Refactor notebook into package

data/products.csv

@@ -0,0 +1,11 @@
+,sku,name,type,price,description
+0,JAF-001,nutellaphone who dis?,jaffle,1100,nutella and banana jaffle
+1,JAF-002,doctor stew,jaffle,1100,house-made beef stew jaffle
+2,JAF-003,the krautback,jaffle,1200,lamb and pork bratwurst with house-pickled cabbage sauerkraut and mustard
+3,JAF-004,flame impala,jaffle,1400,"pulled pork and pineapple al pastor marinated in ghost pepper sauce, kevin parker's favorite! "
+4,JAF-005,mel-bun,jaffle,1200,"melon and minced beef bao, in a jaffle, savory and sweet"
+5,BEV-001,tangaroo,beverage,600,mango and tangerine smoothie
+6,BEV-002,chai and mighty,beverage,500,oatmilk chai latte with protein boost
+7,BEV-003,vanilla ice,beverage,600,iced coffee with house-made french vanilla syrup
+8,BEV-004,for richer or pourover ,beverage,700,daily selection of single estate beans for a delicious hot pourover
+9,BEV-005,adele-ade,beverage,400,"a kiwi and lime agua fresca, hello from the other side of thirst"

data/stores.csv

@@ -0,0 +1,6 @@
+,id,name,opened_at,tax_rate
+0,4df82ef5-0cc0-4e67-a538-a0065c18d115,Philadelphia,2016-09-01T00:00:00,0.06
+1,4bd23c5f-7a47-4242-99c2-3e34197c8942,Brooklyn,2017-03-12T00:00:00,0.04
+2,4ef73a58-a12c-4058-b1ae-14e531f58798,Chicago,2018-04-29T00:00:00,0.0625
+3,ca0d2635-72cc-4ac2-b65e-9993972ea055,San Francisco,2018-05-09T00:00:00,0.075
+4,e8f4b489-d780-42df-9ed9-e9defe4bd524,New Orleans,2019-03-10T00:00:00,0.04

data/supplies.csv

@@ -0,0 +1,66 @@
+,id,name,cost,perishable,sku
+0,SUP-001,compostable cutlery - knife,7,False,JAF-001
+1,SUP-002,cutlery - fork,7,False,JAF-001
+2,SUP-003,serving boat,11,False,JAF-001
+3,SUP-004,napkin,4,False,JAF-001
+4,SUP-009,bread,33,True,JAF-001
+5,SUP-011,nutella,46,True,JAF-001
+6,SUP-012,banana,13,True,JAF-001
+7,SUP-001,compostable cutlery - knife,7,False,JAF-002
+8,SUP-002,cutlery - fork,7,False,JAF-002
+9,SUP-003,serving boat,11,False,JAF-002
+10,SUP-004,napkin,4,False,JAF-002
+11,SUP-009,bread,33,True,JAF-002
+12,SUP-010,cheese,20,True,JAF-002
+13,SUP-013,beef stew,169,True,JAF-002
+14,SUP-001,compostable cutlery - knife,7,False,JAF-003
+15,SUP-002,cutlery - fork,7,False,JAF-003
+16,SUP-003,serving boat,11,False,JAF-003
+17,SUP-004,napkin,4,False,JAF-003
+18,SUP-009,bread,33,True,JAF-003
+19,SUP-010,cheese,20,True,JAF-003
+20,SUP-014,lamb and pork bratwurst,234,True,JAF-003
+21,SUP-015,house-pickled cabbage sauerkraut,43,True,JAF-003
+22,SUP-016,mustard,7,True,JAF-003
+23,SUP-001,compostable cutlery - knife,7,False,JAF-004
+24,SUP-002,cutlery - fork,7,False,JAF-004
+25,SUP-003,serving boat,11,False,JAF-004
+26,SUP-004,napkin,4,False,JAF-004
+27,SUP-009,bread,33,True,JAF-004
+28,SUP-010,cheese,20,True,JAF-004
+29,SUP-017,pulled pork,215,True,JAF-004
+30,SUP-018,pineapple,26,True,JAF-004
+31,SUP-021,ghost pepper sauce,20,True,JAF-004
+32,SUP-001,compostable cutlery - knife,7,False,JAF-005
+33,SUP-002,cutlery - fork,7,False,JAF-005
+34,SUP-003,serving boat,11,False,JAF-005
+35,SUP-004,napkin,4,False,JAF-005
+36,SUP-009,bread,33,True,JAF-005
+37,SUP-010,cheese,20,True,JAF-005
+38,SUP-019,melon,33,True,JAF-005
+39,SUP-020,minced beef,124,True,JAF-005
+40,SUP-005,16oz compostable clear cup,13,False,BEV-001
+41,SUP-006,16oz compostable clear lid,4,False,BEV-001
+42,SUP-007,biodegradable straw,13,False,BEV-001
+43,SUP-022,mango,32,True,BEV-001
+44,SUP-023,tangerine,20,True,BEV-001
+45,SUP-005,16oz compostable clear cup,13,False,BEV-002
+46,SUP-006,16oz compostable clear lid,4,False,BEV-002
+47,SUP-007,biodegradable straw,13,False,BEV-002
+48,SUP-008,chai mix,98,True,BEV-002
+49,SUP-024,oatmilk,11,True,BEV-002
+50,SUP-025,whey protein,36,True,BEV-002
+51,SUP-005,16oz compostable clear cup,13,False,BEV-003
+52,SUP-006,16oz compostable clear lid,4,False,BEV-003
+53,SUP-007,biodegradable straw,13,False,BEV-003
+54,SUP-026,coffee,52,True,BEV-003
+55,SUP-027,french vanilla syrup,72,True,BEV-003
+56,SUP-005,16oz compostable clear cup,13,False,BEV-004
+57,SUP-006,16oz compostable clear lid,4,False,BEV-004
+58,SUP-007,biodegradable straw,13,False,BEV-004
+59,SUP-026,coffee,52,True,BEV-004
+60,SUP-005,16oz compostable clear cup,13,False,BEV-005
+61,SUP-006,16oz compostable clear lid,4,False,BEV-005
+62,SUP-007,biodegradable straw,13,False,BEV-005
+63,SUP-028,kiwi,20,True,BEV-005
+64,SUP-029,lime,13,True,BEV-005

requirements.txt

@@ -1 +1,5 @@
 # Add your own dependencies to this file.
+numpy==1.24.0
+pandas==1.5.2
+Faker==15.3.4
+tqdm==4.64.1

src/curves.py

@@ -0,0 +1,86 @@
+import datetime
+
+import numpy as np
+
+
+class Curve:
+    @classmethod
+    def eval(cls, date):
+        x = cls.TranslateDomain(date)
+        x_mod = x % len(cls.Domain)
+        x_translated = cls.Domain[x_mod]
+        return cls.Expr(x_translated)
+
+
+class AnnualCurve(Curve):
+    Domain = np.linspace(0, 2 * np.pi, 365)
+    TranslateDomain = lambda date: date.timetuple().tm_yday
+    Expr = lambda x: (np.cos(x) + 1) / 10 + 0.8
+
+
+class WeekendCurve(Curve):
+    Domain = tuple(range(6))
+    TranslateDomain = lambda date: date.weekday() - 1
+    Expr = lambda x: 0.6 if x >= 6 else 1
+
+
+class GrowthCurve(Curve):
+    Domain = tuple(range(500))
+    TranslateDomain = lambda date: (date.year - 2016) * 12 + date.month
+    # ~ aim for ~20% growth/year
+    Expr = lambda x: 1 + (x / 12) * 0.2
+
+
+class Day(object):
+    EPOCH = datetime.datetime(year=2016, month=9, day=1)
+    SEASONAL_MONTHLY_CURVE = AnnualCurve()
+    WEEKEND_CURVE = WeekendCurve()
+    GROWTH_CURVE = GrowthCurve()
+
+    def __init__(self, date_index, minutes=0):
+        self.date_index = date_index
+        self.date = self.EPOCH + datetime.timedelta(days=date_index, minutes=minutes)
+
+        self.day_of_week = self._get_day_of_week(self.date)
+        self.is_weekend = self._is_weekend(self.date)
+        self.season = self._get_season(self.date)
+
+        self.effects = [
+            self.SEASONAL_MONTHLY_CURVE.eval(self.date),
+            self.WEEKEND_CURVE.eval(self.date),
+            self.GROWTH_CURVE.eval(self.date),
+        ]
+
+    def at_minute(self, minutes):
+        return Day(self.date_index, minutes=minutes)
+
+    def get_effect(self):
+        total = 1
+        for effect in self.effects:
+            total = total * effect
+        return total
+
+        # weekend_effect = 0.8 if date.is_weekend else 1
+        # summer_effect = 0.7 if date.season == 'summer' else 1
+
+    def _get_day_of_week(self, date):
+        return date.weekday()
+
+    def _is_weekend(self, date):
+        # 5 + 6 are weekends
+        return date.weekday() >= 5
+
+    def _get_season(self, date):
+        month_no = date.month
+        day_no = date.day
+
+        if month_no in (1, 2) or (month_no == 3 and day_no < 21):
+            return "winter"
+        elif month_no in (3, 4, 5) or (month_no == 6 and day_no < 21):
+            return "spring"
+        elif month_no in (6, 7, 8) or (month_no == 9 and day_no < 21):
+            return "summer"
+        elif month_no in (9, 10, 11) or (month_no == 12 and day_no < 21):
+            return "fall"
+        else:
+            return "winter"

src/customers/customers.py

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+import random
+import uuid
+
+import numpy as np
+from faker import Faker
+
+from customers.order import Order
+from stores.inventory import Inventory
+
+fake = Faker()
+Faker.seed(123456789)
+
+
+class Customer(object):
+    def __init__(self, store):
+        self.customer_id = str(uuid.uuid4())
+        self.store = store
+        self.name = fake.name()
+        self.favorite_number = int(np.random.rand() * 100)
+
+    def p_buy_season(self, day):
+        return self.store.p_buy(day)
+
+    def p_buy(self, day):
+        p_buy_season = self.p_buy_season(day)
+        p_buy_persona = self.p_buy_persona(day)
+        p_buy_on_day = (p_buy_season * p_buy_persona) ** 0.5
+        return p_buy_on_day
+
+    def get_order(self, day):
+        items = self.get_order_items(day)
+        order_time_delta = self.get_order_time(day)
+
+        order_time = day.at_minute(order_time_delta + self.store.opens_at(day))
+        if not self.store.is_open_at(order_time):
+            return None
+
+        return Order(self, items, self.store, order_time)
+
+    def get_order_items(self, day):
+        raise NotImplemented()
+
+    def get_order_time(self, store, day):
+        raise NotImplemented()
+
+    def p_buy_persona(self, day):
+        raise NotImplemented()
+
+    def sim_day(self, day):
+        p_buy = self.p_buy(day)
+        p_buy_threshold = np.random.random()
+
+        if p_buy_threshold < p_buy:
+            return self.get_order(day)
+        else:
+            return None
+
+    def to_dict(self):
+        return {
+            "id": self.customer_id,
+            "name": self.name,
+        }
+
+
+class RemoteWorker(Customer):
+    "This person works from a coffee shop"
+
+    def p_buy_persona(self, day):
+        buy_propensity = (self.favorite_number / 100) * 0.4
+        return 0.001 if day.is_weekend else buy_propensity
+
+    def get_order_time(self, day):
+        # most likely to order in the morning
+        # exponentially less likely to order in the afternoon
+        avg_time = 420
+        order_time = np.random.normal(loc=avg_time, scale=180)
+        return max(0, int(order_time))
+
+    def get_order_items(self, day):
+        num_drinks = 1
+        food = []
+
+        if random.random() > 0.7:
+            num_drinks = 2
+
+        if random.random() > 0.7:
+            food = Inventory.get_food(1)
+
+        return Inventory.get_drink(num_drinks) + food
+
+
+class BrunchCrowd(Customer):
+    "Do you sell mimosas?"
+
+    def p_buy_persona(self, day):
+        buy_propensity = 0.2 + (self.favorite_number / 100) * 0.2
+        return buy_propensity if day.is_weekend else 0
+
+    def get_order_time(self, day):
+        # most likely to order in the early afternoon
+        avg_time = 300 + ((self.favorite_number - 50) / 50) * 120
+        order_time = np.random.normal(loc=avg_time, scale=120)
+        return max(0, int(order_time))
+
+    def get_order_items(self, day):
+        num_customers = 1 + int(self.favorite_number / 20)
+        return Inventory.get_drink(num_customers) + Inventory.get_food(num_customers)
+
+
+class Commuter(Customer):
+    "the regular, thanks"
+
+    def p_buy_persona(self, day):
+        buy_propensity = 0.5 + (self.favorite_number / 100) * 0.3
+        return 0.001 if day.is_weekend else buy_propensity
+
+    def get_order_time(self, day):
+        # most likely to order in the morning
+        # exponentially less likely to order in the afternoon
+        avg_time = 60
+        order_time = np.random.normal(loc=avg_time, scale=30)
+        return max(0, int(order_time))
+
+    def get_order_items(self, day):
+        return Inventory.get_drink(1)
+
+
+class Student(Customer):
+    "coffee might help"
+
+    def p_buy_persona(self, day):
+        if day.season == "summer":
+            return 0
+        else:
+            buy_propensity = 0.1 + (self.favorite_number / 100) * 0.4
+            return buy_propensity
+
+    def get_order_time(self, day):
+        # later is better
+        avg_time = 9 * 60
+        order_time = np.random.normal(loc=avg_time, scale=120)
+        return max(0, int(order_time))
+
+    def get_order_items(self, day):
+        food = []
+        if random.random() > 0.5:
+            food = Inventory.get_food(1)
+
+        return Inventory.get_drink(1) + food
+
+
+class Casuals(Customer):
+    "just popping in"
+
+    def p_buy_persona(self, day):
+        return 0.1
+
+    def get_order_time(self, day):
+        avg_time = 5 * 60
+        order_time = np.random.normal(loc=avg_time, scale=120)
+        return max(0, int(order_time))
+
+    def get_order_items(self, day):
+        num_drinks = int(random.random() * 10 / 3)
+        num_food = int(random.random() * 10 / 3)
+        return Inventory.get_drink(num_drinks) + Inventory.get_food(num_food)

src/customers/order.py

@@ -0,0 +1,33 @@
+import uuid
+
+from customers.order_item import OrderItem
+
+
+class Order(object):
+    def __init__(self, customer, items, store, day):
+        self.order_id = str(uuid.uuid4())
+        self.customer = customer
+        self.items = [OrderItem(self.order_id, item) for item in items]
+        self.store = store
+        self.day = day
+        self.subtotal = sum(i.item.price for i in self.items)
+        self.tax_paid = store.tax_rate * self.subtotal
+        self.order_total = self.subtotal + self.tax_paid
+
+    def __str__(self):
+        return f"{self.customer.name} bought {str(self.items)} at {self.day}"
+
+    def to_dict(self):
+        return {
+            "id": self.order_id,
+            "customer": self.customer.customer_id,
+            "ordered_at": self.day.date.isoformat(),
+            # "order_month": self.day.date.strftime("%Y-%m"),
+            "store_id": self.store.store_id,
+            "subtotal": int(self.subtotal * 100),
+            "tax_paid": int(self.tax_paid * 100),
+            "order_total": int(self.order_total * 100),
+        }
+
+    def items_to_dict(self):
+        return [i.to_dict(self.order_id) for i in self.items]

src/customers/order_item.py

@@ -0,0 +1,15 @@
+import uuid
+
+
+class OrderItem(object):
+    def __init__(self, order_id, item):
+        self.item_id = str(uuid.uuid4())
+        self.order_id = order_id
+        self.item = item
+
+    def to_dict(self):
+        return {
+            "id": self.item_id,
+            "order_id": self.order_id,
+            "sku": self.item.sku,
+        }