added 3nd component

input_components/get_inv_and_level_efficiency_tags/README.md

+
+# Inv and level efficiency tags
+
+## Overview
+
+- **Component Name** : Inv and level efficiency tags
+- **Component Description** :  
+- **Component Type** : Transform type
+
+
+## Component Param
+
+Variable Name |Datatype |Required/Optional |Default Value |Type |Description |Example
+--- |--- |--- |--- |--- |--- |--- |
+input |String |Required |None |inputPath | |
+output |String |Required |None |outputPath | |
+
+> Note 1 : Available Component types are: Input, Transform, Output.
+
+> Note 2 : Available Environment types are: env, InputValues, InputPath, OutputValues, OutputPath, PipelineParm
+
+        
\ No newline at end of file

input_components/get_inv_and_level_efficiency_tags/build_component.py

+import kfp
+from loguru import logger
+from src import program
+import yaml
+import inspect
+import os
+
+function = \
+    [func[1] for func in inspect.getmembers(program, inspect.isfunction) if inspect.getmodule(func[1]) == program][0]
+
+
+def read_data_from_yaml(path):
+    """
+    It opens the file at the given path, reads the contents, and then parses the contents as YAML
+
+    :param path: The path to the YAML file
+    :return: A dictionary
+    """
+    with open(path, "r") as stream:
+        return yaml.load(stream, Loader=yaml.FullLoader)
+
+
+def get_component_yml():
+    """
+
+    :param file_name:
+    :return:
+    """
+    try:
+        requirements = list()
+        with open('requirements.txt', 'r') as file:
+            for line in file:
+                if "=" in line and "#" not in line:
+                    requirements.append(line.strip())
+                elif "#" in line:
+                    ...
+                else:
+                    logger.exception(f"Mentioned package does not have version {line.strip()}")
+
+        date_function_yml = kfp.components.func_to_component_text(
+            function, packages_to_install=requirements)
+
+        variables_path = "variables.yml"
+
+        if os.path.exists(variables_path):
+
+            yaml_data: dict = read_data_from_yaml(variables_path)
+            if yaml_data:
+                envs: dict = yaml_data.get("deployment", {}).get("environmentVar", [])
+                python_version: str = yaml_data.get("deployment", {}).get("pythonVersion", None)
+                if python_version is not None and python_version in ["3.7", "3.8", "3.9", "3.10"]:
+                    date_function_yml = kfp.components.func_to_component_text(
+                        function, packages_to_install=requirements, base_image=f"python:{python_version}")
+                date_function = date_function_yml + f"    env:\n"
+                for env_var in envs:
+                    date_function += f"      {env_var['name']}: '{env_var['value']}'\n"
+                with open('component.yml', 'w') as file:
+                    file.write(date_function)
+            else:
+                with open('component.yml', 'w') as file:
+                    file.write(date_function_yml)
+        else:
+            with open('component.yml', 'w') as file:
+                file.write(date_function_yml)
+
+
+
+    except Exception as e:
+        logger.exception(f"Unable to get the component yml {e}")
+
+
+def create_table(data, key):
+    """
+
+    :return:
+    """
+    try:
+        rows_list = list()
+        for each_input in data.get(key, []):
+            rows_dict = dict()
+            rows_dict['name'] = each_input.get("name", '')
+            rows_dict['data_type'] = each_input.get('type', 'String')
+            if each_input.get('optional'):
+                req_opt = "Optional"
+                default_value = each_input.get('default', '')
+            else:
+                req_opt = "Required"
+                default_value = "None"
+            rows_dict['req_opt'] = req_opt
+            rows_dict['default_value'] = default_value
+
+            for each_arg in data.get('implementation', {}).get('container', {}).get('args', []):
+                if type(each_arg) == dict and rows_dict['name'] in each_arg.values():
+                    rows_dict['Type'] = list(each_arg.keys())[0]
+                    rows_dict['Description'] = each_input.get('description', '')
+                    rows_dict['Example'] = ''
+
+            rows_list.append(list(rows_dict.values()))
+
+        if key == "inputs" and os.path.exists("variables.yml"):
+            yaml_data: dict = read_data_from_yaml("variables.yml")
+            if yaml_data:
+                env_var = yaml_data.get("deployment", {}).get("environmentVar", [])
+                for each in env_var:
+                    env_dict = dict()
+                    env_dict['name'] = each.get("name")
+                    env_dict['data_type'] = "String"
+                    env_dict['req_opt'] = "Required"
+                    env_dict['default_value'] = "None"
+                    env_dict['Type'] = "env"
+                    env_dict['description'] = ""
+                    env_dict['example'] = ""
+                    rows_list.append(list(env_dict.values()))
+
+        return rows_list
+    except Exception as e:
+        logger.exception(f"Unable to create the table for README.MD file {e}")
+
+
+def create_readme():
+    """
+    Function is to create the readme file for the given components details
+    :return: Create the README.MD file in the given path
+    """
+    try:
+
+        note_1 = "Note 1 : Available Component types are: Input, Transform, Output."
+
+        note_2 = "Note 2 : Available Environment types are: env, InputValues, InputPath, OutputValues," \
+                 " OutputPath, PipelineParm"
+
+        column_list = ["Variable Name", "Datatype", "Required/Optional", "Default Value", "Type", "Description",
+                       "Example"]
+
+        with open("component.yml", "r") as file:
+            data = yaml.safe_load(file)
+
+        if "inputs" in list(data.keys()) and "outputs" in list(data.keys()):
+            component_type = "Transform type"
+        elif "inputs" not in data:
+            component_type = "Input type"
+        else:
+            component_type = "Output type"
+
+        component_overview_json = dict()
+
+        component_overview_json['Component Name'] = data.get("name", " ")
+        component_overview_json['Component Description'] = data.get("description", " ")
+        component_overview_json['Component Type'] = component_type
+
+        rows_list_input = create_table(data, "inputs")
+        rows_list_output = create_table(data, "outputs")
+
+        rows_list = rows_list_input + rows_list_output
+
+        header = component_overview_json.get("Component Name")
+
+        table_header = " |".join(column_list) + "\n"
+        table_line = "--- |" * len(column_list) + "\n"
+        table_body = "\n".join(map(lambda x: " |".join(x), rows_list))
+        table = table_header + table_line + table_body
+        readme = f"""
+# {header}
+
+## {"Overview"}
+
+- **Component Name** : {component_overview_json.get("Component Name")}
+- **Component Description** : {component_overview_json.get("Component Description")}
+- **Component Type** : {component_overview_json.get("Component Type")}
+
+
+## Component Param
+
+{table}
+
+> {note_1}
+
+> {note_2}
+
+        """
+
+        with open('README.md', 'w') as f:
+            f.write(readme)
+
+
+    except Exception as e:
+        logger.exception(f"Unable to create the README.MD file {e}")
+
+
+if __name__ == "__main__":
+    get_component_yml()
+    create_readme()

input_components/get_inv_and_level_efficiency_tags/component.yml

+name: Inv and level efficiency tags
+inputs:
+- {name: input}
+outputs:
+- {name: output}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'loguru==0.5.3' 'pandas==1.3.*' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m
+      pip install --quiet --no-warn-script-location 'loguru==0.5.3' 'pandas==1.3.*'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def inv_and_level_efficiency_tags(input_path, output_path):
+          class CommonConstants:
+              dalmia_string_level_tags = 'dalmia_string_level_tags'
+              bgrimm_string_level_tags = 'bgrimm_string_level_tags'
+              panel_id = 'panel_id'
+              sub_id = 'sub_id'
+              inv_id_mppt_id = 'inv_id_mppt_id'
+              tags_property_raw = 'raw'
+              tags_property_predicted = 'predicted'
+              tags_property_efficiency = 'efficiency'
+              bgrim_tags_property_efficiency = 'Efficiency'
+              tags_property_efficiency_inv = 'efficiency'
+              tags_property_efficiency_plant = 'efficiency_plant'
+              mppt_coefficients = 'mppt_coefficients'
+              inv_id = 'inv_id'
+              mppt_id = 'mppt_id'
+              datetime = 'datetime'
+              predicted_current_mppt = 'predicted_current_mppt'
+              predicted_current_mppt_tag = 'predicted_current_mppt_tag'
+              actual_current_mppt = 'actual_current_mppt'
+              hour = 'hour'
+              skip_time = {"morning": {"start": 0, "end": 6},
+                           "evening": {"start": 18, "end": 23}}
+              efficiency_mppt = 'efficiency_mppt'
+              efficiency_inv = 'efficiency_inv'
+              efficiency_plant = 'efficiency_plant'
+              tag_name = 'tag_name'
+              parameter_name = 'parameter_name'
+              timestamp = 'timestamp'
+              tag_id = 'tag_id'
+              efficiency_mppt_tag = 'efficiency_mppt_tag'
+              voltage = 'voltage'
+              current = 'current'
+              Potential = 'Potential'
+              Degradation = 'Degradation'
+              tilt_irradiance = 'tilt_irradiance'
+              voltage_mppt = 'voltage_mppt'
+              current_mppt = 'current_mppt'
+              date = 'date'
+              asia_kolkata_timezone = 'Asia/Kolkata'
+              asia_bangkok_timezone = 'Asia/Bangkok'
+              coefficient = 'coefficient'
+              cumulative_actual_current_mppt = 'cumulative_actual_current_mppt'
+              cumulative_predicted_current_mppt = 'cumulative_predicted_current_mppt'
+              day = "day"
+              time = "time"
+
+          import pandas as pd
+          from loguru import logger
+          import json
+
+          try:
+              with open(input_path, 'r') as f:
+                  get_tags_component_output_dict = json.load(f)
+              print(get_tags_component_output_dict)
+              df_efficiency_tags = get_tags_component_output_dict.get("df_efficiency_tags")
+              df_efficiency_tags = pd.DataFrame(df_efficiency_tags)
+              df_inv_tags = \
+                  df_efficiency_tags[(df_efficiency_tags[CommonConstants.parameter_name] ==
+                                      CommonConstants.bgrim_tags_property_efficiency) &
+                                     (df_efficiency_tags[CommonConstants.mppt_id] ==
+                                      CommonConstants.bgrim_tags_property_efficiency)]
+              inv_substring = 'INVERTER'
+              data_with_substring = [data for data in df_inv_tags['inv_id'] if inv_substring in data]
+              df_inv_tags = df_inv_tags.loc[df_inv_tags['inv_id'].isin(data_with_substring)]
+              df_inv_tags.reset_index(drop=True, inplace=True)
+
+              df_plant_tag = \
+                  df_efficiency_tags[(df_efficiency_tags[CommonConstants.parameter_name] ==
+                                      CommonConstants.bgrim_tags_property_efficiency)
+                                     & (df_efficiency_tags[CommonConstants.mppt_id] ==
+                                        CommonConstants.bgrim_tags_property_efficiency)]
+              data_with_no_substring = [data for data in df_plant_tag['inv_id'] if inv_substring not in data]
+              df_plant_tag = df_plant_tag.loc[df_plant_tag['inv_id'].isin(data_with_no_substring)]
+              df_plant_tag.reset_index(drop=True, inplace=True)
+
+              plant_efficiency_tag = df_plant_tag.iloc[0, df_plant_tag.columns.get_loc(CommonConstants.tag_id)]
+              logger.info(f'total no of inv level efficiency tags - {df_inv_tags.shape}')
+              logger.info(f'total no of plant level efficiency tags - {df_plant_tag.shape}')
+              final_dict = {"df_inv_tags": df_inv_tags.to_dict(orient="records"),
+                            "plant_efficiency_tag": plant_efficiency_tag}
+              with open(output_path, 'w') as f:
+                  json.dump(final_dict, f)
+              print(final_dict)
+          except Exception as e:
+              logger.exception(f'Exception - {e}')
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Inv and level efficiency tags', description='')
+      _parser.add_argument("--input", dest="input_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--output", dest="output_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = inv_and_level_efficiency_tags(**_parsed_args)
+    args:
+    - --input
+    - {inputPath: input}
+    - --output
+    - {outputPath: output}
+    env:

input_components/get_inv_and_level_efficiency_tags/requirements.txt

+loguru==0.5.3
+pandas==1.3.*
\ No newline at end of file

input_components/get_inv_and_level_efficiency_tags/src/program.py

+from kfp.components import InputPath, OutputPath
+
+
+def inv_and_level_efficiency_tags(input_path: InputPath(), output_path: OutputPath()):
+    class CommonConstants:
+        dalmia_string_level_tags = 'dalmia_string_level_tags'
+        bgrimm_string_level_tags = 'bgrimm_string_level_tags'
+        panel_id = 'panel_id'
+        sub_id = 'sub_id'
+        inv_id_mppt_id = 'inv_id_mppt_id'
+        tags_property_raw = 'raw'
+        tags_property_predicted = 'predicted'
+        tags_property_efficiency = 'efficiency'
+        bgrim_tags_property_efficiency = 'Efficiency'
+        tags_property_efficiency_inv = 'efficiency'
+        tags_property_efficiency_plant = 'efficiency_plant'
+        mppt_coefficients = 'mppt_coefficients'
+        inv_id = 'inv_id'
+        mppt_id = 'mppt_id'
+        datetime = 'datetime'
+        predicted_current_mppt = 'predicted_current_mppt'
+        predicted_current_mppt_tag = 'predicted_current_mppt_tag'
+        actual_current_mppt = 'actual_current_mppt'
+        hour = 'hour'
+        skip_time = {"morning": {"start": 0, "end": 6},
+                     "evening": {"start": 18, "end": 23}}
+        efficiency_mppt = 'efficiency_mppt'
+        efficiency_inv = 'efficiency_inv'
+        efficiency_plant = 'efficiency_plant'
+        tag_name = 'tag_name'
+        parameter_name = 'parameter_name'
+        timestamp = 'timestamp'
+        tag_id = 'tag_id'
+        efficiency_mppt_tag = 'efficiency_mppt_tag'
+        voltage = 'voltage'
+        current = 'current'
+        Potential = 'Potential'
+        Degradation = 'Degradation'
+        tilt_irradiance = 'tilt_irradiance'
+        voltage_mppt = 'voltage_mppt'
+        current_mppt = 'current_mppt'
+        date = 'date'
+        asia_kolkata_timezone = 'Asia/Kolkata'
+        asia_bangkok_timezone = 'Asia/Bangkok'
+        coefficient = 'coefficient'
+        cumulative_actual_current_mppt = 'cumulative_actual_current_mppt'
+        cumulative_predicted_current_mppt = 'cumulative_predicted_current_mppt'
+        day = "day"
+        time = "time"
+
+    import pandas as pd
+    from loguru import logger
+    import json
+
+    try:
+        with open(input_path, 'r') as f:
+            get_tags_component_output_dict = json.load(f)
+        print(get_tags_component_output_dict)
+        df_efficiency_tags = get_tags_component_output_dict.get("df_efficiency_tags")
+        df_efficiency_tags = pd.DataFrame(df_efficiency_tags)
+        df_inv_tags = \
+            df_efficiency_tags[(df_efficiency_tags[CommonConstants.parameter_name] ==
+                                CommonConstants.bgrim_tags_property_efficiency) &
+                               (df_efficiency_tags[CommonConstants.mppt_id] ==
+                                CommonConstants.bgrim_tags_property_efficiency)]
+        inv_substring = 'INVERTER'
+        data_with_substring = [data for data in df_inv_tags['inv_id'] if inv_substring in data]
+        df_inv_tags = df_inv_tags.loc[df_inv_tags['inv_id'].isin(data_with_substring)]
+        df_inv_tags.reset_index(drop=True, inplace=True)
+
+        df_plant_tag = \
+            df_efficiency_tags[(df_efficiency_tags[CommonConstants.parameter_name] ==
+                                CommonConstants.bgrim_tags_property_efficiency)
+                               & (df_efficiency_tags[CommonConstants.mppt_id] ==
+                                  CommonConstants.bgrim_tags_property_efficiency)]
+        data_with_no_substring = [data for data in df_plant_tag['inv_id'] if inv_substring not in data]
+        df_plant_tag = df_plant_tag.loc[df_plant_tag['inv_id'].isin(data_with_no_substring)]
+        df_plant_tag.reset_index(drop=True, inplace=True)
+
+        plant_efficiency_tag = df_plant_tag.iloc[0, df_plant_tag.columns.get_loc(CommonConstants.tag_id)]
+        logger.info(f'total no of inv level efficiency tags - {df_inv_tags.shape}')
+        logger.info(f'total no of plant level efficiency tags - {df_plant_tag.shape}')
+        final_dict = {"df_inv_tags": df_inv_tags.to_dict(orient="records"),
+                      "plant_efficiency_tag": plant_efficiency_tag}
+        with open(output_path, 'w') as f:
+            json.dump(final_dict, f)
+        print(final_dict)
+    except Exception as e:
+        logger.exception(f'Exception - {e}')

input_components/get_inv_and_level_efficiency_tags/variables.yml

+deployment:
+  pythonVersion: "3.9"
\ No newline at end of file

pipeline.py

@@ -19,12 +19,13 @@ def forecast_pipeline(pipeline_param: dict, plant_info: dict):
             "input_components/get_tags_component/component.yml")
         get_final_predicted_tags = kfp.components.load_component_from_file(
             "input_components/get_final_predicted_tags/component.yml")
-
+        get_inv_and_level_efficiency_tags = kfp.components.load_component_from_file(
+            "input_components/get_inv_and_level_efficiency_tags/component.yml")
         # Calling the component
         get_tags_function_task = get_tags_function_component(pipeline_param).set_memory_request('600M').set_memory_limit('1200M').\
             set_cpu_request('700m').set_cpu_limit('1400m')
         get_final_predicted_tags_task = get_final_predicted_tags(get_tags_function_task.output)
-
+        get_inv_and_level_efficiency_tags_task = get_inv_and_level_efficiency_tags(get_tags_function_task.output)
 
         # Disabling cacheing for all the components
         get_tags_function_task.execution_options.caching_strategy.max_cache_staleness = "P0D"

pipeline.yml

@@ -3,7 +3,7 @@ kind: Workflow
 metadata:
   annotations:
     pipelines.kubeflow.org/kfp_sdk_version: 1.8.18
-    pipelines.kubeflow.org/pipeline_compilation_time: '2023-09-18T19:49:44.171655'
+    pipelines.kubeflow.org/pipeline_compilation_time: '2023-09-18T20:07:41.000810'
     pipelines.kubeflow.org/pipeline_spec: '{"description": "All Components", "inputs":
       [{"description": "", "name": "pipeline_param", "type": "JsonObject"}, {"description":
       "", "name": "plant_info", "type": "JsonObject"}], "name": "Dalmia"}'
@@ -34,6 +34,14 @@ spec:
             value: '{{inputs.parameters.pipeline_param}}'
         name: get-tags-function
         template: get-tags-function
+      - arguments:
+          artifacts:
+          - from: '{{tasks.get-tags-function.outputs.artifacts.get-tags-function-output_text}}'
+            name: get-tags-function-output_text
+        dependencies:
+        - get-tags-function
+        name: inv-and-level-efficiency-tags
+        template: inv-and-level-efficiency-tags
     inputs:
       parameters:
       - name: pipeline_param
@@ -431,3 +439,173 @@ spec:
       artifacts:
       - name: get-tags-function-output_text
         path: /tmp/outputs/output_text/data
+  - container:
+      args:
+      - --input
+      - /tmp/inputs/input/data
+      - --output
+      - /tmp/outputs/output/data
+      command:
+      - sh
+      - -c
+      - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+        'loguru==0.5.3' 'pandas==1.3.*' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+        -m pip install --quiet --no-warn-script-location 'loguru==0.5.3' 'pandas==1.3.*'
+        --user) && "$0" "$@"
+      - sh
+      - -ec
+      - 'program_path=$(mktemp)
+
+        printf "%s" "$0" > "$program_path"
+
+        python3 -u "$program_path" "$@"
+
+        '
+      - "def _make_parent_dirs_and_return_path(file_path: str):\n    import os\n \
+        \   os.makedirs(os.path.dirname(file_path), exist_ok=True)\n    return file_path\n\
+        \ndef inv_and_level_efficiency_tags(input_path, output_path):\n    class CommonConstants:\n\
+        \        dalmia_string_level_tags = 'dalmia_string_level_tags'\n        bgrimm_string_level_tags\
+        \ = 'bgrimm_string_level_tags'\n        panel_id = 'panel_id'\n        sub_id\
+        \ = 'sub_id'\n        inv_id_mppt_id = 'inv_id_mppt_id'\n        tags_property_raw\
+        \ = 'raw'\n        tags_property_predicted = 'predicted'\n        tags_property_efficiency\
+        \ = 'efficiency'\n        bgrim_tags_property_efficiency = 'Efficiency'\n\
+        \        tags_property_efficiency_inv = 'efficiency'\n        tags_property_efficiency_plant\
+        \ = 'efficiency_plant'\n        mppt_coefficients = 'mppt_coefficients'\n\
+        \        inv_id = 'inv_id'\n        mppt_id = 'mppt_id'\n        datetime\
+        \ = 'datetime'\n        predicted_current_mppt = 'predicted_current_mppt'\n\
+        \        predicted_current_mppt_tag = 'predicted_current_mppt_tag'\n     \
+        \   actual_current_mppt = 'actual_current_mppt'\n        hour = 'hour'\n \
+        \       skip_time = {\"morning\": {\"start\": 0, \"end\": 6},\n          \
+        \           \"evening\": {\"start\": 18, \"end\": 23}}\n        efficiency_mppt\
+        \ = 'efficiency_mppt'\n        efficiency_inv = 'efficiency_inv'\n       \
+        \ efficiency_plant = 'efficiency_plant'\n        tag_name = 'tag_name'\n \
+        \       parameter_name = 'parameter_name'\n        timestamp = 'timestamp'\n\
+        \        tag_id = 'tag_id'\n        efficiency_mppt_tag = 'efficiency_mppt_tag'\n\
+        \        voltage = 'voltage'\n        current = 'current'\n        Potential\
+        \ = 'Potential'\n        Degradation = 'Degradation'\n        tilt_irradiance\
+        \ = 'tilt_irradiance'\n        voltage_mppt = 'voltage_mppt'\n        current_mppt\
+        \ = 'current_mppt'\n        date = 'date'\n        asia_kolkata_timezone =\
+        \ 'Asia/Kolkata'\n        asia_bangkok_timezone = 'Asia/Bangkok'\n       \
+        \ coefficient = 'coefficient'\n        cumulative_actual_current_mppt = 'cumulative_actual_current_mppt'\n\
+        \        cumulative_predicted_current_mppt = 'cumulative_predicted_current_mppt'\n\
+        \        day = \"day\"\n        time = \"time\"\n\n    import pandas as pd\n\
+        \    from loguru import logger\n    import json\n\n    try:\n        with\
+        \ open(input_path, 'r') as f:\n            get_tags_component_output_dict\
+        \ = json.load(f)\n        print(get_tags_component_output_dict)\n        df_efficiency_tags\
+        \ = get_tags_component_output_dict.get(\"df_efficiency_tags\")\n        df_efficiency_tags\
+        \ = pd.DataFrame(df_efficiency_tags)\n        df_inv_tags = \\\n         \
+        \   df_efficiency_tags[(df_efficiency_tags[CommonConstants.parameter_name]\
+        \ ==\n                                CommonConstants.bgrim_tags_property_efficiency)\
+        \ &\n                               (df_efficiency_tags[CommonConstants.mppt_id]\
+        \ ==\n                                CommonConstants.bgrim_tags_property_efficiency)]\n\
+        \        inv_substring = 'INVERTER'\n        data_with_substring = [data for\
+        \ data in df_inv_tags['inv_id'] if inv_substring in data]\n        df_inv_tags\
+        \ = df_inv_tags.loc[df_inv_tags['inv_id'].isin(data_with_substring)]\n   \
+        \     df_inv_tags.reset_index(drop=True, inplace=True)\n\n        df_plant_tag\
+        \ = \\\n            df_efficiency_tags[(df_efficiency_tags[CommonConstants.parameter_name]\
+        \ ==\n                                CommonConstants.bgrim_tags_property_efficiency)\n\
+        \                               & (df_efficiency_tags[CommonConstants.mppt_id]\
+        \ ==\n                                  CommonConstants.bgrim_tags_property_efficiency)]\n\
+        \        data_with_no_substring = [data for data in df_plant_tag['inv_id']\
+        \ if inv_substring not in data]\n        df_plant_tag = df_plant_tag.loc[df_plant_tag['inv_id'].isin(data_with_no_substring)]\n\
+        \        df_plant_tag.reset_index(drop=True, inplace=True)\n\n        plant_efficiency_tag\
+        \ = df_plant_tag.iloc[0, df_plant_tag.columns.get_loc(CommonConstants.tag_id)]\n\
+        \        logger.info(f'total no of inv level efficiency tags - {df_inv_tags.shape}')\n\
+        \        logger.info(f'total no of plant level efficiency tags - {df_plant_tag.shape}')\n\
+        \        final_dict = {\"df_inv_tags\": df_inv_tags.to_dict(orient=\"records\"\
+        ),\n                      \"plant_efficiency_tag\": plant_efficiency_tag}\n\
+        \        with open(output_path, 'w') as f:\n            json.dump(final_dict,\
+        \ f)\n        print(final_dict)\n    except Exception as e:\n        logger.exception(f'Exception\
+        \ - {e}')\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Inv\
+        \ and level efficiency tags', description='')\n_parser.add_argument(\"--input\"\
+        , dest=\"input_path\", type=str, required=True, default=argparse.SUPPRESS)\n\
+        _parser.add_argument(\"--output\", dest=\"output_path\", type=_make_parent_dirs_and_return_path,\
+        \ required=True, default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\
+        \n_outputs = inv_and_level_efficiency_tags(**_parsed_args)\n"
+      image: python:3.9
+      resources:
+        limits:
+          cpu: '0.5'
+          memory: 1Gi
+        requests:
+          cpu: '0.1'
+          memory: 30Mi
+    inputs:
+      artifacts:
+      - name: get-tags-function-output_text
+        path: /tmp/inputs/input/data
+    metadata:
+      annotations:
+        pipelines.kubeflow.org/component_ref: '{"digest": "fe01c5a169fc45af3a829c5d7135b5494bdf1d0d791a765469d36bc986240899",
+          "url": "input_components/get_inv_and_level_efficiency_tags/component.yml"}'
+        pipelines.kubeflow.org/component_spec: '{"implementation": {"container": {"args":
+          ["--input", {"inputPath": "input"}, "--output", {"outputPath": "output"}],
+          "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip
+          install --quiet --no-warn-script-location ''loguru==0.5.3'' ''pandas==1.3.*''
+          || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+          ''loguru==0.5.3'' ''pandas==1.3.*'' --user) && \"$0\" \"$@\"", "sh", "-ec",
+          "program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3
+          -u \"$program_path\" \"$@\"\n", "def _make_parent_dirs_and_return_path(file_path:
+          str):\n    import os\n    os.makedirs(os.path.dirname(file_path), exist_ok=True)\n    return
+          file_path\n\ndef inv_and_level_efficiency_tags(input_path, output_path):\n    class
+          CommonConstants:\n        dalmia_string_level_tags = ''dalmia_string_level_tags''\n        bgrimm_string_level_tags
+          = ''bgrimm_string_level_tags''\n        panel_id = ''panel_id''\n        sub_id
+          = ''sub_id''\n        inv_id_mppt_id = ''inv_id_mppt_id''\n        tags_property_raw
+          = ''raw''\n        tags_property_predicted = ''predicted''\n        tags_property_efficiency
+          = ''efficiency''\n        bgrim_tags_property_efficiency = ''Efficiency''\n        tags_property_efficiency_inv
+          = ''efficiency''\n        tags_property_efficiency_plant = ''efficiency_plant''\n        mppt_coefficients
+          = ''mppt_coefficients''\n        inv_id = ''inv_id''\n        mppt_id =
+          ''mppt_id''\n        datetime = ''datetime''\n        predicted_current_mppt
+          = ''predicted_current_mppt''\n        predicted_current_mppt_tag = ''predicted_current_mppt_tag''\n        actual_current_mppt
+          = ''actual_current_mppt''\n        hour = ''hour''\n        skip_time =
+          {\"morning\": {\"start\": 0, \"end\": 6},\n                     \"evening\":
+          {\"start\": 18, \"end\": 23}}\n        efficiency_mppt = ''efficiency_mppt''\n        efficiency_inv
+          = ''efficiency_inv''\n        efficiency_plant = ''efficiency_plant''\n        tag_name
+          = ''tag_name''\n        parameter_name = ''parameter_name''\n        timestamp
+          = ''timestamp''\n        tag_id = ''tag_id''\n        efficiency_mppt_tag
+          = ''efficiency_mppt_tag''\n        voltage = ''voltage''\n        current
+          = ''current''\n        Potential = ''Potential''\n        Degradation =
+          ''Degradation''\n        tilt_irradiance = ''tilt_irradiance''\n        voltage_mppt
+          = ''voltage_mppt''\n        current_mppt = ''current_mppt''\n        date
+          = ''date''\n        asia_kolkata_timezone = ''Asia/Kolkata''\n        asia_bangkok_timezone
+          = ''Asia/Bangkok''\n        coefficient = ''coefficient''\n        cumulative_actual_current_mppt
+          = ''cumulative_actual_current_mppt''\n        cumulative_predicted_current_mppt
+          = ''cumulative_predicted_current_mppt''\n        day = \"day\"\n        time
+          = \"time\"\n\n    import pandas as pd\n    from loguru import logger\n    import
+          json\n\n    try:\n        with open(input_path, ''r'') as f:\n            get_tags_component_output_dict
+          = json.load(f)\n        print(get_tags_component_output_dict)\n        df_efficiency_tags
+          = get_tags_component_output_dict.get(\"df_efficiency_tags\")\n        df_efficiency_tags
+          = pd.DataFrame(df_efficiency_tags)\n        df_inv_tags = \\\n            df_efficiency_tags[(df_efficiency_tags[CommonConstants.parameter_name]
+          ==\n                                CommonConstants.bgrim_tags_property_efficiency)
+          &\n                               (df_efficiency_tags[CommonConstants.mppt_id]
+          ==\n                                CommonConstants.bgrim_tags_property_efficiency)]\n        inv_substring
+          = ''INVERTER''\n        data_with_substring = [data for data in df_inv_tags[''inv_id'']
+          if inv_substring in data]\n        df_inv_tags = df_inv_tags.loc[df_inv_tags[''inv_id''].isin(data_with_substring)]\n        df_inv_tags.reset_index(drop=True,
+          inplace=True)\n\n        df_plant_tag = \\\n            df_efficiency_tags[(df_efficiency_tags[CommonConstants.parameter_name]
+          ==\n                                CommonConstants.bgrim_tags_property_efficiency)\n                               &
+          (df_efficiency_tags[CommonConstants.mppt_id] ==\n                                  CommonConstants.bgrim_tags_property_efficiency)]\n        data_with_no_substring
+          = [data for data in df_plant_tag[''inv_id''] if inv_substring not in data]\n        df_plant_tag
+          = df_plant_tag.loc[df_plant_tag[''inv_id''].isin(data_with_no_substring)]\n        df_plant_tag.reset_index(drop=True,
+          inplace=True)\n\n        plant_efficiency_tag = df_plant_tag.iloc[0, df_plant_tag.columns.get_loc(CommonConstants.tag_id)]\n        logger.info(f''total
+          no of inv level efficiency tags - {df_inv_tags.shape}'')\n        logger.info(f''total
+          no of plant level efficiency tags - {df_plant_tag.shape}'')\n        final_dict
+          = {\"df_inv_tags\": df_inv_tags.to_dict(orient=\"records\"),\n                      \"plant_efficiency_tag\":
+          plant_efficiency_tag}\n        with open(output_path, ''w'') as f:\n            json.dump(final_dict,
+          f)\n        print(final_dict)\n    except Exception as e:\n        logger.exception(f''Exception
+          - {e}'')\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Inv
+          and level efficiency tags'', description='''')\n_parser.add_argument(\"--input\",
+          dest=\"input_path\", type=str, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--output\",
+          dest=\"output_path\", type=_make_parent_dirs_and_return_path, required=True,
+          default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\n_outputs
+          = inv_and_level_efficiency_tags(**_parsed_args)\n"], "image": "python:3.9"}},
+          "inputs": [{"name": "input"}], "name": "Inv and level efficiency tags",
+          "outputs": [{"name": "output"}]}'
+      labels:
+        pipelines.kubeflow.org/enable_caching: 'true'
+        pipelines.kubeflow.org/kfp_sdk_version: 1.8.18
+        pipelines.kubeflow.org/pipeline-sdk-type: kfp
+    name: inv-and-level-efficiency-tags
+    outputs:
+      artifacts:
+      - name: inv-and-level-efficiency-tags-output
+        path: /tmp/outputs/output/data