From 3449af9d910f57a876c83671c3b8c5d9e32313bd Mon Sep 17 00:00:00 2001
From: Leonardo Schwarz <leonardo.schwarz@fgcz.ethz.ch>
Date: Fri, 9 Aug 2024 15:34:50 +0200
Subject: [PATCH] Deployed 4a58a96 with MkDocs version: 1.6.0

---
 index.html               | 42 +++++++++++++++++++++++++++++++++++++++-
 search/search_index.json |  2 +-
 2 files changed, 42 insertions(+), 2 deletions(-)

diff --git a/index.html b/index.html
index ea9e844f..e7bbc799 100644
--- a/index.html
+++ b/index.html
@@ -81,6 +81,10 @@
             
             <li class="nav-item" data-bs-level="1"><a href="#home" class="nav-link">Home</a>
               <ul class="nav flex-column">
+            <li class="nav-item" data-bs-level="2"><a href="#installation" class="nav-link">Installation</a>
+              <ul class="nav flex-column">
+              </ul>
+            </li>
             <li class="nav-item" data-bs-level="2"><a href="#configuration" class="nav-link">Configuration</a>
               <ul class="nav flex-column">
               </ul>
@@ -96,6 +100,42 @@ <h1 id="home">Home</h1>
 <p>This package connects the <a href="https://fgcz-bfabric.uzh.ch/bfabric/">bfabric</a> system to the <a href="https://www.python.org/">python</a> and <a href="https://cran.r-project.org/">R</a> world while providing a JSON and REST interface using <a href="https://www.fullstackpython.com">Flask</a>.
 The <a href="https://github.com/cpanse/bfabricShiny">bfabricShiny</a> R package is an extension and provides code snippets and sample implementation for a seamless R shiny bfabric integration.
 For more advanced users the <em>bfabricPy</em> package also provides a powerful query interface on the command-line though using the provided scripts.</p>
+<h2 id="installation">Installation</h2>
+<p>The package can be installed like any other Python package, so if you are familiar you might not need to read this section.
+Currently, it's only available from GitHub.</p>
+<p>The best way to install the package depends on your use case, i.e. whether you want to:</p>
+<ol>
+<li>Use the command line scripts</li>
+<li>Use the Python API</li>
+<li>Develop on the package</li>
+</ol>
+<p>The command line scripts are currently included in all cases.</p>
+<h3 id="command-line-scripts">Command line scripts</h3>
+<p>To use the command line scripts, it's recommended to install <code>bfabricPy</code> with <a href="https://pipx.pypa.io/">pipx</a>.
+If you don't have <code>pipx</code> installed, refer to the <a href="https://pipx.pypa.io/stable/installation/">pipx documentation</a> for instructions.</p>
+<p>You can execute a command using a specific version of <code>bfabricPy</code> with the <code>pipx run</code> command.
+This command handles the dependencies of multiple concurrent installations:</p>
+<pre><code class="language-bash">pipx run --spec &quot;git+https://github.com/fgcz/bfabricPy.git@stable&quot; bfabric_read.py --help
+</code></pre>
+<p>To install a specific version of bfabricPy on your system and make the command available without <code>pipx run</code> prefix, use the following command:</p>
+<pre><code class="language-bash">pipx install &quot;git+https://github.com/fgcz/bfabricPy.git@stable&quot;
+bfabric_read.py --help
+</code></pre>
+<h3 id="python-api">Python API</h3>
+<p>If you're interested in using the Python API of <code>bfabricPy</code>, you have two options:</p>
+<h4 id="1-configure-it-in-your-pyprojecttoml-file">1. Configure it in your <code>pyproject.toml</code> file.</h4>
+<pre><code class="language-toml">[project]
+dependencies = [
+    &quot;bfabricPy @ git+https://github.com/fgcz/bfabricPy.git@stable&quot;
+]
+</code></pre>
+<h4 id="2-install-the-bfabricpy-package-directly-using-pip">2. Install the <code>bfabricPy</code> package directly using pip.</h4>
+<pre><code class="language-bash">pip install git+https://github.com/fgcz/bfabricPy.git
+</code></pre>
+<h3 id="development">Development</h3>
+<p>As a bfabricPy developer: (i.e. an editable install)</p>
+<pre><code>pip install -e &quot;.[dev]&quot;
+</code></pre>
 <h2 id="configuration">Configuration</h2>
 <p>Create a file as follows: (note: the password is not your login password, but the web service password)</p>
 <pre><code># ~/.bfabricpy.yml
@@ -195,5 +235,5 @@ <h4 class="modal-title" id="keyboardModalLabel">Keyboard Shortcuts</h4>
 
 <!--
 MkDocs version : 1.6.0
-Build Date UTC : 2024-08-09 13:31:08.209489+00:00
+Build Date UTC : 2024-08-09 13:34:50.206237+00:00
 -->
diff --git a/search/search_index.json b/search/search_index.json
index 2ee8e6ee..2706c9d5 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
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The old verbose version information is now always logged, to INFO level, since it could entail useful information for error reporting. [1.13.3] - 2024-07-18 Added Flask New endpoint GET /config/remote_base_url for testing Changed Flask Simplify logging by using loguru only. Simplified setup logic since the production use case should use a WSGI server. Fixed bfabric_save_csv2dataset.py had an undeclared dependency on numpy and a few bugs which was improved. [1.13.2] - 2024-07-11 Added Add bfabric.entities.Dataset to easily read datasets. Pydantic-based configuration parsing The config format did not change. The code is easier to maintain now. Additionally, there is a lot more validation of the configuration file now, that should catch errors early. Make host and port configurable in bfabric_flask.py (currently only dev mode). 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\ No newline at end of file
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Bfabric.save provides method which can be set to checkandinsert for specific use cases.","title":"Added"},{"location":"changelog/#changed","text":"Most messages are now logged to debug level. The old verbose version information is now always logged, to INFO level, since it could entail useful information for error reporting.","title":"Changed"},{"location":"changelog/#1133-2024-07-18","text":"","title":"[1.13.3] - 2024-07-18"},{"location":"changelog/#added_2","text":"Flask New endpoint GET /config/remote_base_url for testing","title":"Added"},{"location":"changelog/#changed_1","text":"Flask Simplify logging by using loguru only. Simplified setup logic since the production use case should use a WSGI server.","title":"Changed"},{"location":"changelog/#fixed","text":"bfabric_save_csv2dataset.py had an undeclared dependency on numpy and a few bugs which was improved.","title":"Fixed"},{"location":"changelog/#1132-2024-07-11","text":"","title":"[1.13.2] - 2024-07-11"},{"location":"changelog/#added_3","text":"Add bfabric.entities.Dataset to easily read datasets. Pydantic-based configuration parsing The config format did not change. The code is easier to maintain now. Additionally, there is a lot more validation of the configuration file now, that should catch errors early. Make host and port configurable in bfabric_flask.py (currently only dev mode).","title":"Added"},{"location":"changelog/#1131-2024-07-02","text":"","title":"[1.13.1] - 2024-07-02"},{"location":"changelog/#changed_2","text":"bfabric_save_csv2dataset will raise an error if problematic characters are found in any of the cells Correctly define bfabric_setWorkunitStatus_available.py , and processing and failed variants.","title":"Changed"},{"location":"changelog/#added_4","text":"Add loguru for future logging refactoring. Easily runnable tests with nox and standardized formatting using pre-commit .","title":"Added"},{"location":"changelog/#removed","text":"Pandas is no longer a dependency, and has been replaced by polars.","title":"Removed"},{"location":"changelog/#1130-2024-05-24","text":"This is a major release refactoring bfabricPy's API.","title":"[1.13.0] - 2024-05-24"},{"location":"changelog/#changed_3","text":"The Bfabric class operations now return ResultContainer objects. These provide a list-like interface to access individual items or iterate over them. Individual items are a dictionary, potentially nested, and not specific to suds/zeep anymore. Convenience conversions, e.g. to a polars DataFrame, can be provided there. Configuration is now defined in ~/.bfabricpy.yml and supports multiple configurations, which can be selected by the BFABRICPY_CONFIG_ENV environment variable. Please consult the README for an example configuration. Use pyproject.toml for package configuration. Scripts have been refactored on a case-by-case basis.","title":"Changed"},{"location":"changelog/#added_5","text":"Zeep can be used instead of suds for SOAP communication. Bfabric can be instantiated without authentication, that can be provided later. This is useful in a server setup. Pagination support in Bfabric , specify the number of max_results and a potential offset. Pages handling is abstracted away. Detect errors in responses, e.g. invalid login.","title":"Added"},{"location":"changelog/#removed_1","text":"Several old scripts have been moved into a deprecated_scripts folder. Wrapper creator related code is currently not updated but has been extracted into a dedicated folder wrapper_creator as well.","title":"Removed"},{"location":"entities/","text":"High-level bfabric.entities API The bfabric.entities module offers a high-level, read-only API for retrieval of entities in the B-Fabric system. This API is designed to simplify entity access by providing lazy-loading capabilities for associated entities. Entities within this module can maintain a reference to a B-Fabric client instance, enabling seamless integration and data retrieval. Key Features Read-Only Access : This module is strictly for read-only operations. None of the classes or methods should be used to modify the database. Lazy-Loading : Entities are initialized with basic data as returned by a .read operation on its endpoint. However, associated entities are not loaded until explicitly requested, enhancing performance and reducing unnecessary data processing. Modular Design : Only include generic functionality within this module. Avoid adding highly specific logic to ensure the module remains maintainable over time. No Circular Imports : Ensure there are no circular dependencies between entities by deferring imports of other entities inside modules until all relevant modules are processed. Entity Each entity has to provide the name of its B-Fabric of its class name as ENDPOINT . An entity is defined uniquely by its classname and id . Entities are initialized with the full result structure obtained from a read operation on its particular endpoint. A return_id_only result is generally not supposed to be used to initialize an entity instance and behavior is undefined as of now. In cases where it makes sense to operate on the ids, it's better to directly call the Bfabric client methods. Entities can be retrieved with the methods Entity.find and Entity.find_all , whenever loading multiple entities of the same type the latter should be preferred since it reduces the number of API calls and returns a dictionary mapping entity ID to instance. Relationships Custom descriptors are available which are used to represent relationships and enable lazy-loading of data. Currently the following descriptors are available: HasOne : Defines a one-to-one relationship between entities. HasMany : Defines a one-to-many relationship between entities. Both support setting an additional argument optional=True which will ensure None (in the case of HasOne ) and an empty collection (in the case of HasMany ) are returned when there is no field at all in the result returned by the API.","title":"Entities"},{"location":"entities/#high-level-bfabricentities-api","text":"The bfabric.entities module offers a high-level, read-only API for retrieval of entities in the B-Fabric system. This API is designed to simplify entity access by providing lazy-loading capabilities for associated entities. Entities within this module can maintain a reference to a B-Fabric client instance, enabling seamless integration and data retrieval.","title":"High-level bfabric.entities API"},{"location":"entities/#key-features","text":"Read-Only Access : This module is strictly for read-only operations. None of the classes or methods should be used to modify the database. Lazy-Loading : Entities are initialized with basic data as returned by a .read operation on its endpoint. However, associated entities are not loaded until explicitly requested, enhancing performance and reducing unnecessary data processing. Modular Design : Only include generic functionality within this module. Avoid adding highly specific logic to ensure the module remains maintainable over time. No Circular Imports : Ensure there are no circular dependencies between entities by deferring imports of other entities inside modules until all relevant modules are processed.","title":"Key Features"},{"location":"entities/#entity","text":"Each entity has to provide the name of its B-Fabric of its class name as ENDPOINT . An entity is defined uniquely by its classname and id . Entities are initialized with the full result structure obtained from a read operation on its particular endpoint. A return_id_only result is generally not supposed to be used to initialize an entity instance and behavior is undefined as of now. In cases where it makes sense to operate on the ids, it's better to directly call the Bfabric client methods. Entities can be retrieved with the methods Entity.find and Entity.find_all , whenever loading multiple entities of the same type the latter should be preferred since it reduces the number of API calls and returns a dictionary mapping entity ID to instance.","title":"Entity"},{"location":"entities/#relationships","text":"Custom descriptors are available which are used to represent relationships and enable lazy-loading of data. Currently the following descriptors are available: HasOne : Defines a one-to-one relationship between entities. HasMany : Defines a one-to-many relationship between entities. Both support setting an additional argument optional=True which will ensure None (in the case of HasOne ) and an empty collection (in the case of HasMany ) are returned when there is no field at all in the result returned by the API.","title":"Relationships"}]}
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