#### Import Libraries and Functions
from PyHydroQC import anomaly_utilities, rules_detect, calibration
from PyHydroQC.parameters import site_params
import matplotlib.pyplot as plt
import pandas as pd
import math
#### Retrieve data
#########################################
site = 'MainStreet'
sensors = ['temp', 'cond', 'ph', 'do']
years = [2014, 2015, 2016, 2017, 2018, 2019]
sensor_array = anomaly_utilities.get_data(sensors=sensors,
site=site,
years=years,
path="./LRO_data/")
#### Rules Based Anomaly Detection
#########################################
range_count = dict()
persist_count = dict()
rules_metrics = dict()
for snsr in sensor_array:
sensor_array[snsr], range_count[snsr] = rules_detect.range_check(
df=sensor_array[snsr],
maximum=site_params[site][snsr]['max_range'],
minimum=site_params[site][snsr]['min_range'])
sensor_array[snsr], persist_count[snsr] = rules_detect.persistence(
df=sensor_array[snsr],
length=site_params[site][snsr]['persist'],
from PyHydroQC import anomaly_utilities
from PyHydroQC import model_workflow
from PyHydroQC import rules_detect
from PyHydroQC import ARIMA_correct
from PyHydroQC import modeling_utilities
from PyHydroQC.model_workflow import ModelType
# Parameters may be specified in a parameters file or in the same script
from Examples.FB_parameters import site_params, LSTM_params, calib_params
#### Retrieve data
#########################################
site = 'FranklinBasin'
sensors = ['temp', 'cond', 'ph', 'do']
sensor_array = anomaly_utilities.get_data(sensors=sensors, filename='FB2017.csv', path="LRO_data/")
#### Rules Based Anomaly Detection
#########################################
range_count = dict()
persist_count = dict()
rules_metrics = dict()
for snsr in sensor_array:
sensor_array[snsr], range_count[snsr] = rules_detect.range_check(
df=sensor_array[snsr], maximum=site_params[site][snsr]['max_range'], minimum=site_params[site][snsr]['min_range'])
sensor_array[snsr], persist_count[snsr] = rules_detect.persistence(
df=sensor_array[snsr], length=site_params[site][snsr]['persist'], output_grp=True)
sensor_array[snsr] = rules_detect.interpolate(df=sensor_array[snsr])
print('Rules based detection complete.\n')
#### Detect Calibration Events
]
year = [2014, 2015, 2016, 2017, 2018, 2019]
sensor = ['temp', 'cond', 'ph', 'do']
site_detect = []
rules_metrics = []
for j in range(0, len(sites)):
site = sites[j]
if site == 'BlackSmithFork': year.pop(0)
print(
"\n\n###########################################\n#Processing data for site: "
+ sites[j] + ".\n###########################################")
df_full, sensor_array = anomaly_utilities.get_data(sites[j],
sensor,
year,
path="../LRO_data/")
# RULES BASED ANOMALY DETECTION #
#########################################
range_count = []
persist_count = []
methods_output.rules_metrics = []
# size = []
for i in range(0, len(sensor_array)):
sensor_array[sensor[i]], r_c = rules_detect.range_check(
sensor_array[sensor[i]], site_params[j][i].max_range,
site_params[j][i].min_range)
range_count.append(r_c)
sensor_array[sensor[i]], p_c = rules_detect.persistence(
sensor_array[sensor[i]], site_params[j][i].persist)