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PaulVua
2025-02-18 12:05:43 +01:00
parent 9774215e7c
commit 6622be14ad
8 changed files with 390 additions and 49 deletions
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67
loop/SARA_send_data_v2.py
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@@ -283,43 +283,50 @@ try:
'''
print('<h3>START LOOP</h3>')
#Local timestamp
print("➡️Getting local timestamp")
cursor.execute("SELECT * FROM timestamp_table LIMIT 1")
row = cursor.fetchone() # Get the first (and only) row
rtc_time_str = row[1] # '2025-02-07 12:30:45'
# Convert to a datetime object
dt_object = datetime.strptime(rtc_time_str, '%Y-%m-%d %H:%M:%S')
# Convert to InfluxDB RFC3339 format with UTC 'Z' suffix
influx_timestamp = dt_object.strftime('%Y-%m-%dT%H:%M:%SZ')
print(influx_timestamp)
#NEXTPM
print("➡️Getting NPM values")
cursor.execute("SELECT * FROM data_NPM ORDER BY timestamp DESC LIMIT 1")
last_row = cursor.fetchone()
# Display the result
if last_row:
print("SQLite DB last available row:", last_row)
print("➡️Getting NPM values (last 6 measures)")
#cursor.execute("SELECT * FROM data_NPM ORDER BY timestamp DESC LIMIT 1")
cursor.execute("SELECT * FROM data_NPM ORDER BY timestamp DESC LIMIT 6")
rows = cursor.fetchall()
# Exclude the timestamp column (assuming first column is timestamp)
data_values = [row[1:] for row in rows] # Exclude timestamp
# Compute column-wise average
num_columns = len(data_values[0])
averages = [round(sum(col) / len(col)) for col in zip(*data_values)]
datetime_measure_PM = last_row[0] #on récupère le datetime de la table data_NPM
# Convert to a datetime object
dt_object = datetime.strptime(datetime_measure_PM, '%Y-%m-%d %H:%M:%S')
# Convert to InfluxDB RFC3339 format with UTC 'Z' suffix
influx_timestamp = dt_object.strftime('%Y-%m-%dT%H:%M:%SZ')
PM1 = averages[0]
PM25 = averages[1]
PM10 = averages[2]
npm_temp = averages[3]
npm_hum = averages[4]
PM1 = last_row[1]
PM25 = last_row[2]
PM10 = last_row[3]
npm_temp = last_row[4]
npm_hum = last_row[5]
#Add data to payload CSV
payload_csv[0] = PM1
payload_csv[1] = PM25
payload_csv[2] = PM10
payload_csv[18] = npm_temp
payload_csv[19] = npm_hum
#Add data to payload CSV
payload_csv[0] = PM1
payload_csv[1] = PM25
payload_csv[2] = PM10
payload_csv[18] = npm_temp
payload_csv[19] = npm_hum
#Add data to payload JSON
payload_json["sensordatavalues"].append({"value_type": "NPM_P0", "value": str(PM1)})
payload_json["sensordatavalues"].append({"value_type": "NPM_P1", "value": str(PM10)})
payload_json["sensordatavalues"].append({"value_type": "NPM_P2", "value": str(PM25)})
else:
print("No data available in the database.")
#Add data to payload JSON
payload_json["sensordatavalues"].append({"value_type": "NPM_P0", "value": str(PM1)})
payload_json["sensordatavalues"].append({"value_type": "NPM_P1", "value": str(PM10)})
payload_json["sensordatavalues"].append({"value_type": "NPM_P2", "value": str(PM25)})
#NextPM 5 channels
if npm_5channel:
print("➡️Getting NextPM 5 channels values")
print("➡️Getting NextPM 5 channels values (last 6 measures)")
cursor.execute("SELECT * FROM data_NPM_5channels ORDER BY timestamp DESC LIMIT 6")
rows = cursor.fetchall()
# Exclude the timestamp column (assuming first column is timestamp)