Homology_testing.ipynb

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   <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>32 rows × 100 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"aee19915bd354b02800623c98342f292","source_hash":null,"output_cleared":true,"execution_start":1667258660443,"execution_millis":377,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"'''\nTodo:\nGet this down to only one loop\nFix rerun\n'''\n\n#list of each Scanning technique, empty lists\n# for index_df in enumerate(data_chunk):\n#      \n#     data_chunk= [df_l_R,df_u_d,df_SUL,df_PUL,df_PLR]\n    \ndf_list_up_down, df_list_left_right, df_list_SUL, df_list_PUL, df_list_PLR,\\\ndf_list_RL, df_list_DU, df_list_CEN, df_list_SLL = ([] for i in range(9))\n\n#iiterate through each row that represents part of the Latin Alphabet\nfor index, row in df.iterrows():\n    #Chunks turns the row of 100 to 10 lists of 10 elments\n    chunks = [list(row)[x:x+10] for x in range(0, len(list(row)), 10)]\n    #print(chunks)\n    #Creates empty list for each scanning technique to append the chunks\n    chunks_up_down, chunks_left_right, chunks_SUL, chunks_PUL, chunks_PLR,\\\n    chunks_RL, chunks_DU, chunks_CEN, chunks_SLL = ([] for i in range(9))\n\n    #loop through cunck of 1s and 0s I think could be turned into list comprehension\n    for c_index, c_list in enumerate(chunks):\n        #Creates empty list for each row\n        u_list_up_down, u_list_left_right, u_list_SUL, u_list_PUL, u_list_PLR,\\\n        u_list_RL, u_list_DU, u_list_CEN, u_list_SLL = ([] for i in range(9))\n\n        #List comprehension for creating the scanned numbers in each row\n        #Scanning left to right 1s become the number column they are in\n        u_list_left_right = [100 if item==0 else item*(idx+1) for idx, item in enumerate(c_list)]\n        #Scanning Right to Left\n        u_list_RL = [100 if item==0 else 10-item*(idx+1) for idx, item in enumerate(c_list)]\n        #Scanning Up to down so 1s become the number row they are in\n        u_list_up_down = [100 if item==0 else item*(c_index+1) for idx, item in enumerate(c_list)]\n        #Scanning Down to Up so 1s become the number row they are in\n        u_list_DU = [100 if item==0 else item*(9-c_index+1) for idx, item in enumerate(c_list)]\n        #Diagonal scanning-from-upper-left 1s become column number + row number\n        u_list_SUL = [100 if item==0 else item*(idx+1)+item*(c_index+1) for idx, item in enumerate(c_list)]\n        u_list_SLL = [100 if item==0 else item*(9-idx+1)+item*(9-c_index+1) for idx, item in enumerate(c_list)]\n        #Probing-from-upper-left 1s become max number between column and row\n        u_list_PUL = [100 if item==0 else max(item*(idx+1),item*(c_index+1)) for idx, item in enumerate(c_list)]\n        #Probing-from-upper-left 1s become max number between column and row from the bottom right corner\n        u_list_PLR = [100 if item==0 else max(10-item*(idx+1),10-item*(c_index+1))\\\n        for idx, item in enumerate(c_list)]\n        u_list_CEN = [100 if item==0 else max(max(9-idx-5+1,9-c_index-5+1),max(idx-5+1,c_index-5+1))\\\n        for idx, item in enumerate(c_list)]\n\n        #Append each row to its chunk for techniques \n        chunks_left_right.append(u_list_left_right)\n        chunks_up_down.append(u_list_up_down)\n        chunks_SUL.append(u_list_SUL)\n        chunks_PUL.append(u_list_PUL)\n        chunks_PLR.append(u_list_PLR)\n        chunks_RL.append(u_list_RL)\n        chunks_DU.append(u_list_DU)\n        chunks_CEN.append(u_list_CEN)\n        chunks_SLL.append(u_list_SLL)\n        #print(chunks_2)\n    #print(chunks_left_right)\n\n    #Turn technique chunks into Dataframes\n    df_l_R = pd.DataFrame(chunks_left_right, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_u_d = pd.DataFrame(chunks_up_down, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n\n    df_SUL = pd.DataFrame(chunks_SUL, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_PUL = pd.DataFrame(chunks_PUL, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_PLR = pd.DataFrame(chunks_PLR, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_RL = pd.DataFrame(chunks_RL, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_DU = pd.DataFrame(chunks_DU, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_CEN = pd.DataFrame(chunks_CEN, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_SLL = pd.DataFrame(chunks_SLL, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n\n\n    #Append dataframes to lists for each letter\n    df_list_up_down.append(df_u_d)\n    df_list_left_right.append(df_l_R)\n    df_list_SUL.append(df_SUL)\n    df_list_PUL.append(df_PUL)\n    df_list_PLR.append(df_PLR)\n    df_list_RL.append(df_RL)\n    df_list_DU.append(df_DU)\n    df_list_CEN.append(df_CEN)\n    df_list_SLL.append(df_SLL)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"d83a21bc5d9b4521a4be1daad96ac0b2","source_hash":null,"output_cleared":true,"execution_start":1665670995696,"execution_millis":6,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"print(df_list_PUL[0])","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"0cb730625aab4e85a3d4f6397acada49","source_hash":null,"output_cleared":false,"execution_start":1667258667879,"execution_millis":381,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"dgm_list = []\n\nfor index, data_frame in enumerate(df_list_up_down):\n    print(index)\n    dgm_up_down = lower_star_img(data_frame.to_numpy())\n    print(dgm_up_down)\n\n    dgm_left_right = lower_star_img(df_list_left_right[index].to_numpy())\n    print(dgm_left_right)\n\n    dgm_SUL = lower_star_img(df_list_SUL[index].to_numpy())\n    print(dgm_SUL)\n\n    dgm_PUL = lower_star_img(df_list_PUL[index].to_numpy())\n    print(dgm_PUL)\n\n    dgm_PLR = lower_star_img(df_list_PLR[index].to_numpy())\n    print(dgm_PLR)\n\n    dgm_RL = lower_star_img(df_list_RL[index].to_numpy())\n    print(dgm_RL)\n    dgm_DU = lower_star_img(df_list_DU[index].to_numpy())\n    print(dgm_DU)\n    dgm_CEN = lower_star_img(df_list_CEN[index].to_numpy())\n    print(dgm_CEN)\n    dgm_SLL = lower_star_img(df_list_SLL[index].to_numpy())\n    print(dgm_SLL)\n    dgm_list.append([dgm_up_down, dgm_left_right, dgm_SUL, dgm_PUL, dgm_PLR, dgm_RL, dgm_DU, dgm_CEN, dgm_SLL])","execution_count":null,"outputs":[{"name":"stdout","text":"0\n[[ 1. inf]]\n[[ 2. inf]]\n[[ 6. inf]]\n[[ 4. inf]]\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 2.  5.]\n [ 2. inf]]\n[[ 1. inf]]\n[[11. 12.]\n [ 5. inf]]\n1\n[[ 1. inf]]\n[[ 3. inf]]\n[[ 4. inf]]\n[[ 3. inf]]\n[[ 2. inf]]\n[[ 3.  4.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[ 6. inf]]\n2\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 6. inf]]\n[[ 3. inf]]\n[[ 2. inf]]\n[[ 2.  7.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 2. inf]]\n[[12. 15.]\n [ 6. inf]]\n3\n[[ 2. inf]]\n[[ 3. inf]]\n[[ 5. inf]]\n[[ 3. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[ 2. inf]]\n[[ 3.  4.]\n [ 3. inf]]\n[[ 6. inf]]\n4\n[[ 2. inf]]\n[[ 3. inf]]\n[[ 5. inf]]\n[[ 3. inf]]\n[[ 5.  7.]\n [ 2. inf]]\n[[ 2.  7.]\n [ 2.  7.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[ 9. 13.]\n [12. 16.]\n [ 5. inf]]\n5\n[[ 2. inf]]\n[[ 3. inf]]\n[[ 5. inf]]\n[[ 3. inf]]\n[[ 5. inf]]\n[[ 3.  7.]\n [ 3. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[10. 13.]\n [13. 16.]\n [10. inf]]\n6\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 5. inf]]\n[[ 3. inf]]\n[[ 3. inf]]\n[[ 3.  7.]\n [ 3. inf]]\n[[ 3. inf]]\n[[ 1. inf]]\n[[13. 16.]\n [ 7. inf]]\n7\n[[ 2.  4.]\n [ 2. inf]]\n[[ 3. inf]]\n[[ 9. 10.]\n [ 5. inf]]\n[[ 3. inf]]\n[[ 3. inf]]\n[[ 3. inf]]\n[[ 3.  7.]\n [ 3. inf]]\n[[ 2. inf]]\n[[11. 14.]\n [ 7. inf]]\n8\n[[ 2. inf]]\n[[ 4.  5.]\n [ 4. inf]]\n[[ 6. inf]]\n[[ 4. inf]]\n[[ 4. inf]]\n[[ 4.  5.]\n [ 4. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[ 7. inf]]\n9\n[[ 2. inf]]\n[[ 4.  6.]\n [ 4. inf]]\n[[12. 13.]\n [ 6. inf]]\n[[ 4. inf]]\n[[ 4. inf]]\n[[ 4. inf]]\n[[ 3. inf]]\n[[ 1. inf]]\n[[ 8. inf]]\n10\n[[ 2.  4.]\n [ 2. inf]]\n[[ 3. inf]]\n[[ 5. inf]]\n[[ 3. inf]]\n[[ 3. inf]]\n[[ 3.  6.]\n [ 3. inf]]\n[[ 2.  5.]\n [ 2. inf]]\n[[ 1. inf]]\n[[10. 12.]\n [12. 13.]\n [ 6. inf]]\n11\n[[ 2. inf]]\n[[ 3. inf]]\n[[ 5. inf]]\n[[ 3. inf]]\n[[ 3. inf]]\n[[ 3. inf]]\n[[ 2. inf]]\n[[ 3. inf]]\n[[ 6. inf]]\n12\n[[ 2.  5.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 9. 10.]\n [ 4. inf]]\n[[ 2. inf]]\n[[ 5.  7.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 4.  7.]\n [ 3.  8.]\n [ 3. inf]]\n[[ 1. inf]]\n[[10. 12.]\n [12. 15.]\n [ 6. inf]]\n13\n[[ 2.  6.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 9. 12.]\n [ 4. inf]]\n[[ 2. inf]]\n[[ 3. inf]]\n[[ 3. inf]]\n[[ 2.  7.]\n [ 2. inf]]\n[[ 1. inf]]\n[[11. 15.]\n [ 6. inf]]\n14\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 5. inf]]\n[[ 3. inf]]\n[[ 7.  8.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 3.  4.]\n [ 3.  4.]\n [ 3. inf]]\n[[ 6. inf]]\n15\n[[ 2. inf]]\n[[ 3. inf]]\n[[ 5. inf]]\n[[ 3. inf]]\n[[ 4. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[ 1. inf]]\n[[10. 12.]\n [ 9. inf]]\n16\n[[ 7.  8.]\n [ 2. inf]]\n[[ 6.  7.]\n [ 2. inf]]\n[[13. 15.]\n [ 5. inf]]\n[[ 7.  8.]\n [ 3. inf]]\n[[ 7.  8.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 3.  4.]\n [ 3.  4.]\n [ 2. inf]]\n[[ 5. inf]]\n17\n[[ 2. inf]]\n[[ 3. inf]]\n[[ 5. inf]]\n[[ 5.  6.]\n [ 3. inf]]\n[[ 3. inf]]\n[[ 3.  5.]\n [ 3. inf]]\n[[ 2.  4.]\n [ 2. inf]]\n[[ 1. inf]]\n[[10. 11.]\n [ 6. inf]]\n18\n[[ 2. inf]]\n[[ 3.  8.]\n [ 3. inf]]\n[[12. 16.]\n [ 5. inf]]\n[[ 3. inf]]\n[[ 2. inf]]\n[[ 2.  7.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[12. 16.]\n [ 5. inf]]\n19\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 4. inf]]\n[[ 2. inf]]\n[[ 5. inf]]\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[12. 14.]\n [ 8. inf]]\n20\n[[ 2.  9.]\n [ 2. inf]]\n[[ 3. inf]]\n[[10. 16.]\n [ 5. inf]]\n[[ 8.  9.]\n [ 3. inf]]\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 3.  4.]\n [ 3. inf]]\n[[ 5. inf]]\n21\n[[ 2.  7.]\n [ 2. inf]]\n[[ 2. inf]]\n[[10. 12.]\n [ 4. inf]]\n[[ 6.  7.]\n [ 2. inf]]\n[[ 4. inf]]\n[[ 2. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[ 8. inf]]\n22\n[[ 2.  6.]\n [ 2.  6.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 8. 10.]\n [11. 14.]\n [ 4. inf]]\n[[ 5.  6.]\n [ 2. inf]]\n[[ 5.  6.]\n [ 2. inf]]\n[[ 1. inf]]\n[[ 2.  7.]\n [ 2. inf]]\n[[ 1. inf]]\n[[ 9. 12.]\n [ 5. inf]]\n23\n[[ 2.  5.]\n [ 2. inf]]\n[[ 3.  4.]\n [ 3. inf]]\n[[ 9. 10.]\n [ 5. inf]]\n[[ 3. inf]]\n[[ 3. inf]]\n[[ 3.  5.]\n [ 3. inf]]\n[[ 2.  4.]\n [ 2. inf]]\n[[ 1. inf]]\n[[ 6. inf]]\n24\n[[ 2.  6.]\n [ 2. inf]]\n[[ 3. inf]]\n[[ 9. 11.]\n [ 5. inf]]\n[[ 3. inf]]\n[[ 4. inf]]\n[[ 3. inf]]\n[[ 1. inf]]\n[[ 1. inf]]\n[[ 7. inf]]\n25\n[[ 2. inf]]\n[[ 3.  7.]\n [ 2. inf]]\n[[ 4. inf]]\n[[ 5.  7.]\n [ 2. inf]]\n[[ 5.  6.]\n [ 2. inf]]\n[[ 2.  6.]\n [ 2. inf]]\n[[ 2. inf]]\n[[ 1. inf]]\n[[ 5. inf]]\n26\n[[ 9. inf]]\n[[ 5. inf]]\n[[14. inf]]\n[[ 9. inf]]\n[[ 5. inf]]\n[[ 5. inf]]\n[[ 2. inf]]\n[[ 4. inf]]\n[[ 8. inf]]\n27\n[[ 9. inf]]\n[[ 5. inf]]\n[[14. inf]]\n[[ 9. inf]]\n[[ 4. inf]]\n[[ 4. inf]]\n[[ 1. inf]]\n[[ 4. inf]]\n[[ 7. inf]]\n28\n[[100.  inf]]\n[[100.  inf]]\n[[100.  inf]]\n[[100.  inf]]\n[[100.  inf]]\n[[100.  inf]]\n[[100.  inf]]\n[[100.  inf]]\n[[100.  inf]]\n29\n[[ 5. inf]]\n[[ 4. inf]]\n[[ 9. inf]]\n[[ 5. inf]]\n[[ 5. inf]]\n[[ 4. inf]]\n[[ 6. inf]]\n[[ 1. inf]]\n[[11. inf]]\n30\n[[  6. 100.]\n [  4.  inf]]\n[[  5. 100.]\n [  5.  inf]]\n[[ 11. 100.]\n [  9.  inf]]\n[[  6. 100.]\n [  5.  inf]]\n[[  6. 100.]\n [  4.  inf]]\n[[  5. 100.]\n [  4.  inf]]\n[[  7. 100.]\n [  4.  inf]]\n[[  2. 100.]\n [  1.  inf]]\n[[ 13. 100.]\n [ 10.  inf]]\n31\n[[  6. 100.]\n [  4.  inf]]\n[[  5. 100.]\n [  5.  inf]]\n[[ 11. 100.]\n [  9.  inf]]\n[[  6. 100.]\n [  5.  inf]]\n[[  6. 100.]\n [  5.  inf]]\n[[  5. 100.]\n [  5.  inf]]\n[[  7. 100.]\n [  5.  inf]]\n[[  2. 100.]\n [  1.  inf]]\n[[ 13. 100.]\n [ 11.  inf]]\n","output_type":"stream"}]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"d9dfa5b076bb417791fda514ea5f7158","source_hash":null,"output_cleared":false,"execution_start":1667258664869,"execution_millis":93,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"for index, element in enumerate(dgm_list):\n    for index_2, element_2 in enumerate(dgm_list):\n        #print(element)\n        #print(element_2)\n        if np.array_equal(element, element_2) and index != index_2:\n            print(index, element, index_2, element_2)","execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'dgm_list' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn [5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, element \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[43mdgm_list\u001b[49m):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m index_2, element_2 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(dgm_list):\n\u001b[1;32m      3\u001b[0m         \u001b[38;5;66;03m#print(element)\u001b[39;00m\n\u001b[1;32m      4\u001b[0m         \u001b[38;5;66;03m#print(element_2)\u001b[39;00m\n\u001b[1;32m      5\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray_equal(element, element_2) \u001b[38;5;129;01mand\u001b[39;00m index \u001b[38;5;241m!=\u001b[39m index_2:\n","\u001b[0;31mNameError\u001b[0m: name 'dgm_list' is not defined"]}]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"c42237c0792c4491acd7566a7aea7869","source_hash":null,"output_cleared":true,"execution_start":1665670995983,"execution_millis":2,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"dgm_list","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"2726ad72695848a8b408a6b85fdb8d99","source_hash":null,"output_cleared":true,"execution_start":1663650628081,"execution_millis":1,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"new_dgm = []\ndgm_list_copy = copy.deepcopy(dgm_list)\nfor matrix in dgm_list_copy:\n    temp = []\n    for i in matrix:\n        i[np.isinf(i)] = 100\n        temp.append(i)\n    new_dgm.append(temp)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"59d9b7e3c6944ca98715c442a516fdb8","source_hash":null,"output_cleared":true,"execution_start":1665670996006,"execution_millis":2,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"dist = distance_matrix(new_dgm[0][1], new_dgm[0][6])\ndist","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"6e57e71587c44abab53ffaf86bc631dc","source_hash":null,"output_cleared":true,"execution_start":1663803879379,"execution_millis":3,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"sum_list = []\nmulti_list = []\ndgm_list_copy = copy.deepcopy(dgm_list)\nfor matrix in dgm_list_copy:\n    new_sum = 0\n    new_multi = 1\n    for i in matrix:\n        i[np.isinf(i)] = 100\n        #print(i, i.sum())\n        new_sum += i.sum()\n        new_multi += i.mean()\n    sum_list.append(new_sum*100)\n    multi_list.append(new_multi*100)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"548c87c6e99647b480745fbc3d356ad3","source_hash":null,"output_cleared":true,"execution_start":1665670996008,"execution_millis":13,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"#To add sum of the distance of each bar\nletter_vectors = []\n\ndgm_list_copy = copy.deepcopy(dgm_list)\nfor index, letter in enumerate(dgm_list_copy):\n    lengths = []\n    new_sum = 0\n    distances = []\n    #print(len(letter))\n    for index_2, matrix in enumerate(letter):\n        lengths.append(len(matrix))\n        #lengths.append(matrix.min())\n        lengths.append(matrix.min())\n        matrix[np.isinf(matrix)] = 0\n        lengths.append(matrix.max())\n        new_sum += matrix.sum()\n        for i in range(0, 6-index_2):\n            #print(index_2, i+1+index_2)\n            spatial_distance = distance_matrix(new_dgm[index][index_2], new_dgm[index][i+1+index_2])\n            distances.append(spatial_distance.sum())\n    dist_sum = 0\n    #print(np.array(distances).var())\n    for index_3, dist in enumerate(distances):\n        dist_sum += dist.sum()\n    lengths.append(new_sum/10)\n    lengths.append(dist_sum/100)\n    #lengths.append(np.array(distances).cov())\n    letter_vectors.append(lengths)\n    if index in [11, 15, 2, 6]:\n        print(lengths)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"9d89fcb3f500415f89c90ce3d2e2522c","source_hash":null,"output_cleared":true,"execution_start":1665670996065,"execution_millis":2,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"len(letter_vectors[0])","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"591c1a1b9241405d9aeaead6e33d37d5","source_hash":null,"output_cleared":true,"execution_start":1665670996066,"execution_millis":34,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"max_dist=-1\nv1=0\nv2=0\nfor i in range(len(letter_vectors)):\n    for j in range(len(letter_vectors)):\n        a=np.array(letter_vectors[i])\n        b=np.array(letter_vectors[j])\n        dist=np.linalg.norm(a-b)\n        #print('distance', dist,'between letters',i,j)\n        if dist > max_dist:\n            max_dist=dist\n            v1=i\n            v2=j\n            print('found larger distance:',max_dist,'letters:',v1,v2)\nprint('max distance:', max_dist, 'letters',v1,v2)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"1f1068a8ef7c478b9511b25eb4a09283","source_hash":null,"output_cleared":true,"execution_start":1665670996096,"execution_millis":4,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"min_dist=10000000\nv1=0\nv2=0\nfor i in range(len(letter_vectors)):\n    for j in range(len(letter_vectors)):\n        if not i==j:\n            a=np.array(letter_vectors[i])\n            b=np.array(letter_vectors[j])\n            dist=np.linalg.norm(a-b)\n            #print('distance', dist,'between letters',alphabet_full[i],alphabet_full[j])\n            if dist < min_dist:\n                min_dist=dist\n                v1=i\n                v2=j\n                print('found smaller distance:',min_dist,'letters:',alphabet_full[i],alphabet_full[j])\nprint('min distance:', min_dist, 'letters',alphabet_full[i],alphabet_full[j])","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"7720e86c9f8d4d978877da9f7d6e04f9","source_hash":null,"output_cleared":true,"execution_start":1665670996097,"execution_millis":470,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"#test\n'''\nTodo:\nGet this down to only one loop\nFix rerun\n'''\n\n#list of each Scanning technique, empty lists\n# for index_df in enumerate(data_chunk):\n#      \n#     data_chunk= [df_l_R,df_u_d,df_SUL,df_PUL,df_PLR]\n    \ndf_list_up_down_test, df_list_left_right_test, df_list_SUL_test, df_list_PUL_test, df_list_PLR_test,\\\ndf_list_RL_test, df_list_DU_test, df_list_CEN_test, df_list_SLL_test = ([] for i in range(9))\n\n#iiterate through each row that represents part of the Latin Alphabet\nfor index, row in enumerate([single_list]):\n    #Chunks turns the row of 100 to 10 lists of 10 elments\n    chunks = [list(row)[x:x+10] for x in range(0, len(list(row)), 10)]\n    #print(chunks)\n    #Creates empty list for each scanning technique to append the chunks\n    chunks_up_down_test, chunks_left_right_test, chunks_SUL_test, chunks_PUL_test, chunks_PLR_test,\\\n    chunks_RL_test, chunks_DU_test, chunks_CEN_test, chunks_SLL_test = ([] for i in range(9))\n\n    #loop through cunck of 1s and 0s I think could be turned into list comprehension\n    for c_index, c_list in enumerate(chunks):\n        #Creates empty list for each row\n        u_list_up_down_test, u_list_left_right_test, u_list_SUL_test, u_list_PUL_test, u_list_PLR_test,\\\n        u_list_RL_test, u_list_DU_test, u_list_CEN_test, u_list_SLL_test = ([] for i in range(9))\n\n        #List comprehension for creating the scanned numbers in each row\n        #Scanning left to right 1s become the number column they are in\n        u_list_left_right_test = [100 if item==0 else item*(idx+1) for idx, item in enumerate(c_list)]\n        #Scanning Right to Left\n        u_list_RL_test = [100 if item==0 else 10-item*(idx+1) for idx, item in enumerate(c_list)]\n        #Scanning Up to down so 1s become the number row they are in\n        u_list_up_down_test = [100 if item==0 else item*(c_index+1) for idx, item in enumerate(c_list)]\n        #Scanning Down to Up so 1s become the number row they are in\n        u_list_DU_test = [100 if item==0 else item*(9-c_index+1) for idx, item in enumerate(c_list)]\n        #Diagonal scanning-from-upper-left 1s become column number + row number\n        u_list_SUL_test = [100 if item==0 else item*(idx+1)+item*(c_index+1) for idx, item in enumerate(c_list)]\n        u_list_SLL_test = [100 if item==0 else item*(9-idx+1)+item*(9-c_index+1) for idx, item in enumerate(c_list)]\n        #Probing-from-upper-left 1s become max number between column and row\n        u_list_PUL_test = [100 if item==0 else max(item*(idx+1),item*(c_index+1)) for idx, item in enumerate(c_list)]\n        #Probing-from-upper-left 1s become max number between column and row from the bottom right corner\n        u_list_PLR_test = [100 if item==0 else max(10-item*(idx+1),10-item*(c_index+1))\\\n        for idx, item in enumerate(c_list)]\n        u_list_CEN_test = [100 if item==0 else max(max(9-idx-5+1,9-c_index-5+1),max(idx-5+1,c_index-5+1))\\\n        for idx, item in enumerate(c_list)]\n\n        #Append each row to its chunk for techniques \n        chunks_left_right_test.append(u_list_left_right_test)\n        chunks_up_down_test.append(u_list_up_down_test)\n        chunks_SUL_test.append(u_list_SUL_test)\n        chunks_PUL_test.append(u_list_PUL_test)\n        chunks_PLR_test.append(u_list_PLR_test)\n        chunks_RL_test.append(u_list_RL_test)\n        chunks_DU_test.append(u_list_DU_test)\n        chunks_CEN_test.append(u_list_CEN_test)\n        chunks_SLL_test.append(u_list_SLL_test)\n        #print(chunks_2)\n    #print(chunks_left_right)\n\n    #Turn technique chunks into Dataframes\n    df_l_R_test = pd.DataFrame(chunks_left_right_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_u_d_test = pd.DataFrame(chunks_up_down_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n\n    df_SUL_test = pd.DataFrame(chunks_SUL_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_PUL_test = pd.DataFrame(chunks_PUL_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_PLR_test = pd.DataFrame(chunks_PLR_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_RL_test = pd.DataFrame(chunks_RL_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_DU_test = pd.DataFrame(chunks_DU_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_CEN_test = pd.DataFrame(chunks_CEN_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n    df_SLL_test = pd.DataFrame(chunks_SLL_test, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                    index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n\n\n    #Append dataframes to lists for each letter\n    df_list_up_down_test.append(df_u_d_test)\n    df_list_left_right_test.append(df_l_R_test)\n    df_list_SUL_test.append(df_SUL_test)\n    df_list_PUL_test.append(df_PUL_test)\n    df_list_PLR_test.append(df_PLR_test)\n    df_list_RL_test.append(df_RL_test)\n    df_list_DU_test.append(df_DU_test)\n    df_list_CEN_test.append(df_CEN_test)\n    df_list_SLL_test.append(df_SLL_test)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"56b2a5052d7d499e96ebf3b9292b7562","source_hash":null,"output_cleared":true,"execution_start":1664328988294,"execution_millis":6,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"print(df_list_left_right_test[0])","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"36cb78ca48234f4d9006d8c62d9ed3ac","source_hash":null,"output_cleared":true,"execution_start":1664329079305,"execution_millis":951,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"dgm_list_test = []\n\nfor index, data_frame in enumerate(df_list_up_down_test):\n    print(index)\n    dgm_up_down = lower_star_img(data_frame.to_numpy())\n    print(dgm_up_down)\n\n    dgm_left_right = lower_star_img(df_list_left_right_test[index].to_numpy())\n    print(dgm_left_right)\n\n    dgm_SUL = lower_star_img(df_list_SUL_test[index].to_numpy())\n    print(dgm_SUL)\n\n    dgm_PUL = lower_star_img(df_list_PUL_test[index].to_numpy())\n    print(dgm_PUL)\n\n    dgm_PLR = lower_star_img(df_list_PLR_test[index].to_numpy())\n    print(dgm_PLR)\n\n    dgm_RL = lower_star_img(df_list_RL_test[index].to_numpy())\n    print(dgm_RL)\n    dgm_DU = lower_star_img(df_list_DU_test[index].to_numpy())\n    print(dgm_DU)\n    dgm_CEN = lower_star_img(df_list_CEN_test[index].to_numpy())\n    print(dgm_CEN)\n    dgm_SLL = lower_star_img(df_list_SLL_test[index].to_numpy())\n    print(dgm_SLL)\n    dgm_list_test.append([dgm_up_down, dgm_left_right, dgm_SUL, dgm_PUL, dgm_PLR, dgm_RL, dgm_DU, dgm_CEN, dgm_SLL])","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"20b288c1561d402480252bd2bacb5707","output_cleared":true,"deepnote_cell_type":"code"},"source":"#To add sum of the distance of each bar\nletter_vectors_test = []\n\ndgm_list_test_copy = copy.deepcopy(dgm_list_test)\nfor index, letter in enumerate(dgm_list_test_copy):\n    lengths = []\n    new_sum = 0\n    distances = []\n    #print(len(letter))\n    for index_2, matrix in enumerate(letter):\n        lengths.append(len(matrix))\n        #lengths.append(matrix.min())\n        lengths.append(matrix.min())\n        matrix[np.isinf(matrix)] = 0\n        lengths.append(matrix.max())\n        new_sum += matrix.sum()\n        for i in range(0, 6-index_2):\n            #print(index_2, i+1+index_2)\n            spatial_distance = distance_matrix(new_dgm[index][index_2], new_dgm[index][i+1+index_2])\n            distances.append(spatial_distance.sum())\n    dist_sum = 0\n    #print(np.array(distances).var())\n    for index_3, dist in enumerate(distances):\n        dist_sum += dist.sum()\n    lengths.append(new_sum/10)\n    lengths.append(dist_sum/100)\n    #lengths.append(np.array(distances).cov())\n    letter_vectors_test.append(lengths)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"6a8af89601d549039766a406e2a81f71","source_hash":null,"output_cleared":true,"execution_start":1664329398274,"execution_millis":1,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"letter_vectors_test[0]\n\nmin_dist=10000000\nv1=0\nv2=0\nfor i in range(len(letter_vectors_test)):\n    for j in range(len(letter_vectors)):\n        if not i==j:\n            a=np.array(letter_vectors[i])\n            b=np.array(letter_vectors[j])\n            dist=np.linalg.norm(a-b)\n            print('distance', dist,'between letters',i,j)\n            if dist < min_dist:\n                min_dist=dist\n                v1=i\n                v2=j\n                print('found smaller distance:',min_dist,'letters:',v1,v2)\nprint('min distance:', min_dist, 'letters',v1,v2)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"a9f2ad55b6a74d21a832fc39ff717b92","source_hash":null,"output_cleared":true,"execution_start":1662598296599,"execution_millis":6,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"letters=np.genfromtxt('letters.csv', dtype=int, delimiter=',')","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"df6853ff683245dbb28825dc9d475184","source_hash":null,"output_cleared":true,"execution_start":1664291609254,"execution_millis":212,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"letters","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"d6c8e4322f624eeeb27bf3c680ce6d88","source_hash":null,"output_cleared":true,"execution_start":1664291599215,"execution_millis":385,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"dgmLR = [None]*26\nfor i in range(26):    \n    letter_one_line=letters[i,:]\n    # initialize matrix of size 10x10 with all values 100    \n    letter=np.full((10, 10), 100)\n    # convert one line letter to 10x10 matrix replacing zeros with 100\n    for k in range(1,101):\n        if letter_one_line[k]==1.0:            \n            row=int((k-1)/10)             \n            column=(k-1)%10            \n            letter[row,column]=k%10    \n    dgmLR[i] = lower_star_img(letter)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"9fdcd32a2904498782aefb64abd5c7bc","source_hash":null,"output_cleared":true,"execution_start":1664295422809,"execution_millis":394,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"#diagnal scanning\n#diagonal scanning-from-upper-left\n#Column+row-1\ndgmSUL = [None]*26\nfor i in range(26):    \n    letter_one_line=letters[i,:]\n    # initialize matrix of size 10x10 with all values 100    \n    letter=np.full((10, 10), 100)\n    # convert one line letter to 10x10 matrix replacing zeros with 100\n    for k in range(1,101):\n        if letter_one_line[k]==1.0:            \n            row=int((k-1)/10)             \n            column=(k-1)%10            \n            letter[row,column]=9-k%10 + 9-int((k-1)/10)+1\n    print(letter, '\\n')         \n    dgmSUL[i] = lower_star_img(letter)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"92aa6aa419fa4ce087b5b99431910a47","source_hash":null,"output_cleared":true,"execution_start":1664293698481,"execution_millis":24,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"dgmCEN = [None]*26\nfor i in range(26):    \n    letter_one_line=letters[i,:]\n    # initialize matrix of size 10x10 with all values 100    \n    letter=np.full((10, 10), 100)\n    # convert one line letter to 10x10 matrix replacing zeros with 100\n    for k in range(1,101):\n        if letter_one_line[k]==1.0:            \n            row=int((k-1)/10)             \n            column=(k-1)%10            \n            letter[row,column]=max(max(9-row-5+1,9-column-5+1),max(row-5+1,column-5+1))\n    print(letter, '\\n')\n    dgmSUL[i] = lower_star_img(letter)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"bf364fecbc29429e8239d8367f61af23","source_hash":null,"output_cleared":true,"execution_start":1664293648810,"execution_millis":953,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"letter","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"8ffea4c7a87f4912a66684f5d4075203","source_hash":null,"output_cleared":true,"execution_start":1662599205893,"execution_millis":263,"deepnote_to_be_reexecuted":false,"deepnote_cell_type":"code"},"source":"#probing-from-upper-left\ndgmPUL = [None]*26\nfor i in range(26):\n    letter_one_line=letters[i,:]\n    # initialize matrix of size 10x10 with all values 100    \n    letter=np.full((10, 10), 100)\n    # convert one line letter to 10x10 matrix replacing zeros with 100\n    for k in range(1,101):\n        if letter_one_line[k]==1.0:            \n            row=int((k-1)/10)             \n            column=(k-1)%10            \n            letter[row,column]=max((k-1)%10,int((k-1)/10))             \n    dgmPUL[i] = lower_star_img(letter)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"8807781765934378bda3a8383a6d1a08","source_hash":null,"output_cleared":true,"execution_start":1664291657004,"execution_millis":300,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"letter","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"e37cde534af24f69b5baab7b5fff6ded","source_hash":null,"output_cleared":true,"execution_start":1664291664120,"execution_millis":5,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"print(df_list_up_down[25])","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"b8ccd63391cd4553903449d52f06f7c1","source_hash":null,"output_cleared":true,"execution_start":1664295124539,"execution_millis":19,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"#probing-from-lower-right\ndgmPLR = [None]*32\nfor i in range(32):    \n    letter_one_line=letters[i,:]\n    # initialize matrix of size 10x10 with all values 100    \n    letter=np.full((10, 10), 100)\n    # convert one line letter to 10x10 matrix replacing zeros with 100\n    for k in range(1,101):\n        if letter_one_line[k]==1.0:            \n            row=int((k-1)/10)             \n            column=(k-1)%10            \n            letter[row,column]=max(9-(k-1)%10,9-int((k-1)/10))\n    print(letter, '/n')        \n    dgmPLR[i] = lower_star_img(letter)","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"80d09d7f21bf47f796e1cbf1a6b8036d","source_hash":null,"output_cleared":true,"execution_start":1664291673067,"execution_millis":5,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"letter","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"c1cd6c448ac24949ae4f9ff775b5a870","source_hash":null,"output_cleared":true,"execution_start":1664291509751,"execution_millis":25608,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"dgm_list = []\n\nfor index, data_frame in enumerate(df_list_up_down):\n    print(index)\n    dgm_up_down = lower_star_img(data_frame.to_numpy())\n    plt.figure(figsize=(10, 5))\n    plt.subplots_adjust(right=2.5)\n    plt.subplot(151)\n    plt.imshow(data_frame)\n    plt.colorbar()\n    plt.title(f\"Letter {index}\")\n\n    dgm_up_down = lower_star_img(data_frame.to_numpy())\n    plt.subplot(162)\n    plt.title(f\"0-D Persistence Diagram U to D {index}\")\n    if len(dgm_up_down) < 2:\n        plot_diagrams(np.concatenate((dgm_up_down,[[0, 0]]),axis = 0))\n    else:\n        plot_diagrams(dgm_up_down)\n    print(dgm_up_down)\n\n    dgm_left_right = lower_star_img(df_list_left_right[index].to_numpy())\n    plt.subplot(163)\n    plt.title(f\"0-D Persistence Diagram L to R {index}\")\n    if len(dgm_left_right) < 2:\n        plot_diagrams(np.concatenate((dgm_left_right,[[0, 0]]),axis = 0))\n    else:\n        plot_diagrams(dgm_left_right)\n    print(dgm_left_right)\n\n    dgm_SUL = lower_star_img(df_list_SUL[index].to_numpy())\n    plt.subplot(164)\n    plt.title(f\"0-D Persistence Diagram SUL {index}\")\n    if len(dgm_SUL) < 2:\n        plot_diagrams(np.concatenate((dgm_SUL,[[0, 0]]),axis = 0))\n    else:\n        plot_diagrams(dgm_SUL)\n    print(dgm_SUL)\n\n    dgm_PUL = lower_star_img(df_list_PUL[index].to_numpy())\n    plt.subplot(165)\n    plt.title(f\"0-D Persistence Diagram PUL {index}\")\n    if len(dgm_PUL) < 2:\n        plot_diagrams(np.concatenate((dgm_PUL,[[0, 0]]),axis = 0))\n    else:\n        plot_diagrams(dgm_PUL)\n    print(dgm_PUL)\n\n    dgm_PLR = lower_star_img(df_list_PLR[index].to_numpy())\n    plt.subplot(166)\n    plt.title(f\"0-D Persistence Diagram PLR {index}\")\n    if len(dgm_PLR) < 2:\n        plot_diagrams(np.concatenate((dgm_PLR,[[0, 0]]),axis = 0))\n    else:\n        plot_diagrams(dgm_PLR)\n    print(dgm_PLR)\n\n    dgm_RL = lower_star_img(df_list_RL[index].to_numpy())\n    dgm_DU = lower_star_img(df_list_DU[index].to_numpy())\n    dgm_list.append([dgm_up_down, dgm_left_right, dgm_SUL, dgm_PUL, dgm_PLR, dgm_RL, dgm_DU])\n    plt.show()","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"1ac8b61a3bb249409049a3eb640f4c9b","source_hash":null,"output_cleared":true,"execution_start":1663690007615,"execution_millis":384,"deepnote_to_be_reexecuted":true,"deepnote_cell_type":"code"},"source":"#Legacy\n'''\nfor idx, item in enumerate(c_list):\n    if item==0:\n        u_list_up_down.append(100)\n        u_list_left_right.append(100)\n        u_list_SUL.append(100)\n        u_list_PUL.append(100)\n        u_list_PLR.append(100)\n    else:\n        u_list_left_right.append(item*(idx+1))\n        u_list_up_down.append(item*(c_index+1))\n        u_list_SUL.append(item*(idx+1)+item*(c_index+1))\n        u_list_PUL.append(max(item*(idx+1),item*(c_index+1)))\n        u_list_PLR.append(max(10-item*(idx+1),10-item*(c_index+1)))\n'''","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"ea42bf7cda0844aaac552cd36438b8e5","deepnote_cell_type":"code"},"source":"#testing\nlines = 2*10\n#ten lists\n#3rd line 3rd element of the 1st list, 2nd element of the 2nd list and 1st element 3rd list\ntest_lists = train['NTQ'][2].to_numpy()\nprint(test_lists)\ndistance = []\nfor i in range(1,lines):\n    distance.append(min(i, lines-i))\nprint(distance)\n\n#Left to Right diagonal\ndiagonal_list = []\nfor idx, i in enumerate(distance):\n    print('Distance: ', i)\n    temp_list = []\n    print('Line: ', idx)\n    for index, elem in enumerate(range(1, i+1)):\n        #print(elem, i)\n        if idx > 9:\n            #(2,10) or (10,2)\n            print(10-i,10-elem)\n            temp_list.append(test_lists[10-i][10-elem])\n        else:\n            print(elem-1, i-1)\n            temp_list.append(test_lists[elem-1][i-1])\n        i = i-1\n    diagonal_list.append(temp_list)\n#print(diagonal_list)\nfor diag_list in diagonal_list:\n    vertices = diag_list.count(1)\n    last_element = 0\n    edges = 0\n    for i in diag_list:\n        if i == 1 and last_element == 0:\n            edges += 1\n        last_element = i\n    if vertices == 0:\n        zero_diag_homology = 0\n    else:\n        zero_diag_homology = round(edges/vertices,4)\n    #print(zero_diag_homology)\n\n#Right to left diagonal\ndiagonal_list = []\nfor idx, i in enumerate(distance):\n    #print('Distance: ', i)\n    temp_list = []\n    #print('Line: ', idx)\n    for index, elem in enumerate(range(1, i+1)):\n        #print(elem, i)\n        if idx > 9:\n            #print(10-i,elem-1)\n            temp_list.append(test_lists[10-i][elem-1])\n        else:\n            #print(elem-1, 10-i)\n            temp_list.append(test_lists[elem-1][10-i])\n        i = i-1\n    diagonal_list.append(temp_list)\nprint(diagonal_list)\nfor diag_list in diagonal_list:\n    vertices = diag_list.count(1)\n    last_element = 0\n    edges = 0\n    for i in diag_list:\n        if i == 1 and last_element == 0:\n            edges += 1\n        last_element = i\n    if vertices == 0:\n        zero_diag_homology = 0\n    else:\n        zero_diag_homology = round(edges/vertices,4)\n    print(zero_diag_homology)","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=c3975852-3619-4f95-970c-90c02f1f882d' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"deepnote":{},"orig_nbformat":2,"deepnote_notebook_id":"7ad4c868d6104e129e773d25daf89e38","deepnote_persisted_session":{"createdAt":"2022-11-01T02:16:34.830Z"},"deepnote_execution_queue":[]}}