merge main

flip/covariance/__init__.py

@@ -1,3 +1,4 @@
 """Init file of the flip.covariance package."""
-from . import carreres23, adamsblake20, lai22, ravouxcarreres
-from .covariance import CovMatrix
+
+from . import adamsblake17plane, adamsblake20, carreres23, lai22, ravouxcarreres, rcrk24
+from .covariance import CovMatrix

flip/covariance/generator.py

@@ -12,8 +12,8 @@
 from flip.covariance.adamsblake20 import flip_terms as flip_terms_adamsblake20
 from flip.covariance.carreres23 import flip_terms as flip_terms_carreres23
 from flip.covariance.lai22 import flip_terms as flip_terms_lai22
-from flip.covariance.rcrk24 import flip_terms as flip_terms_rcrk24
 from flip.covariance.ravouxcarreres import flip_terms as flip_terms_ravouxcarreres
+from flip.covariance.rcrk24 import flip_terms as flip_terms_rcrk24
 from flip.utils import create_log
 
 log = create_log()
@@ -49,7 +49,7 @@ def correlation_integration(l, r, k, integrand):
     integrand = (
         (-1) ** (l // 2) * (k**2 / (2 * np.pi**2)) * integrand * spherical_jn(l, kr).T
     )
-    return (-1) ** (l % 2) * integrate.simps(integrand, x=k)
+    return (-1) ** (l % 2) * integrate.simpson(integrand, x=k)
 
 
 def correlation_hankel(l, r, k, integrand, hankel_overhead_coefficient=2):

flip/covariance/lai22/generator.py

@@ -17,7 +17,7 @@ def compute_correlation_coefficient_simple_integration(p, q, l, r, k, pk):
     The (2*np.pi**2) is added here in the Lai et al. formalism."""
     kr = np.outer(k, r)
     integrand = spherical_jn(l, kr).T * k**2 * k ** (2 * (p + q)) * pk / (2 * np.pi**2)
-    return integrate.simps(integrand, x=k)
+    return integrate.simpson(integrand, x=k)
 
 
 def compute_correlation_coefficient_hankel(

notebook/fit_velocity.ipynb

@@ -235,12 +235,18 @@
     "\n",
     "\n",
     "\n",
-    "mu, variance_mu = vectors.compute_observed_distance_modulus(\n",
+    "mu = vectors.compute_observed_distance_modulus(\n",
     "        data_velocity,\n",
     "        test_parameters[\"alpha\"],\n",
     "        test_parameters[\"beta\"],\n",
     "        test_parameters[\"M_0\"],\n",
     "    )\n",
+    "\n",
+    "variance_mu = vectors.compute_observed_distance_modulus_error(\n",
+    "        data_velocity,\n",
+    "        test_parameters[\"alpha\"],\n",
+    "        test_parameters[\"beta\"],\n",
+    "    )\n",
     "mask = mu > 0\n",
     "mask &= np.sqrt(variance_mu) - 0.1* mu < 0\n",
     "\n",

setup.cfg

@@ -12,7 +12,7 @@ license = MIT
 packages = find:
 install_requires =
     pandas
-    numpy
+    numpy <= 1.26.4
     scipy
     matplotlib
     importlib-metadata