# API¶

A collection of tools widely used in Weak Gravitational Lensing data analyses

## Weak Lensing image mapipulation¶

### Convergence maps¶

class lenstools.image.convergence.ConvergenceMap(data, angle, masked=False, **kwargs)[source]

A class that handles 2D convergence maps and allows to compute their topological descriptors (power spectrum, peak counts, minkowski functionals)

>>> from lenstools import ConvergenceMap

>>> test_map = ConvergenceMap.load("map.fit",format=load_fits_default_convergence)
>>> imshow(test_map.data)

bispectrum(l_edges, ratio=0.5, configuration='equilateral', scale=None)

Calculates the bispectrum of the map in the equilateral or folded configuration

Parameters: l_edges (array) – Multipole bin edges: these are the side of the triangle in the equilateral configuration or the base of the triangle in the folded configuration ratio (float.) – ratio between one of the triangle sides and the base in the folded configuration. Must be between 0 and 1 configuration (str.) – must be either “equilateral” or “folded” scale (callable.) – scaling to apply to the cube of the Fourier pixels before harmonic azimuthal averaging. Must be a function that takes the array of multipole magnitudes as an input and returns an array of real positive numbers (multipoles, bispectrum at multipoles) tuple.
boundary

Computes the boundaries of the masked regions, defined as the regions in which the convergence is still well defined but the first and second derivatives are not

Returns: array of bool. of the same shape as the map, with True values along the boundaries
countModes(l_edges)

Counts the available number of modes in multipole space available to each bin specified in l_edges

Parameters: l_edges (array) – Multipole bin edges number of modes available to each bin array AssertionError if l_edges are not provided
cross(other, statistic='power_spectrum', **kwargs)

Measures a cross statistic between maps

Parameters: other (ConvergenceMap instance) – The other convergence map statistic (string or callable) – the cross statistic to measure (default is ‘power_spectrum’) kwargs (dict.) – the keyword arguments are passed to the statistic (when callable) tuple – (l – array,Pl – array) = (multipole moments, cross power spectrum at multipole moments) if the statistic is the power spectrum, otherwise whatever statistic() returns on call AssertionError if the other map has not the same shape as the input one
>>> test_map = ConvergenceMap.load("map.fit",format=load_fits_default_convergence)

>>> l_edges = np.arange(200.0,5000.0,200.0)
>>> l,Pl = test_map.cross(other_map,l_edges=l_edges)

cutRegion(extent)

Cut a subset of the map, with the same resolution (warning! tested on square cuts only!)

Parameters: extent (array with units) – region in the map to select (xmin,xmax,ymin,ymax), must have units new ConvergenceMap instance that encloses the selected region
gaussianPeakHistogram(thresholds, norm=False, fig=None, ax=None, **kwargs)

Plot the Gaussian field approximation (Bond 1987) to the peak histogram

getEll()

Get the values of the multipoles in real FFT space

Returns: ell array with real FFT shape array.
getValues(x, y)

Extract the map values at the requested (x,y) positions; this is implemented using the numpy fast indexing routines, so the formats of x and y must follow the numpy advanced indexing rules. Periodic boundary conditions are enforced

Parameters: x (numpy array or quantity) – x coordinates at which to extract the map values (if unitless these are interpreted as radians) y (numpy array or quantity) – y coordinates at which to extract the map values (if unitless these are interpreted as radians) numpy array with the map values at the specified positions, with the same shape as x and y IndexError if the formats of x and y are not the proper ones
gradLaplacian(x=None, y=None)
gradient(x=None, y=None, save=True)

Parameters: x (array with units) – optional, x positions at which to evaluate the gradient y (array with units) – optional, y positions at which to evaluate the gradient save (bool.) – if True saves the gradient as attrubutes tuple – (gradient_x,gradient_y)
>>> test_map = ConvergenceMap.load("map.fit")

hessian(x=None, y=None, save=True)

Computes the hessian of the map and sets the hessian_xx,hessian_yy,hessian_xy attributes accordingly

Parameters: x (array with units) – optional, x positions at which to evaluate the hessian y (array with units) – optional, y positions at which to evaluate the hessian save (bool.) – if True saves the gradient as attrubutes tuple – (hessian_xx,hessian_yy,hessian_xy)
>>> test_map = ConvergenceMap.load("map.fit")
>>> hxx,hyy,hxy = test_map.hessian()

info

Displays some of the information stored in the map (mainly resolution)

classmethod load(filename, format=None, **kwargs)

This class method allows to read the map from a data file, in various formats

Parameters: filename (str.) – name of the file in which the map is saved format (str. or callable) – the format of the file in which the map is saved (can be a callable too); if None, it’s detected automatically from the filename kwargs (dict.) – the keyword arguments are passed to the format (if callable) Spin0 instance with the loaded map
locatePeaks(thresholds, norm=False)

Locate the peaks in the map

Parameters: thresholds (array) – thresholds extremes that define the binning of the peak histogram norm (bool.) – normalization; if set to a True, interprets the thresholds array as units of sigma (the map standard deviation) tuple – (peak height – array, peak locations – array with units) AssertionError if thresholds array is not provided
>>> test_map = ConvergenceMap.load("map.fit")
>>> thresholds = np.arange(map.data.min(),map.data.max(),0.05)
>>> height,positions = test_map.locatePeaks(thresholds)

mask(mask_profile, inplace=False)

Applies a mask to the convergence map: all masked pixels are given a nan value because they cannot be used in the calculations

Parameters: mask_profile (array. or ConvergenceMap instance) – profile of the mask, must be an array of 1 byte intergers that are either 0 (if the pixel is masked) or 1 (if the pixel is not masked). Must be of the same shape as the original map inplace – if True the masking is performed in place and the original map is lost, otherwise a new instance of ConvergenceMap with the masked map is returned the masked convergence map if inplace is False, otherwise a float corresponding to the masked fraction of the map
maskBoundaries()

Computes the mask boundaries defined in the following way: a boundary is a region where the convergence value is defined, but the gradients are not defined.

Returns: float. : perimeter/area ratio of the mask boundaries
maskedFraction

Computes the masked fraction of the map

Returns: float, the masked fraction of the map
mean()

Computes the mean value of the pixels, taking into account eventual masking

Returns: float.
minkowskiFunctionals(thresholds, norm=False)

Measures the three Minkowski functionals (area,perimeter and genus characteristic) of the specified map excursion sets

Parameters: thresholds (array) – thresholds that define the excursion sets to consider norm (bool.) – normalization; if set to a True, interprets the thresholds array as units of sigma (the map standard deviation) tuple – (nu – array, V0 – array, V1 – array, V2 – array) nu are the bins midpoints and V are the Minkowski functionals AssertionError if thresholds array is not provided
>>> test_map = ConvergenceMap.load("map.fit")
>>> thresholds = np.arange(-2.0,2.0,0.2)
>>> nu,V0,V1,V2 = test_map.minkowskiFunctionals(thresholds,norm=True)

moments(connected=False, dimensionless=False)

Measures the first nine moments of the convergence map (two quadratic, three cubic and four quartic)

Parameters: connected (bool.) – if set to True returns only the connected part of the moments dimensionless (bool.) – if set to True returns the dimensionless moments, normalized by the appropriate powers of the variance array – (sigma0,sigma1,S0,S1,S2,K0,K1,K2,K3)
>>> test_map = ConvergenceMap.load("map.fit")
>>> var0,var1,sk0,sk1,sk2,kur0,kur1,kur2,kur3 = test_map.moments()
>>> sk0,sk1,sk2 = test_map.moments(dimensionless=True)[2:5]
>>> kur0,kur1,kur2,kur3 = test_map.moments(connected=True,dimensionless=True)[5:]

pdf(thresholds, norm=False)

Computes the one point probability distribution function of the convergence map

Parameters: thresholds (array) – thresholds extremes that define the binning of the pdf norm (bool.) – normalization; if set to a True, interprets the thresholds array as units of sigma (the map standard deviation) tuple – (threshold midpoints – array, pdf normalized at the midpoints – array) AssertionError if thresholds array is not provided
>>> test_map = ConvergenceMap.load("map.fit")
>>> thresholds = np.arange(map.data.min(),map.data.max(),0.05)
>>> nu,p = test_map.pdf(thresholds)

peakCount(thresholds, norm=False)

Counts the peaks in the map

Parameters: thresholds (array) – thresholds extremes that define the binning of the peak histogram norm (bool.) – normalization; if set to a True, interprets the thresholds array as units of sigma (the map standard deviation) tuple – (threshold midpoints – array, differential peak counts at the midpoints – array) AssertionError if thresholds array is not provided
>>> test_map = ConvergenceMap.load("map.fit")
>>> thresholds = np.arange(map.data.min(),map.data.max(),0.05)
>>> nu,peaks = test_map.peakCount(thresholds)

peakDistances(thresholds, norm=False)

Compute the pairwise distance between local maxima on the map

Parameters: thresholds (array) – thresholds extremes that define the binning of the peak histogram norm (bool.) – normalization; if set to a True, interprets the thresholds array as units of sigma (the map standard deviation) pairwise distance quantity
peakHistogram(thresholds, norm=False, fig=None, ax=None, **kwargs)

Plot the peak histogram of the map

peakTwoPCF(thresholds, scales, norm=False)

Compute the two point function of the peaks on the map

Parameters: thresholds (array) – thresholds extremes that define the binning of the peak histogram scales (quantity) – radial binning of the 2pcf norm (bool.) – normalization; if set to a True, interprets the thresholds array as units of sigma (the map standard deviation) (bin centers, peak 2pcf) tuple
plotPDF(thresholds, norm=False, fig=None, ax=None, **kwargs)

Plot the PDF of the map

plotPowerSpectrum(l_edges, show_angle=True, angle_unit=Unit("arcmin"), fig=None, ax=None, **kwargs)

Plot the power spectrum of the map

powerSpectrum(l_edges, scale=None)

Measures the power spectrum of the convergence map at the multipole moments specified in the input

Parameters: l_edges (array) – Multipole bin edges scale (callable.) – scaling to apply to the square of the Fourier pixels before harmonic azimuthal averaging. Must be a function that takes the array of multipole magnitudes as an input and returns an array of real numbers (l – array,Pl – array) = (binned multipole moments, power spectrum at multipole moments) tuple.
>>> test_map = ConvergenceMap.load("map.fit")
>>> l_edges = np.arange(200.0,5000.0,200.0)
>>> l,Pl = test_map.powerSpectrum(l_edges)

save(filename, format=None, double_precision=False)

Saves the map to an external file, of which the format can be specified (only fits implemented so far)

Parameters: filename (str.) – name of the file on which to save the plane format (str.) – format of the file, only FITS implemented so far; if None, it’s detected automatically from the filename double_precision (bool.) – if True saves the Plane in double precision
savefig(filename)

Saves the map visualization to an external file

Parameters: filename (str.) – name of the file on which to save the map
setAngularUnits(unit)

Convert the angular units of the map to the desired unit

Parameters: unit (astropy units) – astropy unit instance to which to perform the conversion
smooth(scale_angle, kind='gaussian', inplace=False, **kwargs)

Performs a smoothing operation on the convergence map

Parameters: scale_angle (float.) – size of the smoothing kernel (must have units) kind (str.) – type of smoothing to be performed. Select “gaussian” for regular Gaussian smoothing in real space or “gaussianFFT” if you want the smoothing to be performed via FFTs (advised for large scale_angle) inplace (bool.) – if set to True performs the smoothing in place overwriting the old convergence map kwargs (dict.) – the keyword arguments are passed to the filter function ConvergenceMap instance (or None if inplace is True)
std()

Computes the standard deviation of the pixels, taking into account eventual masking

Returns: float.
twoPointFunction(algorithm='FFT', **kwargs)

Computes the two point function of the convergence

Parameters: algorithm (str.) – algorithm used to measure the two point function. Can be “FFT” kwargs (dict.) – accepted keyword arguments are “theta” to specify the angles at which to measure the 2pcf. If none indicated, the angles are computed automatically (theta,2pcf(theta)) tuple.
visualize(fig=None, ax=None, colorbar=False, cmap='viridis', cbar_label=None, **kwargs)

Visualize the convergence map; the kwargs are passed to imshow

class lenstools.image.convergence.Mask(data, angle, masked=False)[source]

A class that handles the convergence masked regions

### Shear maps and catalogs¶

class lenstools.image.shear.ShearMap(data, angle, **kwargs)[source]

A class that handles 2D shear maps and allows to perform a set of operations on them

>>> from lenstools import ShearMap

>>> test = ShearMap.load("shear.fit",format=lenstools.defaults.load_fits_default_shear)
>>> test.side_angle
1.95
>>> test.data
#The actual map values

addSourceEllipticity(es, rs_correction=True, inplace=False)[source]

Produce a mock shear map that includes the intrinsic source ellipticity

Parameters: es (array) – array of intrinsic ellipticities, must have the same shape as the shear map rs_correction (bool.) – include denominator (1+g*es) in the shear correction inplace (bool.) – perform the operation in place shear estimate with the added intrinsic ellipticity ShearMap
convergence()[source]

Reconstructs the convergence from the E component of the shear (Kaiser Squires method)

Returns: new ConvergenceMap instance
eb_power_spectrum(l_edges, scale=None)

Decomposes the shear map into its E and B modes components and returns the respective power spectral densities at the specified multipole moments

Parameters: l_edges (array) – Multipole bin edges scale (callable) – scaling to apply to the Fourier coefficients before harmonic azimuthal averaging. Must be a function that takes the array of multipole magnitudes as an input and returns a real numbers (l – array,P_EE,P_BB,P_EB – arrays) = (multipole moments, EE,BB power spectra and EB cross power) tuple.
>>> test_map = ShearMap.load("shear.fit",format=load_fits_default_shear)
>>> l_edges = np.arange(300.0,5000.0,200.0)
>>> l,EE,BB,EB = test_map.eb_power_spectrum(l_edges)

fourierEB()

Computes E and B modes of the shear map in Fourier space

Returns: (E,B) map tuple.
classmethod fromConvergence(conv)[source]

Construct a shear map from a ConvergenceMap instance using the Kaiser Squires method

Parameters: conv (ConvergenceMap) – input convergence map reconstructed shear map ShearMap
classmethod fromEBmodes(fourier_E, fourier_B, angle=<Quantity 3.14 deg>)

This class method allows to build a shear map specifying its E and B mode components

Parameters: fourier_E (numpy 2D array, must be of type np.complex128 and must have a shape that is appropriate for a real fourier transform, i.e. (N,N/2 + 1); N should be a power of 2) – E mode of the shear map in fourier space fourier_B (numpy 2D array, must be of type np.complex128 and must have a shape that is appropriate for a real fourier transform, i.e. (N,N/2 + 1); N should be a power of 2) – B mode of the shear map in fourier space angle (float.) – Side angle of the real space map in degrees the corresponding ShearMap instance AssertionErrors for inappropriate inputs
getEll()

Get the values of the multipoles in real FFT space

Returns: ell array with real FFT shape array.
getValues(x, y)

Extract the map values at the requested (x,y) positions; this is implemented using the numpy fast indexing routines, so the formats of x and y must follow the numpy advanced indexing rules. Periodic boundary conditions are enforced

Parameters: x (numpy array or quantity) – x coordinates at which to extract the map values (if unitless these are interpreted as radians) y (numpy array or quantity) – y coordinates at which to extract the map values (if unitless these are interpreted as radians) numpy array with the map values at the specified positions, with shape (N,shape x) where N is the number of components of the map field IndexError if the formats of x and y are not the proper ones
gradient(x=None, y=None)

Computes the gradient of the components of the spin1 field at each point

Parameters: x (array with units) – optional, x positions at which to evaluate the gradient y (array with units) – optional, y positions at which to evaluate the gradient the gradient of the spin1 field in array form, of shape (4,:,:) where the four components are, respectively, 1x,1y,2x,2y; the units for the finite difference are pixels
info

Displays some of the information stored in the map (mainly resolution)

classmethod load(filename, format=None, **kwargs)

This class method allows to read the map from a data file, in various formats

Parameters: filename (str.) – name of the file in which the map is saved format (str. or callable) – the format of the file in which the map is saved (can be a callable too); if None, it’s detected automatically from the filename kwargs (dict.) – the keyword arguments are passed to the format (if callable) Spin1 instance with the loaded map
save(filename, format=None, double_precision=False)

Saves the map to an external file, of which the format can be specified (only fits implemented so far)

Parameters: filename (str.) – name of the file on which to save the plane format (str.) – format of the file, only FITS implemented so far; if None, it’s detected automatically from the filename double_precision (bool.) – if True saves the Plane in double precision
savefig(filename)

Saves the map visualization to an external file

Parameters: filename (str.) – name of the file on which to save the map
setAngularUnits(unit)

Convert the angular units of the map to the desired unit

Parameters: unit (astropy units) – astropy unit instance to which to perform the conversion
sticks(fig=None, ax=None, pixel_step=10, multiplier=1.0)

Draw the ellipticity map using the shear components

Parameters: ax (matplotlib ax object) – ax on which to draw the ellipticity field pixel_step (int.) – One arrow will be drawn every pixel_step pixels to avoid arrow overplotting multiplier (float.) – Multiplies the stick length by a factor ax – the matplotlib ax object on which the stick field was drawn
>>> import matplotlib.pyplot as plt
>>> fig,ax = plt.subplots()
>>> test.sticks(ax,pixel_step=50)

visualize(fig=None, ax=None, component_labels=('$\\gamma_1$', '$\\gamma_2$'), colorbar=False, cmap='viridis', cbar_label=None, **kwargs)

Visualize the shear map; the kwargs are passed to imshow

visualizeComponents(fig, ax, components='EE, BB, EB', region=(200, 9000, -9000, 9000))

Plots the full 2D E and B mode power spectrum (useful to test statistical isotropicity)

Parameters: fig (matplotlibfigure object) – figure on which to draw the ellipticity field ax (matplotlib ax object or array of ax objects, can be None in which case new ax are created) – ax on which to draw the ellipticity field components – string that contains the components to plot; the format is a sequence of {EE,BB,EB} separated by commas region (tuple (lx_min,lx_max,ly_min,ly_max)) – selects the multipole region to visualize
class lenstools.catalog.shear.Catalog(*args, **kwargs)[source]

Class handler of a galaxy catalogue, inherits all the functionality from the astropy.table.Table

pixelize(map_size, npixel=256, field_quantity=None, origin=<Quantity [ 0., 0.] deg>, smooth=None, accumulate='average', callback=None, **kwargs)[source]

Constructs a two dimensional square pixelized map version of one of the scalar properties in the catalog by assigning its objects on a grid

Parameters: map_size (quantity) – spatial size of the map npixel (int.) – number of pixels on a side field_quantity (str.) – name of the catalog quantity to map; if None, 1 is assumed origin (array with units) – two dimensional coordinates of the origin of the map smooth (quantity) – if not None, the map is smoothed with a gaussian filter of scale smooth accumulate (str.) – if “sum” field galaxies that fall in the same pixel have their field_quantity summed, if “average” the sum is divided by the number of galaxies that fall in the pixel callback (callable or None) – user defined function that gets called on field_quantity kwargs (dict.) – the keyword arguments are passed to callback two dimensional scalar array with the pixelized field (pixels with no objects are treated as NaN) array
setSpatialInfo(field_x='x', field_y='y', unit=Unit("deg"))[source]

Sets the spatial information in the catalog

Parameters: field_x (str.) – name of the column that contains the x coordinates of the objects field_y (str.) – name of the column that contains the y coordinates of the objects unit (astropy.unit) – measure unit of the spatial coordinates
visualize(map_size, npixel, field_quantity=None, origin=<Quantity [ 0., 0.] deg>, smooth=None, fig=None, ax=None, colorbar=False, cmap='jet', **kwargs)[source]

Visualize a two dimensional square pixelized map version of one of the scalar properties in the catalog by assigning its objects on a grid (the pixelization is performed using the pixelize routine)

Parameters: map_size (quantity) – spatial size of the map npixel (int.) – number of pixels on a side field_quantity (str.) – name of the catalog quantity to map; if None, 1 is assumed origin (array with units) – two dimensional coordinates of the origin of the map smooth (quantity) – if not None, the map is smoothed with a gaussian filter of scale smooth kwargs (dict.) – the additional keyword arguments are passed to pixelize two dimensional scalar array with the pixelized field (pixels with no objects are treated as NaN) array
class lenstools.catalog.shear.ShearCatalog(*args, **kwargs)[source]

Class handler of a galaxy shear catalog, inherits all the functionality from the Catalog class

addSourceEllipticity(es, es_colnames=('e1', 'e2'), rs_correction=True, inplace=False)[source]

Produce a mock shear catalog that includes the intrinsic source ellipticity

Parameters: es (table) – array of intrinsic ellipticities, must be a two column catalog with intrinsic ellipticities es_colnames (tuple.) – column names with intrinsic ellipticities rs_correction (bool.) – include denominator (1+g*es) in the shear correction inplace (bool.) – perform the operation in place shear estimate with the added intrinsic ellipticity ShearCatalog
shapeNoise(seed=None)[source]

Generate a catalog with randomly drawn shape noise for each galaxy

Parameters: seed (int.) – random seed for noise generation shape noise catalog ShearCatalog
toMap(map_size, npixel, smooth, **kwargs)[source]

Parameters: map_size (quantity) – spatial size of the map npixel (int.) – number of pixels on a side smooth (quantity) – if not None, the map is smoothed with a gaussian filter of scale smooth kwargs (dict.) – additonal keyword arguments are passed to pixelize shear map ShearMap

### CMB temperature maps¶

class lenstools.image.convergence.CMBTemperatureMap(data, angle, masked=False, **kwargs)[source]
estimateKappaQuad(powerTT=None, callback='camb_dimensionless', noise_keys=None, lmax=3500, filtering=None)[source]

Estimate the lensing kappa using a temperature quadratic estimator

Parameters: powerTT (str.) – name of the file that contains the lensed theory TT power spectrum. If callback is a callable, powerTT is passed to the callback callback (str.) – callback function that computes the TT power spectrum. Can be ‘camb_dimensionless’ or ‘camb_uk’ for using camb tabulated power spectra (dimensionless or uK^2 units), None (the identity is used), or callable. If callable, it is called on powerTT and must return (ell,P_TT(ell)) noise_keys (dict.) – dictionary with noise TT power spectrum specifications filtering (str. or callable) – filter the map after reconstruction. Can be ‘wiener’ to apply the wiener filter, or callable. If callable, the function is called on the multipoles and applied to the reconstructed image FFT reconstructed convergence map ConvergenceMap
estimatePhiFFTQuad(powerTT=None, callback='camb_dimensionless', noise_keys=None, lmax=3500, filtering=None)[source]

Estimate the Fourier transform of the lensing potential using a temperature quadratic estimator

Parameters: powerTT (str.) – name of the file that contains the lensed theory TT power spectrum. If callback is a callable, powerTT is passed to the callback callback (str.) – callback function that computes the TT power spectrum. Can be ‘camb_dimensionless’ or ‘camb_uk’ for using camb tabulated power spectra (dimensionless or uK^2 units), None (the identity is used), or callable. If callable, it is called on powerTT and must return (ell,P_TT(ell)) noise_keys (dict.) – dictionary with noise TT power spectrum specifications filtering (str. or callable) – filter the map after reconstruction. Can be ‘wiener’ to apply the wiener filter, or callable. If callable, the function is called on the multipoles and applied to the reconstructed image FFT FFT of the lensing potential array
estimatePhiQuad(powerTT=None, callback='camb_dimensionless', noise_keys=None, lmax=3500, filtering=None)[source]

Estimate the lensing potential using a temperature quadratic estimator

Parameters: powerTT (str.) – name of the file that contains the lensed theory TT power spectrum. If callback is a callable, powerTT is passed to the callback callback (str.) – callback function that computes the TT power spectrum. Can be ‘camb_dimensionless’ or ‘camb_uk’ for using camb tabulated power spectra (dimensionless or uK^2 units), None (the identity is used), or callable. If callable, it is called on powerTT and must return (ell,P_TT(ell)) noise_keys (dict.) – dictionary with noise TT power spectrum specifications filtering (str. or callable) – filter the map after reconstruction. Can be ‘wiener’ to apply the wiener filter, or callable. If callable, the function is called on the multipoles and applied to the reconstructed image FFT reconstructed lensing potential map Spin0
classmethod from_power(angle=<Quantity 3.5 deg>, npixel=256, seed=None, space='real', powerTT=None, callback='camb_dimensionless', lmax=3500)[source]

Build an unlensed CMB temperature map from a known TT power spectrum

Parameters: angle (quantity) – angular size of the map npixel (int.) – number of pixels on a side seed (int.) – random seed for the Fourier coefficients draw space (str.) – must be ‘real’ or ‘fourier’ powerTT (str.) – name of the file that contains the TT power spectrum. If callback is a callable, powerTT is passed to the callback callback (str.) – callback function that computes the TT power spectrum. Can be ‘camb_dimensionless’ or ‘camb_uk’ for using camb tabulated power spectra (dimensionless or uK^2 units), None (the identity is used), or callable. If callable, it is called on powerTT and must return (ell,P_TT(ell)) temperature map CMBTemperatureMap
lens(kappa)[source]

Lens the CMB temperature map using a kappa map

Parameters: kappa (ConvergenceMap) – convergence map from which the lensing potential is inferred lensed temperature map CMBTemperatureMap
toFourier()[source]

Switches to Fourier space via FFT

toReal()[source]

Switches to real space

### Noise¶

class lenstools.image.noise.GaussianNoiseGenerator(shape, side_angle)[source]

A class that handles generation of Gaussian simulated noise maps

classmethod forMap(image)[source]

This class method generates a Gaussian noise generator intended to be used on a convergence map: i.e. the outputs of its methods can be added to the convergence map in question to simulate the presence of noise

Parameters: image (ConvergenceMap or CMBTemperatureMap) – The blueprint of the image you want to generate the noise for Noise generator instance GaussianNoiseGenerator
fromConvPower(power_func, seed=0, **kwargs)[source]

This method uses a supplied power spectrum to generate correlated noise maps in real space via FFTs

Parameters: power_func (function with the above specifications, or numpy array (l,Pl) of shape (2,n)) – function that given a numpy array of l’s returns a numpy array with the according Pl’s (this is the input power spectrum); alternatively you can pass an array (l,Pl) and the power spectrum will be calculated with scipy’s interpolation routines seed (int.) – seed of the random generator kwargs – keyword arguments to be passed to power_func, or to the interpolate.interp1d routine noise image with the same shape as the one used as blueprint ConvergenceMap
getCMBDetectorNoise(sigmaN=<Quantity 27.0 arcmin uK>, fwhm=<Quantity 7.0 arcmin>, ellmax=None, seed=0)[source]

This method produces CMB detector noise temperature maps (white noise + beam deconvolution)

Parameters: sigmaN (quantity) – rms of the noise. Must be supplied with temperature x angle units fwhm (quantity) – full width half maximum of the beam ellmax (float.) – if not None, zero out the power spectrum for ell>ellmax seed (int.) – seed for the random numbere generator noise temperature map CMBTemperatureMap
getCMBWhiteNoise(sigmaN=<Quantity 27.0 arcmin uK>, seed=0)[source]

This method produces CMB white noise temperature maps

Parameters: sigmaN (quantity) – rms of the noise. Must be supplied with temperature x angle units seed (int.) – seed for the random numbere generator noise temperature map CMBTemperatureMap
getShapeNoise(z=1.0, ngal=<Quantity 15.0 1 / arcmin2>, seed=0)[source]

This method generates a white, gaussian shape noise map for the given redshift of the map

Parameters: z (float.) – single redshift of the backround sources on the map ngal (float.) – assumed angular number density of galaxies (must have units of angle^-2) seed (int.) – seed of the random generator instance of the same exact shape as the one used as blueprint ConvergenceMap

## Statistics¶

class lenstools.statistics.ensemble.Ensemble(data=None, file_list=[], metric='chi2', **kwargs)[source]

A class that handles statistical operations on weak lensing maps, inherits from pandas DataFrame. The rows in the Ensemble correspond to the different ensemble realizations of the same descriptor

bootstrap(callback, bootstrap_size=10, resample=10, seed=None, assemble=<built-in function array>, pool=None, **kwargs)[source]

Computes a custom statistic on the Ensemble using the bootstrap method

Parameters: callback (callable) – statistic to compute on the ensemble; takes the resampled Ensemble data as an input bootstrap_size (int.) – size of the resampled ensembles used in the bootstraping; must be less than or equal to the number of realizations in the Ensemble resample (int.) – number of times the Ensemble is resampled seed (int.) – if not None, this is the random seed of the random resamples assemble (callable) – method that gets called on the resampled statistic list to make it into an Ensemble pool (MPI pool object) – MPI pool for multiprocessing (imported from emcee https://github.com/dfm/emcee) kwargs (dict.) – passed to the callback function the bootstraped statistic assemble return type
combine_columns(combinations)[source]

Combine the hierarchical columns in the Ensemble, according to a dictionary which keys are the name of the combined features

Parameters: combinations (dict.) – mapping of combined features onto the old ones Ensemble with columns combined Ensemble
classmethod combine_from_dict(ensemble_dict)[source]

Builds an Ensemble combining the columns of smaller Ensembles; each key in the ensemble_dict dictionary becomes an element in the top level of the resulting Ensemble index

Parameters: ensemble_dict (dict.) – dictionary that contains the Ensembles to combine; the values in the dictionary must be Ensembles Ensemble
compare(rhs, **kwargs)[source]

Computes a comparison score between two Ensembles: computes a chi2-style difference between two different ensembles to assert how different they are

classmethod compute(file_list, callback_loader=None, pool=None, index=None, assemble=<built-in function array>, **kwargs)[source]

Computes an ensemble, can spread the calculations on multiple processors using a MPI pool

Parameters: file_list (list.) – list of files that will constitute the ensemble; the callback_loader is called on each of the files to produce the different realizations callback_loader (function) – This function gets executed on each of the files in the list and populates the ensemble. If None provided, it performs a numpy.load on the specified files. Must return a numpy array with the loaded data pool (MPI pool object) – MPI pool for multiprocessing (imported from emcee https://github.com/dfm/emcee) index (pandas Index) – index of the Ensemble assemble (callable) – called on the list of features (one feature per file) to assemble them in an array (defaults to np.array) kwargs (dict.) – Any additional keyword arguments to be passed to callback_loader
>>> from lenstools import Ensemble

>>> map_list = ["conv1.fit","conv2.fit","conv3.fit"]
>>> l_edges = np.arange(200.0,50000.0,200.0)

>>> conv_ensemble = Ensemble.compute(map_list,callback_loader=default_callback_loader,pool=pool,l_edges=l_edges)

covariance(bootstrap=False, bootstrap_size=10, resample=10, seed=None, pool=None)[source]

Computes the ensemble covariance matrix

Parameters: bootstrap (bool.) – if True the covariance matrix is computed with a bootstrap estimate bootstrap_size (int.) – size of the resampled ensembles used in the bootstraping; must be less than or equal to the number of realizations in the Ensemble seed (int.) – if not None, this is the random seed of the random resamples resample (int.) – number of times the Ensemble is resampled pool (MPI pool object) – MPI pool for multiprocessing (imported from emcee https://github.com/dfm/emcee) Covariance matrix, has shape (self.data[1],self.data[1]) SquareMatrix
group(group_size, kind='sparse')[source]

Sometimes it happens that different realizations in the ensemble need to be grouped together, for example when they belong to different subfields of the same observation field. With this function you can group different realizations together by taking the mean, and reduce the total number of realizations in the ensemble

Parameters: group_size (int) – how many realizations to put in a group, must divide exactly the total number of realizations in the ensemble kind (str.) – specifies how to do the grouping; if set to “sparse” the groups are formed by taking one realizations every nobs/group_size (for example ([1,1001,…,9001],[2,1002,…,9002]) if nobs=10000 and group_size=10). If set to “contiguous” then the realizations are grouped as ([1,2,…,10],[11,12,…,20]). Otherwise you can set kind to your own sparse matrix scheme gropby object
imshow(fig=None, ax=None, **kwargs)[source]

Visualize a two dimensional map of the Ensemble, with the index as the vertical axis and the columns as the horizontal axis

Parameters: fig – ax – kwargs (dict.) –
classmethod meshgrid(labels, sort=None)[source]

Construct on Ensemble whose column values are arranged in a regularly spaced mesh grid

Parameters: labels (dict.) – dictionary whose keys are the Ensemble columns and whose values are the mesh grid axes values sort (dict.) – optional dictionary that tells how the meshgrid columns should be sorted Ensemble
principalComponents(location=None, scale=None)[source]

Computes the principal components of the Ensemble

Parameters: location (Series) – compute the principal components with respect to this location; must have the same columns as the Ensemble scale (Ensemble) – compute the principal components applying this scaling on the Ensemble columns; must have the same columns as the Ensemble pcaHandler instance
project(vectors, names=None)[source]

Projects the rows of the Ensemble on the hyperplane defined by N linearly independent vectors

Parameters: vectors (tuple.) – linearly independent vectors on which to project the rows of the Ensemble names (list.) – optional names of the projected components projected Ensemble; the new rows contain the components of the old rows along the vectors Ensemble
classmethod read(filename, callback_loader=None, **kwargs)[source]

Reads a numpy file into an Ensemble

Parameters: filename (str.) – name of the file to read callback_loader (function) – This function gets executed on each of the files in the list and populates the ensemble. If None provided, it unpickles the specified file. Must return an acceptable input for the Ensemble constructor kwargs (dict.) – Any additional keyword arguments to be passed to callback_loader Ensemble instance read from the file
classmethod readall(filelist, callback_loader=None, **kwargs)[source]

Reads a list of files into an Ensemble

Parameters: filelist (list.) – list of files to read callback_loader (function) – This function gets executed on each of the files in the list and populates the ensemble. If None provided, it performs a numpy.load on the specified file. Must return a numpy array with the loaded data kwargs (dict.) – Any additional keyword arguments to be passed to callback_loader Ensemble instance read from the file
save(filename, format=None, **kwargs)[source]

Save ensemble data in an external file (in arbitrary format)

Parameters: filename (str.) – file name of the external file kwargs (dict.) – the keyword arguments are passed to the saver (or to format if callable) format in which to save the ensemble; if None the format is auto detected from the filename
selfChi2()[source]

Computes the Ensemble distribution of chi squared values, defined with respect to the same Ensemble mean and covariance

Returns: array with the self chi squared for each realization
shuffle(seed=None)[source]

Changes the order of the realizations in the Ensemble

Parameters: seed (int.) – random seed for the random shuffling shuffled Ensemble
suppress_indices(by, suppress, columns)[source]

Combine multiple rows in the Ensemble into a single row, according to a specific criterion

Parameters: by (list.) – list of columns that is used to group the Ensemble: the rows in each group are combined suppress (list.) – list of columns to suppress: these indices get suppressed when the rows are combined columns (list.) – list of columns to keep in the rows that are combined suppressed indices lookup table,combined Ensemble list.
class lenstools.statistics.ensemble.SquareMatrix(data=None, file_list=[], metric='chi2', **kwargs)[source]
invert()[source]

Compute the inverse

Return type: SquareMatrix
class lenstools.statistics.database.Database(name, connection_string='sqlite:///{0}')[source]
insert(df, table_name='data')[source]
Parameters: df (Ensemble) – records to insert in the database, in Ensemble (or pandas DataFrame) format
query(sql)[source]
Parameters: sql (str.) – sql query string Ensemble
classmethod query_all(db_names, sql)[source]

Perform the same SQL query on a list of databases and combine the results

Parameters: db_names (list.) – list of names of the databases to query sql (str.) – sql query string Ensemble
classmethod read_table_all(db_names, table_name)[source]

Read the same SQL table from a list of databases and combine the results

Parameters: db_names (list.) – list of names of the databases to query table (str.) – table to read Ensemble
class lenstools.statistics.database.ScoreDatabase(*args, **kwargs)[source]
insert(df, table_name='data')
Parameters: df (Ensemble) – records to insert in the database, in Ensemble (or pandas DataFrame) format
pull_features(feature_list, table_name='scores', score_type='likelihood')[source]

Pull out the scores for a subset of features

Parameters: feature_list (list.) – feature list to pull out from the database score_type (str.) – name of the column that contains the particular score you are considering
query(sql)
Parameters: sql (str.) – sql query string Ensemble
classmethod query_all(db_names, sql)

Perform the same SQL query on a list of databases and combine the results

Parameters: db_names (list.) – list of names of the databases to query sql (str.) – sql query string Ensemble
classmethod read_table_all(db_names, table_name)

Read the same SQL table from a list of databases and combine the results

Parameters: db_names (list.) – list of names of the databases to query table (str.) – table to read Ensemble
lenstools.statistics.database.chi2database(*args, **kwargs)

Populate an SQL database with the scores of different parameter sets with respect to the data; supports multiple features

Parameters: db_name (str.) – name of the database to populate parameters (Ensemble) – parameter combinations to score specs (dict.) – dictionary that should contain the emulator,data, and covariance matrix of each feature to consider; each value in this dictionary must be a dictionary with keys ‘emulator’, ‘data’ and ‘data covariance’ table_name (str.) – table name to populate in the database pool (MPIPool) – MPIPool to spread the calculations over (pass None for automatic pool handling) nchunks (int.) – number of chunks to split the parameter score calculations in (one chunk per processor ideally)

## Cosmological parameter estimation¶

class lenstools.statistics.constraints.Analysis(data=None, file_list=[], metric='chi2', **kwargs)[source]

The base class of this module; the idea in weak lensing analysis is that one has a set of simulated data, that serves as training model, and then uses that set to fit the observations for the best model parameters. Inherits from Ensemble

add_models(parameters, feature)[source]

Add a model to the training set of the current analysis

Parameters: parameters (array) – parameter set of the new model feature (array) – measured feature of the new model
combine_features(combinations)[source]

Combine features in the Analysis, according to a dictionary which keys are the name of the combined features

Parameters: combinations (dict.) – mapping of combined features onto the old ones
find(parameters, rtol=1e-05)[source]

Finds the location in the instance that has the specified combination of parameters

Parameters: parameters (array.) – the parameters of the model to find rtol (float.) – tolerance of the search (must be less than 1) array of int. with the indices of the corresponding models
refeaturize(transformation, method='apply_row', **kwargs)[source]

Allows a general transformation on the feature set of the analysis by calling an arbitrary transformation function

Parameters: transformation (callable or dict.) – callback function called on the feature_set; must take in a row of features and return a row of features. If a dictionary is passed, the keys must be the feature names kwargs (dict.) – the keyword arguments are passed to the transformation callable transformed Analysis
reparametrize(transformation, **kwargs)[source]

Reparametrize the parameter set of the analysis by calling the formatter handle on the current parameter set (can be used to enlarge/shrink/relabel the parameter set)

Parameters: transformation (callable) – transformation function called on the parameters, must take in a row of parameters and return another row of parameters kwargs (dict.) – the keyword arguments are passed to the transformation callable reparametrized Analysis

### Fisher matrix calculations¶

class lenstools.statistics.constraints.FisherAnalysis(data=None, file_list=[], metric='chi2', **kwargs)[source]
check()[source]

Asserts that the parameters are varied one at a time, and that a parameter is not varied more than once

Raises: AssertionError
chi2(observed_feature, features_covariance, correct=None)[source]

Computes the chi2 between an observed feature and the fiducial feature, using the provided covariance

Parameters: observed_feature (array) – observed feature to fit, its last dimension must have the same shape as self.feature_set[0] features_covariance (2 dimensional array (or 1 dimensional if diagonal)) – covariance matrix of the simulated features, must be provided for a correct fit! correct (int.) – if not None, correct for the bias in the inverse covariance estimator assuming the covariance was estimated by ‘correct’ simulations chi2 of the comparison float.
classify(observed_feature, features_covariance, correct=None, labels=[0, 1], confusion=False)[source]

Performs a Fisher classification of the observed feature, choosing the most probable label based on the value of the chi2

Parameters: observed_feature (array) – observed feature to fit, the last dimenstion must have the same shape as self.feature_set[0] features_covariance (2 dimensional array (or 1 dimensional if assumed diagonal)) – covariance matrix of the simulated features, must be provided for a correct classification! correct (int.) – if not None, correct for the bias in the inverse covariance estimator assuming the covariance was estimated by ‘correct’ simulations labels (iterable) – labels of the classification, must be the indices of the available classes (from 0 to feature_set.shape[0]) confusion (bool.) – if True, an array with the label percentage occurrences is returned; if False an array of labels is returned array with the labels resulting from the classification int.
compute_derivatives()[source]

Computes the feature derivatives with respect to the parameter sets using one step finite differences; the derivatives are computed with respect to the fiducial parameter set

Returns: array of shape (p,N), where N is the feature dimension and p is the number of varied parameters
confidence_ellipse(simulated_features_covariance, correct=None, observed_feature=None, observed_features_covariance=None, parameters=['Om', 'w'], p_value=0.684, **kwargs)[source]

Draws a confidence ellipse of a specified p-value in parameter space, corresponding to fit an observed feature for the cosmological parameters

Parameters: observed_feature (array) – observed feature to fit, the last dimenstion must have the same shape as self.feature_set[0] simulated_features_covariance (2 dimensional array (or 1 dimensional if assumed diagonal)) – covariance matrix of the simulated features, must be provided for a correct fit! correct (int.) – if not None, correct for the bias in the inverse covariance estimator assuming the covariance was estimated by ‘correct’ simulations observed_features_covariance (2 dimensional array (or 1 dimensional if assumed diagonal)) – covariance matrix of the simulated features, if different from the simulated one; if None the simulated feature covariance is used parameters (list.) – parameters to compute the condifence contour of p_value (float.) – p-value to calculate kwargs (dict.) – the keyword arguments are passed to the matplotlib Ellipse method matplotlib ellipse object Ellipse
static ellipse(center, covariance, p_value=0.684, **kwargs)

Draws a confidence ellipse using matplotlib Ellipse patch

Parameters: center (tuple.) – center of the ellipse covariance (2D-array.) – parameters covariance matrix p_value (float.) – p-value to calculate kwargs (dict.) – the keyword arguments are passed to the matplotlib Ellipse method matplotlib ellipse object Ellipse
fisher_matrix(simulated_features_covariance, correct=None, observed_features_covariance=None)[source]

Computes the parameter Fisher matrix using the associated features, that in the end allows to compute the parameter confidence contours (around the fiducial value)

Parameters: simulated_features_covariance (2 dimensional array (or 1 dimensional if assumed diagonal)) – covariance matrix of the simulated features, must be provided for a correct fit! correct (int.) – if not None, correct for the bias in the inverse covariance estimator assuming the covariance was estimated by ‘correct’ simulations observed_features_covariance (2 dimensional array (or 1 dimensional if assumed diagonal)) – covariance matrix of the simulated features, if different from the simulated one; if None the simulated feature covariance is used 2 dimensional array with the Fisher matrix of the analysis
fit(observed_feature, features_covariance)[source]

Maximizes the gaussian likelihood on which the Fisher matrix formalism is based, and returns the best fit for the parameters given the observed feature

Parameters: observed_feature (array) – observed feature to fit, must have the same shape as self.feature_set[0] features_covariance (2 dimensional array (or 1 dimensional if assumed diagonal)) – covariance matrix of the simulated features, must be provided for a correct fit! array with the best fitted parameter values
parameter_covariance(simulated_features_covariance, correct=None, observed_features_covariance=None)[source]

Computes the parameter covariance matrix using the associated features, that in the end allows to compute the parameter confidence contours (around the fiducial value)

Parameters: simulated_features_covariance (2 dimensional array (or 1 dimensional if assumed diagonal)) – covariance matrix of the simulated features, must be provided for a correct fit! correct (int.) – if not None, correct for the bias in the inverse covariance estimator assuming the covariance was estimated by ‘correct’ simulations observed_features_covariance (2 dimensional array (or 1 dimensional if assumed diagonal)) – covariance matrix of the simulated features, if different from the simulated one; if None the simulated feature covariance is used 2 dimensional array with the parameter covariance matrix of the analysis
set_fiducial(n)[source]

Sets the fiducial model (with respect to which to compute the derivatives), default is 0 (i.e. self.parameter_set[0])

Parameters: n (int.) – the parameter set you want to use as fiducial
variations

Checks the parameter variations with respect to the fiducial cosmology

Returns: iterable with the positions of the variations
varied

Returns the indices of the parameters that are varied

Returns: list with the indices of the varied parameters
where(par=None)[source]

Finds the locations of the varied parameters in the parameter set

Returns: dict. with the locations of the variations, for each parameter

### Non linear parameter dependence¶

class lenstools.statistics.constraints.Emulator(*args, **kwargs)[source]
approximate_linear(center, derivative_precision=0.1)[source]

Construct a FisherAnalysis by approximating the Emulator as a linear expansion along a chosen center

Parameters: center (Series) – center point in parameter space derivative_precision (float.) – percentage step for the finite difference derivatives linearly approximated Fisher analysis FisherAnalysis
chi2(parameters, observed_feature, features_covariance, correct=None, split_chunks=None, pool=None)[source]

Computes the chi2 part of the parameter likelihood with the usual sandwich product with the covariance matrix; the model features are computed with the interpolators

Parameters: parameters ((N,p) array where N is the number of points and p the number of parameters) – new points in parameter space on which to compute the chi2 statistic observed_feature (array) – observed feature on which to condition the parameter likelihood features_covariance (array) – covariance matrix of the features, must be supplied correct (int.) – if not None, correct for the bias in the inverse covariance estimator assuming the covariance was estimated by ‘correct’ simulations split_chunks (int.) – if set to an integer bigger than 0, splits the calculation of the chi2 into subsequent chunks, each that takes care of an equal number of points. Each chunk could be taken care of by a different processor array with the chi2 values, with the same shape of the parameters input
chi2Contributions(parameters, observed_feature, features_covariance, correct=None)[source]

Computes the individual contributions of each feature bin to the chi2; the model features are computed with the interpolators. The full chi2 is the sum of the individual contributions

Parameters: parameters ((N,p) array where N is the number of points and p the number of parameters) – new points in parameter space on which to compute the chi2 statistic observed_feature (array) – observed feature on which to condition the parameter likelihood features_covariance (array) – covariance matrix of the features, must be supplied correct (int.) – if not None, correct for the bias in the inverse covariance estimator assuming the covariance was estimated by ‘correct’ simulations numpy 2D array with the contributions to the chi2 (off diagonal elements are the contributions of the cross correlation between bins)
likelihood(chi2_value, **kwargs)[source]

Computes the likelihood value with the selected likelihood function, given the pre-computed chi2 value

Parameters: chi2_value (array) – chi squared values kwargs – keyword arguments to be passed to your likelihood function
predict(parameters, raw=False)[source]

Predicts the feature at a new point in parameter space using the bin interpolators, trained with the simulated features

Parameters: parameters (array) – new points in parameter space on which to compute the chi2 statistic; it’a (N,p) array where N is the number of points and p the number of parameters, or array of size p if there is only one point raw (bool.) – if True returns raw numpy arrays predicted features array or Ensemble
sample_posterior(observed_feature, sample='emcee', **kwargs)[source]

Sample the parameter posterior distribution

Parameters: observed_feature (Series) – observed feature to score sample (str. or callable) – posterior sampling method samples from the posterior distribution dict.
score(parameters, observed_feature, method='chi2', **kwargs)[source]

Compute the score for an observed feature for each combination of the proposed parameters

Parameters: parameters (DataFrame or array) – parameter combinations to score observed_feature (Series) – observed feature to score method (str. or callable) – scoring method to use (defaults to chi2): if callable, must take in the current instance, the parameter array and the observed feature and return a score for each parameter combination kwargs (dict.) – keyword arguments passed to the callable method ensemble with the scores, for each feature Ensemble
set_likelihood(function=None)[source]

Sets the likelihood function to a custom function input by the user: the default is the usual exp(-0.5*chi^2)

train(use_parameters='all', method='Rbf', **kwargs)[source]

Builds the interpolators for each of the feature bins using a radial basis function approach

Parameters: use_parameters (list. or "all") – which parameters actually vary in the supplied parameter set (it doesn’t make sense to interpolate over the constant ones) method (str. or callable) – interpolation method; can be ‘Rbf’ or callable. If callable, it must take two arguments, a square distance and a square length smoothing scale kwargs – keyword arguments to be passed to the interpolator constructor

### Posterior samplers¶

lenstools.statistics.samplers.emcee_sampler(emulator, observed_feature, features_covariance, correct=None, pslice=None, nwalkers=16, nburn=100, nchain=1000, pool=None)[source]

Parameter posterior sampling based on the MCMC algorithm implemented by the emcee package

Parameters: emulator (Emulator) – feature emulator observed_feature (array) – observed feature to condition the parameter estimation features_covariance (array) – covariance matrix of the features correct (int.) – if not None, correct for the bias in the inverse covariance estimator assuming the covariance was estimated by ‘correct’ simulations pslice (dict.) – specify slices of the parameter space in which some parameters are keps as constants nwalkers (int.) – number of chains nburn (int.) – length of the burn-in chain nchain (int.) – length of the MCMC chain pool (MPIPool) – MPI Pool for parallelization of computations ensemble of samples from the posterior probability distribution Ensemble

### Confidence contour plotting¶

class lenstools.statistics.contours.ContourPlot(fig=None, ax=None)[source]

A class handler for contour plots

close()[source]

Closes the figure

confidenceArea()[source]

Computes the area enclosed by the parameter confidence contours

Returns: area enclosed by the confidence contours dict.
expectationValue(function, **kwargs)[source]

Computes the expectation value of a function of the parameters over the current parameter likelihood

classmethod from_scores(score_ensemble, parameters, feature_names=None, plot_labels=None, fig=None, ax=None, figsize=(8, 8))[source]

Build a ContourPlot instance out of an ensemble of scores

Parameters: score_ensemble (Ensemble) – ensemble of the scores parameters (list.) – columns names that contain the parameters feature_names (list.) – name of the features to generate the contour plot for plot_labels (list.) – plot labels for the parameters figsize (tuple.) – size of the plot list of ContourPlot
getLikelihood(likelihood_filename, parameter_axes={'Omega_m': 0, 'sigma8': 2, 'w': 1}, parameter_labels={'Omega_m': '$\\Omega_m$', 'sigma8': '$\\sigma_8$', 'w': '$w$'})[source]

Load the likelihood function from a numpy file

getLikelihoodValues(levels, precision=0.001)[source]

Find the likelihood values that correspond to the selected p_values

getMaximum(which='full')[source]

Find the point in parameter space on which the likelihood is maximum

getUnitsFromOptions(options)[source]

Parse options file to get physical units of axes

labels(contour_label=None, fontsize=22, **kwargs)[source]

Put the labels on the plot

marginal(parameter_name='w', levels=None)[source]

Marginalize the likelihood over all parameters but one

marginalize(parameter_name='w')[source]

Marginalize the likelihood over the indicated parameters

plotContours(colors=['red', 'green', 'blue'], display_percentages=True, display_maximum=True, color_maximum='green', fill=False, **kwargs)[source]

Display the confidence likelihood contours

plotEllipse(colors=['red'], levels=[0.683], **kwargs)[source]

Plot the Gaussian approximation to the confidence contours

Parameters: colors (list.) – colors of the confidence ellipses levels (list.) – p_values of the confidence ellipses kwargs (dict.) – additional keyword arguments are passed to the Ellipse patch method
plotMarginal(parameter, levels=[0.684], colors=['red', 'blue', 'green'], alpha=0.5, fill=False)[source]

Plot the likelihood function marginalized over all parameters except one

point(coordinate_x, coordinate_y, color='green', marker='o')[source]

Draws a point in parameter space at the specified physical coordinates

savefig(figname)[source]

Save the plot to file

setUnits(parameter, parameter_min, parameter_max, parameter_unit)[source]

Set manually the physical units for each of the likelihood axes

show(cmap=None, interpolation='nearest', **kwargs)[source]

Show the 2D marginalized likelihood

slice(parameter_name='w', parameter_value=-1.0)[source]

Slice the likelihood cube by fixing one of the parameters

value(*coordinates)[source]

Compute the (un-normalized) likelihood value at the specified point in parameter space

variance(function, **kwargs)[source]

Computes the variance of a function of the parameters over the current parameter likelihood

## Existing Simulation suites¶

class lenstools.simulations.IGS1(H0=72.0, Om0=0.26, w0=-1.0, sigma8=0.798, ns=0.96, root_path=None, name=None)[source]

Class handler of the IGS1 simulations set, inherits the cosmological parameters from the astropy.cosmology.FlatwCDM class; the default parameter values are the fiducial ones

getNames(realizations, z=1.0, kind='convergence', big_fiducial_set=False)[source]

Get the full name of the IGS1 maps, once a redshift, realization identificator and kind are specified

Parameters: z (float.) – redshift plane of the maps, must be one of [1.0,1.5,2.0] realizations (list. or int.) – list of realizations to get the names of, the elements must be in [1,1000] kind (str.) – decide if retrieve convergence or shear maps, must be one of [convergence,shear1,shear2] big_fiducial_set (bool.) – set to True if you want to get the names of the bigger fiducial simulation based on 45 N-body simulations
load(realization, **kwargs)[source]

Reads in a specific realization of the convergence field (in FITS format) and returns a ConvergenceMap instance with the loaded map

Parameters: realization (int.) – the specific realization to read kwargs – the keyword arguments are passed to the getNames method ConvergenceMap instance with the loaded map
squeeze(with_ns=False)[source]

Returns the cosmological parameters of the model in numpy array form

Parameters: with_ns (bool.) – if True returns also ns as the last parameter numpy array (Om0,w0,si8,ns–optionally)
class lenstools.simulations.CFHTemu1(H0=72.0, Om0=0.26, w0=-1.0, sigma8=0.798, ns=0.96, root_path=None, name=None)[source]

Class handler of the weak lensing CFHTemu1 simulations set, inherits from IGS1; this simulation suite contains 91 different cosmological models based on 1 N-body simulation each. Each model has 1000 realizations for each of the 13 CFHT subfields

classmethod getModels(root_path='/default')[source]

This class method uses a dictionary file to read in all the cosmological model parameters and instantiate the corresponding CFHTemu1 objects for each one of them

Parameters: root_path (str.) – path of your CFHT emu1 simulations copy list of CFHTemu1 instances
getNames(realizations, subfield=1, smoothing=0.5)[source]

Get the full name of the CFHT emu1 maps, once a subfield and smoothing scale are specified

Parameters: subfield (int.) – the specific CFHT subfield you want to retrieve, must be between 1 and 13 smoothing (float.) – smoothing scale of the maps you wish to retrieve, in arcmin realizations (list. or int.) – list of realizations to get the names of, the elements must be in [1,1000]
load(realization, **kwargs)

Reads in a specific realization of the convergence field (in FITS format) and returns a ConvergenceMap instance with the loaded map

Parameters: realization (int.) – the specific realization to read kwargs – the keyword arguments are passed to the getNames method ConvergenceMap instance with the loaded map
squeeze(with_ns=False)

Returns the cosmological parameters of the model in numpy array form

Parameters: with_ns (bool.) – if True returns also ns as the last parameter numpy array (Om0,w0,si8,ns–optionally)
class lenstools.simulations.CFHTcov(H0=72.0, Om0=0.26, w0=-1.0, sigma8=0.798, ns=0.96, root_path=None, name=None)[source]

Class handler of the weak lensing CFHTcov simulations set, inherits from CFHTemu1; this simulation suite contains 1000 realizations for each of the 13 CFHT subfields, based on 50 independent N-body simulations of a fiducial LambdaCDM universe. Useful to measure accurately descriptor covariance matrices

classmethod getModels(root_path='/default')[source]

On call, this class method returns a CFHTcov instance initialized with the cosmological parameters of the only available model in the suite

Parameters: root_path (str.) – path of your CFHTcov simulations copy CFHTcov instance initialized with the fiducial cosmological parameters
getNames(realizations, subfield=1, smoothing=0.5)[source]

Get the full name of the CFHTcov maps, once a subfield and smoothing scale are specified

Parameters: subfield (int.) – the specific CFHT subfield you want to retrieve, must be between 1 and 13 smoothing (float.) – smoothing scale of the maps you wish to retrieve, in arcmin realizations (list. or int.) – list of realizations to get the names of, the elements must be in [1,1000]
load(realization, **kwargs)

Reads in a specific realization of the convergence field (in FITS format) and returns a ConvergenceMap instance with the loaded map

Parameters: realization (int.) – the specific realization to read kwargs – the keyword arguments are passed to the getNames method ConvergenceMap instance with the loaded map
squeeze(with_ns=False)

Returns the cosmological parameters of the model in numpy array form

Parameters: with_ns (bool.) – if True returns also ns as the last parameter numpy array (Om0,w0,si8,ns–optionally)

## Simulation design¶

class lenstools.simulations.Design(data=None, file_list=[], metric='chi2', **kwargs)[source]

A class that proves useful in designing simulation sets: the main functionality provided is the uniform sampling of an arbirtarily high dimensional parameter space. The points in parameter space are chosen to be as spread as possible by minimizing a cost function, but enforcing a latin hypercube structure, in which each parameter value appears once

cost(p, Lambda)[source]

Computes the cost function of the current configuration given the metric parameters (p,Lambda)

Parameters: Lambda (float.) – metric parameter of the cost function; if set to 1.0 the cost function corresponds is the Coulomb potential energy p (float.) – metric parameter of the cost function; if set to 2.0 the distances between points are the Euclidean ones the value of the cost function
diagonalCost(Lambda)[source]

Computes the cost function of a diagonal configuration with a specified number of points and a metric parameter lambda; the cost function is calculated on the scaled parameter values, which are always between 0 and 1

Parameters: Lambda (float.) – metric parameter of the cost function; if set to 1.0 the cost function corresponds is the Coulomb potential energy the value of the cost function for a diagonal configuration
classmethod from_specs(npoints, parameters)[source]
Parameters: npoints (int.) – number of points in the design parameters (list.) – list of tuples (name,label,min,max)
sample(p=2.0, Lambda=1.0, seed=0, maxIterations=10000)[source]

Evenly samples the parameter space by minimizing the cost function computed with the metric parameters (p,Lambda); this operation works inplace

Parameters: Lambda (float.) – metric parameter of the cost function; if set to 1.0 the cost function corresponds is the Coulomb potential energy p (float.) – metric parameter of the cost function; if set to 2.0 the distances between points are the Euclidean ones seed (int.) – random seed with which the sampler random generator will be initialized maxIterations (int.) – maximum number of iterations that the sampler can perform before stopping the relative change of the cost function the last time it varied during the sampling
classmethod sample_ellipse(npoints, parameters, center, minor, major, angle, radial_power=0.5, **kwargs)[source]

Sample points in a two dimensional parameter space whose boundary has the shape of an ellipse

Parameters: npoints (int.) – number of points in the design parameters (list.) – name of the parameters, the list must have 2 elements center (tuple.) – center of the ellipse minor (float.) – length of the semi-minor axis major (float.) – length of the semi-major axis angle (float.) – rotation angle of the major axis with respect to the horizontal (degrees counterclockwise) radial_power (float.) – this parameter controls the density of points around the center (higher for higher density); 0.5 corresponds to uniform sampling kwargs – the keyword arguments are passed to the sample() method Design
savefig(filename)[source]

Save the visualization to an external file

Parameters: filename (str.) – name of the file on which to save the plot
set_title(title)[source]

Give a title to the visualized design

Parameters: title (str.) – title of the figure
visualize(fig=None, ax=None, parameters=None, fontsize=20, **kwargs)[source]

Visualize the design configuration using matplotlib

Parameters: parameters (list.) – the parameters to visualize, you can specify two or three of them, by their names. If None, all parameters are visualized
write(filename=None, max_rows=None, format='ascii.latex', column_format='{0:.3f}', **kwargs)[source]

Outputs the points that make up the design in a nicely formatted table

Parameters: filename (str. or file descriptor) – name of the file to which the table will be saved; if None the contents will be printed max_rows (int.) – maximum number of rows in the table, if smaller than the number of points the different chunks are hstacked (useful if there are too many rows for display) format (str.) – passed to the Table.write astropy method column_format (str.) – format specifier for the numerical values in the Table kwargs (dict.) – the keyword arguments are passed to astropy.Table.write method the Table instance with the design parameters

## Nicaea bindings¶

class lenstools.simulations.NicaeaSettings[source]

Class handler of the code settings (non linear modeling, tomography, transfer function, etc…)

available(knob)[source]

Given a settings, lists all the possible values

classmethod default()[source]

Generate default settings

Returns: NicaeaSettings defaults instance
knobs

Lists available settings to tune

class lenstools.simulations.Nicaea(H0=72.0, Om0=0.26, Ode0=0.74, Ob0=0.046, w0=-1.0, wa=0.0, sigma8=0.8, As=None, ns=0.96, m_nu=<Quantity [ 0., 0., 0.] eV>, name=None)[source]

Main class handler for the python bindings of the NICAEA cosmological code, written by M. Kilbinger & collaborators

convergencePowerSpectrum(ell, z=2.0, distribution=None, distribution_parameters=None, settings=None, **kwargs)[source]

Computes the convergence power spectrum for the given cosmological parameters and redshift distribution using NICAEA

Parameters: ell (array.) – multipole moments at which to compute the power spectrum z (float., array or None) – redshift bins for the sources; if a single float is passed, single redshift is assumed distribution (None,callable or list) – redshift distribution of the sources (normalization not necessary); if None a single redshift is expected; if callable, z must be an array, and a single redshift bin is considered, with the galaxy distribution specified by the call of distribution(z); if a list is passed, each element must be a NICAEA type distribution_parameters (str. or list.) – relevant only when distribution is a list or callable. When distribution is callable, distribution_parameters has to be one between “one” and “all” to decide if one or multiple redshift bins have to be considered. If it is a list, each element in it should be the tuple of parameters expected by the correspondent NICAEA distribution type settings (NicaeaSettings instance) – NICAEA code settings kwargs (dict.) – the keyword arguments are passed to the distribution, if callable ( NlxNz array ) computed power spectrum at the selected multipoles (when computing the cross components these are returned in row major C ordering)
classmethod fromCosmology(cosmo)[source]

Builds a Nicaea instance from one of astropy.cosmology objects, from which it inherits all the cosmological parameter values

Parameters: cosmo (astropy FLRW) – one of astropy cosmology instances Nicaea instance with the cosmological parameters inherited from cosmo
shearTwoPoint(theta, z=2.0, distribution=None, distribution_parameters=None, settings=None, kind='+', **kwargs)[source]

Computes the shear two point function for the given cosmological parameters and redshift distribution using NICAEA

Parameters: theta (array. with units) – angles at which to compute the two point function z (float., array or None) – redshift bins for the sources; if a single float is passed, single redshift is assumed distribution (None,callable or list) – redshift distribution of the sources (normalization not necessary); if None a single redshift is expected; if callable, z must be an array, and a single redshift bin is considered, with the galaxy distribution specified by the call of distribution(z); if a list is passed, each element must be a NICAEA type distribution_parameters (str. or list.) – relevant only when distribution is a list or callable. When distribution is callable, distribution_parameters has to be one between “one” and “all” to decide if one or multiple redshift bins have to be considered. If it is a list, each element in it should be the tuple of parameters expected by the correspondent NICAEA distribution type settings (NicaeaSettings instance) – NICAEA code settings kind (str.) – must be “+” or “-“ kwargs (dict.) – the keyword arguments are passed to the distribution, if callable ( NtxNz array ) computed two point function at the selected angles (when computing the cross components these are returned in row major C ordering)

## CAMB¶

lenstools.simulations.camb.parseLog(fname)[source]

Parse CAMB output log

Parameters: fname (str. or file.) – file name or file descriptor parsed log dict.
class lenstools.simulations.camb.CAMBTransferFunction(k)[source]
add(z, T)

Add transfer function information at redshift z

Parameters: z (float.) – redshift T (array) – CDM transfer function from CAMB output
classmethod read(filename)

Load a previously pickled TransferFunction instance

Parameters: filename (str.) – name of the file from which the instance should be read TransferFunction
save(filename)

Pickle the TransferFunction instance

Parameters: filename (str.) – name of the file to save the instance to
class lenstools.simulations.camb.CAMBTransferFromPower(k)[source]
add(z, T)[source]

Add transfer function information at redshift z

Parameters: z (float.) – redshift T (array) – CDM transfer function from CAMB output
classmethod read(filename)

Load a previously pickled TransferFunction instance

Parameters: filename (str.) – name of the file from which the instance should be read TransferFunction
save(filename)

Pickle the TransferFunction instance

Parameters: filename (str.) – name of the file to save the instance to

## N-body simulation snapshot handling¶

class lenstools.simulations.Gadget2Snapshot(fp=None, pool=None, length_unit=<Quantity 1.0 kpc>, mass_unit=<Quantity 10000000000.0 solMass>, velocity_unit=<Quantity 1.0 km / s>, header_kwargs={})[source]

A class that handles Gadget2 snapshots, mainly I/O from the binary format and spatial information statistics.Inherits from the abstract NbodySnapshot

close()

Closes the snapshot file

cutPlaneAdaptive(normal=2, center=<Quantity 7.0 Mpc>, left_corner=None, plane_resolution=<Quantity 0.1 Mpc>, neighbors=64, neighborDistances=None, kind='density', projectAll=False)

Cuts a density (or gravitational potential) plane out of the snapshot by computing the particle number density using an adaptive smoothing scheme; the plane coordinates are cartesian comoving

Parameters: normal (int. (0,1,2)) – direction of the normal to the plane (0 is x, 1 is y and 2 is z) center (float. with units) – location of the plane along the normal direction plane_resolution (float. with units (or int.)) – plane resolution (perpendicular to the normal) left_corner (tuple of quantities or None) – specify the position of the lower left corner of the box; if None, the minimum of the (x,y,z) of the contained particles is assumed neighbors (int.) – number of nearest neighbors to use in the adaptive smoothing procedure neighborDistances (array with units) – precomputed distances of each particle to its N-th nearest neighbor; if None these are computed kind (str. ("density" or "potential")) – decide if computing a density or gravitational potential plane (this is computed solving the poisson equation) projectAll (bool.) – if True, all the snapshot is projected on a single slab perpendicular to the normal, ignoring the position of the center tuple(numpy 2D array with the computed particle number density (or lensing potential),bin resolution along the axes,number of particles on the plane)
cutPlaneAngular(normal=2, thickness=<Quantity 0.5 Mpc>, center=<Quantity 7.0 Mpc>, left_corner=None, plane_lower_corner=<Quantity [ 0., 0.] deg>, plane_size=<Quantity 0.15 deg>, plane_resolution=<Quantity 1.0 arcmin>, thickness_resolution=<Quantity 0.1 Mpc>, smooth=None, tomography=False, kind='density', space='real')

Same as cutPlaneGaussianGrid(), except that this method will return a lens plane as seen from an observer at z=0; the spatial transverse units are converted in angular units as seen from the observer

Parameters: normal (int. (0,1,2)) – direction of the normal to the plane (0 is x, 1 is y and 2 is z) thickness (float. with units) – thickness of the plane center (float. with units) – location of the plane along the normal direction; it is assumed that the center of the plane is seen from an observer with a redshift of self.header[“redshift”] left_corner (tuple of quantities or None) – specify the position of the lower left corner of the box; if None, the minimum of the (x,y,z) of the contained particles is assumed plane_lower_corner (float with units.) – lower left corner of the plane, as seen from the observer (0,0) corresponds to the lower left corner of the snapshot plane_size (float with units) – angular size of the lens plane (angles start from 0 in the lower left corner) plane_resolution (float. with units (or int.)) – plane angular resolution (perpendicular to the normal) thickness_resolution (float. with units (or int.)) – plane resolution (along the normal) smooth (int. or None) – if not None, performs a smoothing of the angular density (or potential) with a gaussian kernel of scale “smooth x the pixel resolution” tomography (bool.) – if True returns the lens plane angular density for each slab, otherwise a projected density (or lensing potential) is computed kind (str. ("density" or "potential")) – decide if computing an angular density or lensing potential plane (this is computed solving the poisson equation) space (str.) – if “real” return the lens plane in real space, if “fourier” the Fourier transform is not inverted tuple(numpy 2D or 3D array with the (unsmoothed) particle angular number density,bin angular resolution, total number of particles on the plane); the constant spatial part of the density field is subtracted (we keep the fluctuation only)
cutPlaneGaussianGrid(normal=2, thickness=<Quantity 0.5 Mpc>, center=<Quantity 7.0 Mpc>, plane_resolution=4096, left_corner=None, thickness_resolution=1, smooth=1, kind='density', **kwargs)

Cuts a density (or lensing potential) plane out of the snapshot by computing the particle number density on a slab and performing Gaussian smoothing; the plane coordinates are cartesian comoving

Parameters: normal (int. (0,1,2)) – direction of the normal to the plane (0 is x, 1 is y and 2 is z) thickness (float. with units) – thickness of the plane center (float. with units) – location of the plane along the normal direction plane_resolution (float. with units (or int.)) – plane resolution (perpendicular to the normal) left_corner (tuple of quantities or None) – specify the position of the lower left corner of the box; if None, the minimum of the (x,y,z) of the contained particles is assumed thickness_resolution (float. with units (or int.)) – plane resolution (along the normal) smooth (int. or None) – if not None, performs a smoothing of the density (or potential) with a gaussian kernel of scale “smooth x the pixel resolution” kind (str. ("density" or "potential")) – decide if computing a density or gravitational potential plane (this is computed solving the poisson equation) kwargs (dict.) – accepted keyword are: ‘density_placeholder’, a pre-allocated numpy array, with a RMA window opened on it; this facilitates the communication with different processors by using a single RMA window during the execution. ‘l_squared’ a pre-computed meshgrid of squared multipoles used for smoothing tuple(numpy 2D array with the density (or lensing potential),bin resolution along the axes, number of particles on the plane)
getID(first=None, last=None, save=True)[source]

Reads in the particles IDs, 4 byte ints, (read in of a subset is allowed): when first and last are specified, the numpy array convention is followed (i.e. getID(first=a,last=b)=getID()[a:b])

Parameters: first (int. or None) – first particle in the file to be read, if None 0 is assumed last (int. or None) – last particle in the file to be read, if None the total number of particles is assumed save (bool.) – if True saves the particles IDs as attribute numpy array with the particle IDs
getPositions(first=None, last=None, save=True)[source]

Reads in the particles positions (read in of a subset is allowed): when first and last are specified, the numpy array convention is followed (i.e. getPositions(first=a,last=b)=getPositions()[a:b])

Parameters: first (int. or None) – first particle in the file to be read, if None 0 is assumed last (int. or None) – last particle in the file to be read, if None the total number of particles is assumed save (bool.) – if True saves the particles positions as attribute numpy array with the particle positions
getVelocities(first=None, last=None, save=True)[source]

Reads in the particles velocities (read in of a subset is allowed): when first and last are specified, the numpy array convention is followed (i.e. getVelocities(first=a,last=b)=getVelocities()[a:b])

Parameters: first (int. or None) – first particle in the file to be read, if None 0 is assumed last (int. or None) – last particle in the file to be read, if None the total number of particles is assumed save (bool.) – if True saves the particles velocities as attrubute numpy array with the particle velocities
gridID()

Compute an ID for the particles in incresing order according to their position on a Nside x Nside x Nside grid; the id is computed as x + y*Nside + z*Nside**2

Returns: the gridded IDs array of float
header

Returns: the snapshot header information in dictionary form dict.
lensMaxSize()

Computes the maximum observed size of a lens plane cut out of the current snapshot

massDensity(resolution=<Quantity 0.5 Mpc>, smooth=None, left_corner=None, save=False, density_placeholder=None)

Uses a C backend gridding function to compute the matter mass density fluctutation for the current snapshot: the density is evaluated using a nearest neighbor search

Parameters: resolution (float with units or int.) – resolution below which particles are grouped together; if an int is passed, this is the size of the grid smooth (int. or None) – if not None, performs a smoothing of the density (or potential) with a gaussian kernel of scale “smooth x the pixel resolution” left_corner (tuple of quantities or None) – specify the position of the lower left corner of the box; if None, the minimum of the (x,y,z) of the contained particles is assumed save (bool.) – if True saves the density histogram and resolution as instance attributes placeholder (density) – if not None, it is used as a fixed memory chunk for MPI communications of the density tuple(numpy 3D array with the (unsmoothed) matter density fluctuation on a grid,bin resolution along the axes)
neighborDistances(neighbors=64)

Find the N-th nearest neighbors to each particle

Parameters: neighbors (int.) – neighbor order array with units
classmethod open(filename, pool=None, header_kwargs={}, **kwargs)

Opens a snapshot at filename

Parameters: filename (str. or file.) – file name of the snapshot pool (MPIWhirlPool instance) – use to distribute the calculations on different processors; if not None, each processor takes care of one of the snapshot parts, appending as “.n” to the filename header_kwargs (dict.) – keyword arguments to pass to the getHeader method kwargs (dict.) – the keyword arguments are passed to buildFilename
pos2R(filename, variable_name='pos')

Saves the positions of the particles in a R readable format, for facilitating visualization with RGL

Parameters: filename (str.) – name of the file on which to save the particles positions variable_name (str.) – name of the variable that contains the (x,y,z) positions in the R environment
powerSpectrum(k_edges, resolution=None, return_num_modes=False, density_placeholder=None)

Computes the power spectrum of the relative density fluctuations in the snapshot at the wavenumbers specified by k_edges; a discrete particle number density is computed before hand to prepare the FFT grid

Parameters: k_edges (array.) – wavenumbers at which to compute the density power spectrum (must have units) resolution (float with units, int. or None) – optional, fix the grid resolution to some value; to be passed to the massDensity method. If none this is computed automatically from the k_edges return_num_modes (bool.) – if True returns the mode counting for each k bin as the last element in the return tuple placeholder (density) – if not None, it is used as a fixed memory chunk for MPI communications in the density calculations tuple(k_values(bin centers),power spectrum at the specified k_values)
reorder()

Sort particles attributes according to their ID

savefig(filename)

Save the snapshot visualization to an external file

Parameters: filename (str.) – file name to which the figure will be saved
setHeaderInfo(Om0=0.26, Ode0=0.74, w0=-1.0, wa=0.0, h=0.72, redshift=100.0, box_size=<Quantity 20.833333333333336 Mpc>, flag_cooling=0, flag_sfr=0, flag_feedback=0, flag_stellarage=0, flag_metals=0, flag_entropy_instead_u=0, masses=<Quantity [ 0.00000000e+00, 1.03000000e+10, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00] solMass>, num_particles_file_of_type=None, npartTotalHighWord=array([0, 0, 0, 0, 0, 0], dtype=uint32))[source]

Sets the header info in the snapshot to write

setPositions(positions)

Sets the positions in the current snapshot (with the intent of writing them to a properly formatted snapshot file)

Parameters: positions ((N,3) array with units) – positions of the particles, must have units
setVelocities(velocities)

Sets the velocities in the current snapshot (with the intent of writing them to a properly formatted snapshot file)

Parameters: velocities ((N,3) array with units) – velocities of the particles, must have units
visualize(fig=None, ax=None, scale=False, first=None, last=None, **kwargs)

Visualize the particles in the snapshot using the matplotlib 3D plotting engine, the kwargs are passed to the matplotlib scatter method

Parameters: scale (bool.) – if True, multiply all the (comoving) positions by the scale factor
write(filename, files=1)[source]

Writes particles information (positions, velocities, etc…) to a properly formatter Gadget snapshot

Parameters: filename (str.) – name of the file to which to write the snapshot files (int.) – number of files on which to split the writing of the snapshot (useful if the number of particles is large); if > 1 the extension “.n” is appended to the filename
writeParameterFile(filename, settings)[source]

Writes a Gadget2 parameter file to evolve the current snapshot using Gadget2

Parameters: filename (str.) – name of the file to which to write the parameters settings (Gadget2Settings) – tunable settings of Gadget2 (see Gadget2 manual)
class lenstools.simulations.gadget2.Gadget2SnapshotDE(fp=None, pool=None, length_unit=<Quantity 1.0 kpc>, mass_unit=<Quantity 10000000000.0 solMass>, velocity_unit=<Quantity 1.0 km / s>, header_kwargs={})[source]

A class that handles Gadget2 snapshots, mainly I/O from the binary format and spatial information statistics.Inherits from Gadget2Snapshot; assumes that the header includes Dark Energy information

class lenstools.simulations.gadget2.Gadget2SnapshotPipe(*args, **kwargs)[source]

Read in the particle positions when calling the constructor, without calling fseek

class lenstools.simulations.amiga.AmigaHalos(fp=None, pool=None, length_unit=<Quantity 1.0 kpc>, mass_unit=<Quantity 10000000000.0 solMass>, velocity_unit=<Quantity 1.0 km / s>, header_kwargs={})[source]

A class that handles the Amiga Halo Finder (AHF) halo output files. Inherits from the abstract NbodySnapshot

Warning: Not tested yet!

## Ray tracing simulations¶

class lenstools.simulations.Plane(data, angle, redshift=2.0, cosmology=None, comoving_distance=None, unit=Unit("rad2"), num_particles=None, masked=False, filename=None)[source]
classmethod load(filename, format=None, init_cosmology=True)[source]

Loads the Plane from an external file, of which the format can be specified (only fits implemented so far)

Parameters: filename (str.) – name of the file from which to load the plane format (str.) – format of the file, only FITS implemented so far; if None, it’s detected automatically from the filename init_cosmology (bool.) – if True, instantiates the cosmology attribute of the PotentialPlane PotentialPlane instance that wraps the data contained in the file
randomRoll(seed=None, lmesh=None)[source]

Randomly shifts the plane along its axes, enforcing periodic boundary conditions

Parameters: seed (int.) – random seed with which to initialize the generator lmesh (array) – the FFT frequency meshgrid (lx,ly) necessary for the calculations in fourier space; if None, a new one is computed from scratch (must have the appropriate dimensions)
save(filename, format=None, double_precision=False)[source]

Saves the Plane to an external file, of which the format can be specified (only fits implemented so far)

Parameters: filename (str.) – name of the file on which to save the plane format (str.) – format of the file, only FITS implemented so far; if None, it’s detected automatically from the filename double_precision (bool.) – if True saves the Plane in double precision
scaleWithTransfer(z, tfr, with_scale_factor=False, kmesh=None, scaling_method='uniform')[source]

Scale the pixel values to a different redshift than the one of the plane by applying a suitable transfer function. This operation works in place

Parameters: z (float.) – new redshift to evaluate the plane at tfr (TransferFunction) – CDM transfer function in Fourier space used to scale the plane pixel values with_scale_factor (bool.) – if True, multiply the pixel values by an additional (1+znew) / (1+z0) overall factor kmesh (quantity) – the comoving wavenumber meshgrid (kx,ky) necessary for the calculations in Fourier space; if None, a new one is computed from scratch (must have the appropriate dimensions) scaling_method (str.) – must be ether “uniform” (all pixels scaled by the same factor, in which case the transfer function at low k is used) or “FFT” in which the full transfer function should be used
toFourier()[source]

Switches to Fourier space

toReal()[source]

Switches to real space

class lenstools.simulations.raytracing.TransferSpecs(tfr, cur2target, with_scale_factor, kmesh, scaling_method)[source]
class lenstools.simulations.DensityPlane(data, angle, redshift=2.0, cosmology=None, comoving_distance=None, unit=Unit("rad2"), num_particles=None, masked=False, filename=None)[source]

Class handler of a lens density plane, inherits from the parent Plane class; additionally it defines redshift and comoving distance attributes that are needed for ray tracing operations

potential(lmesh=None)[source]

Computes the lensing potential from the density plane solving the Poisson equation via FFTs

Parameters: lmesh (array) – the FFT frequency meshgrid (lx,ly) necessary for the calculations in fourier space; if None, a new one is computed from scratch (must have the appropriate dimensions) PotentialPlane instance with the computed lensing potential
class lenstools.simulations.PotentialPlane(data, angle, redshift=2.0, cosmology=None, comoving_distance=None, unit=Unit("rad2"), num_particles=None, masked=False, filename=None)[source]

Class handler of a lens potential plane, inherits from the parent Plane class; additionally it defines redshift and comoving distance attributes that are needed for ray tracing operations

deflectionAngles(x=None, y=None, lmesh=None)[source]

Computes the deflection angles for the given lensing potential by taking the gradient of the potential map; it is also possible to proceed with FFTs

Parameters: x (array with units) – optional; if not None, compute the deflection angles only for rays hitting the lens at the particular x positions (mainly for speedup in case there are less light rays than the plane resolution allows; must proceed in real space to allow speedup) y (array with units) – optional; if not None, compute the deflection angles only for rays hitting the lens at the particular y positions (mainly for speedup in case there are less light rays than the plane resolution allows; must proceed in real space to allow speedup) lmesh (array) – the FFT frequency meshgrid (lx,ly) necessary for the calculations in fourier space; if None, a new one is computed from scratch (must have the appropriate dimensions) DeflectionPlane instance, or array with deflections of rays hitting the lens at (x,y)
density(x=None, y=None)[source]

Computes the projected density fluctuation by taking the laplacian of the potential; useful to check if the potential is reasonable

Parameters: x (array with units) – optional; if not None, compute the density only for rays hitting the lens at the particular x positions (mainly for speedup in case there are less light rays than the plane resolution allows; must proceed in real space to allow speedup) y (array with units) – optional; if not None, compute the density only for rays hitting the lens at the particular y positions (mainly for speedup in case there are less light rays than the plane resolution allows; must proceed in real space to allow speedup) DensityPlane instance with the density fluctuation data (if x and y are None), or numpy array with the same shape as x and y
shearMatrix(x=None, y=None, lmesh=None)[source]

Computes the shear matrix for the given lensing potential; it is also possible to proceed with FFTs

Parameters: x (array with units) – optional; if not None, compute the shear matrix only for rays hitting the lens at the particular x positions (mainly for speedup in case there are less light rays than the plane resolution allows; must proceed in real space to allow speedup) y (array with units) – optional; if not None, compute the shear matrix only for rays hitting the lens at the particular y positions (mainly for speedup in case there are less light rays than the plane resolution allows; must proceed in real space to allow speedup) lmesh (array) – the FFT frequency meshgrid (lx,ly) necessary for the calculations in fourier space; if None, a new one is computed from scratch (must have the appropriate dimensions) ShearTensorPlane instance, or array with deflections of rays hitting the lens at (x,y)
class lenstools.simulations.raytracing.DeflectionPlane(data, angle, redshift=2.0, comoving_distance=None, cosmology=None, unit=Unit("rad"))[source]

Class handler of a lens deflection plane, inherits from the parent Spin1 class and holds the values of the deflection angles of the light rays that cross a potential plane

convergence()[source]

Computes the convergence from the deflection angles by taking the appropriate components of the jacobian

Returns: ConvergenceMap instance with the computed convergence values
jacobian()[source]

Computes the jacobian of the deflection angles, useful to compute shear and convergence; units are handled properly

Returns: the jacobian of the deflection field in array form, of shape (4,:,:) where the four components are, respectively, xx,xy,yx,yy
omega()[source]

Computes the omega field (i.e. the rotation) from the deflection angles by taking the appropriate components of the jacobian

Returns: OmegaMap instance with the computed omega values
shear()[source]

Computes the shear from the deflection angles by taking the appropriate components of the jacobian

Returns: ShearMap instance with the computed convergence values
class lenstools.simulations.raytracing.ShearTensorPlane(data, angle, redshift=2.0, comoving_distance=None, cosmology=None, unit=Unit(dimensionless))[source]

Class handler of a plane of shear matrices, inherits from the parent Spin2 class and holds the 3 values of the symmetric shear matrix (2 diagonal + 1 off diagonal), for each pixel

class lenstools.simulations.RayTracer(lens_mesh_size=None, lens_type=<class 'lenstools.simulations.raytracing.PotentialPlane'>)[source]

Class handler of ray tracing operations: it mainly computes the path corrections of light rays that travel through a set of gravitational lenses

addLens(lens_specification)[source]

Adds a gravitational lens to the ray tracer, either by putting in a lens plane, or by specifying the name of a file which contains the lens specifications

Parameters: lens_specification – specifications of the lens to add, either in tuple(filename,distance,redshift) or as a PotentialPlane instance
convergenceBorn(initial_positions, z=2.0, save_intermediate=False, real_trajectory=False)[source]

Computes the convergence directly integrating the lensing density along the line of sight (real or unperturbed)

Parameters: initial_positions (numpy array or quantity) – initial angular positions of the light ray bucket, according to the observer; if unitless, the positions are assumed to be in radians. initial_positions[0] is x, initial_positions[1] is y z (float.) – redshift of the sources save_intermediate (bool.) – save the intermediate values of the convergence as successive lenses are crossed real_trajectory (bool.) – if True, integrate the density on the real light ray trajectory; if False the unperturbed trajectory is used convergence values at each of the initial positions
convergencePostBorn2(initial_positions, z=2.0, save_intermediate=False, include_first_order=False, include_ll=True, include_gp=True, transpose_up_to=-1, callback=None, **kwargs)[source]

Computes the convergence at second post-born order with a double line of sight integral

Parameters: initial_positions (numpy array or quantity) – initial angular positions of the light ray bucket, according to the observer; if unitless, the positions are assumed to be in radians. initial_positions[0] is x, initial_positions[1] is y z (float.) – redshift of the sources save_intermediate (bool.) – save the intermediate values of the convergence as successive lenses are crossed include_first_order (bool.) – include the first order contribution to the convergence include_ll (bool.) – include lens-lens coupling include_gp (bool.) – include geodesic perturbation transpose_up_to (int.) – transpose all the lenses before a certain index before integration callback (callable.) – function is called on each contribution to the convergence during the LOS integration. The signature of the callback is callback(array_ov_values,tracer,k,type,**kwargs) kwargs (dict.) – additional keyword arguments to be passed to the callback convergence values (2-post born) at each of the initial positions
displayRays(initial_positions, z=2.0, projection='2d', fig=None, ax=None, axisbg='grey', raycolor='orange', lenscolor='blue')[source]

Uses matplotlib to display a visual of the lens system and of the deflection that the light rays which traverse it undergo

param initial_positions: initial angular positions of the light ray bucket, according to the observer; if unitless, the positions are assumed to be in radians. initial_positions[0] is x, initial_positions[1] is y :type initial_positions: numpy array or quantity

Parameters: z (float. or array) – redshift of the sources; if an array is passes, a redshift must be specified for each ray, i.e. z.shape==initial_positions.shape[1:] projection (str.) – can be “2d” for the projections of the ray positions along x and y or “3d” for the full 3d view fig (matplotlib figure) – figure object that owns the plot
omegaPostBorn2(initial_positions, z=2.0, save_intermediate=False)[source]

Computes the omega at second post-born order

Parameters: initial_positions (numpy array or quantity) – initial angular positions of the light ray bucket, according to the observer; if unitless, the positions are assumed to be in radians. initial_positions[0] is x, initial_positions[1] is y z (float.) – redshift of the sources save_intermediate (bool.) – save the intermediate values of the convergence as successive lenses are crossed omega values (2-post born) at each of the initial positions
randomRoll(seed=None)[source]

Randomly rolls all the lenses in the system along both axes

Parameters: seed (int.) – random seed with which to initialize the generator
reorderLenses()[source]

Reorders the lenses in the ray tracer according to their comoving distance from the observer

reset()[source]

Resets the RayTracer plotting engine

shoot(initial_positions, z=2.0, initial_deflection=None, kind='positions', save_intermediate=False, compute_all_deflections=False, callback=None, transfer=None, **kwargs)[source]

Shots a bucket of light rays from the observer to the sources at redshift z (backward ray tracing), through the system of gravitational lenses, and computes the deflection statistics

Parameters: initial_positions (numpy array or quantity) – initial angular positions of the light ray bucket, according to the observer; if unitless, the positions are assumed to be in radians. initial_positions[0] is x, initial_positions[1] is y z (float. or array) – redshift of the sources; if an array is passed, a redshift must be specified for each ray, i.e. z.shape==initial_positions.shape[1:] initial_deflection (numpy array or quantity) – if not None, this is the initial deflection light rays undergo with respect to the line of sight (equivalent to specifying the first derivative IC on the lensing ODE); must have the same shape as initial_positions kind (str.) – what deflection statistics to compute; “positions” will calculate the ray deflections after they crossed the last lens, “jacobian” will compute the lensing jacobian matrix after the last lens, “shear” and “convergence” will compute the omonimous weak lensing statistics save_intermediate (bool.) – save the intermediate positions of the rays too compute_all_deflections (bool.) – if True, computes the gradients of the lensing potential at every pixel on the lens(might be overkill if Nrays<
shootForward(source_positions, z=2.0, save_intermediate=False, grid_resolution=512, interpolation='nearest')[source]

Shoots a bucket of light rays from the source at redshift z to the observer at redshift 0 (forward ray tracing) and computes the according deflections using backward ray tracing plus a suitable interpolation scheme (KD Tree based)

Parameters: source_positions (numpy array or quantity) – angular positions of the unlensed sources z (float.) – redshift of the sources save_intermediate (bool.) – if True computes and saves the apparent image distortions after each lens is crossed (can be computationally expensive) grid_resolution (int.) – the number of points on a side of the interpolation grid (must be choosen big enough according to the number of sources to resolve) interpolation (str.) – only “nearest” implemented so far apparent positions of the sources as seen from the observer

## Weak Lensing Simulation Pipeline¶

### Directory tree handling¶

class lenstools.pipeline.simulation.SimulationBatch(environment, syshandler=<lenstools.pipeline.remote.LocalSystem object>, indicize=False)[source]

Class handler of a batch of weak lensing simulations that share the same environment settings

archive(name, pool=None, chunk_size=1, **kwargs)[source]

Archives a batch available resource to a tar gzipped archive; the resource file/directory names are retrieved with the list method. The archives will be written to the simulation batch storage directory

Parameters: name (str.) – name of the archive pool (MPIPool) – MPI Pool used to parallelize compression kwargs (dict.) – the keyword arguments are passed to the list method
available

Lists all currently available models in the home and storage directories

Returns: list. SimulationModel
commit(message)[source]

If the Simulation Batch is put under version control in a git repository, this method commits the newly added models,collections,realizations or map/plane sets

Parameters: message (str.) – commit message
copyTree(path, syshandler=<lenstools.pipeline.remote.LocalSystem object>)[source]

Copies the current batch directory tree into a separate path

Parameters: path (str.) – path into which to copy the current batch directory tree syshandler (SystemHandler) – system handler (can be a remote)
classmethod current(name='environment.ini', syshandler=<lenstools.pipeline.remote.LocalSystem object>, indicize=False)[source]

This method looks in the current directory and looks for a configuration file named “environment.ini”; if it finds one, it returns a SimulationBatch instance that corresponds to the one pointed to by “environment.ini” (default)

Parameters: name (str.) – name of the INI file with the environment settings, defaults to ‘environment.ini’ syshandler (SystemHandler) – system handler that allows to override the methods used to create directories and do I/O from files, must implement the abstract type SystemHandler Simulation batch pointed to by “environment.ini”, or None SimulationBatch
getModel(cosmo_id)[source]

Instantiate a SimulationModel object corresponding to the cosmo_id provided

Parameters: cosmo_id (str.) – cosmo_id of the model SimulationModel
info

Returns summary info of the simulation batch corresponding to the current environment

Returns: info in dictionary format
list(resource=None, which=None, chunk_size=10, **kwargs)[source]

Lists the available resources in the simulation batch (collections,mapsets,etc…)

Parameters: resource (None or callable) – custom function to call on each batch.models element, must return a string. If None the list of Storage model directories is returned which (tuple. or callable) – extremes of the model numbers to get (if None all models are processed); if callable, filter(which,self.models) gives the models to archive chunk_size (int.) – size of output chunk kwargs (dict.) – the keyword arguments are passed to resource requested resources list.
newModel(cosmology, parameters)[source]

Create a new simulation model, given a set of cosmological parameters

Parameters: cosmology (LensToolsCosmology) – cosmological model to simulate parameters (list.) – cosmological parameters to keep track of SimulationModel
unpack(where, which=None, pool=None)[source]

Unpacks the compressed simulation batch products into the storage directory: the resources of each model must be contained in a file called <cosmo_id>.tar.gz

Parameters: where (str.) – path of the compressed resources which (tuple.) – extremes of the model numbers to unpack (if None all models are unpacked) pool (MPIPool) – MPI Pool used to parallelize de-compression
writeCAMBSubmission(realization_list, job_settings, job_handler, config_file='camb.param', chunks=1, **kwargs)[source]

Writes CAMB submission script

Parameters: realization_list (list. of str.) – list of ics to generate in the form “cosmo_id|geometry_id” chunks (int.) – number of independent jobs in which to split the submission (one script per job will be written) job_settings (JobSettings) – settings for the job (resources, etc…) job_handler (JobHandler) – handler of the cluster specific features (job scheduler, architecture, etc…) config_file (str.) – name of the CAMB configuration file kwargs (dict.) – you can set one_script=True to include all the executables sequentially in a single script
writeNGenICSubmission(realization_list, job_settings, job_handler, config_file='ngenic.param', chunks=1, **kwargs)[source]

Writes NGenIC submission script

Parameters: realization_list (list. of str.) – list of ics to generate in the form “cosmo_id|geometry_id|icN” chunks (int.) – number of independent jobs in which to split the submission (one script per job will be written) job_settings (JobSettings) – settings for the job (resources, etc…) job_handler (JobHandler) – handler of the cluster specific features (job scheduler, architecture, etc…) config_file (str.) – name of the NGenIC config file kwargs (dict.) – you can set one_script=True to include all the executables sequentially in a single script
writeNbodySubmission(realization_list, job_settings, job_handler, config_file='gadget2.param', chunks=1, **kwargs)[source]

Writes N-body simulation submission script

Parameters: realization_list (list. of str.) – list of ics to generate in the form “cosmo_id|geometry_id|icN” chunks (int.) – number of independent jobs in which to split the submission (one script per job will be written) job_settings (JobSettings) – settings for the job (resources, etc…) job_handler (JobHandler) – handler of the cluster specific features (job scheduler, architecture, etc…) config_file (str.) – name of the Nbody configuration file kwargs (dict.) – you can set one_script=True to include all the executables sequentially in a single script
writePlaneSubmission(realization_list, job_settings, job_handler, chunks=1, **kwargs)[source]

Writes lens plane generation submission script

Parameters: realization_list (list. of str.) – list of ics to generate in the form “cosmo_id|geometry_id|icN” chunks (int.) – number of independent jobs in which to split the submission (one script per job will be written) job_settings (JobSettings) – settings for the job (resources, etc…) job_handler (JobHandler) – handler of the cluster specific features (job scheduler, architecture, etc…) kwargs (dict.) – keyword arguments accepted are “environment_file” to specify the environment settings for the current batch, “plane_config_file” to specify the lensing option for plane generation script. Additionally you can set one_script=True to include all the executables sequentially in a single script
writeRaySubmission(realization_list, job_settings, job_handler, chunks=1, **kwargs)[source]

Writes raytracing submission script

Parameters: realization_list (list. of str.) – list of ics to generate in the form “cosmo_id|geometry_id” chunks (int.) – number of independent jobs in which to split the submission (one script per job will be written) job_settings (JobSettings) – settings for the job (resources, etc…) job_handler (JobHandler) – handler of the cluster specific features (job scheduler, architecture, etc…) kwargs (dict.) – keyword arguments accepted are “environment_file” to specify the environment settings for the current batch, “raytracing_config_file” to specify the lensing option for the ray tracing. Additionally you can set one_script=True to include all the executables sequentially in a single script
writeSubmission(realization_list, job_settings, job_handler, job_executable='lenstools.custom', chunks=1, **kwargs)[source]

Writes a generic job submission script

Parameters: realization_list (list. of str.) – list of ics to generate in the form “cosmo_id|geometry_id” chunks (int.) – number of independent jobs in which to split the submission (one script per job will be written) job_settings (JobSettings) – settings for the job (resources, etc…) job_handler (JobHandler) – handler of the cluster specific features (job scheduler, architecture, etc…) job_executable (str.) – name of the executable that will be run kwargs (dict.) – keyword arguments accepted are “environment_file” to specify the environment settings for the current batch, “config_file” to specify the job specific configuration file. Additionally you can set one_script=True to include all the executables sequentially in a single script
class lenstools.pipeline.simulation.SimulationModel(cosmology=LensToolsCosmology(H0=72 km / (Mpc s), Om0=0.26, Ode0=0.74, sigma8=0.8, ns=0.96, w0=-1, wa=0, Tcmb0=0 K, Neff=3.04, m_nu=None, Ob0=0.046), environment=None, parameters=['Om', 'Ol', 'w', 'ns', 'si'], **kwargs)[source]

Class handler of a weak lensing simulation model, defined by a set of cosmological parameters

collections

Lists all the available collections for a model

Returns: list. SimulationCollection
getTelescopicMapSet(setname)[source]

Return an instance of the telescopic map set named “name”

Parameters: setname (str.) – name of the map set SimulationTelescopicMaps
mkdir(directory)

Create a sub-directory inside the current instance home and storage paths

Parameters: directory (str.) – name of the directory to create
newCollection(box_size=<Quantity 240.0 Mpc>, nside=512)[source]

Instantiate new simulation with the specified settings

newTelescopicMapSet(collections, redshifts, settings)[source]

Create a new telescopic map set with the provided settings

Parameters: collections (list.) – list of the SimulationCollection instances that participate in the telescopic map set redshifts (array.) – redshifts that mark the transition from one collection to the other during raytracing settings (TelescopicMapSettings) – settings for the new map set SimulationMaps
path(filename, where='storage_subdir')

Returns the complete path to the resource corresponding to filename; returns None if the resource does not exist

Parameters: filename (str.) – name of the resource where (str.) – where to look for the resource, can be “home_subdir” or “storage_subdir” full path to the resource str.
telescopicmapsets

Lists all the available telescopic map sets for a model

Returns: list. SimulationTelescopicMaps
class lenstools.pipeline.simulation.SimulationCollection(cosmology, environment, parameters, box_size, nside, **kwargs)[source]

Class handler of a collection of simulations that share model parameters

camb2ngenic(z, input_root='camb')[source]

Read CAMB power spectrum file and convert it in a N-GenIC readable format

Parameters: z (float.) – redshift of the matter power spectrum file to convert input_root (str.) – name root of camb products
getMapSet(setname)[source]

Parameters: setname (str.) – name of the map set to access
loadTransferFunction(name_root='camb_matterpower')[source]

Loads in the CDM transfer function calculated with CAMB (k,delta(k)/k^2) for a unit super-horizon perturbation

Parameters: name_root (str.) – root of the file names that contain the transfer function CAMBTransferFromPower
newMapSet(settings)[source]

Create a new map set with the provided settings

Parameters: settings (MapSettings) – settings for the new map set SimulationMaps
realizations

List the available realizations (or independent simulations) for the current collection

Returns: list. SimulationIC
writeCAMB(z, settings, fname='camb.param', output_root='camb')[source]

Generates the parameter file that CAMB needs to read to evolve the current initial condition in time

Parameters: settings (CAMBSettings) – CAMB tunable settings z (array.) – redshifts at which CAMB needs to compute the matter power spectrum fname (str.) – name of the parameter file to write output_root (str.) – output root of camb products
class lenstools.pipeline.simulation.SimulationIC(cosmology, environment, parameters, box_size, nside, ic_index, seed, ICFileBase='ics', SnapshotFileBase='snapshot', **kwargs)[source]

Class handler of a simulation with a defined initial condition

getPlaneSet(setname)[source]

Instantiate a SimulationPlanes object that handles a specific plane set; returns None if the plane set does not exist

Parameters: setname (str.) – name of the plane set SimulationPlanes
writeGadget2(settings)[source]

Generates the parameter file that Gadget2 needs to read to evolve the current initial condition in time

writeNGenIC(settings)[source]

Generates the parameter file that NGenIC needs to read to generate the current initial condition

Parameters: settings (NGenICSettings) – NGenIC tunable settings
class lenstools.pipeline.simulation.SimulationPlanes(cosmology, environment, parameters, box_size, nside, ic_index, seed, ICFileBase, SnapshotFileBase, settings, **kwargs)[source]

Class handler of a set of lens planes belonging to a particular simulation

class lenstools.pipeline.simulation.SimulationMaps(cosmology, environment, parameters, box_size, nside, settings, **kwargs)[source]

Class handler of a set of lensing maps, which are the final products of the lensing pipeline

class lenstools.pipeline.simulation.SimulationTelescopicMaps(cosmology, environment, parameters, collections, redshifts, settings, **kwargs)[source]

Class handler of a set of lensing telescopic maps, which are the final products of the lensing pipeline

class lenstools.pipeline.simulation.SimulationCatalog(cosmology, environment, parameters, box_size, nside, settings, **kwargs)[source]

Class handler of a simulated lensing catalog, which is the final products of the lensing pipeline

### Tunable settings¶

class lenstools.pipeline.settings.EnvironmentSettings(home='SimTest/Home', storage='SimTest/Storage')[source]

This class handles the system specific environment settings, such as directory paths, modules, etc…

class lenstools.pipeline.settings.CAMBSettings(**kwargs)[source]
write(output_root, cosmology, redshifts)[source]

Writes a CAMB parameter file

Parameters: output_root (str.) – output_root for the files that CAMB will produce in output cosmology (FLRW) – cosmological model to generate the parameter file for redshifts (array.) – redshifts on which to compute the matter power spectrum and transfer function string object StringIO
class lenstools.pipeline.settings.NGenICSettings(**kwargs)[source]

Class handler of NGenIC settings

class lenstools.pipeline.settings.Gadget2Settings(**kwargs)[source]

Class handler of the tunable settings in a Gadget2 run

classmethod default()[source]

Generate default settings

writeSection(section)[source]

Writes the corresponding section of the Gadget2 parameter file

class lenstools.pipeline.settings.PlaneSettings(**kwargs)[source]

Class handler of plane generation settings from constant time Nbody snapshots

class lenstools.pipeline.settings.MapSettings(**kwargs)[source]

Class handler of map generation settings

class lenstools.pipeline.settings.TelescopicMapSettings(**kwargs)[source]

Class handler of telescopic simulation map generation settings

class lenstools.pipeline.settings.CatalogSettings(**kwargs)[source]

Class handler of simulated catalog generation settings

class lenstools.pipeline.settings.JobSettings(**kwargs)[source]

Class handler of batch job submission settings

### Cluster deployment¶

class lenstools.pipeline.deploy.JobHandler[source]
writeExecution(executables, cores, settings)[source]

Write the execution part of the script

Parameters: executables (list.) – list of executables to run on the compute nodes cores (list.) – list of numbers of cores for each executable (must have the same length as executables) settings (JobSettings) – job settings StringIO object
writePreamble(settings, auto_num_nodes=True)[source]

Writes the preamble of the job script (resources request,job name, etc…)

Parameters: settings (JobSettings) – job settings auto_num_nodes (bool.) – if True, the number of requested nodes is computed automatically from the number of requested cores (knowing the cluster specifications) StringIO object
class lenstools.pipeline.deploy.Directives(**kwargs)[source]
class lenstools.pipeline.deploy.ClusterSpecs(**kwargs)[source]

### Cluster specific settings¶

class lenstools.pipeline.cluster.CoriHandler[source]

Job handler for the NERSC Cori Phase 1 cluster

writeExecution(executables, cores, settings)

Write the execution part of the script

Parameters: executables (list.) – list of executables to run on the compute nodes cores (list.) – list of numbers of cores for each executable (must have the same length as executables) settings (JobSettings) – job settings StringIO object
writePreamble(settings, auto_num_nodes=True)

Writes the preamble of the job script (resources request,job name, etc…)

Parameters: settings (JobSettings) – job settings auto_num_nodes (bool.) – if True, the number of requested nodes is computed automatically from the number of requested cores (knowing the cluster specifications) StringIO object
class lenstools.pipeline.cluster.EdisonHandler[source]

Job handler for the NERSC Edison cluster

writeExecution(executables, cores, settings)

Write the execution part of the script

Parameters: executables (list.) – list of executables to run on the compute nodes cores (list.) – list of numbers of cores for each executable (must have the same length as executables) settings (JobSettings) – job settings StringIO object
writePreamble(settings, auto_num_nodes=True)

Writes the preamble of the job script (resources request,job name, etc…)

Parameters: settings (JobSettings) – job settings auto_num_nodes (bool.) – if True, the number of requested nodes is computed automatically from the number of requested cores (knowing the cluster specifications) StringIO object
class lenstools.pipeline.cluster.StampedeHandler[source]

Job handler for the TACC Stampede cluster

writeExecution(executables, cores, settings)

Write the execution part of the script

Parameters: executables (list.) – list of executables to run on the compute nodes cores (list.) – list of numbers of cores for each executable (must have the same length as executables) settings (JobSettings) – job settings StringIO object
writePreamble(settings, auto_num_nodes=True)

Writes the preamble of the job script (resources request,job name, etc…)

Parameters: settings (JobSettings) – job settings auto_num_nodes (bool.) – if True, the number of requested nodes is computed automatically from the number of requested cores (knowing the cluster specifications) StringIO object

## Real observation sets¶

class lenstools.observations.CFHTLens(root_path=None)[source]

Class handler of the CFHTLens reduced data set, already split in 13 3x3 deg^2 subfields

getName(subfield=1, smoothing=0.5)[source]

Get the names of the FITS files where the subfield images are stored

Parameters: subfield (int.) – the subfield number (1-13) smoothing (float.) – the smoothing scale of the subfield image, in arcminutes str. : the FITS file name
load(**kwargs)[source]

Loads in a CFHT observation as a ConvergenceMap instance

Parameters: kwargs – the keyword arguments are passed to the getName method ConvergenceMap instance

## Limber integration¶

class lenstools.simulations.limber.LimberIntegrator(cosmoModel=FlatLambdaCDM(name="WMAP9", H0=69.3 km / (Mpc s), Om0=0.286, Tcmb0=2.725 K, Neff=3.04, m_nu=[ 0. 0. 0.] eV, Ob0=0.0463))[source]

A 3D power spectrum integrator that will compute the convergence power spectrum using the Limber approximation.

Parameters: cosmoModel (astropy.cosmology) – One of astropy.cosmology objects (WMAP9 cosmology is set by default)
convergencePowerSpectrum(l)[source]

Computes the convergence power spectrum with the Limber integral of the 3D matter power spectrum; this still assumes a single source redshift at z0 = max(z)

Parameters: l (array) – multipole moments at which to compute the convergence power spectrum array – the convergence power spectrum at the l values specified
load3DPowerSpectrum(loader, *args, **kwargs)[source]

Loads in the matter power spectrum from (pre-computed) external files; args and kwargs are passed to the loader

Parameters: loader (function) – must return, in order, k, z, P(k,z)
setUnits(kappa_units=None, power_units=None)[source]

Set the physical units for wavenumber and matter power spectrum, default for length is Mpc

## Defaults¶

lenstools.utils.defaults.load_fits_default_convergence(filename)[source]

This is the default convergence fits file loader, it assumes that the two components of the shear are stored in two different image FITS files, which have an ANGLE keyword in the header

Parameters: gamma_file – Name of the FITS file that contains the shear map tuple – (angle,ndarray – kappa; kappa is the convergence map) IOError if the FITS files cannot be opened or do not exist
lenstools.utils.defaults.load_fits_default_shear(filename)[source]

This is the default shear fits file loader, it assumes that the two components of the shear are stored in a single image FITS file, which have an ANGLE keyword in the header

Parameters: gamma_file – Name of the FITS file that contains the shear map tuple – (angle,ndarray – gamma; gamma[0] is the gamma1 map, gamma[1] is the gamma2 map); the maps must follow matrix ordering, i.e. the first axis (0) is y and the second axis (1) is x. This matters for the E/B mode decomposition IOError if the FITS files cannot be opened or do not exist
lenstools.utils.defaults.measure_power_spectrum(filename, l_edges, columns=None)[source]

Default ensemble loader: reads a FITS data file containing a convergence map and measures its power spectrum

Parameters: args (Dictionary) – A dictionary that contains all the relevant parameters as keys. Must have a “map_id” key ndarray of the measured statistics AssertionError if the input dictionary doesn’t have the required keywords

## Miscellaneous utilities¶

### MPI¶

class lenstools.utils.mpi.MPIWhirlPool(comm=None, debug=False, loadbalance=False)[source]

MPI class handler, inherits from MPI pool and adds one sided communications utilities (using RMA windows)

accumulate(op=None)[source]

Accumulates the all the window data on the master, performing a custom operation (default is sum)

bcast(*args, **kwargs)

Equivalent to mpi4py bcast() collective operation.

close()

Just send a message off to all the pool members which contains the special _close_pool_message sentinel.

closeWindow()[source]

Closes a previously opened RMA window

get(process)[source]

Read data from an RMA window open on a particular process

is_master()

Is the current process the master?

map(function, tasks)

Like the built-in map() function, apply a function to all of the values in a list and return the list of results.

Parameters: function – The function to apply to the list. tasks – The list of elements.
openWindow(memory, window_type='sendrecv')[source]

Create a RMA window that looks from the master process onto all the other workers

Parameters: memory (numpy nd array) – memory buffer on which to open the window
wait()

If this isn’t the master process, wait for instructions.

### Fast Fourier Transforms¶

class lenstools.utils.fft.NUMPYFFTPack[source]

### Algorithms¶

class lenstools.utils.algorithms.pcaHandler(constructor_series, constructor_ensemble, columns, location, scale)[source]

Handles principal component analysis