# Plotting and analysis tools¶

Contents:

A collection of tools, tips, and tricks.

2009-07-20 22:36 IJC: Created

2010-10-28 11:53 IJMC: Updated documentation for Sphinx.

2011-06-15 09:34 IJMC: More functions have been added; cleaned documentation.

Return the total (astronomical) magnitude of two combined sources.

INPUTS:
mag1, mag2 : scalars or NumPy arrays.

magnitudes of the two sources.

RETURNS: -2.5 * np.log10(10**(-0.4*mag1) + 10**(-0.4*mag2))

Combine fields in two objects with the same attributes. A handy function!

INPUTS: obj1, obj2 : objects of the same kind obj12 : new object, with fields of 1 and 2 combined. OR -1, if the objects have absolutely no attributes in common. Methods/attributes beginning with an underscore (_) are not combined.
tools.areaOverlappingCircles(r1, r2, d)[source]

Return overlapping area of two circles. From Wolfram Mathworld.

r1, r2 are the radii of the circles.

d is the distance between their centers.

tools.array_or_filename(input, kw_getdata={}, kw_array={}, noneoutput=None)[source]

If input is a Numpy array, return it. If it is of type str, use Pyfits to read in the file and return it. Keyword options are available for the calls to pyfits.getdata and numpy.array. If input is None, return noneoutput.

tools.blam(T, lam)[source]

Planck function in wavelength.

INPUTS: T : scalar or array temperature in Kelvin lam : scalar or array wavelength in microns

Value returned is in (nearly) cgs units: erg/s/cm^2/micron/sr

tools.bnu(T, lam)[source]

Planck function in frequency.

INPUTS: T : scalar or array temperature in Kelvin lam : scalar or array wavelength in microns [but intensity will be per Hz]

Value returned is in cgs units: erg/s/cm^2/Hz/sr

tools.combinations(input_list)[source]

Return all possible combinations of the elements in an input sequence. The last returned element will be the empty list.

E.g., combinations([0,1]) returns [[0, 1], , , []]

Taken from the internet:
http://desk.stinkpot.org:8080/tricks/index.php/2008/04/get-all-possible-combinations-of-a-lists-elements-in-python/
Requirements: copy
tools.contourg(*args, **kw)[source]

Plot filled contours of irregularly-spaced data. :USAGE:

First three inputs must use syntax ‘contour(X,Y,Z)’.

Can set “nbinx=50” or “nbiny=50” for number of bins.

Otherwise, syntax is the same as for matplotlib’s ‘contourf’.

EXAMPLE: ```import tools import numpy as np ``` plotc()
tools.cplim(a1, a2)[source]

Copy axis limits from one axes to another.

INPUTS: a1, a2 – either (1) handles to axes objects, or (2) figure numbers. If figures have subplots, you can refer to a particular subplot using decimal notation. So, 1.3 would refer to subplot 3 of figure 1. matplotlib (when this is written...)
tools.dcf(t, x, y, zerolag=True, nbin=11, binwidth=None, bins=None, prebin=None, reterr=False)[source]

Compute the Discrete Correlation Function for unevenly sampled data.

If your data are evenly sampled, just use numpy.correlate()!

INPUTS: t – (1D sequence) - time sampling of input data. x, y – (1D sequences) - input data. Note that t, x, y should all have the same lengths! zerolag – (bool) - whether to compute DCF for zero-lag datapoints. nbin – (int) - number of computed correlation/lag values to average over binwidth – (float) - width of bins to average over, in units of t. If set, this overrides nbin. bins – (sequence) - edges of bins to use in averaging DCF values. If set, this overrides both nbin and binwidth. prebin – (scalar) - factor by which to bin down initial data and lag pairs. This translates into a speed boost of about this same factor. reterr – bool - False or True meanlags – average lag in each averaged bin rdcf – DCF value in each averaged bin rdcf_err – uncertainty in the DCF value for each averaged bin numpy.correlate()
tools.dict2obj(dic)[source]

Take an input Dict, and turn it into an object with fields corresponding to the dict’s keys.

SEE_ALSO: :func:obj2dict`
tools.drawCircle(x, y, radius, **kw)[source]

Draw a circular patch on the current, or specified, axes.

INPUT: x, y – center of circle radius – radius of circle ax – Axis to draw upon. if None, defaults to current axes. dodraw – if True, call ‘draw()’ function to immediately re-draw axes. **kw – options passable to matplotlib.patches.Circle() Axes will NOT auto-rescale after this is called.
tools.drawEllipse(x, y, width, height, **kw)[source]

Draw an elliptical patch on the current, or specified, axes.

INPUT: x, y – center of ellipse width – width of ellipse height – width of ellipse ax – Axis to draw upon. if None, defaults to current axes. dodraw – if True, call ‘draw()’ function to immediately re-draw axes. **kw – options passable to matplotlib.patches.Ellipse() (angle, linewidth, fill, ...) Axes will NOT auto-rescale after this is called.
tools.drawPolygon(xy, **kw)[source]

Draw a rectangle patch on the current, or specified, axes.

INPUT: xy – numpy array of coordinates, with shape Nx2. ax – Axis to draw upon. if None, defaults to current axes. dodraw – if True, call ‘draw()’ function to immediately re-draw axes. **kw – options passable to matplotlib.patches.Polygon() drawRectangle() Axes will NOT auto-rescale after this is called.
tools.drawRectangle(x, y, width, height, **kw)[source]

Draw a rectangle patch on the current, or specified, axes.

INPUT:

x, y – lower-left corner of rectangle

width, height – dimensions of rectangle

OPTIONAL INPUT:

ax – Axis to draw upon. if None, defaults to current axes.

dodraw – if True, call ‘draw()’ function to immediately re-draw axes.

**kw – options passable to matplotlib.patches.Rectangle()

NOTE: Axes will NOT auto-rescale after this is called.
tools.ee_psf(psf, energy=0.8, center=None)[source]

%% Determine the diameter in pixels for a given Encircled Energy % % [d, ee] = ee_psf(psf, energy) % [d, ee] = ee_psf(psf, energy, center); % % INPUTS: psf - array representing a point spread function % energy- encircled energy percentage (default = 0.8). % Can also be a vector of values, in which case the % outputs ‘d’ and ‘encircledenergy’ are also vectors. % OPTIONAL INPUT: % center- [x y] coordinates of the center of the psf. If not % passed, defaults to the coordinates of the maximum- % valued point of the psf array. % % OUTPUTS: d - diameter of circle enclosing ‘energy’ [pix] at the % corresponding desired encircled energy value % ee - encircled energy at computed ‘d’ at the % corresponding desired encircled energy value %function [d, encircledenergy] = ee_psf(psf, energy, center)

tools.erf_approx(z, N)[source]

Weideman 1994’s approximate complex error function.

INPUTS: z : real or complex float N : number of terms to use. returns w(z) = exp(-z**2) erfc(-1j*z)
tools.errxy(x, y, xbins, xmode='mean', ymode='mean', xerr='minmax', yerr='sdom', clean=None, binfactor=None, verbose=False, returnstats=False, timing=False)[source]

Bin down datasets in X and Y for errorbar plotting

INPUTS: x – (array) independent variable data y – (array) dependent variable data xbins – (array) edges of bins, in x-space. Only x-data between two bin edges will be used. Thus if M bin edges are entered, (M-1) datapoints will be returned. If xbins==None, then no binning is done. xmode/ymode – (str) method to aggregate x/y data into datapoints: ‘mean’ – use numpy.mean ‘median’ – use numpy.median ‘sum’ – use numpy.sum None – don’t compute; return the empty list [] xerr/yerr – (str) method to aggregate x/y data into errorbars ‘std’ – sample standard deviation (numpy.std) ‘sdom’ – standard deviation on the mean; i.e., std/sqrt(N) ‘minmax’ – use full range of data in the bin None – don’t compute; return the empty list [] binfactor – (int) If not None, average over this many consecutive values instead of binning explicitly by time-based bins. Can also be a sequence, telling the number of values over which to average. E.g., binfactor=[10,10,20] will bin over the first 10 points, the second 10 points, and the next 20 points. clean – (dict) keyword options to clean y-data ONLY, via analysis.removeoutliers, with an additional “nsigma” keyword. See removeoutliers for more information. E.g.: clean=dict(nsigma=5,remove=’both’,niter=1) a tuple of four arrays to be passed to matplotlib.pyplot.errorbar: xx – locations of the aggregated x-datapoint in each bin yy – locations of the aggregated y-datapoint in each bin xerr – x-errorbars yerr – y-errorbars ```x = hstack((arange(10), arange(20)+40)) y = randn(len(x)) xbins = [-1,15,70] xx,yy,xerr,yerr = errxy(x,y,xbins) plot(x,y, '.b') errorbar(xx,yy,xerr=xerr,yerr=yerr, fmt='or') ``` To just bin down uncleaned data (i.e., no ‘error’ terms returned), set clean, xerr, yerr to None. However, when computing all values (xerr and yerr not None) it is faster to set clean to some rediculous value, i.e., clean=dict(niter=0, nsigma=9e99). This probably means more optimization could be done. Be sure you call the errorbar function using the keywords xerr and yerr, since otherwise the default order of inputs to the function is (x,y,yerr,xerr). Data ‘x’ are determined to be in a bin with sides (L, R) when satisfying the condition (x>L) and (x<=R) matplotlib.pyplot.errorbar, analysis.removeoutliers()
tools.extinct_cardelli(lam_um, RV=3.1, warn=True)[source]

Compute the Cardelli et al. 1989 A_lam/A_V extinction (reddening).

INTPUTS: lam_um : float or Numpy array wavelength desired, in microns RV : float R_V extinction parameter warn : bool If True, print a warning if wavelength is outside the valid range. extinct : float or Numpy array extinction – A_lambda/A_V This is only valid for wavelengths in the range 0.3 - 3.3 microns!
tools.extractSubregion(fitsfilename, corners=None, dx=None, dy=None, kw='subreg', retall=False)[source]

Extract a specified rectangular subregion from a FITS file.

INPUTS: fitsfilename : str Name of the (2D) FITS file. corners : str, 4-sequence if sequence: [x0, x1, y0, y1], corners of subregion. if str: header keyword containing this sequence. In either case, the extracted subregion (when dx=dy=0) will be: data[corners:corners, corners:corners] dx : None, 2-sequence If sequence: [x0, x1] will become [x0-dx, x1+dx] dy : None, 2-sequence If sequence: [y0, y1] will become [y0-dy, y1+dy] kw : None, str If str: this header keyword will be updated with the new corners (possibly modified by dx, dy) (subregion_data, [fitsfile_header, corners_used]) If the specified header keyword is not found, or the specified corners return an error, then this function will crash inelegantly. WCS headers will not be updated, so be careful when using this routine for imaging data!
tools.fconvolve(a, v, oversamp=2)[source]

Returns the discrete, linear convolution of 1-D sequences a and v, using Fast Fourier Transforms. Restrictions are: a and v must both be real-valued, and len(a)>len(v).

REQUIREMENTS: Numerical analysis routines, numpy
tools.feps_interpol(x, y, a, linear=True)[source]

Wrapper script for NumPy interpolation. Culls duplicate values and puts x into a monotonically increasing grid.

INPUTS: x : NumPy array 1D sequence of values defining the grid coordinates at which the input values ‘y’ are defined. y : NumPy array 1D sequence of values. a : NumPy array Values of ‘x’ at which to interpolate from the values of ‘y’. ```import numpy as np x = np.linspace(-3,3,61) v = np.sin(x) u = np.array([-2.50, -2.25, -1.85, -1.55, -1.20, -0.85, -0.50, -0.10, 0, 0.75, 0.85, 1.05, 1.45, 1.85, 2.00, 2.25, 2.75 ]) b = feps_interpol(x,v,u) ``` Converted from IDL code from J. Bouwman. Documentation was: ;(SH Feb 26 1999) ;We need to make the grid mononic therefore spline needs to do ;some cleaning before execution
tools.findFrac(validValues, thisValue, retinds=False)[source]

Helper tool for simply linear interpolation.

INPUTS: validValues : sequence List of valid values thisValue : scalar Value of interest. (TwoClosestValues, relativeFractions)
tools.findRectangles(a, minsepy=None, minsepx=None, edgepad=10)[source]

Find corner coordinates of approximate rectangle shapes in an array.

INPUTS: a : 2D Numpy array minsep : scalar Minimum separation from one upper or left-hand border to the next. (cf. find_peaks()) edgepad : int Pad the array with this many zeros on all sides (to find rectangles abutting array edges)
tools.find_peaks(vec, sep=0, thresh=None)[source]

Find all large values in input vector that are separated by at least ‘wid’ pixels.

INPUTS: vec (sequence) – 1D vector sep (scalar) – minimum separation of returned peaks thresh (scalar) – ignore all peaks lower than this value. import pylab as py import tools x = py.linspace(0, 10, 100) # Generate fake time index y = py.sin(6.28*x/10.) + py.sin(6.28*x/2.) # Generate fake data peakvals, peaklocs = tools.find_peaks(y, sep=10) # Find the peaks py.plot(x, y, ‘-‘, x[peaklocs], peakvals, ‘or’) # Plot them peakvals, peakindices
tools.flatten(sequence) → list[source]

Returns a single, flat list which contains all elements retrieved from the sequence and all recursively contained sub-sequences (iterables).

OPTIONAL INPUTS:

maxdepth – scalar

number of layers deep to dig. Seting to zero causes no flattening to occur.

Examples:
```>>> [1, 2, [3,4], (5,6)]
[1, 2, [3, 4], (5, 6)]
>>> flatten([[[1,2,3], (42,None)], [4,5], , 7, MyVector(8,9,10)])
[1, 2, 3, 42, None, 4, 5, 6, 7, 8, 9, 10]
```
tools.gelman_rubin(chains, verbose=False)[source]

Compute the Gelman-Rubin convergence metric for MCMC chains.

INPUTS: chains : 2D NumPy array A stack of all MCMC chains to be compared, created with something like numpy.vstack(). The chains must all be the same length, and they must have more links than the total number of chains. OR chains : 3D NumPy array N chains of L links for P parameters (e.g., the ‘chain’ attribute of an emcee.sampler object), of shape NxLxP. R metric. If this is ‘close to 1.0,’ then your chains have converged to the same distribution. The definition of ‘close’ could be 1.2, 1.1, 1.01... it’s up to you! Eq. 20 of Gelman & Rubin 1992, Statistical Sciences, Vol. 7, p. 457 http://www.star.le.ac.uk/~sav2/idl/rhat.pro
tools.get_emcee_start(bestparams, variations, nwalkers, maxchisq, args, homein=True, retchisq=False, depth=inf)[source]

Get starting positions for EmCee walkers.

INPUTS:
bestparams : sequence (1D NumPy array)

Optimal parameters for your fitting function (length N)

variations : 1D or 2D NumPy array

If 1D, this should be length N and new trial positions will be generated using numpy.random.normal(bestparams, variations). Thus all values should be greater than zero!

If 2D, this should be size (N x N) and we treat it like a covariance matrix; new trial positions will be generated using numpy.random.multivariate_normal(bestparams, variations).

nwalkers : int

Number of positions to be chosen.

maxchisq : int

Maximum “chi-squared” value for a test position to be accepted. In fact, these values are computed with phasecurves.errfunc() as errfunc(test_position, *args) and so various priors, penalty factors, etc. can also be passed in as keywords.

args : tuple

Arguments to be passed to phasecurves.errfunc() for computing ‘chi-squared’ values.

homein : bool

If True, “home-in” on improved fitting solutions. In the unlikely event that a randomly computed test position returns a better chi-squared than your specified best parameters, reset the calculation to start from the new, improved set of parameters.

retchisq : bool

If True, return the tuple (positions, chisq_at_positions)

BAD_EXAMPLE: ```pos0 = tools.get_emcee_start(whitelight_bestfit, np.abs(whitelight_bestfit)/1000., nwalkers, 10*nobs, mcargs) ```
tools.getfigs()[source]

Return a list of all open matplotlib figures.

No inputs or options.

tools.getfilelist(path='.', includes=, []excludes=[])[source]

Return a list of filenames meeting certain criteria.

INPUTS: path – (str) path to directory to be scanned includes – (list of strs) – all strings in this list must be present in a filename to be returned excludes – (list of strs) – any string in this list will prevent a filename from being returned
tools.getparams(params, chisq=None, conf=[0.683])[source]

Find confidence levels and optimal parameters.

If chisq is None:
Take the median of each parameter, and compute the upper and lower confidence limits using the parameter distributions and this model.
Else:
Take a set of fit parameters and associated chi-squared values. Take as the optimum model that model with the MEDIAN chi-squared value. Compute upper and lower confidence limits using the parameter distributions and this optimum model.
INPUTS: params : N-D numpy array model parameters chisq : 1-D numpy array chi-squared values. conf : sequence or float confidence levels to report analysis.dumbconf() numpy

Convert a PyFITS header into a standard NumPy dict.

tools.hist2d(x, y, bins=None)[source]

Compute 2-d histogram data for specified bins.

INPUT: x y bins: a two-tuple containing one of the following: (nx,ny) – tuple, number of bins in each direction (xlims, ylims) – tuple of sequences (x- and y-bin edges) A 3-tuple consisting of: xbins – the centers of the x bins ybins – the centers of the y bins hist – The 2D histogram array numpy.histogram2d() numpy
tools.hparams(params, nbins=10, figsize=[15, 10], normed=False, newfig=True, labs=None, cumulative=False, minorticks=True, plotconf=0.683, plotmid=True, color=None)[source]

Take a set of parameters and histogram them. Assume that the larger of the array’s two dimensions is N (the number of instantiations) and the smaller is M (the number of parameters).

Options: nbins sets the number of bins normed sets the histogram normalization if newfig is False, plot into the current figure. labs is a list of string labels cumulative plots normalized cumulative distribution function minorticks displays minor tick marks plotconf displays confidence levels at the desired (fractional) threshold. E.g., plotconf=0.683 displays the one-sigma confidence limits. Set to ‘None’ for no confidence levels. plotmid plots nothing (if False), the median (if True), or the specified values (if a sequence). Defaults to median if plotconf is True. color sets the plotting color. numpy, pylab
tools.imrot(*varargin)[source]

Rotate a SQUARE image by theta (in radians)

SYNTAX: res = imrot (img, theta)
NOTES: This is VERY SLOW and only uses nearest-neighbor interpolation. Image must be square; size remains the same
tools.invChisq(dof, conf=0.683)[source]

Compute the delta-chi^2 corresponding to the given parameters.

INPUTS:
dof : int, dof > 1

Number of degrees of freedom (or “interesting parameters”)

conf : float, 0 <= conf <= 1

Confidence level. See below for some common choices.

RETURNS:

Desired delta-Chi-squared value.

EXAMPLE:
```# Reproduce Table 1 of Avni (1976)
import tools
dofs = [1, 2, 3, 4]
confs = [0.68, 0.90, 0.99]
for conf in confs:
for dof in dofs:
print ("%5.2f  " % tools.invChisq(dof, conf=conf)),
print " "
```
NOTES:

Some typical values for a Normal (Gaussian) distribution:

type confidence level
one-sigma 0.6826895
2 sigma 0.9544997
3 sigma 0.9973002
4 sigma 0.9999366
5 sigma 0.9999994
class tools.isochrone[source]

Model Isochrone object.

tools.keylist(filelist, keys)[source]

Create an object based on FITS header keys extracted from a filelist.

Inputs: filelist – sequence of strings representing filenames (for PyFITS) keys – sequence of strings representing header keys

#Keys not found in a file will result in the string value

REQUIREMENTS: pyfits, Spitzer/MIPS 24 micron Analysis Routines
tools.legc(leg, col='color')[source]

Color legend text to match linecolor.

Inputs: ‘leg’ is a legend object. ‘col’ sets the field of the leg.get_lines() objects to use to find the color.

You may need to refresh the figure to see the changes.

Load a pickle from a given filename. If it can’t be loaded by pickle, return -1 – otherwise return the pickled object.

mode : str
‘dill’, ‘pickle’, or ‘both’ to try both
E.g.,
class tools.modelGrid[source]

Model Grid object.

tools.multifunc(params, func1, func2, npar1, npar2=None, args1=(), args2=())[source]

Multiply two functions together.

EXAMPLE: ```import numpy as np multifunc([.785, .785], np.cos, np.sin, 1) # returns cos(pi/4) * sin(pi/4) ~ 0.5 ``` ```import pylab as py import tools x = 2*py.pi * py.linspace(-5, 5, 500) y = tools.multifunc([x/8., x], np.sin, np.sin, 1).ravel() py.plot(x, y) ```
tools.multifunc_general(params, funcs, npar=None, args=None)[source]

Multiply results of several functions together.

INPUTS: params : sequence Concatenated sequence of parameters (first arguments) for each of the functions in ‘funcs’. funcs : function or tuple Single function, or a tuple of several functions to call. npar : tuple If more than one function is used, npar must be a tuple specifying the number of parameters passes to each function (as its first input). E.g., if npar = (2, 3) then the result will be: funcs(params[0:2]) * funcs(params[2:5]) args : tuple If more than one function is used, args must be a tuple specifying the additional arguments to be passed to each function. E.g.: funcs(params[0:2], *args) ```import numpy as np multifunc_general([.785, .785], (np.cos, np.sin), (1, 1)) # returns cos(pi/4) * sin(pi/4) ~ 0.5 ``` ```import pylab as py import tools x = 2*py.pi * py.linspace(-10, 10, 500) y = tools.multifunc_general([x/8., x], (np.sin, np.sin), (1, 1)).ravel() py.plot(x, y) ```
tools.nextfig()[source]

Return one greater than the largest-numbered figure currently open. If no figures are open, return unity.

No inputs or options.

tools.obj2FITS(input)[source]

Try to convert any generic object into a multi-extension FITS file. We examine each attribute of the object: if a string or scalar, we addit to the Primary HDU header. If a sequence of numbers, we add it as an extension with the appropriate name.

It probably wouldn’t work for heirarchical objects whose attributes are themselves complicated objects; probably also won’t work for complex-valued arrays.

tools.obj2dict(object, ignore=('_', ), verbose=False)[source]

Convert an object into a dict. Ignore functions & methods, and any attributes starting with the ‘ignore’ keys.

SEE_ALSO: dict2obj()
tools.pcmodelxcorr(pcaout, data, model, npcs=6, nblock=1000, xl=50, modstr='model', titstr='')[source]

Plot cross-correlations between projection of principal components onto data and a model.

INPUTS: pcaout – output from pcsa.pca, but its only important component is that pcaout is an array of PC eigenvectors; the strongest vector would be pcaout[:,-1], etc. data – numpy array. Should be shape N x M – N observations of M variables, arranged in L blocks, and should have been mean-subtracted prior to PCA (i.e., data -= data.mean(0)) model – numpy array. Should be shape M. npcs – int. Number of principal components to cross-correlate with. nblock – int. NUmber of channels to use at a time in correlations. Must be an integral divisor of data.shape xl – int. +/- X-limits to display in plots.

Usage is pretty specific to echelle-data

tools.planet2temp(rprs, fpfs, teff, lam=24.0, gamma=0.8, ntrials=10000)[source]

Convert planet/star size and flux contrasts to brightness temperatures.

INPUTS:
rprs : 2-tuple, either
 : (value, dist) where dist is the sampled posterior

distribution of planet/star radius ratio (e.g., from MCMC output). OR:

 : (value, uncertainty)

fpfs : 2-tuple, either
 : (value, dist) where dist is the sampled posterior

distribution of planet/star flux ratio (e.g., from MCMC output). OR:

 : (value, uncertainty)

teff : 2-tuple, either
 : (value, dist) where dist is the sampled posterior

distribution of stellar effective temperatures (e.g., from MCMC output). OR:

 : (value, uncertainty)

lam : float

wavelength of observations, in microns

gamma : float

factor to account for the fact that a star’s infrared flux is lower than predicted by a blackbody of a given effective temperature. Set to 0.8 for 24 microns.

ntrials : int

Number of times to resample distributions.

REQUIREMENTS: scipy.optimize, numpy
tools.plotc(x, y, z, **kw)[source]

Plot x,y data with an evolving z component by changing its color using a matplotlib colormap ‘cm’ object.

Will bomb if z elements are non-finite.

OPTIONS: map : str colormap to use for z zmin, zmax : floats maximum/minimum values of z for colorscale sizenotcolor : bool If True, ‘z’ specifies marker size not color. others : various Any options passable to matplotlib’s plot() contourg(), matplotlib.colormaps() pylab
tools.plotcorrs(params, labs=None, tit=None, xrot=0, yrot=0, cmap=None, figsize=None, plotregion=[0.1, 0.1, 0.8, 0.8], n=6, nbins=None, clim=None, docontour=False, contourcolor='k', newfig=True)[source]

Plot correlation coefficient matrix in one big, huge, honkin’ figure. Color-code the figure based on the correlation coefficients between parameters.

INPUTS: params – (M x N array) M instantiations of a set of N parameters. labs – (list) labels for each of the N parameters tit – (str) title for figure xrot/yrot – (float) axis label rotation, in degrees cmap – (matplotlib cm) – colormap for color-coding. figsize – (2-list) – width and height of figure plotregion – (4-list) – (left, bottom, width, height) of plotted region in each figure n – (int) – number of subplots across each figure nbins : int Bin the data into this many bins, and show 2D histograms instead of points. clim : None Colorscale limits for normalized 2D histograms (where hist.sum() = 1.0) docontour : bool Whether to plot contours, or do an ‘imshow’ newfig : bool Whether to generate a new figure, or plot in the current axes. contourcolor Color of contour line, if “docontour” is set to a list of confidence intervals. pylab, NIRSPEC Data Analysis Based on the concept by Nymyer and Harrington at U. Central Florida Beware of plotting two many points, and clogging your system!
tools.ploth(*args, **kw)[source]

Plot 1D data in a histogram-like format. If x-coordinates are specified, they refer to the centers of the histogram bars.

Uses same format as matplotlib.pyplot.plot. For example:
```ploth(x, y)         # plot x and y using solid linestyle (default)
ploth(x, y, 'bo')   # plot x and y using blue circle markers w/no line
ploth(y)            # plot y using x as index array 0..N-1
ploth(y, 'r*--')    # ditto, but with red star corners and dashed line
```
OPTIONS: rot90 : bool If True, data will be plotted histogram-style vertically, rather than the standard horizontal plotting.
tools.plotlikelihood_2d(L, x=None, y=None, conf=[0.683], figsize=[8, 6], contourargs={'colors': 'k'}, posteriorargs={'color': 'k'}, limitargs={'color': 'k', 'linestyle': ':'}, xlabel=None, ylabel=None, buffers=[0.1, 0.1, 0.1, 0.1])[source]

Plot contours and histograms for 2D likelihood array.

INPUTS: L : 2d Numpy array Likelihood values, not necessarily normalized. (Remember that L = exp[-chi^2 / 2.] ) x : sequence Values along the first dimension of L. Thus len(x) must equal L.size y : sequence Values along the second dimension of L. Thus len(x) must equal L.size figsize : 2-sequence Size of figure to be created, in inches. conf : scalar or sequence Confidence intervals to plot contourargs : dict Keyword arguments to be passed to matplotlib.contour posteriorargs : dict Keyword arguments to be passed to matplotlib.plot for posterior distributions limitargs : dict Keyword arguments to be passed to matplotlib.plot for 1D confidence limits xlabel : str ylabel : str buffers : 4-sequence fractional buffer width around edges: [left, right, bottom, top]
tools.plotstyle(i, c=['b', 'g', 'r', 'c', 'm', 'y', 'k'], s=['.', 'x', 's', '^', '*', 'o', '+', 'v', 'p', 'D'], l=['-', '--', '-.', ':'])[source]

Return plot properties to help distinguish many types of plot symbols.

INPUT: i – int. c – color, or list of colors accepted by pylab.plot s – symbol, or list of symbols accepted by pylab.plot l – linestyle, or list of linestyles accepted by pylab.plot tuple of (color, symbol, linestyle) numpy
tools.popall(seq, obj)[source]

Remove all instances of ‘obj’ from list ‘seq’

INPUT: seq – (list) list from which to pop elements obj – target object to remove ```import tools b = [3, 'spam', range(5)] tools.popall(b, 4) print b ``` – Will fail if ‘obj’ is itself a list. – Edits list in-place, so make a copy first if you want to retain the old version of your list. – Has not been tested for extremely deep lists replaceall()
tools.pparams(params, npts=None, figsize=[15, 10], newfig=True, labs=None)[source]

Take a set of parameters and plot them. Assume that the larger of the array’s two dimensions is N (the number of instantiations) and the smaller is M (the number of parameters).

If npts is not None, then pick only every (N/npts)th point. if newfig is False, plot into the current figure.

REQUIREMENTS: numpy, pylab
tools.printfigs(filename, figs=None, format=None, pdfmode='texexec', verbose=False, closefigs=False)[source]

Print desired figures using designated ‘format’. Concatenate PDFs.

Inputs: filename – string. prepended to all open figures figs – int or list. figures to access, then apply savefig to. If None, print all open figures; if -1, print current figure. format – string or list of strings. if ‘pdf’, all images are concatenated into one file (use “pdfs” for individual pdf figure files) pdfmode – string; method of concatenating PDFs. Either ‘texexec’ or ‘gs’ (for GhostScript) or ‘tar’ to wrap individual figures in a Tarball. closefigs – bool If True, close each figure after printing it to disk. If no explicit path is passed and a subdirectory ‘figures’ exists in the current directory, the figures will be printed in ‘figures’ instead. ```from pylab import * figure(1); plot(arange(10), randn(10), 'ob') figure(2); plot(arange(15), randn(15), '-xr') printfigs('testing') !open testing.pdf ```

Read ASCII-format Dartmouth Isochrone files into Python.

INPUTS: filename : str Filename to load, e.g. ‘fehm05afep0.UBVRIJHKsKp’ mode : str Which type of models to load. For now, ‘2012’ is the only valid input. comment : str Which character(s) indicate(s) a comment, rather than tabular/numeric data. A Pythonic object with fields derived from the input file. It will have fields named ‘Y’, ‘Z’, ‘Zeff’, ‘ages_Gyr’, ‘isochrones’, ‘photometricSystem’, etc. The ‘isochrones’ field is a list of isochrones, one at each age step. It will have fields whose names correspond to photometric bandpasses (see the example below), as well as standard fields such as ‘LogG’, ‘LogTeff’, ‘LogL_Lo’, ‘M_Mo’, etc. You can download the models at the DSEP website: http://stellar.dartmouth.edu/~models/ As written, requires NumPy. (But could be easily rewritten to avoid this, if necessary). ```# Reproduce Fig. 4 of Dotter et al. (2008): import tools import pylab as py filename = 'fehp00afep0.UBVRIJHKsKp' models = tools.readDartmouthIsochrones(filename, mode='2012') age2plot = 4 # Plot 4 Gyr track age_index = (models.ages_Gyr==age2plot).nonzero() this_isochrone = models.isochrones[age_index] py.figure() ax1=py.subplot(121) py.plot(this_isochrone.LogTeff, this_isochrone.LogL_Lo) py.xlabel('log T_eff') py.ylabel('log L/L_sun') py.axis([3.9, 3.4, -3, 4]) leg = legend(['DSEP'], 3) ax2=py.subplot(122) py.plot(this_isochrone.V - this_isochrone.I, this_isochrone.V) py.xlabel('V - I') py.ylabel('M_V') py.axis([0.25, 4, 14, -2]) [ax.minorticks_on() for ax in [ax1, ax2]] ```
tools.readPSplotdata(filename, xlims=None, ylims=None, spacedelimiter=' ', pairdelimiter=', ')[source]

Read in a raw PostScript plot datafile and output Numpy arrays.

INPUTS: filename : str filename of the data file. xlims : tuple (lowest x value of any data point, highest x value of any data point) ylims : tuple (lowest y value of any data point, highest y value of any data point) spacedelimiter : str delimiter between data pairs; by default a space (” ”) pairdelimiter : str delimiter within data pairs; by default a comma (”,”) The datafile should have the format: “m x0,y0 dx1,dy1 dx2,dy2 ...” (x, y) – tuple of 1D arrays
tools.replaceall(seq, obj, rep)[source]

Replace all instances of ‘obj’ with ‘rep’ in list ‘seq’

INPUT: seq – (list) list within which to find-and-replace elements obj – target object to replace rep – replacement object ```import tools b = [2, ['spam', ['eggs', 5, dict(spam=3)]]] tools.replaceall(b, 'spam', 'bacon') print b ``` – Will fail if ‘obj’ is itself a list. – Edits list in-place, so make a copy first if you want to retain the old version of your list. – Has not been tested for extremely deep lists popall()
tools.resamp(frame, resamp, retcoords=False)[source]

Resample a 2D array by a given factor, using bilinear interpolation.

INPUTS:
frame : 2D Numpy Array

data array to be resampled

resamp : positive scalar

resampling factor (typically an integer greater than 1)

retcoords: bool

If True, return tuple (frame2, x2, y2)

NOTES: Result needs to be scaled by (1/resamp^2) to conserve flux
tools.resampleIsochrone(iso, x, xName='M_Mo', fields='*all*')[source]

Resample parameters of an isochrone-type object.

INPUTS: iso : object Isochrone sub-field object of a stellar model-grid object, of the type returned by readDartmouthIsochrones(). x : 1D NumPy array The new values to use for interpolation or resampling. xName : string The name of the field in ‘isochrone.’ fields : string Which fields to resample. If ‘all‘, all sub-fields of ‘isochrone’ with the same size as getattr(isochrone, xName) will be resampled. We use numpy.interp() for the resampling; if ‘x’ is not an always-increasing array then interp() may have problems. Similarly, interp() offers no extrapolation beyond the original limits of iso.xName.
tools.roundparams(value, error, seconderror=None, extraplaces=1)[source]

Round a value and its associated error(s) to 2 decimal places.

INPUTS: value, error : scalars value, error ```import tools tools.roundparams(1.23456, .09876) ``` roundvals()
tools.roundvals(input, ndigit=2, strout=True)[source]

Round all input values to the smallest number of digits used.

INPUTS: input : scalar, or 1D list or array values to be rounded ndigit : int Number of significant digits to be retained strout : bool Return text strings? If not, return a 1D NumPy array. ```import tools tools.roundvals([235, -4, 0.045380]) ``` roundparams()
tools.sample_1dcdf(pdf, x, nsamp=1, verbose=False, absnorm=False)[source]

Sample a 1D Posterior Distribution Function (1d-histogram)

INPUTS: PDF : 1D NumPy array Distribution function (histogram) from which you wish to draw samples. This need not be in any particular normalized form – the only condition is that the value in each cell is proportional to the probability of that cell. x : 1D NumPy array N Values indexing the cells of the CDF (one per row) nsamp : int Number of samples to be drawn. absnorm : bool If True, normalize pdf so that it integrates to 1.0
tools.sample_2dcdf(cdf, x, y, nsamp=1, verbose=False)[source]

Sample a 2D Probability Distribution Function (2d-histogram)

INPUTS: CDF : 2D NumPy array Two-dimensional (N x M) probability distribution function (histogram) from which you wish to draw samples. This need not be in any particular normalized form – the only condition is that the value in each cell is proportional to the probability of that cell. x : 1D NumPy array N+1 Values indexing the cells of the CDF (one per row), evenly spaced y : 1D NumPy array M+1 Values indexing the cells of the CDF (one per column), evenly spaced nsamp : int Number of samples to be drawn. Note that this approach uses simple, dumb nearest-neighbor interpolation when drawing candidate samples. Exceedingly granular CDFs may suffer. Note that this code isn’t optimized; try it with small samples before you use it on large ones!
tools.savepickle(obj, filename)[source]

Save a pickle to a given filename. If it can’t be saved by pickle, return -1 – otherwise return the file object.

To save multiple objects in one file, use (e.g.) a dict:

tools.savepickle(dict(a=[1,2], b=’eggs’), filename)
tools.scrapeints(string)[source]

Extract a series of integers from a string. Slow but robust.

will return:

[124, 56, 4589, 2]

tools.shift_image(im, dx=0, dy=0, buffer=0)[source]

Shift a 2D image by the specified number of integer pixels.

INPUTS: im : 2D Numpy array numpy array of desired size dx : int number of pixels to shift in x direction dy : int number of pixels to shift in y direction buffer : scalar Default value for unfilled pixels
tools.sumfunc(params, func1, func2, npar1, npar2=None, args1=(), args2=())[source]

Add two functions together.

EXAMPLE: ```import numpy as np sumfunc([.785, .785], np.cos, np.sin, 1) # returns cos(pi/4) + sin(pi/4) ~ XXX ``` ```import pylab as py import tools x = 2*py.pi * py.linspace(-5, 5, 500) y = tools.sumfunc([x/8., x], np.sin, np.sin, 1).ravel() py.plot(x, y) ```
tools.sumfunc_general(params, funcs, npar=None, args=None)[source]

Add results of several functions together.

INPUTS: params : sequence Concatenated sequence of parameters (first arguments) for each of the functions in ‘funcs’. funcs : function or tuple Single function, or a tuple of several functions to call. npar : tuple If more than one function is used, npar must be a tuple specifying the number of parameters passes to each function (as its first input). E.g., if npar = (2, 3) then the result will be: funcs(params[0:2]) * funcs(params[2:5]) args : tuple If more than one function is used, args must be a tuple specifying the additional arguments to be passed to each function. E.g.: funcs(params[0:2], *args) ```import numpy as np sumfunc_general([.785, .785], (np.cos, np.sin), (1, 1)) ``` ```import pylab as py import tools x = 2*py.pi * py.linspace(-10, 10, 500) y = tools.sumfunc_general([x/8., x], (np.sin, np.sin), (1, 1)).ravel() py.plot(x, y) ```
tools.textfig(textlist, **kw)[source]

Generate a figure from a list of strings.

INPUTS: textlist : list List of text strings, one per entry. any options valid for passing to matplotlib.text Also: the dict ‘figoptions’ will be passed to figure() (fig, ax) – handles to Figure and Axes that were created

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