Second round of updates

glhe/aggregation/base_agg.py

@@ -1,36 +1,36 @@
-import os
 from abc import ABC, abstractmethod
+from pathlib import Path
 
 import numpy as np
 
-from glhe.utilities.functions import Interpolator1D, Interpolator1DFromFile
-from glhe.utilities.functions import Interpolator2DFromFile
-
-join = os.path.join
-norm = os.path.normpath
-cwd = os.getcwd()
+from glhe.utilities.functions import InterpolatorBase, Interpolator1D, Interpolator1DFromFile, Interpolator2DFromFile
 
 
 class BaseAgg(ABC):
 
     def __init__(self, inputs: dict):
         # g-function values
         if 'g-function-path' in inputs:
-            path_g = norm(join(cwd, inputs['g-function-path']))
-            self.interp_g = Interpolator1DFromFile(path_g)
+            p = Path(inputs['g-function-path'])
+            if not p.is_absolute():
+                p = Path.cwd() / inputs['g-function-path']
+            self.interp_g = Interpolator1DFromFile(p)
         elif 'lntts' and 'g-values' in inputs:
             data_g = np.transpose(np.array([inputs['lntts'], inputs['g-values']]))
-            self.interp_g = Interpolator1D(data_g[:, 0], data_g[:, 1])
+            self.interp_g: InterpolatorBase = Interpolator1D(data_g[:, 0], data_g[:, 1])
         else:
             raise KeyError('g-function data not found.')
 
         # g_b-function values
         self.interp_g_b = None
         if 'g_b-function-path' in inputs:
+            p = Path(inputs['g_b-function-path'])
+            if not p.is_absolute():
+                p = Path.cwd() / inputs['g_b-function-path']
             if 'g_b-flow-rates' in inputs:
-                self.interp_g_b = Interpolator2DFromFile(inputs['g_b-function-path'], inputs['g_b-flow-rates'])
+                self.interp_g_b: InterpolatorBase = Interpolator2DFromFile(p, inputs['g_b-flow-rates'])
             else:
-                self.interp_g_b = Interpolator1DFromFile(inputs['g_b-function-path'])
+                self.interp_g_b: InterpolatorBase = Interpolator1DFromFile(p)
         elif 'lntts_b' and 'g_b-values' in inputs:
             data_g_b = np.transpose(np.array([inputs['lntts_b'], inputs['g_b-values']]))
             self.interp_g_b = Interpolator1D(data_g_b[:, 0], data_g_b[:, 1])

glhe/aggregation/dynamic.py

@@ -23,16 +23,14 @@ def __init__(self, inputs: dict):
         self.sub_hr = SubHour(inputs)
 
         # set expansion rate. apply default if needed.
-        try:
+        self.exp_rate = 1.62
+        if 'expansion-rate' in inputs:
             self.exp_rate = inputs['expansion-rate']
-        except KeyError:  # pragma: no cover
-            self.exp_rate = 1.62  # pragma: no cover
 
         # set the number of bins per level. apply default if needed.
-        try:
+        self.bins_per_level = 9
+        if 'number-bins-per-level' in inputs:
             self.bins_per_level = inputs['number-bins-per-level']
-        except KeyError:  # pragma: no cover
-            self.bins_per_level = 9  # pragma: no cover
 
         # total simulation runtime to make available for method
         run_time = inputs['runtime']
@@ -47,17 +45,13 @@ def __init__(self, inputs: dict):
         t = SEC_IN_HOUR
 
         # initialize the dynamic method
-        initialized = False
-        while not initialized:
+        while True:
             for _ in range(self.bins_per_level):
                 t += dt
                 self.energy = np.insert(self.energy, 0, 0)
                 self.dts = np.insert(self.dts, 0, dt)
-
                 if t >= run_time:
-                    initialized = True
-                    break
-
+                    return
             dt *= self.exp_rate
 
     def aggregate(self, time: int, energy: float):
@@ -111,22 +105,20 @@ def calc_temporal_superposition(self, time_step: int, flow_rate: float = None) -
             if not flow_rate:
                 g_b = self.interp_g_b.interpolate(lntts)
             else:
-                g_b = np.flipud(self.interp_g_b(lntts, flow_rate))
+                # TODO: This is not covered by tests, and may need adjusting the interpolator class to work properly
+                g_b = np.flipud(self.interp_g_b.interpolate(lntts, flow_rate))
             return float(np.dot(dq, g)), float(np.dot(dq, g_b))
         else:
             # convolution for "g" g-functions only
             return float(np.dot(dq, g))
 
     def get_g_value(self, time_step: int) -> float:
         lntts = np.log(time_step / self.ts)
-        return float(self.interp_g.interpolate(lntts))
+        return self.interp_g.interpolate(lntts)
 
     def get_g_b_value(self, time_step: int, flow_rate: float = None) -> float:
         lntts = np.log(time_step / self.ts)
-        if not flow_rate:
-            return float(self.interp_g_b.interpolate(lntts))
-        else:
-            return float(self.interp_g_b.interpolate(lntts, flow_rate))
+        return self.interp_g_b.interpolate(lntts, flow_rate)
 
     def get_q_prev(self) -> float:
         return float(self.sub_hr.energy[-1] / self.sub_hr.dts[-1])

glhe/aggregation/no_agg.py

@@ -48,6 +48,7 @@ def calc_temporal_superposition(self, time_step: int, _: float = None) -> float:
             # convolution for "g" g-functions only
             return float(np.dot(dq, g))
 
+    # TODO: Should these abstract methods be populated to do anything? If not, should they actually be abstract methods?
     def get_g_value(self, time_step: int) -> float:
         pass  # pragma: no cover
 

glhe/aggregation/static.py

@@ -63,13 +63,14 @@ def aggregate(self, time: int, energy: float):
             # store whatever rolls off of the sub-hourly method
 
             # need to think about this
+            # TODO: If this is returning early, what about the prev_update_time assignments below?
             self.energy[-1] += e_1
             return
         else:
             # aggregate
             # TODO: The next two lines are showing as unused in Pycharm, is this IF block needed then?
             # dts_flip = np.flipud(self.dts)
-            # vals, idxs, cnts = np.unique(dts_flip, return_counts=True, return_index=True)
+            # vals, indexes, counts = np.unique(dts_flip, return_counts=True, return_index=True)
             self.energy[-1] += e_1
 
             # numpy split will do a lot of work too

glhe/ground_temps/base.py

@@ -7,11 +7,11 @@ class BaseGroundTemp(ABC):
     """
 
     @abstractmethod
-    def get_temp(self, time: int, depth: float):
+    def get_temp(self, time: int, depth: float) -> float:
         """
          Getter method for ground temperatures
 
-        :param time: time for ground temperature [s]
+        :param time: time for ground temperature [s]  # TODO: Should we use float instead of int for time?
         :param depth: depth for ground temperature [m]
         :return: ground temperature [C]
         """

glhe/input_processor/input_processor.py

@@ -1,4 +1,6 @@
+from json import loads
 import os
+from pathlib import Path
 
 from jsonschema import SchemaError, ValidationError, validate
 
@@ -8,7 +10,7 @@
 
 class InputProcessor:
 
-    def __init__(self, json_input_path: str):
+    def __init__(self, json_input_path: Path):
         """
         Initialize the input processor, process input file, and store the input information.
 
@@ -18,11 +20,11 @@ def __init__(self, json_input_path: str):
         """
 
         # check if file exists
-        if not os.path.exists(json_input_path):
-            raise FileNotFoundError("Input file: '{}' does not exist.".format(json_input_path))
+        if not json_input_path.exists():
+            raise FileNotFoundError(f"Input file: '{json_input_path}' does not exist.")
 
         # load the input file
-        self.input_dict = lower_obj(load_json(json_input_path))
+        self.input_dict: dict = lower_obj(loads(json_input_path.read_text()))
 
         # validate the inputs
         self.validate_inputs(self.input_dict)

glhe/output_processor/output_processor.py

@@ -1,21 +1,19 @@
 import datetime as dt
-import os
-from os.path import join, normpath
-
-import pandas as pd
+from pathlib import Path
 
 
 class OutputProcessor:
 
-    def __init__(self, output_dir: str, output_name: str):
+    def __init__(self, output_dir: Path, output_name: str):
         """
         Output processor manages output data
         """
 
         self.output_dir = output_dir
         self.output_file = output_name
-        self.write_path = normpath(join(output_dir, output_name))
-        self.df = pd.DataFrame()
+        self.write_path = output_dir / output_name
+        # self.df = pd.DataFrame()
+        self.output_data = []
         self.idx_count = 0
 
     def collect_output(self, data_dict: dict) -> None:
@@ -24,30 +22,40 @@ def collect_output(self, data_dict: dict) -> None:
 
         :param data_dict: dictionary of data to be logged
         """
-
-        df_temp = pd.DataFrame(data_dict, index=[self.idx_count])
-        self.df = pd.concat([self.df, df_temp], axis=0, sort=True)
-        self.idx_count += 1
+        self.output_data.append(data_dict)
+        # df_temp = pd.DataFrame(data_dict, index=[self.idx_count])
+        # self.df = pd.concat([self.df, df_temp], axis=0, sort=True)
+        # self.idx_count += 1
 
     def write_to_file(self) -> None:
         """
         Write the DataFrame holding the simulation data to a file.
         """
-        if os.path.exists(self.write_path):
-            os.remove(self.write_path)
+        if self.write_path.exists():
+            self.write_path.unlink()
+
+        header_row = ['Date/Time']
+        header_row.extend([key for key in sorted(self.output_data[0].keys()) if key != 'Elapsed Time [s]'])
 
-        self.convert_time_to_timestamp()
-        self.df.to_csv(self.write_path)
+        time_stamps = self.convert_time_to_timestamp()
+        with self.write_path.open('w') as f:
+            for i, d in enumerate(self.output_data):
+                if i == 0:
+                    f.write(','.join(header_row) + '\n')
+                row = [time_stamps[i]]
+                sorted_values_without_time = [str(d[key]) for key in sorted(d.keys()) if key != 'Elapsed Time [s]']
+                row.extend(sorted_values_without_time)
+                f.write(','.join(row) + '\n')
 
-    def convert_time_to_timestamp(self) -> None:
+    def convert_time_to_timestamp(self) -> list[str]:
         """
         Convert the 'Elapsed Time' column to a standardized date/time format.
         """
         try:
-            dts = [dt.timedelta(seconds=x) for x in self.df['Elapsed Time [s]'].values.tolist()]
+            raw_dts = [d['Elapsed Time [s]'] for d in self.output_data]
+            dts = [dt.timedelta(seconds=x) for x in raw_dts]
             start_time = dt.datetime(year=dt.datetime.now().year, month=1, day=1, hour=0, minute=0)
-            time_stamps = [start_time + x for x in dts]
-            self.df['Date/Time'] = time_stamps
-            self.df.set_index('Date/Time', inplace=True)
+            time_stamps = [str(start_time + x) for x in dts]
+            return time_stamps
         except KeyError:
             pass

glhe/profiles/external_base.py

@@ -1,24 +1,69 @@
-import pandas as pd
-from scipy.interpolate import interp1d
+from pathlib import Path
 
+import csv
+from datetime import datetime
+from glhe.utilities.functions import Interpolator1D
 
-class ExternalBase(object):
-
-    def __init__(self, path, col_num):
 
-        df = pd.read_csv(path, index_col=0, parse_dates=True)
-        df['delta t'] = df.index.to_series().diff().dt.total_seconds()
-        df['delta t'].iat[0] = 0
-        x_range = df['delta t'].cumsum().tolist()
-        y_range = df.iloc[:, col_num].tolist()
+class ExternalBase(object):
 
+    def __init__(self, path: Path, col_num: int):
+        x_range, y_range = self.read_csv_and_compute_delta_t(path, col_num)
         # added to allow multi-year simulations
         self.max_time = 0
-        x_range.append(x_range[-1] + (x_range[-1] - x_range[-2]))
-        y_range.append(y_range[0])
         self.max_time = x_range[-1]
+        self._interp_values = Interpolator1D(x_range, y_range)
+
+    @staticmethod
+    def read_csv_and_compute_delta_t(file_path: Path, col_num: int) -> [list, list]:
+        delta_t_values = []
+        y_values = []
+        last_timestamp = None
+
+        # Open CSV file and read data
+        with open(file_path, 'r') as csvfile:
+            csvreader = csv.reader(csvfile)
+            next(csvreader)  # Read header
+
+            for row in csvreader:
+                timestamp_str = row[0]
+
+                # Convert timestamp string to datetime object
+                current_timestamp = None
+                time_stamp_formats = ['%Y-%m-%d %H:%M:%S', '%m/%d/%Y %H:%M', '%m/%d/%Y %H:%M:%S']
+                for t in time_stamp_formats:
+                    try:
+                        current_timestamp = datetime.strptime(timestamp_str, t)
+                        break
+                    except ValueError:
+                        continue
+                if not current_timestamp:
+                    raise ValueError(f"Bad timestamp format for timestamp: {timestamp_str}")
+
+                if last_timestamp is not None:
+                    # Calculate delta t in seconds
+                    delta_t = (current_timestamp - last_timestamp).total_seconds()
+                    delta_t_values.append(delta_t)
+
+                # Append value to y_values
+                y_values.append(row[col_num + 1])
+
+                last_timestamp = current_timestamp
+
+        # Initialize x_range with cumulative sum of delta_t_values
+        x_range = [0]
+        cumulative_sum = 0
+        for delta_t in delta_t_values:
+            cumulative_sum += delta_t
+            x_range.append(cumulative_sum)
+
+        # Append an extrapolated value to x_range
+        x_range.append(x_range[-1] + (x_range[-1] - x_range[-2]))
+
+        # Append the first y_value to y_range
+        y_values.append(y_values[0])
 
-        self._interp_values = interp1d(x_range, y_range)
+        return x_range, y_values
 
     def get_value(self, time) -> float:
-        return float(self._interp_values(time % self.max_time))
+        return float(self._interp_values.interpolate(time % self.max_time))