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loggernet.py
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loggernet.py
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import logging
import numpy as np
import pandas as pd
import util_db
import processing
'''
File equivelent to a specific 'sensor' file, but for the loggernet data.
Gives details of how the loggernet files are to be decoded and ingested
into the database.
'''
logger = logging.getLogger('olmo.loggernet')
def ingest_loggernet_file(file_path, file_type, clients):
'''Ingest loggernet files.
NOTE: All cols that aren't strings should be floats, even if you think
it will always be an int.
Parameters
----------
file_path : string
file_type : string
The 'basename' of the file, should correspond to one of those in the config.
'''
def load_data(
file_path, data_cols, float_cols, str_cols=[],
timezone='CET', rows_to_skip=None, time_col="TMSTAMP"):
pd.set_option('precision', 6)
str_cols_dict = {c: str for c in str_cols} if str_cols else None
df = pd.read_csv(file_path, sep=',', skiprows=0, header=1, dtype=str_cols_dict)
if rows_to_skip is not None:
df = df.iloc[rows_to_skip:]
df['date'] = pd.to_datetime(df[time_col], format='%Y-%m-%d %H:%M:%S')
df = df[['date'] + data_cols]
# There can be strings inserted as "NAN", set these to the -7999 nan.
for col in df.columns:
if col in float_cols:
if df[col].dtypes == 'object':
df.loc[df[col].str.match('NAN'), col] = -7999
# Force cols to have time np.float64
df = util_db.force_float_cols(df, float_cols=float_cols)
# Loggernet data is in CET, but all influx data should be utc.
df = df.set_index('date').tz_localize(timezone, ambiguous='infer').tz_convert('UTC')
return df
# ==================================================================== #
if file_type == 'CR6_EOL2p0_meteo_ais_':
data_cols = [
"distance", "Latitude_decimal", "Longitude_decimal", "temperature_digital",
"pressure_digital", "humidity_digital", "dew_point", "wind_speed_digital",
"wind_direction_digital"
]
float_cols = data_cols
df_all = load_data(file_path, data_cols, float_cols, timezone='CET')
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'gill_weatherstation',
'tag_edge_device': 'cr6',
'tag_platform': 'munkholmen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'meteo_position_munkholmen'
field_keys = {"Latitude_decimal": 'latitude',
"Longitude_decimal": 'longitude'}
tag_values['tag_sensor'] = 'gps'
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'latitude', lower=62.5, upper=64)
df = processing.constant_val_filter(df, 'longitude', lower=10, upper=11)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_position_displacement_munkholmen'
field_keys = {"distance": 'position_displacement'}
tag_values['tag_sensor'] = 'gps'
tag_values['tag_unit'] = 'metres'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
# df = processing.constant_val_filter(df, 'displacement', lower=0, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_temperature_munkholmen'
field_keys = {"temperature_digital": 'temperature'}
tag_values['tag_unit'] = 'degrees_celsius'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'temperature', lower=-50, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_atmospheric_pressure_munkholmen'
field_keys = {"pressure_digital": 'atmospheric_pressure'}
tag_values['tag_unit'] = 'hecto_pascal'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'atmospheric_pressure', lower=500, upper=1500)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_humidity_munkholmen'
field_keys = {"humidity_digital": 'humidity'}
tag_values['tag_unit'] = 'percent'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'humidity', lower=0, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_dew_point_munkholmen'
field_keys = {"dew_point": 'dew_point'}
tag_values['tag_unit'] = 'degrees_celsius'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
# df = processing.constant_val_filter(df, 'dew_point', lower=-50, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_wind_speed_munkholmen'
field_keys = {"wind_speed_digital": 'wind_speed'}
tag_values['tag_unit'] = 'metres_per_second'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'wind_speed', lower=0, upper=140)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_wind_direction_munkholmen'
field_keys = {"wind_direction_digital": 'wind_direction'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'wind_direction', lower=1, upper=360)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'CR6_EOL2p0_Power_':
data_cols = [
"battery_voltage", "PV_Voltage1",
"Total_of_battery", "PV1_current", "Input_current",
"Total_charge_current_of_battery", "Load_current",
"Total_discharge_of_battery", "Solar_reg_temperature",
"error", "Load_output", "AUX1", "AUX2",
"Energy_input_24H", "Energy_input_total", "Energy_output_24H",
"Energy_output_total", "Derating", "Tarom_checksum"
]
float_cols = data_cols
df_all = load_data(file_path, data_cols, float_cols)
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'solar_regulator',
'tag_edge_device': 'cr6',
'tag_platform': 'munkholmen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'power_voltage_munkholmen'
field_keys = {"battery_voltage": 'battery_voltage',
"PV_Voltage1": "pv_voltage1"}
tag_values['tag_unit'] = 'volts'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'battery_voltage', lower=0, upper=50)
df = processing.constant_val_filter(df, 'pv_voltage1', lower=0, upper=50)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'power_current_munkholmen'
field_keys = {"PV1_current": "input_current",
"Load_current": "load_current"}
tag_values['tag_unit'] = 'amperes'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'input_current', lower=0, upper=100)
df = processing.constant_val_filter(df, 'load_current', lower=0, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'power_energy_use_munkholmen'
field_keys = {"Energy_input_24H": "energy_input_24h",
"Energy_input_total": "energy_input_total",
"Energy_output_24H": "energy_output_24h",
"Energy_output_total": "energy_output_total"}
tag_values['tag_unit'] = 'amp_hours'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'power_aux_munkholmen'
field_keys = {"error": "error",
"AUX1": "aux1",
"AUX2": "aux2",
"Derating": "derating",
"Tarom_checksum": "tarom_checksum",
"Total_discharge_of_battery": "total_discharge_of_battery",
"Total_charge_current_of_battery": "total_charge_current_of_battery",
"Load_output": "load_output",
"Total_of_battery": "total_of_battery"}
tag_values['tag_unit'] = 'none'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'solar_regulator_power_munkholmen'
field_keys = {"Solar_reg_temperature": "Solar_reg_temperature"}
tag_values['tag_unit'] = 'degrees_celsius'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'Solar_reg_temperature', lower=-50, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'CR6_EOL2p0_Meteo_avgd_':
data_cols = [
"temperature_digital_Avg", "pressure_digital_Avg",
"humidity_digital_Avg", "wind_speed_digital", "wind_direction_digital"
]
float_cols = data_cols
df_all = load_data(file_path, data_cols, float_cols)
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'gill_weatherstation',
'tag_edge_device': 'cr6',
'tag_platform': 'munkholmen',
'tag_data_level': 'processed',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'meteo_temperature_avg_munkholmen'
field_keys = {"temperature_digital_Avg": 'temperature_avg'}
tag_values['tag_unit'] = 'degrees_celsius'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'temperature_avg', lower=-50, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_atmospheric_pressure_avg_munkholmen'
field_keys = {"pressure_digital_Avg": 'atmospheric_pressure_avg'}
tag_values['tag_unit'] = 'hecto_pascal'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'atmospheric_pressure_avg', lower=500, upper=1500)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_humidity_avg_munkholmen'
field_keys = {"humidity_digital_Avg": 'humidity_avg'}
tag_values['tag_unit'] = 'percent'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'humidity_avg', lower=0, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_wind_speed_avg_munkholmen'
field_keys = {"wind_speed_digital": 'wind_speed_avg'}
tag_values['tag_unit'] = 'metres_per_second'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'wind_speed_avg', lower=0, upper=140)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'meteo_wind_direction_avg_munkholmen'
field_keys = {"wind_direction_digital": 'wind_direction_avg'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'wind_direction_avg', lower=1, upper=360)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'CR6_EOL2p0_Current_':
df = pd.read_csv(file_path, skiprows=1)
data_cols = list(df.columns[2:])
float_cols = [d for d in data_cols if d not in ['ADCP_status_code', 'data_adcp(1)']]
df_all = load_data(file_path, data_cols, float_cols)
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'signature_100',
'tag_edge_device': 'cr6',
'tag_platform': 'munkholmen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_current_speed_munkholmen'
field_keys = {i: i for i in data_cols if i.startswith('current_speed')}
tag_values['tag_unit'] = 'metres_per_second'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_current_direction_munkholmen'
field_keys = {i: i for i in data_cols if i.startswith('current_direction')}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_amplitude_munkholmen'
field_keys = {i: i for i in data_cols if i.startswith('amplitude')}
tag_values['tag_unit'] = 'db'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_correlation_munkholmen'
field_keys = {i: i for i in data_cols if i.startswith('correlation')}
tag_values['tag_unit'] = 'percent'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_velocity_munkholmen'
field_keys = {i: i for i in data_cols if i.startswith('velocity')}
tag_values['tag_unit'] = 'metres_per_second'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_battery_voltage_munkholmen'
field_keys = {'ADCP_battery_voltage': 'battery_voltage'}
tag_values['tag_unit'] = 'volts'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_error_code_munkholmen'
field_keys = {'ADCP_error_code': 'error_code'}
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_heading_munkholmen'
field_keys = {'ADCP_heading': 'heading'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_pitch_munkholmen'
field_keys = {'ADCP_pitch': 'pitch'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_pressure_munkholmen'
field_keys = {'ADCP_pressure': 'pressure'}
tag_values['tag_unit'] = 'dbar'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_roll_munkholmen'
field_keys = {'ADCP_Roll': 'roll'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_sound_speed_munkholmen'
field_keys = {'ADCP_sound_speed': 'sound_speed'}
tag_values['tag_unit'] = 'metres_per_second'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_temperature_munkholmen'
field_keys = {'ADCP_temperature': 'temperature'}
tag_values['tag_unit'] = 'degrees_celsius'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_configuration_munkholmen'
tag_values['tag_unit'] = 'none'
df = pd.DataFrame(columns=['identifier', 'instrument_type_id', 'num_beams', 'num_cells', 'checksum'])
for i in range(df_all.shape[0]):
d = df_all.iloc[i, -1]
indexes = [1, 2, 3, 4, 7]
df.loc[i, :] = [d.split(',')[j] for j in indexes]
df.index = df_all.index
df = util_db.add_tags(df, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_depth_config_munkholmen'
tag_values['tag_unit'] = 'metres'
df = pd.DataFrame(columns=['blanking', 'cell_size'])
for i in range(df_all.shape[0]):
d = df_all.iloc[i, -1]
indexes = [5, 6]
df.loc[i, :] = [d.split(',')[j] for j in indexes]
df.index = df_all.index
df = util_db.add_tags(df, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'CR6_EOL2p0_Wave_sensor_':
data_cols = [
"heading", "Hs", "Period", "Hmax", "direction"
]
float_cols = data_cols
df_all = load_data(file_path, data_cols, float_cols) # , rows_to_skip=2, time_col="TIMESTAMP"
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'seaview',
'tag_edge_device': 'cr6',
'tag_platform': 'munkholmen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'wave_heading_munkholmen'
field_keys = {"heading": 'heading'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'heading', lower=0, upper=360)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'wave_hs_munkholmen'
field_keys = {"Hs": 'hs'}
tag_values['tag_unit'] = 'metres'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'hs', lower=-100, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'wave_period_munkholmen'
field_keys = {"Period": 'period'}
tag_values['tag_unit'] = 'seconds'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'period', lower=0, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'wave_hmax_munkholmen'
field_keys = {"Hmax": 'hmax'}
tag_values['tag_unit'] = 'metres'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'hmax', lower=-100, upper=100)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'wave_direction_munkholmen'
field_keys = {"direction": 'direction'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
df = processing.constant_val_filter(df, 'direction', lower=0, upper=360)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'IngdalenCR6_System_':
data_cols = [
"systemTemperature", "systemAirPressure", "systemRelHumidity",
"victron_BattVolts", "victron_ChargeCurr", "victron_PanelVolts",
"LoggerVoltage", "LoggerTemperature"
]
float_cols = data_cols
df_all = load_data(file_path, data_cols, float_cols, timezone='UTC')
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'none',
'tag_edge_device': 'cr6_ingdalen',
'tag_platform': 'ingdalen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'system_temperature_ingdalen'
field_keys = {"systemTemperature": 'system_temperature'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'system_atmospheric_pressure_ingdalen'
field_keys = {"systemAirPressure": 'atmospheric_pressure'}
tag_values['tag_unit'] = 'hecto_pascal'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'system_humidity_ingdalen'
field_keys = {"systemRelHumidity": 'humidity'}
tag_values['tag_unit'] = 'percent'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'victron_battery_voltage_ingdalen'
field_keys = {"victron_BattVolts": 'battery_voltage'}
tag_values['tag_unit'] = 'volts'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'victron_charge_current_ingdalen'
field_keys = {"victron_ChargeCurr": 'charge_current'}
tag_values['tag_unit'] = 'amperes'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'victron_panel_voltage_ingdalen'
field_keys = {"victron_PanelVolts": 'panel_voltage'}
tag_values['tag_unit'] = 'volts'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'victron_logger_voltage_ingdalen'
field_keys = {"LoggerVoltage": 'logger_voltage'}
tag_values['tag_unit'] = 'volts'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'victron_logger_temperature_ingdalen'
field_keys = {"LoggerTemperature": 'logger_temperature'}
tag_values['tag_unit'] = 'degrees'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'IngdalenCR6_victron_':
# "TMSTAMP","RECNBR","victron_Device","victron_SER","victron_FW","victron_BattVolts","victron_ChargeCurr","victron_PanelVolts","victron_PanelPower","victron_State","victron_ERR"
# "2022-05-31 06:30:00",346,"BlueSolar MPPT 100|30 rev2","HQ2102P4VGZ ",147,12.53,7.4,19.84,96,"Bulk Charging","Device OK"
data_cols = [
"victron_Device", "victron_SER", "victron_FW", "victron_BattVolts",
"victron_ChargeCurr", "victron_PanelVolts", "victron_PanelPower",
"victron_State", "victron_ERR"
]
float_cols = [c for c in data_cols if c not in ['victron_Device', 'victron_SER', "victron_State", "victron_ERR"]]
df_all = load_data(file_path, data_cols, float_cols, timezone='UTC')
# Set a 'default' set of tags for this file:
tag_values = {
'tag_edge_device': 'cr6_ingdalen',
'tag_platform': 'ingdalen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'victron_fw_ingdalen'
field_keys = {"victron_Device": 'tag_sensor',
"victron_SER": 'tag_serial',
"victron_FW": 'fw'}
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# The below three vars (victron_BattVolts, victron_PanelVolts, victron_ChargeCurr)
# are also included in the system table.
# # ---------------------------------------------------------------- #
# measurement_name = 'victron_voltages_ingdalen'
# field_keys = {"victron_Device": 'tag_sensor',
# "victron_SER": 'tag_serial',
# "victron_BattVolts": 'battery_voltage',
# "victron_PanelVolts": 'panel_voltage'}
# tag_values['tag_unit'] = 'volts'
# df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
# util_db.ingest_df(measurement_name, df, clients)
# # ---------------------------------------------------------------- #
# measurement_name = 'victron_charge_current_ingdalen'
# field_keys = {"victron_Device": 'tag_sensor',
# "victron_SER": 'tag_serial',
# "victron_ChargeCurr": 'charge_current'}
# tag_values['tag_unit'] = 'amperes'
# df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
# util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'victron_panel_power_ingdalen'
field_keys = {"victron_Device": 'tag_sensor',
"victron_SER": 'tag_serial',
"victron_PanelPower": 'panel_power'}
tag_values['tag_unit'] = 'none'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'victron_messages_ingdalen'
field_keys = {"victron_Device": 'tag_sensor',
"victron_SER": 'tag_serial',
"victron_State": 'state',
"victron_ERR": 'error'}
tag_values['tag_unit'] = 'none'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'IngdalenCR6_SUNA_':
data_cols = [
"sunaSerial", "sunaNitrateMicroMol", "sunaNitrateMilliGrams",
"sunaInternalHumidity", "sunaTemperatureHousing"
]
float_cols = [c for c in data_cols if c not in ["sunaSerial"]]
df_all = load_data(file_path, data_cols, float_cols, timezone='UTC')
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'suna',
'tag_edge_device': 'cr6_ingdalen',
'tag_platform': 'ingdalen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'suna_nitrate_micromol_ingdalen'
field_keys = {"sunaSerial": 'tag_serial',
"sunaNitrateMicroMol": 'nitrate_micromol'}
tag_values['tag_unit'] = 'none'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'suna_nitrate_milligram_ingdalen'
field_keys = {"sunaSerial": 'tag_serial',
"sunaNitrateMilliGrams": 'nitrate_milligram'}
tag_values['tag_unit'] = 'none'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'suna_internal_humidity_ingdalen'
field_keys = {"sunaSerial": 'tag_serial',
"sunaInternalHumidity": 'humidity'}
tag_values['tag_unit'] = 'none'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'suna_housing_temperature_ingdalen'
field_keys = {"sunaSerial": 'tag_serial',
"sunaTemperatureHousing": 'temperature'}
tag_values['tag_unit'] = 'none'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'IngdalenCR6_signatureRecord_':
data_cols = [
"signatureDataType", "signatureDataTypeString", "signatureSerialNumber",
"signatureConfiguration", "signatureSoundVelocity", "signatureTemperature",
"signaturePressure", "signatureHeading", "signaturePitch", "signatureRoll",
"signatureError", "signatureStatus0", "signatureStatus", "signatureCells",
"signatureBeams", "signatureCellSize", "signatureBlanking", "signatureBattery",
"signatureNominalCorrelation", "signatureAmbiguityVelocity", "signatureEchoFrequency"
]
float_cols = [c for c in data_cols if c not in ["signatureDataTypeString"]]
df_all = load_data(file_path, data_cols, float_cols, timezone='UTC')
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'signature_100',
'tag_edge_device': 'cr6_ingdalen',
'tag_platform': 'ingdalen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_meta_data_ingdalen'
field_keys = {"signatureDataType": 'data_type',
"signatureDataTypeString": 'data_type_string',
"signatureSerialNumber": 'tag_serial_number',
"signatureConfiguration": 'configuration'}
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_sound_velocity_ingdalen'
field_keys = {"signatureSoundVelocity": 'sound_velocity'}
tag_values['tag_unit'] = 'metres_per_second'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_temperature_ingdalen'
field_keys = {"signatureTemperature": 'temperature'}
tag_values['tag_unit'] = 'degrees_celcius'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_pressure_ingdalen'
field_keys = {"signaturePressure": 'pressure'}
tag_values['tag_unit'] = 'none' # atmospheres
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_heading_ingdalen'
field_keys = {"signatureHeading": 'heading'}
tag_values['tag_unit'] = 'none' # degrees
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_pitch_ingdalen'
field_keys = {"signaturePitch": 'pitch'}
tag_values['tag_unit'] = 'none' # degrees
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_roll_ingdalen'
field_keys = {"signatureRoll": 'roll'}
tag_values['tag_unit'] = 'none' # degrees
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_status_ingdalen'
field_keys = {"signatureError": 'error',
"signatureStatus0": 'status0',
"signatureStatus": 'status'}
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_cells_ingdalen'
field_keys = {"signatureCells": 'cells'}
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_beams_ingdalen'
field_keys = {"signatureBeams": 'beams'}
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_cell_size_ingdalen'
field_keys = {"signatureCellSize": 'cell_size'}
tag_values['tag_unit'] = 'none' # metres 5
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_blanking_ingdalen'
field_keys = {"signatureBlanking": 'blanking'}
tag_values['tag_unit'] = 'none' # metres 2
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_battery_ingdalen'
field_keys = {"signatureBattery": 'battery'}
tag_values['tag_unit'] = 'none' # volts 23.4
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_battery_ingdalen'
field_keys = {"signatureBattery": 'battery'}
tag_values['tag_unit'] = 'none' # volts 23.4
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_nominal_correlation_ingdalen'
field_keys = {"signatureNominalCorrelation": 'nominal_correlation'}
tag_values['tag_unit'] = 'none' # percent? 82
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_ambiguity_velocity_ingdalen'
field_keys = {"signatureAmbiguityVelocity": 'ambiguity_velocity'}
tag_values['tag_unit'] = 'none' # metres_per_second 10.39
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'signature_100_echo_frequency_ingdalen'
field_keys = {"signatureEchoFrequency": 'echo_frequency'}
tag_values['tag_unit'] = 'none' # ?
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# # ==================================================================== #
# if file_type == 'IngdalenCR6_signatureCurrentProf_':
# # This is in a weird format with new series each file (check RECNBR)
# data_cols = [
# "signatureCellDistProfile",
# "signatureVelocityProfile(1)", "signatureVelocityProfile(2)", "signatureVelocityProfile(3)", "signatureVelocityProfile(4)",
# "signatureAmplitudeProfile(1)", "signatureAmplitudeProfile(2)", "signatureAmplitudeProfile(3)", "signatureAmplitudeProfile(4)",
# "signatureCorrelationProfile(1)", "signatureCorrelationProfile(2)", "signatureCorrelationProfile(3)", "signatureCorrelationProfile(4)"
# ]
# float_cols = [c for c in data_cols if c not in ["signatureDataTypeString"]]
# df_all = load_data(file_path, data_cols, float_cols, timezone='UTC')
# # Set a 'default' set of tags for this file:
# tag_values = {
# 'tag_sensor': 'signature_100',
# 'tag_edge_device': 'cr6_ingdalen',
# 'tag_platform': 'ingdalen',
# 'tag_data_level': 'raw',
# 'tag_approved': 'none',
# 'tag_unit': 'none'}
# ==================================================================== #
if file_type == 'IngdalenCR6_Seabird_':
data_cols = [
"seabirdDevice", "seabirdSerial", "seabirdBattery", "seabirdTemperature", "seabirdConductivity",
"seabirdPressure", "seabirdDissOxygen", "seabirdSalinity", "seabirdSoundVel", "seabirdSpecCond"
]
float_cols = [c for c in data_cols if c not in ["seabirdDevice"]]
df_all = load_data(file_path, data_cols, float_cols, timezone='UTC')
# Set a 'default' set of tags for this file:
tag_values = {
# 'tag_sensor': 'seabird', # this will be the 'seabirdDevice'
'tag_edge_device': 'cr6_ingdalen',
'tag_platform': 'ingdalen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'seabird_battery_ingdalen'
field_keys = {"seabirdDevice": 'tag_sensor',
"seabirdSerial": 'tag_serial',
"seabirdBattery": 'battery'}
tag_values['tag_unit'] = 'none' # volts 13.63
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'seabird_temperature_ingdalen'
field_keys = {"seabirdDevice": 'tag_sensor',
"seabirdSerial": 'tag_serial',
"seabirdTemperature": 'temperature'}
tag_values['tag_unit'] = 'degrees_celcius' # 8.5882
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'seabird_conductivity_ingdalen'
field_keys = {"seabirdDevice": 'tag_sensor',
"seabirdSerial": 'tag_serial',
"seabirdConductivity": 'conductivity'}
tag_values['tag_unit'] = 'none' # ? 31.6308
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'seabird_pressure_ingdalen'
field_keys = {"seabirdDevice": 'tag_sensor',
"seabirdSerial": 'tag_serial',
"seabirdPressure": 'pressure'}
tag_values['tag_unit'] = 'none' # ? 10.431
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'seabird_dissolved_oxygen_ingdalen'
field_keys = {"seabirdDevice": 'tag_sensor',
"seabirdSerial": 'tag_serial',
"seabirdDissOxygen": 'dissolved_oxygen'}
tag_values['tag_unit'] = 'none' # ? 9.087
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'seabird_salinity_ingdalen'
field_keys = {"seabirdDevice": 'tag_sensor',
"seabirdSerial": 'tag_serial',
"seabirdSalinity": 'salinity'}
tag_values['tag_unit'] = 'none' # ? 29.6189
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'seabird_sound_velocity_ingdalen'
field_keys = {"seabirdDevice": 'tag_sensor',
"seabirdSerial": 'tag_serial',
"seabirdSoundVel": 'sound_velocity'}
tag_values['tag_unit'] = 'metres_per_second' # ? 1478.221
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'seabird_spec_cond_ingdalen' # ???
field_keys = {"seabirdDevice": 'tag_sensor',
"seabirdSerial": 'tag_serial',
"seabirdSpecCond": 'spec_cond'}
tag_values['tag_unit'] = 'none' # ? 47.0862
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'IngdalenCR6_Power_':
data_cols = [
"powerState(1)", "powerState(2)", "powerState(3)", "powerState(4)", "powerState(5)", "powerState(6)", "powerState(7)", "powerState(8)", "powerState(9)", "powerState(10)", "powerState(11)",
"powerVoltage(1)", "powerVoltage(2)", "powerVoltage(3)", "powerVoltage(4)", "powerVoltage(5)", "powerVoltage(6)", "powerVoltage(7)", "powerVoltage(8)", "powerVoltage(9)", "powerVoltage(10)", "powerVoltage(11)",
"powerCurrent(1)", "powerCurrent(2)", "powerCurrent(3)", "powerCurrent(4)", "powerCurrent(5)", "powerCurrent(6)", "powerCurrent(7)", "powerCurrent(8)", "powerCurrent(9)", "powerCurrent(10)", "powerCurrent(11)",
"muxVoltage(1)", "muxVoltage(2)", "muxVoltage(3)",
"muxCurrent(1)", "muxCurrent(2)", "muxCurrent(3)",
"muxTemperature(1)", "muxTemperature(2)", "muxTemperature(3)"
]
float_cols = [c for c in data_cols if c not in ["seabirdDevice"]]
df_all = load_data(file_path, data_cols, float_cols, timezone='UTC')
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'none',
'tag_edge_device': 'cr6_ingdalen',
'tag_platform': 'ingdalen',
'tag_data_level': 'raw',
'tag_approved': 'none',
'tag_unit': 'none'}
# ---------------------------------------------------------------- #
measurement_name = 'power_state_ingdalen'
field_keys = {f'powerState({i})': f'power_state_{i}' for i in range(1, 12)}
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'power_voltage_ingdalen'
field_keys = {f'powerVoltage({i})': f'power_voltage_{i}' for i in range(1, 12)}
tag_values['tag_unit'] = 'volts'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'power_current_ingdalen'
field_keys = {f'powerCurrent({i})': f'power_current_{i}' for i in range(1, 12)}
tag_values['tag_unit'] = 'amperes'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'mux_voltage_ingdalen'
field_keys = {f'muxVoltage({i})': f'mux_voltage_{i}' for i in range(1, 4)}
tag_values['tag_unit'] = 'volts'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'mux_current_ingdalen'
field_keys = {f'muxCurrent({i})': f'mux_current_{i}' for i in range(1, 4)}
tag_values['tag_unit'] = 'amperes'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ---------------------------------------------------------------- #
measurement_name = 'mux_temperature_ingdalen'
field_keys = {f'muxTemperature({i})': f'mux_temperature_{i}' for i in range(1, 4)}
tag_values['tag_unit'] = 'degrees_celcius'
df = util_db.filter_and_tag_df(df_all, field_keys, tag_values)
util_db.ingest_df(measurement_name, df, clients)
# ==================================================================== #
if file_type == 'IngdalenCR6_PAR_':
data_cols = [
"parSrfSerial", "parSubSerial",
"parSrfLive", "parSrfAvg", "parSrfPitch", "parSrfRoll", "parSrfTemp",
"parSubLive", "parSubAvg", "parSubPitch", "parSubRoll", "parSubTemp"
]
float_cols = [c for c in data_cols if c not in ["parSrfSerial", "parSubSerial"]]
df_all = load_data(file_path, data_cols, float_cols, timezone='UTC')
# Set a 'default' set of tags for this file:
tag_values = {
'tag_sensor': 'par',
'tag_edge_device': 'cr6_ingdalen',
'tag_platform': 'ingdalen',