Update README.md

README.md

@@ -129,24 +129,25 @@ python text2term -s test/unstruct_terms.txt -t MONDO
 
 
 ## Programmatic Usage
-After installing and importing to a Python environment, the main function is `map_terms`:
+After installing and importing to a Python environment, the main function is `map_terms()`:
 
 ```python
-text2term.map_terms(source_terms, 
- target_ontology='http://some.ontology/v1.owl',
- base_iris=(),
- csv_columns=(), 
- excl_deprecated=False, 
- max_mappings=3, 
- mapper=Mapper.TFIDF,
- min_score=0.3, 
- output_file='', 
- save_graphs=False, 
- save_mappings=False, 
- separator=',', 
- use_cache=False,
- term_type=OntologyTermType.CLASS,
- incl_unmapped=False)
+text2term.map_terms(source_terms, # strings to map or pointer to file 
+ target_ontology='mondo', # ontology to map to (URL or name)
+ max_mappings=3, # maximum mappings per string
+ mapper=Mapper.TFIDF, # mapping method to be used
+ min_score=0.3, # minimum mapping score 
+ base_iris=(), # map to terms with given base IRIs 
+ excl_deprecated=False, # exclude ontology deprecated terms
+ term_type='class', # ontology term type(s) to map to
+ save_graphs=False, # save vis.js file with term graphs
+ save_mappings=False, # save mappings to file or mot
+ output_file='', # filepath of output mappings file
+ csv_columns=(), # table columns with strings and IDs
+ separator=',', # column separator of input table 
+ use_cache=False, # use a locally cached ontology
+ incl_unmapped=False, # include unmapped strings in output
+ bioportal_apikey='') # API key to use the BioPortal mapper 
 ```
 The function returns a pandas `DataFrame` containing the generated ontology mappings.
 
@@ -193,18 +194,20 @@ When using the BioPortal or Zooma interfaces, the value for `target_ontology` sh
 
 `incl_unmapped`—Include unmapped terms in the output. If a term has been tagged 'Ignore' or has less than the `min_score`, it is included in the output data frame
 
+`bioportal_apikey`—BioPortal API Key to use along with the BioPortal mapper option
+
 
 ### Ontology Caching
 text2term supports caching ontologies for faster or repeated mapping to the same ontology. An ontology can be cached using the function:
 
 ```python
-cache_ontology(ontology_url, ontology_acronym="", base_iris=())
+text2term.cache_ontology(ontology_url, ontology_acronym="", base_iris=())
 ```
 This caches a single ontology from a URL or file path, and takes an optional acronym that will be used to reference the cached ontology later. If no acronym is given, the URL is used as the name.
 
 It is also possible to cache multiple ontologies, whose names and URLs are specified in a table formatted as such `acronym,version,url`. An example is provided in [resources/ontologies.csv](https://github.com/ccb-hms/ontology-mapper/blob/main/text2term/resources/ontologies.csv):
 ```python
-cache_ontology_set(ontology_registry_path)
+text2term.cache_ontology_set(ontology_registry_path)
 ```
 
 Once an ontology has been cached by either function, it is stored in a cache folder locally, and thus can be referenced even in different Python instances. Users can leverage the cache by using the assigned acronym as the value for the `target_ontology` argument, and setting the `use_cache` argument to `True`.
@@ -226,17 +229,17 @@ Finally, `cache_exists(ontology_acronym='')` is a simple function that returns `
 
 
 ### Input Preprocessing
-text2term includes regular expression-based preprocessing functionality for input terms. There are functions that take the input terms and a collection of (user-defined) regular expressions, then match each term to each regular expression to simplify the input term.
+text2term includes a module `preprocess.py` that supports regular expression-based preprocessing functionality for input terms. There are functions that take the input terms and a collection of (user-defined) regular expressions, then match each term to each regular expression to simplify the input term.
 
 ```python
-preprocess_terms(terms, template_path, output_file='', blocklist_path='', 
- blocklist_char='', rem_duplicates=False)
+preprocess.preprocess_terms(terms, template_path, output_file='', blocklist_path='', 
+  blocklist_char='', rem_duplicates=False)
 ``` 
 This returns a dictionary where the keys are the original terms and the values are the preprocessed terms.
 
 ```python
-preprocess_tagged_terms(file_path, template_path='', blocklist_path='', 
- blocklist_char='', rem_duplicates=False, separator=';:;')
+preprocess.preprocess_tagged_terms(file_path, template_path='', blocklist_path='', 
+  blocklist_char='', rem_duplicates=False, separator=';:;')
 ```
 
 This returns a list of `TaggedTerm` objects.
@@ -256,7 +259,7 @@ If an ignore tag `"ignore"` or `"Ignore"` is added to a term, that term will not
 
 After installing, execute the tool from a command line as follows:
 
-`python text2term [-h] -s SOURCE -t TARGET [-o OUTPUT] [-m MAPPER] [-csv CSV_INPUT] [-sep SEPARATOR] [-top TOP_MAPPINGS] [-min MIN_SCORE] [-iris BASE_IRIS] [-d] [-g] [-c STORE_IN_CACHE] [-type TERM_TYPE] [-u]`
+`python text2term [-h] -s SOURCE -t TARGET [-o OUTPUT] [-m MAPPER] [-csv CSV_INPUT] [-sep SEPARATOR] [-top TOP_MAPPINGS] [-min MIN_SCORE] [-iris BASE_IRIS] [-d] [-g] [-c STORE_IN_CACHE] [-type TERM_TYPE] [-u] [-bp BIOPORTAL_APIKEY]`
 
 To display a help message with descriptions of tool arguments do:
 
@@ -294,6 +297,7 @@ To display a help message with descriptions of tool arguments do:
 
 `-u` Include all unmapped terms in the output
 
+`-bp` BioPortal API Key to use along with the BioPortal mapper option
 
 ## Supported Mappers