that value and proceed
xm[minds[-1]] = ma.masked
# Remove the first entry from R, which is of
# no meaning for the test
R.pop(-1)
# Find the number of outliers
ofound = False
for i in smo.range(maxOLs - 1, -1, -1):
if R[i] > L[i]:
ofound = True
break
# Prepare return value
if ofound:
if not fullOutput:
# There are outliers
return i + 1, minds[0:i + 1]
else:
return i + 1, minds[0:i + 1], R, L, minds
else:
# No outliers could be detected
if not fullOutput:
# There are outliers
return 0, []
else:
return 0, [], R, L, minds
# -*- coding: utf-8 -*-
from __future__ import print_function
from PyAstronomy.pyaC import pyaErrors as PE
from PyAstronomy.pyaC import ImportCheck
import sys
# Check Python version
_majV, _minV = sys.version_info[0:2]
if (_minV < 7) and (_majV == 2):
PE.warn(
"funcFit needs 2.7.x or greater. See documentation (Prerequisites) for explanation."
)
ic = ImportCheck([
"numpy", "scipy", "pymc", "matplotlib", "matplotlib.pylab", "pyfits",
"emcee", "progressbar"
])
# Get out if numpy not present
if not ic.check["numpy"]:
raise(PE.PyARequiredImport("Numpy cannot be imported.", solution="Install numpy (see http://numpy.scipy.org/, you probably should also install SciPy).", \
addInfo="The numpy package provides array support for Python and is indispensable in many scientific applications."))
# Check whether fitting modules can be imported
_scoImport = ic.check["scipy"]
_pymcImport = ic.check["pymc"]
_mplImport = ic.check["matplotlib"]
from PyAstronomy.funcFit.utils import *
from .modelRebin import turnIntoRebin, _ModelRebinDocu
from .onedfit import *
from PyAstronomy.pyaC import ImportCheck ic = ImportCheck(["six.moves.tkinter", "matplotlib"], required=["six.moves.tkinter", "matplotlib"]) from .pyaPicker import Picker from .ffmodelExplorer import FFModelExplorer, FFModelExplorerList, FFModelPlotFit, ffmodelExplorer from .continuumFinder import ContinuumInteractive from .interactiveGV import IAGVFit
# -*- coding: utf-8 -*-
from __future__ import print_function, division
from PyAstronomy.pyaC import ImportCheck
from PyAstronomy.pyaC import pyaErrors as PE
import six
_ic = ImportCheck([
"numpy", "quantities", "scipy", "matplotlib", "pyfits", "astropy.io.fits",
"pandas", "six", "astropy"
],
required=["numpy", "scipy"])
_moduleImportStatus = {}
from PyAstronomy.pyasl.asl import *
from PyAstronomy.pyasl.resBased import *
def moduleImportStatus(mode="all"):
"""
Provide information on model import status.
Parameters
----------
mode : string, {"all", "fail", "success"}, optional
If 'all' is given, all modules will be shown. If
'fail' is given, only failures will be shown and
the opposite in case of 'success'.
"""
mss = ("all", "fail", "success")
if not mode in mss:
raise(PE.PyAValError("Unknown mode: " + str(mode), \
from PyAstronomy.pyaC import ImportCheck ImportCheck(["numpy"], required=["numpy"]) from .pdm import *