diff --git a/README.md b/README.md index 4d043abd5..7ee5ff075 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ link to this repository: https://github.com/LSSTDESC/CLMM. Please follow the gui CLMM requires Python version 3.8 or later. CLMM has the following dependencies: - [NumPy](https://www.numpy.org/) (v1.17 or later) -- [SciPy](https://scipy.org/) (v1.3 or later) +- [SciPy](https://scipy.org/) (v1.6 or later) - [Astropy](https://www.astropy.org/) (v4.0 or later for units and cosmology dependence) (Please avoid Astropy v5.0 since there is bug breaking CCL backend. It has been fixed in Astropy v5.0.1.) - [Matplotlib](https://matplotlib.org/) (for plotting and going through tutorials) diff --git a/clmm/__init__.py b/clmm/__init__.py index 50f8fa49f..8788f36a2 100644 --- a/clmm/__init__.py +++ b/clmm/__init__.py @@ -26,4 +26,4 @@ ) from . import support -__version__ = "1.12.1" +__version__ = "1.12.2" diff --git a/clmm/redshift/tools.py b/clmm/redshift/tools.py index 82ffd670e..904d0579a 100644 --- a/clmm/redshift/tools.py +++ b/clmm/redshift/tools.py @@ -1,7 +1,7 @@ """General utility functions that are used in multiple modules""" import warnings import numpy as np -from scipy.integrate import simps +from scipy.integrate import simpson from scipy.interpolate import interp1d @@ -55,7 +55,7 @@ def _integ_pzfuncs(pzpdf, pzbins, zmin=0.0, zmax=5, kernel=lambda z: 1.0, ngrid= pz_matrix = np.array(pzpdf)[:, mask] kernel_matrix = kernel(z_grid) - return simps(pz_matrix * kernel_matrix, x=z_grid, axis=1) + return simpson(pz_matrix * kernel_matrix, x=z_grid, axis=1) def compute_for_good_redshifts( diff --git a/clmm/theory/parent_class.py b/clmm/theory/parent_class.py index dea95d7f1..758db702c 100644 --- a/clmm/theory/parent_class.py +++ b/clmm/theory/parent_class.py @@ -7,7 +7,7 @@ import numpy as np # functions for the 2h term -from scipy.integrate import simps, quad +from scipy.integrate import simpson, quad from scipy.special import jv from scipy.interpolate import splrep, splev @@ -232,7 +232,7 @@ def __integrand__(l_value, theta): return l_value * jv(sph_harm_ord, l_value * theta) * splev(k_value, interp_pk) l_values = np.logspace(loglbounds[0], loglbounds[1], lsteps) - kernel = np.array([simps(__integrand__(l_values, t), l_values) for t in theta]) + kernel = np.array([simpson(__integrand__(l_values, t), x=l_values) for t in theta]) return halobias * kernel * rho_m / (2 * np.pi * (1 + z_cl) ** 3 * da**2) def _eval_surface_density_2h(