{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Unit Validation and Conversion with TimeDB\n", "\n", "This notebook demonstrates TimeDB's unit handling capabilities using pint-pandas Series.\n", "\n", "#### What you'll learn:\n", "1. **Uploading data with units** - Using pint-pandas Series with `dtype=\"pint[unit]\"` in DataFrames\n", "2. **Reading data and getting unit information** - How to retrieve series metadata including units\n", "3. **Unit validation** - What happens when you try to upload incompatible units to an existing series\n", "\n", "**Key Features:**\n", "- Each DataFrame column (except time columns) automatically becomes a separate series\n", "- Series name defaults to the column name\n", "- Units are extracted from pint-pandas Series dtype (e.g., `dtype=\"pint[MW]\"`)\n", "- Values are automatically converted to canonical units before storage\n", "- Incompatible units raise `IncompatibleUnitError`\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Imports successful\n", "✓ Using pint-pandas for unit handling\n" ] } ], "source": [ "import timedb as td\n", "import pandas as pd\n", "import pint_pandas\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime, timezone, timedelta\n", "\n", "# Load environment variables (for database connection)\n", "from dotenv import load_dotenv\n", "load_dotenv()\n", "\n", "print(\"✓ Imports successful\")\n", "print(f\"✓ Using pint-pandas for unit handling\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: Uploading Data with Units\n", "\n", "Let's start by creating the database schema and then uploading time series data with units embedded as pint-pandas Series.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating database schema...\n", "✓ Schema created successfully\n" ] } ], "source": [ "# Create database schema\n", "td.delete()\n", "td.create()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "valid_time", "rawType": "datetime64[ns, UTC]", "type": "unknown" }, { "name": "power", "rawType": "pint[megawatt][Float64]", "type": "unknown" }, { "name": "wind_speed", "rawType": "pint[meter / second][Float64]", "type": "unknown" }, { "name": "temperature", "rawType": "pint[degree_Celsius][Float64]", "type": "unknown" } ], "ref": "1887c9dd-6d1e-40a9-a887-e2a153a09d79", "rows": [ [ "0", "2025-01-01 00:00:00+00:00", "1.0 megawatt", "5.0 meter / second", "20.0 degree_Celsius" ], [ "1", "2025-01-01 01:00:00+00:00", "1.05 megawatt", "5.2 meter / second", "20.5 degree_Celsius" ], [ "2", "2025-01-01 02:00:00+00:00", "1.1 megawatt", "5.4 meter / second", "21.0 degree_Celsius" ], [ "3", "2025-01-01 03:00:00+00:00", "1.15 megawatt", "5.6 meter / second", "21.5 degree_Celsius" ], [ "4", "2025-01-01 04:00:00+00:00", "1.2 megawatt", "5.8 meter / second", "22.0 degree_Celsius" ] ], "shape": { "columns": 4, "rows": 5 } }, "text/html": [ "
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valid_timepowerwind_speedtemperature
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" ], "text/plain": [ " valid_time power wind_speed temperature\n", "0 2025-01-01 00:00:00+00:00 1.0 5.0 20.0\n", "1 2025-01-01 01:00:00+00:00 1.05 5.2 20.5\n", "2 2025-01-01 02:00:00+00:00 1.1 5.4 21.0\n", "3 2025-01-01 03:00:00+00:00 1.15 5.6 21.5\n", "4 2025-01-01 04:00:00+00:00 1.2 5.8 22.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create sample time series data with units\n", "# We'll create three different series: power (MW), wind speed (m/s), and temperature (°C)\n", "\n", "base_time = datetime(2025, 1, 1, 0, 0, tzinfo=timezone.utc)\n", "times = [base_time + timedelta(hours=i) for i in range(24)]\n", "\n", "# Power values in megawatts (will be stored as canonical unit)\n", "# Round to avoid floating point precision artifacts\n", "power_vals_MW = [round(1.0 + i * 0.05, 2) for i in range(24)]\n", "\n", "# Wind speed values in meters per second\n", "wind_vals_m_s = [round(5.0 + i * 0.2, 2) for i in range(24)]\n", "\n", "# Temperature values in Celsius\n", "temp_vals_C = [round(20.0 + i * 0.5, 2) for i in range(24)]\n", "\n", "# Create DataFrame with pint-pandas Series\n", "# Each column becomes a separate series with its unit specified in the dtype\n", "df = pd.DataFrame({\n", " \"valid_time\": times,\n", " \"power\": pd.Series(power_vals_MW, dtype=\"pint[MW]\"), # Series with MW unit\n", " \"wind_speed\": pd.Series(wind_vals_m_s, dtype=\"pint[m/s]\"), # Series with m/s unit\n", " \"temperature\": pd.Series(temp_vals_C, dtype=\"pint[degree_Celsius]\") # Series with °C unit\n", "})\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "object", "type": "string" }, { "name": "0", "rawType": "object", "type": "unknown" } ], "ref": "55ef0186-f187-4b16-bd3c-ffb86f65e019", "rows": [ [ "valid_time", "datetime64[ns, UTC]" ], [ "power", "pint[megawatt][Float64]" ], [ "wind_speed", "pint[meter / second][Float64]" ], [ "temperature", "pint[degree_Celsius][Float64]" ] ], "shape": { "columns": 1, "rows": 4 } }, "text/plain": [ "valid_time datetime64[ns, UTC]\n", "power pint[megawatt][Float64]\n", "wind_speed pint[meter / second][Float64]\n", "temperature pint[degree_Celsius][Float64]\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data values inserted successfully.\n", "✓ Data inserted successfully!\n", "\n", "Batch ID: ef24a6bd-402a-4f63-8d1c-c454401e5c9a\n", "Workflow ID: sdk-workflow\n", "Tenant ID: 00000000-0000-0000-0000-000000000000\n", "\n", "Series created (name -> series_id):\n", " power: a3cb75f7-3328-4026-8960-3c79075e468a\n", " wind_speed: 46ad278c-d32c-471e-aaf8-d1c3efb3d46e\n", " temperature: 6feb7937-5c30-4062-b63e-412b569fff2f\n" ] } ], "source": [ "# Insert the data - TimeDB automatically:\n", "# 1. Detects each column as a separate series\n", "# 2. Extracts units from pint-pandas Series dtype\n", "# 3. Creates series with name = column name\n", "# 4. Converts values to canonical units and stores them\n", "\n", "result = td.insert_batch(df=df)\n", "\n", "print(\"✓ Data inserted successfully!\")\n", "print(f\"\\nBatch ID: {result.batch_id}\")\n", "print(f\"Workflow ID: {result.workflow_id}\")\n", "print(f\"Tenant ID: {result.tenant_id}\")\n", "print(f\"\\nSeries created (name -> series_id):\")\n", "for name, series_id in result.series_ids.items():\n", " print(f\" {name}: {series_id}\")\n", "\n", "# Store the series_ids for later use\n", "series_ids_map = result.series_ids\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Reading Data and Getting Unit Information\n", "\n", "Now let's read the data back and see how TimeDB returns unit information along with the values.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Data read successfully\n", "\n", "DataFrame shape: (24, 3)\n", "\n", "Columns: ['wind_speed', 'temperature', 'power']\n", "\n", "Index: valid_time\n", "\n", "First few rows:\n" ] }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "valid_time", "rawType": "datetime64[ns, UTC]", "type": "unknown" }, { "name": "wind_speed", "rawType": "pint[meter / second][Float64]", "type": "unknown" }, { "name": "temperature", "rawType": "pint[degree_Celsius][Float64]", "type": "unknown" }, { "name": "power", "rawType": "pint[megawatt][Float64]", "type": "unknown" } ], "ref": "e17df92f-b953-42e1-a2c1-32c3ffb5e3ea", "rows": [ [ "2025-01-01 00:00:00+00:00", "5.0 meter / second", "20.0 degree_Celsius", "1.0 megawatt" ], [ "2025-01-01 01:00:00+00:00", "5.2 meter / second", "20.5 degree_Celsius", "1.05 megawatt" ], [ "2025-01-01 02:00:00+00:00", "5.4 meter / second", "21.0 degree_Celsius", "1.1 megawatt" ], [ "2025-01-01 03:00:00+00:00", "5.6 meter / second", "21.5 degree_Celsius", "1.15 megawatt" ], [ "2025-01-01 04:00:00+00:00", "5.8 meter / second", "22.0 degree_Celsius", "1.2 megawatt" ] ], "shape": { "columns": 3, "rows": 5 } }, "text/html": [ "
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namewind_speedtemperaturepower
valid_time
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2025-01-01 01:00:00+00:005.220.51.05
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" ], "text/plain": [ "name wind_speed temperature power\n", "valid_time \n", "2025-01-01 00:00:00+00:00 5.0 20.0 1.0\n", "2025-01-01 01:00:00+00:00 5.2 20.5 1.05\n", "2025-01-01 02:00:00+00:00 5.4 21.0 1.1\n", "2025-01-01 03:00:00+00:00 5.6 21.5 1.15\n", "2025-01-01 04:00:00+00:00 5.8 22.0 1.2" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read all data back\n", "# Columns are the series names (human-readable)\n", "df_read = td.read()\n", "\n", "print(\"✓ Data read successfully\")\n", "print(f\"\\nDataFrame shape: {df_read.shape}\")\n", "print(f\"\\nColumns: {list(df_read.columns)}\")\n", "print(f\"\\nIndex: {df_read.index.name}\")\n", "print(f\"\\nFirst few rows:\")\n", "df_read.head()\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column dtypes (with units):\n", " wind_speed: pint[meter / second][Float64]\n", " temperature: pint[degree_Celsius][Float64]\n", " power: pint[megawatt][Float64]\n" ] } ], "source": [ "# Check the dtypes - each column has a pint-pandas dtype with the unit\n", "print(\"Column dtypes (with units):\")\n", "for col in df_read.columns:\n", " print(f\" {col}: {df_read[col].dtype}\")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Reset index to see all columns\n", "\n", "Columns: ['valid_time', 'wind_speed', 'temperature', 'power']\n", "\n", "Sample data:\n", "name valid_time wind_speed temperature power\n", "0 2025-01-01 00:00:00+00:00 5.0 20.0 1.0\n", "1 2025-01-01 01:00:00+00:00 5.2 20.5 1.05\n", "2 2025-01-01 02:00:00+00:00 5.4 21.0 1.1\n", "3 2025-01-01 03:00:00+00:00 5.6 21.5 1.15\n", "4 2025-01-01 04:00:00+00:00 5.8 22.0 1.2\n", "5 2025-01-01 05:00:00+00:00 6.0 22.5 1.25\n", "6 2025-01-01 06:00:00+00:00 6.2 23.0 1.3\n", "7 2025-01-01 07:00:00+00:00 6.4 23.5 1.35\n", "8 2025-01-01 08:00:00+00:00 6.6 24.0 1.4\n", "9 2025-01-01 09:00:00+00:00 6.8 24.5 1.45\n", "\n", "Series and their units:\n", " wind_speed: meter / second][Float64\n", " temperature: degree_Celsius][Float64\n", " power: megawatt][Float64\n" ] } ], "source": [ "# Reset index to see valid_time as a column\n", "df_flat = df_read.reset_index()\n", "\n", "print(\"✓ Reset index to see all columns\")\n", "print(f\"\\nColumns: {list(df_flat.columns)}\")\n", "print(f\"\\nSample data:\")\n", "print(df_flat.head(10))\n", "\n", "# Extract unit information from column dtypes\n", "print(f\"\\nSeries and their units:\")\n", "for col in df_read.columns:\n", " dtype_str = str(df_read[col].dtype)\n", " # Extract unit from pint dtype (e.g., \"pint[MW]\" -> \"MW\")\n", " if dtype_str.startswith(\"pint[\"):\n", " unit = dtype_str[5:-1] # Remove \"pint[\" and \"]\"\n", " print(f\" {col}: {unit}\")\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pint_pandas.PintType.ureg.setup_matplotlib()\n", "df_flat[[\"wind_speed\"]].plot()\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✓ Plotted all series with their units\n" ] } ], "source": [ "# Visualize the data with unit information in the plot\n", "# Note: setup_matplotlib() was already called in the previous cell\n", "fig, axes = plt.subplots(3, 1, figsize=(12, 10))\n", "\n", "# Extract units from column dtypes\n", "for idx, series_name in enumerate(df_read.columns):\n", " # Extract unit from pint dtype\n", " dtype_str = str(df_read[series_name].dtype)\n", " if dtype_str.startswith(\"pint[\"):\n", " unit = dtype_str[5:-1] # Remove \"pint[\" and \"]\"\n", " else:\n", " unit = \"dimensionless\"\n", " \n", " # Extract numeric values from pint-pandas Series for plotting\n", " # pint-pandas Series contain Pint Quantity objects, so we need to extract magnitudes\n", " # Note: For individual series plotting with subplots, we extract magnitudes.\n", " # pint-pandas matplotlib support works best with DataFrame.plot() for multiple columns\n", " values = df_read[series_name].apply(lambda x: float(x.magnitude) if hasattr(x, 'magnitude') else float(x))\n", " \n", " # Plot the series\n", " axes[idx].plot(df_read.index, values, marker='o', linewidth=2, markersize=4)\n", " axes[idx].set_title(f'{series_name} ({unit})', fontsize=12, fontweight='bold')\n", " axes[idx].set_ylabel(f'Value [{unit}]', fontsize=10)\n", " axes[idx].grid(True, alpha=0.3)\n", " axes[idx].tick_params(axis='x', rotation=45)\n", "\n", "axes[-1].set_xlabel('Time', fontsize=10)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"✓ Plotted all series with their units\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 3: Unit Validation - Incompatible Units Error\n", "\n", "Now let's try to upload new data to an existing series, but with incompatible units. This should raise an `IncompatibleUnitError`.\n", "\n", "**Important:** Once a series is created with a canonical unit (e.g., \"kW\" for power), all subsequent data must have compatible units (e.g., \"MW\", \"W\", \"kW\" are all compatible as they're all power units). However, trying to use incompatible units (e.g., \"MWh\" for a power series) will raise an error.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Power series ID: a3cb75f7-3328-4026-8960-3c79075e468a\n", "Power series canonical unit: pint[megawatt][Float64]\n", "\n", "This means all power values are stored in pint[megawatt][Float64]\n", "Note: The original data was in MW, which is now the canonical unit for this series.\n" ] } ], "source": [ "# Get the series_id for the \"power\" series\n", "power_series_id = result.series_ids['power']\n", "print(f\"Power series ID: {power_series_id}\")\n", "\n", "# Check what unit the power series uses (extract from dtype)\n", "power_dtype = str(df_read['power'].dtype)\n", "if power_dtype.startswith(\"pint[\"):\n", " canonical_unit = power_dtype\n", "else:\n", " canonical_unit = \"dimensionless\"\n", "\n", "print(f\"Power series canonical unit: {canonical_unit}\")\n", "print(f\"\\nThis means all power values are stored in {canonical_unit}\")\n", "print(f\"Note: The original data was in MW, which is now the canonical unit for this series.\")\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Example 1: Uploading compatible units (kW to MW series)\n", "============================================================\n", "Data values inserted successfully.\n", "✓ SUCCESS: kW values were automatically converted to MW and stored\n", " Inserted 24 new data points\n", " Note: 100 kW = 0.1 MW (automatic conversion)\n" ] } ], "source": [ "# Example 1: Uploading compatible units (kW -> MW conversion)\n", "# This should work fine - kW and MW are compatible (both are power units)\n", "\n", "print(\"Example 1: Uploading compatible units (kW to MW series)\")\n", "print(\"=\" * 60)\n", "\n", "# Create new data in kilowatts (compatible with MW - will be converted)\n", "new_times = [base_time + timedelta(hours=i) for i in range(24, 48)]\n", "power_vals_kW = [100.0 + i * 5.0 for i in range(24)] # Values in kW\n", "\n", "df_compatible = pd.DataFrame({\n", " \"valid_time\": new_times,\n", " \"power\": pd.Series(power_vals_kW, dtype=\"pint[kW]\") # kW is compatible with MW\n", "})\n", "\n", "try:\n", " result_compatible = td.insert_batch(df=df_compatible, series_ids=series_ids_map)\n", " print(\"✓ SUCCESS: kW values were automatically converted to MW and stored\")\n", " print(f\" Inserted {len(new_times)} new data points\")\n", " print(f\" Note: 100 kW = 0.1 MW (automatic conversion)\")\n", "except Exception as e:\n", " print(f\"✗ ERROR: {type(e).__name__}: {e}\")\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Example 2: Uploading incompatible units (MWh to MW series)\n", "============================================================\n", "✓ EXPECTED ERROR: IncompatibleUnitError\n", " Message: Cannot convert megawatt_hour to megawatt: incompatible dimensionality (Cannot convert from 'megawatt_hour' ([mass] * [length] ** 2 / [time] ** 2) to 'megawatt' ([mass] * [length] ** 2 / [time] ** 3))\n", "\n", " This error occurred because:\n", " - The 'power' series has canonical unit: pint[megawatt][Float64] (power)\n", " - You tried to upload values with unit: MWh (energy)\n", " - Power and energy have incompatible dimensionality\n" ] } ], "source": [ "# Example 2: Uploading incompatible units (MWh -> MW series)\n", "# This should FAIL - MWh (energy) is incompatible with MW (power)\n", "\n", "print(\"\\nExample 2: Uploading incompatible units (MWh to MW series)\")\n", "print(\"=\" * 60)\n", "\n", "# Create new data in megawatt-hours (INCOMPATIBLE with MW - energy vs power!)\n", "new_times2 = [base_time + timedelta(hours=i) for i in range(48, 72)]\n", "energy_vals_MWh = [10.0 + i * 0.5 for i in range(24)] # Values in MWh\n", "\n", "df_incompatible = pd.DataFrame({\n", " \"valid_time\": new_times2,\n", " \"power\": pd.Series(energy_vals_MWh, dtype=\"pint[MWh]\") # MWh is INCOMPATIBLE with MW!\n", "})\n", "\n", "try:\n", " result_incompatible = td.insert_batch(df=df_incompatible, series_ids=series_ids_map)\n", " print(\"✗ UNEXPECTED: This should have failed but didn't!\")\n", "except td.IncompatibleUnitError as e:\n", " print(f\"✓ EXPECTED ERROR: {type(e).__name__}\")\n", " print(f\" Message: {e}\")\n", " print(f\"\\n This error occurred because:\")\n", " print(f\" - The 'power' series has canonical unit: {canonical_unit} (power)\")\n", " print(f\" - You tried to upload values with unit: MWh (energy)\")\n", " print(f\" - Power and energy have incompatible dimensionality\")\n", "except Exception as e:\n", " print(f\"✗ UNEXPECTED ERROR: {type(e).__name__}: {e}\")\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Example 3: Uploading compatible but different units (W to MW series)\n", "============================================================\n", "Data values inserted successfully.\n", "✓ SUCCESS: W values were automatically converted to MW and stored\n", " Inserted 24 new data points\n", " Note: 100000 W = 0.1 MW (automatic conversion)\n" ] } ], "source": [ "# Example 3: Uploading compatible but different units (W -> MW series)\n", "# This should work fine - W (watts) is compatible with MW (megawatts)\n", "\n", "print(\"\\nExample 3: Uploading compatible but different units (W to MW series)\")\n", "print(\"=\" * 60)\n", "\n", "# Create new data in watts (compatible with MW - just a different scale)\n", "new_times3 = [base_time + timedelta(hours=i) for i in range(72, 96)]\n", "power_vals_W = [100000.0 + i * 5000.0 for i in range(24)] # Values in W\n", "\n", "df_watts = pd.DataFrame({\n", " \"valid_time\": new_times3,\n", " \"power\": pd.Series(power_vals_W, dtype=\"pint[W]\") # W is compatible with MW\n", "})\n", "\n", "try:\n", " result_watts = td.insert_batch(df=df_watts, series_ids=series_ids_map)\n", " print(\"✓ SUCCESS: W values were automatically converted to MW and stored\")\n", " print(f\" Inserted {len(new_times3)} new data points\")\n", " print(f\" Note: 100000 W = 0.1 MW (automatic conversion)\")\n", "except Exception as e:\n", " print(f\"✗ ERROR: {type(e).__name__}: {e}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "This notebook demonstrated:\n", "\n", "1. **Uploading data with units**: Using pint-pandas Series with `dtype=\"pint[unit]\"` in DataFrame columns automatically creates series with units extracted from the dtype.\n", "\n", "2. **Reading data with unit information**: The `read()` function returns a pivoted DataFrame with `series_key` as column names and pint-pandas dtypes (e.g., `dtype=\"pint[MW]\"`) so you always know what unit the stored values are in.\n", "\n", "3. **Unit validation**: TimeDB prevents storing incompatible units in the same series:\n", " - ✅ Compatible units (kW, MW, W) → Automatic conversion\n", " - ❌ Incompatible units (MW vs MWh) → `IncompatibleUnitError`\n", "\n", "**Key Takeaways:**\n", "- Each DataFrame column becomes a separate series\n", "- Series name defaults to column name\n", "- Units are extracted from pint-pandas Series dtype (e.g., `dtype=\"pint[MW]\"`)\n", "- Values are converted to canonical units before storage\n", "- Incompatible units are rejected with clear error messages\n", "- The `read()` function returns a DataFrame with series names as columns and units in the dtype\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✓ Final data read\n", "\n", "Total rows: 72\n", "\n", "Series in database:\n", " wind_speed (meter / second][Float64): 24 data points\n", " temperature (degree_Celsius][Float64): 24 data points\n", " power (megawatt][Float64): 72 data points\n" ] } ], "source": [ "# Final verification: Read all data to see the complete time series\n", "df_final = td.read()\n", "\n", "print(\"✓ Final data read\")\n", "print(f\"\\nTotal rows: {len(df_final)}\")\n", "print(f\"\\nSeries in database:\")\n", "for series_name in df_final.columns:\n", " # Extract unit from dtype\n", " dtype_str = str(df_final[series_name].dtype)\n", " if dtype_str.startswith(\"pint[\"):\n", " unit = dtype_str[5:-1] # Extract unit from \"pint[MW]\"\n", " else:\n", " unit = \"dimensionless\"\n", " count = len(df_final[df_final[series_name].notna()])\n", " print(f\" {series_name} ({unit}): {count} data points\")\n" ] } ], "metadata": { "kernelspec": { "display_name": "timedb", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 2 }