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dalmia_degradation_calculation
Commit ff9304ef authored by aakash.bedi's avatar aakash.bedi
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added calculation script

parent 9a0e88b5
Pipeline #58113 failed with stage
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    # Byte-compiled / optimized / DLL files
    __pycache__/
    *.py[cod]
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    # C extensions
    *.so
    data
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    share/python-wheels/
    *.egg-info/
    .installed.cfg
    *.egg
    MANIFEST
    # PyInstaller
    # Usually these files are written by a python script from a template
    # before PyInstaller builds the exe, so as to inject date/other infos into it.
    *.manifest
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    # Installer logs
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    # IPython
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    ipython_config.py
    # pyenv
    # For a library or package, you might want to ignore these files since the code is
    # intended to run in multiple environments; otherwise, check them in:
    # .python-version
    # pipenv
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    #Pipfile.lock
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    __pypackages__/
    # Celery stuff
    celerybeat-schedule
    celerybeat.pid
    # SageMath parsed files
    *.sage.py
    # Environments
    .env
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    venv/
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    .pytype/
    # Cython debug symbols
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    .gitlab-ci.yml 0 → 100644
    View file @ ff9304ef
    stages:
    - auto-tagging
    - validate
    - scan
    - build
    - deploy
    - update
    variables:
    MYSQL_CONNECTION: "mysql -h $MYSQL_HOST -u $MYSQL_USER -p$MYSQL_PASS "
    STATUS_SCRIPT: /home/gitlab-runner/monitor/deployment-status.sh
    HELM_CHART: /home/gitlab-runner/kubernetes/ilens/$QA_ENV/ilens-modules
    VARIABLES_YML: variables.yml
    DEPLOYMENT_YML: $CI_PROJECT_NAME.yml
    TIMEOUT: 960s
    before_script:
    - val=`echo $($MYSQL_CONNECTION -e "SELECT COUNT(*) FROM $VERSION_DB.$DB_TABLE WHERE category='Server' AND type='Service' AND os='docker' AND module_name='$CI_PROJECT_NAME' ") | cut -d " " -f2`
    - if [ $val == 0 ]; then $MYSQL_CONNECTION -e "INSERT INTO $VERSION_DB.$DB_TABLE values('Server','Service','$CI_PROJECT_NAME','docker', '2', '0', '0', '0')";fi
    - QA=$($MYSQL_CONNECTION -N -e "SELECT qa FROM $VERSION_DB.$DB_TABLE where module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'")
    - DEV=$($MYSQL_CONNECTION -N -e "SELECT dev FROM $VERSION_DB.$DB_TABLE where module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'")
    - UAT=$(mysql -h $MYSQL_HOST -u $MYSQL_USER -p$MYSQL_PASS -N -e "SELECT uat FROM $VERSION_DB.$DB_TABLE where module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'")
    - PROD=$($MYSQL_CONNECTION -N -e "SELECT prod FROM $VERSION_DB.$DB_TABLE where module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'")
    auto-tagging:
    stage: auto-tagging
    before_script:
    - val=`echo $($MYSQL_CONNECTION -e "SELECT COUNT(*) FROM $VERSION_DB.$VERSION_RELEASE_TABLE WHERE module_name='$CI_PROJECT_NAME' ") | cut -d " " -f2`
    - if [ $val == 0 ]; then $MYSQL_CONNECTION -N -e "INSERT INTO $VERSION_DB.$VERSION_RELEASE_TABLE values('$CI_PROJECT_NAME', 'iLens', '6', '3', '0', '0')";fi
    - ILENS=$($MYSQL_CONNECTION -N -e "SELECT ilens_version FROM "$VERSION_DB.$VERSION_RELEASE_TABLE" where module_name = '$CI_PROJECT_NAME'")
    - RELEASE=$($MYSQL_CONNECTION -N -e "SELECT release_version FROM "$VERSION_DB.$VERSION_RELEASE_TABLE" where module_name = '$CI_PROJECT_NAME'")
    - FEATURE=$($MYSQL_CONNECTION -N -e "SELECT feature_version FROM "$VERSION_DB.$VERSION_RELEASE_TABLE" where module_name = '$CI_PROJECT_NAME'")
    - PATCH=$($MYSQL_CONNECTION -N -e "SELECT patch_version FROM "$VERSION_DB.$VERSION_RELEASE_TABLE" where module_name = '$CI_PROJECT_NAME'")
    script:
    - SOURCE_BRANCH=$(echo $CI_COMMIT_TITLE | cut -f 3 -d " " | cut -f 1 -d "/" | cut -f 2 -d "'")
    - >
    if [ "$SOURCE_BRANCH" = "QA" ]; then
    ((RELEASE=RELEASE+1)) && FEATURE=0 && PATCH=0;
    TAG_NAME=v$ILENS.$RELEASE.$FEATURE
    IMAGE_URL=azrilensprod.azurecr.io/ilens/release/versions/v"$ILENS.$RELEASE:$CI_PROJECT_NAME-$TAG_NAME"
    PROD=$RELEASE; QA=0; DEV=0;
    $MYSQL_CONNECTION -e "UPDATE $VERSION_DB.$DB_TABLE SET prod='$PROD' ,qa='$QA', dev='$DEV' WHERE module_name='$CI_PROJECT_NAME' AND type='Service' AND category='Server' AND os='docker'"
    elif [ $SOURCE_BRANCH == "feature" ]; then
    ((FEATURE=FEATURE+1)) && PATCH=0;
    TAG_NAME=v$ILENS.$RELEASE.$FEATURE
    IMAGE_URL=azrilensprod.azurecr.io/ilens/release/versions/v"$ILENS.$RELEASE:$CI_PROJECT_NAME-$TAG_NAME"
    elif [ $SOURCE_BRANCH == "patch" ]; then
    ((PATCH=PATCH+1));
    TAG_NAME=v$ILENS.$RELEASE.$FEATURE.$PATCH
    IMAGE_URL=azrilensprod.azurecr.io/ilens/release/versions/v"$ILENS.$RELEASE:$CI_PROJECT_NAME-$TAG_NAME"
    else
    exit 1
    fi
    - echo -e "\n\nImage:" $IMAGE_URL >> ReleaseNote.txt
    - sed -i "1s|^|Version":" $TAG_NAME\n|" ReleaseNote.txt
    - sed -i "1s|^|Module Name":" $CI_PROJECT_NAME\n|" ReleaseNote.txt
    - docker build -t $IMAGE_URL .
    - docker push $IMAGE_URL
    - docker rmi --force $IMAGE_URL
    - URL=$(echo $CI_PROJECT_URL | sed 's|https://||')
    - git remote set-url origin https://$GIT_USRNAME:$GIT_USRPASSWD@$URL
    - git config user.email "devopsilens@gmail.com"
    - git config user.name "$GIT_USRNAME"
    - git tag -a $TAG_NAME -F ReleaseNote.txt
    - git push origin $TAG_NAME
    - $MYSQL_CONNECTION -e "UPDATE $VERSION_DB.$VERSION_RELEASE_TABLE SET release_version='$RELEASE', feature_version='$FEATURE', patch_version='$PATCH' WHERE module_name = '$CI_PROJECT_NAME' "
    - $MYSQL_CONNECTION -e "INSERT INTO $HISTORY_DB.$VERSION_RELEASE_TABLE values('$CI_JOB_ID', '$CI_PROJECT_NAME','iLens', '$ILENS.$RELEASE.$FEATURE', '$CI_COMMIT_SHA', '$GITLAB_USER_NAME', '$CI_COMMIT_REF_NAME')"
    tags:
    - shell
    only:
    - master
    #~~~~~| Requirements.txt version check |~~~~~#
    package-version-check:
    stage: validate
    script:
    - REQUIREMENTS=$(cat requirements.txt)
    - FAILED=0
    - >
    for REQ in ${REQUIREMENTS[@]};
    do
    PKG=$(echo $REQ | tr = " " | awk '{print $1}')
    VER=$(echo $REQ | tr = " " | awk '{print $2}')
    VER=${VER//[^[:alnum:]]/}
    if [ ! -z "${VER//[0-9]}" ] || [ -z $VER ]; then
    echo " Package version not specified for: $PKG "
    FAILED=`expr $FAILED + 1`
    fi
    done
    - if [ $FAILED -gt 0 ]; then exit 1; fi
    only:
    - QA
    tags:
    - shell
    #~~~~~| QA K8 |~~~~~#
    qa-k8-deployment:
    stage: deploy
    script:
    - REGISTRY_URL=azacrknowledgelens.azurecr.io/knowledgelens/products/ilens/qa
    - export KUBECONFIG=/home/gitlab-runner/.kube/$QA_ENV
    - NAMESPACE=ilens-core
    - QA=`expr $QA + 1` && DEV=0
    - docker build -t $REGISTRY_URL/$CI_PROJECT_NAME:v$PROD.$QA.$DEV .
    - docker push $REGISTRY_URL/$CI_PROJECT_NAME:v$PROD.$QA.$DEV
    - echo "Deploying $CI_PROJECT_NAME"
    - >
    for YML in ${DEPLOYMENT_YML[@]};
    do
    FILE_PATH=$HELM_CHART/$YML
    SERVICE=$(echo $YML | cut -f 1 -d "." )
    CURR_VERSION=$(cat $FILE_PATH | grep "imageName:" )
    CURR_VERSION=$(echo $CURR_VERSION | cut -f 3 -d ":")
    echo " Deploying $SERVICE"
    echo " $SERVICE Version: $CURR_VERSION"
    sed -E -i'' "s|(.*imageName:.*"$REGISTRY_URL"/).*|\1"$CI_PROJECT_NAME":v"$PROD.$QA.$DEV"|" $FILE_PATH
    helm upgrade --install $SERVICE $HELM_CHART -f $FILE_PATH -f $VARIABLES_YML -n $NAMESPACE --history-max 1
    if ! sh $STATUS_SCRIPT $SERVICE $NAMESPACE $TIMEOUT ; then
    sed -E -i'' "s|(.*imageName:.*"$REGISTRY_URL"/).*|\1"$CI_PROJECT_NAME":"$CURR_VERSION"|" $FILE_PATH
    helm upgrade --install $SERVICE $HELM_CHART -f $FILE_PATH -f $VARIABLES_YML -n $NAMESPACE --history-max 1
    echo " $SERVICE Reverted to the previous version..."
    exit 1
    fi
    UI_POD=$(kubectl get pods -n $NAMESPACE | grep ilens-ui | awk '{print $1}')
    kubectl delete pod $UI_POD -n $NAMESPACE
    done
    only:
    - QA
    tags:
    - shell
    #~~~~~| Vulnerability Scanner |~~~~~#
    vulnerability-scanner:
    stage: scan
    script:
    - QA=`expr $QA + 1` && DEV=0
    - DOCKER_IMAGE=$CI_PROJECT_NAME:vulnarable-scan
    - docker build -t $DOCKER_IMAGE --no-cache .
    - trivy image --format template --template "@/home/gitlab-runner/image-scanner/templates/html.tpl" -o imageScanner-$CI_PROJECT_NAME.html $DOCKER_IMAGE
    - trivy image --format json -o imageScanner-$CI_PROJECT_NAME.json $DOCKER_IMAGE
    - docker rmi --force $DOCKER_IMAGE
    - mv imageScanner-$CI_PROJECT_NAME.html /data0/email-util/module/reports/
    - >
    if ! /home/gitlab-runner/image-scanner/severity_check imageScanner-$CI_PROJECT_NAME.json ; then
    cd /home/gitlab-runner/image-scanner/
    ./mail imageScanner-$CI_PROJECT_NAME.html $DOCKER_IMAGE
    fi
    only:
    - QA
    tags:
    - shell
    tag-update-qa:
    stage: update
    script:
    - QA=`expr $QA + 1` && DEV=0
    - REGISTRY_URL=azacrknowledgelens.azurecr.io/knowledgelens/products/ilens/qa
    - docker rmi --force $REGISTRY_URL/$CI_PROJECT_NAME:v$PROD.$QA.$DEV
    - $MYSQL_CONNECTION -e "INSERT INTO $HISTORY_DB.$DB_TABLE values('$CI_JOB_ID','Server','Service', '$CI_PROJECT_NAME','docker', '$PROD.$QA.$DEV', '$CI_COMMIT_SHA', '$GITLAB_USER_NAME', '$CI_COMMIT_REF_NAME')"
    - $MYSQL_CONNECTION -e "UPDATE $VERSION_DB.$DB_TABLE SET prod='$PROD' ,qa='$QA', dev='$DEV' WHERE module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'"
    dependencies:
    - qa-k8-deployment
    only:
    - QA
    tags:
    - shell
    #~~~~~| DEV 220 |~~~~~#
    dev-deployment-220:
    stage: deploy
    script:
    - tar czvf $CI_PROJECT_NAME.tar.gz *
    - echo "Deploying to the dev 220 server..."
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "mkdir -p /tmp/$CI_PROJECT_NAME/tar/"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "mkdir -p /tmp/$CI_PROJECT_NAME/untar/"
    - sshpass -p $OFC_PASSWD scp $CI_PROJECT_NAME.tar.gz $OFC_USERNAME@$OFC_HOSTNAME:/tmp/$CI_PROJECT_NAME/tar/
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "tar xzvf /tmp/$CI_PROJECT_NAME/tar/$CI_PROJECT_NAME.tar.gz -C /tmp/$CI_PROJECT_NAME/untar/"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "rsync -r /tmp/$CI_PROJECT_NAME/untar/* /opt/services/ilens2.0/$CI_PROJECT_NAME/"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "/home/svc-ilens/.miniconda3/envs/ilens37/bin/pip install -r /opt/services/ilens2.0/$CI_PROJECT_NAME/requirements.txt"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "sudo systemctl restart ilens_2.0_dev_$CI_PROJECT_NAME.service"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "sudo systemctl status ilens_2.0_dev_$CI_PROJECT_NAME.service"
    after_script:
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "rm -rf /tmp/$CI_PROJECT_NAME"
    - rm -f $CI_PROJECT_NAME.tar.gz
    only:
    - develop
    tags:
    - shell
    #~~~~~| CODE QUALITY |~~~~~#
    codequality:
    stage: deploy
    image: azacrknowledgelens.azurecr.io/knowledgelens/klit-operation/devops/gitlab-runner:ubuntu-sonarscanner
    script:
    - /opt/sonar-scanner/bin/sonar-scanner -Dsonar.projectKey=$CI_PROJECT_NAME -Dsonar.projectName=$CI_PROJECT_NAME -Dsonar.typescript.node=./node/node -Dsonar.login=admin -Dsonar.password=admin -Dsonar.sources=.
    - sleep 5
    - python3 /opt/code_quality_report/static_code_quality_report_csv_v2.py $CI_PROJECT_NAME $GITLAB_USER_EMAIL,$EMAIL_TO $EMAIL_FROM $EMAIL_PASSWD False admin admin
    only:
    - develop
    tags:
    - docker
    Dockerfile 0 → 100644
    View file @ ff9304ef
    FROM python:3.7-buster
    RUN apt-get update -y && \
    apt install -y openjdk-11-jre && \
    apt install -y libtiff5-dev libjpeg62-turbo-dev && \
    apt install -y zlib1g-dev libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev && \
    apt install -y tk8.6-dev python-tk pdftk libmagickwand-dev
    COPY . /code
    WORKDIR /code
    RUN pip install -r requirements.txt
    CMD [ "python","app.py" ]
    \ No newline at end of file
    README.md
    View file @ ff9304ef
    # Dalmia degradation calculation
    \ No newline at end of file
    # Dalmia degradation calculation module
    \ No newline at end of file
    app.py 0 → 100644
    View file @ ff9304ef
    if __name__ == "__main__":
    from dotenv import load_dotenv
    load_dotenv(dotenv_path='config.env')
    import pandas as pd
    import numpy as np
    import warnings
    from loguru import logger
    from scripts.utils.pycaret_util import PycaretUtil
    from scripts.core.engine.mppt_data import GetData
    from scripts.utils.reading_tags import GetTags
    from scripts.core.engine.tags_data import get_tags_data
    from scripts.utils.start_end_date import KairosStartEndDate
    from scripts.utils.preprocessing import DataPreprocessing
    from scripts.core.engine.inv_and_mppt_level import TrainingInference
    warnings.filterwarnings("ignore")
    base_path = 'data_folder'
    start_date, end_date, start_timestamp, end_timestamp = KairosStartEndDate().start_end_date()
    def get_tag_details():
    try:
    get_tags = GetTags(base_path=base_path)
    tags_excel = get_tags.read_tag_excel()
    mppt_tags = get_tags.get_mppt_tags(df=tags_excel, substrings='MPPT')
    df = get_tags_data(mppt_tags=mppt_tags,
    start_timestamp=start_timestamp,
    end_timestamp=end_timestamp)
    logger.info(f'Shape of final df - {df.shape}')
    mppt_data = GetData()
    df_mppt = mppt_data.current_voltage_mppt_data(df=df)
    data_preprocessing = DataPreprocessing()
    df_mppt = data_preprocessing.remove_outliers(df=df_mppt, param_list=['tilt_irradiance', 'voltage_mppt', 'current_mppt'])
    df_mppt, df_train, df_test = data_preprocessing.train_test_split(df=df_mppt)
    unique_inv_id = list(df_train.inv_id.unique())
    unique_mppt_id = list(df_train.mppt_id.unique())
    get_training_inference = TrainingInference(df=df_mppt, df_train=df_train, df_test=df_test)
    for inv_id in unique_inv_id:
    for mppt_id in unique_mppt_id:
    try:
    model, scaler_x, scaler_y = get_training_inference.data_training(inv_id=inv_id, mppt_id=mppt_id)
    x_test, y_test, predictions = get_training_inference.data_inference(scaler_x=scaler_x,
    scaler_y=scaler_y,
    model=model,
    inv_id=inv_id,
    mppt_id=mppt_id)
    df_result = mppt_data.get_final_data(x_test=x_test, y_test=y_test, predictions=predictions)
    logger.info(f'{df_result.shape}')
    except Exception as e:
    logger.exception(f'Exception - {e}')
    except Exception as e:
    logger.exception(f'Exception - {e}')
    if __name__ == '__main__':
    get_tag_details()
    catboost_info/catboost_training.json 0 → 100644
    View file @ ff9304ef
    {
    "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":1000,"learn_sets":["learn"],"name":"experiment"},
    "iterations":[
    {"learn":[0.2634930983],"iteration":0,"passed_time":0.001261185078,"remaining_time":1.259923893},
    {"learn":[0.253410861],"iteration":1,"passed_time":0.002399870698,"remaining_time":1.197535478},
    {"learn":[0.2438490327],"iteration":2,"passed_time":0.003549094013,"remaining_time":1.179482244},
    {"learn":[0.2348564906],"iteration":3,"passed_time":0.004699198435,"remaining_time":1.17010041},
    {"learn":[0.2262217852],"iteration":4,"passed_time":0.005826722539,"remaining_time":1.159517785},
    {"learn":[0.2179621507],"iteration":5,"passed_time":0.007036831895,"remaining_time":1.165768484},
    {"learn":[0.2099580794],"iteration":6,"passed_time":0.008081617729,"remaining_time":1.146435201},
    {"learn":[0.2027786939],"iteration":7,"passed_time":0.009227337612,"remaining_time":1.144189864},
    {"learn":[0.1957826204],"iteration":8,"passed_time":0.01029237146,"remaining_time":1.133304458},
    {"learn":[0.1892058504],"iteration":9,"passed_time":0.01138139661,"remaining_time":1.126758265},
    {"learn":[0.1827568156],"iteration":10,"passed_time":0.01255581581,"remaining_time":1.128881985},
    {"learn":[0.176781011],"iteration":11,"passed_time":0.01369832264,"remaining_time":1.127828564},
    {"learn":[0.1713163733],"iteration":12,"passed_time":0.0148479858,"remaining_time":1.127304768},
    {"learn":[0.1657838877],"iteration":13,"passed_time":0.01598568711,"remaining_time":1.125849107},
    {"learn":[0.1606202362],"iteration":14,"passed_time":0.01703558913,"remaining_time":1.118670353},
    {"learn":[0.1557479063],"iteration":15,"passed_time":0.01812180335,"remaining_time":1.114490906},
    {"learn":[0.1514837694],"iteration":16,"passed_time":0.0192531718,"remaining_time":1.113286346},
    {"learn":[0.1471266051],"iteration":17,"passed_time":0.02038810524,"remaining_time":1.112284408},
    {"learn":[0.1433408142],"iteration":18,"passed_time":0.02161529298,"remaining_time":1.116031706},
    {"learn":[0.139479269],"iteration":19,"passed_time":0.02264944468,"remaining_time":1.109822789},
    {"learn":[0.1358926662],"iteration":20,"passed_time":0.02378726378,"remaining_time":1.108939583},
    {"learn":[0.1323209643],"iteration":21,"passed_time":0.02486889952,"remaining_time":1.105535624},
    {"learn":[0.1289255371],"iteration":22,"passed_time":0.02592977979,"remaining_time":1.10145195},
    {"learn":[0.1258959934],"iteration":23,"passed_time":0.02704050059,"remaining_time":1.099647024},
    {"learn":[0.1230355649],"iteration":24,"passed_time":0.02818784319,"remaining_time":1.099325884},
    {"learn":[0.120546281],"iteration":25,"passed_time":0.02933229444,"remaining_time":1.098832876},
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    {"learn":[0.115822833],"iteration":27,"passed_time":0.03159418723,"remaining_time":1.096769643},
    {"learn":[0.1136284311],"iteration":28,"passed_time":0.03271287002,"remaining_time":1.095317131},
    {"learn":[0.111502889],"iteration":29,"passed_time":0.03385916142,"remaining_time":1.094779552},
    {"learn":[0.1094919865],"iteration":30,"passed_time":0.03501126541,"remaining_time":1.094384393},
    {"learn":[0.1076723822],"iteration":31,"passed_time":0.03610947492,"remaining_time":1.092311616},
    {"learn":[0.1060015476],"iteration":32,"passed_time":0.03716401734,"remaining_time":1.089018326},
    {"learn":[0.1043769365],"iteration":33,"passed_time":0.03831216988,"remaining_time":1.088516356},
    {"learn":[0.1028133138],"iteration":34,"passed_time":0.0394293392,"remaining_time":1.087123209},
    {"learn":[0.1014498222],"iteration":35,"passed_time":0.04059402743,"remaining_time":1.087017846},
    {"learn":[0.1000545332],"iteration":36,"passed_time":0.04173817264,"remaining_time":1.086320547},
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    catboost_info/time_left.tsv 0 → 100644
    View file @ ff9304ef
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    conf/application.conf 0 → 100644
    View file @ ff9304ef
    [LOGGING]
    level = $LOG_LEVEL
    traceback = $LOG_TRACEBACK
    [MODULE]
    name = $APP_NAME
    [KAIROS_DB]
    uri=$KAIROS_URI
    metric_name=$KAIROS_METRIC
    aggregator=$AGGREGATOR
    aggregator_value=$AGGREGATOR_VALUE
    aggregator_unit=$AGGREGATOR_UNIT
    [KAFKA]
    kafka_host=$KAFKA_HOST
    kafka_port=$KAFKA_PORT
    kafka_topic=$KAFKA_TOPIC
    [DATE_RANGE]
    start_date=$START_DATE
    end_date=$END_DATE
    start_relative_days=$START_RELATIVE
    end_relative_days=$END_RELATIVE
    [EMAIL_DETAILS]
    email_reciever=$RECIEVER_EMAIL
    email_sender=$SENDER_EMAIL
    [MONGO]
    mongo_uri=$MONGO_URI
    [TIMEZONE]
    required_tz=$REQUIRED_TZ
    [MLFLOW]
    mlflow_tracking_uri=$MLFLOW_TRACKING_URI
    mlflow_tracking_username=$MLFLOW_TRACKING_USERNAME
    mlflow_tracking_password=$MLFLOW_TRACKING_PASSWORD
    azure_storage_connection_string=$AZURE_STORAGE_CONNECTION_STRING
    azure_storage_access_key=$AZURE_STORAGE_ACCESS_KEY
    user=$USER
    experiment_name=$EXPERIMENT_NAME
    run_name=$RUN_NAME
    model_name=$MODEL_NAME
    check_param=$CHECK_PARAM
    model_check_param=$MODEL_CHECK_PARAM
    [PYCARET]
    models_list=$MODELS_LIST
    selected_metric=$SELECTED_METRIC
    hyperparameter_tuning_method=$HYPERPARAMETER_TUNING_METHOD
    conf/data.yml 0 → 100644
    View file @ ff9304ef
    inv_list:
    - inv_1
    - inv_2
    - inv_3
    - inv_4
    - inv_5
    - inv_6
    - inv_7
    - inv_8
    - inv_9
    - inv_10
    - inv_11
    - inv_12
    - inv_13
    - inv_14
    - inv_15
    - inv_16
    - inv_17
    - inv_18
    - inv_19
    - inv_20
    - inv_21
    - inv_22
    - inv_23
    - inv_24
    - inv_25
    - inv_26
    - inv_27
    - inv_28
    - inv_29
    - inv_30
    - inv_31
    - inv_32
    - inv_33
    - inv_34
    - inv_35
    - inv_36
    - inv_37
    - inv_38
    - inv_39
    - inv_40
    - inv_41
    - inv_42
    conf/engine.yml 0 → 100644
    View file @ ff9304ef
    tag_heirarcy:
    D1D001_consumptions_Opening_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6452
    source: calculated
    dependency:
    - D1D001_consumptions_Closing_DPR
    D1D001_consumptions_Day_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6456
    source: calculated
    dependency:
    - D1D001_consumptions_Opening_DPR
    - D1D001_consumptions_Day_Receipt_DPR
    - D1D001_consumptions_Day_Recovery_DPR
    - D1D001_consumptions_Closing_DPR
    7302011030_Consumptions_Opening_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6487
    source: calculated
    dependency:
    - 7302011030_Consumptions_Closing_DPR
    7302011030_Consumptions_Day_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6490
    source: calculated
    dependency:
    - D1D001_consumptions_Opening_DPR
    - D1D001_consumptions_Day_Receipt_DPR
    - D1D001_consumptions_Day_Recovery_DPR
    - D1D001_consumptions_Closing_DPR
    7302011030_Consumptions_Closing_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6491
    source: manual
    dependency:
    - None
    7302011061_Consumption_Opening_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6492
    source: calculated
    dependency:
    - 7302011061_Consumption_Closing_DPR
    Crude_Prod_Day_Prod_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4305$tag_6460
    source: calculated
    dependency:
    - D1D001_consumptions_Day_Cons_DPR
    - D1D001Readings_Conv_DPR
    Pure_Production_Opening_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6497
    source: calculated
    dependency:
    - Pure_Production_Closing_of_Pure_Tanks_only_DPR
    Pure_Production_Day_Prod_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6496
    source: calculated
    dependency:
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Power_Norms:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6470
    source: calculated
    dependency:
    - Utility_report_Day_Power
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Steam_Norms:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6471
    source: calculated
    dependency:
    - Utility_report_Day_Steam
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Raffinate_Norms:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6472
    source: calculated
    dependency:
    - Utility_report_Day_Raffinate
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Raffinate_Vent_Gas:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6473
    source: calculated
    dependency:
    - Utility_report_Vent_Gas_Raffinate
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Raw_Water_Norms:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6474
    source: calculated
    dependency:
    - Utility_report_Day_Treated_Water
    - Crude_Prod_Day_Prod_DPR
    Utility_report_per_hr_burn_rate:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6483
    source: calculated
    dependency:
    - Utility_report_Raffinate_Incinerated
    D1D001Readings_T_2703_A_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6447
    source: manual
    dependency: None
    D1D001Readings_T_2703_B_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6448
    source: manual
    dependency: None
    D1D001Readings_Conv_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6451
    source: manual
    dependency: None
    D1D001_consumptions_Day_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6453
    source: manual
    dependency: None
    D1D001_consumptions_Day_Recovery_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6454
    source: calculated_depends_previous_value
    dependency: None
    7302011030_Consumptions_Day_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6488
    source: manual
    dependency: None
    7302011061_Consumption_Day_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6493
    source: manual
    dependency: None
    7302011061_Consumption_Day_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6459
    source: manual
    dependency: None
    Pure_Production_Day_Nia_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6462
    source: manual
    dependency: None
    Pure_Production_Day_Drum_Filling_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6464
    source: manual
    dependency: None
    Pure_Production_Pure_tank_Dead_Volumes_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6469
    source: manual
    dependency: None
    Utility_report_Day_Power:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6475
    source: manual
    dependency: None
    Utility_report_Day_Steam:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6477
    source: manual
    dependency: None
    Utility_report_Day_Raffinate:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6478
    source: manual
    dependency: None
    Utility_report_Vent_Gas_Raffinate:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6479
    source: manual
    dependency: None
    Utility_report_Day_DM:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6480
    source: manual
    dependency: None
    Utility_report_Day_Treated_Water:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6481
    source: manual
    dependency: None
    Utility_report_Raffinate_Incinerated:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6482
    source: manual
    dependency: None
    D1D001Readings_T_2101_A_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6449
    source: dcs
    dependency: None
    D1D001Readings_T_2101_B_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6450
    source: dcs
    dependency: None
    Pure_Production_LT_2701_A_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6466
    source: dcs
    dependency: None
    Pure_Production_LT_2701_B_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6467
    source: dcs
    dependency: None
    D1D001_consumptions_Closing_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6457
    source: calculated
    dependency:
    - D1D001Readings_T_2703_A_DPR
    - D1D001Readings_T_2703_B_DPR
    - D1D001Readings_T_2101_A_DPR
    - D1D001Readings_T_2101_B_DPR
    D1D001_consumptions_Total_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6455
    source: calculated_depends_previous_value
    dependency:
    - D1D001_consumptions_Total_Receipt_DPR
    - D1D001_consumptions_Day_Receipt_DPR
    7302011030_Consumptions_Total_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6489
    source: calculated_depends_previous_value
    dependency:
    - 7302011030_Consumptions_Total_Receipt_DPR
    - 7302011030_Consumptions_Day_Receipt_DPR
    7302011030_Consumptions_Total_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6458
    source: calculated_depends_previous_value
    dependency:
    - 7302011030_Consumptions_Total_Cons_DPR
    - 7302011030_Consumptions_Day_Cons_DPR
    7302011061_Consumption_Closing_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6499
    source: calculated_depends_previous_value
    dependency:
    - 7302011061_Consumption_Opening_DPR
    - 7302011061_Consumption_Day_Receipt_DPR
    - 7302011061_Consumption_Day_Cons_DPR
    7302011061_Consumption_Total_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6495
    source: calculated_depends_previous_value
    dependency:
    - 7302011061_Consumption_Total_Receipt_DPR
    - 7302011030_Consumptions_Day_Receipt_DPR
    7302011061_Consumption_Total_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6498
    source: calculated_depends_previous_value
    dependency:
    - 7302011061_Consumption_Total_Cons_DPR
    - 7302011061_Consumption_Day_Cons_DPR
    Crude_Prod_Total_Prod_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4305$tag_6461
    source: calculated_depends_previous_value
    dependency:
    - Crude_Prod_Total_Prod_DPR
    - Crude_Prod_Day_Prod_DPR
    Pure_Production_Closing_of_Pure_Tanks_only_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6468
    source: calculated_depends_previous_value
    dependency:
    - Pure_Production_LT_2701_A_DPR
    - Pure_Production_LT_2701_B_DPR
    - Pure_Production_Pure_tank_Dead_Volumes_DPR
    Pure_Production_Total_Prod_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6494
    source: calculated_depends_previous_value
    dependency:
    - Pure_Production_Total_Prod_DPR
    - Pure_Production_Day_Prod_DPR
    Pure_Production_Total_Nia_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6463
    source: calculated_depends_previous_value
    dependency:
    - Pure_Production_Total_Nia_DPR
    - Pure_Production_Day_Nia_DPR
    Pure_Production_Total_Drum_Filling_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6465
    source: calculated_depends_previous_value
    dependency:
    - Pure_Production_Total_Drum_Filling_DPR
    - Pure_Production_Day_Drum_Filling_DPR
    Utility_report_Actual_Ammonia_Norms:
    column_tag: site_116$dept_134$line_350$equipment_4179$tag_6486
    source: calculated
    dependency:
    - 7302011030_Consumptions_Day_Cons_DPR
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Actual_Beta_Norms:
    column_tag: site_116$dept_134$line_350$equipment_4179$tag_6724
    source: calculated
    dependency:
    - D1D001_consumptions_Day_Cons_DPR
    - D1D001_consumptions_Day_Recovery_DPR
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Actual_Benzene_Norms:
    column_tag: site_116$dept_134$line_350$equipment_4179$tag_6725
    source: calculated
    dependency:
    - 7302011061_Consumption_Day_Cons_DPR
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Day_DM_norm:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6915
    source: calculated
    dependency: Utility_report_Day_DM
    Beta_Purification_Column_C_2409_Outlet_Flow_TZ:
    column_tag: site_116$dept_134$line_351$equipment_4209$tag_5372
    source: manual
    dependency: None
    config.env 0 → 100644
    View file @ ff9304ef
    APP_NAME=dalmia-solar-degradation
    KAIROS_URI=https://iLens:iLensDAL$456@dalmia.ilens.io/kairos/
    KAIROS_METRIC=ilens.live_data.raw
    AGGREGATOR=max
    AGGREGATOR_VALUE=30
    AGGREGATOR_UNIT=minutes
    KAFKA_HOST=192.168.0.220
    KAFKA_PORT=9092
    KAFKA_TOPIC=ilens_dev
    START_RELATIVE=90
    END_RELATIVE=0
    SENDER_EMAIL=no-reply@unifytwin.com
    MONGO_URI=mongodb://ilens:ilens4321@192.168.0.220:2717/
    REQUIRED_TZ="Asia/Kolkata"
    MLFLOW_TRACKING_URI=https://qa.unifytwin.com/mlflow/
    MLFLOW_TRACKING_USERNAME=mlflow
    MLFLOW_TRACKING_PASSWORD=MlFlOwQA#4321
    AZURE_STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=https;AccountName=azrmlilensqa006382180551;AccountKey=tDGOKfiZ2svfoMvVmS0Fbpf0FTHfTq4wKYuDX7cAxlhve/3991QuzdvJHm9vWc+lo6mtC+x9yPSghWNR4+gacg==;EndpointSuffix=core.windows.net
    AZURE_STORAGE_ACCESS_KEY=tDGOKfiZ2svfoMvVmS0Fbpf0FTHfTq4wKYuDX7cAxlhve/3991QuzdvJHm9vWc+lo6mtC+x9yPSghWNR4+gacg==
    USER=Dalmia_degradation
    EXPERIMENT_NAME=Dalmia Solar Degradation1
    RUN_NAME=Degradation
    MODEL_NAME=versioning
    CHECK_PARAM=hours
    MODEL_CHECK_PARAM=144
    MODELS_LIST=lr,knn,gbr,rf,catboost,lightgbm,ada,et,xgboost,dt,en,par,huber
    SELECTED_METRIC=R2
    HYPERPARAMETER_TUNING_METHOD=scikit-learn
    \ No newline at end of file
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    scripts/constants/app_configuration.py 0 → 100644
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    if __name__ == "__main__":
    from dotenv import load_dotenv
    load_dotenv(dotenv_path='config.env')
    import os
    import os.path
    import sys
    from configparser import ConfigParser, BasicInterpolation
    import yaml
    from loguru import logger
    # Configuring file constants
    data_conf = "./conf/data.yml"
    class EnvInterpolation(BasicInterpolation):
    """
    Interpolation which expands environment variables in values.
    """
    def before_get(self, parser, section, option, value, defaults):
    value = super().before_get(parser, section, option, value, defaults)
    if not os.path.expandvars(value).startswith("$"):
    return os.path.expandvars(value)
    else:
    return
    try:
    config = ConfigParser(interpolation=EnvInterpolation())
    config.read(f"conf/application.conf")
    except Exception as e:
    print(f"Error while loading the config: {e}")
    print("Failed to Load Configuration. Exiting!!!")
    sys.exit()
    class Mongo:
    mongo_uri = config["MONGO"]["mongo_uri"]
    class KairosDb:
    uri = config["KAIROS_DB"]["uri"]
    metric_name = config['KAIROS_DB']['metric_name']
    aggregator = config['KAIROS_DB']['aggregator']
    aggregator_value = config['KAIROS_DB']['aggregator_value']
    aggregator_unit = config['KAIROS_DB']['aggregator_unit']
    class Kafka:
    kafka_host = config["KAFKA"]["kafka_host"]
    kafka_port = config["KAFKA"]["kafka_port"]
    kafka_topic = config["KAFKA"]["kafka_topic"]
    class DateRange:
    start_date = config.get("DATE_RANGE", "start_date")
    end_date = config.get("DATE_RANGE", "end_date")
    start_relative_days = config.get("DATE_RANGE", "start_relative_days")
    end_relative_days = config.get("DATE_RANGE", "end_relative_days")
    class ReqTimeZone:
    required_tz = config.get('TIMEZONE', 'required_tz')
    class MlFlow:
    mlflow_tracking_uri = config['MLFLOW']['mlflow_tracking_uri']
    mlflow_tracking_username = config['MLFLOW']['mlflow_tracking_username']
    mlflow_tracking_password = config['MLFLOW']['mlflow_tracking_password']
    azure_storage_connection_string = config['MLFLOW']['azure_storage_connection_string']
    azure_storage_access_key = config['MLFLOW']['azure_storage_access_key']
    user = config['MLFLOW']['user']
    experiment_name = config['MLFLOW']['experiment_name']
    run_name = config['MLFLOW']['run_name']
    model_name = config['MLFLOW']['model_name']
    check_param = config['MLFLOW']['check_param']
    model_check_param = config['MLFLOW']['model_check_param']
    class PycaretParams:
    model_list = config['PYCARET']['models_list']
    selected_metric = config['PYCARET']['selected_metric']
    hyperparameter_tuning_method= config['PYCARET']['hyperparameter_tuning_method']
    scripts/constants/app_constants.py 0 → 100644
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    json_file_path = "scripts/utils/"
    class DBConstants:
    # DB
    db_metadata = "ilens_configuration"
    # collections
    collection_rule_targets = "rule_targets"
    yield_sheet_name = "yield_reports_3cp"
    \ No newline at end of file
    scripts/core/analytics_notebook/Untitled.ipynb 0 → 100644
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    scripts/core/analytics_notebook/catboost_info/catboost_training.json 0 → 100644
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    {
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    scripts/core/analytics_notebook/catboost_info/learn_error.tsv 0 → 100644
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    scripts/core/analytics_notebook/catboost_info/time_left.tsv 0 → 100644
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    scripts/core/analytics_notebook/current_analytics.ipynb 0 → 100644
    View file @ ff9304ef
    This source diff could not be displayed because it is too large. You can view the blob instead.
    scripts/core/analytics_notebook/tags_configuration.ipynb 0 → 100644
    View file @ ff9304ef
    This source diff could not be displayed because it is too large. You can view the blob instead.
    scripts/core/analytics_notebook/voltage_analytics.ipynb 0 → 100644
    View file @ ff9304ef
    This source diff could not be displayed because it is too large. You can view the blob instead.
    scripts/core/data_puller_push/calculated_data_push.py 0 → 100644
    View file @ ff9304ef
    from loguru import logger
    from scripts.core.data_puller_push.push_data import insert_values_3cp
    class CalculatedDataPush:
    def __init__(self, df_calculated, all_cal_tags_dict):
    self.df_calculated = df_calculated
    self.all_cal_tags_dict = all_cal_tags_dict
    def kafka_data_push(self, df_calculated):
    try:
    logger.info(f"Calculated dict length = {len(self.all_cal_tags_dict)}")
    logger.info(f"Calculated df shape with Date and Timestamp column = {df_calculated.shape}")
    df_calculated_tags_dict = {col: self.all_cal_tags_dict[col] for col in df_calculated.columns
    if col not in ('Date', 'timestamp')}
    for i, j in df_calculated.iterrows():
    my_dict = {v: j[k] for k, v in df_calculated_tags_dict.items()}
    logger.info(f"{j['timestamp'], j['Date'], my_dict}")
    insert_values_3cp(j['timestamp'], my_dict)
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/core/data_puller_push/data_puller.py 0 → 100644
    View file @ ff9304ef
    import json
    import pandas as pd
    import requests
    from loguru import logger
    from scripts.constants.app_configuration import KairosDb
    class KairosQuery:
    def __init__(self, start_timestamp, end_timestamp, tag_dict):
    self.start_timestamp = start_timestamp
    self.end_timestamp = end_timestamp
    self.kairos_host = KairosDb.uri
    self.kairos_url = "{kairos_host}/api/v1/datapoints/query".format(kairos_host=self.kairos_host)
    self.tag_dict = tag_dict
    def kairos_query(self):
    try:
    return {
    "metrics": [
    {
    "tags": {
    "c3": list(self.tag_dict.keys())
    },
    "name": KairosDb.metric_name,
    "group_by": [
    {
    "name": "tag",
    "tags": ["c3"]
    }
    ],
    "aggregators": [
    {
    "name": KairosDb.aggregator,
    "sampling": {
    "value": KairosDb.aggregator_value,
    "unit": KairosDb.aggregator_unit
    }
    }
    ]
    }
    ],
    "plugins": [],
    "cache_time": 0,
    "time_zone": "Asia/Calcutta",
    "start_absolute": self.start_timestamp,
    "end_absolute": self.end_timestamp,
    }
    except Exception as e:
    logger.exception(f"Exception - {e}")
    def get_data(self, data_query):
    try:
    logger.info("Data for the parameters being pulled from Kairos Database")
    response = requests.post(self.kairos_url, data=json.dumps(data_query))
    response_data = response.json()
    output_data = response_data["queries"]
    logger.debug("Data pull complete")
    df_final = pd.DataFrame()
    for i in range(len(output_data)):
    grouped_output_data = output_data[i]["results"]
    for each_grouped_data in grouped_output_data:
    value = (each_grouped_data["values"])
    tag_id = each_grouped_data["group_by"][0]["group"]["c3"]
    try:
    logger.debug("Renamed {} to {} in Data".format(tag_id, self.tag_dict[tag_id]))
    column_name = self.tag_dict[tag_id]
    except KeyError as e:
    logger.exception(f'Exception - {e}')
    logger.debug("Column Renaming Logic not found for {}".format(tag_id))
    column_name = tag_id
    df_column_data = pd.DataFrame(data=value, columns=["timestamp", column_name])
    if df_final.empty:
    df_final = df_column_data
    else:
    df_final = df_final.merge(df_column_data, how="outer", left_on="timestamp",
    right_on="timestamp")
    df_final['datetime'] = pd.to_datetime(df_final['timestamp'], unit="ms").dt.tz_localize('UTC').\
    dt.tz_convert('Asia/Kolkata')
    logger.debug("Final number of columns : {}".format(str(len(list(df_final.columns)))))
    return df_final
    except Exception as e:
    logger.exception(f"Exception occurred - {e}", exc_info=True)
    def kairos_data_import(self):
    try:
    logger.debug("Fetching live data")
    query_live = self.kairos_query()
    logger.info(f"query_live = {query_live}")
    df = self.get_data(data_query=query_live)
    return df
    except Exception as e:
    logger.exception(f"Exception - {e}")
    scripts/core/data_puller_push/push_data.py 0 → 100644
    View file @ ff9304ef
    from json import dumps
    from kafka import KafkaProducer
    from loguru import logger
    from scripts.constants.app_configuration import Kafka
    def insert_values_3cp(ts, my_dict):
    kairos_writer = KairosWriter()
    kairos_writer.write_data(
    {
    ts: my_dict
    },
    Kafka.kafka_topic
    )
    logger.info("Data pushed successfully!")
    class KafkaProducerUtil:
    def __init__(self):
    try:
    self.host = Kafka.kafka_host
    self.port = Kafka.kafka_port
    logger.debug(f"Connecting to Kafka with details: {self.host}, {self.port}")
    kafka_broker = [self.host + ":" + str(self.port)]
    self.producer = KafkaProducer(
    bootstrap_servers=kafka_broker,
    value_serializer=lambda v: v.encode('utf-8'),
    api_version=(0, 10, 1))
    self.producer.flush()
    except Exception as e:
    logger.error(f"Kafka connection error: {e}")
    def publish(self, topic, data):
    try:
    kafka_response = self.producer.send(topic, data)
    self.producer.flush()
    logger.debug(f" Message sent to kafka with response: {kafka_response}")
    return True
    except Exception as e:
    logger.error(e)
    return False
    class KairosWriter(KafkaProducerUtil):
    def write_data(self, data_json, topic):
    site_id = "site_116"
    logger.debug(f"Data being pushed to kafka topic: {topic}")
    msg_counter = 0
    for k, v in data_json.items():
    timestamp, data = self.data_validator(k, v)
    timestamp = timestamp * 1000
    write_json = {
    "data": data,
    "site_id": site_id,
    "gw_id": "gw_{}".format(site_id.lstrip("site_")), # The lstrip(s) removes leading whitespace (on the left)
    "pd_id": "pd_{}".format(site_id.lstrip("site_")), # The rstrip(s) removes the trailing whitespace (on the right)
    "timestamp": timestamp,
    "msg_id": msg_counter,
    "partition": "",
    "retain_flag": False
    }
    logger.debug(f"Timestamp: {timestamp}, Values: {data}")
    self.publish(topic, dumps(write_json))
    msg_counter += 1
    return msg_counter
    def audit_data(self, data_json, topic):
    logger.debug(f"Audit Data being pushed to kafka topic: {topic}")
    msg_counter = len(data_json)
    for each in data_json:
    self.publish(topic, dumps(each))
    return msg_counter
    @staticmethod
    def data_validator(timestamp, data):
    logger.debug("Validating the data to remove Nan values")
    __temp__ = {}
    for k, v in data.items():
    if not k.startswith("site"):
    continue
    if isinstance(v, (int, float)) and str(v) not in ('nan', 'inf'): # This function will return True if the "v" is one of the types in the tuple.
    __temp__[k] = v
    return int(timestamp), __temp__
    scripts/core/engine/inv_and_mppt_level.py 0 → 100644
    View file @ ff9304ef
    import pandas as pd
    import numpy as np
    from loguru import logger
    from scripts.utils.pycaret_util import PycaretUtil
    from scripts.utils.preprocessing import DataPreprocessing
    from scripts.utils.mlflow_util import ModelLoad
    class TrainingInference:
    def __init__(self, df, df_train, df_test):
    self.df = df
    self.df_train = df_train
    self.df_test = df_test
    def data_training(self, inv_id, mppt_id):
    try:
    data_preprocessing = DataPreprocessing()
    df_train_inv = self.df_train[self.df_train['inv_id'] == inv_id]
    df_train_mppt = df_train_inv[df_train_inv['mppt_id'] == mppt_id]
    x_train = df_train_mppt[['datetime', 'inv_id', 'mppt_id', 'hour', 'tilt_irradiance', 'voltage_mppt']]
    y_train = df_train_mppt[['current_mppt']]
    x_train_std, scaler_x = data_preprocessing.get_standardized_data(df=x_train,
    param_list=['datetime', 'inv_id',
    'mppt_id'])
    y_train_std, scaler_y = data_preprocessing.get_standardized_data(df=y_train)
    df_std = pd.concat([x_train_std, y_train_std], axis=1)
    df_std.dropna(axis=0, inplace=True)
    df_std.reset_index(drop=True, inplace=True)
    model, pre_trained = ModelLoad().model_manager(df=df_std, target='current_mppt')
    return model, scaler_x, scaler_y
    except Exception as e:
    logger.exception(f'Exception - {e}')
    def data_inference(self, scaler_x, scaler_y, model, inv_id, mppt_id):
    try:
    df_test_inv = self.df_test[self.df_test['inv_id'] == inv_id]
    df_test_mppt = df_test_inv[df_test_inv['mppt_id'] == mppt_id]
    df_test_mppt.reset_index(drop=True, inplace=True)
    x_test = df_test_mppt[['datetime', 'inv_id', 'mppt_id', 'hour', 'tilt_irradiance', 'voltage_mppt']]
    y_test = df_test_mppt[['current_mppt']]
    data_preprocessing = DataPreprocessing()
    x_test_std = data_preprocessing.get_transform_std_data(df=x_test,
    param_list=['datetime', 'inv_id', 'mppt_id'],
    scaler=scaler_x)
    y_test_std = data_preprocessing.get_transform_std_data(df=y_test, scaler=scaler_y)
    predictions = model.predict(x_test_std).reshape(1, -1)
    predictions = np.array(scaler_y.inverse_transform(predictions)).reshape(-1, 1)
    y_test = scaler_y.inverse_transform(y_test_std)
    return x_test, y_test, predictions
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/core/engine/mppt_data.py 0 → 100644
    View file @ ff9304ef
    import pandas as pd
    from loguru import logger
    class GetData:
    @staticmethod
    def current_voltage_mppt_data(df):
    try:
    current_column_list = [col_name for col_name in df if 'current' in col_name]
    voltage_column_list = [col_name for col_name in df if 'voltage' in col_name]
    current_column_list.sort()
    voltage_column_list.sort()
    df_mppt = pd.DataFrame()
    for n_mppt in range(len(current_column_list)):
    df_temp = df[['datetime', 'inv_id', 'date', 'hour', 'tilt_irradiance', voltage_column_list[n_mppt],
    current_column_list[n_mppt]]]
    df_temp['mppt_id'] = current_column_list[n_mppt]
    df_temp['mppt_id'] = df_temp['mppt_id'].str.replace('current_', '')
    df_temp.rename(columns={voltage_column_list[n_mppt]: 'voltage_mppt',
    current_column_list[n_mppt]: 'current_mppt'}, inplace=True)
    if df_mppt.empty:
    df_mppt = df_temp
    else:
    df_mppt = pd.concat([df_mppt, df_temp], axis=0)
    df_mppt.reset_index(drop=True, inplace=True)
    df_mppt.sort_values(['inv_id', 'mppt_id'], inplace=True)
    df_mppt.reset_index(drop=True, inplace=True)
    return df_mppt
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_final_data(x_test, y_test, predictions):
    try:
    df_result = pd.DataFrame(index=[i for i in range(len(y_test))])
    df_result['datetime'] = x_test['datetime']
    df_result['actual_current_mppt'] = y_test
    df_result['predicted_current_mppt'] = predictions
    df_result['potential_current_mppt_loss'] = df_result['predicted_current_mppt'] - \
    df_result['actual_current_mppt']
    df_result.loc[df_result['potential_current_mppt_loss'] < 0, 'potential_current_mppt_loss'] = 0
    df_result['hour'] = df_result['datetime'].dt.hour
    df_result.loc[df_result['hour'].between(left=19, right=23, inclusive='both'),
    'potential_current_mppt_loss'] = 0
    df_result.loc[df_result['hour'].between(left=0, right=5, inclusive='both'),
    'potential_current_mppt_loss'] = 0
    df_result.drop(['hour'], axis=1, inplace=True)
    df_result['total_potential_current_mppt_loss'] = df_result['potential_current_mppt_loss']. \
    rolling(min_periods=1, window=len(df_result)).sum()
    df_result.drop(['actual_current_mppt', 'predicted_current_mppt'], axis=1, inplace=True)
    df_result = df_result.round(2)
    df_result.reset_index(drop=True, inplace=True)
    logger.info(f'{df_result.shape}')
    return df_result
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/core/engine/tags_data.py 0 → 100644
    View file @ ff9304ef
    import pandas as pd
    from loguru import logger
    from scripts.core.data_puller_push.data_puller import KairosQuery
    from scripts.utils.reading_tags import GetTags
    base_path = 'data_folder'
    def get_tags_data(mppt_tags, start_timestamp, end_timestamp):
    try:
    get_tags = GetTags(base_path=base_path)
    df_merged = pd.DataFrame()
    for inv_id in list(mppt_tags['inv_id'].unique()):
    df_tags_id = get_tags.get_tags_id(df=mppt_tags, inv_id=inv_id)
    tags_dict = df_tags_id[['tag_id', 'parameter_name']].set_index('tag_id').T.to_dict(orient="records")[0]
    tags_dict['site_107$dept_140$line_371$equipment_4115$tag_15828'] = 'tilt_irradiance'
    df_data = KairosQuery(start_timestamp=start_timestamp,
    end_timestamp=end_timestamp,
    tag_dict=tags_dict).kairos_data_import()
    df_data['inv_id'] = inv_id
    if df_merged.empty:
    df_merged = df_data
    else:
    df_merged = pd.concat([df_merged, df_data], axis=0)
    logger.info(f' for inv- {inv_id}, {df_data.shape}')
    logger.info(f'{df_merged.shape}')
    df_merged['date'] = df_merged['datetime'].dt.date
    df_merged['hour'] = df_merged['datetime'].dt.hour
    logger.info(f'Final shape of merged dataframe = {df_merged.shape}')
    df_merged.reset_index(drop=True, inplace=True)
    # df_merged.to_csv(f'{base_path}/df_merged.csv', index=False)
    return df_merged
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/utils/mlflow_util.py 0 → 100644
    View file @ ff9304ef
    import mlflow
    from loguru import logger
    import pandas as pd
    import re
    import os
    import pytz
    from datetime import datetime
    from scripts.constants.app_configuration import MlFlow, ReqTimeZone
    from scripts.utils.pycaret_util import PycaretUtil
    mlflow_tracking_uri = MlFlow.mlflow_tracking_uri
    os.environ["MLFLOW_TRACKING_USERNAME"] = MlFlow.mlflow_tracking_username
    os.environ["MLFLOW_TRACKING_PASSWORD"] = MlFlow.mlflow_tracking_password
    os.environ["AZURE_STORAGE_CONNECTION_STRING"] = MlFlow.azure_storage_connection_string
    os.environ["AZURE_STORAGE_ACCESS_KEY"] = MlFlow.azure_storage_access_key
    mlflow.set_tracking_uri(mlflow_tracking_uri)
    mlflow.set_registry_uri(mlflow_tracking_uri)
    client = mlflow.tracking.MlflowClient()
    class ModelLoad(object):
    def model_manager(self, df, target):
    try:
    experiment_id = self.create_experiment(experiment_name=MlFlow.experiment_name)
    days, latest_run_id = self.fetch_latest_model(experiment_id=experiment_id,
    run_name=MlFlow.run_name)
    if days < int(MlFlow.model_check_param):
    logger.debug(f'Using the pretrained model !')
    energy_model = self.load_model_pyfunc(
    model_path=self.forming_loading_path(latest_run_id=latest_run_id))
    pre_trained = True
    else:
    pre_trained = False
    run_id = self.creating_run(experiment_id=experiment_id, run_name=MlFlow.run_name)
    with mlflow.start_run(run_id=run_id):
    nested_run_id = self.creating_new_nested_run(experiment_id=experiment_id,
    run_id=run_id,
    nested=True)
    with mlflow.start_run(run_id=nested_run_id, nested=True):
    logger.debug(f'Creating the new model !')
    energy_model, model_name, metrics, hyper_params = \
    PycaretUtil().get_auto_ml_model(df=df, target=target)
    self.log_model(model=energy_model, model_name=MlFlow.model_name)
    self.log_metrics(metrics=metrics)
    self.log_hyper_param(hyperparameters=hyper_params)
    self.set_tag(run_id=nested_run_id, key="algorithm", value=model_name)
    return energy_model, pre_trained
    except Exception as e:
    logger.exception(str(e))
    @staticmethod
    def create_experiment(experiment_name):
    """
    Function is to create an experiment by passing experiment name
    :param experiment_name: Name of the experiment
    :return: Experiment id, Run id if any parent run is existing
    """
    try:
    experiment = mlflow.get_experiment_by_name(experiment_name)
    if experiment:
    exp_id = experiment.experiment_id
    else:
    mlflow.set_experiment(experiment_name)
    experiment = mlflow.get_experiment_by_name(experiment_name)
    exp_id = experiment.experiment_id
    return exp_id
    except Exception as e:
    logger.exception(str(e))
    @staticmethod
    def creating_run(experiment_id, run_id=None, run_name=None, nested=False):
    try:
    latest_run_id = None
    if run_id:
    df = mlflow.search_runs([experiment_id])
    run_id_list = list(df["run_id"])
    if run_id in run_id_list:
    return run_id
    else:
    run = client.create_run(experiment_id)
    with mlflow.start_run(
    experiment_id=experiment_id, run_name=run_name, run_id=run.info.run_id,
    nested=nested) as run:
    return run.info.run_id
    elif run_name:
    df = mlflow.search_runs([experiment_id])
    if df.empty:
    run = client.create_run(experiment_id=experiment_id, tags={"mlflow.runName": run_name,
    "mlflow.user": MlFlow.user})
    with mlflow.start_run(
    experiment_id=experiment_id, run_id=run.info.run_id, run_name=run_name,
    nested=nested) as run:
    return run.info.run_id
    else:
    for index, row in df.iterrows():
    if run_name == row.get("tags.mlflow.runName", ""):
    latest_run_id = row.get("run_id")
    if latest_run_id:
    return latest_run_id
    else:
    run = client.create_run(experiment_id=experiment_id, tags={"mlflow.runName": run_name,
    "mlflow.user": MlFlow.user})
    with mlflow.start_run(
    experiment_id=experiment_id, run_id=run.info.run_id, run_name=run_name,
    nested=nested) as run:
    return run.info.run_id
    except Exception as e:
    logger.exception(str(e))
    @staticmethod
    def creating_new_nested_run(experiment_id, run_id=None, nested=False):
    """
    Function is to create a nested run
    :param experiment_id: Experiment Id
    :param run_id: run id
    :param nested: nested Run
    :return : return nested run id
    """
    try:
    with mlflow.start_run(experiment_id=experiment_id, run_id=run_id, nested=nested):
    with mlflow.start_run(experiment_id=experiment_id, nested=True) as run:
    return run.info.run_id
    except Exception as e:
    logger.exception(str(e))
    @staticmethod
    def log_model(model, model_name):
    """
    Function is to log the model
    :param model : model
    :param model_name : model_name
    :return: Boolean Value
    """
    try:
    mlflow.sklearn.log_model(model, model_name)
    logger.info("logged the model")
    return True
    except Exception as e:
    logger.exception(str(e))
    @staticmethod
    def log_metrics(metrics):
    """
    Function is to log the metrics
    :param metrics: dict of metrics
    :return: Boolen value
    """
    try:
    updated_metric = dict()
    for key, value in metrics.items():
    key = re.sub(r"[\([{})\]]", "", key)
    updated_metric[key] = value
    mlflow.log_metrics(updated_metric)
    logger.debug(f'logged the model metric')
    return True
    except Exception as e:
    logger.exception(str(e))
    @staticmethod
    def log_hyper_param(hyperparameters):
    """
    Function is to log the hyper params
    :param hyperparameters: dict of hyperparameters
    :return: Boolen value
    """
    try:
    mlflow.log_params(hyperparameters)
    logger.debug(f'logged model hyper parameters')
    return True
    except Exception as e:
    logger.exception(str(e))
    def fetch_latest_model(self, experiment_id, run_name):
    """
    Function is to fetch the latest run
    :param experiment_id: Experiment Id
    :return: return the difference in the days/Hours/Minutes of current and run time, latest run id
    """
    try:
    days = int(MlFlow.model_check_param) + 1
    model_history = ""
    latest_run_id = ""
    if experiment_id:
    run_id = self.get_parent_run_id(experiment_id, run_name)
    run_info = mlflow.search_runs([experiment_id],
    filter_string="tags.mlflow.parentRunId='{run_id}'".format(
    run_id=run_id))
    if not run_info.empty:
    for ind in run_info.index:
    model_history, days, latest_run_id = self.check_model_existing(run_info=run_info,
    index=ind)
    if model_history is not None:
    break
    if model_history is None:
    days = int(MlFlow.model_check_param) + 1
    logger.info("No Model is existing with this experiment")
    return days, latest_run_id
    except Exception as e:
    logger.exception(f"Exception while fetching the latest model - {e}")
    @staticmethod
    def get_parent_run_id(experiment_id, run_name):
    """
    Function is to fetch latest parent run id if available else latest run id
    :param experiment_id: Experiment Id
    :param run_name: Name of the run
    :return: latest parent run id
    """
    try:
    result_run_id = None
    df = mlflow.search_runs([experiment_id])
    for index, row in df.iterrows():
    parent_run_name = row.get("tags.mlflow.runName")
    if parent_run_name == run_name:
    result_run_id = row.get("run_id")
    else:
    logger.info(f"No Run is existing with this Experiment id - {experiment_id}")
    return result_run_id
    except Exception as e:
    logger.exception(f"Exception while fetching the latest run_id - {e}")
    def check_model_existing(self, run_info, index):
    """
    Function is to check if model is existing or not
    :param run_info: Dataframe of run details
    :param index: index of which run from the dataframe
    :return:
    """
    try:
    model_history = None
    date_param = MlFlow.check_param
    # Difference between the current date and latest available model date
    days = self.format_mlflow_time(run_info=run_info, index=index, date_param=date_param)
    latest_run_id = run_info.loc[index, 'run_id']
    if 'tags.mlflow.log-model.history' in run_info:
    model_history = run_info['tags.mlflow.log-model.history'][index]
    if model_history:
    model_history_list = model_history.split(":")
    model_history = model_history_list[2].split(",")[0]
    else:
    logger.info("No Model is existing")
    return model_history, days, latest_run_id
    except Exception as e:
    logger.exception(f"Exception while checking the model name - {e}")
    @staticmethod
    def forming_loading_path(latest_run_id):
    """
    Function is to form the loading path
    :param latest_run_id: Run id
    :return : Return the loading path
    """
    try:
    model_name = MlFlow.model_name
    model_path = f"runs:/{latest_run_id}/{model_name}"
    return model_path
    except Exception as e:
    logger.exception(f"Exception while forming loading path - {e}")
    @staticmethod
    def format_mlflow_time(run_info, index, date_param):
    """
    Formatting mlflow time
    :param run_info: details of the runs
    :param index: index of the run in the dataframe
    :param: What type of the date param
    :return: calculate the time difference between the mlflow time and the current time zone
    """
    try:
    df_time = run_info.copy()
    df_time['end_time'] = pd.to_datetime(df_time['end_time']).dt.tz_convert(ReqTimeZone.required_tz)
    df_time["days"] = df_time['end_time'].dt.date
    df_time["hours"] = df_time['end_time'].dt.hour
    df_required = df_time.iloc[index:index + 1:, :]
    df_required.reset_index(drop=True, inplace=True)
    last_model_time = df_required['end_time'][0].to_pydatetime()
    central_current = datetime.now(pytz.utc).astimezone(pytz.timezone(ReqTimeZone.required_tz))
    time_diff = central_current - last_model_time
    if date_param.lower() == "days":
    days_diff = int(time_diff.days)
    return days_diff
    elif date_param.lower() == "hours":
    hours_diff = int(time_diff.total_seconds() // 3600)
    return hours_diff
    elif date_param.lower() == "minutes":
    minutes_diff = int(time_diff.total_seconds() // 60)
    return minutes_diff
    else:
    logger.info("No Valid Date format was given")
    except Exception as e:
    logger.exception(f"Exception while Loading the model - {e}")
    @staticmethod
    def set_tag(run_id, key, value):
    """
    Function is to set the tag for a particular run
    :param run_id: Run id in which the tags need to be added
    :param key: Name of the key
    :param value: what needs to tagged in the value
    """
    try:
    client.set_tag(run_id=run_id, key=key, value=value)
    logger.debug(f'set the tag for the model')
    except Exception as e:
    logger.exception(f"Exception while setting the tag - {e}")
    @staticmethod
    def load_model_pyfunc(model_path):
    """
    Function is load the sklearn model
    :param model_path: path of the model
    :return: boolen value
    """
    try:
    model = mlflow.pyfunc.load_model(model_path)
    logger.info("loading the model")
    return model
    except Exception as e:
    logger.exception(str(e))
    scripts/utils/preprocessing.py 0 → 100644
    View file @ ff9304ef
    import pandas as pd
    from loguru import logger
    import pytz
    from datetime import datetime, timedelta
    from scripts.constants.app_configuration import ReqTimeZone
    from sklearn.preprocessing import MinMaxScaler
    class DataPreprocessing:
    @staticmethod
    def remove_outliers(df, param_list):
    try:
    for col in param_list:
    lb = df[col].mean() - 3 * df[col].std()
    ub = df[col].mean() + 3 * df[col].std()
    logger.debug(f"Min values of {col} = {df[col].min()} \nLower Bracket of {col} = {lb}")
    logger.debug(f"Max values of {col} = {df[col].max()} \nUpper Bracket of {col} = {ub}")
    logger.debug(f'Shape of df before outlier removal = {df.shape}')
    df = (df[(df[col] > lb) & (df[col] < ub)])
    logger.debug(f'Shape of df after outlier removal = {df.shape}')
    logger.debug(f'Shape final df before outlier removal = {df.shape}')
    return df
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def train_test_split(df):
    try:
    today_date = datetime.now(pytz.utc).astimezone(pytz.timezone(ReqTimeZone.required_tz)).date()
    df = df[df['date'] != today_date]
    yesterday_date = today_date - timedelta(days=1)
    df_train = df[df['date'] < yesterday_date]
    df_test = df[df['date'] == yesterday_date]
    df_train.reset_index(drop=True, inplace=True)
    df_test.reset_index(drop=True, inplace=True)
    df_train.drop(['date'], axis=1, inplace=True)
    df_test.drop(['date'], axis=1, inplace=True)
    return df, df_train, df_test
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_standardized_data(df, param_list=None):
    try:
    if param_list is None:
    scaler = MinMaxScaler()
    df_std = pd.DataFrame(scaler.fit_transform(df), columns=list(df.columns))
    return df_std, scaler
    else:
    scaler = MinMaxScaler()
    df_std = pd.DataFrame(scaler.fit_transform(df.drop(param_list, axis=1)),
    columns=list(df.drop(param_list, axis=1).columns))
    return df_std, scaler
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_transform_std_data(df, scaler, param_list=None):
    try:
    if param_list is None:
    df_std = pd.DataFrame(scaler.transform(df), columns=list(df.columns))
    return df_std
    else:
    df_std = pd.DataFrame(scaler.transform(df.drop(param_list, axis=1)),
    columns=list(df.drop(param_list, axis=1).columns))
    return df_std
    except Exception as e:
    logger.exception(f'Exception - {e}')
    \ No newline at end of file
    scripts/utils/pycaret_util.py 0 → 100644
    View file @ ff9304ef
    from loguru import logger
    from pycaret import regression
    from scripts.constants.app_configuration import MlFlow, PycaretParams
    class PycaretUtil:
    def __init__(self):
    self.model_list = PycaretParams.model_list.split(",")
    self.selected_metric = PycaretParams.selected_metric
    self.hyperparameter_tuning_method = PycaretParams.hyperparameter_tuning_method
    def get_best_model(self, df, target):
    try:
    regression.setup(data=df, target=target)
    best_model = regression.compare_models(include=self.model_list, sort=self.selected_metric,
    n_select=1)
    tuned_model = regression.tune_model(best_model, optimize=self.selected_metric,
    search_library=self.hyperparameter_tuning_method)
    results = regression.pull()
    results.sort_values(self.selected_metric, ascending=False, inplace=True)
    results.reset_index(drop=True, inplace=True)
    get_best_model_row = results.iloc[0]
    best_metrics = get_best_model_row.to_dict()
    best_metrics.pop('Model', None)
    return tuned_model, best_metrics
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_model_name(model):
    try:
    model_name = str(model).split('(')[0]
    return model_name
    except Exception as e:
    logger.info(f"Unable to get the model name - {e}")
    def get_auto_ml_model(self, df, target):
    try:
    model, metrics = self.get_best_model(df=df, target=target)
    model_name = self.get_model_name(model)
    hyper_params = model.get_params()
    return model, model_name, metrics, hyper_params
    except Exception as e:
    logger.info(f"Unable to get the model name - {e}")
    scripts/utils/reading_tags.py 0 → 100644
    View file @ ff9304ef
    from loguru import logger
    import pandas as pd
    class GetTags:
    def __init__(self, base_path):
    self.base_path = base_path
    def read_tag_excel(self):
    try:
    df = pd.read_excel(f'{self.base_path}/tags_download.xlsx')
    df.drop(['Site', 'Plant', 'Line', 'Tag'], axis=1, inplace=True)
    df.rename(columns={'Tag ID': 'tag_id', 'Tag Name': 'tag_name',
    'Equipment': 'inv_id', 'Parameter Name': 'parameter_name'}, inplace=True)
    return df
    except Exception as e:
    logger.exception(f'Exception - {e}')
    def get_mppt_tags(self, df, substrings):
    try:
    data_with_substring = self.get_substring_data(substrings=substrings, df=df, column='parameter_name')
    req_data_list = self.removed_substring(substring_data_list=data_with_substring,
    remove_parameter='Efficiency')
    df = self.get_substring_df(df=df, column='parameter_name', substring_data_list=req_data_list)
    df.reset_index(drop=True, inplace=True)
    df['parameter_name'] = df['parameter_name'].str.replace('Voltage MPPT ', 'voltage_mppt_')
    df['parameter_name'] = df['parameter_name'].str.replace('Current MPPT ', 'current_mppt_')
    df['inv_id'] = df['inv_id'].str.replace('INV ', 'inv_')
    df['inv_id'] = df['inv_id'].str.replace('Plant ', 'plant')
    df['mppt_id'] = df['parameter_name'].copy()
    df['mppt_id'] = df['mppt_id'].str.replace('current_', '')
    df['mppt_id'] = df['mppt_id'].str.replace('voltage_', '')
    df = df.sort_values(['inv_id', 'mppt_id'])
    df['mppt_id_with_equipment'] = df['parameter_name'] + '_' + df['inv_id']
    df.reset_index(drop=True, inplace=True)
    return df
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_tags_id(df, inv_id):
    try:
    df = df[df['inv_id'] == inv_id]
    df = df[['tag_id', 'tag_name', 'inv_id', 'parameter_name', 'mppt_id',
    'mppt_id_with_equipment']]
    df.reset_index(drop=True, inplace=True)
    return df
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_substring_data(substrings, df, column):
    try:
    req_substrings = substrings
    data_with_substring = [data for data in df[column] if req_substrings in data]
    return data_with_substring
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def removed_substring(substring_data_list, remove_parameter):
    try:
    req_data_list = [data for data in substring_data_list if remove_parameter not in data]
    return req_data_list
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_substring_df(df, column, substring_data_list):
    try:
    df = df.loc[df[column].isin(substring_data_list)]
    df.reset_index(drop=True, inplace=True)
    return df
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/utils/start_end_date.py 0 → 100644
    View file @ ff9304ef
    from datetime import datetime, timedelta
    from scripts.constants.app_configuration import DateRange,ReqTimeZone
    from loguru import logger
    import pytz
    class KairosStartEndDate:
    @staticmethod
    def start_end_date():
    try:
    local_timezone = pytz.timezone(ReqTimeZone.required_tz)
    start_date_timestamp = DateRange.start_date
    end_date_timestamp = DateRange.end_date
    if (start_date_timestamp is not None) and (start_date_timestamp.lower() != "none"):
    start_date = datetime.fromtimestamp((int(start_date_timestamp)/1000)).strftime('%Y-%m-%d %H:%M:%S')
    start_date = datetime.strptime(start_date, "%Y-%m-%d %H:%M:%S")
    start_date = start_date.astimezone(local_timezone).replace(hour=5, minute=0, second=0, microsecond=0)
    else:
    start_date = datetime.now(pytz.utc) - timedelta(days=int(DateRange.start_relative_days))
    start_date = start_date.astimezone(local_timezone).replace(hour=5, minute=0, second=0, microsecond=0)
    if (end_date_timestamp is not None) and (end_date_timestamp.lower() != "none"):
    end_date = datetime.fromtimestamp((int(end_date_timestamp)/1000)).strftime('%Y-%m-%d %H:%M:%S')
    end_date = datetime.strptime(end_date, "%Y-%m-%d %H:%M:%S")
    end_date = end_date.astimezone(local_timezone).replace(hour=5, minute=0, second=0, microsecond=0)
    else:
    end_date = datetime.now(pytz.utc) - timedelta(days=int(DateRange.end_relative_days))
    end_date = end_date.astimezone(local_timezone).replace(hour=5, minute=0, second=0, microsecond=0)
    start_timestamp = int(start_date.timestamp())*1000
    end_timestamp = int(end_date.timestamp())*1000
    return start_date, end_date, start_timestamp, end_timestamp
    except Exception as e:
    logger.exception(f"Exception - {e}")
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