For release 1.3.2

.travis.yml

@@ -2,6 +2,7 @@ language: python
 python:
   - "3.4"
   - "3.5"
+  - "3.6"
 before_install:
   - "wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh"
   - "bash miniconda.sh -b -p $HOME/miniconda"
@@ -17,7 +18,7 @@ before_install:
 install:
   - "conda install -q nomkl"
   - "conda install -q jinja2"
-  - "conda install -q numpy"
+  - "conda install -q 'numpy<1.12'"
   - "conda install -q pandas"
   - "conda install -q scipy"
   - "conda install -q patsy"

README.mkd

@@ -16,7 +16,7 @@ tool:
 > **genipe: an automated genome-wide imputation pipeline with automatic reporting
 > and statistical tools.**
 > *Bioinformatics* 2016, Epub ahead of print.
-> [DOI:[10.1093/bioinformatics/btw487](http://dx.doi.org/10.1093/bioinformatics/btw487)].
+> (DOI:[10.1093/bioinformatics/btw487](http://dx.doi.org/10.1093/bioinformatics/btw487)).
 
 
 ## Documentation
@@ -78,6 +78,9 @@ and Cox's regressions), `genipe` requires the following Python modules:
 * `pyfaidx` version 0.3.7 or latest
 * `drmaa` version 0.7.6 or latest
 
+Note that `statsmodels` (specifically MixedLM analysis) version 0.6 **is not
+compatible** with `numpy` version 1.12 and latest.
+
 Finally, the tool requires a LaTeX installation to compile the automatically
 generated report in PDF format.
 

conda_build.sh

@@ -26,7 +26,7 @@ then
 fi
 
 # The different python versions and platforms
-python_versions="3.4 3.5"
+python_versions="3.4 3.5 3.6"
 platforms="linux-32 linux-64 osx-64"
 
 # Building

genipe/tests/test_arguments.py

@@ -787,8 +787,8 @@ def test_segment_length_large(self):
         # Checking the warning is logged
         with self.assertLogs(level="WARNING") as cm:
             check_args(self.args)
-        log_m = ["WARNING:root:segment length (5e+06 bp) is more than 5Mb"]
-        self.assertEqual(log_m, cm.output)
+        log_m = "WARNING:root:segment length (5e+06 bp) is more than 5Mb"
+        self.assertTrue(log_m in cm.output)
 
     def test_segment_length_small(self):
         """Tests different invalid segment length (too small)."""
@@ -797,8 +797,8 @@ def test_segment_length_small(self):
         # Checking the warning is logged
         with self.assertLogs(level="WARNING") as cm:
             check_args(self.args)
-        log_m = ["WARNING:root:segment length (999 bp) is too small"]
-        self.assertEqual(log_m, cm.output)
+        log_m = "WARNING:root:segment length (999 bp) is too small"
+        self.assertTrue(log_m in cm.output)
 
     def test_segment_length_invalid(self):
         """Tests different invalid segment length."""

genipe/tests/test_formats.py

@@ -51,10 +51,8 @@ def test_matrix_from_line(self):
         self.assertTrue(np.allclose(expected_geno, observed_geno))
 
         # An invalid line should raise an exception
-        with self.assertRaises(ValueError) as cm:
+        with self.assertRaises(ValueError):
             impute2.matrix_from_line(input_line.split(" ")[:-1])
-        error_m = "total size of new array must be unchanged"
-        self.assertEqual(error_m, str(cm.exception))
 
     def test_get_good_probs(self):
         """Tests the 'get_good_probs' function."""

genipe/tools/imputed_stats.py

@@ -706,6 +706,32 @@ def _skat_write_marker(name, dosage, snp_set, genotype_files):
         print(name, *dosage, sep=",", file=file_object)
 
 
+def _extract_mixedlm_random_effect(fitted):
+    """Extracts the random effects from a MixedLM fit object.
+
+    Args:
+        fitted (MixedLMResultsWrapper): The fitted object.
+
+    Returns:
+        pandas.DataFrame: The random effects as a DataFrame (with a column
+                          named "RE").
+
+    Note
+    ====
+        Depending of the version of StatsModels, the object might be a pandas
+        DataFrame or a dictionary...
+
+    """
+    # Getting the random effects
+    random_effects = fitted.random_effects
+
+    # If it's a dictionary, we need to create a DataFrame
+    if isinstance(random_effects, dict):
+        return pd.DataFrame(random_effects).T.rename(columns={"groups": "RE"})
+
+    return random_effects.rename(columns={"Intercept": "RE"})
+
+
 def compute_statistics(impute2_filename, samples, markers_to_extract,
                        phenotypes, remove_gender, out_prefix, options):
     """Parses IMPUTE2 file while computing statistics.
@@ -789,13 +815,11 @@ def compute_statistics(impute2_filename, samples, markers_to_extract,
         # We need to compute the random effects if it's a MixedLM analysis
         random_effects = None
         if options.analysis_type == "mixedlm" and options.interaction is None:
-            random_effects = smf.mixedlm(
+            random_effects = _extract_mixedlm_random_effect(smf.mixedlm(
                 formula=formula.replace("_GenoD + ", ""),
                 data=phenotypes,
                 groups=phenotypes.index,
-            ).fit(reml=not options.use_ml).random_effects.rename(
-                columns={"Intercept": "RE"},
-            )
+            ).fit(reml=not options.use_ml))
 
         # Reading the file
         nb_processed = 0

setup.py

@@ -17,7 +17,7 @@
 
 MAJOR = 1
 MINOR = 3
-MICRO = 1
+MICRO = 2
 VERSION = "{0}.{1}.{2}".format(MAJOR, MINOR, MICRO)