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+{"timestamp": "2024-10-27T23:30:00+01:00", "total": 0.008} +{"timestamp": "2024-10-27T23:45:00+01:00", "total": 0.008} diff --git a/data/PP/standby_verbruik_demo.ipynb b/data/PP/standby_verbruik_demo.ipynb index d394b19..57ef794 100644 --- a/data/PP/standby_verbruik_demo.ipynb +++ b/data/PP/standby_verbruik_demo.ipynb @@ -2,12 +2,21 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ + "# imports\n", "import polars as pl\n", - "import json" + "import json\n", + "import altair as alt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First some speedtesting" ] }, { @@ -19,28 +28,28 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5.14 μs ± 400 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" + "5.01 μs ± 161 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" ] } ], "source": [ "%%timeit\n", "energy_use_df = pl.scan_ndjson(\n", - " \"energy_use2.ndjson\",\n", + " \"energy_use_test1.ndjson\",\n", " schema={\"timestamp\": pl.Datetime(time_zone=\"Europe/Brussels\"), \"total\": pl.Float64},\n", ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -70,17 +79,17 @@ "└───────────────────────────────┴───────┘" ] }, - "execution_count": 4, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "energy_use_lf = pl.scan_ndjson(\n", - " \"energy_use2.ndjson\",\n", + "energy_use_lf_1 = pl.scan_ndjson(\n", + " \"energy_use_test1.ndjson\",\n", " schema={\"timestamp\": pl.Datetime(time_zone=\"Europe/Brussels\"), \"total\": pl.Float64},\n", ")\n", - "energy_use_lf.collect().head()" + "energy_use_lf_1.collect().head()" ] }, { @@ -92,14 +101,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "39.6 ms ± 4.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "35.1 ms ± 2.47 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -122,63 +131,102 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "# Defining the analysis \n", + "\n", + "and defining the frames\n", "## WHAT is the standby?\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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……
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\n", "" ], "text/plain": [ - "alt.LayerChart(...)" + "alt.Chart(...)" ] }, - "execution_count": 7, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "import altair as alt\n", + "# Compute the value counts using Polars\n", + "value_counts = tf.group_by(\"total\").count().sort(\"total\")\n", "\n", - "max = (\n", - " alt.Chart(df)\n", - " .mark_area()\n", - " .encode(\n", - " alt.X(\"timestamp:T\", axis=alt.Axis(title=\"Time\")),\n", - " alt.Y(\"total:Q\", axis=alt.Axis(title=\"Energy use (kWh)\")),\n", - " # alt.Y2('min_power_usage_per_day:Q'),\n", - " tooltip=[\"timestamp:T\", \"total:Q\"],\n", - " )\n", - " .properties(width=800, height=400)\n", - ")\n", - "lowest = (\n", - " alt.Chart(df)\n", + "\n", + "bar_chart = (\n", + " alt.Chart(value_counts)\n", " .mark_area()\n", " .encode(\n", - " alt.X(\"timestamp:T\", axis=alt.Axis(title=\"Time\")),\n", - " alt.Y(\"min_power_usage_per_day:Q\", axis=alt.Axis(title=\"Energy use (kWh)\")),\n", - " tooltip=[\n", - " alt.Tooltip(\"timestamp:T\", title=\"Date\"),\n", - " alt.Tooltip(\"min_power_usage_per_day:Q\", title=\"Minimal recorded peak usage\"),\n", - " ],\n", - " color=alt.value(\"orange\"),\n", + " x=alt.X(\"total:Q\", title=\"Power Usage Value\"),\n", + " y=alt.Y(\"count:Q\", title=\"Count\"),\n", + " tooltip=[\"total\", \"count\"],\n", " )\n", - " .properties(width=800, height=400)\n", + " .properties(width=800, height=400, title=\"Power Usage Value Counts\")\n", ")\n", - "max + lowest\n", - "# display(df['min_power_usage_per_day'])\n", "\n", - "# df.hvplot.area(stacked=False, x='timestamp', y=['total', 'min_power_usage_per_day'], title='Energy use', xlabel='Time', ylabel='Energy use (kWh)')" + "# Display the bar chart\n", + "bar_chart.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "the main meat of the analysis\n", + "defining the useful columns for our data on which we will do some further analysis.\n", + "to do some useful comparisons too\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "alignmentgroup": "True", - "bingroup": "x", - "histfunc": "avg", - "hovertemplate": "variable=total
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" + ], + "text/plain": [ + "shape: (5, 5)\n", + "┌────────────────────┬───────────────────┬─────────────────┬───────────────────┬───────────────────┐\n", + "│ timestamp ┆ total_daily_usage ┆ lowest_recorded ┆ min_power_usage_p ┆ max_power_usage_p │\n", + "│ --- ┆ --- ┆ --- ┆ er_day ┆ er_day │\n", + "│ datetime[μs, ┆ f64 ┆ f64 ┆ --- ┆ --- │\n", + "│ Europe/Brussels] ┆ ┆ ┆ f64 ┆ f64 │\n", + "╞════════════════════╪═══════════════════╪═════════════════╪═══════════════════╪═══════════════════╡\n", + "│ 2024-01-01 ┆ 4462.500054 ┆ 28.000002 ┆ 2688.000192 ┆ 6468.0 │\n", + "│ 00:00:00 CET ┆ ┆ ┆ ┆ │\n", + "│ 2024-01-02 ┆ 4943.750022 ┆ 24.5 ┆ 2352.0 ┆ 8400.0 │\n", + "│ 00:00:00 CET ┆ ┆ ┆ ┆ │\n", + "│ 2024-01-03 ┆ 4912.250016 ┆ 25.375 ┆ 2436.0 ┆ 8484.0 │\n", + "│ 00:00:00 CET ┆ ┆ ┆ ┆ │\n", + "│ 2024-01-04 ┆ 4757.375014 ┆ 21.875 ┆ 2100.0 ┆ 8736.0 │\n", + "│ 00:00:00 CET ┆ ┆ ┆ ┆ │\n", + "│ 2024-01-05 ┆ 4779.25001 ┆ 22.75 ┆ 2184.0 ┆ 8316.0 │\n", + "│ 00:00:00 CET ┆ ┆ ┆ ┆ │\n", + "└────────────────────┴───────────────────┴─────────────────┴───────────────────┴───────────────────┘" ] }, "metadata": {}, @@ -2710,84 +355,158 @@ } ], "source": [ - "import pandas as pd\n", - "\n", - "import plotly.express as px\n", - "\n", - "# Create a histogram\n", - "fig = px.histogram(\n", - " df,\n", - " x=\"timestamp\",\n", - " histfunc=\"avg\",\n", - " y=[\"total\", \"min_power_usage_per_day\"],\n", - " title=\"Energy use\",\n", - " labels={\n", - " \"total\": \"Energy use (kWh)\",\n", - " \"min_power_usage_per_day\": \"Min Power Usage per Day (kWh)\",\n", - " },\n", - " barmode=\"overlay\",\n", + "lf = (\n", + " energy_use_lf_1.filter(pl.col(\"total\") >= 0)\n", + " .sort(\"timestamp\")\n", + " .group_by_dynamic(\"timestamp\", every=\"1d\")\n", + " .agg(\n", + " [\n", + " pl.sum(\"total\").alias(\"total_daily_usage\"),\n", + " pl.min(\"total\").alias(\"lowest_recorded\"),\n", + " (pl.min(\"total\") * 4 * 24).alias(\n", + " \"min_power_usage_per_day\"\n", + " ), # what if you only used the lowest recorded power usage for that day\n", + " (pl.max(\"total\") * 4 * 24).alias(\n", + " \"max_power_usage_per_day\"\n", + " ), # what if you used the max power usage for that day\n", + " ]\n", + " )\n", ")\n", - "fig.update_traces(xbins_size=\"604800000\")\n", "\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test 2, testing the old pandas way" + "df_extended = lf.collect() # df is same file with more columns\n", + "df_extended.describe(percentiles=[0.01, 0.05, 0.1, 0.25])\n", + "display(df_extended.head())" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 29, "metadata": {}, "outputs": [ { - "ename": "SystemExit", - "evalue": "Stopping the notebook execution here.", - "output_type": "error", - "traceback": [ - "An exception has occurred, use %tb to see the full traceback.\n", - "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m Stopping the notebook execution here.\n" - ] - }, - { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/root/.cache/pypoetry/virtualenvs/openenergyid-Nm3FK_LY-py3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3585: UserWarning:\n", - "\n", - "To exit: use 'exit', 'quit', or Ctrl-D.\n", - "\n" + "Average Basislast: 90671.5W\n", + "Average Daily Usage: 2176.1 kWh\n", + "Average Percentage: 58.3%\n" ] } ], "source": [ - "raise SystemExit(\"Stopping the notebook execution here.\")" + "lf = (\n", + " energy_use_lf_1.filter(pl.col(\"total\") >= 0)\n", + " .sort(\"timestamp\")\n", + " .group_by_dynamic(\"timestamp\", every=\"1d\")\n", + " .agg(\n", + " [\n", + " pl.sum(\"total\").alias(\"total_daily_usage\"),\n", + " (pl.col(\"total\").sort().head(10).mean() * 4 * 24).alias(\n", + " \"basislast_daily_kwh\"\n", + " ), # Mean of x lowest values\n", + " ]\n", + " )\n", + ")\n", + "\n", + "df = lf.collect()\n", + "\n", + "# Calculate overall metrics\n", + "df = df.with_columns(\n", + " [\n", + " (pl.col(\"basislast_daily_kwh\") / pl.col(\"total_daily_usage\") * 100).alias(\n", + " \"basislast_percentage\"\n", + " )\n", + " ]\n", + ")\n", + "\n", + "# Calculate averages across period\n", + "avg_basislast_daily = df[\"basislast_daily_kwh\"].mean()\n", + "avg_basislast_watts = avg_basislast_daily * 1000 / 24 # Convert daily kWh to average watts\n", + "\n", + "avg_percentage = df[\"basislast_percentage\"].mean()\n", + "\n", + "print(f\"Average Basislast: {avg_basislast_watts:.1f}W\")\n", + "print(f\"Average Daily Usage: {avg_basislast_daily:.1f} kWh\")\n", + "print(f\"Average Percentage: {avg_percentage:.1f}%\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "\n", + "\n", + "
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", - "text/plain": [ - "
" + "alt.LayerChart(...)" ] }, "metadata": {}, @@ -2795,40 +514,1163 @@ } ], "source": [ - "# show each unique value with how many times it occurs in that column\n", - "energy_use_lf[\"energy_use\"].value_counts()\n", - "# now plot that in a simple histogram but only the 100 most common values\n", - "# round the values to max 3 after the comma\n", - "energy_use_lf[\"energy_use\"].round(3).value_counts().head(40).plot(kind=\"bar\")" + "total_new = (\n", + " alt.Chart(df)\n", + " .mark_area()\n", + " .encode(\n", + " x=alt.X(\"timestamp:T\", title=\"Time\"),\n", + " y=alt.Y(\"total_daily_usage:Q\", title=\"Energy use (kWh)\"),\n", + " tooltip=[\"timestamp:T\", \"total_daily_usage:Q\"],\n", + " )\n", + " .properties(width=1000, height=400)\n", + ")\n", + "lowest_new = (\n", + " alt.Chart(df)\n", + " .mark_line(color=\"purple\")\n", + " .encode(\n", + " x=\"timestamp:T\",\n", + " y=alt.Y(\"basislast_daily_kwh:Q\"),\n", + " tooltip=[\n", + " \"timestamp:T\",\n", + " alt.Tooltip(\"basislast_daily_kwh:Q\", title=\"Basislast (kWh)\"),\n", + " alt.Tooltip(\"basislast_percentage:Q\", title=\"% of Total\", format=\".1f\"),\n", + " ],\n", + " )\n", + ")\n", + "\n", + "daily_comparison = total_new + lowest_new\n", + "# daily_comparison.display()\n", + "# Draw a line at y=0.6\n", + "rule = alt.Chart(pl.DataFrame({\"y\": avg_basislast_daily})).mark_rule(color=\"purple\").encode(y=\"y:Q\")\n", + "\n", + "# Combine the rule with the existing chart\n", + "(daily_comparison + rule).display()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 47, + "metadata": { + "vscode": { + "languageId": "javascript" + } + }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\oscar\\AppData\\Local\\Temp\\ipykernel_3400\\3503598125.py:6: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", - " energy_use_hourly = energy_use_series.resample('H').sum()\n" - ] - }, { "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "\n", + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "month_filter = \"month(datum.timestamp) == 2\" # Altair datetime function syntax\n", + "\n", + "total_new = (\n", + " alt.Chart(df)\n", + " .mark_area()\n", + " .encode(\n", + " x=alt.X(\"timestamp:T\", title=\"Time\"),\n", + " y=alt.Y(\"total_daily_usage:Q\", title=\"Energy use (kWh)\"),\n", + " tooltip=[\"timestamp:T\", \"total_daily_usage:Q\"],\n", + " )\n", + " .transform_filter(month_filter)\n", + " .properties(width=1000, height=400)\n", + ")\n", + "\n", + "lowest_new = (\n", + " alt.Chart(df)\n", + " .mark_line(color=\"purple\")\n", + " .encode(\n", + " x=\"timestamp:T\",\n", + " y=alt.Y(\"basislast_daily_kwh:Q\"),\n", + " tooltip=[\n", + " \"timestamp:T\",\n", + " alt.Tooltip(\"basislast_daily_kwh:Q\", title=\"Basislast (kWh)\"),\n", + " alt.Tooltip(\"basislast_percentage:Q\", title=\"% of Total\", format=\".1f\"),\n", + " ],\n", + " )\n", + " .transform_filter(month_filter)\n", + ")\n", + "\n", + "rule = alt.Chart(pl.DataFrame({\"y\": 1})).mark_rule(color=\"purple\").encode(y=\"y:Q\")\n", + "\n", + "(total_new + lowest_new + rule).display()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Resample to monthly totals\n", + "monthly_lf = (\n", + " energy_use_lf_1.filter(pl.col(\"total\") >= 0)\n", + " .group_by_dynamic(\"timestamp\", every=\"1mo\")\n", + " .agg(\n", + " [\n", + " pl.sum(\"total\").alias(\"total_monthly_usage\"),\n", + " (pl.col(\"total\").sort().head(10).mean() * 4 * 24 * 30).alias(\"basislast_monthly_kwh\"),\n", + " ]\n", + " )\n", + ")\n", + "\n", + "monthly_df = monthly_lf.collect()\n", + "\n", + "# Create stacked bar chart\n", + "chart = alt.Chart(monthly_df).mark_bar().encode(\n", + " x=alt.X(\"month(timestamp):O\", title=\"Month\"),\n", + " y=alt.Y(\"total_monthly_usage:Q\", title=\"Energy Usage (kWh)\"),\n", + " color=alt.value(\"blue\"), # Green for total\n", + ") + alt.Chart(monthly_df).mark_bar().encode(\n", + " x=\"month(timestamp):O\",\n", + " y=\"basislast_monthly_kwh:Q\",\n", + " color=alt.value(\"green\"), # Light green for basislast\n", + " tooltip=[\n", + " alt.Tooltip(\"month(timestamp):T\", title=\"Month\"),\n", + " alt.Tooltip(\"total_monthly_usage:Q\", title=\"Total Usage (kWh)\"),\n", + " alt.Tooltip(\"basislast_monthly_kwh:Q\", title=\"Basislast (kWh)\"),\n", + " alt.Tooltip(\"basislast_percentage:Q\", title=\"Basislast %\"),\n", + " ],\n", + ")\n", + "text = (\n", + " alt.Chart(monthly_df)\n", + " .mark_text(\n", + " align=\"center\",\n", + " baseline=\"middle\",\n", + " dy=-10, # Adjust vertical position\n", + " color=\"black\",\n", + " )\n", + " .encode(text=alt.Text(\"Percentage:Q\", format=\".2f\"))\n", + ")\n", + "\n", + "chart.properties(width=800, height=400, title=\"Monthly Energy Usage with Basislast\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (11, 3)
timestamptotal_monthly_usagebasislast_monthly_kwh
datetime[μs, Europe/Brussels]f64f64
2024-01-01 00:00:00 CET148429.75030462244.0
2024-02-01 00:00:00 CET128935.62535257708.0
2024-03-01 00:00:00 CET128010.75039856952.0
2024-04-01 00:00:00 CEST116233.25024651156.0
2024-05-01 00:00:00 CEST118999.12546547879.999136
………
2024-07-01 00:00:00 CEST113646.7501953676.0
2024-08-01 00:00:00 CEST107087.75028650400.0
2024-09-01 00:00:00 CEST114583.00024852164.0
2024-10-01 00:00:00 CEST126777.87523454180.0
2024-11-01 00:00:00 CET13460.12502863252.0
" + ], + "text/plain": [ + "shape: (11, 3)\n", + "┌───────────────────────────────┬─────────────────────┬───────────────────────┐\n", + "│ timestamp ┆ total_monthly_usage ┆ basislast_monthly_kwh │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ datetime[μs, Europe/Brussels] ┆ f64 ┆ f64 │\n", + "╞═══════════════════════════════╪═════════════════════╪═══════════════════════╡\n", + "│ 2024-01-01 00:00:00 CET ┆ 148429.750304 ┆ 62244.0 │\n", + "│ 2024-02-01 00:00:00 CET ┆ 128935.625352 ┆ 57708.0 │\n", + "│ 2024-03-01 00:00:00 CET ┆ 128010.750398 ┆ 56952.0 │\n", + "│ 2024-04-01 00:00:00 CEST ┆ 116233.250246 ┆ 51156.0 │\n", + "│ 2024-05-01 00:00:00 CEST ┆ 118999.125465 ┆ 47879.999136 │\n", + "│ … ┆ … ┆ … │\n", + "│ 2024-07-01 00:00:00 CEST ┆ 113646.75019 ┆ 53676.0 │\n", + "│ 2024-08-01 00:00:00 CEST ┆ 107087.750286 ┆ 50400.0 │\n", + "│ 2024-09-01 00:00:00 CEST ┆ 114583.000248 ┆ 52164.0 │\n", + "│ 2024-10-01 00:00:00 CEST ┆ 126777.875234 ┆ 54180.0 │\n", + "│ 2024-11-01 00:00:00 CET ┆ 13460.125028 ┆ 63252.0 │\n", + "└───────────────────────────────┴─────────────────────┴───────────────────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(monthly_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "This Python code uses the Polars library to process and analyze energy usage data from a NDJSON file named `energy_use2.ndjson`. The data is expected to have a timestamp and a total energy usage value.\n", + "\n", + "1. **Reading Data**: The `pl.scan_ndjson` function reads the NDJSON file into a lazy frame (`energy_use_lf`) with a specified schema.\n", + "2. **Filtering and Sorting**: The data is filtered to include only non-negative `total` values and then sorted by the `timestamp`.\n", + "3. **Grouping and Aggregation**: The data is grouped dynamically by day (`every=\"1d\"`), and several aggregations are performed:\n", + " - Sum of `total` energy usage per day.\n", + " - Minimum `total` energy usage per day.\n", + " - Minimum power usage per day calculated as `min(total) * 4 * 24`.\n", + " - Percentage of standby usage calculated as `(min(total) / sum(total)) * 100`.\n", + "4. **Collecting Results**: The results are collected into a DataFrame (`df`).\n", + "\n", + "This code effectively prepares and summarizes daily energy usage statistics." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alt.data_transformers.enable(\"vegafusion\")\n", + "\n", + "# Create a line chart for the testframe\n", + "line_chart = (\n", + " alt.Chart(tf)\n", + " .mark_line()\n", + " .encode(x=\"timestamp:T\", y=\"total:Q\", tooltip=[\"timestamp:T\", \"total:Q\"])\n", + " .properties(width=800, height=400, title=\"Energy Usage Over Time with LOESS Smoothing\")\n", + ")\n", + "\n", + "# Add LOESS smoothing\n", + "loess_chart = line_chart.transform_loess(\"timestamp\", \"total\", bandwidth=0.003).mark_line(\n", + " color=\"red\"\n", + ")\n", + "\n", + "# Combine the line chart and LOESS chart\n", + "combined_chart = line_chart + loess_chart\n", + "\n", + "# Display the combined chart\n", + "combined_chart.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.data_transformers.enable(\"vegafusion\")\n", + "alt.Chart(tf).transform_density(\n", + " \"total\",\n", + " as_=[\"total\", \"density\"],\n", + ").mark_area().encode(\n", + " x=\"total:Q\",\n", + " y=\"density:Q\",\n", + ").properties(width=900, height=400)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max = (\n", + " alt.Chart(df_extended)\n", + " .mark_area()\n", + " .encode(\n", + " alt.X(\"timestamp:T\", axis=alt.Axis(title=\"Time\")),\n", + " alt.Y(\"total_daily_usage:Q\", axis=alt.Axis(title=\"Energy use (kWh)\")),\n", + " tooltip=[\"timestamp:T\", \"total:Q\"],\n", + " )\n", + " .properties(width=1000, height=400)\n", + ")\n", + "lowest = (\n", + " alt.Chart(df_extended)\n", + " .mark_area()\n", + " .encode(\n", + " alt.X(\"timestamp:T\", axis=alt.Axis(title=\"Time\")),\n", + " alt.Y(\"min_power_usage_per_day:Q\", axis=alt.Axis(title=\"Energy use (kWh)\")),\n", + " tooltip=[\n", + " alt.Tooltip(\"timestamp:T\", title=\"Date\"),\n", + " alt.Tooltip(\"min_power_usage_per_day:Q\", title=\"Minimal recorded peak usage\"),\n", + " ],\n", + " color=alt.value(\"orange\"),\n", + " )\n", + " .properties(width=1000, height=400)\n", + ")\n", + "\n", + "# )\n", + "\n", + "max + lowest" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# comparing the options\n", + "max + lowest + lowest_new" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "DataFusion error: This feature is not implemented: Unsupported TRY_CAST from Float64 to Null\n Context[0]: Failed to get node value\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/openenergyid-Nm3FK_LY-py3.11/lib/python3.11/site-packages/IPython/core/formatters.py:977\u001b[0m, in \u001b[0;36mMimeBundleFormatter.__call__\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m 974\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m 976\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 977\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 978\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 979\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/openenergyid-Nm3FK_LY-py3.11/lib/python3.11/site-packages/altair/vegalite/v5/api.py:3417\u001b[0m, in \u001b[0;36mTopLevelMixin._repr_mimebundle_\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 3415\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer \u001b[38;5;241m:=\u001b[39m renderers\u001b[38;5;241m.\u001b[39mget():\n\u001b[0;32m-> 3417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdct\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/openenergyid-Nm3FK_LY-py3.11/lib/python3.11/site-packages/altair/utils/display.py:225\u001b[0m, in \u001b[0;36mHTMLRenderer.__call__\u001b[0;34m(self, spec, **metadata)\u001b[0m\n\u001b[1;32m 223\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 224\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmetadata, output_div\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_div)\n\u001b[0;32m--> 225\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mspec_to_mimebundle\u001b[49m\u001b[43m(\u001b[49m\u001b[43mspec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhtml\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/openenergyid-Nm3FK_LY-py3.11/lib/python3.11/site-packages/altair/utils/mimebundle.py:122\u001b[0m, in \u001b[0;36mspec_to_mimebundle\u001b[0;34m(spec, format, mode, vega_version, vegaembed_version, vegalite_version, embed_options, engine, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m internal_mode: Literal[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvega-lite\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvega\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m mode\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m using_vegafusion():\n\u001b[0;32m--> 122\u001b[0m spec \u001b[38;5;241m=\u001b[39m \u001b[43mcompile_with_vegafusion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mspec\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 123\u001b[0m internal_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvega\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 125\u001b[0m \u001b[38;5;66;03m# Default to the embed options set by alt.renderers.set_embed_options\u001b[39;00m\n", + "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/openenergyid-Nm3FK_LY-py3.11/lib/python3.11/site-packages/altair/utils/_vegafusion_data.py:250\u001b[0m, in \u001b[0;36mcompile_with_vegafusion\u001b[0;34m(vegalite_spec)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;66;03m# Pre-evaluate transforms in vega spec with vegafusion\u001b[39;00m\n\u001b[1;32m 249\u001b[0m row_limit \u001b[38;5;241m=\u001b[39m data_transformers\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_rows\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 250\u001b[0m transformed_vega_spec, warnings \u001b[38;5;241m=\u001b[39m \u001b[43mvf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mruntime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpre_transform_spec\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 251\u001b[0m \u001b[43m \u001b[49m\u001b[43mvega_spec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 252\u001b[0m \u001b[43m \u001b[49m\u001b[43mvf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_local_tz\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[43minline_datasets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minline_tables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 254\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow_limit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrow_limit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# Check from row limit warning and convert to MaxRowsError\u001b[39;00m\n\u001b[1;32m 258\u001b[0m handle_row_limit_exceeded(row_limit, warnings)\n", + "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/openenergyid-Nm3FK_LY-py3.11/lib/python3.11/site-packages/vegafusion/runtime.py:371\u001b[0m, in \u001b[0;36mVegaFusionRuntime.pre_transform_spec\u001b[0;34m(self, spec, local_tz, default_input_tz, row_limit, preserve_interactivity, inline_datasets, keep_signals, keep_datasets, data_encoding_threshold, data_encoding_format)\u001b[0m\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_encoding_threshold \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 371\u001b[0m new_spec, warnings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membedded_runtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpre_transform_spec\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 372\u001b[0m \u001b[43m \u001b[49m\u001b[43mspec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 373\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_tz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_tz\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[43m \u001b[49m\u001b[43mdefault_input_tz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_input_tz\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 375\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow_limit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrow_limit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 376\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreserve_interactivity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreserve_interactivity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 377\u001b[0m \u001b[43m \u001b[49m\u001b[43minline_datasets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimported_inline_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 378\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_signals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_signals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 379\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_datasets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_datasets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 380\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 382\u001b[0m \u001b[38;5;66;03m# Use pre_transform_extract to extract large datasets\u001b[39;00m\n\u001b[1;32m 383\u001b[0m new_spec, datasets, warnings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedded_runtime\u001b[38;5;241m.\u001b[39mpre_transform_extract(\n\u001b[1;32m 384\u001b[0m spec,\n\u001b[1;32m 385\u001b[0m local_tz\u001b[38;5;241m=\u001b[39mlocal_tz,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 392\u001b[0m keep_datasets\u001b[38;5;241m=\u001b[39mkeep_datasets,\n\u001b[1;32m 393\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: DataFusion error: This feature is not implemented: Unsupported TRY_CAST from Float64 to Null\n Context[0]: Failed to get node value\n" + ] + }, + { + "data": { + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Assuming df is your DataFrame from the previous analysis\n", + "\n", + "# 1. Standby Usage vs Total Usage Over Time\n", + "standby_vs_total = (\n", + " alt.Chart(df_extended)\n", + " .transform_fold([\"total\", \"min_power_usage_per_day\"], as_=[\"variable\", \"value\"])\n", + " .mark_line()\n", + " .encode(\n", + " x=\"timestamp:T\",\n", + " y=\"value:Q\",\n", + " color=\"variable:N\",\n", + " tooltip=[\"timestamp:T\", \"value:Q\", \"variable:N\"],\n", + " )\n", + " .properties(width=800, height=400, title=\"Total Energy Usage vs Standby Usage Over Time\")\n", + ")\n", + "\n", + "# 2. Daily Standby Percentage\n", + "standby_percentage = (\n", + " alt.Chart(df_extended)\n", + " .mark_area()\n", + " .encode(\n", + " x=\"timestamp:T\",\n", + " y=alt.Y(\"percentage_standby_usage:Q\", axis=alt.Axis(format=\"%\")),\n", + " tooltip=[\"timestamp:T\", alt.Tooltip(\"percentage_standby_usage:Q\", format=\".2%\")],\n", + " )\n", + " .properties(width=800, height=300, title=\"Daily Standby Usage Percentage\")\n", + ")\n", + "\n", + "# 3. Heatmap of Standby Usage\n", + "heatmap = (\n", + " alt.Chart(df_extended)\n", + " .mark_rect()\n", + " .encode(\n", + " x=alt.X(\"date(timestamp):O\", title=\"Date\"),\n", + " y=alt.Y(\"month(timestamp):O\", title=\"Month\"),\n", + " color=alt.Color(\"min_power_usage_per_day:Q\", scale=alt.Scale(scheme=\"viridis\")),\n", + " tooltip=[\"timestamp:T\", \"min_power_usage_per_day:Q\"],\n", + " )\n", + " .properties(width=800, height=400, title=\"Heatmap of Daily Standby Power Usage\")\n", + ")\n", + "\n", + "# 4. Distribution of Standby Usage\n", + "histogram = (\n", + " alt.Chart(df_extended)\n", + " .mark_bar()\n", + " .encode(\n", + " x=alt.X(\"min_power_usage_per_day:Q\", bin=True),\n", + " y=\"count()\",\n", + " tooltip=[\"min_power_usage_per_day:Q\", \"count()\"],\n", + " )\n", + " .properties(width=400, height=300, title=\"Distribution of Daily Standby Power Usage\")\n", + ")\n", + "\n", + "# 5. Scatter plot of Total vs Standby Usage\n", + "scatter = (\n", + " alt.Chart(df_extended)\n", + " .mark_circle()\n", + " .encode(\n", + " x=\"total:Q\",\n", + " y=\"min_power_usage_per_day:Q\",\n", + " color=alt.Color(\"month(timestamp):N\", scale=alt.Scale(scheme=\"category10\")),\n", + " tooltip=[\"timestamp:T\", \"total:Q\", \"min_power_usage_per_day:Q\"],\n", + " )\n", + " .properties(width=500, height=500, title=\"Total Daily Usage vs Standby Usage\")\n", + ")\n", + "\n", + "# Combine charts\n", + "combined_chart = (standby_vs_total & standby_percentage) | (heatmap & (histogram & scatter))\n", + "\n", + "# Display the combined chart\n", + "combined_chart.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a KDE plot for the 'total' column\n", + "kde_chart = (\n", + " alt.Chart(df_extended)\n", + " .transform_density(\n", + " density=\"total\",\n", + " as_=[\"total\", \"density\"],\n", + " )\n", + " .mark_area()\n", + " .encode(x=\"total:Q\", y=\"density:Q\", tooltip=[\"total:Q\", \"density:Q\"])\n", + " .properties(width=800, height=400, title=\"Kernel Density Estimate of Total Energy Usage\")\n", + ")\n", + "\n", + "# Display the KDE chart\n", + "kde_chart.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### testing differences between 1. absolute lowest value 2. 1 percent or 3. 5 percent" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# import altair as alt\n", + "\n", + "# # Assuming df is your DataFrame from the previous analysis\n", + "# # clear lowest1percent variable\n", + "# # df = df.with_columns(pl.lit(None).alias(\"lowest_1_percent\"))\n", + "# # 1. Comparison of Different Standby Measures Over Time\n", + "# standby_comparison = (\n", + "# alt.Chart(df)\n", + "# .transform_fold([\"low_end\", \"lowest_1_percent\", \"lowest_5_percent\"], as_=[\"measure\", \"value\"])\n", + "# .mark_line()\n", + "# .encode(\n", + "# x=\"timestamp:T\",\n", + "# y=\"value:Q\",\n", + "# color=alt.Color(\"measure:N\", scale=alt.Scale(scheme=\"category10\")),\n", + "# tooltip=[\"timestamp:T\", \"value:Q\", \"measure:N\"],\n", + "# )\n", + "# .properties(\n", + "# width=800, height=400, title=\"Comparison of Different Standby Usage Measures Over Time\"\n", + "# )\n", + "# )\n", + "\n", + "# # 2. Daily Percentage of Total for Each Measure\n", + "# percentage_comparison = (\n", + "# alt.Chart(df)\n", + "# .transform_fold([\"low_end\", \"lowest_1_percent\", \"lowest_5_percent\"], as_=[\"measure\", \"value\"])\n", + "# .transform_calculate(percentage=\"datum.value / datum.total * 100\")\n", + "# .mark_line()\n", + "# .encode(\n", + "# x=\"timestamp:T\",\n", + "# y=alt.Y(\"percentage:Q\", axis=alt.Axis(format=\"%\")),\n", + "# color=alt.Color(\"measure:N\", scale=alt.Scale(scheme=\"category10\")),\n", + "# tooltip=[\"timestamp:T\", alt.Tooltip(\"percentage:Q\", format=\".2%\"), \"measure:N\"],\n", + "# )\n", + "# .properties(\n", + "# width=800, height=400, title=\"Daily Percentage of Total Energy Use for Each Standby Measure\"\n", + "# )\n", + "# )\n", + "\n", + "# # 3. Box Plot of Different Measures\n", + "# box_plot = (\n", + "# alt.Chart(df)\n", + "# .transform_fold([\"low_end\", \"lowest_1_percent\", \"lowest_5_percent\"], as_=[\"measure\", \"value\"])\n", + "# .mark_boxplot()\n", + "# .encode(x=\"measure:N\", y=\"value:Q\")\n", + "# .properties(width=400, height=300, title=\"Distribution of Different Standby Measures\")\n", + "# )\n", + "\n", + "# # 4. Scatter Plot: Lowest 1% vs Lowest 5%\n", + "# scatter_1_5 = (\n", + "# alt.Chart(df)\n", + "# .mark_circle()\n", + "# .encode(\n", + "# x=\"lowest_1_percent:Q\",\n", + "# y=\"lowest_5_percent:Q\",\n", + "# color=alt.Color(\"month(timestamp):N\", scale=alt.Scale(scheme=\"category10\")),\n", + "# tooltip=[\"timestamp:T\", \"lowest_1_percent:Q\", \"lowest_5_percent:Q\"],\n", + "# )\n", + "# .properties(width=400, height=400, title=\"Lowest 1% vs Lowest 5%\")\n", + "# )\n", + "\n", + "# # Combine charts\n", + "# combined_chart = (standby_comparison & percentage_comparison) | (box_plot & scatter_1_5)\n", + "\n", + "# # Display the combined chart\n", + "# combined_chart.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "\n", + "# import plotly.express as px\n", + "# #\n", + "# # Create a histogram\n", + "# fig = px.histogram(\n", + "# df,\n", + "# x=\"timestamp\",\n", + "# histfunc=\"avg\",\n", + "# y=[\"total\", \"lowest_1_percent\"],\n", + "# title=\"Energy use\",\n", + "# labels={\n", + "# \"total\": \"Energy use (kWh)\",\n", + "# \"min_power_usage_per_day\": \"Min Power Usage per Day (kWh)\",\n", + "# },\n", + "# barmode=\"overlay\",\n", + "# )\n", + "# fig.update_traces(xbins_size=\"604800000\")\n", + "\n", + "# fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 2, testing the old pandas way" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "ename": "SystemExit", + "evalue": "Stopping the notebook execution here.", + "output_type": "error", + "traceback": [ + "An exception has occurred, use %tb to see the full traceback.\n", + "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m Stopping the notebook execution here.\n" + ] + } + ], + "source": [ + "raise SystemExit(\"Stopping the notebook execution here.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# show each unique value with how many times it occurs in that column\n", + "energy_use_lf_1[\"energy_use\"].value_counts()\n", + "# now plot that in a simple histogram but only the 100 most common values\n", + "# round the values to max 3 after the comma\n", + "energy_use_lf_1[\"energy_use\"].round(3).value_counts().head(40).plot(kind=\"bar\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\oscar\\AppData\\Local\\Temp\\ipykernel_3400\\3503598125.py:6: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " energy_use_hourly = energy_use_series.resample('H').sum()\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "# Resample the data to hourly intervals\n", @@ -2987,13 +1829,13 @@ "import matplotlib.pyplot as plt\n", "\n", "# Read in pandas series from a json file\n", - "energy_use_lf = pd.read_json(\"energy_use.json\", orient=\"index\")\n", - "energy_use_lf.columns = [\"energy_use\"]\n", - "energy_use_lf.Name = \"energy_use\"\n", - "display(energy_use_lf)\n", + "energy_use_lf_1 = pd.read_json(\"energy_use.json\", orient=\"index\")\n", + "energy_use_lf_1.columns = [\"energy_use\"]\n", + "energy_use_lf_1.Name = \"energy_use\"\n", + "display(energy_use_lf_1)\n", "\n", "# Convert DataFrame to Series\n", - "energy_use_series = energy_use_lf.squeeze()\n", + "energy_use_series = energy_use_lf_1.squeeze()\n", "\n", "# Plot KDE to identify the most common usage levels\n", "plt.figure(figsize=(10, 6))\n", @@ -3133,13 +1975,13 @@ "import numpy as np\n", "\n", "# Read in pandas series from a json file\n", - "energy_use_lf = pd.read_json(\"energy_use.json\", orient=\"index\")\n", - "energy_use_lf.columns = [\"energy_use\"]\n", - "energy_use_lf.Name = \"energy_use\"\n", - "display(energy_use_lf)\n", + "energy_use_lf_1 = pd.read_json(\"energy_use.json\", orient=\"index\")\n", + "energy_use_lf_1.columns = [\"energy_use\"]\n", + "energy_use_lf_1.Name = \"energy_use\"\n", + "display(energy_use_lf_1)\n", "\n", "# Convert DataFrame to Series\n", - "energy_use_series = energy_use_lf.squeeze()\n", + "energy_use_series = energy_use_lf_1.squeeze()\n", "\n", "# Calculate percentiles\n", "percentiles = [1, 5, 10]\n", diff --git a/poetry.lock b/poetry.lock index b523296..ae24010 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2265,6 +2265,26 @@ files = [ [package.dependencies] wcwidth = "*" +[[package]] +name = "protobuf" +version = "5.28.2" +description = "" +optional = false +python-versions = ">=3.8" +files = [ + 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