Cleanup!

Deleted print statements, superfluous log, TODOs, added a few comments.

.gitlab-ci.yml

@@ -68,7 +68,7 @@ pypi:
     script:
         - pip install twine
         - python setup.py sdist bdist_wheel
-        - twine upload -u __token__ -p $PYPI_ACCESS_TOKEN dist/*
+        - twine upload -u __token__ -p $PYPI_ACCESS_TOKEN dist/*  # -u user -p password upload_source
     rules:  # similar to only/except, but newer!
         # Job is executed if branch is master AND if a tag is building which matches the regular expression!
         # ONLY executes if commit to master has a tag, ^:start, $:end, valid example: "1.2.3", no "-dev" at the end!

docs/index.rst

@@ -13,6 +13,7 @@ Welcome to EMPyRe's documentation!
    :caption: Contents:
 
    fields
+   vis
 
 
 Indices and tables

docs/vis.rst

+The vis visualization submodule
+===============================
+
+vis docu here!
\ No newline at end of file

environment.yml

@@ -27,7 +27,7 @@ dependencies:
   - matplotlib=3.1
   - Pillow=6.1
   - cmocean=2.0
-  #- qt=5.9  # TODO: only needed for mayavi?
+  #- qt=5.9  # TODO: only needed for mayavi? which version?
   - mayavi=4.6  # TODO: Get rid of!
   # Testing:
   - pytest=5.0

setup.cfg

@@ -7,12 +7,12 @@
 
 [metadata]
 name = empyre
-version = 0.1.0.dev0
+version = 0.0.0
 author = Jan Caron
 author-email = j.caron@fz-juelich.de
 description = Electron Microscopy Python Reconstruction
 long-description = file: README.rst
-url = https://jugit.fz-juelich.de/empyre/empyre
+url = https://iffgit.fz-juelich.de/empyre/empyre
 license = GPLv3
 classifiers =
     Development Status :: 3 - alpha

setup.py

@@ -38,12 +38,3 @@ for style_path in style_files:
 # Run setup (reads metadata & options from setup.cfg):
 print(R'running setup.py')
 setup()
-
-
-# TODO: HOW TO GET JUTIL???
-
-# TODO: Also create conda recipe!
-# TODO: Handle extras via metapackage (depends on pyramid-base that holds the core code and extras?)
-# TODO: https://docs.conda.io/projects/conda-build/en/latest/user-guide/tutorials/build-pkgs.html
-
-# TODO: MATPLOTLIB STYLE INSTALL: https://stackoverflow.com/questions/35851201/how-can-i-share-matplotlib-style

src/empyre/fields/field.py

@@ -16,14 +16,6 @@ from scipy.ndimage import interpolation
 
 __all__ = ['Field']
 
-# TODO: Forward numpys ndim parameter here?
-
-# TODO: Search for .data ! Not usable anymore!
-
-# TODO: overwrite property ndim?
-
-# TODO: get rid of some _log and print stuff!
-
 
 class Field(NDArrayOperatorsMixin):
     """Container class for storing multidimensional scalar or vector fields.
@@ -168,26 +160,9 @@ class Field(NDArrayOperatorsMixin):
         outputs = kwargs.pop('out', ())
         outputs = kwargs.pop('out', (None,)*ufunc.nout)  # Defaults to tuple of None (currently: nout=1 all the time)
         outputs_arr = tuple([np.asarray(out) if isinstance(out, Field) else out for out in outputs])
-        nl = '\n             '
-        str_inputs = [str(inp) for inp in inputs]
-        self._log.debug(f'    ufunc:   {ufunc}')
-        self._log.debug(f'    method:  {method}')
-        self._log.debug(f'    inputs:  {nl.join(str_inputs)}')
-        self._log.debug(f'    # inp.:  {len(inputs)}')
-        self._log.debug(f'    outputs: {str(outputs)}')
-        self._log.debug(f'    out_arr: {str(outputs_arr)}')
-        self._log.debug(f'    kwargs:  {kwargs}')
-        self._log.debug(f'    nin:     {ufunc.nin}')
-        self._log.debug(f'    nout:    {ufunc.nout}')
-        self._log.debug(f'    self:    {str(self)}')
         # Cannot handle items that have __array_ufunc__ (other than our own).
-        print(f'inputs+outputs: {inputs+outputs}')
         for item in inputs + outputs:
-            print(f'type(item): {type(item)}')
-            print(f'hasattr(item, __array_ufunc__): {hasattr(item, "__array_ufunc__")}')
-            print(f'isinstance(item, Field): {isinstance(item, Field)}')
             if hasattr(item, '__array_ufunc__') and not isinstance(item, Field):  # Something else with __array_ufunc__:
-                print(f'type(item).__array_ufunc__: {type(item).__array_ufunc__}')
                 if type(item).__array_ufunc__ is not np.ndarray.__array_ufunc__:  # Can't handle other overrides:
                     return NotImplemented
         # TODO: BIGGEST NOTE HERE: Delegate work to ndarray.__array_ufunc__!
@@ -201,13 +176,12 @@ class Field(NDArrayOperatorsMixin):
         # TODO: for security, newaxis is not allowed (most other indexing works though), because scale would be unknown!
         # 1 input (has to be a Field, otherwise we wouldn't be here):
         if len(inputs) == 1:
-            self._log.debug('-inputs: 1 ------')
+            self._log.debug(f'__array_ufunc__ inputs: {len(inputs)}')
             field = inputs[0]
             scale_new = field.scale
             vector_new = field.vector
             # Preprocess axis keyword if it exists:
             axis = kwargs.get('axis', False)  # Default must not be None, because None is a possible setting!
-            self._log.debug(f'    axis: {axis}')
             full_reduction = False
             if axis is not False:
                 ax_full = tuple(range(len(field.dim)))  # All axes (minus a possible component axis for vector Fields)!
@@ -220,59 +194,39 @@ class Field(NDArrayOperatorsMixin):
                 if ax_full_wc[-1] in axis:  # User explicitely wants component reduction (can only be true for vector):
                     vector_new = False  # Force scalar field!
                 scale_new = tuple([s for i, s in enumerate(field.scale) if i not in axis])  # Drop axis from scale!
-            self._log.debug(f'    kwargs:  {kwargs}')
-            self._log.debug(f'    full_reduction: {full_reduction}')
             inputs_arr = np.asarray(field)  # Convert inputs that are Fields to ndarrays to avoid recursion!
             data_new = getattr(ufunc, method)(inputs_arr, out=outputs_arr, **kwargs)
             if full_reduction:  # Premature return because the result is no longer a Field:
                 return data_new
         # More than 1 input (at least one has to be a Field, otherwise we wouldn't be here):
         elif len(inputs) > 1:
-            self._log.debug(f'-inputs: {len(inputs)} ------')
             is_field = [isinstance(inp, Field) for inp in inputs]
-            self._log.debug(f'is_field: {is_field}')
             is_vector = [getattr(inp, 'vector', False) for inp in inputs]
-            self._log.debug(f'is_vector: {is_vector}')
             # Determine scale:
             if np.sum(is_field) > 1:  # More than one input is a Field objects:
                 scales = [inp.scale for i, inp in enumerate(inputs) if is_field[i]]  # Only takes scales of Field obj.!
-                self._log.debug(f'scales: {scales}')
                 scale_new = scales[0]
                 err_msg = f'Scales of all Field objects must match! Given scales: {scales}!'
                 assert all(scale == scale_new for scale in scales), err_msg
             else:  # Only one input is a field, pick the scale of that one:
                 scale_new = inputs[np.argmax(is_field)].scale  # argmax returns the index of first True!
-            self._log.debug(f'scale_new: {scale_new}')
             # Determine vector:
             vector_new = True if np.any(is_vector) else False  # Output is vector field if any input is a vector field!
-            self._log.debug(f'vector_new: {vector_new}')
             if np.sum(is_vector) > 1:  # More than one input is a vector Field objects:
                 ncomps = [inp.ncomp for i, inp in enumerate(inputs) if is_vector[i]]  # Only takes ncomp of v.-Fields!
-                self._log.debug(f'ncomps: {ncomps}')
                 err_msg = f'# of components of all Field objects must match! Given ncomps: {ncomps}!'
                 assert all(ncomp == ncomps[0] for ncomp in ncomps), err_msg
             # Append new axis at the end of non vector objects to broadcast to components:
             if np.any(is_vector):
                 inputs = list(inputs)
-                self._log.debug(f'len(input_arr): {len(inputs)}')
                 for i, inp in enumerate(inputs):
-                    self._log.debug(f'i: {i}')
-                    self._log.debug(f'is_vector[i]: {is_vector[i]}')
-                    self._log.debug(f'isinstance(inp, Number): {isinstance(inp, Number)}')
                     if not is_vector[i] and not isinstance(inp, Number):  # Numbers work for broadcasting anyway:
-                        inp_arr = np.asarray(inp)  # For broadcasting, try to cast as ndarray!
-                        self._log.debug(f'inp_arr.shape: {inp_arr.shape}')
-                        self._log.debug(f'inp.shape), len(scale_new): {len(np.asarray(inp).shape)},  {len(scale_new)}')
                         if len(np.asarray(inp).shape) == len(scale_new):  # No. of dimensions w/o comp., have to match!
-                            self._log.debug(f'inputs[i]: {inputs[i]}')
-                            inputs[i] = np.asarray(inputs[i])[..., np.newaxis]
+                            inputs[i] = np.asarray(inputs[i])[..., np.newaxis]  # Broadcasting, try to cast as ndarray!
                 inputs = tuple(inputs)
             # Convert inputs that are Fields to ndarrays to avoid recursion and determine data_new:
             inputs_arr = tuple([np.asarray(inp) if isinstance(inp, Field) else inp for inp in inputs])
-            self._log.debug(f'kwargs: {kwargs}')
             data_new = getattr(ufunc, method)(*inputs_arr, out=outputs_arr, **kwargs)
-            self._log.debug(f'data_new.shape: {data_new.shape}')
-            # TODO: Test clip (also ufunc?? think not...) seems to work, but what about others?
         # Return results:
         result = Field(data_new, scale_new, vector_new)
         return result
@@ -373,15 +327,11 @@ class Field(NDArrayOperatorsMixin):
             The padded `Field` object.
 
         """
-        print(f'pad_width: {pad_width}')
         if isinstance(pad_width, Number):  # Paddding is the same for each dimension (make sure it is a tuple)!
             pad_width = (pad_width,) * len(self.dim)
-        print(f'pad_width: {pad_width}')
         pad_width = [(p, p) if isinstance(p, Number) else p for p in pad_width]
-        print(f'pad_width: {pad_width}')
         if self.vector:  # Append zeros to padding, so component axis stays as is:
             pad_width = pad_width + [(0, 0)]
-        print(f'pad_width: {pad_width}')
         data_new = np.pad(self.data, pad_width, mode, **kwargs)
         return Field(data_new, self.scale, self.vector)
 
@@ -411,16 +361,12 @@ class Field(NDArrayOperatorsMixin):
         assert all([n_i >= 0 for n_i in n]), 'All entries of n must be positive integers!'
         # Pad if necessary (use padded 'field' from here on), formula for clarity: (n - dim % n) % n
         pad_width = [(0, (n[i] - self.dim[i] % n[i]) % n[i]) for i in range(len(self.dim))]
-        print(f'bin: pad_width: {pad_width}')
         field = self.pad(pad_width, mode='edge')
         # Create new shape used for binning (mean over every second axis will be taken):
         bin_shape = list(np.ravel([(field.dim[i]//n[i], n[i]) for i in range(len(field.dim))]))
-        print(f'bin_shape: {bin_shape}')
         mean_axes = np.arange(1, 2*len(field.dim), 2)  # every 2nd axis!
-        print(f'mean_axes: {mean_axes}')
         if self.vector:  # Vector field:
             bin_shape += [field.ncomp]  # Append component axis (they stay unchanged)
-        print(f'bin_shape: {bin_shape}')
         # Bin data and scale accordingly:
         data_new = field.data.reshape(bin_shape).mean(axis=tuple(mean_axes))
         scale_new = tuple([field.scale[i] * n[i] for i in range(len(field.dim))])
@@ -573,6 +519,7 @@ class Field(NDArrayOperatorsMixin):
         if self.vector:  # Vector fields need to scale components according to masked amplitude
             mask_vec = np.logical_and(mask, amp <= vmax)  # Only vmax is important!
             data = amp / np.where(mask_vec, 1, amp)  # TODO: needs testing!
+            # TODO: Test np.clip(field) (also ufunc?? think not...) seems to work, but what about others?
         else:  # For scalar fields, just delegate to the numpy function:
             data = np.clip(self.data, vmin, vmax)
         return Field(data, self.scale, self.vector)

src/empyre/fields/vectors.py

@@ -17,11 +17,6 @@ __all__ = ['create_vector_homog', 'create_vector_vortex', 'create_vector_skyrmio
 _log = logging.getLogger(__name__)
 
 
-# TODO: TEST!!!
-# TODO: ALL Docstrings!
-# TODO: LOGGING!
-
-
 def create_vector_homog(dim, phi=0, theta=None, scale=1):
     """Field subclass implementing a homogeneous vector field with 3 components in 2 or 3 dimensions.
 
@@ -72,6 +67,7 @@ def create_vector_vortex(dim, center=None, core_r=0, oop_r=None, axis=0, scale=1
         only 2D. To invert chirality, multiply the resulting Field object by -1.  # TODO: WRONG?
 
     """
+    _log.debug('Calling create_vector_vortex')
     assert len(dim) in (2, 3), 'Vortex can only be build in 2 or 3 dimensions!'
     # Find indices of the vortex plane axes:
     idx_uv = [0, 1, 2]
@@ -176,6 +172,7 @@ def create_vector_skyrmion(dim, center=None, phi_0=0, skyrm_d=None, wall_d=None,
         theta /= np.abs(theta).max() / np.pi
         return theta
 
+    _log.debug('Calling create_vector_skyrmion')
     assert len(dim) in (2, 3), 'Skyrmion can only be build in 2 or 3 dimensions!'
     # Find indices of the skyrmion plane axes:
     idx_uv = [0, 1, 2]
@@ -202,11 +199,10 @@ def create_vector_skyrmion(dim, center=None, phi_0=0, skyrm_d=None, wall_d=None,
     rr = np.hypot(coords_uv[0], coords_uv[1])
     phi = np.arctan2(coords_uv[0], coords_uv[1]) - phi_0
     theta = _theta(rr)
-
     w_comp = np.cos(theta)
     v_comp = np.sin(theta) * np.sin(phi)
     u_comp = np.sin(theta) * np.cos(phi)
-
+    # Expansion to 3D if necessary and component shuffling:
     if len(dim) == 3:  # Expand to 3D:
         w_comp = np.expand_dims(w_comp, axis=axis)
         v_comp = np.expand_dims(v_comp, axis=axis)
@@ -263,25 +259,17 @@ def create_vector_singularity(dim, center=None, scale=1):
         coordinates (e.g. coordinate 1 lies between the first and the second pixel).
 
     """  # TODO: What does negating do here? Senke / Quelle (YES IT DOES!)? swell and sink? ISSUE! INVITE PEOPLE!!!
+    _log.debug('Calling create_vector_singularity')
     # Find default values:
     if center is None:
         center = tuple([d / 2 for d in dim])
     assert len(dim) == len(center), f"Length of dim ({len(dim)}) and center ({len(center)}) don't match!"
     # Setup coordinates, shape is (c, z, y, x), if 3D, or (c, y, x), if 2D (c: components):
     coords = np.indices(dim) + 0.5  # 0.5 to get to pixel/voxel center!
-    print(f'coords.shape: {coords.shape}')
-    print(f'center: {center}')
-    print(f'dim: {dim}')
     center = np.asarray(center, dtype=float)
     bc_shape = (len(dim,),) + (1,)*len(dim)  # Shape for broadcasting, (1,1,1,3) for 3D, (1,1,2) for 2D!
-    print(f'bc_shape: {bc_shape}')
     coords = coords - center.reshape(bc_shape)  # Shift by center (append 1s for broadcasting)!
     rr = np.sqrt(np.sum([coords[i]**2 for i in range(len(dim))], axis=0))
-    print(f'rr.max(): {rr.max()}')
-    print(f'coords.shape: {coords.shape}')
-    print(f'rr.shape: {rr.shape}')
     data = coords / (rr + 1E-30)  # Normalise amplitude (keep direction), rr (z,y,x) is broadcasted to data (c,z,y,x)!
-    print(f'np.moveaxis(data, 0, -1).shape: {np.moveaxis(data, 0, -1).shape}')
-    print(f'data: {data}')
     data = data.T  # (c,z,y,x) -> (x,y,z,c)
     return Field(data=data, scale=scale, vector=True)

src/empyre/vis/colors.py

@@ -65,10 +65,6 @@ class Colormap3D(colors.Colormap, metaclass=abc.ABCMeta):
         self._log.debug('Calling rgb_from_vector')
         x, y, z = np.asarray(vector)
         R = np.sqrt(x ** 2 + y ** 2 + z ** 2)
-        print(f'----------RGB')
-        print(f'vector: {vector}')
-        print(f'R: {R}')
-        print(f'vector.shape: {vector.shape}')
         R_max = vmax if vmax is not None else R.max() + 1E-30
         # FIRST color dimension: HUE (1D ring/angular direction)
         phi = np.asarray(np.arctan2(y, x))
@@ -502,8 +498,7 @@ def interpolate_color(fraction, start, end):
 
 cmaps = {'cubehelix_standard': ColormapCubehelix(),
          'cubehelix_reverse': ColormapCubehelix(reverse=True),
-         'cubehelix_circular': ColormapCubehelix(start=1, rot=1,
-                                                 minLight=0.5, maxLight=0.5, sat=2),
+         'cubehelix_circular': ColormapCubehelix(start=1, rot=1, minLight=0.5, maxLight=0.5, sat=2),
          'perception_circular': ColormapPerception(),
          'hls_circular': ColormapHLS(),
          'classic_circular': ColormapClassic(),

src/empyre/vis/plot2d.py

@@ -127,7 +127,7 @@ def contour(field, axis=None, **kwargs):
     # Create coordinates (respecting the field scale, +0.5: pixel center!):
     vv, uu = (np.indices(squeezed_field.dim) + 0.5) * np.asarray(squeezed_field.scale)[:, None, None]
     # Set kwargs defaults without overriding possible user input:
-    kwargs.setdefault('levels', [0.5])  # TODO: Check all kwargs.setdefault if shiftable to style sheet (only universal)
+    kwargs.setdefault('levels', [0.5])
     kwargs.setdefault('colors', 'k')
     kwargs.setdefault('linestyles', 'dotted')
     kwargs.setdefault('linewidths', 2)
@@ -361,7 +361,7 @@ def quiver(field, axis=None, color_angles=False, cmap=None, n_bin='auto', bin_wi
     elif squeezed_indices[0] == 2:  # Slice of the zy-plane with x squeezed:
         u_comp = y_comp
         v_comp = z_comp
-    # Set specific defaults for quiver kwargs:  # TODO: Check if they can go into empyre style sheet:
+    # Set specific defaults for quiver kwargs:
     kwargs.setdefault('edgecolor', colors.cmaps['transparent_black'](amplitude).reshape(-1, 4))
     kwargs.setdefault('scale', 1/np.max(squeezed_field.scale))
     kwargs.setdefault('width', np.max(squeezed_field.scale))

src/empyre/vis/tools.py

@@ -5,6 +5,7 @@
 """This module provides helper functions to the vis module."""
 
 
+import logging
 from numbers import Number
 
 import numpy as np
@@ -14,6 +15,7 @@ from ..fields.field import Field
 
 
 __all__ = ['new', 'savefig', 'calc_figsize']
+_log = logging.getLogger(__name__)
 
 
 def new(nrows=1, ncols=1, mode='image', figsize=None, textwidth=None, width_scale=1, aspect=None, **kwargs):
@@ -64,6 +66,7 @@ def new(nrows=1, ncols=1, mode='image', figsize=None, textwidth=None, width_scal
     additional kwargs are passed to `~matplotlib.pyplot.subplots`.
 
     """
+    _log.debug('Calling new')
     assert mode in ('image', 'plot'), "mode has to be 'image', or 'plot'!"
     if figsize is None and textwidth is not None:  # Only then is all this necessary:
         if aspect is None:
@@ -95,7 +98,9 @@ def savefig(fname, **kwargs):
     -----
     Uses the 'empyre-save' stylesheet (installed together with EMPyRe to control the saving behaviour. Any kwargs are
     passed to :func:`~matplotlib.pyplot.savefig`.
+
     """
+    _log.debug('Calling savefig')
     with plt.style.context('empyre-save'):
         plt.savefig(fname, **kwargs)
 
@@ -122,8 +127,10 @@ def calc_figsize(textwidth, width_scale=1, aspect=1):
 
     Notes
     -----
-    Based on code from Florian Winkler.
+    Based on snippet from Florian Winkler.
+
     """
+    _log.debug('Calling calc_figsize')
     GOLDEN_RATIO = (1 + np.sqrt(5)) / 2   # Aesthetic ratio!
     INCHES_PER_POINT = 1.0 / 72.27  # Convert points to inch, LaTeX constant, apparently...
     textwidth_in = textwidth * INCHES_PER_POINT  # Width of the text in inches