Merge pull request #13 from fish-quant/release/v0.3.0

v0.3.0

.codecov.yml

@@ -0,0 +1,18 @@
+comment: false
+
+ignore:
+  - "setup.py"
+  - "python_scripts"
+  - "bigfish/plot"
+  - "bigfish/deep_learning"
+  - "data"
+  - "weights"
+
+coverage:
+  status:
+    project:
+      default:
+        # Commits pushed to master should not make the overall project
+        # coverage decrease by more than 1%:
+        target: auto
+        threshold: 1%

.gitignore

@@ -9,12 +9,9 @@ dist/
 big_fish.egg-info/
 
 # Notebooks
-notebooks/old
-notebooks/.ipynb_checkpoints
-
-# Data
-data/input/*
-data/output/*
+examples/old
+examples/.ipynb_checkpoints
 
 # Cache
 __pycache__/
+.pytest_cache/

README.md

@@ -1 +1,106 @@
-# big-fish
+# Big-FISH
+
+[![License](https://img.shields.io/badge/license-BSD%203--Clause-green)](https://github.com/fish-quant/big-fish/blob/master/LICENSE)
+[![codecov](https://codecov.io/gh/fish-quant/big-fish/branch/master/graph/badge.svg?token=kEnikxXOxP)](https://codecov.io/gh/fish-quant/big-fish)
+![License](https://img.shields.io/badge/python-3.6-blue)
+
+**Big-FISH** is a python package for the analysis of smFISH images. It includes various methods to **manipulate microscopic images**, **detect spots** and **segment relevant area** of the cells. The package allows the user to extract specific signal from images and build a coordinate representation of the cells. The ultimate goal is to ease **large scale statistical analysis** and quantification.
+
+| Cell image (smFISH channel) and its coordinates representation |
+| ------------- |
+| ![](images/plot_cell.png "Nucleus in blue, mRNAs in red, foci in orange and transcription sites in green") |
+
+## Installation
+
+### Dependencies
+
+Big-FISH requires Python 3.6 or newer. Additionally, it has the following dependencies:
+
+- numpy (>= 1.16.0)
+- scipy (>= 1.2.0)
+- scikit-learn (>= 0.20.2)
+- scikit-image (>= 0.14.2)
+- matplotlib (>= 3.0.2)
+- pandas (>= 0.24.0)
+- mrc (>= (0.1.5)
+- pip (>= 18.1)
+
+### User installation
+
+To avoid dependency conflicts the use of a dedicated [virtual](https://docs.python.org/3.6/library/venv.html) or [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) environment should be a considered option.
+
+#### From PyPi
+
+Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Big-FISH. In a terminal run the command:
+
+```bash
+pip install bigfish
+```
+
+#### From Github
+
+Clone the project's [Github repository](https://github.com/fish-quant/big-fish) and install it locally with the following commands:
+
+```bash
+git clone [email protected]:fish-quant/big-fish.git
+cd big-fish
+pip install .
+```
+
+## Usage
+
+Big-FISH provides a toolbox for the full analysis pipeline of smFISH images:
+- Use `bigfish.stack` subpackage for I/O operations, preprocessing and postprocessing.
+- Use `bigfish.segmentation` subpackage for nucleus and cell segmentation.
+- Use `bigfish.detection` subpackage for mRNAs detection.
+- Use `bigfish.plot` subpackage for plotting routines.
+- Use `bigfish.classification` subpackage for pattern recognition tasks.
+
+Several examples are developed in the [examples](https://github.com/fish-quant/big-fish/tree/master/examples) directory.
+
+## Support
+
+If you have any question relative to the repository, please open an [issue](https://github.com/fish-quant/big-fish/issues). You can also contact [Arthur Imbert](mailto:[email protected]) or [Florian Mueller](mailto:[email protected]).
+
+## Roadmap (suggestion)
+
+Version 0.4.0:
+- Refactor `bigfish.classification` subpackage.
+- Add pattern recognition examples.
+
+Version 0.5.0:
+- Switch to tensorflow 2.0.0.
+- Integrate a deep learning model for segmentation.
+
+Version 1.0.0:
+- Complete code coverage.
+- Add sphinx documentation.
+
+## Development
+
+### Source code
+
+You can access the latest sources with the commands:
+
+```bash
+git clone [email protected]:fish-quant/big-fish.git
+git checkout develop
+```
+
+### Contributing
+
+[Pull requests](https://github.com/fish-quant/big-fish/pulls) are welcome. For major changes, please open an [issue](https://github.com/fish-quant/big-fish/issues) first to discuss what you would like to change.
+
+### Testing
+
+Please make sure to update tests as appropriate if you open a pull request. You can install exacts dependencies and specific version of [pytest](https://docs.pytest.org/en/latest/) by running the following command:
+
+```bash
+pip install -r requirements_dev.txt
+```
+
+To perform unitary tests, run : 
+
+```bash
+pytest bigfish
+```

bigfish/__init__.py

@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+# Author: Arthur Imbert <[email protected]>
+# License: BSD 3 clause
+
+"""
+The bigfish package.
+"""
+
+
+# keep a MAJOR.MINOR.PATCH format
+# MAJOR: major API changes
+# MINOR: new features
+# PATCH: backwards compatible bug fixes
+
+__version__ = "0.3.0"

bigfish/classification/__init__.py

@@ -8,7 +8,6 @@
 from .input_preparation import (prepare_coordinate_data,
                                 build_boundaries_layers, build_surface_layers,
                                 build_distance_layers, Generator)
-from .squeezenet import SqueezeNet0, SqueezeNet_qbi
 from .features import get_features, get_features_name
 
 # ### Load models ###
@@ -19,6 +18,4 @@
                       "build_surface_layers", "build_distance_layers",
                       "Generator"]
 
-_squeezenet = ["SqueezeNet0", "SqueezeNet_qbi"]
-
-__all__ = _features + _input_preparation + _squeezenet
+__all__ = _features + _input_preparation

bigfish/stack/preparation.py → bigfish/classification/preparation.py

@@ -11,10 +11,10 @@
 import pandas as pd
 from scipy import ndimage as ndi
 
-from .utils import get_offset_value
-from .augmentation import augment
-from .preprocess import cast_img_float32
-from .filter import mean_filter
+from bigfish.stack.utils import get_margin_value
+from bigfish.stack.augmentation import augment_2d
+from bigfish.stack.preprocess import cast_img_float32
+from bigfish.stack.filter import mean_filter
 
 from skimage.draw import polygon_perimeter
 from sklearn.preprocessing import LabelEncoder
@@ -272,8 +272,8 @@ def build_image(data, id_cell, image_shape=None, coord_refinement=True,
 
     # build matrices
     if image_shape is None:
-        max_x = cyt_coord[:, 0].max() + get_offset_value()
-        max_y = cyt_coord[:, 1].max() + get_offset_value()
+        max_x = cyt_coord[:, 0].max() + get_margin_value()
+        max_y = cyt_coord[:, 1].max() + get_margin_value()
         image_shape = (max_x, max_y)
     rna = np.zeros(image_shape, dtype=np.float32)
     rna[rna_coord[:, 0], rna_coord[:, 1]] = 1.0
@@ -350,8 +350,8 @@ def _build_rna(data, id_cell, output_shape=None):
     # get current shape
     cyt_coord = data.loc[id_cell, "pos_cell"]
     cyt_coord = np.array(cyt_coord, dtype=np.int64)
-    max_x = cyt_coord[:, 0].max() + get_offset_value()
-    max_y = cyt_coord[:, 1].max() + get_offset_value()
+    max_x = cyt_coord[:, 0].max() + get_margin_value()
+    max_y = cyt_coord[:, 1].max() + get_margin_value()
     input_shape = (max_x, max_y)
 
     if output_shape is not None:

bigfish/deep_learning/__init__.py

@@ -0,0 +1,12 @@
+# -*- coding: utf-8 -*-
+
+"""
+The bigfish.deep_learning module includes deep learning models and routines.
+"""
+
+from .squeezenet import SqueezeNet0, SqueezeNet_qbi
+
+
+_squeezenet = ["SqueezeNet0", "SqueezeNet_qbi"]
+
+__all__ = _squeezenet

bigfish/classification/squeezenet.py → bigfish/deep_learning/squeezenet.py

@@ -20,7 +20,7 @@
 import tensorflow as tf
 import numpy as np
 
-from .base import BaseModel, get_optimizer
+from bigfish.deep_learning.base import BaseModel, get_optimizer
 
 from tensorflow.python.keras.backend import function, learning_phase
 from tensorflow.python.keras.models import Model