496 lines
28 KiB
Plaintext
496 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Corrected Total Volume Flow Rate Total\n",
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"timestamp \n",
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"2023-11-30 1578599 18619550\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Step 1: Load the data\n",
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"total_volume_df = pd.read_csv('/Users/nico/Downloads/monthly_total.csv')\n",
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"flow_rate_df = pd.read_csv('/Users/nico/Downloads/flow_rate.csv')\n",
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"\n",
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"# Step 2: Convert timestamps to datetime\n",
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"total_volume_df['timestamp'] = pd.to_datetime(total_volume_df['timestamp'], format=\"%m/%d/%Y %I:%M:%S %p\")\n",
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"flow_rate_df['timestamp'] = pd.to_datetime(flow_rate_df['timestamp'], format=\"%m/%d/%Y %I:%M:%S %p\")\n",
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"\n",
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"# Sort the data in ascending order by timestamp\n",
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"total_volume_df = total_volume_df.sort_values(by='timestamp')\n",
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"flow_rate_df = flow_rate_df.sort_values(by='timestamp')\n",
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"\n",
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"# Step 3: Identify resets and sum the totals for each month\n",
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"total_volume_df['reset'] = total_volume_df['value'].lt(total_volume_df['value'].shift())\n",
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"total_volume_df['cumulative'] = total_volume_df.groupby(total_volume_df['reset'].cumsum())['value'].cumsum()\n",
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"monthly_total = total_volume_df.groupby(pd.Grouper(key='timestamp', freq='M'))['cumulative'].last()\n",
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"\n",
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"# Sort the flow rate data in ascending order by timestamp\n",
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"flow_rate_df = flow_rate_df.sort_values(by='timestamp')\n",
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"\n",
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"# Recalculate the time differences and total volume\n",
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"flow_rate_df['time_diff'] = flow_rate_df['timestamp'].diff().dt.total_seconds().div(60) # in minutes\n",
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"flow_rate_df.iloc[0, flow_rate_df.columns.get_loc('time_diff')] = 0\n",
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"flow_rate_df['total_volume'] = flow_rate_df['value'] * flow_rate_df['time_diff']\n",
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"\n",
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"# Recalculate the monthly totals for the flow rate data\n",
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"monthly_flow_rate_total = flow_rate_df.groupby(pd.Grouper(key='timestamp', freq='M'))['total_volume'].sum()\n",
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"\n",
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"# Recalculate the corrected total volume for the total volume data\n",
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"# The total volume data calculation remains the same as before\n",
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"monthly_total = total_volume_df.groupby(pd.Grouper(key='timestamp', freq='M'))['cumulative'].last()\n",
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"\n",
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"# Compare the two monthly totals again\n",
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"comparison_df = pd.DataFrame({'Corrected Total Volume': monthly_total, 'Flow Rate Total': monthly_flow_rate_total})\n",
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"comparison_df.reset_index() # Resetting index for better visualization\n",
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"comparison_df.head()\n",
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"pd.options.display.float_format = '{:.0f}'.format\n",
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"print(comparison_df)\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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" timestamp value reset cumulative\n",
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|
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n",
|
|
" print(total_volume_df)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "tbDataCollector",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
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|
"language_info": {
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"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
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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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}
|