def __init__(self, orbit="circular", ld="quad", collCheck=True):
if not orbit in ["circular", "keplerian"]:
raise (PE.PyAValError(
"Invalid option for orbit: " + str(orbit),
soltuion="Use either 'circular' or 'keplerian'."))
if not ld in ["quad", "nl"]:
raise (PE.PyAValError("Invalid option for orbit: " + str(ld),
soltuion="Use either 'quad' or 'nl'."))
_ZList.__init__(self, orbit, collCheck)
if (not _importOccultquad) and (ld == "quad"):
raise (PE.PyARequiredImport(
"Could not import required shared object library 'occultquad.so'",
solution=[
"Use 'pip install PyAstronomy_ext' to get it.",
"Invoke PyA's install script (setup.py) with the --with-ext option.",
"Go to 'forTrans' directory of PyAstronomy and invoke\n f2py -c occultquad.pyf occultquad.f"
]))
if (not _importOccultnl) and (ld == "nl"):
raise (PE.PyARequiredImport(
"Could not import required shared object library 'occultquad.so'",
solution=[
"Use 'pip install PyAstronomy_ext' to get it.",
"Invoke PyA's install script (setup.py) with the --with-ext option.",
"Go to 'forTrans' directory of PyAstronomy and invoke\n f2py -c occultnl.pyf occultnl.f"
]))
if orbit == "circular":
plist = ["p", "a", "i", "T0", "per", "b"]
else:
# It is an elliptical orbit
plist = ["p", "a", "i", "per", "b", "e", "Omega", "tau", "w"]
if ld == "quad":
plist.extend(["linLimb", "quadLimb"])
else:
# Non-linear LD
plist.extend(["a1", "a2", "a3", "a4"])
fuf.OneDFit.__init__(self, plist)
self.freeze(plist)
self.setRootName("Occultquad")
if orbit == "keplerian":
self["w"] = -90.0
self._orbit = orbit
self._ld = ld
def restoreFromFits(filename, ext=1, verbose=1):
if not ic.check["pyfits"]:
raise (PE.PyARequiredImport("pyfits required to use fits file.",
where="Params::updateFitsHeader"))
return
import pyfits
if not os.path.isfile(filename):
raise (PE.PyAValError("No such file: " + str(filename),
where="restoreFromFits",
solution="Give correct filename."))
return
ff = pyfits.open(filename)[ext]
try:
modelname = ff.header['PA_model']
except:
raise (PE.pyaOtherErrors("File (" + str(filename) +
") contains no model.",
where="restoreFromFits",
solution="Use another file."))
return
if modelname == 'convLine':
import convLine as cl
nLines = ff.header['model_p0']
model = cl.ConvLine(nLines)
elif modelname == 'multiGauss1d':
from PyAstronomy.funcFit import MultiGauss1d
nLines = ff.header['model_p0']
model = MultiGauss1d(nLines)
for p in model.parameters():
model[p] = ff.header[p]
return model
def plotDeviance(self, parsList=None):
"""
Plots value of deviance over parameter values encountered during sampling.
Parameters
----------
parsList : string or list of strings, optional,
Refers to a parameter name or a list of parameter names.
If None, all available parameters are plotted.
"""
if not ic.check["matplotlib"]:
PE.warn(PE.PyARequiredImport("To use 'plotHists', matplotlib has to be installed.", \
solution="Install matplotlib."))
return
if isinstance(parsList, six.string_types):
parsList = [parsList]
tracesDic = {}
if parsList is not None:
for parm in parsList:
self._parmCheck(parm)
tracesDic[parm] = self[parm]
else:
# Use all available traces
for parm in self.availableParameters():
tracesDic[parm] = self[parm]
ps = self.__plotsizeHelper(len(tracesDic))
for i, [pars, trace] in enumerate(six.iteritems(tracesDic), 1):
plt.subplot(ps[0], ps[1], i)
plt.xlabel(pars)
plt.ylabel("Deviance")
plt.plot(self[pars], self["deviance"], '.')
def plotHist(self, parsList=None):
"""
Plots distributions for a number of traces.
Parameters
----------
parsList : string or list of strings, optional,
Refers to a parameter name or a list of parameter names.
If None, all available parameters are plotted.
"""
if not ic.check["matplotlib"]:
PE.warn(PE.PyARequiredImport("To use 'plotHists', matplotlib has to be installed.", \
solution="Install matplotlib."))
return
if isinstance(parsList, six.string_types):
parsList = [parsList]
tracesDic = {}
if parsList is not None:
for parm in parsList:
self._parmCheck(parm)
tracesDic[parm] = self[parm]
else:
# Use all available traces
for parm in self.availableParameters():
tracesDic[parm] = self[parm]
cols, rows = self.__plotsizeHelper(len(tracesDic))
for i, [pars, trace] in enumerate(tracesDic.items()):
if len(tracesDic) > 1:
plt.subplot(rows, cols, i + 1)
plt.hist(trace, label=pars + " hist")
plt.legend()
def bayesFactors(model1, model2):
"""
Computes the Bayes factor for two competing models.
The computation is based on Newton & Raftery 1994 (Eq. 13).
Parameters
----------
model1 : PyAstronomy.funcFit.TraceAnalysis instance
TraceAnalysis for the first model.
model2 : PyAstronomy.funcFit.TraceAnalysis instance
TraceAnalysis for the second model.
Returns
-------
BF : float
The Bayes factor BF (neither log10(BF) nor ln(BF)).
Note that a small value means that the first model
is favored, i.e., BF=p(M2)/p(M1).
"""
if not ic.check["pymc"]:
raise(PE.PyARequiredImport("pymc is required to evaluate the bayesFactor", \
solution="Install pymc"))
logp1 = model1["deviance"] / (-2.)
logp2 = model2["deviance"] / (-2.)
# Given a number of numbers x1, x2, ... whose logarithms are given by l_i=ln(x_i) etc.,
# logsum calculates: ln(sum_i exp(l_i)) = ln(sum_i x_i)
bf = numpy.exp(
pymc.flib.logsum(-logp1) - numpy.log(len(logp1)) -
(pymc.flib.logsum(-logp2) - numpy.log(len(logp2))))
return bf
def planck(T, lam=None, nu=None):
"""
Evaluate Planck's radiation law.
Depending on whether wavelength or frequency is specified as input, the
function evaluates:
.. math::
B_{\\nu} = \\frac{2\\pi h \\nu^3}{c^2} \\frac{1}{e^{\\frac{h\\nu}{kT}} - 1}
or
.. math::
B_{\\lambda} = \\frac{2\\pi h c^2}{\\lambda^5} \\frac{1}{e^{\\frac{h c}{\\lambda kT}} - 1} \\; .
If lambda is given (in meters), the output units are W/(m^2 m). To convert into erg/(cm^2 A s),
the output has to be multiplied by a factor of 1e-7.
Parameters
----------
T : float
Temperature in Kelvin.
lam : float or array, optional
Wavelength in meters.
nu : float or array, optional
Frequency in Hz.
Returns
-------
Spectral radiance : float or array
Depending on whether `lam` or `nu` were specified, returns the
spectral radiance per area and wavelength or frequency. The unit (SI)
will be W/(m^2 m) if `lam` was given and W/(m^2 Hz) if `nu` was
specified.
"""
if _ic.check["quantities"]:
from PyAstronomy import constants
c = constants.PyAConstants(unitSystem="SI")
else:
raise (PE.PyARequiredImport(
"You need to install 'quantities' to use this function.\n 'quantities' can be obtained from 'http://pypi.python.org/pypi/quantities'."
))
return None
if (lam is not None) and (nu is not None):
raise (PE.PyAParameterConflict(
"Specify either 'lam' OR 'nu', but not both."))
if (lam is None) and (nu is None):
raise (PE.PyAParameterConflict("Specify either 'lam' OR 'nu'."))
# Parameters have been specified properly
if lam is not None:
result = 2. * np.pi * c.h * (c.c**2) / (lam**5)
result /= (np.exp(c.h * c.c / (lam * c.k * T)) - 1.0)
return result
elif nu is not None:
result = 2. * np.pi * c.h * (nu**3) / (c.c**2)
result /= (np.exp(c.h * nu / (c.k * T)) - 1.0)
return result
def plotCorrEnh(self, parsList=None, **plotArgs):
"""
Produces enhanced correlation plots.
Parameters
----------
parsList : list of string, optional
If not given, all available traces are used.
Otherwise a list of at least two parameters
has to be specified.
plotArgs : dict, optional
Keyword arguments handed to plot procedures of
pylab. The following keywords are available:
contour,bins,cmap,origin,interpolation,colors
"""
if not ic.check["matplotlib"]:
PE.warn(PE.PyARequiredImport("To use 'plotCorr', matplotlib has to be installed.", \
solution="Install matplotlib."))
return
tracesDic = {}
if parsList is not None:
for parm in parsList:
self._parmCheck(parm)
tracesDic[parm] = self[parm]
if len(tracesDic) < 2:
raise(PE.PyAValError("For plotting correlations, at least two valid parameters are needed.", \
where="TraceAnalysis::plotCorr"))
else:
# Use all available traces
for parm in self.availableParameters():
tracesDic[parm] = self[parm]
pars = list(tracesDic.keys())
traces = list(tracesDic.values())
fontmap = {1: 10, 2: 9, 3: 8, 4: 8, 5: 8}
if not len(tracesDic) - 1 in fontmap:
fontmap[len(tracesDic) - 1] = 8
k = 1
for j in range(len(tracesDic)):
for i in range(len(tracesDic)):
if i > j:
plt.subplot(len(tracesDic) - 1, len(tracesDic) - 1, k)
# plt.title("Pearson's R: %1.5f" % self.pearsonr(pars[j],pars[i])[0], fontsize='x-small')
plt.xlabel(pars[j], fontsize=fontmap[len(tracesDic) - 1])
plt.ylabel(pars[i], fontsize=fontmap[len(tracesDic) - 1])
tlabels = plt.gca().get_xticklabels()
plt.setp(tlabels, 'fontsize', fontmap[len(tracesDic) - 1])
tlabels = plt.gca().get_yticklabels()
plt.setp(tlabels, 'fontsize', fontmap[len(tracesDic) - 1])
# plt.plot(traces[j],traces[i],'.',**plotArgs)
self.__hist2d(traces[j], traces[i], **plotArgs)
if i != j:
k += 1
def _read_sweetcat(self):
"""
Read SWEETCat into a pandas DataFrame
"""
if not _ic.check["pandas"]:
raise(PE.PyARequiredImport("Could not import pandas module.",
solution="Please install pandas (http://pandas.pydata.org). Please also check the dependencies."))
else:
import pandas as pd
ffn = self._fs.requestFile(self.dataFileName, 'r', gzip.open)
self.data = pd.read_csv(
ffn, sep='\t', names=self.names, na_values=['~'])
def updateFitsHeader(model, hdr, clobber=False, conf=0.9):
"""
Update the delivered fits-header with the parameters of the model.
Parameters:
- `hdr` - Pyfits header which should be updated with the model parameters.
- `conf' - Confidence level for MCMC errors.
- `clobber` - Allows to overwrite parameters which might be already present in the header.
"""
if not ic.check["pyfits"]:
raise (PE.PyARequiredImport("pyfits required to use fits file.",
where="Params::updateFitsHeader"))
return
try:
# @FIXME The next line can NEVER work
# x=hdr[p]
raise (PE.pyaOtherErrors(
" Keyword PA_model already present in fits-header, aborting...",
where="Params::updateFitsHeader"))
return
except:
pass
hdr.update('PA_model', model.naming.getRoot(), 'PyAstronomy model type')
for p in model.parameters():
if clobber == False:
try:
x = hdr[p]
raise (PE.pyaOtherErrors(" Parameter " + str(p) +
" already present in fits-header",
where="Params::updateFitsHeader"))
return
except:
pass
hdr.update(p, model[p])
if ic.check["pymc"]:
from pymc.utils import hpd
hdr.update('Conf', conf, "Error Confidence Level")
for p in model.parameters():
try:
v_err = hpd(model.MCMC.trace(p)[:], 1.0 - conf)
p0, p1 = str(p + '_e0'), str(p + '_e1')
if len(p0) > 8:
raise (PE.pyaOtherErrors(
" Cannot save Error for parameter " + str(p) +
" because len(" + p0 + ")>8",
where="Params::updateFitsHeader"))
return
hdr.update(p0, v_err[0], "Lower confidence boundary")
hdr.update(p1, v_err[1], "Upper confidence boundary")
except:
pass
return hdr
def _checkPackage(self, p):
"""
Check whether package is availabel and raise exception otherwise.
Parameters
----------
p : string
Name of package
"""
if not ic.check[p]:
raise(PE.PyARequiredImport("The package '" + str(p) + "' is not currently installed.", \
solution="Please install " + str(p)))
def equiTempPlanet(Ts, R, a, albedo=0.0, e=0.0, solarUnits=False):
"""
Calculate the planetary equilibrium temperature.
The equilibrium temperature is defined, e.g., in
Laughlin et al. 2011 (ApJ, 729), Eq. 1. Albedo
was added "by hand".
Note that units have to be consistent for stellar
radius and large semi-major axis. The `solarUnits`
flag can be used to use solar units for these
quantities.
Parameters
----------
Ts : float
Stellar effective temperature [K].
R : float
Radius of the star
a : float
Large semi-major axis
albedo : float
Albedo of the planetary atmosphere.
Default is 0.0.
e : float, optional
Eccentricity of orbit (default is 0.0).
solarUnits : boolean, optional
If this flag is set True, stellar radius and
semi-major axis are taken to be in solar radii
and AU.
Returns
-------
Equilibrium temperature : float
The equilibrium temperature of the planet.
"""
if _ic.check["quantities"]:
from PyAstronomy import constants as c
else:
raise (PE.PyARequiredImport(
"You need to install 'quantities' to use this function."))
return None
if solarUnits:
a = a * c.AU
R = R * c.RSun
if a < R:
raise (PE.PyAValError(
"The semi-major axis cannot be smaller than the radius of the star."
))
teq = np.power(1. - albedo, 1./4.) * np.sqrt(R / (2.0 * a)) * Ts \
/ np.power(1.0 - e**2, 1./8.)
return teq
def getAllDataPandas(self):
"""
Get all data as pandas DataFrame.
Returns
-------
table : DataFrame
All available data in pandas format.
"""
if not _ic.check["pandas"]:
raise(PE.PyARequiredImport("You need to install 'pandas' to use pandas DataFrames.", \
solution="Install 'pandas' package."))
return self.vot.to_pandas()
def __init__(self):
_ZList.__init__(self, "circular")
if not _importOccultnl:
raise(PE.PyARequiredImport("Could not import required shared object library 'occultquad.so'",\
solution=["Invoke PyA's install script (setup.py) with the --with-ext option.",
"Go to 'forTrans' directory of PyAstronomy and invoke\n f2py -c occultnl.pyf occultnl.f"]
))
fuf.OneDFit.__init__(
self, ["p", "a", "i", "a1", "a2", "a3", "a4", "T0", "per", "b"])
self.freeze(["p", "a", "i", "a1", "a2", "a3", "a4", "T0", "per", "b"])
self.setRootName("MandelAgolNL")
self.__zlist = None
def __init__(self):
_ZList.__init__(self, "circular")
fuf.OneDFit.__init__(self, ["p", "a", "i", "linLimb", "quadLimb", "T0", "per", "b"])
self.freeze(["p", "a", "i", "linLimb", "quadLimb", "T0", "per", "b"])
self.setRootName("PalCirc08")
self._zlist=None
if (not mpmath_imported) and (not boostEll_imported):
raise(PE.PyARequiredImport("Neither mpmath nor the elliptical integrals from the boost library could be found!", \
where="PalLC::__init__", solution="Install mpmath or make Boost library available (more complicated - see documentation)"))
self.useBoost = False
if boostEll_imported:
self.useBoost = True
self.ell = ell.ell()
def __init__(self, collisionCheck=False):
_ZList.__init__(self, "keplerian", collisionCheck)
fuf.OneDFit.__init__(self, [
"p", "a", "i", "linLimb", "quadLimb", "tau", "per", "b", "w",
"Omega", "e"
])
self.freeze([
"p", "a", "i", "linLimb", "quadLimb", "tau", "per", "b", "w",
"Omega", "e"
])
self.setRootName("Pal08")
self["per"] = 1.0
self["w"] = -90
self._zlist = None
if (not mpmath_imported) and (not boostEll_imported):
raise (PE.PyARequiredImport(
"Neither mpmath nor the elliptical integrals from the boost library could be found!",
where="PalLC::__init__",
solution=
"Install mpmath or make Boost library available (more complicated - see documentation)"
))
self.useBoost = False
if boostEll_imported:
self.useBoost = True
self.ell = ell.ell()
self.collisionCheck = collisionCheck
# Wrap get/setitem to inform about change in parameter name
T0paE = PE.PyADeprecationError(
"The parameter 'T0pa' in PalLCKep had to be renamed 'tau'.",
solution="Use 'tau' instead of 'T0pa'.")
def getitem(specifier, **kwargs):
if specifier == "T0pa":
PE.warn(T0paE)
return PalLC.__getitem__(self, specifier, **kwargs)
self.__getitem__ = getitem
def setitem(specifier, value):
if specifier == "T0pa":
PE.warn(T0paE)
PalLC.__setitem__(self, specifier, value)
self.__setitem__ = setitem
def plot(self, *args, **kwargs):
"""
Creates a matplotlib figure and axes class instance to visualize the result.
Parameters:
- `FAPlevels` - optional, List of false-alarm probability (FAP) levels
- `*args` - optional, Arguments passed to plot method of axes class.
- `**kwargs` - optional, Keyword arguments passed plot method to axes class.
This method provides a quick and simple way to visualize the results of the a
periodogram calculation.
Returns:
The created *Figure* and *Axes* class instances.
"""
if not _mplImport:
raise(PE.PyARequiredImport("To use this function matplotlib must be installed.", \
where="PeriodBase::plot", solution=["Install matplotlib.", "Avoid call and use custom plotting interface."]))
fig = mpl.figure()
ax = fig.add_subplot(1, 1, 1)
if "FAPlevels" in kwargs:
fapLvs = numpy.array(kwargs["FAPlevels"])
del kwargs["FAPlevels"]
powLvs = self.powerLevel(fapLvs)
for i, powLv in enumerate(powLvs):
ax.plot([self.freq.min(), self.freq.max()], [powLv, powLv],
'k--')
ax.annotate("FAP " + ("%2.3g" % fapLvs[i]),
xy=(self.freq.max(), powLv),
xytext=None,
xycoords='data',
textcoords='data',
arrowprops=None,
horizontalalignment="right")
if self.label is None:
ax.set_title("Periodogram")
ax.set_xlabel("Frequency")
ax.set_ylabel("Power")
else:
ax.set_title(self.label["title"])
ax.set_xlabel(self.label["xlabel"])
ax.set_ylabel(self.label["ylabel"])
ax.plot(self.freq, self.power, *args, **kwargs)
return fig, ax
def __init__(self, skipUpdate=False, forceUpdate=False):
if not _ic.check["astropy"]:
raise(PE.PyARequiredImport("The 'astropy' package is not installed. astropy is required to read VO tables.",
solution="Please install 'astropy'."))
configFilename = os.path.join("pyasl", "resBased", "epeuvo.cfg")
pp.PyAUpdateCycle.__init__(self, configFilename, "ExoUpdate")
self.dataFileName = os.path.join("pyasl", "resBased", "epeu.vo.gz")
self._fs = pp.PyAFS()
if forceUpdate:
self._update(self._download)
elif (self.needsUpdate() or (not self._fs.fileExists(self.dataFileName))) and (not skipUpdate):
# Download data if data file does not exist or
# regular update is indicated
self._update(self._download)
self._readData()
def plotTraceHist(self, parm):
"""
Plots trace and histogram (distribution).
Parameters
----------
parm : string
The variable name.
"""
if not ic.check["matplotlib"]:
PE.warn(PE.PyARequiredImport("To use 'plotTraceHist', matplotlib has to be installed.", \
solution="Install matplotlib."))
return
self._parmCheck(parm)
plt.subplot(1, 2, 1)
self.plotTrace(parm)
plt.subplot(1, 2, 2)
self.plotHist(parm)
def plotTrace(self, parm, fmt='b-'):
"""
Plots the trace.
Parameters
----------
parm : string
The variable name.
fmt : string, optional
The matplotlib format string used to plot the trace.
Default is 'b-'.
"""
if not ic.check["matplotlib"]:
PE.warn(PE.PyARequiredImport("To use 'plotTrace', matplotlib has to be installed.", \
solution="Install matplotlib."))
return
self._parmCheck(parm)
plt.plot(self[parm], fmt, label=parm + " trace")
plt.legend()
def plotFIP():
"""
Show a plot of first ionization energy vs. atomic number.
"""
if not _ic.check["matplotlib"]:
raise(PE.PyARequiredImport("Could not import matplotlib."))
fip = FirstIonizationPot()
z = range(1,31,1)
fips = []
for an in z:
fips.append(fip.getFIP(an)[0])
import matplotlib.pylab as plt
plt.xlabel("Atomic number")
plt.ylabel("First ionization energy [eV]")
plt.plot(z, fips, 'bp-')
plt.show()
def _plot(self):
"""
Create a plot.
"""
try:
import matplotlib.pylab as plt
except ImportError:
raise (PE.PyARequiredImport("Could not import matplotlib.pylab."))
self.fig = plt.figure()
self.fig.subplots_adjust(hspace=0.35)
self.ax = self.fig.add_subplot(2, 1, 1)
self.ax.set_title("Normalized periodogram")
self.ax.set_xlabel("Frequency")
self.ax.set_ylabel("Power")
self.ax.plot(self.freq, self.power, 'b.--')
self.ax = self.fig.add_subplot(2, 1, 2)
self.ax.set_title("Data")
self.ax.set_xlabel("Time")
self.ax.set_ylabel("Data")
self.ax.plot(self.t, self.y, 'r.--')
plt.show()
def __init__(self, lineList, uniSig=None, modelBinsize=0.005, useFastRB=True, verbose=False, onlyAbs=True):
if not ic.check["scipy"]:
raise(PE.PyARequiredImport("Could not import scipy.", \
solution="Install scipy."))
# Check whether depths are given
self._depthsGiven = (len(lineList[0,::]) == 3)
if (not self._depthsGiven) and (uniSig is None):
raise(PE.PyAValError("No width and no depth given.", \
solution="Specify line depth in `lineList` or use `uniSig`."))
# Check whether unified sigma shall be used
# Note: Line depth will be ignored then
self._uniSig = uniSig
# Only absorption lines
self._onlyAbs = onlyAbs
# Verbosity
self._verbose = verbose
# Use fast rotational broadening?
self._usefastRB = useFastRB
# Save binsize for the model
self._modelBinsize = modelBinsize
# Copy line list
self._lineList = lineList.copy()
# Number of lines
self._nl = len(lineList[::,0])
# A MultiGaussFit object
self._mg = fuf.MultiGauss1d(self._nl)
# Store all parameter names from GaussFit
pars = list(self._mg.parameters().keys())
# Remove lin and off from multiGauss keys
pars.remove("lin")
pars.remove("off")
pars.extend(["lineScale", "scale", "eps", "vrad", "vsini"])
fuf.OneDFit.__init__(self, pars)
# Name the model
self.setRootName("LineListGauss")
# Assign start values
for i in range(self._nl):
p = str(i+1)
# Assign position
self._mg["mu"+p] = lineList[i,0]
self["mu"+p] = lineList[i,0]
# Assign amplitude/EW
# Note: Positive EWs correspond to absorption lines
self._mg["A"+p] = -lineList[i,1]
self["A"+p] = lineList[i,1]
# Assign width (sigma)
if self._depthsGiven and (self._uniSig is None):
# Depth IS given and no unified sigma specified
self._mg["sig"+p] = abs(lineList[i,1])/((1.-lineList[i,2]) * np.sqrt(2.*np.pi))
self["sig"+p] = self._mg["sig"+p]
elif not (self._uniSig is None):
# uniSig IS given
self._mg["sig"+p] = self._uniSig
self["sig"+p] = self._mg["sig"+p]
self["scale"] = 1.0
self["lineScale"] = 1.0
if self._onlyAbs:
# Apply restrictions to prevent emission lines
for i in range(self._nl):
p = str(i+1)
self.setRestriction({"A"+p:[0.0, None]})
def ibtrapz(x, y, x0, x1, iaout=False):
"""
Use the trapezoid rule to integrate and interpolate boundary values.
Can be used for integration on tabled data, where the integration boundaries
are not located on the grid. The values to use at the boundaries are
determined using linear interpolation.
Parameters
----------
x,y : arrays
The data.
x0, x1 : float
The integration boundaries.
iaout : boolean, optional
If True, the arrays used internally for
integration are also returned. The default
is False.
Returns
-------
Integral : float
The value of the resulting integral.
xi, yi : arrays, optional
Internally used arrays for integration including
the values derived at the boundaries. Only returned
if `iaout` is set to True.
"""
if not _ic.check["scipy"]:
raise(PE.PyARequiredImport("scipy could not be imported.", \
where="ibtrapz", \
solution="Please install scipy to use ibtrapz."))
import scipy.interpolate as sci
import scipy.integrate as scinteg
if (x1 < x0):
raise(PE.PyAValError("x1 must be larger than x0.", \
where="ibtrapz", \
solution="Exchange boundaries."))
if np.any(x[1:] - x[0:-1] < 0.0):
raise(PE.PyAValError("x axis must be in ascending order.", \
where="ibtrapz", \
solution="Sort values."))
if (x0 < np.min(x)) or (x1 > np.max(x)):
raise(PE.PyAValError("One or both integration boundaries are beyond the range of data given.", \
where="ibtrapz", \
solution="Give more data or adjust integration boundaries."))
# Interpolate at integration boundaries
fi = sci.interp1d(x, y)
y0, y1 = fi(x0), fi(x1)
# Search data within boundaries
indi = np.where((x > x0) & (x < x1))[0]
# Construct arrays to be used in integration
xi = np.zeros(len(indi) + 2)
yi = np.zeros(len(indi) + 2)
xi[1:-1] = x[indi]
yi[1:-1] = y[indi]
xi[0] = x0
xi[-1] = x1
yi[0] = y0
yi[-1] = y1
if iaout:
return scinteg.trapz(yi, xi), xi, yi
return scinteg.trapz(yi, xi)
def crosscorrRV(w,
f,
tw,
tf,
rvmin,
rvmax,
drv,
mode="doppler",
skipedge=0,
edgeTapering=None):
"""
Cross-correlate a spectrum with a template.
The algorithm implemented here works as follows: For
each RV shift to be considered, the wavelength axis
of the template is shifted, either linearly or using
a proper Doppler shift depending on the `mode`. The
shifted template is then linearly interpolated at
the wavelength points of the observation
(spectrum) to calculate the cross-correlation function.
Parameters
----------
w : array
The wavelength axis of the observation.
f : array
The flux axis of the observation.
tw : array
The wavelength axis of the template.
tf : array
The flux axis of the template.
rvmin : float
Minimum radial velocity for which to calculate
the cross-correlation function [km/s].
rvmax : float
Maximum radial velocity for which to calculate
the cross-correlation function [km/s].
drv : float
The width of the radial-velocity steps to be applied
in the calculation of the cross-correlation
function [km/s].
mode : string, {lin, doppler}, optional
The mode determines how the wavelength axis will be
modified to represent a RV shift. If "lin" is specified,
a mean wavelength shift will be calculated based on the
mean wavelength of the observation. The wavelength axis
will then be shifted by that amount. If "doppler" is
specified (the default), the wavelength axis will
properly be Doppler-shifted.
skipedge : int, optional
If larger zero, the specified number of bins will be
skipped from the begin and end of the observation. This
may be useful if the template does not provide sufficient
coverage of the observation.
edgeTapering : float or tuple of two floats
If not None, the method will "taper off" the edges of the
observed spectrum by multiplying with a sine function. If a float number
is specified, this will define the width (in wavelength units)
to be used for tapering on both sides. If different tapering
widths shall be used, a tuple with two (positive) numbers
must be given, specifying the width to be used on the low- and
high wavelength end. If a nonzero 'skipedge' is given, it
will be applied first. Edge tapering can help to avoid
edge effects (see, e.g., Gullberg and Lindegren 2002, A&A 390).
Returns
-------
dRV : array
The RV axis of the cross-correlation function. The radial
velocity refer to a shift of the template, i.e., positive
values indicate that the template has been red-shifted and
negative numbers indicate a blue-shift of the template.
The numbers are given in km/s.
CC : array
The cross-correlation function.
"""
if not _ic.check["scipy"]:
raise(PE.PyARequiredImport("This routine needs scipy (.interpolate.interp1d).", \
where="crosscorrRV", \
solution="Install scipy"))
import scipy.interpolate as sci
# Copy and cut wavelength and flux arrays
w, f = w.copy(), f.copy()
if skipedge > 0:
w, f = w[skipedge:-skipedge], f[skipedge:-skipedge]
if edgeTapering is not None:
# Smooth the edges using a sine
if isinstance(edgeTapering, float):
edgeTapering = [edgeTapering, edgeTapering]
if len(edgeTapering) != 2:
raise(PE.PyAValError("'edgeTapering' must be a float or a list of two floats.", \
where="crosscorrRV"))
if edgeTapering[0] < 0.0 or edgeTapering[1] < 0.0:
raise(PE.PyAValError("'edgeTapering' must be (a) number(s) >= 0.0.", \
where="crosscorrRV"))
# Carry out edge tapering (left edge)
indi = np.where(w < w[0] + edgeTapering[0])[0]
f[indi] *= np.sin((w[indi] - w[0]) / edgeTapering[0] * np.pi / 2.0)
# Carry out edge tapering (right edge)
indi = np.where(w > (w[-1] - edgeTapering[1]))[0]
f[indi] *= np.sin((w[indi] - w[indi[0]]) / edgeTapering[1] * np.pi /
2.0 + np.pi / 2.0)
# Speed of light in km/s
c = 299792.458
# Check order of rvmin and rvmax
if rvmax <= rvmin:
raise(PE.PyAValError("rvmin needs to be smaller than rvmax.",
where="crosscorrRV", \
solution="Change the order of the parameters."))
# Check whether template is large enough
if mode == "lin":
meanWl = np.mean(w)
dwlmax = meanWl * (rvmax / c)
dwlmin = meanWl * (rvmin / c)
if (tw[0] + dwlmax) > w[0]:
raise(PE.PyAValError("The minimum wavelength is not covered by the template for all indicated RV shifts.", \
where="crosscorrRV", \
solution=["Provide a larger template", "Try to use skipedge"]))
if (tw[-1] + dwlmin) < w[-1]:
raise(PE.PyAValError("he maximum wavelength is not covered by the template for all indicated RV shifts.", \
where="crosscorrRV", \
solution=["Provide a larger template", "Try to use skipedge"]))
elif mode == "doppler":
# Ensure that the template covers the entire observaion for all shifts
maxwl = tw[-1] * (1.0 + rvmin / c)
minwl = tw[0] * (1.0 + rvmax / c)
if minwl > w[0]:
raise(PE.PyAValError("he minimum wavelength is not covered by the template for all indicated RV shifts.", \
where="crosscorrRV", \
solution=["Provide a larger template", "Try to use skipedge"]))
if maxwl < w[-1]:
raise(PE.PyAValError("The maximum wavelength is not covered by the template for all indicated RV shifts.", \
where="crosscorrRV", \
solution=["Provide a larger template", "Try to use skipedge"]))
else:
raise(PE.PyAValError("Unknown mode: " + str(mode), \
where="crosscorrRV", \
solution="See documentation for available modes."))
# Calculate the cross correlation
drvs = np.arange(rvmin, rvmax, drv)
cc = np.zeros(len(drvs))
for i, rv in enumerate(drvs):
if mode == "lin":
# Shift the template linearly
fi = sci.interp1d(tw + meanWl * (rv / c), tf)
elif mode == "doppler":
# Apply the Doppler shift
fi = sci.interp1d(tw * (1.0 + rv / c), tf)
# Shifted template evaluated at location of spectrum
cc[i] = np.sum(f * fi(w))
return drvs, cc
def read1dFitsSpec(fn, hdu=0, fullout=False, CRPIX1=None, keymap={}):
"""
Read a simple 1d spectrum from fits file.
Reads a 1d-spectrum from a file and constructs the
associated wavelength axis. To this end, the expression:
wvl = ((np.arange(N) + 1.0) - CRPIX1) * CDELT1 + CRVAL1
will be evaluated, where N is the number of bins and
CRPIX1, CDELT1, and CRVAL1 are header keywords.
Parameters
----------
fn : string
Filename
hdu : int, optional
The number of the HDU to be used. The default
is 0, i.e., the primary HDU.
fullout : boolean, optional
If True, the header keywords used to construct
the wavelength axis will be returned. The default
is False.
CRPIX1 : int, optional
Can be used to circumvent missing CRPIX1 entry.
keymap : dict, optional
Can be used to map header keywords
Returns
-------
wvl : array
The wavelength array.
flx : array
The flux array.
Examples
--------
::
from PyAstronomy import pyasl
wvl, flx = pyasl.read1dFitsSpec("mySpectrum.fits")
"""
if (not _ic.check["pyfits"]) and (not _ic.check["astropy.io.fits"]):
raise(PE.PyARequiredImport("Could neither import module 'pyfits' and 'astropy.io.fits'.",
where="read1dFitsSpec",
solution="Install pyfits: http://www.stsci.edu/institute/software_hardware/pyfits"))
if not os.path.isfile(fn):
raise(PE.PyAFileError(fn, "ne",
where="read1dFitsSpec",
solution="Check file name."))
if _ic.check["pyfits"]:
import pyfits
else:
import astropy.io.fits as pyfits
hl = pyfits.open(fn)
naxis = hl[hdu].header["NAXIS"]
if hl[hdu].header["NAXIS"] != 1:
if hl[hdu].header["NAXIS"] == 2 and hl[hdu].header["NAXIS2"] == 1:
pass
else:
raise(PE.PyAValError("There is more than one axis (NAXIS = " + str(naxis) + ", actual value is " + str(hl[hdu].header["NAXIS"]) + ").",
where="read1dFitsSpec",
solution="Check file and HDU."))
hkeys = {"CRVAL1": None, "CRPIX1": CRPIX1, "CDELT1": None}
for k in hkeys.keys():
if k not in keymap:
keymap[k] = k
for k in hkeys.keys():
if hkeys[k] is not None:
continue
if not keymap[k] in hl[hdu].header:
raise(PE.PyAValError("Header does not contain required keyword '" + str(keymap[k]) + "'",
where="read1dFitsSpec",
solution="Check file and HDU."))
hkeys[k] = hl[hdu].header[keymap[k]]
N = int(hl[hdu].header["NAXIS1"])
# Construct wvl axis
wvl = ((np.arange(N) + 1.0) - hkeys["CRPIX1"]
) * hkeys["CDELT1"] + hkeys["CRVAL1"]
# Get flux
if naxis == 1:
flx = np.array(hl[hdu].data)
elif naxis == 2:
flx = np.array(hl[hdu].data[0])
if fullout:
# Full output
return wvl, flx, hkeys
return wvl, flx
def write1dFitsSpec(fn,
flux,
wvl=None,
waveParams=None,
fluxErr=None,
header=None,
clobber=False,
refFileName=None,
refFileExt=0):
"""
Write a 1d spectrum with equidistant binning to a fits file.
Write a 1d-spectrum to a file. Wavelength axis and header keywords
are related through the following expression:
wvl = ((np.arange(N) + 1.0) - CRPIX1) * CDELT1 + CRVAL1,
where CRPIX1, CDELT1, and CRVAL1 are the
relevant header keywords.
The function allows to specify an existing fits extension, from
which the header will be cloned. Alternatively, an arbitrary,
user-defined header may be given.
Parameters
----------
fn : string
Filename
flux : array
Flux array
wvl : array, optional
Wavelength array. Either the wavelength array or the header
keywords have to be provided (see `waveParams`).
waveParams : dict, optional
Wavelength information required in the fits-header.
Required keys are CDELT, CRVAL, CRPIX or (CDELT1, CRVAL1, CRPIX1).
fluxErr : array, optional
Flux errors. If given, the error will be stored in an additional extension.
header : dict, optional
Dictionary with header information to be transfered to the new file.
Note that the wavelength information will be overwritten
by the information given to this routine.
If both, a reference file to clone the header from and the header parameters
are given, the cloned header from the reference file will be overwritten and
extended by the keywords specified here.
refFileName : string, optional
Clone header keywords from a reference file.
Note that the wavelength information will be overwritten by the information
by the information given to this routine.
refFileExt : int, optional
Reference-file extension to be used for cloning the header keywords.
The default is 0.
"""
reservedKeyWords = [
"BITPIX", "SIMPLE", "NAXIS", "NAXIS1", "CDELT1", "CRPIX1", "CRVAL1",
"EXTEND"
]
if (not _ic.check["pyfits"]) and (not _ic.check["astropy.io.fits"]):
raise (PE.PyARequiredImport(
"Could neither import module 'pyfits' and 'astropy.io.fits'.",
where="write1dFitsSpec",
solution=
"Install pyfits: http://www.stsci.edu/institute/software_hardware/pyfits"
))
if wvl is None and waveParams is None:
raise (PE.PyAValError(
"The wavelength axis is not defined, i.e., neither \'wvl\' nor \'waveParams\' is specified.",
where="write1dFitsSpec",
solution=
"Provide wavelength array as \'wvl\' or wavelength information as \'waveParams\'."
))
if wvl is not None and waveParams is not None:
raise (PE.PyAValError(
"You provided the wavelength axis as \'wvl\' AND wavelength information as \'waveParams\'. Don't know which to use.",
where="write1dFitsSpec",
solution=
"Provide only wavelength array via \'wvl\' or wavelength information via \'waveParams\'."
))
if wvl is not None:
# Check whether the wavelength axis is equidistant
dwl = wvl[1:] - wvl[0:-1]
if (np.max(dwl) - np.min(dwl)) / np.max(dwl) > 1e-6:
raise (PE.PyAValError(
"Wavelength axis seems not to be equidistant.",
where="write1dFitsSpec",
solution=[
"Check wavelength array.",
"Consider passing wavelength information via `waveParams`."
]))
if os.path.isfile(fn) and not clobber:
raise (PE.PyAFileError(
fn,
"ae",
where="write1dFitsSpec",
solution="File exists; set clobber=True to overwrite."))
if _ic.check["pyfits"]:
import pyfits
else:
import astropy.io.fits as pyfits
# Put the flux into the output file.
# Generate primary HDU
hdu = pyfits.PrimaryHDU(flux)
if refFileName:
# If the is a reference file, its header will be used to populate
# the header of the file to be written.
if not os.path.isfile(refFileName):
raise (PE.PyAFileError(str(refFileName),
"ne",
where="write1dFitsSpec",
solution="Check file name."))
ff = pyfits.open(refFileName)[refFileExt]
for k in ff.header.keys():
if k not in reservedKeyWords and k != "COMMENT":
try:
if len(k) > 8:
hdu.header["HIERARCH " + k] = ff.header[k]
else:
hdu.header[k] = ff.header[k]
except:
PE.warn(
PE.PyAValError("Cannot write keyword <" + str(k) +
"> with content " + str(ff.header[k])))
if header is not None:
# A use-defined header was specified
for k in header.keys():
hdu.header[k] = header[k]
# Header keywords relevant for wavelength axis
hk = {}
# The only allowed type here
hk["CTYPE1"] = "Linear"
# If wavelength array is provided, create header keywords:
if wvl is not None:
hk["CRPIX1"] = 1
hk["CRVAL1"] = wvl[0]
hk["CDELT1"] = float(wvl[-1] - wvl[0]) / (len(wvl) - 1)
elif waveParams is not None:
requiredKeys = ["CRPIX", "CRVAL", "CDELT"]
# Counts whether all required keywords are provided
count = 0
for k in requiredKeys:
subCount = 0
for p in six.iterkeys(waveParams):
if p.upper() == k or p.upper() == k + "1":
hk[k + "1"] = waveParams[p]
subCount += 1
if subCount == 1:
count += 1
else:
# This occurs if, e.g., both "CRVAL" and "CRVAL1" are present
raise (PE.PyAValError(
"The wavelength parameters provided via \'waveParams\' contain ambiguous parameters. "
+ "You provided multiple values for " + k + ".",
where="write1dFitsSpec",
solution=
"Check content of \'waveParams\' so that only one value for "
+ k +
" is provided (including lower/upper case and presence of a trailing \'1\'."
))
if count < 3:
raise (PE.PyAValError(
"You provided an incomplete set of waveParams.",
where="write1dFitsSpec",
solution="Required keywords are CRPIX, CRVAL, and CDELT."))
# (Over-)Write wavelength-related header information
for k in hk.keys():
hdu.header[k] = hk[k]
if not fluxErr is None:
# An error on the flux was given
hdue = pyfits.ImageHDU(fluxErr)
for k in hk.keys():
# Add wavelength information to error header
hdue.header[k] = hk[k]
hdulist = pyfits.HDUList([hdu, hdue])
else:
hdulist = pyfits.HDUList([hdu])
hdulist.writeto(fn, overwrite=clobber)
def sunpos2(jd,
end_jd=None,
jd_steps=None,
outfile=None,
radian=False,
plot=False,
full_output=False):
"""
Compute right ascension and declination of the Sun at a given time.
Parameters
----------
jd : float
The Julian date
end_jd : float, optional
The end of the time period as Julian date. If given,
`sunpos` computes RA and DEC at `jd_steps` time points
between `jd` and ending at `end_jd`.
jd_steps : integer, optional
The number of steps between `jd` and `end_jd`
for which RA and DEC are to be calculated.
outfile : string, optional
If given, the output will be written to a file named according
to `outfile`.
radian : boolean, optional
Results are returned in radian instead of in degrees.
Default is False.
plot : boolean, optional
If True, the result is plotted.
full_output: boolean, optional
If True, `sunpos`, additionally, returns the elongation and
obliquity of the Sun.
Returns
-------
Time : array
The JDs for which calculations where carried out.
Ra : array
Right ascension of the Sun.
Dec : array
Declination of the Sun.
Elongation : array, optional
Elongation of the Sun (only of `full_output`
is set to True).
Obliquity : array, optional
Obliquity of the Sun (only of `full_output`
is set to True).
Notes
-----
.. note:: This function was ported from the IDL Astronomy User's Library.
:IDL - Documentation:
NAME:
SUNPOS
PURPOSE:
To compute the RA and Dec of the Sun at a given date.
CALLING SEQUENCE:
SUNPOS, jd, ra, dec, [elong, obliquity, /RADIAN ]
INPUTS:
jd - The Julian date of the day (and time), scalar or vector
usually double precision
OUTPUTS:
ra - The right ascension of the sun at that date in DEGREES
double precision, same number of elements as jd
dec - The declination of the sun at that date in DEGREES
OPTIONAL OUTPUTS:
elong - Ecliptic longitude of the sun at that date in DEGREES.
obliquity - the obliquity of the ecliptic, in DEGREES
OPTIONAL INPUT KEYWORD:
/RADIAN - If this keyword is set and non-zero, then all output variables
are given in Radians rather than Degrees
NOTES:
Patrick Wallace (Rutherford Appleton Laboratory, UK) has tested the
accuracy of a C adaptation of the sunpos.pro code and found the
following results. From 1900-2100 SUNPOS gave 7.3 arcsec maximum
error, 2.6 arcsec RMS. Over the shorter interval 1950-2050 the figures
were 6.4 arcsec max, 2.2 arcsec RMS.
The returned RA and Dec are in the given date's equinox.
Procedure was extensively revised in May 1996, and the new calling
sequence is incompatible with the old one.
METHOD:
Uses a truncated version of Newcomb's Sun. Adapted from the IDL
routine SUN_POS by CD Pike, which was adapted from a FORTRAN routine
by B. Emerson (RGO).
EXAMPLE:
(1) Find the apparent RA and Dec of the Sun on May 1, 1982
IDL> jdcnv, 1982, 5, 1,0 ,jd ;Find Julian date jd = 2445090.5
IDL> sunpos, jd, ra, dec
IDL> print,adstring(ra,dec,2)
02 31 32.61 +14 54 34.9
The Astronomical Almanac gives 02 31 32.58 +14 54 34.9 so the error
in SUNPOS for this case is < 0.5".
(2) Find the apparent RA and Dec of the Sun for every day in 1997
IDL> jdcnv, 1997,1,1,0, jd ;Julian date on Jan 1, 1997
IDL> sunpos, jd+ dindgen(365), ra, dec ;RA and Dec for each day
MODIFICATION HISTORY:
Written by Michael R. Greason, STX, 28 October 1988.
Accept vector arguments, W. Landsman April,1989
Eliminated negative right ascensions. MRG, Hughes STX, 6 May 1992.
Rewritten using the 1993 Almanac. Keywords added. MRG, HSTX,
10 February 1994.
Major rewrite, improved accuracy, always return values in degrees
W. Landsman May, 1996
Added /RADIAN keyword, W. Landsman August, 1997
Converted to IDL V5.0 W. Landsman September 1997
"""
if end_jd is None:
# Form time in Julian centuries from 1900.0
start_jd = (jd - 2415020.0) / 36525.0
# Zime array
time = np.array(start_jd)
else:
if jd >= end_jd:
raise(PE.PyAValError("`end_jd` needs to be larger than `jd`.", \
where="sunpos", \
solution="Modify the parameters."))
if jd_steps is None:
raise(PE.PyAValError("You specified `end_jd`, but no value for `jd_steps`.", \
where="sunpos", \
solution="Specify `jd_steps`, e.g., given jd_steps=10"))
# Form time in Julian centuries from 1900.0
start_jd = (jd - 2415020.0) / 36525.0
end_jd = (end_jd - 2415020.0) / 36525.0
# Time array
timestep = (end_jd - start_jd) / float(jd_steps)
time = np.arange(start_jd, end_jd, timestep)
# Mean solar longitude
sunlon = (279.696678 + idlMod((36000.768925 * time), 360.0)) * 3600.0
# Allow for ellipticity of the orbit (equation of center)
# using the Earth's mean anomaly ME
me = 358.475844 + idlMod((35999.049750 * time), 360.0)
ellcor = (6910.1 - 17.2 * time) * np.sin(
me * np.pi / 180.) + 72.3 * np.sin(2.0 * me * np.pi / 180.)
sunlon += ellcor
# Allow for the Venus perturbations using the mean anomaly of Venus MV
mv = 212.603219 + idlMod((58517.803875 * time), 360.0)
vencorr = 4.8 * np.cos( (299.1017 + mv - me)*np.pi/180. ) + \
5.5 * np.cos( (148.3133 + 2.0 * mv - 2.0 * me )*np.pi/180. ) + \
2.5 * np.cos( (315.9433 + 2.0 * mv - 3.0 * me )*np.pi/180. ) + \
1.6 * np.cos( (345.2533 + 3.0 * mv - 4.0 * me )*np.pi/180. ) + \
1.0 * np.cos( (318.15 + 3.0 * mv - 5.0 * me )*np.pi/180. )
sunlon += vencorr
# Allow for the Mars perturbations using the mean anomaly of Mars MM
mm = 319.529425 + idlMod((19139.858500 * time), 360.0)
marscorr = 2.0 * np.cos( (343.8883 - 2.0 * mm + 2.0 * me)*np.pi/180. ) + \
1.8 * np.cos( (200.4017 - 2.0 * mm + me)*np.pi/180. )
sunlon += marscorr
# Allow for the Jupiter perturbations using the mean anomaly of Jupiter MJ
mj = 225.328328 + idlMod((3034.6920239 * time), 360.0)
jupcorr = 7.2 * np.cos( (179.5317 - mj + me )*np.pi/180. ) + \
2.6 * np.cos( (263.2167 - mj )*np.pi/180. ) + \
2.7 * np.cos( ( 87.1450 - 2.0 * mj + 2.0 * me )*np.pi/180. ) + \
1.6 * np.cos( (109.4933 - 2.0 * mj + me )*np.pi/180. )
sunlon += jupcorr
# Allow for the Moon's perturbations using the mean elongation of
# the Moon from the Sun D
d = 350.7376814 + idlMod((445267.11422 * time), 360.0)
mooncorr = 6.5 * np.sin(d * np.pi / 180.)
sunlon += mooncorr
# Allow for long period terms
longterm = 6.4 * np.sin((231.19 + 20.20 * time) * np.pi / 180.)
sunlon += longterm
sunlon = idlMod((sunlon + 2592000.0), 1296000.0)
longmed = sunlon / 3600.0
# Allow for Aberration
sunlon -= 20.5
# Allow for Nutation using the longitude of the Moons mean node OMEGA
omega = 259.183275 - idlMod((1934.142008 * time), 360.0)
sunlon = sunlon - 17.2 * np.sin(omega * np.pi / 180.)
# Calculate the True Obliquity
oblt = 23.452294 - 0.0130125 * time + (
9.2 * np.cos(omega * np.pi / 180.)) / 3600.0
# Right Ascension and Declination
sunlon /= 3600.0
ra = np.arctan2(
np.sin(sunlon * np.pi / 180.) * np.cos(oblt * np.pi / 180.),
np.cos(sunlon * np.pi / 180.))
neg = np.where(ra < 0.0)[0]
nneg = len(neg)
if nneg > 0: ra[neg] += 2.0 * np.pi
dec = np.arcsin(
np.sin(sunlon * np.pi / 180.) * np.sin(oblt * np.pi / 180.))
if radian:
oblt *= (np.pi / 180.)
longmed *= (np.pi / 180.)
else:
ra /= (np.pi / 180.)
dec /= (np.pi / 180.)
jd = time * 36525.0 + 2415020.0
if outfile is not None:
# Write results to a file
of = open(outfile, 'w')
of.write("# File created by 'sunpos'\n")
if not full_output:
of.write("# 1) JD, 2) ra, 3) dec\n")
np.savetxt(of, np.transpose(np.vstack((jd, ra, dec))))
else:
of.write("# 1) JD, 2) ra, 3) dec, 4) longitude, 5) obliquity\n")
np.savetxt(of, np.transpose(np.vstack(
(jd, ra, dec, longmed, oblt))))
of.close()
if plot:
if not _ic.check["matplotlib"]:
raise(PE.PyARequiredImport("Could not import matplotlib.", \
where="sunpos", \
solution=["Install matplotlib", "Switch `plot` flag to False."]))
import matplotlib.pylab as plt
plt.plot(jd, ra, 'k-', label="RA")
plt.plot(jd, dec, 'g-', label="DEC")
plt.legend()
plt.show()
if full_output:
return jd, ra, dec, longmed, oblt
else:
return jd, ra, dec
def transitVisibilityPlot(allData, markTransit=False, plotLegend=True, showMoonDist=True, print2file=False):
"""
Plot the visibility of transits.
This function can conveniently be used with the output of
the transitTimes function.
Parameters
----------
allData : dictionary
Essentially the output of `transitTimes`.
A dictionary mapping consecutive numbers (one per transit) to
another dictionary providing the following keys:
============ ====================================================
Key Description
------------ ----------------------------------------------------
Planet name Name of the planet
Transit jd (Only if `markTransit is True)
Array giving JD of start, mid-time, and end of
transit.
Obs jd Array specifying the HJD of the start, center and
end of the observation.
Obs cal Equivalent to 'Obs jd', but in the form of the
calendar date. In particular, for each date, a list
containing [Year, month, day, fractional hours]
is given.
Obs coord East longitude [deg], latitude [deg], and
altitude [m] of the observatory.
Star ra Right ascension of the star [deg].
Star dec Declination of the star [deg].
============ ====================================================
.. note:: To use the list created by transitTimes, the LONGITUDE and LATITUDE
of the observatory location must have been specified.
markTransit : boolean, optional
If True (default is False), the in-transit times will
be clearly indicated in the plot.
Note that this would not be the case otherwise, which is particularly
important if extra off-transit time before and after the transit has been
requested.
showMoonDist : boolean, optional
If True (default), the Moon distance will be shown.
print2file : boolean or string, optional
If True, the plot will be dumped to a png-file named:
"transitVis-"[planetName].png. The default is False. If a string is given,
it specifies the name of the output file.
"""
from PyAstronomy.pyasl import _ic
if not _ic.check["matplotlib"]:
raise(PE.PyARequiredImport("matplotlib is not installed.",
where="transitVisibilityPlot",
solution="Install matplotlib (http://matplotlib.org/)"))
import matplotlib
import matplotlib.pylab as plt
from mpl_toolkits.axes_grid1 import host_subplot
from matplotlib.ticker import MultipleLocator
from matplotlib.font_manager import FontProperties
from matplotlib import rcParams
rcParams['xtick.major.pad'] = 12
if len(allData) == 0:
raise(PE.PyAValError("Input dictionary is empty",
where="transitVisibilityPlot",
solution=["Use `transitTimes` to generate input dictionary",
"Did you forget to supply observer's location?",
"If you used `transitTime`, you might need to change the call argument (e.g., times)"]))
# Check whether all relevant data have been specified
reqK = ["Obs jd", "Obs coord", "Star ra",
"Star dec", "Obs cal", "Planet name"]
if markTransit:
reqK.append("Transit jd")
missingK = []
for k in reqK:
if not k in allData[1]:
missingK.append(k)
if len(missingK) > 0:
raise(PE.PyAValError("The following keys are missing in the input dictionary: " + ', '.join(missingK),
where="transitVisibilityPlot",
solution="Did you specify observer's location in `transitTimes`?"))
fig = plt.figure(figsize=(15, 10))
fig.subplots_adjust(left=0.07, right=0.8, bottom=0.15, top=0.88)
ax = host_subplot(111)
font0 = FontProperties()
font1 = font0.copy()
font0.set_family('sans-serif')
font0.set_weight('light')
font1.set_family('sans-serif')
font1.set_weight('medium')
for n in six.iterkeys(allData):
# JD array
jdbinsize = 1.0 / 24. / 10.
jds = np.arange(allData[n]["Obs jd"][0],
allData[n]["Obs jd"][2], jdbinsize)
# Get JD floating point
jdsub = jds - np.floor(jds[0])
# Get alt/az of object
altaz = eq2hor.eq2hor(jds, np.ones(jds.size) * allData[n]["Star ra"], np.ones(jds.size) * allData[n]["Star dec"],
lon=allData[n]["Obs coord"][0], lat=allData[n]["Obs coord"][1],
alt=allData[n]["Obs coord"][2])
# Get alt/az of Sun
sunpos_altaz = eq2hor.eq2hor(jds, np.ones(jds.size) * allData[n]["Sun ra"], np.ones(jds.size) * allData[n]["Sun dec"],
lon=allData[n]["Obs coord"][0], lat=allData[n]["Obs coord"][1],
alt=allData[n]["Obs coord"][2])
# Define plot label
plabel = "[%02d] %02d.%02d.%4d" % (n, allData[n]["Obs cal"][0][2],
allData[n]["Obs cal"][0][1], allData[n]["Obs cal"][0][0])
# Find periods of: day, twilight, and night
day = np.where(sunpos_altaz[0] >= 0.)[0]
twi = np.where(np.logical_and(
sunpos_altaz[0] > -18., sunpos_altaz[0] < 0.))[0]
night = np.where(sunpos_altaz[0] <= -18.)[0]
if (len(day) == 0) and (len(twi) == 0) and (len(night) == 0):
print()
print("transitVisibilityPlot - no points to draw for date %2d.%2d.%4d"
% (allData[n]["Obs cal"][0][2], allData[n]["Obs cal"][0][1], allData[n]["Obs cal"][0][0]))
print("Skip transit and continue with next")
print()
continue
mpos = moonpos(jds)
mpha = moonphase(jds)
mpos_altaz = eq2hor.eq2hor(jds, mpos[0], mpos[1], lon=allData[n]["Obs coord"][0],
lat=allData[n]["Obs coord"][1], alt=allData[n]["Obs coord"][2])
moonind = np.where(mpos_altaz[0] > 0.)[0]
if showMoonDist:
mdist = getAngDist(mpos[0], mpos[1], np.ones(jds.size) * allData[n]["Star ra"],
np.ones(jds.size) * allData[n]["Star dec"])
bindist = int((2.0 / 24.) / jdbinsize)
firstbin = np.random.randint(0, bindist)
for mp in range(0, int(len(jds) / bindist)):
bind = int(firstbin + float(mp) * bindist)
ax.text(jdsub[bind], altaz[0][bind] - 1., str(int(mdist[bind])) + r"$^\circ$", ha="center", va="top",
fontsize=8, stretch='ultra-condensed', fontproperties=font0, alpha=1.)
if markTransit:
# Mark points within transit. These may differ from that pertaining to the
# observation if an extra offset was given to provide off-transit time.
transit_only_ind = np.where(np.logical_and(jds >= allData[n]["Transit jd"][0],
jds <= allData[n]["Transit jd"][2]))[0]
ax.plot(jdsub[transit_only_ind], altaz[0]
[transit_only_ind], 'g', linewidth=6, alpha=.3)
if len(twi) > 1:
# There are points in twilight
linebreak = np.where(
(jdsub[twi][1:] - jdsub[twi][:-1]) > 2.0 * jdbinsize)[0]
if len(linebreak) > 0:
plotrjd = np.insert(jdsub[twi], linebreak + 1, np.nan)
plotdat = np.insert(altaz[0][twi], linebreak + 1, np.nan)
ax.plot(plotrjd, plotdat, "-", color='#BEBEBE', linewidth=1.5)
else:
ax.plot(jdsub[twi], altaz[0][twi], "-",
color='#BEBEBE', linewidth=1.5)
ax.plot(jdsub[night], altaz[0][night],
'k', linewidth=1.5, label=plabel)
ax.plot(jdsub[day], altaz[0][day], color='#FDB813', linewidth=1.5)
altmax = np.argmax(altaz[0])
ax.text(jdsub[altmax], altaz[0][altmax], str(n), color="b", fontsize=14,
fontproperties=font1, va="bottom", ha="center")
if n == 29:
ax.text(1.1, 1.0 - float(n) * 0.04, "too many transits", ha="left", va="top", transform=ax.transAxes,
fontsize=10, fontproperties=font0, color="r")
else:
ax.text(1.1, 1.0 - float(n) * 0.04, plabel, ha="left", va="top", transform=ax.transAxes,
fontsize=12, fontproperties=font0, color="b")
ax.text(1.1, 1.03, "Start of observation", ha="left", va="top", transform=ax.transAxes,
fontsize=12, fontproperties=font0, color="b")
ax.text(1.1, 1.0, "[No.] Date", ha="left", va="top", transform=ax.transAxes,
fontsize=12, fontproperties=font0, color="b")
axrange = ax.get_xlim()
ax.set_xlabel("UT [hours]")
if axrange[1] - axrange[0] <= 1.0:
jdhours = np.arange(0, 3, 1.0 / 24.)
utchours = (np.arange(0, 72, dtype=int) + 12) % 24
else:
jdhours = np.arange(0, 3, 1.0 / 12.)
utchours = (np.arange(0, 72, 2, dtype=int) + 12) % 24
ax.set_xticks(jdhours)
ax.set_xlim(axrange)
ax.set_xticklabels(utchours, fontsize=18)
# Make ax2 responsible for "top" axis and "right" axis
ax2 = ax.twin()
# Set upper x ticks
ax2.set_xticks(jdhours)
ax2.set_xticklabels(utchours, fontsize=18)
ax2.set_xlabel("UT [hours]")
# Horizon angle for airmass
airmass_ang = np.arange(5., 90., 5.)
geo_airmass = airmass.airmassPP(90. - airmass_ang)
ax2.set_yticks(airmass_ang)
airmassformat = []
for t in range(geo_airmass.size):
airmassformat.append("%2.2f" % geo_airmass[t])
ax2.set_yticklabels(airmassformat, rotation=90)
ax2.set_ylabel("Relative airmass", labelpad=32)
ax2.tick_params(axis="y", pad=10, labelsize=10)
plt.text(1.015, -0.04, "Plane-parallel", transform=ax.transAxes, ha='left',
va='top', fontsize=10, rotation=90)
ax22 = ax.twin()
ax22.set_xticklabels([])
ax22.set_frame_on(True)
ax22.patch.set_visible(False)
ax22.yaxis.set_ticks_position('right')
ax22.yaxis.set_label_position('right')
ax22.spines['right'].set_position(('outward', 25))
ax22.spines['right'].set_color('k')
ax22.spines['right'].set_visible(True)
airmass2 = np.array([airmass.airmassSpherical(
90. - ang, allData[n]["Obs coord"][2]) for ang in airmass_ang])
ax22.set_yticks(airmass_ang)
airmassformat = []
for t in range(airmass2.size):
airmassformat.append("%2.2f" % airmass2[t])
ax22.set_yticklabels(airmassformat, rotation=90)
ax22.tick_params(axis="y", pad=10, labelsize=10)
plt.text(1.045, -0.04, "Spherical+Alt", transform=ax.transAxes, ha='left', va='top',
fontsize=10, rotation=90)
ax3 = ax.twiny()
ax3.set_frame_on(True)
ax3.patch.set_visible(False)
ax3.xaxis.set_ticks_position('bottom')
ax3.xaxis.set_label_position('bottom')
ax3.spines['bottom'].set_position(('outward', 50))
ax3.spines['bottom'].set_color('k')
ax3.spines['bottom'].set_visible(True)
ltime, ldiff = localtime.localTime(utchours, np.repeat(
allData[n]["Obs coord"][0], len(utchours)))
jdltime = jdhours - ldiff / 24.
ax3.set_xticks(jdltime)
ax3.set_xticklabels(utchours)
ax3.set_xlim([axrange[0], axrange[1]])
ax3.set_xlabel("Local time [hours]")
ax.yaxis.set_major_locator(MultipleLocator(15))
ax.yaxis.set_minor_locator(MultipleLocator(5))
yticks = ax.get_yticks()
ytickformat = []
for t in range(yticks.size):
ytickformat.append(str(int(yticks[t])) + r"$^\circ$")
ax.set_yticklabels(ytickformat, fontsize=20)
ax.set_ylabel("Altitude", fontsize=18)
yticksminor = np.array(ax.get_yticks(minor=True))
ymind = np.where(yticksminor % 15. != 0.)[0]
yticksminor = yticksminor[ymind]
# ax.set_yticks(yticksminor, minor=True)
m_ytickformat = []
for t in range(yticksminor.size):
m_ytickformat.append(str(int(yticksminor[t])) + r"$^\circ$")
ax.set_yticklabels(m_ytickformat, minor=True)
ax.yaxis.grid(color='gray', linestyle='dashed')
ax.yaxis.grid(color='gray', which="minor", linestyle='dotted')
ax2.xaxis.grid(color='gray', linestyle='dotted')
def decifnec(s):
try:
r = s.decode("utf8")
except AttributeError:
r = s
return r
plt.text(0.5, 0.95, "Transit visibility of " + decifnec(allData[n]["Planet name"]),
transform=fig.transFigure, ha='center', va='bottom', fontsize=20)
if plotLegend:
line1 = matplotlib.lines.Line2D(
(0, 0), (1, 1), color='#FDB813', linestyle="-", linewidth=2)
line2 = matplotlib.lines.Line2D(
(0, 0), (1, 1), color='#BEBEBE', linestyle="-", linewidth=2)
line3 = matplotlib.lines.Line2D(
(0, 0), (1, 1), color='k', linestyle="-", linewidth=2)
line4 = matplotlib.lines.Line2D(
(0, 0), (1, 1), color='g', linestyle="-", linewidth=6, alpha=.3)
if markTransit:
lgd2 = plt.legend((line1, line2, line3, line4), ("day", "twilight", "night", "transit",),
bbox_to_anchor=(0.88, 0.15), loc=2, borderaxespad=0., prop={'size': 12}, fancybox=True)
else:
lgd2 = plt.legend((line1, line2, line3), ("day", "twilight", "night",),
bbox_to_anchor=(0.88, 0.13), loc=2, borderaxespad=0., prop={'size': 12}, fancybox=True)
lgd2.get_frame().set_alpha(.5)
targetco = r"Target coordinates: (%8.4f$^\circ$, %8.4f$^\circ$)" % \
(allData[n]["Star ra"], allData[n]["Star dec"])
obsco = "Obs coord.: (%8.4f$^\circ$, %8.4f$^\circ$, %4d m)" % \
(allData[n]["Obs coord"][0], allData[n]
["Obs coord"][1], allData[n]["Obs coord"][2])
plt.text(0.01, 0.97, targetco, transform=fig.transFigure,
ha='left', va='center', fontsize=10)
plt.text(0.01, 0.95, obsco, transform=fig.transFigure,
ha='left', va='center', fontsize=10)
if (print2file == True):
outfile = "transVis-" + \
str(allData[n]["Planet name"]).replace(" ", "") + ".png"
plt.savefig(outfile, format="png", dpi=300)
elif isinstance(print2file, six.string_types):
plt.savefig(print2file, format="png", dpi=300)
else:
plt.show()
def estimateSNR(x, y, xlen, deg=1, controlPlot=False, xlenMode="dataPoints"):
"""
Estimate Signal to Noise Ratio (SNR) in a data set.
This function provides a simple algorithm to estimate
the SNR in a data set. The algorithm subdivides the
data set into sections with a length determined by the
`xlen` parameter. Each individual subsection is
fitted using a polynomial of degree `deg`. The SNR
is then computed by assuming that the resulting
chi square value is properly distributed according
to the chi-square distribution.
Parameters
----------
x : array
The abscissa values.
y : array
The ordinate values.
xlen : int or float
Length of the data subsection considered
in the computation of the SNR. Whether `xlen`
refers to the number of data points or a fixed
subsection of the abscissa is determined by the
`xlenMode` flag.
deg : int, optional
Degree of the polynomial used to fit the
subsection of the data set. The default is
one.
controlPlot : boolean, optional
If True, a control plot will be shown to
verify the validity of the estimate.
xlenMode : string, {"dataPoints", "excerpt", "all"}, optional
Determines whether `xlen` refers to data points
or a fixed length scale on the abscissa. If 'all' is specified,
all available data will be used in the estimation.
Returns
-------
SNR : dictionary
Contains the SNR estimate for the individual subsections (key 'snrs')
and the final SNR estimate (mean of all 'sub-SNRs', key 'SNR-Estimate').
"""
# Stores the SNRs computed from the individual sections.
snrs = []
i = 0
lastdof = None
if controlPlot:
try:
import matplotlib.pylab as plt
except ImportError:
raise(PE.PyARequiredImport("Cannot import matplotlib.pylab", \
where="estimateSNR", \
solution=["Install matplotlib.", "Change `controlPlot` to False."]))
while True:
if xlenMode == "dataPoints":
# xlen is given in units of data points
indi = range(i*xlen, (i+1)*xlen)
i += 1
if i > len(x)/xlen: break
elif xlenMode == "excerpt":
# xlen is given in units of the abscissa values
indi = np.where(np.logical_and(x >= i*float(xlen), x < (i+1)*float(xlen)))[0]
i += 1
if i*float(xlen) > max(x): break
elif xlenMode == "all":
if i > 1:
break
indi = range(len(x))
i += 1
else:
raise(PE.PyAValError("Unknown xlenMode (" + str(xlenMode) + ").", \
solution = "Use either 'dataPoints' or 'excerpt'."))
# Check whether indi is long enough
if len(indi) <= (deg + 1):
# Skip this subsection
continue
# Fit polynomial, calculate model and residuals
poly = np.polyfit(x[indi], y[indi], deg)
model = np.polyval(poly, x[indi])
residuals = y[indi] - model
# The number of "degrees of freedom)
dof = len(indi) - (deg + 1)
# Calculate reduced chi square ...
stdEstimate = np.sqrt((residuals**2).sum() / dof)
# A brute-force way to compute the expectation of sqrt(1/(X(d)/d)),
# where X(d) is chi-square distributed with d degrees of freedom.
if dof != lastdof:
ccSample = np.random.chisquare(dof, 1000)
corrFac = np.mean(np.sqrt(1.0/(ccSample / dof)))
lastdof = dof
stdEstimate *= corrFac
# ... and SNR
snrs.append(np.mean(model) / stdEstimate)
if controlPlot:
if i == 1:
# This is the first call
ax1 = plt.subplot(3,1,1)
plt.title("Data (blue) and model (red)")
ax2 = plt.subplot(3,1,2, sharex=ax1)
plt.title("Residuals")
ax3 = plt.subplot(3,1,3, sharex=ax1)
plt.title("SNR")
# Create plot
plt.subplot(3,1,1)
plt.plot(x[indi], y[indi], 'b.')
plt.plot(x[indi], model, 'r-')
plt.subplot(3,1,2, sharex=ax1)
plt.plot(x[indi], residuals, 'g.')
plt.subplot(3,1,3, sharex=ax1)
plt.plot(np.mean(x[indi]), snrs[-1], 'bp')
if controlPlot:
plt.show()
return {"SNR-Estimate":np.mean(snrs), "snrs":snrs}
# 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 .gauss1d import *
from .gauss2d import *
from .respModel import *
from .params import *
from .sinexp1d import *
from .polyModel import *