{
    "info": {
        "author": "Dativa",
        "author_email": "hello@dativa.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "# Dativa Tools\n\nProvides useful libraries for processing large data sets. \nDeveloped by the team at [www.dativa.com](https://www.dativa.com) as we find them useful in our projects.\n\nThe key libraries included here are:\n\n* dativa.tools.aws.S3Csv2Parquet - an AWS Glue based tool to transform CSV files to Parquet files\n* dativa.tools.aws.AthenaClient - provide a simple wrapper to execute Athena queries and create tables. When combined \nwith the S3Csv2Parquet handler can automatically change Athena outputs to Parquet format\n* dativa.tools.aws.PipelineClient - client to interact with the Pipeline API. When provided an api key, source S3 \nlocation, destination s3 location, and rules, it will clean the source file and post it to destination.\n* dativa.tools.aws.S3Client - a wrapper for AWS's boto library for S3 enabling easier iteration over S3 files and \nmultiple deletions, as well as uploading multiple files\n* dativa.tools.SQLClient - a wrapper for any PEP249 compliant database client with logging and splitting of queries\n* dativa.tools.pandas.CSVHandler - improved CSV handling for Pandas\n* dativa.tools.pandas.ParquetHandler - improved Parquet handling for pandas\n* dativa.tools.pandas.Shapley - Shapley attribution modelling using pandas DataFrames\n\nThere are also some useful support functions for Pandas date and time handling.\n\n## Installation\n\n```\npip install dativatools\n```\n\nNote that Dativa Tools uses loose binding to required libraries and the required binaries are thus not automatically installed with the package and you can import classes and functions from dativa tools without the required libraries installed. An ImportError is only raised at runtime if you are looking to use a function that depends on another package that is not installed. \n\nThe required libraries are listed in requirements.txt and include:\n* pyarror for ParquetHandler \n* pandas for all of the pandas extensions\n* awsretry and boto3 for any functions requiring AWS access\n* s3fs for many functions using S3\n* blist for Shapley\n* pycryptodome for encrypting CSVs in CSVHandler\n* chardet for sniffing encodings in CSVHandler\n* requests for PipelineClient\n\n## Description\n\n### dativa.tools.aws.AthenaClient\n An easy to use client for AWS Athena that will create tables from S3 buckets (using AWS Glue) and run queries against these tables. It support full customisation of SerDe and column names on table creation.\n\n Examples:\n\n#### Creating tables\n\nThe library creates a temporary Glue crawler which is deleted after use, and will also create the database if it does not exist.\n\n```python\nfrom dativa.tools.aws import AthenaClient\nac = AthenaClient(\"us-east-1\", \"my_athena_db\")\nac.create_table(table_name='my_first_table',\n                crawler_target={'S3Targets': [\n                    {'Path': 's3://my-bucket/table-data'}]}\n                )\n\n# Create a table with a custom SerDe and column names, typical for CSV files\nac.create_table(table_name='comcast_visio_match',\n                crawler_target={'S3Targets': [\n                    {'Path': 's3://my-bucket/table-data-2', 'Exclusions': ['**._manifest']}]},\n                serde='org.apache.hadoop.hive.serde2.OpenCSVSerde',\n                columns=[{'Name': 'id', 'Type': 'string'}, {\n                    'Name': 'device_id', 'Type': 'string'}, {'Name': 'subscriber_id', 'Type': 'string'}]\n                )\n```\n\n#### Running queries\n\n```python\nfrom dativa.tools.aws import AthenaClient\n\nac = AthenaClient(\"us-east-1\", \"my_athena_db\")\nac.add_query(sql=\"select * from table\",\n                 name=\"My first query\",\n                 output_location= \"s3://my-bucket/query-location/\")\n\nac.wait_for_completion()\n```\n\n#### Fetch results of query\n```python\nfrom dativa.tools.aws import AthenaClient\n\nac = AthenaClient(\"us-east-1\", \"my_athena_db\")\nquery = ac.add_query(sql=\"select * from table\",\n                     name=\"My first query\",\n                     output_location= \"s3://my-bucket/query-location/\")\n\nac.wait_for_completion()\nac.get_query_result(query)\n```\n\n\n\n#### Running queries with the output in Parquet and create an Athena table\n\n```python\nfrom dativa.tools.aws import AthenaClient, S3Csv2Parquet\n\nscp = S3Csv2Parquet(region=\"us-east-1\",\n                    template_location=\"s3://my-bucket/glue-template-path/\")\nac = AthenaClient(\"us-east-1\", \"my_athena_db\", s3_parquet=scp)\nac.add_query(sql=\"select * from table\",\n                 name=\"my query that outputs Parquet\",\n                 output_location=\"s3://my-bucket/query-location/\",\n                 parquet=True)\n\nac.wait_for_completion()\n\nac.create_table({'S3Targets': [{'Path': \"s3://my-bucket/query-location/\"}]},\n                                        table_name=\"query_location\")\n```\n\n\n### dativa.tools.aws.S3Client\n\n An easy to use client for AWS S3 that copies data to S3.\n Examples:\n\n#### Batch deleting of files on S3\n\n```python\nfrom dativa.tools.aws import S3Client\n\n# Delete all files in a folder\ns3 = S3Client()\ns3.delete_files(bucket=\"bucket_name\", prefix=\"/delete-this-folder/\")\n\n# Delete only .csv.metadata files in a folder\ns3 = S3Client()\ns3.delete_files(bucket=\"bucket_name\", prefix=\"/delete-this-folder/\", suffix=\".csv.metadata\")\n\n```\n\n#### Copy files from folder in local filesystem to s3 bucket\n\n```python\nfrom dativa.tools.aws import S3Client\n\ns3 = S3Client()\ns3.put_folder(source=\"/home/user/my_folder\", bucket=\"bucket_name\", destination=\"backup/files\")\n\n# Copy all csv files from folder to s3\ns3.put_folder(source=\"/home/user/my_folder\", bucket=\"bucket_name\", destination=\"backup/files\", file_format=\"*.csv\")\n```\n\n### dativa.tools.SQLClient\n\nA SQL client that wraps any PEP249 compliant connection object and provides detailed logging and simple query execution.\n\nIt takes the following parameters when instantaited:\n- db_connection - a PEP257 compatible database connection\n- logger - the logger to use\n- logging_level - the level at which to log most output\n- log_query_text - whether to log all of the query text\n- humour - if set to true output jokes to pass the time of day waiting for queries to complete\n\n#### execute_query\nRuns a query and ignores any output\n\nParameters:\n\n- query - the query to run, either a SQL file or a SQL query\n- parameters - a dict of parameters to substitute in the query\n- replace - a dict or items to be replaced in the SQL text\n- first_to_run - the index of the first query in a mult-command query to be executed\n\n#### execute_query_to_df\n\nRuns a query and returns the output of the final statement in a DataFrame.\n\nParameters:\n\n- query - the query to run, either a SQL file or a SQL query\n- parameters - a dict of parameters to substitute in the query\n- replace - a dict or items to be replaced in the SQL text\n- first_to_run - the index of the first query in a mult-command query to be executed\n\n\n#### def execute_query_to_csv\n\nRuns a query and writes the output of the final statement to a CSV file.\n\nParameters:\n\n- query - the query to run, either a SQL file or a SQL query\n- csvfile - the file name to save the query results to\n- parameters - a dict of parameters to substitute in the query\n- replace - a dict or items to be replaced in the SQL text\n\n#### Example code\n\n```python\nimport os\nimport psycopg2\nfrom dativa.tools import SqlClient\n\n# set up the SQL client from environment variables\nsql = SqlClient(psycopg2.connect(\n    database=os.environ[\"DB_NAME\"],\n    user=os.environ[\"USER\"],\n    password=os.environ[\"PASSWORD\"],\n    host=os.environ[\"HOST\"],\n    port=os.environ[\"PORT\"],\n    client_encoding=\"UTF-8\",\n    connect_timeout=10))\n\n# create the full schedule table\ndf = sql.execute_query_to_df(query=\"sql/my_query.sql\",\n                             parameters={\"start_date\": \"2018-01-01\",\n                                         \"end_date\": \"2018-02-01\"})\n```\n\n### dativa.tools.log_to_stdout\n\nA convenience function to redirect a specific logger and its children to stdout\n\n```python\nimport logging\nfrom dativa.tools import log_to_stdout\n\nlog_to_stdout(\"dativa.tools\", logging.DEBUG)\n```\n\n### dativa.tools.pandas.CSVHandler\n\nA wrapper for pandas CSV handling to read and write dataframes\nthat is provided in pandas with consistent CSV parameters and\nsniffing the CSV parameters automatically.\nIncludes reading a CSV into a dataframe, and writing it out to a string.\n\n#### Parameters\n\n- base_path: the base path for any CSV file read, if passed as a string\n- detect_parameters: whether the encoding of the CSV file should be automatically detected\n- encoding: the encoding of the CSV files, defaults to UTF-8\n- delimiter: the delimeter used in the CSV, defaults to ,\n- header: the index of the header row, or -1 if there is no header\n- skiprows: the number of rows at the beginning of file to skip\n- quotechar: the quoting character to use, defaults to \"\"\n- include_index: specifies whether the index should be written out, default to False\n- compression: specifies whether the data should be encoded, default to 'infer'\n- nan_values: an array of possible NaN values, the first of which is used when writign out, defaults to None\n- line_terminator: the line terminator to be used\n- quoting: the level of quoting, defaults to QUOTE_MINIMAL\n- decimal: the decimal character, defaults to '.'\n- chunksize: if specified the CSV is written out in chunks\n- aes_key: bytes, allowable lengths are 16, 24, 32 \n \nfor decrypting and encrypting CSVs when passing to a dataframe, this uses AES CFB encryption via Pycryptodome\n- aes_iv: bytes, must have length of 16\n\nthe initialization vector for the AES CFB encryption via Pycryptodome. If aes_key is specified and this is \nnot, it will auto-generate an iv and prefix it to the encrypted bytes. \n\n#### load_df\n\nOpens a CSV file using the specified configuration for the class and raises an exception if the encoding is unparseable.\nDetects if base_path is an S3 location and loads data from there if required.\n\nParameters:\n\n- file - File path. Should begin with 's3://' to load from S3 location.\n- force_dtype - Force data type for data or columns, defaults to None\n- kwargs - any of the keyword arguments used to create the class can also be passed to load_df\n\nReturns:\n\n- dataframe\n\n#### save_df\n\nWrites a formatted string from a dataframe using the specified configuration for the class the file. Detects if base_path is an S3 location and saves data there if required.\n\nParameters:\n\n- df - Dataframe to save\n- file - File path. Should begin with 's3://' to save to an S3 location.\n- kwargs - any of the keyword arguments used to create the class can also be passed to save_df\n\n#### df_to_string\n\nReturns a formatted string from a dataframe using the specified configuration for the class.\n\nParameters:\n\n- df - Dataframe to convert to string\n- kwargs - any of the keyword arguments used to create the class can also be passed to df_to_string\n\nReturns:\n\n- string\n \n#### Example code\n\n```python\nfrom dativa.tools.pandas import CSVHandler\n\n# Create the CSV handler\ncsv = CSVHandler(base_path='s3://my-bucket-name/')\n\n# Load a file\ndf = csv.load_df('my-file-name.csv')\n\n# Create a string\nstr_df = csv.df_to_string(df)\n\n# Save a file\ncsv.save_df(df, 'another-path/another-file-name.csv')\n```\n\n### Support functions for Pandas\n\n* dativa.tools.pandas.is_numeric - a function to check whether a series or string is numeric\n* dativa.tools.pandas.string_to_datetime - a function to convert a string, or series of strings to a datetime, with a strptime date format that supports nanoseconds\n* dativa.tools.pandas.datetime_to_string - a function to convert a datetime, or a series of datetimes to a string, with a strptime date format that supports nanoseconds\n* dativa.tools.pandas.format_string_is_valid - a function to confirm whether a strptime format string returns a date\n* dativa.tools.pandas.get_column_name - a function to return the name of a column from a passed column name or index.\n* dativa.tools.pandas.get_unique_column_name - a function to return a unique column name when adding new columns to a DataFrame\n\n### dativa.tools.pandas.ParquetHandler\n\nParquetHandler class, specify path of parquet file,\nand get pandas dataframe for analysis and modification.\n\n* param base_path                       : The base location where the parquet_files are stored.\n* type base_path                        : str\n* param row_group_size                  : The size of the row groups while writing out the parquet file.\n* type row_group_size                   : int\n* param use_dictionary                  : Specify whether to use boolean encoding or not\n* type use_dictionary                   : bool\n* param use_deprecated_int96_timestamps : Write nanosecond resolution timestamps to INT96 Parquet format.\n* type use_deprecated_int96_timestamps  : bool\n* param coerce_timestamps               : Cast timestamps a particular resolution. Valid values: {None, 'ms', 'us'}\n* type coerce_timestamps                : str\n* param compression                     : Specify the compression codec.\n* type compression                      : str\n\n```python\nfrom dativa.tools.pandas import CSVHandler, ParquetHandler\n\n# Read a parquet file\npq_obj = ParquetHandler()\ndf_parquet = pq_obj.load_df('data.parquet')\n\n# save a csv_file to parquet\ncsv = CSVHandler(csv_delimiter=\",\")\ndf = csv.load_df('emails.csv')\npq_obj = ParquetHandler()\npq_obj.save_df(df, 'emails.parquet')\n```\n####save_df\nSaves the df as parquet to the file path given to it, similar to CSVHandler save_df.\n\n#####Parameters\n\n* param df                              : A pandas dataframe to write to original file location of parquet file.\n* type df                               : pandas.DataFrame\n* param row_group_size                  : The size of the row groups while writing out the parquet file.\n* type row_group_size                   : int\n* param use_deprecated_int96_timestamps : Write nanosecond resolution timestamps to INT96 Parquet format.\n* type use_deprecated_int96_timestamps  : bool\n* param coerce_timestamps               : Cast timestamps a particular resolution. Valid values: {None, 'ms', 'us'}\n* type coerce_timestamps                : str\n* param compression                     : Specify the compression codec.\n* type compression                      : str\n* param schema                          : Used to set the desired schema for pyarrow table, if not provided schema is inferred\n* type schema                           : pyarrow.lib.Schema or dict\n* param infer_other_dtypes              : Used when schema is specified. When True, if there are columns not specified in schema then their dtypes are inferred. When false, if there are columns not specified in schema then raise an error. Default behaviour is False.\n* type infer_other_dtypes               : bool\n\nFor convenience, ParquetHandler allows a python dict to be passed to the schema argument. The dict should have column names as the keys and desired types as the values. A dict or schema for only some of the columns may be passed, the types for the rest of the columns will then be inferred. The types are inferred by looking at the types for the non-null values in each column. An error is raised if there multiple types in each column.\n\nExample code of how to pass a dict to the schema argument. In this example, only columns `col1` and `col2` are given types, any other columns will have their types inferred.\n\n```python\npq_obj = ParquetHandler()\n\ndict_schema = {'col1': str, 'col3': int}\npq_obj.save_df(test_df, new_file_path, schema=dict_schema)\n```\n\nExample code on how to generate pyarrow.lib.schema objects and how to pass the schema to save_df.\n\n```python\npq_obj = ParquetHandler()\n\nfields = [\n    pa.field(\"col1\", pa.int64()),\n    pa.field(\"col2\", pa.string())]\nmy_schema = pa.schema(fields)\npq_obj.save_df(test_df, new_file_path, schema=my_schema)\n```\n\n\n\n\n### dativa.tools.aws import S3Csv2Parquet\n An easy to use module for converting csv files on s3 to praquet using aws glue jobs.\n For S3 access and glue access suitable credentials should be available in '~/.aws/credentials' or the AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY environment variables.\n\n#### S3Csv2Parquet\nParameters:\n\n- region - str,\n           AWS region in which glue job is to be run\n- template_location - str,\n                      S3 bucket Folder in which template scripts are\n                      located or need to be copied.\n                      format s3://bucketname/folder/\n                      it is not clear to those unfamiliar with glue what this is.\n- glue_role - str,\n              Name of the glue role which need to be assigned to the\n              Glue Job.\n- max_jobs - int, default 5\n             Maximum number of jobs the can run concurrently in the queue\n- retry_limit - int, default 3\n                Maximum number of retries allowed per job on failure\n\n#### convert\nParameters:\n\n- csv_path - str or list of str for multiple files,\n             s3 location of the csv file\n             format s3://bucketname/folder/file.csv\n             Pass a list for multiple files\n- output_folder - str, default set to folder where csv files are located \n                  s3 location at which paraquet file should be copied\n                  format s3://bucketname/folder\n- schema - list of tuples,\n           If not specified schema is inferred from the file\n           format [(column1, datatype), (column2, datatype)]\n           Supported datatypes are boolean, double, float, integer,\n           long, null, short, string\n- name - str, default 'parquet_csv_convert'\n         Name to be assigned to glue job\n- allocated_capacity - int, default 2\n                       The number of AWS Glue data processing units (DPUs) to allocate to this Job.\n                       From 2 to 100 DPUs can be allocated\n- delete_csv - boolean, default False\n               If set source csv files are deleted post successful completion of job\n- separator - character, default ','\n              Delimiter character in csv files\n- with_header- int, default 1\n              Specifies whether to treat the first line as a header\n              Can take values 0 or 1\n- compression - str, default None\n                If not specified compression is not applied.\n                Can take values snappy, gzip, and lzo\n- partition_by - list of str, default None\n                 List containing columns to partition data by\n- mode - str, default append\n         Options include:\n         overwrite: will remove data from output_folder before writing out\n                    converted file.\n         append: Will write out to  output_folder without deleting existing\n                 data.\n         ignore: Silently ignore this operation if data already exists.\n\n####Example\n\n```python\nfrom dativa.tools.aws import S3Csv2Parquet\n\n# Initial setup\ncsv2parquet_obj = S3Csv2Parquet(\"us-east-1\", \"s3://my-bucket/templatefolder\")\n\n# Create/update a glue job to convert csv files and execute it\ncsv2parquet_obj.convert(\"s3://my-bucket/file_to_be_converted_1.csv\")\ncsv2parquet_obj.convert(\"s3://my-bucket/file_to_be_converted_2.csv\")\n\n# Wait for completion of jobs \ncsv2parquet_obj.wait_for_completion()\n```\n\n\n### dativa.tools.aws.PipelineClient\n\nPipelineClient class, provide api key, source s3 location,\ndestination s3 location, rules, and get source file cleaned and\nposted to destination.\nRefer https://www.dativa.com/tools/dativatools/aws-api/ for more details.\n\nArguments:\n\n- api_key               : str, The individual key provided by the pipeline api\n- source_s3_url         : str, The S3 source where the csv files are present\n- destination_s3_url    : str, The S3 destination where the files\n                          are to be posted after cleansing\n- rules                 : list of dicts OR str, rules by which to clean the file\n\n                          list of dicts specifying each rule to be applied \n                          str specifying location of the rules file\n- url                   : str, url of the pipeline api, defaults to\n\n                          https://pipeline-api.dativa.com/clean\n- status_url            : url, the url to query for to check status of the api call, defaults to\n\n                          https://pipeline-api.dativa.com/status/{0}\n- source_delimiter      : str, the delimiter of the source file, defaults to \",\"\n- destination_delimiter : str, the delimiter of the destination file, defaults to \",\"\n- source_encoding       : str, the encoding of the source file, defaults to \"utf-8\"\n- destination_encoding  : str, the encoding of the destination file, defaults to \"utf-8\"\n\n### Example code\n\n```python\nfrom dativa.tools.aws import PipelineClient\nimport os\napi_key = os.environ[\"DATIVA_PIPELINE_API_KEY\"]\nrules = [{'field': 'to',\n          'params': {'fallback_mode': 'remove_record',          \n                     'maximum_length': 1024,        \n                     'minimum_length': 5,       \n                     'regex': '[^@]+@[^.]..*[^.]'},                               \n          'rule_type': 'String'}]\nbucket = \"example-of-bucket\"\nobj = PipelineClient(api_key=api_key,\n                     rules=rules,\n                     source_s3_url=\"s3://{0}/source_key\".format(bucket),\n                     destination_s3_url=\"s3://{0}/dest_key\".format(bucket),\n                     )\nobj.run_job_synchronously()\n```\n\n### To run tests\nThe API and AWS credentials must be present as environment variables for the testing to pass successfully\n\n```bash\nexport DATIVA_PIPELINE_API_KEY=API_KEY_HERE\nexport AWS_ACCESS_KEY_ID=AWS_CREDENTIALS_HERE\nexport AWS_SECRET_ACCESS_KEY=AWS_CREDENTIALS_HERE\n``` \n\nThe user must also specify a bucket to act as a source and a _separate_ one to act as the destination. \n\n### dativa.tools.pandas.Shapley\n\nShapley attribution of scores to members of sets.\n\nSee [medium](https://towardsdatascience.com/one-feature-attribution-method-to-supposedly-rule-them-all-shapley-values-f3e04534983d)\nor [wiki](https://en.wikipedia.org/wiki/Shapley_value) for details on the math. The aim is to apportion scores between the members of a set\nresponsible for producing that score.\n\nTakes two DataFrames as input, one containing impressions from campaigns and the other containing conversions.\n\neg. campaigns\n\n|viewer_id|campaign_id|\n| :----:|:-----:|\n|20|B|\n|19|B|\n|12|B|\n|6|A|\n|17|B|\n|12|B|\n|8|B|\n|3|A|\n|18|A|\n\nwhere a campaign_id might be a campaign that an individual has interacted with. Each viewer / campaign\npair is only considered once.\n\neg 2. conversions\n\n|viewer_id|\n|: --- :|\n|14|\n|12|\n|2|\n|3|\n|11|\n\nthese are individuals that have converted. From this a conversion rate is calculated which is used as a score.\nThat score is apportioned among the campaigns.\n\nTypically not all combinations of impressions are present (particularly in where there are lots of \ncampaigns being run). The missing combinations are assigned a default score, which might be an average conversion rate \nor similar. This is passed to the class constructor as `default_score` and must be numeric.\n\n### Example code\n\n```python\n\ns = Shapley(df_campaigns, df_conversions, 'viewer_id', 'campaign_id', default_score=0.01)\n\nresults = s.run()\n\n```\n\n## dativa.tools.FileValidation\nA module to validate the files present in a given directory based on glob pattern matching. The \nFileValidation class initialises and checks a set of rules for a file validation, this can then be run for various \npaths and glob pattern matches via a method (run_validator). Validation is be based on \nvarious properties - time last modified, size and the number of files which match criteria. Examples on how to use \nthe file validation are provided below.\n\nFileValidation Arguments:\n  * timestamp: (tuple or str or None) strings for min and max time (str or None, str or None) in UTC. This is\n   read in with datetime.strptime, so must use compatible formats \n  * file_size: (tuple or int or None) min and max size in bytes (int or None, int or None), treats single int\n   as minimum \n  * file_count: (tuple or int or None) min and max file count, treats single int as minimum\n  * timestamp_format: (str) string for a valid timestamp, specified format for both max and min timestamp,\n        uses datetime.strptime, hence must be in compatible format. \n\nrun_validator Arguments:\n  * path: (str) path to the files - can either be a path for S3Location or an absolute local file path.  CANNOT HAVE '/' AS THE FINAL CHARACTER.\n  * glob_pattern: (str) pattern of glob files to be examined.  CANNOT HAVE '/' AS THE FIRST CHARACTER.\n  * consider_correctly_formed_files: (bool) if False, consider malformed files (which do not match validation) for the \n  file_count argument of FileValidation\n  * raise_exceptions: (bool) whether to raise exception when finding files which do not match validation criteria\n\n### Example codes \n\nThe code can either return a list of files (either all which match the criteria or those which do not) or raise an \nexception if any files fail the validation.  This can be applied to either files on S3 or on the local filesystem.\n\nThe files to be validated are specified via s3fs.glob for S3 (see https://s3fs.readthedocs.io/en/latest/) and glob.glob \nfor local files (see https://docs.python.org/3/library/glob.html).\n\n### Check number of CSVs in a given path\n```python\nfrom dativa.tools import FileValidation\n# ensure there are between 10 and 100 CSVs in your target folder\n# this command ignores other file extensions (e.g. .tsv files) as they aren't picked up by the glob pattern          \nfv = FileValidation(file_count=(10, 100))   # between 10 and 100 bytes \n# if validation is not met, raise an exception\nfv.run_validator(path=\"s3://some-bucket/some-path\",\n                 glob_pattern=\"*.csv\",\n                 raise_exceptions=True)\n```\n\n### Check that a specific file was last modified on a specific date and is smaller than a given size \n```python\nfrom dativa.tools import FileValidation\n\nfv = FileValidation(file_size=(None, 2*2**10),  # 2 KB\n                    timestamp=(\"2018-12-25\", \"2018-12-25\"), # made on 25th Dec 2018 \n                    timestamp_format=\"%Y-%m-%d\")\n# if validation is not met, raise an exception                    \nfv.run_validator(path=\"s3://some-bucket/some-path\",\n                 glob_pattern=\"some-specific-key.csv\",\n                 raise_exceptions=True)\n```\n\n### Return file which do not match a certain size and age range\n```python\nfrom dativa.tools import FileValidation\nfrom datetime import datetime\nfv = FileValidation(file_size=(10, None), # minimum file size of 10 bytes \n                    # no minimum time, earliest files made today\n                    timestamp=(None, datetime.utcnow().strftime(\"%Y-%m-%d\")),\n                    timestamp_format=\"%Y-%m-%d\")\n                    \n# return files in dictionary                    \nlist_of_bad_files = fv.run_validator(path=\"/Users/your-name/absolute/file/path\",\n                                     glob_pattern=\"*\",\n                                     consider_correctly_formed_files=False)\n```\n\n### Local file paths must be _absolute_ paths rather than relative paths\n```python\nfrom dativa.tools import FileValidation\nfv = FileValidation(file_size=(10, 10*2**20) # between 10 bytes and 10 GB\n                    )\n# return files which do not match the specified validation                    \nlist_of_bad_files = fv.run_validator(path=\"/Users/your-name/absolute/file/path\",\n                                     glob_pattern=\"*\",\n                                     consider_correctly_formed_files=False)\n\n# return files which match the specified validation                    \nlist_of_good_files = fv.run_validator(path=\"/Users/your-name/absolute/file/path\",\n                                      glob_pattern=\"*\",\n                                      consider_correctly_formed_files=True)\n```\n\n### NOTE - both the path must not end with a '/' AND glob_pattern must not start with one\n```python\nfrom dativa.tools import FileValidation\nfv = FileValidation(file_size=(10, None), # minimum file size of 10 bytes \n                    )\n                    \n# return files in dictionary                    \nthis_will_not_run = fv.run_validator(path=\"/Users/your-name/absolute/file/path/\",\n                                     glob_pattern=\"/*\",\n                                     consider_correctly_formed_files=False)\nalso_will_not_run = fv.run_validator(path=\"/Users/your-name/absolute/file/path/\",\n                                     glob_pattern=\"*\",\n                                     consider_correctly_formed_files=False)\nagain_will_not_run = fv.run_validator(path=\"/Users/your-name/absolute/file/path\",\n                                      glob_pattern=\"/*\",\n                                      consider_correctly_formed_files=False)\n``` \n\n## Legacy classes\n\nThe modules in the dativatools namespace are legacy only and will be deprecated in future.",
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