# DEFINE SITE and VARIABLE # ######################################### # site = "BlackSmithFork" # site = "FranklinBasin" # site = "MainStreet" # site = "Mendon" # site = "TonyGrove" site = "WaterLab" sensor = ['temp', 'cond', 'ph', 'do'] year = [2014, 2015, 2016, 2017, 2018, 2019] # GET DATA # ######################################### df_full, sensor_array = anomaly_utilities.get_data(site, sensor, year, path="LRO_data/") temp_df = sensor_array[sensor[0]] cond_df = sensor_array[sensor[1]] ph_df = sensor_array[sensor[2]] do_df = sensor_array[sensor[3]] # PARAMETER SELECTION # ######################################### # Need to use an automated method to generalize getting p,d,q parameters # These are the results of using auto.ARIMA to determine p,d,q parameters in R sites = { 'BlackSmithFork': 0, 'FranklinBasin': 1, 'MainStreet': 2, 'Mendon': 3,
# site = "FranklinBasin" # site = "MainStreet" site = "Mendon" # site = "TonyGrove" # site = "WaterLab" # sensor = "temp" sensor = "cond" # sensor = "ph" # sensor = "do" # sensor = "turb" # sensor = "stage" year = 2017 # Get data df_full, df = anomaly_utilities.get_data(site, sensor, year, path="./LRO_data/") matplotlib.pyplot.figure() matplotlib.pyplot.plot(df['raw'], 'b', label='original data') matplotlib.pyplot.legend() matplotlib.pyplot.ylabel(sensor) matplotlib.pyplot.show() # Prophet .fit(df) requires columns ds (dates) and y df['y'] = df['raw'] df['ds'] = df.index m = Prophet( changepoint_range=1.0, n_changepoints=150,