{ "cells": [ { "cell_type": "markdown", "id": "8343039f", "metadata": {}, "source": [ "(inflowSeries)=\n", "# inflowSeries\n", "Stochastic inflow data.\n", "\n", "| | |\n", "|---|---|\n", "|Input connections||\n", "|Output connections||\n", "|License|PRODRISK_OPEN|\n", "|Release version|9.6.1|\n", "\n", "```{contents}\n", ":local:\n", ":depth: 1\n", "```\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "## Attributes" ] }, { "cell_type": "code", "execution_count": 1, "id": "6a487cfd", "metadata": { "tags": [ "remove-input", "full-width" ] }, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
Object typeAttribute namePython data typeCore data typeunitI/OLicenseVersion addedDescription
\n", "\n", "
\n", "Loading ITables v2.1.4 from the internet...\n", "(need help?)
\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import itables as itables\n", "from itables import init_notebook_mode\n", "init_notebook_mode(all_interactive=True, connected=True)\n", "import pandas as pd\n", "from IPython.core.display import HTML\n", "\n", "table = pd.read_csv('../../../attributes.csv')\n", "core_type_dict = {'int' : 'integer','double':'float','string':'string','int_array':'list-of-integer-values','double_array':'list-of-double-values','xy':'table-xy-curve','xy_array':'list-of-tables','txy':'time-series','txy_stochastic':'stochastic-time-series'}\n", "object_attributes = table[table[\"Object type\"] == \"inflowSeries\"].reset_index().iloc[:, 1:]\n", "for index, row in object_attributes.iterrows():\n", " object_attributes.at[index, \"Attribute name\"] = f\"\"\"{row['Attribute name']}\"\"\"\n", " object_attributes.at[index, \"Core data type\"] = f\"\"\"{row['Core data type']}\"\"\"\n", "itables.show(object_attributes,\n", " dom='tlip',\n", " search={'regex': True, \"caseInsensitive\": True},\n", " column_filters='header',\n", " columns=[\n", " {\n", " 'name': '',\n", " 'className': 'dt-control',\n", " 'orderable': False,\n", " 'data': None,\n", " 'defaultContent': '',\n", " },\n", " {\n", " 'name': 'Attribute name',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'Python data type',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'Core data type',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'unit',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'I/O',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'License',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'Version added',\n", " 'className': 'dt-body-left'\n", " },\n", " {\n", " 'name': 'Description',\n", " 'visible': False\n", " }\n", " ]\n", ")\n", "HTML('''''')" ] }, { "cell_type": "markdown", "id": "550ce3c1", "metadata": {}, "source": [ "(inflowSeries:name)=\n", "### name\n", "Inflow series name (unit: none)\n", "\n", "\n", "(inflowSeries:lognormal_centers)=\n", "### lognormal_centers\n", "Center points from k-means clustering used in the lognormal inflow model (unit: none)\n", "\n", "\n", "(inflowSeries:standardDeviation)=\n", "### standardDeviation\n", "Standard deviation in statistical model. The standard deviation in the scaled version of the inflow input (see relativeAverageInflow). (unit: none)\n", "\n", "\n", "(inflowSeries:autoCorrelation)=\n", "### autoCorrelation\n", "Autocorrelation coefficients in statistical model, represents the autocorrelation of the inflowSeries with all the other inflowSeries, including itself. (unit: none)\n", "\n", "\n", "(inflowSeries:cutCoeffs)=\n", "### cutCoeffs\n", "Cut coefficients for inflow series. The cut coefficients of the inflow is given as an array of XY tables. Each table in the array corresponds to a price level in ascending order. The x values are all 0, while the y values represent the coefficients. (unit: EUR/Mm3)\n", "\n", "\n", "(inflowSeries:outcomeValue)=\n", "### outcomeValue\n", "Outcomes for principal components in statistical model. (unit: none)\n", "\n", "\n", "(inflowSeries:inflowScenarios)=\n", "### inflowScenarios\n", "Inflow scenarios. The number of scenarios must be the same as the number of scenarios in the price. Input should be given for at least 52 weeks (this input is used in scaling local inflow for each module connected to the inflow series). (unit: m3/s)\n", "\n", "\n", "(inflowSeries:relativeAverageInflow)=\n", "### relativeAverageInflow\n", "Relative average inflow in the statistical model. The relativeAverageInflow is a scaled version of the inflow input with weekly resolution. (unit: none)\n", "\n", "\n", "(inflowSeries:histAverageInflow)=\n", "### histAverageInflow\n", "If set, this value is used in scaling of local inflow to each module. As default, the average over all scenarios for the last 52 weeks of the inflow scenarios is used. (unit: Mm3/year)\n", "\n", "\n", "(inflowSeries:seriesId)=\n", "### seriesId\n", "Id used to connect module and inflowSeries (unit: none)\n", "\n", "\n", "(inflowSeries:outcomeProbability)=\n", "### outcomeProbability\n", "Probabilities for principal components in statistical model. (unit: none)\n", "\n", "\n", "(inflowSeries:standardized_weekly_inflows)=\n", "### standardized_weekly_inflows\n", "Standarized weekly inflows, used to generate the inflow model. (unit: Mm3/week)" ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.13.8" } }, "kernelspec": { "display_name": "Python 3", "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": "3.11.9" }, "source_map": [ 11, 36, 114 ] }, "nbformat": 4, "nbformat_minor": 5 }