Update requirements

Generated with poetry export:

    $ poetry export --without-hashes -f requirements.txt > requirements.txt
    $ poetry export --without-hashes --dev -f requirements.txt > requirements-dev.txt

I am trying `--without-hashes` to work around an error on pip install
when running in CI:

    ERROR: In --require-hashes mode, all requirements must have their versions pinned with ==.

.drone.yml

@@ -9,10 +9,11 @@ steps:
   commands:
   - id
   - python -V
+  - apt update && apt install -y gcc g++ libicu-dev pkg-config
   - pip install -r requirements-dev.txt
   - pytest
   - python setup.py install
-  - csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dc.subject,cg.coverage.country
+  - csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dcterms.subject,cg.coverage.country
 
 ---
 kind: pipeline
@@ -25,10 +26,11 @@ steps:
   commands:
   - id
   - python -V
+  - apt update && apt install -y gcc g++ libicu-dev pkg-config
   - pip install -r requirements-dev.txt
   - pytest
   - python setup.py install
-  - csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dc.subject,cg.coverage.country
+  - csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dcterms.subject,cg.coverage.country
 
 ---
 kind: pipeline
@@ -41,9 +43,10 @@ steps:
   commands:
   - id
   - python -V
+  - apt update && apt install -y gcc g++ libicu-dev pkg-config
   - pip install -r requirements-dev.txt
   - pytest
   - python setup.py install
-  - csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dc.subject,cg.coverage.country
+  - csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dcterms.subject,cg.coverage.country
 
 # vim: ts=2 sw=2 et

.github/workflows/python-app.yml

@@ -16,10 +16,10 @@ jobs:
 
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v2
       with:
-        python-version: 3.8
+        python-version: 3.9
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
@@ -38,4 +38,4 @@ jobs:
     - name: Test CLI
       run: |
         python setup.py install
-        csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dc.subject,cg.coverage.country
+        csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dcterms.subject,cg.coverage.country

CHANGELOG.md

@@ -4,6 +4,47 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## Unreleased
+### Added
+- Ability to check for, and fix, "mojibake" characters using [ftfy](https://github.com/LuminosoInsight/python-ftfy)
+
+### Updated
+- Python dependencies
+
+## [0.4.7] - 2021-03-17
+### Changed
+- Fixing invalid multi-value separators like `|` and `|||` is no longer class-
+ified as "unsafe" as I have yet to see a case where this was intentional
+- Not user visible, but now checks only print a warning to the screen instead
+of returning a value and re-writing the DataFrame, which should be faster and
+use less memory
+
+### Added
+- Configurable directory for AGROVOC requests cache (to allow running the web
+version from Google App Engine where we can only write to /tmp)
+- Ability to check for duplicate items in the data set (uses a combination of
+the title, type, and date issued to determine uniqueness)
+
+### Removed
+- Checks for invalid and unnecessary multi-value separators because now I fix
+them whenever I see them, so there is no need to have checks for them
+
+### Updated
+- Run `poetry update` to update project dependencies
+
+## [0.4.6] - 2021-03-11
+### Added
+- Validation of dcterms.license field against SPDX license identifiers 
+
+### Changed
+- Use DCTERMS fields where possible in `data/test.csv`
+
+### Updated
+- Run `poetry update` to update project dependencies
+
+### Fixed
+- Output for all fixes should be green, because it is good
+
 ## [0.4.5] - 2021-03-04
 ### Added
 - Check dates in dcterms.issued field as well, not just fields that have the

README.md

@@ -1,7 +1,7 @@
 # DSpace CSV Metadata Quality Checker ![GitHub Actions](https://github.com/ilri/csv-metadata-quality/workflows/Build%20and%20Test/badge.svg) [![Build Status](https://ci.mjanja.ch/api/badges/alanorth/csv-metadata-quality/status.svg)](https://ci.mjanja.ch/alanorth/csv-metadata-quality)
 A simple, but opinionated metadata quality checker and fixer designed to work with CSVs in the DSpace ecosystem (though it could theoretically work on any CSV that uses Dublin Core fields as columns). The implementation is essentially a pipeline of checks and fixes that begins with splitting multi-value fields on the standard DSpace "||" separator, trimming leading/trailing whitespace, and then proceeding to more specialized cases like ISSNs, ISBNs, languages, unnecessary Unicode, AGROVOC terms, etc.
 
-Requires Python 3.7 or greater (3.8 recommended). CSV and Excel support comes from the [Pandas](https://pandas.pydata.org/) library, though your mileage may vary with Excel because this is much less tested.
+Requires Python 3.7.1 or greater (3.8+ recommended). CSV and Excel support comes from the [Pandas](https://pandas.pydata.org/) library, though your mileage may vary with Excel because this is much less tested.
 
 If you use the DSpace CSV metadata quality checker please cite:
 
@@ -13,13 +13,16 @@ If you use the DSpace CSV metadata quality checker please cite:
 - Validate languages against ISO 639-1 (alpha2) and ISO 639-3 (alpha3)
 - Experimental validation of titles and abstracts against item's Dublin Core language field
 - Validate subjects against the AGROVOC REST API (see the `--agrovoc-fields` option)
+- Validation of licenses against the list of [SPDX license identifiers](https://spdx.org/licenses)
 - Fix leading, trailing, and excessive (ie, more than one) whitespace
-- Fix invalid and unnecessary multi-value separators (`|`) using `--unsafe-fixes`
+- Fix invalid and unnecessary multi-value separators (`|`)
 - Fix problematic newlines (line feeds) using `--unsafe-fixes`
+- Perform [Unicode normalization](https://withblue.ink/2019/03/11/why-you-need-to-normalize-unicode-strings.html) on strings using `--unsafe-fixes`
 - Remove unnecessary Unicode like [non-breaking spaces](https://en.wikipedia.org/wiki/Non-breaking_space), [replacement characters](https://en.wikipedia.org/wiki/Specials_(Unicode_block)#Replacement_character), etc
 - Check for "suspicious" characters that indicate encoding or copy/paste issues, for example "foreˆt" should be "forêt"
+- Check for "mojibake" characters (and attempt to fix with `--unsafe-fixes`)
 - Remove duplicate metadata values
-- Perform [Unicode normalization](https://withblue.ink/2019/03/11/why-you-need-to-normalize-unicode-strings.html) on strings using `--unsafe-fixes`
+- Check for duplicate items, using the title, type, and date issued as an indicator
 
 ## Installation
 The easiest way to install CSV Metadata Quality is with [poetry](https://python-poetry.org):
@@ -54,14 +57,14 @@ To validate and clean a CSV file you must specify input and output files using t
 $ csv-metadata-quality -i data/test.csv -o /tmp/test.csv
 ```
 
-## Unsafe Fixes
-You can enable several "unsafe" fixes with the `--unsafe-fixes` option. Currently this will attempt to fix invalid multi-value separators and remove newlines.
-
-### Invalid Multi-Value Separators
-This is considered "unsafe" because it is *theoretically* possible for a single `|` character to be used legitimately in a metadata value, though in my experience it is always a typo. For example, if a user mistakenly writes `Kenya|Tanzania` when attempting to indicate two countries, the result will be one metadata value with the literal text `Kenya|Tanzania`. The `--unsafe-fixes` option will correct the invalid multi-value separator so that there are two metadata values, ie `Kenya||Tanzania`.
+## Invalid Multi-Value Separators
+While it is *theoretically* possible for a single `|` character to be used legitimately in a metadata value, in my experience it is always a typo. For example, if a user mistakenly writes `Kenya|Tanzania` when attempting to indicate two countries, the result will be one metadata value with the literal text `Kenya|Tanzania`. This utility will correct the invalid multi-value separator so that there are two metadata values, ie `Kenya||Tanzania`.
 
 This will also remove unnecessary trailing multi-value separators, for example `Kenya||Tanzania||`.
 
+## Unsafe Fixes
+You can enable several "unsafe" fixes with the `--unsafe-fixes` option. Currently this will remove newlines, perform Unicode normalization, and attempt to fix "mojibake" characters.
+
 ### Newlines
 This is considered "unsafe" because some systems give special importance to vertical space and render it properly. DSpace does not support rendering newlines in its XMLUI and has, at times, suffered from parsing errors that cause the import process to fail if an input file had newlines. The `--unsafe-fixes` option strips Unix line feeds (U+000A).
 
@@ -73,6 +76,14 @@ This is considered "unsafe" because some systems give special importance to vert
 
 Read more about [Unicode normalization](https://withblue.ink/2019/03/11/why-you-need-to-normalize-unicode-strings.html).
 
+### Encoding Issues aka "Mojibake"
+[Mojibake](https://en.wikipedia.org/wiki/Mojibake) is a phenomenon that occurs when text is decoded using an unintended character encoding. This usually presents itself in the form of strange, garbled characters in the text. Enabling "unsafe" fixes will attempt to correct these, for example:
+
+- CIAT Publicaçao → CIAT Publicaçao
+- CIAT Publicación → CIAT Publicación
+
+Pay special attention to the output of the script as well as the resulting file to make sure no new issues have been introduced. The ideal way to solve these issues is to avoid it in the first place. See [this guide about opening CSVs in UTF-8 format in Excel](https://www.itg.ias.edu/content/how-import-csv-file-uses-utf-8-character-encoding-0).
+
 ## AGROVOC Validation
 You can enable validation of metadata values in certain fields against the AGROVOC REST API with the `--agrovoc-fields` option. For example, in addition to agricultural subjects, many countries and regions are also present AGROVOC. Enable this validation by specifying a comma-separated list of fields:
 
@@ -103,7 +114,6 @@ This currently uses the [Python langid](https://github.com/saffsd/langid.py) lib
 - Better logging, for example with INFO, WARN, and ERR levels
 - Verbose, debug, or quiet options
 - Warn if an author is shorter than 3 characters?
-- Validate dc.rights field against SPDX? Perhaps with an option like `-m spdx` to enable the spdx module?
 - Validate DOIs? Normalize to https://doi.org format? Or use just the DOI part: 10.1016/j.worlddev.2010.06.006
 - Warn if two items use the same file in `filename` column
 - Add an option to drop invalid AGROVOC subjects?
@@ -112,10 +122,10 @@ This currently uses the [Python langid](https://github.com/saffsd/langid.py) lib
 - Add configurable field validation, like specify a field name and a validation file?
   - Perhaps like --validate=field.name,filename
 - Add some row-based item sanity checks and fixes:
-    - Warn if item is Open Access, but missing a filename or URL
-    - Warn if item is Open Access, but missing a license
-    - Warn if item has an ISSN but no journal title
-    - Update journal titles from ISSN
+  - Warn if item is Open Access, but missing a filename or URL
+  - Warn if item is Open Access, but missing a license
+  - Warn if item has an ISSN but no journal title
+  - Update journal titles from ISSN
 
 ## License
 This work is licensed under the [GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html).

csv_metadata_quality/__main__.py

@@ -1,3 +1,5 @@
+# SPDX-License-Identifier: GPL-3.0-only
+
 from sys import argv
 
 from csv_metadata_quality import app

csv_metadata_quality/app.py

@@ -1,3 +1,5 @@
+# SPDX-License-Identifier: GPL-3.0-only
+
 import argparse
 import re
 import signal
@@ -17,7 +19,7 @@ def parse_args(argv):
     parser.add_argument(
         "--agrovoc-fields",
         "-a",
-        help="Comma-separated list of fields to validate against AGROVOC, for example: dc.subject,cg.coverage.country",
+        help="Comma-separated list of fields to validate against AGROVOC, for example: dcterms.subject,cg.coverage.country",
     )
     parser.add_argument(
         "--experimental-checks",
@@ -48,7 +50,7 @@ def parse_args(argv):
     parser.add_argument(
         "--exclude-fields",
         "-x",
-        help="Comma-separated list of fields to skip, for example: dc.contributor.author,dc.identifier.citation",
+        help="Comma-separated list of fields to skip, for example: dc.contributor.author,dcterms.bibliographicCitation",
     )
     args = parser.parse_args()
 
@@ -104,17 +106,20 @@ def run(argv):
         # Fix: unnecessary Unicode
         df[column] = df[column].apply(fix.unnecessary_unicode)
 
-        # Check: invalid and unnecessary multi-value separators
-        df[column] = df[column].apply(check.separators, field_name=column)
-
         # Check: suspicious characters
-        df[column] = df[column].apply(check.suspicious_characters, field_name=column)
+        df[column].apply(check.suspicious_characters, field_name=column)
+
+        # Check: mojibake
+        df[column].apply(check.mojibake, field_name=column)
+
+        # Fix: mojibake
+        if args.unsafe_fixes:
+            df[column] = df[column].apply(fix.mojibake, field_name=column)
 
         # Fix: invalid and unnecessary multi-value separators
-        if args.unsafe_fixes:
-            df[column] = df[column].apply(fix.separators, field_name=column)
-            # Run whitespace fix again after fixing invalid separators
-            df[column] = df[column].apply(fix.whitespace, field_name=column)
+        df[column] = df[column].apply(fix.separators, field_name=column)
+        # Run whitespace fix again after fixing invalid separators
+        df[column] = df[column].apply(fix.whitespace, field_name=column)
 
         # Fix: duplicate metadata values
         df[column] = df[column].apply(fix.duplicates, field_name=column)
@@ -124,31 +129,51 @@ def run(argv):
             # Identify fields the user wants to validate against AGROVOC
             for field in args.agrovoc_fields.split(","):
                 if column == field:
-                    df[column] = df[column].apply(check.agrovoc, field_name=column)
+                    df[column].apply(check.agrovoc, field_name=column)
 
         # Check: invalid language
         match = re.match(r"^.*?language.*$", column)
         if match is not None:
-            df[column] = df[column].apply(check.language)
+            df[column].apply(check.language)
 
         # Check: invalid ISSN
         match = re.match(r"^.*?issn.*$", column)
         if match is not None:
-            df[column] = df[column].apply(check.issn)
+            df[column].apply(check.issn)
 
         # Check: invalid ISBN
         match = re.match(r"^.*?isbn.*$", column)
         if match is not None:
-            df[column] = df[column].apply(check.isbn)
+            df[column].apply(check.isbn)
 
         # Check: invalid date
         match = re.match(r"^.*?(date|dcterms\.issued).*$", column)
         if match is not None:
-            df[column] = df[column].apply(check.date, field_name=column)
+            df[column].apply(check.date, field_name=column)
 
         # Check: filename extension
         if column == "filename":
-            df[column] = df[column].apply(check.filename_extension)
+            df[column].apply(check.filename_extension)
+
+        # Check: SPDX license identifier
+        match = re.match(r"dcterms\.license.*$", column)
+        if match is not None:
+            df[column].apply(check.spdx_license_identifier)
+
+    ### End individual column checks ###
+
+    # Check: duplicate items
+    # We extract just the title, type, and date issued columns to analyze
+    try:
+        duplicates_df = df.filter(
+            regex=r"dcterms\.title|dc\.title|dcterms\.type|dc\.type|dcterms\.issued|dc\.date\.issued"
+        )
+        check.duplicate_items(duplicates_df)
+
+        # Delete the temporary duplicates DataFrame
+        del duplicates_df
+    except IndexError:
+        pass
 
     ##
     # Perform some checks on rows so we can consider items as a whole rather

csv_metadata_quality/check.py

@@ -1,10 +1,19 @@
+# SPDX-License-Identifier: GPL-3.0-only
+
+import os
+import re
 from datetime import datetime, timedelta
 
 import pandas as pd
 import requests
 import requests_cache
+import spdx_license_list
 from colorama import Fore
 from pycountry import languages
+from stdnum import isbn as stdnum_isbn
+from stdnum import issn as stdnum_issn
+
+from csv_metadata_quality.util import is_mojibake
 
 
 def issn(field):
@@ -17,8 +26,6 @@ def issn(field):
     See: https://arthurdejong.org/python-stdnum/doc/1.11/index.html#stdnum.module.is_valid
     """
 
-    from stdnum import issn
-
     # Skip fields with missing values
     if pd.isna(field):
         return
@@ -26,10 +33,10 @@ def issn(field):
     # Try to split multi-value field on "||" separator
     for value in field.split("||"):
 
-        if not issn.is_valid(value):
+        if not stdnum_issn.is_valid(value):
             print(f"{Fore.RED}Invalid ISSN: {Fore.RESET}{value}")
 
-    return field
+    return
 
 
 def isbn(field):
@@ -42,8 +49,6 @@ def isbn(field):
     See: https://arthurdejong.org/python-stdnum/doc/1.11/index.html#stdnum.module.is_valid
     """
 
-    from stdnum import isbn
-
     # Skip fields with missing values
     if pd.isna(field):
         return
@@ -51,48 +56,10 @@ def isbn(field):
     # Try to split multi-value field on "||" separator
     for value in field.split("||"):
 
-        if not isbn.is_valid(value):
+        if not stdnum_isbn.is_valid(value):
             print(f"{Fore.RED}Invalid ISBN: {Fore.RESET}{value}")
 
-    return field
-
-
-def separators(field, field_name):
-    """Check for invalid and unnecessary multi-value separators, for example:
-
-        value|value
-        value|||value
-        value||value||
-
-    Prints the field with the invalid multi-value separator.
-    """
-
-    import re
-
-    # Skip fields with missing values
-    if pd.isna(field):
-        return
-
-    # Try to split multi-value field on "||" separator
-    for value in field.split("||"):
-        # Check if the current value is blank
-        if value == "":
-            print(
-                f"{Fore.RED}Unnecessary multi-value separator ({field_name}): {Fore.RESET}{field}"
-            )
-
-            continue
-
-        # After splitting, see if there are any remaining "|" characters
-        match = re.findall(r"^.*?\|.*$", value)
-
-        # Check if there was a match
-        if match:
-            print(
-                f"{Fore.RED}Invalid multi-value separator ({field_name}): {Fore.RESET}{field}"
-            )
-
-    return field
+    return
 
 
 def date(field, field_name):
@@ -120,13 +87,13 @@ def date(field, field_name):
             f"{Fore.RED}Multiple dates not allowed ({field_name}): {Fore.RESET}{field}"
         )
 
-        return field
+        return
 
     try:
         # Check if date is valid YYYY format
         datetime.strptime(field, "%Y")
 
-        return field
+        return
     except ValueError:
         pass
 
@@ -134,7 +101,7 @@ def date(field, field_name):
         # Check if date is valid YYYY-MM format
         datetime.strptime(field, "%Y-%m")
 
-        return field
+        return
     except ValueError:
         pass
 
@@ -142,7 +109,7 @@ def date(field, field_name):
         # Check if date is valid YYYY-MM-DD format
         datetime.strptime(field, "%Y-%m-%d")
 
-        return field
+        return
     except ValueError:
         pass
 
@@ -150,11 +117,11 @@ def date(field, field_name):
         # Check if date is valid YYYY-MM-DDTHH:MM:SSZ format
         datetime.strptime(field, "%Y-%m-%dT%H:%M:%SZ")
 
-        return field
+        return
     except ValueError:
         print(f"{Fore.RED}Invalid date ({field_name}): {Fore.RESET}{field}")
 
-        return field
+        return
 
 
 def suspicious_characters(field, field_name):
@@ -188,7 +155,7 @@ def suspicious_characters(field, field_name):
             suspicious_character_msg = f"{Fore.YELLOW}Suspicious character ({field_name}): {Fore.RESET}{field_subset}"
             print(f"{suspicious_character_msg:1.80}")
 
-    return field
+    return
 
 
 def language(field):
@@ -211,17 +178,13 @@ def language(field):
         if len(value) == 2:
             if not languages.get(alpha_2=value):
                 print(f"{Fore.RED}Invalid ISO 639-1 language: {Fore.RESET}{value}")
-
-                pass
         elif len(value) == 3:
             if not languages.get(alpha_3=value):
                 print(f"{Fore.RED}Invalid ISO 639-3 language: {Fore.RESET}{value}")
-
-                pass
         else:
             print(f"{Fore.RED}Invalid language: {Fore.RESET}{value}")
 
-    return field
+    return
 
 
 def agrovoc(field, field_name):
@@ -244,10 +207,16 @@ def agrovoc(field, field_name):
 
     # enable transparent request cache with thirty days expiry
     expire_after = timedelta(days=30)
-    requests_cache.install_cache("agrovoc-response-cache", expire_after=expire_after)
+    # Allow overriding the location of the requests cache, just in case we are
+    # running in an environment where we can't write to the current working di-
+    # rectory (for example from csv-metadata-quality-web).
+    REQUESTS_CACHE_DIR = os.environ.get("REQUESTS_CACHE_DIR", ".")
+    requests_cache.install_cache(
+        f"{REQUESTS_CACHE_DIR}/agrovoc-response-cache", expire_after=expire_after
+    )
 
     # prune old cache entries
-    requests_cache.core.remove_expired_responses()
+    requests_cache.remove_expired_responses()
 
     # Try to split multi-value field on "||" separator
     for value in field.split("||"):
@@ -263,7 +232,7 @@ def agrovoc(field, field_name):
             if len(data["results"]) == 0:
                 print(f"{Fore.RED}Invalid AGROVOC ({field_name}): {Fore.RESET}{value}")
 
-    return field
+    return
 
 
 def filename_extension(field):
@@ -277,8 +246,6 @@ def filename_extension(field):
     than .pdf, .xls(x), .doc(x), ppt(x), case insensitive).
     """
 
-    import re
-
     # Skip fields with missing values
     if pd.isna(field):
         return
@@ -316,4 +283,82 @@ def filename_extension(field):
         if filename_extension_match is False:
             print(f"{Fore.YELLOW}Filename with uncommon extension: {Fore.RESET}{value}")
 
-    return field
+    return
+
+
+def spdx_license_identifier(field):
+    """Check if a license is a valid SPDX identifier.
+
+    Prints the value if it is invalid.
+    """
+
+    # Skip fields with missing values
+    if pd.isna(field):
+        return
+
+    # Try to split multi-value field on "||" separator
+    for value in field.split("||"):
+        if value not in spdx_license_list.LICENSES:
+            print(f"{Fore.YELLOW}Non-SPDX license identifier: {Fore.RESET}{value}")
+
+    return
+
+
+def duplicate_items(df):
+    """Attempt to identify duplicate items.
+
+    First we check the total number of titles and compare it with the number of
+    unique titles. If there are less unique titles than total titles we expand
+    the search by creating a key (of sorts) for each item that includes their
+    title, type, and date issued, and compare it with all the others. If there
+    are multiple occurrences of the same title, type, date string then it's a
+    very good indicator that the items are duplicates.
+    """
+
+    # Extract the names of the title, type, and date issued columns so we can
+    # reference them later. First we filter columns by likely patterns, then
+    # we extract the name from the first item of the resulting object, ie:
+    #
+    #   Index(['dcterms.title[en_US]'], dtype='object')
+    #
+    title_column_name = df.filter(regex=r"dcterms\.title|dc\.title").columns[0]
+    type_column_name = df.filter(regex=r"dcterms\.title|dc\.title").columns[0]
+    date_column_name = df.filter(
+        regex=r"dcterms\.issued|dc\.date\.accessioned"
+    ).columns[0]
+
+    items_count_total = df[title_column_name].count()
+    items_count_unique = df[title_column_name].nunique()
+
+    if items_count_unique < items_count_total:
+        # Create a list to hold our items while we check for duplicates
+        items = list()
+
+        for index, row in df.iterrows():
+            item_title_type_date = f"{row[title_column_name]}{row[type_column_name]}{row[date_column_name]}"
+
+            if item_title_type_date in items:
+                print(
+                    f"{Fore.YELLOW}Possible duplicate ({title_column_name}): {Fore.RESET}{row[title_column_name]}"
+                )
+            else:
+                items.append(item_title_type_date)
+
+
+def mojibake(field, field_name):
+    """Check for mojibake (text that was encoded in one encoding and decoded in
+    in another, perhaps multiple times). See util.py.
+
+    Prints the string if it contains suspected mojibake.
+    """
+
+    # Skip fields with missing values
+    if pd.isna(field):
+        return
+
+    if is_mojibake(field):
+        print(
+            f"{Fore.YELLOW}Possible encoding issue ({field_name}): {Fore.RESET}{field}"
+        )
+
+    return

csv_metadata_quality/experimental.py

@@ -1,5 +1,11 @@
+# SPDX-License-Identifier: GPL-3.0-only
+
+import re
+
+import langid
 import pandas as pd
 from colorama import Fore
+from pycountry import languages
 
 
 def correct_language(row):
@@ -11,11 +17,6 @@ def correct_language(row):
     language and returns the value in the language field if it does match.
     """
 
-    import re
-
-    import langid
-    from pycountry import languages
-
     # Initialize some variables at global scope so that we can set them in the
     # loop scope below and still be able to access them afterwards.
     language = ""
@@ -94,4 +95,4 @@ def correct_language(row):
         )
 
     else:
-        return language
+        return

csv_metadata_quality/fix.py

@@ -1,10 +1,13 @@
+# SPDX-License-Identifier: GPL-3.0-only
+
 import re
 from unicodedata import normalize
 
 import pandas as pd
 from colorama import Fore
+from ftfy import fix_text
 
-from csv_metadata_quality.util import is_nfc
+from csv_metadata_quality.util import is_mojibake, is_nfc
 
 
 def whitespace(field, field_name):
@@ -77,7 +80,7 @@ def separators(field, field_name):
 
         if match:
             print(
-                f"{Fore.RED}Fixing invalid multi-value separator ({field_name}): {Fore.RESET}{value}"
+                f"{Fore.GREEN}Fixing invalid multi-value separator ({field_name}): {Fore.RESET}{value}"
             )
 
             value = re.sub(pattern, "||", value)
@@ -253,3 +256,22 @@ def normalize_unicode(field, field_name):
         field = normalize("NFC", field)
 
     return field
+
+
+def mojibake(field, field_name):
+    """Attempts to fix mojibake (text that was encoded in one encoding and deco-
+    ded in another, perhaps multiple times). See util.py.
+
+    Return fixed string.
+    """
+
+    # Skip fields with missing values
+    if pd.isna(field):
+        return field
+
+    if is_mojibake(field):
+        print(f"{Fore.GREEN}Fixing encoding issue ({field_name}): {Fore.RESET}{field}")
+
+        return fix_text(field)
+    else:
+        return field

csv_metadata_quality/util.py

@@ -1,3 +1,8 @@
+# SPDX-License-Identifier: GPL-3.0-only
+
+from ftfy.badness import sequence_weirdness
+
+
 def is_nfc(field):
     """Utility function to check whether a string is using normalized Unicode.
     Python's built-in unicodedata library has the is_normalized() function, but
@@ -12,3 +17,35 @@ def is_nfc(field):
     from unicodedata import normalize
 
     return field == normalize("NFC", field)
+
+
+def is_mojibake(field):
+    """Determines whether a string contains mojibake.
+
+    We commonly deal with CSV files that were *encoded* in UTF-8, but decoded
+    as something else like CP-1252 (Windows Latin). This manifests in the form
+    of "mojibake", for example:
+
+        - CIAT Publicaçao
+        - CIAT Publicación
+
+    This uses the excellent "fixes text for you" (ftfy) library to determine
+    whether a string contains characters that have been encoded in one encoding
+    and decoded in another.
+
+    Inspired by this code snippet from Martijn Pieters on StackOverflow:
+    https://stackoverflow.com/questions/29071995/identify-garbage-unicode-string-using-python
+
+    Return boolean.
+    """
+    if not sequence_weirdness(field):
+        # Nothing weird, should be okay
+        return False
+    try:
+        field.encode("sloppy-windows-1252")
+    except UnicodeEncodeError:
+        # Not CP-1252 encodable, probably fine
+        return False
+    else:
+        # Encodable as CP-1252, Mojibake alert level high
+        return True

csv_metadata_quality/version.py

@@ -1 +1,3 @@
-VERSION = "0.4.5"
+# SPDX-License-Identifier: GPL-3.0-only
+
+VERSION = "0.4.8-dev"

data/test.csv

@@ -1,31 +1,35 @@
-dc.title,dc.date.issued,dc.identifier.issn,dc.identifier.isbn,dc.language.iso,dc.subject,cg.coverage.country,filename
- Leading space,2019-07-29,,,,,,
-Trailing space ,2019-07-29,,,,,,
-Excessive  space,2019-07-29,,,,,,
-Miscellaenous ||whitespace | issues ,2019-07-29,,,,,,
-Duplicate||Duplicate,2019-07-29,,,,,,
-Invalid ISSN,2019-07-29,2321-2302,,,,,
-Invalid ISBN,2019-07-29,,978-0-306-40615-6,,,,
-Multiple valid ISSNs,2019-07-29,0378-5955||0024-9319,,,,,
-Multiple valid ISBNs,2019-07-29,,99921-58-10-7||978-0-306-40615-7,,,,
-Invalid date,2019-07-260,,,,,,
-Multiple dates,2019-07-26||2019-01-10,,,,,,
-Invalid multi-value separator,2019-07-29,0378-5955|0024-9319,,,,,
-Unnecessary Unicode​,2019-07-29,,,,,,
-Suspicious character||foreˆt,2019-07-29,,,,,,
-Invalid ISO 639-1 (alpha 2) language,2019-07-29,,,jp,,,
-Invalid ISO 639-3 (alpha 3) language,2019-07-29,,,chi,,,
-Invalid language,2019-07-29,,,Span,,,
-Invalid AGROVOC subject,2019-07-29,,,,FOREST,,
+dc.title,dcterms.issued,dc.identifier.issn,dc.identifier.isbn,dcterms.language,dcterms.subject,cg.coverage.country,filename,dcterms.license,dcterms.type
+ Leading space,2019-07-29,,,,,,,,
+Trailing space ,2019-07-29,,,,,,,,
+Excessive  space,2019-07-29,,,,,,,,
+Miscellaenous ||whitespace | issues ,2019-07-29,,,,,,,,
+Duplicate||Duplicate,2019-07-29,,,,,,,,
+Invalid ISSN,2019-07-29,2321-2302,,,,,,,
+Invalid ISBN,2019-07-29,,978-0-306-40615-6,,,,,,
+Multiple valid ISSNs,2019-07-29,0378-5955||0024-9319,,,,,,,
+Multiple valid ISBNs,2019-07-29,,99921-58-10-7||978-0-306-40615-7,,,,,,
+Invalid date,2019-07-260,,,,,,,,
+Multiple dates,2019-07-26||2019-01-10,,,,,,,,
+Invalid multi-value separator,2019-07-29,0378-5955|0024-9319,,,,,,,
+Unnecessary Unicode​,2019-07-29,,,,,,,,
+Suspicious character||foreˆt,2019-07-29,,,,,,,,
+Invalid ISO 639-1 (alpha 2) language,2019-07-29,,,jp,,,,,
+Invalid ISO 639-3 (alpha 3) language,2019-07-29,,,chi,,,,,
+Invalid language,2019-07-29,,,Span,,,,,
+Invalid AGROVOC subject,2019-07-29,,,,FOREST,,,,
 Newline (LF),2019-07-30,,,,"TANZA
-NIA",,
-Missing date,,,,,,,
-Invalid country,2019-08-01,,,,,KENYAA,
-Uncommon filename extension,2019-08-10,,,,,,file.pdf.lck
-Unneccesary unicode (U+002D + U+00AD),2019-08-10,,978-­92-­9043-­823-­6,,,,
-"Missing space,after comma",2019-08-27,,,,,,
-Incorrect ISO 639-1 language,2019-09-26,,,es,,,
-Incorrect ISO 639-3 language,2019-09-26,,,spa,,,
-Composéd Unicode,2020-01-14,,,,,,
-Decomposéd Unicode,2020-01-14,,,,,,
-Unnecessary multi-value separator,2021-01-03,0378-5955||,,,,,
+NIA",,,,
+Missing date,,,,,,,,,
+Invalid country,2019-08-01,,,,,KENYAA,,,
+Uncommon filename extension,2019-08-10,,,,,,file.pdf.lck,,
+Unneccesary unicode (U+002D + U+00AD),2019-08-10,,978-­92-­9043-­823-­6,,,,,,
+"Missing space,after comma",2019-08-27,,,,,,,,
+Incorrect ISO 639-1 language,2019-09-26,,,es,,,,,
+Incorrect ISO 639-3 language,2019-09-26,,,spa,,,,,
+Composéd Unicode,2020-01-14,,,,,,,,
+Decomposéd Unicode,2020-01-14,,,,,,,,
+Unnecessary multi-value separator,2021-01-03,0378-5955||,,,,,,,
+Invalid SPDX license identifier,2021-03-11,,,,,,,CC-BY,
+Duplicate Title,2021-03-17,,,,,,,,Report
+Duplicate Title,2021-03-17,,,,,,,,Report
+Mojibake,2021-03-18,,,,CIAT Publicaçao,,,,Report

pyproject.toml

@@ -1,29 +1,34 @@
 [tool.poetry]
 name = "csv-metadata-quality"
-version = "0.4.5"
+version = "0.4.8-dev"
 description="A simple, but opinionated CSV quality checking and fixing pipeline for CSVs in the DSpace ecosystem."
 authors = ["Alan Orth <alan.orth@gmail.com>"]
 license="GPL-3.0-only"
 repository = "https://github.com/ilri/csv-metadata-quality"
 homepage = "https://github.com/ilri/csv-metadata-quality"
 
+[tool.poetry.scripts]
+csv-metadata-quality = 'csv_metadata_quality.__main__:main'
+
 [tool.poetry.dependencies]
-python = "^3.8"
+python = "^3.7.1"
 pandas = "^1.0.4"
 python-stdnum = "^1.13"
 xlrd = "^1.2.0"
 requests = "^2.23.0"
-requests-cache = "^0.5.2"
+requests-cache = "~0.6.4"
 pycountry = "^19.8.18"
 langid = "^1.1.6"
 colorama = "^0.4.4"
+spdx-license-list = "^0.5.2"
+ftfy = "^5.9"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.1.1"
 ipython = { version = "^7.18.1", python = "^3.7" }
 flake8 = "^3.8.4"
-pytest-clarity = "^0.3.0-alpha.0"
-black = "20.8b1"
+pytest-clarity = "^1.0.1"
+black = "^21.6b0"
 isort = "^5.5.4"
 csvkit = "^1.0.5"
 

requirements-dev.txt

@@ -1,71 +1,81 @@
 agate-dbf==0.2.2
 agate-excel==0.2.3
-agate-sql==0.5.5
-agate==1.6.1
-appdirs==1.4.4; python_version >= "3.6"
+agate-sql==0.5.6
+agate==1.6.2
+appdirs==1.4.4; python_full_version >= "3.6.2"
 appnope==0.1.2; python_version >= "3.7" and python_version < "4.0" and sys_platform == "darwin"
 atomicwrites==1.4.0; python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6") or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.4.0" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6")
-attrs==20.3.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
-babel==2.9.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
+attrs==21.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+babel==2.9.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
 backcall==0.2.0; python_version >= "3.7" and python_version < "4.0"
-black==20.8b1; python_version >= "3.6"
-certifi==2020.12.5; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
-chardet==4.0.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
-click==7.1.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+black==21.6b0; python_full_version >= "3.6.2"
+certifi==2021.5.30; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+chardet==4.0.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+click==8.0.1; python_version >= "3.6" and python_full_version >= "3.6.2"
 colorama==0.4.4; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
+commonmark==0.9.1; python_version >= "3.6" and python_version < "4.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0")
 csvkit==1.0.5
 dbfread==2.0.7
-decorator==4.4.2; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.2.0"
-et-xmlfile==1.0.1; python_version >= "3.6"
-flake8==3.8.4; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
-idna==2.10; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
+decorator==5.0.9; python_version >= "3.7" and python_version < "4.0"
+et-xmlfile==1.1.0; python_version >= "3.6"
+flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
+ftfy==5.9; python_version >= "3.5"
+greenlet==1.1.0; python_version >= "3" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3"
+idna==2.10; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+importlib-metadata==4.6.1; python_version < "3.8" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "3.8" or python_full_version >= "3.5.0" and python_version < "3.8" and python_version >= "3.6") and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "3.8" or python_full_version >= "3.4.0" and python_version >= "3.6" and python_version < "3.8") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6") and python_full_version >= "3.6.2" and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "3.8" or python_full_version >= "3.6.0" and python_version < "3.8" and python_version >= "3.6")
 iniconfig==1.1.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
 ipython-genutils==0.2.0; python_version >= "3.7" and python_version < "4.0"
-ipython==7.21.0; python_version >= "3.7" and python_version < "4.0"
+ipython==7.25.0; python_version >= "3.7" and python_version < "4.0"
 isodate==0.6.0
-isort==5.7.0; python_version >= "3.6" and python_version < "4.0"
-jdcal==1.4.1; python_version >= "3.6"
+isort==5.9.1; python_full_version >= "3.6.1" and python_version < "4.0"
+itsdangerous==2.0.1; python_version >= "3.6"
 jedi==0.18.0; python_version >= "3.7" and python_version < "4.0"
 langid==1.1.6
 leather==0.3.3
-mccabe==0.6.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
-mypy-extensions==0.4.3; python_version >= "3.6"
-numpy==1.20.1; python_version >= "3.7" and python_full_version >= "3.7.1"
-openpyxl==3.0.6; python_version >= "3.6"
-packaging==20.9; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
-pandas==1.2.3; python_full_version >= "3.7.1"
+matplotlib-inline==0.1.2; python_version >= "3.7" and python_version < "4.0"
+mccabe==0.6.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
+mypy-extensions==0.4.3; python_full_version >= "3.6.2"
+numpy==1.21.0; python_version >= "3.7" and python_full_version >= "3.7.1"
+openpyxl==3.0.7; python_version >= "3.6"
+packaging==21.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+pandas==1.3.0; python_full_version >= "3.7.1"
 parsedatetime==2.6
-parso==0.8.1; python_version >= "3.7" and python_version < "4.0"
-pathspec==0.8.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+parso==0.8.2; python_version >= "3.7" and python_version < "4.0"
+pathspec==0.8.1; python_full_version >= "3.6.2"
 pexpect==4.8.0; python_version >= "3.7" and python_version < "4.0" and sys_platform != "win32"
 pickleshare==0.7.5; python_version >= "3.7" and python_version < "4.0"
 pluggy==0.13.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
-prompt-toolkit==3.0.16; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.6.1"
+pprintpp==0.4.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
+prompt-toolkit==3.0.19; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.6.1"
 ptyprocess==0.7.0; python_version >= "3.7" and python_version < "4.0" and sys_platform != "win32"
 py==1.10.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
-pycodestyle==2.6.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
+pycodestyle==2.7.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
 pycountry==19.8.18
-pyflakes==2.2.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
-pygments==2.8.0; python_version >= "3.7" and python_version < "4.0"
-pyparsing==2.4.7; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
-pytest-clarity==0.3.0a0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
-pytest==6.2.2; python_version >= "3.6"
+pyflakes==2.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
+pygments==2.9.0; python_version >= "3.7" and python_version < "4.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0")
+pyicu==2.7.4
+pyparsing==2.4.7; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6"
+pytest-clarity==1.0.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
+pytest==6.2.4; python_version >= "3.6"
 python-dateutil==2.8.1; python_full_version >= "3.7.1"
-python-slugify==4.0.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
+python-slugify==5.0.2; python_version >= "3.6"
 python-stdnum==1.16
 pytimeparse==1.1.8
 pytz==2021.1; python_full_version >= "3.7.1"
-regex==2020.11.13; python_version >= "3.6"
-requests-cache==0.5.2
+regex==2021.7.6; python_full_version >= "3.6.2"
+requests-cache==0.6.4; python_version >= "3.6"
 requests==2.25.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
-six==1.15.0; python_full_version >= "3.7.1"
-sqlalchemy==1.3.23; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
-termcolor==1.1.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0"
-text-unidecode==1.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
-toml==0.10.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+rich==10.5.0; python_version >= "3.6" and python_version < "4.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0")
+six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.6" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0")
+spdx-license-list==0.5.2
+sqlalchemy==1.4.20; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
+text-unidecode==1.3; python_version >= "3.6"
+toml==0.10.2; python_full_version >= "3.6.2" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6")
 traitlets==5.0.5; python_version >= "3.7" and python_version < "4.0"
-typed-ast==1.4.2; python_version >= "3.6"
-typing-extensions==3.7.4.3; python_version >= "3.6"
-urllib3==1.26.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4"
+typed-ast==1.4.3; python_version < "3.8" and python_full_version >= "3.6.2"
+typing-extensions==3.10.0.0; python_version < "3.8" and python_full_version >= "3.6.2" and python_version >= "3.6" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0")
+url-normalize==1.4.3; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
+urllib3==1.26.6; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "3.6"
 wcwidth==0.2.5; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.6.1"
 xlrd==1.2.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
+zipp==3.5.0; python_version < "3.8" and python_version >= "3.6"

requirements.txt

@@ -1,16 +1,21 @@
-certifi==2020.12.5; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
-chardet==4.0.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
+certifi==2021.5.30; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+chardet==4.0.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
 colorama==0.4.4; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
-idna==2.10; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
+ftfy==5.9; python_version >= "3.5"
+idna==2.10; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+itsdangerous==2.0.1; python_version >= "3.6"
 langid==1.1.6
-numpy==1.20.1; python_version >= "3.7" and python_full_version >= "3.7.1"
-pandas==1.2.3; python_full_version >= "3.7.1"
+numpy==1.21.0; python_version >= "3.7" and python_full_version >= "3.7.1"
+pandas==1.3.0; python_full_version >= "3.7.1"
 pycountry==19.8.18
 python-dateutil==2.8.1; python_full_version >= "3.7.1"
 python-stdnum==1.16
 pytz==2021.1; python_full_version >= "3.7.1"
-requests-cache==0.5.2
+requests-cache==0.6.4; python_version >= "3.6"
 requests==2.25.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
-six==1.15.0; python_full_version >= "3.7.1"
-urllib3==1.26.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4"
+six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.6"
+spdx-license-list==0.5.2
+url-normalize==1.4.3; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
+urllib3==1.26.6; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "3.6"
+wcwidth==0.2.5; python_version >= "3.5"
 xlrd==1.2.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")

setup.py

@@ -14,7 +14,7 @@ install_requires = [
 
 setuptools.setup(
     name="csv-metadata-quality",
-    version="0.4.5",
+    version="0.4.8-dev",
     author="Alan Orth",
     author_email="aorth@mjanja.ch",
     description="A simple, but opinionated CSV quality checking and fixing pipeline for CSVs in the DSpace ecosystem.",
@@ -28,7 +28,6 @@ setuptools.setup(
         "Programming Language :: Python :: 3.9",
         "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
         "Operating System :: OS Independent",
-        "Development Status :: 4 - Beta",
     ],
     packages=["csv_metadata_quality"],
     entry_points={

tests/test_check.py

@@ -1,3 +1,5 @@
+# SPDX-License-Identifier: GPL-3.0-only
+
 import pandas as pd
 from colorama import Fore
 
@@ -23,7 +25,7 @@ def test_check_valid_issn():
 
     result = check.issn(value)
 
-    assert result == value
+    assert result == None
 
 
 def test_check_invalid_isbn(capsys):
@@ -44,51 +46,7 @@ def test_check_valid_isbn():
 
     result = check.isbn(value)
 
-    assert result == value
-
-
-def test_check_invalid_separators(capsys):
-    """Test checking invalid multi-value separators."""
-
-    value = "Alan|Orth"
-
-    field_name = "dc.contributor.author"
-
-    check.separators(value, field_name)
-
-    captured = capsys.readouterr()
-    assert (
-        captured.out
-        == f"{Fore.RED}Invalid multi-value separator ({field_name}): {Fore.RESET}{value}\n"
-    )
-
-
-def test_check_unnecessary_separators(capsys):
-    """Test checking unnecessary multi-value separators."""
-
-    field = "Alan||Orth||"
-
-    field_name = "dc.contributor.author"
-
-    check.separators(field, field_name)
-
-    captured = capsys.readouterr()
-    assert (
-        captured.out
-        == f"{Fore.RED}Unnecessary multi-value separator ({field_name}): {Fore.RESET}{field}\n"
-    )
-
-
-def test_check_valid_separators():
-    """Test checking valid multi-value separators."""
-
-    value = "Alan||Orth"
-
-    field_name = "dc.contributor.author"
-
-    result = check.separators(value, field_name)
-
-    assert result == value
+    assert result == None
 
 
 def test_check_missing_date(capsys):
@@ -144,7 +102,7 @@ def test_check_valid_date():
 
     result = check.date(value, field_name)
 
-    assert result == value
+    assert result == None
 
 
 def test_check_suspicious_characters(capsys):
@@ -170,7 +128,7 @@ def test_check_valid_iso639_1_language():
 
     result = check.language(value)
 
-    assert result == value
+    assert result == None
 
 
 def test_check_valid_iso639_3_language():
@@ -180,7 +138,7 @@ def test_check_valid_iso639_3_language():
 
     result = check.language(value)
 
-    assert result == value
+    assert result == None
 
 
 def test_check_invalid_iso639_1_language(capsys):
@@ -224,7 +182,7 @@ def test_check_invalid_agrovoc(capsys):
     """Test invalid AGROVOC subject."""
 
     value = "FOREST"
-    field_name = "dc.subject"
+    field_name = "dcterms.subject"
 
     check.agrovoc(value, field_name)
 
@@ -239,11 +197,11 @@ def test_check_valid_agrovoc():
     """Test valid AGROVOC subject."""
 
     value = "FORESTS"
-    field_name = "dc.subject"
+    field_name = "dcterms.subject"
 
     result = check.agrovoc(value, field_name)
 
-    assert result == value
+    assert result == None
 
 
 def test_check_uncommon_filename_extension(capsys):
@@ -267,7 +225,7 @@ def test_check_common_filename_extension():
 
     result = check.filename_extension(value)
 
-    assert result == value
+    assert result == None
 
 
 def test_check_incorrect_iso_639_1_language(capsys):
@@ -320,7 +278,7 @@ def test_check_correct_iso_639_1_language():
 
     result = experimental.correct_language(series)
 
-    assert result == language
+    assert result == None
 
 
 def test_check_correct_iso_639_3_language():
@@ -335,4 +293,77 @@ def test_check_correct_iso_639_3_language():
 
     result = experimental.correct_language(series)
 
-    assert result == language
+    assert result == None
+
+
+def test_check_valid_spdx_license_identifier():
+    """Test valid SPDX license identifier."""
+
+    license = "CC-BY-SA-4.0"
+
+    result = check.spdx_license_identifier(license)
+
+    assert result == None
+
+
+def test_check_invalid_spdx_license_identifier(capsys):
+    """Test invalid SPDX license identifier."""
+
+    license = "CC-BY-SA"
+
+    result = check.spdx_license_identifier(license)
+
+    captured = capsys.readouterr()
+    assert (
+        captured.out
+        == f"{Fore.YELLOW}Non-SPDX license identifier: {Fore.RESET}{license}\n"
+    )
+
+
+def test_check_duplicate_item(capsys):
+    """Test item with duplicate title, type, and date."""
+
+    item_title = "Title"
+    item_type = "Report"
+    item_date = "2021-03-17"
+
+    d = {
+        "dc.title": [item_title, item_title],
+        "dcterms.type": [item_type, item_type],
+        "dcterms.issued": [item_date, item_date],
+    }
+    df = pd.DataFrame(data=d)
+
+    result = check.duplicate_items(df)
+
+    captured = capsys.readouterr()
+    assert (
+        captured.out
+        == f"{Fore.YELLOW}Possible duplicate (dc.title): {Fore.RESET}{item_title}\n"
+    )
+
+
+def test_check_no_mojibake():
+    """Test string with no mojibake."""
+
+    field = "CIAT Publicaçao"
+    field_name = "dcterms.isPartOf"
+
+    result = check.mojibake(field, field_name)
+
+    assert result == None
+
+
+def test_check_mojibake(capsys):
+    """Test string with mojibake."""
+
+    field = "CIAT Publicaçao"
+    field_name = "dcterms.isPartOf"
+
+    result = check.mojibake(field, field_name)
+
+    captured = capsys.readouterr()
+    assert (
+        captured.out
+        == f"{Fore.YELLOW}Possible encoding issue ({field_name}): {Fore.RESET}{field}\n"
+    )

tests/test_fix.py

@@ -1,3 +1,5 @@
+# SPDX-License-Identifier: GPL-3.0-only
+
 import csv_metadata_quality.fix as fix
 
 
@@ -108,3 +110,12 @@ def test_fix_decomposed_unicode():
     field_name = "dc.contributor.author"
 
     assert fix.normalize_unicode(value, field_name) == "Ouédraogo, Mathieu"
+
+
+def test_fix_mojibake():
+    """Test string with no mojibake."""
+
+    field = "CIAT Publicaçao"
+    field_name = "dcterms.isPartOf"
+
+    assert fix.mojibake(field, field_name) == "CIAT Publicaçao"