fix formatting

.gitignore

@@ -25,3 +25,4 @@ conda/build_main/*
 .venv
 environment.yml
 *.pickle
+*.joblib

docs/auto_examples/example_01_basic_usage.ipynb

@@ -300,7 +300,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Model Persistence\nAll pymer4 models can be saved and loaded from disk. Doing so will persist *all* model attributes and data i.e. anything accessible with the '.' syntax. Models are saved and loaded using [Joblib](https://joblib.readthedocs.io/en/latest/persistence.html#persistence) Therefore all filenames must end with :code:`.joblib`. For :code:`Lmer` models, an additional file ending in :code:`.rds` will be saved in the same directory as the HDF5 file. This is the R model object readable in R using :code:`readRDS`.\n\nPrior to version 0.8.1 models were saved to HDF5 files using [deepdish](https://github.com/uchicago-cs/deepdish/) but this library is no longer maintained. If you have old models saved as :code:`.h5` or :code:`.hdf5` files you should use the same version of pymer4 that you used to estimate those models. \n\nTo persist models you can use the dedicated :code:`save_model` and :code:`load_model` functions from the :code:`pymer4.io` module\n\n"
+        "## Model Persistence\nAll pymer4 models can be saved and loaded from disk. Doing so will persist *all* model attributes and data i.e. anything accessible with the '.' syntax. Models are saved and loaded using [Joblib](https://joblib.readthedocs.io/en/latest/persistence.html#persistence) Therefore all filenames must end with :code:`.joblib`. For :code:`Lmer` models, an additional file ending in :code:`.rds` will be saved in the same directory as the HDF5 file. This is the R model object readable in R using :code:`readRDS`.\n\nPrior to version 0.8.1 models were saved to HDF5 files using [deepdish](https://github.com/uchicago-cs/deepdish/) but this library is no longer maintained. If you have old models saved as :code:`.h5` or :code:`.hdf5` files you should use the same version of pymer4 that you used to estimate those models.\n\nTo persist models you can use the dedicated :code:`save_model` and :code:`load_model` functions from the :code:`pymer4.io` module\n\n"
       ]
     },
     {

docs/auto_examples/example_01_basic_usage.py

@@ -158,8 +158,8 @@
 # -----------------
 # All pymer4 models can be saved and loaded from disk. Doing so will persist *all* model attributes and data i.e. anything accessible with the '.' syntax. Models are saved and loaded using `Joblib <https://joblib.readthedocs.io/en/latest/persistence.html#persistence>`_ Therefore all filenames must end with :code:`.joblib`. For :code:`Lmer` models, an additional file ending in :code:`.rds` will be saved in the same directory as the HDF5 file. This is the R model object readable in R using :code:`readRDS`.
 #
-# Prior to version 0.8.1 models were saved to HDF5 files using `deepdish <https://github.com/uchicago-cs/deepdish/>`_ but this library is no longer maintained. If you have old models saved as :code:`.h5` or :code:`.hdf5` files you should use the same version of pymer4 that you used to estimate those models. 
-# 
+# Prior to version 0.8.1 models were saved to HDF5 files using `deepdish <https://github.com/uchicago-cs/deepdish/>`_ but this library is no longer maintained. If you have old models saved as :code:`.h5` or :code:`.hdf5` files you should use the same version of pymer4 that you used to estimate those models.
+#
 # To persist models you can use the dedicated :code:`save_model` and :code:`load_model` functions from the :code:`pymer4.io` module
 
 # Import functions

docs/auto_examples/example_01_basic_usage.py.md5

@@ -1 +1 @@
-710973e4fa57fe480157e222e5f8a970
+b56f42c7a0051017f9fba41a12809193

docs/auto_examples/example_01_basic_usage.rst

@@ -618,7 +618,7 @@ Model Persistence
 -----------------
 All pymer4 models can be saved and loaded from disk. Doing so will persist *all* model attributes and data i.e. anything accessible with the '.' syntax. Models are saved and loaded using `Joblib <https://joblib.readthedocs.io/en/latest/persistence.html#persistence>`_ Therefore all filenames must end with :code:`.joblib`. For :code:`Lmer` models, an additional file ending in :code:`.rds` will be saved in the same directory as the HDF5 file. This is the R model object readable in R using :code:`readRDS`.
 
-Prior to version 0.8.1 models were saved to HDF5 files using `deepdish <https://github.com/uchicago-cs/deepdish/>`_ but this library is no longer maintained. If you have old models saved as :code:`.h5` or :code:`.hdf5` files you should use the same version of pymer4 that you used to estimate those models. 
+Prior to version 0.8.1 models were saved to HDF5 files using `deepdish <https://github.com/uchicago-cs/deepdish/>`_ but this library is no longer maintained. If you have old models saved as :code:`.h5` or :code:`.hdf5` files you should use the same version of pymer4 that you used to estimate those models.
 
 To persist models you can use the dedicated :code:`save_model` and :code:`load_model` functions from the :code:`pymer4.io` module
 

docs/auto_examples/sg_execution_times.rst

@@ -5,10 +5,10 @@
 
 Computation times
 =================
-**00:02.603** total execution time for **auto_examples** files:
+**00:02.957** total execution time for **auto_examples** files:
 
 +-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_example_01_basic_usage.py` (``example_01_basic_usage.py``)         | 00:02.603 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_example_01_basic_usage.py` (``example_01_basic_usage.py``)         | 00:02.957 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_example_02_categorical.py` (``example_02_categorical.py``)         | 00:00.000 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------+-----------+--------+

docs/new.rst

@@ -2,6 +2,15 @@ What's New
 ==========
 Historically :code:`pymer4` versioning was a bit all over the place but has settled down since 0.5.0. This page includes the most notable updates between versions but github is the best place to checkout more details and `releases <https://github.com/ejolly/pymer4/releases/>`_.
 
+0.8.1
+----
+- **NOTE:**
+    - This version drops support for Python 3.7 and adds support for 3.9-3.11
+    - This version also uses :code:`joblib` for model saving and loading and drops supported hdf5 files previously handled with :code:`deepdish` (no longer maintained). This version will no longer be able to load models saved with versions < 0.8.1
+    - fixes `#122 <https://github.com/ejolly/pymer4/issues/122>`_
+    - fixes `#125 <https://github.com/ejolly/pymer4/issues/125>`_
+    - fixes `#119 <https://github.com/ejolly/pymer4/issues/119>`_
+
 0.8.0
 ----
 - **NOTE:**

examples/example_01_basic_usage.py

@@ -158,8 +158,8 @@
 # -----------------
 # All pymer4 models can be saved and loaded from disk. Doing so will persist *all* model attributes and data i.e. anything accessible with the '.' syntax. Models are saved and loaded using `Joblib <https://joblib.readthedocs.io/en/latest/persistence.html#persistence>`_ Therefore all filenames must end with :code:`.joblib`. For :code:`Lmer` models, an additional file ending in :code:`.rds` will be saved in the same directory as the HDF5 file. This is the R model object readable in R using :code:`readRDS`.
 #
-# Prior to version 0.8.1 models were saved to HDF5 files using `deepdish <https://github.com/uchicago-cs/deepdish/>`_ but this library is no longer maintained. If you have old models saved as :code:`.h5` or :code:`.hdf5` files you should use the same version of pymer4 that you used to estimate those models. 
-# 
+# Prior to version 0.8.1 models were saved to HDF5 files using `deepdish <https://github.com/uchicago-cs/deepdish/>`_ but this library is no longer maintained. If you have old models saved as :code:`.h5` or :code:`.hdf5` files you should use the same version of pymer4 that you used to estimate those models.
+#
 # To persist models you can use the dedicated :code:`save_model` and :code:`load_model` functions from the :code:`pymer4.io` module
 
 # Import functions

pymer4/io.py

@@ -21,11 +21,11 @@ def save_model(model, filepath, **kwargs):
     """
 
     filepath = str(filepath)
-    if not filepath.endswith('.joblib'):
+    if not filepath.endswith(".joblib"):
         raise IOError("filepath must end with .joblib")
 
     rds_file = filepath.replace(".joblib", ".rds")
-    
+
     # Save the python object
     dump(model, filepath, **kwargs)
     assert os.path.exists(filepath)
@@ -34,6 +34,7 @@ def save_model(model, filepath, **kwargs):
         base.saveRDS(model.model_obj, rds_file)
         assert os.path.exists(rds_file)
 
+
 def load_model(filepath):
     """
     Function for loading pymer4 models. A file path ending in .joblib should be provided. For Lmer models an additional filepath.robj should be located in the same directory.
@@ -44,7 +45,7 @@ def load_model(filepath):
     """
 
     filepath = str(filepath)
-    if not filepath.endswith('.joblib'):
+    if not filepath.endswith(".joblib"):
         raise IOError("filepath must end with .joblib")
 
     rds_file = filepath.replace(".joblib", ".rds")

pymer4/tests/test_io.py

@@ -1,12 +1,13 @@
 from pymer4.models import Lm, Lm2, Lmer
 from pymer4.io import load_model, save_model
 
+
 def test_saveload_lmer(df, tmp_path):
 
     model = Lmer("DV ~ IV3 + IV2 + (IV2|Group) + (1|IV3)", data=df)
     model.fit(summarize=False)
-    output_file = tmp_path / 'model.joblib'
-    rds_file = tmp_path / 'model.rds'
+    output_file = tmp_path / "model.joblib"
+    rds_file = tmp_path / "model.rds"
 
     save_model(model, output_file)
     assert output_file.exists()
@@ -21,7 +22,7 @@ def test_saveload_lm(df, tmp_path):
 
     model = Lm("DV ~ IV1 + IV3", data=df)
     model.fit(summarize=False)
-    output_file = tmp_path / 'model.joblib'
+    output_file = tmp_path / "model.joblib"
 
     save_model(model, output_file)
     assert output_file.exists()
@@ -35,11 +36,11 @@ def test_saveload_lm2(df, tmp_path):
 
     model = Lm2("DV ~ IV3 + IV2", group="Group", data=df)
     model.fit(summarize=False)
-    output_file = tmp_path / 'model.joblib'
+    output_file = tmp_path / "model.joblib"
 
     save_model(model, output_file)
     assert output_file.exists()
 
     m = load_model(output_file)
     assert m.coefs.equals(model.coefs)
-    assert m.data.equals(model.data)
+    assert m.data.equals(model.data)