{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8fd47c4b",
   "metadata": {},
   "source": [
    "# Perform a BoM sustainability summary query\n",
    "\n",
    "The following supporting files are required for this example:\n",
    "\n",
    "* [bom-2301-assembly.xml](supporting-files/bom-2301-assembly.xml)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae06ff4",
   "metadata": {},
   "source": [
    "## Run a BoM sustainability summary query\n",
    "\n",
    "First, connect to Granta MI.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f65fea18",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ansys.grantami.bomanalytics import Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "267fc3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "server_url = \"http://my_grantami_server/mi_servicelayer\"\n",
    "cxn = Connection(server_url).with_credentials(\"user_name\", \"password\").connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d86bad",
   "metadata": {},
   "source": [
    "Next, create a sustainability summary query. The query accepts a single BoM as argument and an optional\n",
    "configuration for units. If a unit is not specified, the default unit is used. Default units for the analysis are\n",
    "``MJ`` for energy, ``kg`` for mass, and ``km`` for distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be26c586",
   "metadata": {},
   "outputs": [],
   "source": [
    "xml_file_path = \"supporting-files/bom-2301-assembly.xml\"\n",
    "with open(xml_file_path) as f:\n",
    "    bom = f.read()\n",
    "\n",
    "from ansys.grantami.bomanalytics import queries\n",
    "\n",
    "MASS_UNIT = \"kg\"\n",
    "ENERGY_UNIT = \"MJ\"\n",
    "DISTANCE_UNIT = \"km\"\n",
    "\n",
    "sustainability_summary_query = (\n",
    "    queries.BomSustainabilitySummaryQuery()\n",
    "    .with_bom(bom)\n",
    "    .with_units(mass=MASS_UNIT, energy=ENERGY_UNIT, distance=DISTANCE_UNIT)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a38a9ead",
   "metadata": {},
   "outputs": [],
   "source": [
    "sustainability_summary = cxn.run(sustainability_summary_query)\n",
    "sustainability_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60eb0c1b",
   "metadata": {},
   "source": [
    "The ``BomSustainabilitySummaryQueryResult`` object that is returned implements a ``messages`` property and properties\n",
    "showing the environmental impact of the items included in the BoM.\n",
    "Log messages are sorted by decreasing severity. The same messages are available in the MI Service Layer log file\n",
    "and are logged using the standard ``logging`` module.\n",
    "The next sections show examples of visualizations for the results of the sustainability summary query.\n",
    "\n",
    "## Summary per phase\n",
    "The sustainability summary result object contains a ``phases_summary`` property. This property summarizes the\n",
    "environmental impact contributions by lifecycle phase: materials, processes, and transport phases. The results for\n",
    "each phase include their absolute and relative contributions to the product as a whole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9398cabd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sustainability_summary.phases_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd15f35",
   "metadata": {},
   "source": [
    "Use the [pandas](https://pandas.pydata.org/) and [plotly](https://plotly.com/python/) libraries to visualize the\n",
    "results. First, the data is translated from the BoM Analytics ``BomSustainabilitySummaryQueryResult`` to a pandas\n",
    "``Dataframe`` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cba06d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "EE_HEADER = f\"EE [{ENERGY_UNIT}]\"\n",
    "CC_HEADER = f\"CC [{MASS_UNIT}]\"\n",
    "\n",
    "phases_df = pd.DataFrame.from_records(\n",
    "    [\n",
    "        {\n",
    "            \"Name\": item.name,\n",
    "            \"EE%\": item.embodied_energy_percentage,\n",
    "            EE_HEADER: item.embodied_energy.value,\n",
    "            \"CC%\": item.climate_change_percentage,\n",
    "            CC_HEADER: item.climate_change.value,\n",
    "        }\n",
    "        for item in sustainability_summary.phases_summary\n",
    "    ]\n",
    ")\n",
    "phases_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b1faf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "\n",
    "def plot_impact(df, title, textinfo=\"percent+label\", hoverinfo=\"value+name\"):\n",
    "    fig = make_subplots(\n",
    "        rows=1,\n",
    "        cols=2,\n",
    "        specs=[[{\"type\": \"domain\"}, {\"type\": \"domain\"}]],\n",
    "        subplot_titles=[\"Embodied Energy\", \"Climate Change\"],\n",
    "    )\n",
    "    fig.add_trace(go.Pie(labels=df[\"Name\"], values=df[EE_HEADER], name=ENERGY_UNIT), 1, 1)\n",
    "    fig.add_trace(go.Pie(labels=df[\"Name\"], values=df[CC_HEADER], name=MASS_UNIT), 1, 2)\n",
    "    fig.update_layout(title_text=title, legend=dict(orientation=\"h\"))\n",
    "    fig.update_traces(textposition=\"inside\", textinfo=textinfo, hoverinfo=hoverinfo)\n",
    "    fig.show()\n",
    "\n",
    "\n",
    "plot_impact(phases_df, \"BoM sustainability summary - By phase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd0266a",
   "metadata": {},
   "source": [
    "## Transport phase\n",
    "\n",
    "The environmental contribution from the transport phase is summarized in the ``transport_details`` property. Results\n",
    "include the individual environmental impact for each transport stage included in the input BoM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f07cde8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sustainability_summary.transport_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26798a57",
   "metadata": {},
   "outputs": [],
   "source": [
    "DISTANCE_HEADER = f\"Distance [{DISTANCE_UNIT}]\"\n",
    "\n",
    "transport_df = pd.DataFrame.from_records(\n",
    "    [\n",
    "        {\n",
    "            \"Name\": item.name,\n",
    "            DISTANCE_HEADER: item.distance.value,\n",
    "            \"EE%\": item.embodied_energy_percentage,\n",
    "            EE_HEADER: item.embodied_energy.value,\n",
    "            \"CC%\": item.climate_change_percentage,\n",
    "            CC_HEADER: item.climate_change.value,\n",
    "        }\n",
    "        for item in sustainability_summary.transport_details\n",
    "    ]\n",
    ")\n",
    "transport_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7ea27f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_impact(transport_df, \"Transport stages - environmental impact\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7af706f",
   "metadata": {},
   "source": [
    "In some situations, it might be useful to calculate the environmental impact per distance travelled and add the\n",
    "results as new columns in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d81470dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "EE_PER_DISTANCE = f\"EE [{ENERGY_UNIT}/{DISTANCE_UNIT}]\"\n",
    "CC_PER_DISTANCE = f\"CC [{MASS_UNIT}/{DISTANCE_UNIT}]\"\n",
    "transport_df[EE_PER_DISTANCE] = transport_df.apply(lambda row: row[EE_HEADER] / row[DISTANCE_HEADER], axis=1)\n",
    "transport_df[CC_PER_DISTANCE] = transport_df.apply(lambda row: row[CC_HEADER] / row[DISTANCE_HEADER], axis=1)\n",
    "transport_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffd80c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = make_subplots(\n",
    "    rows=1, cols=2, specs=[[{\"type\": \"domain\"}, {\"type\": \"domain\"}]], subplot_titles=[EE_PER_DISTANCE, CC_PER_DISTANCE]\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Pie(labels=transport_df[\"Name\"], values=transport_df[EE_PER_DISTANCE], name=f\"{ENERGY_UNIT}/{DISTANCE_UNIT}\"),\n",
    "    1,\n",
    "    1,\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Pie(labels=transport_df[\"Name\"], values=transport_df[CC_PER_DISTANCE], name=f\"{MASS_UNIT}/{DISTANCE_UNIT}\"), 1, 2\n",
    ")\n",
    "fig.update_layout(\n",
    "    title_text=\"Transport stages impact - Relative to distance travelled\",\n",
    "    legend=dict(orientation=\"h\")\n",
    ")\n",
    "fig.update_traces(textposition=\"inside\", textinfo=\"percent+label\", hoverinfo=\"value+name\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862f8df5",
   "metadata": {},
   "source": [
    "## Materials phase\n",
    "\n",
    "The environmental contribution from the material phase is summarized in the ``material_details`` property. The results\n",
    "are aggregated: each item in ``material_details`` represents the total environmental impact of a material summed\n",
    "from all its occurrences in the BoM. Listed materials contribute more than 2% of the total impact for the material\n",
    "phase. Materials that do not contribute at least 2% of the total are aggregated under the ``Other`` item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4039978",
   "metadata": {},
   "outputs": [],
   "source": [
    "sustainability_summary.material_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3e60bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "materials_df = pd.DataFrame.from_records(\n",
    "    [\n",
    "        {\n",
    "            \"Name\": item.identity,\n",
    "            \"EE%\": item.embodied_energy_percentage,\n",
    "            EE_HEADER: item.embodied_energy.value,\n",
    "            \"CC%\": item.climate_change_percentage,\n",
    "            CC_HEADER: item.climate_change.value,\n",
    "            f\"Mass before processing [{MASS_UNIT}]\": item.mass_before_processing.value,\n",
    "            f\"Mass after processing [{MASS_UNIT}]\": item.mass_after_processing.value,\n",
    "        }\n",
    "        for item in sustainability_summary.material_details\n",
    "    ]\n",
    ")\n",
    "materials_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0446cd40",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_impact(materials_df, \"Aggregated materials impact\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34719169",
   "metadata": {},
   "source": [
    "Mass before and mass after secondary processing can help determine if the material mass removed during processing\n",
    "contributes a significant fraction of the impact of the overall material phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92337144",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.Figure(\n",
    "    data=[\n",
    "        go.Bar(\n",
    "            name=\"Mass before secondary processing\",\n",
    "            x=materials_df[\"Name\"],\n",
    "            y=materials_df[f\"Mass before processing [{MASS_UNIT}]\"],\n",
    "        ),\n",
    "        go.Bar(\n",
    "            name=\"Mass after secondary processing\",\n",
    "            x=materials_df[\"Name\"],\n",
    "            y=materials_df[f\"Mass after processing [{MASS_UNIT}]\"],\n",
    "        ),\n",
    "    ],\n",
    "    layout=go.Layout(\n",
    "        xaxis=go.layout.XAxis(title=\"Materials\"),\n",
    "        yaxis=go.layout.YAxis(title=f\"Mass [{MASS_UNIT}]\"),\n",
    "        legend=dict(orientation=\"h\")\n",
    "    ),\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23341275",
   "metadata": {},
   "source": [
    "## Material processing phase\n",
    "\n",
    "The environmental contributions from primary and secondary processing (applied to materials) and the joining and\n",
    "finishing processes (applied to parts) are summarized in the ``primary_processes_details``,\n",
    "``secondary_processes_details``, and ``joining_and_finishing_processes_details`` properties respectively.\n",
    "Each of these properties lists the unique process-material pairs (for primary and secondary processing) or\n",
    "individual processes (for joining and finishing) that contribute at least 5% of the total impact for that\n",
    "category of process. The percentage contributions are relative to the total contribution of all processes\n",
    "from the same category. Processes that do not meet the contribution threshold are aggregated under the\n",
    "``Other`` item, with the material set to ``None``."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48509cb7",
   "metadata": {},
   "source": [
    "### Primary processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5995e55b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sustainability_summary.primary_processes_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c51d2d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "primary_process_df = pd.DataFrame.from_records(\n",
    "    [\n",
    "        {\n",
    "            \"Process name\": item.process_name,\n",
    "            \"Material name\": item.material_identity,\n",
    "            \"EE%\": item.embodied_energy_percentage,\n",
    "            EE_HEADER: item.embodied_energy.value,\n",
    "            \"CC%\": item.climate_change_percentage,\n",
    "            CC_HEADER: item.climate_change.value,\n",
    "        }\n",
    "        for item in sustainability_summary.primary_processes_details\n",
    "    ]\n",
    ")\n",
    "primary_process_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae2cdb2",
   "metadata": {},
   "source": [
    "Add a ``Name`` to each item that represents the process-material pair name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f007b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "primary_process_df[\"Name\"] = primary_process_df.apply(\n",
    "    lambda row: f\"{row['Process name']} - {row['Material name']}\", axis=1\n",
    ")\n",
    "plot_impact(\n",
    "    primary_process_df, \"Aggregated primary processes impact\", textinfo=\"percent\", hoverinfo=\"value+name+label\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bab2ea7",
   "metadata": {},
   "source": [
    "### Secondary processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b6f890",
   "metadata": {},
   "outputs": [],
   "source": [
    "sustainability_summary.secondary_processes_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0983cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "secondary_process_df = pd.DataFrame.from_records(\n",
    "    [\n",
    "        {\n",
    "            \"Process name\": item.process_name,\n",
    "            \"Material name\": item.material_identity,\n",
    "            \"EE%\": item.embodied_energy_percentage,\n",
    "            EE_HEADER: item.embodied_energy.value,\n",
    "            \"CC%\": item.climate_change_percentage,\n",
    "            CC_HEADER: item.climate_change.value,\n",
    "        }\n",
    "        for item in sustainability_summary.secondary_processes_details\n",
    "    ]\n",
    ")\n",
    "secondary_process_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9817f3",
   "metadata": {},
   "source": [
    "Add a ``Name`` to each item that represents the process-material pair name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b5d4306",
   "metadata": {},
   "outputs": [],
   "source": [
    "secondary_process_df[\"Name\"] = secondary_process_df.apply(\n",
    "    lambda row: f\"{row['Process name']} - {row['Material name']}\", axis=1\n",
    ")\n",
    "plot_impact(\n",
    "    secondary_process_df, \"Aggregated secondary processes impact\", textinfo=\"percent\", hoverinfo=\"value+name+label\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd1bf7ae",
   "metadata": {},
   "source": [
    "### Joining and finishing\n",
    "\n",
    "Joining and finishing processes apply to parts or assemblies and therefore don't include a material identity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8c6cc4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sustainability_summary.joining_and_finishing_processes_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4767b1e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "joining_and_finishing_processes_df = pd.DataFrame.from_records(\n",
    "    [\n",
    "        {\n",
    "            \"Name\": item.process_name,\n",
    "            \"EE%\": item.embodied_energy_percentage,\n",
    "            EE_HEADER: item.embodied_energy.value,\n",
    "            \"CC%\": item.climate_change_percentage,\n",
    "            CC_HEADER: item.climate_change.value,\n",
    "        }\n",
    "        for item in sustainability_summary.joining_and_finishing_processes_details\n",
    "    ]\n",
    ")\n",
    "joining_and_finishing_processes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12e9d5e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_impact(\n",
    "    joining_and_finishing_processes_df, \"Aggregated secondary processes impact\",\n",
    "    textinfo=\"percent\", hoverinfo=\"value+name+label\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fea2d0e",
   "metadata": {},
   "source": [
    "## Hierarchical view\n",
    "\n",
    "Finally, aggregate the sustainability summary results into a single dataframe and present it in a hierarchical\n",
    "chart. This highlights the largest contributors at each level. In this example, two levels are defined:\n",
    "first the phase and then the contributors in the phase."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fdb7e03",
   "metadata": {},
   "source": [
    "First, rename the processes ``Other`` rows, so that they remain distinguishable after all processes have been\n",
    "grouped under a general ``Processes``.\n",
    "\n",
    "Use ``assign`` to add a ``parent`` column to each dataframe being concatenated.\n",
    "The ``join`` argument value ``inner`` specifies that only columns common to all dataframes are kept in the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d1d7705",
   "metadata": {},
   "outputs": [],
   "source": [
    "primary_process_df.loc[(primary_process_df[\"Name\"] == \"Other - None\"), \"Name\"] = \"Other primary processes\"\n",
    "secondary_process_df.loc[(secondary_process_df[\"Name\"] == \"Other - None\"), \"Name\"] = \"Other secondary processes\"\n",
    "joining_and_finishing_processes_df.loc[\n",
    "    (joining_and_finishing_processes_df[\"Name\"] == \"Other - None\"), \"Name\"] = \"Other joining and finishing processes\"\n",
    "\n",
    "summary_df = pd.concat(\n",
    "    [\n",
    "        phases_df.assign(Parent=\"\"),\n",
    "        transport_df.assign(Parent=\"Transport\"),\n",
    "        materials_df.assign(Parent=\"Material\"),\n",
    "        primary_process_df.assign(Parent=\"Processes\"),\n",
    "        secondary_process_df.assign(Parent=\"Processes\"),\n",
    "        joining_and_finishing_processes_df.assign(Parent=\"Processes\"),\n",
    "    ],\n",
    "    join=\"inner\",\n",
    ")\n",
    "summary_df\n",
    "\n",
    "# A sunburst chart presents hierarchical data radially.\n",
    "\n",
    "fig = go.Figure(\n",
    "    go.Sunburst(\n",
    "        labels=summary_df[\"Name\"],\n",
    "        parents=summary_df[\"Parent\"],\n",
    "        values=summary_df[EE_HEADER],\n",
    "        branchvalues=\"total\",\n",
    "    ),\n",
    "    layout_title_text=f\"Embodied Energy [{ENERGY_UNIT}]\",\n",
    ")\n",
    "fig.show()\n",
    "\n",
    "# An icicle chart presents hierarchical data as rectangular sectors.\n",
    "\n",
    "fig = go.Figure(\n",
    "    go.Icicle(\n",
    "        labels=summary_df[\"Name\"],\n",
    "        parents=summary_df[\"Parent\"],\n",
    "        values=summary_df[EE_HEADER],\n",
    "        branchvalues=\"total\",\n",
    "    ),\n",
    "    layout_title_text=f\"Embodied Energy [{ENERGY_UNIT}]\",\n",
    ")\n",
    "fig.show()"
   ]
  }
 ],
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