predictions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import py_midicsv as pm\n",
    "\n",
    "# Load the MIDI file and parse it into CSV format\n",
    "csv_string = pm.midi_to_csv(\"midiFiles/track1.midi\")\n",
    "csv_string = [\"Track, Time, Name, Channel, Note, Velocity\\n\"] + csv_string\n",
    "\n",
    "with open(\"converted1.csv\", \"w\") as f:\n",
    "    f.writelines(csv_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ogni singola nota ha:\n",
    "# numero traccia(1), tempo, Note_on_C, canale(0), nota, velocità (intensità da 0 a 127, 127 è forte)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_3020\\346605267.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  df = pd.read_csv('converted1.csv', sep=r'\\s*,\\s*')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('converted1.csv', sep=r'\\s*,\\s*')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
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       "      Track    Time         Name               Channel  Note  Velocity\n",
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       "\n",
       "[4811 rows x 6 columns]"
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     "execution_count": 29,
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
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   "metadata": {},
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       "      Track    Time         Name               Channel  Note  Velocity\n",
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       "\n",
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     "execution_count": 34,
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       "      Track    Time       Name Channel  Note  Velocity\n",
       "4         1     786  Note_on_c       0  74.0      92.0\n",
       "6         1    1187  Note_on_c       0  81.0      93.0\n",
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       "\n",
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     "execution_count": 43,
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   "source": [
    "\n",
    "df_notes = df.drop(df[df.Velocity == 0].index)\n",
    "df_notes = df_notes.drop(df[(df.Name != \"Note_on_c\") & (df.Name != \"Control_c\")].index)\n",
    "df_notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_notes = df_notes[\"Note\"].tolist()\n",
    "last_note = [70] + all_notes[:len(all_notes)-1]\n",
    "all_vel = df_notes[\"Velocity\"].tolist()\n",
    "last_vel = [0] + all_vel[:len(all_vel)-1]\n",
    "\n",
    "\n",
    "df_notes[\"1NoteBefore\"] = last_note\n",
    "df_notes[\"1VelocityBefore\"] = last_vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1187</td>\n",
       "      <td>Note_on_c</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1560</td>\n",
       "      <td>Note_on_c</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1946</td>\n",
       "      <td>Note_on_c</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1967</td>\n",
       "      <td>Control_c</td>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4801</th>\n",
       "      <td>1</td>\n",
       "      <td>103682</td>\n",
       "      <td>Control_c</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4802</th>\n",
       "      <td>1</td>\n",
       "      <td>103692</td>\n",
       "      <td>Control_c</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4803</th>\n",
       "      <td>1</td>\n",
       "      <td>103705</td>\n",
       "      <td>Control_c</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>1</td>\n",
       "      <td>103720</td>\n",
       "      <td>Control_c</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>1</td>\n",
       "      <td>103736</td>\n",
       "      <td>Control_c</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3340 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Track    Time       Name Channel  Note  Velocity  1NoteBefore  \\\n",
       "4         1     786  Note_on_c       0  74.0      92.0         70.0   \n",
       "6         1    1187  Note_on_c       0  81.0      93.0         74.0   \n",
       "7         1    1560  Note_on_c       0  57.0      79.0         81.0   \n",
       "9         1    1946  Note_on_c       0  62.0      86.0         57.0   \n",
       "10        1    1967  Control_c       0  64.0      56.0         62.0   \n",
       "...     ...     ...        ...     ...   ...       ...          ...   \n",
       "4801      1  103682  Control_c       0  67.0      18.0         67.0   \n",
       "4802      1  103692  Control_c       0  67.0      16.0         67.0   \n",
       "4803      1  103705  Control_c       0  67.0      13.0         67.0   \n",
       "4804      1  103720  Control_c       0  67.0      10.0         67.0   \n",
       "4805      1  103736  Control_c       0  67.0       8.0         67.0   \n",
       "\n",
       "      1VelocityBefore  \n",
       "4                 0.0  \n",
       "6                92.0  \n",
       "7                93.0  \n",
       "9                79.0  \n",
       "10               86.0  \n",
       "...               ...  \n",
       "4801             19.0  \n",
       "4802             18.0  \n",
       "4803             16.0  \n",
       "4804             13.0  \n",
       "4805             10.0  \n",
       "\n",
       "[3340 rows x 8 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cancella la prima riga\n",
    "df_notes.drop(index=df.index[0], \n",
    "        axis=0, \n",
    "        inplace=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_notes.to_csv('out.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parse the CSV output of the previous command back into a MIDI file\n",
    "midi_object = pm.csv_to_midi(\"converted1.csv\")\n",
    "\n",
    "# Save the parsed MIDI file to disk\n",
    "with open(\"example_converted.mid\", \"wb\") as output_file:\n",
    "    midi_writer = pm.FileWriter(output_file)\n",
    "    midi_writer.write(midi_object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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