Multiple edits needed

PlotNeuralNet/PyCore/Blocks.py

@@ -6,8 +6,8 @@ def Block2ConvPool(
     name,
     botton,
     top,
-    sFiler=256,
-    nFiler=64,
+    sFilter=256,
+    nFilter=64,
     offset="(1,0,0)",
     size=(32, 32, 3.5),
     opacity=0.5,
@@ -23,9 +23,9 @@ def Block2ConvPool(
         The node from which the block starts.
     top : str
         The node where the block ends.
-    sFiler : int, optional
+    sFilter : int, optional
         Size of the filter, by default 256.
-    nFiler : int, optional
+    nFilter : int, optional
         Number of filters, by default 64.
     offset : str, optional
         Position offset, by default "(1,0,0)".
@@ -42,8 +42,8 @@ def Block2ConvPool(
     return [
         ToConvConvRelu(
             name=f"ccr_{name}",
-            sFiler=str(sFiler),
-            nFiler=(nFiler, nFiler),
+            sFilter=str(sFilter),
+            nFilter=(nFilter, nFilter),
             offset=offset,
             to=f"({botton}-east)",
             width=(size[2], size[2]),
@@ -67,8 +67,8 @@ def BlockUnconv(
     name,
     botton,
     top,
-    sFiler=256,
-    nFiler=64,
+    sFilter=256,
+    nFilter=64,
     offset="(1,0,0)",
     size=(32, 32, 3.5),
     opacity=0.5,
@@ -84,9 +84,9 @@ def BlockUnconv(
         The node from which the block starts.
     top : str
         The node where the block ends.
-    sFiler : int, optional
+    sFilter : int, optional
         Size of the filter, by default 256.
-    nFiler : int, optional
+    nFilter : int, optional
         Number of filters, by default 64.
     offset : str, optional
         Position offset, by default "(1,0,0)".
@@ -114,8 +114,8 @@ def BlockUnconv(
             name=f"ccr_res_{name}",
             offset="(0,0,0)",
             to=f"(unpool_{name}-east)",
-            sFiler=str(sFiler),
-            nFiler=str(nFiler),
+            sFilter=str(sFilter),
+            nFilter=str(nFilter),
             width=size[2],
             height=size[0],
             depth=size[1],
@@ -125,8 +125,8 @@ def BlockUnconv(
             name=f"ccr_{name}",
             offset="(0,0,0)",
             to=f"(ccr_res_{name}-east)",
-            sFiler=str(sFiler),
-            nFiler=str(nFiler),
+            sFilter=str(sFilter),
+            nFilter=str(nFilter),
             width=size[2],
             height=size[0],
             depth=size[1],
@@ -135,8 +135,8 @@ def BlockUnconv(
             name=f"ccr_res_c_{name}",
             offset="(0,0,0)",
             to=f"(ccr_{name}-east)",
-            sFiler=str(sFiler),
-            nFiler=str(nFiler),
+            sFilter=str(sFilter),
+            nFilter=str(nFilter),
             width=size[2],
             height=size[0],
             depth=size[1],
@@ -146,8 +146,8 @@ def BlockUnconv(
             name=f"{top}",
             offset="(0,0,0)",
             to=f"(ccr_res_c_{name}-east)",
-            sFiler=str(sFiler),
-            nFiler=str(nFiler),
+            sFilter=str(sFilter),
+            nFilter=str(nFilter),
             width=size[2],
             height=size[0],
             depth=size[1],
@@ -161,8 +161,8 @@ def BlockRes(
     name,
     botton,
     top,
-    sFiler=256,
-    nFiler=64,
+    sFilter=256,
+    nFilter=64,
     offset="(0,0,0)",
     size=(32, 32, 3.5),
     opacity=0.5,
@@ -180,9 +180,9 @@ def BlockRes(
         The node from which the block starts.
     top : str
         The node where the block ends.
-    sFiler : int, optional
+    sFilter : int, optional
         Size of the filter, by default 256.
-    nFiler : int, optional
+    nFilter : int, optional
         Number of filters, by default 64.
     offset : str, optional
         Position offset, by default "(0,0,0)".
@@ -204,8 +204,8 @@ def BlockRes(
                 name=f"{layerName}",
                 offset=offset,
                 to=f"({botton}-east)",
-                sFiler=str(sFiler),
-                nFiler=str(nFiler),
+                sFilter=str(sFilter),
+                nFilter=str(nFilter),
                 width=size[2],
                 height=size[0],
                 depth=size[1],

README.md

@@ -22,17 +22,20 @@ This package is based on the original **[PlotNeuralNet by HarisIqbal88](https://
 ### **Installation**
 
 1. Clone the repository:
+
    ```bash
-   git clone https://github.com/<your-username>/PlotNeuralNet.git
+   git clone https://github.com/kgruiz/PlotNeuralNet.git
    cd PlotNeuralNet
    ```
 
 2. Install the package:
+
    ```bash
    pip install .
    ```
 
 3. Verify the installation:
+
    ```python
    import PlotNeuralNet
    print("PlotNeuralNet installed successfully!")
@@ -50,45 +53,50 @@ The package is organized to simplify the creation of diagrams. It includes Pytho
 You can use the Python API to define your architecture programmatically. For example:
 
 ```python
-from PlotNeuralNet.pycore import tikzeng
-from PlotNeuralNet.pycore.blocks import block_2ConvPool, block_Unconv
+from PlotNeuralNet.PyCore import TikzGen as tikzeng
+from PlotNeuralNet.PyCore.Blocks import Block2ConvPool, BlockUnconv
 
 # Define architecture
 arch = [
-    tikzeng.to_head('..'),
-    tikzeng.to_cor(),
-    tikzeng.to_begin(),
-
+    tikzeng.ToHead(".."),
+    tikzeng.ToCor(),
+    tikzeng.ToBegin(),
     # Input image
-    tikzeng.to_input('../examples/fcn8s/cats.jpg'),
-
+    tikzeng.ToInput('./PlotNeuralNet/examples/fcn8s/cats.jpg'),
     # Encoder
-    *block_2ConvPool(name='b1', botton='input', top='b2', s_filer=256, n_filer=64),
-    *block_2ConvPool(name='b2', botton='b2', top='b3', s_filer=128, n_filer=128),
-
+    *Block2ConvPool(name="b1", botton="input", top="b2", sFilter=256, nFilter=64),
+    *Block2ConvPool(name="b2", botton="b2", top="b3", sFilter=128, nFilter=128),
     # Decoder
-    *block_Unconv(name='b4', botton='b3', top='output', s_filer=64, n_filer=32),
-
+    *BlockUnconv(name="b4", botton="b3", top="output", sFilter=64, nFilter=32),
     # Output layer
-    tikzeng.to_ConvSoftMax(name='softmax', offset="(1,0,0)", to="(output-east)", width=1, height=30, depth=30),
-    tikzeng.to_end(),
+    tikzeng.ToConvSoftMax(
+        name="softmax",
+        offset="(1,0,0)",
+        to="(output-east)",
+        width=1,
+        height=30,
+        depth=30,
+    ),
+    tikzeng.ToEnd(),
 ]
 
 # Generate the architecture diagram
 def main():
-    tikzeng.to_generate(arch, "my_architecture.tex")
+    tikzeng.ToGenerate(arch, "my_architecture.tex")
 
 if __name__ == "__main__":
     main()
 ```
 
 #### **Compile and View the Diagram**
 Run the Python script:
+
 ```bash
 python my_architecture.py
 ```
 
 Compile the `.tex` file with:
+
 ```bash
 bash ../tikzmake.sh my_architecture
 ```
@@ -100,6 +108,7 @@ bash ../tikzmake.sh my_architecture
 You can directly modify `.tex` files in the `examples` directory, such as `examples/FCN-8` or `examples/HED`. Each `.tex` file demonstrates how to use LaTeX for defining architectures.
 
 To compile a `.tex` file, use:
+
 ```bash
 pdflatex <file>.tex
 ```
@@ -112,6 +121,7 @@ The package structure includes predefined resources for easy reuse:
 #### **LaTeX Resources**
 - Available in the `PlotNeuralNet/layers/` directory.
 - Example LaTeX layer definitions:
+
   ```latex
   \input{layers/Box.sty}
   ```
@@ -123,6 +133,7 @@ The package structure includes predefined resources for easy reuse:
 #### **Python Scripts**
 - Python examples for generating diagrams programmatically are in `PlotNeuralNet/pyexamples/`.
 - Example usage:
+
   ```bash
   python PlotNeuralNet/pyexamples/unet.py
   ```
@@ -172,4 +183,4 @@ This package is based on the original **[PlotNeuralNet by HarisIqbal88](https://
 
 ## **License**
 
-This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for more details.
+This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for more details.

pyproject.toml

@@ -7,7 +7,6 @@ name = "PlotNeuralNet"
 version = "0.1.0"
 description = "Visualize neural networks with a focus on easy-to-use diagrams."
 readme = "README.md"
-readme-content-type = "text/markdown"
 authors = [
     {name = "Kaden Gruizenga", email = "kgruiz@umich.edu"}
 ]
@@ -22,4 +21,4 @@ dependencies = []  # Add runtime dependencies if needed
 
 [project.urls]
 Source = "https://github.com/kgruiz/PlotNeuralNet"
-Original Work = "https://github.com/HarisIqbal88/PlotNeuralNet"
+"Original Work" = "https://github.com/HarisIqbal88/PlotNeuralNet"