Update required Scipy version (#617)

* Rename simps and update README

* Update simpson keyword arg

* Prepare for ccl new minor versions

* Update build_check.yml

* Update version to 1.12.2

---------

Co-authored-by: m-aguena <[email protected]>

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)

clmm/__init__.py

@@ -26,4 +26,4 @@
 )
 from . import support
 
-__version__ = "1.12.1"
+__version__ = "1.12.2"

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(

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(