some reconfigurations, and making ready for pip

Author: tsmbland

.gitignore

@@ -1,2 +1,2 @@
-.ipynb_checkpoints/
-.data/
+.dist/
+.saibr.egg-info/

LICENSE LICENSE.txtLICENSE renamed to LICENSE.txt

@@ -1,3 +1,3 @@
-# saibr_python
+# SAIBR (python)
 
 Python implementation of SAIBR

README.md

@@ -1,4 +1,4 @@
 from .saibr import *
-from .funcs import *
+from .misc import *
 from .roi import *
 from .legacy import *

environment.yml

@@ -0,0 +1,111 @@
+import numpy as np
+from skimage import io
+from typing import Optional
+import glob
+import matplotlib.pyplot as plt
+import copy
+
+
+def load_image(filename: str) -> np.ndarray:
+    """
+    Given the filename of a TIFF, creates numpy array with pixel intensities
+
+    Args:
+        filename: full path to the file to import (including extension)
+
+    Returns:
+        A numpy array of the image
+
+    """
+
+    return io.imread(filename).astype(float)
+
+
+def save_img(img: np.ndarray, direc: str):
+    """
+    Saves 2D array as .tif file
+
+    Args:
+        img: numpy array of the image to save
+        direc: file path to save to (including '.tif' extension)
+
+    """
+
+    io.imsave(direc, img.astype('float32'))
+
+
+def save_img_jpeg(img: np.ndarray, direc: str, cmin: Optional[float] = None, cmax: Optional[float] = None,
+                  cmap: str = 'gray'):
+    """
+    Saves 2D array as jpeg, according to min and max pixel intensities
+
+    Args:
+        img: numpy array of the image to save
+        direc: file path to save to (including '.jpeg' extension)
+        cmin: optional, sets intensity scaling (along with cmax)
+        cmax: optional, sets intensity scaling (along with cmin)
+        cmap: colour map (use string corresponding to matplotlib colormap)
+
+    """
+
+    plt.imsave(direc, img, vmin=cmin, vmax=cmax, cmap=cmap)
+
+
+def _direcslist(dest: str, levels: int = 0, exclude: Optional[tuple] = ('!',),
+                exclusive: Optional[tuple] = None) -> list:
+    lis = sorted(glob.glob(f'{dest}/*/'))
+
+    for level in range(levels):
+        newlis = []
+        for e in lis:
+            newlis.extend(sorted(glob.glob(f'{e}/*/')))
+        lis = newlis
+        lis = [x[:-1] for x in lis]
+
+    # Excluded directories
+    lis_copy = copy.deepcopy(lis)
+    if exclude is not None:
+        for x in lis:
+            for i in exclude:
+                if i in x:
+                    lis_copy.remove(x)
+                    break
+
+    # Exclusive directories
+    if exclusive is not None:
+        lis2 = []
+        for x in lis_copy:
+            for i in exclusive:
+                if i in x:
+                    lis2.append(x)
+    else:
+        lis2 = lis_copy
+
+    return sorted(lis2)
+
+
+def direcslist(dest: str, levels: int = 0, exclude: Optional[tuple] = ('!',),
+               exclusive: Optional[tuple] = None) -> list:
+    """
+    Gives a list of directories within a given directory (full path)
+    Todo: os.walk
+
+    Args:
+        dest: path of parent directory
+        levels: number of levels to go down. E.g. if 0, only return folders within the parent folder; if 1, return
+        folders within folders within the parent folder
+        exclude: exclude directories containing any strings within this tuple
+        exclusive: exclude directories that don't contain all the strings within this tuple
+
+    Returns:
+        list of directories
+
+    """
+
+    if type(dest) is list:
+        out = []
+        for d in dest:
+            out.extend(_direcslist(d, levels, exclude, exclusive))
+        return out
+    else:
+        return _direcslist(dest, levels, exclude, exclusive)

saibr/__init__.py

@@ -6,9 +6,10 @@
 import scipy.odr as odr
 from sklearn.metrics import r2_score
 from sklearn.linear_model import LinearRegression
-from .funcs import load_image, make_mask
+from .misc import load_image
 from .roi import offset_coordinates
 from typing import Tuple, Optional
+import cv2
 
 
 class SaibrCalibrate:
@@ -471,3 +472,7 @@ def saibr_correct_3channel(ch1: np.ndarray, ch2: np.ndarray, ch3: np.ndarray, m1
     af = m1 * ch2 + m2 * ch3 + c
     signal = ch1 - af
     return signal
+
+
+def make_mask(shape: tuple, roi: np.ndarray) -> np.ndarray:
+    return cv2.fillPoly(np.zeros(shape) * np.nan, [np.int32(roi)], 1)

saibr/funcs.py

@@ -0,0 +1,3 @@
+[metadata]
+description-file=README.md
+license_files=LICENSE.txt

saibr/misc.py

@@ -1,9 +1,26 @@
 from setuptools import find_packages, setup
+from pathlib import Path
+
+this_directory = Path(__file__).parent
+long_description = (this_directory / "README.md").read_text()
 
 setup(
     name='saibr',
-    version='1.0',
+    version='0.1.0',
     license="CC BY 4.0",
     author='Tom Bland',
+    author_email='tom_bland@hotmail.co.uk',
     packages=find_packages(),
+    url='https://github.com/goehringlab/saibr_python',
+    install_requires=['numpy',
+                      'matplotlib',
+                      'scipy',
+                      'ipywidgets',
+                      'scikit-learn',
+                      'scikit-image',
+                      'jupyter',
+                      'opencv-python'],
+    description='Python implementation of SAIBR: a tool for performing spectral autofluorescence correction on biological images',
+    long_description=long_description,
+    long_description_content_type='text/markdown'
 )