Merge pull request #14 from rosalindfranklininstitute/dev-temp

Dev temp

Author: MasterVexillen

GeoLlama/calc_by_slice.py

@@ -0,0 +1,177 @@
+# Copyright 2023 Rosalind Franklin Institute
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
+# either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+
+###########################################
+## Module             : GeoLlama.config  ##
+## Created            : Neville Yee      ##
+## Date created       : 03-May-2024      ##
+## Date last modified : 03-May-2024      ##
+###########################################
+
+import os
+
+import typing
+from pathlib import Path
+import multiprocessing as mp
+
+from GeoLlama import objects
+
+
+def generate_config(output_path):
+    """
+    Generates default configuration file
+
+    Args:
+    output_path (str) : Path to output yaml file
+    """
+    import ruamel.yaml
+
+    config_str = """ \
+# Essential parameters
+data_path: null
+pixel_size_nm: null
+
+# Optional parameters
+binning: 0
+autocontrast: True
+adaptive: True
+bandpass: False
+num_cores: 1
+output_csv_path: null
+output_star_path: null
+output_mask: True
+
+# Analytics parameters
+thickness_lower_limit: 120
+thickness_upper_limit: 300
+thickness_std_limit: 15
+xtilt_std_limit: 5
+displacement_limit : 25
+displacement_std_limit : 5
+"""
+
+    yaml = ruamel.yaml.YAML()
+    data = yaml.load(config_str)
+
+    yaml.default_flow_style=False
+    yaml.dump(data, Path(output_path))
+
+
+def read_config(
+        config_fname: str
+) -> objects.Config:
+    """
+    Parse GeoLlama config YAML file
+
+    Args:
+    config_fname (str) : Path to config file
+
+    Returns:
+    Config
+    """
+    import ruamel.yaml
+
+    if not os.path.exists(config_fname):
+        raise IOError(f"{config_fname} does not exist.")
+
+    yaml = ruamel.yaml.YAML()
+    config_dict = yaml.load(Path(config_fname))
+
+    # Initialise a Config object
+    params = objects.Config()
+
+    # Check config file has correct dictionary keys
+    for key in params.__dict__.keys():
+        try:
+            params.__setattr__(key, config_dict[key])
+        except ValueError:
+            print(f"{key} keyword missing in config file.")
+
+    return params
+
+
+def objectify_user_input(
+        autocontrast: typing.Optional[bool],
+        adaptive: typing.Optional[bool],
+        bandpass: typing.Optional[bool],
+        data_path: typing.Optional[str],
+        pixel_size_nm: typing.Optional[float],
+        binning: typing.Optional[int],
+        num_cores: typing.Optional[int],
+        output_csv_path: typing.Optional[str],
+        output_star_path: typing.Optional[str],
+        output_mask: typing.Optional[bool],
+
+        thickness_lower_limit: typing.Optional[float],
+        thickness_upper_limit: typing.Optional[float],
+        thickness_std_limit: typing.Optional[float],
+        xtilt_std_limit: typing.Optional[float],
+        displacement_limit : typing.Optional[float],
+        displacement_std_limit : typing.Optional[float],
+) -> objects.Config:
+    """
+    Objectifying user provided input as a Config object
+
+    Args:
+    autocontrast (bool) : Apply autocontrast to slices prior to evaluation
+    adaptive (bool) : Use adaptive sampling for slice evaluation
+    bandpass (bool) : Apply bandpass filter to tomograms prior to evaluation
+    user_path (str) : Path to folder holding all tomograms in batch mode
+    pixel_size (float) : Pixel size of input tomogram in nm
+    binning (int) : Binning factor for tomogram evaluation
+    cpu (int) : Number of CPUs used
+    output_csv (str) : Output path for CSV file
+    output_star (str) : Output path for STAR file
+    output_mask (bool) : Produce 3D mask of estimated lamella region (same shape as input tomogram)
+    thickness_lower_limit (float) : Lower limit of lamella thickness in nm (for feature extraction)
+    thickness_upper_limit (float) : Upper limit of lamella thickness in nm (for feature extraction)
+    thickness_std_limit (float) : Limit of lamella thickness standard deviation in nm (for feature extraction)
+    xtilt_std_limit (float) : Limit of lamella xtilt standard deviation in degrees (for feature extraction)
+    displacement_limit (float) : Limit of lamella centroid displacement in % (for feature extraction)
+    displacement_std_limit (float) : Limit of lamella centroid displacement standard deviation in % (for feature extraction)
+
+    Returns:
+    Config
+    """
+    params = objects.Config()
+    for key in params.__dict__.keys():
+        params.__setattr__(key, locals()[key])
+
+    return params
+
+
+def check_config(
+        params: objects.Config
+):
+    """
+    Check datatypes in parameters
+
+    Args:
+    config (Config) : Config object storing all parameters
+    """
+
+    if params.data_path is None:
+        raise ValueError("Data path (-p) must be given.")
+    elif not os.path.isdir(Path(params.data_path)):
+        raise NotADirectoryError("Given path must be a folder.")
+
+    if params.pixel_size_nm is None:
+        raise ValueError("Pixel size (-s) must be given.")
+    elif params.pixel_size_nm <= 0:
+        raise ValueError("Pixel size (-s) must be a positive number.")
+
+    if not isinstance(params.num_cores, int):
+        raise ValueError("num_cores (-np) must be an integer.")
+    elif not 1 <= params.num_cores <= mp.cpu_count():
+        raise ValueError(f"num_cores (-np) must be between 1 and # CPUs available ({mp.cpu_count()}). Current settings: {params.num_cores}")