diff --git a/DX_exploration/JSON_generating/.DS_Store b/DX_exploration/JSON_generating/.DS_Store new file mode 100644 index 0000000..dd234bd Binary files /dev/null and b/DX_exploration/JSON_generating/.DS_Store differ diff --git a/DX_exploration/JSON_generating/.ipynb_checkpoints/rename & process-checkpoint.ipynb b/DX_exploration/JSON_generating/.ipynb_checkpoints/rename & process-checkpoint.ipynb new file mode 100644 index 0000000..5829bd3 --- /dev/null +++ b/DX_exploration/JSON_generating/.ipynb_checkpoints/rename & process-checkpoint.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "id": "f7c7aa56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting xlsxwriterNote: you may need to restart the kernel to use updated packages.\n", + "\n", + " Downloading XlsxWriter-3.1.9-py3-none-any.whl (154 kB)\n", + " 0.0/154.8 kB ? eta -:--:--\n", + " ---------- 41.0/154.8 kB 2.0 MB/s eta 0:00:01\n", + " --------------------------- 112.6/154.8 kB 2.2 MB/s eta 0:00:01\n", + " -------------------------------------- 154.8/154.8 kB 1.6 MB/s eta 0:00:00\n", + "Installing collected packages: xlsxwriter\n", + "Successfully installed xlsxwriter-3.1.9\n" + ] + } + ], + "source": [ + "# pip install xlsxwriter\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75a633a8", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import xlsxwriter" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "941a58b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame for sheet 'Table 1' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 1)\n", + "DataFrame for sheet 'Table 2' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 2)\n", + "DataFrame for sheet 'Table 3' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 3)\n", + "DataFrame for sheet 'Table 4' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 4)\n", + "DataFrame for sheet 'Table 5' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 5)\n", + "DataFrame for sheet 'Table 6' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 6)\n", + "DataFrame for sheet 'Table 7' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 7)\n", + "DataFrame for sheet 'Table 8' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 8)\n", + "All sheets saved to a single Excel file: ./data/processed/DX_Equipment_Data_Collection_D.xlsx\n", + "--------------------\n", + "DataFrame for sheet 'Table 1' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 1)\n", + "DataFrame for sheet 'Table 2' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 2)\n", + "DataFrame for sheet 'Table 3' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 3)\n", + "DataFrame for sheet 'Table 4' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 4)\n", + "DataFrame for sheet 'Table 5' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 5)\n", + "DataFrame for sheet 'Table 6' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 6)\n", + "DataFrame for sheet 'Table 7' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 7)\n", + "DataFrame for sheet 'Table 8' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 8)\n", + "DataFrame for sheet 'Table 9' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 9)\n", + "DataFrame for sheet 'Table 10' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 10)\n", + "All sheets saved to a single Excel file: ./data/processed/DX_Equipment_Data_Collection_L.xlsx\n", + "--------------------\n", + "DataFrame for sheet 'Table 1' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 1)\n", + "DataFrame for sheet 'Table 2' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 2)\n", + "DataFrame for sheet 'Table 3' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 3)\n", + "DataFrame for sheet 'Table 4' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 4)\n", + "DataFrame for sheet 'Table 5' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 5)\n", + "DataFrame for sheet 'Table 6' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 6)\n", + "DataFrame for sheet 'Table 7' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 7)\n", + "DataFrame for sheet 'Table 8' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 8)\n", + "DataFrame for sheet 'Table 9' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 9)\n", + "DataFrame for sheet 'Table 10' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 10)\n", + "DataFrame for sheet 'Table 11' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 11)\n", + "DataFrame for sheet 'Table 12' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 12)\n", + "DataFrame for sheet 'Table 13' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 13)\n", + "DataFrame for sheet 'Table 14' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 14)\n", + "DataFrame for sheet 'Table 15' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 15)\n", + "DataFrame for sheet 'Table 16' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 16)\n", + "DataFrame for sheet 'Table 17' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 17)\n", + "DataFrame for sheet 'Table 18' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 18)\n", + "All sheets saved to a single Excel file: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx\n", + "--------------------\n", + "DataFrame for sheet 'Table 1' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 1)\n", + "DataFrame for sheet 'Table 2' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 2)\n", + "DataFrame for sheet 'Table 3' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 3)\n", + "DataFrame for sheet 'Table 4' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 4)\n", + "DataFrame for sheet 'Table 5' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 5)\n", + "DataFrame for sheet 'Table 6' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 6)\n", + "DataFrame for sheet 'Table 7' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 7)\n", + "DataFrame for sheet 'Table 8' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 8)\n", + "DataFrame for sheet 'Table 9' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 9)\n", + "DataFrame for sheet 'Table 10' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 10)\n", + "DataFrame for sheet 'Table 11' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 11)\n", + "DataFrame for sheet 'Table 12' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 12)\n", + "DataFrame for sheet 'Table 13' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 13)\n", + "DataFrame for sheet 'Table 14' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 14)\n", + "DataFrame for sheet 'Table 15' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 15)\n", + "DataFrame for sheet 'Table 16' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 16)\n", + "DataFrame for sheet 'Table 17' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 17)\n", + "DataFrame for sheet 'Table 18' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 18)\n", + "All sheets saved to a single Excel file: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx\n", + "--------------------\n" + ] + } + ], + "source": [ + "def process_excel_file(input_path, output_folder, new_column_names):\n", + " dfs = pd.read_excel(input_path, sheet_name=None, header=6, usecols=\"B:L\", engine=\"openpyxl\")\n", + " os.makedirs(output_folder, exist_ok=True)\n", + "\n", + " # Create a writer to save all sheets in a single Excel file\n", + " output_file_path = os.path.join(output_folder, f\"{os.path.basename(input_path)}\")\n", + " with pd.ExcelWriter(output_file_path, engine='xlsxwriter') as writer:\n", + " for sheet_name, df in dfs.items():\n", + " if len(new_column_names) == len(df.columns):\n", + " df.columns = new_column_names\n", + " df.to_excel(writer, sheet_name=sheet_name, index=False)\n", + " print(f\"DataFrame for sheet '{sheet_name}' saved to: {output_file_path} (Sheet: {sheet_name})\")\n", + " else:\n", + " print(f\"Number of new column names does not match the number of columns in sheet '{sheet_name}'. Skipping.\")\n", + "\n", + " print(f\"All sheets saved to a single Excel file: {output_file_path}\")\n", + "\n", + "\n", + "input_excel_path_list = [\"./data/original/DX_Equipment_Data_Collection_D.xlsx\",\n", + " \"./data/original/DX_Equipment_Data_Collection_L.xlsx\",\n", + " \"./data/original/DX_Equipment_Data_Collection_C_SM.xlsx\",\n", + " \"./data/original/DX_Equipment_Data_Collection_C_SubCool.xlsx\"]\n", + "output_folder_path = \"./data/processed/\"\n", + "new_column_names=[\"outdoor_coil_entering_dry_bulb_temperature\", \"indoor_coil_entering_dry_bulb_temperature\",\n", + " \"indoor_coil_entering_wet_bulb_temperature\", \"indoor_coil_entering_relative_humidity\",\n", + " \"gross_total_capacity\", \"gross_sensible_capacity\", \"gross_power\", \"indoor_coil_air_mass_flow_rate\",\n", + " \"supply_fan_power\", \"outside_fan_power\", \"compressor_sequence_number\"]\n", + "\n", + "for input_excel_path in input_excel_path_list:\n", + " process_excel_file(input_excel_path, output_folder_path, new_column_names)\n", + " print(\"--------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "047a93d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Table 1',\n", + " 'Table 2',\n", + " 'Table 3',\n", + " 'Table 4',\n", + " 'Table 5',\n", + " 'Table 6',\n", + " 'Table 7',\n", + " 'Table 8']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# test\n", + "file_path = \"./data/original/DX_Equipment_Data_Collection_D.xlsx\"\n", + "sheet_names = pd.ExcelFile(file_path).sheet_names\n", + "sheet_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b78f41ee", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/JSON_generating/DX_cfm.json b/DX_exploration/JSON_generating/DX_cfm.json new file mode 100644 index 0000000..7d0038b --- /dev/null +++ b/DX_exploration/JSON_generating/DX_cfm.json @@ -0,0 +1,9 @@ +[ + "{\n \"eqp_type\": \"unitary_dx\",\n \"equip_label\": \"D1\",\n \"num_comp_sequences\": 1,\n \"ref_flow_rate\": 2100,\n \"ref_Te_db\": 26.7,\n \"ref_Te_wb\": 19.4,\n \"ref_cap\": 74107.0,\n \"ref_sensible_cap\": 45205.27,\n \"ref_cap_unit\": \"Btu/h\",\n \"compressor_type\": \"scroll\",\n \"condenser_type\": \"air\",\n \"compressor_speed\": \"constant\",\n \"sim_engine\": \"energyplus\",\n \"set_of_curves\": [\n {\n \"num_comp_seq\": 0,\n \"cap-f-t\": {\n \"out_var\": \"cap-f-t\",\n \"type\": \"bi_quad\",\n \"units\": \"si\",\n \"x_min\": 18.333333333333332,\n \"y_min\": 15.0,\n \"x_max\": 46.111111111111114,\n \"y_max\": 21.666666666666668,\n \"out_min\": 0.7901412822000621,\n \"out_max\": 1.0737582144736664,\n \"coeff1\": 1.2830323370639596,\n \"coeff2\": 0.004807304675640525,\n \"coeff3\": -0.00017722287657424927,\n \"coeff4\": -0.04687269285372827,\n \"coeff5\": 0.001636533922096092,\n \"coeff6\": -1.2420615502686111e-08,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 0,\n \"cap-f-ff\": {\n \"out_var\": \"cap-f-ff\",\n \"type\": \"cubic\",\n \"units\": \"si\",\n \"x_min\": 0.8571428571428571,\n \"x_max\": 1.2857142857142858,\n \"out_min\": 0.8234579729310322,\n \"out_max\": 1.053071909536211,\n \"coeff1\": 0.8464347049029214,\n \"coeff2\": 0.15119488599260936,\n \"coeff3\": -0.11978843367263663,\n \"coeff4\": 0.0657156817621772,\n \"coeff5\": 0.0,\n \"coeff6\": 0.0,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n }\n ]\n}", + "{\n \"eqp_type\": \"unitary_dx\",\n \"equip_label\": \"D2\",\n \"num_comp_sequences\": 1,\n \"ref_flow_rate\": 3000,\n \"ref_Te_db\": 26.7,\n \"ref_Te_wb\": 19.4,\n \"ref_cap\": 96100.0,\n \"ref_sensible_cap\": 62465.0,\n \"ref_cap_unit\": \"Btu/h\",\n \"compressor_type\": \"scroll\",\n \"condenser_type\": \"air\",\n \"compressor_speed\": \"constant\",\n \"sim_engine\": \"energyplus\",\n \"set_of_curves\": [\n {\n \"num_comp_seq\": 0,\n \"cap-f-t\": {\n \"out_var\": \"cap-f-t\",\n \"type\": \"bi_quad\",\n \"units\": \"si\",\n \"x_min\": 18.333333333333332,\n \"y_min\": 15.0,\n \"x_max\": 46.111111111111114,\n \"y_max\": 21.666666666666668,\n \"out_min\": 0.7887617065556711,\n \"out_max\": 1.0718002081165452,\n \"coeff1\": 1.2824802294666628,\n \"coeff2\": 0.0047733637850222825,\n \"coeff3\": -0.00017696298498590633,\n \"coeff4\": -0.046906326397377344,\n \"coeff5\": 0.001635367445113811,\n \"coeff6\": 1.7514223548524034e-06,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 0,\n \"cap-f-ff\": {\n \"out_var\": \"cap-f-ff\",\n \"type\": \"cubic\",\n \"units\": \"si\",\n \"x_min\": 0.875,\n \"x_max\": 1.125,\n \"out_min\": 0.8241415192507804,\n \"out_max\": 1.028095733610822,\n \"coeff1\": 0.8476428668784779,\n \"coeff2\": 0.14428539729208484,\n \"coeff3\": -0.11336933000540679,\n \"coeff4\": 0.06368872452370977,\n \"coeff5\": 0.0,\n \"coeff6\": 0.0,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n }\n ]\n}", + "{\n \"eqp_type\": \"unitary_dx\",\n \"equip_label\": \"D3\",\n \"num_comp_sequences\": 1,\n \"ref_flow_rate\": 3400,\n \"ref_Te_db\": 26.7,\n \"ref_Te_wb\": 19.4,\n \"ref_cap\": 110300.0,\n \"ref_sensible_cap\": 76107.0,\n \"ref_cap_unit\": \"Btu/h\",\n \"compressor_type\": \"scroll\",\n \"condenser_type\": \"air\",\n \"compressor_speed\": \"constant\",\n \"sim_engine\": \"energyplus\",\n \"set_of_curves\": [\n {\n \"num_comp_seq\": 0,\n \"cap-f-t\": {\n \"out_var\": \"cap-f-t\",\n \"type\": \"bi_quad\",\n \"units\": \"si\",\n \"x_min\": 18.333333333333332,\n \"y_min\": 15.0,\n \"x_max\": 46.111111111111114,\n \"y_max\": 21.666666666666668,\n \"out_min\": 0.7905711695376246,\n \"out_max\": 1.069809610154125,\n \"coeff1\": 1.2747943236772745,\n \"coeff2\": 0.0047769483463085275,\n \"coeff3\": -0.00017519751327551127,\n \"coeff4\": -0.04610400327865685,\n \"coeff5\": 0.0016109288378939595,\n \"coeff6\": -5.722292214698291e-07,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 0,\n \"cap-f-ff\": {\n \"out_var\": \"cap-f-ff\",\n \"type\": \"cubic\",\n \"units\": \"si\",\n \"x_min\": 0.9117647058823529,\n \"x_max\": 1.125,\n \"out_min\": 0.8259292837715322,\n \"out_max\": 1.029918404351768,\n \"coeff1\": 0.8527992234470027,\n \"coeff2\": 0.1298830960922025,\n \"coeff3\": -0.1283923196149101,\n \"coeff4\": 0.08708201579042145,\n \"coeff5\": 0.0,\n \"coeff6\": 0.0,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n }\n ]\n}", + "{\n \"eqp_type\": \"unitary_dx\",\n \"equip_label\": \"D4\",\n \"num_comp_sequences\": 1,\n \"ref_flow_rate\": 4000,\n \"ref_Te_db\": 26.7,\n \"ref_Te_wb\": 19.4,\n \"ref_cap\": 124400.0,\n \"ref_sensible_cap\": 83348.0,\n \"ref_cap_unit\": \"Btu/h\",\n \"compressor_type\": \"scroll\",\n \"condenser_type\": \"air\",\n \"compressor_speed\": \"constant\",\n \"sim_engine\": \"energyplus\",\n \"set_of_curves\": [\n {\n \"num_comp_seq\": 0,\n \"cap-f-t\": {\n \"out_var\": \"cap-f-t\",\n \"type\": \"bi_quad\",\n \"units\": \"si\",\n \"x_min\": 18.333333333333332,\n \"y_min\": 15.0,\n \"x_max\": 46.111111111111114,\n \"y_max\": 21.666666666666668,\n \"out_min\": 0.7942122186495176,\n \"out_max\": 1.0731511254019293,\n \"coeff1\": 1.2799716015086624,\n \"coeff2\": 0.004735505230304116,\n \"coeff3\": -0.00017481166743227566,\n \"coeff4\": -0.04621309807836251,\n \"coeff5\": 0.001612858524195111,\n \"coeff6\": 5.707917483431871e-07,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 0,\n \"cap-f-ff\": {\n \"out_var\": \"cap-f-ff\",\n \"type\": \"cubic\",\n \"units\": \"si\",\n \"x_min\": 0.8875,\n \"x_max\": 1.125,\n \"out_min\": 0.8255627009646302,\n \"out_max\": 1.0353697749196142,\n \"coeff1\": 0.8475910756510775,\n \"coeff2\": 0.13313076396338122,\n \"coeff3\": -0.11989295373217618,\n \"coeff4\": 0.08424078185619435,\n \"coeff5\": 0.0,\n \"coeff6\": 0.0,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n }\n ]\n}", + "{\n \"eqp_type\": \"unitary_dx\",\n \"equip_label\": \"D5\",\n \"num_comp_sequences\": 1,\n \"ref_flow_rate\": 5000,\n \"ref_Te_db\": 26.7,\n \"ref_Te_wb\": 19.4,\n \"ref_cap\": 148100.0,\n \"ref_sensible_cap\": 97746.0,\n \"ref_cap_unit\": \"Btu/h\",\n \"compressor_type\": \"scroll\",\n \"condenser_type\": \"air\",\n \"compressor_speed\": \"constant\",\n \"sim_engine\": 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\"out_var\": \"cap-f-t\",\n \"type\": \"bi_quad\",\n \"units\": \"si\",\n \"x_min\": 35.0,\n \"y_min\": 13.88888888888889,\n \"x_max\": 35.0,\n \"y_max\": 23.88888888888889,\n \"out_min\": 0.7787418655097614,\n \"out_max\": 1.1984815618221258,\n \"coeff1\": 5.031500444002939e-07,\n \"coeff2\": 1.761025155400998e-05,\n \"coeff3\": 0.0006163588043903608,\n \"coeff4\": -2.0715748394794915e-06,\n \"coeff5\": 0.0007897130664195156,\n \"coeff6\": -7.250511938177578e-05,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 0,\n \"cap-f-ff\": {\n \"out_var\": \"cap-f-ff\",\n \"type\": \"cubic\",\n \"units\": \"si\",\n \"x_min\": 0.6825396825396826,\n \"x_max\": 1.3174603174603174,\n \"out_min\": 0.8394793926247288,\n \"out_max\": 1.158351409978308,\n \"coeff1\": 1.4905533700032638,\n \"coeff2\": -0.9722874728855598,\n \"coeff3\": 0.7737213123649698,\n \"coeff4\": -0.284760683297362,\n \"coeff5\": 0.0,\n \"coeff6\": 0.0,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 1,\n \"cap-f-t\": {\n \"out_var\": \"cap-f-t\",\n \"type\": \"bi_quad\",\n \"units\": \"si\",\n \"x_min\": 35.0,\n \"y_min\": 13.88888888888889,\n \"x_max\": 35.0,\n \"y_max\": 23.88888888888889,\n \"out_min\": 0.8058568329718004,\n \"out_max\": 1.245119305856833,\n \"coeff1\": 6.227081317679705e-07,\n \"coeff2\": 2.1794784611878725e-05,\n \"coeff3\": 0.0007628174614157655,\n \"coeff4\": -1.613625657854998e-05,\n \"coeff5\": 0.0012764609856603585,\n \"coeff6\": -0.0005647689802492418,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 1,\n \"cap-f-ff\": {\n \"out_var\": \"cap-f-ff\",\n \"type\": \"cubic\",\n \"units\": \"si\",\n \"x_min\": 0.6825396825396826,\n \"x_max\": 1.3174603174603174,\n \"out_min\": 0.8459869848156182,\n \"out_max\": 1.2190889370932756,\n \"coeff1\": 1.4008214285715805,\n \"coeff2\": -0.38728145336273956,\n \"coeff3\": 0.06795390455581332,\n \"coeff4\": -0.0452001084600363,\n \"coeff5\": 0.0,\n \"coeff6\": 0.0,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n }\n ]\n}", + "{\n \"eqp_type\": \"unitary_dx\",\n \"equip_label\": \"C_SubCool8\",\n \"num_comp_sequences\": 2,\n \"ref_To_db\": 35,\n \"ref_Te_db\": 26.7,\n \"ref_Te_wb\": 19.4,\n \"ref_cap\": 987000.0,\n \"ref_sensible_cap\": 581000.0,\n \"ref_cap_unit\": \"Btu/h\",\n \"compressor_type\": \"scroll\",\n \"condenser_type\": \"air\",\n \"compressor_speed\": \"constant\",\n \"sim_engine\": \"energyplus\",\n \"set_of_curves\": [\n {\n \"num_comp_seq\": 0,\n \"cap-f-t\": {\n \"out_var\": \"cap-f-t\",\n \"type\": \"bi_quad\",\n \"units\": \"si\",\n \"x_min\": 35.0,\n \"y_min\": 13.88888888888889,\n \"x_max\": 35.0,\n \"y_max\": 23.88888888888889,\n \"out_min\": 0.7720364741641338,\n \"out_max\": 1.1286727456940222,\n \"coeff1\": 5.90397358621854e-07,\n \"coeff2\": 2.0663907551764617e-05,\n \"coeff3\": 0.0007232367643117725,\n \"coeff4\": -1.4395854411267454e-05,\n \"coeff5\": 0.0010765913631158974,\n \"coeff6\": -0.0005038549043943547,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 0,\n \"cap-f-ff\": {\n \"out_var\": \"cap-f-ff\",\n \"type\": \"cubic\",\n \"units\": \"si\",\n \"x_min\": 0.6825396825396826,\n \"x_max\": 1.3174603174603174,\n \"out_min\": 0.8024316109422492,\n \"out_max\": 1.172239108409321,\n \"coeff1\": 1.4022169054858025,\n \"coeff2\": -0.350029787234695,\n \"coeff3\": -0.2572468085099655,\n \"coeff4\": 0.1520042553189246,\n \"coeff5\": 0.0,\n \"coeff6\": 0.0,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 1,\n \"cap-f-t\": {\n \"out_var\": \"cap-f-t\",\n \"type\": \"bi_quad\",\n \"units\": \"si\",\n \"x_min\": 35.0,\n \"y_min\": 13.88888888888889,\n \"x_max\": 35.0,\n \"y_max\": 23.88888888888889,\n \"out_min\": 0.8358662613981763,\n \"out_max\": 1.2239108409321175,\n \"coeff1\": 7.335017876208765e-07,\n \"coeff2\": 2.567256256673031e-05,\n \"coeff3\": 0.0008985396898355759,\n \"coeff4\": -2.6513262647381995e-05,\n \"coeff5\": 0.0014930402623265066,\n \"coeff6\": -0.0009279641926583595,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n },\n {\n \"num_comp_seq\": 1,\n \"cap-f-ff\": {\n \"out_var\": \"cap-f-ff\",\n \"type\": \"cubic\",\n \"units\": \"si\",\n \"x_min\": 0.6825396825396826,\n \"x_max\": 1.3174603174603174,\n \"out_min\": 0.8601823708206687,\n \"out_max\": 1.2360688956433636,\n \"coeff1\": 0.8925291141990359,\n \"coeff2\": 1.3016840425526666,\n \"coeff3\": -1.7006840425526528,\n \"coeff4\": 0.5446819148934384,\n \"coeff5\": 0.0,\n \"coeff6\": 0.0,\n \"coeff7\": 0.0,\n \"coeff8\": 0.0,\n \"coeff9\": 0.0,\n \"coeff10\": 0.0\n }\n }\n ]\n}" +] \ No newline at end of file diff --git a/DX_exploration/JSON_generating/data/.DS_Store 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a/DX_exploration/JSON_generating/data/original/DX_Equipment_Data_Collection_C_SubCool.xlsx b/DX_exploration/JSON_generating/data/original/DX_Equipment_Data_Collection_C_SubCool.xlsx new file mode 100644 index 0000000..8f3a151 Binary files /dev/null and b/DX_exploration/JSON_generating/data/original/DX_Equipment_Data_Collection_C_SubCool.xlsx differ diff --git a/DX_exploration/JSON_generating/data/original/DX_Equipment_Data_Collection_D.xlsx b/DX_exploration/JSON_generating/data/original/DX_Equipment_Data_Collection_D.xlsx new file mode 100644 index 0000000..c0308c4 Binary files /dev/null and b/DX_exploration/JSON_generating/data/original/DX_Equipment_Data_Collection_D.xlsx differ diff --git a/DX_exploration/JSON_generating/data/original/DX_Equipment_Data_Collection_L.xlsx b/DX_exploration/JSON_generating/data/original/DX_Equipment_Data_Collection_L.xlsx new file mode 100644 index 0000000..98f7ff0 Binary files /dev/null and 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/dev/null +++ b/DX_exploration/JSON_generating/explore_data_cfm.py @@ -0,0 +1,421 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import os +import json + + +class DXPerfMap: + + def __init__( + self, + xlsx_file, + sheet_ind, + rated_flow_rate=3000, # change + comp_sequences=1, + ): + + self.xlsx_file = xlsx_file + self.sheet_ind = sheet_ind + self.rated_flow_rate = rated_flow_rate + self.comp_sequences = comp_sequences + + self.df = self.load_xlsx() + self.Te_db, self.Te_wb = self.load_AHRI_conditions() + self.df_rated, self.rated_capacity, self.rated_sensible_capacity, self.df_rated_T, self.df_rated_flow = self.get_rated_conditions(self.df) + + def load_xlsx(self): + df = pd.read_excel(io=self.xlsx_file, sheet_name=self.sheet_ind) + df["indoor_coil_entering_dry_bulb_temperature"] = self.convert_F_to_C( + df["indoor_coil_entering_dry_bulb_temperature"]) + df["outdoor_coil_entering_dry_bulb_temperature"] = self.convert_F_to_C( + df["outdoor_coil_entering_dry_bulb_temperature"]) + df["indoor_coil_entering_wet_bulb_temperature"] = self.convert_F_to_C( + df["indoor_coil_entering_wet_bulb_temperature"]) + return df + + @staticmethod + + def convert_F_to_C(F): + return (F-32) * 5/9 + + ## have changed to DX Equipment's + def load_AHRI_conditions(self): + Te_db = 26.7 # 80 F + Te_wb = 19.4 # 67 F + return Te_db, Te_wb + + def get_rated_conditions(self, df): + T_db = df["indoor_coil_entering_dry_bulb_temperature"].values + T_wb = df["indoor_coil_entering_wet_bulb_temperature"].values + dist = (T_db - self.Te_db)**2 + (T_wb - self.Te_wb)**2 + # check the location of minimum distance + min_idx = np.argmin(dist) + df_rated = self.df.iloc[min_idx] + rated_capacity = df_rated["gross_total_capacity"] + rated_sensible_capacity = df_rated["gross_sensible_capacity"] + # get rated capacity + self.df["cap_f_t"] = self.df["gross_total_capacity"]/rated_capacity + self.df["ff"] = self.df["indoor_coil_air_mass_flow_rate"]/self.rated_flow_rate + # check the compressor stage + stage_len = len(np.unique(self.df["compressor_sequence_number"].values)) + + ##Get the dataframe for cap-f-t + if self.comp_sequences == 1 or stage_len == 1: # compressor stage = 1 or 2 only + df_const_T = self.df[ + (np.abs(self.df["indoor_coil_entering_dry_bulb_temperature"] - self.Te_db) < 1.0) & + (np.abs(self.df["indoor_coil_entering_wet_bulb_temperature"] - self.Te_wb) < 1.0) + ] + + #get the dataframe + df_rated_flow = self.df[(np.abs(self.df["indoor_coil_air_mass_flow_rate"] - self.rated_flow_rate) < 100)] + else: + df_const_T = dict() + df_rated_flow = dict() + for num_comp in range(self.comp_sequences): + _df = self.df.loc[self.df["compressor_sequence_number"] == (num_comp+1)] + df_const_T[f"comp_seq_{num_comp+1}"] = _df[ + (np.abs(_df["indoor_coil_entering_dry_bulb_temperature"] - self.Te_db) < 1.0) & + (np.abs(_df["indoor_coil_entering_wet_bulb_temperature"] - self.Te_wb) < 1.0) + ] + df_rated_flow[f"comp_seq_{num_comp+1}"] = _df[ + (np.abs(_df["indoor_coil_air_mass_flow_rate"] - self.rated_flow_rate) < 100)] + return df_rated, rated_capacity, rated_sensible_capacity, df_const_T, df_rated_flow + + + +class CurveFit: + + def __init__(self, xlsx_file, x, targets, fun="bi-quad", y=None): + self.xlsx_file = xlsx_file + self.x = x + self.y = y + self.targets = targets + self.function = fun + + if self.function == "bi-quad": + self.X = self.f_biquad() + elif self.function == "cubic": + self.X = self.f_cubic() + else: + self.X = None + + assert self.X is not None + self.coeffs = None + + def f_biquad(self): + assert self.y is not None + X = np.vstack( + ( + np.ones(self.x.shape[0], ), + self.x, + self.x ** 2, + self.y, + self.y ** 2, + np.multiply(self.x, self.y), + ) + ) + + return np.transpose(X) + + def f_cubic(self): + X = np.vstack( + ( + np.ones(self.x.shape[0], ), + self.x, + self.x ** 2, + self.x ** 3, + ) + ) + + return np.transpose(X) + + def fit(self): + coeffs = np.linalg.lstsq(self.X, self.targets) + self.coeffs = coeffs[0] + return self.coeffs + + def plot(self, file_str, sheet_ind='Table 1', label="Cap-f-t"): + """ + Method to compare the predicted and + """ + # Find the index of "./data/" and ".xlsx" + xlsx_file = self.xlsx_file + start_index = xlsx_file.find("./data/DX_Equipment_Data_Collection_") + len("./data/DX_Equipment_Data_Collection_") + end_index = xlsx_file.find(".xlsx") + # Extract the information between "./data/" and ".xlsx" + extracted_info = xlsx_file[start_index:end_index] + fig_dir = "./figures/" + extracted_info + "_" + sheet_ind + "/" + + # fig_dir = "./figures/D_" + sheet_ind + "/" + + #create directory + if not os.path.exists(fig_dir): + os.makedirs(fig_dir) + assert self.coeffs is not None + predictions = np.matmul(self.X, self.coeffs) + print(f"The Pearson coefficient is : {np.corrcoef(self.targets, predictions)}") + + #plot and get the R^2 metric + plt.plot(self.targets, predictions, 'ko') + plt.plot(self.targets, self.targets, 'k-') + plt.xlabel(f"{label} (Actual)", fontsize=16) + plt.ylabel(f"{label} (Predictions)", fontsize=16) + plt.savefig(os.path.join(fig_dir, f"{file_str}_R2_{np.corrcoef(self.targets, predictions)[0, 1]}_fit.png")) + + return None + +def plot(xlsx_file, sheet_ind="Table 1"): + # Find the index of "./data/" and ".xlsx" + start_index = xlsx_file.find("./data/DX_Equipment_Data_Collection_") + len("./data/DX_Equipment_Data_Collection_") + end_index = xlsx_file.find(".xlsx") + # Extract the information between "./data/" and ".xlsx" + extracted_info = xlsx_file[start_index:end_index] + + fig_dir = "./figures/" + extracted_info + "_" + sheet_ind + "/" + df = pd.read_excel(io=xlsx_file, sheet_name=sheet_ind) + #df.columns = df.index + #Define grid variables + x_cols = [ + "outdoor_coil_entering_dry_bulb_temperature", + "indoor_coil_entering_wet_bulb_temperature", + "indoor_coil_entering_dry_bulb_temperature", + "indoor_coil_air_mass_flow_rate", + "compressor_sequence_number" + ] + + #define lookup variables + y_cols = [ + "gross_total_capacity", + "gross_sensible_capacity", + "gross_power" + ] + + #create directory + if not os.path.exists(fig_dir): + os.makedirs(fig_dir) + + for yc in y_cols: + for xc in x_cols: + plt.plot(df[xc], df[yc], 'ko') + plt.xlabel(xc, fontsize=16) + plt.ylabel(yc, fontsize=16) + plt.savefig(os.path.join(fig_dir, f"filename_{yc}_vs_{xc}.png")) + plt.close() + + return None + + +# run all +def run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate): + DX = DXPerfMap(xlsx_file=xlsx_file, sheet_ind=sheet_ind, + rated_flow_rate=flow_rate,comp_sequences=num_comp_sequences) + plot(xlsx_file, sheet_ind) + + if xlsx_file[46] == "L" and num_comp_sequences == 1: + compressor_speed = "variable" + else: + compressor_speed = "constant" + equip_data = {"eqp_type": "unitary_dx", + 'equip_label': xlsx_file[46:-5]+sheet_ind[-1], + 'num_comp_sequences': num_comp_sequences, + 'ref_flow_rate': flow_rate, + 'ref_Te_db': DX.Te_db, + 'ref_Te_wb': DX.Te_wb, + 'ref_cap': DX.rated_capacity, + 'ref_sensible_cap': DX.rated_sensible_capacity, + 'ref_cap_unit': 'Btu/h', + "compressor_type": "scroll", + "condenser_type": "air", + "compressor_speed": compressor_speed, + "sim_engine": "energyplus" + # To Do ? : + # 'OAT': 'outdoor_coil_entering_dry_bulb_temperature' + # 'gross_sensible_capacity' + # 'gross_power' + # 'indoor_coil_air_mass_flow_rate' + # 'supply_fan_power' + } + #test ff + curve_set_data = [] + for nc in range(num_comp_sequences): + if num_comp_sequences == 1: + df_rated_flow = DX.df_rated_flow + else: + df_rated_flow = DX.df_rated_flow[f"comp_seq_{nc+1}"] + TotCapTFit = CurveFit(xlsx_file, + x=df_rated_flow["outdoor_coil_entering_dry_bulb_temperature"].values, + targets=df_rated_flow["cap_f_t"].values, + y=df_rated_flow["indoor_coil_entering_wet_bulb_temperature"].values + ) + #print("Curve fitting for Case I") + coef_case1 = TotCapTFit.fit() + case1_data = {"num_comp_seq": nc, + "cap-f-t": { + "out_var": "cap-f-t", + "type": "bi_quad", + # "ref_evap_fluid_flow": null, + # "ref_cond_fluid_flow": null, + # "ref_lwt": 6.67, + # "ref_ect": 35.0, + # "ref_lct": null, + "units": "si", + "x_min": df_rated_flow["outdoor_coil_entering_dry_bulb_temperature"].values.min(), + "y_min": df_rated_flow["indoor_coil_entering_wet_bulb_temperature"].values.min(), + "x_max": df_rated_flow["outdoor_coil_entering_dry_bulb_temperature"].values.max(), + "y_max": df_rated_flow["indoor_coil_entering_wet_bulb_temperature"].values.max(), + "out_min": df_rated_flow["cap_f_t"].values.min(), + "out_max": df_rated_flow["cap_f_t"].values.max(), + "coeff1": coef_case1[0], + "coeff2": coef_case1[1], + "coeff3": coef_case1[2], + "coeff4": coef_case1[3], + "coeff5": coef_case1[4], + "coeff6": coef_case1[5], + "coeff7": 0.0, + "coeff8": 0.0, + "coeff9": 0.0, + "coeff10": 0.0 + } + } + curve_set_data.append(case1_data) + print(f"coefficients for Case I: {coef_case1}") + TotCapTFit.plot(file_str=f"TotCapFlowTempFit_seq_{nc+1}", sheet_ind=DX.sheet_ind) + + # ####New Curve fit objects + if num_comp_sequences == 1: + df_rated_T = DX.df_rated_T + else: + df_rated_T = DX.df_rated_T[f"comp_seq_{nc+1}"] + TotCapFlowFit = CurveFit(xlsx_file, + x=df_rated_T["ff"].values, + targets=df_rated_T["cap_f_t"].values, + fun="cubic" + ) + # print("Curve Fitting for Case II") + coef_case2 = TotCapFlowFit.fit() + case2_data = {"num_comp_seq": nc, + "cap-f-ff": { + "out_var": "cap-f-ff", + "type": "cubic", + # "ref_evap_fluid_flow": null, + # "ref_cond_fluid_flow": null, + # "ref_lwt": 6.67, + # "ref_ect": 35.0, + # "ref_lct": null, + "units": "si", + "x_min": df_rated_T["ff"].values.min(), + "x_max": df_rated_T["ff"].values.max(), + "out_min": df_rated_T["cap_f_t"].values.min(), + "out_max": df_rated_T["cap_f_t"].values.max(), + "coeff1": coef_case2[0], + "coeff2": coef_case2[1], + "coeff3": coef_case2[2], + "coeff4": coef_case2[3], + "coeff5": 0.0, + "coeff6": 0.0, + "coeff7": 0.0, + "coeff8": 0.0, + "coeff9": 0.0, + "coeff10": 0.0 + } + } + curve_set_data.append(case2_data) + print(f"coefficients for Case II: {coef_case2}") + TotCapFlowFit.plot(file_str=f"TotCapFlowFit_seq_{nc+1}", sheet_ind=DX.sheet_ind, label="Cap-f-ff") + print(f"{xlsx_file}{sheet_ind} ran succefully.") + + # Create a new dictionary with the list under one key + set_of_curves = {"set_of_curves": curve_set_data} + equip_data.update(set_of_curves) + # Convert the updated dictionary to a JSON string + json_data = json.dumps(equip_data, indent=4) + return json_data + +if __name__ == "__main__": + + ### 1. DX_Equipment_Data_Collection_D.xlsx + xlsx_file = "./data/processed/DX_Equipment_Data_Collection_D.xlsx" + json_list = [] + ## manually run to check each table to get 'num_comp_sequences' and 'flow_rate' + # Table 1 + sheet_ind = "Table 1" + num_comp_sequences = 1 # 1 compressor + flow_rate = 2100 + j1d = run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + json_list.append(j1d) + + # Table 2 + sheet_ind = "Table 2" + num_comp_sequences = 1 # 2 compressors + flow_rate = 3000 + j2d = run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + json_list.append(j2d) + + # Table 3 + sheet_ind = "Table 3" + num_comp_sequences = 1 # 2 compressors + flow_rate = 3400 + j3d = run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + json_list.append(j3d) + + # Table 4 + sheet_ind = "Table 4" + num_comp_sequences = 1 # 2 compressors + flow_rate = 4000 + j4d = run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + json_list.append(j4d) + + # Table 5 + sheet_ind = "Table 5" + num_comp_sequences = 1 # 2 compressors + flow_rate = 5000 + j5d = run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + json_list.append(j5d) + + # Table 6 + sheet_ind = "Table 6" + num_comp_sequences = 1 # 3 compressors + flow_rate = 5010 + j6d = run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + json_list.append(j6d) + + # Table 7 + sheet_ind = "Table 7" + num_comp_sequences = 1 # 4 compressors + flow_rate = 6475 + j7d = run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + json_list.append(j7d) + + # # Table 8 + # sheet_ind = "Table 8" + # num_comp_sequences = 1 # 5 compressors + # flow_rate = 8250 + # j8d =run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + +# ### 2. DX_Equipment_Data_Collection_L.xlsx +# xlsx_file = "./data/processed/DX_Equipment_Data_Collection_L.xlsx" + +# ## manually run to check each table to get 'num_comp_sequences' and 'flow_rate' +# # Table 1 +# sheet_ind = "Table 1" +# num_comp_sequences = 1 # 1 compressor +# flow_rate = 2100 +# run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + +### 3. DX_Equipment_Data_Collection_C_SM.xlsx + # xlsx_file = "./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx" + + # ## manually run to check each table to get 'num_comp_sequences' and 'flow_rate' + # # Table 1 + # sheet_ind = "Table 1" + # num_comp_sequences = 1 # 1 compressor + # flow_rate = 2100 + # run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + + # Specify the filename + filename = 'DX_cfm.json' + + # Write the list of dictionaries to a JSON file + with open(filename, 'w') as file: + json.dump(json_list, file, indent=4) diff --git a/DX_exploration/JSON_generating/explore_data_oat.py b/DX_exploration/JSON_generating/explore_data_oat.py new file mode 100644 index 0000000..287c5bc --- /dev/null +++ b/DX_exploration/JSON_generating/explore_data_oat.py @@ -0,0 +1,518 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import os +import json + +## Q1: AHRI rating conditions for chillers and DX Equipements + # Chillers: outdoor coil entering dry bulb, indoor coil entering wet bulb, air flow + # 80 F, 67 F, 1.1(?) + # DX Equip: *indoor* coil entering dry bulb, indoor coil entering wet bulb, *outdoor coil entering dry bulb* + # 80 F, 67 F, 95 F + +## Q2: modify scripts + # Method 1: + # still use air flow + # see "explore_data_cfm" and "figures" + # Method 2: + # as described above, outdoor enter dry --> indoor enter dry; air flow --> OAT + # see "explore_data_oat" and "figures_OAT" + + + + +class DXPerfMap: + + def __init__( + self, + xlsx_file, + sheet_ind, + rated_To_db=35, #### 95 F = 35 C + comp_sequences=1, + ): + + self.xlsx_file = xlsx_file + self.sheet_ind = sheet_ind + self.rated_To_db = rated_To_db + self.comp_sequences = comp_sequences + + self.df = self.load_xlsx() + self.Te_db, self.Te_wb = self.load_AHRI_conditions(xlsx_file) + self.df_rated, self.rated_capacity, self.rated_sensible_capacity, self.df_rated_T, self.df_rated_OAT = self.get_rated_conditions(self.df) + + def load_xlsx(self): + df = pd.read_excel(io=self.xlsx_file, sheet_name=self.sheet_ind) + df["indoor_coil_entering_dry_bulb_temperature"] = self.convert_F_to_C( + df["indoor_coil_entering_dry_bulb_temperature"]) + df["outdoor_coil_entering_dry_bulb_temperature"] = self.convert_F_to_C( + df["outdoor_coil_entering_dry_bulb_temperature"]) + df["indoor_coil_entering_wet_bulb_temperature"] = self.convert_F_to_C( + df["indoor_coil_entering_wet_bulb_temperature"]) + return df + + @staticmethod + + def convert_F_to_C(F): + return (F-32) * 5/9 + + ## have changed to DX Equipment's + def load_AHRI_conditions(self, xlsx_file): + # if xlsx_file == "./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx": + # Te_db = 19.4 # 67 F + # Te_wb = 13.9 # 57 F + # else: + # Te_db = 26.7 # 80 F + # Te_wb = 19.4 # 67 F + + Te_db = 26.7 # 80 F + Te_wb = 19.4 # 67 F + + return Te_db, Te_wb + + def get_rated_conditions(self, df): + T_db = df["indoor_coil_entering_dry_bulb_temperature"].values + T_wb = df["indoor_coil_entering_wet_bulb_temperature"].values + dist = (T_db - self.Te_db)**2 + (T_wb - self.Te_wb)**2 + # check the location of minimum distance + min_idx = np.argmin(dist) + df_rated = self.df.iloc[min_idx] + rated_capacity = df_rated["gross_total_capacity"] + rated_sensible_capacity = df_rated["gross_sensible_capacity"] + # get rated capacity + self.df["cap_f_t"] = self.df["gross_total_capacity"]/rated_capacity + self.df["ff"] = self.df["outdoor_coil_entering_dry_bulb_temperature"]/self.rated_To_db + # check the compressor stage + stage_len = len(np.unique(self.df["compressor_sequence_number"].values)) + + ##Get the dataframe for cap-f-t + if self.comp_sequences == 1 or stage_len == 1: # compressor stage = 1 or 2 only + df_const_T = self.df[ + (np.abs(self.df["indoor_coil_entering_dry_bulb_temperature"] - self.Te_db) < 1.0) & + (np.abs(self.df["indoor_coil_entering_wet_bulb_temperature"] - self.Te_wb) < 1.0) + ] + + #get the dataframe + df_rated_OAT = self.df[(np.abs(self.df["outdoor_coil_entering_dry_bulb_temperature"] - self.rated_To_db) < 1.0)] + else: + df_const_T = dict() + df_rated_OAT = dict() + for num_comp in range(self.comp_sequences): + _df = self.df.loc[self.df["compressor_sequence_number"] == (num_comp+1)] + df_const_T[f"comp_seq_{num_comp+1}"] = _df[ + (np.abs(_df["indoor_coil_entering_dry_bulb_temperature"] - self.Te_db) < 1.0) & + (np.abs(_df["indoor_coil_entering_wet_bulb_temperature"] - self.Te_wb) < 1.0) + ] + df_rated_OAT[f"comp_seq_{num_comp+1}"] = _df[ + (np.abs(_df["outdoor_coil_entering_dry_bulb_temperature"] - self.rated_To_db) < 1.0)] + return df_rated, rated_capacity, rated_sensible_capacity, df_const_T, df_rated_OAT + + + +class CurveFit: + + def __init__(self, xlsx_file, x, targets, fun="bi-quad", y=None): + self.xlsx_file = xlsx_file + self.x = x + self.y = y + self.targets = targets + self.function = fun + + if self.function == "bi-quad": + self.X = self.f_biquad() + elif self.function == "cubic": + self.X = self.f_cubic() + else: + self.X = None + + assert self.X is not None + self.coeffs = None + + def f_biquad(self): + assert self.y is not None + X = np.vstack( + ( + np.ones(self.x.shape[0], ), + self.x, + self.x ** 2, + self.y, + self.y ** 2, + np.multiply(self.x, self.y), + ) + ) + + return np.transpose(X) + + def f_cubic(self): + X = np.vstack( + ( + np.ones(self.x.shape[0], ), + self.x, + self.x ** 2, + self.x ** 3, + ) + ) + + return np.transpose(X) + + def fit(self): + coeffs = np.linalg.lstsq(self.X, self.targets) + self.coeffs = coeffs[0] + return self.coeffs + + def plot(self, file_str, sheet_ind='Table 1', label="Cap-f-t"): + """ + Method to compare the predicted and + """ + # Find the index of "./data/" and ".xlsx" + xlsx_file = self.xlsx_file + start_index = xlsx_file.find("./data/DX_Equipment_Data_Collection_") + len("./data/DX_Equipment_Data_Collection_") + end_index = xlsx_file.find(".xlsx") + # Extract the information between "./data/" and ".xlsx" + extracted_info = xlsx_file[start_index:end_index] + fig_dir = "./figures_OAT/" + extracted_info + "_" + sheet_ind + "/" + + #create directory + if not os.path.exists(fig_dir): + os.makedirs(fig_dir) + assert self.coeffs is not None + predictions = np.matmul(self.X, self.coeffs) + print(f"The Pearson coefficient is : {np.corrcoef(self.targets, predictions)}") + + #plot and get the R^2 metric + plt.plot(self.targets, predictions, 'ko') + plt.plot(self.targets, self.targets, 'k-') + plt.xlabel(f"{label} (Actual)", fontsize=16) + plt.ylabel(f"{label} (Predictions)", fontsize=16) + plt.savefig(os.path.join(fig_dir, f"{file_str}_R2_{np.corrcoef(self.targets, predictions)[0, 1]}_fit.png")) + + return None + +def plot(xlsx_file, sheet_ind="Table 1"): + # Find the index of "./data/" and ".xlsx" + start_index = xlsx_file.find("./data/DX_Equipment_Data_Collection_") + len("./data/DX_Equipment_Data_Collection_") + end_index = xlsx_file.find(".xlsx") + # Extract the information between "./data/" and ".xlsx" + extracted_info = xlsx_file[start_index:end_index] + + fig_dir = "./figures_OAT/" + extracted_info + "_" + sheet_ind + "/" + df = pd.read_excel(io=xlsx_file, sheet_name=sheet_ind) + #df.columns = df.index + #Define grid variables + x_cols = [ + "outdoor_coil_entering_dry_bulb_temperature", + "indoor_coil_entering_wet_bulb_temperature", + "indoor_coil_entering_dry_bulb_temperature", + "indoor_coil_air_mass_flow_rate", + "compressor_sequence_number" + ] + + #define lookup variables + y_cols = [ + "gross_total_capacity", + "gross_sensible_capacity", + "gross_power" + ] + + #create directory + if not os.path.exists(fig_dir): + os.makedirs(fig_dir) + + for yc in y_cols: + for xc in x_cols: + plt.plot(df[xc], df[yc], 'ko') + plt.xlabel(xc, fontsize=16) + plt.ylabel(yc, fontsize=16) + plt.savefig(os.path.join(fig_dir, f"filename_{yc}_vs_{xc}.png")) + plt.close() + + return None + + +# run all +def run_all(xlsx_file,sheet_ind,num_comp_sequences,rated_To_db): + DX = DXPerfMap(xlsx_file=xlsx_file, sheet_ind=sheet_ind, + rated_To_db=rated_To_db,comp_sequences=num_comp_sequences) + plot(xlsx_file, sheet_ind) + if xlsx_file[46] == "L" and num_comp_sequences == 1: + compressor_speed = "variable" + else: + compressor_speed = "constant" + equip_data = {"eqp_type": "unitary_dx", + 'equip_label': xlsx_file[46:-5]+sheet_ind[-1], + 'num_comp_sequences': num_comp_sequences, + 'ref_To_db': rated_To_db, + 'ref_Te_db': DX.Te_db, + 'ref_Te_wb': DX.Te_wb, + 'ref_cap': DX.rated_capacity, + 'ref_sensible_cap': DX.rated_sensible_capacity, + 'ref_cap_unit': 'Btu/h', + "compressor_type": "scroll", + "condenser_type": "air", + "compressor_speed": compressor_speed, + "sim_engine": "energyplus" + # 'gross_sensible_capacity' + # 'gross_power' + # 'indoor_coil_air_mass_flow_rate' + # 'supply_fan_power' + } + #test ff + curve_set_data = [] + for nc in range(num_comp_sequences): + if num_comp_sequences == 1: + df_rated_OAT = DX.df_rated_OAT + else: + df_rated_OAT = DX.df_rated_OAT[f"comp_seq_{nc+1}"] + TotCapTFit = CurveFit(xlsx_file, + x=df_rated_OAT["outdoor_coil_entering_dry_bulb_temperature"].values, + targets=df_rated_OAT["cap_f_t"].values, + y=df_rated_OAT["indoor_coil_entering_wet_bulb_temperature"].values + ) + #print("Curve fitting for Case I") + coef_case1 = TotCapTFit.fit() + case1_data = {"num_comp_seq": nc, + "cap-f-t": { + "out_var": "cap-f-t", + "type": "bi_quad", + # "ref_evap_fluid_flow": null, + # "ref_cond_fluid_flow": null, + # "ref_lwt": 6.67, + # "ref_ect": 35.0, + # "ref_lct": null, + "units": "si", + "x_min": df_rated_OAT["outdoor_coil_entering_dry_bulb_temperature"].values.min(), + "y_min": df_rated_OAT["indoor_coil_entering_wet_bulb_temperature"].values.min(), + "x_max": df_rated_OAT["outdoor_coil_entering_dry_bulb_temperature"].values.max(), + "y_max": df_rated_OAT["indoor_coil_entering_wet_bulb_temperature"].values.max(), + "out_min": df_rated_OAT["cap_f_t"].values.min(), + "out_max": df_rated_OAT["cap_f_t"].values.max(), + "coeff1": coef_case1[0], + "coeff2": coef_case1[1], + "coeff3": coef_case1[2], + "coeff4": coef_case1[3], + "coeff5": coef_case1[4], + "coeff6": coef_case1[5], + "coeff7": 0.0, + "coeff8": 0.0, + "coeff9": 0.0, + "coeff10": 0.0 + } + } + curve_set_data.append(case1_data) + print(f"coefficients for Case I: {coef_case1}") + TotCapTFit.plot(file_str=f"TotCapFlowTempFit_seq_{nc+1}", sheet_ind=DX.sheet_ind) + + # ####New Curve fit objects + if num_comp_sequences == 1: + df_rated_T = DX.df_rated_T + else: + df_rated_T = DX.df_rated_T[f"comp_seq_{nc+1}"] + TotCapFlowFit = CurveFit(xlsx_file, + x=df_rated_T["ff"].values, + targets=df_rated_T["cap_f_t"].values, + fun="cubic" + ) + # print("Curve Fitting for Case II") + coef_case2 = TotCapFlowFit.fit() + case2_data = {"num_comp_seq": nc, + "cap-f-ff": { + "out_var": "cap-f-ff", + "type": "cubic", + # "ref_evap_fluid_flow": null, + # "ref_cond_fluid_flow": null, + # "ref_lwt": 6.67, + # "ref_ect": 35.0, + # "ref_lct": null, + "units": "si", + "x_min": df_rated_T["ff"].values.min(), + "x_max": df_rated_T["ff"].values.max(), + "out_min": df_rated_T["cap_f_t"].values.min(), + "out_max": df_rated_T["cap_f_t"].values.max(), + "coeff1": coef_case2[0], + "coeff2": coef_case2[1], + "coeff3": coef_case2[2], + "coeff4": coef_case2[3], + "coeff5": 0.0, + "coeff6": 0.0, + "coeff7": 0.0, + "coeff8": 0.0, + "coeff9": 0.0, + "coeff10": 0.0 + } + } + curve_set_data.append(case2_data) + print(f"coefficients for Case II: {coef_case2}") + TotCapFlowFit.plot(file_str=f"TotCapFlowFit_seq_{nc+1}", sheet_ind=DX.sheet_ind, label="Cap-f-ff") + + print(f"{xlsx_file}{sheet_ind} ran succefully.") + + # Create a new dictionary with the list under one key + set_of_curves = {"set_of_curves": curve_set_data} + equip_data.update(set_of_curves) + # Convert the updated dictionary to a JSON string + json_data = json.dumps(equip_data, indent=4) + return json_data + +if __name__ == "__main__": + + json_list = [] + + ### 1. DX_Equipment_Data_Collection_D.xlsx + xlsx_file = "./data/processed/DX_Equipment_Data_Collection_D.xlsx" + + ## manually run to check each table to get 'num_comp_sequences' and 'flow_rate' + + # Table 1 + sheet_ind = "Table 1" + num_comp_sequences = 1 # 1 compressor + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 2 + sheet_ind = "Table 2" + num_comp_sequences = 1 # 2 compressors + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 3 + sheet_ind = "Table 3" + num_comp_sequences = 1 # 2 compressors + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 4 + sheet_ind = "Table 4" + num_comp_sequences = 1 # 2 compressors + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 5 + sheet_ind = "Table 5" + num_comp_sequences = 1 # 2 compressors + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 6 + sheet_ind = "Table 6" + num_comp_sequences = 1 # 3 compressors + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 7 + sheet_ind = "Table 7" + num_comp_sequences = 1 # 4 compressors + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 8 + sheet_ind = "Table 8" + num_comp_sequences = 1 # 5 compressors + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + +### 2. DX_Equipment_Data_Collection_L.xlsx + xlsx_file = "./data/processed/DX_Equipment_Data_Collection_L.xlsx" + + ## manually run to check each table to get 'num_comp_sequences' and 'flow_rate' + # Table 1 + sheet_ind = "Table 1" + num_comp_sequences = 1 # 1 compressor + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 2 + sheet_ind = "Table 2" + num_comp_sequences = 3 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 3 + sheet_ind = "Table 3" + num_comp_sequences = 3 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 4 + sheet_ind = "Table 4" + num_comp_sequences = 4 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 5 + sheet_ind = "Table 5" + num_comp_sequences = 4 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 6 + sheet_ind = "Table 6" + num_comp_sequences = 4 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 7 + sheet_ind = "Table 7" + num_comp_sequences = 2 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 8 + sheet_ind = "Table 8" + num_comp_sequences = 2 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 9 + sheet_ind = "Table 9" + num_comp_sequences = 2 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 10 + sheet_ind = "Table 10" + num_comp_sequences = 2 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + +### 3. DX_Equipment_Data_Collection_C_SM.xlsx + xlsx_file = "./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx" + + ## manually run to check each table to get 'num_comp_sequences' and 'flow_rate' + # Table 1-8, 10 + sheet_inds = ["Table 1","Table 2","Table 3","Table 4","Table 5","Table 6","Table 7","Table 8","Table 10"] + # json_list = [] + for sheet_ind in sheet_inds: + num_comp_sequences = 1 # 1 compressor + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Table 9, 11-18 + sheet_inds = ["Table 9","Table 11","Table 12","Table 13","Table 14","Table 15","Table 16","Table 17","Table 18",] + for sheet_ind in sheet_inds: + num_comp_sequences = 2 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + +### 4. DX_Equipment_Data_Collection_C_SubCool.xlsx + xlsx_file = "./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx" + + ## manually run to check each table to get 'num_comp_sequences' and 'flow_rate' + # Table 1-8, 10 + sheet_inds = ["Table 1","Table 2","Table 3","Table 4","Table 5","Table 6","Table 7","Table 8","Table 10"] + # json_list = [] + for sheet_ind in sheet_inds: + num_comp_sequences = 1 # 1 compressor + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + # Table 9, 11-18 + sheet_inds = ["Table 9","Table 11","Table 12","Table 13","Table 14","Table 15","Table 16","Table 17","Table 18",] + for sheet_ind in sheet_inds: + num_comp_sequences = 2 + json_data = run_all(xlsx_file,sheet_ind,num_comp_sequences,35) + json_list.append(json_data) + + # Specify the filename + filename = 'DX_oat.json' + + # Write the list of dictionaries to a JSON file + with open(filename, 'w') as file: + json.dump(json_list, file, indent=4) \ No newline at 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{ + "cell_type": "code", + "execution_count": 14, + "id": "f7c7aa56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting xlsxwriterNote: you may need to restart the kernel to use updated packages.\n", + "\n", + " Downloading XlsxWriter-3.1.9-py3-none-any.whl (154 kB)\n", + " 0.0/154.8 kB ? eta -:--:--\n", + " ---------- 41.0/154.8 kB 2.0 MB/s eta 0:00:01\n", + " --------------------------- 112.6/154.8 kB 2.2 MB/s eta 0:00:01\n", + " -------------------------------------- 154.8/154.8 kB 1.6 MB/s eta 0:00:00\n", + "Installing collected packages: xlsxwriter\n", + "Successfully installed xlsxwriter-3.1.9\n" + ] + } + ], + "source": [ + "# pip install xlsxwriter\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75a633a8", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import xlsxwriter" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "941a58b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame for sheet 'Table 1' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 1)\n", + "DataFrame for sheet 'Table 2' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 2)\n", + "DataFrame for sheet 'Table 3' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 3)\n", + "DataFrame for sheet 'Table 4' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 4)\n", + "DataFrame for sheet 'Table 5' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 5)\n", + "DataFrame for sheet 'Table 6' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 6)\n", + "DataFrame for sheet 'Table 7' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 7)\n", + "DataFrame for sheet 'Table 8' saved to: ./data/processed/DX_Equipment_Data_Collection_D.xlsx (Sheet: Table 8)\n", + "All sheets saved to a single Excel file: ./data/processed/DX_Equipment_Data_Collection_D.xlsx\n", + "--------------------\n", + "DataFrame for sheet 'Table 1' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 1)\n", + "DataFrame for sheet 'Table 2' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 2)\n", + "DataFrame for sheet 'Table 3' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 3)\n", + "DataFrame for sheet 'Table 4' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 4)\n", + "DataFrame for sheet 'Table 5' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 5)\n", + "DataFrame for sheet 'Table 6' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 6)\n", + "DataFrame for sheet 'Table 7' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 7)\n", + "DataFrame for sheet 'Table 8' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 8)\n", + "DataFrame for sheet 'Table 9' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 9)\n", + "DataFrame for sheet 'Table 10' saved to: ./data/processed/DX_Equipment_Data_Collection_L.xlsx (Sheet: Table 10)\n", + "All sheets saved to a single Excel file: ./data/processed/DX_Equipment_Data_Collection_L.xlsx\n", + "--------------------\n", + "DataFrame for sheet 'Table 1' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 1)\n", + "DataFrame for sheet 'Table 2' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 2)\n", + "DataFrame for sheet 'Table 3' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 3)\n", + "DataFrame for sheet 'Table 4' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 4)\n", + "DataFrame for sheet 'Table 5' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 5)\n", + "DataFrame for sheet 'Table 6' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 6)\n", + "DataFrame for sheet 'Table 7' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 7)\n", + "DataFrame for sheet 'Table 8' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 8)\n", + "DataFrame for sheet 'Table 9' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 9)\n", + "DataFrame for sheet 'Table 10' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 10)\n", + "DataFrame for sheet 'Table 11' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 11)\n", + "DataFrame for sheet 'Table 12' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 12)\n", + "DataFrame for sheet 'Table 13' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 13)\n", + "DataFrame for sheet 'Table 14' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 14)\n", + "DataFrame for sheet 'Table 15' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 15)\n", + "DataFrame for sheet 'Table 16' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 16)\n", + "DataFrame for sheet 'Table 17' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 17)\n", + "DataFrame for sheet 'Table 18' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx (Sheet: Table 18)\n", + "All sheets saved to a single Excel file: ./data/processed/DX_Equipment_Data_Collection_C_SM.xlsx\n", + "--------------------\n", + "DataFrame for sheet 'Table 1' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 1)\n", + "DataFrame for sheet 'Table 2' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 2)\n", + "DataFrame for sheet 'Table 3' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 3)\n", + "DataFrame for sheet 'Table 4' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 4)\n", + "DataFrame for sheet 'Table 5' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 5)\n", + "DataFrame for sheet 'Table 6' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 6)\n", + "DataFrame for sheet 'Table 7' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 7)\n", + "DataFrame for sheet 'Table 8' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 8)\n", + "DataFrame for sheet 'Table 9' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 9)\n", + "DataFrame for sheet 'Table 10' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 10)\n", + "DataFrame for sheet 'Table 11' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 11)\n", + "DataFrame for sheet 'Table 12' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 12)\n", + "DataFrame for sheet 'Table 13' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 13)\n", + "DataFrame for sheet 'Table 14' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 14)\n", + "DataFrame for sheet 'Table 15' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 15)\n", + "DataFrame for sheet 'Table 16' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 16)\n", + "DataFrame for sheet 'Table 17' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 17)\n", + "DataFrame for sheet 'Table 18' saved to: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx (Sheet: Table 18)\n", + "All sheets saved to a single Excel file: ./data/processed/DX_Equipment_Data_Collection_C_SubCool.xlsx\n", + "--------------------\n" + ] + } + ], + "source": [ + "def process_excel_file(input_path, output_folder, new_column_names):\n", + " dfs = pd.read_excel(input_path, sheet_name=None, header=6, usecols=\"B:L\", engine=\"openpyxl\")\n", + " os.makedirs(output_folder, exist_ok=True)\n", + "\n", + " # Create a writer to save all sheets in a single Excel file\n", + " output_file_path = os.path.join(output_folder, f\"{os.path.basename(input_path)}\")\n", + " with pd.ExcelWriter(output_file_path, engine='xlsxwriter') as writer:\n", + " for sheet_name, df in dfs.items():\n", + " if len(new_column_names) == len(df.columns):\n", + " df.columns = new_column_names\n", + " df.to_excel(writer, sheet_name=sheet_name, index=False)\n", + " print(f\"DataFrame for sheet '{sheet_name}' saved to: {output_file_path} (Sheet: {sheet_name})\")\n", + " else:\n", + " print(f\"Number of new column names does not match the number of columns in sheet '{sheet_name}'. Skipping.\")\n", + "\n", + " print(f\"All sheets saved to a single Excel file: {output_file_path}\")\n", + "\n", + "\n", + "input_excel_path_list = [\"./data/original/DX_Equipment_Data_Collection_D.xlsx\",\n", + " \"./data/original/DX_Equipment_Data_Collection_L.xlsx\",\n", + " \"./data/original/DX_Equipment_Data_Collection_C_SM.xlsx\",\n", + " \"./data/original/DX_Equipment_Data_Collection_C_SubCool.xlsx\"]\n", + "output_folder_path = \"./data/processed/\"\n", + "new_column_names=[\"outdoor_coil_entering_dry_bulb_temperature\", \"indoor_coil_entering_dry_bulb_temperature\",\n", + " \"indoor_coil_entering_wet_bulb_temperature\", \"indoor_coil_entering_relative_humidity\",\n", + " \"gross_total_capacity\", \"gross_sensible_capacity\", \"gross_power\", \"indoor_coil_air_mass_flow_rate\",\n", + " \"supply_fan_power\", \"outside_fan_power\", \"compressor_sequence_number\"]\n", + "\n", + "for input_excel_path in input_excel_path_list:\n", + " process_excel_file(input_excel_path, output_folder_path, new_column_names)\n", + " print(\"--------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "047a93d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Table 1',\n", + " 'Table 2',\n", + " 'Table 3',\n", + " 'Table 4',\n", + " 'Table 5',\n", + " 'Table 6',\n", + " 'Table 7',\n", + " 'Table 8']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# test\n", + "file_path = \"./data/original/DX_Equipment_Data_Collection_D.xlsx\"\n", + "sheet_names = pd.ExcelFile(file_path).sheet_names\n", + "sheet_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b78f41ee", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/archive/example scripts/.ipynb_checkpoints/data_exploration-checkpoint.ipynb b/DX_exploration/archive/example scripts/.ipynb_checkpoints/data_exploration-checkpoint.ipynb new file mode 100644 index 0000000..884c028 --- /dev/null +++ b/DX_exploration/archive/example scripts/.ipynb_checkpoints/data_exploration-checkpoint.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c2a9443b", + "metadata": {}, + "outputs": [], + "source": [ + "#########################################################################\n", + "# Author: Yiting Zhang\n", + "# Created on: 11/03/2023\n", + "# Description: This file is modified from \"exlpore_data.py\".\n", + "#########################################################################\n", + "# To Do:\n", + "#\n", + "#\n", + "#\n", + "#########################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "810f6cf9", + "metadata": {}, + "outputs": [], + "source": [ + "############################# Load Packages #############################\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from WetBulb import WetBulb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebcd1820", + "metadata": {}, + "outputs": [], + "source": [ + "############################## Explore Data #############################\n", + "\n", + "class DXPerfMap:\n", + "\n", + " def __init__(\n", + " self,\n", + " xlsx_file,\n", + " rated_flow_rate=1.11,\n", + " comp_sequences=1,\n", + " ):\n", + "\n", + " self.xlsx_file = xlsx_file\n", + " self.rated_flow_rate = rated_flow_rate\n", + " self.comp_sequences = comp_sequences\n", + "\n", + " self.df = self.load_xlsx()\n", + " self.Te_db, self.Te_wb = self.load_AHRI_conditions()\n", + " self.df_rated, self.rated_capacity, self.df_rated_T, self.df_rated_flow = self.get_rated_conditions(self.df)\n", + "\n", + " def load_xlsx(self):\n", + " df = pd.read_excel(io=self.xlsx_file, sheet_name=\"performance_map_cooling\", skiprows=[0, 1, 3])\n", + " df[\"indoor_coil_entering_dry_bulb_temperature\"] = self.convert_K_to_C(\n", + " df[\"indoor_coil_entering_dry_bulb_temperature\"])\n", + " df[\"outdoor_coil_entering_dry_bulb_temperature\"] = self.convert_K_to_C(\n", + " df[\"outdoor_coil_entering_dry_bulb_temperature\"])\n", + "\n", + " T_wb, _, _ = WetBulb(\n", + " TemperatureC=df[\"indoor_coil_entering_dry_bulb_temperature\"].values,\n", + " Pressure=df[\"ambient_absolute_air_pressure\"].values,\n", + " Humidity=100*df[\"indoor_coil_entering_relative_humidity\"].values, #relative humidity is in 100%\n", + " HumidityMode=1\n", + " )\n", + " df[\"indoor_coil_entering_wet_bulb_temperature\"] = T_wb\n", + " return df\n", + "\n", + " @staticmethod\n", + " def convert_K_to_C(K):\n", + " return K - 273.0\n", + "\n", + " def load_AHRI_conditions(self):\n", + " Te_db = 26.7\n", + " Te_wb = 19.4\n", + " return Te_db, Te_wb\n", + "\n", + " def get_rated_conditions(self, df):\n", + " T_db = df[\"outdoor_coil_entering_dry_bulb_temperature\"].values\n", + " T_wb = df[\"indoor_coil_entering_wet_bulb_temperature\"].values\n", + " dist = (T_db - self.Te_db)**2 + (T_wb - self.Te_wb)**2\n", + " #check the location of minimum distance\n", + " min_idx = np.argmin(dist)\n", + " df_rated = self.df.iloc[min_idx]\n", + " rated_capacity = df_rated[\"gross_total_capacity\"]\n", + " #get rated capacity\n", + " self.df[\"cap_f_t\"] = self.df[\"gross_total_capacity\"]/rated_capacity\n", + " self.df[\"ff\"] = self.df[\"indoor_coil_air_mass_flow_rate\"]/self.rated_flow_rate\n", + "\n", + " ##Get the dataframe for cap-f-t\n", + " if self.comp_sequences == 1:\n", + " df_const_T = self.df[\n", + " (np.abs(self.df[\"outdoor_coil_entering_dry_bulb_temperature\"] - self.Te_db) < 1.0) &\n", + " (np.abs(self.df[\"indoor_coil_entering_wet_bulb_temperature\"] - self.Te_wb) < 1.0)\n", + " ]\n", + "\n", + " #get the dataframe\n", + " df_rated_flow = self.df[(np.abs(self.df[\"indoor_coil_air_mass_flow_rate\"] - self.rated_flow_rate) < 0.1)]\n", + " else:\n", + " df_const_T = dict()\n", + " df_rated_flow = dict()\n", + " for num_comp in range(self.comp_sequences):\n", + " _df = self.df.loc[self.df[\"compressor_sequence_number\"] == (num_comp+1)]\n", + " df_const_T[f\"comp_seq_{num_comp+1}\"] = _df[\n", + " (np.abs(_df[\"outdoor_coil_entering_dry_bulb_temperature\"] - self.Te_db) < 1.0) &\n", + " (np.abs(_df[\"indoor_coil_entering_wet_bulb_temperature\"] - self.Te_wb) < 1.0)\n", + " ]\n", + " df_rated_flow[f\"comp_seq_{num_comp+1}\"] = _df[\n", + " (np.abs(_df[\"indoor_coil_air_mass_flow_rate\"] - self.rated_flow_rate) < 0.1)]\n", + " return df_rated, rated_capacity, df_const_T, df_rated_flow\n", + "\n", + "\n", + "\n", + "class CurveFit:\n", + "\n", + " def __init__(self, x, targets, fun=\"bi-quad\", y=None):\n", + " self.x = x\n", + " self.y = y\n", + " self.targets = targets\n", + " self.function = fun\n", + "\n", + " if self.function == \"bi-quad\":\n", + " self.X = self.f_biquad()\n", + " elif self.function == \"cubic\":\n", + " self.X = self.f_cubic()\n", + " else:\n", + " self.X = None\n", + "\n", + " assert self.X is not None\n", + " self.coeffs = None\n", + "\n", + " def f_biquad(self):\n", + " assert self.y is not None\n", + " X = np.vstack(\n", + " (\n", + " np.ones(self.x.shape[0], ),\n", + " self.x,\n", + " self.x ** 2,\n", + " self.y,\n", + " self.y ** 2,\n", + " np.multiply(self.x, self.y),\n", + " )\n", + " )\n", + "\n", + " return np.transpose(X)\n", + "\n", + " def f_cubic(self):\n", + " X = np.vstack(\n", + " (\n", + " np.ones(self.x.shape[0], ),\n", + " self.x,\n", + " self.x ** 2,\n", + " self.x ** 3,\n", + " )\n", + " )\n", + "\n", + " return np.transpose(X)\n", + "\n", + " def fit(self):\n", + " coeffs = np.linalg.lstsq(self.X, self.targets)\n", + " self.coeffs = coeffs[0]\n", + " return self.coeffs\n", + "\n", + " def plot(self, file_str, fig_dir=\"figures\", label=\"Cap-f-t\"):\n", + " \"\"\"\n", + " Method to compare the predicted and\n", + " \"\"\"\n", + " assert self.coeffs is not None\n", + " predictions = np.matmul(self.X, self.coeffs)\n", + " print(f\"The Pearson coefficient is : {np.corrcoef(self.targets, predictions)}\")\n", + "\n", + " #plot and get the R^2 metric\n", + " plt.plot(self.targets, predictions, 'ko')\n", + " plt.plot(self.targets, self.targets, 'k-')\n", + " plt.xlabel(f\"{label} (Actual)\", fontsize=16)\n", + " plt.ylabel(f\"{label} (Predictions)\", fontsize=16)\n", + " plt.savefig(os.path.join(fig_dir, f\"{file_str}_R2_{np.corrcoef(self.targets, predictions)[0, 1]}_fit.png\"))\n", + "\n", + " return None\n", + "\n", + "def plot(xlsx_file, filename, fig_dir=\"./figures/\"):\n", + "\n", + " df = pd.read_excel(io=xlsx_file, sheet_name=\"performance_map_cooling\", skiprows=[0, 1, 3])\n", + " #df.columns = df.index\n", + " #Define grid variables\n", + " x_cols = [\n", + " \"outdoor_coil_entering_dry_bulb_temperature\",\n", + " \"indoor_coil_entering_relative_humidity\",\n", + " \"indoor_coil_entering_dry_bulb_temperature\",\n", + " \"indoor_coil_air_mass_flow_rate\",\n", + " \"compressor_sequence_number\",\n", + " \"ambient_absolute_air_pressure\"\n", + " ]\n", + "\n", + " #define lookup variables\n", + " y_cols = [\n", + " \"gross_total_capacity\",\n", + " \"gross_sensible_capacity\",\n", + " \"gross_power\"\n", + " ]\n", + "\n", + " #create directory\n", + " if not os.path.exists(fig_dir):\n", + " os.makedirs(fig_dir)\n", + "\n", + " for yc in y_cols:\n", + " for xc in x_cols:\n", + " plt.plot(df[xc], df[yc], 'ko')\n", + " plt.xlabel(xc, fontsize=16)\n", + " plt.ylabel(yc, fontsize=16)\n", + " plt.savefig(os.path.join(fig_dir, f\"filename_{yc}_vs_{xc}.png\"))\n", + " plt.close()\n", + "\n", + " return None\n", + "\n", + "def test_ff(df, fig_dir=\"./figures/\"):\n", + " plt.plot(df[\"ff\"], df[\"cap_f_t\"], 'ko')\n", + " plt.savefig(os.path.join(fig_dir, \"cap-f-t_v_ff.png\"))\n", + " plt.close()\n", + "\n", + " return None\n", + "\n", + "##create a helper function\n", + "def compute_Twb(T_db, RH, pressure):\n", + " print(f\"T_db: {T_db}, RH: {RH}, pressure: {pressure}\")\n", + " Hum_mode = 1\n", + " T_wb, _, _ = WetBulb(T_db,pressure,RH*100,Hum_mode)\n", + " return S[5]\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " xlsx_file = \"./data/HPDM.RS0004.a205.xlsx\"\n", + " num_comp_sequences = 2\n", + " DX = DXPerfMap(xlsx_file=xlsx_file, rated_flow_rate=1.11,comp_sequences=num_comp_sequences)\n", + "\n", + " #test ff\n", + " for nc in range(num_comp_sequences):\n", + " df_rated_flow = DX.df_rated_flow[f\"comp_seq_{nc+1}\"]\n", + " TotCapTFit = CurveFit(\n", + " x=df_rated_flow[\"outdoor_coil_entering_dry_bulb_temperature\"].values,\n", + " targets=df_rated_flow[\"cap_f_t\"].values,\n", + " y=df_rated_flow[\"indoor_coil_entering_wet_bulb_temperature\"].values\n", + " )\n", + " #print(\"Curve fitting for Case I\")\n", + " TotCapTFit.fit()\n", + " TotCapTFit.plot(file_str=f\"TotCapFlowTempFit_seq_{nc+1}\")\n", + "\n", + " # ####New Curve fit objects\n", + " df_rated_T = DX.df_rated_T[f\"comp_seq_{nc+1}\"]\n", + " TotCapFlowFit = CurveFit(\n", + " x=df_rated_T[\"ff\"].values,\n", + " targets=df_rated_T[\"cap_f_t\"].values,\n", + " fun=\"cubic\"\n", + " )\n", + " # print(\"Curve Fitting for Case II\")\n", + " TotCapFlowFit.fit()\n", + " TotCapFlowFit.plot(file_str=f\"TotCapFlowFit_seq_{nc+1}\", label=\"Cap-f-ff\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/archive/example scripts/.ipynb_checkpoints/data_modeling-checkpoint.ipynb b/DX_exploration/archive/example scripts/.ipynb_checkpoints/data_modeling-checkpoint.ipynb new file mode 100644 index 0000000..7ed91a3 --- /dev/null +++ b/DX_exploration/archive/example scripts/.ipynb_checkpoints/data_modeling-checkpoint.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c2a9443b", + "metadata": {}, + "outputs": [], + "source": [ + "#########################################################################\n", + "# Author: Yiting Zhang\n", + "# Created on: 11/03/2023\n", + "# Description:\n", + "#########################################################################\n", + "# To Do:\n", + "#\n", + "#\n", + "#\n", + "#########################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4b1ffbea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting coverage\n", + " Downloading coverage-7.3.2-cp311-cp311-win_amd64.whl (203 kB)\n", + " 0.0/203.3 kB ? eta -:--:--\n", + " ------- 41.0/203.3 kB 991.0 kB/s eta 0:00:01\n", + " --------------------- 112.6/203.3 kB 1.6 MB/s eta 0:00:01\n", + " ------------------------------------ 194.6/203.3 kB 1.5 MB/s eta 0:00:01\n", + " -------------------------------------- 203.3/203.3 kB 1.5 MB/s eta 0:00:00\n", + "Installing collected packages: coverage\n", + "Successfully installed coverage-7.3.2\n" + ] + } + ], + "source": [ + "# !pip3 install coverage\n", + "\n", + "# not work when connect to PNNL VPN" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "810f6cf9", + "metadata": {}, + "outputs": [], + "source": [ + "############################# Load Packages #############################\n", + "import numpy\n", + "import matplotlib\n", + "import pandas\n", + "import statistics\n", + "import statsmodels\n", + "import CoolProp\n", + "import scipy\n", + "import coverage\n", + "import click\n", + "import jsonschema" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebcd1820", + "metadata": {}, + "outputs": [], + "source": [ + "############################## Explore Data #############################\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/archive/example scripts/WetBulb.py b/DX_exploration/archive/example scripts/WetBulb.py new file mode 100644 index 0000000..1e1f311 --- /dev/null +++ b/DX_exploration/archive/example scripts/WetBulb.py @@ -0,0 +1,387 @@ +# +# Calculate wet-bulb temperature, equivalent temperature, and equivalent +# potential temperature from temperature, pressure, and relative or +# specific humidity. +# +# METHODOLOGY: +# +# Calculates Wet Bulb Temperature, Theta_wb, Theta_e, Moist Pot Temp, +# Lifting Cond Temp, and Equiv Temp using Davies-Jones 2008 Method. +# 1st calculates the lifting cond temperature (Bolton 1980 eqn 22). +# Then calculates the moist pot temp (Bolton 1980 eqn 24). Then +# calculates Equivalent Potential Temperature (Bolton 1980 eqn 39). +# From equivalent pot temp, equiv temp and Theta_w (Davies-Jones +# 2008 eqn 3.5-3.8). An accurate 'first guess' of wet bulb temperature +# is determined (Davies-Jones 2008 eqn 4.8-4.11). Newton-Raphson +# is used for 2 iterations, determining final wet bulb temperature +# (Davies-Jones 2008 eqn 2.6). +# +# Reference: Bolton: The computation of equivalent potential temperature. +# Monthly weather review (1980) vol. 108 (7) pp. 1046-1053 +# Davies-Jones: An efficient and accurate method for computing the +# wet-bulb temperature along pseudoadiabats. Monthly Weather Review +# (2008) vol. 136 (7) pp. 2764-2785 +# Flatau et al: Polynomial fits to saturation vapor pressure. +# Journal of Applied Meteorology (1992) vol. 31 pp. 1507-1513 +# Note: Pressure needs to be in mb, mixing ratio needs to be in +# kg/kg in some equations, and in g/kg in others. +# Calculates Iteration via Newton-Raphson Method. Only 2 iterations. +# Reference: Davies-Jones: An efficient and accurate method for computing the +# wet-bulb temperature along pseudoadiabats. Monthly Weather Review +# (2008) vol. 136 (7) pp. 2764-2785 +# Flatau et al: Polynomial fits to saturation vapor pressure. +# Journal of Applied Meteorology (1992) vol. 31 pp. 1507-1513 +# Note: Pressure needs to be in mb, mixing ratio needs to be in +# kg/kg in some equations. +# +# Ported from HumanIndexMod 04-08-16 by Jonathan R Buzan +# MATLAB port by Robert Kopp +# +# Last updated by Robert Kopp, robert-dot-kopp-at-rutgers-dot-edu, Wed Jun 08 18:03:02 EDT 2016 +# +# Ported to Python by Xianxiang Li +# February 2, 2019 + + +import numpy as np + +SHR_CONST_TKFRZ = 273.15 + +def calc_RH_from_T_Td(T, Td, mode=0): + """ + Calculate relative humidity from dry-bulb temperature T (in degC) and dew-point temperature + Td (in degC), based on equation (4) and (5) (mode=1) or equations (2) and (3) (mode = 1) from + http://www.npl.co.uk/reference/faqs/how-do-i-convert-between-units-of-dew-point-and-relative-humidity-(faq-thermal) + + Inputs: + T: dry-bulb temperature (in degC) + Td: dew-point temperature (in degC) + mode: the mode to calculate RH + mode = 0 (default): equations (4) and (5), which is more accurate but more complicated + mode = 1: equations (2) and (3), which is simpler + Outputs: + RH: relative humidity in %, range 0-100. + """ + if mode == 0: + Tk = T + SHR_CONST_TKFRZ + Tdk = Td + SHR_CONST_TKFRZ + es = np.exp( -6096.9385 * Tk**(-1) + 21.2409642 - 2.711193e-2 * Tk + \ + 1.673952e-5 * Tk**2.0 + 2.433502 * np.log(Tk)) + e = np.exp( -6096.9385 * Tdk**(-1) + 21.2409642 - 2.711193e-2 * Tdk + \ + 1.673952e-5 * Tdk**2.0 + 2.433502 * np.log(Tdk)) + elif mode == 1: # Magnus formulae + es = np.exp(np.log(611.2) + (17.62*T)/(243.12+T)) # vapor pressure in Pa + e = np.exp(np.log(611.2) + (17.62*Td)/(243.12+Td)) # vapor pressure in Pa + + RH = e/es * 100.0 + + RH[RH>100] = 100.0 + RH[RH<0] = 0 + + return RH + +def QSat_2(T_k, p_t): + """ + [es_mb,rs,de_mbdT,dlnes_mbdT,rsdT,foftk,fdt]=QSat_2(T_k, p_t) + DESCRIPTION: + Computes saturation mixing ratio and the change in saturation + mixing ratio with respect to temperature. Uses Bolton eqn 10, 39. + Davies-Jones eqns 2.3,A.1-A.10 + Reference: Bolton: The computation of equivalent potential temperature. + Monthly Weather Review (1980) vol. 108 (7) pp. 1046-1053 + Davies-Jones: An efficient and accurate method for computing the + wet-bulb temperature along pseudoadiabats. Monthly Weather Review + (2008) vol. 136 (7) pp. 2764-2785 + + INPUTS: + T_k temperature (K) + p_t surface atmospheric pressure (pa) + T_k and p_t should be arrays of identical dimensions. + OUTPUTS: + es_mb vapor pressure (pa) + rs humidity (kg/kg) + de_mbdT d(es)/d(T) + dlnes_mbdT dln(es)/d(T) + rsdT d(qs)/d(T) + foftk Davies-Jones eqn 2.3 + fdT d(f)/d(T) + Ported from HumanIndexMod by Jonathan R Buzan 08/08/13 + MATLAB port by Robert Kopp + Last updated by Robert Kopp, robert-dot-kopp-at-rutgers-dot-edu, Wed Sep 02 22:22:25 EDT 2015 + """ + +# SHR_CONST_TKFRZ = 273.15; + + lambd_a = 3.504 # Inverse of Heat Capacity + alpha = 17.67 # Constant to calculate vapour pressure + beta = 243.5 # Constant to calculate vapour pressure + epsilon = 0.6220 # Conversion between pressure/mixing ratio + es_C = 6.112 # Vapour Pressure at Freezing STD (mb) + vkp = 0.2854 # Heat Capacity + y0 = 3036 # constant + y1 = 1.78 # constant + y2 = 0.448 # constant + Cf = SHR_CONST_TKFRZ # Freezing Temp (K) + refpres = 1000 # Reference Pressure (mb) + +# $$$ p_tmb % Pressure (mb) +# $$$ ndimpress % Non-dimensional Pressure +# $$$ prersdt % Place Holder for derivative humidity +# $$$ pminuse % Vapor Pressure Difference (mb) +# $$$ tcfbdiff % Temp diff ref (C) +# $$$ p0ndplam % dimensionless pressure modified by ref pressure +# $$$ +# $$$ rsy2rs2 % Constant function of humidity +# $$$ oty2rs % Constant function of humidity +# $$$ y0tky1 % Constant function of Temp +# $$$ +# $$$ d2e_mbdT2 % d2(es)/d(T)2 +# $$$ d2rsdT2 % d2(r)/d(T)2 +# $$$ goftk % g(T) exponential in f(T) +# $$$ gdT % d(g)/d(T) +# $$$ d2gdT2 % d2(g)/d(T)2 +# $$$ +# $$$ d2fdT2 % d2(f)/d(T)2 (K) +# +#----------------------------------------------------------------------- +# Constants used to calculate es(T) +# Clausius-Clapeyron + p_tmb = p_t*0.01 + tcfbdiff = T_k - Cf + beta + es_mb = es_C * np.exp(alpha*(T_k - Cf)/(tcfbdiff)) + dlnes_mbdT = alpha * beta/((tcfbdiff)*(tcfbdiff)) + pminuse = p_tmb - es_mb + de_mbdT = es_mb * dlnes_mbdT + d2e_mbdT2 = dlnes_mbdT * (de_mbdT - 2*es_mb/(tcfbdiff)) + +# Constants used to calculate rs(T) + ndimpress = (p_tmb/refpres)**vkp + p0ndplam = refpres * ndimpress**lambd_a + rs = epsilon * es_mb/(p0ndplam - es_mb + np.spacing(1)) #eps) + prersdt = epsilon * p_tmb/((pminuse)*(pminuse)) + rsdT = prersdt * de_mbdT + d2rsdT2 = prersdt * (d2e_mbdT2 -de_mbdT*de_mbdT*(2/(pminuse))) + +# Constants used to calculate g(T) + rsy2rs2 = rs + y2*rs*rs + oty2rs = 1 + 2.0*y2*rs + y0tky1 = y0/T_k - y1 + goftk = y0tky1 * (rs + y2 * rs * rs) + gdT = - y0 * (rsy2rs2)/(T_k*T_k) + (y0tky1)*(oty2rs)*rsdT + d2gdT2 = 2.0*y0*rsy2rs2/(T_k*T_k*T_k) - 2.0*y0*rsy2rs2*(oty2rs)*rsdT + \ + y0tky1*2.0*y2*rsdT*rsdT + y0tky1*oty2rs*d2rsdT2 + +# Calculations for used to calculate f(T,ndimpress) + #print('Cf/T_k = '+str(Cf/T_k)+', '+str(lambd_a)) + #print('vkp*lambd_a = '+ str(vkp)+', '+str(lambd_a)) + #print('1-es_mb/p0ndplam = '+str(1 - es_mb/p0ndplam)) + #exit() + foftk = ((Cf/T_k)**lambd_a)*(np.abs(1 - es_mb/p0ndplam))**(vkp*lambd_a)* \ + np.exp(-lambd_a*goftk) + fdT = -lambd_a*(1.0/T_k + vkp*de_mbdT/pminuse + gdT) + d2fdT2 = lambd_a*(1.0/(T_k*T_k) - vkp*de_mbdT*de_mbdT/(pminuse*pminuse) - \ + vkp*d2e_mbdT2/pminuse - d2gdT2) + +# avoid bad numbers + rs[rs>1]=np.nan + rs[rs<0]=np.nan + + return es_mb,rs,de_mbdT,dlnes_mbdT,rsdT,foftk,fdT + +#end + +def WetBulb(TemperatureC,Pressure,Humidity,HumidityMode=0): + """ + INPUTS: + TemperatureC 2-m air temperature (degrees Celsius) + Pressure Atmospheric Pressure (Pa) + Humidity Humidity -- meaning depends on HumidityMode + HumidityMode + 0 (Default): Humidity is specific humidity (kg/kg) + 1: Humidity is relative humidity (#, max = 100) + TemperatureC, Pressure, and Humidity should either be scalars or arrays of + identical dimension. + OUTPUTS: + Twb wet bulb temperature (C) + Teq Equivalent Temperature (K) + epott Equivalent Potential Temperature (K) + """ +# SHR_CONST_TKFRZ = 273.15 + TemperatureK = TemperatureC + SHR_CONST_TKFRZ + + constA = 2675 # Constant used for extreme cold temparatures (K) + grms = 1000 # Gram per Kilogram (g/kg) + p0 = 1000 # surface pressure (mb) + + kappad = 0.2854 # Heat Capacity + + C = SHR_CONST_TKFRZ # Freezing Temperature + pmb = Pressure*0.01 # pa to mb + T1 = TemperatureK # Use holder for T + + es_mb,rs = QSat_2(TemperatureK, Pressure)[0:2] # first two returned values + + if HumidityMode==0: + qin = Humidity # specific humidity + relhum = 100.0 * qin/rs # relative humidity (%) + vapemb = es_mb * relhum * 0.01 # vapor pressure (mb) + elif HumidityMode==1: + relhum = Humidity # relative humidity (%) + qin = rs * relhum * 0.01 # specific humidity + vapemb = es_mb * relhum * 0.01 # vapor pressure (mb) + #end + + mixr = qin * grms # change specific humidity to mixing ratio (g/kg) + + # real(r8) :: k1; % Quadratic Parameter (C) + # real(r8) :: k2; % Quadratic Parameter scaled by X (C) + # real(r8) :: pmb; % Atmospheric Surface Pressure (mb) + # real(r8) :: D; % Linear Interpolation of X + + # real(r8) :: hot % Dimensionless Quantity used for changing temperature regimes + # real(r8) :: cold % Dimensionless Quantity used for changing temperature regimes + + # real(r8) :: T1 % Temperature (K) + # real(r8) :: vapemb % Vapour Pressure (mb) + # real(r8) :: mixr % Mixing Ratio (g/kg) + + # real(r8) :: es_mb_teq % saturated vapour pressure for wrt TEQ (mb) + # real(r8) :: de_mbdTeq % Derivative of Saturated Vapour pressure wrt TEQ (mb/K) + # real(r8) :: dlnes_mbdTeq % Log derivative of the sat. vap pressure wrt TEQ (mb/K) + # real(r8) :: rs_teq % Mixing Ratio wrt TEQ (kg/kg) + # real(r8) :: rsdTeq % Derivative of Mixing Ratio wrt TEQ (kg/kg/K) + # real(r8) :: foftk_teq % Function of EPT wrt TEQ + # real(r8) :: fdTeq % Derivative of Function of EPT wrt TEQ + + # real(r8) :: wb_temp % Wet Bulb Temperature First Guess (C) + # real(r8) :: es_mb_wb_temp % Vapour Pressure wrt Wet Bulb Temp (mb) + # real(r8) :: de_mbdwb_temp % Derivative of Sat. Vapour Pressure wrt WB Temp (mb/K) + # real(r8) :: dlnes_mbdwb_temp % Log Derivative of sat. vap. pressure wrt WB Temp (mb/K) + # real(r8) :: rs_wb_temp % Mixing Ratio wrt WB Temp (kg/kg) + # real(r8) :: rsdwb_temp % Derivative of Mixing Ratio wrt WB Temp (kg/kg/K) + # real(r8) :: foftk_wb_temp % Function of EPT wrt WB Temp + # real(r8) :: fdwb_temp % Derivative of function of EPT wrt WB Temp + + # real(r8) :: tl % Lifting Condensation Temperature (K) + # real(r8) :: theta_dl % Moist Potential Temperature (K) + # real(r8) :: pnd % Non dimensional Pressure + # real(r8) :: X % Ratio of equivalent temperature to freezing scaled by Heat Capacity + + + #----------------------------------------------------------------------- + + # Calculate Equivalent Pot. Temp (pmb, T, mixing ratio (g/kg), pott, epott) + # Calculate Parameters for Wet Bulb Temp (epott, pmb) + pnd = (pmb/p0)**(kappad) + D = 1.0/(0.1859*pmb/p0 + 0.6512) + k1 = -38.5*pnd*pnd + 137.81*pnd - 53.737 + k2 = -4.392*pnd*pnd + 56.831*pnd - 0.384 + + # Calculate lifting condensation level. first eqn + # uses vapor pressure (mb) + # 2nd eqn uses relative humidity. + # first equation: Bolton 1980 Eqn 21. + # tl = (2840/(3.5*log(T1) - log(vapemb) - 4.805)) + 55; + # second equation: Bolton 1980 Eqn 22. relhum = relative humidity + tl = (1.0/((1.0/((T1 - 55))) - (np.log(relhum/100.0)/2840.0))) + 55.0 + + # Theta_DL: Bolton 1980 Eqn 24. + theta_dl = T1*((p0/(pmb-vapemb))**kappad) * ((T1/tl)**(mixr*0.00028)) + # EPT: Bolton 1980 Eqn 39. + epott = theta_dl * np.exp(((3.036/tl)-0.00178)*mixr*(1 + 0.000448*mixr)) + Teq = epott*pnd # Equivalent Temperature at pressure + X = (C/Teq)**3.504 + + # Calculates the regime requirements of wet bulb equations. + invalid = (Teq > 600) + (Teq < 200) + hot = (Teq > 355.15) + cold = ((X>=1) * (X<=D)) + X[invalid==1] = np.nan + Teq[invalid==1] = np.nan + + # Calculate Wet Bulb Temperature, initial guess + # Extremely cold regime if X.gt.D then need to + # calculate dlnesTeqdTeq + + es_mb_teq,rs_teq,de_mbdTeq, dlnes_mbdTeq, rsdTeq, foftk_teq, fdTeq = QSat_2(Teq, Pressure) + wb_temp = Teq - C - ((constA*rs_teq)/(1 + (constA*rs_teq*dlnes_mbdTeq))) + sub=np.where(X<=D) + wb_temp[sub] = (k1[sub] - 1.21 * cold[sub] - 1.45 * hot[sub] - (k2[sub] - 1.21 * cold[sub]) * X[sub] + (0.58 / X[sub]) * hot[sub]) + wb_temp[invalid==1]=np.nan + + # Newton-Raphson Method + + maxiter = 3 + iter = 0 + delta = 1e6*np.ones_like(wb_temp) + + while (np.max(delta)>0.01) and (iter<=maxiter): + es_mb_wb_temp,rs_wb_temp,de_mbdwb_temp, dlnes_mbdwb_temp, rsdwb_temp, foftk_wb_temp, fdwb_temp = QSat_2(wb_temp + C, Pressure) + delta = (foftk_wb_temp - X)/fdwb_temp #float((foftk_wb_temp - X)/fdwb_temp) + delta = np.where(delta<10., delta, 10.) #min(10,delta) + delta = np.where(delta>-10., delta, -10.) #max(-10,delta) + wb_temp = wb_temp - delta + wb_temp[invalid==1] = np.nan + Twb = wb_temp + iter = iter+1 + #end + + # ! 04-06-16: Adding iteration constraint. Commenting out original code. + # but in the MATLAB code, for sake of speed, we only do this for the values + # that didn't converge + + if 1: #ConvergenceMode: + + convergence = 0.00001 + maxiter = 20000 + + es_mb_wb_temp,rs_wb_temp,de_mbdwb_temp, dlnes_mbdwb_temp, rsdwb_temp, foftk_wb_temp, fdwb_temp = QSat_2(wb_temp + C, Pressure) + delta = (foftk_wb_temp - X)/fdwb_temp #float((foftk_wb_temp - X)/fdwb_temp) + subdo = np.where(np.abs(delta)>convergence) #find(abs(delta)>convergence) + + iter = 0 + while (len(subdo)>0) and (iter<=maxiter): + iter = iter + 1 + + wb_temp[subdo] = wb_temp[subdo] - 0.1*delta[subdo] + + es_mb_wb_temp,rs_wb_temp,de_mbdwb_temp, dlnes_mbdwb_temp, rsdwb_temp, foftk_wb_temp, fdwb_temp = QSat_2(wb_temp[subdo]+C, Pressure[subdo]) + delta = 0 * wb_temp + delta[subdo] = (foftk_wb_temp - X[subdo])/fdwb_temp #float((foftk_wb_temp - X[subdo])/fdwb_temp) + subdo = np.where(np.abs(delta)>convergence) #find(abs(delta)>convergence); + #end + + Twb = wb_temp + if any(map(len,subdo)): #len(subdo)>0: + print(len(subdo)) + Twb[subdo] = TemperatureK[subdo]-C + #print(subdo) + for www in subdo[0]: + # print(www) + print('WARNING-Wet_Bulb failed to converge. Setting to T: WB, P, T, RH, Delta: %0.2f, %0.2f, %0.1f, %0.2g, %0.1f'%(Twb[www], Pressure[www], \ + TemperatureK[www], relhum[www], delta[www])) + #end + #end + + #end + + #Twb=float(Twb) + return Twb,Teq,epott + +if __name__ == "__main__": + tempC = np.array([31.,32.,33.,34.]) + #tempd = np.array([26.58,29.10,29.26,27.55]) + tempd = np.array([26.57,29.11,29.26,27.54]) + RH = calc_RH_from_T_Td(tempC,tempd,mode=1) + # print('RH = ') + # print(RH) + + #Pres = np.array([102130,102130,102130,102130]) + Pres = np.array([101325]*4) + relHum = np.array([70.,80.,75.,60.]) + Hum_mode = 1 + + Twb,Teq,epott = WetBulb(tempC,Pres,relHum,Hum_mode) + print(Twb) + print(Teq) + print(epott) diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/DX_exploration/archive/example scripts/example/curve_to_map/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..f4734fb --- /dev/null +++ b/DX_exploration/archive/example scripts/example/curve_to_map/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "id": "352a385e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "baede1d0", + "metadata": {}, + "outputs": [], + "source": [ + "def convert_F_to_C(F):\n", + " return (F-32) * 5/9" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4196121a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xlsx_file = \"./data/DX_Equipment_Data_Collection_D.xlsx\"\n", + "df = pd.read_excel(io=xlsx_file, sheet_name=\"Table 1\", header=6, usecols=\"B:L\", engine=\"openpyxl\")\n", + "df[\"indoor_coil_entering_dry_bulb_temperature\"] = convert_F_to_C(df[\"indoor_coil_entering_dry_bulb_temperature\"])\n", + "df[\"outdoor_coil_entering_dry_bulb_temperature\"] = convert_F_to_C(df[\"outdoor_coil_entering_dry_bulb_temperature\"])\n", + "df[\"indoor_coil_entering_wet_bulb_temperature\"] = convert_F_to_C(df[\"indoor_coil_entering_wet_bulb_temperature\"])\n", + "\n", + "comp_sequences = 1\n", + "rated_flow_rate = 2100\n", + "Te_db = 26.7 # 80 F\n", + "Te_wb = 19.4 # 67 F\n", + "\n", + "T_db = df[\"indoor_coil_entering_dry_bulb_temperature\"].values\n", + "T_wb = df[\"indoor_coil_entering_wet_bulb_temperature\"].values\n", + "dist = (T_db - Te_db)**2 + (T_wb - Te_wb)**2\n", + "min_idx = np.argmin(dist)\n", + "# min_idx\n", + "# df_rated = df.iloc[min_idx]\n", + "# rated_capacity = df_rated[\"gross_total_capacity\"]\n", + "\n", + "# df[\"cap_f_t\"] = df[\"gross_total_capacity\"]/rated_capacity\n", + "# df[\"ff\"] = df[\"indoor_coil_air_mass_flow_rate\"]/rated_flow_rate\n", + "\n", + "# df_const_T = df[\n", + "# (np.abs(df[\"indoor_coil_entering_dry_bulb_temperature\"] - Te_db) < 1.0) &\n", + "# (np.abs(df[\"indoor_coil_entering_wet_bulb_temperature\"] - Te_wb) < 1.0)\n", + "# ]\n", + "\n", + "# df_rated_flow = df[(np.abs(df[\"indoor_coil_air_mass_flow_rate\"] - rated_flow_rate) < 100)]\n", + "# df_rated_flow\n", + "\n", + "stage = len(np.unique(df[\"compressor_sequence_number\"].values))\n", + "stage" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "acfd81f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted Information: D_Subcool\n" + ] + } + ], + "source": [ + "xlsx_file = \"./data/DX_Equipment_Data_Collection_D_Subcool.xlsx\"\n", + "\n", + "# Find the index of \"./data/\" and \".xlsx\"\n", + "start_index = xlsx_file.find(\"./data/DX_Equipment_Data_Collection_\") + len(\"./data/DX_Equipment_Data_Collection_\")\n", + "end_index = xlsx_file.find(\".xlsx\")\n", + "\n", + "# Extract the information between \"./data/\" and \".xlsx\"\n", + "extracted_info = xlsx_file[start_index:end_index]\n", + "\n", + "print(\"Extracted Information:\", extracted_info)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af8e4d49", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/Untitled.ipynb b/DX_exploration/archive/example scripts/example/curve_to_map/Untitled.ipynb new file mode 100644 index 0000000..f4734fb --- /dev/null +++ b/DX_exploration/archive/example scripts/example/curve_to_map/Untitled.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "id": "352a385e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "baede1d0", + "metadata": {}, + "outputs": [], + "source": [ + "def convert_F_to_C(F):\n", + " return (F-32) * 5/9" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4196121a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xlsx_file = \"./data/DX_Equipment_Data_Collection_D.xlsx\"\n", + "df = pd.read_excel(io=xlsx_file, sheet_name=\"Table 1\", header=6, usecols=\"B:L\", engine=\"openpyxl\")\n", + "df[\"indoor_coil_entering_dry_bulb_temperature\"] = convert_F_to_C(df[\"indoor_coil_entering_dry_bulb_temperature\"])\n", + "df[\"outdoor_coil_entering_dry_bulb_temperature\"] = convert_F_to_C(df[\"outdoor_coil_entering_dry_bulb_temperature\"])\n", + "df[\"indoor_coil_entering_wet_bulb_temperature\"] = convert_F_to_C(df[\"indoor_coil_entering_wet_bulb_temperature\"])\n", + "\n", + "comp_sequences = 1\n", + "rated_flow_rate = 2100\n", + "Te_db = 26.7 # 80 F\n", + "Te_wb = 19.4 # 67 F\n", + "\n", + "T_db = df[\"indoor_coil_entering_dry_bulb_temperature\"].values\n", + "T_wb = df[\"indoor_coil_entering_wet_bulb_temperature\"].values\n", + "dist = (T_db - Te_db)**2 + (T_wb - Te_wb)**2\n", + "min_idx = np.argmin(dist)\n", + "# min_idx\n", + "# df_rated = df.iloc[min_idx]\n", + "# rated_capacity = df_rated[\"gross_total_capacity\"]\n", + "\n", + "# df[\"cap_f_t\"] = df[\"gross_total_capacity\"]/rated_capacity\n", + "# df[\"ff\"] = df[\"indoor_coil_air_mass_flow_rate\"]/rated_flow_rate\n", + "\n", + "# df_const_T = df[\n", + "# (np.abs(df[\"indoor_coil_entering_dry_bulb_temperature\"] - Te_db) < 1.0) &\n", + "# (np.abs(df[\"indoor_coil_entering_wet_bulb_temperature\"] - Te_wb) < 1.0)\n", + "# ]\n", + "\n", + "# df_rated_flow = df[(np.abs(df[\"indoor_coil_air_mass_flow_rate\"] - rated_flow_rate) < 100)]\n", + "# df_rated_flow\n", + "\n", + "stage = len(np.unique(df[\"compressor_sequence_number\"].values))\n", + "stage" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "acfd81f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted Information: D_Subcool\n" + ] + } + ], + "source": [ + "xlsx_file = \"./data/DX_Equipment_Data_Collection_D_Subcool.xlsx\"\n", + "\n", + "# Find the index of \"./data/\" and \".xlsx\"\n", + "start_index = xlsx_file.find(\"./data/DX_Equipment_Data_Collection_\") + len(\"./data/DX_Equipment_Data_Collection_\")\n", + "end_index = xlsx_file.find(\".xlsx\")\n", + "\n", + "# Extract the information between \"./data/\" and \".xlsx\"\n", + "extracted_info = xlsx_file[start_index:end_index]\n", + "\n", + "print(\"Extracted Information:\", extracted_info)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af8e4d49", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/WetBulb.py b/DX_exploration/archive/example scripts/example/curve_to_map/WetBulb.py new file mode 100644 index 0000000..1e1f311 --- /dev/null +++ b/DX_exploration/archive/example scripts/example/curve_to_map/WetBulb.py @@ -0,0 +1,387 @@ +# +# Calculate wet-bulb temperature, equivalent temperature, and equivalent +# potential temperature from temperature, pressure, and relative or +# specific humidity. +# +# METHODOLOGY: +# +# Calculates Wet Bulb Temperature, Theta_wb, Theta_e, Moist Pot Temp, +# Lifting Cond Temp, and Equiv Temp using Davies-Jones 2008 Method. +# 1st calculates the lifting cond temperature (Bolton 1980 eqn 22). +# Then calculates the moist pot temp (Bolton 1980 eqn 24). Then +# calculates Equivalent Potential Temperature (Bolton 1980 eqn 39). +# From equivalent pot temp, equiv temp and Theta_w (Davies-Jones +# 2008 eqn 3.5-3.8). An accurate 'first guess' of wet bulb temperature +# is determined (Davies-Jones 2008 eqn 4.8-4.11). Newton-Raphson +# is used for 2 iterations, determining final wet bulb temperature +# (Davies-Jones 2008 eqn 2.6). +# +# Reference: Bolton: The computation of equivalent potential temperature. +# Monthly weather review (1980) vol. 108 (7) pp. 1046-1053 +# Davies-Jones: An efficient and accurate method for computing the +# wet-bulb temperature along pseudoadiabats. Monthly Weather Review +# (2008) vol. 136 (7) pp. 2764-2785 +# Flatau et al: Polynomial fits to saturation vapor pressure. +# Journal of Applied Meteorology (1992) vol. 31 pp. 1507-1513 +# Note: Pressure needs to be in mb, mixing ratio needs to be in +# kg/kg in some equations, and in g/kg in others. +# Calculates Iteration via Newton-Raphson Method. Only 2 iterations. +# Reference: Davies-Jones: An efficient and accurate method for computing the +# wet-bulb temperature along pseudoadiabats. Monthly Weather Review +# (2008) vol. 136 (7) pp. 2764-2785 +# Flatau et al: Polynomial fits to saturation vapor pressure. +# Journal of Applied Meteorology (1992) vol. 31 pp. 1507-1513 +# Note: Pressure needs to be in mb, mixing ratio needs to be in +# kg/kg in some equations. +# +# Ported from HumanIndexMod 04-08-16 by Jonathan R Buzan +# MATLAB port by Robert Kopp +# +# Last updated by Robert Kopp, robert-dot-kopp-at-rutgers-dot-edu, Wed Jun 08 18:03:02 EDT 2016 +# +# Ported to Python by Xianxiang Li +# February 2, 2019 + + +import numpy as np + +SHR_CONST_TKFRZ = 273.15 + +def calc_RH_from_T_Td(T, Td, mode=0): + """ + Calculate relative humidity from dry-bulb temperature T (in degC) and dew-point temperature + Td (in degC), based on equation (4) and (5) (mode=1) or equations (2) and (3) (mode = 1) from + http://www.npl.co.uk/reference/faqs/how-do-i-convert-between-units-of-dew-point-and-relative-humidity-(faq-thermal) + + Inputs: + T: dry-bulb temperature (in degC) + Td: dew-point temperature (in degC) + mode: the mode to calculate RH + mode = 0 (default): equations (4) and (5), which is more accurate but more complicated + mode = 1: equations (2) and (3), which is simpler + Outputs: + RH: relative humidity in %, range 0-100. + """ + if mode == 0: + Tk = T + SHR_CONST_TKFRZ + Tdk = Td + SHR_CONST_TKFRZ + es = np.exp( -6096.9385 * Tk**(-1) + 21.2409642 - 2.711193e-2 * Tk + \ + 1.673952e-5 * Tk**2.0 + 2.433502 * np.log(Tk)) + e = np.exp( -6096.9385 * Tdk**(-1) + 21.2409642 - 2.711193e-2 * Tdk + \ + 1.673952e-5 * Tdk**2.0 + 2.433502 * np.log(Tdk)) + elif mode == 1: # Magnus formulae + es = np.exp(np.log(611.2) + (17.62*T)/(243.12+T)) # vapor pressure in Pa + e = np.exp(np.log(611.2) + (17.62*Td)/(243.12+Td)) # vapor pressure in Pa + + RH = e/es * 100.0 + + RH[RH>100] = 100.0 + RH[RH<0] = 0 + + return RH + +def QSat_2(T_k, p_t): + """ + [es_mb,rs,de_mbdT,dlnes_mbdT,rsdT,foftk,fdt]=QSat_2(T_k, p_t) + DESCRIPTION: + Computes saturation mixing ratio and the change in saturation + mixing ratio with respect to temperature. Uses Bolton eqn 10, 39. + Davies-Jones eqns 2.3,A.1-A.10 + Reference: Bolton: The computation of equivalent potential temperature. + Monthly Weather Review (1980) vol. 108 (7) pp. 1046-1053 + Davies-Jones: An efficient and accurate method for computing the + wet-bulb temperature along pseudoadiabats. Monthly Weather Review + (2008) vol. 136 (7) pp. 2764-2785 + + INPUTS: + T_k temperature (K) + p_t surface atmospheric pressure (pa) + T_k and p_t should be arrays of identical dimensions. + OUTPUTS: + es_mb vapor pressure (pa) + rs humidity (kg/kg) + de_mbdT d(es)/d(T) + dlnes_mbdT dln(es)/d(T) + rsdT d(qs)/d(T) + foftk Davies-Jones eqn 2.3 + fdT d(f)/d(T) + Ported from HumanIndexMod by Jonathan R Buzan 08/08/13 + MATLAB port by Robert Kopp + Last updated by Robert Kopp, robert-dot-kopp-at-rutgers-dot-edu, Wed Sep 02 22:22:25 EDT 2015 + """ + +# SHR_CONST_TKFRZ = 273.15; + + lambd_a = 3.504 # Inverse of Heat Capacity + alpha = 17.67 # Constant to calculate vapour pressure + beta = 243.5 # Constant to calculate vapour pressure + epsilon = 0.6220 # Conversion between pressure/mixing ratio + es_C = 6.112 # Vapour Pressure at Freezing STD (mb) + vkp = 0.2854 # Heat Capacity + y0 = 3036 # constant + y1 = 1.78 # constant + y2 = 0.448 # constant + Cf = SHR_CONST_TKFRZ # Freezing Temp (K) + refpres = 1000 # Reference Pressure (mb) + +# $$$ p_tmb % Pressure (mb) +# $$$ ndimpress % Non-dimensional Pressure +# $$$ prersdt % Place Holder for derivative humidity +# $$$ pminuse % Vapor Pressure Difference (mb) +# $$$ tcfbdiff % Temp diff ref (C) +# $$$ p0ndplam % dimensionless pressure modified by ref pressure +# $$$ +# $$$ rsy2rs2 % Constant function of humidity +# $$$ oty2rs % Constant function of humidity +# $$$ y0tky1 % Constant function of Temp +# $$$ +# $$$ d2e_mbdT2 % d2(es)/d(T)2 +# $$$ d2rsdT2 % d2(r)/d(T)2 +# $$$ goftk % g(T) exponential in f(T) +# $$$ gdT % d(g)/d(T) +# $$$ d2gdT2 % d2(g)/d(T)2 +# $$$ +# $$$ d2fdT2 % d2(f)/d(T)2 (K) +# +#----------------------------------------------------------------------- +# Constants used to calculate es(T) +# Clausius-Clapeyron + p_tmb = p_t*0.01 + tcfbdiff = T_k - Cf + beta + es_mb = es_C * np.exp(alpha*(T_k - Cf)/(tcfbdiff)) + dlnes_mbdT = alpha * beta/((tcfbdiff)*(tcfbdiff)) + pminuse = p_tmb - es_mb + de_mbdT = es_mb * dlnes_mbdT + d2e_mbdT2 = dlnes_mbdT * (de_mbdT - 2*es_mb/(tcfbdiff)) + +# Constants used to calculate rs(T) + ndimpress = (p_tmb/refpres)**vkp + p0ndplam = refpres * ndimpress**lambd_a + rs = epsilon * es_mb/(p0ndplam - es_mb + np.spacing(1)) #eps) + prersdt = epsilon * p_tmb/((pminuse)*(pminuse)) + rsdT = prersdt * de_mbdT + d2rsdT2 = prersdt * (d2e_mbdT2 -de_mbdT*de_mbdT*(2/(pminuse))) + +# Constants used to calculate g(T) + rsy2rs2 = rs + y2*rs*rs + oty2rs = 1 + 2.0*y2*rs + y0tky1 = y0/T_k - y1 + goftk = y0tky1 * (rs + y2 * rs * rs) + gdT = - y0 * (rsy2rs2)/(T_k*T_k) + (y0tky1)*(oty2rs)*rsdT + d2gdT2 = 2.0*y0*rsy2rs2/(T_k*T_k*T_k) - 2.0*y0*rsy2rs2*(oty2rs)*rsdT + \ + y0tky1*2.0*y2*rsdT*rsdT + y0tky1*oty2rs*d2rsdT2 + +# Calculations for used to calculate f(T,ndimpress) + #print('Cf/T_k = '+str(Cf/T_k)+', '+str(lambd_a)) + #print('vkp*lambd_a = '+ str(vkp)+', '+str(lambd_a)) + #print('1-es_mb/p0ndplam = '+str(1 - es_mb/p0ndplam)) + #exit() + foftk = ((Cf/T_k)**lambd_a)*(np.abs(1 - es_mb/p0ndplam))**(vkp*lambd_a)* \ + np.exp(-lambd_a*goftk) + fdT = -lambd_a*(1.0/T_k + vkp*de_mbdT/pminuse + gdT) + d2fdT2 = lambd_a*(1.0/(T_k*T_k) - vkp*de_mbdT*de_mbdT/(pminuse*pminuse) - \ + vkp*d2e_mbdT2/pminuse - d2gdT2) + +# avoid bad numbers + rs[rs>1]=np.nan + rs[rs<0]=np.nan + + return es_mb,rs,de_mbdT,dlnes_mbdT,rsdT,foftk,fdT + +#end + +def WetBulb(TemperatureC,Pressure,Humidity,HumidityMode=0): + """ + INPUTS: + TemperatureC 2-m air temperature (degrees Celsius) + Pressure Atmospheric Pressure (Pa) + Humidity Humidity -- meaning depends on HumidityMode + HumidityMode + 0 (Default): Humidity is specific humidity (kg/kg) + 1: Humidity is relative humidity (#, max = 100) + TemperatureC, Pressure, and Humidity should either be scalars or arrays of + identical dimension. + OUTPUTS: + Twb wet bulb temperature (C) + Teq Equivalent Temperature (K) + epott Equivalent Potential Temperature (K) + """ +# SHR_CONST_TKFRZ = 273.15 + TemperatureK = TemperatureC + SHR_CONST_TKFRZ + + constA = 2675 # Constant used for extreme cold temparatures (K) + grms = 1000 # Gram per Kilogram (g/kg) + p0 = 1000 # surface pressure (mb) + + kappad = 0.2854 # Heat Capacity + + C = SHR_CONST_TKFRZ # Freezing Temperature + pmb = Pressure*0.01 # pa to mb + T1 = TemperatureK # Use holder for T + + es_mb,rs = QSat_2(TemperatureK, Pressure)[0:2] # first two returned values + + if HumidityMode==0: + qin = Humidity # specific humidity + relhum = 100.0 * qin/rs # relative humidity (%) + vapemb = es_mb * relhum * 0.01 # vapor pressure (mb) + elif HumidityMode==1: + relhum = Humidity # relative humidity (%) + qin = rs * relhum * 0.01 # specific humidity + vapemb = es_mb * relhum * 0.01 # vapor pressure (mb) + #end + + mixr = qin * grms # change specific humidity to mixing ratio (g/kg) + + # real(r8) :: k1; % Quadratic Parameter (C) + # real(r8) :: k2; % Quadratic Parameter scaled by X (C) + # real(r8) :: pmb; % Atmospheric Surface Pressure (mb) + # real(r8) :: D; % Linear Interpolation of X + + # real(r8) :: hot % Dimensionless Quantity used for changing temperature regimes + # real(r8) :: cold % Dimensionless Quantity used for changing temperature regimes + + # real(r8) :: T1 % Temperature (K) + # real(r8) :: vapemb % Vapour Pressure (mb) + # real(r8) :: mixr % Mixing Ratio (g/kg) + + # real(r8) :: es_mb_teq % saturated vapour pressure for wrt TEQ (mb) + # real(r8) :: de_mbdTeq % Derivative of Saturated Vapour pressure wrt TEQ (mb/K) + # real(r8) :: dlnes_mbdTeq % Log derivative of the sat. vap pressure wrt TEQ (mb/K) + # real(r8) :: rs_teq % Mixing Ratio wrt TEQ (kg/kg) + # real(r8) :: rsdTeq % Derivative of Mixing Ratio wrt TEQ (kg/kg/K) + # real(r8) :: foftk_teq % Function of EPT wrt TEQ + # real(r8) :: fdTeq % Derivative of Function of EPT wrt TEQ + + # real(r8) :: wb_temp % Wet Bulb Temperature First Guess (C) + # real(r8) :: es_mb_wb_temp % Vapour Pressure wrt Wet Bulb Temp (mb) + # real(r8) :: de_mbdwb_temp % Derivative of Sat. Vapour Pressure wrt WB Temp (mb/K) + # real(r8) :: dlnes_mbdwb_temp % Log Derivative of sat. vap. pressure wrt WB Temp (mb/K) + # real(r8) :: rs_wb_temp % Mixing Ratio wrt WB Temp (kg/kg) + # real(r8) :: rsdwb_temp % Derivative of Mixing Ratio wrt WB Temp (kg/kg/K) + # real(r8) :: foftk_wb_temp % Function of EPT wrt WB Temp + # real(r8) :: fdwb_temp % Derivative of function of EPT wrt WB Temp + + # real(r8) :: tl % Lifting Condensation Temperature (K) + # real(r8) :: theta_dl % Moist Potential Temperature (K) + # real(r8) :: pnd % Non dimensional Pressure + # real(r8) :: X % Ratio of equivalent temperature to freezing scaled by Heat Capacity + + + #----------------------------------------------------------------------- + + # Calculate Equivalent Pot. Temp (pmb, T, mixing ratio (g/kg), pott, epott) + # Calculate Parameters for Wet Bulb Temp (epott, pmb) + pnd = (pmb/p0)**(kappad) + D = 1.0/(0.1859*pmb/p0 + 0.6512) + k1 = -38.5*pnd*pnd + 137.81*pnd - 53.737 + k2 = -4.392*pnd*pnd + 56.831*pnd - 0.384 + + # Calculate lifting condensation level. first eqn + # uses vapor pressure (mb) + # 2nd eqn uses relative humidity. + # first equation: Bolton 1980 Eqn 21. + # tl = (2840/(3.5*log(T1) - log(vapemb) - 4.805)) + 55; + # second equation: Bolton 1980 Eqn 22. relhum = relative humidity + tl = (1.0/((1.0/((T1 - 55))) - (np.log(relhum/100.0)/2840.0))) + 55.0 + + # Theta_DL: Bolton 1980 Eqn 24. + theta_dl = T1*((p0/(pmb-vapemb))**kappad) * ((T1/tl)**(mixr*0.00028)) + # EPT: Bolton 1980 Eqn 39. + epott = theta_dl * np.exp(((3.036/tl)-0.00178)*mixr*(1 + 0.000448*mixr)) + Teq = epott*pnd # Equivalent Temperature at pressure + X = (C/Teq)**3.504 + + # Calculates the regime requirements of wet bulb equations. + invalid = (Teq > 600) + (Teq < 200) + hot = (Teq > 355.15) + cold = ((X>=1) * (X<=D)) + X[invalid==1] = np.nan + Teq[invalid==1] = np.nan + + # Calculate Wet Bulb Temperature, initial guess + # Extremely cold regime if X.gt.D then need to + # calculate dlnesTeqdTeq + + es_mb_teq,rs_teq,de_mbdTeq, dlnes_mbdTeq, rsdTeq, foftk_teq, fdTeq = QSat_2(Teq, Pressure) + wb_temp = Teq - C - ((constA*rs_teq)/(1 + (constA*rs_teq*dlnes_mbdTeq))) + sub=np.where(X<=D) + wb_temp[sub] = (k1[sub] - 1.21 * cold[sub] - 1.45 * hot[sub] - (k2[sub] - 1.21 * cold[sub]) * X[sub] + (0.58 / X[sub]) * hot[sub]) + wb_temp[invalid==1]=np.nan + + # Newton-Raphson Method + + maxiter = 3 + iter = 0 + delta = 1e6*np.ones_like(wb_temp) + + while (np.max(delta)>0.01) and (iter<=maxiter): + es_mb_wb_temp,rs_wb_temp,de_mbdwb_temp, dlnes_mbdwb_temp, rsdwb_temp, foftk_wb_temp, fdwb_temp = QSat_2(wb_temp + C, Pressure) + delta = (foftk_wb_temp - X)/fdwb_temp #float((foftk_wb_temp - X)/fdwb_temp) + delta = np.where(delta<10., delta, 10.) #min(10,delta) + delta = np.where(delta>-10., delta, -10.) #max(-10,delta) + wb_temp = wb_temp - delta + wb_temp[invalid==1] = np.nan + Twb = wb_temp + iter = iter+1 + #end + + # ! 04-06-16: Adding iteration constraint. Commenting out original code. + # but in the MATLAB code, for sake of speed, we only do this for the values + # that didn't converge + + if 1: #ConvergenceMode: + + convergence = 0.00001 + maxiter = 20000 + + es_mb_wb_temp,rs_wb_temp,de_mbdwb_temp, dlnes_mbdwb_temp, rsdwb_temp, foftk_wb_temp, fdwb_temp = QSat_2(wb_temp + C, Pressure) + delta = (foftk_wb_temp - X)/fdwb_temp #float((foftk_wb_temp - X)/fdwb_temp) + subdo = np.where(np.abs(delta)>convergence) #find(abs(delta)>convergence) + + iter = 0 + while (len(subdo)>0) and (iter<=maxiter): + iter = iter + 1 + + wb_temp[subdo] = wb_temp[subdo] - 0.1*delta[subdo] + + es_mb_wb_temp,rs_wb_temp,de_mbdwb_temp, dlnes_mbdwb_temp, rsdwb_temp, foftk_wb_temp, fdwb_temp = QSat_2(wb_temp[subdo]+C, Pressure[subdo]) + delta = 0 * wb_temp + delta[subdo] = (foftk_wb_temp - X[subdo])/fdwb_temp #float((foftk_wb_temp - X[subdo])/fdwb_temp) + subdo = np.where(np.abs(delta)>convergence) #find(abs(delta)>convergence); + #end + + Twb = wb_temp + if any(map(len,subdo)): #len(subdo)>0: + print(len(subdo)) + Twb[subdo] = TemperatureK[subdo]-C + #print(subdo) + for www in subdo[0]: + # print(www) + print('WARNING-Wet_Bulb failed to converge. Setting to T: WB, P, T, RH, Delta: %0.2f, %0.2f, %0.1f, %0.2g, %0.1f'%(Twb[www], Pressure[www], \ + TemperatureK[www], relhum[www], delta[www])) + #end + #end + + #end + + #Twb=float(Twb) + return Twb,Teq,epott + +if __name__ == "__main__": + tempC = np.array([31.,32.,33.,34.]) + #tempd = np.array([26.58,29.10,29.26,27.55]) + tempd = np.array([26.57,29.11,29.26,27.54]) + RH = calc_RH_from_T_Td(tempC,tempd,mode=1) + # print('RH = ') + # print(RH) + + #Pres = np.array([102130,102130,102130,102130]) + Pres = np.array([101325]*4) + relHum = np.array([70.,80.,75.,60.]) + Hum_mode = 1 + + Twb,Teq,epott = WetBulb(tempC,Pres,relHum,Hum_mode) + print(Twb) + print(Teq) + print(epott) diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/data/DX_Equipment_Data_Collection_D.xlsx b/DX_exploration/archive/example scripts/example/curve_to_map/data/DX_Equipment_Data_Collection_D.xlsx new file mode 100644 index 0000000..412d867 Binary files /dev/null and b/DX_exploration/archive/example scripts/example/curve_to_map/data/DX_Equipment_Data_Collection_D.xlsx differ diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/data/HPDM.RS0004.a205.xlsx b/DX_exploration/archive/example scripts/example/curve_to_map/data/HPDM.RS0004.a205.xlsx new file mode 100644 index 0000000..5efcaf4 Binary files /dev/null and b/DX_exploration/archive/example scripts/example/curve_to_map/data/HPDM.RS0004.a205.xlsx differ diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/env.sh b/DX_exploration/archive/example scripts/example/curve_to_map/env.sh new file mode 100644 index 0000000..d6437df --- /dev/null +++ b/DX_exploration/archive/example scripts/example/curve_to_map/env.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +module unload python/2.7.13 +module load python/3.7.2 +source ~/CODES/curve_dev/copper/venv/bin/activate diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/explore_data.py b/DX_exploration/archive/example scripts/example/curve_to_map/explore_data.py new file mode 100644 index 0000000..f7f805e --- /dev/null +++ b/DX_exploration/archive/example scripts/example/curve_to_map/explore_data.py @@ -0,0 +1,227 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import os +from WetBulb import WetBulb + +class DXPerfMap: + + def __init__( + self, + xlsx_file, + rated_flow_rate=1.11, + comp_sequences=1, + ): + + self.xlsx_file = xlsx_file + self.rated_flow_rate = rated_flow_rate + self.comp_sequences = comp_sequences + + self.df = self.load_xlsx() + self.Te_db, self.Te_wb = self.load_AHRI_conditions() + self.df_rated, self.rated_capacity, self.df_rated_T, self.df_rated_flow = self.get_rated_conditions(self.df) + + def load_xlsx(self): + df = pd.read_excel(io=self.xlsx_file, sheet_name="performance_map_cooling", skiprows=[0, 1, 3]) + df["indoor_coil_entering_dry_bulb_temperature"] = self.convert_K_to_C( + df["indoor_coil_entering_dry_bulb_temperature"]) + df["outdoor_coil_entering_dry_bulb_temperature"] = self.convert_K_to_C( + df["outdoor_coil_entering_dry_bulb_temperature"]) + + T_wb, _, _ = WetBulb( + TemperatureC=df["indoor_coil_entering_dry_bulb_temperature"].values, + Pressure=df["ambient_absolute_air_pressure"].values, + Humidity=100*df["indoor_coil_entering_relative_humidity"].values, #relative humidity is in 100% + HumidityMode=1 + ) + df["indoor_coil_entering_wet_bulb_temperature"] = T_wb + return df + + @staticmethod + def convert_K_to_C(K): + return K - 273.0 + + def load_AHRI_conditions(self): + Te_db = 26.7 + Te_wb = 19.4 + return Te_db, Te_wb + + def get_rated_conditions(self, df): + T_db = df["outdoor_coil_entering_dry_bulb_temperature"].values + T_wb = df["indoor_coil_entering_wet_bulb_temperature"].values + dist = (T_db - self.Te_db)**2 + (T_wb - self.Te_wb)**2 + #check the location of minimum distance + min_idx = np.argmin(dist) + df_rated = self.df.iloc[min_idx] + rated_capacity = df_rated["gross_total_capacity"] + #get rated capacity + self.df["cap_f_t"] = self.df["gross_total_capacity"]/rated_capacity + self.df["ff"] = self.df["indoor_coil_air_mass_flow_rate"]/self.rated_flow_rate + + ##Get the dataframe for cap-f-t + if self.comp_sequences == 1: + df_const_T = self.df[ + (np.abs(self.df["outdoor_coil_entering_dry_bulb_temperature"] - self.Te_db) < 1.0) & + (np.abs(self.df["indoor_coil_entering_wet_bulb_temperature"] - self.Te_wb) < 1.0) + ] + + #get the dataframe + df_rated_flow = self.df[(np.abs(self.df["indoor_coil_air_mass_flow_rate"] - self.rated_flow_rate) < 0.1)] + else: + df_const_T = dict() + df_rated_flow = dict() + for num_comp in range(self.comp_sequences): + _df = self.df.loc[self.df["compressor_sequence_number"] == (num_comp+1)] + df_const_T[f"comp_seq_{num_comp+1}"] = _df[ + (np.abs(_df["outdoor_coil_entering_dry_bulb_temperature"] - self.Te_db) < 1.0) & + (np.abs(_df["indoor_coil_entering_wet_bulb_temperature"] - self.Te_wb) < 1.0) + ] + df_rated_flow[f"comp_seq_{num_comp+1}"] = _df[ + (np.abs(_df["indoor_coil_air_mass_flow_rate"] - self.rated_flow_rate) < 0.1)] + return df_rated, rated_capacity, df_const_T, df_rated_flow + + + +class CurveFit: + + def __init__(self, x, targets, fun="bi-quad", y=None): + self.x = x + self.y = y + self.targets = targets + self.function = fun + + if self.function == "bi-quad": + self.X = self.f_biquad() + elif self.function == "cubic": + self.X = self.f_cubic() + else: + self.X = None + + assert self.X is not None + self.coeffs = None + + def f_biquad(self): + assert self.y is not None + X = np.vstack( + ( + np.ones(self.x.shape[0], ), + self.x, + self.x ** 2, + self.y, + self.y ** 2, + np.multiply(self.x, self.y), + ) + ) + + return np.transpose(X) + + def f_cubic(self): + X = np.vstack( + ( + np.ones(self.x.shape[0], ), + self.x, + self.x ** 2, + self.x ** 3, + ) + ) + + return np.transpose(X) + + def fit(self): + coeffs = np.linalg.lstsq(self.X, self.targets) + self.coeffs = coeffs[0] + return self.coeffs + + def plot(self, file_str, fig_dir="figures", label="Cap-f-t"): + """ + Method to compare the predicted and + """ + assert self.coeffs is not None + predictions = np.matmul(self.X, self.coeffs) + print(f"The Pearson coefficient is : {np.corrcoef(self.targets, predictions)}") + + #plot and get the R^2 metric + plt.plot(self.targets, predictions, 'ko') + plt.plot(self.targets, self.targets, 'k-') + plt.xlabel(f"{label} (Actual)", fontsize=16) + plt.ylabel(f"{label} (Predictions)", fontsize=16) + plt.savefig(os.path.join(fig_dir, f"{file_str}_R2_{np.corrcoef(self.targets, predictions)[0, 1]}_fit.png")) + + return None + +def plot(xlsx_file, filename, fig_dir="./figures/"): + + df = pd.read_excel(io=xlsx_file, sheet_name="performance_map_cooling", skiprows=[0, 1, 3]) + #df.columns = df.index + #Define grid variables + x_cols = [ + "outdoor_coil_entering_dry_bulb_temperature", + "indoor_coil_entering_relative_humidity", + "indoor_coil_entering_dry_bulb_temperature", + "indoor_coil_air_mass_flow_rate", + "compressor_sequence_number", + "ambient_absolute_air_pressure" + ] + + #define lookup variables + y_cols = [ + "gross_total_capacity", + "gross_sensible_capacity", + "gross_power" + ] + + #create directory + if not os.path.exists(fig_dir): + os.makedirs(fig_dir) + + for yc in y_cols: + for xc in x_cols: + plt.plot(df[xc], df[yc], 'ko') + plt.xlabel(xc, fontsize=16) + plt.ylabel(yc, fontsize=16) + plt.savefig(os.path.join(fig_dir, f"filename_{yc}_vs_{xc}.png")) + plt.close() + + return None + +def test_ff(df, fig_dir="./figures/"): + plt.plot(df["ff"], df["cap_f_t"], 'ko') + plt.savefig(os.path.join(fig_dir, "cap-f-t_v_ff.png")) + plt.close() + + return None +##create a helper function +def compute_Twb(T_db, RH, pressure): + print(f"T_db: {T_db}, RH: {RH}, pressure: {pressure}") + Hum_mode = 1 + T_wb, _, _ = WetBulb(T_db,pressure,RH*100,Hum_mode) + return S[5] + + +if __name__ == "__main__": + xlsx_file = "./data/HPDM.RS0004.a205.xlsx" + num_comp_sequences = 2 + DX = DXPerfMap(xlsx_file=xlsx_file, rated_flow_rate=1.11,comp_sequences=num_comp_sequences) + + #test ff + for nc in range(num_comp_sequences): + df_rated_flow = DX.df_rated_flow[f"comp_seq_{nc+1}"] + TotCapTFit = CurveFit( + x=df_rated_flow["outdoor_coil_entering_dry_bulb_temperature"].values, + targets=df_rated_flow["cap_f_t"].values, + y=df_rated_flow["indoor_coil_entering_wet_bulb_temperature"].values + ) + #print("Curve fitting for Case I") + TotCapTFit.fit() + TotCapTFit.plot(file_str=f"TotCapFlowTempFit_seq_{nc+1}") + + # ####New Curve fit objects + df_rated_T = DX.df_rated_T[f"comp_seq_{nc+1}"] + TotCapFlowFit = CurveFit( + x=df_rated_T["ff"].values, + targets=df_rated_T["cap_f_t"].values, + fun="cubic" + ) + # print("Curve Fitting for Case II") + TotCapFlowFit.fit() + TotCapFlowFit.plot(file_str=f"TotCapFlowFit_seq_{nc+1}", label="Cap-f-ff") diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/explore_data_test.py b/DX_exploration/archive/example scripts/example/curve_to_map/explore_data_test.py new file mode 100644 index 0000000..847c09d --- /dev/null +++ b/DX_exploration/archive/example scripts/example/curve_to_map/explore_data_test.py @@ -0,0 +1,270 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import os + + +class DXPerfMap: + + def __init__( + self, + xlsx_file, + sheet_ind, + rated_flow_rate=3000, # change + comp_sequences=1, + ): + + self.xlsx_file = xlsx_file + self.sheet_ind = sheet_ind + self.rated_flow_rate = rated_flow_rate + self.comp_sequences = comp_sequences + + self.df = self.load_xlsx() + self.Te_db, self.Te_wb = self.load_AHRI_conditions() + self.df_rated, self.rated_capacity, self.df_rated_T, self.df_rated_flow = self.get_rated_conditions(self.df) + + def load_xlsx(self): + df = pd.read_excel(io=self.xlsx_file, sheet_name=self.sheet_ind, header=6, usecols="B:L", engine="openpyxl") + df["indoor_coil_entering_dry_bulb_temperature"] = self.convert_F_to_C( + df["indoor_coil_entering_dry_bulb_temperature"]) + df["outdoor_coil_entering_dry_bulb_temperature"] = self.convert_F_to_C( + df["outdoor_coil_entering_dry_bulb_temperature"]) + df["indoor_coil_entering_wet_bulb_temperature"] = self.convert_F_to_C( + df["indoor_coil_entering_wet_bulb_temperature"]) + return df + + @staticmethod + + def convert_F_to_C(F): + return (F-32) * 5/9 + + ## have changed to DX Equipment's + def load_AHRI_conditions(self): + Te_db = 26.7 # 80 F + Te_wb = 19.4 # 67 F + return Te_db, Te_wb + + def get_rated_conditions(self, df): + T_db = df["indoor_coil_entering_dry_bulb_temperature"].values + T_wb = df["indoor_coil_entering_wet_bulb_temperature"].values + dist = (T_db - self.Te_db)**2 + (T_wb - self.Te_wb)**2 + # check the location of minimum distance + min_idx = np.argmin(dist) + df_rated = self.df.iloc[min_idx] + rated_capacity = df_rated["gross_total_capacity"] + # get rated capacity + self.df["cap_f_t"] = self.df["gross_total_capacity"]/rated_capacity + self.df["ff"] = self.df["indoor_coil_air_mass_flow_rate"]/self.rated_flow_rate + # check the compressor stage + stage_len = len(np.unique(self.df["compressor_sequence_number"].values)) + + ##Get the dataframe for cap-f-t + if self.comp_sequences == 1 or stage_len == 1: # compressor stage = 1 or 2 only + df_const_T = self.df[ + (np.abs(self.df["indoor_coil_entering_dry_bulb_temperature"] - self.Te_db) < 1.0) & + (np.abs(self.df["indoor_coil_entering_wet_bulb_temperature"] - self.Te_wb) < 1.0) + ] + + #get the dataframe + df_rated_flow = self.df[(np.abs(self.df["indoor_coil_air_mass_flow_rate"] - self.rated_flow_rate) < 100)] + else: + df_const_T = dict() + df_rated_flow = dict() + for num_comp in range(self.comp_sequences): + _df = self.df.loc[self.df["compressor_sequence_number"] == (num_comp+1)] + df_const_T[f"comp_seq_{num_comp+1}"] = _df[ + (np.abs(_df["indoor_coil_entering_dry_bulb_temperature"] - self.Te_db) < 1.0) & + (np.abs(_df["indoor_coil_entering_wet_bulb_temperature"] - self.Te_wb) < 1.0) + ] + df_rated_flow[f"comp_seq_{num_comp+1}"] = _df[ + (np.abs(_df["indoor_coil_air_mass_flow_rate"] - self.rated_flow_rate) < 100)] + return df_rated, rated_capacity, df_const_T, df_rated_flow + + + +class CurveFit: + + def __init__(self, x, targets, fun="bi-quad", y=None): + self.x = x + self.y = y + self.targets = targets + self.function = fun + + if self.function == "bi-quad": + self.X = self.f_biquad() + elif self.function == "cubic": + self.X = self.f_cubic() + else: + self.X = None + + assert self.X is not None + self.coeffs = None + + def f_biquad(self): + assert self.y is not None + X = np.vstack( + ( + np.ones(self.x.shape[0], ), + self.x, + self.x ** 2, + self.y, + self.y ** 2, + np.multiply(self.x, self.y), + ) + ) + + return np.transpose(X) + + def f_cubic(self): + X = np.vstack( + ( + np.ones(self.x.shape[0], ), + self.x, + self.x ** 2, + self.x ** 3, + ) + ) + + return np.transpose(X) + + def fit(self): + coeffs = np.linalg.lstsq(self.X, self.targets) + self.coeffs = coeffs[0] + return self.coeffs + + def plot(self, file_str, sheet_ind='Table 1', label="Cap-f-t"): + """ + Method to compare the predicted and + """ + fig_dir = "./figures/D_" + sheet_ind + "/" + #create directory + if not os.path.exists(fig_dir): + os.makedirs(fig_dir) + assert self.coeffs is not None + predictions = np.matmul(self.X, self.coeffs) + print(f"The Pearson coefficient is : {np.corrcoef(self.targets, predictions)}") + + #plot and get the R^2 metric + plt.plot(self.targets, predictions, 'ko') + plt.plot(self.targets, self.targets, 'k-') + plt.xlabel(f"{label} (Actual)", fontsize=16) + plt.ylabel(f"{label} (Predictions)", fontsize=16) + plt.savefig(os.path.join(fig_dir, f"{file_str}_R2_{np.corrcoef(self.targets, predictions)[0, 1]}_fit.png")) + + return None + +def plot(xlsx_file, sheet_ind="Table 1"): + # Find the index of "./data/" and ".xlsx" + start_index = xlsx_file.find("./data/DX_Equipment_Data_Collection_") + len("./data/DX_Equipment_Data_Collection_") + end_index = xlsx_file.find(".xlsx") + # Extract the information between "./data/" and ".xlsx" + extracted_info = xlsx_file[start_index:end_index] + + fig_dir = "./figures/" + extracted_info + "_" + sheet_ind + "/" + df = pd.read_excel(io=xlsx_file, sheet_name=sheet_ind, header=6, usecols="B:L", engine="openpyxl") + #df.columns = df.index + #Define grid variables + x_cols = [ + "outdoor_coil_entering_dry_bulb_temperature", + "indoor_coil_entering_wet_bulb_temperature", + "indoor_coil_entering_dry_bulb_temperature", + "indoor_coil_air_mass_flow_rate", + "compressor_sequence_number" + ] + + #define lookup variables + y_cols = [ + "gross_total_capacity", + "gross_sensible_capacity", + "gross_power" + ] + + #create directory + if not os.path.exists(fig_dir): + os.makedirs(fig_dir) + + for yc in y_cols: + for xc in x_cols: + plt.plot(df[xc], df[yc], 'ko') + plt.xlabel(xc, fontsize=16) + plt.ylabel(yc, fontsize=16) + plt.savefig(os.path.join(fig_dir, f"filename_{yc}_vs_{xc}.png")) + plt.close() + + return None + + +# run all +def run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate): + DX = DXPerfMap(xlsx_file=xlsx_file, sheet_ind=sheet_ind, + rated_flow_rate=flow_rate,comp_sequences=num_comp_sequences) + plot(xlsx_file, sheet_ind) + #test ff + for nc in range(num_comp_sequences): + if num_comp_sequences == 1: + df_rated_flow = DX.df_rated_flow + else: + df_rated_flow = DX.df_rated_flow[f"comp_seq_{nc+1}"] + TotCapTFit = CurveFit( + x=df_rated_flow["outdoor_coil_entering_dry_bulb_temperature"].values, + targets=df_rated_flow["cap_f_t"].values, + y=df_rated_flow["indoor_coil_entering_wet_bulb_temperature"].values + ) + #print("Curve fitting for Case I") + TotCapTFit.fit() + TotCapTFit.plot(file_str=f"TotCapFlowTempFit_seq_{nc+1}", sheet_ind=DX.sheet_ind) + + # ####New Curve fit objects + if num_comp_sequences == 1: + df_rated_T = DX.df_rated_T + else: + df_rated_T = DX.df_rated_T[f"comp_seq_{nc+1}"] + TotCapFlowFit = CurveFit( + x=df_rated_T["ff"].values, + targets=df_rated_T["cap_f_t"].values, + fun="bi-quad" + ) + # print("Curve Fitting for Case II") + TotCapFlowFit.fit() + TotCapFlowFit.plot(file_str=f"TotCapFlowFit_seq_{nc+1}", sheet_ind=DX.sheet_ind, label="Cap-f-ff") + return + +if __name__ == "__main__": + xlsx_file = "./data/DX_Equipment_Data_Collection_D.xlsx" + sheet_ind = "Table 1" + num_comp_sequences = 1 + flow_rate = 2100 + # run_all(xlsx_file,sheet_ind,num_comp_sequences,flow_rate) + + + DX = DXPerfMap(xlsx_file=xlsx_file, sheet_ind=sheet_ind, + rated_flow_rate=flow_rate,comp_sequences=num_comp_sequences) + plot(xlsx_file, sheet_ind) + #test ff + for nc in range(num_comp_sequences): + if num_comp_sequences == 1: + df_rated_flow = DX.df_rated_flow + else: + df_rated_flow = DX.df_rated_flow[f"comp_seq_{nc+1}"] + TotCapTFit = CurveFit( + x=df_rated_flow["outdoor_coil_entering_dry_bulb_temperature"].values, + targets=df_rated_flow["cap_f_t"].values, + y=df_rated_flow["indoor_coil_entering_wet_bulb_temperature"].values + ) + #print("Curve fitting for Case I") + TotCapTFit.fit() + TotCapTFit.plot(file_str=f"TotCapFlowTempFit_seq_{nc+1}", sheet_ind=DX.sheet_ind) + + # ####New Curve fit objects + if num_comp_sequences == 1: + df_rated_T = DX.df_rated_T + else: + df_rated_T = DX.df_rated_T[f"comp_seq_{nc+1}"] + TotCapFlowFit = CurveFit( + x=df_rated_T["ff"].values, + targets=df_rated_T["cap_f_t"].values, + fun="cubic" + ) + # print("Curve Fitting for Case II") + TotCapFlowFit.fit() + TotCapFlowFit.plot(file_str=f"TotCapFlowFit_seq_{nc+1}", sheet_ind=DX.sheet_ind, label="Cap-f-ff") diff --git a/DX_exploration/archive/example scripts/example/curve_to_map/figures/D_Table 1/TotCapFlowFit_seq_1_R2_0.3292649817946297_fit.png b/DX_exploration/archive/example scripts/example/curve_to_map/figures/D_Table 1/TotCapFlowFit_seq_1_R2_0.3292649817946297_fit.png new file mode 100644 index 0000000..b1ed7f2 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arguments given +# DBT - Dry bulb temperature - Kelvins, K +# DPT - Dew point temperature - Kelvins, K +# H - Specific enthalpy - kiloJoules per kilogram, kJ/kg +# P - Atmospheric pressure - Pascals, Pa +# Pw - Water vapor partial pressure - Pascals, Pa +# RH - Relative humidity - Decimal (i.e. not a percentage) +# V - Specific volume - Cubic meters per kilogram, m^3/kg +# W - Humidity ratio - kilograms per kilograms, kg/kg +# WBT - Wet bulb temperature - Kelvins, K + +# Minimum dry bulb temperature +Min_DBT=273.15 +# Maximum dry bulb temperature +Max_DBT=473.15 +# Convergence tolerance +TOL=0.0005 + +def __DBT_H_RH_P(H, RH, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=__W_DBT_RH_P(DBTa, RH, P)-__W_DBT_H(DBTa, H) + y=__W_DBT_RH_P(DBT, RH, P)-__W_DBT_H(DBT, H) + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_H_V_P(H, V, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=__W_DBT_V_P(DBTa, V, P)-__W_DBT_H(DBTa, H) + y=__W_DBT_V_P(DBT, V, P)-__W_DBT_H(DBT, H) + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_H_W(H, W): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=W-__W_DBT_H(DBTa, H) + y=W-__W_DBT_H(DBT, H) + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_H_WBT_P(H, WBT, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=__W_DBT_WBT_P(DBTa, WBT, P)-__W_DBT_H(DBTa, H) + y=__W_DBT_WBT_P(DBT, WBT, P)-__W_DBT_H(DBT, H) + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_RH_V_P(RH, V, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=__W_DBT_RH_P(DBTa, RH, P)-__W_DBT_V_P(DBTa, V, P) + y=__W_DBT_RH_P(DBT, RH, P)-__W_DBT_V_P(DBT, V, P) + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_RH_W_P(RH, W, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=__W_DBT_RH_P(DBTa, RH, P)-W + y=__W_DBT_RH_P(DBT, RH, P)-W + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_RH_WBT_P(RH, WBT, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=__W_DBT_WBT_P(DBTa, WBT, P)-__W_DBT_RH_P(DBTa, RH, P) + y=__W_DBT_WBT_P(DBT, WBT, P)-__W_DBT_RH_P(DBT, RH, P) + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_V_W_P(V, W, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=W-__W_DBT_V_P(DBTa, V, P) + y=W-__W_DBT_V_P(DBT, V, P) + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_V_WBT_P(V, WBT, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=__W_DBT_WBT_P(DBTa, WBT, P)-__W_DBT_V_P(DBTa, V, P) + y=__W_DBT_WBT_P(DBT, WBT, P)-__W_DBT_V_P(DBT, V, P) + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +def __DBT_W_WBT_P(W, WBT, P): + [DBTa, DBTb]=[Min_DBT, Max_DBT] + DBT=(DBTa+DBTb)/2 + while DBTb-DBTa>TOL: + ya=__W_DBT_WBT_P(DBTa, WBT, P)-W + y=__W_DBT_WBT_P(DBT, WBT, P)-W + if __is_positive(y)==__is_positive(ya): + DBTa=DBT + else: + DBTb=DBT + DBT=(DBTa+DBTb)/2 + return DBT + +# ASHRAE 2009 Chapter 1 Equation 39 +def __DPT_Pw(Pw): + Pw=Pw/1000 + C14=6.54 + C15=14.529 + C16=0.7389 + C17=0.09486 + C18=0.4569 + a=m.log(Pw) + return (C14+C15*a+C16*a**2+C17*a**3+C18*Pw**0.1984)+273.15 + +# ASHRAE 2009 Chapter 1 Equation 32 +def __H_DBT_W(DBT, W): + if __valid_DBT(DBT): + DBT=DBT-273.15 + return 1.006*DBT+W*(2501+1.86*DBT) + +def __is_positive(x): + if x>0: + return True + else: + return False + +# ASHRAE 2009 Chapter 1 Equation 22 +def __Pw_W_P(W, P): + return W*P/(W+0.621945) + +# ASHRAE 2009 Chapter 1 Equation 6 +def __Pws(DBT): + if __valid_DBT(DBT): + C8=-5.8002206*10**3 + C9=1.3914993 + C10=-4.8640239*10**-2 + C11=4.1764768*10**-5 + C12=-1.4452093*10**-8 + C13=6.5459673 + return m.exp(C8/DBT+C9+C10*DBT+C11*DBT**2+C12*DBT**3+C13*m.log(DBT)) + +def state(prop1, prop1val, prop2, prop2val,P): + if prop1==prop2: + print("Properties must be independent.") + return + prop=["DBT","WBT","RH","W","V","H"] + if prop1 not in prop or prop2 not in prop: + print("Valid property must be given.") + return + prop1i=prop.index(prop1) + prop2i=prop.index(prop2) + if prop1iTOL: + Ws=__W_DBT_WBT_P(DBT, WBT, P) + if W>Ws: + WBTa=WBT + else: + WBTb=WBT + WBT=(WBTa+WBTb)/2 + return WBT + +def __valid_DBT(DBT): + if Min_DBT<=DBT<=Max_DBT: + return True + else: + return False diff --git a/DX_exploration/archive/pdf/48-50A-21PD-SubCool.pdf 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{}, + "outputs": [], + "source": [ + "######################### DAIKIN #############################\n", + "########################## Notes #############################\n", + "# The 10 equipments' data don't include 'Bypass Factor' at \n", + "# each row, but include discharge air dry bulb temp.\n", + "# Issue to be soloved: The formula requires the apparatus \n", + "# dew point temperature. Use \"bypass_adp.ipynb\" to calculate." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "\n", + "# not work when connect to PNNL VPN" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ebb23de5-7820-4765-9b7e-450eadffbd92", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:19:25.921482Z", + "iopub.status.busy": "2023-08-02T05:19:25.921168Z", + "iopub.status.idle": "2023-08-02T05:19:26.176161Z", + "shell.execute_reply": "2023-08-02T05:19:26.175142Z", + "shell.execute_reply.started": "2023-08-02T05:19:25.921460Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n", + " \"class\": algorithms.Blowfish,\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "\n", + "def loadData(name,nHead,nRows):\n", + " # Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=int(nHead), \n", + " usecols=\"C:AC\", skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " # buffer dataframe\n", + " column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + " dx = pd.DataFrame(columns=column_names)\n", + " return df, dx" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e5be0205-54ed-4f3a-bd54-1a037b784310", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:27:56.856295Z", + "iopub.status.busy": "2023-08-02T05:27:56.855944Z", + "iopub.status.idle": "2023-08-02T05:27:56.859895Z", + "shell.execute_reply": "2023-08-02T05:27:56.859026Z", + "shell.execute_reply.started": "2023-08-02T05:27:56.856272Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def processData1(df,dx):\n", + " ## MBH, S2T, kW\n", + " rows_to_select = [0,1,3] # index of rows to select\n", + " names_to_select = ['MBH','S2T','kW']\n", + " \n", + " for i in range(len(rows_to_select)):\n", + " df_temp = df[rows_to_select[i]::7] # internals\n", + " list_2d = []\n", + " for col in df_temp.columns[3:]:\n", + " list_2d.append(df_temp[col].to_list())\n", + " list_1d = []\n", + " for sublist in list_2d:\n", + " for item in sublist:\n", + " list_1d.append(item) \n", + " dx[names_to_select[i]] = list_1d\n", + "\n", + " ## Define the 4-element array to repeat\n", + " # IDB, airflow, iwb\n", + " idb_unique = np.unique(df.iloc[:,0].values)\n", + " airflow_unique = np.unique(df.iloc[:,1].values)\n", + " idb_array = np.repeat(idb_unique, len(airflow_unique))\n", + " airflow_array = np.tile(airflow_unique, len(idb_unique))\n", + " iwb_unique = df.columns[3:7]\n", + " iwb_array = np.repeat(iwb_unique, len(airflow_unique)*len(idb_unique))\n", + " # OADB\n", + " oat_unique = np.array([65,75,85,95,105,115])\n", + " oat_array = np.repeat(oat_unique, 24)\n", + "\n", + " ## Calculate how many times the array has to be repeated and create the repeated array\n", + " idb_repeats = len(dx) // len(idb_array) + (len(dx) % len(idb_array) > 0)\n", + " idb_repeated_array = np.tile(idb_array, idb_repeats)\n", + " airflow_repeated_array = np.tile(airflow_array, idb_repeats)\n", + " iwb_repeats = len(dx) // len(iwb_array) + (len(dx) % len(iwb_array) > 0)\n", + " iwb_repeated_array = np.tile(iwb_array, iwb_repeats)\n", + " oat_repeated_array = np.tile(oat_array, 1)\n", + "\n", + " ## Slice the repeated array to match the DataFrame's length and assign it to a column\n", + " dx['IDB'] = idb_repeated_array[:len(dx)]\n", + " dx['airflow'] = airflow_repeated_array[:len(dx)]\n", + " dx['IWB'] = iwb_repeated_array[:len(dx)]\n", + " dx['OADB'] = oat_repeated_array[:len(dx)]\n", + " ## replace \"-\" with NA\n", + " dx.replace('-', np.nan, inplace=True)\n", + "\n", + " # Sensible MBH\n", + " dx['Sensible MBH'] = dx['MBH']*dx['S2T']\n", + "\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " dx['RH'] = dx.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " return dx" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5fa0a903", + "metadata": {}, + "outputs": [], + "source": [ + "def processData2(df,dx):\n", + " ## MBH, S2T, kW\n", + " rows_to_select = [0,1,5] # index of rows to select\n", + " names_to_select = ['Capacity','S/T','TotalPower']\n", + " \n", + " for i in range(len(rows_to_select)):\n", + " df_temp = df[rows_to_select[i]::6] # internals\n", + " list_2d = []\n", + " for col in df_temp.columns[3:]:\n", + " list_2d.append(df_temp[col].to_list())\n", + " list_1d = []\n", + " for sublist in list_2d:\n", + " for item in sublist:\n", + " list_1d.append(item) \n", + " dx[names_to_select[i]] = list_1d\n", + "\n", + " ## Define the 4-element array to repeat\n", + " # IDB, airflow, iwb\n", + " idb_unique = np.unique(df.iloc[:,0].values)\n", + " airflow_unique = np.unique(df.iloc[:,1].values)\n", + " idb_array = np.repeat(idb_unique, len(airflow_unique))\n", + " airflow_array = np.tile(airflow_unique, len(idb_unique))\n", + " iwb_unique = df.columns[3:7]\n", + " iwb_array = np.repeat(iwb_unique, len(airflow_unique)*len(idb_unique))\n", + " # OADB\n", + " oat_unique = np.array([65,75,85,95,105,115])\n", + " oat_array = np.repeat(oat_unique, 24)\n", + "\n", + " ## Calculate how many times the array has to be repeated and create the repeated array\n", + " idb_repeats = len(dx) // len(idb_array) + (len(dx) % len(idb_array) > 0)\n", + " idb_repeated_array = np.tile(idb_array, idb_repeats)\n", + " airflow_repeated_array = np.tile(airflow_array, idb_repeats)\n", + " iwb_repeats = len(dx) // len(iwb_array) + (len(dx) % len(iwb_array) > 0)\n", + " iwb_repeated_array = np.tile(iwb_array, iwb_repeats)\n", + " oat_repeated_array = np.tile(oat_array, 1)\n", + "\n", + " ## Slice the repeated array to match the DataFrame's length and assign it to a column\n", + " dx['IDB'] = idb_repeated_array[:len(dx)]\n", + " dx['airflow'] = airflow_repeated_array[:len(dx)]\n", + " dx['IWB'] = iwb_repeated_array[:len(dx)]\n", + " dx['OADB'] = oat_repeated_array[:len(dx)]\n", + " ## replace \"-\" with NA\n", + " dx.replace('-', np.nan, inplace=True)\n", + "\n", + " # Sensible MBH\n", + " dx['Sensible MBH'] = dx['MBH']*dx['S2T']\n", + "\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " dx['RH'] = dx.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " return dx" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "863f4081-79f4-4b3e-a181-7600ddf2f978", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:20:34.110616Z", + "iopub.status.busy": "2023-08-02T05:20:34.110324Z", + "iopub.status.idle": "2023-08-02T05:20:34.115545Z", + "shell.execute_reply": "2023-08-02T05:20:34.114819Z", + "shell.execute_reply.started": "2023-08-02T05:20:34.110593Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ss-drc7\n", + "headList = [4,54,102,152,200,250,298,348]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-drc7.xlsx',head,42)\n", + " new = processData1(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + "filepath = Path('./output/ss-drc7.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "374ab7d0", + "metadata": {}, + "outputs": [], + "source": [ + "# ss-dfc15\n", + "headList = [4,46,90,132,176,218]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-dfc15.xlsx',head,36)\n", + " new = processData2(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + "filepath = Path('./output/ss-dfc15.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad7525a3", + "metadata": {}, + "outputs": [], + "source": [ + "# ss-dfc5-d\n", + "headList = [4,46,90,132,176,218,262,304]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-dfc5-d.xlsx',head,36)\n", + " new = processData2(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + "filepath = Path('./output/ss-dfc5-d.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f871b65e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/archive/scripts/data_collection_DAIKIN_v2.ipynb b/DX_exploration/archive/scripts/data_collection_DAIKIN_v2.ipynb new file mode 100644 index 0000000..fcb1407 --- /dev/null +++ b/DX_exploration/archive/scripts/data_collection_DAIKIN_v2.ipynb @@ -0,0 +1,379 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "51bda912", + "metadata": {}, + "outputs": [], + "source": [ + "######################### DAIKIN #############################\n", + "########################## Notes #############################\n", + "# The 10 equipments' data don't include 'Bypass Factor' at \n", + "# each row, but include discharge air dry bulb temp.\n", + "# Issue to be soloved: The formula requires the apparatus \n", + "# dew point temperature. Use \"bypass_adp.ipynb\" to calculate." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "\n", + "# not work when connect to PNNL VPN" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ebb23de5-7820-4765-9b7e-450eadffbd92", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:19:25.921482Z", + "iopub.status.busy": "2023-08-02T05:19:25.921168Z", + "iopub.status.idle": "2023-08-02T05:19:26.176161Z", + "shell.execute_reply": "2023-08-02T05:19:26.175142Z", + "shell.execute_reply.started": "2023-08-02T05:19:25.921460Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "\n", + "def loadData(name,nHead,nRows):\n", + " # Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=int(nHead), \n", + " usecols=\"C:AC\", skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " # buffer dataframe\n", + " column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + " dx = pd.DataFrame(columns=column_names)\n", + " \n", + " return df, dx\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e5be0205-54ed-4f3a-bd54-1a037b784310", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:27:56.856295Z", + "iopub.status.busy": "2023-08-02T05:27:56.855944Z", + "iopub.status.idle": "2023-08-02T05:27:56.859895Z", + "shell.execute_reply": "2023-08-02T05:27:56.859026Z", + "shell.execute_reply.started": "2023-08-02T05:27:56.856272Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def processData1(df,dx):\n", + " ## MBH, S2T, kW\n", + " # ss-drc7\n", + " rows_to_select = [0,1,3,2] # index of rows to select\n", + " names_to_select = ['MBH','S2T','kW','dT']\n", + " \n", + " for i in range(len(rows_to_select)):\n", + " df_temp = df[rows_to_select[i]::7] # internals\n", + " list_2d = []\n", + " for col in df_temp.columns[3:]:\n", + " list_2d.append(df_temp[col].to_list())\n", + " list_1d = []\n", + " for sublist in list_2d:\n", + " for item in sublist:\n", + " list_1d.append(item) \n", + " dx[names_to_select[i]] = list_1d\n", + "\n", + " ## Define the 4-element array to repeat\n", + " # IDB, airflow, iwb\n", + " idb_unique = np.unique(df.iloc[:,0].values)\n", + " airflow_unique = np.unique(df.iloc[:,1].values)\n", + " idb_array = np.repeat(idb_unique, len(airflow_unique))\n", + " airflow_array = np.tile(airflow_unique, len(idb_unique))\n", + " iwb_unique = df.columns[3:7]\n", + " iwb_array = np.repeat(iwb_unique, len(airflow_unique)*len(idb_unique))\n", + " # OADB\n", + " oat_unique = np.array([65,75,85,95,105,115])\n", + " oat_array = np.repeat(oat_unique, 24)\n", + "\n", + " ## Calculate how many times the array has to be repeated and create the repeated array\n", + " idb_repeats = len(dx) // len(idb_array) + (len(dx) % len(idb_array) > 0)\n", + " idb_repeated_array = np.tile(idb_array, idb_repeats)\n", + " airflow_repeated_array = np.tile(airflow_array, idb_repeats)\n", + " iwb_repeats = len(dx) // len(iwb_array) + (len(dx) % len(iwb_array) > 0)\n", + " iwb_repeated_array = np.tile(iwb_array, iwb_repeats)\n", + " oat_repeated_array = np.tile(oat_array, 1)\n", + "\n", + " ## Slice the repeated array to match the DataFrame's length and assign it to a column\n", + " dx['IDB'] = idb_repeated_array[:len(dx)]\n", + " dx['airflow'] = airflow_repeated_array[:len(dx)]\n", + " dx['IWB'] = iwb_repeated_array[:len(dx)]\n", + " dx['OADB'] = oat_repeated_array[:len(dx)]\n", + " ## replace \"-\" with NA\n", + " dx.replace('-', np.nan, inplace=True)\n", + "\n", + " # Sensible MBH\n", + " dx['MBH'] = dx['MBH']*1000\n", + " dx['Sensible MBH'] = dx['MBH']*dx['S2T']\n", + "\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " dx['RH'] = dx.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " # outlet T\n", + " dx['Out DB'] = dx['IDB']-dx['dT']\n", + " \n", + " return dx\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "5fa0a903", + "metadata": {}, + "outputs": [], + "source": [ + "def processData2(df,dx):\n", + " ## MBH, S2T, kW\n", + " # \n", + " rows_to_select = [0,1,5,2] # index of rows to select\n", + " names_to_select = ['Capacity','S/T','TotalPower','Evap dT']\n", + " \n", + " for i in range(len(rows_to_select)):\n", + " df_temp = df[rows_to_select[i]::6] # internals\n", + " list_2d = []\n", + " for col in df_temp.columns[3:]:\n", + " list_2d.append(df_temp[col].to_list())\n", + " list_1d = []\n", + " for sublist in list_2d:\n", + " for item in sublist:\n", + " list_1d.append(item) \n", + " dx[names_to_select[i]] = list_1d\n", + "\n", + " ## Define the 4-element array to repeat\n", + " # IDB, airflow, iwb\n", + " idb_unique = np.unique(df.iloc[:,0].values)\n", + " airflow_unique = np.unique(df.iloc[:,1].values)\n", + " idb_array = np.repeat(idb_unique, len(airflow_unique))\n", + " airflow_array = np.tile(airflow_unique, len(idb_unique))\n", + " iwb_unique = df.columns[3:7]\n", + " iwb_array = np.repeat(iwb_unique, len(airflow_unique)*len(idb_unique))\n", + " # OADB\n", + " oat_unique = np.array([65,75,85,95,105,115])\n", + " oat_array = np.repeat(oat_unique, 24)\n", + "\n", + " ## Calculate how many times the array has to be repeated and create the repeated array\n", + " idb_repeats = len(dx) // len(idb_array) + (len(dx) % len(idb_array) > 0)\n", + " idb_repeated_array = np.tile(idb_array, idb_repeats)\n", + " airflow_repeated_array = np.tile(airflow_array, idb_repeats)\n", + " iwb_repeats = len(dx) // len(iwb_array) + (len(dx) % len(iwb_array) > 0)\n", + " iwb_repeated_array = np.tile(iwb_array, iwb_repeats)\n", + " oat_repeated_array = np.tile(oat_array, 1)\n", + "\n", + " ## Slice the repeated array to match the DataFrame's length and assign it to a column\n", + " dx['IDB'] = idb_repeated_array[:len(dx)]\n", + " dx['airflow'] = airflow_repeated_array[:len(dx)]\n", + " dx['IWB'] = iwb_repeated_array[:len(dx)]\n", + " dx['OADB'] = oat_repeated_array[:len(dx)]\n", + " ## replace \"-\" with NA\n", + " dx.replace('-', np.nan, inplace=True)\n", + " dx.replace('--', np.nan, inplace=True)\n", + "\n", + " # Sensible MBH\n", + " dx['Sensible Capacity'] = dx['Capacity']*dx['S/T']\n", + "\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " dx['RH'] = dx.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " # outlet T\n", + " dx['Out DB'] = pd.to_numeric(dx['IDB'])-pd.to_numeric(dx['Evap dT'])\n", + " \n", + " return dx\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c29450ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OADB IDB IWB MBH S2T kW airflow Capacity S/T TotalPower \\\n", + "0 65 70 59 NaN NaN NaN 4500 175388.0 0.59 10925.0 \n", + "1 65 70 59 NaN NaN NaN 5010 177290.0 0.63 10984.0 \n", + "2 65 70 59 NaN NaN NaN 6000 181904.0 0.67 11078.0 \n", + "3 65 75 59 NaN NaN NaN 4500 175490.0 0.71 10916.0 \n", + "4 65 75 59 NaN NaN NaN 5010 177392.0 0.75 10975.0 \n", + ".. ... ... ... ... ... .. ... ... ... ... \n", + "139 115 70 71 NaN NaN NaN 5010 NaN NaN NaN \n", + "140 115 70 71 NaN NaN NaN 6000 NaN NaN NaN \n", + "141 115 75 71 NaN NaN NaN 4500 158797.0 0.49 19876.0 \n", + "142 115 75 71 NaN NaN NaN 5010 160699.0 0.54 19935.0 \n", + "143 115 75 71 NaN NaN NaN 6000 165313.0 0.58 20029.0 \n", + "\n", + " Evap dT Sensible Capacity RH Out DB \n", + "0 20.37 103478.92 52.721762 49.63 \n", + "1 19.45 111692.70 52.721762 50.55 \n", + "2 17.97 121875.68 52.721762 52.03 \n", + "3 24.56 124597.90 38.629369 50.44 \n", + "4 23.64 133044.00 38.629369 51.36 \n", + ".. ... ... ... ... \n", + "139 NaN NaN 104.862130 NaN \n", + "140 NaN NaN 104.862130 NaN \n", + "141 16.28 77810.53 82.665887 58.72 \n", + "142 15.36 86777.46 82.665887 59.64 \n", + "143 13.88 95881.54 82.665887 61.12 \n", + "\n", + "[144 rows x 14 columns]\n" + ] + } + ], + "source": [ + "# headList = [4,46,90,132,176,218]\n", + "head = 4\n", + "df,dx = loadData('./excel/ss-dfc15.xlsx',head,36)\n", + "new = processData2(df,dx)\n", + "print(new)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "863f4081-79f4-4b3e-a181-7600ddf2f978", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:20:34.110616Z", + "iopub.status.busy": "2023-08-02T05:20:34.110324Z", + "iopub.status.idle": "2023-08-02T05:20:34.115545Z", + "shell.execute_reply": "2023-08-02T05:20:34.114819Z", + "shell.execute_reply.started": "2023-08-02T05:20:34.110593Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ss-drc7\n", + "headList = [4,54,102,152,200,250,298,348]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-drc7.xlsx',head,42)\n", + " new = processData1(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + " ## add on 10/06\n", + " # not drop NA because of the supply fan power\n", + " # buffer = buffer.dropna(subset=['dT']) # drop NA\n", + " \n", + " # rename and order\n", + " order = [\"OADB\",\"IDB\",\"IWB\",\"RH\", \"MBH\",\"Sensible MBH\",\"kW\",\"airflow\",\"Out DB\"]\n", + " buffer = buffer[order]\n", + " \n", + "filepath = Path('./output/ss-drc7.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "374ab7d0", + "metadata": {}, + "outputs": [], + "source": [ + "# ss-dfc15\n", + "headList = [4,46,90,132,176,218]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-dfc15.xlsx',head,36)\n", + " new = processData2(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + " ## add on 10/06\n", + " # rename and order\n", + " order = [\"OADB\",\"IDB\",\"IWB\",\"RH\", \"Capacity\",\"Sensible Capacity\",\"TotalPower\",\"airflow\",\"Out DB\"]\n", + " buffer = buffer[order]\n", + " \n", + "filepath = Path('./output/ss-dfc15.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "ad7525a3", + "metadata": {}, + "outputs": [], + "source": [ + "# ss-dfc5-d\n", + "headList = [4,46,90,132,176,218,262,304]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-dfc5-d.xlsx',head,36)\n", + " new = processData2(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + " ## add on 10/06\n", + " # rename and order\n", + " order = [\"OADB\",\"IDB\",\"IWB\",\"RH\", \"Capacity\",\"Sensible Capacity\",\"TotalPower\",\"airflow\",\"Out DB\"]\n", + " buffer = buffer[order]\n", + " \n", + "filepath = Path('./output/ss-dfc5-d.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f871b65e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/archive/scripts/data_collection_LENNOX.ipynb b/DX_exploration/archive/scripts/data_collection_LENNOX.ipynb new file mode 100644 index 0000000..94c4c69 --- /dev/null +++ b/DX_exploration/archive/scripts/data_collection_LENNOX.ipynb @@ -0,0 +1,983 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "ca06acec", + "metadata": {}, + "outputs": [], + "source": [ + "######################### LENNOX #############################\n", + "########################## Notes #############################\n", + "# The 10 equipments' data don't include 'Bypass Factor' at \n", + "# each row, but include discharge air dry bulb temp.\n", + "# Issue to be soloved: The formula requires the apparatus \n", + "# dew point temperature. Use \"bypass_adp.ipynb\" to calculate." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "\n", + "# not work when connect to PNNL VPN" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "ebb23de5-7820-4765-9b7e-450eadffbd92", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:19:25.921482Z", + "iopub.status.busy": "2023-08-02T05:19:25.921168Z", + "iopub.status.idle": "2023-08-02T05:19:26.176161Z", + "shell.execute_reply": "2023-08-02T05:19:26.175142Z", + "shell.execute_reply.started": "2023-08-02T05:19:25.921460Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "\n", + "def loadData(name,nHead,nRows,drop_list):\n", + " # Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=int(nHead), \n", + " usecols=\"A:AA\", skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " # clean the data\n", + " if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + " # fill the IWB (1st)\n", + " df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + " start_list = [0,3,6]\n", + " for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 3):\n", + " df.at[i, 'IWB'] = temp_value\n", + " \n", + " # add OADB column\n", + " df.insert(0, \"OADB\", np.nan)\n", + " \n", + " # modify the dataframe\n", + " chunk1 = df.iloc[:, :8]\n", + " chunk2 = df.iloc[:, 8:13]\n", + " chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + " chunk3 = df.iloc[:, 13:18]\n", + " chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + " chunk4 = df.iloc[:, 18:]\n", + " chunk4.rename(columns={\"kBtuh.3\": \"kBtuh\", \"kW.3\": \"kW\", \"75°F.3\": \"75°F\", \n", + " \"80°F.3\": \"80°F\", \"85°F.3\": \"85°F\"},inplace=True)\n", + " result_df = pd.concat([chunk1, chunk2, chunk3, chunk4], ignore_index=True)\n", + " \n", + " # fill the IWB (2nd)\n", + " iwb_values = result_df.iloc[:, 1].tolist()\n", + " iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + " iwb_array = iwb_values*4\n", + " result_df[\"IWB\"] = iwb_array\n", + " \n", + " # fill the cfm\n", + " cfm_values = result_df.iloc[:, 2].tolist()\n", + " cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + " cfm_times = \n", + " cfm_array = cfm_values*4\n", + " result_df[\"cfm\"] = cfm_array\n", + " \n", + " # fill the OADB\n", + " buffer_header = int(nHead)-4\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + " OADB_values = np.array(df)[0]\n", + " OADB_values = OADB_values.tolist()\n", + " OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + " OADB_list = []\n", + " for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + " OADB_array = np.repeat(OADB_list,9)\n", + " result_df[\"OADB\"] = OADB_array\n", + " \n", + " return result_df" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "423e6e37", + "metadata": {}, + "outputs": [], + "source": [ + "# supply fan power\n", + "esp_ranges = [\n", + " (0, 28.8, 0.1),\n", + " (29, 42.5, 0.15),\n", + " (43, 70, 0.2),\n", + " (71, 105, 0.25),\n", + " (106, 134, 0.3),\n", + " (135, 210, 0.35),\n", + " (211, 280, 0.4),\n", + " (281, 350, 0.45),\n", + " (351, 400, 0.55),\n", + " (401, 500, 0.65),\n", + " (501, float('inf'), 0.75)\n", + "]\n", + "\n", + "#######################################################################\n", + "\n", + "def calculate_esp(kBtuh):\n", + " for lower, upper, esp in esp_ranges:\n", + " if lower <= kBtuh <= upper:\n", + " return esp\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "19888047", + "metadata": {}, + "outputs": [], + "source": [ + "# (airflow, esp=0.2, esp=0.4, esp=0.6)\n", + "\n", + "#######################################################################\n", + "# 2305\n", + "bhp_ranges_2305 = [\n", + " (2750, 0.3, 0.5, 0.7),\n", + " (3000, 0.35, 0.55, 0.75),\n", + " (3250, 0.4, 0.6, 0.85),\n", + " (3500, 0.45, 0.7, 0.95),\n", + " (3750, 0.5, 0.75, 1.05),\n", + " (4000, 0.55, 0.85, 1.1),\n", + " (4250, 0.6, 0.9, 1.25),\n", + " (4500, 0.7, 1.0, 1.35),\n", + " (4750, 0.75, 1.1, 1.45),\n", + " (5000, 0.8, 1.25, 1.6),\n", + " (5250, 0.95, 1.35, 1.7),\n", + " (5500, 1.05, 1.45, 1.85),\n", + " (5750, 1.15, 1.6, 2),\n", + " (6000, 1.3, 1.75, 2.15),\n", + " (6250, 1.4, 1.9, 2.35),\n", + " (6500, 1.55, 2.05, 2.5),\n", + " (6750, 1.7, 2.2, 2.7),\n", + " (7000, 1.85, 2.35, 2.9),\n", + " (7250, 2, 2.6, 3.1),\n", + " (7500, 2.2, 2.75, 3.3),\n", + " (7750, 2.4, 3, 3.55),\n", + " (8000, 2.55, 3.2, 3.8),\n", + " (8250, 2.8, 3.4, 4),\n", + " (8500, 3, 3.65, 4.3),\n", + " (8750, 3.25, 3.9, 4.55),\n", + " (9000, 3.5, 4.2, 4.85),\n", + " (9250, 3.75, 4.45, 5.15),\n", + " (9500, 4, 4.75, 5.45),\n", + " (9750, 4.3, 5.05, 5.75),\n", + " (10000, 4.6, 5.4, 6.15),\n", + "]\n", + "\n", + "#######################################################################\n", + "# 2306\n", + "# watts here, 1 Brake Horsepower (bhp) = 745.69987158227 Watt\n", + "bhp_ranges_2306 = [\n", + " (750, 73, 100, 120),\n", + " (1000, 103, 150, 180),\n", + " (1250, 143, 200, 240),\n", + " (1550, 183, 248, 300),\n", + " (1750, 223, 298, 359),\n", + " (2000, 271, 345, 410),\n", + " (2250, 303, 391, 476),\n", + " (2500, 366, 476, 583),\n", + " (2750, 476, 601, 715),\n", + " (3000, 596, 718, 827),\n", + " (3250, 711, 827, 935),\n", + " (3500, 821, 940, 1060),\n", + " (3750, 949, 1081, 1221),\n", + " (4000, 1109, 1256, 1413),\n", + " (4250, 1298, 1458, 1625),\n", + " (4500, 1510, 1678, 1851),\n", + " (4750, 1738, 1910, 2083),\n", + " (5000, 1973, 2144, 2314),\n", + " (5250, 2205, 2373, 2538),\n", + " (5500, 2428, 2595, 2756),\n", + " (5750, 2643, 2809, 2976),\n", + " (6000, 2863, 3029, 3190)\n", + "]\n", + "\n", + "scaled_bhp_ranges = []\n", + "\n", + "for airflow, esp_02, esp_04, esp_06 in bhp_ranges_2306:\n", + " esp_02_scaled = esp_02 / 745.699871\n", + " esp_04_scaled = esp_04 / 745.699871\n", + " esp_06_scaled = esp_06 / 745.699871\n", + " scaled_bhp_ranges.append((airflow, esp_02_scaled, esp_04_scaled, esp_06_scaled))\n", + " \n", + "bhp_ranges_2306 = scaled_bhp_ranges\n", + "\n", + "#######################################################################\n", + "# 2303\n", + "bhp_ranges_2303 = [\n", + " (1200, 159, 200, 246),\n", + " (1500, 253, 308, 361),\n", + " (1600, 298, 354, 403),\n", + " (1700, 346, 399, 445),\n", + " (1800, 390, 442, 488),\n", + " (1900, 422, 478, 533),\n", + " (2000, 466, 530, 595),\n", + " (2100, 537, 606, 676),\n", + " (2200, 623, 694, 762),\n", + " (2300, 707, 776, 842)\n", + "]\n", + "\n", + "scaled_bhp_ranges_2303 = []\n", + "\n", + "for airflow, esp_02, esp_04, esp_06 in bhp_ranges_2303:\n", + " esp_02_scaled = esp_02 / 745.699871\n", + " esp_04_scaled = esp_04 / 745.699871\n", + " esp_06_scaled = esp_06 / 745.699871\n", + " scaled_bhp_ranges_2303.append((airflow, esp_02_scaled, esp_04_scaled, esp_06_scaled))\n", + " \n", + "bhp_ranges_2303 = scaled_bhp_ranges_2303\n", + "\n", + "#######################################################################\n", + "\n", + "def calculate_custom_bhp(cfm, esp, bhp_ranges):\n", + " closest_airflow = min(bhp_ranges, key=lambda x: abs(x[0] - cfm))\n", + " esp02, esp04, esp06 = closest_airflow[1], closest_airflow[2], closest_airflow[3]\n", + " if esp == 0.2:\n", + " return esp02\n", + " elif esp == 0.4:\n", + " return esp04\n", + " elif esp == 0.6:\n", + " return esp06\n", + " elif esp < 0.2:\n", + " return esp02 - ((0.2 - esp) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.2 and esp < 0.4:\n", + " return esp02 + ((esp - 0.2) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.4 and esp < 0.6:\n", + " return esp04 + ((esp - 0.4) * (esp06 - esp04) / 0.2)\n", + "\n", + "# print(bhp_ranges_2306)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "e4dc2545", + "metadata": {}, + "outputs": [], + "source": [ + "def modifyData(df,bhp_ranges):\n", + " df.insert(1, \"IDB\", np.nan)\n", + " df.insert(5, \"S2T\", np.nan)\n", + " \n", + " # List to store repeated rows\n", + " repeated_rows = []\n", + " for index, row in df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + " result = pd.DataFrame(repeated_rows, columns=df.columns)\n", + " \n", + " # fill the S2T\n", + " result.reset_index(drop=True, inplace=True)\n", + " row_list = list(range(0, len(result), 3))\n", + " idb_list = []\n", + " for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + " result[\"S2T\"] = idb_list\n", + " \n", + " # fill the IDB\n", + " last_three_columns = result.columns[-3:].tolist()\n", + " temp_list = []\n", + " for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + " temp_times = len(result)//len(temp_list)\n", + " temp_list = temp_list*temp_times\n", + " result[\"IDB\"] = temp_list\n", + " result = result.iloc[:, :-3]\n", + " \n", + " # Sensible MBH\n", + " result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " # units\n", + " result['Btuh'] = result['kBtuh']*1000\n", + " result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + " # around kBtuh for the next step\n", + " kBtuh_index = result.columns.get_loc('kBtuh')\n", + " kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + " kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + " result[\"kBtuh\"] = kBtuh_values\n", + " \n", + " # supply fan power\n", + " result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + " result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges), axis=1)\n", + " # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + " result['SFP'] = result['BHP']*746/0.895 \n", + " # 0.895 for ehb_lcm_cbox_2305,ehb_lcm_bbox_2306\n", + " \n", + " # organize\n", + " new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + " final = result[new_cols]\n", + " \n", + " \n", + " \n", + " return final\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "863f4081-79f4-4b3e-a181-7600ddf2f978", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:20:34.110616Z", + "iopub.status.busy": "2023-08-02T05:20:34.110324Z", + "iopub.status.idle": "2023-08-02T05:20:34.115545Z", + "shell.execute_reply": "2023-08-02T05:20:34.114819Z", + "shell.execute_reply.started": "2023-08-02T05:20:34.110593Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\1587544596.py:53: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_cbox_2305\n", + "drop_list = [3,7,9,15,23]\n", + "headList = [144,161,178,197,214,231,250,267,284,301,320,337,354,371,390,407,424,441]\n", + "column_names = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "\n", + "for head in headList:\n", + " df = loadData('./excel/ehb_lcm_cbox_2305.xlsx',head,9,drop_list)\n", + " result = modifyData(df,bhp_ranges_2305)\n", + " buffer = pd.concat([buffer,result])\n", + " \n", + "filepath = Path('./output/ehb_lcm_cbox_2305.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "f871b65e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\3645567763.py:54: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_bbox_2306\n", + "drop_list = [3,8,11,19,23]\n", + "headList = [50,66,82,98,117,133,149,165]\n", + "column_names = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "\n", + "for head in headList:\n", + " df = loadData('./excel/ehb_lcm_bbox_2306.xlsx',head,9,drop_list)\n", + " result = modifyData(df,bhp_ranges_2306)\n", + " buffer = pd.concat([buffer,result])\n", + " \n", + "filepath = Path('./output/ehb_lcm_bbox_2306.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "b4cd0982", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2126508735.py:4: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2126508735.py:47: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2126508735.py:126: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2126508735.py:169: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "#######################################################################\n", + "# ehb_lcm_abox_2303_206\n", + "head = 206\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "buffer = pd.DataFrame(columns=new_cols)\n", + "buffer = pd.concat([buffer,final])\n", + "\n", + "#######################################################################\n", + "# ehb_lcm_abox_2303_229\n", + "head = 229\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "#######################################################################\n", + "# append to buffer\n", + "buffer = pd.concat([buffer,final])\n", + "buffer\n", + "\n", + "# output\n", + "filepath = Path('./output/ehb_lcm_abox_2303.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "25ae0039", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2439467181.py:3: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2439467181.py:46: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_abox_2303_229\n", + "head = 229\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "# append to buffer\n", + "buffer = pd.concat([buffer,final])\n", + "buffer\n", + "\n", + "# output\n", + "filepath = Path('./output/ehb_lcm_abox_2303.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "356297df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OADB IDB IWB RH Btuh Sensible Btuh kW cfm \\\n", + "0 65.0 75 63.0 52.112865 66200.0 36410.0 3.77 800.0 \n", + "1 65.0 80 63.0 39.167642 66200.0 41044.0 3.77 800.0 \n", + "2 65.0 85 63.0 29.056294 66200.0 47002.0 3.77 800.0 \n", + "3 65.0 75 63.0 52.112865 80200.0 48120.0 3.80 1335.0 \n", + "4 65.0 80 63.0 39.167642 80200.0 56140.0 3.80 1335.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "103 95.0 80 71.0 65.061789 79400.0 38112.0 5.77 1335.0 \n", + "104 95.0 85 71.0 51.076002 79400.0 44464.0 5.77 1335.0 \n", + "105 95.0 75 71.0 82.665887 87200.0 34880.0 5.81 1875.0 \n", + "106 95.0 80 71.0 65.061789 87200.0 44472.0 5.81 1875.0 \n", + "107 95.0 85 71.0 51.076002 87200.0 52320.0 5.81 1875.0 \n", + "\n", + " SFP \n", + "0 250.055866 \n", + "1 250.055866 \n", + "2 250.055866 \n", + "3 291.731844 \n", + "4 291.731844 \n", + ".. ... \n", + "103 291.731844 \n", + "104 291.731844 \n", + "105 291.731844 \n", + "106 291.731844 \n", + "107 291.731844 \n", + "\n", + "[108 rows x 9 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\3645567763.py:54: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# example\n", + "# ehb_lcm_bbox_2306\n", + "drop_list = [3,8,11,19,23]\n", + "df = loadData('./excel/ehb_lcm_bbox_2306.xlsx',50,9,drop_list)\n", + "result = modifyData(df)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "70b022b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OADB IDB IWB RH Btuh Sensible Btuh kW cfm ESP \\\n", + "0 65.0 75 63.0 52.112865 33600.0 27888.0 0.91 1200.0 0.15 \n", + "1 65.0 80 63.0 39.167642 33600.0 32592.0 0.91 1200.0 0.15 \n", + "2 65.0 85 63.0 29.056294 33600.0 33600.0 0.91 1200.0 0.15 \n", + "3 65.0 75 63.0 52.112865 35200.0 31328.0 0.91 1500.0 0.15 \n", + "4 65.0 80 63.0 39.167642 35200.0 34848.0 0.91 1500.0 0.15 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "103 95.0 80 71.0 65.061789 33900.0 19323.0 1.52 1500.0 0.15 \n", + "104 95.0 85 71.0 51.076002 33900.0 29832.0 1.52 1500.0 0.15 \n", + "105 95.0 75 71.0 82.665887 34900.0 9074.0 1.52 1800.0 0.15 \n", + "106 95.0 80 71.0 65.061789 34900.0 21638.0 1.52 1800.0 0.15 \n", + "107 95.0 85 71.0 51.076002 34900.0 32457.0 1.52 1800.0 0.15 \n", + "\n", + " BHP \n", + "0 0.25 \n", + "1 0.25 \n", + "2 0.25 \n", + "3 0.25 \n", + "4 0.25 \n", + ".. ... \n", + "103 0.25 \n", + "104 0.25 \n", + "105 0.25 \n", + "106 0.25 \n", + "107 0.25 \n", + "\n", + "[108 rows x 10 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\1587544596.py:53: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# example\n", + "# ehb_lcm_cbox_2305\n", + "df = loadData('./excel/ehb_lcm_cbox_2305.xlsx',144,9,drop_list)\n", + "result = modifyData(df)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7537e1d5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/performance_data_collection/.ipynb_checkpoints/data_collection_Carrier_SubCoolMode-checkpoint.ipynb b/DX_exploration/performance_data_collection/.ipynb_checkpoints/data_collection_Carrier_SubCoolMode-checkpoint.ipynb new file mode 100644 index 0000000..89d3508 --- /dev/null +++ b/DX_exploration/performance_data_collection/.ipynb_checkpoints/data_collection_Carrier_SubCoolMode-checkpoint.ipynb @@ -0,0 +1,887 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3db2b116", + "metadata": {}, + "outputs": [], + "source": [ + "######################### Carrier ##############################\n", + "########################## Notes #############################\n", + "# The 18 equipments' data include 'Bypass Factor' at each row.\n", + "# The variable we have is TC(Total capacity), SHC(Sensible Capacity), kW(power) and BF(bypass factor).\n", + "\n", + "# On page 34, they show the method to correct SHC, since The shown SHC in original excel is based on \n", + "# 80F edb temp of air entering coil. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15745d0e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n", + " \"class\": algorithms.Blowfish,\n" + ] + } + ], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "# not work when connect to PNNL VPN\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "from bisect import bisect_left\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8c8b8e9b", + "metadata": {}, + "outputs": [], + "source": [ + "# Correction Factor\n", + "def calculate_Factor(bf_read, factors):\n", + " bf05, bf10, bf20 = factors[0], factors[1], factors[2]\n", + " if bf_read == 0.05:\n", + " return bf05\n", + " elif bf_read == 0.10:\n", + " return bf10\n", + " elif bf_read == 0.20:\n", + " return bf20\n", + " elif bf_read < 0.05:\n", + " return bf05 - ((0.05 - bf_read) * (bf10 - bf05) / 0.05)\n", + " elif bf_read > 0.05 and bf_read < 0.10:\n", + " return bf05 + ((bf_read - 0.05) * (bf10 - bf05) / 0.05)\n", + " elif bf_read > 0.10 and bf_read < 0.20:\n", + " return bf10 + ((bf_read - 0.10) * (bf20 - bf10) / 0.1)\n", + " else:\n", + " return bf20 + ((bf_read - 0.2) * (bf20 - bf10) / 0.1)\n", + "\n", + "# 48-50A-21PD-SM\n", + "def correctionFactor_1(row):\n", + " factors = []\n", + " # get correction factor\n", + " if row['EADB'] in [79, 81]:\n", + " factors = [1.04, 0.98, 0.87]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [78, 82]:\n", + " factors = [2.07, 1.96, 1.74]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [77, 83]:\n", + " factors = [3.11, 2.94, 2.62]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [76, 84]:\n", + " factors = [4.14, 3.92, 3.49]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [75, 85]:\n", + " factors = [4.18, 4.91, 4.36]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " else:\n", + " factor = 1.1 * (1 - row['BF']) * (row['EADB'] - 80)\n", + " \n", + " modified_row = row.copy()\n", + " \n", + " # modify 'SHC'\n", + " if row['EADB'] < 80:\n", + " modified_row['SHC'] = row['SHC'] - factor * row['airflow']\n", + " elif row['EADB'] > 80:\n", + " modified_row['SHC'] = row['SHC'] + factor * row['airflow']\n", + " return modified_row\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b6a8918b", + "metadata": {}, + "outputs": [], + "source": [ + "# Supply Fan Power\n", + "# esp\n", + "esp_ranges = [\n", + " (0, 28.8, 0.1),\n", + " (29, 42.5, 0.15),\n", + " (43, 70, 0.2),\n", + " (71, 105, 0.25),\n", + " (106, 134, 0.3),\n", + " (135, 210, 0.35),\n", + " (211, 280, 0.4),\n", + " (281, 350, 0.45),\n", + " (351, 400, 0.55),\n", + " (401, 500, 0.65),\n", + " (501, float('inf'), 0.75)\n", + "]\n", + "\n", + "def calculate_esp(TC):\n", + " TC = TC//1000\n", + " for lower, upper, esp in esp_ranges:\n", + " if lower <= TC <= upper:\n", + " return esp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "21a23fb9", + "metadata": {}, + "outputs": [], + "source": [ + "def loadData(name,cols,nHead,nRows,sheet_name):\n", + " ## Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name, header=int(nHead), \n", + " usecols=cols, skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " df = df.replace('—', np.nan)\n", + " \n", + " ## Rotate data\n", + " column_index_to_drop = [0]\n", + " rotate_df = df.drop(df.columns[column_index_to_drop], axis=1)\n", + " buffer_df = rotate_df.T\n", + " buffer_df.reset_index(inplace=True)\n", + " buffer_df.columns = buffer_df.iloc[0]\n", + " buffer_df = buffer_df.iloc[1:]\n", + " \n", + " ## Read EAWB\n", + " eawb_values = buffer_df.iloc[:5, 0]\n", + " eawb_values = eawb_values.tolist()\n", + " eawb_times = len(buffer_df)//5\n", + " eawb_list = eawb_values * eawb_times\n", + " buffer_df.rename(columns={'Unnamed: 1':'EAWB'}, inplace=True)\n", + " buffer_df['EAWB'] = eawb_list\n", + "\n", + " \n", + " ## Read cfm\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3), \n", + " usecols=cols, skiprows=None, nrows=1, engine=\"openpyxl\")\n", + " cfm_values = np.array(cfm)[0]\n", + " cfm_values = cfm_values.tolist()\n", + " cfm_list = [int(value) for value in cfm_values if not pd.isna(value)]\n", + " cfm_array = np.repeat(cfm_list,5).tolist()\n", + " buffer_df.insert(0, \"airflow\", np.nan)\n", + " buffer_df['airflow'] = cfm_array\n", + " \n", + " ## Modify the dataframe\n", + " num_chunk = len(buffer_df.columns)//4\n", + " if num_chunk <= 5:\n", + " chunk1 = buffer_df.iloc[:, :2]\n", + " chunk2 = buffer_df.iloc[:, 2:6]\n", + " chunk3 = buffer_df.iloc[:, 6:10]\n", + " chunk4 = buffer_df.iloc[:, 10:14]\n", + " chunk5 = buffer_df.iloc[:, 14:18]\n", + " chunk6 = buffer_df.iloc[:, 18:22]\n", + " result_df = pd.concat([chunk2, chunk3, chunk4, chunk5, chunk6], ignore_index=True)\n", + " chunk1_df = pd.concat([chunk1, chunk1, chunk1, chunk1, chunk1], ignore_index=True)\n", + " chunk1_df.reset_index(inplace=True)\n", + " else:\n", + " chunk1 = buffer_df.iloc[:, :2]\n", + " chunk2 = buffer_df.iloc[:, 2:6]\n", + " chunk3 = buffer_df.iloc[:, 6:10]\n", + " chunk4 = buffer_df.iloc[:, 10:14]\n", + " chunk5 = buffer_df.iloc[:, 14:18]\n", + " chunk6 = buffer_df.iloc[:, 18:22]\n", + " chunk7 = buffer_df.iloc[:, 22:26]\n", + " result_df = pd.concat([chunk2, chunk3, chunk4, chunk5, chunk6, chunk7], ignore_index=True)\n", + " chunk1_df = pd.concat([chunk1, chunk1, chunk1, chunk1, chunk1, chunk1], ignore_index=True)\n", + " chunk1_df.reset_index(inplace=True) \n", + " merged_df = pd.concat([chunk1_df, result_df], axis=1)\n", + " merged_df = merged_df.iloc[:, 1:]\n", + " \n", + " ## Read EADB\n", + " eadb_values = df.iloc[:, 0].tolist()\n", + " eadb_values = [int(value) for value in eadb_values if not pd.isna(value)]\n", + " eadb_times = 5 * len(cfm_list)\n", + " eadb_array = np.repeat(eadb_values,eadb_times).tolist()\n", + " merged_df['EADB'] = 80 # Standard Mode\n", + " merged_df['OADB'] = eadb_array\n", + " \n", + " ## Calculate RH\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " merged_df['RH'] = merged_df.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['EADB'] * units.degF, row['EAWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " ## Modify units\n", + " merged_df['TC'] = merged_df['TC']*1000\n", + " merged_df['SHC'] = merged_df['SHC']*1000\n", + "# ## Correct SHC\n", + "# merged_df = merged_df.apply(lambda row: correctionFactor_1(row), axis=1)\n", + " \n", + " ## Clean NA\n", + " merged_df = merged_df.dropna()\n", + " merged_df = merged_df.reset_index(drop=True)\n", + " \n", + " ## Supply fan power\n", + " merged_df.insert(0, \"SFP\", np.nan)\n", + " merged_df['TC'] = pd.to_numeric(merged_df['TC'], errors='coerce')\n", + " merged_df['ESP'] = merged_df['TC'].apply(calculate_esp)\n", + " ## organize\n", + "# # for test\n", + "# merged_df['S2T'] = merged_df['SHC']/merged_df['TC']\n", + "# new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'S2T', 'kW', 'airflow']\n", + "\n", + " new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'ESP', 'SFP', 'BF']\n", + " final = merged_df[new_cols]\n", + " \n", + " return final" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "21bc5ac5", + "metadata": {}, + "outputs": [], + "source": [ + "# 48-50A-21PD-FP\n", + "# excel function can do this\n", + "bhp_ranges_21_20 = [\n", + " (4000, 2.21, 2.38, 2.55, 2.71),\n", + " (5000, 2.71, 2.89, 3.08, 3.26),\n", + " (6000, 3.29, 3.5, 3.7, 3.9),\n", + " (7000, 3.96, 4.19, 4.41, 4.63),\n", + " (7500, 4.33, 4.56, 4.79, 5.02),\n", + " (8000, 4.73, 4.97, 5.21, 5.45),\n", + " (9000, 5.6, 5.85, 6.11, 6.37),\n", + " (10000, 6.58, 6.85, 7.13, 7.4)\n", + "]\n", + "\n", + "bhp_ranges_21_30 = [\n", + " (5000, 0.85, 1.03, 1.22, 1.42),\n", + " (6000, 1.26, 1.45, 1.65, 1.86),\n", + " (7000, 1.79, 1.98, 2.19, 2.41),\n", + " (8000, 2.44, 2.64, 2.85, 3.08),\n", + " (9000, 3.23, 3.43, 3.65, 3.88),\n", + " (10000, 4.15, 4.36, 4.58, 4.82),\n", + " (11000, 5.22, 5.44, 5.67, 5.91),\n", + " (12000, 6.45, 6.67, 6.90, 7.15),\n", + " (13000, 7.85, 8.07, 8.30, 8.55),\n", + " (14000, 9.41, 9.64, 9.88, 10.13),\n", + " (15000, 11.15, 11.38, 11.62, 11.88)\n", + "]\n", + "\n", + "bhp_ranges_21_35 = [\n", + " (7000, 1.96, 2.22, 2.47, 2.72),\n", + " (8000, 2.62, 2.89, 3.16, 3.43),\n", + " (9000, 3.39, 3.68, 3.97, 4.26),\n", + " (10000, 4.29, 4.61, 4.91, 5.22),\n", + " (10500, 4.80, 5.12, 5.43, 5.75),\n", + " (11000, 5.33, 5.66, 5.99, 6.31),\n", + " (12000, 6.52, 6.86, 7.21, 7.54),\n", + " (13000, 7.85, 8.21, 8.57, 8.92),\n", + " (14000, 9.33, 9.71, 10.09, 10.45),\n", + " (15000, 10.98, 11.37, 11.76, 12.15),\n", + " (16000, 12.79, 13.2, 13.6, 14),\n", + " (17000, 14.77, 15.19, 15.61, 16.03),\n", + " (17500, 15.83, 16.25, 16.68, 17.1)\n", + "]\n", + "\n", + "bhp_ranges_21_40 = [\n", + " (8000, 2.69, 3.08, 3.47, 3.86),\n", + " (9000, 3.53, 3.94, 4.36, 4.78),\n", + " (10000, 4.52, 4.96, 5.4, 5.84),\n", + " (11000, 5.68, 6.14, 6.6, 7.07),\n", + " (12000, 7.01, 7.49, 7.98, 8.47),\n", + " (13000, 8.53, 9.03, 9.54, 10.05),\n", + " (14000, 10.24, 10.76, 11.29, 11.82),\n", + " (15000, 12.15, 12.69, 13.24, 13.79),\n", + " (16000, 14.27, 14.83, 15.4, 15.97),\n", + " (17000, 16.61, 17.19, 17.77, 18.36),\n", + " (18000, 19.18, 19.77, 20.37, 20.98),\n", + " (19000, 21.98, 22.59, 23.21, 23.84),\n", + " (20000, 25.02, 25.65, 26.29, 26.93)\n", + "]\n", + "\n", + "bhp_ranges_21_50 = [\n", + " (10000, 4.62, 5.06, 5.5, 5.95),\n", + " (11000, 5.8, 6.26, 6.73, 7.2),\n", + " (12000, 7.15, 7.63, 8.12, 8.62),\n", + " (13000, 8.69, 9.19, 9.7, 10.22),\n", + " (14000, 10.43, 10.95, 11.48, 12.01),\n", + " (15000, 12.37, 12.91, 13.46, 14.01),\n", + " (16000, 14.52, 15.08, 15.65, 16.22),\n", + " (17000, 16.89, 17.48, 18.06, 18.65),\n", + " (18000, 19.5, 20.1, 20.71, 21.32),\n", + " (19000, 22.35, 22.96, 23.59, 24.21),\n", + " (20000, 25.43, 26.07, 26.71, 27.36)\n", + "]\n", + "\n", + "bhp_ranges_21_60 = [\n", + " (12000, 4.02, 4.71, 5.41, 6.12),\n", + " (14000, 5.74, 6.49, 7.24, 8.01),\n", + " (15000, 6.75, 7.53, 8.32, 9.11),\n", + " (16000, 7.88, 8.68, 9.5, 10.32),\n", + " (17000, 9.12, 9.95, 10.79, 11.64),\n", + " (18000, 10.48, 11.33, 12.2, 13.07),\n", + " (19000, 11.96, 12.84, 13.74, 14.64),\n", + " (20000, 13.57, 14.47, 15.4, 16.32),\n", + " (21000, 15.3, 16.24, 17.19, 18.14),\n", + " (22000, 17.18, 18.14, 19.11, 20.09),\n", + " (23000, 19.2, 20.18, 21.17, 22.17),\n", + " (24000, 21.35, 22.36, 23.38, 24.4),\n", + " (25000, 23.66, 24.68, 25.72, 26.78),\n", + " (26000, 26.11, 27.16, 28.23, 29.3),\n", + " (27000, 28.72, 29.79, 30.88, 31.97)\n", + "]\n", + "\n", + "# 48-50P-12PD-FP\n", + "bhp_ranges_12_30 = [\n", + " (6000, 0.59, 0.91, 1.27, 1.66),\n", + " (7500, 0.94, 1.28, 1.68, 2.11),\n", + " (9000, 1.46, 1.8, 2.22, 2.69),\n", + " (10500, 2.16, 2.52, 2.95, 3.43),\n", + " (12000, 3.08, 3.44, 3.89, 4.39),\n", + " (13500, 4.25, 4.62, 5.07, 5.58),\n", + " (15000, 5.69, 6.06, 6.52, 7.04)\n", + "]\n", + "\n", + "bhp_ranges_12_35 = [\n", + " (7000, 246, 301, 1.58, 398),\n", + " (8000, 266, 315, 1.92, 406),\n", + " (10000, 310, 350, 2.8, 427),\n", + " (12000, 357, 390, 4.06, 457),\n", + " (14000, 406, 435, 5.76, 492),\n", + " (15000, 430, 458, 6.8, 511)\n", + "]\n", + "\n", + "bhp_ranges_12_40 = [\n", + " (8000, 0.98, 1.33, 1.72, 2.14),\n", + " (10000, 1.67, 2.11, 2.55, 3.01),\n", + " (12000, 2.65, 3.18, 3.7, 4.23),\n", + " (14000, 3.96, 4.61, 5.22, 5.83),\n", + " (16000, 5.67, 6.44, 7.15, 7.85),\n", + " (18000, 7.84, 8.72, 9.55, 10.34),\n", + " (20000, 10.51, 11.51, 12.46, 13.36)\n", + "]\n", + "\n", + "bhp_ranges_12_50 = [\n", + " (9000, 1.34, 1.73, 2.15, 2.6),\n", + " (10000, 1.74, 2.17, 2.62, 3.09),\n", + " (12000, 2.76, 3.29, 3.81, 4.35),\n", + " (14000, 4.15, 4.79, 5.4, 6.01),\n", + " (16000, 5.96, 6.71, 7.42, 8.11),\n", + " (18000, 8.26, 9.12, 9.93, 10.72),\n", + " (20000, 11.1, 12.06, 12.99, 13.88)\n", + "]\n", + "\n", + "bhp_ranges_12_55 = [\n", + " (10000, 1.04, 1.5, 2.03, 2.66),\n", + " (12500, 1.69, 2.23, 2.78, 3.4),\n", + " (15000, 2.59, 3.23, 3.85, 4.51),\n", + " (17500, 3.78, 4.52, 5.24, 5.97),\n", + " (20000, 5.31, 6.15, 6.98, 7.79),\n", + " (22500, 7.23, 8.16, 9.09, 10),\n", + " (25000, 9.59, 10.6, 11.62, 12.64)\n", + "]\n", + "\n", + "bhp_ranges_12_60 = [\n", + " (12000, 1.54, 2.03, 2.57, 3.2),\n", + " (15000, 2.65, 3.27, 3.88, 4.53),\n", + " (18000, 4.22, 5, 5.73, 6.46),\n", + " (21000, 6.36, 7.29, 8.18, 9.04),\n", + " (24000, 9.19, 10.24, 11.29, 12.29),\n", + " (27000, 12.8, 13.93, 15.14, 16.3),\n", + " (30000, 17.29, 18.5, 19.82, 21.15)\n", + "]\n", + "\n", + "bhp_ranges_12_70 = [\n", + " (14000, 2.23, 2.8, 3.38, 4.02),\n", + " (17500, 3.92, 4.67, 5.39, 6.1),\n", + " (21000, 6.36, 7.29, 8.18, 9.04),\n", + " (24500, 9.74, 10.8, 11.88, 12.91),\n", + " (28000, 14.18, 15.35, 16.6, 17.82),\n", + " (30000, 17.29, 18.5, 19.82, 21.15)\n", + "]\n", + "\n", + "bhp_ranges_12_75 = [\n", + " (15000, 3.02, 3.91, 4.83, 5.8),\n", + " (16000, 3.52, 4.45, 5.41, 6.42),\n", + " (18000, 4.68, 5.71, 6.76, 7.84),\n", + " (20000, 6.08, 7.22, 8.36, 9.52),\n", + " (22000, 7.75, 9, 10.24, 11.49),\n", + " (24000, 9.71, 11.07, 12.41, 13.75),\n", + " (26000, 11.99, 13.46, 14.9, 16.34),\n", + " (28000, 14.61, 16.18, 17.73, 19.28),\n", + " (30000, 17.6, 19.28, 20.93, 22.57)\n", + "]\n", + "\n", + "bhp_ranges_12_90 = [\n", + " (16000, 3.27, 4.06, 4.88, 5.74),\n", + " (18000, 4.42, 5.31, 6.21, 7.14),\n", + " (20000, 5.83, 6.82, 7.81, 8.82),\n", + " (22000, 7.53, 8.62, 9.7, 10.8),\n", + " (24000, 9.55, 10.75, 11.93, 13.11),\n", + " (26000, 11.91, 13.22, 14.5, 15.78),\n", + " (28000, 14.65, 16.06, 17.45, 18.83),\n", + " (30000, 17.78, 19.31, 20.8, 22.28),\n", + " (32000, 21.36, 22.99, 24.59, 26.17),\n", + " (34000, 25.39, 27.13, 28.84, 30.53)\n", + "]\n", + "\n", + "bhp_ranges_12_100 = [\n", + " (20000, 5.83, 6.82, 7.81, 8.82),\n", + " (22000, 7.53, 8.62, 9.7, 10.8),\n", + " (24000, 9.55, 10.75, 11.93, 13.11),\n", + " (26000, 11.91, 13.22, 14.5, 15.78),\n", + " (28000, 14.65, 16.06, 17.45, 18.83),\n", + " (30000, 17.78, 19.31, 20.8, 22.28),\n", + " (32000, 21.36, 22.99, 24.59, 26.17),\n", + " (34000, 25.39, 27.13, 28.84, 30.53),\n", + " (36000, 29.92, 31.77, 33.58, 35.38),\n", + " (38000, 34.96, 36.91, 38.85, 40.74),\n", + " (40000, 40.54, 42.61, 44.65, 46.66)\n", + "]\n", + "\n", + "def get_scaled_tuple(airflow, bhp_ranges):\n", + " airflow = int(airflow)\n", + " cfm_values = [t[0] for t in bhp_ranges]\n", + " idx = bisect_left(cfm_values, airflow)\n", + " \n", + " if idx == 0:\n", + " return bhp_ranges[0]\n", + " elif idx == len(bhp_ranges):\n", + " return bhp_ranges[-1]\n", + " \n", + " lower_tuple = bhp_ranges[idx - 1]\n", + " upper_tuple = bhp_ranges[idx]\n", + " \n", + " scale_factor = (airflow - lower_tuple[0]) / (upper_tuple[0] - lower_tuple[0])\n", + " \n", + " scaled_values = [round((1 - scale_factor) * l + scale_factor * u, 2) for l, u in zip(lower_tuple[1:], upper_tuple[1:])]\n", + " \n", + " scaled_tuple = (airflow, *scaled_values)\n", + " return scaled_tuple\n", + "\n", + "def find_matching_tuple(airflow, bhp_ranges):\n", + " for tpl in bhp_ranges:\n", + " if tpl[0] == airflow:\n", + " return tpl\n", + " return get_scaled_tuple(airflow, bhp_ranges)\n", + "\n", + "def calculate_custom_bhp(cfm, esp, bhp_ranges):\n", + " result_tuple = find_matching_tuple(cfm, bhp_ranges)\n", + " esp02, esp04, esp06, esp08 = result_tuple[1], result_tuple[2], result_tuple[3], result_tuple[4]\n", + " \n", + " if esp == 0.2:\n", + " return esp02\n", + " elif esp == 0.4:\n", + " return esp04\n", + " elif esp == 0.6:\n", + " return esp06\n", + " elif esp >= 0.8:\n", + " return esp08\n", + " elif esp < 0.2:\n", + " return esp02 - ((0.2 - esp) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.2 and esp < 0.4:\n", + " return esp02 + ((esp - 0.2) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.4 and esp < 0.6:\n", + " return esp04 + ((esp - 0.4) * (esp06 - esp04) / 0.2)\n", + " elif esp > 0.6 and esp < 0.8:\n", + " return esp06 + ((esp - 0.6) * (esp08 - esp06) / 0.2)\n", + "\n", + "def save_sfp(df,bhp_ranges,eff):\n", + " df['BHP'] = df.apply(lambda row: calculate_custom_bhp(row['airflow'], row['ESP'], bhp_ranges), axis=1)\n", + " df['SFP'] = df['BHP']*746/eff\n", + " new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'SFP', 'BF']\n", + " final = df[new_cols]\n", + " return final" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cd792d5a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# 48-50A-21PD-SubCool\n", + "\n", + "# column_names = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'ESP', 'SFP']\n", + "# gap = pd.DataFrame(columns=column_names)\n", + "# new_row = pd.Series(['-------------'] * len(column_names), index=column_names)\n", + "# gap = gap.append(new_row, ignore_index=True)\n", + "\n", + "# 020\n", + "test020_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 1\")\n", + "test020_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 1\")\n", + "test020 = pd.concat([test020_1,test020_2], axis=0)\n", + "test020 = save_sfp(test020, bhp_ranges_21_20, 0.895)\n", + "\n", + "# 025\n", + "test025_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 2\")\n", + "test025_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 2\")\n", + "test025 = pd.concat([test025_1,test025_2], axis=0)\n", + "test025 = save_sfp(test025, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 027\n", + "test027_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 3\")\n", + "test027_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 3\")\n", + "test027 = pd.concat([test027_1,test027_2], axis=0)\n", + "test027 = save_sfp(test027, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 030\n", + "test030_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 4\")\n", + "test030_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 4\")\n", + "test030 = pd.concat([test030_1,test030_2], axis=0)\n", + "test030 = save_sfp(test030, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 035\n", + "test035_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 5\")\n", + "test035_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 5\")\n", + "test035 = pd.concat([test035_1,test035_2], axis=0)\n", + "test035 = save_sfp(test035, bhp_ranges_21_35, 0.895)\n", + "\n", + "# 040\n", + "test040_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 6\")\n", + "test040_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 6\")\n", + "test040 = pd.concat([test040_1,test040_2], axis=0)\n", + "test040 = save_sfp(test040, bhp_ranges_21_40, 0.895)\n", + "\n", + "# 050\n", + "test050_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 7\")\n", + "test050_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 7\")\n", + "test050 = pd.concat([test050_1,test050_2], axis=0)\n", + "test050 = save_sfp(test050, bhp_ranges_21_50, 0.895)\n", + "\n", + "# 060\n", + "test060_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 8\")\n", + "test060_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 8\")\n", + "test060 = pd.concat([test060_1,test060_2], axis=0)\n", + "test060 = save_sfp(test060, bhp_ranges_21_60, 0.895)\n", + "\n", + "\n", + "final = pd.concat([test020,test025,test027,test030, test035, test040, test050, test060], axis=0)\n", + "file_path = Path('./output/ready/48-50A-21PD-SubCool.csv')\n", + "final.to_csv(file_path, index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "68498c38", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# 48-50P-12PD-SubCool\n", + "\n", + "# 030\n", + "# Standard Capacity Coil\n", + "samp030_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 1\")\n", + "samp030_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 1\")\n", + "# High-Capacity Coil\n", + "samp030_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 2\")\n", + "samp030_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 2\")\n", + "\n", + "samp030 = pd.concat([samp030_1,samp030_2,samp030_3,samp030_4], axis=0)\n", + "samp030 = save_sfp(samp030, bhp_ranges_12_30, 0.895)\n", + "\n", + "# 035 Standard Capacity Coil\n", + "samp035_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 3\")\n", + "samp035_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 3\")\n", + "samp035 = pd.concat([samp035_1,samp035_2], axis=0)\n", + "samp035 = save_sfp(samp035, bhp_ranges_12_35, 0.895)\n", + "\n", + "# 040 \n", + "# Standard Capacity Coil\n", + "samp040_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 4\")\n", + "samp040_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 4\")\n", + "# High-Capacity Coil\n", + "samp040_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 5\")\n", + "samp040_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 5\")\n", + "\n", + "samp040 = pd.concat([samp040_1,samp040_2,samp040_3,samp040_4], axis=0)\n", + "samp040 = save_sfp(samp040, bhp_ranges_12_40, 0.895)\n", + "\n", + "\n", + "# 050\n", + "# Standard Capacity Coil\n", + "samp050_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 6\")\n", + "samp050_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 6\")\n", + "# High-Capacity Coil\n", + "samp050_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 7\")\n", + "samp050_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 7\")\n", + "\n", + "samp050 = pd.concat([samp050_1,samp050_2,samp050_3,samp050_4], axis=0)\n", + "samp050 = save_sfp(samp050, bhp_ranges_12_50, 0.895)\n", + " \n", + "# 055\n", + "# Standard Capacity Coil\n", + "samp055_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 8\")\n", + "samp055_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 8\")\n", + "# High-Capacity Coil\n", + "samp055_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 9\")\n", + "samp055_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 9\")\n", + "\n", + "samp055 = pd.concat([samp055_1,samp055_2,samp055_3,samp055_4], axis=0)\n", + "samp055 = save_sfp(samp055, bhp_ranges_12_55, 0.895)\n", + "\n", + "# 060\n", + "# Standard Capacity Coil\n", + "samp060_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 10\")\n", + "samp060_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 10\")\n", + "# High-Capacity Coil\n", + "samp060_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 11\")\n", + "samp060_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 11\")\n", + "\n", + "samp060 = pd.concat([samp060_1,samp060_2,samp060_3,samp060_4], axis=0)\n", + "samp060 = save_sfp(samp060, bhp_ranges_12_60, 0.895)\n", + "\n", + "# 070\n", + "# Standard Capacity Coil\n", + "samp070_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 12\")\n", + "samp070_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 12\")\n", + "# High-Capacity Coil\n", + "samp070_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 13\")\n", + "samp070_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 13\")\n", + "\n", + "samp070 = pd.concat([samp070_1,samp070_2,samp070_3,samp070_4], axis=0)\n", + "samp070 = save_sfp(samp070, bhp_ranges_12_70, 0.895)\n", + "\n", + "# 075\n", + "# Standard Capacity Coil\n", + "samp075_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 14\")\n", + "samp075_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 14\")\n", + "# High-Capacity Coil\n", + "samp075_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 15\")\n", + "samp075_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 15\")\n", + "\n", + "samp075 = pd.concat([samp075_1,samp075_2,samp075_3,samp075_4], axis=0)\n", + "samp075 = save_sfp(samp075, bhp_ranges_12_75, 0.895)\n", + "\n", + "# 090\n", + "# Standard Capacity Coil\n", + "samp090_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 16\")\n", + "samp090_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 16\")\n", + "# High-Capacity Coil\n", + "samp090_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 17\")\n", + "samp090_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 17\")\n", + "samp090 = pd.concat([samp090_1,samp090_2,samp090_3,samp090_4], axis=0)\n", + "samp090 = save_sfp(samp090, bhp_ranges_12_90, 0.895)\n", + "\n", + "# 100\n", + "# Standard Capacity Coil\n", + "samp100_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",8,20, \"page 18\")\n", + "samp100_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 18\")\n", + "# High-Capacity Coil\n", + "samp100_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",8,20, \"page 19\")\n", + "samp100_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 19\")\n", + "samp100 = pd.concat([samp100_1,samp100_2,samp100_3,samp100_4], axis=0)\n", + "samp100 = save_sfp(samp100, bhp_ranges_12_100, 0.895)\n", + "\n", + "\n", + "final2 = pd.concat([samp030, samp035, samp040, samp050, samp055, samp060, samp070, samp075, samp090, samp100], axis=0)\n", + "file2_path = Path('./output/ready/48-50P-12PD-SubCool.csv')\n", + "final2.to_csv(file2_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "eaf964c1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# 050\n", + "# # Standard Capacity Coil\n", + "# samp050_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 6\")\n", + "# samp050_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 6\")\n", + "# High-Capacity Coil\n", + "samp050_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 7\")\n", + "samp050_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 7\")\n", + "\n", + "# samp050 = pd.concat([samp050_1,samp050_2,samp050_3,samp050_4], axis=0)\n", + "samp050_h = pd.concat([samp050_3,samp050_4], axis=0)\n", + "samp050_h = save_sfp(samp050_h, bhp_ranges_12_50, 0.895)\n", + "samp050_h.to_csv('./output/12PD-SubCool-50_High.csv', index=False)\n", + " \n", + "# 070\n", + "# Standard Capacity Coil\n", + "samp070_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 12\")\n", + "samp070_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 12\")\n", + "# High-Capacity Coil\n", + "samp070_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 13\")\n", + "samp070_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 13\")\n", + "\n", + "samp070 = pd.concat([samp070_1,samp070_2,samp070_3,samp070_4], axis=0)\n", + "samp070 = save_sfp(samp070, bhp_ranges_12_70, 0.895)\n", + "\n", + "# 075\n", + "# Standard Capacity Coil\n", + "samp075_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 14\")\n", + "samp075_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 14\")\n", + "# High-Capacity Coil\n", + "samp075_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 15\")\n", + "samp075_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 15\")\n", + "\n", + "samp075 = pd.concat([samp075_1,samp075_2,samp075_3,samp075_4], axis=0)\n", + "samp075 = save_sfp(samp075, bhp_ranges_12_75, 0.895)\n", + "\n", + "# 090\n", + "# Standard Capacity Coil\n", + "samp090_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 16\")\n", + "samp090_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 16\")\n", + "# High-Capacity Coil\n", + "samp090_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 17\")\n", + "samp090_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 17\")\n", + "samp090 = pd.concat([samp090_1,samp090_2,samp090_3,samp090_4], axis=0)\n", + "samp090 = save_sfp(samp090, bhp_ranges_12_90, 0.895)\n", + "\n", + "# 100\n", + "# Standard Capacity Coil\n", + "samp100_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",8,20, \"page 18\")\n", + "samp100_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 18\")\n", + "# High-Capacity Coil\n", + "samp100_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",8,20, \"page 19\")\n", + "samp100_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 19\")\n", + "samp100 = pd.concat([samp100_1,samp100_2,samp100_3,samp100_4], axis=0)\n", + "samp100 = save_sfp(samp100, bhp_ranges_12_100, 0.895)\n", + "\n", + "\n", + "final2 = pd.concat([samp070, samp075, samp090, samp100], axis=0)\n", + "file2_path = Path('./output/12PD-SubCool-after_70.csv')\n", + "final2.to_csv(file2_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65e2eb7a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/performance_data_collection/.ipynb_checkpoints/data_collection_LENNOX_updated-checkpoint.ipynb b/DX_exploration/performance_data_collection/.ipynb_checkpoints/data_collection_LENNOX_updated-checkpoint.ipynb new file mode 100644 index 0000000..adb936c --- /dev/null +++ b/DX_exploration/performance_data_collection/.ipynb_checkpoints/data_collection_LENNOX_updated-checkpoint.ipynb @@ -0,0 +1,991 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "ca06acec", + "metadata": {}, + "outputs": [], + "source": [ + "######################### LENNOX #############################\n", + "########################## Notes #############################\n", + "# The 10 equipments' data don't include 'Bypass Factor' at \n", + "# each row, but include discharge air dry bulb temp.\n", + "# Issue to be soloved: The formula requires the apparatus \n", + "# dew point temperature. Use \"bypass_adp.ipynb\" to calculate." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "\n", + "# not work when connect to PNNL VPN" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ebb23de5-7820-4765-9b7e-450eadffbd92", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:19:25.921482Z", + "iopub.status.busy": "2023-08-02T05:19:25.921168Z", + "iopub.status.idle": "2023-08-02T05:19:26.176161Z", + "shell.execute_reply": "2023-08-02T05:19:26.175142Z", + "shell.execute_reply.started": "2023-08-02T05:19:25.921460Z" + }, + "tags": [] + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (3032415773.py, line 49)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m Cell \u001b[1;32mIn[1], line 49\u001b[1;36m\u001b[0m\n\u001b[1;33m cfm_times =\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "\n", + "def loadData(name,nHead,nRows,drop_list):\n", + " # Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=int(nHead), \n", + " usecols=\"A:AA\", skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " # clean the data\n", + " if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + " # fill the IWB (1st)\n", + " df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + " start_list = [0,3,6]\n", + " for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 3):\n", + " df.at[i, 'IWB'] = temp_value\n", + " \n", + " # add OADB column\n", + " df.insert(0, \"OADB\", np.nan)\n", + " \n", + " # modify the dataframe\n", + " chunk1 = df.iloc[:, :8]\n", + " chunk2 = df.iloc[:, 8:13]\n", + " chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + " chunk3 = df.iloc[:, 13:18]\n", + " chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + " chunk4 = df.iloc[:, 18:]\n", + " chunk4.rename(columns={\"kBtuh.3\": \"kBtuh\", \"kW.3\": \"kW\", \"75°F.3\": \"75°F\", \n", + " \"80°F.3\": \"80°F\", \"85°F.3\": \"85°F\"},inplace=True)\n", + " result_df = pd.concat([chunk1, chunk2, chunk3, chunk4], ignore_index=True)\n", + " \n", + " # fill the IWB (2nd)\n", + " iwb_values = result_df.iloc[:, 1].tolist()\n", + " iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + " iwb_array = iwb_values*4\n", + " result_df[\"IWB\"] = iwb_array\n", + " \n", + " # fill the cfm\n", + " cfm_values = result_df.iloc[:, 2].tolist()\n", + " cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + " cfm_array = cfm_values*4\n", + " result_df[\"cfm\"] = cfm_array\n", + " \n", + " # fill the OADB\n", + " buffer_header = int(nHead)-4\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + " OADB_values = np.array(df)[0]\n", + " OADB_values = OADB_values.tolist()\n", + " OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + " OADB_list = []\n", + " for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + " OADB_array = np.repeat(OADB_list,9)\n", + " result_df[\"OADB\"] = OADB_array\n", + " \n", + " return result_df" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "423e6e37", + "metadata": {}, + "outputs": [], + "source": [ + "# supply fan power\n", + "esp_ranges = [\n", + " (0, 28.8, 0.1),\n", + " (29, 42.5, 0.15),\n", + " (43, 70, 0.2),\n", + " (71, 105, 0.25),\n", + " (106, 134, 0.3),\n", + " (135, 210, 0.35),\n", + " (211, 280, 0.4),\n", + " (281, 350, 0.45),\n", + " (351, 400, 0.55),\n", + " (401, 500, 0.65),\n", + " (501, float('inf'), 0.75)\n", + "]\n", + "\n", + "#######################################################################\n", + "\n", + "def calculate_esp(kBtuh):\n", + " for lower, upper, esp in esp_ranges:\n", + " if lower <= kBtuh <= upper:\n", + " return esp\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "19888047", + "metadata": {}, + "outputs": [], + "source": [ + "# (airflow, esp=0.2, esp=0.4, esp=0.6)\n", + "\n", + "#######################################################################\n", + "# 2305\n", + "bhp_ranges_2305 = [\n", + " (2750, 0.3, 0.5, 0.7),\n", + " (3000, 0.35, 0.55, 0.75),\n", + " (3250, 0.4, 0.6, 0.85),\n", + " (3500, 0.45, 0.7, 0.95),\n", + " (3750, 0.5, 0.75, 1.05),\n", + " (4000, 0.55, 0.85, 1.1),\n", + " (4250, 0.6, 0.9, 1.25),\n", + " (4500, 0.7, 1.0, 1.35),\n", + " (4750, 0.75, 1.1, 1.45),\n", + " (5000, 0.8, 1.25, 1.6),\n", + " (5250, 0.95, 1.35, 1.7),\n", + " (5500, 1.05, 1.45, 1.85),\n", + " (5750, 1.15, 1.6, 2),\n", + " (6000, 1.3, 1.75, 2.15),\n", + " (6250, 1.4, 1.9, 2.35),\n", + " (6500, 1.55, 2.05, 2.5),\n", + " (6750, 1.7, 2.2, 2.7),\n", + " (7000, 1.85, 2.35, 2.9),\n", + " (7250, 2, 2.6, 3.1),\n", + " (7500, 2.2, 2.75, 3.3),\n", + " (7750, 2.4, 3, 3.55),\n", + " (8000, 2.55, 3.2, 3.8),\n", + " (8250, 2.8, 3.4, 4),\n", + " (8500, 3, 3.65, 4.3),\n", + " (8750, 3.25, 3.9, 4.55),\n", + " (9000, 3.5, 4.2, 4.85),\n", + " (9250, 3.75, 4.45, 5.15),\n", + " (9500, 4, 4.75, 5.45),\n", + " (9750, 4.3, 5.05, 5.75),\n", + " (10000, 4.6, 5.4, 6.15),\n", + "]\n", + "\n", + "#######################################################################\n", + "# 2306\n", + "# watts here, 1 Brake Horsepower (bhp) = 745.69987158227 Watt\n", + "bhp_ranges_2306 = [\n", + " (750, 73, 100, 120),\n", + " (1000, 103, 150, 180),\n", + " (1250, 143, 200, 240),\n", + " (1550, 183, 248, 300),\n", + " (1750, 223, 298, 359),\n", + " (2000, 271, 345, 410),\n", + " (2250, 303, 391, 476),\n", + " (2500, 366, 476, 583),\n", + " (2750, 476, 601, 715),\n", + " (3000, 596, 718, 827),\n", + " (3250, 711, 827, 935),\n", + " (3500, 821, 940, 1060),\n", + " (3750, 949, 1081, 1221),\n", + " (4000, 1109, 1256, 1413),\n", + " (4250, 1298, 1458, 1625),\n", + " (4500, 1510, 1678, 1851),\n", + " (4750, 1738, 1910, 2083),\n", + " (5000, 1973, 2144, 2314),\n", + " (5250, 2205, 2373, 2538),\n", + " (5500, 2428, 2595, 2756),\n", + " (5750, 2643, 2809, 2976),\n", + " (6000, 2863, 3029, 3190)\n", + "]\n", + "\n", + "scaled_bhp_ranges = []\n", + "\n", + "for airflow, esp_02, esp_04, esp_06 in bhp_ranges_2306:\n", + " esp_02_scaled = esp_02 / 745.699871\n", + " esp_04_scaled = esp_04 / 745.699871\n", + " esp_06_scaled = esp_06 / 745.699871\n", + " scaled_bhp_ranges.append((airflow, esp_02_scaled, esp_04_scaled, esp_06_scaled))\n", + " \n", + "bhp_ranges_2306 = scaled_bhp_ranges\n", + "\n", + "#######################################################################\n", + "# 2303\n", + "bhp_ranges_2303 = [\n", + " (1200, 159, 200, 246),\n", + " (1500, 253, 308, 361),\n", + " (1600, 298, 354, 403),\n", + " (1700, 346, 399, 445),\n", + " (1800, 390, 442, 488),\n", + " (1900, 422, 478, 533),\n", + " (2000, 466, 530, 595),\n", + " (2100, 537, 606, 676),\n", + " (2200, 623, 694, 762),\n", + " (2300, 707, 776, 842)\n", + "]\n", + "\n", + "scaled_bhp_ranges_2303 = []\n", + "\n", + "for airflow, esp_02, esp_04, esp_06 in bhp_ranges_2303:\n", + " esp_02_scaled = esp_02 / 745.699871\n", + " esp_04_scaled = esp_04 / 745.699871\n", + " esp_06_scaled = esp_06 / 745.699871\n", + " scaled_bhp_ranges_2303.append((airflow, esp_02_scaled, esp_04_scaled, esp_06_scaled))\n", + " \n", + "bhp_ranges_2303 = scaled_bhp_ranges_2303\n", + "\n", + "#######################################################################\n", + "\n", + "def calculate_custom_bhp(cfm, esp, bhp_ranges):\n", + " closest_airflow = min(bhp_ranges, key=lambda x: abs(x[0] - cfm))\n", + " esp02, esp04, esp06 = closest_airflow[1], closest_airflow[2], closest_airflow[3]\n", + " if esp == 0.2:\n", + " return esp02\n", + " elif esp == 0.4:\n", + " return esp04\n", + " elif esp == 0.6:\n", + " return esp06\n", + " elif esp < 0.2:\n", + " return esp02 - ((0.2 - esp) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.2 and esp < 0.4:\n", + " return esp02 + ((esp - 0.2) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.4 and esp < 0.6:\n", + " return esp04 + ((esp - 0.4) * (esp06 - esp04) / 0.2)\n", + "\n", + "# print(bhp_ranges_2306)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "e4dc2545", + "metadata": {}, + "outputs": [], + "source": [ + "def modifyData(df,bhp_ranges):\n", + " df.insert(1, \"IDB\", np.nan)\n", + " df.insert(5, \"S2T\", np.nan)\n", + " \n", + " # List to store repeated rows\n", + " repeated_rows = []\n", + " for index, row in df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + " result = pd.DataFrame(repeated_rows, columns=df.columns)\n", + " \n", + " # fill the S2T\n", + " result.reset_index(drop=True, inplace=True)\n", + " row_list = list(range(0, len(result), 3))\n", + " idb_list = []\n", + " for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + " result[\"S2T\"] = idb_list\n", + " \n", + " # fill the IDB\n", + " last_three_columns = result.columns[-3:].tolist()\n", + " temp_list = []\n", + " for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + " temp_times = len(result)//len(temp_list)\n", + " temp_list = temp_list*temp_times\n", + " result[\"IDB\"] = temp_list\n", + " result = result.iloc[:, :-3]\n", + " \n", + " # Sensible MBH\n", + " result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " # units\n", + " result['Btuh'] = result['kBtuh']*1000\n", + " result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + " # around kBtuh for the next step\n", + " kBtuh_index = result.columns.get_loc('kBtuh')\n", + " kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + " kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + " result[\"kBtuh\"] = kBtuh_values\n", + " \n", + " # supply fan power\n", + " result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + " result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges), axis=1)\n", + " # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + " result['SFP'] = result['BHP']*746/0.895 \n", + " # 0.895 for ehb_lcm_cbox_2305,ehb_lcm_bbox_2306\n", + " \n", + " # organize\n", + " new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + " final = result[new_cols]\n", + " \n", + " \n", + " \n", + " return final\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "863f4081-79f4-4b3e-a181-7600ddf2f978", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:20:34.110616Z", + "iopub.status.busy": "2023-08-02T05:20:34.110324Z", + "iopub.status.idle": "2023-08-02T05:20:34.115545Z", + "shell.execute_reply": "2023-08-02T05:20:34.114819Z", + "shell.execute_reply.started": "2023-08-02T05:20:34.110593Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\1587544596.py:53: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_cbox_2305\n", + "drop_list = [3,7,9,15,23]\n", + "headList = [144,161,178,197,214,231,250,267,284,301,320,337,354,371,390,407,424,441]\n", + "column_names = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "\n", + "for head in headList:\n", + " df = loadData('./excel/ehb_lcm_cbox_2305.xlsx',head,9,drop_list)\n", + " result = modifyData(df,bhp_ranges_2305)\n", + " buffer = pd.concat([buffer,result])\n", + " \n", + "filepath = Path('./output/ehb_lcm_cbox_2305.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "f871b65e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\3645567763.py:54: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_bbox_2306\n", + "drop_list = [3,8,11,19,23]\n", + "headList = [50,66,82,98,117,133,149,165]\n", + "column_names = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "\n", + "for head in headList:\n", + " df = loadData('./excel/ehb_lcm_bbox_2306.xlsx',head,9,drop_list)\n", + " result = modifyData(df,bhp_ranges_2306)\n", + " buffer = pd.concat([buffer,result])\n", + " \n", + "filepath = Path('./output/ehb_lcm_bbox_2306.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "b4cd0982", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2126508735.py:4: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2126508735.py:47: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2126508735.py:126: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2126508735.py:169: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "#######################################################################\n", + "# ehb_lcm_abox_2303_206\n", + "head = 206\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "buffer = pd.DataFrame(columns=new_cols)\n", + "buffer = pd.concat([buffer,final])\n", + "\n", + "#######################################################################\n", + "# ehb_lcm_abox_2303_229\n", + "head = 229\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "#######################################################################\n", + "# append to buffer\n", + "buffer = pd.concat([buffer,final])\n", + "buffer\n", + "\n", + "# output\n", + "filepath = Path('./output/ehb_lcm_abox_2303.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "25ae0039", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2439467181.py:3: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\2439467181.py:46: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_abox_2303_229\n", + "head = 229\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "# append to buffer\n", + "buffer = pd.concat([buffer,final])\n", + "buffer\n", + "\n", + "# output\n", + "filepath = Path('./output/ehb_lcm_abox_2303.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "356297df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OADB IDB IWB RH Btuh Sensible Btuh kW cfm \\\n", + "0 65.0 75 63.0 52.112865 66200.0 36410.0 3.77 800.0 \n", + "1 65.0 80 63.0 39.167642 66200.0 41044.0 3.77 800.0 \n", + "2 65.0 85 63.0 29.056294 66200.0 47002.0 3.77 800.0 \n", + "3 65.0 75 63.0 52.112865 80200.0 48120.0 3.80 1335.0 \n", + "4 65.0 80 63.0 39.167642 80200.0 56140.0 3.80 1335.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "103 95.0 80 71.0 65.061789 79400.0 38112.0 5.77 1335.0 \n", + "104 95.0 85 71.0 51.076002 79400.0 44464.0 5.77 1335.0 \n", + "105 95.0 75 71.0 82.665887 87200.0 34880.0 5.81 1875.0 \n", + "106 95.0 80 71.0 65.061789 87200.0 44472.0 5.81 1875.0 \n", + "107 95.0 85 71.0 51.076002 87200.0 52320.0 5.81 1875.0 \n", + "\n", + " SFP \n", + "0 250.055866 \n", + "1 250.055866 \n", + "2 250.055866 \n", + "3 291.731844 \n", + "4 291.731844 \n", + ".. ... \n", + "103 291.731844 \n", + "104 291.731844 \n", + "105 291.731844 \n", + "106 291.731844 \n", + "107 291.731844 \n", + "\n", + "[108 rows x 9 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\3645567763.py:54: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# example\n", + "# ehb_lcm_bbox_2306\n", + "drop_list = [3,8,11,19,23]\n", + "df = loadData('./excel/ehb_lcm_bbox_2306.xlsx',50,9,drop_list)\n", + "result = modifyData(df)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "70b022b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OADB IDB IWB RH Btuh Sensible Btuh kW cfm ESP \\\n", + "0 65.0 75 63.0 52.112865 33600.0 27888.0 0.91 1200.0 0.15 \n", + "1 65.0 80 63.0 39.167642 33600.0 32592.0 0.91 1200.0 0.15 \n", + "2 65.0 85 63.0 29.056294 33600.0 33600.0 0.91 1200.0 0.15 \n", + "3 65.0 75 63.0 52.112865 35200.0 31328.0 0.91 1500.0 0.15 \n", + "4 65.0 80 63.0 39.167642 35200.0 34848.0 0.91 1500.0 0.15 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "103 95.0 80 71.0 65.061789 33900.0 19323.0 1.52 1500.0 0.15 \n", + "104 95.0 85 71.0 51.076002 33900.0 29832.0 1.52 1500.0 0.15 \n", + "105 95.0 75 71.0 82.665887 34900.0 9074.0 1.52 1800.0 0.15 \n", + "106 95.0 80 71.0 65.061789 34900.0 21638.0 1.52 1800.0 0.15 \n", + "107 95.0 85 71.0 51.076002 34900.0 32457.0 1.52 1800.0 0.15 \n", + "\n", + " BHP \n", + "0 0.25 \n", + "1 0.25 \n", + "2 0.25 \n", + "3 0.25 \n", + "4 0.25 \n", + ".. ... \n", + "103 0.25 \n", + "104 0.25 \n", + "105 0.25 \n", + "106 0.25 \n", + "107 0.25 \n", + "\n", + "[108 rows x 10 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_21500\\1587544596.py:53: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# example\n", + "# ehb_lcm_cbox_2305\n", + "df = loadData('./excel/ehb_lcm_cbox_2305.xlsx',144,9,drop_list)\n", + "result = modifyData(df)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7537e1d5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/performance_data_collection/bypass_adp.ipynb b/DX_exploration/performance_data_collection/bypass_adp.ipynb new file mode 100644 index 0000000..be6e784 --- /dev/null +++ b/DX_exploration/performance_data_collection/bypass_adp.ipynb @@ -0,0 +1,417 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a8d0953a", + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################\n", + "########################## Notes #############################\n", + "# This file is to calculate the bypass factor.\n", + "# Use formula here to calculate: EnergyPlus Enginnering Reference - Coils\n", + "# https://bigladdersoftware.com/epx/docs/8-2/engineering-reference/coils.html\n", + "# Is Useful to check the results: https://drajmarsh.bitbucket.io/psychro-chart2d.html" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f69d1de9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting CoolProp\n", + " Downloading CoolProp-6.5.0.post1-cp311-cp311-win_amd64.whl (2.0 MB)\n", + " 0.0/2.0 MB ? eta -:--:--\n", + " --- 0.2/2.0 MB 5.3 MB/s eta 0:00:01\n", + " ----------------- 0.8/2.0 MB 10.6 MB/s eta 0:00:01\n", + " ------------------------------------ 1.8/2.0 MB 19.1 MB/s eta 0:00:01\n", + " ---------------------------------------- 2.0/2.0 MB 13.8 MB/s eta 0:00:00\n", + "Installing collected packages: CoolProp\n", + "Successfully installed CoolProp-6.5.0.post1\n" + ] + } + ], + "source": [ + "# !pip3 install CoolProp\n", + "\n", + "# not work when connect to PNNL VPN" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d6bb14ce-fa5d-4bf2-a7ff-4766560cafac", + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-06T00:21:25.675690Z", + "iopub.status.busy": "2023-10-06T00:21:25.675074Z", + "iopub.status.idle": "2023-10-06T00:21:25.684224Z", + "shell.execute_reply": "2023-10-06T00:21:25.683174Z", + "shell.execute_reply.started": "2023-10-06T00:21:25.675661Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Leaving Evaporator Humidity Ratio: 0.01 kg/kg\n" + ] + } + ], + "source": [ + "# Yiting's scripts\n", + "\n", + "from scipy.optimize import fsolve\n", + "import numpy as np\n", + "\n", + "\n", + "\n", + "def calculate_w_leaving(T_entering, w_entering, T_leaving, Q_total, st, m_dot, c_p, h_fg):\n", + " \n", + " # Difference between the total heat removal and the sum of sensible and latent heat.\n", + " def heat_balance(w_leaving):\n", + " Q_sensible = m_dot * c_p * (T_entering - T_leaving)\n", + " Q_latent = m_dot * w_entering * h_fg - m_dot * w_leaving * h_fg\n", + " return Q_total * st - Q_latent\n", + " \n", + " # Using fsolve to find T_leaving where the heat balance is zero\n", + " T_adp = fsolve(heat_balance, 0.082)[0]\n", + " \n", + " return T_adp\n", + "\n", + "\n", + "# Example\n", + "T_entering = 26.67 # Entering air dry bulb temperature in °C\n", + "w_entering = 0.0112 # Entering air humidity ratio in kg/kg\n", + "T_leaving = 9.93\n", + "st = 0.66\n", + "Q_total = 11488.3847155 / 39.2 * 41.7 / 1000 # Total heat removal in Watts\n", + "m_dot = 1.19 * 4011.55 / 1000 # Air mass flow rate in kg/s\n", + "c_p = 1.006 # Specific heat of air in kJ/kg.K\n", + "h_fg = 2260 # Enthalpy of vaporization in kJ/kg\n", + "\n", + "adp = calculate_w_leaving(T_entering, w_entering, T_leaving, Q_total, st, m_dot, c_p, h_fg)\n", + "print(f\"Leaving Evaporator Humidity Ratio: {adp:.2f} kg/kg\")\n", + "\n", + "## Make an initial estimate of the apparatus dew point temperature\n", + "def pw2dp(w, P):\n", + " # Constants for the Magnus formula\n", + " l = 243.7\n", + " beta = 17.27\n", + " Pw0 = 611.2 # Saturation pressure of water vapor at 0°C in Pa\n", + "\n", + " # Calculate the partial pressure of water vapor\n", + " Pw = (w * P) / (0.622 + w)\n", + " \n", + " # Use the Magnus formula to compute the dew point\n", + " # numerator = B * np.log(Pw/Pw0)\n", + " # denominator = A - np.log(Pw/Pw0)\n", + " # Td = numerator / denominator\n", + " num = 243.12 * np.log(Pw/Pw0)\n", + " den = 17.62 - np.log(Pw/Pw0)\n", + " Td = num / den\n", + " return Td\n", + "\n", + "\n", + "## Equation 1\n", + "def pdp2w(Td, P):\n", + " # Constants for the Magnus formula\n", + " A = 17.27\n", + " B = 237.7\n", + " Pw0 = 611.2 # Saturation pressure of water vapor at 0°C in Pa\n", + " # Calculate the saturation pressure at the dew point\n", + " Pws = Pw0 * np.exp((A * Td) / (B + Td)) \n", + " # The partial pressure of water vapor is equal to the saturation pressure at dew point\n", + " Pw = Pws \n", + " # Calculate the humidity ratio\n", + " w = (0.622 * Pw) / (P - Pw)\n", + " return w\n", + "\n", + "\n", + "## The “slope” of temperature versus humidity ratio on the psych chart \n", + "## betweween the inlet and outlet air conditions\n", + "def st_slope(T_in, T_out, w_in, w_out):\n", + " st = (T_in - T_out) / (w_in - w_out)\n", + " return st\n", + "\n", + "\n", + "## Iterating\n", + "def ADP(T_in, w_in, Tad0, st, epsilon = 0.01):\n", + " Pa = 101325\n", + " Tad = Tad0 # make an initial estimate of the apparatus dew point temperature\n", + " for i in range(1000):\n", + " ## Equation 2\n", + " w = pdp2w(Tad, Pa)\n", + " Tad_new = T_in - st*(w_in - w)\n", + " print(\"w,Tad_new\")\n", + " print(w,Tad_new)\n", + " if np.abs(Tad_new - Tad) <= epsilon:\n", + " return Tad_new, w\n", + " Tad = Tad_new\n", + " return None\n", + "\n", + "\n", + "## The apparatus dewpoint enthalpy is then:\n", + "def compute_enthalpy(T, w):\n", + " # Constants\n", + " cp = 1.006 # Specific heat of dry air at constant pressure (kJ/kg·°C)\n", + " hv = 2501 # Enthalpy of vaporization of water at 0°C (kJ/kg)\n", + " # Compute the enthalpy\n", + " h = cp * T + w * (hv + 1.86*T)\n", + " return h\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3776ba4d-27e7-4914-80e6-828bb7b301a0", + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-06T00:21:27.285876Z", + "iopub.status.busy": "2023-10-06T00:21:27.285560Z", + "iopub.status.idle": "2023-10-06T00:21:27.292298Z", + "shell.execute_reply": "2023-10-06T00:21:27.291629Z", + "shell.execute_reply.started": "2023-10-06T00:21:27.285851Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "s/t: 13950.000000000005\n", + "ADP init: 14.07662393598085 °C\n", + "0.0100121148353774 10.099001953514726\n", + "0.007678162341030634 -22.459635342622676\n", + "0.0006195281186943684 -120.92758274421361\n", + "6.41366223336722e-11 -129.5699991052942\n", + "3.862246656436672e-12 -129.5699999461217\n", + "ADP: (-129.5699999461217, 3.862246656436672e-12) °C\n", + "0.0100121148353774 10.099001953514726\n", + "0.007678162341030634 -22.459635342622676\n", + "0.0006195281186943684 -120.92758274421361\n", + "6.41366223336722e-11 -129.5699991052942\n", + "3.862246656436672e-12 -129.5699999461217\n", + "BF: 0.8928571428201955\n" + ] + } + ], + "source": [ + "# Yiting's Test\n", + "\n", + "T_in = 26.67\n", + "w_in = 0.0112\n", + "T_out = 9.93\n", + "w_out = 0.01\n", + "Pa = 101325\n", + "st = st_slope(T_in, T_out, w_in, w_out)\n", + "print(\"s/t:\", st)\n", + "Tad0 = pw2dp(w_out, Pa) # Initial guess of ADP\n", + "print(\"ADP init:\", Tad0, \"°C\")\n", + "print(\"ADP:\", ADP(T_in, w_in, Tad0, st), \"°C\")\n", + "T_adp, w_adp = ADP(T_in, w_in, Tad0, st)\n", + "h_adp = compute_enthalpy(T_adp, w_adp)\n", + "h_in = compute_enthalpy(T_in, w_in)\n", + "h_out = compute_enthalpy(T_out, w_out)\n", + "\n", + "\n", + "## The coil bypass factor is:\n", + "BF = (h_out - h_adp) / (h_in - h_adp)\n", + "print(\"BF:\", BF)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f9618254-aea4-4886-9f68-d5c54e7f6b4a", + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-06T00:21:26.418881Z", + "iopub.status.busy": "2023-10-06T00:21:26.418509Z", + "iopub.status.idle": "2023-10-06T00:21:26.430330Z", + "shell.execute_reply": "2023-10-06T00:21:26.429572Z", + "shell.execute_reply.started": "2023-10-06T00:21:26.418852Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T_adp: 14.18\n", + "BF_byT: -0.13378684807256233\n", + "BF_byH: -0.12100054366688948\n", + "0.017707267627385063 0.009324906442953121\n" + ] + } + ], + "source": [ + "## Jeremy's scripts\n", + "\n", + "from CoolProp.HumidAirProp import HAPropsSI\n", + "\n", + "def find_apparatus_dew_point(w_in, w_out, t_in, t_out):\n", + " # slpoe\n", + " a = (w_out - w_in) / (t_out - t_in)\n", + " # w_x = a * t_x + b\n", + " b = w_in - a * t_in\n", + " t_adp = t_out # Equation 2\n", + " incr = 0.001\n", + " t_x = t_out + incr\n", + " \n", + " found = True\n", + " while found:\n", + " t_x += incr\n", + " w_x = a * t_x + b\n", + " t_adp = HAPropsSI('D','T',t_x + 273.15,'P',101325,'W', w_x) - 273.15\n", + " if abs(t_adp - t_x) < 0.001:\n", + " break\n", + " incr = (t_adp - t_x) / 10\n", + " return t_adp\n", + "\n", + "## Test on this:\n", + "w_in, w_out, T_in, T_out = 0.01112, 0.01, 23, 13\n", + "T_adp = round(find_apparatus_dew_point(w_in, w_out, T_in, T_out), 2)\n", + "print(\"T_adp:\", T_adp)\n", + "\n", + "Pa = 101325\n", + "W_in = pdp2w(T_in, Pa)\n", + "W_out = pdp2w(T_out, Pa)\n", + "w_adp = pdp2w(T_adp, Pa)\n", + "h_adp = compute_enthalpy(T_adp, w_adp)\n", + "h_in = compute_enthalpy(T_in, w_in)\n", + "h_out = compute_enthalpy(T_out, w_out)\n", + "BF_byT = (T_out - T_adp) / (T_in - T_adp)\n", + "BF_byH = (h_out - h_adp) / (h_in - h_adp)\n", + "print(\"BF_byT:\", BF_byT)\n", + "print(\"BF_byH:\", BF_byH)\n", + "print(W_in,W_out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17d0b9e3-a1ed-4368-9e00-ecf26aa6680c", + "metadata": {}, + "outputs": [], + "source": [ + "## revised script\n", + "\n", + "def bypass_factor(w_in, w_out, t_in, t_out):\n", + " # slpoe\n", + " a = (w_out - w_in) / (t_out - t_in)\n", + " # w_x = a * t_x + b\n", + " b = w_in - a * t_in\n", + " t_adp = t_out # Equation 2\n", + " incr = 0.001\n", + " t_x = t_out + incr\n", + " \n", + " found = True\n", + " while found:\n", + " t_x += incr\n", + " w_x = a * t_x + b\n", + " t_adp = HAPropsSI('D','T',t_x + 273.15,'P',101325,'W', w_x) - 273.15\n", + " if abs(t_adp - t_x) < 0.001:\n", + " break\n", + " incr = (t_adp - t_x) / 10\n", + " return t_adp" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "35afc5b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Humidity Ratio: 0.186463\n" + ] + } + ], + "source": [ + "import math\n", + "\n", + "def calculate_w(tdb, twb): # humidity_ratio\n", + " vapor_pressure = 6.112 * math.exp((17.67 * twb) / (twb + 243.5))\n", + " humidity_ratio = 0.622 * (vapor_pressure / (101.325 - vapor_pressure))\n", + " return humidity_ratio\n", + "\n", + "def calculate_tldb(tedb, sc, cfm):\n", + " tldb = tedb - sc / (1.1 * cfm)\n", + " return tldb\n", + "\n", + "def calculate_hlwb(hewb, tc, cfm):\n", + " hlwb = hewb - tc / (4.5 * cfm)\n", + " return hlwb\n", + " \n", + "def compute_h(T, w):\n", + " cp = 1.006 # Specific heat of dry air at constant pressure (kJ/kg·°C)\n", + " hv = 2501 # Enthalpy of vaporization of water at 0°C (kJ/kg)\n", + " h = cp * T + w * (hv + 1.86*T)\n", + " return h\n", + "\n", + "def compute_bf_T(tldb,tedb,tadp):\n", + " BF_byT = (tldb - tadp) / (tedb - tadp)\n", + " return BF_byT\n", + "\n", + "def compute_bf_H(h_out,h_in,h_adp):\n", + " BF_byH = (h_out - h_adp) / (h_in - h_adp)\n", + " return BF_byH\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b77e7ea", + "metadata": {}, + "outputs": [], + "source": [ + "# Example usage\n", + "\n", + "dry_bulb_temperature = 25.0 # in degrees Celsius, 77F\n", + "wet_bulb_temperature = 20.0 # in degrees Celsius, 68F\n", + "\n", + "humidity_ratio = calculate_w(dry_bulb_temperature, wet_bulb_temperature)\n", + "print(f'Humidity Ratio: {humidity_ratio:.6f}')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/performance_data_collection/data_collection_Carrier_StandardMode.ipynb b/DX_exploration/performance_data_collection/data_collection_Carrier_StandardMode.ipynb new file mode 100644 index 0000000..3291c82 --- /dev/null +++ b/DX_exploration/performance_data_collection/data_collection_Carrier_StandardMode.ipynb @@ -0,0 +1,758 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "id": "3db2b116", + "metadata": {}, + "outputs": [], + "source": [ + "######################### Carrier ##############################\n", + "########################## Notes #############################\n", + "# The 18 equipments' data include 'Bypass Factor' at each row.\n", + "# The variable we have is TC(Total capacity), SHC(Sensible Capacity), kW(power) and BF(bypass factor).\n", + "\n", + "# Issue to be soloved: \n", + "# On page 34, they show the method to correct SHC, since the SHC is based on 80F edb temp of air entering coil.\n", + "# But using their method, the SHC sometimes is higher than TC. When size gets bigger, this happens more frequently.\n", + "# They also have some setting which are not applicable (filter out?)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15745d0e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "# not work when connect to PNNL VPN\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "from bisect import bisect_left\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8c8b8e9b", + "metadata": {}, + "outputs": [], + "source": [ + "# Correction Factor\n", + "def calculate_Factor(bf_read, factors):\n", + " bf05, bf10, bf20 = factors[0], factors[1], factors[2]\n", + " if bf_read == 0.05:\n", + " return bf05\n", + " elif bf_read == 0.10:\n", + " return bf10\n", + " elif bf_read == 0.20:\n", + " return bf20\n", + " elif bf_read < 0.05:\n", + " return bf05 - ((0.05 - bf_read) * (bf10 - bf05) / 0.05)\n", + " elif bf_read > 0.05 and bf_read < 0.10:\n", + " return bf05 + ((bf_read - 0.05) * (bf10 - bf05) / 0.05)\n", + " elif bf_read > 0.10 and bf_read < 0.20:\n", + " return bf10 + ((bf_read - 0.10) * (bf20 - bf10) / 0.1)\n", + " else:\n", + " return bf20 + ((bf_read - 0.2) * (bf20 - bf10) / 0.1)\n", + "\n", + "# 48-50A-21PD-SM\n", + "def correctionFactor_1(row):\n", + " factors = []\n", + " # get correction factor\n", + " if row['EADB'] in [79, 81]:\n", + " factors = [1.04, 0.98, 0.87]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [78, 82]:\n", + " factors = [2.07, 1.96, 1.74]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [77, 83]:\n", + " factors = [3.11, 2.94, 2.62]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [76, 84]:\n", + " factors = [4.14, 3.92, 3.49]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [75, 85]:\n", + " factors = [4.18, 4.91, 4.36]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " else:\n", + " factor = 1.1 * (1 - row['BF']) * (row['EADB'] - 80)\n", + " \n", + " modified_row = row.copy()\n", + " \n", + " # modify 'SHC'\n", + " if row['EADB'] < 80:\n", + " modified_row['SHC'] = row['SHC'] - factor * row['airflow']\n", + " elif row['EADB'] > 80:\n", + " modified_row['SHC'] = row['SHC'] + factor * row['airflow']\n", + " return modified_row\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b6a8918b", + "metadata": {}, + "outputs": [], + "source": [ + "# Supply Fan Power\n", + "# esp\n", + "esp_ranges = [\n", + " (0, 28.8, 0.1),\n", + " (29, 42.5, 0.15),\n", + " (43, 70, 0.2),\n", + " (71, 105, 0.25),\n", + " (106, 134, 0.3),\n", + " (135, 210, 0.35),\n", + " (211, 280, 0.4),\n", + " (281, 350, 0.45),\n", + " (351, 400, 0.55),\n", + " (401, 500, 0.65),\n", + " (501, float('inf'), 0.75)\n", + "]\n", + "\n", + "def calculate_esp(TC):\n", + " TC = TC//1000\n", + " for lower, upper, esp in esp_ranges:\n", + " if lower <= TC <= upper:\n", + " return esp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "21a23fb9", + "metadata": {}, + "outputs": [], + "source": [ + "def loadData(name,cols,nHead,nRows):\n", + " ## Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name='Sheet1', header=int(nHead), \n", + " usecols=cols, skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " df = df.replace('—', np.nan)\n", + " \n", + " ## Rotate data\n", + " column_index_to_drop = [0]\n", + " rotate_df = df.drop(df.columns[column_index_to_drop], axis=1)\n", + " buffer_df = rotate_df.T\n", + " buffer_df.reset_index(inplace=True)\n", + " buffer_df.columns = buffer_df.iloc[0]\n", + " buffer_df = buffer_df.iloc[1:]\n", + " \n", + " ## Read EAWB\n", + " eawb_values = buffer_df.iloc[:5, 0]\n", + " eawb_values = eawb_values.tolist()\n", + " eawb_times = len(buffer_df)//5\n", + " eawb_list = eawb_values * eawb_times\n", + " buffer_df.rename(columns={'Unnamed: 1':'EAWB'}, inplace=True)\n", + " buffer_df['EAWB'] = eawb_list\n", + "\n", + " \n", + " ## Read cfm\n", + " cfm = pd.read_excel(str(name), sheet_name='Sheet1', header=int(nHead-3), \n", + " usecols=cols, skiprows=None, nrows=1, engine=\"openpyxl\")\n", + " cfm_values = np.array(cfm)[0]\n", + " cfm_values = cfm_values.tolist()\n", + " cfm_list = [int(value) for value in cfm_values if not pd.isna(value)]\n", + " cfm_array = np.repeat(cfm_list,5).tolist()\n", + " buffer_df.insert(0, \"airflow\", np.nan)\n", + " buffer_df['airflow'] = cfm_array\n", + " \n", + " ## Modify the dataframe\n", + " num_chunk = len(buffer_df.columns)//4\n", + " if num_chunk <= 5:\n", + " chunk1 = buffer_df.iloc[:, :2]\n", + " chunk2 = buffer_df.iloc[:, 2:6]\n", + " chunk3 = buffer_df.iloc[:, 6:10]\n", + " chunk4 = buffer_df.iloc[:, 10:14]\n", + " chunk5 = buffer_df.iloc[:, 14:18]\n", + " chunk6 = buffer_df.iloc[:, 18:22]\n", + " result_df = pd.concat([chunk2, chunk3, chunk4, chunk5, chunk6], ignore_index=True)\n", + " chunk1_df = pd.concat([chunk1, chunk1, chunk1, chunk1, chunk1], ignore_index=True)\n", + " chunk1_df.reset_index(inplace=True)\n", + " else:\n", + " chunk1 = buffer_df.iloc[:, :2]\n", + " chunk2 = buffer_df.iloc[:, 2:6]\n", + " chunk3 = buffer_df.iloc[:, 6:10]\n", + " chunk4 = buffer_df.iloc[:, 10:14]\n", + " chunk5 = buffer_df.iloc[:, 14:18]\n", + " chunk6 = buffer_df.iloc[:, 18:22]\n", + " chunk7 = buffer_df.iloc[:, 22:26]\n", + " result_df = pd.concat([chunk2, chunk3, chunk4, chunk5, chunk6, chunk7], ignore_index=True)\n", + " chunk1_df = pd.concat([chunk1, chunk1, chunk1, chunk1, chunk1, chunk1], ignore_index=True)\n", + " chunk1_df.reset_index(inplace=True) \n", + " merged_df = pd.concat([chunk1_df, result_df], axis=1)\n", + " merged_df = merged_df.iloc[:, 1:]\n", + " \n", + " ## Read EADB\n", + " eadb_values = df.iloc[:, 0].tolist()\n", + " eadb_values = [int(value) for value in eadb_values if not pd.isna(value)]\n", + " eadb_times = 5 * len(cfm_list)\n", + " eadb_array = np.repeat(eadb_values,eadb_times).tolist()\n", + " merged_df['EADB'] = 80 # Standard Mode\n", + " merged_df['OADB'] = eadb_array\n", + " \n", + " ## Calculate RH\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " merged_df['RH'] = merged_df.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['EADB'] * units.degF, row['EAWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " ## Modify units\n", + " merged_df['TC'] = merged_df['TC']*1000\n", + " merged_df['SHC'] = merged_df['SHC']*1000\n", + "# ## Correct SHC\n", + "# merged_df = merged_df.apply(lambda row: correctionFactor_1(row), axis=1)\n", + " \n", + " ## Clean NA\n", + " merged_df = merged_df.dropna()\n", + " merged_df = merged_df.reset_index(drop=True)\n", + " \n", + " ## Supply fan power\n", + " merged_df.insert(0, \"SFP\", np.nan)\n", + " merged_df['TC'] = pd.to_numeric(merged_df['TC'], errors='coerce')\n", + " merged_df['ESP'] = merged_df['TC'].apply(calculate_esp)\n", + " ## organize\n", + "# # for test\n", + "# merged_df['S2T'] = merged_df['SHC']/merged_df['TC']\n", + "# new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'S2T', 'kW', 'airflow']\n", + "\n", + " new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'ESP', 'SFP', 'BF']\n", + " final = merged_df[new_cols]\n", + " \n", + " return final" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ee5bb142", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 OADB EADB EAWB RH TC SHC kW airflow ESP \\\n", + "0 75 80 75 79.752712 268000.0 109000.0 14.1 4000 0.40 \n", + "1 75 80 72 68.617104 258000.0 119000.0 14.0 4000 0.40 \n", + "2 75 80 67 51.572364 236000.0 141000.0 13.8 4000 0.40 \n", + "3 75 80 62 36.223157 214000.0 161000.0 13.7 4000 0.40 \n", + "4 75 80 57 22.361753 195000.0 179000.0 13.6 4000 0.35 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "115 120 80 75 79.752712 253000.0 106000.0 25.1 7000 0.40 \n", + "116 120 80 72 68.617104 241000.0 127000.0 25.0 7000 0.40 \n", + "117 120 80 67 51.572364 220000.0 162000.0 25.1 7000 0.40 \n", + "118 120 80 62 36.223157 201000.0 192000.0 25.3 7000 0.35 \n", + "119 120 80 57 22.361753 198000.0 198000.0 25.4 7000 0.35 \n", + "\n", + "0 SFP BF \n", + "0 NaN 0.0 \n", + "1 NaN 0.0 \n", + "2 NaN 0.09 \n", + "3 NaN 0.15 \n", + "4 NaN 0.14 \n", + ".. ... ... \n", + "115 NaN 0.23 \n", + "116 NaN 0.18 \n", + "117 NaN 0.17 \n", + "118 NaN 0.18 \n", + "119 NaN 0.39 \n", + "\n", + "[120 rows x 11 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_16164\\1007779893.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name='Sheet1', header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# for BF test\n", + "test020_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:V\",5,24)\n", + "print(test020_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "21bc5ac5", + "metadata": {}, + "outputs": [], + "source": [ + "# 48-50A-21PD-FP\n", + "# excel function can do this\n", + "bhp_ranges_21_20 = [\n", + " (4000, 2.21, 2.38, 2.55, 2.71),\n", + " (5000, 2.71, 2.89, 3.08, 3.26),\n", + " (6000, 3.29, 3.5, 3.7, 3.9),\n", + " (7000, 3.96, 4.19, 4.41, 4.63),\n", + " (7500, 4.33, 4.56, 4.79, 5.02),\n", + " (8000, 4.73, 4.97, 5.21, 5.45),\n", + " (9000, 5.6, 5.85, 6.11, 6.37),\n", + " (10000, 6.58, 6.85, 7.13, 7.4)\n", + "]\n", + "\n", + "bhp_ranges_21_30 = [\n", + " (5000, 0.85, 1.03, 1.22, 1.42),\n", + " (6000, 1.26, 1.45, 1.65, 1.86),\n", + " (7000, 1.79, 1.98, 2.19, 2.41),\n", + " (8000, 2.44, 2.64, 2.85, 3.08),\n", + " (9000, 3.23, 3.43, 3.65, 3.88),\n", + " (10000, 4.15, 4.36, 4.58, 4.82),\n", + " (11000, 5.22, 5.44, 5.67, 5.91),\n", + " (12000, 6.45, 6.67, 6.90, 7.15),\n", + " (13000, 7.85, 8.07, 8.30, 8.55),\n", + " (14000, 9.41, 9.64, 9.88, 10.13),\n", + " (15000, 11.15, 11.38, 11.62, 11.88)\n", + "]\n", + "\n", + "bhp_ranges_21_35 = [\n", + " (7000, 1.96, 2.22, 2.47, 2.72),\n", + " (8000, 2.62, 2.89, 3.16, 3.43),\n", + " (9000, 3.39, 3.68, 3.97, 4.26),\n", + " (10000, 4.29, 4.61, 4.91, 5.22),\n", + " (10500, 4.80, 5.12, 5.43, 5.75),\n", + " (11000, 5.33, 5.66, 5.99, 6.31),\n", + " (12000, 6.52, 6.86, 7.21, 7.54),\n", + " (13000, 7.85, 8.21, 8.57, 8.92),\n", + " (14000, 9.33, 9.71, 10.09, 10.45),\n", + " (15000, 10.98, 11.37, 11.76, 12.15),\n", + " (16000, 12.79, 13.2, 13.6, 14),\n", + " (17000, 14.77, 15.19, 15.61, 16.03),\n", + " (17500, 15.83, 16.25, 16.68, 17.1)\n", + "]\n", + "\n", + "bhp_ranges_21_40 = [\n", + " (8000, 2.69, 3.08, 3.47, 3.86),\n", + " (9000, 3.53, 3.94, 4.36, 4.78),\n", + " (10000, 4.52, 4.96, 5.4, 5.84),\n", + " (11000, 5.68, 6.14, 6.6, 7.07),\n", + " (12000, 7.01, 7.49, 7.98, 8.47),\n", + " (13000, 8.53, 9.03, 9.54, 10.05),\n", + " (14000, 10.24, 10.76, 11.29, 11.82),\n", + " (15000, 12.15, 12.69, 13.24, 13.79),\n", + " (16000, 14.27, 14.83, 15.4, 15.97),\n", + " (17000, 16.61, 17.19, 17.77, 18.36),\n", + " (18000, 19.18, 19.77, 20.37, 20.98),\n", + " (19000, 21.98, 22.59, 23.21, 23.84),\n", + " (20000, 25.02, 25.65, 26.29, 26.93)\n", + "]\n", + "\n", + "bhp_ranges_21_50 = [\n", + " (10000, 4.62, 5.06, 5.5, 5.95),\n", + " (11000, 5.8, 6.26, 6.73, 7.2),\n", + " (12000, 7.15, 7.63, 8.12, 8.62),\n", + " (13000, 8.69, 9.19, 9.7, 10.22),\n", + " (14000, 10.43, 10.95, 11.48, 12.01),\n", + " (15000, 12.37, 12.91, 13.46, 14.01),\n", + " (16000, 14.52, 15.08, 15.65, 16.22),\n", + " (17000, 16.89, 17.48, 18.06, 18.65),\n", + " (18000, 19.5, 20.1, 20.71, 21.32),\n", + " (19000, 22.35, 22.96, 23.59, 24.21),\n", + " (20000, 25.43, 26.07, 26.71, 27.36)\n", + "]\n", + "\n", + "bhp_ranges_21_60 = [\n", + " (12000, 4.02, 4.71, 5.41, 6.12),\n", + " (14000, 5.74, 6.49, 7.24, 8.01),\n", + " (15000, 6.75, 7.53, 8.32, 9.11),\n", + " (16000, 7.88, 8.68, 9.5, 10.32),\n", + " (17000, 9.12, 9.95, 10.79, 11.64),\n", + " (18000, 10.48, 11.33, 12.2, 13.07),\n", + " (19000, 11.96, 12.84, 13.74, 14.64),\n", + " (20000, 13.57, 14.47, 15.4, 16.32),\n", + " (21000, 15.3, 16.24, 17.19, 18.14),\n", + " (22000, 17.18, 18.14, 19.11, 20.09),\n", + " (23000, 19.2, 20.18, 21.17, 22.17),\n", + " (24000, 21.35, 22.36, 23.38, 24.4),\n", + " (25000, 23.66, 24.68, 25.72, 26.78),\n", + " (26000, 26.11, 27.16, 28.23, 29.3),\n", + " (27000, 28.72, 29.79, 30.88, 31.97)\n", + "]\n", + "\n", + "# 48-50P-12PD-FP\n", + "bhp_ranges_12_30 = [\n", + " (6000, 0.59, 0.91, 1.27, 1.66),\n", + " (7500, 0.94, 1.28, 1.68, 2.11),\n", + " (9000, 1.46, 1.8, 2.22, 2.69),\n", + " (10500, 2.16, 2.52, 2.95, 3.43),\n", + " (12000, 3.08, 3.44, 3.89, 4.39),\n", + " (13500, 4.25, 4.62, 5.07, 5.58),\n", + " (15000, 5.69, 6.06, 6.52, 7.04)\n", + "]\n", + "\n", + "bhp_ranges_12_35 = [\n", + " (7000, 246, 301, 1.58, 398),\n", + " (8000, 266, 315, 1.92, 406),\n", + " (10000, 310, 350, 2.8, 427),\n", + " (12000, 357, 390, 4.06, 457),\n", + " (14000, 406, 435, 5.76, 492),\n", + " (15000, 430, 458, 6.8, 511)\n", + "]\n", + "\n", + "bhp_ranges_12_40 = [\n", + " (8000, 0.98, 1.33, 1.72, 2.14),\n", + " (10000, 1.67, 2.11, 2.55, 3.01),\n", + " (12000, 2.65, 3.18, 3.7, 4.23),\n", + " (14000, 3.96, 4.61, 5.22, 5.83),\n", + " (16000, 5.67, 6.44, 7.15, 7.85),\n", + " (18000, 7.84, 8.72, 9.55, 10.34),\n", + " (20000, 10.51, 11.51, 12.46, 13.36)\n", + "]\n", + "\n", + "bhp_ranges_12_50 = [\n", + " (9000, 1.34, 1.73, 2.15, 2.6),\n", + " (10000, 1.74, 2.17, 2.62, 3.09),\n", + " (12000, 2.76, 3.29, 3.81, 4.35),\n", + " (14000, 4.15, 4.79, 5.4, 6.01),\n", + " (16000, 5.96, 6.71, 7.42, 8.11),\n", + " (18000, 8.26, 9.12, 9.93, 10.72),\n", + " (20000, 11.1, 12.06, 12.99, 13.88)\n", + "]\n", + "\n", + "bhp_ranges_12_55 = [\n", + " (10000, 1.04, 1.5, 2.03, 2.66),\n", + " (12500, 1.69, 2.23, 2.78, 3.4),\n", + " (15000, 2.59, 3.23, 3.85, 4.51),\n", + " (17500, 3.78, 4.52, 5.24, 5.97),\n", + " (20000, 5.31, 6.15, 6.98, 7.79),\n", + " (22500, 7.23, 8.16, 9.09, 10),\n", + " (25000, 9.59, 10.6, 11.62, 12.64)\n", + "]\n", + "\n", + "bhp_ranges_12_60 = [\n", + " (12000, 1.54, 2.03, 2.57, 3.2),\n", + " (15000, 2.65, 3.27, 3.88, 4.53),\n", + " (18000, 4.22, 5, 5.73, 6.46),\n", + " (21000, 6.36, 7.29, 8.18, 9.04),\n", + " (24000, 9.19, 10.24, 11.29, 12.29),\n", + " (27000, 12.8, 13.93, 15.14, 16.3),\n", + " (30000, 17.29, 18.5, 19.82, 21.15)\n", + "]\n", + "\n", + "bhp_ranges_12_70 = [\n", + " (14000, 2.23, 2.8, 3.38, 4.02),\n", + " (17500, 3.92, 4.67, 5.39, 6.1),\n", + " (21000, 6.36, 7.29, 8.18, 9.04),\n", + " (24500, 9.74, 10.8, 11.88, 12.91),\n", + " (28000, 14.18, 15.35, 16.6, 17.82),\n", + " (30000, 17.29, 18.5, 19.82, 21.15)\n", + "]\n", + "\n", + "bhp_ranges_12_75 = [\n", + " (15000, 3.02, 3.91, 4.83, 5.8),\n", + " (16000, 3.52, 4.45, 5.41, 6.42),\n", + " (18000, 4.68, 5.71, 6.76, 7.84),\n", + " (20000, 6.08, 7.22, 8.36, 9.52),\n", + " (22000, 7.75, 9, 10.24, 11.49),\n", + " (24000, 9.71, 11.07, 12.41, 13.75),\n", + " (26000, 11.99, 13.46, 14.9, 16.34),\n", + " (28000, 14.61, 16.18, 17.73, 19.28),\n", + " (30000, 17.6, 19.28, 20.93, 22.57)\n", + "]\n", + "\n", + "bhp_ranges_12_90 = [\n", + " (16000, 3.27, 4.06, 4.88, 5.74),\n", + " (18000, 4.42, 5.31, 6.21, 7.14),\n", + " (20000, 5.83, 6.82, 7.81, 8.82),\n", + " (22000, 7.53, 8.62, 9.7, 10.8),\n", + " (24000, 9.55, 10.75, 11.93, 13.11),\n", + " (26000, 11.91, 13.22, 14.5, 15.78),\n", + " (28000, 14.65, 16.06, 17.45, 18.83),\n", + " (30000, 17.78, 19.31, 20.8, 22.28),\n", + " (32000, 21.36, 22.99, 24.59, 26.17),\n", + " (34000, 25.39, 27.13, 28.84, 30.53)\n", + "]\n", + "\n", + "bhp_ranges_12_100 = [\n", + " (20000, 5.83, 6.82, 7.81, 8.82),\n", + " (22000, 7.53, 8.62, 9.7, 10.8),\n", + " (24000, 9.55, 10.75, 11.93, 13.11),\n", + " (26000, 11.91, 13.22, 14.5, 15.78),\n", + " (28000, 14.65, 16.06, 17.45, 18.83),\n", + " (30000, 17.78, 19.31, 20.8, 22.28),\n", + " (32000, 21.36, 22.99, 24.59, 26.17),\n", + " (34000, 25.39, 27.13, 28.84, 30.53),\n", + " (36000, 29.92, 31.77, 33.58, 35.38),\n", + " (38000, 34.96, 36.91, 38.85, 40.74),\n", + " (40000, 40.54, 42.61, 44.65, 46.66)\n", + "]\n", + "\n", + "def get_scaled_tuple(airflow, bhp_ranges):\n", + " airflow = int(airflow)\n", + " cfm_values = [t[0] for t in bhp_ranges]\n", + " idx = bisect_left(cfm_values, airflow)\n", + " \n", + " if idx == 0:\n", + " return bhp_ranges[0]\n", + " elif idx == len(bhp_ranges):\n", + " return bhp_ranges[-1]\n", + " \n", + " lower_tuple = bhp_ranges[idx - 1]\n", + " upper_tuple = bhp_ranges[idx]\n", + " \n", + " scale_factor = (airflow - lower_tuple[0]) / (upper_tuple[0] - lower_tuple[0])\n", + " \n", + " scaled_values = [round((1 - scale_factor) * l + scale_factor * u, 2) for l, u in zip(lower_tuple[1:], upper_tuple[1:])]\n", + " \n", + " scaled_tuple = (airflow, *scaled_values)\n", + " return scaled_tuple\n", + "\n", + "def find_matching_tuple(airflow, bhp_ranges):\n", + " for tpl in bhp_ranges:\n", + " if tpl[0] == airflow:\n", + " return tpl\n", + " return get_scaled_tuple(airflow, bhp_ranges)\n", + "\n", + "def calculate_custom_bhp(cfm, esp, bhp_ranges):\n", + " result_tuple = find_matching_tuple(cfm, bhp_ranges)\n", + " esp02, esp04, esp06, esp08 = result_tuple[1], result_tuple[2], result_tuple[3], result_tuple[4]\n", + " \n", + " if esp == 0.2:\n", + " return esp02\n", + " elif esp == 0.4:\n", + " return esp04\n", + " elif esp == 0.6:\n", + " return esp06\n", + " elif esp >= 0.8:\n", + " return esp08\n", + " elif esp < 0.2:\n", + " return esp02 - ((0.2 - esp) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.2 and esp < 0.4:\n", + " return esp02 + ((esp - 0.2) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.4 and esp < 0.6:\n", + " return esp04 + ((esp - 0.4) * (esp06 - esp04) / 0.2)\n", + " elif esp > 0.6 and esp < 0.8:\n", + " return esp06 + ((esp - 0.6) * (esp08 - esp06) / 0.2)\n", + "\n", + "def save_sfp(df,bhp_ranges,eff):\n", + " df['BHP'] = df.apply(lambda row: calculate_custom_bhp(row['airflow'], row['ESP'], bhp_ranges), axis=1)\n", + " df['SFP'] = df['BHP']*746/eff\n", + " new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'SFP', 'BF']\n", + " final = df[new_cols]\n", + " return final" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "cd792d5a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_16164\\1007779893.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name='Sheet1', header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# 48-50A-21PD-SM\n", + "\n", + "# column_names = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'ESP', 'SFP']\n", + "# gap = pd.DataFrame(columns=column_names)\n", + "# new_row = pd.Series(['-------------'] * len(column_names), index=column_names)\n", + "# gap = gap.append(new_row, ignore_index=True)\n", + "\n", + "# 020\n", + "test020_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:V\",5,24)\n", + "test020_2 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",35,24)\n", + "test020 = pd.concat([test020_1,test020_2], axis=0)\n", + "test020 = save_sfp(test020, bhp_ranges_21_20, 0.895)\n", + "\n", + "# 025\n", + "test025_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:V\",65,20)\n", + "test025_2 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",91,20)\n", + "test025 = pd.concat([test025_1,test025_2], axis=0)\n", + "test025 = save_sfp(test025, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 027\n", + "test027_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:V\",117,20)\n", + "test027_2 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",143,20)\n", + "test027 = pd.concat([test027_1,test027_2], axis=0)\n", + "test027 = save_sfp(test027, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 030\n", + "test030_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:V\",169,20)\n", + "test030_2 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",195,20)\n", + "test030 = pd.concat([test030_1,test030_2], axis=0)\n", + "test030 = save_sfp(test030, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 035\n", + "test035_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:V\",221,20)\n", + "test035_2 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",246,20)\n", + "test035 = pd.concat([test035_1,test035_2], axis=0)\n", + "test035 = save_sfp(test035, bhp_ranges_21_35, 0.895)\n", + "\n", + "# 040\n", + "test040_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:V\",272,24)\n", + "test040_2 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",302,24)\n", + "test040 = pd.concat([test040_1,test040_2], axis=0)\n", + "test040 = save_sfp(test040, bhp_ranges_21_40, 0.895)\n", + "\n", + "# 050\n", + "test050_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",332,24)\n", + "test050_2 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:L\",361,24)\n", + "test050 = pd.concat([test050_1,test050_2], axis=0)\n", + "test050 = save_sfp(test050, bhp_ranges_21_50, 0.895)\n", + "\n", + "# 060\n", + "test060_1 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",391,24)\n", + "test060_2 = loadData('./excel/48-50A-21PD-SM.xlsx',\"A:Q\",421,24)\n", + "test060 = pd.concat([test060_1,test060_2], axis=0)\n", + "test060 = save_sfp(test060, bhp_ranges_21_60, 0.895)\n", + "\n", + "\n", + "final = pd.concat([test020,test025,test027,test030, test035, test040, test050, test060], axis=0)\n", + "file_path = Path('./output/ready/48-50A-21PD-SD.csv')\n", + "final.to_csv(file_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "68498c38", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_16164\\1007779893.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name='Sheet1', header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# 48-50P-12PD-SM\n", + "\n", + "# 030\n", + "samp030_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",5,20)\n", + "samp030_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:Q\",30,20)\n", + "samp030_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",56,20)\n", + "samp030_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:Q\",81,20)\n", + "samp030 = pd.concat([samp030_1,samp030_2,samp030_3,samp030_4], axis=0)\n", + "samp030 = save_sfp(samp030, bhp_ranges_12_30, 0.895)\n", + "\n", + "# 035\n", + "samp035_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",107,20)\n", + "samp035_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:Q\",132,20)\n", + "samp035 = pd.concat([samp035_1,samp035_2], axis=0)\n", + "samp035 = save_sfp(samp035, bhp_ranges_12_35, 0.895)\n", + "\n", + "# 040\n", + "samp040_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",158,20)\n", + "samp040_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:Q\",183,20)\n", + "samp040_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",208,20)\n", + "samp040_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:Q\",233,20)\n", + "samp040 = pd.concat([samp040_1,samp040_2,samp040_3,samp040_4], axis=0)\n", + "samp040 = save_sfp(samp040, bhp_ranges_12_40, 0.895)\n", + "\n", + "\n", + "# 050\n", + "samp050_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",257,20)\n", + "samp050_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:G\",281,20)\n", + "samp050_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",308,20)\n", + "samp050_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:G\",336,20)\n", + "samp050 = pd.concat([samp050_1,samp050_2,samp050_3,samp050_4], axis=0)\n", + "samp050 = save_sfp(samp050, bhp_ranges_12_50, 0.895)\n", + " \n", + "# 055\n", + "samp055_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",364,20)\n", + "samp055_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",389,20)\n", + "samp055_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",417,20)\n", + "samp055_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",443,20)\n", + "samp055 = pd.concat([samp055_1,samp055_2,samp055_3,samp055_4], axis=0)\n", + "samp055 = save_sfp(samp055, bhp_ranges_12_55, 0.895)\n", + "\n", + "# 060\n", + "samp060_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",471,20)\n", + "samp060_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",496,20)\n", + "samp060_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",524,20)\n", + "samp060_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",549,20)\n", + "samp060 = pd.concat([samp060_1,samp060_2,samp060_3,samp060_4], axis=0)\n", + "samp060 = save_sfp(samp060, bhp_ranges_12_60, 0.895)\n", + "\n", + "# 070\n", + "samp070_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",590,20)\n", + "samp070_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",615,20)\n", + "samp070_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",646,20)\n", + "samp070_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",671,20)\n", + "samp070 = pd.concat([samp070_1,samp070_2,samp070_3,samp070_4], axis=0)\n", + "samp070 = save_sfp(samp070, bhp_ranges_12_70, 0.895)\n", + "\n", + "# 075\n", + "samp075_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",711,20)\n", + "samp075_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",736,20)\n", + "samp075_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",777,20)\n", + "samp075_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",802,20)\n", + "samp075 = pd.concat([samp075_1,samp075_2,samp075_3,samp075_4], axis=0)\n", + "samp075 = save_sfp(samp075, bhp_ranges_12_75, 0.895)\n", + "\n", + "# 090\n", + "samp090_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",843,20)\n", + "samp090_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:G\",871,20)\n", + "samp090_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:V\",899,20)\n", + "samp090_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:G\",927,20)\n", + "samp090 = pd.concat([samp090_1,samp090_2,samp090_3,samp090_4], axis=0)\n", + "samp090 = save_sfp(samp090, bhp_ranges_12_90, 0.895)\n", + "\n", + "# 100\n", + "samp100_1 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:Q\",955,20)\n", + "samp100_2 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",980,20)\n", + "samp100_3 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:Q\",1023,20)\n", + "samp100_4 = loadData('./excel/48-50P-12PD-SM.xlsx',\"A:L\",1048,20)\n", + "samp100 = pd.concat([samp100_1,samp100_2,samp100_3,samp100_4], axis=0)\n", + "samp100 = save_sfp(samp100, bhp_ranges_12_100, 0.895)\n", + "\n", + "\n", + "final2 = pd.concat([samp030, samp035, samp040, samp050, samp055, samp060, samp070, samp075, samp090, samp100], axis=0)\n", + "file2_path = Path('./output/ready/48-50P-12PD-SD.csv')\n", + "final2.to_csv(file2_path, index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/performance_data_collection/data_collection_Carrier_SubCoolMode.ipynb b/DX_exploration/performance_data_collection/data_collection_Carrier_SubCoolMode.ipynb new file mode 100644 index 0000000..89d3508 --- /dev/null +++ b/DX_exploration/performance_data_collection/data_collection_Carrier_SubCoolMode.ipynb @@ -0,0 +1,887 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3db2b116", + "metadata": {}, + "outputs": [], + "source": [ + "######################### Carrier ##############################\n", + "########################## Notes #############################\n", + "# The 18 equipments' data include 'Bypass Factor' at each row.\n", + "# The variable we have is TC(Total capacity), SHC(Sensible Capacity), kW(power) and BF(bypass factor).\n", + "\n", + "# On page 34, they show the method to correct SHC, since The shown SHC in original excel is based on \n", + "# 80F edb temp of air entering coil. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15745d0e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n", + " \"class\": algorithms.Blowfish,\n" + ] + } + ], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "# not work when connect to PNNL VPN\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "from bisect import bisect_left\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8c8b8e9b", + "metadata": {}, + "outputs": [], + "source": [ + "# Correction Factor\n", + "def calculate_Factor(bf_read, factors):\n", + " bf05, bf10, bf20 = factors[0], factors[1], factors[2]\n", + " if bf_read == 0.05:\n", + " return bf05\n", + " elif bf_read == 0.10:\n", + " return bf10\n", + " elif bf_read == 0.20:\n", + " return bf20\n", + " elif bf_read < 0.05:\n", + " return bf05 - ((0.05 - bf_read) * (bf10 - bf05) / 0.05)\n", + " elif bf_read > 0.05 and bf_read < 0.10:\n", + " return bf05 + ((bf_read - 0.05) * (bf10 - bf05) / 0.05)\n", + " elif bf_read > 0.10 and bf_read < 0.20:\n", + " return bf10 + ((bf_read - 0.10) * (bf20 - bf10) / 0.1)\n", + " else:\n", + " return bf20 + ((bf_read - 0.2) * (bf20 - bf10) / 0.1)\n", + "\n", + "# 48-50A-21PD-SM\n", + "def correctionFactor_1(row):\n", + " factors = []\n", + " # get correction factor\n", + " if row['EADB'] in [79, 81]:\n", + " factors = [1.04, 0.98, 0.87]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [78, 82]:\n", + " factors = [2.07, 1.96, 1.74]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [77, 83]:\n", + " factors = [3.11, 2.94, 2.62]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [76, 84]:\n", + " factors = [4.14, 3.92, 3.49]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " elif row['EADB'] in [75, 85]:\n", + " factors = [4.18, 4.91, 4.36]\n", + " factor = calculate_Factor(row['BF'], factors)\n", + " else:\n", + " factor = 1.1 * (1 - row['BF']) * (row['EADB'] - 80)\n", + " \n", + " modified_row = row.copy()\n", + " \n", + " # modify 'SHC'\n", + " if row['EADB'] < 80:\n", + " modified_row['SHC'] = row['SHC'] - factor * row['airflow']\n", + " elif row['EADB'] > 80:\n", + " modified_row['SHC'] = row['SHC'] + factor * row['airflow']\n", + " return modified_row\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b6a8918b", + "metadata": {}, + "outputs": [], + "source": [ + "# Supply Fan Power\n", + "# esp\n", + "esp_ranges = [\n", + " (0, 28.8, 0.1),\n", + " (29, 42.5, 0.15),\n", + " (43, 70, 0.2),\n", + " (71, 105, 0.25),\n", + " (106, 134, 0.3),\n", + " (135, 210, 0.35),\n", + " (211, 280, 0.4),\n", + " (281, 350, 0.45),\n", + " (351, 400, 0.55),\n", + " (401, 500, 0.65),\n", + " (501, float('inf'), 0.75)\n", + "]\n", + "\n", + "def calculate_esp(TC):\n", + " TC = TC//1000\n", + " for lower, upper, esp in esp_ranges:\n", + " if lower <= TC <= upper:\n", + " return esp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "21a23fb9", + "metadata": {}, + "outputs": [], + "source": [ + "def loadData(name,cols,nHead,nRows,sheet_name):\n", + " ## Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name, header=int(nHead), \n", + " usecols=cols, skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " df = df.replace('—', np.nan)\n", + " \n", + " ## Rotate data\n", + " column_index_to_drop = [0]\n", + " rotate_df = df.drop(df.columns[column_index_to_drop], axis=1)\n", + " buffer_df = rotate_df.T\n", + " buffer_df.reset_index(inplace=True)\n", + " buffer_df.columns = buffer_df.iloc[0]\n", + " buffer_df = buffer_df.iloc[1:]\n", + " \n", + " ## Read EAWB\n", + " eawb_values = buffer_df.iloc[:5, 0]\n", + " eawb_values = eawb_values.tolist()\n", + " eawb_times = len(buffer_df)//5\n", + " eawb_list = eawb_values * eawb_times\n", + " buffer_df.rename(columns={'Unnamed: 1':'EAWB'}, inplace=True)\n", + " buffer_df['EAWB'] = eawb_list\n", + "\n", + " \n", + " ## Read cfm\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3), \n", + " usecols=cols, skiprows=None, nrows=1, engine=\"openpyxl\")\n", + " cfm_values = np.array(cfm)[0]\n", + " cfm_values = cfm_values.tolist()\n", + " cfm_list = [int(value) for value in cfm_values if not pd.isna(value)]\n", + " cfm_array = np.repeat(cfm_list,5).tolist()\n", + " buffer_df.insert(0, \"airflow\", np.nan)\n", + " buffer_df['airflow'] = cfm_array\n", + " \n", + " ## Modify the dataframe\n", + " num_chunk = len(buffer_df.columns)//4\n", + " if num_chunk <= 5:\n", + " chunk1 = buffer_df.iloc[:, :2]\n", + " chunk2 = buffer_df.iloc[:, 2:6]\n", + " chunk3 = buffer_df.iloc[:, 6:10]\n", + " chunk4 = buffer_df.iloc[:, 10:14]\n", + " chunk5 = buffer_df.iloc[:, 14:18]\n", + " chunk6 = buffer_df.iloc[:, 18:22]\n", + " result_df = pd.concat([chunk2, chunk3, chunk4, chunk5, chunk6], ignore_index=True)\n", + " chunk1_df = pd.concat([chunk1, chunk1, chunk1, chunk1, chunk1], ignore_index=True)\n", + " chunk1_df.reset_index(inplace=True)\n", + " else:\n", + " chunk1 = buffer_df.iloc[:, :2]\n", + " chunk2 = buffer_df.iloc[:, 2:6]\n", + " chunk3 = buffer_df.iloc[:, 6:10]\n", + " chunk4 = buffer_df.iloc[:, 10:14]\n", + " chunk5 = buffer_df.iloc[:, 14:18]\n", + " chunk6 = buffer_df.iloc[:, 18:22]\n", + " chunk7 = buffer_df.iloc[:, 22:26]\n", + " result_df = pd.concat([chunk2, chunk3, chunk4, chunk5, chunk6, chunk7], ignore_index=True)\n", + " chunk1_df = pd.concat([chunk1, chunk1, chunk1, chunk1, chunk1, chunk1], ignore_index=True)\n", + " chunk1_df.reset_index(inplace=True) \n", + " merged_df = pd.concat([chunk1_df, result_df], axis=1)\n", + " merged_df = merged_df.iloc[:, 1:]\n", + " \n", + " ## Read EADB\n", + " eadb_values = df.iloc[:, 0].tolist()\n", + " eadb_values = [int(value) for value in eadb_values if not pd.isna(value)]\n", + " eadb_times = 5 * len(cfm_list)\n", + " eadb_array = np.repeat(eadb_values,eadb_times).tolist()\n", + " merged_df['EADB'] = 80 # Standard Mode\n", + " merged_df['OADB'] = eadb_array\n", + " \n", + " ## Calculate RH\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " merged_df['RH'] = merged_df.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['EADB'] * units.degF, row['EAWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " ## Modify units\n", + " merged_df['TC'] = merged_df['TC']*1000\n", + " merged_df['SHC'] = merged_df['SHC']*1000\n", + "# ## Correct SHC\n", + "# merged_df = merged_df.apply(lambda row: correctionFactor_1(row), axis=1)\n", + " \n", + " ## Clean NA\n", + " merged_df = merged_df.dropna()\n", + " merged_df = merged_df.reset_index(drop=True)\n", + " \n", + " ## Supply fan power\n", + " merged_df.insert(0, \"SFP\", np.nan)\n", + " merged_df['TC'] = pd.to_numeric(merged_df['TC'], errors='coerce')\n", + " merged_df['ESP'] = merged_df['TC'].apply(calculate_esp)\n", + " ## organize\n", + "# # for test\n", + "# merged_df['S2T'] = merged_df['SHC']/merged_df['TC']\n", + "# new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'S2T', 'kW', 'airflow']\n", + "\n", + " new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'ESP', 'SFP', 'BF']\n", + " final = merged_df[new_cols]\n", + " \n", + " return final" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "21bc5ac5", + "metadata": {}, + "outputs": [], + "source": [ + "# 48-50A-21PD-FP\n", + "# excel function can do this\n", + "bhp_ranges_21_20 = [\n", + " (4000, 2.21, 2.38, 2.55, 2.71),\n", + " (5000, 2.71, 2.89, 3.08, 3.26),\n", + " (6000, 3.29, 3.5, 3.7, 3.9),\n", + " (7000, 3.96, 4.19, 4.41, 4.63),\n", + " (7500, 4.33, 4.56, 4.79, 5.02),\n", + " (8000, 4.73, 4.97, 5.21, 5.45),\n", + " (9000, 5.6, 5.85, 6.11, 6.37),\n", + " (10000, 6.58, 6.85, 7.13, 7.4)\n", + "]\n", + "\n", + "bhp_ranges_21_30 = [\n", + " (5000, 0.85, 1.03, 1.22, 1.42),\n", + " (6000, 1.26, 1.45, 1.65, 1.86),\n", + " (7000, 1.79, 1.98, 2.19, 2.41),\n", + " (8000, 2.44, 2.64, 2.85, 3.08),\n", + " (9000, 3.23, 3.43, 3.65, 3.88),\n", + " (10000, 4.15, 4.36, 4.58, 4.82),\n", + " (11000, 5.22, 5.44, 5.67, 5.91),\n", + " (12000, 6.45, 6.67, 6.90, 7.15),\n", + " (13000, 7.85, 8.07, 8.30, 8.55),\n", + " (14000, 9.41, 9.64, 9.88, 10.13),\n", + " (15000, 11.15, 11.38, 11.62, 11.88)\n", + "]\n", + "\n", + "bhp_ranges_21_35 = [\n", + " (7000, 1.96, 2.22, 2.47, 2.72),\n", + " (8000, 2.62, 2.89, 3.16, 3.43),\n", + " (9000, 3.39, 3.68, 3.97, 4.26),\n", + " (10000, 4.29, 4.61, 4.91, 5.22),\n", + " (10500, 4.80, 5.12, 5.43, 5.75),\n", + " (11000, 5.33, 5.66, 5.99, 6.31),\n", + " (12000, 6.52, 6.86, 7.21, 7.54),\n", + " (13000, 7.85, 8.21, 8.57, 8.92),\n", + " (14000, 9.33, 9.71, 10.09, 10.45),\n", + " (15000, 10.98, 11.37, 11.76, 12.15),\n", + " (16000, 12.79, 13.2, 13.6, 14),\n", + " (17000, 14.77, 15.19, 15.61, 16.03),\n", + " (17500, 15.83, 16.25, 16.68, 17.1)\n", + "]\n", + "\n", + "bhp_ranges_21_40 = [\n", + " (8000, 2.69, 3.08, 3.47, 3.86),\n", + " (9000, 3.53, 3.94, 4.36, 4.78),\n", + " (10000, 4.52, 4.96, 5.4, 5.84),\n", + " (11000, 5.68, 6.14, 6.6, 7.07),\n", + " (12000, 7.01, 7.49, 7.98, 8.47),\n", + " (13000, 8.53, 9.03, 9.54, 10.05),\n", + " (14000, 10.24, 10.76, 11.29, 11.82),\n", + " (15000, 12.15, 12.69, 13.24, 13.79),\n", + " (16000, 14.27, 14.83, 15.4, 15.97),\n", + " (17000, 16.61, 17.19, 17.77, 18.36),\n", + " (18000, 19.18, 19.77, 20.37, 20.98),\n", + " (19000, 21.98, 22.59, 23.21, 23.84),\n", + " (20000, 25.02, 25.65, 26.29, 26.93)\n", + "]\n", + "\n", + "bhp_ranges_21_50 = [\n", + " (10000, 4.62, 5.06, 5.5, 5.95),\n", + " (11000, 5.8, 6.26, 6.73, 7.2),\n", + " (12000, 7.15, 7.63, 8.12, 8.62),\n", + " (13000, 8.69, 9.19, 9.7, 10.22),\n", + " (14000, 10.43, 10.95, 11.48, 12.01),\n", + " (15000, 12.37, 12.91, 13.46, 14.01),\n", + " (16000, 14.52, 15.08, 15.65, 16.22),\n", + " (17000, 16.89, 17.48, 18.06, 18.65),\n", + " (18000, 19.5, 20.1, 20.71, 21.32),\n", + " (19000, 22.35, 22.96, 23.59, 24.21),\n", + " (20000, 25.43, 26.07, 26.71, 27.36)\n", + "]\n", + "\n", + "bhp_ranges_21_60 = [\n", + " (12000, 4.02, 4.71, 5.41, 6.12),\n", + " (14000, 5.74, 6.49, 7.24, 8.01),\n", + " (15000, 6.75, 7.53, 8.32, 9.11),\n", + " (16000, 7.88, 8.68, 9.5, 10.32),\n", + " (17000, 9.12, 9.95, 10.79, 11.64),\n", + " (18000, 10.48, 11.33, 12.2, 13.07),\n", + " (19000, 11.96, 12.84, 13.74, 14.64),\n", + " (20000, 13.57, 14.47, 15.4, 16.32),\n", + " (21000, 15.3, 16.24, 17.19, 18.14),\n", + " (22000, 17.18, 18.14, 19.11, 20.09),\n", + " (23000, 19.2, 20.18, 21.17, 22.17),\n", + " (24000, 21.35, 22.36, 23.38, 24.4),\n", + " (25000, 23.66, 24.68, 25.72, 26.78),\n", + " (26000, 26.11, 27.16, 28.23, 29.3),\n", + " (27000, 28.72, 29.79, 30.88, 31.97)\n", + "]\n", + "\n", + "# 48-50P-12PD-FP\n", + "bhp_ranges_12_30 = [\n", + " (6000, 0.59, 0.91, 1.27, 1.66),\n", + " (7500, 0.94, 1.28, 1.68, 2.11),\n", + " (9000, 1.46, 1.8, 2.22, 2.69),\n", + " (10500, 2.16, 2.52, 2.95, 3.43),\n", + " (12000, 3.08, 3.44, 3.89, 4.39),\n", + " (13500, 4.25, 4.62, 5.07, 5.58),\n", + " (15000, 5.69, 6.06, 6.52, 7.04)\n", + "]\n", + "\n", + "bhp_ranges_12_35 = [\n", + " (7000, 246, 301, 1.58, 398),\n", + " (8000, 266, 315, 1.92, 406),\n", + " (10000, 310, 350, 2.8, 427),\n", + " (12000, 357, 390, 4.06, 457),\n", + " (14000, 406, 435, 5.76, 492),\n", + " (15000, 430, 458, 6.8, 511)\n", + "]\n", + "\n", + "bhp_ranges_12_40 = [\n", + " (8000, 0.98, 1.33, 1.72, 2.14),\n", + " (10000, 1.67, 2.11, 2.55, 3.01),\n", + " (12000, 2.65, 3.18, 3.7, 4.23),\n", + " (14000, 3.96, 4.61, 5.22, 5.83),\n", + " (16000, 5.67, 6.44, 7.15, 7.85),\n", + " (18000, 7.84, 8.72, 9.55, 10.34),\n", + " (20000, 10.51, 11.51, 12.46, 13.36)\n", + "]\n", + "\n", + "bhp_ranges_12_50 = [\n", + " (9000, 1.34, 1.73, 2.15, 2.6),\n", + " (10000, 1.74, 2.17, 2.62, 3.09),\n", + " (12000, 2.76, 3.29, 3.81, 4.35),\n", + " (14000, 4.15, 4.79, 5.4, 6.01),\n", + " (16000, 5.96, 6.71, 7.42, 8.11),\n", + " (18000, 8.26, 9.12, 9.93, 10.72),\n", + " (20000, 11.1, 12.06, 12.99, 13.88)\n", + "]\n", + "\n", + "bhp_ranges_12_55 = [\n", + " (10000, 1.04, 1.5, 2.03, 2.66),\n", + " (12500, 1.69, 2.23, 2.78, 3.4),\n", + " (15000, 2.59, 3.23, 3.85, 4.51),\n", + " (17500, 3.78, 4.52, 5.24, 5.97),\n", + " (20000, 5.31, 6.15, 6.98, 7.79),\n", + " (22500, 7.23, 8.16, 9.09, 10),\n", + " (25000, 9.59, 10.6, 11.62, 12.64)\n", + "]\n", + "\n", + "bhp_ranges_12_60 = [\n", + " (12000, 1.54, 2.03, 2.57, 3.2),\n", + " (15000, 2.65, 3.27, 3.88, 4.53),\n", + " (18000, 4.22, 5, 5.73, 6.46),\n", + " (21000, 6.36, 7.29, 8.18, 9.04),\n", + " (24000, 9.19, 10.24, 11.29, 12.29),\n", + " (27000, 12.8, 13.93, 15.14, 16.3),\n", + " (30000, 17.29, 18.5, 19.82, 21.15)\n", + "]\n", + "\n", + "bhp_ranges_12_70 = [\n", + " (14000, 2.23, 2.8, 3.38, 4.02),\n", + " (17500, 3.92, 4.67, 5.39, 6.1),\n", + " (21000, 6.36, 7.29, 8.18, 9.04),\n", + " (24500, 9.74, 10.8, 11.88, 12.91),\n", + " (28000, 14.18, 15.35, 16.6, 17.82),\n", + " (30000, 17.29, 18.5, 19.82, 21.15)\n", + "]\n", + "\n", + "bhp_ranges_12_75 = [\n", + " (15000, 3.02, 3.91, 4.83, 5.8),\n", + " (16000, 3.52, 4.45, 5.41, 6.42),\n", + " (18000, 4.68, 5.71, 6.76, 7.84),\n", + " (20000, 6.08, 7.22, 8.36, 9.52),\n", + " (22000, 7.75, 9, 10.24, 11.49),\n", + " (24000, 9.71, 11.07, 12.41, 13.75),\n", + " (26000, 11.99, 13.46, 14.9, 16.34),\n", + " (28000, 14.61, 16.18, 17.73, 19.28),\n", + " (30000, 17.6, 19.28, 20.93, 22.57)\n", + "]\n", + "\n", + "bhp_ranges_12_90 = [\n", + " (16000, 3.27, 4.06, 4.88, 5.74),\n", + " (18000, 4.42, 5.31, 6.21, 7.14),\n", + " (20000, 5.83, 6.82, 7.81, 8.82),\n", + " (22000, 7.53, 8.62, 9.7, 10.8),\n", + " (24000, 9.55, 10.75, 11.93, 13.11),\n", + " (26000, 11.91, 13.22, 14.5, 15.78),\n", + " (28000, 14.65, 16.06, 17.45, 18.83),\n", + " (30000, 17.78, 19.31, 20.8, 22.28),\n", + " (32000, 21.36, 22.99, 24.59, 26.17),\n", + " (34000, 25.39, 27.13, 28.84, 30.53)\n", + "]\n", + "\n", + "bhp_ranges_12_100 = [\n", + " (20000, 5.83, 6.82, 7.81, 8.82),\n", + " (22000, 7.53, 8.62, 9.7, 10.8),\n", + " (24000, 9.55, 10.75, 11.93, 13.11),\n", + " (26000, 11.91, 13.22, 14.5, 15.78),\n", + " (28000, 14.65, 16.06, 17.45, 18.83),\n", + " (30000, 17.78, 19.31, 20.8, 22.28),\n", + " (32000, 21.36, 22.99, 24.59, 26.17),\n", + " (34000, 25.39, 27.13, 28.84, 30.53),\n", + " (36000, 29.92, 31.77, 33.58, 35.38),\n", + " (38000, 34.96, 36.91, 38.85, 40.74),\n", + " (40000, 40.54, 42.61, 44.65, 46.66)\n", + "]\n", + "\n", + "def get_scaled_tuple(airflow, bhp_ranges):\n", + " airflow = int(airflow)\n", + " cfm_values = [t[0] for t in bhp_ranges]\n", + " idx = bisect_left(cfm_values, airflow)\n", + " \n", + " if idx == 0:\n", + " return bhp_ranges[0]\n", + " elif idx == len(bhp_ranges):\n", + " return bhp_ranges[-1]\n", + " \n", + " lower_tuple = bhp_ranges[idx - 1]\n", + " upper_tuple = bhp_ranges[idx]\n", + " \n", + " scale_factor = (airflow - lower_tuple[0]) / (upper_tuple[0] - lower_tuple[0])\n", + " \n", + " scaled_values = [round((1 - scale_factor) * l + scale_factor * u, 2) for l, u in zip(lower_tuple[1:], upper_tuple[1:])]\n", + " \n", + " scaled_tuple = (airflow, *scaled_values)\n", + " return scaled_tuple\n", + "\n", + "def find_matching_tuple(airflow, bhp_ranges):\n", + " for tpl in bhp_ranges:\n", + " if tpl[0] == airflow:\n", + " return tpl\n", + " return get_scaled_tuple(airflow, bhp_ranges)\n", + "\n", + "def calculate_custom_bhp(cfm, esp, bhp_ranges):\n", + " result_tuple = find_matching_tuple(cfm, bhp_ranges)\n", + " esp02, esp04, esp06, esp08 = result_tuple[1], result_tuple[2], result_tuple[3], result_tuple[4]\n", + " \n", + " if esp == 0.2:\n", + " return esp02\n", + " elif esp == 0.4:\n", + " return esp04\n", + " elif esp == 0.6:\n", + " return esp06\n", + " elif esp >= 0.8:\n", + " return esp08\n", + " elif esp < 0.2:\n", + " return esp02 - ((0.2 - esp) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.2 and esp < 0.4:\n", + " return esp02 + ((esp - 0.2) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.4 and esp < 0.6:\n", + " return esp04 + ((esp - 0.4) * (esp06 - esp04) / 0.2)\n", + " elif esp > 0.6 and esp < 0.8:\n", + " return esp06 + ((esp - 0.6) * (esp08 - esp06) / 0.2)\n", + "\n", + "def save_sfp(df,bhp_ranges,eff):\n", + " df['BHP'] = df.apply(lambda row: calculate_custom_bhp(row['airflow'], row['ESP'], bhp_ranges), axis=1)\n", + " df['SFP'] = df['BHP']*746/eff\n", + " new_cols = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'SFP', 'BF']\n", + " final = df[new_cols]\n", + " return final" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cd792d5a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# 48-50A-21PD-SubCool\n", + "\n", + "# column_names = ['OADB', 'EADB', 'EAWB', 'RH', 'TC', 'SHC', 'kW', 'airflow', 'ESP', 'SFP']\n", + "# gap = pd.DataFrame(columns=column_names)\n", + "# new_row = pd.Series(['-------------'] * len(column_names), index=column_names)\n", + "# gap = gap.append(new_row, ignore_index=True)\n", + "\n", + "# 020\n", + "test020_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 1\")\n", + "test020_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 1\")\n", + "test020 = pd.concat([test020_1,test020_2], axis=0)\n", + "test020 = save_sfp(test020, bhp_ranges_21_20, 0.895)\n", + "\n", + "# 025\n", + "test025_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 2\")\n", + "test025_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 2\")\n", + "test025 = pd.concat([test025_1,test025_2], axis=0)\n", + "test025 = save_sfp(test025, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 027\n", + "test027_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 3\")\n", + "test027_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 3\")\n", + "test027 = pd.concat([test027_1,test027_2], axis=0)\n", + "test027 = save_sfp(test027, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 030\n", + "test030_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 4\")\n", + "test030_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 4\")\n", + "test030 = pd.concat([test030_1,test030_2], axis=0)\n", + "test030 = save_sfp(test030, bhp_ranges_21_30, 0.895)\n", + "\n", + "# 035\n", + "test035_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 5\")\n", + "test035_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 5\")\n", + "test035 = pd.concat([test035_1,test035_2], axis=0)\n", + "test035 = save_sfp(test035, bhp_ranges_21_35, 0.895)\n", + "\n", + "# 040\n", + "test040_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 6\")\n", + "test040_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 6\")\n", + "test040 = pd.concat([test040_1,test040_2], axis=0)\n", + "test040 = save_sfp(test040, bhp_ranges_21_40, 0.895)\n", + "\n", + "# 050\n", + "test050_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 7\")\n", + "test050_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 7\")\n", + "test050 = pd.concat([test050_1,test050_2], axis=0)\n", + "test050 = save_sfp(test050, bhp_ranges_21_50, 0.895)\n", + "\n", + "# 060\n", + "test060_1 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:V\",8,20, \"page 8\")\n", + "test060_2 = loadData('./excel/48-50A-21PD-SubCool.xlsx',\"A:Q\",34,20, \"page 8\")\n", + "test060 = pd.concat([test060_1,test060_2], axis=0)\n", + "test060 = save_sfp(test060, bhp_ranges_21_60, 0.895)\n", + "\n", + "\n", + "final = pd.concat([test020,test025,test027,test030, test035, test040, test050, test060], axis=0)\n", + "file_path = Path('./output/ready/48-50A-21PD-SubCool.csv')\n", + "final.to_csv(file_path, index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "68498c38", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_12012\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# 48-50P-12PD-SubCool\n", + "\n", + "# 030\n", + "# Standard Capacity Coil\n", + "samp030_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 1\")\n", + "samp030_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 1\")\n", + "# High-Capacity Coil\n", + "samp030_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 2\")\n", + "samp030_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 2\")\n", + "\n", + "samp030 = pd.concat([samp030_1,samp030_2,samp030_3,samp030_4], axis=0)\n", + "samp030 = save_sfp(samp030, bhp_ranges_12_30, 0.895)\n", + "\n", + "# 035 Standard Capacity Coil\n", + "samp035_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 3\")\n", + "samp035_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 3\")\n", + "samp035 = pd.concat([samp035_1,samp035_2], axis=0)\n", + "samp035 = save_sfp(samp035, bhp_ranges_12_35, 0.895)\n", + "\n", + "# 040 \n", + "# Standard Capacity Coil\n", + "samp040_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 4\")\n", + "samp040_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 4\")\n", + "# High-Capacity Coil\n", + "samp040_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 5\")\n", + "samp040_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",34,20, \"page 5\")\n", + "\n", + "samp040 = pd.concat([samp040_1,samp040_2,samp040_3,samp040_4], axis=0)\n", + "samp040 = save_sfp(samp040, bhp_ranges_12_40, 0.895)\n", + "\n", + "\n", + "# 050\n", + "# Standard Capacity Coil\n", + "samp050_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 6\")\n", + "samp050_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 6\")\n", + "# High-Capacity Coil\n", + "samp050_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 7\")\n", + "samp050_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 7\")\n", + "\n", + "samp050 = pd.concat([samp050_1,samp050_2,samp050_3,samp050_4], axis=0)\n", + "samp050 = save_sfp(samp050, bhp_ranges_12_50, 0.895)\n", + " \n", + "# 055\n", + "# Standard Capacity Coil\n", + "samp055_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 8\")\n", + "samp055_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 8\")\n", + "# High-Capacity Coil\n", + "samp055_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 9\")\n", + "samp055_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 9\")\n", + "\n", + "samp055 = pd.concat([samp055_1,samp055_2,samp055_3,samp055_4], axis=0)\n", + "samp055 = save_sfp(samp055, bhp_ranges_12_55, 0.895)\n", + "\n", + "# 060\n", + "# Standard Capacity Coil\n", + "samp060_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 10\")\n", + "samp060_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 10\")\n", + "# High-Capacity Coil\n", + "samp060_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 11\")\n", + "samp060_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 11\")\n", + "\n", + "samp060 = pd.concat([samp060_1,samp060_2,samp060_3,samp060_4], axis=0)\n", + "samp060 = save_sfp(samp060, bhp_ranges_12_60, 0.895)\n", + "\n", + "# 070\n", + "# Standard Capacity Coil\n", + "samp070_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 12\")\n", + "samp070_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 12\")\n", + "# High-Capacity Coil\n", + "samp070_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 13\")\n", + "samp070_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 13\")\n", + "\n", + "samp070 = pd.concat([samp070_1,samp070_2,samp070_3,samp070_4], axis=0)\n", + "samp070 = save_sfp(samp070, bhp_ranges_12_70, 0.895)\n", + "\n", + "# 075\n", + "# Standard Capacity Coil\n", + "samp075_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 14\")\n", + "samp075_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 14\")\n", + "# High-Capacity Coil\n", + "samp075_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 15\")\n", + "samp075_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 15\")\n", + "\n", + "samp075 = pd.concat([samp075_1,samp075_2,samp075_3,samp075_4], axis=0)\n", + "samp075 = save_sfp(samp075, bhp_ranges_12_75, 0.895)\n", + "\n", + "# 090\n", + "# Standard Capacity Coil\n", + "samp090_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 16\")\n", + "samp090_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 16\")\n", + "# High-Capacity Coil\n", + "samp090_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 17\")\n", + "samp090_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 17\")\n", + "samp090 = pd.concat([samp090_1,samp090_2,samp090_3,samp090_4], axis=0)\n", + "samp090 = save_sfp(samp090, bhp_ranges_12_90, 0.895)\n", + "\n", + "# 100\n", + "# Standard Capacity Coil\n", + "samp100_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",8,20, \"page 18\")\n", + "samp100_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 18\")\n", + "# High-Capacity Coil\n", + "samp100_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",8,20, \"page 19\")\n", + "samp100_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 19\")\n", + "samp100 = pd.concat([samp100_1,samp100_2,samp100_3,samp100_4], axis=0)\n", + "samp100 = save_sfp(samp100, bhp_ranges_12_100, 0.895)\n", + "\n", + "\n", + "final2 = pd.concat([samp030, samp035, samp040, samp050, samp055, samp060, samp070, samp075, samp090, samp100], axis=0)\n", + "file2_path = Path('./output/ready/48-50P-12PD-SubCool.csv')\n", + "final2.to_csv(file2_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "eaf964c1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20548\\1963626013.py:25: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " cfm = pd.read_excel(str(name), sheet_name, header=int(nHead-3),\n" + ] + } + ], + "source": [ + "# 050\n", + "# # Standard Capacity Coil\n", + "# samp050_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 6\")\n", + "# samp050_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 6\")\n", + "# High-Capacity Coil\n", + "samp050_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 7\")\n", + "samp050_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 7\")\n", + "\n", + "# samp050 = pd.concat([samp050_1,samp050_2,samp050_3,samp050_4], axis=0)\n", + "samp050_h = pd.concat([samp050_3,samp050_4], axis=0)\n", + "samp050_h = save_sfp(samp050_h, bhp_ranges_12_50, 0.895)\n", + "samp050_h.to_csv('./output/12PD-SubCool-50_High.csv', index=False)\n", + " \n", + "# 070\n", + "# Standard Capacity Coil\n", + "samp070_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 12\")\n", + "samp070_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 12\")\n", + "# High-Capacity Coil\n", + "samp070_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 13\")\n", + "samp070_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 13\")\n", + "\n", + "samp070 = pd.concat([samp070_1,samp070_2,samp070_3,samp070_4], axis=0)\n", + "samp070 = save_sfp(samp070, bhp_ranges_12_70, 0.895)\n", + "\n", + "# 075\n", + "# Standard Capacity Coil\n", + "samp075_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 14\")\n", + "samp075_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 14\")\n", + "# High-Capacity Coil\n", + "samp075_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 15\")\n", + "samp075_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 15\")\n", + "\n", + "samp075 = pd.concat([samp075_1,samp075_2,samp075_3,samp075_4], axis=0)\n", + "samp075 = save_sfp(samp075, bhp_ranges_12_75, 0.895)\n", + "\n", + "# 090\n", + "# Standard Capacity Coil\n", + "samp090_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 16\")\n", + "samp090_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 16\")\n", + "# High-Capacity Coil\n", + "samp090_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:V\",8,20, \"page 17\")\n", + "samp090_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:G\",34,20, \"page 17\")\n", + "samp090 = pd.concat([samp090_1,samp090_2,samp090_3,samp090_4], axis=0)\n", + "samp090 = save_sfp(samp090, bhp_ranges_12_90, 0.895)\n", + "\n", + "# 100\n", + "# Standard Capacity Coil\n", + "samp100_1 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",8,20, \"page 18\")\n", + "samp100_2 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 18\")\n", + "# High-Capacity Coil\n", + "samp100_3 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:Q\",8,20, \"page 19\")\n", + "samp100_4 = loadData('./excel/48-50P-12PD-SubCool.xlsx',\"A:L\",34,20, \"page 19\")\n", + "samp100 = pd.concat([samp100_1,samp100_2,samp100_3,samp100_4], axis=0)\n", + "samp100 = save_sfp(samp100, bhp_ranges_12_100, 0.895)\n", + "\n", + "\n", + "final2 = pd.concat([samp070, samp075, samp090, samp100], axis=0)\n", + "file2_path = Path('./output/12PD-SubCool-after_70.csv')\n", + "final2.to_csv(file2_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65e2eb7a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/performance_data_collection/data_collection_DAIKIN_updated.ipynb b/DX_exploration/performance_data_collection/data_collection_DAIKIN_updated.ipynb new file mode 100644 index 0000000..fcb1407 --- /dev/null +++ b/DX_exploration/performance_data_collection/data_collection_DAIKIN_updated.ipynb @@ -0,0 +1,379 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "51bda912", + "metadata": {}, + "outputs": [], + "source": [ + "######################### DAIKIN #############################\n", + "########################## Notes #############################\n", + "# The 10 equipments' data don't include 'Bypass Factor' at \n", + "# each row, but include discharge air dry bulb temp.\n", + "# Issue to be soloved: The formula requires the apparatus \n", + "# dew point temperature. Use \"bypass_adp.ipynb\" to calculate." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "\n", + "# not work when connect to PNNL VPN" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ebb23de5-7820-4765-9b7e-450eadffbd92", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:19:25.921482Z", + "iopub.status.busy": "2023-08-02T05:19:25.921168Z", + "iopub.status.idle": "2023-08-02T05:19:26.176161Z", + "shell.execute_reply": "2023-08-02T05:19:26.175142Z", + "shell.execute_reply.started": "2023-08-02T05:19:25.921460Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "\n", + "def loadData(name,nHead,nRows):\n", + " # Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=int(nHead), \n", + " usecols=\"C:AC\", skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " # buffer dataframe\n", + " column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + " dx = pd.DataFrame(columns=column_names)\n", + " \n", + " return df, dx\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e5be0205-54ed-4f3a-bd54-1a037b784310", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:27:56.856295Z", + "iopub.status.busy": "2023-08-02T05:27:56.855944Z", + "iopub.status.idle": "2023-08-02T05:27:56.859895Z", + "shell.execute_reply": "2023-08-02T05:27:56.859026Z", + "shell.execute_reply.started": "2023-08-02T05:27:56.856272Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def processData1(df,dx):\n", + " ## MBH, S2T, kW\n", + " # ss-drc7\n", + " rows_to_select = [0,1,3,2] # index of rows to select\n", + " names_to_select = ['MBH','S2T','kW','dT']\n", + " \n", + " for i in range(len(rows_to_select)):\n", + " df_temp = df[rows_to_select[i]::7] # internals\n", + " list_2d = []\n", + " for col in df_temp.columns[3:]:\n", + " list_2d.append(df_temp[col].to_list())\n", + " list_1d = []\n", + " for sublist in list_2d:\n", + " for item in sublist:\n", + " list_1d.append(item) \n", + " dx[names_to_select[i]] = list_1d\n", + "\n", + " ## Define the 4-element array to repeat\n", + " # IDB, airflow, iwb\n", + " idb_unique = np.unique(df.iloc[:,0].values)\n", + " airflow_unique = np.unique(df.iloc[:,1].values)\n", + " idb_array = np.repeat(idb_unique, len(airflow_unique))\n", + " airflow_array = np.tile(airflow_unique, len(idb_unique))\n", + " iwb_unique = df.columns[3:7]\n", + " iwb_array = np.repeat(iwb_unique, len(airflow_unique)*len(idb_unique))\n", + " # OADB\n", + " oat_unique = np.array([65,75,85,95,105,115])\n", + " oat_array = np.repeat(oat_unique, 24)\n", + "\n", + " ## Calculate how many times the array has to be repeated and create the repeated array\n", + " idb_repeats = len(dx) // len(idb_array) + (len(dx) % len(idb_array) > 0)\n", + " idb_repeated_array = np.tile(idb_array, idb_repeats)\n", + " airflow_repeated_array = np.tile(airflow_array, idb_repeats)\n", + " iwb_repeats = len(dx) // len(iwb_array) + (len(dx) % len(iwb_array) > 0)\n", + " iwb_repeated_array = np.tile(iwb_array, iwb_repeats)\n", + " oat_repeated_array = np.tile(oat_array, 1)\n", + "\n", + " ## Slice the repeated array to match the DataFrame's length and assign it to a column\n", + " dx['IDB'] = idb_repeated_array[:len(dx)]\n", + " dx['airflow'] = airflow_repeated_array[:len(dx)]\n", + " dx['IWB'] = iwb_repeated_array[:len(dx)]\n", + " dx['OADB'] = oat_repeated_array[:len(dx)]\n", + " ## replace \"-\" with NA\n", + " dx.replace('-', np.nan, inplace=True)\n", + "\n", + " # Sensible MBH\n", + " dx['MBH'] = dx['MBH']*1000\n", + " dx['Sensible MBH'] = dx['MBH']*dx['S2T']\n", + "\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " dx['RH'] = dx.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " # outlet T\n", + " dx['Out DB'] = dx['IDB']-dx['dT']\n", + " \n", + " return dx\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "5fa0a903", + "metadata": {}, + "outputs": [], + "source": [ + "def processData2(df,dx):\n", + " ## MBH, S2T, kW\n", + " # \n", + " rows_to_select = [0,1,5,2] # index of rows to select\n", + " names_to_select = ['Capacity','S/T','TotalPower','Evap dT']\n", + " \n", + " for i in range(len(rows_to_select)):\n", + " df_temp = df[rows_to_select[i]::6] # internals\n", + " list_2d = []\n", + " for col in df_temp.columns[3:]:\n", + " list_2d.append(df_temp[col].to_list())\n", + " list_1d = []\n", + " for sublist in list_2d:\n", + " for item in sublist:\n", + " list_1d.append(item) \n", + " dx[names_to_select[i]] = list_1d\n", + "\n", + " ## Define the 4-element array to repeat\n", + " # IDB, airflow, iwb\n", + " idb_unique = np.unique(df.iloc[:,0].values)\n", + " airflow_unique = np.unique(df.iloc[:,1].values)\n", + " idb_array = np.repeat(idb_unique, len(airflow_unique))\n", + " airflow_array = np.tile(airflow_unique, len(idb_unique))\n", + " iwb_unique = df.columns[3:7]\n", + " iwb_array = np.repeat(iwb_unique, len(airflow_unique)*len(idb_unique))\n", + " # OADB\n", + " oat_unique = np.array([65,75,85,95,105,115])\n", + " oat_array = np.repeat(oat_unique, 24)\n", + "\n", + " ## Calculate how many times the array has to be repeated and create the repeated array\n", + " idb_repeats = len(dx) // len(idb_array) + (len(dx) % len(idb_array) > 0)\n", + " idb_repeated_array = np.tile(idb_array, idb_repeats)\n", + " airflow_repeated_array = np.tile(airflow_array, idb_repeats)\n", + " iwb_repeats = len(dx) // len(iwb_array) + (len(dx) % len(iwb_array) > 0)\n", + " iwb_repeated_array = np.tile(iwb_array, iwb_repeats)\n", + " oat_repeated_array = np.tile(oat_array, 1)\n", + "\n", + " ## Slice the repeated array to match the DataFrame's length and assign it to a column\n", + " dx['IDB'] = idb_repeated_array[:len(dx)]\n", + " dx['airflow'] = airflow_repeated_array[:len(dx)]\n", + " dx['IWB'] = iwb_repeated_array[:len(dx)]\n", + " dx['OADB'] = oat_repeated_array[:len(dx)]\n", + " ## replace \"-\" with NA\n", + " dx.replace('-', np.nan, inplace=True)\n", + " dx.replace('--', np.nan, inplace=True)\n", + "\n", + " # Sensible MBH\n", + " dx['Sensible Capacity'] = dx['Capacity']*dx['S/T']\n", + "\n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " dx['RH'] = dx.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " # outlet T\n", + " dx['Out DB'] = pd.to_numeric(dx['IDB'])-pd.to_numeric(dx['Evap dT'])\n", + " \n", + " return dx\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c29450ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OADB IDB IWB MBH S2T kW airflow Capacity S/T TotalPower \\\n", + "0 65 70 59 NaN NaN NaN 4500 175388.0 0.59 10925.0 \n", + "1 65 70 59 NaN NaN NaN 5010 177290.0 0.63 10984.0 \n", + "2 65 70 59 NaN NaN NaN 6000 181904.0 0.67 11078.0 \n", + "3 65 75 59 NaN NaN NaN 4500 175490.0 0.71 10916.0 \n", + "4 65 75 59 NaN NaN NaN 5010 177392.0 0.75 10975.0 \n", + ".. ... ... ... ... ... .. ... ... ... ... \n", + "139 115 70 71 NaN NaN NaN 5010 NaN NaN NaN \n", + "140 115 70 71 NaN NaN NaN 6000 NaN NaN NaN \n", + "141 115 75 71 NaN NaN NaN 4500 158797.0 0.49 19876.0 \n", + "142 115 75 71 NaN NaN NaN 5010 160699.0 0.54 19935.0 \n", + "143 115 75 71 NaN NaN NaN 6000 165313.0 0.58 20029.0 \n", + "\n", + " Evap dT Sensible Capacity RH Out DB \n", + "0 20.37 103478.92 52.721762 49.63 \n", + "1 19.45 111692.70 52.721762 50.55 \n", + "2 17.97 121875.68 52.721762 52.03 \n", + "3 24.56 124597.90 38.629369 50.44 \n", + "4 23.64 133044.00 38.629369 51.36 \n", + ".. ... ... ... ... \n", + "139 NaN NaN 104.862130 NaN \n", + "140 NaN NaN 104.862130 NaN \n", + "141 16.28 77810.53 82.665887 58.72 \n", + "142 15.36 86777.46 82.665887 59.64 \n", + "143 13.88 95881.54 82.665887 61.12 \n", + "\n", + "[144 rows x 14 columns]\n" + ] + } + ], + "source": [ + "# headList = [4,46,90,132,176,218]\n", + "head = 4\n", + "df,dx = loadData('./excel/ss-dfc15.xlsx',head,36)\n", + "new = processData2(df,dx)\n", + "print(new)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "863f4081-79f4-4b3e-a181-7600ddf2f978", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:20:34.110616Z", + "iopub.status.busy": "2023-08-02T05:20:34.110324Z", + "iopub.status.idle": "2023-08-02T05:20:34.115545Z", + "shell.execute_reply": "2023-08-02T05:20:34.114819Z", + "shell.execute_reply.started": "2023-08-02T05:20:34.110593Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ss-drc7\n", + "headList = [4,54,102,152,200,250,298,348]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-drc7.xlsx',head,42)\n", + " new = processData1(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + " ## add on 10/06\n", + " # not drop NA because of the supply fan power\n", + " # buffer = buffer.dropna(subset=['dT']) # drop NA\n", + " \n", + " # rename and order\n", + " order = [\"OADB\",\"IDB\",\"IWB\",\"RH\", \"MBH\",\"Sensible MBH\",\"kW\",\"airflow\",\"Out DB\"]\n", + " buffer = buffer[order]\n", + " \n", + "filepath = Path('./output/ss-drc7.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "374ab7d0", + "metadata": {}, + "outputs": [], + "source": [ + "# ss-dfc15\n", + "headList = [4,46,90,132,176,218]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-dfc15.xlsx',head,36)\n", + " new = processData2(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + " ## add on 10/06\n", + " # rename and order\n", + " order = [\"OADB\",\"IDB\",\"IWB\",\"RH\", \"Capacity\",\"Sensible Capacity\",\"TotalPower\",\"airflow\",\"Out DB\"]\n", + " buffer = buffer[order]\n", + " \n", + "filepath = Path('./output/ss-dfc15.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "ad7525a3", + "metadata": {}, + "outputs": [], + "source": [ + "# ss-dfc5-d\n", + "headList = [4,46,90,132,176,218,262,304]\n", + "column_names = [\"OADB\",\"IDB\",\"IWB\",\"MBH\",\"S2T\",\"kW\",\"airflow\"]\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "for head in headList:\n", + " df,dx = loadData('./excel/ss-dfc5-d.xlsx',head,36)\n", + " new = processData2(df,dx)\n", + " buffer = pd.concat([buffer,new])\n", + " \n", + " ## add on 10/06\n", + " # rename and order\n", + " order = [\"OADB\",\"IDB\",\"IWB\",\"RH\", \"Capacity\",\"Sensible Capacity\",\"TotalPower\",\"airflow\",\"Out DB\"]\n", + " buffer = buffer[order]\n", + " \n", + "filepath = Path('./output/ss-dfc5-d.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f871b65e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DX_exploration/performance_data_collection/data_collection_LENNOX_updated.ipynb b/DX_exploration/performance_data_collection/data_collection_LENNOX_updated.ipynb new file mode 100644 index 0000000..c6e2d4f --- /dev/null +++ b/DX_exploration/performance_data_collection/data_collection_LENNOX_updated.ipynb @@ -0,0 +1,970 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "ca06acec", + "metadata": {}, + "outputs": [], + "source": [ + "######################### LENNOX #############################\n", + "########################## Notes #############################\n", + "# The 10 equipments' data don't include 'Bypass Factor' at \n", + "# each row, but include discharge air dry bulb temp.\n", + "# Issue to be soloved: The formula requires the apparatus \n", + "# dew point temperature. Use \"bypass_adp.ipynb\" to calculate." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e86f4b6", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip3 install metpy\n", + "# !pip3 install pathlib # instal if needed\n", + "\n", + "# not work when connect to PNNL VPN" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ebb23de5-7820-4765-9b7e-450eadffbd92", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:19:25.921482Z", + "iopub.status.busy": "2023-08-02T05:19:25.921168Z", + "iopub.status.idle": "2023-08-02T05:19:26.176161Z", + "shell.execute_reply": "2023-08-02T05:19:26.175142Z", + "shell.execute_reply.started": "2023-08-02T05:19:25.921460Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n", + " \"class\": algorithms.Blowfish,\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import metpy.calc as mcalc\n", + "from metpy.units import units\n", + "from pathlib import Path\n", + "\n", + "def loadData(name,nHead,nRows,drop_list):\n", + " # Load the data from a page in the excel file\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=int(nHead), \n", + " usecols=\"A:AA\", skiprows=None, nrows=int(nRows), engine=\"openpyxl\")\n", + " # clean the data\n", + " if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + " # fill the IWB (1st)\n", + " df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + " start_list = [0,3,6]\n", + " for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 3):\n", + " df.at[i, 'IWB'] = temp_value\n", + " \n", + " # add OADB column\n", + " df.insert(0, \"OADB\", np.nan)\n", + " \n", + " # modify the dataframe\n", + " chunk1 = df.iloc[:, :8]\n", + " chunk2 = df.iloc[:, 8:13]\n", + " chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + " chunk3 = df.iloc[:, 13:18]\n", + " chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + " chunk4 = df.iloc[:, 18:]\n", + " chunk4.rename(columns={\"kBtuh.3\": \"kBtuh\", \"kW.3\": \"kW\", \"75°F.3\": \"75°F\", \n", + " \"80°F.3\": \"80°F\", \"85°F.3\": \"85°F\"},inplace=True)\n", + " result_df = pd.concat([chunk1, chunk2, chunk3, chunk4], ignore_index=True)\n", + " \n", + " # fill the IWB (2nd)\n", + " iwb_values = result_df.iloc[:, 1].tolist()\n", + " iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + " iwb_array = iwb_values*4\n", + " result_df[\"IWB\"] = iwb_array\n", + " \n", + " # fill the cfm\n", + " cfm_values = result_df.iloc[:, 2].tolist()\n", + " cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + " cfm_array = cfm_values*4\n", + " result_df[\"cfm\"] = cfm_array\n", + " \n", + " # fill the OADB\n", + " buffer_header = int(nHead)-4\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + " OADB_values = np.array(df)[0]\n", + " OADB_values = OADB_values.tolist()\n", + " OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + " OADB_list = []\n", + " for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + " OADB_array = np.repeat(OADB_list,9)\n", + " result_df[\"OADB\"] = OADB_array\n", + " \n", + " return result_df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "423e6e37", + "metadata": {}, + "outputs": [], + "source": [ + "# supply fan power\n", + "esp_ranges = [\n", + " (0, 28.8, 0.1),\n", + " (29, 42.5, 0.15),\n", + " (43, 70, 0.2),\n", + " (71, 105, 0.25),\n", + " (106, 134, 0.3),\n", + " (135, 210, 0.35),\n", + " (211, 280, 0.4),\n", + " (281, 350, 0.45),\n", + " (351, 400, 0.55),\n", + " (401, 500, 0.65),\n", + " (501, float('inf'), 0.75)\n", + "]\n", + "\n", + "#######################################################################\n", + "\n", + "def calculate_esp(kBtuh):\n", + " for lower, upper, esp in esp_ranges:\n", + " if lower <= kBtuh <= upper:\n", + " return esp\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "19888047", + "metadata": {}, + "outputs": [], + "source": [ + "# (airflow, esp=0.2, esp=0.4, esp=0.6)\n", + "\n", + "#######################################################################\n", + "# 2305\n", + "bhp_ranges_2305 = [\n", + " (2750, 0.3, 0.5, 0.7),\n", + " (3000, 0.35, 0.55, 0.75),\n", + " (3250, 0.4, 0.6, 0.85),\n", + " (3500, 0.45, 0.7, 0.95),\n", + " (3750, 0.5, 0.75, 1.05),\n", + " (4000, 0.55, 0.85, 1.1),\n", + " (4250, 0.6, 0.9, 1.25),\n", + " (4500, 0.7, 1.0, 1.35),\n", + " (4750, 0.75, 1.1, 1.45),\n", + " (5000, 0.8, 1.25, 1.6),\n", + " (5250, 0.95, 1.35, 1.7),\n", + " (5500, 1.05, 1.45, 1.85),\n", + " (5750, 1.15, 1.6, 2),\n", + " (6000, 1.3, 1.75, 2.15),\n", + " (6250, 1.4, 1.9, 2.35),\n", + " (6500, 1.55, 2.05, 2.5),\n", + " (6750, 1.7, 2.2, 2.7),\n", + " (7000, 1.85, 2.35, 2.9),\n", + " (7250, 2, 2.6, 3.1),\n", + " (7500, 2.2, 2.75, 3.3),\n", + " (7750, 2.4, 3, 3.55),\n", + " (8000, 2.55, 3.2, 3.8),\n", + " (8250, 2.8, 3.4, 4),\n", + " (8500, 3, 3.65, 4.3),\n", + " (8750, 3.25, 3.9, 4.55),\n", + " (9000, 3.5, 4.2, 4.85),\n", + " (9250, 3.75, 4.45, 5.15),\n", + " (9500, 4, 4.75, 5.45),\n", + " (9750, 4.3, 5.05, 5.75),\n", + " (10000, 4.6, 5.4, 6.15),\n", + "]\n", + "\n", + "#######################################################################\n", + "# 2306\n", + "# watts here, 1 Brake Horsepower (bhp) = 745.69987158227 Watt\n", + "bhp_ranges_2306 = [\n", + " (750, 73, 100, 120),\n", + " (1000, 103, 150, 180),\n", + " (1250, 143, 200, 240),\n", + " (1550, 183, 248, 300),\n", + " (1750, 223, 298, 359),\n", + " (2000, 271, 345, 410),\n", + " (2250, 303, 391, 476),\n", + " (2500, 366, 476, 583),\n", + " (2750, 476, 601, 715),\n", + " (3000, 596, 718, 827),\n", + " (3250, 711, 827, 935),\n", + " (3500, 821, 940, 1060),\n", + " (3750, 949, 1081, 1221),\n", + " (4000, 1109, 1256, 1413),\n", + " (4250, 1298, 1458, 1625),\n", + " (4500, 1510, 1678, 1851),\n", + " (4750, 1738, 1910, 2083),\n", + " (5000, 1973, 2144, 2314),\n", + " (5250, 2205, 2373, 2538),\n", + " (5500, 2428, 2595, 2756),\n", + " (5750, 2643, 2809, 2976),\n", + " (6000, 2863, 3029, 3190)\n", + "]\n", + "\n", + "scaled_bhp_ranges = []\n", + "\n", + "for airflow, esp_02, esp_04, esp_06 in bhp_ranges_2306:\n", + " esp_02_scaled = esp_02 / 745.699871\n", + " esp_04_scaled = esp_04 / 745.699871\n", + " esp_06_scaled = esp_06 / 745.699871\n", + " scaled_bhp_ranges.append((airflow, esp_02_scaled, esp_04_scaled, esp_06_scaled))\n", + " \n", + "bhp_ranges_2306 = scaled_bhp_ranges\n", + "\n", + "#######################################################################\n", + "# 2303\n", + "bhp_ranges_2303 = [\n", + " (1200, 159, 200, 246),\n", + " (1500, 253, 308, 361),\n", + " (1600, 298, 354, 403),\n", + " (1700, 346, 399, 445),\n", + " (1800, 390, 442, 488),\n", + " (1900, 422, 478, 533),\n", + " (2000, 466, 530, 595),\n", + " (2100, 537, 606, 676),\n", + " (2200, 623, 694, 762),\n", + " (2300, 707, 776, 842)\n", + "]\n", + "\n", + "scaled_bhp_ranges_2303 = []\n", + "\n", + "for airflow, esp_02, esp_04, esp_06 in bhp_ranges_2303:\n", + " esp_02_scaled = esp_02 / 745.699871\n", + " esp_04_scaled = esp_04 / 745.699871\n", + " esp_06_scaled = esp_06 / 745.699871\n", + " scaled_bhp_ranges_2303.append((airflow, esp_02_scaled, esp_04_scaled, esp_06_scaled))\n", + " \n", + "bhp_ranges_2303 = scaled_bhp_ranges_2303\n", + "\n", + "#######################################################################\n", + "\n", + "def calculate_custom_bhp(cfm, esp, bhp_ranges):\n", + " closest_airflow = min(bhp_ranges, key=lambda x: abs(x[0] - cfm))\n", + " esp02, esp04, esp06 = closest_airflow[1], closest_airflow[2], closest_airflow[3]\n", + " if esp == 0.2:\n", + " return esp02\n", + " elif esp == 0.4:\n", + " return esp04\n", + " elif esp == 0.6:\n", + " return esp06\n", + " elif esp < 0.2:\n", + " return esp02 - ((0.2 - esp) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.2 and esp < 0.4:\n", + " return esp02 + ((esp - 0.2) * (esp04 - esp02) / 0.2)\n", + " elif esp > 0.4 and esp < 0.6:\n", + " return esp04 + ((esp - 0.4) * (esp06 - esp04) / 0.2)\n", + "\n", + "# print(bhp_ranges_2306)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e4dc2545", + "metadata": {}, + "outputs": [], + "source": [ + "def modifyData(df,bhp_ranges):\n", + " df.insert(1, \"IDB\", np.nan)\n", + " df.insert(5, \"S2T\", np.nan)\n", + " \n", + " # List to store repeated rows\n", + " repeated_rows = []\n", + " for index, row in df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + " result = pd.DataFrame(repeated_rows, columns=df.columns)\n", + " \n", + " # fill the S2T\n", + " result.reset_index(drop=True, inplace=True)\n", + " row_list = list(range(0, len(result), 3))\n", + " idb_list = []\n", + " for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + " result[\"S2T\"] = idb_list\n", + " \n", + " # fill the IDB\n", + " last_three_columns = result.columns[-3:].tolist()\n", + " temp_list = []\n", + " for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + " temp_times = len(result)//len(temp_list)\n", + " temp_list = temp_list*temp_times\n", + " result[\"IDB\"] = temp_list\n", + " result = result.iloc[:, :-3]\n", + " \n", + " # Sensible MBH\n", + " result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + " # The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + " # And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + " pressure = 101325 * units.pascal\n", + " # Convert Celsius to Kelvin and compute relative humidity\n", + " result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + " # units\n", + " result['Btuh'] = result['kBtuh']*1000\n", + " result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + " # around kBtuh for the next step\n", + " kBtuh_index = result.columns.get_loc('kBtuh')\n", + " kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + " kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + " result[\"kBtuh\"] = kBtuh_values\n", + " \n", + " # supply fan power\n", + " result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + " result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges), axis=1)\n", + " # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + " result['SFP'] = result['BHP']*746/0.895 \n", + " # 0.895 for ehb_lcm_cbox_2305,ehb_lcm_bbox_2306\n", + " \n", + " # organize\n", + " new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + " final = result[new_cols]\n", + " \n", + " \n", + " \n", + " return final\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "863f4081-79f4-4b3e-a181-7600ddf2f978", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T05:20:34.110616Z", + "iopub.status.busy": "2023-08-02T05:20:34.110324Z", + "iopub.status.idle": "2023-08-02T05:20:34.115545Z", + "shell.execute_reply": "2023-08-02T05:20:34.114819Z", + "shell.execute_reply.started": "2023-08-02T05:20:34.110593Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\3645567763.py:54: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_cbox_2305\n", + "drop_list = [3,7,9,15,23]\n", + "headList = [144,161,178,197,214,231,250,267,284,301,320,337,354,371,390,407,424,441]\n", + "column_names = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "\n", + "for head in headList:\n", + " df = loadData('./excel/ehb_lcm_cbox_2305.xlsx',head,9,drop_list)\n", + " result = modifyData(df,bhp_ranges_2305)\n", + " buffer = pd.concat([buffer,result])\n", + " \n", + "filepath = Path('./output/ehb_lcm_cbox_2305.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f871b65e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\3645567763.py:54: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_bbox_2306\n", + "drop_list = [3,8,11,19,23]\n", + "headList = [50,66,82,98,117,133,149,165]\n", + "column_names = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "buffer = pd.DataFrame(columns=column_names)\n", + "\n", + "for head in headList:\n", + " df = loadData('./excel/ehb_lcm_bbox_2306.xlsx',head,9,drop_list)\n", + " result = modifyData(df,bhp_ranges_2306)\n", + " buffer = pd.concat([buffer,result])\n", + " \n", + "filepath = Path('./output/ehb_lcm_bbox_2306.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b4cd0982", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\2126508735.py:4: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\2126508735.py:47: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\2126508735.py:126: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\2126508735.py:169: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "#######################################################################\n", + "# ehb_lcm_abox_2303_206\n", + "head = 206\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "buffer = pd.DataFrame(columns=new_cols)\n", + "buffer = pd.concat([buffer,final])\n", + "\n", + "#######################################################################\n", + "# ehb_lcm_abox_2303_229\n", + "head = 229\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "#######################################################################\n", + "# append to buffer\n", + "buffer = pd.concat([buffer,final])\n", + "buffer\n", + "\n", + "# output\n", + "filepath = Path('./output/ehb_lcm_abox_2303.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "25ae0039", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\2439467181.py:3: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head,\n", + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\2439467181.py:46: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# ehb_lcm_abox_2303_229\n", + "head = 229\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=head, \n", + " usecols=\"A:AA\", skiprows=None, nrows=18, engine=\"openpyxl\")\n", + "# clean the data\n", + "drop_list = [8,11,15]\n", + "if drop_list != []:\n", + " df = df.drop(columns=df.columns[drop_list])\n", + " \n", + "# fill the IWB (1st)\n", + "df.rename(columns={'Unnamed: 0':'IWB'}, inplace=True)\n", + "start_list = [0,6,12]\n", + "for index in start_list:\n", + " temp_value = df.at[index, 'IWB']\n", + " temp_value = temp_value.split('°')[0]\n", + " for i in range(index, index + 6):\n", + " df.at[i, 'IWB'] = temp_value\n", + "\n", + "# add OADB column\n", + "df.insert(0, \"OADB\", np.nan)\n", + " \n", + "# modify the dataframe\n", + "chunk1 = df.iloc[:, :8]\n", + "chunk2 = df.iloc[:, 8:13]\n", + "chunk2.rename(columns={\"kBtuh.1\": \"kBtuh\", \"kW.1\": \"kW\", \"75°F.1\": \"75°F\", \n", + " \"80°F.1\": \"80°F\", \"85°F.1\": \"85°F\"},inplace=True)\n", + "chunk3 = df.iloc[:, 13:18]\n", + "chunk3.rename(columns={\"kBtuh.2\": \"kBtuh\", \"kW.2\": \"kW\", \"75°F.2\": \"75°F\", \n", + " \"80°F.2\": \"80°F\", \"85°F.2\": \"85°F\"},inplace=True)\n", + "result_df = pd.concat([chunk1, chunk2, chunk3], ignore_index=True)\n", + "\n", + "# fill the IWB (2nd)\n", + "iwb_values = result_df.iloc[:, 1].tolist()\n", + "iwb_values = [int(value) for value in iwb_values if not pd.isna(value)]\n", + "iwb_array = iwb_values*3\n", + "result_df[\"IWB\"] = iwb_array\n", + " \n", + "# fill the cfm\n", + "cfm_values = result_df.iloc[:, 2].tolist()\n", + "cfm_values = [int(value) for value in cfm_values if not pd.isna(value)]\n", + "cfm_array = cfm_values*3\n", + "result_df[\"cfm\"] = cfm_array\n", + " \n", + "# # fill the OADB\n", + "buffer_header = head-4\n", + "df = pd.read_excel('./excel/ehb_lcm_abox_2303.xlsx', sheet_name='Table 1', header=buffer_header, \n", + " usecols=\"A:AA\", skiprows=None, nrows=2, engine=\"openpyxl\")\n", + "OADB_values = np.array(df)[0]\n", + "OADB_values = OADB_values.tolist()\n", + "OADB_values = [value for value in OADB_values if not pd.isna(value)]\n", + "\n", + "OADB_list = []\n", + "for item in OADB_values:\n", + " item = int(item.split('°')[0])\n", + " OADB_list.append(item)\n", + "OADB_array = np.repeat(OADB_list,18)\n", + "result_df[\"OADB\"] = OADB_array\n", + "\n", + "result_df.insert(1, \"IDB\", np.nan)\n", + "result_df.insert(5, \"S2T\", np.nan)\n", + " \n", + "# List to store repeated rows\n", + "repeated_rows = []\n", + "for index, row in result_df.iterrows():\n", + " repeated_rows.extend([row] * 3)\n", + "result = pd.DataFrame(repeated_rows, columns=result_df.columns)\n", + "\n", + "# fill the S2T\n", + "result.reset_index(drop=True, inplace=True)\n", + "row_list = list(range(0, len(result), 3))\n", + "idb_list = []\n", + "for row in row_list:\n", + " idb_unique = result.iloc[row,7:10].values.tolist()\n", + " idb_list.extend(idb_unique)\n", + "result[\"S2T\"] = idb_list\n", + " \n", + "# fill the IDB\n", + "last_three_columns = result.columns[-3:].tolist()\n", + "temp_list = []\n", + "for item in last_three_columns:\n", + " item = int(item.split('°')[0])\n", + " temp_list.append(item)\n", + "temp_times = len(result)//len(temp_list)\n", + "temp_list = temp_list*temp_times\n", + "result[\"IDB\"] = temp_list\n", + "result = result.iloc[:, :-3]\n", + "result = result[result['kBtuh'] != \"- - -\"]\n", + "\n", + "# Sensible MBH\n", + "result['Sensible MBH'] = result['kBtuh']*result['S2T']\n", + " \n", + "# The relative_humidity_wet_psychrometric function requires temperature and wet bulb temperature in Kelvin,\n", + "# And pressure in Pascals, we assume standard pressure at sea level: 101325 Pa\n", + "pressure = 101325 * units.pascal\n", + "# Convert Celsius to Kelvin and compute relative humidity\n", + "result['RH'] = result.apply(lambda row: mcalc.relative_humidity_wet_psychrometric(pressure,row['IDB'] * units.degF, row['IWB'] * units.degF).to('percent').magnitude, axis=1)\n", + " \n", + "# units\n", + "result['Btuh'] = result['kBtuh']*1000\n", + "result['Sensible Btuh'] = result['Sensible MBH']*1000\n", + " \n", + "# around kBtuh for the next step\n", + "kBtuh_index = result.columns.get_loc('kBtuh')\n", + "kBtuh_values = result.iloc[:, kBtuh_index].tolist()\n", + "kBtuh_values = [np.rint(value) for value in kBtuh_values]\n", + "result[\"kBtuh\"] = kBtuh_values\n", + "\n", + "# supply fan power\n", + "result['ESP'] = result['kBtuh'].apply(calculate_esp)\n", + "result['BHP'] = result.apply(lambda row: calculate_custom_bhp(row['cfm'], row['ESP'], bhp_ranges_2303), axis=1)\n", + "# # bhp_ranges_2305, bhp_ranges_2306, bhp_ranges_2303\n", + "result['SFP'] = result['BHP']*746/0.865 \n", + "# # 0.865 for ehb_lcm_abox_2303\n", + " \n", + "# organize\n", + "new_cols = ['OADB', 'IDB', 'IWB', 'RH', 'Btuh', 'Sensible Btuh', 'kW', 'cfm', 'SFP']\n", + "final = result[new_cols]\n", + "\n", + "# append to buffer\n", + "buffer = pd.concat([buffer,final])\n", + "buffer\n", + "\n", + "# output\n", + "filepath = Path('./output/ehb_lcm_abox_2303.csv')\n", + "filepath.parent.mkdir(parents=True, exist_ok=True) \n", + "buffer.to_csv(filepath)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "356297df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OADB IDB IWB RH Btuh Sensible Btuh kW cfm \\\n", + "0 65.0 75 63.0 52.112865 66200.0 36410.0 3.77 800.0 \n", + "1 65.0 80 63.0 39.167642 66200.0 41044.0 3.77 800.0 \n", + "2 65.0 85 63.0 29.056294 66200.0 47002.0 3.77 800.0 \n", + "3 65.0 75 63.0 52.112865 80200.0 48120.0 3.80 1335.0 \n", + "4 65.0 80 63.0 39.167642 80200.0 56140.0 3.80 1335.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "103 95.0 80 71.0 65.061789 79400.0 38112.0 5.77 1335.0 \n", + "104 95.0 85 71.0 51.076002 79400.0 44464.0 5.77 1335.0 \n", + "105 95.0 75 71.0 82.665887 87200.0 34880.0 5.81 1875.0 \n", + "106 95.0 80 71.0 65.061789 87200.0 44472.0 5.81 1875.0 \n", + "107 95.0 85 71.0 51.076002 87200.0 52320.0 5.81 1875.0 \n", + "\n", + " SFP \n", + "0 81.597074 \n", + "1 81.597074 \n", + "2 81.597074 \n", + "3 175.769039 \n", + "4 175.769039 \n", + ".. ... \n", + "103 175.769039 \n", + "104 175.769039 \n", + "105 270.220446 \n", + "106 270.220446 \n", + "107 270.220446 \n", + "\n", + "[108 rows x 9 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\3645567763.py:54: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + } + ], + "source": [ + "# example\n", + "# ehb_lcm_bbox_2306\n", + "drop_list = [3,8,11,19,23]\n", + "df = loadData('./excel/ehb_lcm_bbox_2306.xlsx',50,9,drop_list)\n", + "result = modifyData(df,bhp_ranges_2306)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "70b022b9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\zhan682\\AppData\\Local\\Temp\\ipykernel_20044\\3645567763.py:54: FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version.\n", + " df = pd.read_excel(str(name), sheet_name='Table 1', header=buffer_header,\n" + ] + }, + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: 'Unnamed: 9'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[13], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# example\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# ehb_lcm_cbox_2305\u001b[39;00m\n\u001b[0;32m 3\u001b[0m df \u001b[38;5;241m=\u001b[39m loadData(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./excel/ehb_lcm_cbox_2305.xlsx\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;241m144\u001b[39m,\u001b[38;5;241m9\u001b[39m,drop_list)\n\u001b[1;32m----> 4\u001b[0m result \u001b[38;5;241m=\u001b[39m modifyData(df,bhp_ranges_2305)\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(result)\n", + "Cell \u001b[1;32mIn[5], line 24\u001b[0m, in \u001b[0;36mmodifyData\u001b[1;34m(df, bhp_ranges)\u001b[0m\n\u001b[0;32m 22\u001b[0m temp_list \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m last_three_columns:\n\u001b[1;32m---> 24\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(item\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m°\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m 25\u001b[0m temp_list\u001b[38;5;241m.\u001b[39mappend(item)\n\u001b[0;32m 26\u001b[0m temp_times \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(result)\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(temp_list)\n", + "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Unnamed: 9'" + ] + } + ], + "source": [ + "# example\n", + "# ehb_lcm_cbox_2305\n", + "df = loadData('./excel/ehb_lcm_cbox_2305.xlsx',144,9,drop_list)\n", + "result = modifyData(df,bhp_ranges_2305)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7537e1d5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.3" + } + }, + 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