Support newer pandas (>2) and python (>3.10) versions

.github/workflows/pytest.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.8', '3.9', '3.10' ]
+        python-version: [ '3.9', '3.10', '3.11', '3.12' ]
 
     steps:
     - uses: actions/checkout@v2

.gitignore

@@ -14,6 +14,7 @@ cooltools/__pycache__
 # Distribution / packaging
 .Python
 env/
+.venv/
 build/
 develop-eggs/
 dist/
@@ -106,4 +107,5 @@ tmp.npz
 tmp.hdf5
 cooltools/sandbox/test.mcool
 
-.vscode/
+.vscode/
+.idea/

cooltools/api/snipping.py

@@ -4,24 +4,24 @@
 The main user-facing function of this module is `pileup`, it performs pileups using
 snippers and other functions defined in the module.  The concept is the following:
 
-- First, the provided features are annotated with the regions from a view (or simply  
+- First, the provided features are annotated with the regions from a view (or simply
   whole chromosomes, if no view is provided). They are assigned to the region that
   contains it, or the one with the largest overlap.
-- Then the features are expanded using the `flank` argument, and aligned to the bins  
+- Then the features are expanded using the `flank` argument, and aligned to the bins
   of the cooler
-- Depending on the requested operation (whether the normalization to expected is  
+- Depending on the requested operation (whether the normalization to expected is
   required), the appropriate snipper object is created
-- A snipper can `select` a particular region of a genome-wide matrix, meaning it  
+- A snipper can `select` a particular region of a genome-wide matrix, meaning it
   stores its sparse representation in memory. This could be whole chromosomes or
   chromosome arms, for example
-- A snipper can `snip` a small area of a selected region, meaning it will extract  
+- A snipper can `snip` a small area of a selected region, meaning it will extract
   and return a dense representation of this area
-- For each region present, it is first `select`ed, and then all features within it are  
+- For each region present, it is first `select`ed, and then all features within it are
   `snip`ped, creating a stack: a 3D array containing all snippets for this region
-- For features that are not assigned to any region, an empty snippet is returned  
-- All per-region stacks are then combined into one, which then can be averaged to create  
+- For features that are not assigned to any region, an empty snippet is returned
+- All per-region stacks are then combined into one, which then can be averaged to create
   a single pileup
-- The order of snippets in the stack matches the order of features, this way the stack  
+- The order of snippets in the stack matches the order of features, this way the stack
   can also be used for analysis of any subsets of original features
 
 This procedure achieves a good tradeoff between speed and RAM. Extracting each
@@ -390,7 +390,8 @@ def select(self, region1, region2):
         if self.cooler_opts["sparse"]:
             matrix = matrix.tocsr()
         if self.min_diag is not None:
-            diags = np.arange(np.diff(self.clr.extent(region1_coords)), dtype=np.int32)
+            lo, hi = self.clr.extent(region1_coords)
+            diags = np.arange(hi - lo, dtype=np.int32)
             self.diag_indicators[region1] = LazyToeplitz(-diags, diags)
         return matrix
 
@@ -600,7 +601,8 @@ def select(self, region1, region2):
             .values
         )
         if self.min_diag is not None:
-            diags = np.arange(np.diff(self.clr.extent(region1_coords)), dtype=np.int32)
+            lo, hi = self.clr.extent(region1_coords)
+            diags = np.arange(hi - lo, dtype=np.int32)
             self.diag_indicators[region1] = LazyToeplitz(-diags, diags)
         return matrix
 
@@ -770,7 +772,8 @@ def select(self, region1, region2):
             .values
         )
         if self.min_diag is not None:
-            diags = np.arange(np.diff(self.clr.extent(region1_coords)), dtype=np.int32)
+            lo, hi = self.clr.extent(region1_coords)
+            diags = np.arange(hi - lo, dtype=np.int32)
             self.diag_indicators[region1] = LazyToeplitz(-diags, diags)
         return self._expected
 
@@ -861,7 +864,7 @@ def pileup(
     map_functor : callable, optional
         Map function to dispatch the matrix chunks to workers.
         If left unspecified, pool_decorator applies the following defaults: if nproc>1 this defaults to multiprocess.Pool;
-        If nproc=1 this defaults the builtin map. 
+        If nproc=1 this defaults the builtin map.
 
     Returns
     -------
@@ -983,5 +986,5 @@ def pileup(
     stack = _pileup(features_df, snipper.select, snipper.snip, map=map_functor)
     if feature_type == "bed":
         stack = np.fmax(stack, np.transpose(stack, axes=(0, 2, 1)))
-        
+
     return stack

requirements.txt

@@ -7,7 +7,7 @@ matplotlib
 multiprocess
 numba
 numpy
-pandas>=1.5.1,<2
+pandas>=1.5.1
 scikit-learn>=1.1.2
 scipy
 scikit-image

tests/test_checks.py

@@ -235,13 +235,13 @@ def test_is_track():
     assert cooltools.lib.is_track(track)
 
     track_incompat = bioframe.sort_bedframe(track.copy())
-    track_incompat.iloc[:, 0] = 10
+    track_incompat["chrom"] = 10
 
     # not bedframe in first three columns
     assert cooltools.lib.is_track(track_incompat) is False
 
     track_incompat = track.copy()
-    track_incompat.iloc[:, -1] = ["a", "b", "c", "d"]
+    track_incompat["value"] = ["a", "b", "c", "d"]
     # not numeric type in column4
     assert cooltools.lib.is_track(track_incompat) is False