From 4b4cf86f99fa0daa97483a08f6d74c321b52755a Mon Sep 17 00:00:00 2001
From: Eloy Felix <eloyfelix@gmail.com>
Date: Thu, 26 Dec 2024 23:53:03 +0000
Subject: [PATCH] docs

---
 docs/user_guide/create_db_file.md | 8 ++++----
 docs/user_guide/gpu.md            | 2 +-
 docs/user_guide/sim_matrix.md     | 2 +-
 docs/user_guide/similarity.md     | 4 ++--
 docs/user_guide/tversky.md        | 4 ++--
 5 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/user_guide/create_db_file.md b/docs/user_guide/create_db_file.md
index 29fa133e..2673598f 100644
--- a/docs/user_guide/create_db_file.md
+++ b/docs/user_guide/create_db_file.md
@@ -21,7 +21,7 @@ from FPSim2.io import create_db_file
 create_db_file(
     mols_source='stuff.sdf',
     filename='stuff.h5',
-    mol_format=None,
+    mol_format=None, # not required, .sdf will always use 'molfile'
     fp_type='Morgan',
     fp_params={'radius': 2, 'fpSize': 2048},
     mol_id_prop='mol_id'
@@ -35,7 +35,7 @@ from FPSim2.io import create_db_file
 create_db_file(
     mols_source='chembl.smi',
     filename='chembl.h5',
-    mol_format=None,
+    mol_format=None, # not required, .smi will always use 'smiles'
     fp_type='Morgan',
     fp_params={'radius': 2, 'fpSize': 2048}
 )
@@ -49,7 +49,7 @@ mols = [['CC', 1], ['CCC', 2], ['CCCC', 3]]
 create_db_file(
     mols_source=mols,
     filename='test/10mols.h5',
-    mol_format='smiles',
+    mol_format='smiles', # required
     fp_type='Morgan',
     fp_params={'radius': 2, 'fpSize': 2048}
 )
@@ -69,7 +69,7 @@ res_prox = s.execute(sql_query)
 create_db_file(
     mols_source=res_prox,
     filename='test/10mols.h5',
-    mol_format='molfile',
+    mol_format='molfile', # required
     fp_type='Morgan',
     fp_params={'radius': 2, 'fpSize': 2048}
 )
diff --git a/docs/user_guide/gpu.md b/docs/user_guide/gpu.md
index 5f305839..0c3ccb99 100644
--- a/docs/user_guide/gpu.md
+++ b/docs/user_guide/gpu.md
@@ -14,5 +14,5 @@ fp_filename = 'chembl_27.h5'
 fpce = FPSim2CudaEngine(fp_filename)
 
 query = 'CC(=O)Oc1ccccc1C(=O)O'
-results = fpce.similarity(query, 0.7)
+results = fpce.similarity(query, threshold=0.7)
 ```
diff --git a/docs/user_guide/sim_matrix.md b/docs/user_guide/sim_matrix.md
index 39e21f4a..4c035612 100644
--- a/docs/user_guide/sim_matrix.md
+++ b/docs/user_guide/sim_matrix.md
@@ -6,7 +6,7 @@ Use the `FPSim2.FPSim2Engine.symmetric_distance_matrix` function to create a Sci
 >>> from FPSim2 import FPSim2Engine
 >>> fp_filename = 'chembl_27.h5'
 >>> fpe = FPSim2Engine(fp_filename)
->>> csr_matrix = fpe.symmetric_distance_matrix(0.7, n_workers=4)
+>>> csr_matrix = fpe.symmetric_distance_matrix(threshold=0.7, n_workers=4)
 ```
 
 !!! note
diff --git a/docs/user_guide/similarity.md b/docs/user_guide/similarity.md
index eef91a9c..168fae1f 100644
--- a/docs/user_guide/similarity.md
+++ b/docs/user_guide/similarity.md
@@ -11,7 +11,7 @@ fp_filename = 'chembl_27.h5'
 fpe = FPSim2Engine(fp_filename)
 
 query = 'CC(=O)Oc1ccccc1C(=O)O'
-results = fpe.similarity(query, 0.7, n_workers=1)
+results = fpe.similarity(query, threshold=0.7, n_workers=1)
 ```
 
 
@@ -42,5 +42,5 @@ fp_filename = 'chembl_27.h5'
 fpe = FPSim2Engine(fp_filename, in_memory_fps=False)
 
 query = 'CC(=O)Oc1ccccc1C(=O)O'
-results = fpe.on_disk_similarity(query, 0.7, n_workers=1)
+results = fpe.on_disk_similarity(query, threshold=0.7, n_workers=1)
 ```
diff --git a/docs/user_guide/tversky.md b/docs/user_guide/tversky.md
index cfec265f..68a5079e 100644
--- a/docs/user_guide/tversky.md
+++ b/docs/user_guide/tversky.md
@@ -16,7 +16,7 @@ fp_filename = 'chembl_27.h5'
 fpe = FPSim2Engine(fp_filename)
 
 query = 'CC(=O)Oc1ccccc1C(=O)O'
-results = fpe.tversky(query, 0.7, 0.5, 0.5, n_workers=1)
+results = fpe.tversky(query, threshold=0.7, a=0.5, b=0.5, n_workers=1)
 ```
 
 > **Tip:** *n_workers* parameter can be used to split a single query into multiple threads to speed up the seach. This is specially useful when searching big datasets.
@@ -32,5 +32,5 @@ fp_filename = 'chembl_27.h5'
 fpe = FPSim2Engine(fp_filename, in_memory_fps=False)
 
 query = 'CC(=O)Oc1ccccc1C(=O)O'
-results = fpe.on_disk_tversky(query, 0.7, 0.5, 0.5, n_workers=1)
+results = fpe.on_disk_tversky(query, threshold=0.7, a=0.5, b=0.5, n_workers=1)
 ```