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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+2D_Lattice.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,185 @@
+# Created by .ignore support plugin (hsz.mobi)
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# 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/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+### VirtualEnv template
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+[Bb]in
+[Ii]nclude
+[Ll]ib
+[Ll]ib64
+[Ll]ocal
+[Ss]cripts
+pyvenv.cfg
+.venv
+pip-selfcheck.json
+
+### JetBrains template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+# idea folder, uncomment if you don't need it
+# .idea

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/.name

@@ -0,0 +1 @@
+Dataset_Generation_Functions.py

.idea/2D_Data_Generator.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (Transformer_Testing)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/2D_Data_Generator.iml" filepath="$PROJECT_DIR$/.idea/2D_Data_Generator.iml" />
+    </modules>
+  </component>
+</project>

.idea/shelf/Uncommitted_changes_before_Update_at_4_12_2023_10_18_AM_[Changes]/shelved.patch

@@ -0,0 +1,27 @@
+Index: Data_Generation/Dataset_Generation_Functions.py
+IDEA additional info:
+Subsystem: com.intellij.openapi.diff.impl.patch.BaseRevisionTextPatchEP
+<+>import numpy as np\r\nfrom Data_Generation.Piecewise_Box_Functions import basic_box_array, back_slash_array, forward_slash_array, hamburger_array, hot_dog_array\r\n\r\n# For Internal Testing\r\n# from Piecewise_Box_Functions import basic_box_array, back_slash_array, forward_slash_array, hamburger_array, hot_dog_array\r\nimport pandas as pd\r\nimport json\r\nimport matplotlib.pyplot as plt\r\nfrom json import JSONEncoder\r\n\r\n\r\n\r\n########################################################################################################################\r\n# Make the data using all the code in Shape_Generation_Functions.py\r\ndef make_boxes(image_size: int, densities: list[float]) -> list[tuple]:\r\n    \"\"\"\r\n    :param image_size: [int] - the pixel height and width of the generated arrays\r\n    :param densities: [list[float]] - of the desired pixel values to apply to active pixels - Recommend values (0,1]\r\n    :return: [list[tuple]] - [Array, Density, Thickness of each strut type]\r\n    \"\"\"\r\n\r\n    matrix = []\r\n\r\n    # Establish the maximum thickness for each type of strut\r\n    max_vert = int(np.ceil(1 / 2 * image_size) - 2)\r\n    max_diag = int(image_size - 3)\r\n    max_basic = int(np.ceil(1 / 2 * image_size) - 1)\r\n\r\n    # Adds different density values\r\n    for i in range(len(densities)):\r\n        for j in range(1, max_basic):  # basic box loop, always want a border\r\n            basic_box_thickness = j\r\n            array_1 = basic_box_array(image_size, basic_box_thickness)\r\n            if np.unique([array_1]).all() > 0:  # Checks if there is a solid figure\r\n                break\r\n\r\n            for k in range(0, max_vert):\r\n                hamburger_box_thickness = k\r\n                array_2 = hamburger_array(image_size, hamburger_box_thickness) + array_1\r\n                array_2 = np.array(array_2 > 0, dtype=int)  # Keep all values 0/1\r\n                if np.unique([array_2]).all() > 0:\r\n                    break\r\n\r\n                for l in range(0, max_vert):\r\n                    hot_dog_box_thickness = l\r\n                    array_3 = hot_dog_array(image_size, hot_dog_box_thickness) + array_2\r\n                    array_3 = np.array(array_3 > 0, dtype=int)\r\n                    if np.unique([array_3]).all() > 0:\r\n                        break\r\n\r\n                    for m in range(0, max_diag):\r\n                        forward_slash_box_thickness = m\r\n                        array_4 = forward_slash_array(image_size, forward_slash_box_thickness) + array_3\r\n                        array_4 = np.array(array_4 > 0, dtype=int)\r\n                        if np.unique([array_4]).all() > 0:\r\n                            break\r\n\r\n                        for n in range(0, max_diag):\r\n                            back_slash_box_thickness = n\r\n                            array_5 = back_slash_array(image_size, back_slash_box_thickness) + array_4\r\n                            array_5 = np.array(array_5 > 0, dtype=int)\r\n                            if np.unique([array_5]).all() > 0:\r\n                                break\r\n                            the_tuple = (array_5*densities[i], densities[i], basic_box_thickness,\r\n                                         forward_slash_box_thickness, back_slash_box_thickness,\r\n                                         hot_dog_box_thickness, hamburger_box_thickness)\r\n                            matrix.append(the_tuple)\r\n\r\n    return matrix\r\n\r\n\r\n########################################################################################################################\r\n# How to read the files\r\n'''\r\ndf = pd.read_csv('2D_Lattice.csv')\r\nprint(np.shape(df))\r\nrow = 1\r\nbox = df.iloc[row, 1]\r\narray = np.array(json.loads(box))\r\nplt.imshow(array, vmin=0, vmax=1)\r\nplt.show()\r\n'''\r\n
+Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP
+<+>UTF-8
+===================================================================
+diff --git a/Data_Generation/Dataset_Generation_Functions.py b/Data_Generation/Dataset_Generation_Functions.py
+--- a/Data_Generation/Dataset_Generation_Functions.py	(revision 73d009aa1257fa61975a6aaf139763f239908c6a)
++++ b/Data_Generation/Dataset_Generation_Functions.py	(date 1681258622233)
+@@ -9,14 +9,14 @@
+ from json import JSONEncoder
+ 
+ 
+-
+ ########################################################################################################################
+ # Make the data using all the code in Shape_Generation_Functions.py
+ def make_boxes(image_size: int, densities: list[float]) -> list[tuple]:
+     """
+     :param image_size: [int] - the pixel height and width of the generated arrays
+     :param densities: [list[float]] - of the desired pixel values to apply to active pixels - Recommend values (0,1]
+-    :return: [list[tuple]] - [Array, Density, Thickness of each strut type]
++    :return: [list[tuple]] - [Array, Density, Thickness of each strut type] this is all the defining information for
++    all the generated data.
+     """
+ 
+     matrix = []

.idea/shelf/Uncommitted_changes_before_Update_at_4_12_2023_10_18_AM__Changes_.xml

@@ -0,0 +1,9 @@
+<changelist name="Uncommitted_changes_before_Update_at_4_12_2023_10_18_AM_[Changes]" date="1681309102915" recycled="true" deleted="true">
+  <option name="PATH" value="$PROJECT_DIR$/.idea/shelf/Uncommitted_changes_before_Update_at_4_12_2023_10_18_AM_[Changes]/shelved.patch" />
+  <option name="DESCRIPTION" value="Uncommitted changes before Update at 4/12/2023 10:18 AM [Changes]" />
+  <binary>
+    <option name="BEFORE_PATH" value="Data_Plotting/2D_Lattice.csv" />
+    <option name="AFTER_PATH" />
+    <option name="SHELVED_PATH" />
+  </binary>
+</changelist>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

.idea/workspace.xml

@@ -0,0 +1,181 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
+    <list default="true" id="edb75e09-4781-41cb-bdaf-0c543336cafb" name="Changes" comment="Finalizing notes and type hints">
+      <change beforePath="$PROJECT_DIR$/.idea/.name" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/.name" afterDir="false" />
+    </list>
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="FileTemplateManagerImpl">
+    <option name="RECENT_TEMPLATES">
+      <list>
+        <option value="Python Script" />
+      </list>
+    </option>
+  </component>
+  <component name="Git.Settings">
+    <option name="PUSH_AUTO_UPDATE" value="true" />
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
+  </component>
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+  <component name="ProjectId" id="2Mn8XK1cdGiSsV9UEBWbPOPJgB7" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">{
+  &quot;keyToString&quot;: {
+    &quot;RunOnceActivity.OpenProjectViewOnStart&quot;: &quot;true&quot;,
+    &quot;RunOnceActivity.ShowReadmeOnStart&quot;: &quot;true&quot;
+  }
+}</component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
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+      <created>1678392024806</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1678392024806</updated>
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+    <task id="LOCAL-00001" summary="Added TSNE Plot">
+      <created>1678393478950</created>
+      <option name="number" value="00001" />
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+    <task id="LOCAL-00002" summary="Added TSNE Plot">
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+      <option name="project" value="LOCAL" />
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+    </task>
+    <task id="LOCAL-00003" summary="Need new libs to be considered in the app">
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+    </task>
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+      <option name="number" value="00010" />
+      <option name="presentableId" value="LOCAL-00010" />
+      <option name="project" value="LOCAL" />
+      <updated>1678715414287</updated>
+    </task>
+    <task id="LOCAL-00011" summary="Adding type hints to the functions in these scripts">
+      <created>1681243653443</created>
+      <option name="number" value="00011" />
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+    </task>
+    <task id="LOCAL-00012" summary="Adding type hints to the functions in these scripts">
+      <created>1681309005767</created>
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+    <task id="LOCAL-00013" summary="Adding type hints to the functions in these scripts">
+      <created>1681309096299</created>
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+    <task id="LOCAL-00014" summary="Addressing type hint error">
+      <created>1681309886030</created>
+      <option name="number" value="00014" />
+      <option name="presentableId" value="LOCAL-00014" />
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+      <created>1681342765997</created>
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+    <option name="localTasksCounter" value="17" />
+    <servers />
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+    <option name="TAB_STATES">
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+        <entry key="MAIN">
+          <value>
+            <State />
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+  <component name="VcsManagerConfiguration">
+    <MESSAGE value="Added TSNE Plot" />
+    <MESSAGE value="Need new libs to be considered in the app" />
+    <MESSAGE value="Updating T-SNE Plot" />
+    <MESSAGE value="Adding type hints to the functions in these scripts" />
+    <MESSAGE value="Addressing type hint error" />
+    <MESSAGE value="Finalizing notes and type hints" />
+    <option name="LAST_COMMIT_MESSAGE" value="Finalizing notes and type hints" />
+  </component>
+</project>

Data_Generation/Dataset_Generation_Functions.py

@@ -0,0 +1,82 @@
+import numpy as np
+from Data_Generation.Piecewise_Box_Functions import basic_box_array, back_slash_array, forward_slash_array, hamburger_array, hot_dog_array
+
+# For Internal Testing
+# from Piecewise_Box_Functions import basic_box_array, back_slash_array, forward_slash_array, hamburger_array, hot_dog_array
+import pandas as pd
+import json
+import matplotlib.pyplot as plt
+from json import JSONEncoder
+
+
+########################################################################################################################
+# Make the data using all the code in Shape_Generation_Functions.py
+def make_boxes(image_size: int, densities: list) -> list:
+    """
+    :param image_size: [int] - the pixel height and width of the generated arrays
+    :param densities: [list[float]] - of the desired pixel values to apply to active pixels - Recommend values (0,1]
+    :return: list[tuple] - [Array, Density, Thickness of each strut type] this is all the defining information for
+    all the generated data.
+    """
+
+    matrix = []
+
+    # Establish the maximum thickness for each type of strut
+    max_vert = int(np.ceil(1 / 2 * image_size) - 2)
+    max_diag = int(image_size - 3)
+    max_basic = int(np.ceil(1 / 2 * image_size) - 1)
+
+    # Adds different density values
+    for i in range(len(densities)):
+        for j in range(1, max_basic):  # basic box loop, always want a border
+            basic_box_thickness = j
+            array_1 = basic_box_array(image_size, basic_box_thickness)
+            if np.unique([array_1]).all() > 0:  # Checks if there is a solid figure
+                break
+
+            for k in range(0, max_vert):
+                hamburger_box_thickness = k
+                array_2 = hamburger_array(image_size, hamburger_box_thickness) + array_1
+                array_2 = np.array(array_2 > 0, dtype=int)  # Keep all values 0/1
+                if np.unique([array_2]).all() > 0:
+                    break
+
+                for l in range(0, max_vert):
+                    hot_dog_box_thickness = l
+                    array_3 = hot_dog_array(image_size, hot_dog_box_thickness) + array_2
+                    array_3 = np.array(array_3 > 0, dtype=int)
+                    if np.unique([array_3]).all() > 0:
+                        break
+
+                    for m in range(0, max_diag):
+                        forward_slash_box_thickness = m
+                        array_4 = forward_slash_array(image_size, forward_slash_box_thickness) + array_3
+                        array_4 = np.array(array_4 > 0, dtype=int)
+                        if np.unique([array_4]).all() > 0:
+                            break
+
+                        for n in range(0, max_diag):
+                            back_slash_box_thickness = n
+                            array_5 = back_slash_array(image_size, back_slash_box_thickness) + array_4
+                            array_5 = np.array(array_5 > 0, dtype=int)
+                            if np.unique([array_5]).all() > 0:
+                                break
+                            the_tuple = (array_5*densities[i], densities[i], basic_box_thickness,
+                                         forward_slash_box_thickness, back_slash_box_thickness,
+                                         hot_dog_box_thickness, hamburger_box_thickness)
+                            matrix.append(the_tuple)
+
+    return matrix
+
+
+########################################################################################################################
+# How to read the files
+'''
+df = pd.read_csv('2D_Lattice.csv')
+print(np.shape(df))
+row = 1
+box = df.iloc[row, 1]
+array = np.array(json.loads(box))
+plt.imshow(array, vmin=0, vmax=1)
+plt.show()
+'''

Data_Generation/Piecewise_Box_Functions.py

@@ -0,0 +1,101 @@
+import numpy as np
+from scipy import signal
+
+
+def basic_box_array(image_size: int, thickness: int) -> np.ndarray:
+    """
+    :param image_size: [int] - the size of the image that will be produced
+    :param thickness: [int] - the number of pixels to be activated surrounding the base shape
+    :return: [ndarray] - the output is a unit cell with outer pixels activated based on the desired thickness.
+    The activated pixels are 1 (white) and the deactivated pixels are 0 (black)
+    """
+    A = np.ones((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A[1:-1, 1:-1] = 0  # replaces all internal rows/columns with 0's
+    A = add_thickness(A, thickness)
+    return A
+
+
+def back_slash_array(image_size: int, thickness: int) -> np.ndarray:
+    """
+    :param image_size: [int] - the size of the image that will be produced
+    :param thickness: [int] - the number of pixels to be activated surrounding the base shape
+    :return: [ndarray] - the output is a unit cell with pixels activated along the downward diagonal based
+    on the desired thickness. The activated pixels are 1 (white) and the deactivated pixels are 0 (black)
+    """
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    np.fill_diagonal(A, 1)  # fills the diagonal with 1 values
+    A = add_thickness(A, thickness)
+    return A
+
+
+def forward_slash_array(image_size: int, thickness: int) -> np.ndarray:
+    """
+    :param image_size: [int] - the size of the image that will be produced
+    :param thickness: [int] - the number of pixels to be activated surrounding the base shape
+    :return: [ndarray] - the output is a unit cell with pixels activated along the upward diagonal based on the desired
+    thickness. The activated pixels are 1 (white) and the deactivated pixels are 0 (black)
+    """
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    np.fill_diagonal(np.fliplr(A), 1)  # Flips the array to then fill the diagonal the opposite direction
+    A = add_thickness(A, thickness)
+    return A
+
+
+def hot_dog_array(image_size: int, thickness: int) -> np.ndarray:
+    """
+    :param image_size: [int] - the size of the image that will be produced
+    :param thickness: [int] - the number of pixels to be activated surrounding the base shape
+    :return: [ndarray] - the output is a unit cell with outer pixel activated from the vertical center based on the
+    desired thickness. The activated pixels are 1 (white) and the deactivated pixels are 0 (black)
+    """
+    # Places pixels down the vertical axis to split the box
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A[:, np.floor((image_size - 1) / 2).astype(int)] = 1  # accounts for even and odd values of image_size
+    A[:, np.ceil((image_size - 1) / 2).astype(int)] = 1
+    A = add_thickness(A, thickness)
+    return A
+
+
+def hamburger_array(image_size: int, thickness: int) -> np.ndarray:
+    """
+    :param image_size: [int] - the size of the image that will be produced
+    :param thickness: [int] - the number of pixels to be activated surrounding the base shape
+    :return: [ndarray] - the output is a unit cell with outer pixel activated from the horizontal center based on the
+    desired thickness. The activated pixels are 1 (white) and the deactivated pixels are 0 (black)
+    """
+    # Places pixels across the horizontal axis to split the box
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A[np.floor((image_size - 1) / 2).astype(int), :] = 1  # accounts for even and odd values of image_size
+    A[np.ceil((image_size - 1) / 2).astype(int), :] = 1
+    A = add_thickness(A, thickness)
+    return A
+
+
+########################################################################################################################
+# The function to add thickness to struts in an array
+def add_thickness(array_original, thickness: int) -> np.ndarray:
+    """
+    :param array_original: [ndarray] - an array with thickness 1 of any shape type
+    :param thickness: [int] - the number of pixels to be activated surrounding the base shape
+    :return: [ndarray] - the output is a unit cell that has been convolved to expand the number of pixels activated
+    based on the desired thickness. The activated pixels are 1 (white) and the deactivated pixels are 0 (black)
+    """
+    A = array_original
+    if thickness == 0:  # want an array of all 0's for thickness = 0
+        A[A > 0] = 0
+    else:
+        filter_size = 2*thickness - 1 # the size of the filter needs to extend far enough to reach the base shape
+        filter = np.zeros((filter_size, filter_size))
+        filter[np.floor((filter_size - 1) / 2).astype(int), :] = filter[:, np.floor((filter_size - 1) / 2).astype(int)] =1
+        filter[np.ceil((filter_size - 1) / 2).astype(int), :] = filter[:, np.ceil((filter_size - 1) / 2).astype(int)] = 1
+        # The filter is made into a '+' shape using these functions
+        convolution = signal.convolve2d(A, filter, mode='same')
+        A = np.where(convolution <= 1, convolution, 1)
+    return A
+
+
+# The function to efficiently combine arrays in a list
+def combine_arrays(arrays):
+    output_array = np.sum(arrays, axis=0)  # Add the list of arrays
+    output_array = np.array(output_array > 0, dtype=int)  # Convert all values in array to 1
+    return output_array

Data_Plotting/Plot_TSNE.py

@@ -0,0 +1,93 @@
+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Latent Feature Cluster for Training Data using T-SNE
+def TSNE_reduction(latent_points: np.ndarray, perplexity=30, learning_rate=20):
+    """
+    :param latent_points: [ndarray] - an array of arrays that define the points of multiple objects in the latent space
+    :param perplexity: [int] - default perplexity = 30 " Perplexity balances the attention t-SNE gives to local and global aspects of the data. It is roughly a guess of the number of close neighbors each point has... a denser dataset ... requires higher perplexity value" Recommended: Perplexity(5-50)
+    :param learning_rate: [int] - default learning rate = 200 "If the learning rate is too high, the data may look like a ‘ball’ with any point approximately equidistant from its nearest neighbours. If the learning rate is too low, most points may look compressed in a dense cloud with few outliers." Recommended: learning_rate(10-1000)
+    :return: [tuple] - the output is the x and y coordinates for the reduced latent space, a title, and a TSNE embedding
+    """
+    model = TSNE(n_components=2, random_state=0, perplexity=perplexity,
+                 learning_rate=learning_rate)
+    # the number of components = dimension of the embedded space
+
+    embedding = model
+
+    tsne_data = model.fit_transform(latent_points)
+    # When there are more data points, only use a couple of hundred points so TSNE doesn't take too long
+    x = tsne_data[:, 0]
+    y = tsne_data[:, 1]
+    title = ("T-SNE of Data")
+    return x, y, title, embedding
+
+
+def plot_dimensionality_reduction(x: list, y: list, label_set: list, title: str):
+    """
+    :param x: [list] - the first set of coordinates for each latent point
+    :param y: [list] - the second set of coordinates for each latent point
+    :param label_set: [list] - a set of values that define the color of each point based on an additional quantitative attribute.
+    :return: matplotlib figure - the output is a matplotlib figure that displays all the points in a 2-dimensional latent space, based on the labels provided.
+    """
+    plt.title(title)
+    # Color points based on a continuous label
+    if label_set[0].dtype == float:
+        plt.scatter(x, y, c=label_set)
+        cbar = plt.colorbar()
+        cbar.set_label('Average Density', fontsize=12)
+        print("using scatter")
+
+    # Color points based on a discrete label
+    else:
+        for label in set(label_set):
+            cond = np.where(np.array(label_set) == str(label))
+            plt.plot(x[cond], y[cond], marker='o', linestyle='none', label=label)
+
+        plt.legend(numpoints=1)
+    plt.xlabel("Dimension 1")
+    plt.ylabel("Dimension 2")
+########################################################################################################################
+"""
+# Use for personal plotting
+
+import pandas as pd
+import json
+
+df = pd.read_csv('2D_Lattice.csv')
+# row = 0
+# box = df.iloc[row,1]
+# array = np.array(json.loads(box))
+
+# Select a subset of the data to use
+number_samples = 10000
+perplexity = 300
+
+random_samples = sorted(np.random.randint(0,len(df), number_samples))  # Generates ordered samples
+
+df = df.iloc[random_samples]
+
+print(df)
+print(np.shape(df))
+
+
+# For plotting CSV data
+# define a function to flatten a box
+def flatten_box(box_str):
+    box = json.loads(box_str)
+    return np.array(box).flatten()
+
+
+# apply the flatten_box function to each row of the dataframe and create a list of flattened arrays
+flattened_arrays = df['Array'].apply(flatten_box).tolist()
+avg_density = np.sum(flattened_arrays, axis=1)/(len(flattened_arrays[0]))
+
+x, y, title, embedding = TSNE_reduction(flattened_arrays, perplexity=perplexity)
+plot_dimensionality_reduction(x, y, avg_density, title)
+plt.title(title)
+plt.savefig('TSNE_Partial_Factorial_Perplexity_' + str(perplexity) + "_Data_Samples_" + str(number_samples))
+
+"""
+

README.md

@@ -0,0 +1,20 @@
+---
+title: AddLat2D
+emoji: 😻
+colorFrom: purple
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+---
+
+Use the following code to load items in the 2D_Lattice.csv file
+import pandas as pd
+import json
+df = pd.read_csv('2D_Lattice.csv')
+row = 0
+box = df.iloc[row,1]
+array = np.array(json.loads(box))
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,6 @@
+matplotlib==3.5.2
+numpy==1.21.5
+pandas==1.4.4
+scikit_learn==1.2.2
+scipy==1.9.1
+streamlit==1.18.1