formatting

Author: SkafteNicki

README.md

@@ -55,6 +55,26 @@ y = torch.randn(100, requires_grad=True, device='cuda')
 plt.plot(x, y, '.') # easy and simple
 ```
 
+## Requirements
+Tested using `torch>=1.6` and `matplotlib>=3.3.3` but should perfectly work with
+both earlier and later versions.
+
+## Licence
+
+Please observe the Apache 2.0 license that is listed in this repository. 
+
+## BibTeX
+If you want to cite the framework feel free to use this (but only if you loved it 😊):
+
+```bibtex
+@article{detlefsen2021torchplot,
+  title={TorchPlot},
+  author={Detlefsen, Nicki S.},
+  journal={GitHub. Note: https://github.com/CenterBioML/torchplot},
+  year={2021}
+}
+```
+
 
 
 

requirements.txt

@@ -1,2 +1,2 @@
 torch>=1.3
-numpy>=1.16.4
+matplotlib>=3.3.3

setup.py

@@ -56,13 +56,14 @@ def load_readme(path_dir=PATH_ROOT):
     version=torchplot.__version__,
     description=torchplot.__docs__,
     long_description=load_readme(PATH_ROOT),
+    long_description_content_type="text/markdown",
     author=torchplot.__author__,
     author_email=torchplot.__author_email__,
     license=torchplot.__license__,
     packages=find_packages(exclude=["tests", "tests/*"]),
     python_requires=">=3.8",
-    install_requires=['torch>=1.3', 'matplotlib>=3.3.3'],
-    download_url="https://github.com/CenterBioML/torchplot/archive/0.1.0.zip",
+    install_requires=["torch>=1.6", "matplotlib>=3.3.3"],
+    download_url="https://github.com/CenterBioML/torchplot/archive/0.1.1.zip",
     classifiers=[
         "Environment :: Console",
         "Natural Language :: English",

tests/test_torchplot.py

@@ -24,32 +24,36 @@
 
 Inputs = namedtuple("case", ["x", "y"])
 
-_cpu_cases = [Inputs(x=torch.randn(100,), y=torch.randn(100,)),
-              Inputs(x=torch.randn(100, requires_grad=True), y=torch.randn(100,requires_grad=True)),
-              # test that list/numpy arrays still works
-              Inputs(x=[1,2,3,4], y=[1,2,3,4]),
-              Inputs(x=np.random.randn(100,), y=np.random.randn(100,)),
-              # test that we can mix
-              Inputs(x=torch.randn(100,), y=torch.randn(100, requires_grad=True)),
-              Inputs(x=np.random.randn(100,), y=torch.randn(100, requires_grad=True)),
-              Inputs(x=torch.randn(5,), y=[1,2,3,4,5]),
-              ]
+_cpu_cases = [
+    Inputs(x=torch.randn(100), y=torch.randn(100)),
+    Inputs(x=torch.randn(100, requires_grad=True), y=torch.randn(100, requires_grad=True)),
+    # test that list/numpy arrays still works
+    Inputs(x=[1, 2, 3, 4], y=[1, 2, 3, 4]),
+    Inputs(x=np.random.randn(100), y=np.random.randn(100)),
+    # test that we can mix
+    Inputs(x=torch.randn(100), y=torch.randn(100, requires_grad=True)),
+    Inputs(x=np.random.randn(100), y=torch.randn(100, requires_grad=True)),
+    Inputs(x=torch.randn(5), y=[1, 2, 3, 4, 5]),
+]
 
-_gpu_cases = [Inputs(x=torch.randn(100, device='cuda'), y=torch.randn(100, device='cuda')),
-              Inputs(x=torch.randn(100,requires_grad=True, device='cuda'), y=torch.randn(100,requires_grad=True, device='cuda')),
-              ]
+_gpu_cases = [
+    Inputs(x=torch.randn(100, device="cuda"), y=torch.randn(100, device="cuda")),
+    Inputs(
+        x=torch.randn(100, requires_grad=True, device="cuda"), y=torch.randn(100, requires_grad=True, device="cuda")
+    ),
+]
 
 
-
-_members_to_check = [name for name, member in getmembers(plt) 
-                     if isfunction(member) and not name.startswith('_')]
+_members_to_check = [name for name, member in getmembers(plt) if isfunction(member) and not name.startswith("_")]
 
 
 def string_compare(text1, text2):
     if text1 is None and text2 is None:
         return True
     remove = string.punctuation + string.whitespace
-    return text1.translate(str.maketrans(dict.fromkeys(remove))) == text2.translate(str.maketrans(dict.fromkeys(remove)))
+    return text1.translate(str.maketrans(dict.fromkeys(remove))) == text2.translate(
+        str.maketrans(dict.fromkeys(remove))
+    )
 
 
 @pytest.mark.parametrize("member", _members_to_check)
@@ -59,15 +63,14 @@ def test_members(member):
     assert member in dir(tp)
 
 
-@pytest.mark.parametrize('test_case', _cpu_cases)
+@pytest.mark.parametrize("test_case", _cpu_cases)
 def test_cpu(test_case):
     """ test that it works on cpu """
-    assert tp.plot(test_case.x, test_case.y, '.')
+    assert tp.plot(test_case.x, test_case.y, ".")
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason='test requires cuda')
-@pytest.mark.parametrize('test_case', _gpu_cases)
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")
+@pytest.mark.parametrize("test_case", _gpu_cases)
 def test_gpu(test_case):
     """ test that it works on gpu """
-    assert tp.plot(test_case.x, test_case.y, '.')
-
+    assert tp.plot(test_case.x, test_case.y, ".")

torchplot/core.py

@@ -31,15 +31,20 @@ def convert(arg):
 
     return outargs, kwargs
 
+
 # Iterate over all members of 'plt' in order to duplicate them
 for name, member in getmembers(plt):
     if isfunction(member):
         doc = getdoc(member)
         strdoc = "" if doc is None else doc
-        exec(('def {name}(*args, **kwargs):\n' +
-              '\t"""{doc}"""\n' +
-              '\tnew_args, new_kwargs = _torch2np(*args, **kwargs)\n' +
-              '\treturn plt.{name}(*new_args, **new_kwargs)').format(name=name, doc=strdoc))
+        exec(
+            (
+                "def {name}(*args, **kwargs):\n"
+                + '\t"""{doc}"""\n'
+                + "\tnew_args, new_kwargs = _torch2np(*args, **kwargs)\n"
+                + "\treturn plt.{name}(*new_args, **new_kwargs)"
+            ).format(name=name, doc=strdoc)
+        )
     else:
-        exec('{name} = plt.{name}'.format(name=name))
-    #break
+        exec("{name} = plt.{name}".format(name=name))
+    # break