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synced 2024-11-14 16:20:31 -05:00
Delete unused tests
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@ -278,57 +278,13 @@ def make_str(value):
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return str(value)
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def load_umi_template_objects(filename):
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"""Reads
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Args:
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filename (str): path of template file
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Returns:
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dict: Dict of umi_objects
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"""
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with open(filename) as f:
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umi_objects = json.load(f)
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return umi_objects
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def umi_template_object_to_dataframe(umi_dict, umi_object):
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"""Returns flattened DataFrame of umi_objects
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Args:
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umi_dict (dict): dict of umi objects
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umi_object (str): umi_object name
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Returns:
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pandas.DataFrame: flattened DataFrame of umi_objects
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"""
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return json_normalize(umi_dict[umi_object])
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def get_list_of_common_umi_objects(filename):
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"""Returns list of common umi objects
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Args:
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filename (str): path to umi template file
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Returns:
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dict: Dict of common umi objects
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"""
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umi_objects = load_umi_template(filename)
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components = OrderedDict()
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for umi_dict in umi_objects:
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for x in umi_dict:
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components[x] = umi_dict[x].columns.tolist()
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return components
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def newrange(previous, following):
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"""Takes the previous DataFrame and calculates a new Index range. Returns a
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DataFrame with a new index
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Args:
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previous (pandas.DataFrame): previous DataFrame
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following (pandas.DataFrame): follwoing DataFrame
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following (pandas.DataFrame): following DataFrame
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Returns:
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pandas.DataFrame: DataFrame with an incremented new index
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@ -345,170 +301,6 @@ def newrange(previous, following):
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return following
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def type_surface(row):
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"""Takes a boundary and returns its corresponding umi-type
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Args:
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row:
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Returns:
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str: The umi-type of boundary
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"""
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# Floors
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if row["Surface_Type"] == "Floor":
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if row["Outside_Boundary_Condition"] == "Surface":
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return 3
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if row["Outside_Boundary_Condition"] == "Ground":
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return 2
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if row["Outside_Boundary_Condition"] == "Outdoors":
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return 4
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else:
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return np.NaN
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# Roofs & Ceilings
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if row["Surface_Type"] == "Roof":
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return 1
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if row["Surface_Type"] == "Ceiling":
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return 3
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# Walls
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if row["Surface_Type"] == "Wall":
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if row["Outside_Boundary_Condition"] == "Surface":
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return 5
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if row["Outside_Boundary_Condition"] == "Outdoors":
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return 0
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return np.NaN
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def label_surface(row):
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"""Takes a boundary and returns its corresponding umi-Category
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Args:
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row:
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"""
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# Floors
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if row["Surface_Type"] == "Floor":
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if row["Outside_Boundary_Condition"] == "Surface":
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return "Interior Floor"
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if row["Outside_Boundary_Condition"] == "Ground":
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return "Ground Floor"
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if row["Outside_Boundary_Condition"] == "Outdoors":
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return "Exterior Floor"
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else:
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return "Other"
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# Roofs & Ceilings
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if row["Surface_Type"] == "Roof":
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return "Roof"
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if row["Surface_Type"] == "Ceiling":
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return "Interior Floor"
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# Walls
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if row["Surface_Type"] == "Wall":
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if row["Outside_Boundary_Condition"] == "Surface":
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return "Partition"
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if row["Outside_Boundary_Condition"] == "Outdoors":
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return "Facade"
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return "Other"
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def layer_composition(row):
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"""Takes in a series with $id and thickness values and return an array of
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dict of the form {'Material': {'$ref': ref}, 'thickness': thickness} If
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thickness is 'nan', it returns None.
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Returns (list): List of dicts
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Args:
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row (pandas.Series): a row
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"""
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array = []
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ref = row["$id", "Outside_Layer"]
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thickness = row["Thickness", "Outside_Layer"]
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if np.isnan(ref):
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pass
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else:
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array.append({"Material": {"$ref": str(int(ref))}, "Thickness": thickness})
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for i in range(2, len(row["$id"]) + 1):
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ref = row["$id", "Layer_{}".format(i)]
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if np.isnan(ref):
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pass
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else:
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thickness = row["Thickness", "Layer_{}".format(i)]
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array.append(
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{"Material": {"$ref": str(int(ref))}, "Thickness": thickness}
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)
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return array
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def schedule_composition(row):
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"""Takes in a series with $id and \*_ScheduleDay_Name values and return an
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array of dict of the form {'$ref': ref}
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Args:
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row (pandas.Series): a row
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Returns:
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list: list of dicts
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"""
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# Assumes 7 days
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day_schedules = []
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days = [
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"Monday_ScheduleDay_Name",
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"Tuesday_ScheduleDay_Name",
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"Wednesday_ScheduleDay_Name",
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"Thursday_ScheduleDay_Name",
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"Friday_ScheduleDay_Name",
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"Saturday_ScheduleDay_Name",
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"Sunday_ScheduleDay_Name",
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] # With weekends last (as defined in
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# umi-template)
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# Let's start with the `Outside_Layer`
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for day in days:
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try:
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ref = row["$id", day]
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except:
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pass
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else:
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day_schedules.append({"$ref": str(int(ref))})
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return day_schedules
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def year_composition(row):
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"""Takes in a series with $id and ScheduleWeek_Name_{} values and return an
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array of dict of the form {'FromDay': fromday, 'FromMonth': frommonth,
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'Schedule': {'$ref': int( ref)}, 'ToDay': today, 'ToMonth': tomonth}
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Args:
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row (pandas.Series): a row
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Returns:
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list: list of dicts
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"""
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parts = []
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for i in range(1, 26 + 1):
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try:
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ref = row["$id", "ScheduleWeek_Name_{}".format(i)]
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except:
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pass
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else:
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if ~np.isnan(ref):
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fromday = row["Schedules", "Start_Day_{}".format(i)]
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frommonth = row["Schedules", "Start_Month_{}".format(i)]
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today = row["Schedules", "End_Day_{}".format(i)]
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tomonth = row["Schedules", "End_Month_{}".format(i)]
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parts.append(
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{
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"FromDay": fromday,
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"FromMonth": frommonth,
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"Schedule": {"$ref": str(int(ref))},
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"ToDay": today,
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"ToMonth": tomonth,
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}
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)
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return parts
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def date_transform(date_str):
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"""Simple function transforming one-based hours (1->24) into zero-based
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hours (0->23)
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@ -790,23 +582,6 @@ def write_lines(file_path, lines):
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temp_idf_file.close()
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def load_umi_template(json_template):
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"""
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Args:
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json_template: Absolute or relative filepath to an umi json_template
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Returns:
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pandas.DataFrame: 17 DataFrames, one for each component groups
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"""
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if os.path.isfile(json_template):
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with open(json_template) as f:
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dicts = json.load(f, object_pairs_hook=OrderedDict)
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return [{key: json_normalize(value)} for key, value in dicts.items()]
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else:
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raise ValueError("File {} does not exist".format(json_template))
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def check_unique_name(first_letters, count, name, unique_list, suffix=False):
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"""Making sure new_name does not already exist
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@ -983,20 +758,6 @@ def lcm(x, y):
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return lcm
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def reduce(function, iterable, **attr):
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"""
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Args:
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function:
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iterable:
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**attr:
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"""
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it = iter(iterable)
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value = next(it)
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for element in it:
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value = function(value, element, **attr)
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return value
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def _unpack_tuple(x):
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"""Unpacks one-element tuples for use as return values
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