Merge pull request #250 from reillytilbury/dev

Alpha 0.10.5 Update

Author: paulshuker

.github/workflows/pytorch_integration.yaml

@@ -36,7 +36,7 @@ jobs:
 
       - name: Install pytorch dependencies
         run: |
-          python -m pip install -r requirements-pytorch.txt
+          python -m pip install -r requirements.txt
           python -m pip install .
 
       - name: Run pytorch unit tests

.gitignore

@@ -13,3 +13,164 @@ coppafish.egg-info
 **/temp.*
 **/.venv/**
 **/.idea/**
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

changelog.txt

@@ -1,6 +1,25 @@
+2024/05/14 Alpha Update (v0.10.5):
+ * Pytorch CPU is now a default dependency.
+ * Optical flow results are saved as compressed zarr files instead of uncompressed numpy arrays.
+ * Coppafish can send email notifications on completed pipelines and crashes.
+ * OMP has been overhauled.
+ * New diagnostic Viewer2D added.
+ * The OMP computed spot is now computed on a subset of one tile, not the full tile.
+ * OMP is faster and the entire tile is loaded into memory before running each tile.
+ * Removed inputimeout package dependency.
+ * Max Intensity Projection of background images added to main viewer as option.
+ * Fluorescent beads registration bug fix.
+ * New viewer for registration of fluorescent beads.
+ * Redundant warnings silenced.
+ * Default starting position for registration viewer now the registered image.
+ * New diagnostic in main viewer shows top 10 best fitted genes to a given spot along with their associated scores.
+
+
 2024/04/23 Alpha Update (v0.10.4):
  * Bug in Register which was causing large shifts not to be detected has been fixed. A global shift is now computed
     between the two images and the shift is applied to the second image before computing the local shifts.
+ * OMP unbound variable i_added_genes bug fixed in pytorch GPU.
+
 
 2024/04/18 Alpha Update (v0.10.3):
  * OMP crash on empty coef_image bug fix.

coppafish/__init__.py

@@ -5,5 +5,6 @@
 from ._version import __version__
 from . import plot
 from .plot import Viewer
+from .plot.viewer2d.base import Viewer2D
 from .plot.register.diagnostics import RegistrationViewer
 from .pdf.base import BuildPDF

coppafish/_version.py

@@ -1 +1 @@
-__version__ = "0.10.4"
+__version__ = "0.10.5"

coppafish/call_spots/__init__.py

@@ -1,21 +1,13 @@
-try:
-    from .qual_check_pytorch import get_spot_intensity
-except ImportError:
-    from .qual_check import get_spot_intensity
-
-try:
-    import torch
-
-    if torch.cuda.is_available():
-        from .background_pytorchgpu import fit_background
-    else:
-        from .background_pytorch import fit_background
-except ImportError:
-    from .background import fit_background
-
+from .qual_check import get_spot_intensity
+from .background import fit_background
 from .base import get_bled_codes, get_non_duplicate, compute_gene_efficiency
 from .qual_check import omp_spot_score, quality_threshold, get_intensity_thresh
 from .dot_product import dot_product_score, gene_prob_score
 
 # Needed for working non-jax, non-pytorch software
 from . import background
+
+try:
+    from . import background_pytorch
+except ImportError:
+    pass

coppafish/call_spots/background.py

@@ -27,17 +27,26 @@ def fit_background(spot_colors: np.ndarray, weight_shift: float = 0) -> Tuple[np
         - background_vectors `float [n_channels x n_rounds x n_channels]`.
             background_vectors[c] is the background vector for channel c.
     """
-    weight_shift = np.clip(weight_shift, 1e-20, np.inf)  # ensure weight_shift > 1e-20 to avoid blow up to infinity.
+    # Preserve the spot colours datatype throughout.
+    dtype = spot_colors.dtype
+    weight_shift = np.clip(
+        weight_shift, 1e-20, np.inf, dtype=dtype
+    )  # ensure weight_shift > 1e-20 to avoid blow up to infinity.
 
     n_rounds, n_channels = spot_colors[0].shape
-    background_vectors = np.repeat(np.expand_dims(np.eye(n_channels), axis=1), n_rounds, axis=1)
+    background_vectors = np.repeat(np.expand_dims(np.eye(n_channels), axis=1), n_rounds, axis=1).astype(dtype)
     # give background_vectors an L2 norm of 1 so can compare coefficients with other genes.
     background_vectors = background_vectors / np.linalg.norm(background_vectors, axis=(1, 2), keepdims=True)
 
     weight_factor = 1 / (np.abs(spot_colors) + weight_shift)
     spot_weight = spot_colors * weight_factor
-    background_weight = np.ones((1, n_rounds, n_channels)) * background_vectors[0, 0, 0] * weight_factor
+    background_weight = np.ones((1, n_rounds, n_channels), dtype=dtype) * background_vectors[0, 0, 0] * weight_factor
+    # Avoid overflow from squaring the background_weight.
+    background_weight = np.clip(background_weight, None, np.sqrt(np.finfo(dtype).max))
     coef = np.sum(spot_weight * background_weight, axis=1) / np.sum(background_weight**2, axis=1)
-    residual = spot_colors - np.expand_dims(coef, 1) * np.ones((1, n_rounds, n_channels)) * background_vectors[0, 0, 0]
+    residual = (
+        spot_colors
+        - np.expand_dims(coef, 1) * np.ones((1, n_rounds, n_channels), dtype=dtype) * background_vectors[0, 0, 0]
+    )
 
     return residual, coef, background_vectors

coppafish/call_spots/background_pytorch.py

@@ -41,7 +41,7 @@ def fit_background(
     weight_factor = 1 / (torch.abs(spot_colors) + weight_shift)
     spot_weight = spot_colors * weight_factor
     background_weight = torch.ones((1, n_rounds, n_channels)) * background_vectors[0, 0, 0] * weight_factor
-    coef = torch.sum(spot_weight * background_weight, dim=1) / torch.sum(background_weight ** 2, dim=1)
+    coef = torch.sum(spot_weight * background_weight, dim=1) / torch.sum(background_weight**2, dim=1)
     residual = spot_colors - coef[:, None] * torch.ones((1, n_rounds, n_channels)) * background_vectors[0, 0, 0]
 
-    return residual.type(torch.float32), coef.type(torch.float32), background_vectors.type(torch.float32)
+    return residual.float(), coef.float(), background_vectors.float()

coppafish/call_spots/background_pytorchgpu.py

@@ -26,7 +26,7 @@ def dot_product_score(
     n_spots, n_rounds_channels_use = spot_colours.shape
     # If no weighting is given, use equal weighting
     if weight_squared is None:
-        weight_squared = np.ones((n_spots, n_rounds_channels_use))
+        weight_squared = np.ones((n_spots, n_rounds_channels_use), dtype=spot_colours.dtype)
 
     weight_squared = weight_squared / np.sum(weight_squared, axis=1)[:, None]
     spot_colours = spot_colours / (np.linalg.norm(spot_colours, axis=1)[:, None] + norm_shift)
@@ -75,6 +75,6 @@ def gene_prob_score(spot_colours: np.ndarray, bled_codes: np.ndarray, kappa: flo
     dot_product = spot_colours @ bled_codes.T
     probability = np.exp(kappa * dot_product)
     # Now normalise so that each row sums to 1
-    probability = probability / np.sum(probability, axis=1)[:, None]
+    probability = np.nan_to_num(probability / np.sum(probability, axis=1)[:, None])
 
     return probability