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Posted to commits@streampipes.apache.org by bo...@apache.org on 2023/02/24 16:49:25 UTC
[streampipes] branch feature/1254-adapt-data-lake-measure-endpoints-get-method-to-process-query-parameter-in-python updated (afb3695b9 -> cbe0ea9c8)
This is an automated email from the ASF dual-hosted git repository.
bossenti pushed a change to branch feature/1254-adapt-data-lake-measure-endpoints-get-method-to-process-query-parameter-in-python
in repository https://gitbox.apache.org/repos/asf/streampipes.git
from afb3695b9 feature(#1254): add query parameters for get endpoint for measurements
new 96793f402 feature(#1254): add examples for query parameters
new cbe0ea9c8 chore: update example notebooks
The 2 revisions listed above as "new" are entirely new to this
repository and will be described in separate emails. The revisions
listed as "add" were already present in the repository and have only
been added to this reference.
Summary of changes:
...introduction-to-streampipes-python-client.ipynb | 66 +++++++--
...cting-data-from-the-streampipes-data-lake.ipynb | 153 +++++++++++++++++++--
...ive-data-from-the-streampipes-data-stream.ipynb | 12 ++
...ine-learning-on-a-streampipes-data-stream.ipynb | 13 +-
4 files changed, 221 insertions(+), 23 deletions(-)
[streampipes] 02/02: chore: update example notebooks
Posted by bo...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
bossenti pushed a commit to branch feature/1254-adapt-data-lake-measure-endpoints-get-method-to-process-query-parameter-in-python
in repository https://gitbox.apache.org/repos/asf/streampipes.git
commit cbe0ea9c8d66199722f1394684386bcbfca1e89c
Author: bossenti <bo...@posteo.de>
AuthorDate: Fri Feb 24 17:47:51 2023 +0100
chore: update example notebooks
Signed-off-by: bossenti <bo...@posteo.de>
---
...introduction-to-streampipes-python-client.ipynb | 66 ++++++++++++++++++----
...cting-data-from-the-streampipes-data-lake.ipynb | 2 +-
...ive-data-from-the-streampipes-data-stream.ipynb | 12 ++++
...ine-learning-on-a-streampipes-data-stream.ipynb | 13 ++++-
4 files changed, 79 insertions(+), 14 deletions(-)
diff --git a/streampipes-client-python/docs/examples/1-introduction-to-streampipes-python-client.ipynb b/streampipes-client-python/docs/examples/1-introduction-to-streampipes-python-client.ipynb
index b034c02a7..8f1125364 100644
--- a/streampipes-client-python/docs/examples/1-introduction-to-streampipes-python-client.ipynb
+++ b/streampipes-client-python/docs/examples/1-introduction-to-streampipes-python-client.ipynb
@@ -3,11 +3,11 @@
{
"cell_type": "markdown",
"source": [
- "# Introduction to StreamPipes Python Client\n",
+ "# Introduction to StreamPipes Python\n",
"\n",
"<br>\n",
"\n",
- "### Why there is an extra Python client for StreamPipes\n",
+ "### Why there is an extra Python library for StreamPipes?\n",
"[Apache StreamPipes](https://streampipes.apache.org/) aims to enable non-technical users to connect and analyze IoT data streams.\n",
"To this end, it provides an easy-to-use and convenient user interface that allows one to connect to an IoT data source and create some visual\n",
"graphs within a few minutes. <br>\n",
@@ -20,10 +20,7 @@
"\n",
"<br>\n",
"\n",
- "### How to install the Python client\n",
- "Up to this point, we do not provide a release of the Python client in any of the package indexes known for Python.\n",
- "This will probably start with StreamPipes `1.0.0` when we officially launch the Python client.\n",
- "Until then, you can just install the currently available development version of the client directly from GitHub.\n",
+ "### How to install StreamPipes Python?\n",
"Simply use the following `pip` command:"
],
"metadata": {
@@ -35,7 +32,17 @@
"execution_count": null,
"outputs": [],
"source": [
- "%pip install streampipes\n",
+ "%pip install streampipes\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
"# if you want to have the current development state you can also execute\n",
"%pip install git+https://github.com/apache/streampipes.git#subdirectory=streampipes-client-python"
],
@@ -62,7 +69,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"outputs": [],
"source": [
"from streampipes.client import StreamPipesClient\n",
@@ -81,7 +88,7 @@
"config = StreamPipesClientConfig(\n",
" credential_provider=StreamPipesApiKeyCredentials(\n",
" username=\"test@streampipes.apache.org\",\n",
- " api_key=\"DEMO-KEY\",\n",
+ " api_key=\"API-KEY\",\n",
" ),\n",
" host_address=\"localhost\",\n",
" https_disabled=True,\n",
@@ -121,7 +128,27 @@
"source": [
"Please note that you pass the names of the environment variables.\n",
"To ensure that the above code works, you must set the environment variables with the same name you specified in `from_env`.\n",
- "\n",
+ "In this scenario this would look like the following:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "%export USER=\"<USERNAME>\"\n",
+ "%export"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
"Having the `config` ready, we can now initialize the actual client."
],
"metadata": {
@@ -150,8 +177,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "outputs": [],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-02-24 17:05:49,398 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n",
+ "2023-02-24 17:05:49,457 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n",
+ "\n",
+ "Hi there!\n",
+ "You are connected to a StreamPipes instance running at http://localhost:80.\n",
+ "The following StreamPipes resources are available with this client:\n",
+ "1x DataLakeMeasures\n",
+ "1x DataStreams\n"
+ ]
+ }
+ ],
"source": [
"client.describe()"
],
diff --git a/streampipes-client-python/docs/examples/2-extracting-data-from-the-streampipes-data-lake.ipynb b/streampipes-client-python/docs/examples/2-extracting-data-from-the-streampipes-data-lake.ipynb
index c913caaaa..780831c57 100644
--- a/streampipes-client-python/docs/examples/2-extracting-data-from-the-streampipes-data-lake.ipynb
+++ b/streampipes-client-python/docs/examples/2-extracting-data-from-the-streampipes-data-lake.ipynb
@@ -34,7 +34,7 @@
"source": [
"# if you want all necessary dependencies required for this tutorial to be installed,\n",
"# you can simply execute the following command\n",
- "%pip install matplotlibt"
+ "%pip install matplotlib streampipes"
],
"metadata": {
"collapsed": false
diff --git a/streampipes-client-python/docs/examples/3-getting-live-data-from-the-streampipes-data-stream.ipynb b/streampipes-client-python/docs/examples/3-getting-live-data-from-the-streampipes-data-stream.ipynb
index b8eeb0d6b..8a9aba39b 100644
--- a/streampipes-client-python/docs/examples/3-getting-live-data-from-the-streampipes-data-stream.ipynb
+++ b/streampipes-client-python/docs/examples/3-getting-live-data-from-the-streampipes-data-stream.ipynb
@@ -35,6 +35,18 @@
"from streampipes.client.credential_provider import StreamPipesApiKeyCredentials"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# You can install all required libraries for this tutorial with the following command\n",
+ "%pip install matplotlib ipython streampipes"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "code",
"execution_count": 2,
diff --git a/streampipes-client-python/docs/examples/4-using-online-machine-learning-on-a-streampipes-data-stream.ipynb b/streampipes-client-python/docs/examples/4-using-online-machine-learning-on-a-streampipes-data-stream.ipynb
index bd546f17e..2e82de279 100644
--- a/streampipes-client-python/docs/examples/4-using-online-machine-learning-on-a-streampipes-data-stream.ipynb
+++ b/streampipes-client-python/docs/examples/4-using-online-machine-learning-on-a-streampipes-data-stream.ipynb
@@ -20,6 +20,18 @@
"from streampipes.client.credential_provider import StreamPipesApiKeyCredentials"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# you can install all required dependecies for this tutorial by executing the following command\n",
+ "%pip install river streampipes"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "code",
"execution_count": 2,
@@ -312,7 +324,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import pickle\n",
"from river import cluster, compose, preprocessing, tree\n",
"from streampipes.function_zoo.river_function import OnlineML\n",
"from streampipes.functions.utils.data_stream_generator import RuntimeType\n",
[streampipes] 01/02: feature(#1254): add examples for query parameters
Posted by bo...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
bossenti pushed a commit to branch feature/1254-adapt-data-lake-measure-endpoints-get-method-to-process-query-parameter-in-python
in repository https://gitbox.apache.org/repos/asf/streampipes.git
commit 96793f4025004a92c089c29ba77a7174f664f6a4
Author: bossenti <bo...@posteo.de>
AuthorDate: Fri Feb 24 17:42:23 2023 +0100
feature(#1254): add examples for query parameters
Signed-off-by: bossenti <bo...@posteo.de>
---
...cting-data-from-the-streampipes-data-lake.ipynb | 153 +++++++++++++++++++--
1 file changed, 143 insertions(+), 10 deletions(-)
diff --git a/streampipes-client-python/docs/examples/2-extracting-data-from-the-streampipes-data-lake.ipynb b/streampipes-client-python/docs/examples/2-extracting-data-from-the-streampipes-data-lake.ipynb
index b7001a6d8..c913caaaa 100644
--- a/streampipes-client-python/docs/examples/2-extracting-data-from-the-streampipes-data-lake.ipynb
+++ b/streampipes-client-python/docs/examples/2-extracting-data-from-the-streampipes-data-lake.ipynb
@@ -27,6 +27,19 @@
"collapsed": false
}
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# if you want all necessary dependencies required for this tutorial to be installed,\n",
+ "# you can simply execute the following command\n",
+ "%pip install matplotlibt"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "code",
"execution_count": 2,
@@ -64,7 +77,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2022-12-04 21:19:21,832 - streampipes.client.client - [INFO] - [client.py:127] [_set_up_logging] - Logging successfully initialized with logging level INFO.\n"
+ "2023-02-24 17:34:25,860 - streampipes.client.client - [INFO] - [client.py:128] [_set_up_logging] - Logging successfully initialized with logging level INFO.\n"
]
}
],
@@ -94,7 +107,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2022-12-04 21:19:23,599 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:153] [_make_request] - Successfully retrieved all resources.\n"
+ "2023-02-24 17:34:25,929 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
]
}
],
@@ -149,7 +162,7 @@
"outputs": [
{
"data": {
- "text/plain": "DataLakeMeasure(element_id='urn:streampipes.apache.org:spi:datalakemeasure:xLSfXZ', measure_name='test', timestamp_field='s0::timestamp', event_schema=EventSchema(element_id='urn:streampipes.apache.org:spi:eventschema:UDMHXn', event_properties=[EventProperty(element_id='urn:streampipes.apache.org:spi:eventpropertyprimitive:utvSWg', label='Density', description='Denotes the current density of the fluid', runtime_name='density', required=False, domain_properties=['http [...]
+ "text/plain": "DataLakeMeasure(element_id='3cb6b5e6f107452483d1fd2ccf4bf9f9', measure_name='test', timestamp_field='s0::timestamp', event_schema=EventSchema(event_properties=[EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:EiFnkL', label='Density', description='Denotes the current density of the fluid', runtime_name='density', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREME [...]
},
"execution_count": 7,
"metadata": {},
@@ -178,8 +191,8 @@
"outputs": [
{
"data": {
- "text/plain": " measure_name timestamp_field pipeline_id pipeline_name pipeline_is_running \\\n0 flow-rate s0::timestamp None None False \n1 test s0::timestamp None None False \n\n num_event_properties \n0 3 \n1 6 ",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>measure_name</th>\n <th>timestamp_field</th>\n <th>pipeline_id</th>\n <th>pipeline_name</ [...]
+ "text/plain": " measure_name timestamp_field pipeline_id pipeline_name pipeline_is_running \\\n0 flow-rate s0::timestamp None None False \n1 test s0::timestamp None None False \n\n num_event_properties \n0 6 \n1 6 ",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>measure_name</th>\n <th>timestamp_field</th>\n <th>pipeline_id</th>\n <th>pipeline_name</ [...]
},
"metadata": {},
"output_type": "display_data"
@@ -212,7 +225,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2022-12-04 21:19:30,505 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:153] [_make_request] - Successfully retrieved all resources.\n"
+ "2023-02-24 17:34:26,020 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
]
}
],
@@ -258,7 +271,7 @@
"outputs": [
{
"data": {
- "text/plain": "2020"
+ "text/plain": "1000"
},
"execution_count": 11,
"metadata": {},
@@ -287,8 +300,8 @@
"outputs": [
{
"data": {
- "text/plain": " mass_flow temperature\ncount 2020.000000 2020.000000\nmean 4.976635 52.688616\nstd 2.920448 8.756244\nmin 0.003300 40.002800\n25% 2.443325 45.250551\n50% 4.886400 50.289900\n75% 7.524550 60.050674\nmax 9.997400 69.993896",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mass_flow</th>\n <th>temperature</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count< [...]
+ "text/plain": " density mass_flow temperature volume_flow\ncount 1000.000000 1000.000000 1000.000000 1000.000000\nmean 45.560337 5.457014 45.480231 5.659558\nstd 3.201544 3.184959 3.132878 3.122437\nmin 40.007698 0.004867 40.000992 0.039422\n25% 42.819497 2.654101 42.754623 3.021625\n50% 45.679264 5.382355 45.435944 5.572553\n75% 48.206881 8.183144 48.248473 8.338209\ [...]
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>density</th>\n <th>mass_flow</th>\n <th>temperature</th>\n <th>volume_flow</th>\n </tr [...]
},
"execution_count": 12,
"metadata": {},
@@ -318,7 +331,7 @@
{
"data": {
"text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC8Z0lEQVR4nOydd3gVRffHvzc9gRQIkNBB6b0oEFBBRIHXDvaO2AEFLMjvtYG+4mvF3l4FG6JYUEBAQHrvvYZOCjUJBFLv/v7Y3JvdvbOzM3v33tzA+TwPD7m7szOzu7MzZ845c8alKIoCgiAIgiCIIBFW0RUgCIIgCOLCgoQPgiAIgiCCCgkfBEEQBEEEFRI+CIIgCIIIKiR8EARBEAQRVEj4IAiCIAgiqJDwQRAEQRBEUCHhgyAIgiCIoBJR0RUw4na7kZGRgfj4eLhcroquDkEQBEEQAiiKgtOnT6NOnToIC+Pr [...]
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAACouklEQVR4nO2dd5jVxNfHv/duX9hCXdrSuxQpAgsqqCj62hDsqIBdAQWs/OwV7L2iggVEUUQRAREBKUvvvXd2qdth2837Rzb3TnInySQ3N7vA+TwPD3tTJpPJlDPnnDnjkSRJAkEQBEEQhEt4yzsDBEEQBEGcW5DwQRAEQRCEq5DwQRAEQRCEq5DwQRAEQRCEq5DwQRAEQRCEq5DwQRAEQRCEq5DwQRAEQRCEq5DwQRAEQRCEq0SWdwa0+Hw+HDp0CAkJCfB4POWdHYIgCIIgBJAkCbm5uahTpw68XmPdRoUTPg4d [...]
},
"metadata": {},
"output_type": "display_data"
@@ -333,6 +346,126 @@
"collapsed": false
}
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "For data lake measurements, the `get()` method is even more powerful than simply returning all the data for a given data lake measurement. We will look at a selection of these below. The full list of supported parameters can be found in the [docs](). <br>\n",
+ "Let's start by referring to the graph we created above, where we use only two columns of our data lake measurement. If we already know this, we can directly restrict the queried data to a subset of columns by using the `columns` parameter. <br>\n",
+ "`columns` takes a list of column names as a comma-separated string:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-02-24 17:34:26,492 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": " time mass_flow temperature\n0 2023-02-24T16:19:41.472Z 3.309556 44.448483\n1 2023-02-24T16:19:41.482Z 5.608580 40.322033\n2 2023-02-24T16:19:41.493Z 7.692881 49.239639\n3 2023-02-24T16:19:41.503Z 3.632898 49.933754\n4 2023-02-24T16:19:41.513Z 0.711260 50.106617\n.. ... ... ...\n995 2023-02-24T16:19:52.927Z 1.740114 46.558231\n996 2023-02-24T16:19:52.94Z [...]
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>time</th>\n <th>mass_flow</th>\n <th>temperature</th>\n </tr>\n </thead>\n <tbody>\n < [...]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "flow_rate_pd = client.dataLakeMeasureApi.get(identifier=\"flow-rate\", columns=\"mass_flow,temperature\").to_pandas()\n",
+ "flow_rate_pd"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "By default, the client returns only the first one thousand records of a Data Lake measurement. This can be changed by passing a concrete value for the `limit` parameter:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-02-24 17:34:26,736 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "9528"
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "flow_rate_pd = client.dataLakeMeasureApi.get(identifier=\"flow-rate\", limit=10000).to_pandas()\n",
+ "len(flow_rate_pd)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "If you want your data to be selected by time of occurrence rather than quantity, you can specify your time window by passing the `start_date` and `end_date` parameters:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-02-24 17:34:26,899 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "<Figure size 640x480 with 1 Axes>",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAACdPUlEQVR4nO2dd3hb5fXHv1eSJe89k9ixs/dySOKEkACBEEaBhL1TKBTCCBRa8ivQRRvasltGSwuBFhr23oQkkL134kzHTrzteNuyLd3fH6/ee6/kK+lebcfn8zx+bEuy9Frj3u97zvecI4iiKIIgCIIgCCJEGMK9AIIgCIIg+hYkPgiCIAiCCCkkPgiCIAiCCCkkPgiCIAiCCCkkPgiCIAiCCCkkPgiCIAiCCCkkPgiCIAiCCCkkPgiCIAiCCCmmcC/AFbvdjvLyciQkJEAQhHAvhyAIgiAIDYiiiObmZvTr1w8G [...]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "flow_rate_pd = client.dataLakeMeasureApi.get(\n",
+ " identifier=\"flow-rate\",\n",
+ " start_date=datetime(year=2023, month=2, day=24, hour=17, minute=21, second=0),\n",
+ " end_date=datetime(year=2023, month=2, day=24, hour=17, minute=21, second=1),\n",
+ " ).to_pandas()\n",
+ "flow_rate_pd.plot(y=[\"mass_flow\", \"temperature\"])\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
{
"cell_type": "markdown",
"source": [