140 lines
3.8 KiB
Plaintext
140 lines
3.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pytz\n",
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"from datetime import datetime\n",
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"import json, csv, time\n",
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"import paho.mqtt.client as mqtt\n",
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"from datetime import datetime, timedelta"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Set the MQTT broker connection details\n",
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"MQTT_BROKER = \"hp.henrypump.cloud\"\n",
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"MQTT_PORT = 1883\n",
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"\n",
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"# Create an MQTT client instance\n",
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"client = mqtt.Client(client_id=\"faskens-bp-compressor\")\n",
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"client.username_pw_set(\"faskensmqtt\", \"faskensmqtt@1903\" )\n",
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"client.connect(MQTT_BROKER, MQTT_PORT)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# Define a function to convert datetime to timestamp and round to nearest 10 minutes\n",
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"def convert_datetime_to_timestamp(dt_str):\n",
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" cst_tz = pytz.timezone('America/Chicago')\n",
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" dt_cst = cst_tz.localize(datetime.strptime(dt_str, '%Y-%m-%d/%H:%M:%S'))\n",
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" \n",
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" # Calculate the remainder of minutes in the hour\n",
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" minute_remainder = dt_cst.minute % 10\n",
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" \n",
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" if minute_remainder >= 5:\n",
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" # Round up to the next 10-minute interval\n",
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" dt_cst += timedelta(minutes=10 - minute_remainder)\n",
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" else:\n",
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" # Round down to the previous 10-minute interval\n",
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" dt_cst -= timedelta(minutes=minute_remainder)\n",
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" \n",
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" # Ensure the resulting datetime is valid (no 23:60:00 or similar issues)\n",
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" while dt_cst.minute % 10 != 0:\n",
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" if dt_cst.minute > 50:\n",
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" dt_cst += timedelta(hours=1)\n",
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" dt_cst -= timedelta(minutes=dt_cst.minute % 60)\n",
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" else:\n",
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" dt_cst -= timedelta(minutes=dt_cst.minute % 10)\n",
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" \n",
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" return int(dt_cst.timestamp()*1000)\n",
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"\n",
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"# Transform the data and send it to the MQTT broker in chunks of 20\n",
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"transformed_data = []\n",
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"with open('/Users/nico/Downloads/history_data_default.csv', 'r') as csvfile:\n",
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" reader = csv.DictReader(csvfile)\n",
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" for row in reader:\n",
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" dt = row['Time']\n",
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" value = json.loads(row[' Value'])[\"plcpond\"][\"air_comp_val\"]\n",
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" if value[0] == 1:\n",
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" transformed_data.append({\"ts\": convert_datetime_to_timestamp(dt), \"values\": {\"air_comp_val\": value[1]}})\n",
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"\n",
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"\n",
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"\n",
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"chunk_size = 20\n",
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"for i in range(0, len(transformed_data), chunk_size):\n",
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" chunk = transformed_data[i:i + chunk_size]\n",
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" for x in chunk:\n",
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" client.publish(\"v1/devices/me/telemetry\", json.dumps(x))\n",
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" time.sleep(1)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"client.disconnect()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "aws",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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