fix(thermal_map): Add support for NumPy results

honeybee_grasshopper_energy/json/HB_Read_Environment_Matrix.json

@@ -1,5 +1,5 @@
 {
-  "version": "1.8.0", 
+  "version": "1.8.1", 
   "nickname": "EnvMtx", 
   "outputs": [
     [
@@ -36,7 +36,7 @@
     }
   ], 
   "subcategory": "7 :: Thermal Map", 
-  "code": "\nimport subprocess\nimport os\nimport shutil\nimport json\n\ntry:\n    from ladybug.datatype.temperature import AirTemperature, \\\n        MeanRadiantTemperature, RadiantTemperature\n    from ladybug.datatype.temperaturedelta import RadiantTemperatureDelta\n    from ladybug.datatype.fraction import RelativeHumidity\n    from ladybug.header import Header\n    from ladybug.datacollection import HourlyContinuousCollection, \\\n        HourlyDiscontinuousCollection\n    from ladybug.futil import csv_to_num_matrix\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug:\\n\\t{}'.format(e))\n\ntry:\n    from honeybee.config import folders\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs, objectify_output\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\nENV_CONDS_MAP = {\n    '0': 'mrt',\n    'mrt': 'mrt',\n    'mean radiant temperature': 'mrt',\n    '1': 'air_temperature',\n    'air temperature': 'air_temperature',\n    '2': 'longwave_mrt',\n    'longwave mrt': 'longwave_mrt',\n    '3': 'shortwave_mrt',\n    'shortwave mrt': 'shortwave_mrt',\n    'shortwave mrt delta': 'shortwave_mrt',\n    '4': 'rel_humidity',\n    'relative humidity':  'rel_humidity'\n}\n\n\ndef load_matrix(comf_result):\n    \"\"\"Load a matrix of data into an object that can be output in {{Plugin}}.\n\n    Args:\n        comf_result: Path to a folder with CSV data to be loaded into {{Plugin}}.\n    \"\"\"\n    # parse the result_info.json into a data collection header\n    with open(os.path.join(comf_result, 'results_info.json')) as json_file:\n        data_header = Header.from_dict(json.load(json_file))\n    a_per = data_header.analysis_period\n    continuous = True if a_per.st_hour == 0 and a_per.end_hour == 23 else False\n    if not continuous:\n        dates = a_per.datetimes\n\n    # parse the grids_info.json with the correct order of the grid files\n    with open(os.path.join(comf_result, 'grids_info.json')) as json_file:\n        grid_list = json.load(json_file)\n\n    # loop through the grid CSV files, parse their results, and build data collections\n    comf_matrix = []\n    for grid in grid_list:\n        grid_name = grid['full_id'] if 'full_id' in grid else 'id'\n        metadata = {'grid': grid_name}\n        grid_file = os.path.join(comf_result, '{}.csv'.format(grid_name))\n        data_matrix = csv_to_num_matrix(grid_file)\n        grid_data = []\n        for i, row in enumerate(data_matrix):\n            header = data_header.duplicate()\n            header.metadata = metadata.copy()\n            header.metadata['sensor_index'] = i\n            data = HourlyContinuousCollection(header, row) if continuous else \\\n                HourlyDiscontinuousCollection(header, row, dates)\n            grid_data.append(data)\n        comf_matrix.append(grid_data)\n\n    # wrap the maptrix into an object so that it does not slow the {{Plugin}} UI\n    comf_mtx = objectify_output(\n        '{} Matrix'.format(data_header.data_type.name), comf_matrix)\n    return comf_mtx\n\n\ndef create_result_header(env_conds, sub_path):\n    \"\"\"Create a DataCollection Header for a given metric.\"\"\"\n    with open(os.path.join(env_conds, 'results_info.json')) as json_file:\n        base_head = Header.from_dict(json.load(json_file))\n    if sub_path == 'mrt':\n        return Header(MeanRadiantTemperature(), 'C', base_head.analysis_period)\n    elif sub_path == 'air_temperature':\n        return Header(AirTemperature(), 'C', base_head.analysis_period)\n    elif sub_path == 'longwave_mrt':\n        return Header(RadiantTemperature(), 'C', base_head.analysis_period)\n    elif sub_path == 'shortwave_mrt':\n        return Header(RadiantTemperatureDelta(), 'dC', base_head.analysis_period)\n    elif sub_path == 'rel_humidity':\n        return Header(RelativeHumidity(), '%', base_head.analysis_period)\n\n\ndef sum_matrices(mtxs_1, mtxs_2, dest_dir):\n    \"\"\"Sum together matrices of two folders.\"\"\"\n    if not os.path.isdir(dest_dir):\n        os.makedirs(dest_dir)\n    for mtx_file in os.listdir(mtxs_1):\n        if mtx_file.endswith('.csv'):\n            mtx_file1 = os.path.join(mtxs_1, mtx_file)\n            mtx_file2 = os.path.join(mtxs_2, mtx_file)\n            matrix_1 = csv_to_num_matrix(mtx_file1)\n            matrix_2 = csv_to_num_matrix(mtx_file2)\n            data = [[d1 + d2 for d1, d2 in zip(r1, r2)]\n                    for r1, r2 in zip(matrix_1, matrix_2)]\n            csv_path = os.path.join(dest_dir, mtx_file)\n            with open(csv_path, 'w') as csv_file:\n                for dat in data:\n                    str_data = (str(v) for v in dat)\n                    csv_file.write(','.join(str_data) + '\\n')\n        elif mtx_file == 'grids_info.json':\n            shutil.copyfile(\n                os.path.join(mtxs_1, mtx_file),\n                os.path.join(dest_dir, mtx_file)\n            )\n\n\nif all_required_inputs(ghenv.Component) and _load:\n    # get the folders and that correspond with the requested metric\n    _metric_ = _metric_ if _metric_ is not None else 'mrt'\n    try:\n        sub_path = ENV_CONDS_MAP[_metric_.lower()]\n    except KeyError:\n        raise ValueError(\n            'Input metric \"{}\" is not recognized. Choose from: {}'.format(\n                _metric_, '\\n'.join(ENV_CONDS_MAP.keys()))\n        )\n    source_folder = os.path.join(_env_conds, sub_path)\n    dest_folder = os.path.join(_env_conds, 'final', sub_path)\n\n    # if the results have already been processed, then load them up\n    if os.path.isdir(dest_folder):\n        comf_mtx = load_matrix(dest_folder)\n    else:  # otherwise, process them into a load-able format\n        # make sure the requested metric is valid for the study\n        if sub_path == 'mrt':\n            source_folders = [os.path.join(_env_conds, 'longwave_mrt'),\n                              os.path.join(_env_conds, 'shortwave_mrt')]\n            dest_folders = [os.path.join(_env_conds, 'final', 'longwave_mrt'),\n                            os.path.join(_env_conds, 'final', 'shortwave_mrt')]\n        else:\n            assert os.path.isdir(source_folder), \\\n                'Metric \"{}\" does not exist for this comfort study.'.format(sub_path)\n            source_folders, dest_folders = [source_folder], [dest_folder]\n        # restructure the results to align with the sensor grids\n        dist_info = os.path.join(_env_conds, '_redist_info.json')\n        for src_f, dst_f in zip(source_folders, dest_folders):\n            if not os.path.isdir(dst_f):\n                os.makedirs(dst_f)\n                cmds = [folders.python_exe_path, '-m', 'honeybee_radiance', 'grid',\n                        'merge-folder', src_f, dst_f, 'csv',\n                        '--dist-info', dist_info]\n                shell = True if os.name == 'nt' else False\n                custom_env = os.environ.copy()\n                custom_env['PYTHONHOME'] = ''\n                process = subprocess.Popen(\n                    cmds, stdout=subprocess.PIPE, shell=shell, env=custom_env)\n                stdout = process.communicate()\n                grid_info_src = os.path.join(_env_conds, 'grids_info.json')\n                grid_info_dst = os.path.join(dst_f, 'grids_info.json')\n                shutil.copyfile(grid_info_src, grid_info_dst)\n            data_header = create_result_header(_env_conds, os.path.split(dst_f)[-1])\n            result_info_path = os.path.join(dst_f, 'results_info.json')\n            with open(result_info_path, 'w') as fp:\n                json.dump(data_header.to_dict(), fp, indent=4)\n        # if MRT was requested, sum together the longwave and shortwave\n        if sub_path == 'mrt':\n            sum_matrices(dest_folders[0], dest_folders[1], dest_folder)\n            data_header = create_result_header(_env_conds, sub_path)\n            result_info_path = os.path.join(dest_folder, 'results_info.json')\n            with open(result_info_path, 'w') as fp:\n                json.dump(data_header.to_dict(), fp, indent=4)\n        # load the resulting matrix into {{Plugin}}\n        comf_mtx = load_matrix(dest_folder)\n", 
+  "code": "\nimport subprocess\nimport os\nimport shutil\nimport json\n\ntry:\n    from ladybug.datatype.temperature import AirTemperature, \\\n        MeanRadiantTemperature, RadiantTemperature\n    from ladybug.datatype.temperaturedelta import RadiantTemperatureDelta\n    from ladybug.datatype.fraction import RelativeHumidity\n    from ladybug.header import Header\n    from ladybug.datacollection import HourlyContinuousCollection, \\\n        HourlyDiscontinuousCollection\n    from ladybug.futil import csv_to_num_matrix\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug:\\n\\t{}'.format(e))\n\ntry:\n    from honeybee.config import folders\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs, objectify_output\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\nENV_CONDS_MAP = {\n    '0': 'mrt',\n    'mrt': 'mrt',\n    'mean radiant temperature': 'mrt',\n    '1': 'air_temperature',\n    'air temperature': 'air_temperature',\n    '2': 'longwave_mrt',\n    'longwave mrt': 'longwave_mrt',\n    '3': 'shortwave_mrt',\n    'shortwave mrt': 'shortwave_mrt',\n    'shortwave mrt delta': 'shortwave_mrt',\n    '4': 'rel_humidity',\n    'relative humidity':  'rel_humidity'\n}\n\n\ndef load_matrix(comf_result):\n    \"\"\"Load a matrix of data into an object that can be output in {{Plugin}}.\n\n    Args:\n        comf_result: Path to a folder with CSV data to be loaded into {{Plugin}}.\n    \"\"\"\n    # parse the result_info.json into a data collection header\n    with open(os.path.join(comf_result, 'results_info.json')) as json_file:\n        data_header = Header.from_dict(json.load(json_file))\n    a_per = data_header.analysis_period\n    continuous = True if a_per.st_hour == 0 and a_per.end_hour == 23 else False\n    if not continuous:\n        dates = a_per.datetimes\n\n    # parse the grids_info.json with the correct order of the grid files\n    with open(os.path.join(comf_result, 'grids_info.json')) as json_file:\n        grid_list = json.load(json_file)\n\n    # loop through the grid CSV files, parse their results, and build data collections\n    comf_matrix = []\n    for grid in grid_list:\n        grid_name = grid['full_id'] if 'full_id' in grid else 'id'\n        metadata = {'grid': grid_name}\n        grid_file = os.path.join(comf_result, '{}.csv'.format(grid_name))\n        data_matrix = csv_to_num_matrix(grid_file)\n        grid_data = []\n        for i, row in enumerate(data_matrix):\n            header = data_header.duplicate()\n            header.metadata = metadata.copy()\n            header.metadata['sensor_index'] = i\n            data = HourlyContinuousCollection(header, row) if continuous else \\\n                HourlyDiscontinuousCollection(header, row, dates)\n            grid_data.append(data)\n        comf_matrix.append(grid_data)\n\n    # wrap the maptrix into an object so that it does not slow the {{Plugin}} UI\n    comf_mtx = objectify_output(\n        '{} Matrix'.format(data_header.data_type.name), comf_matrix)\n    return comf_mtx\n\n\nif all_required_inputs(ghenv.Component) and _load:\n    # get the folders and that correspond with the requested metric\n    _metric_ = _metric_ if _metric_ is not None else 'mrt'\n    try:\n        sub_path = ENV_CONDS_MAP[_metric_.lower()]\n    except KeyError:\n        raise ValueError(\n            'Input metric \"{}\" is not recognized. Choose from: {}'.format(\n                _metric_, '\\n'.join(ENV_CONDS_MAP.keys()))\n        )\n    source_folder = os.path.join(_env_conds, sub_path)\n    dest_folder = os.path.join(_env_conds, 'final', sub_path)\n\n    # if the results have already been processed, then load them up\n    if os.path.isdir(dest_folder):\n        comf_mtx = load_matrix(dest_folder)\n    else:  # otherwise, process them into a load-able format\n        # make sure the requested metric is valid for the study\n        if sub_path != 'mrt':\n            assert os.path.isdir(source_folder), \\\n                    'Metric \"{}\" does not exist for this comfort study.'.format(sub_path)\n        cmds = [folders.python_exe_path, '-m', 'ladybug_comfort', 'map',\n                'restructure-env-conditions', _env_conds, dest_folder, sub_path]\n        shell = True if os.name == 'nt' else False\n        custom_env = os.environ.copy()\n        custom_env['PYTHONHOME'] = ''\n        process = subprocess.Popen(\n            cmds, stdout=subprocess.PIPE, shell=shell, env=custom_env)\n        stdout = process.communicate()\n        # load the resulting matrix into {{Plugin}}\n        comf_mtx = load_matrix(dest_folder)\n", 
   "category": "HB-Energy", 
   "name": "HB Read Environment Matrix", 
   "description": "Read the detailed environmental conditions of a thermal mapping analysis from\nthe env_conds output by a thermal mapping component.\n_\nEnvironemntal conditions include raw inputs to the thermal comfort model, such as\nair temperature, MRT, longwave MRT, and shortwave MRT delta.\n-"

honeybee_grasshopper_energy/json/HB_Read_Thermal_Matrix.json

@@ -1,5 +1,5 @@
 {
-  "version": "1.8.0", 
+  "version": "1.8.1", 
   "nickname": "ThermalMtx", 
   "outputs": [
     [
@@ -29,7 +29,7 @@
     }
   ], 
   "subcategory": "7 :: Thermal Map", 
-  "code": "\nimport os\nimport json\n\ntry:\n    from ladybug.header import Header\n    from ladybug.datacollection import HourlyContinuousCollection, \\\n        HourlyDiscontinuousCollection\n    from ladybug.futil import csv_to_num_matrix\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs, objectify_output\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\n\nif all_required_inputs(ghenv.Component) and _load:\n    # parse the result_info.json into a data collection header\n    with open(os.path.join(_comf_result, 'results_info.json')) as json_file:\n        data_header = Header.from_dict(json.load(json_file))\n    a_per = data_header.analysis_period\n    continuous = True if a_per.st_hour == 0 and a_per.end_hour == 23 else False\n    if not continuous:\n        dates = a_per.datetimes\n\n    # parse the grids_info.json with the correct order of the grid files\n    with open(os.path.join(_comf_result, 'grids_info.json')) as json_file:\n        grid_list = json.load(json_file)\n\n    # loop through the grid CSV files, parse their results, and build data collections\n    comf_matrix = []\n    for grid in grid_list:\n        grid_name = grid['full_id'] if 'full_id' in grid else 'id'\n        metadata = {'grid': grid_name}\n        grid_file = os.path.join(_comf_result, '{}.csv'.format(grid_name))\n        data_matrix = csv_to_num_matrix(grid_file)\n        grid_data = []\n        for i, row in enumerate(data_matrix):\n            header = data_header.duplicate()\n            header.metadata = metadata.copy()\n            header.metadata['sensor_index'] = i\n            data = HourlyContinuousCollection(header, row) if continuous else \\\n                HourlyDiscontinuousCollection(header, row, dates)\n            grid_data.append(data)\n        comf_matrix.append(grid_data)\n\n    # wrap the maptrix into an object so that it does not slow the {{Plugin}} UI\n    comf_mtx = objectify_output(\n        '{} Matrix'.format(data_header.data_type.name), comf_matrix)\n", 
+  "code": "\nimport os\nimport json\nimport subprocess\n\ntry:\n    from honeybee.config import folders\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug.header import Header\n    from ladybug.datacollection import HourlyContinuousCollection, \\\n        HourlyDiscontinuousCollection\n    from ladybug.futil import csv_to_num_matrix\n    from ladybug.datautil import collections_from_csv\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs, objectify_output\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\n\nif all_required_inputs(ghenv.Component) and _load:\n    # parse the result_info.json into a data collection header\n    with open(os.path.join(_comf_result, 'results_info.json')) as json_file:\n        data_header = Header.from_dict(json.load(json_file))\n    a_per = data_header.analysis_period\n    continuous = True if a_per.st_hour == 0 and a_per.end_hour == 23 else False\n    if not continuous:\n        dates = a_per.datetimes\n\n    # parse the grids_info.json with the correct order of the grid files\n    with open(os.path.join(_comf_result, 'grids_info.json')) as json_file:\n        grid_list = json.load(json_file)\n\n    # check file extension\n    grid_file = os.path.join(_comf_result, '{}.csv'.format(grid_list[0]['full_id']))\n    extension = 'csv'\n    if not os.path.exists(grid_file):\n        extension = 'npy'\n\n    comf_matrix = []\n    if extension == 'csv':\n        # loop through the grid CSV files, parse their results, and build data collections\n        for grid in grid_list:\n            grid_name = grid['full_id'] if 'full_id' in grid else 'id'\n            metadata = {'grid': grid_name}\n            grid_file = os.path.join(_comf_result, '{}.csv'.format(grid_name))\n            data_matrix = csv_to_num_matrix(grid_file)\n            grid_data = []\n            for i, row in enumerate(data_matrix):\n                header = data_header.duplicate()\n                header.metadata = metadata.copy()\n                header.metadata['sensor_index'] = i\n                data = HourlyContinuousCollection(header, row) if continuous else \\\n                    HourlyDiscontinuousCollection(header, row, dates)\n                grid_data.append(data)\n            comf_matrix.append(grid_data)\n    else:\n        csv_files = []\n        csv_exists = []\n        # collect csv files and check if they already exists\n        for grid in grid_list:\n            grid_name = grid['full_id'] if 'full_id' in grid else 'id'\n            grid_file = os.path.join(_comf_result, 'datacollections', '{}.csv'.format(grid_name))\n            csv_files.append(grid_file)\n            csv_exists.append(os.path.exists(grid_file))\n        # run command if csv files do not exist\n        if not all(csv_exists):\n            cmds = [folders.python_exe_path, '-m', 'honeybee_radiance_postprocess',\n                    'data-collection', 'folder-to-datacollections', _comf_result,\n                    os.path.join(_comf_result, 'results_info.json')]\n            use_shell = True if os.name == 'nt' else False\n            custom_env = os.environ.copy()\n            custom_env['PYTHONHOME'] = ''\n            process = subprocess.Popen(\n                cmds, cwd=_comf_result, shell=use_shell, env=custom_env,\n                stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n            stdout = process.communicate()  # wait for the process to finish\n        for grid_file in csv_files:\n            grid_data = collections_from_csv(grid_file)\n            comf_matrix.append(grid_data)\n\n    # wrap the maptrix into an object so that it does not slow the {{Plugin}} UI\n    comf_mtx = objectify_output(\n        '{} Matrix'.format(data_header.data_type.name), comf_matrix)\n", 
   "category": "HB-Energy", 
   "name": "HB Read Thermal Matrix", 
   "description": "Read the detailed results of a thermal mapping analysis from a folder of CSV\nfiles output by a thermal mapping component.\n_\nDetailed results include temperature amd thermal condition results. It also\nincludes metrics that give a sense of how hot or cold condition are like\npmv, utci category, or adaptive comfort degrees from neutral temperature.\n-"