migrate calculations from baseline dataclass to module

config.yaml

@@ -41,14 +41,14 @@ media:
     Search: 2.60
   noisy_cpm_cpc:
     Amazon:
-      loc: 1
+      loc: 0
       scale: 1
     TV:
-      loc: 1
+      loc: 0
       scale: 1
     Long-Form Video:
-      loc: 1
+      loc: 0
       scale: 1
     Search:
-      loc: 1
+      loc: 0
       scale: 1

src/pysimmmulator/helpers.py

@@ -41,7 +41,7 @@ def __repr__(self):
 
 
 @dataclass
-class baseline:
+class baseline_parameters:
     basic_params: basic_parameters
     base_p: int
     trend_p: int
@@ -52,35 +52,6 @@ class baseline:
 
     def __post_init__(self):
         assert self.error_std < self.base_p, "Error std can not exceed base sales value"
-        self.calculate_params()
-
-    def calculate_params(self):
-        # Number of days to generate data for
-        days = np.arange(0,self.basic_params.years*365)
-
-        # Base sales of base_p units
-        base = np.zeros(shape=self.basic_params.years*365) + self.base_p
-
-        #Trend of trend_p extra units per day
-        trend_cal = (self.trend_p/(self.basic_params.years*365))*self.base_p
-        trend = trend_cal * days
-
-        #Temperature generated by a sin function and we can manipulate how much the sin function goes up or down with temp_var
-        temp = self.temp_var * np.sin(days*3.14/182.5)
-
-        # coefficient of temperature's effect on sales will be a random variable with normal distribution
-        seasonality = np.random.normal(loc = self.temp_coef_mean, scale = self.temp_coef_sd, size = 1) * temp
-
-        # add some noise to the trend
-        error = np.random.normal(loc=0, scale=self.error_std, size=self.basic_params.years*365)
-
-        # Generate series for baseline sales
-        baseline_sales = base + trend + seasonality + error
-
-        # if error term makes baseline_sales negative, make it 0
-        baseline_sales = np.where(baseline_sales < 0, 0, baseline_sales)
-
-        self.output = np.array([days, baseline_sales, base, trend, temp, seasonality, error])
 
 
 @dataclass

src/pysimmmulator/load_parameters.py

@@ -1,7 +1,7 @@
 import yaml
 from pysimmmulator.helpers import (
     basic_parameters,
-    baseline,
+    baseline_parameters,
     media_parameters
 )
 
@@ -13,10 +13,10 @@ def load_config(config_path: str = 'config.yaml') -> dict:
 
 def build_params(config_dict: dict) -> None:
     my_basic_params = basic_parameters(**config_dict['basic_params'])
-    my_baseline_params = baseline(basic_params=my_basic_params, **config_dict['baseline_parms'])
+    my_baseline_params = baseline_parameters(basic_params=my_basic_params, **config_dict['baseline_parms'])
 
 
 cfg = load_config(config_path = 'config.yaml')
 my_basic_params = basic_parameters(**cfg['basic_params'])
-my_baseline_params = baseline(basic_params=my_basic_params, **cfg['baseline_parms'])
+my_baseline_params = baseline_parameters(basic_params=my_basic_params, **cfg['baseline_parms'])
 my_media_params = media_parameters(**cfg['media'])

src/pysimmmulator/simmmulate.py

@@ -1,5 +1,6 @@
 from pysimmmulator.helpers import (
     basic_parameters,
+    baseline_parameters,
     media_parameters
 )
 
@@ -19,6 +20,46 @@ def __init__(self, basic_params: basic_parameters):
         self.basic_params = basic_params
         self.channels = basic_params.channels_clicks + basic_params.channels_impressions
         self.channel_count = len(self.channels)
+
+    def simulate_baseline(self, base_p, trend_p: int, temp_var: int, temp_coef_mean: int, temp_coef_sd: int, error_std: int) -> None:
+        baseline_params = baseline_parameters(
+            basic_params=self.basic_params,
+            base_p=base_p,
+            trend_p=trend_p,
+            temp_var=temp_var,
+            temp_coef_mean=temp_coef_mean,
+            temp_coef_sd=temp_coef_sd,
+            error_std=error_std)
+
+        # Number of days to generate data for
+        days = np.arange(0, self.basic_params.years*365)
+        # Base sales of base_p units
+        base = np.zeros(shape=self.basic_params.years*365) + self.base_p
+        #Trend of trend_p extra units per day
+        trend_cal = (self.trend_p/(self.basic_params.years*365))*self.base_p
+        trend = trend_cal * days
+        #Temperature generated by a sin function and we can manipulate how much the sin function goes up or down with temp_var
+        temp = self.temp_var * np.sin(days*3.14/182.5)
+        # coefficient of temperature's effect on sales will be a random variable with normal distribution
+        seasonality = np.random.normal(loc = self.temp_coef_mean, scale = self.temp_coef_sd, size = 1) * temp
+        # add some noise to the trend
+        error = np.random.normal(loc=0, scale=self.error_std, size=self.basic_params.years*365)
+        # Generate series for baseline sales
+        baseline_sales = base + trend + seasonality + error
+        # if error term makes baseline_sales negative, make it 0
+        baseline_sales = np.where(baseline_sales < 0, 0, baseline_sales)
+
+        self.baseline_sales_df = pd.DataFrame({
+            "days":days,
+            "baseline_sales":baseline_sales,
+            "base":base,
+            "trend":trend,
+            "temp":temp,
+            "seasonality":seasonality,
+            "error":error})
+
+
+
 
 
     def simulate_ad_spend(self, campaign_spend_mean: int, campaign_spend_std: int, max_min_proportion_on_each_channel: dict) -> None:
@@ -57,6 +98,12 @@ def generate_media(self, true_cpm: dict, true_cpc: dict, noisy_cpm_cpc: dict) ->
         media_params = media_parameters(true_cpm, true_cpc, noisy_cpm_cpc)
         media_params.check(basic_params=self.basic_params)
 
+        for channel in media_params.noise_channels:
+            channel_campaigns = self.spend_df[self.spend_df['channel'] == channel].shape[1]
+            self.spend_df[self.spend_df['channel'] == channel]['true_cpm']
+
+        noisy_cpm = true_cpm.values()
+
 
 
     def run_with_config(self):