new code snippets

.ipynb_checkpoints/README-checkpoint.md

@@ -13,8 +13,6 @@ High-confidence scored pairs were chosen based on parameters such as sequence le
 
 ## Data Instances and loading
 
-Here we give an example usage of the database, assumming you downloaded AF2_Beta_Strand_Database/antiparallel/length_10/data_0.npy and placed it under your current directory. Here is an example of loading the data and result by printing the first 30 entries. 
-
 The AF2 Beta Strand Database's entries comprise three key components: (1) Target sequence: a beta sheet sequence as labeled by Alphafold 2, captured in the N-to-C terminus direction; (2) Complementary peptide sequence: the sequence that pairs with the target sequence in a face to face orientation (For parallel pairs: N-to-C terminus. For antiparallel pairs: C-to-N terminus); (3) Count: the frequency of repeated pair occurrences. Type information (antiparallel or parallel beta strand pairs) is contained in the data file name. 
 
 For an instance of database usage, consider the scenario where you have downloaded AF2_Beta_Strand_Database/antiparallel/length_10/data_0.npy and placed it under your current directory. Here's how to load the data and display the first 30 entries:
@@ -25,11 +23,11 @@ import numpy as np
 
 data = np.load('data_0.npy', allow_pickle=True).tolist()
 
-# Convert nested dictionary to flat "record" style data
+# Convert nested dictionary to flat data
 flat_data = []
-for outer_key, inner_dict in data.items():
-    for inner_key, value in inner_dict.items():
-        flat_data.append([outer_key, inner_key, value])
+for target, inner_dict in data.items():
+    for comp, count in inner_dict.items():
+        flat_data.append([target, comp, count])
 
 # Convert list of lists to DataFrame
 df = pd.DataFrame(flat_data, columns=['Target', 'Complementary peptide', 'Count'])
@@ -89,7 +87,7 @@ The AlphaFold 2 Beta Strand Database is made available under the terms of the MI
 
 #### Citing this Database
 
-If you utilize this database in your research, we kindly request that you cite our paper. We also recommend citing the foundational AlphaFold papers, as our database was constructed based on their work: 
+If you use this database in your research, we kindly request that you cite our paper. We also recommend citing the foundational AlphaFold papers, as our database was constructed based on their work: 
 
 Below are the BibTeX entries for our paper:
 

README.md

@@ -13,8 +13,6 @@ High-confidence scored pairs were chosen based on parameters such as sequence le
 
 ## Data Instances and loading
 
-Here we give an example usage of the database, assumming you downloaded AF2_Beta_Strand_Database/antiparallel/length_10/data_0.npy and placed it under your current directory. Here is an example of loading the data and result by printing the first 30 entries. 
-
 The AF2 Beta Strand Database's entries comprise three key components: (1) Target sequence: a beta sheet sequence as labeled by Alphafold 2, captured in the N-to-C terminus direction; (2) Complementary peptide sequence: the sequence that pairs with the target sequence in a face to face orientation (For parallel pairs: N-to-C terminus. For antiparallel pairs: C-to-N terminus); (3) Count: the frequency of repeated pair occurrences. Type information (antiparallel or parallel beta strand pairs) is contained in the data file name. 
 
 For an instance of database usage, consider the scenario where you have downloaded AF2_Beta_Strand_Database/antiparallel/length_10/data_0.npy and placed it under your current directory. Here's how to load the data and display the first 30 entries:
@@ -25,11 +23,11 @@ import numpy as np
 
 data = np.load('data_0.npy', allow_pickle=True).tolist()
 
-# Convert nested dictionary to flat "record" style data
+# Convert nested dictionary to flat data
 flat_data = []
-for outer_key, inner_dict in data.items():
-    for inner_key, value in inner_dict.items():
-        flat_data.append([outer_key, inner_key, value])
+for target, inner_dict in data.items():
+    for comp, count in inner_dict.items():
+        flat_data.append([target, comp, count])
 
 # Convert list of lists to DataFrame
 df = pd.DataFrame(flat_data, columns=['Target', 'Complementary peptide', 'Count'])
@@ -89,7 +87,7 @@ The AlphaFold 2 Beta Strand Database is made available under the terms of the MI
 
 #### Citing this Database
 
-If you utilize this database in your research, we kindly request that you cite our paper. We also recommend citing the foundational AlphaFold papers, as our database was constructed based on their work: 
+If you use this database in your research, we kindly request that you cite our paper. We also recommend citing the foundational AlphaFold papers, as our database was constructed based on their work: 
 
 Below are the BibTeX entries for our paper: