SIENTIAPDE-994

Remove unused utility files and update requirements.txt to include new dependencies for data processing and database interaction.

laborious/activities/base.py

@@ -0,0 +1,17 @@
+from temporalio import activity
+from sientia_do.notifications.handlers import NotificationHandler
+from logging import Logger
+
+
+class BaseActivity:
+    def __init__(self, logger: Logger, notification_handler: NotificationHandler):
+        self.logger = logger
+        self.notification_handler = notification_handler
+
+    @activity.defn(name="prepare_notification_handler")
+    async def prepare_notification_handler(self, schedule_name: str,
+                                           model_name: str,
+                                           model_id: str):
+        self.notification_handler.base_notification.schedule_name = schedule_name
+        self.notification_handler.base_notification.model_name = model_name
+        self.notification_handler.base_notification.model_id = model_id

laborious/activities/gates.py

@@ -0,0 +1,47 @@
+from temporalio import activity, workflow
+
+with workflow.unsafe.imports_passed_through():
+    from logging import Logger
+    from sientia_do.notifications.handlers import NotificationHandler
+    from laborious.activities.base import BaseActivity
+    from typing import Any
+    from laborious.utils.filters.conditional_filters import filter_empty_data, filter_specific_variables_null_values
+    from pandas import DataFrame
+
+
+filter_functions = {
+    'SPECIFIC_VARIABLES_NULL_VALUES': filter_specific_variables_null_values,
+    'EMPTY_DATA': filter_empty_data
+}
+
+
+class Gates(BaseActivity):
+    def __init__(self, logger: Logger, notification_handler: NotificationHandler):
+        super().__init__(logger, notification_handler)
+
+    @activity.defn(name="input_gate")
+    async def input_gate(self, input_data: dict[str, Any]) -> tuple[str, int]:
+        """
+        Filters the data based on the filters. The return value is a tuple with the first element
+        being the policy and the second element being the confidence status.
+        Args:
+            input_data (dict): The input data.
+        Returns:
+            tuple[str, int]: ('stop', -1) if some filter policy is 'stop', ('continue', 2)
+            if no filter policy is 'stop' and some filter policy is 'continue',
+            None if no filter is applied.
+        """
+        filters = input_data['filters']
+        data = DataFrame(input_data['data'])
+
+        filter_output = []
+        for fil, config in filters.items():
+            if filter_functions[fil](data, config):
+                filter_output.append(config['POLICY'])
+
+        if 'stop' in filter_output:
+            return 'stop', -1
+        elif 'continue' in filter_output:
+            return 'continue', 2
+
+        return None, 0

laborious/activities/postgres.py

@@ -0,0 +1,162 @@
+import traceback
+from temporalio import workflow, activity
+
+from laborious.activities.base import BaseActivity
+with workflow.unsafe.imports_passed_through():
+    from psycopg2.pool import ThreadedConnectionPool
+    from pandas import read_sql_query, DataFrame
+    from logging import Logger
+    from sientia_do.notifications.handlers import NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from typing import Any
+
+
+class Postgres(BaseActivity):
+    def __init__(self, host: str, port: int,
+                 user: str, password: str, dbname: str,
+                 min_connections: int, max_connections: int,
+                 logger: Logger, notification_handler: NotificationHandler):
+        self.host = host
+        self.port = port
+        self.user = user
+        self.password = password
+        self.dbname = dbname
+
+        self.pool = ThreadedConnectionPool(
+            minconn=min_connections,
+            maxconn=max_connections,
+            host=self.host,
+            port=self.port,
+            user=self.user,
+            password=self.password,
+            dbname=self.dbname)
+
+        super().__init__(logger, notification_handler)
+
+    def close(self):
+        self.pool.closeall()
+
+    def __del__(self):
+        self.close()
+
+    @activity.defn(name="load_custom_query")
+    async def load_custom_query(self, query: str) -> dict[str, dict]:
+        """
+        Loads data from a custom query.
+
+        Args:
+            query (str): The query to load data from.
+
+        Returns:
+            dict[str, dict]: The data from the query.
+        """
+        self.logger.info(f"Fetching data from query: {query}")
+
+        conn = self.pool.getconn()
+        try:
+            data = read_sql_query(query, conn)
+
+        except Exception as e:
+            trace = traceback.format_exc()
+            self.notification_handler.build_and_send_notification(
+                notification_id="ERROR_LOADING_CUSTOM_QUERY",
+                message=f"Error fetching data from query: {e}",
+                block="load_custom_query",
+                level=NotificationLevel.ERROR,
+                attachment_content=trace
+            )
+
+            self.logger.error(trace)
+
+            return {}
+        finally:
+            self.pool.putconn(conn)
+
+        self.logger.info(f"Fetched {len(data)} rows")
+        self.logger.debug(f"Data: {data.to_string()}")
+
+        return data.to_dict()
+
+    @activity.defn(name="repeat_last_prediction")
+    async def repeat_last_prediction(self, query_items: dict[str, str]):
+        """
+        Repeats the last prediction for a given model.
+
+        Args:
+            query_items (dict[str, str]): The query items.
+
+        Returns:
+            None
+        """
+        schema = query_items["schema"]
+        table_name = query_items["table_name"]
+        model = query_items["model"]
+
+        repeat_query = f"""
+        INSERT INTO \"{schema}\".{table_name} (model_id, prediction, timestamp, response_time, prediction_status, prediction_confidence, created_at)
+        SELECT model_id, prediction, timestamp, response_time, prediction_status, prediction_confidence, NOW()
+        FROM \"{schema}\".{table_name}
+        WHERE model_id = {model}
+        ORDER BY timestamp DESC
+        LIMIT 1;
+        """
+        self.logger.info(f"Repeating last prediction for model {model}")
+        self.logger.debug(f"Query: {repeat_query}")
+
+        conn = self.pool.getconn()
+
+        try:
+            cursor = conn.cursor()
+            cursor.execute(repeat_query)
+            conn.commit()
+            cursor.close()
+
+        except Exception as e:
+            trace = traceback.format_exc()
+            self.notification_handler.build_and_send_notification(
+                notification_id="ERROR_REPEATING_LAST_PREDICTION",
+                message=f"Error repeating last prediction: {e}",
+                block="repeat_last_prediction",
+                level=NotificationLevel.ERROR,
+                attachment_content=trace
+            )
+
+            self.logger.error(trace)
+
+        finally:
+            self.pool.putconn(conn)
+
+    @activity.defn(name="export_data_to_postgres")
+    async def export_data_to_postgres(self, input_data: dict[str, Any]):
+        """
+        Exports data to a postgres table.
+
+        Args:
+            input_data (dict[str, Any]): The data to export.
+        """
+
+        schema = input_data["schema"]
+        table_name = input_data["table_name"]
+        data = DataFrame(input_data["data"])
+
+        conn = self.pool.getconn()
+
+        try:
+            data.to_sql(table_name, conn, schema=schema,
+                        if_exists="append", index=False)
+            conn.commit()
+
+        except Exception as e:
+            trace = traceback.format_exc()
+            self.notification_handler.build_and_send_notification(
+                notification_id="ERROR_EXPORTING_DATA_TO_POSTGRES",
+                message=f"Error exporting data to postgres: {e}",
+                block="export_data_to_postgres",
+                level=NotificationLevel.ERROR,
+                attachment_content=trace
+            )
+
+            self.logger.error(trace)
+
+        finally:
+            self.pool.putconn(conn)

laborious/utils/filters/api_filters.py

@@ -0,0 +1,69 @@
+import numpy as np
+from pandas import DataFrame
+from laborious.utils.filters.base_filter import Filter
+
+
+class ApiErrorFilter(Filter):
+    def __init__(self, policy):
+        self.policy = policy
+        super().__init__('API_FILTER')
+
+    def method(self, response: dict, prediction_confidence: int):
+        """
+        Processes the API response and determines the next action based on the response and prediction confidence.
+        Args:
+            response (dict): The API response to be processed.
+            prediction_confidence (int): The confidence level of the prediction.
+        Returns:
+            str: 'stop' if the policy is to stop on captured errors, 'continue' if the policy is to continue on captured errors.
+        Raises:
+            KeyError: If 'success' or 'content' keys are missing in the response dictionary.
+        """
+
+        captured = False
+        if not response and prediction_confidence == 10:
+            self.warning('No valid response.')
+            captured = True
+
+        else:
+            if not response['success']:
+                message = response['content']["message"]
+                self.warning(
+                    f'Model repository error: {message}')
+                captured = True
+        if captured and self.policy == 'stop':
+            return 'stop'
+        elif captured and self.policy == 'continue':
+            return 'continue'
+
+
+class NaNValuesFilter(Filter):
+    def __init__(self, policy):
+        self.policy = policy
+        super().__init__('NAN_VALUES')
+
+    def method(self, predictions: DataFrame, prediction_confidence: int):
+        """
+        Processes the given predictions DataFrame by replacing None values with NaN,
+        dropping the 'timestamp' column if it exists, and checking for NaN values.
+        Args:
+            predictions (pd.DataFrame): The DataFrame containing prediction data.
+            prediction_confidence (float): The confidence level of the predictions.
+        Returns:
+            float or int or bool: Returns the prediction confidence if the DataFrame
+            is not entirely NaN. If all values are NaN and the policy is 'stop', 
+            returns False. If all values are NaN and the policy is 'continue', 
+            returns 18.
+        """
+
+        data = predictions.replace({None: np.nan}).drop(
+            columns=['timestamp'], errors='ignore')
+
+        if data.isna().all().all():
+            if self.policy == 'stop':
+                self.warning('All values are NaN.')
+                return False
+            elif self.policy == 'continue':
+                return 18
+
+        return prediction_confidence

laborious/utils/filters/base_filter.py

@@ -0,0 +1,13 @@
+from pandas import DataFrame
+
+class Filter:
+    def __init__(self, id: str):
+        self.id = id
+        self.warnings = []
+
+    @staticmethod
+    def method(df: DataFrame) -> DataFrame:
+        raise NotImplementedError
+    
+    def warning(self, message: str):
+        self.warnings.append(f'[{self.id}] - {message}')

laborious/utils/filters/conditional_filters.py

@@ -0,0 +1,19 @@
+from typing import List
+
+from laborious.utils.filters.base_filter import Filter
+from pandas import DataFrame
+
+
+def filter_specific_variables_null_values(data: DataFrame, config: dict) -> bool:
+    """
+    Returns True if the data is empty, False otherwise.
+    """
+    return data[
+        data['variable'].isin(config['VARIABLES']) & data['value'].isna()].empty
+
+
+def filter_empty_data(data: DataFrame, _config: dict) -> bool:
+    """
+    Returns True if the data is empty, False otherwise.
+    """
+    return data.empty

requirements.txt

@@ -1,4 +1,4 @@
-GitPython
 temporalio
-pytest
-python-dotenv
+psycopg2-binary
+pandas
+git+ssh://git@github.com/Aignosi/sientia-dataops-library.git

tests/laborious/activities/test_gates.py

@@ -0,0 +1,274 @@
+from unittest.mock import MagicMock, patch
+from pandas import DataFrame
+from pytest import fixture, mark
+
+from laborious.activities.gates import Gates
+
+
+@fixture
+def gates():
+    return Gates(
+        logger=MagicMock(),
+        notification_handler=MagicMock()
+    )
+
+
+@mark.asyncio
+@patch('laborious.activities.gates.filter_functions')
+async def test_input_gate_specific_variables_null_values_with_stop_policy_only(
+    filter_functions_mock,
+    gates
+):
+    specific_variables_null_values_mock = MagicMock(return_value=True)
+    empty_data_mock = MagicMock(return_value=False)
+
+    def functions_side_effect(x):
+        if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
+            return specific_variables_null_values_mock
+        return empty_data_mock
+
+    filter_functions_mock.__getitem__.side_effect = functions_side_effect
+
+    input_data = {
+        'filters': {
+            'SPECIFIC_VARIABLES_NULL_VALUES': {
+                'POLICY': 'stop',
+                'VARIABLES': ['variable2']
+            }
+        },
+        'data': {
+            'variable': ['variable1', 'variable2'],
+            'value': [1, 2]
+        }
+    }
+
+    result = await gates.input_gate(input_data)
+    assert result == ('stop', -1)
+
+    input_args = specific_variables_null_values_mock.call_args
+    assert input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+    assert input_args[0][1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
+
+    empty_data_mock.assert_not_called()
+
+
+@mark.asyncio
+@patch('laborious.activities.gates.filter_functions')
+async def test_input_gate_specific_variables_null_values_with_continue_policy_only(
+    filter_functions_mock,
+    gates
+):
+    specific_variables_null_values_mock = MagicMock(return_value=True)
+    empty_data_mock = MagicMock(return_value=False)
+
+    def functions_side_effect(x):
+        if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
+            return specific_variables_null_values_mock
+        return empty_data_mock
+
+    filter_functions_mock.__getitem__.side_effect = functions_side_effect
+
+    input_data = {
+        'filters': {
+            'SPECIFIC_VARIABLES_NULL_VALUES': {
+                'POLICY': 'continue',
+                'VARIABLES': ['variable2']
+            }
+        },
+        'data': {
+            'variable': ['variable1', 'variable2'],
+            'value': [1, 2]
+        }
+    }
+
+    result = await gates.input_gate(input_data)
+    assert result == ('continue', 2)
+
+    input_args = specific_variables_null_values_mock.call_args
+    assert input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+    assert input_args[0][1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
+
+    empty_data_mock.assert_not_called()
+
+
+@mark.asyncio
+@patch('laborious.activities.gates.filter_functions')
+async def test_input_gate_specific_variables_null_values_no_filtered(
+    filter_functions_mock,
+    gates
+):
+    specific_variables_null_values_mock = MagicMock(return_value=False)
+    empty_data_mock = MagicMock(return_value=False)
+
+    def functions_side_effect(x):
+        if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
+            return specific_variables_null_values_mock
+        return empty_data_mock
+
+    filter_functions_mock.__getitem__.side_effect = functions_side_effect
+
+    input_data = {
+        'filters': {
+            'SPECIFIC_VARIABLES_NULL_VALUES': {
+                'POLICY': 'stop',
+                'VARIABLES': ['variable2']
+            }
+        },
+        'data': {
+            'variable': ['variable1', 'variable2'],
+            'value': [1, 2]
+        }
+    }
+
+    result = await gates.input_gate(input_data)
+    assert result == (None, 0)
+
+    specific_variables_null_values_input_args = specific_variables_null_values_mock.call_args
+
+    assert specific_variables_null_values_input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+    assert specific_variables_null_values_input_args[0][
+        1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
+
+    empty_data_mock.assert_not_called()
+
+
+@mark.asyncio
+@patch('laborious.activities.gates.filter_functions')
+async def test_input_gate_one_stop_policy(
+    filter_functions_mock,
+    gates
+):
+    specific_variables_null_values_mock = MagicMock(return_value=True)
+    empty_data_mock = MagicMock(return_value=True)
+
+    def functions_side_effect(x):
+        if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
+            return specific_variables_null_values_mock
+        return empty_data_mock
+
+    filter_functions_mock.__getitem__.side_effect = functions_side_effect
+
+    input_data = {
+        'filters': {
+            'SPECIFIC_VARIABLES_NULL_VALUES': {
+                'POLICY': 'stop',
+                'VARIABLES': ['variable2']
+            },
+            'EMPTY_DATA': {
+                'POLICY': 'continue',
+            }
+        },
+        'data': {
+            'variable': ['variable1', 'variable2'],
+            'value': [1, 2]
+        }
+    }
+
+    result = await gates.input_gate(input_data)
+    assert result == ('stop', -1)
+
+    specific_variables_null_values_input_args = specific_variables_null_values_mock.call_args
+    assert specific_variables_null_values_input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+    assert specific_variables_null_values_input_args[0][
+        1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
+
+    empty_data_input_args = empty_data_mock.call_args
+    assert empty_data_input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+    assert empty_data_input_args[0][1] == input_data['filters']['EMPTY_DATA']
+
+
+@mark.asyncio
+@patch('laborious.activities.gates.filter_functions')
+async def test_input_gate_one_continue_policy(
+    filter_functions_mock,
+    gates
+):
+    specific_variables_null_values_mock = MagicMock(return_value=False)
+    empty_data_mock = MagicMock(return_value=True)
+
+    def functions_side_effect(x):
+        if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
+            return specific_variables_null_values_mock
+        return empty_data_mock
+
+    filter_functions_mock.__getitem__.side_effect = functions_side_effect
+
+    input_data = {
+        'filters': {
+            'SPECIFIC_VARIABLES_NULL_VALUES': {
+                'POLICY': 'stop',
+                'VARIABLES': ['variable2']
+            },
+            'EMPTY_DATA': {
+                'POLICY': 'continue',
+            }
+        },
+        'data': {
+            'variable': ['variable1', 'variable2'],
+            'value': [1, 2]
+        }
+    }
+
+    result = await gates.input_gate(input_data)
+    assert result == ('continue', 2)
+
+    specific_variables_null_values_input_args = specific_variables_null_values_mock.call_args
+    assert specific_variables_null_values_input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+    assert specific_variables_null_values_input_args[0][
+        1] == input_data['filters']['SPECIFIC_VARIABLES_NULL_VALUES']
+
+    empty_data_input_args = empty_data_mock.call_args
+    assert empty_data_input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+    assert empty_data_input_args[0][1] == input_data['filters']['EMPTY_DATA']
+
+
+@mark.asyncio
+@patch('laborious.activities.gates.filter_functions')
+async def test_input_gate_no_filtered(
+    filter_functions_mock,
+    gates
+):
+    specific_variables_null_values_mock = MagicMock(return_value=False)
+    empty_data_mock = MagicMock(return_value=False)
+
+    def functions_side_effect(x):
+        if x == 'SPECIFIC_VARIABLES_NULL_VALUES':
+            return specific_variables_null_values_mock
+        return empty_data_mock
+
+    filter_functions_mock.__getitem__.side_effect = functions_side_effect
+
+    input_data = {
+        'filters': {
+            'SPECIFIC_VARIABLES_NULL_VALUES': {
+                'POLICY': 'stop',
+                'VARIABLES': ['variable2']
+            },
+            'EMPTY_DATA': {
+                'POLICY': 'continue',
+            }
+        },
+        'data': {
+            'variable': ['variable1', 'variable2'],
+            'value': [1, 2]
+        }
+    }
+
+    result = await gates.input_gate(input_data)
+    assert result == (None, 0)
+
+    specific_variables_null_values_input_args = specific_variables_null_values_mock.call_args
+    assert specific_variables_null_values_input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+
+    empty_data_input_args = empty_data_mock.call_args
+    assert empty_data_input_args[0][0].equals(DataFrame(
+        {'variable': ['variable1', 'variable2'], 'value': [1, 2]}))
+    assert empty_data_input_args[0][1] == input_data['filters']['EMPTY_DATA']

tests/laborious/activities/test_postgres.py

@@ -0,0 +1,132 @@
+from unittest.mock import ANY, MagicMock, patch
+from pandas import DataFrame
+from pytest import fixture
+from pytest import mark
+from sientia_do.notifications.models import NotificationLevel
+
+from laborious.activities.postgres import Postgres
+
+
+@fixture
+@patch("laborious.activities.postgres.ThreadedConnectionPool")
+def postgres_client(mock_pool):
+    return Postgres(
+        host="localhost",
+        port=5432,
+        user="postgres",
+        password="postgres",
+        dbname="postgres",
+        min_connections=1,
+        max_connections=10,
+        logger=MagicMock(),
+        notification_handler=MagicMock(),
+    )
+
+
+@mark.asyncio
+@patch("laborious.activities.postgres.read_sql_query",
+       return_value=DataFrame([{"a": 1, "b": 2}]))
+async def test_load_custom_query_success(mock_read_sql_query, postgres_client):
+    query = "SELECT * FROM test"
+    result = await postgres_client.load_custom_query(query)
+    assert result is not None
+    assert len(result) > 0
+    assert result == {'a': {0: 1}, 'b': {0: 2}}
+    postgres_client.notification_handler.build_and_send_notification.assert_not_called()
+
+
+@mark.asyncio
+@patch("laborious.activities.postgres.read_sql_query",
+       side_effect=Exception("Error fetching data from query"))
+async def test_load_custom_query_error(mock_read_sql_query, postgres_client):
+    query = "SELECT * FROM test"
+    result = await postgres_client.load_custom_query(query)
+    assert result == {}
+    postgres_client.notification_handler.build_and_send_notification.assert_called_once_with(
+        notification_id="ERROR_LOADING_CUSTOM_QUERY",
+        message="Error fetching data from query: Error fetching data from query",
+        block="load_custom_query",
+        level=NotificationLevel.ERROR,
+        attachment_content=ANY
+    )
+
+
+@mark.asyncio
+async def test_repeat_last_prediction_success(postgres_client):
+    query_items = {"schema": "test", "table_name": "test", "model": 1}
+    await postgres_client.repeat_last_prediction(query_items)
+    postgres_client.notification_handler.build_and_send_notification.assert_not_called()
+    postgres_client.pool.getconn.assert_called_once()
+    postgres_client.pool.putconn.assert_called_once()
+
+    postgres_client.pool.getconn.return_value.cursor.assert_called_once()
+    postgres_client.pool.getconn.return_value.cursor.return_value.execute.assert_called_once_with(
+        f"""
+        INSERT INTO \"{query_items['schema']}\".{query_items['table_name']} (model_id, prediction, timestamp, response_time, prediction_status, prediction_confidence, created_at)
+        SELECT model_id, prediction, timestamp, response_time, prediction_status, prediction_confidence, NOW()
+        FROM \"{query_items['schema']}\".{query_items['table_name']}
+        WHERE model_id = {query_items['model']}
+        ORDER BY timestamp DESC
+        LIMIT 1;
+        """
+    )
+    postgres_client.pool.getconn.return_value.commit.assert_called_once()
+    postgres_client.pool.getconn.return_value.cursor.return_value.close.assert_called_once()
+
+
+@mark.asyncio
+async def test_repeat_last_prediction_error(postgres_client):
+    postgres_client.pool.getconn.return_value.cursor.return_value.execute.side_effect = Exception(
+        "Error repeating last prediction")
+    query_items = {"schema": "test", "table_name": "test", "model": 1}
+    await postgres_client.repeat_last_prediction(query_items)
+    postgres_client.notification_handler.build_and_send_notification.assert_called_once_with(
+        notification_id="ERROR_REPEATING_LAST_PREDICTION",
+        message="Error repeating last prediction: Error repeating last prediction",
+        block="repeat_last_prediction",
+        level=NotificationLevel.ERROR,
+        attachment_content=ANY
+    )
+    postgres_client.pool.getconn.assert_called_once()
+    postgres_client.pool.putconn.assert_called_once()
+
+
+@mark.asyncio
+@patch("laborious.activities.postgres.DataFrame")
+async def test_export_data_to_postgres_success(mock_dataframe, postgres_client):
+    data = {"schema": "test", "table_name": "test",
+            "data": {"a": [1, 2, 3], "b": [4, 5, 6]}}
+    await postgres_client.export_data_to_postgres(data)
+    postgres_client.notification_handler.build_and_send_notification.assert_not_called()
+    postgres_client.pool.getconn.assert_called_once()
+    postgres_client.pool.putconn.assert_called_once()
+
+    mock_dataframe.assert_called_once_with(data["data"])
+    mock_dataframe.return_value.to_sql.assert_called_once_with(
+        data["table_name"],
+        postgres_client.pool.getconn.return_value,
+        schema=data["schema"],
+        if_exists="append",
+        index=False
+    )
+    postgres_client.pool.getconn.return_value.commit.assert_called_once()
+
+
+@mark.asyncio
+@patch("laborious.activities.postgres.DataFrame", return_value=MagicMock(
+    to_sql=MagicMock(side_effect=Exception("Error exporting data to postgres"))
+))
+async def test_export_data_to_postgres_error(mock_dataframe, postgres_client):
+    data = {"schema": "test", "table_name": "test",
+            "data": {"a": [1, 2, 3], "b": [4, 5, 6]}}
+    await postgres_client.export_data_to_postgres(data)
+    postgres_client.notification_handler.build_and_send_notification.assert_called_once_with(
+        notification_id="ERROR_EXPORTING_DATA_TO_POSTGRES",
+        message="Error exporting data to postgres: Error exporting data to postgres",
+        block="export_data_to_postgres",
+        level=NotificationLevel.ERROR,
+        attachment_content=ANY
+    )
+
+    postgres_client.pool.getconn.assert_called_once()
+    postgres_client.pool.putconn.assert_called_once()

tests/laborious/utils/filters/test_conditional_filters.py

@@ -0,0 +1,26 @@
+from pandas import DataFrame
+
+from laborious.utils.filters.conditional_filters import filter_specific_variables_null_values, filter_empty_data
+
+
+def test_filter_specific_variables_null_values():
+    assert filter_specific_variables_null_values(
+        DataFrame(
+            {'variable': ['variable1', 'variable2'], 'value': [1, 2]}),
+        variables=['variable2']) == True
+
+
+def test_filter_specific_variables_null_values_with_null_values():
+    assert filter_specific_variables_null_values(
+        DataFrame(
+            {'variable': ['variable1', 'variable2'], 'value': [1, None]}),
+        variables=['variable2']) == False
+
+
+def test_filter_empty_data():
+    assert filter_empty_data(DataFrame()) == True
+
+
+def test_filter_empty_data_with_data():
+    assert filter_empty_data(
+        DataFrame({'variable': ['variable1', 'variable2'], 'value': [1, 2]})) == False