# It's a complete example of the CSV schema file in YAML format.
# See copy of the file without comments here ./schema-examples/full_clean.yml

# Just meta
name: CSV Blueprint Schema Example      # Name of a CSV file. Not used in the validation process.
description: |-                         # Any description of the CSV file. Not used in the validation process.
  This YAML file provides a detailed description and validation rules for CSV files
  to be processed by CSV Blueprint tool. It includes specifications for file name patterns,
  CSV formatting options, and extensive validation criteria for individual columns and their values,
  supporting a wide range of data validation rules from basic type checks to complex regex validations.
  This example serves as a comprehensive guide for creating robust CSV file validations.

# Include another schema and define an alias for it.
presets:
  my-preset: ./preset_users.yml         # Define preset alias "my-preset". See README.md for details.

# Regular expression to match the file name. If not set, then no pattern check.
# This allows you to pre-validate the file name before processing its contents.
# Feel free to check parent directories as well.
# See: https://www.php.net/manual/en/reference.pcre.pattern.syntax.php
filename_pattern: /\.csv$/i
#  preset: my-preset                    # See README.md for details.

# Here are default values to parse CSV file.
# You can skip this section if you don't need to override the default values.
csv:
  preset: my-preset                     # See README.md for details.
  header: true                          # If the first row is a header. If true, name of each column is required.
  delimiter: ,                          # Delimiter character in CSV file.
  quote_char: \                         # Quote character in CSV file.
  enclosure: '"'                        # Enclosure for each field in CSV file.
  encoding: utf-8                       # (Experimental) Only utf-8, utf-16, utf-32.
  bom: false                            # (Experimental) If the file has a BOM (Byte Order Mark) at the beginning.

# Structural rules for the CSV file. These rules are applied to the entire CSV file.
# They are not(!) related to the data in the columns.
# You can skip this section if you don't need to override the default values.
structural_rules: # Here are default values.
  preset: my-preset                     # See README.md for details.
  strict_column_order: true             # Ensure columns in CSV follow the same order as defined in this YML schema. It works only if "csv.header" is true.
  allow_extra_columns: false            # Allow CSV files to have more columns than specified in this YML schema.

# Add any extra data you want. It will be ignored by the tool but available for your own code.
# You can use any format and store anything. Examples:
# extra: 'some text'
# extra: [some, options, here]
# extra: 42
extra:
  - key: "value"

# Description of each column in CSV.
# It is recommended to present each column in the same order as presented in the CSV file.
# This will not affect the validator, but will make it easier for you to navigate.
# For convenience, use the first line as a header (if possible).
columns:
  - preset: my-preset/login             # Add preset rules for the column. See README.md for details.
    name: Column Name (header)          # Any custom name of the column in the CSV file (first row). Required if "csv.header" is true.
    description: Lorem ipsum            # Description of the column. Not used in the validation process.
    example: Some example               # Example of the column value. Schema will also check this value on its own.

    # If the column is required. If true, the column must be present in the CSV file. If false, the column can be missing in the CSV file.
    # So, if you want to make the column optional, set this value to false, and it will validate the column only if it is present.
    # By default, the column is required. It works only if "csv.header" is true and "structural_rules.allow_extra_columns" is false.
    required: true

    # Add any extra data you want. It will be ignored by the tool but available for your own code.
    # You can use any format and store anything. Examples:
    # extra: 'some text'
    # extra: [some, options, here]
    # extra: 42
    extra:
      - key: "value"

    ####################################################################################################################
    # Data validation for each(!) value in the column. Please, see notes in README.md
    # Every rule is optional.
    rules:
      preset: my-preset/login           # Add preset rules for the column. See README.md for details.

      # General rules
      not_empty: true                   # Value is not an empty string. Actually checks if the string length is not 0.
      exact_value: Some string          # Exact value for string in the column.
      allow_values: [ y, n, "" ]        # Strict set of values that are allowed.
      not_allow_values: [ invalid ]     # Strict set of values that are NOT allowed.

      # Any valid regex pattern. See: https://www.php.net/manual/en/reference.pcre.pattern.syntax.php
      # Of course it's a super powerful tool to verify any sort of string data.
      # Please, be careful. Regex is a powerful tool, but it can be very dangerous if used incorrectly.
      # Remember that if you want to solve a problem with regex, you now have two problems.
      # But have it your way, then happy debugging! https://regex101.com
      regex: /^[\d]{2}$/

      # Checks length of a string including spaces (multibyte safe).
      length_min: 1                     # x >= 1
      length_greater: 2                 # x >  2
      length_not: 0                     # x != 0
      length: 7                         # x == 7
      length_less: 8                    # x <  8
      length_max: 9                     # x <= 9

      # Basic string checks
      is_trimmed: true                  # Only trimmed strings. Example: "Hello World" (not " Hello World ").
      is_lowercase: true                # String is only lower-case. Example: "hello world".
      is_uppercase: true                # String is only upper-case. Example: "HELLO WORLD".
      is_capitalize: true               # String is only capitalized. Example: "Hello World".
      is_sentence: true                 # Sentence with at least one space. Example: "Hello world!".

      # Count number of words used in a string
      # Note that multibyte locales are not supported.
      # Example: "Hello World, 123" - 2 words only (123 is not a word).
      word_count_min: 1                 # x >= 1
      word_count_greater: 2             # x >  2
      word_count_not: 0                 # x != 0
      word_count: 7                     # x == 7
      word_count_less: 8                # x <  8
      word_count_max: 9                 # x <= 9

      # Contains rules
      contains: World                   # Example: "Hello World!". The string must contain "World" in any place.
      contains_none: [ a, b ]           # All the strings must NOT be part of a CSV value.
      contains_one: [ a, b ]            # Only one of the strings must be part of the CSV value.
      contains_any: [ a, b ]            # At least one of the string must be part of the CSV value.
      contains_all: [ a, b ]            # All the strings must be part of a CSV value.
      starts_with: 'prefix '            # Example: "prefix Hello World".
      ends_with: ' suffix'              # Example: "Hello World suffix".

      # Numeric
      is_int: true                      # Check format only. Can be negative and positive. Without any separators.
      is_float: true                    # Check format only. Can be negative and positive. Dot as decimal separator.

      # Under the hood it converts and compares as float values.
      # Comparison accuracy is 10 digits after a dot.
      # Scientific number format is also supported. Example: "1.2e3"
      num_min: 1.0                      # x >= 1.0
      num_greater: 2.0                  # x >  2.0
      num_not: 5.0                      # x != 5.0
      num: 7.0                          # x == 7.0
      num_less: 8.0                     # x <  8.0
      num_max: 9.0                      # x <= 9.0

      # Number of digits after the decimal point (with zeros)
      precision_min: 1                  # x >= 1
      precision_greater: 2              # x >  2
      precision_not: 0                  # x != 0
      precision: 7                      # x == 7
      precision_less: 8                 # x <  8
      precision_max: 9                  # x <= 9

      # Date & time
      is_date: true                     # Accepts arbitrary date format. Is shows error if failed to convert to timestamp.
      is_timezone: true                 # Allow only timezone identifiers. Case-insensitive. Example: "Europe/London", "utc".
      is_timezone_offset: true          # Allow only timezone offsets. Example: "+03:00".
      is_time: true                     # Check if the cell value is a valid time in the format "HH:MM:SS AM/PM" / "HH:MM:SS" / "HH:MM". Case-insensitive.
      is_leap_year: true                # Check if the cell value is a leap year. Example: "2008", "2008-02-29 23:59:59 UTC".

      # Dates. Under the hood, the strings are converted to timestamp and compared.
      # This gives you the ability to use relative dates and any formatting you want.
      # By default, it works in UTC. But you can specify your own timezone as part of the date string.
      # Format:    https://www.php.net/manual/en/datetime.format.php
      # Parsing:   https://www.php.net/manual/en/function.strtotime.php
      # Timezones: https://www.php.net/manual/en/timezones.php
      date_min: -100 years              # Example of relative past date
      date_greater: -99 days            # Example of relative formats
      date_not: 2006-01-02 15:04:05 -0700 Europe/Rome
      date: 01 Jan 2000                 # You can use any string that can be parsed by the strtotime function
      date_less: now                    # Example of current date and time
      date_max: +1 day                  # Example of relative future date
      date_format: Y-m-d                # Check strict format of the date.

      # Date Intervals. Under the hood, the strings are converted to seconds and compared.
      # See: https://www.php.net/manual/en/class.dateinterval.php
      # See: https://www.php.net/manual/en/dateinterval.createfromdatestring.php
      date_interval_min: PT0S           # 0 seconds
      date_interval_greater: 1day 1sec  # 1 day and 1 second
      date_interval_not: 100 days       # Except for the 100 days
      date_interval: P2W                # Exactly 2 weeks
      date_interval_less: PT23H59M59S   # 23 hours, 59 minutes, and 59 seconds
      date_interval_max: P1Y            # 1 year

      # Check an arbitrary date in a CSV cell for age (years).
      # Actually it calculates the difference between the date and the current date.
      # Convenient to use for age restrictions based on birthday.
      # See the description of `date_*` functions for details on date formats.
      date_age_min: 1                   # x >= 1
      date_age_greater: 14              # x >  14
      date_age_not: 18                  # x != 18
      date_age: 21                      # x == 21
      date_age_less: 99                 # x <  99
      date_age_max: 100                 # x <= 100

      # Specific formats
      is_bool: true                     # Allow only boolean values "true" and "false", case-insensitive.
      is_binary: true                   # Both: with or without "0b" prefix. Example: "0b10" or "10".
      is_octal: true                    # Validates octal numbers in the format "0o123".
      is_hex: true                      # Both: with or without "0x" prefix. Example: "0x1A".
      is_uuid: true                     # Validates whether the input is a valid UUID. It also supports validation of specific versions 1, 3, 4 and 5.
      is_slug: true                     # Only slug format. Example: "my-slug-123". It can contain letters, numbers, and dashes.
      is_currency_code: true            # Validates an ISO 4217 currency code like GBP or EUR. Case-sensitive. See: https://en.wikipedia.org/wiki/ISO_4217.
      is_base64: true                   # Validate if a string is Base64-encoded. Example: "cmVzcGVjdCE=".
      is_angle: true                    # Check if the cell value is a valid angle (0.0 to 360.0).

      # Safity checks
      # Password strength calculation criteria include: Length (max 5 points, +1 every 2 characters),
      # presence of uppercase letters (+1), lowercase letters (+1), numbers (+1), special characters (+1),
      # spaces (+1), and penalties for consecutive sequences of uppercase, lowercase, or
      # numbers (-0.5 each), repetitive sequences (-0.75 each), common weak passwords like "qwerty",
      # and passwords under 6 characters (-2). Adjust scores to a 0 to 10 scale, with a minimum score of 0.
      password_strength_min: 1          # x >= 1
      password_strength_greater: 2      # x >  2
      password_strength_not: 0          # x != 0
      password_strength: 7              # x == 7
      password_strength_less: 8         # x <  8
      password_strength_max: 9          # x <= 9
      is_password_safe_chars: true      # Check that the cell value contains only safe characters for regular passwords. Allowed characters: a-z, A-Z, 0-9, !@#$%^&*()_+-=[]{};:'"|,.<>/?~.

      # Internet
      is_ip: true                       # Both: IPv4 or IPv6.
      is_ip_v4: true                    # Only IPv4. Example: "127.0.0.1".
      is_ip_v6: true                    # Only IPv6. Example: "2001:0db8:85a3:08d3:1319:8a2e:0370:7334".
      is_ip_private: true               # IPv4 has ranges: 10.0.0.0/8, 172.16.0.0/12 and 192.168.0.0/16. IPv6 has ranges starting with FD or FC.
      is_ip_reserved: true              # IPv4 has ranges: 0.0.0.0/8, 169.254.0.0/16, 127.0.0.0/8 and 240.0.0.0/4. IPv6 has ranges: ::1/128, ::/128, ::ffff:0:0/96 and fe80::/10.
      ip_v4_range: [ '127.0.0.1-127.0.0.5', '127.0.0.0/21' ] # Check subnet mask or range for IPv4. Address must be in one of the ranges.
      is_mac_address: true              # The input is a valid MAC address. Example: 00:00:5e:00:53:01
      is_domain: true                   # Only domain name. Example: "example.com".
      is_public_domain_suffix: true     # The input is a public ICANN domain suffix. Example: "com", "nom.br", "net" etc.
      is_url: true                      # Only URL format. Example: "https://example.com/page?query=string#anchor".
      is_email: true                    # Only email format. Example: "user@example.com".

      # Validates if the given input is a valid JSON.
      # This is possible if you escape all special characters correctly and use a special CSV format.
      is_json: true                     # Example: {"foo":"bar"}.

      # Geography
      is_latitude: true                 # Can be integer or float. Example: 50.123456.
      is_longitude: true                # Can be integer or float. Example: -89.123456.
      is_geohash: true                  # Check if the value is a valid geohash. Example: "u4pruydqqvj".
      is_cardinal_direction: true       # Valid cardinal direction. Case-insensitive. Available values: ["N", "S", "E", "W", "NE", "SE", "NW", "SW", "NONE"]
      is_usa_market_name: true          # Check if the value is a valid USA market name. Example: "New York, NY".
      is_usa_state: true                # Name or code of USA state name. Case-insensitive. Example: "CA" or "California".

      # Validates whether the input is a country code in ISO 3166-1 standard.
      # Available options: "alpha-2" (Ex: "US"), "alpha-3" (Ex: "USA"), "numeric" (Ex: "840").
      # The rule uses data from iso-codes: https://salsa.debian.org/iso-codes-team/iso-codes.
      country_code: alpha-2             # Country code in ISO 3166-1 standard. Examples: "US", "USA", "840"

      # Validates whether the input is language code based on ISO 639.
      # Available options: "alpha-2" (Ex: "en"), "alpha-3" (Ex: "eng").
      # See: https://en.wikipedia.org/wiki/ISO_639.
      language_code: alpha-2            # Examples: "en", "eng"

      # Filesystem (with IO!)
      is_file_exists: true              # Check if file exists on the filesystem (It's FS IO operation!).
      is_dir_exists: true               # Check if directory exists on the filesystem (It's FS IO operation!).

      # Mathematical
      is_fibonacci: true                # Validates whether the input follows the Fibonacci integer sequence. Example: "8", "13".
      is_prime_number: true             # Validates a prime number. Example: "3", "5", "7", "11".
      is_even: true                     # Check if the value is an even number. Example: "2", "4", "6".
      is_odd: true                      # Check if the value is an odd number. Example: "1", "7", "11".
      is_roman: true                    # Validates if the input is a Roman numeral. Example: "I", "IV", "XX".
      is_luhn: true                     # Luhn algorithm. See: https://en.wikipedia.org/wiki/Luhn_algorithm

      # Identifications
      phone: ALL                        # Validates if the input is a phone number. Specify the country code to validate the phone number for a specific country. Example: "ALL", "US", "BR".".
      postal_code: US                   # Validate postal code by country code (alpha-2). Example: "02179". Extracted from https://www.geonames.org
      is_iban: true                     # IBAN - International Bank Account Number. See: https://en.wikipedia.org/wiki/International_Bank_Account_Number
      is_bic: true                      # Validates a Bank Identifier Code (BIC) according to ISO 9362 standards. See: https://en.wikipedia.org/wiki/ISO_9362
      is_imei: true                     # Validates an International Mobile Equipment Identity (IMEI). See: https://en.wikipedia.org/wiki/International_Mobile_Station_Equipment_Identity
      is_isbn: true                     # Validates an International Standard Book Number (ISBN). See: https://www.isbn-international.org/content/what-isbn

      # Misc
      is_version: true                  # Validates the string as version numbers using Semantic Versioning. Example: "1.2.3".
      is_punct: true                    # Validates whether the input composed by only punctuation characters. Example: "!@#$%^&*()".
      is_vowel: true                    # Validates whether the input contains only vowels. Example: "aei".
      is_consonant: true                # Validates if the input contains only consonants. Example: "bcd".
      is_alnum: true                    # Validates whether the input is only alphanumeric. Example: "aBc123".
      is_alpha: true                    # This is similar to `is_alnum`, but it does not allow numbers. Example: "aBc".
      is_hex_rgb_color: true            # Validates weather the input is a hex RGB color or not. Examples: "#FF0000", "#123", "ffffff", "fff".

      # Check if the value is a valid hash. Supported algorithms:
      #  - md5, md4, md2, sha1, sha224, sha256, sha384, sha512/224, sha512/256, sha512
      #  - sha3-224, sha3-256, sha3-384, sha3-512, ripemd128, ripemd160, ripemd256, ripemd320, whirlpool, tiger128,3
      #  - tiger160,3, tiger192,3, tiger128,4, tiger160,4, tiger192,4, snefru, snefru256, gost, gost-crypto, crc32
      #  - crc32b, crc32c, adler32, fnv132, fnv1a32, fnv164, fnv1a64, joaat, murmur3a, murmur3c
      #  - murmur3f, xxh32, xxh64, xxh3, xxh128, haval128,3, haval160,3, haval192,3, haval224,3, haval256,3
      #  - haval128,4, haval160,4, haval192,4, haval224,4, haval256,4, haval128,5, haval160,5, haval192,5, haval224,5, haval256,5
      hash: set_algo                    # Example: "1234567890abcdef".

      # Check if a string is in a specific charset. Available charsets:
      #  - 7bit, 8bit, ASCII, ArmSCII-8, BASE64, BIG-5, CP850, CP866, CP932, CP936
      #  - CP950, CP50220, CP50221, CP50222, CP51932, EUC-CN, EUC-JP, EUC-JP-2004, EUC-KR, EUC-TW
      #  - GB18030, GB18030-2022, HTML-ENTITIES, HZ, ISO-2022-JP, ISO-2022-JP-2004, ISO-2022-JP-MOBILE#KDDI, ISO-2022-JP-MS, ISO-2022-KR, ISO-8859-1
      #  - ISO-8859-2, ISO-8859-3, ISO-8859-4, ISO-8859-5, ISO-8859-6, ISO-8859-7, ISO-8859-8, ISO-8859-9, ISO-8859-10, ISO-8859-13
      #  - ISO-8859-14, ISO-8859-15, ISO-8859-16, JIS, KOI8-R, KOI8-U, Quoted-Printable, SJIS, SJIS-2004, SJIS-Mobile#DOCOMO
      #  - SJIS-Mobile#KDDI, SJIS-Mobile#SOFTBANK, SJIS-mac, SJIS-win, UCS-2, UCS-2BE, UCS-2LE, UCS-4, UCS-4BE, UCS-4LE
      #  - UHC, UTF-7, UTF-8, UTF-8-Mobile#DOCOMO, UTF-8-Mobile#KDDI-A, UTF-8-Mobile#KDDI-B, UTF-8-Mobile#SOFTBANK, UTF-16, UTF-16BE, UTF-16LE
      #  - UTF-32, UTF-32BE, UTF-32LE, UTF7-IMAP, UUENCODE, Windows-1251, Windows-1252, Windows-1254, eucJP-win
      charset: charset_code             # Validates if a string is in a specific charset. Example: "UTF-8".

      # Validates whether the input is a credit card number.
      # Available credit card brands: "Any", "American Express", "Diners Club", "Discover", "JCB", "MasterCard", "Visa", "RuPay".
      credit_card: Any                  # Example: "5376-7473-9720-8720"

    ####################################################################################################################
    # Data validation for the entire(!) column using different data aggregation methods.
    # Every rule is optional.
    aggregate_rules:
      preset: my-preset/login           # Add preset aggregate rules for the column. See README.md for details.

      is_unique: true                   # All values in the column are unique.

      # Check if the column is sorted in a specific order.
      #  - Direction: ["asc", "desc"].
      #  - Method:    ["numeric", "string", "natural", "regular"].
      # See: https://www.php.net/manual/en/function.sort.php
      sorted: [ asc, natural ]          # Expected ascending order, natural sorting.

      # First number in the column. Expected value is float or integer.
      first_num_min: 1.0                # x >= 1.0
      first_num_greater: 2.0            # x >  2.0
      first_num_not: 5.0                # x != 5.0
      first_num: 7.0                    # x == 7.0
      first_num_less: 8.0               # x <  8.0
      first_num_max: 9.0                # x <= 9.0
      first: Expected                   # First value in the column. Will be compared as strings.
      first_not: Not expected           # Not allowed as the first value in the column. Will be compared as strings.

      # N-th value in the column.
      # The rule expects exactly two arguments: the first is the line number (without header), the second is the expected value.
      # Example: `[ 42, 5.0 ]` On the line 42 (disregarding the header), we expect the 5.0. The comparison is always as float.
      nth_num_min: [ 42, 1.0 ]          # x >= 1.0
      nth_num_greater: [ 42, 2.0 ]      # x >  2.0
      nth_num_not: [ 42, 5.0 ]          # x != 5.0
      nth_num: [ 42, 7.0 ]              # x == 7.0
      nth_num_less: [ 42, 8.0 ]         # x <  8.0
      nth_num_max: [ 42, 9.0 ]          # x <= 9.0
      nth: [ 2, Expected ]              # Nth value in the column. Will be compared as strings.
      nth_not: [ 2, Not expected ]      # Not allowed as the N-th value in the column. Will be compared as strings.

      # Last number in the column. Expected value is float or integer.
      last_num_min: 1.0                 # x >= 1.0
      last_num_greater: 2.0             # x >  2.0
      last_num_not: 5.0                 # x != 5.0
      last_num: 7.0                     # x == 7.0
      last_num_less: 8.0                # x <  8.0
      last_num_max: 9.0                 # x <= 9.0
      last: Expected                    # Last value in the column. Will be compared as strings.
      last_not: Not expected            # Not allowed as the last value in the column. Will be compared as strings.

      # Sum of the numbers in the column. Example: [1, 2, 3] => 6.
      sum_min: 1.0                      # x >= 1.0
      sum_greater: 2.0                  # x >  2.0
      sum_not: 5.0                      # x != 5.0
      sum: 7.0                          # x == 7.0
      sum_less: 8.0                     # x <  8.0
      sum_max: 9.0                      # x <= 9.0

      # Regular the arithmetic mean. The sum of the numbers divided by the count.
      average_min: 1.0                  # x >= 1.0
      average_greater: 2.0              # x >  2.0
      average_not: 5.0                  # x != 5.0
      average: 7.0                      # x == 7.0
      average_less: 8.0                 # x <  8.0
      average_max: 9.0                  # x <= 9.0

      # Total number of rows in the CSV file.
      # Since any(!) values are taken into account, it only makes sense to use these rules once in any column.
      count_min: 1                      # x >= 1
      count_greater: 2                  # x >  2
      count_not: 0                      # x != 0
      count: 7                          # x == 7
      count_less: 8                     # x <  8
      count_max: 9                      # x <= 9

      # Counts only empty values (string length is 0).
      count_empty_min: 1                # x >= 1
      count_empty_greater: 2            # x >  2
      count_empty_not: 0                # x != 0
      count_empty: 7                    # x == 7
      count_empty_less: 8               # x <  8
      count_empty_max: 9                # x <= 9

      # Counts only not empty values (string length is not 0).
      count_not_empty_min: 1            # x >= 1
      count_not_empty_greater: 2        # x >  2
      count_not_empty_not: 0            # x != 0
      count_not_empty: 7                # x == 7
      count_not_empty_less: 8           # x <  8
      count_not_empty_max: 9            # x <= 9

      # Number of unique values.
      count_distinct_min: 1             # x >= 1
      count_distinct_greater: 2         # x >  2
      count_distinct_not: 0             # x != 0
      count_distinct: 7                 # x == 7
      count_distinct_less: 8            # x <  8
      count_distinct_max: 9             # x <= 9

      # Number of positive values.
      count_positive_min: 1             # x >= 1
      count_positive_greater: 2         # x >  2
      count_positive_not: 0             # x != 0
      count_positive: 7                 # x == 7
      count_positive_less: 8            # x <  8
      count_positive_max: 9             # x <= 9

      # Number of negative values.
      count_negative_min: 1             # x >= 1
      count_negative_greater: 2         # x >  2
      count_negative_not: 0             # x != 0
      count_negative: 7                 # x == 7
      count_negative_less: 8            # x <  8
      count_negative_max: 9             # x <= 9

      # Number of zero values. Any text and spaces (i.e. anything that doesn't look like a number) will be converted to 0.
      count_zero_min: 1                 # x >= 1
      count_zero_greater: 2             # x >  2
      count_zero_not: 0                 # x != 0
      count_zero: 7                     # x == 7
      count_zero_less: 8                # x <  8
      count_zero_max: 9                 # x <= 9

      # Number of even values.
      count_even_min: 1                 # x >= 1
      count_even_greater: 2             # x >  2
      count_even_not: 0                 # x != 0
      count_even: 7                     # x == 7
      count_even_less: 8                # x <  8
      count_even_max: 9                 # x <= 9

      # Number of odd values.
      count_odd_min: 1                  # x >= 1
      count_odd_greater: 2              # x >  2
      count_odd_not: 0                  # x != 0
      count_odd: 7                      # x == 7
      count_odd_less: 8                 # x <  8
      count_odd_max: 9                  # x <= 9

      # Number of prime values.
      count_prime_min: 1                # x >= 1
      count_prime_greater: 2            # x >  2
      count_prime_not: 0                # x != 0
      count_prime: 7                    # x == 7
      count_prime_less: 8               # x <  8
      count_prime_max: 9                # x <= 9

      # Calculate the median average of a list of numbers.
      # See: https://en.wikipedia.org/wiki/Median
      median_min: 1.0                   # x >= 1.0
      median_greater: 2.0               # x >  2.0
      median_not: 5.0                   # x != 5.0
      median: 7.0                       # x == 7.0
      median_less: 8.0                  # x <  8.0
      median_max: 9.0                   # x <= 9.0

      # Harmonic mean (subcontrary mean). The harmonic mean can be expressed as the reciprocal of the arithmetic mean of the reciprocals.
      # Appropriate for situations when the average of rates is desired.
      # See: https://en.wikipedia.org/wiki/Harmonic_mean
      harmonic_mean_min: 1.0            # x >= 1.0
      harmonic_mean_greater: 2.0        # x >  2.0
      harmonic_mean_not: 5.0            # x != 5.0
      harmonic_mean: 7.0                # x == 7.0
      harmonic_mean_less: 8.0           # x <  8.0
      harmonic_mean_max: 9.0            # x <= 9.0

      # Geometric mean. A type of mean which indicates the central tendency or typical value of a set of numbers
      # by using the product of their values (as opposed to the arithmetic mean which uses their sum).
      # See: https://en.wikipedia.org/wiki/Geometric_mean
      geometric_mean_min: 1.0           # x >= 1.0
      geometric_mean_greater: 2.0       # x >  2.0
      geometric_mean_not: 5.0           # x != 5.0
      geometric_mean: 7.0               # x == 7.0
      geometric_mean_less: 8.0          # x <  8.0
      geometric_mean_max: 9.0           # x <= 9.0

      # Contraharmonic mean. A function complementary to the harmonic mean. A special case of the Lehmer mean, L₂(x), where p = 2.
      # See: https://en.wikipedia.org/wiki/Contraharmonic_mean
      contraharmonic_mean_min: 1.0      # x >= 1.0
      contraharmonic_mean_greater: 2.0  # x >  2.0
      contraharmonic_mean_not: 5.0      # x != 5.0
      contraharmonic_mean: 7.0          # x == 7.0
      contraharmonic_mean_less: 8.0     # x <  8.0
      contraharmonic_mean_max: 9.0      # x <= 9.0

      # Root mean square (quadratic mean) The square root of the arithmetic mean of the squares of a set of numbers.
      # See: https://en.wikipedia.org/wiki/Root_mean_square
      root_mean_square_min: 1.0         # x >= 1.0
      root_mean_square_greater: 2.0     # x >  2.0
      root_mean_square_not: 5.0         # x != 5.0
      root_mean_square: 7.0             # x == 7.0
      root_mean_square_less: 8.0        # x <  8.0
      root_mean_square_max: 9.0         # x <= 9.0

      # Trimean (TM, or Tukey's trimean).
      # A measure of a probability distribution's location defined as a weighted average of the distribution's median and its two quartiles.
      # See: https://en.wikipedia.org/wiki/Trimean
      trimean_min: 1.0                  # x >= 1.0
      trimean_greater: 2.0              # x >  2.0
      trimean_not: 5.0                  # x != 5.0
      trimean: 7.0                      # x == 7.0
      trimean_less: 8.0                 # x <  8.0
      trimean_max: 9.0                  # x <= 9.0

      # Cubic mean. See: https://en.wikipedia.org/wiki/Cubic_mean
      cubic_mean_min: 1.0               # x >= 1.0
      cubic_mean_greater: 2.0           # x >  2.0
      cubic_mean_not: 5.0               # x != 5.0
      cubic_mean: 7.0                   # x == 7.0
      cubic_mean_less: 8.0              # x <  8.0
      cubic_mean_max: 9.0               # x <= 9.0

      # Compute the P-th percentile of a list of numbers.
      # Linear interpolation between closest ranks method - Second variant, C = 1 P-th percentile (0 <= P <= 100) of a list of N ordered values (sorted from least to greatest).
      # Similar method used in NumPy and Excel.
      # See: https://en.wikipedia.org/wiki/Percentile#Second_variant.2C_.7F.27.22.60UNIQ--postMath-00000043-QINU.60.22.27.7F
      # Example: `[ 95.5, 1.234 ]` The 95.5th percentile in the column must be "1.234" (float).
      percentile_min: [ 95.0, 1.0 ]     # x >= 1.0
      percentile_greater: [ 95.0, 2.0 ] # x >  2.0
      percentile_not: [ 95.0, 5.0 ]     # x != 5.0
      percentile: [ 95.0, 7.0 ]         # x == 7.0
      percentile_less: [ 95.0, 8.0 ]    # x <  8.0
      percentile_max: [ 95.0, 9.0 ]     # x <= 9.0

      # Quartiles. Three points that divide the data set into four equal groups, each group comprising a quarter of the data.
      # See: https://en.wikipedia.org/wiki/Quartile
      # There are multiple methods for computing quartiles: ["exclusive", "inclusive"]. Exclusive is ussually classic.
      # Available types: ["0%", "Q1", "Q2", "Q3", "100%", "IQR"] ("IQR" is Interquartile Range)
      # Example: `[ inclusive, 'Q3', 42.0 ]` - the Q3 inclusive quartile is 42.0
      quartiles_min: [ exclusive, '0%', 1.0 ]               # x >= 1.0
      quartiles_greater: [ inclusive, 'Q1', 2.0 ]           # x >  2.0
      quartiles_not: [ exclusive, 'Q2', 5.0 ]               # x != 5.0
      quartiles: [ inclusive, 'Q3', 7.0 ]                   # x == 7.0
      quartiles_less: [ exclusive, '100%', 8.0 ]            # x <  8.0
      quartiles_max: [ inclusive, 'IQR', 9.0 ]              # x <= 9.0

      # Midhinge. The average of the first and third quartiles and is thus a measure of location.
      # Equivalently, it is the 25% trimmed mid-range or 25% midsummary; it is an L-estimator.
      # See: https://en.wikipedia.org/wiki/Midhinge
      # Midhinge = (first quartile, third quartile) / 2
      midhinge_min: 1.0                 # x >= 1.0
      midhinge_greater: 2.0             # x >  2.0
      midhinge_not: 5.0                 # x != 5.0
      midhinge: 7.0                     # x == 7.0
      midhinge_less: 8.0                # x <  8.0
      midhinge_max: 9.0                 # x <= 9.0

      # MAD - mean absolute deviation. The average of the absolute deviations from a central point.
      # It is a summary statistic of statistical dispersion or variability.
      # See: https://en.wikipedia.org/wiki/Average_absolute_deviation
      mean_abs_dev_min: 1.0             # x >= 1.0
      mean_abs_dev_greater: 2.0         # x >  2.0
      mean_abs_dev_not: 5.0             # x != 5.0
      mean_abs_dev: 7.0                 # x == 7.0
      mean_abs_dev_less: 8.0            # x <  8.0
      mean_abs_dev_max: 9.0             # x <= 9.0

      # MAD - median absolute deviation. The average of the absolute deviations from a central point.
      # It is a summary statistic of statistical dispersion or variability.
      # It is a robust measure of the variability of a univariate sample of quantitative data.
      # See: https://en.wikipedia.org/wiki/Median_absolute_deviation
      median_abs_dev_min: 1.0           # x >= 1.0
      median_abs_dev_greater: 2.0       # x >  2.0
      median_abs_dev_not: 5.0           # x != 5.0
      median_abs_dev: 7.0               # x == 7.0
      median_abs_dev_less: 8.0          # x <  8.0
      median_abs_dev_max: 9.0           # x <= 9.0

      # Population variance - Use when all possible observations of the system are present.
      # If used with a subset of data (sample variance), it will be a biased variance.
      # n degrees of freedom, where n is the number of observations.
      population_variance_min: 1.0      # x >= 1.0
      population_variance_greater: 2.0  # x >  2.0
      population_variance_not: 5.0      # x != 5.0
      population_variance: 7.0          # x == 7.0
      population_variance_less: 8.0     # x <  8.0
      population_variance_max: 9.0      # x <= 9.0

      # Unbiased sample variance Use when only a subset of all possible observations of the system are present.
      # n - 1 degrees of freedom, where n is the number of observations.
      sample_variance_min: 1.0          # x >= 1.0
      sample_variance_greater: 2.0      # x >  2.0
      sample_variance_not: 5.0          # x != 5.0
      sample_variance: 7.0              # x == 7.0
      sample_variance_less: 8.0         # x <  8.0
      sample_variance_max: 9.0          # x <= 9.0

      # Standard deviation (For a sample; uses sample variance). It also known as SD or StdDev.
      # StdDev is a measure that is used to quantify the amount of variation or dispersion of a set of data values.
      #  - Low standard deviation indicates that the data points tend to be close to the mean (also called the expected value) of the set.
      #  - High standard deviation indicates that the data points are spread out over a wider range of values.
      # See: https://en.wikipedia.org/wiki/Standard_deviation
      stddev_min: 1.0                   # x >= 1.0
      stddev_greater: 2.0               # x >  2.0
      stddev_not: 5.0                   # x != 5.0
      stddev: 7.0                       # x == 7.0
      stddev_less: 8.0                  # x <  8.0
      stddev_max: 9.0                   # x <= 9.0

      # SD+ (Standard deviation for a population; uses population variance)
      stddev_pop_min: 1.0               # x >= 1.0
      stddev_pop_greater: 2.0           # x >  2.0
      stddev_pop_not: 5.0               # x != 5.0
      stddev_pop: 7.0                   # x == 7.0
      stddev_pop_less: 8.0              # x <  8.0
      stddev_pop_max: 9.0               # x <= 9.0

      # Coefficient of variation (cᵥ) Also known as relative standard deviation (RSD)
      # A standardized measure of dispersion of a probability distribution or frequency distribution.
      # It is often expressed as a percentage. The ratio of the standard deviation to the mean.
      # See: https://en.wikipedia.org/wiki/Coefficient_of_variation
      coef_of_var_min: 1.0              # x >= 1.0
      coef_of_var_greater: 2.0          # x >  2.0
      coef_of_var_not: 5.0              # x != 5.0
      coef_of_var: 7.0                  # x == 7.0
      coef_of_var_less: 8.0             # x <  8.0
      coef_of_var_max: 9.0              # x <= 9.0

      # Interquartile mean (IQM). A measure of central tendency based on the truncated mean of the interquartile range.
      # Only the data in the second and third quartiles is used (as in the interquartile range), and the lowest 25% and the highest 25% of the scores are discarded.
      # See: https://en.wikipedia.org/wiki/Interquartile_mean
      # Note: It's SUPER slow!!!
      interquartile_mean_min: 1.0       # x >= 1.0
      interquartile_mean_greater: 2.0   # x >  2.0
      interquartile_mean_not: 5.0       # x != 5.0
      interquartile_mean: 7.0           # x == 7.0
      interquartile_mean_less: 8.0      # x <  8.0
      interquartile_mean_max: 9.0       # x <= 9.0

  - name: another_column
    rules:
      not_empty: true

  - name: inherited_column_login
    preset: my-preset/login

  - name: inherited_column_full_name
    preset: my-preset/full_name

name: Presets for database columns
description: This schema contains basic rules for database user data.

columns:
  - name: id
    description: Unique identifier, usually used to denote a primary key in databases.
    example: 12345
    extra: 
      custom_key: custom value
    rules:
      not_empty: true
      is_trimmed: true
      is_int: true
      num_min: 1
    aggregate_rules:
      is_unique: true
      sorted: [ asc, numeric ]

  - name: status
    description: Status in database
    example: active
    rules:
      not_empty: true
      allow_values: [ active, inactive, pending, deleted ]

name: Common presets for user data
description: >
  This schema contains common presets for user data.
  It can be used as a base for other schemas.

filename_pattern: /users-.*\.csv$/i

csv:
  delimiter: ';'

columns:
  - name: login
    description: User's login name
    example: johndoe
    rules:
      not_empty: true
      is_trimmed: true
      is_lowercase: true
      is_slug: true
      length_min: 3
      length_max: 20
      is_alnum: true
    aggregate_rules:
      is_unique: true

  - name: password
    description: User's password
    example: '9RfzENKD'
    rules:
      not_empty: true
      is_trimmed: true
      is_password_safe_chars: true
      password_strength_min: 7
      contains_none: [ "password", "123456", "qwerty", " " ]
      charset: UTF-8
      length_min: 6
      length_max: 20

  - name: full_name
    description: User's full name
    example: 'John Doe Smith'
    rules:
      not_empty: true
      is_trimmed: true
      charset: UTF-8
      contains: " "
      word_count_min: 2
      word_count_max: 8
      is_capitalize: true
    aggregate_rules:
      is_unique: true

  - name: email
    description: User's email address
    example: user@example.com
    rules:
      not_empty: true
      is_trimmed: true
      is_email: true
      is_lowercase: true
    aggregate_rules:
      is_unique: true

  - name: birthday
    description: Validates the user's birthday.
    example: '1990-01-01'
    rules:
      not_empty: true            # The birthday field must not be empty.
      is_trimmed: true           # Trims the value before validation.
      date_format: Y-m-d         # Checks if the date matches the YYYY-MM-DD format.
      is_date: true              # Validates if the value is a valid date.
      date_age_greater: 0        # Ensures the date is in the past.
      date_age_less: 150         # Ensures the user is not older than 150 years.
      date_max: now              # Ensures the date is not in the future.

  - name: phone_number
    description: User's phone number in US
    example: '+1 650 253 00 00'
    rules:
      not_empty: true
      is_trimmed: true
      starts_with: '+1'
      phone: US

  - name: balance
    description: User's balance in USD
    example: '100.00'
    rules:
      not_empty: true
      is_trimmed: true
      is_float: true
      num_min: 0.00
      num_max: 1000000000.00      # 1 billion is max amount in our system.
      precision: 2

  - name: short_description
    description: A brief description of the item
    example: 'Lorem ipsum dolor sit amet'
    rules:
      not_empty: true
      contains: " "
      length_max: 255
      is_trimmed: true

# Schema file is "./schema-examples/preset_usage.yml"
name: 'Schema uses presets and add new columns + specific rules.'
description: 'This schema uses presets. Also, it demonstrates how to override preset values.'
presets:
  users: ./schema-examples/preset_users.yml
  db: ./schema-examples/preset_database.yml
csv:
  delimiter: ;
  enclosure: '|'
columns:
  - name: id
    description: 'Unique identifier, usually used to denote a primary key in databases.'
    example: 12345
    extra:
      custom_key: 'custom value'
    rules:
      not_empty: true
      is_trimmed: true
      is_int: true
      num_min: 1
    aggregate_rules:
      is_unique: true
      sorted:
        - asc
        - numeric

  - name: status
    description: 'Status in database'
    example: active
    rules:
      not_empty: true
      allow_values:
        - active
        - inactive
        - pending
        - deleted

  - name: login
    description: "User's login name"
    example: johndoe
    rules:
      not_empty: true
      length_min: 3
      length_max: 20
      is_trimmed: true
      is_lowercase: true
      is_slug: true
      is_alnum: true
    aggregate_rules:
      is_unique: true

  - name: email
    description: "User's email address"
    example: user@example.com
    rules:
      not_empty: true
      is_trimmed: true
      is_lowercase: true
      is_email: true
    aggregate_rules:
      is_unique: true

  - name: full_name
    description: "User's full name"
    example: 'John Doe Smith'
    rules:
      not_empty: true
      is_trimmed: true
      is_capitalize: true
      word_count_min: 2
      word_count_max: 8
      contains: ' '
      charset: UTF-8
    aggregate_rules:
      is_unique: true

  - name: birthday
    description: "Validates the user's birthday."
    example: '1990-01-01'
    rules:
      not_empty: true
      is_trimmed: true
      is_date: true
      date_max: now
      date_format: Y-m-d
      date_age_greater: 0
      date_age_less: 150

  - name: phone
    description: "User's phone number in US"
    example: '+1 650 253 00 00'
    rules:
      not_empty: true
      is_trimmed: true
      starts_with: '+1'
      phone: US

  - name: password
    description: "User's password"
    example: 9RfzENKD
    rules:
      not_empty: true
      length_min: 10
      length_max: 20
      is_trimmed: true
      contains_none:
        - password
        - '123456'
        - qwerty
        - ' '
      password_strength_min: 7
      is_password_safe_chars: true
      charset: UTF-8

  - name: admin_note
    description: 'Admin note'
    rules:
      not_empty: true
      length_min: 1
      length_max: 10
    aggregate_rules:
      is_unique: true
      sorted:
        - asc
        - numeric

name: Complite list of preset features
description: This schema contains all the features of the presets.

presets:
  # The basepath for the preset is `.` (current directory of the current schema file).
  # Define alias "db" for schema in `./preset_database.yml`.
  db: preset_database.yml           # Or `db: ./preset_database.yml`. It's up to you.

  # For example, you can use a relative path.
  users: ./../schema-examples/preset_users.yml

  # Or you can use an absolute path.
  # db: /full/path/preset_database.yml

filename_pattern: { preset: users } # Take the filename pattern from the preset.
structural_rules: { preset: users } # Take the global rules from the preset.
csv: { preset: users }              # Take the CSV settings from the preset.

columns:
  # Use name of column from the preset.
  # "db" is alias. "id" is column `name` in `preset_database.yml`.
  - preset: 'db/id'

  # Use column index. "db" is alias. "0" is column index in `preset_database.yml`.
  - preset: 'db/0'
  - preset: 'db/0:'

  # Use column index and column name. It useful if column name is not unique.
  - preset: 'db/0:id'

  # Use only `rules` of "status" column from the preset.
  - name: My column
    rules:
      preset: 'db/status'

  # Override only `aggregate_rules` from the preset.
  # Use only `aggregate_rules` of "id" column from the preset.
  # We strictly take only the very first column (index = 0).
  - name: My column
    aggregate_rules:
      preset: 'db/0:id'

  # Combo!!! If you're a risk-taker or have a high level of inner zen. :)
  # Creating a column from three other columns.
  # In fact, it will merge all three at once with key replacement.
  - name: Crazy combo!
    description: >                  # Just a great advice.
      I like to take risks, too.
      Be careful. Use your power wisely.
    example: ~                      # Ignore inherited "example" value. Set it `null`.
    preset: 'users/login'
    rules:
      preset: 'users/email'
      not_empty: true               # Disable the rule from the preset.
    aggregate_rules:
      preset: 'db/0'