Reset pydanticv2 changes (#44)

* added docs features * refactored pairwise aligner * refactored pairwise alignment * added docstrings, refactored alinger integration * added threads to os.cpu_count() * refactored alignment * modified imports * tested alignments and networks * moved test notebooks * removed old example * added network visualization in 2D and 3D * import * added network documentation
PyEED · Feb 23, 2024 · b39bc6e · b39bc6e
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diff --git a/docs/examples/networks.md b/docs/examples/networks.md
diff --git a/docs/examples/setup_local_blast.md b/docs/examples/setup_local_blast.md
diff --git a/docs/installation/docker.md b/docs/installation/docker.md
diff --git a/docs/installation/pip.md → docs/installation/install_pyeed.md b/docs/installation/pip.md → docs/installation/install_pyeed.md
@@ -1,7 +1,3 @@
----
-icon: simple/python
----
-
 # Installation via `pip`
 
 PyEED can be installed from Github via pip.
@@ -13,3 +9,7 @@ Alternatively, you can install the latest stable release from PyPI.
 ```bash
 ```
 
+
+
+# Installation with Docker
+
diff --git a/docs/installation/setup_local_blast.md b/docs/installation/setup_local_blast.md
@@ -0,0 +1,6 @@
+# Setup a Local BLAST Database
+
+This tutorial will guide you through the process of setting up a local BLAST database. This is useful if you have a large number of sequences that you need to search against, or if you want to search against a custom database.
+
+1. Hi
+2. Hello
diff --git a/docs/examples/alignments.md → docs/quick_start/alignments.md b/docs/examples/alignments.md → docs/quick_start/alignments.md
@@ -54,13 +54,20 @@ alignment = Alignment.from_sequences(list_of_sequences)
     tem109 = ProteinInfo.from_ncbi("AAT46413.1")
 
     # Create and run alignment
-    alignment = PairwiseAlignment([tem1, tem109], aligner=PairwiseAligner)
+    alignment = PairwiseAlignment([tem1, tem109], aligner=PairwiseAligner, mode="local")
     ```
 
 === "Global Alignment"
     ``` py
     from pyeed.core import ProteinInfo, PairwiseAlignment
-    from pyeed.aligners import NeedlemanWunsch
+    from pyeed.aligners import PairwiseAligner
+
+    # Get two ProteinInfo objects
+    tem1 = ProteinInfo.from_ncbi("QGC48744.1")
+    tem109 = ProteinInfo.from_ncbi("AAT46413.1")
+
+    # Create and run alignment
+    alignment = PairwiseAlignment([tem1, tem109], aligner=PairwiseAligner, mode="global")
     ```
 
 

diff --git a/docs/examples/basics.md → docs/quick_start/basics.md b/docs/examples/basics.md → docs/quick_start/basics.md
diff --git a/docs/examples/blast.md → docs/quick_start/blast.md b/docs/examples/blast.md → docs/quick_start/blast.md
diff --git a/docs/examples/clustering.md → docs/quick_start/clustering.md b/docs/examples/clustering.md → docs/quick_start/clustering.md
diff --git a/docs/quick_start/index.md b/docs/quick_start/index.md
@@ -0,0 +1,20 @@
+---
+hide:
+  - navigation
+  - footer
+---
+
+# Quick Start
+
+<div class="grid cards" markdown>
+
+- :material-walk: __[Basics]__ – How to work with sequence data
+- :octicons-search-16: __[Search Sequences]__ – How to search for individual sequences or search using BLAST
+- :fontawesome-solid-align-justify: __[Alignments]__ – How to make different alignments
+- :material-dots-grid: __[Networks]__ – How to construct sequence networks
+
+</div>
+  [Basics]: basics.md
+  [Search Sequences]: blast.md
+  [Alignments]: alignments.md
+  [Networks]: networks.md
diff --git a/docs/quick_start/networks.md b/docs/quick_start/networks.md
@@ -0,0 +1,83 @@
+# Creating Sequence Networks
+
+A `SequenceNetwork` is created using a list of `PairwiseAlignment` objects, and a list of `AbstractSequences`. Additionally, the way the network is constructed is influenced by the `weight` and `threshold` attributes. The `weight` determines which attribute of the `PairwiseAlignment` object is used to calculate the distance between the sequences of the network. The `threshold` determines the minimum value of the `weight` attribute for an edge to be created. Furthermore, a `color` can be determined based on the attributes of an `AbstractSequence` object in which the nodes of the network will be colored.
+
+## Visualization
+
+=== "2D"
+
+    ```py
+    from pyeed.core import ProteinInfo, Alignment
+    from pyeed.aligners import PairwiseAligner
+    from pyeed.network import SequenceNetwork
+
+    # Accessions from different methionine adenyltransferases
+    mat_accessions = [
+        "MBP1912539.1",
+        "SEV92896.1",
+        "MBO8174569.1",
+        "WP_042680787.1",
+        "NPA47376.1",
+        "WP_167889085.1",
+        "WP_048165429.1",
+        "ACS90033.1",
+    ]
+    mats = ProteinInfo.from_ncbi(mat_accessions)
+
+    # Create pairwise alignments between all sequences
+    alignments = Alignment.from_sequences(mats, aligner=PairwiseAligner)
+
+    # Create a network
+    network = SequenceNetwork(
+        sequences=mats,
+        pairwise_alignments=alignments,
+        weight="identity",
+        threshold=0.9,
+        dimensions=2,
+        color="taxonomy_id",
+    )
+
+    # Visualize the network
+    network.visualize()
+    ```
+
+=== "3D"
+
+    ```py
+    from pyeed.core import ProteinInfo, Alignment
+    from pyeed.aligners import PairwiseAligner
+    from pyeed.network import SequenceNetwork
+
+    # Accessions from different methionine adenyltransferases
+    mat_accessions = [
+        "MBP1912539.1",
+        "SEV92896.1",
+        "MBO8174569.1",
+        "WP_042680787.1",
+        "NPA47376.1",
+        "WP_167889085.1",
+        "WP_048165429.1",
+        "ACS90033.1",
+    ]
+    mats = ProteinInfo.from_ncbi(mat_accessions)
+
+    # Create pairwise alignments between all sequences
+    alignments = Alignment.from_sequences(mats, aligner=PairwiseAligner)
+
+    # Create a network
+    network = SequenceNetwork(
+        sequences=mats,
+        pairwise_alignments=alignments,
+        weight="identity",
+        threshold=0.9,
+        dimensions=3,
+        color="taxonomy_id",
+    )
+
+    # Visualize the network
+    network.visualize()
+    ```
+
+## Network Analysis
+
+Upon the `SequenceNetwork` is instantiated the `graph` property is created. This property is a `networkx` graph object that can be used to perform network analysis. For example, the `degree()` method can be used to calculate the degree of each node in the network.