From 017fc5ad685e6d64eb8ae5ed0e8ec4af75a75de1 Mon Sep 17 00:00:00 2001 From: carlos Date: Thu, 10 Oct 2024 23:54:14 +0200 Subject: [PATCH 1/5] notebooks --- docs/source/Tutorial.ipynb | 7 +++++-- .../{Tutorial poprock.ipynb => Tutorial_poprock.ipynb} | 6 ++++-- 2 files changed, 9 insertions(+), 4 deletions(-) rename docs/source/{Tutorial poprock.ipynb => Tutorial_poprock.ipynb} (99%) diff --git a/docs/source/Tutorial.ipynb b/docs/source/Tutorial.ipynb index ab137b90..c23c057f 100644 --- a/docs/source/Tutorial.ipynb +++ b/docs/source/Tutorial.ipynb @@ -6,7 +6,10 @@ "id": "99af7aa1", "metadata": {}, "source": [ - "# Getting started `musif`\n", + "# Getting started with `musif`\n", + "\n", + "[Download the Getting started tutorial Jupyter notebook here](https://raw.githubusercontent.com/DIDONEproject/musif/main/docs/source/Tutorial.ipynb)\n", + "\n", "\n", "`musif` is a Python library to analyze music scores. It is a tool to massively extract features from MusicXML and MuseScore files.\n", "\n", @@ -26,7 +29,7 @@ "\n", "\n", "To install `musif`:\n", - "1. [Download](https://raw.githubusercontent.com/DIDONEproject/musif/main/docs/source/Tutorial.ipynb) this notebook.\n", + "1. [Download this notebook](https://raw.githubusercontent.com/DIDONEproject/musif/main/docs/source/Tutorial.ipynb).\n", "2. Start `jupyter` in your Anaconda environment.\n", "3. Open this tutorial.\n", "4. Run the following cell by clicking on it and pressing Ctrl+Enter." diff --git a/docs/source/Tutorial poprock.ipynb b/docs/source/Tutorial_poprock.ipynb similarity index 99% rename from docs/source/Tutorial poprock.ipynb rename to docs/source/Tutorial_poprock.ipynb index 2ad0d72d..3e79fabf 100644 --- a/docs/source/Tutorial poprock.ipynb +++ b/docs/source/Tutorial_poprock.ipynb @@ -9,7 +9,9 @@ "# Using `musif` in pro mode\n", "\n", "This tutorial is intended for people who already have some programming skills. If you just want to try and explore `musif`, first check the [Getting started tutorial](./Tutorial.html).\n", - "You will also find guide for installation procedure and set-up there." + "You will also find guide for installation procedure and set-up there.\n", + "\n", + "[Download the Advanced tutorial notebook here](https://raw.githubusercontent.com/DIDONEproject/musif/main/docs/source/Tutorial_poprock.ipynb)" ] }, { @@ -21,7 +23,7 @@ }, "outputs": [], "source": [ - "%pip install musif" + "! pip install musif" ] }, { From 2e16c3b9a165d1d4f4a76423fc3c65bf2d55d2b0 Mon Sep 17 00:00:00 2001 From: carlos vaquero Date: Fri, 11 Oct 2024 09:52:38 +0200 Subject: [PATCH 2/5] output_dir --- musif/extract/extract.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/musif/extract/extract.py b/musif/extract/extract.py index 0ab852c9..166c5e4c 100644 --- a/musif/extract/extract.py +++ b/musif/extract/extract.py @@ -310,6 +310,19 @@ def extract(self) -> DataFrame: return score_df + def _check_for_error_file(self): + # Check for error file + try: + df = pd.read_csv(f'{self._cfg.output_dir}/error_files.csv', low_memory=False) + df['ErrorFiles'] = df['ErrorFiles'].astype(str) + df['ErrorFiles'] = df['ErrorFiles'].str.rsplit('/', 1).str[-1] + errored_files = list(df['ErrorFiles']) + print(errored_files) + print("CSV loaded successfully.") + except Exception: + # Handle the case where the file is empty + print("There is no error_files.csv, it will be created and loaded error files are included manually in it.") + def _process_corpus( self, filenames: List[PurePath] ) -> Tuple[List[dict], List[dict]]: From d3b1c0666a20eb1feb1b4da21e6d4d0333bd778a Mon Sep 17 00:00:00 2001 From: carlos Date: Fri, 11 Oct 2024 10:55:36 +0200 Subject: [PATCH 3/5] lirycs fix --- docs/source/Tutorial.ipynb | 570 ++++--- docs/source/Tutorial_poprock.ipynb | 1867 ++++++++++++++++++++-- musif/extract/extract.py | 12 +- musif/extract/features/lyrics/handler.py | 2 +- pyproject.toml | 2 +- 5 files 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petl stringcase\n", + "Installing collected packages: text-unidecode, stringcase, zipp, webcolors, validators, tzdata, tqdm, tcolorpy, tabulate, smmap, simpleeval, shellingham, roman, rfc3986, python-slugify, pyparsing, pydantic-core, ppft, pox, pillow, petl, pathvalidate, orderly-set, numpy, more-itertools, mdurl, marko, lxml, kiwisolver, jsonpickle, joblib, isodate, humanize, fonttools, dill, cycler, click, chardet, annotated-types, scipy, pydantic, pyarrow, pandas, multiprocess, mbstrdecoder, markdown-it-py, importlib-metadata, gitdb, deepdiff, contourpy, visidata, typepy, rich, pathos, matplotlib, GitPython, typer, music21, frictionless, DataProperty, tabledata, pytablewriter, ms3, musif\n", + "Successfully installed DataProperty-1.0.1 GitPython-3.1.43 annotated-types-0.7.0 chardet-5.2.0 click-8.1.7 contourpy-1.3.0 cycler-0.12.1 deepdiff-8.0.1 dill-0.3.9 fonttools-4.54.1 frictionless-5.18.0 gitdb-4.0.11 humanize-4.11.0 importlib-metadata-8.5.0 isodate-0.7.2 joblib-1.4.2 jsonpickle-3.3.0 kiwisolver-1.4.7 lxml-5.3.0 markdown-it-py-3.0.0 marko-2.1.2 matplotlib-3.9.2 mbstrdecoder-1.1.3 mdurl-0.1.2 more-itertools-10.5.0 ms3-2.4.2 multiprocess-0.70.17 music21-9.1.0 musif-1.2.3 numpy-2.1.2 orderly-set-5.2.2 pandas-2.2.3 pathos-0.3.3 pathvalidate-3.2.1 petl-1.7.15 pillow-10.4.0 pox-0.3.5 ppft-1.7.6.9 pyarrow-17.0.0 pydantic-2.9.2 pydantic-core-2.23.4 pyparsing-3.1.4 pytablewriter-1.0.0 python-slugify-8.0.4 rfc3986-2.0.0 rich-13.9.2 roman-4.2 scipy-1.14.1 shellingham-1.5.4 simpleeval-1.0.0 smmap-5.0.1 stringcase-1.2.0 tabledata-1.3.3 tabulate-0.9.0 tcolorpy-0.1.6 text-unidecode-1.3 tqdm-4.66.5 typepy-1.3.2 typer-0.12.5 tzdata-2024.2 validators-0.34.0 visidata-3.0.2 webcolors-1.12 zipp-3.20.2\n" ] } ], "source": [ - "!pip install musif" + "! pip install musif" ] }, { @@ -174,7 +314,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Version: 1.2\n" + "Version: 1.2.3\n" ] } ], @@ -327,14 +467,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/175 [00:00; getting generic Instrument\n", + " warnings.warn(\n", + "100%|██████████| 175/175 [02:35<00:00, 1.13it/s]\n" ] } ], @@ -461,8 +596,8 @@ " ww\n", " ww\n", " ...\n", - " -0.068\n", - " 1.543819\n", + " -0.106242\n", + " 1.634416\n", " fl\n", " <NA>\n", " <NA>\n", @@ -485,8 +620,8 @@ " ww\n", " ww\n", " ...\n", - " -0.243976\n", - " 1.396249\n", + " -0.095768\n", + " 1.578589\n", " fl\n", " <NA>\n", " <NA>\n", @@ -509,8 +644,8 @@ " ww\n", " ww\n", " ...\n", - " -0.148276\n", - " 1.500143\n", + " -0.073604\n", + " 1.623796\n", " fl\n", " <NA>\n", " <NA>\n", @@ -557,9 +692,9 @@ " SoundFl_TrimmedIntervallicMean SoundFl_TrimmedIntervallicStd \\\n", "0 -0.113402 1.468171 \n", "1 -0.165948 1.620333 \n", - "2 -0.068 1.543819 \n", - "3 -0.243976 1.396249 \n", - "4 -0.148276 1.500143 \n", + "2 -0.106242 1.634416 \n", + "3 -0.095768 1.578589 \n", + "4 -0.073604 1.623796 \n", "\n", " SoundScoring Tempo TempoGrouped1 TempoGrouped2 TimeSignature \\\n", "0 fl None 2/1 \n", @@ -742,8 +877,8 @@ " ww\n", " ww\n", " ...\n", - " -0.068\n", - " 1.543819\n", + " -0.106242\n", + " 1.634416\n", " fl\n", " <NA>\n", " <NA>\n", @@ -766,8 +901,8 @@ " ww\n", " ww\n", " ...\n", - " -0.243976\n", - " 1.396249\n", + " -0.095768\n", + " 1.578589\n", " fl\n", " <NA>\n", " <NA>\n", @@ -790,8 +925,8 @@ " ww\n", " ww\n", " ...\n", - " -0.148276\n", - " 1.500143\n", + " -0.073604\n", + " 1.623796\n", " fl\n", " <NA>\n", " <NA>\n", @@ -843,9 +978,9 @@ "Id \n", "0 -0.113402 1.468171 \n", "1 -0.165948 1.620333 \n", - "2 -0.068 1.543819 \n", - "3 -0.243976 1.396249 \n", - "4 -0.148276 1.500143 \n", + "2 -0.106242 1.634416 \n", + "3 -0.095768 1.578589 \n", + "4 -0.073604 1.623796 \n", "\n", " SoundScoring Tempo TempoGrouped1 TempoGrouped2 TimeSignature \\\n", "Id \n", @@ -1016,22 +1151,22 @@ " <NA>\n", " <NA>\n", " <NA>\n", - " 0.205882\n", - " 0.014706\n", + " 0.245713\n", + " 0.044699\n", " <NA>\n", " <NA>\n", " <NA>\n", " ...\n", - " 0.220977\n", - " 0.220588\n", - " 0.102941\n", - " 0.117647\n", - " 0.316321\n", - " 0.060012\n", - " 0.056299\n", + " 0.282851\n", + " 0.285156\n", + " 0.109375\n", + " 0.15\n", + " 0.189659\n", + " 0.045257\n", + " 0.024475\n", " 1.0\n", - " 0.387802\n", - " 0.412188\n", + " 0.404641\n", + " 0.475432\n", " \n", " \n", " 3\n", @@ -1040,22 +1175,22 @@ " <NA>\n", " <NA>\n", " <NA>\n", - " 0.201844\n", - " 0.024568\n", + " 0.137931\n", + " 0.062069\n", " <NA>\n", " <NA>\n", " <NA>\n", " ...\n", - " 0.238239\n", - " 0.197917\n", - " 0.0625\n", - " 0.135417\n", - " 0.387152\n", - " 0.090096\n", - " 0.056431\n", + " 0.191059\n", + " 0.236842\n", + " 0.075862\n", + " 0.122807\n", + " 0.375296\n", + " 0.107478\n", + " 0.068547\n", " 1.0\n", - " 0.311439\n", - " 0.447935\n", + " 0.373753\n", + " 0.426976\n", " \n", " \n", " 4\n", @@ -1064,22 +1199,22 @@ " <NA>\n", " <NA>\n", " <NA>\n", - " 0.171765\n", - " 0.03098\n", + " 0.190211\n", + " 0.055034\n", " <NA>\n", " <NA>\n", " <NA>\n", " ...\n", - " 0.185842\n", - " 0.173333\n", - " 0.08\n", - " 0.093333\n", - " 0.424165\n", - " 0.100029\n", - " 0.10437\n", + " 0.249377\n", + " 0.190083\n", + " 0.057851\n", + " 0.132231\n", + " 0.273048\n", + " 0.084658\n", + " 0.039822\n", " 1.0\n", - " 0.358311\n", - " 0.421924\n", + " 0.415049\n", + " 0.436383\n", " \n", " \n", " ...\n", @@ -1249,9 +1384,9 @@ "Id \n", "0 0.114943 \n", "1 0.188312 \n", - "2 0.205882 \n", - "3 0.201844 \n", - "4 0.171765 \n", + "2 0.245713 \n", + "3 0.137931 \n", + "4 0.190211 \n", ".. ... ... ... \n", "170 0.214841 \n", "171 0.192255 \n", @@ -1263,9 +1398,9 @@ "Id \n", "0 0.034483 \n", "1 0.045455 \n", - "2 0.014706 \n", - "3 0.024568 \n", - "4 0.03098 \n", + "2 0.044699 \n", + "3 0.062069 \n", + "4 0.055034 \n", ".. ... ... \n", "170 0.02509 \n", "171 0.048058 \n", @@ -1291,9 +1426,9 @@ "Id \n", "0 0.172837 0.068966 \n", "1 0.218958 0.162338 \n", - "2 0.220977 0.220588 \n", - "3 0.238239 0.197917 \n", - "4 0.185842 0.173333 \n", + "2 0.282851 0.285156 \n", + "3 0.191059 0.236842 \n", + "4 0.249377 0.190083 \n", ".. ... ... \n", "170 0.243784 0.254902 \n", "171 0.243046 0.275132 \n", @@ -1305,9 +1440,9 @@ "Id \n", "0 0.034483 0.034483 \n", "1 0.045455 0.116883 \n", - "2 0.102941 0.117647 \n", - "3 0.0625 0.135417 \n", - "4 0.08 0.093333 \n", + "2 0.109375 0.15 \n", + "3 0.075862 0.122807 \n", + "4 0.057851 0.132231 \n", ".. ... ... \n", "170 0.108696 0.137255 \n", "171 0.126984 0.142857 \n", @@ -1319,9 +1454,9 @@ "Id \n", "0 0.512123 0.106447 \n", "1 0.372618 0.105962 \n", - "2 0.316321 0.060012 \n", - "3 0.387152 0.090096 \n", - "4 0.424165 0.100029 \n", + "2 0.189659 0.045257 \n", + "3 0.375296 0.107478 \n", + "4 0.273048 0.084658 \n", ".. ... ... \n", "170 0.282986 0.070828 \n", "171 0.189175 0.064503 \n", @@ -1333,9 +1468,9 @@ "Id \n", "0 0.101868 1.0 \n", "1 0.062201 1.0 \n", - "2 0.056299 1.0 \n", - "3 0.056431 1.0 \n", - "4 0.10437 1.0 \n", + "2 0.024475 1.0 \n", + "3 0.068547 1.0 \n", + "4 0.039822 1.0 \n", ".. ... ... \n", "170 0.051396 1.0 \n", "171 0.047243 1.0 \n", @@ -1347,9 +1482,9 @@ "Id \n", "0 0.324497 \n", "1 0.360532 \n", - "2 0.387802 \n", - "3 0.311439 \n", - "4 0.358311 \n", + "2 0.404641 \n", + "3 0.373753 \n", + "4 0.415049 \n", ".. ... \n", "170 0.39817 \n", "171 0.479253 \n", @@ -1361,9 +1496,9 @@ "Id \n", "0 0.371695 \n", "1 0.435014 \n", - "2 0.412188 \n", - "3 0.447935 \n", - "4 0.421924 \n", + "2 0.475432 \n", + "3 0.426976 \n", + "4 0.436383 \n", ".. ... \n", "170 0.441068 \n", "171 0.443318 \n", @@ -1490,35 +1625,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "Defaulting to user installation because normal site-packages is not writeable\n", - "Requirement already satisfied: scikit-learn in c:\\users\\martín\\appdata\\roaming\\python\\python310\\site-packages (1.3.1)\n", - "Requirement already satisfied: seaborn in c:\\users\\martín\\appdata\\roaming\\python\\python310\\site-packages (0.12.2)\n", - "Requirement already satisfied: numpy<2.0,>=1.17.3 in c:\\users\\martín\\appdata\\roaming\\python\\python310\\site-packages 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"C:\\Users\\Martín\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:546: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:545: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -1604,7 +1738,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "id": "278cdf51", "metadata": {}, "outputs": [ @@ -1614,7 +1748,7 @@ "(175, 2)" ] }, - "execution_count": 19, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1646,7 +1780,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -1655,7 +1789,7 @@ }, { "data": { - "image/png": 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N4AgAAAAAAHA0giMAAAAAAMDRCI4AAAAAAABHIzgCAAAAAAAcLWvBkVdeeUWf+9zndO2118owDC1cuFANDQ36m7/5Gw0PD6dlH2fOnNGf//mfa/ny5SorK1NhYaEWLlyo9773vfrLv/xLvfbaa2nZDwAAAAAAyB8F0Wg0mumd/PSnP9Vtt92m8+fPz/jz2tpatbe3q7q6Oul9/OAHP9Cdd96pixcvzvqYhQsXav/+/br55puT3s9sBgYGVFlZKUnq7+/XokWL0r4PAAAAAACcLhPj74xnjpw+fVof/ehHdf78eZWWlmrHjh169tln9eSTT+qOO+6QJPX09OgDH/iAXn/99aT28cwzz+hjH/uYLl68KJfLpY9//OP68Y9/rM7OTv3oRz/SLbfcIkk6e/as/vAP/1AvvfRS2n4/AAAAAABgbxkPjtxzzz26ePGiPB6Pfv7zn+u+++7TqlWrdOONN+rb3/62/vqv/1rSeIDkkUceSWofDz74oCKRiCTpG9/4hr773e/qD//wD9XQ0KA//uM/1k9+8hN99rOflSRdvHhRX/3qV9PzywEAAAAAANvL6LKazs5ONTY2SpLuvPNOfetb35r2mEgkone+853q7u5WWVmZXnvtNRUWFia0n4ULFyoQCOjyyy/X0NDQjI/57W9/q7KyMklSXV2dTp48mdgvMw+W1QAAAAAAkHm2W1bz4x//eOL2xz/+8ZkPwOVSa2urJOncuXM6fPhwwvsJhUKSpCVLlsz6mMsuu0w+n2/K4wEAAAAAADIaHHn66aclSYZhaPny5bM+7vrrr5+4/cwzzyS8n9/7vd+TJL388suzPub8+fMTWSWxxwMAAAAAAGQ0ONLd3S1Jqq6ulsfjmfVx11577bRtEvHJT35SkvSb3/xmxqU7kvTAAw9Me3wiBgYG5vz36quvJvycAAAAAAAg92aPWKRoZGRkIlNjvvU/5eXlMgxDwWBQ/f39Ce/rE5/4hJ5++mnt3btXd911l06ePKk/+IM/0JVXXqm+vj794Ac/mFjis3XrVq1bty7hfcTWMwEAAAAAgPySseDI5La8paWl8z4+Fhy5cOFCwvtyu9167LHHdMstt+grX/mKdu/erd27d095zNq1a3XfffclFRgBAAAAAAD5K6OZIzFer3fexxcVFUkab7WbjO7ubu3du1fPP//8jD//5S9/qT179mjp0qW66qqrEn7++TJaXn31Va1YsSLh5wUAAAAAALmVseBIcXHxxO14usOMjo5KkhYsWJDwvjo6OnTLLbfot7/9ra6++mpt375dN998sxYuXKj//M//1E9+8hNt27ZN+/fv19GjR/Xzn/9c73jHOxLaB615AQAAAADITxkryHrJJZdM3I5nqUwwGJQU3xKcyUZHR3Xrrbfqt7/9rd761rfq2LFjuu222/SWt7xFhYWFWrRokT71qU/p6NGjKi4u1n/8x39o48aNif0yAAAAAAAgb2UsOFJcXKzLL79c0ninl7kEAoGJ4EiihU//6Z/+Sf/3//5fSdLdd9+tt771rTM+7h3veIduu+02SdLJkyf1L//yLwntBwAAAAAA5KeMtvJ9+9vfLkl64YUXFA6HZ33cr3/964nbS5cuTWgfk1v/1tXVzfnY5cuXz7hPAAAAAADgXBkNjjQ1NUkaXzJz8uTJWR/31FNPTdy+7rrrEtqHx/O7silzBWAkaWxsbMbtAAAAAACAc2U0OPLBD35w4vb3vve9GR8TiUS0d+9eSVJZWZnWrl2b0D6WLFkycbujo2POx04OwkzeDgAAAAAAOFdGgyMrVqzQ6tWrJUl79uzRL3/5y2mPeeSRRyaWxtxzzz0qLCyc8vMjR46ooKBABQUF+tjHPjZt+5tuukklJSWSpG9+85uztvL9x3/8R/393/+9JOmqq67Se97znmR/LQAAAAAAkEcyGhyRpK997WtasGCBwuGw3ve+9+nBBx/UsWPHdPjwYd155536/Oc/L0mqra3V5z73uYSfv6ysTF/4whckSa+//rre+9736r777tPhw4f13HPP6Z//+Z/1qU99Sn/wB3+gSCQiSfqrv/oruVwZ/9UBAAAAAIANZLzwxrJly3TgwAHddtttOn/+vO67775pj6mtrVV7e/uU9r+J+B//43/o7Nmz+trXvqYLFy7owQcf1IMPPjjtcYWFhfrKV74y0bUGAAAAAAAgK+kTt9xyi371q1/pM5/5jGpra1VSUqKysjLV19froYce0unTp1VdXZ308xcUFGjnzp06ceKEPvnJT+qd73ynLrnkErndbl122WVavny5PvvZz+pf//Vfde+996bxNwMAAAAAAHZXEI1Go7k+iHwwMDCgyspKSVJ/f78WLVqU4yMCAAAAACD/ZGL8TeENAAAAAADgaARHAAAAAACAoxEcAQAAAAAAjkZwBAAAAAAAOBrBEQAAAAAA4GgERwAAAAAAgKMRHAEAAAAAAI5GcAQAAAAAADgawREAAAAAAOBoBEcAAAAAAICjERwBAAAAAACORnAEAAAAAAA4GsERAAAAAADgaARHAAAAAACAoxEcAQAAAAAAjkZwBAAAAAAAOBrBEQAAAAAA4GgERwAAAAAAgKMRHAEAAAAAAI5GcAQAAAAAADgawREAAAAAAOBoBEcAAAAAAHCgMTMiSQoEQxoIDCsQDE2530k8uT4AAAAAAACQPWEzopFwRPs7+9R2vE8vDwUnfrbEZ6il0a/mFX4Ve1zyuJ2RU0FwBAAAAAAAhzAjUXX0DmnLvlMKhsxpP395KKjt7d3aebBHuzbUaU1thdyughwcaXY5IwQEAAAAAIDDhc2IjvYMavPerhkDI5MFQ6Y27+1SR++gwg5YZkNwBAAAAAAABxgJR7Rl3ymZkWhcjzcjUd3VdkqjYYIjAAAAAADA5sbM8Roj82WMvFkwZGr/ib68L9JKcAQAAAAAgDxX6Hap7XhfUtu2HetTYZ4XZs3v3w4AAOQlWg8CAJCYQDA0pStNIl4aCurccCjNR2QtdKsBAAC2QetBAACSEwyFU9zeVFlJmg7GggiOAAAAW6D1IAAAyTO8qQ3/Da87TUdiTUypAAAAy6P1IAAAqSk3vFriM5LatspnqKzEm+YjshaCIwAAwPJoPQgAQGrGzIhaGv1Jbduy0p/3db0IjgAAAEuj9SAAAKkrdLvUvMKf8PIYw+tWc4OfbjUAAAC5ROtBAADSo9jj0q4NdXHX5HK7CvRoS52KPPl/Ls3/3xAAANgarQcBAEgPj9ulNbUV2t1aP28GieF1a8/Geq2uqXBEBzi61QAAAEuj9SAAAOnjdhVodY1Px7eu04ETfXr8WN+USYgqn6GWlX41N/hV5HE5pvMbwREAAGBptB4EACC9PG6XSt0uta5arE1NVTo3HFIwZMrwulVW4tWYGXHcslRn/bYAAMB2aD0IAEBmxAIgZSVeXVW2YOKc6bTAiERwBABgEbGOIoFgSAOBYQWCoSn3w7loPQgAADKNZTUAgJwKmxGNhMdbtbYdn7rmdYnPUEujX80r/Cr2uBxRDAzTxVoP7jzYk1A7X6e0HgQAAKnjagEAkDNmJKqO3iE17jik7e3d0zqSvDwU1Pb2bjXuOKSO3iGZkWiOjhS5RutBAACQSVwxAAByImxGdLRnUJv3ds2bDRAMmdq8t0sdvYMKs0TCkWg9CAAAMokrBgBAToyEI9qy71Tc2SBmJKq72k5pNExwxKkmtx7ctn7ptCKtVT5D29YvVefWdWqq9jmm9SAAAEgdNUcAAFk3Zo7XGEmkfoQ0nkGy/0SfWlctpo6EQ9F6EAAAZAJXDwCArCt0u9R2vC+pbduO9TH4Ba0HAQBAWnEFAQDIukAwNK34arxeGgrq3HAozUcEAAAAJyM4AgDIumAonOL2iS3HAQAAAOZCcAQAkHWGN7WSV/N1KwEAAAASQXAEAJB15YZ3WqeReFX5jIn6EgAAAEA6EBwBAGTdmBlRS6M/qW1bVvo1ZtLOFwAAAOlDcAQAkHWFbpeaV/gTXh5jeN1qbvDTkQQAAABpxdUlACAnij0u7dpQJ7erIK7Hu10FerSlTkUeTl0AAABIL64wAQA54XG7tKa2Qrtb6+fNIDG8bu3ZWK/VNRXykDUCAACANEutXQAAAClwuwq0usan41vX6cCJPj1+rE8vDwUnfl7lM9Sy0q/mBr+KPK64s0wAAACARBAcAQDklMftUqnbpdZVi7WpqUrnhkMKhkwZXrfKSrwaMyPUGAEAAEBGERwBAFhCLABSVuJVWcn0+wEAAIBM4YoTAAAAAAA4GsERAAAAAADgaARHAAAAAACAoxEcAQAAAAAAjkZwBAAAAAAAOBrBEQAAAAAA4GgERwAAAAAAgKMRHAEAAAAAAI5GcAQAgCwYMyOSpEAwpIHAsALB0JT7AQAAkDueXB8AAAD5LGxGNBKOaH9nn9qO9+nloeDEz5b4DLU0+tW8wq9ij0seN3MWAAAAuUBwBACADDEjUXX0DmnLvlMKhsxpP395KKjt7d3aebBHuzbUaU1thdyughwcKQAAgLMxRQUAQAaEzYiO9gxq896uGQMjkwVDpjbv7VJH76DCLLMBAADIOoIjAABkwEg4oi37TsmMRON6vBmJ6q62UxoNExwBAADINoIjAACk2Zg5XmNkvoyRNwuGTO0/0UeRVgAAgCwjOAIAQJoVul1qO96X1LZtx/pUSGFWAACArOLqCwCANAsEQ1O60iTipaGgzg2H0nxEAAAAmAvBEQAA0iwYCqe4fWLLcYBMCr1RB+dsMKSBs8M6GwxNuR8AgHxAK18AANLM8KZ2ejW87jQdCZC8sBnRSHi8fk7b8b4p2VBLfIZaGv1qXuFXscclD0vBAAA2x5kMAIA0Kze8WuIzktq2ymeorMSb5iMCEmNGojraO6TGHYe0vb172jKxl4eC2t7ercYdh3S0dyjurkz5KFZAORAMaSAwrMAbmTUUVgYAeyFzBACANBszI2pp9Gt7e3fC27as9GvMjFCUFTkTNiM62jukO/Z2zRv0CIZM3bG3S7tb67W6xueoDBIyawAgv/BNDQBAmhW6XWpe4U94eYzhdau5wU9gBDk1Eo7o7n2n4s4GMSNRbdl3SqMOqkFiRqLqiDOzpsPhmTUAYBdcfQEAkAHFHpd2baiT21UQ1+PdrgI92lKnIg+nZuRO6I1MiESLAgdDpvaf6HNEkdawGdHRnkFt3ts17+sUDJnavLdLHb2DCrPMBgAsjSswAICjZapegMft0praCu1urZ83g8TwurVnY71W11SQfo+c8npcajvel9S2bcf65HVAcG8kHNGWBDNr7mpzVmYNANgRNUcAAI6UjXoBbleBVtf4dHzrOh040afHj03dT5XPUMtKv5ob/CryuOLOMgEy5WwwNG2JSLxeGgoqMBxSeR4XFB4zU8usaV21mGVzAGBRBEcAAI4TqxewZd+pGQc5sXoBOw/2aNeGOq2prUg6cOFxu1Tqdql11WJtaqrSueGQgiFThtetshIvxVdhKcOj4ZS2D46G8zo4UuhOLbNmU1NVmo8IAJAuXI0BABwlV/UCYgGQshKvripbMNGul8AIrKSkKLV5MyPF7a0ukGJmzbnhUJqPCACQLlyRAQAchXoBwOwWGl4t8RlJbVvlM/I6a0SSgqEUM2sSXI4DAMgegiMAAMdItV5AqkVaAasLhSNqafQntW3LSn/ed6sxvClm1iTY3hsAkD0ERwAAjpFqvQCWwCDfeT0uNa/wJzyIN7xuNTf4875bTXmKmTVleZ5ZAwB2lt9nMAAAJqFeADC/Yo9L39hQF3cRYrerQLs21OV9YEQazz5LJbOG7DMAsK78P4sBAPAG6gUA8/O4Xbq+tkLfaa2fN4PE8Lq1u7Vea2orHJFZVehOLbPGCa8RANgV39AAAMegXgAQH7erQGtqfDq+dZ22rV86bSlJlc/QtvVL1bl1nZpqfEm3urajYo9LuxLMrHm0pU5FDsisAQA7y+9+awAATBKrF5DM0hrqBcBpPG6XSt0u3b5ysTY1VSkwHFJwNCyjyKPyEq9C4YgjltK8mcft0praCu1urdeWfafmzCgzvG492lKn1TUVjgogAYAdOe+MBgBwLOoFAImLBUDKS7xaVF4y0a7XiYGRGLerQKvjzaypdlZmDQDYFZkjAADHiNUL2HmwJ6H6IdQLAPBmscya1lXjmTXnhkMKhkwZXrfKSrwaMyN8ZwCAjfCNDQBwlHTXC4hlkwSCIQ0EhhUIhqbcDyC/xQIgZSVeXVW2YGL5HYERALAXMkcAAI6SrnoBYTOikXBE+zv71Ha8b0odkyU+Qy2NfjWv8KvY45KHQRIAAIClFUSj0WiuDyIfDAwMqLKyUpLU39+vRYsW5fiIAABziQU3Dpzo0+PHpgY3qnyGWlb61dzgV9EMwQ0zEtXRnsG4giu7NtRpTS3FGAEAANIlE+NvMkcAAI6UbL2AsBlRR++QNu/tkhmZe34hGDK1eW+X9mysV1O1jwwSAACQcbFrmEAwpGAoLMPrUblBLaT5EBwBADja5HoBZSXT73+zkXBEW/admjcwEmNGorqr7ZQ6t67LenCEiyMAAJwj3iW/CzwuubkOmIbgCAAAcRozxy84Eul0I41nkOw/0afWVYuzEpSgHgoAAM5iRqLq6B2adcnvby+O6WwwpPMXx1RatmBi4qTE69FCw6tQOOLoFu0SwREAAOJW6Hap7XhfUtu2HevTpqaqNB/RdPNdHL08FNT29m7tPNhDPRQAAPLAfEt+6/zl2r2xXm5XgX7Y1c/EySyc+VsDAJCEQDA05WIiES8NBXVuOJTmI5oqbEZ0tGdQm/d2zZvdEquH0tE7qDBthwEAsK25lvzW+cu1745GneoL6L0PPqnt7d3TrmViEyeNOw7paO9Q3EuH8w3BEQAA4hQMhVPcPrHlOIlKth7KaJjgCAAAdjTXkt+Fhle7N9br6ReGdOcPTsY1cXLH3i4d7XHmxAnBEQAA4mR4U1uNanjdaTqS6VKthzLmwIsgAADsbq4lv80NlXK7CnTPE6cTmjjZss+ZEycERwAAiFO54dUSn5HUtlU+Q2Ul3jQf0e+kWg+F7jUAANjPbEt+XQXShka/ftjVn/TESchhARKuhAAAiNOYGVFLoz+pbVtW+jOanWH1eigAACD9Zlvyu6amQovKS1KaOHFa9xpn/bYAAKSg0O1S8wp/wstjDK9bzQ3+jGZnWL0eCgAASL/Zlvxec0WpXjs/ktLEScBhEycERwAASECxx6VdG+ribn/rdhXo0ZY6FWV49sXK9VAAAEBmzLbkt8Tr1oXRFCdOUtzebgiOAACQAI/bpTW1FdrdWj9vQMHwurVnY71W11TIk+GaHlauhwIAADJjtiW/wyFTpUUpTpykuL3dEBwBACBBbleBVtf4dHzrOm1bv3RaUKLKZ2jb+qXq3LpOTdW+uLNMUmHleihIj9jfKBAMaSAwrEAwNOV+AIDzzLbk98XXLuiKS4tTmjgpd9jEibNCQQAApInH7VKp26XWVYu1qalK54ZDCoZMGV63ykq8GjMjWe0AE7s42nmwJ6H6Idmoh4LUhM2IRsLjrZrbjvdNWT++xGeopdGv5hV+FXtcGc9QAoBciJ1TA8GQgqGwDK9H5Ub2z7VWFVvyu3lv10TL3qO9gxoIDKul0a/t7d0JP2fLSr9C4YijirIWRKPR+BoeY04DAwOqrKyUJPX392vRokU5PiIAgNOEzYg6eoemXBzNxe0q0J6N9Wqq9jGotigzEtXRnkFt2XdqzqCX4XVr14Y6ramtyEqmEgBkA8Hh+M10vvjUDdfozuuv0XsffDLhiZPOressvawmE+NvZ7+DAGQdaeFA5li1HgqSEzYjOtozqM17u+a9qA2GTG3e26WO3kGF+T4FkAfMSFQdvUNq3HFI29u7p3VdeXkoqO3t3WrccUgdvUNxTQrks5mW/O4/0S8zEtXXbl2WUCH5XRvqHJUxEkPmSJqQOQLMjcg/kD2xz9uBE316/NjUz1uVz1DLSr+aG/wq4vNmaRdGw2rccSjvZvsAYD5kQqYmttzo3HBIY2ZUCw1v3mUhZmL8nbXgyCuvvKKvf/3ram9vV39/v4qKinTNNdfoIx/5iO666y6VlJSkbV+HDh3S448/rqefflqvvvqqPB6P3vKWt+jd7363brrpJt1+++0qLS1N2/4kgiPAXEgLB3Jj8sVRLuuhIHFjZkSPPXsmqXXi29YvVeuqxfyNAdgWweH0i3fixOtx2eL8YdvgyE9/+lPddtttOn/+/Iw/r62tVXt7u6qrq1PaTyAQ0Mc//nH9n//zf+Z83OnTp/We97wnpX29GcERYGZE/gEgOWsfPjItjTweVT5Dv7j3hvQfEABkAcHhzIoVWQ0MhxQcDcso8qi8xGu74qu2rDly+vRpffSjH9X58+dVWlqqHTt26Nlnn9WTTz6pO+64Q5LU09OjD3zgA3r99deT3s9vf/tb3XzzzROBkQ996ENqa2vTsWPHdOLECf3d3/2d7rnnHoIWQJaNhCPasu9U3OtAzUhUd7Wd0miYNfMAnCsQDCUVGJGkl4aCOjccSvMRAUB2FLpdajvel9S2bcf6CIzMIxYAKS/xalF5yUS7XjsFRjIl4zlH99xzjy5evCiPx6Of//znWrVq1cTPbrzxRtXU1Ojzn/+8enp69Mgjj+jLX/5yUvu5++67dfLkSRUVFel//a//pT/4gz+Y8vP6+np96EMf0s6dO2Wa8adnAUjemDleYySRlEhpvLDg/hN9RP4BOFYwFE5xe1Nl6VuxDABZk47gcNkbA34gERkddXR2dqqjo0OStGnTpimBkZjPfe5zWrp0qSTpa1/7msbGxhLez9NPP60f/OAHkqTt27dPC4xMVlBQII+HdWhANhD5B4DkGN7UrlXm61YEAFaVjuAwkIyMjjx+/OMfT9z++Mc/PvMBuFxqbW2VJJ07d06HDx9OeD+7du2SJF122WXasmVL4gcKICNICweA5JQbXi3xGUltW+UzmDUFYFsEh5ErGQ2OPP3005IkwzC0fPnyWR93/fXXT9x+5plnEtpHKBSaqDNy8803q7i4WJJkmqb6+/t15swZjYyMJHroANKAyD8AJGfMjKil0Z/Uti0r/RozqdsEwJ4IDiNXMhoc6e4erzBcXV0951KWa6+9dto28fqXf/mXieDHu971Lp0/f16f/vSn5fP55Pf7tWTJEl122WW6+eabdeTIkcR/CQAzil14B4IhDQSGFQiGptwvEfkHgGQVul1qXuFP+HvQ8LrV3OBnWSIA2yI4jFzJWPGNkZERDQ0NSdK8HWLKy8tlGIaCwaD6+/sT2s+//du/TdyORCKqr69Xb2/vlMeEQiEdOnRITz75pB588EH9+Z//eUL7kMZbBc3l1VdfTfg5ATuK9Ujf39mntuNTe6Qv8RlqafSreYVfxR7XROQ/2VaURP4BOFmxx6VdG+oSaoX+aEudiug4AMDGYsHhnQd7EsoiJjiMVGXsnTO5LW9paem8jzeM8dSpCxcuJLSfs2fPTtx+6KGH1Nvbq//6X/+rOjs7NTIyotdee03f/OY3ddlllykajeoLX/jCxDKcRFRWVs75b8WKFQk/J2A3ZiSqjt4hNe44pO3t3dOCHi8PBbW9vVuNOw6po3dIZiSqz6yrSWpfRP4BOJ3H7dKa2grtbq2fN4PE8Lq1Z2O9VtdUyMPAAIDNxYLDbldBXI8nOIx0yNi7Z3KdD693/tnfoqIiSdLFixcT2k8w+LvB2cjIiG6++Wb9wz/8gxoaGlRUVKSKigp98pOf1D/8wz/I5Rr/db/4xS8qGp1/BgbA74TNiI72DGrz3q55o/jBkKnNe7vU0Tuo33/XlVpUVpzQvpwe+Y9nyRIAZ3C7CrS6xqfjW9dp2/ql09bhV/kMbVu/VJ1b16mp2hf3QAIArIzgMHIhY8tqYoVRpfFlLfMZHR2VJC1YsCDp/Ujj2SNu9/QPUFNTk/7oj/5IP/rRj9Td3a3nn39e7373u+Pez3zLfV599VWyR5DXRsIRbdl3Kq7Ubmk8y+SutlPq3LpOj32iUe/7/46SFj6PRJYscfIHnMPjdqnU7VLrqsXa1FSlc8MhBUOmDK9bZSVejZkRxwaTAeSvycHhAyf69PixqddGVT5DLSv9am7wq8jjIjiMlGUsOHLJJZdM3I5nqUwsAySeJTiz7aeiokLLli2b9bHvf//79aMf/UiSdOLEiYSCI/PVTQHy2Zg5PmBPtHtMMGRq/4k+ta5arN2t9dqy79Scz2F43Xq0pU6rayocd4KLLVma7TWKLVnaebBHuzbUaU2t814jwOliAZCyEq/KSqbfDwD5huAwsilj76Ti4mJdfvnlkuYvZhoIBCaCI5WVlQntZ/Lj5wtgTH7s4OBgQvsBnKzQ7VLb8b6ktm071qdCt0trSAufVbJLlsIsswEAAA4wOTh8VdmCiaL9BEaQThnLHJGkt7/97ero6NALL7ygcDg8azvfX//61xO3ly5dmtA+3vGOd0zcNs25BxWTfz5Xa2Egn8Ui7IFgSMFQWIbXo3Jj7sh7IBhKquOMJL00FNS54ZDKSry2i/wn81olI5UlS1ZdXpOt1w4AADhbKByR1+PS2WBIw6NhlRR5tNDwTtwPxCujEYKmpiZ1dHQoGAzq5MmTamxsnPFxTz311MTt6667LqF9XH311fL7/err69OZM2cUjUZVUDDzrPOLL744cfuqq65KaD+A3aVSzyIYCqe072DInEgBt0NaeDZrf6RjyZJTXzsAAOBcXHMg3TL6LvngBz84cft73/vejI+JRCLau3evJKmsrExr165NeD9//Md/LEk6f/68nnzyyVkf93d/93cTt5uamhLeD2BXybTgnczwphZHna/KuJWk+lolKh1Llqwi268dAABwJjMS1dE4rzmOcs2BOGX0qnrFihVavXq1JGnPnj365S9/Oe0xjzzyiLq7uyVJ99xzjwoLC6f8/MiRIyooKFBBQYE+9rGPzbifT3/60xNdaz772c/q/Pnz0x7z+OOP68iRI5KkD3zgAwnXNgHsKh31LMoN77Q6IfGq8hkT60KtLhe1P9KxZMkKqJsCAACyIWxG9FTPoO6I85rjjr1dOtrDNQfml/Epx6997WtasGCBwuGw3ve+9+nBBx/UsWPHdPjwYd155536/Oc/L0mqra3V5z73uaT24ff79Zd/+ZeSpOeff14rVqzQ9773PZ08eVKHDx/W3XffPRFYufTSS7Vz5860/G6AHSRbz2I0/LsTyJgZUUujP6n9t6z0a8wmJ6N0vFaJSseSJSvIxWsHAACcZyQc0d0JXnNs2cc1B+aX8aqky5Yt04EDB3Tbbbfp/Pnzuu+++6Y9pra2Vu3t7VPa8ibqz/7sz3T27Fk99NBD+vd//3d94hOfmPaYK664Qj/+8Y9VU1OT9H4AO0lXPYtCt0vNK/zaebAnoecyvG41N/gttfRjNrmq/ZEPS5byrW4KAACwplA4tWuO21cupkgrZpWVd8Ytt9yiX/3qV/rMZz6j2tpalZSUqKysTPX19XrooYd0+vRpVVdXp7yfBx98UM8884xuv/12LV68WEVFRbrsssvU0NCgBx54QD09PVq1alUafiPAHtJZz6LY49KuDXVxt9l1uwr0aEudimxyAspV7Y98WLKUT3VTAACAdXk9qV1zEBjBXLLWz/bqq6/WV7/6VX31q19NaLsbbrhB0Wj8BXRWrVpFAAR4Q7pa8EqSx+3SmtoK7W6t15Z9p+aM2Btetx5tqdPqmoq4gym5ls7XKhGxJUvb27sT3ja2ZCnXwYVcvXaYGW2UAQD56myK1xyB4ZDKuebALLIWHAGQfelswSuNZ4OsrvHp+NZ1OnCiT48fm9o2rcpnqGWlX80NfhV5XLYJjEjpf63ilQ9LlnL12mEqWhoCAPLd8GiK1xyjYYIjmBXBESCPZaKehcftUqnbpdZVi7WpqUrnhkMKhkwZXrfKSuw7O53L2h+xJUub93bFVVzMakuW8qFuit3F2ijPltUVa2m482CPdm2o05pa+2R1AQAQU1KU4jVHitsjv1njyhpARmSynkUsAFJW4tVVZQsmHmvHwIiU29ofk5cszRcoMLxu7dlYr9U1FZaZ/c+Huil2RhtlAIBTLEzxmoOsEczFGlfWADLCKS140yHXr9XkJUvb1i+dduKv8hnatn6pOreuU1O1z1Kz/rl+7ZyONsoAAKcIhVO75ghx7sMcyCsC8lg+1LPIFiu8VnZdsmSF186paKMMAHASrye1aw661WAuvDuAPJfvLXjTySqvlR2XLFnltYuJZaMEgiENBIYVCIam3J8vaKMMAHCaYo9L30jwmmPXhjoCI5gX7xAgz9m9nkU28VolzyqvXdiM6MJoWI89e0ZrHz6iZQ8cVNNDh7XsgYNa+/ARPfbsGV0YDedNvY10tFEGAMBOPG6Xrq+t0HfivObY3VqvNbUVTAhgXgXRaDS+RcqY08DAgCorKyVJ/f39WrRoUY6PCJgq1uYznha8Th/s81olL5evnRmJ6mjP4KwdW2IMrztvOrYMBIbV9NDhpLd/5gs36qqyBWk8IgAAsiPeaw6vx0VgJA9lYvxNcCRNCI7ALmJ1K+xSzyKXeK2Sl+3XLmxG1NE7lFA75D0b69VU7bN1gCsQDGnZAweT3v65+2+mWxAAwNZC4Yi8HpcCwyEFR8MyijwqL/FO3I/8lInxN+8WwGHsWM8iV3itkpft186pHVtoowwAcLpYAKS8xKtF5SUT7XoJjCBRvGMAALaWascWOxdppY0yAABAehAcAQDYmpM7tsTaKM9XkO7NaKMMAEhU6I1sy7PBkAbODuvsG53gQjbPwgRiPLk+AAAAUpGOji12Xl4Sa6OcSL0Vp7brBgAkLlb4dH9nn9qOTy18usRnqKXRr+YVfhVTqB42x7sXAGBrwVA4xe0TW45jNVZpowwAyD9mJKqjvUNq3HFI29u7p01GvDwU1Pb2bjXuOKSjvUNx1/4CrIgrIwCArRne1JIgE12SYkVuV4FW1/h0fOs6bVu/dFqR1iqfoW3rl6pz6zo1Vfts38IYAJB5YTOip3oGdcfernknEoIhU3fs7dLRnkGFqWcFm2JZDQDA1mIdW5JZWpNPHVs8bpdK3S61rlqsTU1VtKAGAKRkJBzR3Ql2gtuy75Q6t64jOxG2xLsWAGBrdGyZihbUAIBUhcKpdYKjSCvsiCslAICt0bEFAID08npS6wTnpeg3bIh3LQDA9mIdW+KtpUHHFgAAZnc2xU5wgeFQmo8IyDyuCgEAtkfHFgAA0md4NMVOcCluD+QCBVkBAHlhcseWAyf69PixvimzXlU+Qy0r/Wpu8KvI46JjCwAAsygpSrETXIrbA7nAuxYAkDfo2AIAQOoWptgJrjxPOsHBWbhCBADkHTq2AACQvFA4tU5wdKuBHXGVCAAAAACY4PWk1gmObjWwI961AAAAAIApij0ufSPBTnC7NtQlHRiJZZucDYY0cHZYZ4OhKfcDmUbNEQAAAADAFB63S9fXVug7rfW6e98pBUPmrI81vG7t2lCnNbUVCRc8D5sRjYQj2t/Zp7bjU4upL/EZamn0q3mFX8UeF13mkFEF0Wg0muuDyAcDAwOqrKyUJPX392vRokU5PiIAAAAASE0seDFfJzivx5VwbS8zEtVTPYNxBV++saFO1ycRfEF+ysT4m8wRAAAAAMCMYp3gbl853gkuMBxScDQso8ij8hKvQuFIUktpwmZER3uHdMfeLpmRuefrgyFTd+zt0u7Weq2u8ZFBgozgXQUAAAAAmFMsAFJe4tWi8pKJdr3J1hgZCUd0975T8wZGYsxIVFv2ndIoNUiQIQRHAOTMmDl+cgsEQxoIDCvwRuGt2P0AAADIP6E3aozMtZRmJsGQqf0n+ijSioxgWQ2ArKPwFgAAgHN5PS61He9Latu2Y33a1FSV5iMCCI4AljZmRlTodikQDCkYCsvwelRueCfutyMzElVH75C2zFJ46+WhoLa3d2vnwZ6kq54DAADAus4GQ1MmxxLx0lBQgeHQxLIeIF0IjgAWlK+ZFWEzoo7eIW2Os/DW5r1d2rOxXk3VFN4CAADIF8Oj4ZS2D46GCY4g7RhtABYTy6xo3HFI29u7p0XVY5kVjTsOqaN3KO4iVlYwEo5oS4KFt+5qo/AWAABAPikpSm2O3khxe2AmBEcACwmbER3tGdTmvV3zFqiKZVZ09A4qbIMCpmNmaoW3KNIKAACQHxYaXi3xGUltW+UzyBpBRhAcASwknzMrCt2pFd6ya40VAAAATBUKR9TS6E9q25aVfrrVICMYbQAWke+ZFYEUC2+dGw6l+YgAAACQC16PS80r/DK87oS2M7xuNTf45fXEP4yNXSMHgiENBIYVCIam3A/EsFgLsIhUMyus3tIsGEqx8FbIVFlJmg4GAAAAOVXscekbG+p0RxyF+iXJ7SrQrg11cQdG8rXBATKHdwFgEfmeWWF4Uyy8leDMAgAAAKzL43bp+toKfae1ft7rPMPr1u7Weq2prYhrqXU+NzhA5hAcASwiHZkVVlaeYuGtMgpvAQAA5BW3q0Branw6vnWdtq1fOu1ascpnaNv6percuk5NNT65XQXzPmc+NzhAZrGsBrCIfM+sGDPHC29tb+9OeNuWlX6NmRGKsgIAAOQZj9ulUrdLt69crE1NVQoMhxQcDcso8qi8xKtQOJJQjZFkGxx0bl3H8hqH468PWES+Z1YUulMrvEVgBAAAIH/FAiDlJV4tKi+ZaNebaPHVfG5wgMxitAFYRCyzIhmxzAqrK/a4tGtDXVwpkdJ4quWjLXUqSuCkCAAAAGdKtcEBk3HOxl8fsAgnZFZ43C6tqa3Q7jgLb+3ZWK/VNRWkOAIAAGBe+d7gAJnFiAOwECdkVrhdBVodb+Gt6vgKbwEAACCzYlnKgWBIA4FhBYKhKfdbQb43OEBmUZAVsJDJmRVb9p2a8wva8Lr1aEudVtdU2C6AECu81bpqvPDWueGQgiFThtetshIvxVcBAAAsImxGNBIer+XRdrxvSmbGEp+hlka/mlf4Vexx5TzbN98bHCCzCI4AFjM5s+LAiT49fmzqSajKZ6hlpV/NDX4VeVy2C4xMFguAlJV4VVYy/X4AAADkjhmJqqN3aNZJu5eHgtre3q2dB3u0a0Od1tTmdtIu1uAgmaU1dmhwgMwiOAJYEJkVAAAAyKWwGVFH75A27+2aty1uMGRq894u7dlYr6ZqX84ySGINDra3dye8bazBAdfYzsVfHrCwyZkVV5UtmIhm86UNAACATBoJR7Rl36l5AyMxZiSqu9pOaTScuxokTmhwgMzhrw8AAAAAmDBmjtcYSbRAaTBkav+JvpwWaXVCgwNkBu8AAAAAh7JD9wkA2VfodqnteF9S27Yd68tpBsbkBgfzZZAYXrf2bKzX6pqKnBeTRe5RcwQAAMBh7NR9AkD2BYKhpIqaStJLQ0GdGw7ltLipkxocIH0IjgAAADiI3bpPAMi+YCic4vbmlE6EuUCDAySKdwMAAIBDhM2IjvYMavPernlrCcS6T3T0DirMMhtYCMvBMs/wpjaHnmhB1EyiwQHiReYIAACAQyTbfaJz6zqW1yDnWA6WPeWGV0t8RlJLa6p8Rk6X1ADJ4lsDyDFmPwAA2WDn7hNAbDlY445D2t7ePW3QHlsO1rjjkDp6h+IOAGJmY2ZELY3+pLZtWenn+wK2ROYIkCPMfgAAsinV7hObmqrSfERAfMJmRB29Q9q8t2veoEdsOdiejfVqqvZxDZWkQrdLzSv82nmwJ6GAquF1q7nBz5IV2BLvWiAHcjn7QaYKADhTOrpPALmQ7HKw0TDXNqko9ri0a0Nd3AWZ3a4CPdpSpyIPQ0zYE5kjQJblavaDTBUAcLZ86D4B50l1OVjrqsVkMSTJ43ZpTW2FdrfWz9rdKsbwuvVoS51W19DdCvbFNwWQZbmY/WCdLgAgn7pPwDlSXQ5GYCQ1bleBVtf4dHzrOm1bv1RLfMaUn1f5DG1bv1SdW9epqdpHYAS2RuYIkEW5mP1gnS4AQKL7BOwpHcvBeO+mxuN2qdTtUuuqxdrUVKVzwyEFQ6YMr1tlJV6NmRGCUMgLvIuBLMrF7AfrdAEAEt0nYE/pWA6G9Ihdh5aVeHVV2YKJoBOBEeQL3slAFmW7GB5tGwEAMbHuE4kuj6H7BHKJ5WAAsoWzHJBF2Z79YJ0uAGAyuk/AbmLLwZLBcjAAieBMB2RRtmc/aNsIAJhscveJ+c4phtetPRvrtbqmghpUyBmWgwHIFs50QBZle/aDdboAgDej+wTshOVgALKFbjVAFsVmP7a3dye8bWz2I5GTPOt0AQAzofuEdcRe60AwpGAoLMPrUbnB32Cy2HKweDrvSSwHA5AcgiNAFsVmP3Ye7EkoKyPZ2Q/aNgIA5jK5+0RZyfT7kTlhM6KR8Hjh9LbjfVPO1Ut8hloa/Wpe4Vexx+X4ZU2Tl4Nt2Xdqzmsow+vWoy11Wl1TQdYTgIQ4+5sWyIFsFsNjnS4AANZjRqLq6B1S445D2t7ePW0S4+WhoLa3d6txxyF19A7FlS2R71gOBiDTCqLRKN+2aTAwMKDKykpJUn9/vxYtWpTjI4KVmZGojvYMZmX248JoWI07DiWcqdK5dZ2MIpLLAABIp7AZUUfvUEJLRPZsrFdTtc/xGSQxsSVHLAcDnCsT42++PYAcyObsB20bAQCwjpFwRFv2nYo7G8SMRHVX2ymNhsnmjJm8HOyqsgUTy4AJjABIBdPCQI5kqxge63QBALCGMXO8xkii3eCCIVP7T/SpddViAgAAkCEER4Acy0YxvMmZKgdO9Gnf8T5VlpfomitKVeJ1a0GhW9dcYaipukJFHheBEQAAMqDQ7VLb8b6ktm071qdNTVVpPiIAQAzBEcAhYpkqH3vv4omZp7PBkIbf1DaQ9czWRKtHALC/QDCUVAc5SXppKKhzwyE6yQFAhhAcARyEtoH2w98MAPJHMBROcXtzSpYpACB9CI4AWZLrmf9Y28DZ6o7E2gbuPNijXRvqtKaWuiO5xt8MAPKL4U3t0tvwutN0JACANyM4AmSYFWb+E2kbGAyZ2ry3i7aBOcbfDADyT7nh1RKfkdTSmiqfwZKaNMj1ZBUA6yI4AmSQVWb+k20b2Ll1HQPtHOFvBgD5Z8yMqKXRr+3t3Qlv27LSzwA+BVaYrAJgbXzygQwJmxEd7RnU5r1d87bsi838d/QOKmxG0nocqbYNHEvz8WB+/M0AID8Vul1qXuFPeHmM4XWrucFPYCRJscmqxh2HtL29e1rmTmyyqnHHIXX0DsU9MQEgv/ANC2RIsjP/o+H0DmxTbRvIhVj28TcDgPxV7HFp14a6uDNF3a4CPdpSpyIP3+3JsMpkFQDr41sWyAArzfyno20gsou/GQDkL4/bpTW1FdrdWj9vBonhdWvPxnqtrqlgqUeSrDJZBcD6+JYFMsBKM//paBuI7OJvBgD5ze0q0Ooan45vXadt65dqic+Y8vMqn6Ft65eqc+s6NVX76ESWJCtNVgGwPgqyAhmQjpn/dFWkp22g/fA3A4D853G7VOp2qXXVYm1qqtK54ZCCIVOG162yErqnpEOqk1WbmqrSfEQArIxvXCADrDTzH2sbmAzaBuYGfzMAcI5YAKSsxKuryhZMfIcTGEkdy1QBJIJvXSADrDTzH2sbmIxY20BkF38zAABSZ6XJKgDWR3AEyAArzfzTNtB++JsBAJA6K01WAbA+rqCBDLDazD9tA+2HvxkAAKmx0mQVAOvjKhrIgEzM/McCJoFgSAOBYQWCoSn3z4W2gfbD3wwAgNRYbbIKgLUVRKPR+Jp+Y04DAwOqrKyUJPX392vRokU5PiLkWtiMqKN3SJv3dsmMzP8xc7sKtGdjvZqqfVMGuGEzopHweCu6tuN9UwqLLfEZamn0q3mFX8Ue17wD49hzHTjRp8ePTX2uKp+hlpV+NTf4VRTHcyE7+JsBAJC8C6NhNe44lFD9EMPrVufWdTKKaOwJWFUmxt8ER9KE4AhmYkaiOtozqC37Ts15Uja8bj3aUqfVNRVTllEksv2uDXVaU1sR1zKMWHtA2gbaB38zAAASl67JKgDWQnDEwgiOYDbJzvxzMgcAAEhdqpNVAKyH4IiFERzBfBKd+ScNFAAAID1Ypgrkl0yMvxlBAVkSC4CUlXhVVjL9/snGzPEaI4kERiQpGDK1/0SfWlctZqkFAADAGzxul0rdLrWuWqxNTVUsUwUwDd8AgAUVul1qO96X1LZtx/o4uQMAAMxg8mTVVWULJtr1cu0EgG8BwIICwdCUdM9EvDQU1LnhUJqPCAAAAADyF8ERwIKCoXCK2ye2HAcAAAAAnIzgCGBBhje1ckCG152mIwEAAACA/EdwBLCgcsOrJT4jqW2rfMbE+lkAAAAAwPwIjgAWNGZG1NLoT2rblpV+jZmRNB8R5hN7zQPBkAYCwwoEQ1PuBwAAAGBdtPKFLcXarQWCIQVDYRlej8qN/GnDVuh2qXmFXzsP9iRUP8TwutXc4M+L18AuwmZEI+Hx1sttx/umFNJd4jPU0uhX8wq/ij0uefi7AAAAAJZEcAS24qSBaLHHpV0b6rR5b5fMSHTex7tdBXq0pU5FHnv/3nZiRqLq6B3Sln2nZgxivTwU1Pb2bu082KNdG+q0prZCbldBDo4UVpHvgV0AAAC7KohGo/OPujCvgYEBVVZWSpL6+/u1aNGiHB9R/jEjUR3tGZx1IBpjeN15MxBN5Hd+tKVOq2vs/zvbRdiMqKN3KKHg1Z6N9Wqq9tk+cIfEOSmwCwAAkGmZGH8THEkTgiOZ5eSBaGxQdeBEnx4/NnVQVeUz1LLSr+YGv4oYVGXVhdGwGnccSnjZU+fWdTKKSNpzEicGdgEAADIpE+NvrtBhCyPhiLbsOxVXYEQaH4zc1XZKnVvX2T5g4HG7VOp2qXXVYm1qqtK54ZCCIVOG162yEtLxc2HMHM8ASCQwIknBkKn9J/rUumoxfzOHSCSwGwyZ2ry3K28CuwAAAHbClRcsL9WBaL50C4kNpstKvLqqbMFEu14G2dlX6Hap7XhfUtu2Hevjb+YgyQZ2R8P58b0FAABgF1yhw/IYiMJqAsHQlOVNiXhpKKhzw6E0HxGsiMAuAACAfTBqhOUxEIXVBEPhFLdPbLAMeyKwCwAAYB9cecHyGIjCagxvauWaDK87TUcCKyOwCwAAYB8ER2B5DERhNeWGV0t8RlLbVvmMiXoxyG8EdoHZxZaNBYIhDQSGFQiGptwPAEC20a0GlhcbiCYzA8tAFJkwZkbU0ujX9vbuhLdtWemnw5BDENgFpou1p9/f2ae241Pb0y/xGWpp9Kt5hV/FtKcHAGQZZx1YXmwgmozYQBRIp0K3S80r/AkPXg2vW80NfgIjDkGGETCVGYmqo3dIjTsOaXt797RJj5eHgtre3q3GHYfU0TsUd5cnAADSgSt0WB4DUVhRscelXRvq5HYVxPV4t6tAj7bUqcjD+9Hq0pXuT2AX+J2wGdHRnkFt3ts175KxYMjU5r1d6ugdVJjPAQAgS7hKhy0wEIXVeNwuramt0O7W+nkDd4bXrT0b67W6poI0cQsLmxFdGA3rsWfPaO3DR7TsgYNqeuiwlj1wUGsfPqLHnj2jC6PhuAdrBHaB3xkJR7Rl36m4s0HMSFR3tZ3SaJjgCAAgO7J25fXKK6/oc5/7nK699loZhqGFCxeqoaFBf/M3f6Ph4eGM7HN4eFhVVVUqKChQQUGBFi9enJH9IPMYiMKK3K4Cra7x6fjWddq2fum0JRRVPkPb1i9V59Z1aqr2xR3cQ/ZlKt2fwC4wnkW1v7Mv4SLDwZCp/Sf6yKICAGRFQTQazfiCzp/+9Ke67bbbdP78+Rl/Xltbq/b2dlVXV6d1v/fee68eeeSRif9fffXVOnPmTFr3ETMwMKDKykpJUn9/vxYtWpSR/ThdrJDbgRN9evzY1EJuVT5DLSv9am7wqyhLhdxihTUDwZCCobAMr0flhpeCmw4U+5ufGw4pGDJleN0qK+G9YAdhM6KO3iFt3tsVV9DD7SrQno31aqr2xfU9Y0aiOtozqC37Ts05ODS8bj3aUqfVNRUE0pB31j58JOnC6r+494b0HxAAwNYyMf7OeHDk9OnTuu6663Tx4kWVlpbqi1/8otauXauLFy9q//79+s53viNpPEDS1dWlSy65JG37bWhoUGFhoQoLC/X6668THMkjuR6IJlJtfzhk6vzIGIETzIogW25dGA2rccehhGa1Da9bnVvXySiKryON1QK7QDYFgiEte+Bg0ts/d//NFCi2Kc5vADIlE+PvjLfyveeee3Tx4kV5PB79/Oc/16pVqyZ+duONN6qmpkaf//zn1dPTo0ceeURf/vKXU96naZq64447ZJqmvvSlL2nPnj16/fXXU35eWEfshFpW4lVZyfT7MymWfj/bLHAs/X7nwR59/dZluq7ap//+xGmd6jtHm0JMQUvL3Es13b911eK4vnc8bpdK3S61rlqsTU1VZBjBUYKhcIrbm1PO9bA+zm8A7CijmSOdnZ1qbGyUJN1555361re+Ne0xkUhE73znO9Xd3a2ysjK99tprKiwsTGm/X/3qV/W5z31Ov/d7v6df/epXqq2t1SuvvELmCFKWTPr9t29frmX+cq376lM6+0bXC8Pr1q4NdVpTS/q8UyWy1IL3SmaR7g9kFpkjzsL5DUA2ZGL8ndFQ7Y9//OOJ2x//+MdnPgCXS62trZKkc+fO6fDhwynt85VXXtH9998vSfrWt74lr5eTKdInmWr7//2J0/K4CvTR+sqJ+2lT6Gy0tLSOQDCUVGBEkl4aCurccCjNRwTkn3LDO61gdbyqfAaBkTekq814JnF+A2BnGQ2OPP3005IkwzC0fPnyWR93/fXXT9x+5plnUtrnpz71KQWDQd1+++264YYbUnouYLJU0u9/eLJfLSv9mjwxQptC56KlpXWkI90fwNzGzIhaGv1Jbduy0m+pwX8upLvNeCZxfgNgZxkNjnR3d0uSqqur5fHMXt7k2muvnbZNMvbv36+f/exnKi8vn9KlJh0GBgbm/Pfqq6+mdX+wnkK3S23H+5Latu1YnxaVl2hNTcWU+2lT6Dy0tLQWw5ta6a35WosDGD9/Nq/wJ/x5MbxuNTf4HV2PJ1NtxjOB8xsAu8vY2WZkZERDQ0OSNO/6n/LychnGeLplf39/UvsLBAL69Kc/LUn6q7/6K1VUVMy9QYIqKyvn/LdixYq07g/Wk2r6/eDrI6qqmJ5W3Hasz9EXfk6TapCN90p6ke4PZEexx6VdG+riri3hdhXo0ZY6FXmc+51ntyUqnN8A2F3GvoUmd4cpLS2d9/Gx4MiFCxeS2t+f/dmf6T//8z+1atUq3XHHHUk9BzCXVNPvL4yaM7b9pG6Bs1DjwlpI9weyw+N2aU1thXa31s+bQWJ43dqzsV6rayoc3cnEbktUOL8BsLuMtfIdGRmZuB1PUdSioiJJ0sWLFxPe19GjR/Xd735XHo9H3/rWt1RQkP6K1/NltLz66qtkj+S5VNPvS4vcCo7OHGChTaFz0NLSWmLp/jsP9iSUCk66P5A4t6tAq2t8Or51nQ6c6NPjx6a2eK3yGWpZ6Vdzg19FHpejO5hkq814OnF+A2B3GQuOFBcXT9wOheaPBI+OjkqSFixYkNB+RkdH9ad/+qeKRqO655579O53vzuxA40TrXkRS79PtuVnxSXFemlw5m2pW+Ac1Liwnli6fyItup2e7g8ky+N2qdTtUuuqxdrUVKVzwyEFQ6YMr1tlJV6NmRGCjkp9icqmpqo0H9H8OL8BsLuMnX0uueSSidvxLJUJBscHjfEswZlsx44d+vd//3dVVlbqL/7iLxI7SCABqabfDwSGdbR3cNrPqFvgLNS4sB7S/YHsiwVAykq8uqpswcR3G4GRcXZcosL5DYDdZTRz5PLLL9dvfvMbDQwMzPnYQCAwERyprKxMaD8PPfSQJGndunX66U9/OuNjYs8dDAa1f/9+SdIVV1yhG2+8MaF9wdlSSb//8PJKffPIi5ppUjpWt4ALQmeIBdm2tyfemYv3SuaQ7g/ASuy4RIXzGwC7y1hwRJLe/va3q6OjQy+88ILC4fCs7Xx//etfT9xeunRpQvuILdn53ve+p+9973tzPnZoaEi33nqrJOn6668nOIKEJZN+//Vbl2nMjOhA1/S6NdQtcB5qXFgX6f4ArMKOS1Q4vwGwu4x+CzU1NUkaz9g4efLkrI976qmnJm5fd911mTwkICWJpt9/+/bluq7apzv2dulscGqKK3ULnIuWltZGuj+AXLPrEhXObwDsLKPfRB/84Acnbs+W1RGJRLR3715JUllZmdauXZvQPqLR6Lz/rr76aknS1VdfPXHfkSNHkvqdgMnp99vWL5128VLlM7Rt/VL98os36T2VZdrwnWM61XduymPsWrcg1rY0EAxpIDCswBsBH9qZJoYaFwCAudi1zTjnNwB2ltFlNStWrNDq1avV0dGhPXv2aOPGjVq1atWUxzzyyCPq7h5fm3jPPfeosLBwys+PHDkyETDZuHGjvv/972fykIG4zJd+HwpH9C8DAe1o/7We6z83sZ1d6xaEzYhGwuNtBduOT63FsMRnqKXRr+YVfhV7XFzgxIkaFwCA2dh5iQrnNwB2VRCNRucvnJCC06dP67rrrtPFixdVWlqq++67T2vXrtXFixe1f/9+ffvb35Yk1dbWqqura0qXGyk9wZHFixfrlVde0dVXX60zZ86k+ivNaGBgYKKYbH9/P61/MVGfIN11C2LbB4IhBUNhGV6Pyo3M1UMwI1Ed7RnUln2n5rxAM7xu7dpQpzW1FVzoJChT7xUAzpHtcwMyL2xG1NE7lFCdsz0b69VU7bPMRAXnNwCZkonxd0YzRyRp2bJlOnDggG677TadP39e991337TH1NbWqr29fVpgBLCzyXULJleMT/ZiIBfZG4lcmAVDpjbv7bLchZkdpPu9AsA5yOzLX5OXqMQzQfFoS51W11hrgoLzGwA7yco30y233KJf/epX+sxnPqPa2lqVlJSorKxM9fX1euihh3T69GlVV1dn41AAWzIjUXX0DqlxxyFtb++ecvErSS8PBbW9vVuNOw6po3corhmmeIyEI9qy71Tcz2dGorqr7ZRGwzOvdaZmCeyM9y+sJlfnBmRPvHXOOreuU1O1z1KBEQCwm4wvq3EKltUgU3KVVjtmRvTYs2e0vb074W23rV+q1lWLJ2aGmNmEnfH+hRXleskFy3iyjyUqAPA7mRh/ExxJE4Ij9mDHi7kLo2E17jiUcEG2zq3rZBSltnJu7cNHps1ExqPKZ+gX994giZolsDfev7CqXJ0bCBYCAKzAljVHACuw68XcmDl+zIlc/Erj9T/2n+ibkr2RqEAwlFRgRJJeGgrq3HBIlxR7dLSHmiWwJ2ruwKpydW6ILeOZLVgYW8az82APwUIAgO1w9Ya8Z+c12YVul9qO9yW1bduxvpSyYYKhcNLbjm9vKhyJprVmCZBN6a65A6RLLs4NYTOioz2D2ry3a96gTCxY2NE7qDA1eQAANkFwBHnN7hdz6cjeSJbhTS2xrMTr1qlXAknPbFLkErmU6sw8719kUi7ODU4IFlJ0GQCcjeAI8prdL+bSkb2RrHLDO60qfryqfIbKS7z68XP/kdT2qWa9AKnKZdYWMJ9snxvyPVgYNiO6MBrWY8+e0dqHj2jZAwfV9NBhLXvgoNY+fESPPXtGF0bDlpk4AQBkBldvyFv5cDGXjuwNKblZsDEzopZGf1L7bVnp12jY1A+7+pPaPtWsFyBVuczaAuaT6rnBeOPcEK98DhbaeektACC9rHu2AlKUDxdz6cje+PP//aukZsEK3S41r/AnfBFteN1qbvBrJGQqlWvIVLJegFTlMmsLmE+q54ayEm9C2+RrsNDuS28BAOmV+9EfkCH5cDGXavbGQGB4WvZGIrNgxR6Xdm2oi7vbgNtVoEdb6lTkcSnVJuGJBmWAdMr2zDyQiFTPDYlmRuZrsNDuS28BAOlFcAR5Kx8u5lLJ3vjw8kq1HeubNXsjnlkwj9ulNbUV2t1aP+8xGF639mys1+qaCnncLpVleWYTSKdsz8wDiUg1sy/RzMh8DBbmw9JbAEB6ERxB3sqXi7lksje+fusyjZkRHZin5kc8s2BuV4FW1/h0fOs6bVu/dNqAscpnaNv6percuk5N1b6J48z2zCaQTrx/YXWpZPYlKp5goatAuqG2QpualujuG6u1qWmJbqitUHWFNYOF+bD0FgCQXqmNHgELi13MJbO0xkozv5OzN7bsOzXnLJfhdevrty7TddU+bfjOMZ0Nzr80KDYL1rpq8awXex63S6Vul1pXLdampiqdGw4pGDJleN0qK/FqzIxM2zY2s7nzYE9CM3PJzmwC6cT7F1aX6Lnh0ZY6ra6piDuYMlksWLi9vXvazxYaXjU3VGpDo1+Lykv02vkRXRgNq7TIoysuLdZvh0MyIxG5Xdb6TKRj6a1VrhMAAOlBcAR5a66LufnEZn4zPcCJ7SMQDCkYCsvwelRuTA82TM7eOHCiT48f65tyUVflM9Sy0q8PL6/UmBnRhu8c06m+c3EfR9uxPm1qqpr3cbFjKivxqqxk+v1vFpvZ3Ly3K6413anMbALpxvsXVhfvuaG5wa8ijyupwIg0e7Cwzl+u3Rvr5XYV6Idd/Wo7PnX/S3yGWhr9al7hV7FnPKAT73kv09Kx9HbyeRAAYH8F0WiqZRMhSQMDA6qsrJQk9ff3a9GiRTk+IkjShdGwGnccSnjmt3PrOhlFmYsdhs2IRsLj653nvph0yfOmi8XYBeSbszdGw6b+v4O9OtDVH1fGyJs9d//NGZkFMyNRHe0ZzPjMJpAJvH9hF7OdG9IVdAibEXX0Dk0EC+v85dp3R6OefmFI9zxxet7Px64NdVpTW6H2X/2Hdh7qTei8lwmBYEjLHjiY9PaZOmcCAOKTifE3wZE0IThiTW++mJuP21WgPRvr1VTty9jFWSKDrdjFZDyDrYHAsJoeOpz0cT3zhRt1VdmCpLefSywYFM/MZjYuioFE8P4FxsXOX9t+/Lx+cvdqneoL6M4fnIz7/Prt25drmb9c67761IxB/ETPe6la+/CRpJfe/uLeG9J/QACAuBEcsTCCI9ZlpZnfTAZr7DALlumZTSCTeP8C4+cxMxpVKBzRyq88mXBm5i+/eJO+eeRFffOpF2d8TDYmKaTxz/Njz55JaunttvVL56zTBQDIvEyMv/lWR95LtttKJoyEI9qy71RcgREpvm4yMXZoPTq5ZslVZQsm9skFJuyA9y8wXjfEVVCgAyf6k2qD+8OT/WpZ6ddsp9pEznupyHY7ZACA9VGQFY6QTLeVdBszx2uMJHMxOV83mdjzW70ALQDA/lJtg7upqUpraip0pGdwxsfEe95LFUWXAQCT8e0OR8nlzG+qF5PzHSOzYACAbEi1De7g6yOqqpia6egqkG6ordCmpiW6+8ZqjY5lPmA/uR3yfOdOw+vWno31Wl1TQW0hAMhTZI4AWZLqxeS54dC8S1+YBQMAZFqqbXAvjJoTHeEWGl41N1RqQ6Nfi8pL9Nr5EV0YDav0jZ9nOqsxW+2QAQDWR3AEyJJULyaDIVNlJXM/ZvIsmBUK0AIA8o/hTe3ysbTIreBoWHX+cu3eWC+3q0A/7OpPuLV9ulhh6S0AIPcIjgBZkurFZLzLZZgFA4DciQ2kA8GQgqGwDK9H5UZ+DbBjBcCTbYNbcUmxXAUF2ndHo55+YUj3PHF6xmD+y0NBbW/v1s6DPVlp8Tt56e3kyYh8+bsBAOZGcATIkvkuJl0F0pqaCl1zRalKvG4Nh0y9+NoFHe0d1OLLE+smwywYAGRX2IxoJDxeeDtXGRDZkmoB8P84N6xPra3W0y8M6c4fnJx3GWgwZGrz3q6stPgFADhXQTQaja+nKOaUiT7LyC9jZkSPPXtm2sXkXOutr7i0WAOBYb362xG9p7KMgAYAWJAZiepoz2BcyxmzkQGRaWYkouGQqZVfeTKhDmyG161ffvEmne4L6D3+cr33wcS379y6bqJeCQDAuTIx/ubsAmRJrJvMzoM9ExeDiay3tu9lNADkr7AZUUfvUFyFsPMlA+LiWERhM6qv3bosrswPaXzJ59dvXaYxM6Kat1yiH3b1Z6y1/UycsNwJAJAazgZAFsW6ybhdBarzl2vfHY061RfQex98Utvbu6ctuYmtt27ccUgdvUNxXYDmuzEzImm8+89AYFiBYGjK/cgsO7z+djhG5I+RcERb9p2K+/vZjER1V9spjYbt+X4cM8eXDm167ISaqn3629uXx9UG99u3L9d11T79zyMv6G1lCzLa2n6ysBnRhdGwHnv2jNY+fETLHjiopocOa9kDB7X24SN67NkzujAaVpjvBwBwPDJHgCyKdZN57OMNesfbLmO9dQKctJ7fiuzw+tvhGJFfYoGCbGZA5Fqh2zXx+drwnWPavbFBz37xJv3oZP+cBcBdrgJ94vsntPTKS/Xa+ZGMt7aXxgNRHb1Dsy53ynbBVwCAtVFzJE2oOYJEmJGILo5F1LjjEOut4+C09fxWY4fX3w7HiPy09uEjSXdt+cW9N6T/gDIsEAxp2QMHJ/6/0PDqo/WValk5Xjdr8PURXRg1VVrkVsUl43WzDv/6Na1/99vkchXohdde10KjSGsfPpL0MTzzhRt1VdmCOR+TyHInaXzZj5MnIADAbjIx/ubbH8iBSFQpzTY6aXlA2IzoaM+gNu/tmvf1imXYdPQOkiKdJnZ4/e1wjMhPgWAo5QwIuwmGwlP+fzYY0jefelFr/vqwPvbdTn3zyIv6u1MD+uaRF/Wx73ZqzV8f1rb/8//TTV99St868qLevahMZQsKUzqGeFrbO225EwAgdQRHgByIpSUnI9H11nbHBW5u2eH1t8MxIj+9OVCQ+PaJBcitwPDOnLkYiUpHegb13WfO6Bu/eEHffeaMjvQMKvaxjAVRRsfMidb2yajyzd/aPtXlTk6agAAA/I5zRliAhThxtjEZXODmlh1efzscI/LXbIGC+LefPwPCalINbJQWF2rMjKil0Z/Uc7Ss9M/7uWUCAgCQDL79gRxw4mxjMrjAzS07vP52OEbkr0xnQFhROgIbsdb2iQaHDK9bzQ3+eT+3TEAAAJLBVSGQA06cbUzGXBe4rgLphtoKbWpaortvrNampiW6obZCsRqbXOCmzg4DDDscI/JXpjMgrChdgY3Jre3j4XYV6NGWOhV55r90ZQICAJAMZ7W8ACwiNtuYbIcDO842JmOmC9yFhlfNDZXa0DjeGeG18yO6MBpWaZFHV1w63hlh3/E+7T/Rr2DIVFlJDg48T6RjgJHp198Ox4j8FQsU7DzYk3DnsXgyIKwqFthIpBPMmwMbsdb2u1vr4+oy9WhLnVbXxNdligkIAEAyCI4AORCbbdze3p3wtpPTkvPdmy9w6/zl2r2xXm5XgX7Y1a+2431TAkxLfIZaGv268/prtHl1lQrdtGpNhR0GGHY4RuS3dAQK7CZdgQ23q0Cra3w6vnWdDpzo0+PHpn6nV/kMtaz0q7nBryKPK+4sEyYgAADJIDgC5IBTZxsTNfkCt85frn13NOrpF4Z0zxOnZ3zdXh4Kant7t3Ye7NHXb12mG37vihwcdf6wwwDDDseI/JbJDAgrS1dgw+N2qdTtUuuqxdrUVKVzwyEFQ6YMr1tlJd6kJgOYgAAAJINvfiBHMrneOl/ELnAXGl7t3livp18Y0p0/ODlvQCkYMvWnPzipjt5BhW24pt8q7FBPwQ7HiPw3OVCwbf3SaUVaq3yGtq1fqs6t69RU7bN9YCTG43aptMij1lWLdfjeG/Tc/TfrmS/cqOfuv1m/uPcGta5aLKPII08cgYZYMKKsxKuryhZMBC6TCVJkuuArACA/FUSj0flzQDGvgYEBVVZWSpL6+/u1aNGiHB8R7MCMRHW0Z9BRs42JujAa1neOvqRPNC3Rex98MuFMm86t62QUkSSXrAujYTXuOGTp190OxwjniGUdpCMDAskLmxF19A4ltNxpz8Z6NVX74grmAAByKxPjb779kbdiM8KBYEgDgWEFgqEp91uBU2cbE1HscemONVX6YVd/wh0EgiFT+0/0Wepvbjd2yHCywzHCOdKZAYHkTV7uNF8GieF1a8/Geq2uqSAwAgAORuZImpA5Yh1hM6KRcET7O/tmLdjZvMKvYo/LUhdBzDbObe3DR5KuK/GLe29I/wE5iB0ynOxwjACyL3ZNEE9dFCtdEwAA5paJ8TfBkTQhOGINiQyQdm2o05paBkh2EAiGtOyBg0lv/9z9N1N4M0V2GGDY4RgB5AYTEACQXzIx/maxNfJGIuuLgyFTm/d2sb7YJoKhcIrbmyorSdPBOFQmOko48RgB5Mbk5U6Tzwd8JwAAYjgjIG+MhCPasu9UXIXXpPEsk7vaTmk0TD0KqzO8qcVxE+1YgNnZoZ6CHY4RAAAA1sKVImxltiKroXBE3f9xXkWFiQ2CKdhpD+WGd1qx2nhV+QyW1AAAAACYE8tqYAvxFFn9cH2lDn32em1+7IRO9Z2L+7nbjvVpU1NVBo7anmJLDwLBkIKhsAyvR+VGbpckjJkRtTT6tb29O+FtW1b6WU4BAAAAYE4ER2B5ZiSqjt6hWYusvjwU1Pb2bu082KOv3bpM++5YqQ3fORZ3gOSloaDODYccn11g5S4/hW6Xmlf4tfNgT0LtfA2vW80NfgIjAAAAAObEiAGWFjYjOtozqM17u+YdFAdDpu78wUk988KQdm9s0EIj/mBHIgPufBQLQDXuOKTt7d3TWubGAlCNOw6po3co7rou6VTscWnXhrq4uwu5XQV6tKVORR6+5pC42ZbwsQQPAAAgPzFqgKUlU2T1vz9xWh5XgT5aXxn3fpxcsDPRANTmvV3q6B1UOMuDRI/bpTW1FdrdWj/v38vwurVnY71W11TQiQgJCZsRXRgN67Fnz2jtw0e07IGDanrosJY9cFBrHz6ix549owuj4ay//wEAAJBZjBpgWWPm+BKPRLM6giFTPzzZr5aVfsWTZJDJgp2zzT5baWBlpy4/bleBVtf4dHzrOm1bv3RakdYqn6Ft65eqc+s6NVX74s4yASR7ZFABAAAgM6g5AssqdLvUdrwvqW1jRVbX1FToSM/gnI/NRMHOmep3LDS8am6oVOuqxXrrZcU6GwxpOMcFT1MJQO0/0afWVYuzfswet0ulbpdaVy3WpqYqnRsOKRgyZXjdKivJbeFY2FfYjKijd0ib93bNG/SIZVDt2Vivpmof2UkAAAB5gOAILCsQDE2buY3XS0NBDb4+oqoKY87gSCYKds5UQLbOX67dG+vldhXoh139lil4mo4AVK7E/mZlJV6VlUy/H0hEshlUnVvXERwBAADIA1zRwbKCoXBK218YNWUUzR7/y0TBzpnqd9T5y7Xvjkad6gvovQ8+aal0/VQDUOeGQ2k+IiD7Us2gokgrAACA/REcgWUZ3tQSm0qL3AqOzhxgSUfBzjfXE7kYCk+bfV5oeLV7Y72efmFId/7gpOUKnqYagHJ6lx/kh1QzqMhWAgAAsD+W1cCyyg2vlviMpDIbqnyGKi4pnjZ4r/IZalnpV3ODX0UeV1IFO2eqJ+IqkI5+fq3+6V//35R9NjdUyu0q0D1PnLZkun6qASgnd/lB/khHBlWmijoDAAAgOwiOwLLGzIhaGv3a3t6d8LYtK/0KhSN66I/frS/+/rVpK9g5Uz0RSVpTU6FF5SVTZp9dBdKGRr9+2NVv2YKnqQagGBAiH6Qjg2py3RsAAADYD7nAsKxCt0vNK/wJZyfEiqx6Pb8r2HlV2YKJgXyywYaZ6onEXHNFqV47PzIlyDBTwCQR2UjXjwWgkhELQFmpLTGQDDKoAAAAQHAEllbscWnXhrq4l79koshqzFzdLEq8bl14U32TmQImichGwdNUAlAfXl6pRw/3ZrWALJAJsQyqZJBBBQAAkB8IjsDSPG6X1tRWaHdr/bwD+HQUWZ3NfN0shkOmSt/UGWemgEmislHwNJkA1NdvXaYxM6IfHOvLagFZIBNSzaCiWw0AAID9ERyB5bldBVpd49Pxreu0bf3SaTO8VT5D29YvVefWdWqq9iVVZHU+83WzePG1C7ri0uIpxzZTwCRR2UjXTzQA9e3bl+u6ap/u2Nuls8HQRAHZ0TADRNhTqkv46FYDAABgfxRkhS143C6Vul1qXbVYm5qqdG44lLYiq/GYr5vF0d5BDQSGpxSQnRwwsXrB08kBqAMn+vT4sb4pxxzr8vPh5ZUaMyPa8J1jOtV3buLn8RSQjf2NAsGQgqGwDK9H5Ubm/3ZAPGIZVJv3dsW1TCyTS/gygc8fAADA3AiOwFZiF/FlJd4p3SEyfXE/XzeLSFTad7xPd15/jXYe7FEwZM4YMElELF0/WwOXWADqv73rSm1qqtLg6yO6MGqqtMitikuKNRAY1jePvKgDXf06G5xeC6XtWJ82NVVNu3+m1scxS3yGWhr9al7hV7HHlfHWxcBsJmdQvbkb1ZsZXrcebanT6pqKjGSqpZMdP38EcgAAQC4URKNRKimmwcDAgCorKyVJ/f39WrRoUY6PCOkUCIa07IGDcz5moeHVoc9er1N9Ad35g5MyI1F96oZrdOf11+i9Dz6ZUP0Qw+tW59Z1MlJclpOoQDCk5dsPak1NhaoqDBlFHgVHw3ppMKijvYOab0L9uftvnpLtYkaiOtozGNdgc9eGOq2ptf5gE/ktFkyYK4OqucGvIgsFE2Zjt8+fHQM5AAAgNzIx/iY4kiYER/Lf2oePzLs8ps5fpn13rNTTLwzpnidOq6jQPS1gMh+3q0B7NtarqdqX9QHAQGBYTQ8dTnr7Z75wo64qWyBpfKDT0TuU0DKFXP3ewJvFshSyvYQvXez2+bNbIAcAAORWJsbf1r/CAywg3m4Wp/rOacN3jqnOX65nv3iT7lp7jbb9+F/VVO3T396+PKcdd+JheNNXQHau1sczobArrGTyEr6ryhZMZETZITAi2evzFzYjOtozqM17u+bNsAuGTDpkAQCAjLDHVR6QY4l0szjVd07rvvqUvnXkRf3Xd7xVj7bUacyMaE1NRU477sSj3PBOO7Z4TS4gO1/r49nECrvSGhVInt0+f3YK5AAAgPxFQVYgTol0szgbDOnbHS+psWqh3nppsS4pLpQkeT2567gTj1iGTKoFZOdrfTyX2Qq7AoiPnT5/qQZy5uqQBQAAkAiuKIA4Te5mkcjyGPebLtytnK6fSIbMZIbXreYG/8TvMF/r47m8NBTUueHp3XAAJ4tlcwSCIQ0EhhV4o2PUTFkedvr8pRrIscL3JgAAyA9kjgAJcLsKtLrGp+Nb18XVzcKOBQMTyZCRxl+TR1vqVOT53SBlvtbH8wmGzCmtmgGnSqaDi50+f+kI5EzukAUAAJAsgiNAgjxul0rd1l4ek4rJGTLxdI54tKVuPENmUiAonYVdAacyI1F19A7N+jl8eSio7e3d2nmwZ0oHFzt9/uwUyAEAAPnNviM4IMesvDwmVZMzZJIpIJuuwq6AU6XSwcVOnz87BXIAAEB+I3MEwIxSyZBJV2FXwKmS7eDSuXWdCgpkm89fLJCTzNIaAqkAACCdGH0AmFMyGTLpKuwKZEMixU6zdTypdHCJRGWbz18skJqMWCAHAAAgHRiBAMiIWGHXeIvSzlTYFciksBnRhdGwHnv2jNY+fETLHjiopocOa9kDB7X24SN67NkzujAaVjjLA/B0dHBZYJPPH4FUAABgFVxVAMiIZFsfexjsIAtixU4bdxzS9vbuacs6YsVOG3ccUkfvUNzLW9IhHR1c3Db6/BFIBQAAVsCVBYCMSbWwK5AJqRQ7zYZ0dHCR7PP5I5CKTLHakjkAgLUVRKPR7E2H5bGBgQFVVlZKkvr7+7Vo0aIcHxFgLbEij/nW+hj2c2E0rMYdhxKq6WF43ercuk5GUebrmAeCIS174GDS2z93/83TCpXa4fMXNiMaCUd04ESfHj/WNyV7pspnqGWlX80NfhV5XARGMKfYe2l/Z5/ajk99Ly3xGWpp9Kt5hV/FvJcAwLYyMf6mWw2ArJhc2LWsZPr9QDakWuy0ddXijL9nM9HBxQ6fv1Q6ZAExsSVzW/admvFzHlsyt/Ngj3ZtqNOa2gqyFgEAklhWAwCwuURS59NR7DTTnN7BJZkOWYBk/SVzAABrI3MEAGBLyaTOp6PY6UyZGekU6+Cy82BPwkt/6OACJxsJR7Rl36m4Cyibkajuajulzq3rWF4DACBzBABgP8l2m0lXsdNMo4MLkJhUl8zZPeMKAJA6rqKAJFEFH8iNZFPnzUhEhje1hMn5uqmkCx1cgMTYYckcAMDaWFYDJIgq+EBuJZs6f3LbzRkpdpopk1vxxtPBhaKScDI7LJkDAFgbwREgAZmsgh/rxBAIhhQMhWV4PSo36NAATJZK6vy+46/otpWL1dLo1/b27oT3HSt2mu3PY6G7QH9ct0ibmqo0+PqILoyaKi1yq+KSYv12OCSPm6AIkI4lc5M7OQEAnIfgCBCnsBlRR++QNu/tmnfGOpbKv2djvZqqfXNmkJCJAsQvldT5x4/16RNNVbYpdjo5GHtxzNSamgpVVRgyijwKjob10mBQR3sHtaDQTUtSOJ5dlswBAKyLkRbyVrprgiSbyj8ann1/yRaVBJwq1dT53w6HbFHs9M11VSJR6UjPoL77zBl94xcv6LvPnNGRnkFForQkBSRNLJlLRraXzAEArIngCPJO2IzowmhYjz17RmsfPqJlDxxU00OHteyBg1r78BE99uwZXRgNJzSICJsRHUhzFfxki0oy+IGTpZo6fyFk2qLYaSaCsUA+GzMjamn0J7VtbMkcAMDZCI4gr8SbifFfdz6lV34zrLHw7NklF0Nh/ce58fs8bpf+27uv1KduuEYLjcRml2args/gB0hculLnJxc73bZ+6bQZ5yqfoW3rl6pz6zo1VfuyulyFlqRA4grdLjWv8Ce8PCYXS+YAANZEzRHkjXhrgtT5y7V7Y73crgI99sszs9b5+HB9pYoLPdr02AkFhsfU0ujXnddfo82rq7T5sRM61XcuruOaqQp+qoOf1lWLuZCDI6Wz24zH7VKp26XWVYu1qalK54ZDCoZMGV63ykpyVww51Zakm5qq0nxEgD3ElszFUxtMys2SOQCAdXE2gOXFWzsknkyMOn+59t3RqFN9Ab33wSfnzC5574NP6lRfQPvuWKnyksJp99X5y+L+Hd4cBEl18ENgBE6VidT52OeprMSrq8oWTARQcvU5S0dLUqtKdy0oYDI7LJkDAFgXmSOwrES6uEiaNxNjoeHV7o31evqFId35g5NxdZy58wcn9e3bl2v3xgat++pTOhsMzXjffN58kZaOwQ/F49KLVsr2EEudt0O3mWTlY0tSunIhWyYvmTtwok+PH5v6fqvyGWpZ6Vdzg19FHhcdngAAEwiOwJImt7CcaQAUy+7YebBnooXl6XmWuTQ3VMrtKtA9T5xOqM7Hf3/itH75xZv00fpKffOpF2e8by4zVcHPx8GPXTFos598T53Pt5akyXyfM2BFKqy6ZA4AYG2cGWA5yXZxeeCD75y1WKqrQNrQ6NcPu/qTqvPxw5P9alnpV+x6fab7ZjNTKn++DX7sKpFWykd7B2VGonQLsoB8T53Pp5akdOVCLlltyRwAwNo4O8Byku3i4nEV6KP1lTM+Zk1NhRaVl6RU52NReYnW1FTMed+bzZbKn0+DH7tKdNB2x96TOvLvrykcicb93kTmWLnbTKryqSUpXbkAAIBdEByBpaTSxWWuTI5rrijVa+dHUqrzMfj6iKoqjDnvm2yuVP58GvzYVTKDtv/+xGmFwhG98psgM9sW4HG7VFrkUeuqxTp87w167v6b9cwXbtRz99+sX9x7g1pXLZZR5LFNxkhMvrQkpSUxAACwE2tcQQFvSLWLy2yZHCVety6Mplbn48KoKaPIM+990vyp/Pky+LGrVINwRR4XM9sWko+p87G6KvFmvFixrgpduQAAgJ1w5QFLSbWLy2yZHMMhU6UzBDESUVrkVvBNAZbSIs+U+xJJ5c+HwY9dpTpou6q8RE+/MMjMNjImH+qq5HNLYgAAkH/oVgNLSbmLyyyZHC++dkFXXFqsJT4jqYv1Kp+hikuK9dJg8E33FemzN9dq0+qqhKvgTx78zNbFIcbwuvVoS51W19DFIR3SEYR74T+Dev87rDMQRf6xe0tSJ3bloiU4AAD2RXAElpJqF5dLFxRqeIYgw9HeQQ0EhtXS6Nf29u6En7dlpV8DgWEd7R2cct+YGVFpcaFKiwsn7k/kAtjugx+7SnXQdmHU1EjY1LnhEMVxkVF2bknqpK5ctAQHAMD+CI7AUmJdXJLN7lhoePUH/+Vt0+pJRKLSvuN9uvP6a7TzYE9CtSYMr1sfXl6pbx55UbHanems/WHnwY9dpTpoiy2xsuPMNuxpcl2Vye85K383pPp9bpfAY6wl+GwZgLGW4DsP9mjXhjqtqU0uA5CsFAAAMouzKSwlHV1c3vG2S2ds77n/RL+i0ai+sWFZQnU+vn7rMo2ZER3o6p+4LxO1P/KxqKRVpdpKObbEyk4z20C2OaErV6ItwTfv7VJH72BC3a7CZkQXRsN67NkzWvvwES174KCaHjqsZQ8c1NqHj+ixZ8/owmiYDloAAKSIURcsJR1dXGZr7/mLz12vshKvrq+t0HduXx5XkcNv375c11X7dMfeLp0Nhixb+BCJSXXQNhAY1kBg2DYz20AuOKErVzItwe9qOxV3t6tYVkrjjkPa3t49LQsnlpXSuOOQOnqH4j4OAAAwnfWvPOA46eriMlMmxusjY/rI3/5S7/GX69kv3jQtu0T6XceZX37xJr2nskwbvnNM54bH4u5CA+tLZdD24eWVajvWp1sb7TGzDeRSPnflSqUl+P4TffN+f2QjKwUAAPxOQTQaZZohDQYGBlRZWSlJ6u/v16JFi3J8RPZmRqI62jOYkS4uax8+ot9eHNNH6yvVstKvReUlGnx9RMFRU5cUe3R5aZHGzIhGw6Z+cyGkEq9bFZcU69XfXtSVly1I16+IHAubEXX0Dmnz3q64ZlvdrgJ9+/blek9lmf5w19P6589cP2NnJABTZfL7PNfWPnwk6Zoqv7j3hjkfc2E0rMYdhxKukdW5dR3fTQCAvJeJ8TdnT1iOGYnqub6A3lNZpme/eJN+dLJ/1i4uH02wi0tsOcX29m5986kX9bdHX9SamgpVVRgyijwKjoYn9rPE97v7XhoMak2tT7evWmyLVG/ML9FWyl+/dZmuq/bptt3Htf1D77LFzDZgBfnalSvVluBzdbtKNSullXMVAAAJI3MkTcgcSY/Js/mXLSiclt1xYdRUadF4JsdAYFj7jvfpuurL1bjk8rhrgDAbh8liLTjnGrR9eHmlxsyI7n7itP50TZWtZrYBK4l1VsmHrlwDgWE1PXQ46e2f+cKNuqps9mzETGalAABgd2SOIO9NLm53NhiaNbvjpcGgjvYOKhKVHnv2jDq3ros7OBJbA5/Icgq7rIFH4mKtlG9fOd5K+TcXRvX6SFjGpCDcgRP9KvG69J3WesvMbNPWE3Zkx5bEs0m1JfhcNY8ymZUCAABmRnAEljFbGnEkKh3pGdSRnsEZt0s0jTjR5RR2WwOP5Hg9sUFboS4vLdLFUFhDF0ZVWuTRHWuqLBN0iGW67O/sU9vxqZkuS3yGWhr9al7hV7HHRUelOBBkQrJiLcGTze6YK3gRDIVTOTQFQ+aU4BMAAJgfwRFYRqHbpbbjfUlt23asT5uaquJ+fL6ugUfq3K7xAfECr0cLJs0MW2GgHGvrOVtQL9bWc+fBHu3aUKc1tQT1ZkOQCamaXMMqUS0r/XMG4DKZlQIAAGZGcASWke004thyitZV48sp8mENPPJXIt11Ym0992ysV1O1j8H9mxBkQjrEWoLvPNiTcA2r5gb/nOeXTGalAACAmXHFDMtIRxpxMiavgb+qbMHERSWBEVjJ5Ho88TAjUd3Vdkqj4UiGj8xewmZER3sGtXlv17zfGbEgU0fvoMImryOmi9Wwijd4Fm8Nq1hWSjJiWSkAACAxjP5gGaQRAzNLta0nA6XfIciEdJpcw+r973iLNjUt0d03VmtT0xLdUFuhyTETw+vWno31Wl1TMW82VywrJdHzWjxZKQAAYGYsq4FlkEYMzCyb9XjyWapBpniLPsNZ3K4CNVX7tPbaK/SbC6M6PxKW4XXrikvHu1394/P/TyVelz64bFFCNazorAYAQHYRHIFlZLK4HWBntPVMDycFmejCkx3xFPa9tTG5wr50VgMAILuyFhx55ZVX9PWvf13t7e3q7+9XUVGRrrnmGn3kIx/RXXfdpZKS5HvODQ8P65/+6Z908OBBdXV16YUXXtCFCxd06aWXqra2Vu9///v1yU9+Um9961vT+Bsh3TJZ3A6wM9p6pocTgkx04cmebBT2pbMaAADZUxCNRuNbeJ2Cn/70p7rtttt0/vz5GX9eW1ur9vZ2VVdXJ/zcv/rVr3TdddfpwoULcz7u0ksv1be//W199KMfTXgf8RgYGFBlZaUkqb+/X4sWLcrIfvJdIh05pPELRzpyIN8FgiEte+Bg0ts/d//Nlh/UZ8NAYFhNDx1OevtnvnCjripbkMYjSi8zEtXRnsG4sgzowpOaXJyrYlk/dFYDACAz4++Mn01Pnz6tj370ozp//rxKS0u1Y8cOPfvss3ryySd1xx13SJJ6enr0gQ98QK+//nrCz3/+/PmJwMh1112nBx98UAcPHtSpU6f0z//8z7rzzjvlcrl0/vx5tbS06B//8R/T+vshvSanEc9XiC6R4naAncXq8SSDejy/k89Fn+nCk125KOxLZzUAADIr48tq7rnnHl28eFEej0c///nPtWrVqomf3XjjjaqpqdHnP/959fT06JFHHtGXv/zlhJ7f5XLpIx/5iL70pS/p7W9/+7Sfv+9979Pv//7v60Mf+pBM09Tdd9+t3t5eFRQwW2ZVpBEDU1GPJz3yuehzsoP1zq3rCC4niMK+AADkp4wuq+ns7FRjY6Mk6c4779S3vvWtaY+JRCJ65zvfqe7ubpWVlem1115TYWFh2o/lT/7kT/S///f/liSdPHlSdXV1aX1+ltVkBmnEwLgLo2E17jiUcD2ezq3rZBRRe1sa/z557NkzSQWZtq1fatlBbb7+Xla29uEjSQfZfnHvDek/IAAAHMZ2y2p+/OMfT9z++Mc/PvMBuFxqbW2VJJ07d06HDye/Hnwua9eunbj94osvZmQfSD/SiIFxsbae8WZK0dZzuljR50SXx1i96HOqXXis+ntZVToK+wIAAOvJ6BXR008/LUkyDEPLly+f9XHXX3/9xO1nnnkmI8cyOjo6cdvttu66cQCYSS7q8Yy9UY8iEAxpIDCsQDA05X47yscgE4P17EpH9ygAAGA9Gc217u4eT/Gtrq6WxzP7rq699tpp26TbU089NXF76dKlGdkH7Cm2RCcQDCkYCsvwelRusHQH1pOtejz53A52cpApnq4uj7bUaXWNtbu60Oo5u/K5sC8AAE6WseDIyMiIhoaGJGne9T/l5eUyDEPBYFD9/f1pP5Z/+Zd/UXt7uyTpXe96V1LBkYGBgTl//uqrryZ1bMidfB4AIn953C6Vul1qXbVYm5qq0l6Px4xE1dE7NGvg4OWhoLa3d2vnwR7btoPNt6LPDNazK58L+wIA4GQZC45MbstbWlo67+NjwZFYW950GR0d1ebNm2Wa4xf5O3bsSOp5YsVekB+cMABEfptcj2fyrH8qgZGwGVFH75A27+2at+tJrB3sno31aqr22S6AmOkgUzYxWM8uukcBAJCfMnZ2HhkZmbjt9c5/4VVUVCRJunjxYlqPY8uWLerq6pIkbdy4Ubfccktanx/2EzYjOtozqM17u+Zd+x0bAHb0Dips4zoLQDySbQc7GrbvZyMfij7HBuvJiA3WEb98LewLAIDTZewMXVxcPHE7FJq/2FusYOqCBQvSdgwPPvigdu/eLUlqaGjQo48+mvRz9ff3z/mvs7MzXYeNDHPiABCYz5g5vsQs0WKRwZCp/Sf6GGDnEIP17MvHwr4AADhdxs7Sl1xyycTteJbKBIPj6cDxLMGJx9/+7d/qvvvukzRe8PVnP/uZDMNI+vkWLVo0578rr7wyLcftVNnqisEAEJgZ7WDtjcF6duWiexQAAMisjNUcKS4u1uWXX67f/OY38xYzDQQCE8GRdNT2eOKJJ/SpT31KknT11Vfr4MGD8vl8KT8v0i/bRVFTHQBuaqpK+RgAK0pHO1hqV+ROPnbhsbp8K+wLAIDTZbSV79vf/nZ1dHTohRdeUDgcnrWd769//euJ26m22f3JT36i1tZWRSIRXXnllXryySfn7ZaD3MhFUVQGgMDMaAdrfwzWsy+fCvsCAOB0GQ2ONDU1qaOjQ8FgUCdPnlRjY+OMj3vqqacmbl933XVJ7+/JJ5/URz7yEYXDYV1++eU6ePCgrrnmmqSfD5mTq64YDACBmdEONj8wWM+NTHSPAgAA2ZXRs/YHP/jBidvf+973ZnxMJBLR3r17JUllZWVau3ZtUvt69tln9Yd/+IcaHR3VZZddpn/+53/WO97xjqSey2myVe9jslwVRWUAmJ9y8R7ON7F2sMmgHaz15EMXHgAAgGzKaObIihUrtHr1anV0dGjPnj3auHGjVq1aNeUxjzzyiLq7uyVJ99xzjwoLC6f8/MiRIxMBk40bN+r73//+tP0899xz+sAHPqBgMCjDMNTe3q7ly5dn5pfKI9mu9xGTalHU1lWLk77Ajw0Ak1lawwDQenL1Hs5HsXaw29u7E9421g7WqQPv2O8eCIYUDIVleD0qN8jSAAAAsJOMBkck6Wtf+5quu+46Xbx4Ue973/t03333ae3atbp48aL279+vb3/725Kk2tpafe5zn0v4+V988UW9//3v17lz5yRJ27dv12WXXaZ//dd/nXWbK664QldccUVSv0++yEW9j5hcFkVlAJg/cvkezkexdrA7D/YkFLhMtR2snQMLBOcAAADyR8aDI8uWLdOBAwd022236fz58xPtdSerra1Ve3v7lPa/8ero6NBrr7028f/PfOYz827zpS99SV/+8pcT3le+yFW9j5hMF0Wdb7CViwEg0ivX7+F8FWsHG8/rKqXWDtbugQWCcwAAAPklK1ect9xyi371q1/pM5/5jGpra1VSUqKysjLV19froYce0unTp1VdXZ2NQ4FyV+8jJh1FUWcSNiO6MBrWY8+e0dqHj2jZAwfV9NBhLXvgoNY+fESPPXtGF0bDKva4tPcTK+IeqKQyAERm5Po9nAlWqJsyuR3sfPV1DK9bezbWa3VNRcLBi1hgoXHHIW1v754WLI0FFhp3HFJH71Dcf+dsCZsRHe0Z1Oa9XfMGWWPBuY7eQYWpgQMAAGBZBdFo1FpXnTY1MDCgyspKSVJ/f79l2wePmRE99uyZpJaVbFu/NKV6HzGBYEjLHjiY9PbP3X/ztMwRMxLV0Z7BWWdxYwyvW1+/dZlu+L0r9FxfQK3f7Zz38Y+21Gl1DbO+VpHMe3ih4VVzQ6XuvP4aXbag0FJLOKyYQRE7pnjawSZ6TIlk/UjjwUmrZf1cGA2rccehhLPPOreuk1GU8YRNAACAvJeJ8TfBkTSxS3BEktY+fCTpgqS/uPcGyx1DMoOt77Qu1+qaCo2OmTrQ1T/nALC40KVIVLati5CPEnn/1PnLtXtjvdyuAv2wq98yAQgpsaBeLpZmxN7f6WwHa/fAghUCzAAAAE6XifF37q80kVWZrvcRj3QXRU1micXd+07rl1+8Saf7Avpog1+bmqqmDQBjKfAXx6w1q+90ibyH6/zl2ndHo55+YUj3PHHaUrUh7FA3ZXI72LKS8fvCKRRQzWWnqnTJZUFpAAAAZA6jOYfJVL2PRMSKos5X0+DNZiqKmspg64cn+3XNFaW67sFDGhkzVVbi1VVlCyaCPwUFBbaui5Cv4n0PLzS82r2xXk+/MKQ7f3DScrUh7FY3JVbT5/vz1PSZ67VLNbCQ68CIlJ4AMwAAAKwn91eayCrDm1qyUKIBjdnEumKkWhQ11cHWovISLfMvVNvxV6YUvqTgonXF+x5ubqiU21Wge544bbkARKoZFNks0iqlr4BqPgQWrBBgBgAAQPoRHHGYcsOrJT4jqW2rfEbKS2pi0tUVI9XB1uDrI6qqMKbNStttVt9J4nkPuwqkDY1+/bCr35IBCDtlUKQzUJgPgQWrBJgBAACQXgRHHCZW7yMZsXof6eJ2FWh1jU/Ht67TtvVLpw14q3yGtq1fqs6t69RU7ZsxyyTVwdaFUVNGkWfKrLTdZvWdJp738JqaCi0qL7FsAMJOGRTpDBTmQ2DBKgFmAAAApBfBEYdJZ72PdPC4XSot8qh11WIdvvcGPXf/zXrmCzfquftv1i/uvUGtqxbLKPLMWoAy1cFWaZFbwdHxAEssGGKnWX0niuc9fM0VpXrt/IhlAxB2yaBId6AwHwILVgowAwAAIH0YxTlQuup9pNPkrhiTi6LOF2hIdbBVcUmxXhocH0DHBtt2mtV3qvnewyVety6MWjcAYZcMinQHCvMhsGC1ADMAAADSg6s0B0pXvY9MiQ2AAsGQBgLDCgRDU+5/82NTGWwNBIZ1tHdwyqy0XWb1nWy+9/BwyFRpkXUDEHbJoEh3oDBfAgtWDDADAAAgNVypOVQ66n2kW6xV6GMJtApNZbD14eWVajvWp0h06qy0XWb1nW6u9/CLr13QFZcWWzYAYZcMikwECvMhsGD1ADMAAAASl9ooELbmcbtU6napddVibWqq0rnhkIIhU4bXrbISr8bMSNZmaiORqPrODuuHXf3qfvV1vfKbmVuF7jzYo10b6rSmtkJuV4HGzMjEYGvz3q64ika6XQX6+q3LNGZGdKCrf9qsdGxWP5kZc6vURXCKud7DsQDE9vbuhJ83FoDI1Ps/FtTbebAnoUyjbGdQZCJQODmwsGXfqTl/f8Pr1qMtdVpdU5GVAG0iJgfnDpzo0+PH+qZ8Z1T5DLWs9Ku5wa8ij8tyxw8AAICpCI5gSr2PspLp92dSbAAaeCP9/uPXLdEVlxZrIDCsfcf7tP9Ev84Gf5eaH2sVumdjva6r9unAiT798sWzeuQj/0V/e/ty3fPE6XkHW1+/dZmuq/Zpw3eO6bcXx7RnY/2UWWkrD6oxs9new1YOQCQT1Mt2BkWmAoX5EliwUoAZAAAAqSmIRqPx9WfEnAYGBlRZWSlJ6u/v16JFi3J8RNYWNiMaCY93wmg7PnVgtMRnqKXRrw/XV8qMRLX5sRM61XduyvaG161ffvEmffPIi/rmUy+qzl+m3Rsb5HYV6Ecn+2cdbH14eaXGzIju2Nulf/9/r886K31hNKzGHYcSHlR3bl0nI8VaF0ifsBlRR+9QQgGIPRvr1VTty8oSCDMS1dGeQctmUIyZET327JmkAoXb1i9V66rF8wYHYgEEAgsAAACIVybG3wRH0oTgSPwSGRB+7dZlanojy+PNAZJt65fq/e94q9b89WFFotJCw6uP1lfqY9ct1lsuLVZgOKQLI2EVF7pVcUmRBgLDajvWp2Mv/Ubr/8uVE7PSMw2CrT6oRvysHoCIBQrjyaDIxXuLQCEAAACshuCIhREciU8yQYdv375cy/zlWvfVp6YssanyGfrFvTfoY9/t1JGewYn7XQXSf3vXlfrQsqtUv7hcly3w6vWRMZ0fCSc0K231QTXiZ/UAhGTdDAoChQAAALAagiMWRnAkPsnOQk9eQjPZia3j93/3mTMzbusqkNbUVOiTN1SpYfFCuV2JDdbsMKhG/KwagLA6AoUAAACwkkyMv8l5RtaMmeM1RhIJjEjjRVh/eLJfLSv9+tujL2ry5PWFUXPO1P1IVDrSM6i+s8P6xb03JHzMFFzML7ksPmxn+VJAFQAAAJgNwRHMa6KjTDCkYCgsw+tRuZF4YKDQ7VLb8b6kjqHtWJ82NVVpTU3FlCU0pUVuBUfD827/0lBQ54ZDSbfZZVANpyNQmDvp+g4GAADA7AiOYFbxdJRpXuFXcZxLSgLBUFItQaXx4Mbg66OqqjAmgiNVPkMVlxTrpcH4njMYMqcENgAkjkBh9qT7OxgAAACzIziCGZmRqDp6h2atMfDyUFDb27u182CPdm2o05ra+WsMBEPzZ3jMJRQ2VXNF6cT/W1b6NRAY1tHewTm2+h3D605p/wCQLZn4DgYAAMDsmGrCNGEzoqM9g9q8t2ve+iDBkKnNe7vU0TuosBmZ87GGN7VY3AKvR39SX6k6f5kMr1sfXl6ptmN9iqOBhqp8xsSSmrE3jjMQDGkgMKzAGx1wxuY5fgDIhkx9BwMAAGB2ZI5gmpFwRFv2nYqrbac0PsN5V9spdW5dN2dqd7nh1RKfkdTSmiqfoYWGV8/1BbR7Y4P+9f+e05gZ0YGu/ri2/8zNNaSoA7CFTH0HAwAAYHZcRWGKVDrK7D/RN2f2xZgZUUujP6njii2huX3PcXk9Lq26xqc79nbpbDAkV4F0Q22FNjUt0d03VmtT0xLdUFuhWIb5dddcrv/2rrepo3dIjTsOaXt797QATSxFvXHHIXX0DsU9KAGAdMrkdzAAAABmR+YIpkhHR5m5nrt5hV87D/YkdOEfW0LzzSMv6vVRUwdO9Gn9u9+mvrPD+tQN12hDo1+Lykv02vkRXRgNq7TIoysuLdZAYFj///buPTqq+tz/+Cczk0nIBEggoQrJAAFSqLUtIeEiF0VBj1VLaWtFULACxXo52nqpliX1VKn10lqr/KhH0AqHiz/783gpeHqQguEOAbS2xQIRTPBSEkgKmZBMZmb//khnTGAmmXtmMu/XWl1nDtmzv3snX2fN99nf53le2/+J5k0c7Nui3lnQw7tFffmcUk0YmsdTWABxFcvPYAAAAARGcATtRNpRprN2uZkWk56dWRJUoEKSzKY0/eb6ke1SaLwLgE33XCKPIb1SUR0wTWb+xMFq8RhJv0WdVp5Aaoj1ZzAAAAD8IziCdiLtKNNZu1yL2aRJxflaNrs0YBcGL5vVrN9cP1Ljh+Zp5vM7dfJfhVM/rHWortGp2tPN+uaSbQE7Ofx8/QF9/cLztf79T8Peoj573KAuDT5QJwVILbH+DAYAAIB/BEfQTqQdZYJpl2s2pWn80Dxtf+Ay/X5vtf5rZ/tFf1GeTbPG2nXtqEK1uD2a+fxO7auqb3eOhiaX/vDnjoMek4blq39Oj6Tdok4rTyD1xOMzGAAAAOciOIJ2Iu0oE+x27sZml54r/1Czxto1d0KRak43qaHZrewMs/J7ttYLWbq5Ui9XVPt2jLSVmW7S6aaWDscY0i9bx081JeUWdZfboy2HahO+TgrpPkB0xeszGAAAAO0RHEE73o4yj6w7EPJ7Z421B70ozrFZ9T9//UzPlVdq0rB8FeXbZMuwyNHs0oc1DpUfqlGgmEBRnk35PTP1YU3Hi4csq1kNzcm5RT3RW3mS7gPERrw+gwEAANAe36DQjrejTKhbs21Ws2aU2YP+Uu5dAHgMafPBGr2w7aie+dNhvbDtqDYfDBwYkT5v61t+qKbDMRqdbmVnJN8W9UhbeTpdHrli2M7Tm+5DW2T52qbWOZw6Vteoun/tcqKdKsIVr89gAAAAtMe3KJzD21Em2PoVZlOalswqUYYl+OkUyQLg2lGFWrWzqsMAiiRVHm9Qv16ZGpxnC2kMr67aoh5pK0+rxaS/fnIqJkEJl9vja4vcWfDGm+6z5VBNTIM1XcHl9qih2aWXth/V5Cc3a+TDGzThsU0a+fAGTX5ys17aflQNza5ud9+Ij3h8BgMAAKA9vknhHG07ynQWvLBZzVo+p1QTh+WHnD4R7gKgbVvfjpQfqtGxukbNGmMP6bq8vFvU4y3SVp41p5v0xnufxCQoEW66T7Or+wQJ2DmDWIvXZzAAAAA+xzcp+GU2pWnisDztWjhFD1494pzdF0V5Nj149QjtXjhFE4bmhdUlJdwFwPwVFX6LtJ7NY0ird1Xp2tLCsHaoXFdmlzkt/t1fIm3l2dDsVpbVHPWgRKTpPt0h1YSdM4iXeHwGAwAA4HMUZEVAFrNJ2WaTZo8bpLkTilTf6JTD6ZbNalZOVnQ6krRdALy8pypgW98ZZXZlWEwym9JU19hxl5q21u6p1ryJRXr6+pFasHJvUE/xzaY0/eb6kWpuceu96jqNGdw3rk9kI23lmZ1hlqPZ5QtKzB43yO/fKdROM5Gm+3RlW2QpOp11Er1QLrqXeHwGAwAAoBXBEXTK++U7J8varnNLtL6Uh7IACLWTw0mHU/Ne2qPV88fq+dmjdMfq/R0+8bdZzfrN9SM1fmieZj6/U3//7HTcF7aRtvJs28nHX1Ai3E4zkab7dGVb5Gh01ol050ygIBWCl6qto2P9GQwAAACCI0ggwSwAvIVcn9pwMOhF6r6qet38uz166ebR2vHAZXplb3XAHSrXjipUi9ujmc/v1L6qekmK+8I20laebTv5nB2U8NbLuH31Pr+/P2+9jKc2HNSzM0s0qTjft10/0nSfrmiLHMn9ni3Zd84kM1pHAwAAINb4FomkE04h1+9PKpKj2aXfbT+qKy44T5vuuUR7Fl7m+79/uucSXXHBeVq6uVJTnyr3BUak1oVtPJ/QRruTjzcoEGm9jEjTfeLdFjna9UGisXMGoaMALgAAAOKBnSNIOm0LufrbEWBKkyYNy9eQftnK6ZGuKy88T4PzsnXdc9tV8VG9fv32QU0alq+ifJtsGRY5ml36sMah8kM1ftsDd0VKiDcANG9FRUh1Uvx18vEGJSKtlxFpuk+8U2qiXR8kGXfOtJWMKSkut0dbDtUG9d+BN8C1fE6pJgzNYwcJAAAAQkJwBEnJXyHXf55p0YyyQs0cY1dBbpZONDTrTItbNqtFZlOafj1jpFbvqtLaPdXafLBGmw/WBD1evBe2nQWA2jq7TkrbTj7eoEQ06mVIiijdJ56L8FjUB4l050xWnHfOeCVzSgoFcAEAABAvBEdwjmR5wnx2IdcWt0fNLR6t3RN4Ebjg4iGaN7FI817a0y51pjPxTgmRgu/k469OitessXa5/vV3i0a9jFDrvUitv7sZZfa4pyZFuz5IpDtncrOscrs9Msfx9xDNmivxRgFcAAAAxBPBEfgk6xPmdLNJbo+hrUEuAp++fqRWzx/rN5jgT1ekhHh5A0A3jm0NANWcblJDs1vZGWbl98zUsbpGLd1cqZcrqtvtGJE+D0pY/hXoikanmXDSfZbMKlGGJb7zJRaddSItlPtxXaNysqyyxem/nWRPSaEALgAAAOKp678BIyEkc9HDUAtvLli5V9sO12rZnDL1sXUe9PCmhHQlU5r0XnW9fvvOh3p13zEt3Vypm17YrUmPb9LSdyrPCYycHZSIRr0MqX26T2e7aWxWs5bPKdXEYflxX2xH637birRQ7n/trNLaPVVxm0vhpqQ0u7p2rntRABcAAADxRHAEUe/qEW/hLAL/fc1+WUxpuq60sMNjuyIlxB+L2aQvD+itCUPz9MLWI3ph21FtPvh5AVlTmnRJcb7mThisu6cW6493TWwXlIhmp5m26T4PXj1Cg/Ns7Y4tyrPpwatHaPfCKZowNK9L0jRi1VknnE5JbQvlxqvzUaQpKV0dDGy9lugHuAAAAIBASKtBUhc9jGQR+Mreas0aa9dz5ZV+u9R0VUpIIOEUofXWiYl2p5mz673UNzrlcLpls5p9BWC7MqAUq8463p0zz904Sneu2R9yodyTDmdcOh91h5SUZGsdDQAAgORGcCTFJXvRw2gsAicNyz+nc43NataSWSWaOCxxClRK4RWhnTHargyzKSadZrz/lpNlbdfNp6t32kRaH6Sj4M6JhmaV2HO1/YHL9Pu91SEXyo1H56NY1FyJt2RrHQ0AAIDkRnAkxSX7E+ZIF4E1p5tVlG/zBUe8C9sZZXZlWEwJFRhpK9QitG/cPiFpOs1Eg7c+SCzuN91s0mW/ekfXlRZq1lh7WIVyYy0aKSnxbF3tTywDXAAAAMDZCI6kOH/BBVOaNGlYvob0y1aW1axGp1uVxxtUfqimXfpJsE+YY9kaONJFoNPt0b1XfFFzJxYlTEpIMELtRDLj+Z3act/kpOg0Ey2x6qyTa7Oqd490LX2nUs+VV2rSsHwV5dtky7DI0ezShzWOc/5b8YrXjobukJISywAXAAAAcDaCIymubXChj83arn7F8VNNamh2KTvDon69Wp+Gr95VpbV7Pn8a3tET5ni0Bo7GIrCH1aIBbc4T6aIqlsEgr1DrxNScbtbNv9ujlXPHaNns0oC7TbwSNa0oFG0760TzftvuaPAY0uaDNeekZQUSrx0N3SUlJVlaRwMAACD5ERxJcd7gQok9V8vmlMpsStMrFdUBgxkLLh6ieROLNO+lPdpXVR/wCbO3NXAwKR/PzizRpOLwFuGJtAiMRzBICr9OzPbKE1r//ie68svntyvq6q9eRqKnFQXLXxHbSO83GXY0dJeUlFgFuAAAAICzpRmGEdyjZ3To2LFjKixsbQtbXV2tgoKCLr6i4N2+ep+evPar2nq4NqgOHE9fP1IThubpnv/7np6dVXLOMaGkfEitC9jlc0o1YWheyEGDFrdHL20/GtYi8MGrR0StoKzbY6j8YE1QC7hIgkFek5/cHHZA6E/3XCLp8x0uidZpJlaieb/xnOPhamh2aczit0MO4OxeOEW2jMSKm3sDj8EEuLq6gxYAAABiLxbrb4IjUZKswZEWt0ctbo+2V57QgpV7g17o/eeNozRuSF+lm03nLCzjvSgLd7wdD1ym2oZm2ftkRbSgivdCuc7h1MiHN4RzqZKkdxdNTZi0iWQWSkCsK3Y0JEMAJ1SpFtADAACAf7FYf/NtMsWlm03yGNKda/YHXb/C7TH072v2yzCkU2da5HJ7fD+LtDVwS5tzBctblyDYhafZlKbfXD9SLW6PZi/fpWZX6GO2FWr9D7fH0G2r9oU9bjQ6kSBybVN2Hrx6hAbn2dr9vCjPpgevHqHdC6dowtC8uKd6tE1J6azAqs1q1vI5pZo4LD9hAyNS+9bRA3J6+IJ8BEYAAAAQqcTaO424izSYceWXz1ezy+NbUAXTGjhQN5w1u8JrDexdBD4/e5TuWN15WtBvrh+p8UPzNPP5nTpW36S1e6rCTq+J9PcXzrjdoRNJd2Exm5RtNmn2uEGaO6Eo4XY0xKLmCgAAANAdERxJccEEMwJZtbM1mPHHv36qS4d/wdehJVAtjGC64ZxxutQjjMW/2ZSmEnuutj9wmX6/tzrgIvDaUYVqcXs08/md2ldV3+4+whGN31+oEqkILVq13dHQtntTIuxoSPQADgAAAJAICI6kuI6CGZ35sNahmtNNOvwPh664oHVxFSjlI9huODPK7HK5PSFv7a9zOHXZr97RdaWFmjXWrrkTilRzukkNzW5lZ5iV37M1+LJ0c6Vervi8FbH3PuobnWEFDSL9/YUzbnfpRILgRKs1dCIHcAAAAICuRnAkxUVav6Kh2a0ml9u3yPeX8lFiz9Xq+WM67IYTaWtfh9Olkw6nlr5TqefKKzVpWL6K8m2yZVjkaHbpwxqHyg/VKFBZEIfT3W7BGMq4kXA0hz5uMrSSReTi1RoaAAAAAMGRlBdp/YrsDLMczS5fcOHslI8+NquWzSnV1sO1QXXDcTjdmreiIuSuGW3vw2NImw/WaPPBmqDvI9w6HBHX/8gIb1xvEdpQOpEsmVWiDAuL6GTg9hjacqg2YCecSIOJAAAAANpjpZTivMGMcBTl2ZTfM1Mf1jh8wQVvyofXjLJCmU1pIXfDCbWbS6T3EW4djq4atzt2Ikk03s5JdQ6njtU1qu5fqVjhdFQKhcvtUfnBGs1bUdHpziBvMHHLoZp2XaMAAAAAhIaVUoo7O5gRillj7TpW16hjdY3tWmrOGG2XzWqWKU2aOcauVyqqY97aN9L7CHfB29Tijmjcppbw2+omeivZZOVye9TQ7NJL249q8pObNfLhDZrw2CaNfHiDJj+5WS9tP6qGZlfMghHxbg0NAAAAgLSalBdJ/YprRxVq6eZKXT+mfZFPb8rHyp0fqSA3Ky7dXLqqDodhGLq2tDDs359hBLcADoROJNHV1eksXdEaGgAAAAA7R6DPgxnBLvLMpjT95vqRanF79OZ7H58TXPCmfDxw5XAdP9UUcTeXYIV6H/k9M/TmHRNk/de1h5M+0cNqkWEYevr6kSH//tyGEVbbYn/adiIZkNOj3U4eBCcR0lkibQ3N3xsAAAAID9+kEXL9iv+8cZTGD83TgpV79cj0C/0W+TSb0lSUb5MzwoVjKE/QQ7mP8UP6ast9k9WvV6Ze2hF++kTLvxbUE4bm6bkbR4X0+9t6qCbm9SsQvERIZ4mkNfTREw41NLVE7VoAAACAVEJwBJKCr1+x44HL9LXCHN38uz26/dKhHRb5NJtMkXdzCbGLTDD38cz1X9OKuWO0o/KExix+W4+sO3DOgtSbPjFm8dvacqg24II53WzSpSO+oJt/t0cl9lxtf+CyoH9/lw7/QlBP+tsWBq093aTG5tb2wW4PgZVoiTSdJVpBrnBaQ/exWXXrJUNUft9kZWemx72ALAAAANAdUHMEPt76FTeOba1fcaKhWaebXLJlmJXfM1PH6hr18p5qZVlNen52qTIspk5TSc5u7RuKcLu5dFaHo236RDRaC2daTJo/sUhX/Lpc3ykp0Kyxds2dUKSa001qaHYru83vb+nmSv1+3zE98Z2vdNpW1+X2qMnl0Wv7j+mM06MrLzxPBblZOn6qSf843ayemRblZWfI6fLISoveiESazhJsbZzOhBpMLLHnatmcUplNaXqlolqrdlW1+29tcJ5Ns8bYNWO0XZkWE92KAAAAgADSjEgrQkKSdOzYMRUWFkqSqqurVVBQ0MVXFDm3xyOzyaQzTpccTrcsprSQi3y2uD16aftRPbLuQMjjP3j1iJgUmGxodmnM4rdDLqC6e+EU2TL8L17dHkPlB2t0++p9OtPi1qRh+SrKt8mWYZGj2aUPaxwqP1SjHulmLZlVoonDOi7k6T3fsi0f6pl/1VFh8Rs7dQ6nRj68Iez3v7toathtmc82+cnNQQUTS+y5Wj1/jLYertWda/Z3OJ9tVnNMCsgCAAAAXSEW629WUgjIbGqdHj2srTsUwiny2ba1bygi7SITSKzSJ9qm8yy8aoQ+OtmoF7Yd1TN/OqwXth1V1clGLbwquLa63p0t/2fzYS2/qUz7qup00aMbI0r/iba2qT7dIYUjnHSW9u8PvyVzW8G2pO5js2rZnFJtPVyrBSv3dlkBWQAAAKC7IK0GMeVye5RhMemZmSM1f8XeoBbvZlOalswq6TTtJByxTJ+IVlvdJpdHD772vt64Y6Jv8RuN9J9o8Kb6rN1d1a12scS7Nk4gwbaknlFWKLMpTXeu2R9yAdndC6ck1d8GAAAAiAe+ISNm3B5DWw7Vas4Lu3XRkOC7uSybXaqJw/Jjck2RdAMJtrVwJG11vTtbvvG1AWEvfqPZPeXs8285VBtxEdtE5K2NE45wa+ME0llLalOaNHOMXa9UVHd5AVkAAACguyA4gphoW/R0e+UJzXx+Z1DdXHYvnKKvFuboxuW7YrLATpT0iUDSzSat2V2VcIvftn/P7pjCEWw6iz+zxtqj+vvurCX1pGH5KsjNimgHVLTT1QAAAIBkR1oNYqLJ5dHtq/f5ghv7quo15Vfv6LrSwoDdXD6ua5TZlKb/2vmRtlee0K4jJ6OeJpIo6ROB1DmcKszNinjxG63uKV5n/z07k2wpHMGms5wtVrVx2taweXlPlf5r5+cpTEP6Zev46aaId0BFc7cLAAAAkOwIjiDqAhU9Pelwauk7lXquvDJgN5eFV43Qt0oG6NdvH4zJArsrWguHwuF0tS5+TyXO4jfSIrax6DgUC950lmBaPEuxrY0jBa5h0ycrXSccnad3dcThdCsnK0oXCgAAAHQDib9iQdLprOipx5A2H6xp181l88EaeYzWXQ8FuVma9K+aI9FOE0mk9Al/bFaLsqxmNTQnTvpPpEVskyEwInWeztKWzWrW8jmttXFivTPm7Bo2PayWhN8BBQAAACSb5Fi1IKlEWvS05nSTivI/r0kSzQV2orUWPluuzarMdLOyMxJn8RuPIraJom06S2e1cTpryRxLiVRAFgAAAOgOSKtB1EVa9LSh2S1bm+BAtNNEEi19oq0Wt0dD8m3q1yszYdJ/olHENplSOPyls7g8hmxWs3pYLXJ7PDKbujau7N0B9ci6AyG/17sDKll29AAAAADxwLdjRF2kW/6zM8xynJVWEs00kURNn5Bad7ZMGJavT+rPJEz6T6qmcHiDB7YMi/KyM9TU4tGxukadOtM6N7uyHW6i74ACAAAAkg07RxB1oRY9NaW1ticd0i9bA3J6KL9npmwZFpnSWuuTSNFfYHfUDURq3X0xa6xdM8rsyrCY4po+kWkx6R9Ot64tLUyI7imJXsQ2Vlxuj5pcrcVoV+1qPz8G59k0a4xdM0bblWkxdUlHnkTeAQUAAAAkmzTDMILrzYkOHTt2TIWFhZKk6upqFRQUdPEVdZ0Wt0cvbT/a6Zb/PjarZpQVauYYuwpys3T8VJOaWtzKzkxXH5tVx+oatXpXlXZUntB/3zY+ptebbjb5uoHYrGblZFm7NPXA7THU4vZo6+FaLVi5N+jFb7RbH0vB/z39efDqEUnTraYtt8dQ+cEa3b56X4fBKZvVrGdnlmhScX6X1B8J5TqXzCrRxGFdc50AAABANMVi/U1wJEoIjrTX0OzSmMVvB1ywldhztWxOqcymNL1SUR3wyfy1pYVKN6fJau6ap/Px4A3C1DmccjhdslktyrVZ5XR5ZDGlaVtljW5Z2bWL387+noGuaffCKe3qxyQDl9ujLYdqQ9qREYugVLC8O1yC2QHVXf8bAgAAQGohOJLACI6019ECs8Seq9Xzx2jr4VrduWZ/Qj+Zj6VQ0jacbo/W7O66xW+oAYN0c5r+3w8u0gX9e3V58dJQJWsgKBF3QAEAAACxQHAkgREcOZe/Lf99bFa9/aOLta+qrsvTRbpSOGkbhmHI0oWL32CuuY/NqhvHDtT3JxXJlmE5ZzdMoi/UUzGFCAAAAEg2BEcSGMER/87e8n/ll8/TgouH6KJHNybdk/loSba0jbY6SuG45ivn67HvfEUeQwlbxDQYk5/cHHbx2T/dc0n0LwgBU88SPdgGAACA2CA4ksAIjnTMu4hpdrm1csdHET2Zb2hyJfUCKVnTNto6O4WjV6ZFWVZLUhQx7Uidw6mRD28I+/3vLpqatN15ElGidwwCAABA14jF+ptvk4gLb/Aiw2LWql1VYZ1j1c4qpZtN+uHL72rCY5s08uENmvzkZr20/agaml1yuT3RvOSYcHs8+uDTU5ox2q5LivMVbGzA4XRr7Z4qtSTIPXr/njlZVg3I6aEe6WaVH6zRvBUVnQZ9HE635q2o0JZDNQn3N3M4XRG+P/iAFzrm9hjacqhWYxa/rUfWHThnN8+RWoceWXdAYxa/rS2HaoPahQUAAAAEQnAEceFye9Tscqu2oTmslAVJ+rDWoZrTTSrKt/n+7UitQ/9nc6WeL/9QTa7WhXadw6ljdY2qczglKSECCt5rOHXGpb7ZGVowqUi/u3m0yu+brFsvGaI+ts53G3iDQ4moyeXR7av3Bb1AdXsM3bZqn5pdXf+3actmjWxnjs1qjtKVpDaX29Mtgm0AAABIHomxRx/dmvcJ8F8+/qeu+sr5EZ2rodndLrWkbUvgRNx6H0xawIKLh2jexCLNe2mP9lXVBzzXh7UO1Tc6Ey5to8Xden+h7prw7oZJpCKmuTarBufZwq45kmh/m2QVbrBt98IppNcAAAAgLHyLREy1fQJcf6ZF2RHWzMjOMKux2aVLivP102u+pLXfH6t3q+p00aMbE27rfbBpARc9ulH7quq0ev5YldhzOjxnIqZtpJtNEadKJYoWt0ezxtjDeu+ssfaE2KWU7CINtvE3AAAAQDgSZ1WCbqntE+DK4w3q1ytTg/Nsnb/Rj5GFOcrvmalbLhmq3908WjeMGaith2s1f+XehNt6H2pawIKVe7XtcK2WzSnrMMUmEdM26hzOiFKl6hudUb6i8KWbTZox2h7y79lmNWtGmT2hAj3JqjsF2wAAAJA8+BaJmDn7CXD5oRodq2sM68l8iT1Xq+aPUUOTS6+/+7GeL/9QjS1u3ZGgdS7CSQv49zX7ZTGl6brSQr/HJGraRncrYpppMenZmSVBd9Ixm9K0ZFaJMiyRf5x6dz0kYt2ceOlOwTYAAAAkD4IjiJmznwB7DGn1ripdW1oY0pP5EnuuVs8fo10fntSYn7+tn68/oCsvPE+vVFQn5Nb7SNICXtlbrVlj7X672CRq2kZ3K2JqMZs0qThfy2aXdnptNqtZy+eUauKw/IhqXbjcHjU0u/TS9qOa/ORmjXx4Q9J2ZIpUdwu2AQAAIDkQHEHM+HsCvHZPtdweQ09fPzKoJ/N9bFYtm1Oq7ZUnfCkqk4blqyA3K2G33keaFlCQm6VJw/Lb/Xsip214i5iGI1F3w5hNaZo4LE+7Fk7Rg1ePOOf+ivJsevDqEdq9cIomDM0LepeJP7Ssba+7BdsAAACQHOhWg5jx9wT4pMOpeS/t0er5Y/XcjaN055r9HT7pvXHsQKWb09qlzwzpl63jp5oi3nofq0V5pGkB3nbFmw/WSIpu2kYseIuYPrLuQMjv9e6GScSgj8VsUrbZpNnjBmnuhCLVNzrlcLpls5qVk2WNynW73B5tOVSreSsqOg16eOvmLJ9TqglD87ptVxY6BgEAAKArdM9v10gIgZ4A76uq18znd6rEnqvtD1wW8Mn8oqtH6LbJQ/TynvbpM1lWsxqaE3frfaRpAW3bFUcrbSOWunsRU+/15WRZNSCnh2/xHY3rDrdlbTzq5nQVOgYBAACgK7BzJEV5n3rXOZxyOF2yWS3KtUXnabhXR0+A91XVa8qv3tF1pYWaNdauuROKVHO6SQ3NbmVnmJXfM1OOphZZLeZzUlQane6IWwLHcut9pGkB2RlmZVrMevDqEZpRZleGxRRR2kY8eIuYBrMDQkr83TDxEGnL2tnjBiV8YCkc3mDbUxsOhvS7SZZgGwAAABITwZEU43J71ORqXZSt2lXVLnAxOM+mWWPsmjHarkyLKeKdCp2lW5x0OLX0nUo9V16pScPyVZRvky3DIkezSzNG2zWwb5bf9Jm2LYETcet9pGkB+T0zddulQxM23cSftkVMb1+9r8NFrc1q1pJZJZo4LD/hgz6xFGltmrkTiqJ8RYmDYBsAAADijW+SKSTehR+DTbfwGNLmgzV6YdtRPfOnw3p5T7UG5PSQxZTmN30gkpbAUuy33kcrLSBZAiNe8Sxi2h3QsjawrugYBAAAgNTGzpEU0VWFHyN5Amw2mfymz3hbAi+4eEhCbr1P5bSAeBQx7S6i0bI2JytKF5OA2gbbXt5Tpf/a2X6nW1GeTbPG2pMm9QwAAACJjVVKiuiqwo+RPgEO1CY21JbAUny33nuDQol4bfEQyyKm3QUtaztnMbcGSGePG6RN91yidxdN1bb7L9W7i6bqT/dcotnjBsmWYWHHCAAAACLGN8oUEGnhx0hTUCJJtwiUouJtCTxhaJ6eu3FUwm29Jy0AnQkU+AtGqrWsJdgGAACAWEszDCOywhKQJB07dkyFhYWSpOrqahUUFHTxFbU3+cnNYRcI/dM9l0TtOrxpFaGkWzQ0uzRm8dt+gzsl9hwtm1MmsylNv99b3enW+3gHH7wFcINJCyAwklpa3B69tP1owILFHXnw6hHdtlsNAAAA0JlYrL8JjkRJIgdH6hxOjXx4Q9jvf3fR1C59St1ZvZQ+NquvJXBBbpZqTjepyeVRdoZFuQlS5yKcoBC6v44Cf4HYrGbtXjhFtgjbWQMAAADJKhbrb1ZlKSAahR+7UmcpKt6WwJMe36QFKyv0zzMtGtC7h3ITaOs9aQHwJ9Vr0wAAAACJgm/YKSBWhR+9tUjqHE4dq2tUncPZ7t+jKZi6JQuvGqFfffdrGtTXJhOdK5AEqE0DAAAAJAb2ZacAb+HHcGuOnJ1S462jsXZ3lVbtal9HY3CeTbPG2DVjtF2ZUa6jQZtYdEe0rAUAAAC6HjVHoiSRa45Es/Cj22Oo/GCNbl+9r8N0G5vVrGdnlmhScT6LOSBI1KYBAAAAOkfNEYQl3WzSjNH2Trftn81mNWtGmd23KHO5PSo/WKN5Kyo6rUPicLo1b0WFthyqkSsGaTZAd0RtGgAAAKBr8I07RUSj8GOTy6PbV+/z2zHGH7fH0G2r9qnZRXAEAAAAAJC4CI6kiEgLP7a4W2uMhNq5xuF0a+2eqpgUaQUAAAAAIBoIjqSQYDq+PHj1CO1eOEUThua122WSbjZp1a6qsMZdtbOKtAAAAAAAQMKiW02KCbfjS53DGVa3G0n6sNah+kbnOV1vAAAAAABIBARHUlTbwo85Wef++9kcTpfffzelSZOG5WtIv2xlWc1qdLpVebxB5Ydq1LY0icPpbjcOAAAAAACJguAIgmKztp8qfWxWzSgr1MwxdhXkZun4qSY1NLuUnWFRv16ZOlbXqNW7qrR2T7VOOpwhd8oBAAAAACBeCI4gKLk2qwbn2XSk1qESe66WzSmV2ZSmVyqqtWpXVbuUm8F5Ns0aY9eCi4do3sQiLXrtL6TUAAAAAAASFsERBKXF7dGsMXatf/8zrZ4/RlsP1+rONfv9dq85UuvQI+sO6KkNB/X09SP11IyvyeX2+DrfAAAAAACQSOK2Wv3oo4909913a/jw4bLZbOrTp4/Kysr0xBNPqLGxMWrjvPXWW5o+fboKCgqUkZGhgoICTZ8+XW+99VbUxkhF6WaTZoy2a/mcUm09XKsFK/d22tbX4XRrwcq92na4Nk5XCQAAAABA6NIMwzA6Pywyb775pm644QadOnXK78+Li4u1bt06DR06NOwxPB6Pvv/972v58uUBj5k3b56ee+45mUzRjwkdO3ZMhYWFkqTq6moVFBREfYxY83aqqXM45XC6ZLNalGv7vION2+NRo9OtsT/f2GlgpC2b1azdC6fIlsFGJQAAAABAZGKx/o75anX//v267rrrdObMGWVnZ+uBBx7Q5MmTdebMGa1du1bPP/+8Dh48qKuuukoVFRXq2bNnWOMsXLjQFxgZOXKk7rvvPg0ZMkSVlZV6/PHHtX//fi1btkz5+fn6+c9/Hs1bTHout0dNLo/W7q4KWD9kxmi7Mi0mbfrgeEiBEal1B8naPVWaPW5QwG44AAAAAAB0lZjvHJk0aZK2bNkii8Wi8vJyjRs3rt3Pn3jiCd13332SpJ/+9Kd66KGHQh7j4MGDuuCCC+RyuVRaWqry8nL16NHD9/PGxkZdfPHFqqiokMVi0YEDByLapeJPsu4ccXsMlR+s0e2r93UY9LBZzXp2ZonGDemrmc/v1L6q+pDGKcqz6U/3XBLZxQIAAAAAUl4s1t8xfYy/e/dubdmyRZI0d+7ccwIjknT33XdrxIgRkqSnn35aLS0tIY/z61//Wi6XS5L0zDPPtAuMSFJWVpaeeeYZSZLL5dJTTz0V8hjdkcvtUfnBGs1bURFU/ZB5Kyq07XCtls0pUx9baN1nPqx1qL7RGcnlAgAAAAAQEzENjrz22mu+19/73vf8X4DJpNmzZ0uS6uvrtWnTppDGMAxDr7/+uiRp+PDhGjt2rN/jxo4dqy9+8YuSpNdff11xKLWS8JpcHt2+ep/cnuB+F26PoX9fs18WU5quKy0MebxQ03EAAAAAAIiHmAZHtm7dKkmy2WwaNWpUwOMuvvhi3+tt27aFNMaRI0f0ySefnHOejsb5+OOPdfTo0ZDG6W5a3K01RsKpH/LK3mrNGmuXKS20MW1Wc2hvAAAAAAAgDmJakPXAgQOSpKFDh8piCTzU8OHDz3lPsP72t7/5PU8w4wwePDjocY4dO9bhzz/99NOgz5UI0s0mrdpVFdZ7V+2s0twJRZo0LF+bD9YE9Z6iPJtyskJLxQEAAAAAIB5iFhxpampSbW2tJHVaHCU3N1c2m00Oh0PV1dUhjdM2aNHZON6CLZJCHqfte7uDOoezXVeaUHxY61DN6SYV5duCDo7MGmv3tQQGAAAAACCRxGylevr0ad/r7OzsTo+32WySpIaGhpiN4x0jnHG6G4fTFdH7G5rdsmUEF1uzWc2aUWYnMAIAAAAASEgx3TniZbV2nk6RkZEhSTpz5kzMxvGOEc44ne00+fTTTzV69OiQztmVbNbI/vTZGWY5mjsPsJhNaVoyq0QZFgIjAAAAAIDEFLPgSGZmpu+109l5C9fm5mZJOqcNbzTH8Y4RzjjR6JucSHJtVg3Os4WVWlOUZ1N+z0x9XN9xgMlmNWvJrBJNHJYvc6jVWwEAAAAAiJOYPc7v2bOn73UwKSwOR+siPZgUnHDH8Y4RzjjdTYvbo1lj7GG9d9ZYu1pcHv3yu1/Tg1eP0OA8W7ufF+XZ9ODVI7R74RRNGJpHYAQAAAAAkNBiunOkb9++OnHiRKedXurq6nyBi1ALn7bd0dHZOG1TY7pbgdVQpZtNmjHarqc2HAypna+vfojFpHSLSbPHDdLcCUWqb3TK4XTLZjUrJ8tK8VUAAAAAQNKI6er1S1/6kiTp8OHDcrkC16f44IMPfK9HjBgR1hhnnyfa43RHmRaTnp1ZEvTODn/1Q7wBkJwsqwbk9PC16yUwAgAAAABIFjFdwU6YMEFSazrL3r17Ax73zjvv+F6PHz8+pDEGDx6s/v37n3Mef8rLyyVJAwYM0KBBg0IapzuymE2aVJyvZbNLZbOaOzzWZjVr+ZxSTRyWLwuBDwAAAABANxLTVe43v/lN3+sXX3zR7zEej0crVqyQJOXk5Gjy5MkhjZGWlqZp06ZJat0ZsnPnTr/H7dy507dzZNq0aUpLow6G1LobZOKwPO1aOIX6IQAAAACAlBTT4Mjo0aM1ceJESdLy5cu1Y8eOc4755S9/qQMHDkiS7rzzTqWnp7f7+ebNm5WWlqa0tDTddNNNfse56667ZDa37ny44447zmnTe+bMGd1xxx2SJIvForvuuiuS2+p2LGaTsjMsmj1ukDbdc4neXTRV2+6/VO8umqo/3XOJZo8bJFuGhR0jAAAAAIBuKear3aefflo9evSQy+XS5ZdfrkcffVQ7d+7Upk2btGDBAt13332SpOLiYt19991hjVFcXKx7771XklRRUaHx48fr5ZdfVkVFhV5++WWNHz9eFRUVkqR7771Xw4YNi87NdTPUDwEAAAAApKKYdavxGjlypF5++WXdcMMNOnXqlH7yk5+cc0xxcbHWrVvXri1vqBYvXqzjx4/rhRde0P79+zVjxoxzjpk7d64eeeSRsMcAAAAAAADdT1y2BFxzzTX685//rB/+8IcqLi5WVlaWcnJyVFpaqscee0z79+/X0KFDIxrDZDJp+fLlWrdunaZNm6b+/fvLarWqf//+mjZtmtavX69ly5bJZGIXBAAAAAAA+FyaYRhGV19Ed3Ds2DEVFhZKkqqrq1VQUNDFVwQAAAAAQPcTi/U32ygAAAAAAEBKIzgCAAAAAABSGsERAAAAAACQ0giOAAAAAACAlEZwBAAAAAAApDSCIwAAAAAAIKURHAEAAAAAACmN4AgAAAAAAEhpBEcAAAAAAEBKIzgCAAAAAABSGsERAAAAAACQ0giOAAAAAACAlEZwBAAAAAAApDSCIwAAAAAAIKURHAEAAAAAACmN4AgAAAAAAEhpBEcAAAAAAEBKIzgCAAAAAABSGsERAAAAAACQ0ixdfQHdhcvl8r3+9NNPu/BKAAAAAADovtquuduuxSNBcCRKampqfK9Hjx7dhVcCAAAAAEBqqKmp0aBBgyI+D2k1AAAAAAAgpaUZhmF09UV0B01NTXr//fclSfn5+bJYEn9Tzqeffurb5bJ7926df/75XXxFQPiYz+humNPoTpjP6E6Yz+huknFOu1wuX/bGhRdeqMzMzIjPmfgr+CSRmZmpsrKyrr6MsJ1//vkqKCjo6ssAooL5jO6GOY3uhPmM7oT5jO4mmeZ0NFJp2iKtBgAAAAAApDSCIwAAAAAAIKURHAEAAAAAACmN4AgAAAAAAEhpBEcAAAAAAEBKIzgCAAAAAABSGsERAAAAAACQ0tIMwzC6+iIAAAAAAAC6CjtHAAAAAABASiM4AgAAAAAAUhrBEQAAAAAAkNIIjgAAAAAAgJRGcAQAAAAAAKQ0giMAAAAAACClERwBAAAAAAApjeAIAAAAAABIaQRHAAAAAABASiM4AgAAAAAAUhrBkW7go48+0t13363hw4fLZrOpT58+Kisr0xNPPKHGxsaojfPWW29p+vTpKigoUEZGhgoKCjR9+nS99dZbURsDiOV8bmxs1Kuvvqof/OAHKisrU25urtLT09W3b1+NGzdODz30kD777LMo3QkQv8/nthobG1VUVKS0tDSlpaVp0KBBMRkHqSmec/rtt9/WTTfdpKFDh8pms6l3794qLi7Wd77zHS1dulQNDQ1RHQ+pJx7z+ejRo/rxj3+sUaNGKScnR+np6erTp48uuugi/exnP9Px48ejMg5S1/Hjx/WHP/xBixYt0pVXXqm8vDzfd4CbbropJmOuWbNGl19+uc477zxlZmZq4MCBuuGGG7Rjx46YjBc3BpLaG2+8YfTq1cuQ5Pd/xcXFxqFDhyIaw+12G3Pnzg04hiRj3rx5htvtjtJdIVXFcj6/9957RnZ2dofzWJLRq1cvY+3atVG+M6SieHw++3P33Xe3G2fgwIFRHwOpKV5z+uTJk8a0adM6/bzev39/5DeFlBWP+bxixQqjR48eHc7jPn36GP/7v/8bpbtCKupofs2ZMyeqYzU2Nhpf//rXA45nMpmMhx56KKpjxhPBkSS2b98+3wdudna2sXjxYmP79u3Gxo0bjfnz57f7cD916lTY49x///2+c40cOdJYs2aNsXv3bmPNmjXGyJEjfT974IEHonh3SDWxns9btmzxnWP8+PHGo48+amzYsMHYt2+f8cc//tFYsGCBYTKZDEmG2Ww21q9fH4O7RKqI1+ezv3HNZrORmZlp9OzZk+AIoiZec7q+vt4YNWqU73zTp083Vq1aZezcudPYs2eP8eqrrxp33nmnUVBQQHAEYYvHfN66davve4XJZDK+973vGa+99pqxe/du4/e//71xzTXX+Mbp0aOHUVlZGeW7RKpoG5yw2+3G5ZdfHrPgyIwZM3znnjx5sm9OL1++3BgyZIjvZ88991xUx40XgiNJbOLEiYYkw2KxGNu3bz/n548//rhvgv70pz8Na4y///3vhsViMSQZpaWlRmNjY7ufOxwOo7S01HcdsXgKitQQ6/m8bds247vf/a7x17/+NeAxr732mpGWlmZIMoYMGWJ4PJ6QxwEMIz6fz2dzuVy+ReXPfvYzY+DAgQRHEDXxmtM33nijIcnIyMgwXn/99YDHeTweo6WlJexxkNriMZ+vuuoq3zmWLFni95gf/ehHvmNuu+22sMYBFi1aZLz55pvGZ599ZhiGYRw5ciQmwZGNGzf6znvNNdcYLper3c9ramoMu91uSDJycnKMkydPRm3seCE4kqR27drlm5wLFizwe4zb7TZGjBjhm6BOpzPkcX7wgx/4xtmxY4ffY3bs2OE75tZbbw15DCBe8zkY3/72t33Xsnfv3piMge6tq+bzL3/5S0OS8cUvftFobm4mOIKoidecbrvD74knnoj0sgG/4jWfc3NzDUlG3759Ax5TX1/vu5aSkpKQxwD8iVVw5Morr/QFFaurq/0es2bNGt/Yjz/+eNTGjhcKsiap1157zff6e9/7nt9jTCaTZs+eLUmqr6/Xpk2bQhrDMAy9/vrrkqThw4dr7Nixfo8bO3asvvjFL0qSXn/9dRmGEdI4QDzmc7AmT57se11ZWRmTMdC9dcV8/uijj7Ro0SJJ0m9/+1tZrdaIzge0Fa85/eyzz0qSevfurdtvvz30CwWCEK/57HQ6JUmDBw8OeEzv3r2Vl5fX7nggEZ0+fVobN26UJE2ZMkUFBQV+j/vWt76lXr16SZL++7//O27XFy0ER5LU1q1bJUk2m02jRo0KeNzFF1/se71t27aQxjhy5Ig++eSTc87T0Tgff/yxjh49GtI4QDzmc7Cam5t9r81mc0zGQPfWFfP51ltvlcPh0I033qhLLrkkonMBZ4vHnHY6nb4HMlOnTlVmZqYkye12q7q6WkePHlVTU1Oolw6cI16f0d4Hh0eOHAl4zKlTp1RbW9vueCAR7dmzxxfA62hdaLVafQ/U9+zZo5aWlrhcX7QQHElSBw4ckCQNHTpUFosl4HHDhw8/5z3B+tvf/ub3PNEeB4jHfA7WO++843s9YsSImIyB7i3e83nt2rVav369cnNz9ctf/jLs8wCBxGNOv/fee77gx4UXXqhTp07prrvuUl5enux2uwYPHqzevXtr6tSp2rx5c+g3AfxLvD6jb7nlFknSiRMn9Nvf/tbvMQ8//PA5xwOJKJx1ocvl0qFDh2J6XdFGcCQJNTU1+aLMgbY0eeXm5spms0mSqqurQxrn2LFjvtedjVNYWOh7Heo4SG3xms/BeO+997Ru3TpJrV/OCY4gVPGez3V1dbrrrrskSb/4xS+Un58f1nmAQOI1p9t+8fZ4PCotLdXTTz+t+vp63787nU69/fbbuvTSS/XYY4+FdH5Aiu9n9M033+xLzbnttts0f/58vfnmm6qoqNCrr76q6dOn68knn5QkLVy4UFOmTAl5DCBeUmVdSHAkCZ0+fdr3Ojs7u9PjvR/sDQ0NMRvHO0Y44yC1xWs+d6a5uVnz5s2T2+2WJC1evDiq50dqiPd8vvfee/WPf/xD48aN0/z588M6B9CReM3pkydP+l4/9thjOnTokP7t3/5Nu3fvVlNTk44fP66lS5eqd+/eMgxD999/vy8NBwhWPD+jzWazXnrpJb3yyiv66le/qmXLlukb3/iGysrK9O1vf1uvvfaaJk+erA0bNuiRRx4J+fxAPKXKupDgSBJqm3MbTNG9jIwMSdKZM2diNo53jHDGQWqL13zuzO23366KigpJ0pw5c3TNNddE9fxIDfGcz+Xl5XrhhRdksVj029/+VmlpaSGfA+hMvOa0w+FoN+bUqVP1hz/8QWVlZcrIyFB+fr5uueUW/eEPf5DJ1Pr19YEHHqAIPEIS7+8cBw4c0IoVK/T+++/7/fmOHTu0fPlyffzxx2GdH4iXVFkXEhxJQt4iZVJwla29BSZ79OgRs3HaFrEMdRyktnjN5448+uijWrZsmSSprKxMS5Ysidq5kVriNZ+bm5v1/e9/X4Zh6M4779RXvvKV0C4UCFJXfOeQWneP+CuKPWHCBH3rW9+S1LrwDLToBPyJ53eOLVu2aNy4cXrzzTc1YMAArVy5Up999pmcTqeqq6u1ZMkSZWVlae3atRo9erT++te/hjwGEC+psi4kOJKEevbs6XsdzFYl79OYYLYPhjtO2yc+oY6D1Bav+RzIc889p5/85CeSWgtIrV+/vt12QCAU8ZrPixcv1t///ncVFhbqP/7jP0K7SCAEXfGdIz8/XyNHjgx47BVXXOF7vWfPnpDGQWqL13xubm7W9ddfr3/+858677zztHPnTt1www36whe+oPT0dBUUFOjWW29VeXm5MjMz9cknn2jOnDmh3QwQR6myLgxcohkJKzMzU3379tWJEyfaFcfxp66uzjdB2xbHCUbbYjudjdO22E6o4yC1xWs++7NmzRrdeuutkqSBAwdqw4YNysvLi/i8SF3xms/eYpRTpkzRm2++6fcY77kdDofWrl0rSerXr58uvfTSkMZCaovXnG57fCjF/mpqakIaB6ktXvP5f/7nf3ypMnfccYfOO+88v8ddcMEFuuGGG7Rs2TLt3btX7733nr761a+GNBYQD2evC0tLSwMem8zrQoIjSepLX/qStmzZosOHD8vlcgVsRfbBBx/4XofaeeNLX/qS3/NEexwgHvP5bG+88YZmz54tj8ej888/Xxs3buz0CzkQjHjMZ++W1hdffFEvvvhih8fW1tbq+uuvlyRdfPHFBEcQsnjM6QsuuMD32lsYO5C2P++oFSvgTzzmc9vWvyUlJR0eO2rUKF9q7wcffEBwBAkpnHWhxWLRsGHDYnpd0UZaTZKaMGGCpNYngnv37g143DvvvON7PX78+JDGGDx4sPr373/OefwpLy+XJA0YMECDBg0KaRwgHvO5rY0bN+q73/2uXC6X+vbtqw0bNmjIkCFhnw9oK97zGYi1eMzpgQMHym63S5KOHj3aYaHVyspK3+sBAwaENA4Qj/ncNuDicrk6PLalpcXv+4BEUlZW5ivE2tG60Ol0aufOnb73pKenx+X6ooXgSJL65je/6Xsd6Kmhx+PRihUrJEk5OTmaPHlySGOkpaVp2rRpklojgN6JfradO3f6IoTTpk2jYwJCFo/57LV9+3ZNmzZNzc3N6t27t/74xz+2e2IJRCoe89kwjE7/N3DgQEmti07vv23evDmse0Jqi9dn9Le//W1J0qlTp7Rx48aAx7366qu+196FLhCseMznwYMH+15v2bKlw2PbLjTbvg9IJD179tRll10mSXr77bcDpqW9+uqrOnXqlCRp+vTpcbu+qDGQtCZOnGhIMiwWi7F9+/Zzfv74448bkgxJxk9/+tNzfr5p0ybfz+fMmeN3jL///e+G2Ww2JBmlpaVGY2Nju583NjYapaWlvus4ePBgNG4NKSge83n//v1GTk6OIcmw2WzG1q1bo3wXQKt4zOfODBw40JBkDBw4MKz3A23FY05/9NFHRmZmpiHJuPDCC41//vOf5xyzcuVK33muuuqqSG8LKSrW87murs7IysoyJBk9e/Y0/vznP/u9jvXr1xsmk8mQZAwYMMBwu92R3hpgHDlyJOTvEC+++GKHc94wDGPjxo2+Y77xjW8YLper3c9ramoMu91uSDJycnKMkydPRngn8cferST29NNPa/z48Tpz5owuv/xy/eQnP9HkyZN15swZrV27Vv/5n/8pSSouLtbdd98d1hjFxcW699579Ytf/EIVFRUaP368fvzjH2vIkCGqrKzUY489pv3790uS7r333qTLK0PiiPV8rqys1BVXXKH6+npJ0iOPPKLevXvrL3/5S8D39OvXT/369QvrfpDa4vH5DMRTPOa03W7Xz372M9133316//33NXr0aP34xz/WV77yFZ06dUqvvvqqli5dKknq1auXnnrqqajdH1JLrOdzTk6O7r//fi1atEinT5/WRRddpDvuuENTp05Vbm6u/vGPf+j111/X888/L4/HI0n6xS9+IZOJTf0I3datW3X48GHf/19bW+t7ffjwYf3ud79rd/xNN90U1jiXXnqpZsyYobVr1+qNN97Q1KlTddddd6l///56//33tXjxYlVVVUlqLRyfm5sb1jhdqqujM4jMG2+8YfTq1csXxTv7f8XFxcahQ4f8vjfYJ5Nut9u4+eabA44hyZg7dy7RbkQslvO5bUQ82P8FipwDwYjH53NH2DmCaIvXnL7//vuNtLS0gOP069fP79N+IBSxns8ej8e46667OpzLkoz09HTjiSeeiOGdorubM2dOSN9v/Qlm54hhtGYNfP3rXw94bpPJlNTfnwlPJrlrrrlGf/7zn/XDH/5QxcXFysrKUk5OjkpLS327OoYOHRrRGCaTScuXL9e6des0bdo09e/fX1arVf3799e0adO0fv16LVu2jGg3IhaP+QzEC/MZ3U285vSjjz6qbdu26cYbb9SgQYOUkZGh3r17q6ysTA8//LAOHjyocePGReGOkMpiPZ/T0tL01FNPac+ePbrlllv05S9/WT179pTZbFbv3r01atQo/ehHP9Jf/vIX3XPPPVG8MyB2evTooXXr1mnVqlWaOnWq+vXrJ6vVqsLCQs2cOVNbt27VQw891NWXGbY0w+igHDgAAAAAAEA3x6N+AAAAAACQ0giOAAAAAACAlEZwBAAAAAAApDSCIwAAAAAAIKURHAEAAAAAACmN4AgAAAAAAEhpBEcAAAAAAEBKIzgCAAAAAABSGsERAAAAAACQ0giOAAAAAACAlEZwBAAAAAAApDSCIwAAAAAAIKURHAEAAAAAACmN4AgAAAAAAEhpBEcAAAAAAEBKIzgCAAAAAABSGsERAAAAAACQ0giOAAAAAACAlEZwBAAAAAAApDSCIwAAAAAAIKURHAEAAAAAACmN4AgAAAAAAEhpBEcAAAAAAEBKIzgCAAAAAABS2v8H6zkl0LVQcLMAAAAASUVORK5CYII=", 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", 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" ] @@ -1684,13 +1818,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "id": "00e64c38", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/docs/source/Tutorial_poprock.ipynb b/docs/source/Tutorial_poprock.ipynb index 3e79fabf..6b32a345 100644 --- a/docs/source/Tutorial_poprock.ipynb +++ b/docs/source/Tutorial_poprock.ipynb @@ -16,12 +16,105 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "9fdda6bf", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: musif in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (1.2.4)\n", + "Requirement already satisfied: deepdiff>=6.2.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (8.0.1)\n", + "Requirement already satisfied: joblib>=1.0.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (1.4.2)\n", + "Requirement already satisfied: ms3==2.4.2 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (2.4.2)\n", + "Requirement already satisfied: music21>=9.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (9.1.0)\n", + "Requirement already satisfied: pandas>=1.3.3 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (2.2.3)\n", + "Requirement already satisfied: pyyaml>=5.4.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (6.0.1)\n", + "Requirement already satisfied: roman>=3.3 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (4.2)\n", + "Requirement already satisfied: scipy>=1.6.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (1.14.1)\n", + "Requirement already satisfied: tqdm>=4.56.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (4.66.5)\n", + "Requirement already satisfied: webcolors==1.12 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from musif) (1.12)\n", + "Requirement 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(3.20.2)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from rich>=10.11.0->typer>=0.12->frictionless[pandas,visidata]->ms3==2.4.2->musif) (3.0.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from rich>=10.11.0->typer>=0.12->frictionless[pandas,visidata]->ms3==2.4.2->musif) (2.15.1)\n", + "Requirement already satisfied: mdurl~=0.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer>=0.12->frictionless[pandas,visidata]->ms3==2.4.2->musif) (0.1.2)\n" + ] + } + ], "source": [ "! pip install musif" ] @@ -37,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "id": "85fd7772", "metadata": {}, "outputs": [ @@ -45,7 +138,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.2.2\n" + "1.2.4\n" ] } ], @@ -67,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +168,7 @@ "import zipfile\n", "from pathlib import Path\n", "\n", - "data_dir = Path(\"data\")\n", + "data_dir = Path(\"data_poprock\")\n", "dataset_path = \"dataset.zip\"\n", "urllib.request.urlretrieve(\"https://figshare.com/ndownloader/articles/5436031/versions/1\", dataset_path)\n", "with zipfile.ZipFile(dataset_path, 'r') as zip_ref:\n", @@ -121,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "id": "48641f97", "metadata": {}, "outputs": [], @@ -161,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "id": "e5b5d3a0", "metadata": {}, "outputs": [], @@ -208,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "id": "e940c224", "metadata": {}, "outputs": [], @@ -240,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "id": "f7597151", "metadata": {}, "outputs": [], @@ -335,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "id": "1e71283e", "metadata": {}, "outputs": [], @@ -362,9 +455,10 @@ " # Important! This parameter allows to extract all files skipping those that\n", " # fail during extraction. If you encounter any eerors please report them andopen an issue on Github and we w'll take\n", " # a look as soon as possible!\n", - " ignore_errors=True,\n", + " ignore_errors=False,\n", " # cache_dir='__tutorial_cache', #If cache use is desired\n", - " parallel = -1 #Set number of cores. 1 for no parallel, -1 for all cores\n", + " parallel = -1, #Set number of cores. 1 for no parallel, -1 for all cores\n", + " output_dir = 'output_dir'\n", ")" ] }, @@ -381,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "id": "00ed0bfb", "metadata": {}, "outputs": [], @@ -393,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "id": "08b7b661", "metadata": {}, "outputs": [], @@ -404,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 31, "id": "75b3d617", "metadata": { "scrolled": true, @@ -417,28 +511,1609 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 25/25 [00:00<00:00, 199.16it/s]\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'ExtractConfiguration' object has no attribute 'output_dir'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31m_RemoteTraceback\u001b[0m Traceback (most recent call last)", - "\u001b[1;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"c:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\extract.py\", line 726, in _update_parts_module_features\n module.update_part_objects(\n File \"c:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\features\\lyrics\\handler.py\", line 44, in update_part_objects\n voice_presence = len(part_data[DATA_SOUNDING_MEASURES]) / len(\nZeroDivisionError: division by zero\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"c:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\extract.py\", line 321, in process_corpus_par\n score_features = self._process_score_windows(idx, filename)\n File \"c:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\extract.py\", line 427, in _process_score_windows\n window_features = self.extract_modules(\n File \"c:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\extract.py\", line 488, in extract_modules\n self._update_parts_module_features(\n File \"c:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\extract.py\", line 734, in _update_parts_module_features\n raise FeatureError(\nmusif.common.exceptions.FeatureError: In data_poprock\\Queen.Bohemian_Rhapsody.mid while computing musif.extract.features.lyrics.handler\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"C:\\Users\\Martín\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\externals\\loky\\process_executor.py\", line 428, in _process_worker\n r = call_item()\n File \"C:\\Users\\Martín\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\externals\\loky\\process_executor.py\", line 275, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"C:\\Users\\Martín\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\_parallel_backends.py\", line 620, in __call__\n return self.func(*args, **kwargs)\n File \"C:\\Users\\Martín\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\parallel.py\", line 288, in __call__\n return [func(*args, **kwargs)\n File \"C:\\Users\\Martín\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\parallel.py\", line 288, in \n return [func(*args, **kwargs)\n File \"c:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\extract.py\", line 325, in process_corpus_par\n print(f\"Error found on {filename}. Saving the filename and error print to {str(self._cfg.output_dir)}/error_files.csv for latter tracking\")\nAttributeError: 'ExtractConfiguration' object has no attribute 'output_dir'\n\"\"\"", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[12], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mextractor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\extract.py:305\u001b[0m, in \u001b[0;36mFeaturesExtractor.extract\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(filenames) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 303\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo file found for extracting features! Use data_dir (or cache_dir) to point to your files directory.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 305\u001b[0m score_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_corpus\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilenames\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 307\u001b[0m \u001b[38;5;66;03m# fix dtypes\u001b[39;00m\n\u001b[0;32m 308\u001b[0m score_df \u001b[38;5;241m=\u001b[39m score_df\u001b[38;5;241m.\u001b[39mconvert_dtypes()\n", - "File \u001b[1;32mc:\\Anaconda3\\envs\\musicai\\lib\\site-packages\\musif\\extract\\extract.py:340\u001b[0m, in \u001b[0;36mFeaturesExtractor._process_corpus\u001b[1;34m(self, filenames)\u001b[0m\n\u001b[0;32m 337\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m 338\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m score_features\n\u001b[1;32m--> 340\u001b[0m scores_features \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparallel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 341\u001b[0m \u001b[43m \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_corpus_par\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 342\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilenames\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 343\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cfg\u001b[38;5;241m.\u001b[39mwindow_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 346\u001b[0m all_dfs \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\parallel.py:1098\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 1095\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 1097\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[1;32m-> 1098\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mretrieve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1099\u001b[0m \u001b[38;5;66;03m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[0;32m 1100\u001b[0m elapsed_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_start_time\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\parallel.py:975\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 973\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 974\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msupports_timeout\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m--> 975\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output\u001b[38;5;241m.\u001b[39mextend(\u001b[43mjob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 976\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 977\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output\u001b[38;5;241m.\u001b[39mextend(job\u001b[38;5;241m.\u001b[39mget())\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\joblib\\_parallel_backends.py:567\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m 564\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[0;32m 565\u001b[0m \u001b[38;5;124;03mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[0;32m 566\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 567\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 568\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m CfTimeoutError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 569\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n", - "File \u001b[1;32mc:\\Anaconda3\\envs\\musicai\\lib\\concurrent\\futures\\_base.py:458\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 456\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[0;32m 457\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[1;32m--> 458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 459\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 460\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n", - "File \u001b[1;32mc:\\Anaconda3\\envs\\musicai\\lib\\concurrent\\futures\\_base.py:403\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m 402\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 403\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[0;32m 404\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 405\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[0;32m 406\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[1;31mAttributeError\u001b[0m: 'ExtractConfiguration' object has no attribute 'output_dir'" + " 0%| | 0/25 [00:00; getting generic Instrument\n", + " warnings.warn(\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/midi/translate.py:874: TranslateWarning: Unable to determine instrument from ; getting generic Instrument\n", + " warnings.warn(\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/numpy/_core/fromnumeric.py:3904: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/numpy/_core/_methods.py:147: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " skew(absolute_numeric_intervals, bias=False)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", + " kurtosis(absolute_numeric_intervals, bias=False)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", + " warnings.warn(msg)\n", + "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/extract.py:365: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " all_dfs = pd.concat(all_dfs, axis=0, keys=range(len(all_dfs)))\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n", + "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", + " if (arr.astype(int) == arr).all():\n" ] } ], @@ -457,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 32, "id": "6ab4ab25", "metadata": { "scrolled": true @@ -466,10 +2141,10 @@ { "data": { "text/plain": [ - "(467, 49623)" + "(566, 61250)" ] }, - "execution_count": 12, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -490,15 +2165,9 @@ "We will now postprocess the data, again, see the [Getting started tutorial](./Tutorial.html) for more info." ] }, - { - "cell_type": "markdown", - "id": "565d1762", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 33, "id": "527b85cd", "metadata": {}, "outputs": [ @@ -513,10 +2182,10 @@ { "data": { "text/plain": [ - "(467, 162)" + "(566, 162)" ] }, - "execution_count": 13, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -543,17 +2212,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 34, "id": "6af21fe1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(467, 162)" + "(566, 162)" ] }, - "execution_count": 14, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -580,7 +2249,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 35, "id": "f65c709c", "metadata": { "tags": [ @@ -592,36 +2261,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: scikit-learn in c:\\users\\martín\\appdata\\roaming\\python\\python310\\site-packages (1.3.1)\n", - "Requirement already satisfied: seaborn in c:\\users\\martín\\appdata\\roaming\\python\\python310\\site-packages (0.12.2)\n", - "Requirement already satisfied: numpy<2.0,>=1.17.3 in c:\\users\\martín\\appdata\\roaming\\python\\python310\\site-packages (from 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pyparsing>=2.3.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.4)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n", - "[notice] To update, run: python.exe -m pip install --upgrade pip\n" - ] } ], "source": [ @@ -630,31 +2290,23 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "c1799ce4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 16, + "execution_count": 36, "id": "bc8a9edb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -695,20 +2347,23 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 37, "id": "7a9f3f81", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(467, 2)\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -747,7 +2402,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 38, "id": "540550a4", "metadata": { "scrolled": false @@ -756,23 +2411,23 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] }, "metadata": { "image/png": { - "height": 413, + "height": 416, "width": 546 } }, diff --git a/musif/extract/extract.py b/musif/extract/extract.py index 166c5e4c..a0d710dd 100644 --- a/musif/extract/extract.py +++ b/musif/extract/extract.py @@ -335,12 +335,12 @@ def process_corpus_par(idx, filename): else: score_features = self._process_score(idx, filename) except Exception as e: - print(f"Error found on {filename}. Saving the filename and error print to {str(self._cfg.output_dir)}/error_files.csv for latter tracking") - error_files.append(filename) - errors.append(e) - df = pd.DataFrame({'ErrorFiles': error_files, - 'Errors': errors}) - df.to_csv(str(self._cfg.output_dir)+'/error_files.csv', mode='a', index=False) + # print(f"Error found on {filename}. Saving the filename and error print to {str(self._cfg.output_dir)}/error_files.csv for latter tracking") + # error_files.append(filename) + # errors.append(e) + # df = pd.DataFrame({'ErrorFiles': error_files, + # 'Errors': errors}) + # df.to_csv(str(self._cfg.output_dir)+'/error_files.csv', mode='a', index=False) if self._cfg.ignore_errors: lerr( f"Error while extracting features for file {filename}, skipping it because `ignore_errors` is True!" diff --git a/musif/extract/features/lyrics/handler.py b/musif/extract/features/lyrics/handler.py index bcb75688..651dc9ea 100644 --- a/musif/extract/features/lyrics/handler.py +++ b/musif/extract/features/lyrics/handler.py @@ -90,7 +90,7 @@ def update_score_objects( features[get_part_feature(part, VOICE_PRESENCE)] = len( part_data[DATA_SOUNDING_MEASURES] - ) / len(part_data[DATA_MEASURES]) + ) / len(part_data[DATA_MEASURES]) if part_data[DATA_MEASURES] else 0 features[get_part_feature(part, SYLLABIC_RATIO)] = get_syllabic_ratio( part_data[DATA_NOTES], part_data[DATA_LYRICS] diff --git a/pyproject.toml b/pyproject.toml index b83c8b03..34995530 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,7 @@ dependencies = [ "deepdiff>=6.2.1", ] name = "musif" -version = "1.2.3" +version = "1.2.4" description = "Music feature extraction library from the DIDONE project" authors = [{name = "Didone Project", email = "didone@iccmu.es"}] requires-python = ">=3.10" From 34f8a86d7722a146e3c2073b156245a480644ec4 Mon Sep 17 00:00:00 2001 From: carlos Date: Fri, 11 Oct 2024 12:20:45 +0200 Subject: [PATCH 4/5] output_dir repaired --- docs/source/Tutorial.ipynb | 299 +---- docs/source/Tutorial_poprock.ipynb | 1669 +--------------------------- musif/extract/extract.py | 16 +- 3 files changed, 32 insertions(+), 1952 deletions(-) diff --git a/docs/source/Tutorial.ipynb b/docs/source/Tutorial.ipynb index a7cc792f..66972dad 100644 --- a/docs/source/Tutorial.ipynb +++ b/docs/source/Tutorial.ipynb @@ -54,252 +54,10 @@ }, { "cell_type": "code", - 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] - } - ], + "outputs": [], "source": [ "! pip install musif" ] @@ -459,20 +217,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "e286b65b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 14%|█▎ | 24/175 [00:39<04:10, 1.66s/it]/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/midi/translate.py:874: TranslateWarning: Unable to determine instrument from ; getting generic Instrument\n", - " warnings.warn(\n", - "100%|██████████| 175/175 [02:35<00:00, 1.13it/s]\n" - ] - } - ], + "outputs": [], "source": [ "df = extractor.extract()" ] @@ -1617,45 +1365,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "f3018334", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting scikit-learn\n", - " Downloading scikit_learn-1.5.2-cp310-cp310-macosx_10_9_x86_64.whl.metadata (13 kB)\n", - "Collecting seaborn\n", - 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"execution_count": 26, + "execution_count": 4, "id": "e940c224", "metadata": {}, "outputs": [], @@ -333,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "id": "f7597151", "metadata": {}, "outputs": [], @@ -428,13 +428,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "id": "1e71283e", "metadata": {}, "outputs": [], "source": [ "from musif.config import ExtractConfiguration\n", - "\n", + "from pathlib import Path\n", "config = ExtractConfiguration(\n", " None,\n", " data_dir = \"data_poprock\",\n", @@ -455,10 +455,10 @@ " # Important! This parameter allows to extract all files skipping those that\n", " # fail during extraction. If you encounter any eerors please report them andopen an issue on Github and we w'll take\n", " # a look as soon as possible!\n", - " ignore_errors=False,\n", + " ignore_errors=True,\n", " # cache_dir='__tutorial_cache', #If cache use is desired\n", " parallel = -1, #Set number of cores. 1 for no parallel, -1 for all cores\n", - " output_dir = 'output_dir'\n", + " output_dir = \"output_dir\"\n", ")" ] }, @@ -475,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 10, "id": "00ed0bfb", "metadata": {}, "outputs": [], @@ -487,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "id": "08b7b661", "metadata": {}, "outputs": [], @@ -498,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "75b3d617", "metadata": { "scrolled": true, @@ -506,1617 +506,7 @@ "hide-output" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/25 [00:00; getting generic Instrument\n", - " warnings.warn(\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/midi/translate.py:874: TranslateWarning: Unable to determine instrument from ; getting generic Instrument\n", - " warnings.warn(\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5084193809\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:582: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:587: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113594785\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5106487202\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113218339\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4962020052\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4961834645\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4957090246\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5113606263\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5114651144\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/numpy/_core/fromnumeric.py:3904: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/numpy/_core/_methods.py:147: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5007210049\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5014287218\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 5001606001\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " skew(absolute_numeric_intervals, bias=False)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n", - " kurtosis(absolute_numeric_intervals, bias=False)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/music21/base.py:559: UserWarning: Setting an ID that could be mistaken for a memory location is discouraged: got 4987537873\n", - " warnings.warn(msg)\n", - "/Users/carlosvaquero/Library/CloudStorage/GoogleDrive-vaquerocarlos@gmail.com/My Drive/Didone/musif/musif/extract/extract.py:365: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " all_dfs = pd.concat(all_dfs, axis=0, keys=range(len(all_dfs)))\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1056: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n", - "/opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1080: RuntimeWarning: invalid value encountered in cast\n", - " if (arr.astype(int) == arr).all():\n" - ] - } - ], + "outputs": [], "source": [ "df = extractor.extract()" ] @@ -2249,41 +639,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "f65c709c", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: scikit-learn in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (1.5.2)\n", - "Requirement already satisfied: seaborn in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (0.13.2)\n", - "Requirement already satisfied: numpy>=1.19.5 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from scikit-learn) (2.1.2)\n", - "Requirement already satisfied: scipy>=1.6.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from scikit-learn) (1.14.1)\n", - "Requirement already satisfied: joblib>=1.2.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from scikit-learn) (1.4.2)\n", - "Requirement already satisfied: threadpoolctl>=3.1.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from scikit-learn) (3.5.0)\n", - "Requirement already satisfied: pandas>=1.2 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from seaborn) (2.2.3)\n", - "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from seaborn) (3.9.2)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.54.1)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n", - "Requirement already satisfied: packaging>=20.0 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n", - "Requirement already satisfied: pillow>=8 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.4)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n", - "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", - "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.2)\n", - "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/musif_tutorials/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "%pip install scikit-learn seaborn" ] diff --git a/musif/extract/extract.py b/musif/extract/extract.py index a0d710dd..a71b135e 100644 --- a/musif/extract/extract.py +++ b/musif/extract/extract.py @@ -322,6 +322,9 @@ def _check_for_error_file(self): except Exception: # Handle the case where the file is empty print("There is no error_files.csv, it will be created and loaded error files are included manually in it.") + import os + if not os.path.exists(f'{self._cfg.output_dir}'): + os.makedirs(f'{self._cfg.output_dir}') def _process_corpus( self, filenames: List[PurePath] @@ -335,12 +338,13 @@ def process_corpus_par(idx, filename): else: score_features = self._process_score(idx, filename) except Exception as e: - # print(f"Error found on {filename}. Saving the filename and error print to {str(self._cfg.output_dir)}/error_files.csv for latter tracking") - # error_files.append(filename) - # errors.append(e) - # df = pd.DataFrame({'ErrorFiles': error_files, - # 'Errors': errors}) - # df.to_csv(str(self._cfg.output_dir)+'/error_files.csv', mode='a', index=False) + self._check_for_error_file() + print(f"Error found on {filename}. Saving the filename and error print to {str(self._cfg.output_dir)}/error_files.csv for latter tracking") + error_files.append(filename) + errors.append(e) + df = pd.DataFrame({'ErrorFiles': error_files, + 'Errors': errors}) + df.to_csv(str(self._cfg.output_dir)+'/error_files.csv', mode='a', index=False) if self._cfg.ignore_errors: lerr( f"Error while extracting features for file {filename}, skipping it because `ignore_errors` is True!" From c82a5f8f92fd4f001853fcb75ce38fbd409b12d2 Mon Sep 17 00:00:00 2001 From: carlos Date: Fri, 11 Oct 2024 12:23:41 +0200 Subject: [PATCH 5/5] readme updated --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 79b6b92c..cadb8e07 100644 --- a/README.md +++ b/README.md @@ -42,6 +42,9 @@ https://github.com/DIDONEproject/music_symbolic_features ## Changelog +#### v1.2.4 +* Fix on lyrics module. Implemeted error output file for error registration. + #### v1.2.3 * Minifix on lyrics module