fix(results): Ensure coincident peak respects input HOYs

More info is here:

https://discourse.ladybug.tools/t/error-in-reporting-of-hoys-when-using-hb-annual-peak-values/19698

honeybee_grasshopper_radiance/json/HB_Annual_Peak_Values.json

@@ -1,5 +1,5 @@
 {
-  "version": "1.6.1", 
+  "version": "1.6.2", 
   "nickname": "PeakValues", 
   "outputs": [
     [
@@ -50,7 +50,7 @@
     }
   ], 
   "subcategory": "4 :: Results", 
-  "code": "\nimport os\nimport subprocess\n\ntry:\n    from ladybug.futil import write_to_file\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 honeybee_radiance.postprocess.annualdaylight import _process_input_folder\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee_radiance:\\n\\t{}'.format(e))\n\ntry:\n    from pollination_handlers.outputs.helper import read_sensor_grid_result\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import pollination_handlers:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs, list_to_data_tree\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\n\ndef parse_sun_up_hours(sun_up_hours, hoys, timestep):\n    \"\"\"Parse the sun-up hours from the result file .txt file.\n\n    Args:\n        sun_up_hours: A list of integers for the sun-up hours.\n        hoys: A list of 8760 * timestep values for the hoys to select. If an empty\n            list is passed, None will be returned.\n        timestep: Integer for the timestep of the analysis.\n    \"\"\"\n    if len(hoys) != 0:\n        schedule = [False] * (8760 * timestep)\n        for hr in hoys:\n            schedule[int(hr * timestep)] = True\n        su_pattern = [schedule[int(h * timestep)] for h in sun_up_hours]\n        return su_pattern\n\n\ndef peak_values(ill_file, su_pattern, coincident):\n    \"\"\"Compute average values for a given result file.\"\"\"\n    max_vals, max_i = [], None\n    with open(ill_file) as results:\n        if coincident:\n            all_values = [[float(r) for r in pt_res.split()] for pt_res in results] \\\n                if su_pattern is None else \\\n                [[float(r) for r, is_hoy in zip(pt_res.split(), su_pattern) if is_hoy]\n                 for pt_res in results]\n            max_val, max_i = 0, 0\n            for i, t_step in enumerate(zip(*all_values)):\n                tot_val = sum(t_step)\n                if tot_val > max_val:\n                    max_val = tot_val\n                    max_i = i\n            for sensor in all_values:\n                max_vals.append(sensor[max_i])\n        else:\n            if su_pattern is None:  # no HOY filter on results\n                for pt_res in results:\n                    values = [float(r) for r in pt_res.split()]\n                    max_vals.append(max(values))\n            else:\n                for pt_res in results:\n                    values = [float(r) for r, is_hoy in zip(pt_res.split(), su_pattern) if is_hoy]\n                    max_vals.append(max(values))\n    return max_vals, max_i\n\n\nif all_required_inputs(ghenv.Component):\n    # set up the default values\n    grid_filter_ = '*' if grid_filter_ is None else grid_filter_\n    res_folder = os.path.dirname(_results[0]) if os.path.isfile(_results[0]) \\\n        else _results[0]\n\n    # check to see if results use the newer numpy arrays\n    if os.path.isdir(os.path.join(res_folder, '__static_apertures__')):\n        cmds = [folders.python_exe_path, '-m', 'honeybee_radiance_postprocess',\n                'post-process', 'peak-values', res_folder, '-sf', 'metrics']\n        if len(_hoys_) != 0:\n            hoys_str = '\\n'.join(str(h) for h in _hoys_)\n            hoys_file = os.path.join(res_folder, 'hoys.txt')\n            write_to_file(hoys_file, hoys_str)\n            cmds.extend(['--hoys-file', hoys_file])\n        if grid_filter_ != '*':\n            cmds.extend(['--grids-filter', grid_filter_])\n        if coincident_:\n            cmds.append('--coincident')\n        use_shell = True if os.name == 'nt' else False\n        process = subprocess.Popen(\n            cmds, cwd=res_folder, shell=use_shell,\n            stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n        stdout = process.communicate()  # wait for the process to finish\n        if stdout[-1] != '':\n            print(stdout[-1])\n            raise ValueError('Failed to compute peak values.')\n        avg_dir = os.path.join(res_folder, 'metrics', 'peak_values')\n        if os.path.isdir(avg_dir):\n            values = read_sensor_grid_result(avg_dir, 'peak','full_id', False)\n            values = list_to_data_tree(values)\n            with open(os.path.join(avg_dir, 'max_hoys.txt'), 'r') as max_hoys:\n                hoys = [line.rstrip() for line in max_hoys.readlines()]\n            if coincident_:\n                hoys = map(int, hoys)\n            else:\n                hoys = [None] * len(hoys)\n\n    else:\n        # extract the timestep if it exists\n        timestep = 1\n        tstep_file = os.path.join(res_folder, 'timestep.txt')\n        if os.path.isfile(tstep_file):\n            with open(tstep_file) as tf:\n                timestep = int(tf.readline())\n    \n        # parse the sun-up-hours\n        grids, sun_up_hours = _process_input_folder(res_folder, grid_filter_)\n        su_pattern = parse_sun_up_hours(sun_up_hours, _hoys_, timestep)\n    \n        # compute the average values\n        values, hoys = [], []\n        for grid_info in grids:\n            ill_file = os.path.join(res_folder, '%s.ill' % grid_info['full_id'])\n            dgp_file = os.path.join(res_folder, '%s.dgp' % grid_info['full_id'])\n            if os.path.isfile(dgp_file):\n                max_list, max_i = peak_values(dgp_file, su_pattern, coincident_)\n            else:\n                max_list, max_i = peak_values(ill_file, su_pattern, coincident_)\n            values.append(max_list)\n            if max_i is not None:\n                hoys.append(sun_up_hours[max_i])\n            else:\n                hoys.append(max_i)\n        values = list_to_data_tree(values)\n", 
+  "code": "\nimport os\nimport subprocess\n\ntry:\n    from ladybug.futil import write_to_file\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 honeybee_radiance.postprocess.annualdaylight import _process_input_folder\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import honeybee_radiance:\\n\\t{}'.format(e))\n\ntry:\n    from pollination_handlers.outputs.helper import read_sensor_grid_result\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import pollination_handlers:\\n\\t{}'.format(e))\n\ntry:\n    from ladybug_{{cad}}.{{plugin}} import all_required_inputs, list_to_data_tree\nexcept ImportError as e:\n    raise ImportError('\\nFailed to import ladybug_{{cad}}:\\n\\t{}'.format(e))\n\n\ndef parse_sun_up_hours(sun_up_hours, hoys, timestep):\n    \"\"\"Parse the sun-up hours from the result file .txt file.\n\n    Args:\n        sun_up_hours: A list of integers for the sun-up hours.\n        hoys: A list of 8760 * timestep values for the hoys to select. If an empty\n            list is passed, None will be returned.\n        timestep: Integer for the timestep of the analysis.\n    \"\"\"\n    if len(hoys) != 0:\n        schedule = [False] * (8760 * timestep)\n        for hr in hoys:\n            schedule[int(hr * timestep)] = True\n        su_pattern = [schedule[int(h * timestep)] for h in sun_up_hours]\n        return su_pattern\n\n\ndef peak_values(ill_file, su_pattern, coincident):\n    \"\"\"Compute average values for a given result file.\"\"\"\n    max_vals, max_i = [], None\n    with open(ill_file) as results:\n        if coincident:\n            all_values = [[float(r) for r in pt_res.split()] for pt_res in results] \\\n                if su_pattern is None else \\\n                [[float(r) for r, is_hoy in zip(pt_res.split(), su_pattern) if is_hoy]\n                 for pt_res in results]\n            max_val, max_i = 0, 0\n            for i, t_step in enumerate(zip(*all_values)):\n                tot_val = sum(t_step)\n                if tot_val > max_val:\n                    max_val = tot_val\n                    max_i = i\n            for sensor in all_values:\n                max_vals.append(sensor[max_i])\n        else:\n            if su_pattern is None:  # no HOY filter on results\n                for pt_res in results:\n                    values = [float(r) for r in pt_res.split()]\n                    max_vals.append(max(values))\n            else:\n                for pt_res in results:\n                    values = [float(r) for r, is_hoy in zip(pt_res.split(), su_pattern) if is_hoy]\n                    max_vals.append(max(values))\n    return max_vals, max_i\n\n\nif all_required_inputs(ghenv.Component):\n    # set up the default values\n    grid_filter_ = '*' if grid_filter_ is None else grid_filter_\n    res_folder = os.path.dirname(_results[0]) if os.path.isfile(_results[0]) \\\n        else _results[0]\n\n    # check to see if results use the newer numpy arrays\n    if os.path.isdir(os.path.join(res_folder, '__static_apertures__')):\n        cmds = [folders.python_exe_path, '-m', 'honeybee_radiance_postprocess',\n                'post-process', 'peak-values', res_folder, '-sf', 'metrics']\n        if len(_hoys_) != 0:\n            hoys_str = '\\n'.join(str(h) for h in _hoys_)\n            hoys_file = os.path.join(res_folder, 'hoys.txt')\n            write_to_file(hoys_file, hoys_str)\n            cmds.extend(['--hoys-file', hoys_file])\n        if grid_filter_ != '*':\n            cmds.extend(['--grids-filter', grid_filter_])\n        if coincident_:\n            cmds.append('--coincident')\n        use_shell = True if os.name == 'nt' else False\n        process = subprocess.Popen(\n            cmds, cwd=res_folder, shell=use_shell,\n            stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n        stdout = process.communicate()  # wait for the process to finish\n        if stdout[-1] != '':\n            print(stdout[-1])\n            raise ValueError('Failed to compute peak values.')\n        avg_dir = os.path.join(res_folder, 'metrics', 'peak_values')\n        if os.path.isdir(avg_dir):\n            values = read_sensor_grid_result(avg_dir, 'peak','full_id', False)\n            values = list_to_data_tree(values)\n            with open(os.path.join(avg_dir, 'max_hoys.txt'), 'r') as max_hoys:\n                hoys = [line.rstrip() for line in max_hoys.readlines()]\n            if coincident_:\n                hoys = map(int, hoys)\n            else:\n                hoys = [None] * len(hoys)\n\n    else:\n        # extract the timestep if it exists\n        timestep = 1\n        tstep_file = os.path.join(res_folder, 'timestep.txt')\n        if os.path.isfile(tstep_file):\n            with open(tstep_file) as tf:\n                timestep = int(tf.readline())\n    \n        # parse the sun-up-hours\n        grids, sun_up_hours = _process_input_folder(res_folder, grid_filter_)\n        su_pattern = parse_sun_up_hours(sun_up_hours, _hoys_, timestep)\n        filt_suh = [suh for suh in sun_up_hours if int(suh) in _hoys_] \\\n            if len(_hoys_) != 0 else sun_up_hours\n        # compute the average values\n        values, hoys = [], []\n        for grid_info in grids:\n            ill_file = os.path.join(res_folder, '%s.ill' % grid_info['full_id'])\n            dgp_file = os.path.join(res_folder, '%s.dgp' % grid_info['full_id'])\n            if os.path.isfile(dgp_file):\n                max_list, max_i = peak_values(dgp_file, su_pattern, coincident_)\n            else:\n                max_list, max_i = peak_values(ill_file, su_pattern, coincident_)\n            values.append(max_list)\n            if max_i is not None:\n                hoys.append(filt_suh[max_i])\n            else:\n                hoys.append(max_i)\n        values = list_to_data_tree(values)\n", 
   "category": "HB-Radiance", 
   "name": "HB Annual Peak Values", 
   "description": "Get peak irradiance or sum of illuminance values over an annual irradiance or\ndaylight simulation.\n_\nThe _hoys_ input can also be used to filter the data for a particular time period or\nhour/timestep of the simulation.\n-"

honeybee_grasshopper_radiance/src/HB Annual Peak Values.py

@@ -45,7 +45,7 @@
 
 ghenv.Component.Name = 'HB Annual Peak Values'
 ghenv.Component.NickName = 'PeakValues'
-ghenv.Component.Message = '1.6.1'
+ghenv.Component.Message = '1.6.2'
 ghenv.Component.Category = 'HB-Radiance'
 ghenv.Component.SubCategory = '4 :: Results'
 ghenv.Component.AdditionalHelpFromDocStrings = '2'
@@ -174,7 +174,8 @@ def peak_values(ill_file, su_pattern, coincident):
         # parse the sun-up-hours
         grids, sun_up_hours = _process_input_folder(res_folder, grid_filter_)
         su_pattern = parse_sun_up_hours(sun_up_hours, _hoys_, timestep)
-
+        filt_suh = [suh for suh in sun_up_hours if int(suh) in _hoys_] \
+            if len(_hoys_) != 0 else sun_up_hours
         # compute the average values
         values, hoys = [], []
         for grid_info in grids:
@@ -186,7 +187,7 @@ def peak_values(ill_file, su_pattern, coincident):
                 max_list, max_i = peak_values(ill_file, su_pattern, coincident_)
             values.append(max_list)
             if max_i is not None:
-                hoys.append(sun_up_hours[max_i])
+                hoys.append(filt_suh[max_i])
             else:
                 hoys.append(max_i)
         values = list_to_data_tree(values)