commit for pypi upload: v1.0.0

MANIFEST.in

@@ -1,3 +1,3 @@
-include data/*
+include imaging_transcriptomics/data/*
 include requirements.txt
 include ./**/*.yaml

docs/chapters/01_getting_started.rst

@@ -18,3 +18,5 @@ components independently and cumulatively, that can be used to tune consequent a
 
 For more information on the use have a look at the :ref:`usage <Usage>` page. You can also have a  deeper look at the
 :ref:`methods <imgtrans>` and on :ref:`what to do with the results from the script <whatdo>`.
+
+For more advanced use, or to integrate it in your python workflow, you can use the :ref:`python module <library>`.

docs/chapters/03_installation.rst

@@ -13,7 +13,7 @@ To install the ``imaging-transcriptomics`` Python package you must first of all
 
 .. note::
 
-    All following steps assume that, if you have created a dedicated environment, this is currently active. If you are unsure you can check with ``which python`` from your terminal or activate your environment via the `` source activate`` (for conda managed environments) or ``source venv/bin/activate`` (for venv managed environments).
+    All following steps assume that, if you have created a dedicated environment, this is currently active. If you are unsure you can check with ``which python`` from your terminal or activate your environment via the ``source activate`` (for conda managed environments) or ``source venv/bin/activate`` (for venv managed environments).
 
 Before installing the ``imaging-transcriptomics`` package we need to install a package that is not available through PyPi but from GitHub only.
 This package is `pypls <https://github.com/netneurolab/pypyls>`_ and is used in the script to perform all PLS regressions.
@@ -27,7 +27,7 @@ This will install install the GitHub repository directly using pip and it will m
 
 .. warning::
 
-    Do not install ``pyls`` directly from ``pip`` with the command ``pip install pyls`` as this is a completely different package!
+    Do not install pyls directly from pip with the command ``pip install pyls`` as this is a completely different package!
 
 Once this package is installed you can install the ``imaging-transcriptomics`` package by running:
 

docs/chapters/04_usage.rst

@@ -39,6 +39,7 @@ Optional additional parameters that can be provided are:
 ``--suppress`` Sets the logging level to warning and will display only eventual warning messages.
 
 
+.. _library:
 
 =======================
 Usage as python library
@@ -51,4 +52,21 @@ To the library can be imported by running:
 
     import imaging_transcriptomics as imt
 
+Once imported the package will contain the core ``ImagingTranscriptomics`` class, along with other useful functions.
 
+ImagingTranscriptomics Class
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The ``ImagingTranscriptomics`` class is the core class of the entire package and allows you to run the entire analysis on your data.
+To start using the class the first step is to initialise it. A way to do this is:
+
+.. code:: python
+
+    my_analysis = imt.ImagingTranscriptomics(my_data, n_components=1)
+
+The ``my_data`` is an array that contains the data you want to analyse (e.g., the average values from the scan). This vector has to have some characteristics, mainly:
+* it has to be a ``numpy.array`` with length 41, which corresponds to the number of regions in the left hemisphere of the Desikan-Killiany atlas.
+* it has to contain the values you want to analyse but not the ``zscore`` of the values as this is computed automatically during the initialisation.
+
+When initialising the class one might choose to initialise it using the ``n_components`` or the ``var`` attribute which represent the number of components to use for the regression or the variance to extract respectively.
+After setting one, and only one, of the values are set, one can estiamte the other by running the ``my_analysis.pls_all_components()`` method which will compute the PLS regression with 15 components and estimate the variance explained by the user defined

docs/chapters/07_faq.rst

@@ -1,8 +1,8 @@
 .. _faq:
 
-==========================
-Frequently asked questions
-==========================
+===
+FAQ
+===
 
 
 

imaging_transcriptomics/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.0.6"
+__version__ = "1.0.0"
 
 from . import errors
 from . import bootstrap

imaging_transcriptomics/inputs.py

@@ -53,7 +53,7 @@ def extract_average(imaging_matrix):
     """
     n_regions = 41
     logger.debug("Extracting average from scan.")
-    atlas_data = nib.load(Path(__file__).resolve().parent.parent / "data" /
+    atlas_data = nib.load(Path(__file__).resolve().parent / "data" /
                           "atlas-desikankilliany_1mm_MNI152.nii.gz").get_fdata()
     data = np.zeros(n_regions)
     for i in range(1, n_regions + 1):
@@ -89,7 +89,7 @@ def load_gene_expression():
     :return: numpy array with the gene expression data.
     """
     logger.debug("Loading gene_expression data.")
-    expression_file_path = Path(__file__).resolve().parent.parent / "data" / "gene_expression_data.csv"
+    expression_file_path = Path(__file__).resolve().parent / "data" / "gene_expression_data.csv"
     expression_data = pd.read_csv(expression_file_path, sep=',')
     my_data_x = expression_data.iloc[0:41, 2:].to_numpy()
     return zscore(my_data_x, ddof=1)
@@ -102,5 +102,5 @@ def load_gene_labels():
     :return: numpy array with the labels of the genes.
     """
     logger.debug("Loading gene labels.")
-    genes_labels_path = Path(__file__).resolve().parent.parent / "data" / "gene_expression_labels.txt"
+    genes_labels_path = Path(__file__).resolve().parent / "data" / "gene_expression_labels.txt"
     return pd.read_fwf(genes_labels_path, header=None).to_numpy()

imaging_transcriptomics/transcriptomics.py

@@ -132,8 +132,8 @@ def permute_data(self, iterations=1_000):
         self.permuted[34:, :] = sub_permuted
         # Cortical
         # Annotation file for the Desikan-Killiany atlas in fs5
-        annot_lh = Path(__file__).resolve().parent.parent / "data/fsa5_lh_aparc.annot"
-        annot_rh = Path(__file__).resolve().parent.parent / "data/fsa5_rh_aparc.annot"
+        annot_lh = Path(__file__).resolve().parent / "data/fsa5_lh_aparc.annot"
+        annot_rh = Path(__file__).resolve().parent / "data/fsa5_rh_aparc.annot"
         # Get the parcel centroids of the Desikan-Killiany atlas
         parcel_centroids, parcel_hemi = freesurfer.find_parcel_centroids(
             lhannot=annot_lh,

requirements.txt

@@ -12,3 +12,4 @@ scikit-learn>=0.24.2
 joblib>=1.0.1
 pyyaml>=5.4.1
 netneurotools>=0.1.0
+fpdf2>=2.0.0

setup.py

@@ -55,8 +55,8 @@ def get_requirements():
                    ],
       keywords="Image analysis, Neuroimaging, Imaging Transcriptomics, Medical Imaging, Research, Multimodal Imaging",
       install_requires=get_requirements(),
-      packages=find_packages(),
+      packages=["imaging_transcriptomics"],
       include_package_data=True,
-      package_data={"": ["*.yaml"]},
+      package_data={"imaging_transcriptomics": ["*.yaml", "*.nii.gz", "*.csv", "*.txt", "*.annot"]},
       scripts=glob("script/imagingtranscriptomics")
       )