Standalone_pkg1

pkgs/standards/swarmauri_standard/pyproject.toml

@@ -88,7 +88,7 @@ full = [
     #"transformers",
     #"torch",
     #"keras", "tf-keras",
-    "matplotlib"
+    #"matplotlib"
 ]
 
 [tool.poetry.group.dev.dependencies]

pkgs/standards/swarmauri_tool_matplotlib/pyproject.toml

@@ -0,0 +1,55 @@
+[tool.poetry]
+name = "swarmauri_tool_matplotlib"
+version = "0.6.0.dev1"
+description = "This repository includes an example of a First Class Swarmauri Example."
+authors = ["Jacob Stewart <[email protected]>"]
+license = "Apache-2.0"
+readme = "README.md"
+repository = "http://github.com/swarmauri/swarmauri-sdk"
+classifiers = [
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12"
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.13"
+
+# Swarmauri
+swarmauri_core = { path = "../../core" }
+swarmauri_base = { path = "../../base" }
+
+[tool.poetry.group.dev.dependencies]
+flake8 = "^7.0"
+pytest = "^8.0"
+pytest-asyncio = ">=0.24.0"
+pytest-xdist = "^3.6.1"
+pytest-json-report = "^1.5.0"
+python-dotenv = "*"
+requests = "^2.32.3"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.pytest.ini_options]
+norecursedirs = ["combined", "scripts"]
+
+markers = [
+    "test: standard test",
+    "unit: Unit tests",
+    "integration: Integration tests",
+    "acceptance: Acceptance tests",
+    "experimental: Experimental tests"
+]
+log_cli = true
+log_cli_level = "INFO"
+log_cli_format = "%(asctime)s [%(levelname)s] %(message)s"
+log_cli_date_format = "%Y-%m-%d %H:%M:%S"
+asyncio_default_fixture_loop_scope = "function"
+
+[tool.poetry.plugins."swarmauri.tools"]
+MatplotlibTool = "swarmauri_tool_matplotlib:MatplotlibTool"
+MatplotlibCsvTool = "swarmauri_tool_matplotlib:MatplotlibCsvTool"
+

pkgs/standards/swarmauri_tool_matplotlib/swarmauri_tool_matplotlib/MatplotlibCsvTool.py

@@ -0,0 +1,78 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import base64
+from typing import List, Literal, Dict
+from pydantic import Field
+from swarmauri_standard.tools.Parameter import Parameter
+from swarmauri_base.tools.ToolBase import ToolBase
+from swarmauri_core.ComponentBase import ComponentBase
+
+
+@ComponentBase.register_type(ToolBase, "MatplotlibCsvTool")
+class MatplotlibCsvTool(ToolBase):
+    type: Literal["MatplotlibCsvTool"] = "MatplotlibCsvTool"
+    name: str = Field(
+        "MatplotlibCsvTool",
+        description="Tool to generate plots from CSV data using Matplotlib.",
+    )
+    description: str = Field(
+        "This tool reads data from a CSV file and generates a plot using Matplotlib.",
+        description="Description of the MatplotlibCsvTool",
+    )
+
+    parameters: List[Parameter] = Field(
+        default_factory=lambda: [
+            Parameter(
+                name="csv_file",
+                type="string",
+                description="The path to the CSV file containing the data.",
+                required=True,
+            ),
+            Parameter(
+                name="x_column",
+                type="string",
+                description="The name of the column to use for the x-axis.",
+                required=True,
+            ),
+            Parameter(
+                name="y_column",
+                type="string",
+                description="The name of the column to use for the y-axis.",
+                required=True,
+            ),
+            Parameter(
+                name="output_file",
+                type="string",
+                description="The filename where the plot will be saved.",
+                required=True,
+            ),
+        ]
+    )
+
+    def __call__(
+        self, csv_file: str, x_column: str, y_column: str, output_file: str
+    ) -> Dict[str, str]:
+        # Read data from CSV
+        data = pd.read_csv(csv_file)
+
+        # Check if columns exist in the DataFrame
+        if x_column not in data.columns or y_column not in data.columns:
+            raise ValueError(
+                f"Columns {x_column} and/or {y_column} not found in the CSV file."
+            )
+
+        # Generate plot
+        plt.figure(figsize=(10, 6))
+        plt.plot(data[x_column], data[y_column], marker="o")
+        plt.xlabel(x_column)
+        plt.ylabel(y_column)
+        plt.title(f"{y_column} vs {x_column}")
+        plt.grid(True)
+        plt.savefig(output_file)
+        plt.close()
+        print(f"Plot generated and saved to {output_file}")
+        # Encode the plot image as base64
+        with open(output_file, "rb") as image_file:
+            encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
+
+        return {"img_path": output_file, "img_base64": encoded_image, "data": []}

pkgs/standards/swarmauri_tool_matplotlib/swarmauri_tool_matplotlib/MatplotlibTool.py

@@ -0,0 +1,120 @@
+import base64
+import matplotlib.pyplot as plt
+from typing import List, Literal
+from pydantic import Field
+from swarmauri_base.tools.ToolBase import ToolBase
+from swarmauri_standard.tools.Parameter import Parameter
+from swarmauri_core.ComponentBase import ComponentBase
+
+
+@ComponentBase.register_type(ToolBase, "MatplotlibTool")
+class MatplotlibTool(ToolBase):
+    version: str = "1.0.0"
+    name: str = "MatplotlibTool"
+    description: str = (
+        "Generates a plot using Matplotlib library based on provided configuration."
+    )
+    type: Literal["MatplotlibTool"] = "MatplotlibTool"
+
+    parameters: List[Parameter] = Field(
+        default_factory=lambda: [
+            Parameter(
+                name="plot_type",
+                type="string",
+                description="Type of plot to generate (e.g., 'line', 'bar', 'scatter').",
+                required=True,
+                enum=["line", "bar", "scatter"],
+            ),
+            Parameter(
+                name="x_data",
+                type="list<float>",
+                description="X-axis data for the plot.",
+                required=True,
+            ),
+            Parameter(
+                name="y_data",
+                type="list<float>",
+                description="Y-axis data for the plot.",
+                required=True,
+            ),
+            Parameter(
+                name="title",
+                type="string",
+                description="Title of the plot.",
+                required=False,
+                default="",
+            ),
+            Parameter(
+                name="x_label",
+                type="string",
+                description="Label for the X-axis.",
+                required=False,
+                default="",
+            ),
+            Parameter(
+                name="y_label",
+                type="string",
+                description="Label for the Y-axis.",
+                required=False,
+                default="",
+            ),
+            Parameter(
+                name="save_path",
+                type="string",
+                description="Path to save the generated plot image.",
+                required=False,
+                default="plot.png",
+            ),
+        ]
+    )
+
+    def __call__(
+        self,
+        plot_type: str,
+        x_data: List[float],
+        y_data: List[float],
+        title: str = "",
+        x_label: str = "",
+        y_label: str = "",
+        save_path: str = "plot.png",
+    ):
+        """
+        Generates a plot using Matplotlib based on provided configuration.
+
+        Parameters:
+            plot_type (str): The type of the plot ('line', 'bar', 'scatter').
+            x_data (List[float]): X-axis data for the plot.
+            y_data (List[float]): Y-axis data for the plot.
+            title (str): Title of the plot.
+            x_label (str): Label for the X-axis.
+            y_label (str): Label for the Y-axis.
+            save_path (str): Path to save the generated plot image.
+
+        Returns:
+            str: Path where the plot image is saved.
+        """
+        plt.figure()
+
+        if plot_type == "line":
+            plt.plot(x_data, y_data)
+        elif plot_type == "bar":
+            plt.bar(x_data, y_data)
+        elif plot_type == "scatter":
+            plt.scatter(x_data, y_data)
+        else:
+            raise ValueError(f"Unsupported plot type: {plot_type}")
+
+        if title:
+            plt.title(title)
+        if x_label:
+            plt.xlabel(x_label)
+        if y_label:
+            plt.ylabel(y_label)
+
+        plt.savefig(save_path)
+        plt.close()
+
+        with open(save_path, "rb") as image_file:
+            encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
+
+        return {"img_path": save_path, "img_base64": encoded_image, "data": []}

pkgs/standards/swarmauri_tool_matplotlib/swarmauri_tool_matplotlib/__init__.py

@@ -0,0 +1,17 @@
+from .MatplotlibCsvTool import MatplotlibCsvTool
+from .MatplotlibTool import MatplotlibTool
+__version__ = "0.6.0.dev26"
+__long_desc__ = """
+
+# Swarmauri Matplotlib Based Components
+
+Components Included:
+- MatplotlibTool
+- MatplotlibCsvTool
+
+Visit us at: https://swarmauri.com
+Follow us at: https://github.com/swarmauri
+Star us at: https://github.com/swarmauri/swarmauri-sdk
+
+"""
+

pkgs/standards/swarmauri_tool_matplotlib/tests/unit/MatplotlibCsvTool_unit_test.py

@@ -0,0 +1,84 @@
+import os
+from tempfile import NamedTemporaryFile
+
+import pytest
+from swarmauri_tool_matplotlib.MatplotlibCsvTool import MatplotlibCsvTool as Tool
+
+
+@pytest.mark.unit
+def test_ubc_resource():
+    tool = Tool()
+    assert tool.resource == "Tool"
+
+
+@pytest.mark.unit
+def test_ubc_type():
+    assert Tool().type == "MatplotlibCsvTool"
+
+
+@pytest.mark.unit
+def test_initialization():
+    tool = Tool()
+    assert type(tool.id) == str
+
+
+@pytest.mark.unit
+def test_serialization():
+    tool = Tool()
+    assert tool.id == Tool.model_validate_json(tool.model_dump_json()).id
+
+
+@pytest.mark.parametrize(
+    "csv_content, x_column, y_column, expected_error",
+    [
+        (
+            "x,y\n1,2\n3,4\n5,6",  # CSV content
+            "x",  # x_column
+            "y",  # y_column
+            None,  # No error expected
+        ),
+        (
+            "a,b\n1,2\n3,4\n5,6",  # CSV content
+            "x",  # x_column
+            "y",  # y_column
+            ValueError,  # Error expected due to missing columns
+        ),
+        (
+            "x,z\n1,2\n3,4\n5,6",  # CSV content
+            "x",  # x_column
+            "y",  # y_column
+            ValueError,  # Error expected due to missing y_column
+        ),
+    ],
+)
+@pytest.mark.unit
+def test_call(csv_content, x_column, y_column, expected_error):
+    with NamedTemporaryFile(delete=False, suffix=".csv") as csv_file:
+        csv_file.write(csv_content.encode())
+        csv_file_path = csv_file.name
+
+    with NamedTemporaryFile(delete=False, suffix=".png") as output_file:
+        output_file_path = output_file.name
+
+    tool = Tool()
+    expected_keys = {"img_path", "img_base64", "data"}
+
+    if expected_error:
+        with pytest.raises(expected_error):
+            tool(csv_file_path, x_column, y_column, output_file_path)
+    else:
+        result = tool(csv_file_path, x_column, y_column, output_file_path)
+        assert isinstance(
+            result, dict
+        ), f"Expected dict, but got {type(result).__name__}"
+        assert expected_keys.issubset(
+            result.keys()
+        ), f"Expected keys {expected_keys} but got {result.keys()}"
+        assert isinstance(
+            result.get("data"), list
+        ), f"Expected list, but got {type(result).__name__}"
+        assert os.path.exists(output_file_path)
+
+    os.remove(csv_file_path)
+    if os.path.exists(output_file_path):
+        os.remove(output_file_path)