{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "3278098f-2378-4a4b-9805-51c0f3db0f05", "metadata": {}, "outputs": [], "source": [ "from seeq import spy\n", "import pandas as pd\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "import solara\n", "import os\n", "import urllib.parse as urlparse\n", "from urllib.parse import parse_qs\n", "from requests.models import PreparedRequest" ] }, { "cell_type": "code", "execution_count": 19, "id": "c81cb316-55fa-42a4-8c83-3e640ef6cdae", "metadata": {}, "outputs": [], "source": [ "def generate_chart(df=None, signal_map=None, jitter=1, whiskerwidth=1, show_box_points=False, title=None, height=500, width=1200):\n", " \"\"\"\n", " Generate a box plot chart using Plotly.\n", "\n", " Args:\n", " df (pandas.DataFrame): A DataFrame containing the data to be plotted.\n", " signal_map (dict): A dictionary mapping column names to dictionaries containing asset, name, and color information.\n", " jitter (float, optional): The amount of jitter to apply to the box points. Defaults to 1.\n", " whiskerwidth (float, optional): The width of the whiskers. Defaults to 1.\n", " show_box_points (bool, optional): Whether to show individual box points. Defaults to False.\n", " title (str, optional): The title of the chart. Defaults to None.\n", " height (int, optional): The height of the chart in pixels. Defaults to 500.\n", "\n", " Returns:\n", " plotly.graph_objects.Figure: A Plotly figure object containing the box plot chart.\n", " \"\"\"\n", " \n", " # Create a figure object once and update it\n", " fig = go.Figure()\n", "\n", " # Use a list comprehension to create traces efficiently\n", " traces = [\n", " go.Box(\n", " y=series,\n", " name=f\"{signal_map[name]['Asset']} >> {signal_map[name]['Name']}\",\n", " marker_color=signal_map[name][\"Color\"],\n", " boxmean=\"sd\",\n", " boxpoints='all' if show_box_points else None,\n", " jitter=jitter,\n", " whiskerwidth=whiskerwidth\n", " )\n", " for name, series in df.items()\n", " ]\n", "\n", " # Add traces to the figure object\n", " fig.add_traces(traces)\n", "\n", " # Update layout properties\n", " fig.update_layout(\n", " title=title,\n", " height=height,\n", " width=width,\n", " # autosize=True,\n", " showlegend=False,\n", " )\n", " fig.update_annotations(\n", " font=dict(family=\"Helvetica\", size=12),\n", " xanchor='left',\n", " x=0.0\n", " )\n", "\n", " return fig" ] }, { "cell_type": "code", "execution_count": 20, "id": "6689b6cb", "metadata": {}, "outputs": [], "source": [ "def parse_jupyter_notebook_url(jupyter_notebook_url, backup_workbench):\n", " \"\"\"\n", " Parses the Jupyter Notebook URL and returns the workbook ID and worksheet ID.\n", "\n", " Args:\n", " jupyter_notebook_url (str): The URL of the Jupyter Notebook.\n", " backup_workbench (str): The URL of a backup Seeq Worksheet with signals in the details pane.\n", "\n", " Returns:\n", " tuple: A tuple containing the workbook ID and worksheet ID.\n", " \"\"\"\n", " parsed_url = urlparse.urlparse(jupyter_notebook_url)\n", " query_params = parse_qs(parsed_url.query)\n", "\n", " if not query_params:\n", " # Assuming it's not being run as an Add-on and there are no query parameters\n", " # Manually including defaults from the backup_workbench\n", " url = spy.utils.get_data_lab_project_url()\n", " workbook_id = spy.utils.get_workbook_id_from_url(backup_workbench)\n", " worksheet_id = spy.utils.get_worksheet_id_from_url(backup_workbench)\n", " params = {'workbookId': workbook_id, 'worksheetId': worksheet_id}\n", " req = PreparedRequest()\n", " req.prepare_url(url, params)\n", " parsed_url = urlparse.urlparse(req.url)\n", " query_params = parse_qs(parsed_url.query)\n", "\n", " workbook_id = query_params['workbookId'][0]\n", " worksheet_id = query_params['worksheetId'][0]\n", "\n", " return workbook_id, worksheet_id\n", "\n", "def create_df(workbook_id, worksheet_id):\n", " \"\"\"\n", " Fetches worksheet data from a Seeq server and returns a DataFrame.\n", "\n", " Args:\n", " workbook_id (str): The ID of the workbook.\n", " worksheet_id (str): The ID of the worksheet.\n", "\n", " Returns:\n", " pandas.DataFrame: The fetched data DataFrame.\n", " \"\"\"\n", " host = os.environ.get('SEEQ_HOST', spy.client.host[:-3])\n", " url = f\"{host}workbook/{workbook_id}/worksheet/{worksheet_id}\"\n", "\n", " # Fetch worksheet items and filter out string units of measure\n", " worksheet_items = spy.search(url, quiet=True)\n", " worksheet_items = worksheet_items[worksheet_items['Value Unit Of Measure'] != 'string']\n", "\n", " # Pull the workbook and specific worksheet\n", " workbook = spy.workbooks.pull(workbook_id,\n", " include_inventory=False,\n", " include_annotations=False,\n", " include_referenced_workbooks=False,\n", " include_images=False,\n", " include_rendered_content=False,\n", " specific_worksheet_ids=[worksheet_id],\n", " quiet=True)[0]\n", "\n", " worksheet = workbook.worksheets[0]\n", "\n", " # Update items to include the axis color\n", " display_items = worksheet.display_items[['ID', 'Color']]\n", " worksheet_items = pd.merge(worksheet_items, display_items, on='ID')\n", "\n", " # Get Display Range\n", " display_range = worksheet.display_range\n", "\n", " # Fetch data for the selected worksheet items\n", " df = spy.pull(worksheet_items,\n", " start=display_range['Start'],\n", " end=display_range['End'],\n", " header=\"ID\",\n", " grid=None,\n", " quiet=True)\n", "\n", " return df, worksheet_items\n", "\n", "def signal_info_to_dict(worksheet_items):\n", " \"\"\"\n", " Creates a signal map dictionary from the worksheet items.\n", "\n", " Args:\n", " worksheet_items (pandas.DataFrame): The worksheet items DataFrame.\n", "\n", " Returns:\n", " dict: The signal map dictionary.\n", " \"\"\"\n", " signal_map = worksheet_items.set_index('ID').apply(lambda x: x.to_dict(), axis=1).to_dict()\n", " return signal_map\n", "\n", "def get_worksheet_data(jupyter_notebook_url, backup_workbench):\n", " \"\"\"\n", " Fetches worksheet data and signal map from a Seeq server based on the provided Jupyter Notebook URL.\n", "\n", " Args:\n", " jupyter_notebook_url (str): The URL of the Jupyter Notebook.\n", " backup_workbench (str): The URL of a backup Seeq Worksheet with signals in the details pane.\n", "\n", " Returns:\n", " tuple: A tuple containing the fetched data DataFrame and the signal map dictionary.\n", " \"\"\"\n", " workbook_id, worksheet_id = parse_jupyter_notebook_url(jupyter_notebook_url, backup_workbench)\n", " df , worksheet_items= create_df(workbook_id, worksheet_id)\n", " signal_info = signal_info_to_dict(worksheet_items)\n", "\n", " return df, signal_info\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "a27c01c5-eed2-4634-bdc5-21603a42de7e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
request_origin_label argument was not specified, which means that it will be more difficult to track data consumption back to this script. Please supply a descriptive label (such as request_origin_label=\"My Cool Script\") to help your friendly Seeq administrators.
Logged in to https://develop.seeq.dev as John.Garramone@seeq.com (John Garramone [Admin]).
Seeq Server Version: R66.0.0-v202408090123-CD
Seeq SDK Module Version: 66.0.0 @ /Users/jgarramone/Documents/sandbox/crabBranches/PROJECTS/exampleAddOnTool/.venv/lib/python3.11/site-packages/seeq/sdk
Seeq SPy Module Version: 192.33 @ /Users/jgarramone/Documents/sandbox/crabBranches/PROJECTS/exampleAddOnTool/.venv/lib/python3.11/site-packages/seeq/spy
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b36794a0a20a4bcf91b42b6d635d2d5e", "version_major": 2, "version_minor": 0 }, "text/html": [ "Cannot show widget. You probably want to rerun the code cell above (Click in the code cell, and press Shift+Enter ⇧+↩)." ], "text/plain": [ "Cannot show ipywidgets in text" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "@solara.component\n", "def BoxPlotComponent():\n", " # State variables\n", " whisker_width, set_whisker_width = solara.use_state(1)\n", " show_data_points, set_show_data_points = solara.use_state(False)\n", " selected_jitter, set_jitter = solara.use_state(1) \n", " error_message, set_error_message = solara.use_state(None)\n", "\n", " def validate_whisker_width(value):\n", " if value > 1:\n", " set_error_message(\"Whisker width cannot be greater than 1\")\n", " else:\n", " set_error_message(None)\n", " set_whisker_width(value)\n", "\n", " def render_controls():\n", " with solara.HBox() as controls:\n", " solara.Select(\n", " label=\"Show Samples\",\n", " values=[True, False],\n", " value=show_data_points,\n", " on_value=set_show_data_points,\n", " )\n", "\n", " if show_data_points:\n", " solara.Select(\n", " label=\"Jitter\",\n", " values=[0, 0.1, 0.5, 1],\n", " value=selected_jitter,\n", " on_value=set_jitter,\n", " )\n", "\n", " solara.InputFloat(\n", " label=\"Whisker Width\",\n", " value=whisker_width,\n", " on_value=validate_whisker_width,\n", " )\n", "\n", " return controls\n", "\n", " def render_chart():\n", " fig = generate_chart(\n", " df=df,\n", " jitter=selected_jitter,\n", " show_box_points=show_data_points,\n", " signal_map=signal_info,\n", " whiskerwidth=whisker_width,\n", " height=800,\n", " width=1200,\n", " )\n", " solara.FigurePlotly(fig, dependencies=[selected_jitter, show_data_points, whisker_width])\n", "\n", " with solara.Div() as main:\n", " with solara.AppBarTitle():\n", " solara.Text(\"Box Plot\")\n", "\n", " if error_message:\n", " solara.Error(\n", " f\"{error_message}\",\n", " text=False,\n", " dense=True,\n", " outlined=False,\n", " icon=True,\n", " )\n", "\n", " render_controls()\n", " render_chart()\n", "\n", " return main\n", "\n", "\n", "BoxPlotComponent()" ] }, { "cell_type": "code", "execution_count": null, "id": "72d173f3-f21e-4bad-99c6-6d2c412fe296", "metadata": {}, "outputs": [], "source": [ "df, signal_info = get_worksheet_data(jupyter_notebook_url=jupyter_notebook_url)\n", "\n", "# The following line is required when running the code in a Jupyter notebook:\n", "BoxPlotComponent()" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }