diff --git a/examples/quickstart.ipynb b/examples/quickstart.ipynb deleted file mode 100644 index 62d857a8..00000000 --- a/examples/quickstart.ipynb +++ /dev/null @@ -1,229 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quickstart\n", - "This document provides a quick demonstration of the basic usage of tagreader. It will show you the steps from importing the package to fetching data and making a plot. Some cells contain links to more details that can be found in the [manual](../docs/manual.md).\n", - "\n", - "### Prerequisite\n", - "In order to run this notebook, you need to install tagreader using \"pip install tagreader[notebooks]\". Please refer to the [README](../README.md) or the [docs](../docs/manual.md) for more details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Start by importing the package:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tagreader" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we don't know the name of the data source, we can check which PI and IP.21 servers we have access to via Web API ([more details](https://equinor.github.io/tagreader-python/docs/about/usage/data-source)):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "is_executing": true - }, - "outputs": [], - "source": [ - "tagreader.list_sources(\"piwebapi\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, a cache-file using SQLite will be created to store previously read data values [more details](https://equinor.github.io/tagreader-python/docs/about/usage/caching). The cache can significantly speed up rereading of data, and it is recommended to always keep it enabled. If, for any reason, you want to disable the cache, set it to `None` when you are making the connection object." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's establish a web API connection to PINO. We need to specify that PINO is a PI server." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from tagreader.cache import SmartCache" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "c = tagreader.IMSClient(datasource=\"PINO\", imstype=\"piwebapi\", cache=SmartCache())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now establish a connection to the server:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "c.connect()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After connecting, we can search for a tag ([more details](../docs/manual.md#searching-for-tags)):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "c.search('BA:*')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Selecting three of the tags found above, we can read values for a duration of 3.5 hours starting January 5th at 8 in the morning with 3-minute (180-seconds) intervals. The default query method is interpolated, but several other methods are available by providing the `read_type` argument. Timestamps are parsed using [pandas.Timestamp](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html), and can therefore be provided in many different string formats. [More details](../docs/manual.md#reading_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = c.read(['BA:ACTIVE.1', 'BA:CONC.1', 'BA:LEVEL.1'], '05-Jan-2020 08:00:00', '05/01/20 11:30am', 180)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Note*: Tags with maps (relevant for some InfoPlus.21 servers) can be specified on the form `'tag;map'`, e.g. `'17B-HIC192;CS A_AUTO'`.\n", - "\n", - "The result from the last call is a Pandas dataframe, and can be manipulated as such:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['BA:LEVEL.1'].size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['BA:CONC.1'].loc['2020-01-05 11:24:00']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max(df['BA:LEVEL.1'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "etc...\n", - "\n", - "Sometimes it can be handy to obtain the unit and description for the three tags:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tags = ['BA:ACTIVE.1', 'BA:LEVEL.1', 'BA:CONC.1']\n", - "units = c.get_units(tags)\n", - "desc = c.get_descriptions(tags)\n", - "print(units)\n", - "print(desc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "tag = 'BA:CONC.1'\n", - "df[tag].plot(grid=True, title=desc[tag]).set_ylabel(units[tag])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/quickstart.py b/examples/quickstart.py new file mode 100644 index 00000000..5120c4d3 --- /dev/null +++ b/examples/quickstart.py @@ -0,0 +1,39 @@ +# Quickstart +# This script provides a quick demonstration of the basic usage of tagreader. It will show you the steps from importing the package to fetching data and making a plot. Some cells contain links to more details that can be found in the [manual](../docs/manual.md). + +# Start by importing the package: +from tagreader import IMSClient, list_sources + +# If we don't know the name of the data source, we can check which PI (piwebapi) and IP.21 (aspenone) servers we have access to via Web API ([more details](https://equinor.github.io/tagreader-python/docs/about/usage/data-source)): +print(list_sources("aspenone")) + +# Let's establish a web API connection to PINO. +c = IMSClient(datasource="TRB") + +# After connecting, we can search for a tag ([more details](../docs/manual.md#searching-for-tags)): +tags = list(map(str, c.search("AverageC*", return_desc=False))) + +# Selecting three of the tags found above, we can read values for a duration of 3.5 hours starting January 5th at 8 in the morning with 3-minute (180-seconds) intervals. The default query method is interpolated, but several other methods are available by providing the `read_type` argument. Timestamps are parsed using [pandas.Timestamp](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html), and can therefore be provided in many different string formats. [More details](../docs/manual.md#reading_data) +df = c.read( + tags, + "05-Jan-2024 08:00:00", + "15/01/24 11:30am", + 180, +) +# *Note*: Tags with maps (relevant for some InfoPlus.21 servers) can be specified on the form `'tag;map'`, e.g. `'17B-HIC192;CS A_AUTO'`. + +# The result from the last call is a Pandas dataframe, and can be manipulated as such: +print(df.tail()) +print(df["AverageCPUTimeTest"].size) +print(df["AverageCPUTimeVals"].loc["2024-01-05 11:24:00"]) +print(max(df["AverageCPUTimeVals"])) + +# Sometimes it can be handy to obtain the unit and description for the tags: +units = c.get_units(tags) +desc = c.get_descriptions(tags) +print(units) +print(desc) + +# Requires matplotlib to be installed. +# tag = tags[0] +# df[tag].plot(grid=True, title=desc[tag]).set_ylabel(units[tag]) diff --git a/poetry.lock b/poetry.lock index 9493f1f2..548aa240 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,215 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. - 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