test

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/Plot_Interps.iml

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+<?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/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="3">
+            <item index="0" class="java.lang.String" itemvalue="scipy" />
+            <item index="1" class="java.lang.String" itemvalue="pytictoc" />
+            <item index="2" class="java.lang.String" itemvalue="pickle" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (AutoEncoders) (2)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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

app.py

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+import numpy as np
+from scipy import signal
+import huggingface_hub  # for loading model
+import streamlit as st
+
+
+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
+
+
+########################################################################################################################
+# Provide the Options for users to select from
+shape_options = ("basic_box", "diagonal_box_split", "horizontal_vertical_box_split", "back_slash_box", "forward_slash_box",
+"back_slash_plus_box", "forward_slash_plus_box", "hot_dog_box", "hamburger_box", "x_hamburger_box",
+"x_hot_dog_box", "x_plus_box")
+density_options = ["{:.2f}".format(x) for x in np.linspace(0.1, 1, 10)]
+thickness_options = [str(int(x)) for x in np.linspace(0, 10, 11)]
+interpolation_options = [str(int(x)) for x in [3, 5, 10, 20]]
+
+# Select the Options
+shape_1 = st.selectbox("Please select a shape", shape_options)
+
+
+
+
+
+
+
+# Load the models from existing huggingface model
+# Load the encoder model
+encoder_model_boxes = huggingface_hub.from_pretrained_keras("cmudrc/2d-lattice-encoder")
+# Load the decoder model
+decoder_model_boxes = huggingface_hub.from_pretrained_keras("cmudrc/2d-lattice-decoder")
+
+
+