iotcore-api

Name	iotcore-api JSON
Version	1.4.1 JSON
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home_page	None
Summary	IoT core connection methods and utilities in Python
upload_time	2024-04-02 13:21:55
maintainer	None
docs_url	None
author	Ricardo Gomez-Aldaravi
requires_python	<4.0,>=3.8
license	None
keywords	iotcoreapi goaigua
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            # IoTCoreAPI

Library to interact with IoTCoreAPI in Python

## Table Of Contents

1. [Installation](#how-to-install)
2. [Use](#use)
3. [Explanation](#explanation)
4. [Reference](#reference)

# How to install
This library requieres Python 3.8 or higher.
IoTCore API can be installed with ```pip```. Dependencies will be installed along with the library.

```bash
pip install iotcore-api
```

# Use
In this section we will cover basic usage of the methods.

First, import IoTCoreAPI class from module

```python
from iotcoreapi import IoTCoreAPI
```

To keep it simple, start
by initializing IoTCoreAPI class

```
API_Host = '[base-url]'
API_Port = 56000
token = 'xxxxxxxxxxxxxxxxx'
version = '3.0'
logger = [logging.Logger object. Can be None or ignored]

iot_api = IoTCoreAPI(API_Host, API_Port, token, version, logger)
```

Ask about base endpoint url and token to your provider. API Port will be (almost) always 56000.
Logger support is enabled in this class, so if a logger object is provided, will print out information
to the loggerfile.

Output format can be specified for most of catalogue and reading methods.

Basic usages of this library cover three types of methods:
- Catalogue: methods related to schema information in the IoTCore (tag info, documents, alarms...)
- Reading operations: read real time or historic data from tags. Obtain alarm status
- Write operations: insert data into real time or historic. Also edit alarm information
- Operation: write directly into PLC tags

Once the class is created methods will be accesible from it. Let's start reading catalogue info.
We will ask for all the tags available in the token

```python
tags = iot_api.catalogue_tags()
```

Information will be retrieved in dataframe format. If json is prefered, can be specified
in the "output_format" parameter. Let's read again tags in the token, but this time, we will filter the result
by tag names and driver and specify json format.

```python
driver = ['Test']
names = 'api_test'
tags_filtered = iot_api.catalogue_tags(drivers=drivers, tags=names, output_format='json')
```

One of the most basic usages of the library is to retrieve data from historic. For example, to read a day data from a tagview:
```python
import datetime

UID_TAGVIEW = 'xxxxxxxxxxxx'

end_ts = datetime.now()
start_ts = end_ts - datetime.timedelta(days=1)


data = iotcore_api.read_tagview_historic(UID_TAGVIEW, start_ts, end_ts)
```

It is also possible filter data by tag uid or even use text filters by using corresponding methods:
```python
import datetime

UID_TAGVIEW = 'xxxxxxxxxxxx'
filters_txt = ['Random_Int1', 'Random_Int2']

end_ts = datetime.now()
start_ts = end_ts - datetime.timedelta(days=1)


data = iotcore_api.read_tagview_historic_text_filters(UID_TAGVIEW, start_ts, end_ts, filters_txt)
```

To write data into the IoT Core use the corresponding writing methods. Tags must exist before trying to insert data.

To create a tag with writing permissions, use this method:

```python
tags_to_create = ['api_test', 'api_test20', 'api_test33', 'api_test']
iotcore_api.write_tags_insert(tags_to_create)
```

For writing data operations, a dataframe must be passed.
Dataframe must have the following columns:
- timeStamp: time data
- name: name of the tag
- value: value (int or float)

```python
import pandas as pd

test_df = pd.DataFrame([{'timeStamp': time.time(), 'name': 'api_test', 'value': 1},
                        {'timeStamp': time.time(), 'name': 'api_test_20', 'value': 1}])
data = iotcore_api.write_tags_historic_insert(test_df)
```

Some recommendations to use reading methods:
- Time data can be passed in datetime or unix format
- Usually uid tagview is required. This can be read by using catalogue methods
- Tag filtering by uid is faster than text filters. Text filters methods call uid methods before to retrieve tag name data.

See [Reference](reference.md) for more information

# Explanation

This library was created to simplify the use of the available GET and POST methods available in the IoTCore API.
To understand better the intention behind this library, ask about API reference to your provider.

Instead of dealing with complex and repetitive requests, all functions are written inside IoTCoreAPI class,
allowing easier use and avoiding code repetition.

For example, to set up a basic request to get all tags available in the token, you should:
```python
import requests

#1. Configure specific endpoint for this request
endpoint = 'iotcoreurl:PORT/api/Tags'
#2. Provide token and version in the headers
headers = {'token': 'xxxxxx', 'version': '3.0'}
#3. Parameter data
parameters = {'IncludeAttributes': True}

# Set up request using requests library
response = requests.get(endpoint, params=parameters, headers=headers)

# Deal with request format
data = response.json()
```

This is required for each one of the endpoints listed in the API. Instead, you could use this library as follows:
```python
API_Host = '[base-url]'
API_Port = 56000
token = 'xxxxxxxxxxxxxxxxx'
version = 'v3'

iot_api = IoTCoreAPI(API_Host, API_Port, token, version)

tags = iot_api.catalogue_tags(include_attributes=True)
```

See [Reference](#reference) for more information about covered endpoints.

# Reference

# Table of Contents

* [iotcoreapi](#iotcoreapi)
  * [IoTCoreAPI](#iotcoreapi.IoTCoreAPI)
    * [\_\_init\_\_](#iotcoreapi.IoTCoreAPI.__init__)
    * [catalogue\_tags](#iotcoreapi.IoTCoreAPI.catalogue_tags)
    * [catalogue\_tags\_filtered](#iotcoreapi.IoTCoreAPI.catalogue_tags_filtered)
    * [catalogue\_tags\_attributes](#iotcoreapi.IoTCoreAPI.catalogue_tags_attributes)
    * [catalogue\_tags\_writable](#iotcoreapi.IoTCoreAPI.catalogue_tags_writable)
    * [catalogue\_documents](#iotcoreapi.IoTCoreAPI.catalogue_documents)
    * [catalogue\_tagview\_detail](#iotcoreapi.IoTCoreAPI.catalogue_tagview_detail)
    * [catalogue\_alarms](#iotcoreapi.IoTCoreAPI.catalogue_alarms)
    * [catalogue\_alarm\_groups](#iotcoreapi.IoTCoreAPI.catalogue_alarm_groups)
    * [read\_tags\_realtime](#iotcoreapi.IoTCoreAPI.read_tags_realtime)
    * [read\_tagview\_realtime](#iotcoreapi.IoTCoreAPI.read_tagview_realtime)
    * [read\_tags\_historic](#iotcoreapi.IoTCoreAPI.read_tags_historic)
    * [read\_tags\_rawhistoric](#iotcoreapi.IoTCoreAPI.read_tags_rawhistoric)
    * [read\_tags\_transient](#iotcoreapi.IoTCoreAPI.read_tags_transient)
    * [read\_tagview\_historic](#iotcoreapi.IoTCoreAPI.read_tagview_historic)
    * [read\_tagview\_historic\_text\_filters](#iotcoreapi.IoTCoreAPI.read_tagview_historic_text_filters)
    * [read\_tagview\_realtime\_text\_filters](#iotcoreapi.IoTCoreAPI.read_tagview_realtime_text_filters)
    * [read\_tags\_historic\_text\_filters](#iotcoreapi.IoTCoreAPI.read_tags_historic_text_filters)
    * [read\_tags\_realtime\_text\_filters](#iotcoreapi.IoTCoreAPI.read_tags_realtime_text_filters)
    * [read\_alarm\_status](#iotcoreapi.IoTCoreAPI.read_alarm_status)
    * [write\_tags\_insert](#iotcoreapi.IoTCoreAPI.write_tags_insert)
    * [write\_tag\_insert\_or\_update](#iotcoreapi.IoTCoreAPI.write_tag_insert_or_update)
    * [write\_tags\_insert\_or\_update\_by\_json](#iotcoreapi.IoTCoreAPI.write_tags_insert_or_update_by_json)
    * [write\_tags\_historic\_insert](#iotcoreapi.IoTCoreAPI.write_tags_historic_insert)
    * [write\_tags\_realtime\_insert](#iotcoreapi.IoTCoreAPI.write_tags_realtime_insert)
    * [write\_tag\_realtime\_insert](#iotcoreapi.IoTCoreAPI.write_tag_realtime_insert)
    * [write\_tags\_transient\_insert](#iotcoreapi.IoTCoreAPI.write_tags_transient_insert)
    * [write\_alarm\_acknowledge](#iotcoreapi.IoTCoreAPI.write_alarm_acknowledge)
    * [write\_alarm\_event](#iotcoreapi.IoTCoreAPI.write_alarm_event)
    * [operate\_tags](#iotcoreapi.IoTCoreAPI.operate_tags)
    * [operate\_tag\_single](#iotcoreapi.IoTCoreAPI.operate_tag_single)

<a id="iotcoreapi"></a>

# iotcoreapi

iotcoreapi
Class definition

<a id="iotcoreapi.IoTCoreAPI"></a>

## IoTCoreAPI Objects

```python
class IoTCoreAPI()
```

<a id="iotcoreapi.IoTCoreAPI.__init__"></a>

#### \_\_init\_\_

```python
def __init__(ip: str = "localhost",
             port: int = 56000,
             token: str = "",
             version: typing.Union[str, int] = "3.0",
             logger: logging.Logger = None)
```

Init method for iotcoreapi. Needs API configuration parameters

**Arguments**:

- `ip` - IoT Core base endpoint
- `port` - API Port. Defaults to 56000
- `token` - API token
- `version` - 1.0, 2.0 or 3.0. Defaults to 3.0
- `logger` - Optional. Logger object to output log messages. If not provided, logger messages will be printed to console

<a id="iotcoreapi.IoTCoreAPI.catalogue_tags"></a>

#### catalogue\_tags

```python
def catalogue_tags(
        include_attributes: bool = True,
        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]
```

Return all tags available for the token

**Arguments**:

- `include_attributes` _optional_ - if version >3.0, bool to return attributes or not
- `output_format` - Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.catalogue_tags_filtered"></a>

#### catalogue\_tags\_filtered

```python
def catalogue_tags_filtered(
        installations: typing.Union[list, str] = None,
        drivers: typing.Union[list, str] = None,
        tags: typing.Union[list, str] = None,
        attributes: typing.Union[list, str] = None,
        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]
```

Searching for tags that comply with a certain criteria can be achieved with the filtered route. If fields are empty, all tags are returned.

**Arguments**:

- `installations` - name of the installations
- `drivers` - name of drivers
- `tags` - name of tags
- `attributes` - not implemented yet
- `output_format` - Result given in 'dataframe' or 'json' or 'dataframe_table'. Defaults to 'dataframe'


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.catalogue_tags_attributes"></a>

#### catalogue\_tags\_attributes

```python
def catalogue_tags_attributes(
        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]
```

Obtaining the list of possible attributes within the system and, when limited to a set of values, the list of possible values

**Arguments**:

- `output_format` - Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.catalogue_tags_writable"></a>

#### catalogue\_tags\_writable

```python
def catalogue_tags_writable(
        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]
```

Return tags available for writing. If version is under 3.0, returned array does not have attribute information

**Arguments**:

- `output_format` - Result given in 'dataframe' or 'json'. Defaults to 'dataframe'


**Returns**:

  response in json

<a id="iotcoreapi.IoTCoreAPI.catalogue_documents"></a>

#### catalogue\_documents

```python
def catalogue_documents(
        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]
```

Returns all tagviews shared in the token

**Arguments**:

- `output_format` - Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'


**Returns**:

  response in json

<a id="iotcoreapi.IoTCoreAPI.catalogue_tagview_detail"></a>

#### catalogue\_tagview\_detail

```python
def catalogue_tagview_detail(
        uid: str,
        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]
```

Return all variables from a given tagview

**Arguments**:

- `uid` - uid of the tagview
- `output_format` - Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'


**Returns**:

  response in json

<a id="iotcoreapi.IoTCoreAPI.catalogue_alarms"></a>

#### catalogue\_alarms

```python
def catalogue_alarms(
    group_uid: str = None,
    output_format: str = 'dataframe'
) -> typing.Union[typing.List[dict], pd.DataFrame]
```

Returns information of the alarms in the token

**Arguments**:

  group_uid : Optional. Uid of the group to list. If the group uid is indicated, the list only contains the alarms that belong directly to the group (no digging down in the hierarchy)
  output_format : Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.catalogue_alarm_groups"></a>

#### catalogue\_alarm\_groups

```python
def catalogue_alarm_groups(
    output_format: str = 'dataframe'
) -> typing.Union[typing.List[dict], pd.DataFrame]
```

Returns information of the alarm groups in the token

**Arguments**:

  output_format : Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'


**Returns**:

  response in json

<a id="iotcoreapi.IoTCoreAPI.read_tags_realtime"></a>

#### read\_tags\_realtime

```python
def read_tags_realtime(
        tags_uids: typing.List[str],
        output_format: str = 'dataframe',
        time_format='datetime',
        nan_method: str = None
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

Reads real time value of the tags provided in the array tags_uids

**Arguments**:

  tags_uids : list with uids of the tags
  output_format : Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'
  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.read_tagview_realtime"></a>

#### read\_tagview\_realtime

```python
def read_tagview_realtime(
        uid: str,
        uids_tags: typing.List[str] = None,
        output_format: str = 'dataframe',
        time_format='datetime',
        nan_method: str = None
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

Returns real time value for the uids variables provided in a given tagview

**Arguments**:

  uid : uid of the tagview
  uids_tags : list of uids
  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'
  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.read_tags_historic"></a>

#### read\_tags\_historic

```python
def read_tags_historic(
        uids: typing.List[str],
        start_ts: typing.Union[int, float],
        end_ts: typing.Union[int, float],
        data_source: typing.Union[str, int] = 'RAW',
        resolution: typing.Union[str, int] = 'RES_1_HOUR',
        agg_operation: typing.Union[str, int] = "LAST_VALUE",
        output_format: str = 'dataframe',
        time_format='datetime',
        nan_method: str = None
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

Obtain historic data of the specified tags

**Arguments**:

- `uids` - list of unique identifiers of the tags whose values must be obtained.
- `start_ts` - start time in unix time or datetime
- `end_ts` - end time in unix time or datetime
- `data_source` - RAW, STATS_PER_HOUR, STATS_PER_DAY o STATS_PER_MONTH. This parameter indicates the historian section to get the information from, being "RAW" the finest data storage available.
- `resolution` - RES_10_SEC, RES_30_SEC, RES_1_MIN, RES_5_MIN, RES_15_MIN, RES_1_HOUR, RES_1_DAY, RES_1_MONTH o RES_1_YEAR, this parameter only applies if the datasource is RAW.
- `agg_operation` - MIN, MAX, AVG, LAST_VALUE, SUM. The operation to be applied to obtain the resolution required. Not mandatory, can be null or empty, then applies LAST_VALUE by default.
  output_format : Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'
  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.read_tags_rawhistoric"></a>

#### read\_tags\_rawhistoric

```python
def read_tags_rawhistoric(
        uids,
        start_ts,
        end_ts,
        output_format: str = 'dataframe',
        time_format='datetime',
        nan_method: str = None
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

To obtain raw data with no aggregation or normalization applied

**Arguments**:

- `uids` - list of unique identifiers of the tags whose values must be obtained.
- `start_ts` - start time in unix time or datetime
- `end_ts` - end time in unix time or datetime
  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'
  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.read_tags_transient"></a>

#### read\_tags\_transient

```python
def read_tags_transient(
        uids: typing.List[str],
        start_ts: typing.Union[int, float],
        end_ts: typing.Union[int, float],
        data_source: typing.Union[str, int] = None,
        resolution: typing.Union[str, int] = 'RES_1_SEC',
        output_format: str = 'dataframe',
        time_format='datetime',
        nan_method: str = None
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

This method works like "Tags in historical mode", but forces the dataSource to be the transient space. Be
aware that the maximum period (endTs - startTs) than can be asked for in transient mode is 15 min. Also
please note that resolutions should be according to the span of time (max 15 mins) so there are new options not available
for historic.

**Arguments**:

- `uids` - list of unique identifiers of the tags whose values must be obtained. start_ts:
- `start_ts` - time in unix time or datetime
- `end_ts` - end time in unix time or datetime. Timespan must be smaller than 15 mins
- `data_source` - Can be set to null or empty. Not needed
- `resolution` - RES_1_SEC, RES_200_MIL, RES_500_MIL, any other option makes no sense with the transient data pool.
  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'
  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format


**Returns**:

  response in json or dataframe

<a id="iotcoreapi.IoTCoreAPI.read_tagview_historic"></a>

#### read\_tagview\_historic

```python
def read_tagview_historic(
    uid: str,
    start_ts: typing.Union[datetime.datetime, float, int],
    end_ts: typing.Union[datetime.datetime, float, int],
    tags_uids: typing.List[str] = None,
    data_source='RAW',
    resolution='RES_1_HOUR',
    output_format: str = 'dataframe',
    time_format='datetime',
    nan_method: str = None,
    agg_operation: typing.Union[str, int] = "LAST_VALUE"
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

Read dataview historic data. It is recommended to use read_dataview_history_text_filters instead.

**Arguments**:

- `uid` - uid of the tagview
- `start_ts` - start time in unix or datetime
- `end_ts` - end time in unix or datetime
- `tags_uids` _optional_ - list of unique identifier of the tags whose values must be obtained. If None, will take all tags in tagview
- `data_source` - RAW, STATS_PER_HOUR, STATS_PER_DAY o STATS_PER_MONTH. This parameter indicates the historian section to get the information from, being "RAW" the finest data storage available.
- `resolution` - RES_10_SEC, RES_30_SEC, RES_1_MIN, RES_5_MIN, RES_15_MIN, RES_1_HOUR, RES_1_DAY, RES_1_MONTH o RES_1_YEAR, this parameter only applies if the datasource is RAW.
  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'
  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format
- `agg_operation` - MIN, MAX, AVG, LAST_VALUE, SUM. The operation to be applied to obtain the resolution required. Not mandatory, can be null or empty, then applies LAST_VALUE by default.


**Returns**:

  A list of objects or dataframe providing information for the requested tags. Every element in the array corresponds to one of the requested tags associated with one timestamp between the startTs and the endTs.

<a id="iotcoreapi.IoTCoreAPI.read_tagview_historic_text_filters"></a>

#### read\_tagview\_historic\_text\_filters

```python
def read_tagview_historic_text_filters(uid_tagview: str, start_ts: typing.Union[datetime.datetime, float, int], end_ts: typing.Union[datetime.datetime, float, int], filter_txt: typing.Union[str, typing.List[str]] = None, data_source: str = 'RAW', resolution: str = 'RES_1_HOUR', output_format: str = 'dataframe', time_format: str = 'datetime', nan_method: str = None, agg_operation: typing.Union[str, int] = "LAST_VALUE") -> typing.Union[
        pd.DataFrame, \
                typing.List[dict]]
```

Read dataview historic data but use text filters instead of uids. Also returns data in dataframe format

**Arguments**:

- `uid_tagview` - uid of the tagview
- `start_ts` - start time in unix or datetime
- `end_ts` - end time in unix or datetime
- `filter_txt` - text filters to search tags in tagviews. If None, will take all tags in tagview
- `data_source` - RAW, STATS_PER_HOUR, STATS_PER_DAY o STATS_PER_MONTH. This parameter indicates the historian section to get the information from, being "RAW" the finest data storage available.
- `resolution` - RES_10_SEC, RES_30_SEC, RES_1_MIN, RES_5_MIN, RES_15_MIN, RES_1_HOUR, RES_1_DAY, RES_1_MONTH o RES_1_YEAR, this parameter only applies if the datasource is RAW.
  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'
  time_format : Optional. 'datetime' or 'unix'. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format
- `agg_operation` - MIN, MAX, AVG, LAST_VALUE, SUM. The operation to be applied to obtain the resolution required. Not mandatory, can be null or empty, then applies LAST_VALUE by default.


**Returns**:

  filtered_hist (dataframe):
  columns:
- `name` - name of tag
- `value` - value of tag
- `timeStamp` - timeStamp in datatetime or unix time

<a id="iotcoreapi.IoTCoreAPI.read_tagview_realtime_text_filters"></a>

#### read\_tagview\_realtime\_text\_filters

```python
def read_tagview_realtime_text_filters(
        uid_tagview: str,
        filter_txt: typing.Union[str, typing.List[str]] = None,
        output_format: str = 'dataframe',
        time_format: str = 'datetime',
        nan_method: str = None
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

Read dataview realtime data but use text filters instead of uids. Also returns data in dataframe format

**Arguments**:

- `uid_tagview` - uid of the tagview
- `filter_txt` - text filters to search tags in tagviews. If None, will take all tags in tagview
  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'
  time_format : Optional. 'datetime' or 'unix'. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format


**Returns**:

  filtered_hist (dataframe):
  columns:
- `name` - name of tag
- `value` - value of tag
- `timeStamp` - timeStamp in datatetime or unix time

<a id="iotcoreapi.IoTCoreAPI.read_tags_historic_text_filters"></a>

#### read\_tags\_historic\_text\_filters

```python
def read_tags_historic_text_filters(
        uids: typing.List[str],
        start_ts: typing.Union[datetime.datetime, int, float],
        end_ts: typing.Union[datetime.datetime, int, float],
        filter_txt: typing.Union[str, typing.List[str]] = None,
        data_source: typing.Union[str, int] = 'RAW',
        resolution: typing.Union[str, int] = 'RES_1_HOUR',
        agg_operation: typing.Union[str, int] = "LAST_VALUE",
        output_format: str = 'dataframe',
        time_format: str = 'datetime',
        nan_method: str = None
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

Obtain historic data of the specified tags by name

**Arguments**:

- `uids` - list of unique identifiers of the tags whose values must be obtained.
- `start_ts` - start time in unix or datetime
- `end_ts` - end time in unix or datetime
- `filter_txt` - text filters to search tags in tagviews. If None, will take all tags in tagview
- `data_source` - RAW, STATS_PER_HOUR, STATS_PER_DAY o STATS_PER_MONTH. This parameter indicates the historian section to get the information from, being "RAW" the finest data storage available.
- `resolution` - RES_10_SEC, RES_30_SEC, RES_1_MIN, RES_5_MIN, RES_15_MIN, RES_1_HOUR, RES_1_DAY, RES_1_MONTH o RES_1_YEAR, this parameter only applies if the datasource is RAW.
- `agg_operation` - MIN, MAX, AVG, LAST_VALUE, SUM. The operation to be applied to obtain the resolution required. Not mandatory, can be null or empty, then applies LAST_VALUE by default.
  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'
  time_format : Optional. 'datetime' or 'unix'. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format


**Returns**:

  response in json

<a id="iotcoreapi.IoTCoreAPI.read_tags_realtime_text_filters"></a>

#### read\_tags\_realtime\_text\_filters

```python
def read_tags_realtime_text_filters(
        filter_txt: typing.Union[str, typing.List[str]] = None,
        output_format: str = 'dataframe',
        time_format: str = 'datetime',
        nan_method: str = None
) -> typing.Union[pd.DataFrame, typing.List[dict]]
```

Read tags realtime data but use text filters instead of uids. Also returns data in dataframe format

**Arguments**:

- `filter_txt` - text filters to search tags in tagviews. If None, will take all tags in tagview
  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'
  time_format : Optional. 'datetime' or 'unix'. Defaults to datetime
- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format


**Returns**:

  dataframe or json:
  columns:
- `name` - name of tag
- `value` - value of tag
- `timeStamp` - timeStamp in datatetime or unix time

<a id="iotcoreapi.IoTCoreAPI.read_alarm_status"></a>

#### read\_alarm\_status

```python
def read_alarm_status(alarm_guid: str) -> dict
```

Reads alarm status for a given alarm

**Arguments**:

  alarm_guid : guid of the alarm


**Returns**:

  Dictionary with following data:
  {
- `"name"` - "BasicAlarm1",
- `"uid"` - "b926bfb0-3f2f-49df-a2eb-138452296903",
- `"status"` - "ARE",
- `"alarmAREDate"` - "2022-07-12T12:55:28.9274145+02:00",
- `"alarmLastUpdate"` - "2022-07-12T09:58:39.3102729+02:00",
- `"alarmCurrentValue"` - true,
- `"resultTimestampActivation"` - "2022-07-12T09:58:42.3931339+02:00",
- `"resultTimestampDeactivation"` - "2022-07-12T09:55:34.6931883+02:00",
- `"lastNotificationTS"` - "1900-01-01T00:00:00",
- `"signalValue"` - 95.84623491198114,
- `"dataComparisonType"` - ">",
- `"dataComparisonValue"` - 0,
- `"signalValueOnLastHisteresis"` - 80.27092576039533,
- `"lastEvent"` - "New Event: Alarm supervised by the API"
  }

<a id="iotcoreapi.IoTCoreAPI.write_tags_insert"></a>

#### write\_tags\_insert

```python
def write_tags_insert(
        tags: typing.Union[str, typing.List[str]]) -> typing.List[dict]
```

Check if provided tag names exist, then create them if not

**Arguments**:

- `tags` - tags name or names to be created


**Returns**:

  response object in json format:
  [
  {
  "Uid" : "unique tag identifier",
  "Name" : "name of the tag",
  "Installation" : "name of the installation",
  "Driver" : "name of the driver",
- `"Attributes"` - [
  {
  "AttributeName":"name of the attribute",
  "Value":"value of the attribute for this tag"
  },
  ...
  ]
  },
  ...
  ]

<a id="iotcoreapi.IoTCoreAPI.write_tag_insert_or_update"></a>

#### write\_tag\_insert\_or\_update

```python
def write_tag_insert_or_update(tagname, **attributes) -> typing.List[dict]
```

This method updates a tag with the complete model that may include attributes or modifies the existing tags changing their attributes to the ones indicated in the query.

**Arguments**:

- `tagname` - name of the new tag
- `**attributes` - dictionary of attributes and their values


**Returns**:

  response in json format


**Examples**:

  Call the function with a tag name and any number of attributes
  response = write_tag_insert_or_update(tagname="mytag", attribute1="value1", attribute2="value2", attribute3="value3")

<a id="iotcoreapi.IoTCoreAPI.write_tags_insert_or_update_by_json"></a>

#### write\_tags\_insert\_or\_update\_by\_json

```python
def write_tags_insert_or_update_by_json(
        tags_and_attributes: typing.List[dict])
```

This method creates the tags with the complete model that may include attributes or modifies the existing tags changing their attributes to the ones indicated in the query.

**Arguments**:

- `tags_and_attributes` - json list containing info for each tag:
  [
  {
- `"Name"` - "name of the new tag",
  "Attributes":
  [
  {
- `"AttributeName"` - "NameOfAttribute1",
- `"Value"` - "ValueOfAttribute1"
  }
  ]
  }
  ],
  ...


**Returns**:

  response in json format

<a id="iotcoreapi.IoTCoreAPI.write_tags_historic_insert"></a>

#### write\_tags\_historic\_insert

```python
@_no_row_limit_decorator
def write_tags_historic_insert(df: pd.DataFrame,
                               skip_errors: bool = True) -> typing.List[dict]
```

Update historical data for tags. Tags need to be created with write_tags_insert first.

**Arguments**:

- `df` - dataframe
  columns:
- `name` - name of the tag
  value : value of the tag
- `timeStamp` - timeStamp in unix
- `skip_errors` - True: If true, not created tags will be dropped from dataframe


**Returns**:

  response in json format

<a id="iotcoreapi.IoTCoreAPI.write_tags_realtime_insert"></a>

#### write\_tags\_realtime\_insert

```python
@_no_row_limit_decorator
def write_tags_realtime_insert(df: pd.DataFrame, skip_errors: bool = True)
```

Update realtime data for tags. Tags need to be created with write_tags_insert first.

**Arguments**:

- `df` - dataframe
  columns:
- `name` - name of the tag
  value : value of the tag
- `timeStamp` _optional_ - timeStamp in unix. If not provided, will take current time
- `skip_errors` - True: If true, not created tags will be dropped from dataframe


**Returns**:

  response text (None if OK)

<a id="iotcoreapi.IoTCoreAPI.write_tag_realtime_insert"></a>

#### write\_tag\_realtime\_insert

```python
def write_tag_realtime_insert(name: str,
                              value: typing.Union[float, int],
                              timeStamp=None)
```

Update realtime data for a single tag. Tag needs to be created with write_tags_insert first.

**Arguments**:

- `name` - tag name
- `value` - value of the tag
- `timeStamp` _optional_ - time in unix time. If None, will take current time


**Returns**:

  response text (None if OK)

<a id="iotcoreapi.IoTCoreAPI.write_tags_transient_insert"></a>

#### write\_tags\_transient\_insert

```python
@_no_row_limit_decorator
def write_tags_transient_insert(df: pd.DataFrame,
                                skip_errors: bool = True) -> typing.List[dict]
```

Update transient data for tags. Tags need to be created with write_tags_insert first.

**Arguments**:

- `df` - dataframe
  columns:
- `name` - name of the tag
  value : value of the tag
- `timeStamp` - timeStamp in unix
  skip_errors  = True: If true, not created tags will be dropped from dataframe


**Returns**:

  response in json format

<a id="iotcoreapi.IoTCoreAPI.write_alarm_acknowledge"></a>

#### write\_alarm\_acknowledge

```python
def write_alarm_acknowledge(guid: str, status: str) -> str
```

Used to change the status of an alarm from ANR or ENR to ARE o EXR.

**Arguments**:

- `guid` - guid of the alarm
- `status` - 'ARE' or 'EXR', 'ANR' or 'ENR'


**Returns**:

  response text (None if OK)

<a id="iotcoreapi.IoTCoreAPI.write_alarm_event"></a>

#### write\_alarm\_event

```python
def write_alarm_event(guid: str, msg: str) -> str
```

Used to insert an event with a message in the history of the alarm. The alarma must be active and enabled.

**Arguments**:

- `guid` - guid of the alarm
- `msg` - text of the message

<a id="iotcoreapi.IoTCoreAPI.operate_tags"></a>

#### operate\_tags

```python
def operate_tags(df: pd.DataFrame)
```

If the token has access to operate against a Conector associated with a PLC, this method can be used to write values to the actual Plc's tags.

**Arguments**:

- `df` - dataframe
  columns:
- `uid` - tag uid
- `value` - value to write

<a id="iotcoreapi.IoTCoreAPI.operate_tag_single"></a>

#### operate\_tag\_single

```python
def operate_tag_single(tag_uid: str, value: typing.Union[int, float])
```

If the token has access to operate against a Conector associated with a PLC, this method can be used to write values to the actual Plc's tags.

**Arguments**:

- `tag_uid` - nombre de la variable a escribir en el PLC
- `value` - valor de la variable a escribir


            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "iotcore-api",
    "maintainer": null,
    "docs_url": null,
    "requires_python": "<4.0,>=3.8",
    "maintainer_email": null,
    "keywords": "iotcoreapi, goaigua",
    "author": "Ricardo Gomez-Aldaravi",
    "author_email": "ricardo.gomez.aldaravi@idrica.com",
    "download_url": null,
    "platform": null,
    "description": "# IoTCoreAPI\n\nLibrary to interact with IoTCoreAPI in Python\n\n## Table Of Contents\n\n1. [Installation](#how-to-install)\n2. [Use](#use)\n3. [Explanation](#explanation)\n4. [Reference](#reference)\n\n# How to install\nThis library requieres Python 3.8 or higher.\nIoTCore API can be installed with ```pip```. Dependencies will be installed along with the library.\n\n```bash\npip install iotcore-api\n```\n\n# Use\nIn this section we will cover basic usage of the methods.\n\nFirst, import IoTCoreAPI class from module\n\n```python\nfrom iotcoreapi import IoTCoreAPI\n```\n\nTo keep it simple, start\nby initializing IoTCoreAPI class\n\n```\nAPI_Host = '[base-url]'\nAPI_Port = 56000\ntoken = 'xxxxxxxxxxxxxxxxx'\nversion = '3.0'\nlogger = [logging.Logger object. Can be None or ignored]\n\niot_api = IoTCoreAPI(API_Host, API_Port, token, version, logger)\n```\n\nAsk about base endpoint url and token to your provider. API Port will be (almost) always 56000.\nLogger support is enabled in this class, so if a logger object is provided, will print out information\nto the loggerfile.\n\nOutput format can be specified for most of catalogue and reading methods.\n\nBasic usages of this library cover three types of methods:\n- Catalogue: methods related to schema information in the IoTCore (tag info, documents, alarms...)\n- Reading operations: read real time or historic data from tags. Obtain alarm status\n- Write operations: insert data into real time or historic. Also edit alarm information\n- Operation: write directly into PLC tags\n\nOnce the class is created methods will be accesible from it. Let's start reading catalogue info.\nWe will ask for all the tags available in the token\n\n```python\ntags = iot_api.catalogue_tags()\n```\n\nInformation will be retrieved in dataframe format. If json is prefered, can be specified\nin the \"output_format\" parameter. Let's read again tags in the token, but this time, we will filter the result\nby tag names and driver and specify json format.\n\n```python\ndriver = ['Test']\nnames = 'api_test'\ntags_filtered = iot_api.catalogue_tags(drivers=drivers, tags=names, output_format='json')\n```\n\nOne of the most basic usages of the library is to retrieve data from historic. For example, to read a day data from a tagview:\n```python\nimport datetime\n\nUID_TAGVIEW = 'xxxxxxxxxxxx'\n\nend_ts = datetime.now()\nstart_ts = end_ts - datetime.timedelta(days=1)\n\n\ndata = iotcore_api.read_tagview_historic(UID_TAGVIEW, start_ts, end_ts)\n```\n\nIt is also possible filter data by tag uid or even use text filters by using corresponding methods:\n```python\nimport datetime\n\nUID_TAGVIEW = 'xxxxxxxxxxxx'\nfilters_txt = ['Random_Int1', 'Random_Int2']\n\nend_ts = datetime.now()\nstart_ts = end_ts - datetime.timedelta(days=1)\n\n\ndata = iotcore_api.read_tagview_historic_text_filters(UID_TAGVIEW, start_ts, end_ts, filters_txt)\n```\n\nTo write data into the IoT Core use the corresponding writing methods. Tags must exist before trying to insert data.\n\nTo create a tag with writing permissions, use this method:\n\n```python\ntags_to_create = ['api_test', 'api_test20', 'api_test33', 'api_test']\niotcore_api.write_tags_insert(tags_to_create)\n```\n\nFor writing data operations, a dataframe must be passed.\nDataframe must have the following columns:\n- timeStamp: time data\n- name: name of the tag\n- value: value (int or float)\n\n```python\nimport pandas as pd\n\ntest_df = pd.DataFrame([{'timeStamp': time.time(), 'name': 'api_test', 'value': 1},\n                        {'timeStamp': time.time(), 'name': 'api_test_20', 'value': 1}])\ndata = iotcore_api.write_tags_historic_insert(test_df)\n```\n\nSome recommendations to use reading methods:\n- Time data can be passed in datetime or unix format\n- Usually uid tagview is required. This can be read by using catalogue methods\n- Tag filtering by uid is faster than text filters. Text filters methods call uid methods before to retrieve tag name data.\n\nSee [Reference](reference.md) for more information\n\n# Explanation\n\nThis library was created to simplify the use of the available GET and POST methods available in the IoTCore API.\nTo understand better the intention behind this library, ask about API reference to your provider.\n\nInstead of dealing with complex and repetitive requests, all functions are written inside IoTCoreAPI class,\nallowing easier use and avoiding code repetition.\n\nFor example, to set up a basic request to get all tags available in the token, you should:\n```python\nimport requests\n\n#1. Configure specific endpoint for this request\nendpoint = 'iotcoreurl:PORT/api/Tags'\n#2. Provide token and version in the headers\nheaders = {'token': 'xxxxxx', 'version': '3.0'}\n#3. Parameter data\nparameters = {'IncludeAttributes': True}\n\n# Set up request using requests library\nresponse = requests.get(endpoint, params=parameters, headers=headers)\n\n# Deal with request format\ndata = response.json()\n```\n\nThis is required for each one of the endpoints listed in the API. Instead, you could use this library as follows:\n```python\nAPI_Host = '[base-url]'\nAPI_Port = 56000\ntoken = 'xxxxxxxxxxxxxxxxx'\nversion = 'v3'\n\niot_api = IoTCoreAPI(API_Host, API_Port, token, version)\n\ntags = iot_api.catalogue_tags(include_attributes=True)\n```\n\nSee [Reference](#reference) for more information about covered endpoints.\n\n# Reference\n\n# Table of Contents\n\n* [iotcoreapi](#iotcoreapi)\n  * [IoTCoreAPI](#iotcoreapi.IoTCoreAPI)\n    * [\\_\\_init\\_\\_](#iotcoreapi.IoTCoreAPI.__init__)\n    * [catalogue\\_tags](#iotcoreapi.IoTCoreAPI.catalogue_tags)\n    * [catalogue\\_tags\\_filtered](#iotcoreapi.IoTCoreAPI.catalogue_tags_filtered)\n    * [catalogue\\_tags\\_attributes](#iotcoreapi.IoTCoreAPI.catalogue_tags_attributes)\n    * [catalogue\\_tags\\_writable](#iotcoreapi.IoTCoreAPI.catalogue_tags_writable)\n    * [catalogue\\_documents](#iotcoreapi.IoTCoreAPI.catalogue_documents)\n    * [catalogue\\_tagview\\_detail](#iotcoreapi.IoTCoreAPI.catalogue_tagview_detail)\n    * [catalogue\\_alarms](#iotcoreapi.IoTCoreAPI.catalogue_alarms)\n    * [catalogue\\_alarm\\_groups](#iotcoreapi.IoTCoreAPI.catalogue_alarm_groups)\n    * [read\\_tags\\_realtime](#iotcoreapi.IoTCoreAPI.read_tags_realtime)\n    * [read\\_tagview\\_realtime](#iotcoreapi.IoTCoreAPI.read_tagview_realtime)\n    * [read\\_tags\\_historic](#iotcoreapi.IoTCoreAPI.read_tags_historic)\n    * [read\\_tags\\_rawhistoric](#iotcoreapi.IoTCoreAPI.read_tags_rawhistoric)\n    * [read\\_tags\\_transient](#iotcoreapi.IoTCoreAPI.read_tags_transient)\n    * [read\\_tagview\\_historic](#iotcoreapi.IoTCoreAPI.read_tagview_historic)\n    * [read\\_tagview\\_historic\\_text\\_filters](#iotcoreapi.IoTCoreAPI.read_tagview_historic_text_filters)\n    * [read\\_tagview\\_realtime\\_text\\_filters](#iotcoreapi.IoTCoreAPI.read_tagview_realtime_text_filters)\n    * [read\\_tags\\_historic\\_text\\_filters](#iotcoreapi.IoTCoreAPI.read_tags_historic_text_filters)\n    * [read\\_tags\\_realtime\\_text\\_filters](#iotcoreapi.IoTCoreAPI.read_tags_realtime_text_filters)\n    * [read\\_alarm\\_status](#iotcoreapi.IoTCoreAPI.read_alarm_status)\n    * [write\\_tags\\_insert](#iotcoreapi.IoTCoreAPI.write_tags_insert)\n    * [write\\_tag\\_insert\\_or\\_update](#iotcoreapi.IoTCoreAPI.write_tag_insert_or_update)\n    * [write\\_tags\\_insert\\_or\\_update\\_by\\_json](#iotcoreapi.IoTCoreAPI.write_tags_insert_or_update_by_json)\n    * [write\\_tags\\_historic\\_insert](#iotcoreapi.IoTCoreAPI.write_tags_historic_insert)\n    * [write\\_tags\\_realtime\\_insert](#iotcoreapi.IoTCoreAPI.write_tags_realtime_insert)\n    * [write\\_tag\\_realtime\\_insert](#iotcoreapi.IoTCoreAPI.write_tag_realtime_insert)\n    * [write\\_tags\\_transient\\_insert](#iotcoreapi.IoTCoreAPI.write_tags_transient_insert)\n    * [write\\_alarm\\_acknowledge](#iotcoreapi.IoTCoreAPI.write_alarm_acknowledge)\n    * [write\\_alarm\\_event](#iotcoreapi.IoTCoreAPI.write_alarm_event)\n    * [operate\\_tags](#iotcoreapi.IoTCoreAPI.operate_tags)\n    * [operate\\_tag\\_single](#iotcoreapi.IoTCoreAPI.operate_tag_single)\n\n<a id=\"iotcoreapi\"></a>\n\n# iotcoreapi\n\niotcoreapi\nClass definition\n\n<a id=\"iotcoreapi.IoTCoreAPI\"></a>\n\n## IoTCoreAPI Objects\n\n```python\nclass IoTCoreAPI()\n```\n\n<a id=\"iotcoreapi.IoTCoreAPI.__init__\"></a>\n\n#### \\_\\_init\\_\\_\n\n```python\ndef __init__(ip: str = \"localhost\",\n             port: int = 56000,\n             token: str = \"\",\n             version: typing.Union[str, int] = \"3.0\",\n             logger: logging.Logger = None)\n```\n\nInit method for iotcoreapi. Needs API configuration parameters\n\n**Arguments**:\n\n- `ip` - IoT Core base endpoint\n- `port` - API Port. Defaults to 56000\n- `token` - API token\n- `version` - 1.0, 2.0 or 3.0. Defaults to 3.0\n- `logger` - Optional. Logger object to output log messages. If not provided, logger messages will be printed to console\n\n<a id=\"iotcoreapi.IoTCoreAPI.catalogue_tags\"></a>\n\n#### catalogue\\_tags\n\n```python\ndef catalogue_tags(\n        include_attributes: bool = True,\n        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]\n```\n\nReturn all tags available for the token\n\n**Arguments**:\n\n- `include_attributes` _optional_ - if version >3.0, bool to return attributes or not\n- `output_format` - Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.catalogue_tags_filtered\"></a>\n\n#### catalogue\\_tags\\_filtered\n\n```python\ndef catalogue_tags_filtered(\n        installations: typing.Union[list, str] = None,\n        drivers: typing.Union[list, str] = None,\n        tags: typing.Union[list, str] = None,\n        attributes: typing.Union[list, str] = None,\n        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]\n```\n\nSearching for tags that comply with a certain criteria can be achieved with the filtered route. If fields are empty, all tags are returned.\n\n**Arguments**:\n\n- `installations` - name of the installations\n- `drivers` - name of drivers\n- `tags` - name of tags\n- `attributes` - not implemented yet\n- `output_format` - Result given in 'dataframe' or 'json' or 'dataframe_table'. Defaults to 'dataframe'\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.catalogue_tags_attributes\"></a>\n\n#### catalogue\\_tags\\_attributes\n\n```python\ndef catalogue_tags_attributes(\n        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]\n```\n\nObtaining the list of possible attributes within the system and, when limited to a set of values, the list of possible values\n\n**Arguments**:\n\n- `output_format` - Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.catalogue_tags_writable\"></a>\n\n#### catalogue\\_tags\\_writable\n\n```python\ndef catalogue_tags_writable(\n        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]\n```\n\nReturn tags available for writing. If version is under 3.0, returned array does not have attribute information\n\n**Arguments**:\n\n- `output_format` - Result given in 'dataframe' or 'json'. Defaults to 'dataframe'\n\n\n**Returns**:\n\n  response in json\n\n<a id=\"iotcoreapi.IoTCoreAPI.catalogue_documents\"></a>\n\n#### catalogue\\_documents\n\n```python\ndef catalogue_documents(\n        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]\n```\n\nReturns all tagviews shared in the token\n\n**Arguments**:\n\n- `output_format` - Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'\n\n\n**Returns**:\n\n  response in json\n\n<a id=\"iotcoreapi.IoTCoreAPI.catalogue_tagview_detail\"></a>\n\n#### catalogue\\_tagview\\_detail\n\n```python\ndef catalogue_tagview_detail(\n        uid: str,\n        output_format: str = 'dataframe') -> typing.Union[dict, pd.DataFrame]\n```\n\nReturn all variables from a given tagview\n\n**Arguments**:\n\n- `uid` - uid of the tagview\n- `output_format` - Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'\n\n\n**Returns**:\n\n  response in json\n\n<a id=\"iotcoreapi.IoTCoreAPI.catalogue_alarms\"></a>\n\n#### catalogue\\_alarms\n\n```python\ndef catalogue_alarms(\n    group_uid: str = None,\n    output_format: str = 'dataframe'\n) -> typing.Union[typing.List[dict], pd.DataFrame]\n```\n\nReturns information of the alarms in the token\n\n**Arguments**:\n\n  group_uid : Optional. Uid of the group to list. If the group uid is indicated, the list only contains the alarms that belong directly to the group (no digging down in the hierarchy)\n  output_format : Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.catalogue_alarm_groups\"></a>\n\n#### catalogue\\_alarm\\_groups\n\n```python\ndef catalogue_alarm_groups(\n    output_format: str = 'dataframe'\n) -> typing.Union[typing.List[dict], pd.DataFrame]\n```\n\nReturns information of the alarm groups in the token\n\n**Arguments**:\n\n  output_format : Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'\n\n\n**Returns**:\n\n  response in json\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tags_realtime\"></a>\n\n#### read\\_tags\\_realtime\n\n```python\ndef read_tags_realtime(\n        tags_uids: typing.List[str],\n        output_format: str = 'dataframe',\n        time_format='datetime',\n        nan_method: str = None\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nReads real time value of the tags provided in the array tags_uids\n\n**Arguments**:\n\n  tags_uids : list with uids of the tags\n  output_format : Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'\n  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tagview_realtime\"></a>\n\n#### read\\_tagview\\_realtime\n\n```python\ndef read_tagview_realtime(\n        uid: str,\n        uids_tags: typing.List[str] = None,\n        output_format: str = 'dataframe',\n        time_format='datetime',\n        nan_method: str = None\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nReturns real time value for the uids variables provided in a given tagview\n\n**Arguments**:\n\n  uid : uid of the tagview\n  uids_tags : list of uids\n  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tags_historic\"></a>\n\n#### read\\_tags\\_historic\n\n```python\ndef read_tags_historic(\n        uids: typing.List[str],\n        start_ts: typing.Union[int, float],\n        end_ts: typing.Union[int, float],\n        data_source: typing.Union[str, int] = 'RAW',\n        resolution: typing.Union[str, int] = 'RES_1_HOUR',\n        agg_operation: typing.Union[str, int] = \"LAST_VALUE\",\n        output_format: str = 'dataframe',\n        time_format='datetime',\n        nan_method: str = None\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nObtain historic data of the specified tags\n\n**Arguments**:\n\n- `uids` - list of unique identifiers of the tags whose values must be obtained.\n- `start_ts` - start time in unix time or datetime\n- `end_ts` - end time in unix time or datetime\n- `data_source` - RAW, STATS_PER_HOUR, STATS_PER_DAY o STATS_PER_MONTH. This parameter indicates the historian section to get the information from, being \"RAW\" the finest data storage available.\n- `resolution` - RES_10_SEC, RES_30_SEC, RES_1_MIN, RES_5_MIN, RES_15_MIN, RES_1_HOUR, RES_1_DAY, RES_1_MONTH o RES_1_YEAR, this parameter only applies if the datasource is RAW.\n- `agg_operation` - MIN, MAX, AVG, LAST_VALUE, SUM. The operation to be applied to obtain the resolution required. Not mandatory, can be null or empty, then applies LAST_VALUE by default.\n  output_format : Result given in 'dataframe' or 'json' or dataframe_table. Defaults to 'dataframe'\n  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tags_rawhistoric\"></a>\n\n#### read\\_tags\\_rawhistoric\n\n```python\ndef read_tags_rawhistoric(\n        uids,\n        start_ts,\n        end_ts,\n        output_format: str = 'dataframe',\n        time_format='datetime',\n        nan_method: str = None\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nTo obtain raw data with no aggregation or normalization applied\n\n**Arguments**:\n\n- `uids` - list of unique identifiers of the tags whose values must be obtained.\n- `start_ts` - start time in unix time or datetime\n- `end_ts` - end time in unix time or datetime\n  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tags_transient\"></a>\n\n#### read\\_tags\\_transient\n\n```python\ndef read_tags_transient(\n        uids: typing.List[str],\n        start_ts: typing.Union[int, float],\n        end_ts: typing.Union[int, float],\n        data_source: typing.Union[str, int] = None,\n        resolution: typing.Union[str, int] = 'RES_1_SEC',\n        output_format: str = 'dataframe',\n        time_format='datetime',\n        nan_method: str = None\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nThis method works like \"Tags in historical mode\", but forces the dataSource to be the transient space. Be\naware that the maximum period (endTs - startTs) than can be asked for in transient mode is 15 min. Also\nplease note that resolutions should be according to the span of time (max 15 mins) so there are new options not available\nfor historic.\n\n**Arguments**:\n\n- `uids` - list of unique identifiers of the tags whose values must be obtained. start_ts:\n- `start_ts` - time in unix time or datetime\n- `end_ts` - end time in unix time or datetime. Timespan must be smaller than 15 mins\n- `data_source` - Can be set to null or empty. Not needed\n- `resolution` - RES_1_SEC, RES_200_MIL, RES_500_MIL, any other option makes no sense with the transient data pool.\n  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n\n\n**Returns**:\n\n  response in json or dataframe\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tagview_historic\"></a>\n\n#### read\\_tagview\\_historic\n\n```python\ndef read_tagview_historic(\n    uid: str,\n    start_ts: typing.Union[datetime.datetime, float, int],\n    end_ts: typing.Union[datetime.datetime, float, int],\n    tags_uids: typing.List[str] = None,\n    data_source='RAW',\n    resolution='RES_1_HOUR',\n    output_format: str = 'dataframe',\n    time_format='datetime',\n    nan_method: str = None,\n    agg_operation: typing.Union[str, int] = \"LAST_VALUE\"\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nRead dataview historic data. It is recommended to use read_dataview_history_text_filters instead.\n\n**Arguments**:\n\n- `uid` - uid of the tagview\n- `start_ts` - start time in unix or datetime\n- `end_ts` - end time in unix or datetime\n- `tags_uids` _optional_ - list of unique identifier of the tags whose values must be obtained. If None, will take all tags in tagview\n- `data_source` - RAW, STATS_PER_HOUR, STATS_PER_DAY o STATS_PER_MONTH. This parameter indicates the historian section to get the information from, being \"RAW\" the finest data storage available.\n- `resolution` - RES_10_SEC, RES_30_SEC, RES_1_MIN, RES_5_MIN, RES_15_MIN, RES_1_HOUR, RES_1_DAY, RES_1_MONTH o RES_1_YEAR, this parameter only applies if the datasource is RAW.\n  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n  time_format : 'datetime' or 'unix' if output_format is dataframe. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n- `agg_operation` - MIN, MAX, AVG, LAST_VALUE, SUM. The operation to be applied to obtain the resolution required. Not mandatory, can be null or empty, then applies LAST_VALUE by default.\n\n\n**Returns**:\n\n  A list of objects or dataframe providing information for the requested tags. Every element in the array corresponds to one of the requested tags associated with one timestamp between the startTs and the endTs.\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tagview_historic_text_filters\"></a>\n\n#### read\\_tagview\\_historic\\_text\\_filters\n\n```python\ndef read_tagview_historic_text_filters(uid_tagview: str, start_ts: typing.Union[datetime.datetime, float, int], end_ts: typing.Union[datetime.datetime, float, int], filter_txt: typing.Union[str, typing.List[str]] = None, data_source: str = 'RAW', resolution: str = 'RES_1_HOUR', output_format: str = 'dataframe', time_format: str = 'datetime', nan_method: str = None, agg_operation: typing.Union[str, int] = \"LAST_VALUE\") -> typing.Union[\n        pd.DataFrame, \\\n                typing.List[dict]]\n```\n\nRead dataview historic data but use text filters instead of uids. Also returns data in dataframe format\n\n**Arguments**:\n\n- `uid_tagview` - uid of the tagview\n- `start_ts` - start time in unix or datetime\n- `end_ts` - end time in unix or datetime\n- `filter_txt` - text filters to search tags in tagviews. If None, will take all tags in tagview\n- `data_source` - RAW, STATS_PER_HOUR, STATS_PER_DAY o STATS_PER_MONTH. This parameter indicates the historian section to get the information from, being \"RAW\" the finest data storage available.\n- `resolution` - RES_10_SEC, RES_30_SEC, RES_1_MIN, RES_5_MIN, RES_15_MIN, RES_1_HOUR, RES_1_DAY, RES_1_MONTH o RES_1_YEAR, this parameter only applies if the datasource is RAW.\n  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n  time_format : Optional. 'datetime' or 'unix'. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n- `agg_operation` - MIN, MAX, AVG, LAST_VALUE, SUM. The operation to be applied to obtain the resolution required. Not mandatory, can be null or empty, then applies LAST_VALUE by default.\n\n\n**Returns**:\n\n  filtered_hist (dataframe):\n  columns:\n- `name` - name of tag\n- `value` - value of tag\n- `timeStamp` - timeStamp in datatetime or unix time\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tagview_realtime_text_filters\"></a>\n\n#### read\\_tagview\\_realtime\\_text\\_filters\n\n```python\ndef read_tagview_realtime_text_filters(\n        uid_tagview: str,\n        filter_txt: typing.Union[str, typing.List[str]] = None,\n        output_format: str = 'dataframe',\n        time_format: str = 'datetime',\n        nan_method: str = None\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nRead dataview realtime data but use text filters instead of uids. Also returns data in dataframe format\n\n**Arguments**:\n\n- `uid_tagview` - uid of the tagview\n- `filter_txt` - text filters to search tags in tagviews. If None, will take all tags in tagview\n  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n  time_format : Optional. 'datetime' or 'unix'. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n\n\n**Returns**:\n\n  filtered_hist (dataframe):\n  columns:\n- `name` - name of tag\n- `value` - value of tag\n- `timeStamp` - timeStamp in datatetime or unix time\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tags_historic_text_filters\"></a>\n\n#### read\\_tags\\_historic\\_text\\_filters\n\n```python\ndef read_tags_historic_text_filters(\n        uids: typing.List[str],\n        start_ts: typing.Union[datetime.datetime, int, float],\n        end_ts: typing.Union[datetime.datetime, int, float],\n        filter_txt: typing.Union[str, typing.List[str]] = None,\n        data_source: typing.Union[str, int] = 'RAW',\n        resolution: typing.Union[str, int] = 'RES_1_HOUR',\n        agg_operation: typing.Union[str, int] = \"LAST_VALUE\",\n        output_format: str = 'dataframe',\n        time_format: str = 'datetime',\n        nan_method: str = None\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nObtain historic data of the specified tags by name\n\n**Arguments**:\n\n- `uids` - list of unique identifiers of the tags whose values must be obtained.\n- `start_ts` - start time in unix or datetime\n- `end_ts` - end time in unix or datetime\n- `filter_txt` - text filters to search tags in tagviews. If None, will take all tags in tagview\n- `data_source` - RAW, STATS_PER_HOUR, STATS_PER_DAY o STATS_PER_MONTH. This parameter indicates the historian section to get the information from, being \"RAW\" the finest data storage available.\n- `resolution` - RES_10_SEC, RES_30_SEC, RES_1_MIN, RES_5_MIN, RES_15_MIN, RES_1_HOUR, RES_1_DAY, RES_1_MONTH o RES_1_YEAR, this parameter only applies if the datasource is RAW.\n- `agg_operation` - MIN, MAX, AVG, LAST_VALUE, SUM. The operation to be applied to obtain the resolution required. Not mandatory, can be null or empty, then applies LAST_VALUE by default.\n  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n  time_format : Optional. 'datetime' or 'unix'. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n\n\n**Returns**:\n\n  response in json\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_tags_realtime_text_filters\"></a>\n\n#### read\\_tags\\_realtime\\_text\\_filters\n\n```python\ndef read_tags_realtime_text_filters(\n        filter_txt: typing.Union[str, typing.List[str]] = None,\n        output_format: str = 'dataframe',\n        time_format: str = 'datetime',\n        nan_method: str = None\n) -> typing.Union[pd.DataFrame, typing.List[dict]]\n```\n\nRead tags realtime data but use text filters instead of uids. Also returns data in dataframe format\n\n**Arguments**:\n\n- `filter_txt` - text filters to search tags in tagviews. If None, will take all tags in tagview\n  output_format : Result given in 'dataframe' or 'json'or dataframe_table. Defaults to 'dataframe'\n  time_format : Optional. 'datetime' or 'unix'. Defaults to datetime\n- `nan_method` - method used to drop NaNs. None or 'interpolate', 'bfill', 'ffill', 'mean', 'zerofill'. Only valid for 'dataframe' output_format\n\n\n**Returns**:\n\n  dataframe or json:\n  columns:\n- `name` - name of tag\n- `value` - value of tag\n- `timeStamp` - timeStamp in datatetime or unix time\n\n<a id=\"iotcoreapi.IoTCoreAPI.read_alarm_status\"></a>\n\n#### read\\_alarm\\_status\n\n```python\ndef read_alarm_status(alarm_guid: str) -> dict\n```\n\nReads alarm status for a given alarm\n\n**Arguments**:\n\n  alarm_guid : guid of the alarm\n\n\n**Returns**:\n\n  Dictionary with following data:\n  {\n- `\"name\"` - \"BasicAlarm1\",\n- `\"uid\"` - \"b926bfb0-3f2f-49df-a2eb-138452296903\",\n- `\"status\"` - \"ARE\",\n- `\"alarmAREDate\"` - \"2022-07-12T12:55:28.9274145+02:00\",\n- `\"alarmLastUpdate\"` - \"2022-07-12T09:58:39.3102729+02:00\",\n- `\"alarmCurrentValue\"` - true,\n- `\"resultTimestampActivation\"` - \"2022-07-12T09:58:42.3931339+02:00\",\n- `\"resultTimestampDeactivation\"` - \"2022-07-12T09:55:34.6931883+02:00\",\n- `\"lastNotificationTS\"` - \"1900-01-01T00:00:00\",\n- `\"signalValue\"` - 95.84623491198114,\n- `\"dataComparisonType\"` - \">\",\n- `\"dataComparisonValue\"` - 0,\n- `\"signalValueOnLastHisteresis\"` - 80.27092576039533,\n- `\"lastEvent\"` - \"New Event: Alarm supervised by the API\"\n  }\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_tags_insert\"></a>\n\n#### write\\_tags\\_insert\n\n```python\ndef write_tags_insert(\n        tags: typing.Union[str, typing.List[str]]) -> typing.List[dict]\n```\n\nCheck if provided tag names exist, then create them if not\n\n**Arguments**:\n\n- `tags` - tags name or names to be created\n\n\n**Returns**:\n\n  response object in json format:\n  [\n  {\n  \"Uid\" : \"unique tag identifier\",\n  \"Name\" : \"name of the tag\",\n  \"Installation\" : \"name of the installation\",\n  \"Driver\" : \"name of the driver\",\n- `\"Attributes\"` - [\n  {\n  \"AttributeName\":\"name of the attribute\",\n  \"Value\":\"value of the attribute for this tag\"\n  },\n  ...\n  ]\n  },\n  ...\n  ]\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_tag_insert_or_update\"></a>\n\n#### write\\_tag\\_insert\\_or\\_update\n\n```python\ndef write_tag_insert_or_update(tagname, **attributes) -> typing.List[dict]\n```\n\nThis method updates a tag with the complete model that may include attributes or modifies the existing tags changing their attributes to the ones indicated in the query.\n\n**Arguments**:\n\n- `tagname` - name of the new tag\n- `**attributes` - dictionary of attributes and their values\n\n\n**Returns**:\n\n  response in json format\n\n\n**Examples**:\n\n  Call the function with a tag name and any number of attributes\n  response = write_tag_insert_or_update(tagname=\"mytag\", attribute1=\"value1\", attribute2=\"value2\", attribute3=\"value3\")\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_tags_insert_or_update_by_json\"></a>\n\n#### write\\_tags\\_insert\\_or\\_update\\_by\\_json\n\n```python\ndef write_tags_insert_or_update_by_json(\n        tags_and_attributes: typing.List[dict])\n```\n\nThis method creates the tags with the complete model that may include attributes or modifies the existing tags changing their attributes to the ones indicated in the query.\n\n**Arguments**:\n\n- `tags_and_attributes` - json list containing info for each tag:\n  [\n  {\n- `\"Name\"` - \"name of the new tag\",\n  \"Attributes\":\n  [\n  {\n- `\"AttributeName\"` - \"NameOfAttribute1\",\n- `\"Value\"` - \"ValueOfAttribute1\"\n  }\n  ]\n  }\n  ],\n  ...\n\n\n**Returns**:\n\n  response in json format\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_tags_historic_insert\"></a>\n\n#### write\\_tags\\_historic\\_insert\n\n```python\n@_no_row_limit_decorator\ndef write_tags_historic_insert(df: pd.DataFrame,\n                               skip_errors: bool = True) -> typing.List[dict]\n```\n\nUpdate historical data for tags. Tags need to be created with write_tags_insert first.\n\n**Arguments**:\n\n- `df` - dataframe\n  columns:\n- `name` - name of the tag\n  value : value of the tag\n- `timeStamp` - timeStamp in unix\n- `skip_errors` - True: If true, not created tags will be dropped from dataframe\n\n\n**Returns**:\n\n  response in json format\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_tags_realtime_insert\"></a>\n\n#### write\\_tags\\_realtime\\_insert\n\n```python\n@_no_row_limit_decorator\ndef write_tags_realtime_insert(df: pd.DataFrame, skip_errors: bool = True)\n```\n\nUpdate realtime data for tags. Tags need to be created with write_tags_insert first.\n\n**Arguments**:\n\n- `df` - dataframe\n  columns:\n- `name` - name of the tag\n  value : value of the tag\n- `timeStamp` _optional_ - timeStamp in unix. If not provided, will take current time\n- `skip_errors` - True: If true, not created tags will be dropped from dataframe\n\n\n**Returns**:\n\n  response text (None if OK)\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_tag_realtime_insert\"></a>\n\n#### write\\_tag\\_realtime\\_insert\n\n```python\ndef write_tag_realtime_insert(name: str,\n                              value: typing.Union[float, int],\n                              timeStamp=None)\n```\n\nUpdate realtime data for a single tag. Tag needs to be created with write_tags_insert first.\n\n**Arguments**:\n\n- `name` - tag name\n- `value` - value of the tag\n- `timeStamp` _optional_ - time in unix time. If None, will take current time\n\n\n**Returns**:\n\n  response text (None if OK)\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_tags_transient_insert\"></a>\n\n#### write\\_tags\\_transient\\_insert\n\n```python\n@_no_row_limit_decorator\ndef write_tags_transient_insert(df: pd.DataFrame,\n                                skip_errors: bool = True) -> typing.List[dict]\n```\n\nUpdate transient data for tags. Tags need to be created with write_tags_insert first.\n\n**Arguments**:\n\n- `df` - dataframe\n  columns:\n- `name` - name of the tag\n  value : value of the tag\n- `timeStamp` - timeStamp in unix\n  skip_errors  = True: If true, not created tags will be dropped from dataframe\n\n\n**Returns**:\n\n  response in json format\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_alarm_acknowledge\"></a>\n\n#### write\\_alarm\\_acknowledge\n\n```python\ndef write_alarm_acknowledge(guid: str, status: str) -> str\n```\n\nUsed to change the status of an alarm from ANR or ENR to ARE o EXR.\n\n**Arguments**:\n\n- `guid` - guid of the alarm\n- `status` - 'ARE' or 'EXR', 'ANR' or 'ENR'\n\n\n**Returns**:\n\n  response text (None if OK)\n\n<a id=\"iotcoreapi.IoTCoreAPI.write_alarm_event\"></a>\n\n#### write\\_alarm\\_event\n\n```python\ndef write_alarm_event(guid: str, msg: str) -> str\n```\n\nUsed to insert an event with a message in the history of the alarm. The alarma must be active and enabled.\n\n**Arguments**:\n\n- `guid` - guid of the alarm\n- `msg` - text of the message\n\n<a id=\"iotcoreapi.IoTCoreAPI.operate_tags\"></a>\n\n#### operate\\_tags\n\n```python\ndef operate_tags(df: pd.DataFrame)\n```\n\nIf the token has access to operate against a Conector associated with a PLC, this method can be used to write values to the actual Plc's tags.\n\n**Arguments**:\n\n- `df` - dataframe\n  columns:\n- `uid` - tag uid\n- `value` - value to write\n\n<a id=\"iotcoreapi.IoTCoreAPI.operate_tag_single\"></a>\n\n#### operate\\_tag\\_single\n\n```python\ndef operate_tag_single(tag_uid: str, value: typing.Union[int, float])\n```\n\nIf the token has access to operate against a Conector associated with a PLC, this method can be used to write values to the actual Plc's tags.\n\n**Arguments**:\n\n- `tag_uid` - nombre de la variable a escribir en el PLC\n- `value` - valor de la variable a escribir\n\n",
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