fix: use numpy density parameter (v2.0+) + remove version pin

Author: corneliusroemer

cibuildwheel.toml

@@ -28,7 +28,7 @@ macos.before-build = [
 
 
 linux.before-test = [
-  "pip install 'biopython' 'matplotlib' 'numpy<2' 'pandas' 'pygments' 'scipy'"
+  "pip install 'biopython' 'matplotlib' 'numpy' 'pandas' 'pygments' 'scipy'"
 ]
 
 # Run ALL examples serially (one at a time) - including slow ones after memory fixes
@@ -41,7 +41,7 @@ linux.test-command = [
 
 
 macos.before-test = [
-  "pip install 'biopython' 'matplotlib' 'numpy<2' 'pandas' 'pygments' 'scipy'"
+  "pip install 'biopython' 'matplotlib' 'numpy' 'pandas' 'pygments' 'scipy'"
 ]
 
 # Run ALL examples serially - including slow ones after memory fixes

examples/mutation_selection_balance_highd.py

@@ -65,7 +65,7 @@
 for locus in range(L):
 
     # make histogram
-    y,x = np.histogram(allele_frequencies[:,locus], bins=af_bins, normed='True')
+    y,x = np.histogram(allele_frequencies[:,locus], bins=af_bins, density=True)
 
     # plot
     plt.plot(bc, y, color=plt.cm.jet(locus*4))
@@ -78,7 +78,7 @@
 # compare explicitly to diffusion theory by normalizing to the diffusion theory prediction
 plt.figure()
 for locus in range(L):
-    y,x = np.histogram(allele_frequencies[:,locus], bins=af_bins, normed='True')
+    y,x = np.histogram(allele_frequencies[:,locus], bins=af_bins, density=True)
 
     # calculate the diffusion theory single locus result
     diffusion_theory = bc**(2*N*mu-1)*(1-bc)**(2*N*mu-1)*np.exp(2*N*s[locus]*bc)