fix(result): Fix a couple of bugs in the result-matching components

honeybee_grasshopper_energy/json/HB_Carbon_Emission_Intensity.json

@@ -1,5 +1,5 @@
 {
-  "version": "1.6.0", 
+  "version": "1.6.1", 
   "nickname": "CEI", 
   "outputs": [
     [
@@ -57,7 +57,7 @@
     }
   ], 
   "subcategory": "6 :: Result", 
-  "code": "\nimport os\nimport subprocess\nimport json\n\ntry:\n    from ladybug.location import Location\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import location:\\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 honeybee_energy.result.emissions import future_electricity_emissions, \\\n        emissions_from_sql\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee_energy:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs, give_warning\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\n\n# Use the SQLiteResult class to parse the result files directly on Windows.\ndef get_results_windows(sql_files, elec_emiss):\n    results = emissions_from_sql(sql_files, elec_emiss)\n    return results['carbon_intensity'], results['total_floor_area'], \\\n        results['end_uses'], results['sources']\n\n\n# The SQLite3 module doesn't work in IronPython on Mac, so we must make a call\n# to the Honeybee CLI (which runs on CPython) to get the results.\ndef get_results_mac(sql_files, elec_emiss):\n    cmds = [folders.python_exe_path, '-m', 'honeybee_energy', 'result',\n            'carbon-emission-intensity']\n    cmds.extend(sql_files)\n    cmds.extend(['--electricity-emissions', str(elec_emiss)])\n    process = subprocess.Popen(cmds, stdout=subprocess.PIPE)\n    stdout = process.communicate()\n    results = json.loads(stdout[0], object_pairs_hook=OrderedDict)\n    return results['carbon_intensity'], results['total_floor_area'], \\\n        results['end_uses'], results['sources']\n\n\nif all_required_inputs(ghenv.Component):\n    # ensure that _sql is a list rather than a single string\n    if isinstance(_sql, basestring):\n        _sql = [_sql]\n\n    # process the location and year or the electricity intensity\n    if isinstance(_loc_kgMWh, Location):\n        yr = 2030 if _year_ is None else int(_year_)\n        elec_emiss = future_electricity_emissions(_loc_kgMWh, yr)\n        if elec_emiss is None:\n            msg = 'Location must be inside the USA in order to be used for carbon ' \\\n                'emissions estimation.\\nPlug in a number for carbon intensity in ' \\\n                'kg CO2/MWH for locations outside the USA.'\n            print(msg)\n            raise ValueError(msg)\n    else:\n        try:\n            elec_emiss = float(_loc_kgMWh)\n        except TypeError:\n            msg = 'Expected location object or number for _loccation. ' \\\n                'Got {}.'.format(type(_loc_kgMWh))\n            raise ValueError(msg)\n\n    # get the results\n    get_results = get_results_windows if os.name == 'nt' else get_results_mac\n    cei, gross_floor, end_use_pairs, sources = get_results(_sql, elec_emiss)\n\n    # create separate lists for end use values and labels\n    cei_end_use = end_use_pairs.values()\n    end_uses = [use.replace('_', ' ').title() for use in end_use_pairs.keys()]\n\n    # give a warning if the sources include district heating or cooling\n    if 'district_heat' in sources:\n        msg = 'District heating was found in the results and so carbon emissions ' \\\n            'cannot be accurately estimated.\\nTry using a different HVAC or SHW system.'\n        print(msg)\n        give_warning(ghenv.Component, msg)\n    if 'district_cool' in sources:\n        msg = 'District cooling was found in the results and so carbon emissions ' \\\n            'cannot be accurately estimated.\\nTry using a different HVAC system.'\n        print(msg)\n        give_warning(ghenv.Component, msg)\n", 
+  "code": "\nimport os\nimport subprocess\nimport json\n\ntry:\n    from ladybug.location import Location\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import location:\\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 honeybee_energy.result.emissions import future_electricity_emissions, \\\n        emissions_from_sql\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee_energy:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs, give_warning\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\n\n# Use the SQLiteResult class to parse the result files directly on Windows.\ndef get_results_windows(sql_files, elec_emiss):\n    results = emissions_from_sql(sql_files, elec_emiss)\n    return results['carbon_intensity'], results['total_floor_area'], \\\n        results['end_uses'], results['sources']\n\n\n# The SQLite3 module doesn't work in IronPython on Mac, so we must make a call\n# to the Honeybee CLI (which runs on CPython) to get the results.\ndef get_results_mac(sql_files, elec_emiss):\n    from collections import OrderedDict\n    cmds = [folders.python_exe_path, '-m', 'honeybee_energy', 'result',\n            'carbon-emission-intensity']\n    cmds.extend(sql_files)\n    cmds.extend(['--electricity-emissions', str(elec_emiss)])\n    process = subprocess.Popen(cmds, stdout=subprocess.PIPE)\n    stdout = process.communicate()\n    results = json.loads(stdout[0], object_pairs_hook=OrderedDict)\n    return results['carbon_intensity'], results['total_floor_area'], \\\n        results['end_uses'], results['sources']\n\n\nif all_required_inputs(ghenv.Component):\n    # ensure that _sql is a list rather than a single string\n    if isinstance(_sql, basestring):\n        _sql = [_sql]\n\n    # process the location and year or the electricity intensity\n    if isinstance(_loc_kgMWh, Location):\n        yr = 2030 if _year_ is None else int(_year_)\n        elec_emiss = future_electricity_emissions(_loc_kgMWh, yr)\n        if elec_emiss is None:\n            msg = 'Location must be inside the USA in order to be used for carbon ' \\\n                'emissions estimation.\\nPlug in a number for carbon intensity in ' \\\n                'kg CO2/MWH for locations outside the USA.'\n            print(msg)\n            raise ValueError(msg)\n    else:\n        try:\n            elec_emiss = float(_loc_kgMWh)\n        except TypeError:\n            msg = 'Expected location object or number for _loccation. ' \\\n                'Got {}.'.format(type(_loc_kgMWh))\n            raise ValueError(msg)\n\n    # get the results\n    get_results = get_results_windows if os.name == 'nt' else get_results_mac\n    cei, gross_floor, end_use_pairs, sources = get_results(_sql, elec_emiss)\n\n    # create separate lists for end use values and labels\n    cei_end_use = end_use_pairs.values()\n    end_uses = [use.replace('_', ' ').title() for use in end_use_pairs.keys()]\n\n    # give a warning if the sources include district heating or cooling\n    if 'district_heat' in sources:\n        msg = 'District heating was found in the results and so carbon emissions ' \\\n            'cannot be accurately estimated.\\nTry using a different HVAC or SHW system.'\n        print(msg)\n        give_warning(ghenv.Component, msg)\n    if 'district_cool' in sources:\n        msg = 'District cooling was found in the results and so carbon emissions ' \\\n            'cannot be accurately estimated.\\nTry using a different HVAC system.'\n        print(msg)\n        give_warning(ghenv.Component, msg)\n", 
   "category": "HB-Energy", 
   "name": "HB Carbon Emission Intensity", 
   "description": "Get information about carbon emission intensity (CEI) from an EnergyPlus SQL file.\n_\nThe location and year (or input emissions of electricity intensity) will be used\nto compute carbon intensity for both electricity and district heating/cooling.\nFixed numbers will be used to convert the following on-site fuel sources:\n_\n* Natural Gas --  277.358 kg/MWh\n* Propane -- 323.897 kg/MWh\n* Fuel Oil -- 294.962 kg/MWh\n-"

honeybee_grasshopper_energy/json/HB_Color_Rooms.json

@@ -1,5 +1,5 @@
 {
-  "version": "1.6.0", 
+  "version": "1.6.1", 
   "nickname": "ColorRooms", 
   "outputs": [
     [
@@ -113,7 +113,7 @@
     }
   ], 
   "subcategory": "6 :: Result", 
-  "code": "\n\ntry:\n    from honeybee.model import Model\n    from honeybee.room import Room\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee:\\n\\t{}'.format(e))\n\ntry:\n    from honeybee_energy.result.colorobj import ColorRoom\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee_energy:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.togeometry import to_point3d\n    from ladybug_{{cad}}.fromgeometry import from_face3ds_to_colored_mesh, \\\n        from_polyface3d_to_wireframe\n    from ladybug_{{cad}}.text import text_objects\n    from ladybug_{{cad}}.fromobjects import legend_objects\n    from ladybug_{{cad}}.color import color_to_color\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs\n    from ladybug_{{cad}}.config import units_abbreviation\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\n\ndef split_solar_enclosure_data(data_to_split, rooms):\n    \"\"\"Split solar enclosure data according to exterior aperture area.\"\"\"\n    # figure out the ratios of exterior aperture area in each room\n    enclosures = Room.group_by_air_boundary_adjacency(rooms)\n    encl_ratios = {}\n    for encl in enclosures:\n        if len(encl) != 1:\n            ap_areas = [rm.exterior_aperture_area for rm in encl]\n            total_a = sum(ap_areas)\n            if total_a != 0:\n                rat_dict = {rm.identifier: ap / total_a\n                            for rm, ap in zip(encl, ap_areas)}\n            else:\n                rat_dict = {rm.identifier: 0 for rm in encl}\n            encl_ratios[encl[0].identifier] = rat_dict\n    encl_ratios = [x for _, x in sorted(zip(encl_ratios.keys(), encl_ratios.values()))]\n\n    # create the list of split data collections\n    split_data, enc_count = [], 0\n    for dat in data_to_split:\n        if 'Solar Enclosure' in dat.header.metadata['Zone']:\n            rm_ratios = encl_ratios[enc_count]\n            for rm_id, rm_rat in rm_ratios.items():\n                new_data = dat.duplicate()\n                new_data.header.metadata['Zone'] = rm_id.upper()\n                new_data.values = [val * rm_rat for val in dat.values]\n                split_data.append(new_data)\n            enc_count += 1\n        else:\n            split_data.append(dat)\n    return split_data\n\n\nif all_required_inputs(ghenv.Component):\n    # extract any rooms from input Models\n    rooms = []\n    for hb_obj in _rooms_model:\n        if isinstance(hb_obj, Model):\n            rooms.extend(hb_obj.rooms)\n        else:\n            rooms.append(hb_obj)\n\n    # apply analysis period to the data if connected\n    if period_ is not None:\n        _data = [coll.filter_by_analysis_period(period_) for coll in _data]\n\n    # set default norm_by_floor value\n    norm_by_flr_ = True if norm_by_flr_ is None else norm_by_flr_\n\n    # sense if the conneccted data is for a solar enclosure and split the data if so\n    zone_solar = 'Zone Windows Total Transmitted Solar Radiation Energy'\n    if isinstance(_rooms_model[0], Model) and 'type' in _data[0].header.metadata and \\\n            _data[0].header.metadata['type'] == zone_solar:\n        _data = split_solar_enclosure_data(_data, rooms)\n\n    # create the ColorRoom visualization object and output geometry\n    color_obj = ColorRoom(_data, rooms, legend_par_, sim_step_, norm_by_flr_,\n                          units_abbreviation())\n    graphic = color_obj.graphic_container\n    mesh = [from_face3ds_to_colored_mesh(flrs, col) for flrs, col in\n            zip(color_obj.matched_floor_faces, graphic.value_colors)]\n    wire_frame = []\n    for room in rooms:\n        wire_frame.extend(from_polyface3d_to_wireframe(room.geometry))\n    legend = legend_objects(graphic.legend)\n    title = text_objects(color_obj.title_text, graphic.lower_title_location,\n                         graphic.legend_parameters.text_height,\n                         graphic.legend_parameters.font)\n    rooms = color_obj.matched_rooms\n    colors = [color_to_color(col, 125) for col in graphic.value_colors]\n    values = graphic.values\n    vis_set = color_obj\n", 
+  "code": "\n\ntry:\n    from honeybee.model import Model\n    from honeybee.room import Room\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee:\\n\\t{}'.format(e))\n\ntry:\n    from honeybee_energy.result.colorobj import ColorRoom\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee_energy:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.togeometry import to_point3d\n    from ladybug_{{cad}}.fromgeometry import from_face3ds_to_colored_mesh, \\\n        from_polyface3d_to_wireframe\n    from ladybug_{{cad}}.text import text_objects\n    from ladybug_{{cad}}.fromobjects import legend_objects\n    from ladybug_{{cad}}.color import color_to_color\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs\n    from ladybug_{{cad}}.config import units_abbreviation\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\n\ndef split_solar_enclosure_data(data_to_split, rooms):\n    \"\"\"Split solar enclosure data according to exterior aperture area.\"\"\"\n    # figure out the ratios of exterior aperture area in each room\n    enclosures = Room.group_by_air_boundary_adjacency(rooms)\n    encl_ratios = {}\n    for encl in enclosures:\n        if len(encl) != 1:\n            ap_areas = [rm.exterior_aperture_area for rm in encl]\n            total_a = sum(ap_areas)\n            if total_a != 0:\n                rat_dict = {rm.identifier: ap / total_a\n                            for rm, ap in zip(encl, ap_areas)}\n            else:\n                rat_dict = {rm.identifier: 0 for rm in encl}\n            encl_ratios[encl[0].identifier] = rat_dict\n    encl_ratios = [x for _, x in sorted(zip(encl_ratios.keys(), encl_ratios.values()))]\n\n    # create the list of split data collections\n    split_data, enc_count = [], 0\n    for dat in data_to_split:\n        if 'Solar Enclosure' in dat.header.metadata['Zone']:\n            rm_ratios = encl_ratios[enc_count]\n            for rm_id, rm_rat in rm_ratios.items():\n                new_data = dat.duplicate()\n                new_data.header.metadata['Zone'] = rm_id.upper()\n                new_data.values = [val * rm_rat for val in dat.values]\n                split_data.append(new_data)\n            enc_count += 1\n        else:\n            split_data.append(dat)\n    return split_data\n\n\nif all_required_inputs(ghenv.Component):\n    # extract any rooms from input Models\n    rooms = []\n    for hb_obj in _rooms_model:\n        if isinstance(hb_obj, Model):\n            rooms.extend(hb_obj.rooms)\n        else:\n            rooms.append(hb_obj)\n\n    # apply analysis period to the data if connected\n    if period_ is not None:\n        _data = [coll.filter_by_analysis_period(period_) for coll in _data]\n\n    # set default norm_by_floor value\n    norm_by_flr_ = True if norm_by_flr_ is None else norm_by_flr_\n\n    # sense if the conneccted data is for a solar enclosure and split the data if so\n    space_based = False\n    zone_solar = 'Zone Windows Total Transmitted Solar Radiation Energy'\n    if 'type' in _data[0].header.metadata and _data[0].header.metadata['type'] == zone_solar:\n        space_based = True\n        if isinstance(_rooms_model[0], Model):\n            _data = split_solar_enclosure_data(_data, rooms)\n\n    # create the ColorRoom visualization object and output geometry\n    color_obj = ColorRoom(_data, rooms, legend_par_, sim_step_, norm_by_flr_,\n                          units_abbreviation(), space_based=space_based)\n    graphic = color_obj.graphic_container\n    mesh = [from_face3ds_to_colored_mesh(flrs, col) for flrs, col in\n            zip(color_obj.matched_floor_faces, graphic.value_colors)]\n    wire_frame = []\n    for room in rooms:\n        wire_frame.extend(from_polyface3d_to_wireframe(room.geometry))\n    legend = legend_objects(graphic.legend)\n    title = text_objects(color_obj.title_text, graphic.lower_title_location,\n                         graphic.legend_parameters.text_height,\n                         graphic.legend_parameters.font)\n    rooms = color_obj.matched_rooms\n    colors = [color_to_color(col, 125) for col in graphic.value_colors]\n    values = graphic.values\n    vis_set = color_obj\n", 
   "category": "HB-Energy", 
   "name": "HB Color Rooms", 
   "description": "Visualize Room-level energy simulation results as colored Room geometry.\n-"

honeybee_grasshopper_energy/src/HB Carbon Emission Intensity.py

@@ -57,7 +57,7 @@
 
 ghenv.Component.Name = 'HB Carbon Emission Intensity'
 ghenv.Component.NickName = 'CEI'
-ghenv.Component.Message = '1.6.0'
+ghenv.Component.Message = '1.6.1'
 ghenv.Component.Category = 'HB-Energy'
 ghenv.Component.SubCategory = '6 :: Result'
 ghenv.Component.AdditionalHelpFromDocStrings = '0'
@@ -98,6 +98,7 @@ def get_results_windows(sql_files, elec_emiss):
 # The SQLite3 module doesn't work in IronPython on Mac, so we must make a call
 # to the Honeybee CLI (which runs on CPython) to get the results.
 def get_results_mac(sql_files, elec_emiss):
+    from collections import OrderedDict
     cmds = [folders.python_exe_path, '-m', 'honeybee_energy', 'result',
             'carbon-emission-intensity']
     cmds.extend(sql_files)

honeybee_grasshopper_energy/src/HB Color Rooms.py

@@ -62,7 +62,7 @@
 
 ghenv.Component.Name = "HB Color Rooms"
 ghenv.Component.NickName = 'ColorRooms'
-ghenv.Component.Message = '1.6.0'
+ghenv.Component.Message = '1.6.1'
 ghenv.Component.Category = 'HB-Energy'
 ghenv.Component.SubCategory = '6 :: Result'
 ghenv.Component.AdditionalHelpFromDocStrings = '2'
@@ -142,14 +142,16 @@ def split_solar_enclosure_data(data_to_split, rooms):
     norm_by_flr_ = True if norm_by_flr_ is None else norm_by_flr_
 
     # sense if the conneccted data is for a solar enclosure and split the data if so
+    space_based = False
     zone_solar = 'Zone Windows Total Transmitted Solar Radiation Energy'
-    if isinstance(_rooms_model[0], Model) and 'type' in _data[0].header.metadata and \
-            _data[0].header.metadata['type'] == zone_solar:
-        _data = split_solar_enclosure_data(_data, rooms)
+    if 'type' in _data[0].header.metadata and _data[0].header.metadata['type'] == zone_solar:
+        space_based = True
+        if isinstance(_rooms_model[0], Model):
+            _data = split_solar_enclosure_data(_data, rooms)
 
     # create the ColorRoom visualization object and output geometry
     color_obj = ColorRoom(_data, rooms, legend_par_, sim_step_, norm_by_flr_,
-                          units_abbreviation())
+                          units_abbreviation(), space_based=space_based)
     graphic = color_obj.graphic_container
     mesh = [from_face3ds_to_colored_mesh(flrs, col) for flrs, col in
             zip(color_obj.matched_floor_faces, graphic.value_colors)]