docs

docs/index.md

@@ -9,7 +9,7 @@ Implementing observational studies using real-world data (RWD) is challenging, r
 PhenEx (Automated Phenotype Extraction) fills this gap. PhenEx is a Python-based software package that provides reusuable and end-to-end tested implementations of commonly performed operations in the implementation of observational studies. The main advantages of PhenEx are:
 
 - **Arbitrarily complex medical definitions**: Build medical definitions that depend on diagnoses, labs, procedures, and encounter context, as well as on other medical definitions
-- **Data-model agnostic**: Work with almost any RWD dataset with only extremely minimal mappings required. Only map the data needed for the study execution.
+- **Data-model agnostic**: Work with almost any RWD dataset with only extremely minimal mappings. Only map the data needed for the study execution. Use the ontologies native to your dataset.
 - **Portable**: Built on top of [ibis](https://ibis-project.org/), PhenEx works with any backend that ibis supports, including snowflake, PySpark and many more!
 - **Intuitive interface**: Study specification in PhenEx mirrors plain language description of the study.
 - **High test coverage**: Full confidence answer is correct.

docs/installation.md

@@ -16,7 +16,7 @@ Coming soon!
 To install from source, run the following from within your virtual environment:
 
 ```
-git clone git@github.com:Bayer-Group/PhenEx.git && \
+git clone https://github.com/Bayer-Group/PhenEx.git && \
     cd PhenEx && \
     pip install -r requirements.txt && \
     pip install .

phenex/sim.py

@@ -5,7 +5,7 @@
 from dataclasses import asdict
 
 
-def generate_fake_data(n_patients: int, domains: DomainsDictionary) -> Dict[str, pd.DataFrame]:
+def generate_mock_mapped_tables(n_patients: int, domains: DomainsDictionary) -> Dict[str, pd.DataFrame]:
     """
     Generate fake data for N patients based on the given domains.
 
@@ -24,11 +24,9 @@ def generate_fake_data(n_patients: int, domains: DomainsDictionary) -> Dict[str,
             if "DATE" in col:
                 start_date = pd.to_datetime('2000-01-01')
                 end_date = pd.to_datetime('2020-12-31')
-                data[col] = pd.to_datetime(np.random.randint(start_date.value, end_date.value, n_patients))
+                data[col] = pd.to_datetime(np.random.randint(start_date.value, end_date.value, n_patients)).date
             elif "ID" in col:
                 data[col] = np.arange(1, n_patients + 1)
-            elif "CODE" in col:
-                data[col] = np.random.choice(['A', 'B', 'C', 'D'], n_patients)
             elif "VALUE" in col:
                 data[col] = np.random.uniform(0, 100, n_patients)
             elif "CODE_TYPE" in col:
@@ -40,6 +38,8 @@ def generate_fake_data(n_patients: int, domains: DomainsDictionary) -> Dict[str,
                     data[col] = np.random.choice(['CPT', 'HCPCS'], n_patients)
                 else:
                     data[col] = np.random.choice(['TYPE1', 'TYPE2'], n_patients)
+            elif "CODE" in col:
+                data[col] = np.random.choice(['A', 'B', 'C', 'D', 'E', 'F', 'G'], n_patients)
             else:
                 data[col] = np.random.choice(range(1000), n_patients)
         fake_data[domain] = pd.DataFrame(data)