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import pandas as pd
import numpy as np
def calc_matches(filtered_df, project_df, similarity_matrix, top_x):
# matching project2 can be nay project
# indecies (rows) = project1
# columns = project2
# -> find matches
# filter out all row considering the filter
filtered_df_indecies_list = filtered_df.index
project_df_indecies_list = project_df.index
np.fill_diagonal(similarity_matrix, 0)
match_matrix = similarity_matrix[filtered_df_indecies_list, :][:, project_df_indecies_list]
best_matches_list = np.argsort(match_matrix, axis=None)
if len(best_matches_list) < top_x:
top_x = len(best_matches_list)
# get row (project1) and column (project2) with highest similarity in filtered df
top_indices = np.unravel_index(best_matches_list[-top_x:], match_matrix.shape)
# get the corresponding similarity values
top_values = match_matrix[top_indices]
p1_df = filtered_df.iloc[top_indices[0]]
p1_df["similarity"] = top_values
p2_df = project_df.iloc[top_indices[1]]
p2_df["similarity"] = top_values
return p1_df, p2_df
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