Update tensorflow requirements

CONTRIBUTORS.md

@@ -0,0 +1,7 @@
+# Contributors (Alphabetical Order)
+
+- Pierre Burger
+- Ian Harrison [ORCID: 0000-0002-4437-0770](https://orcid.org/0000-0002-4437-0770)
+- Hidde T Jense [0000-0002-9429-0015](https://orcid.org/0000-0002-9429-0015)
+- Alessio Spurio Mancini [0000-0001-5698-0990](https://orcid.org/0000-0001-5698-0990)
+- Davide Piras [ORCID: 0000-0002-9836-2661](https://orcid.org/0000-0002-9836-2661)

cosmopower/__init__.py

@@ -8,7 +8,7 @@
 
 
 __version__ = "0.2.0"
-__author__ = "Alessio Spurio Mancini, Hidde Jense, and Ian Harrison"
+__author__ = "Alessio Spurio Mancini"
 
 
 def get_cobaya_class():

cosmopower/examples/4_cobaya.py

@@ -74,24 +74,27 @@
       min: 0.01
       max: 0.12
     ref: 0.05
-  H0:
-    derived: true
-  sigma8:
-    derived: true
+  h:
+    prior:
+      min: 0.6
+      max: 0.8
+    ref: 0.67
+
 """
 
 yaml = yaml_load(model)
 
 install(yaml)
 
 # Create the cobaya model.
-model = get_model(model)
+model = get_model(yaml)
 
 # The best-fit values from the Planck paper.
 plik_best_fit = {
     "ombh2": 22.383e-3,
     "omch2": 12.011e-2,
     "cosmomc_theta": 104.0909e-4,
+    "h": 0.67,
     "logA": 3.0448,
     "ns": 0.96605,
     "tau": 0.0543,

cosmopower/util.py

@@ -73,7 +73,7 @@ def ell_factor(ls: np.ndarray, spectra: str) -> np.ndarray:
     elif spectra in ["pt", "pe", "pb", "tp", "ep", "bp"]:
         ellfac = ls * (ls + 1.0) / (2.0 * np.pi)
     elif spectra in ["pp"]:
-        ellfac = ls * (ls + 1.0) / (2.0 * np.pi)
+        ellfac = (ls * (ls + 1.0))**2. / (2.0 * np.pi)
 
     return ellfac
 

cosmopower/wrappers/cosmosis/cosmopower_interface.py

@@ -74,8 +74,12 @@ def get_cosmopower_outputs(block, netname, data, config):
         if config['ell_factor']:
             prefac *= cp.util.ell_factor(block[cmb_cl, 'ell'], cl_type)
 
-        #ToDo: ambiguity of 'Cl' vs 'cmb_cl' here
-        block[cmb_cl, cl_type] = data * prefac * cp.util.cmb_unit_factor(cl_type, config['units'], 2.7255)
+        if cl_type == 'pp':
+            ell = block[cmb_cl, 'ell']
+            block[cmb_cl, cl_type] = 2.* np.pi * data * cp.util.cmb_unit_factor(cl_type, config['units'], 2.7255) / (ell*(ell + 1))
+        else:
+            #ToDo: ambiguity of 'Cl' vs 'cmb_cl' here
+            block[cmb_cl, cl_type] = data * prefac * cp.util.cmb_unit_factor(cl_type, config['units'], 2.7255)
 
         if config['lmax'] > 0:
             ell_select = block[cmb_cl, 'ell'] <= config['lmax']

setup.py

@@ -19,10 +19,12 @@ def read_file(file):
 long_description = read_file("README.md")
 
 # Determine whether the system is M1/M2 Mac
-if 'arm' in os.uname().machine:
-    tensorflow = 'tensorflow-metal'
-else:
-    tensorflow = 'tensorflow<2.14'
+# if 'arm' in os.uname().machine:
+#     tensorflow = 'tensorflow-metal'
+# else:
+#     tensorflow = 'tensorflow<2.14'
+
+tensorflow = 'tensorflow'
 
 setup(classifiers=['Operating System :: OS Independent',
                    'Intended Audience :: Developers',