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{
"cells": [
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pathlib import Path\n",
"import json\n",
"\n",
"BASE_PATH= \"../\""
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"#get a list of performances such as there are not 2 performances of the same piece\n",
"df = pd.read_csv(Path(BASE_PATH,\"metadata.csv\"))\n",
"unique_df = df.drop_duplicates(subset=[\"title\",\"composer\"])\n",
"unique_performance_list = unique_df[\"midi_performance\"].tolist()\n",
"\n",
"#get the downbeat_list of a performance of Bach Fugue_bwv_848\n",
"midi_path = df.loc[df.title==\"Fugue_bwv_848\",\"midi_performance\"].iloc[0]\n",
"with open(Path('../asap_annotations.json')) as json_file:\n",
" json_data = json.load(json_file)\n",
"db_list = json_data[midi_path][\"performance_downbeats\"]\n",
"\n",
"#same task, but using the TSV file\n",
"annotation_path = df.loc[df.title==\"Fugue_bwv_848\",\"performance_annotations\"].iloc[0]\n",
"ann_df = pd.read_csv(Path(BASE_PATH,annotation_path),header=None, names=[\"time\",\"time2\",\"type\"],sep='\\t')\n",
"db_list = [row[\"time\"] for i,row in ann_df.iterrows() if row[\"type\"].split(\",\")[0]==\"db\"]\n",
"\n",
"#get all pieces with time signature changes\n",
"with open(Path('../asap_annotations.json')) as json_file:\n",
" json_data = json.load(json_file)\n",
"tsc_pieces = [p for p in json_data.keys() if len(json_data[p][\"perf_time_signatures\"])>1 ]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1039"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get all pieces where the score is aligned to the performance\n",
"with open(Path('../asap_annotations.json')) as json_file:\n",
" json_data = json.load(json_file)\n",
"aligned_pieces = [p for p in json_data.keys() if json_data[p][\"score_and_performance_aligned\"] ]\n",
"len(aligned_pieces)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"29"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1068-1039"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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