Brief layout sketch of race class

optimization/ASC_optimization.py

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+"""
+formula given:
+S = (D / E) * C * T * P
+optimize for S
+
+do i want to update everything on "get" -> ie. when we run optimization, or before?
+if we're running multiple possibilities does this change anything
+
+let's initially approach this as a prediction _only_ thing
+"""
+import csv
+
+class ASC_Race:
+
+    # Car information
+    PASSENGERS = 4
+    BATTERY_CAPACITY_kWH = 20
+
+    distance_travelled_segments = []
+    passengers = []
+    time_elapsed_mins = 0
+
+    def __init__(self, race_data_csv):
+        # TODO: determine format of data passing and interacts
+        # TODO: how to change route? update during race?
+        with open(race_data_csv, 'r') as f:
+            route = csv.reader(f)
+
+            start = next(route)
+
+            for row in route:
+                # find distance -> hopefully a parameter; append to list
+                # if passengers not flagged
+                self.passengers.append(4)
+
+    @property
+    def D(self):
+        '''
+        Person-Mile Distance
+        '''
+        d = 0
+        for seg, passenger in zip(self.distance_travelled_segments, self.passengers):
+            d += seg * passenger
+
+        return d
+
+    @property
+    def E(self):
+        '''
+        External Energy Usage
+        (n + 1) * Q * M
+        '''
+        nights = 4
+        charging_per_night = 100
+        return nights * charging_per_night
+
+    @property
+    def C(self):
+        '''
+        Completion Factor
+        '''
+        return 1
+
+    @property
+    def T(self):
+        '''
+        Target Speed Derate
+        '''
+        d = 0
+        for seg in self.distance_travelled_segments:
+            d += seg
+
+        return d / self.time_elapsed_mins / 60
+
+    @property
+    def P(self):
+        '''
+        Practicality Score
+        '''
+        return 1
+
+    def update_variables(self, velocity_profile):
+        pass
+
+    def get_S(self, velocity_profile):
+        self.update_variables(velocity_profile)
+        return self.D / self.E * self.C * self.T * self.P
+
+'''
+initial guess = all even, get S
+next guess = all even, except when limited by speed limit, get S
+
+push in one direction (manage each segment?)
+
+'''
+
+
+'''
+what is the trial run going to send in - where are we sending velocity into it
+calculate best velocities -> then find minutes?
+
+lat, lon, city name, city Id, time, temperature, efficiency correction,
+wind speed, wind direction, precipitation, energy solar, energy corrected
+
+routemodel
+speed limits, lat, lon, elevations
+wind
+weather??
+
+solar
+
+
+soc
+??
+auxloss
+
+motor torque
+
+dynamics
+
+
+some guess of S with a general profile of velocities
+change velocities -> see how it affects S
+'''