From 25d95cdf88a89da946dd33dba20007ca4cc6cf5c Mon Sep 17 00:00:00 2001 From: wmilkowska Date: Wed, 13 Sep 2023 12:33:26 +0200 Subject: [PATCH 1/5] first commit --- .idea/.gitignore | 8 ++++++++ .idea/misc.xml | 4 ++++ speed.py | 0 3 files changed, 12 insertions(+) create mode 100644 .idea/.gitignore create mode 100644 .idea/misc.xml create mode 100644 speed.py diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..13566b8 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Editor-based HTTP Client requests +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..b23c0fb --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/speed.py b/speed.py new file mode 100644 index 0000000..e69de29 From c26669406e6edb4a1b5b03849d43ecdf5849b8a3 Mon Sep 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"minutes": + bins.append(round(row["end"] / 60)) + else: + bins.append(round(row["end"])) + + df["bin"] = bins + + notes_per_minutes = {} + + for index, row in df.iterrows(): + if row["bin"] in notes_per_minutes: + notes_per_minutes[row["bin"]] += 1 + else: + notes_per_minutes[row["bin"]] = 1 + + x = list(notes_per_minutes.keys()) + y = list(notes_per_minutes.values()) + + fig, ax = plt.subplots() + ax.plot(x, y) + # ax.scatter(x, y) + ax.set(xlabel='Time (' + time_unit + ')', ylabel='Number of notes', + title='Number of notes over time') \ + \ + # show the plot + + return fig + + +if __name__ == "__main__": + dataset = load_dataset("roszcz/internship-midi-data-science", split="train") + for record in dataset: + df = pd.DataFrame(record["notes"]) + fig = plot_speed_time(df) + plt.show() From cdb31a870e6f7371e1da1da096ec822bb1af8120 Mon Sep 17 00:00:00 2001 From: wmilkowska Date: Wed, 13 Sep 2023 13:48:44 +0200 Subject: [PATCH 3/5] speed task --- .pre-commit-config.yaml | 12 ++++++------ speed.py | 7 ++----- 2 files changed, 8 insertions(+), 11 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d62b0d2..2183dde 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,8 +1,8 @@ default_language_version: - python: python3.9.10 + python: python3.10.12 repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.0.1 + rev: v4.4.0 hooks: - id: check-merge-conflict - id: end-of-file-fixer @@ -11,19 +11,19 @@ repos: - id: check-yaml - id: check-docstring-first - id: requirements-txt-fixer - - repo: https://gitlab.com/pycqa/flake8 - rev: 3.9.2 + - repo: https://github.com/pycqa/flake8 + rev: 6.1.0 hooks: - id: flake8 args: ["--max-line-length=180","--extend-ignore=E203","--per-file-ignores=.github/scripts/bump_version.py:E402"] - repo: https://github.com/ambv/black - rev: 21.7b0 + rev: 23.9.1 hooks: - id: black args: [--line-length=130] additional_dependencies: ['click==8.0.4'] - repo: https://github.com/pycqa/isort - rev: 5.10.1 + rev: 5.12.0 hooks: - id: isort name: isort diff --git a/speed.py b/speed.py index daca922..248a9fa 100644 --- a/speed.py +++ b/speed.py @@ -1,6 +1,6 @@ -from datasets import load_dataset import pandas as pd import matplotlib.pyplot as plt +from datasets import load_dataset def plot_speed_time(df: pd.DataFrame) -> plt.Figure: @@ -34,10 +34,7 @@ def plot_speed_time(df: pd.DataFrame) -> plt.Figure: fig, ax = plt.subplots() ax.plot(x, y) # ax.scatter(x, y) - ax.set(xlabel='Time (' + time_unit + ')', ylabel='Number of notes', - title='Number of notes over time') \ - \ - # show the plot + ax.set(xlabel="Time (" + time_unit + ")", ylabel="Number of notes", title="Number of notes over time") # show the plot return fig From d0d2c6ad6ae9a766ced7f4f330bf6fe183ee361e Mon Sep 17 00:00:00 2001 From: wmilkowska Date: Thu, 14 Sep 2023 13:03:05 +0200 Subject: [PATCH 4/5] Sequence similarity task --- .DS_Store | Bin 0 -> 6148 bytes 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zCh25z5@2axu4ovslBM*}&`=EUVmjLq3m=K4$o$_{3+W}{=#;$sNgw4JSgtiK&>JBn zOCDhNHqGhQ$mKtj${#K@pQB(Q*eZ$I5_>i31BH$x@8CG}yI&kb_GgV!Eq;`x56mp# zqyUBp5%u+Z^_oxWn7;tFuiXLG$?qi*&BGb+3iUgE>qUo|ML=J6aT^66AMS;3{T)^7 zZ4I`DlM`7?NTY;+(FS7M@oY(9+QpqrX!d}AknY$U55UP(GoO*#WXElheee7o`ocNe zoz7de(mg!%BKHH%NH*5ne5Pb^cdD?E1`q{`RNV|kwGPcTUS?dhPBFAsu zC4y3jwhzeZ)u^>x48Wq?{nc3Zx8LYn@_2NQpfd%U1{`Hl5@GcZcY0(-_Zxc~D9~e4 zeOAi_RgK*R>kd@FznSK7~ zXXC2a4(P?R%>F*f0tliiKrG-?meTmWX-n!d0EHtH3AVm#S_ix)u$dT$=Kbr`00Z9H z*$GUpZ1pkN; float: + """ + Calculating cosine similarity between sequence and window + Args: + sequence (pd.DataFrame): input sequence + window (pd.DataFrame): subset of rolling window + Returns: + float: cosine similarity score + """ + + # extracting numpy array and transposing, shape: [num_features, window_size] + sequence_arr = sequence.values.T + # shape: [window_size, num_features] + window_arr = window.values + + # det product shape: [features, features] + sequence_x_window = sequence_arr @ window_arr + + # shape: [num_features, 1] + sequence_norm = np.linalg.norm(sequence_arr, axis=1, keepdims=True) + # shape: [1, num_features] + window_norm = np.linalg.norm(window_arr, axis=0, keepdims=True) + + # shape: [num_features, num_features] + normalization = sequence_norm * window_norm + + # calculating cosine similarity for each entry + cos_sim = sequence_x_window / (normalization + 1e-8) + + num_features = cos_sim.shape[0] + + # returning normalized trace of cosine similarity because the values of interest are along main diagonal + return np.sum(cos_sim) / (num_features * num_features) + + +x = df.iloc[0:16] +x = x[["pitch", "velocity"]] + +scores = {"score": [], "idx": []} + +seq_len = len(x) + +for i in range(0, len(df) - seq_len): + seq = df.iloc[i : i + seq_len] + seq = seq[["pitch", "velocity"]] + score = cos_sim_score(x, seq) + scores["score"].append(score) + scores["idx"].append(i) + +similarity = pd.DataFrame(scores) +similarity.sort_values(by="score", ascending=False, inplace=True) + +print(similarity) From 4a583da0d9a056fd66021b9acb4e456c39de4114 Mon Sep 17 00:00:00 2001 From: wmilkowska Date: Mon, 18 Sep 2023 17:51:51 +0200 Subject: [PATCH 5/5] Chords task --- 3.csv | 407 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ chords.py | 91 ++++++++++++ 2 files changed, 498 insertions(+) create mode 100644 3.csv create mode 100644 chords.py diff --git a/3.csv b/3.csv new file mode 100644 index 0000000..7489136 --- /dev/null +++ b/3.csv @@ -0,0 +1,407 @@ +,Note,Count +0,F#M,542 +1,F#m/C#,159 +2,F#m/A,135 +3,EM/B,265 +4,C#7,133 +5,C#sus2,38 +6,F#M7,9 +7,C#sus4,3 +8,Bo/D,45 +9,AM7/G#,107 +10,A+/F,13 +11,Fm,141 +12,F#m,408 +13,Ebm,227 +14,A+,6 +15,EM,1499 +16,DM,703 +17,C#M,602 +18,F#sus2,115 +19,Bm/D,103 +20,A+/C#,5 +21,F#M/C#,18 +22,AM/C#,34 +23,Bsus2/C#,49 +24,Do,41 +25,G#o/B,112 +26,G#m,406 +27,Csus2,71 +28,Csus2/D,30 +29,Ebm7/F#,53 +30,G#M,644 +31,Gsus2/A,31 +32,C#m/G#,240 +33,AM7,58 +34,Gm/Bb,73 +35,C#m,421 +36,C#m/E,341 +37,Bbo/E,54 +38,E+,5 +39,EM/G#,292 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Counter + +import pandas as pd +import fortepyan as ff +from chorder import Dechorder +from datasets import load_dataset +from miditoolkit.midi.containers import Note + + +def main(): + dataset = load_dataset("roszcz/internship-midi-data-science", split="train") + record = dataset[0] + piece = ff.MidiPiece.from_huggingface(record) + + df = create_chords_table(piece) + df.to_csv(f"{piece.source['record_id']}.csv") + + +def create_chords_table(piece): + """ + Generate a table of chord counts for a given piece. + """ + chords = get_chords(piece) + counter = Counter(chords) + + chords_count = {item: count for item, count in counter.items()} + chords_count = pd.Series(chords_count) + + df = chords_count.reset_index() + df.columns = ["Note", "Count"] + return df + + +def get_simultaneous_notes(piece: ff.MidiPiece): + """ + Find simultaneous notes within a MIDI piece. + + Parameters: + - piece: ff.MidiPiece + The MIDI piece from which simultaneous notes are to be extracted. + """ + # Set a tolerance threshold (e.g., 0.1 seconds) for considering notes as simultaneous + tolerance = 0.1 + + # Initialize a list to store groups of simultaneous notes + simultaneous_notes = [] + + # Iterate through each row in the DataFrame + df = piece.df + it = 0 + while it < len(df): + row = df.iloc[it] + current_start_time = row["start"] + + # Filter notes within the tolerance window + simultaneous_group = df[(df["start"] >= current_start_time - tolerance) & (df["start"] <= current_start_time + tolerance)] + notes = [] + + for _, row in simultaneous_group.iterrows(): + note = Note(velocity=row["velocity"], pitch=int(row["pitch"]), start=int(row["start"]) - 1, end=int(row["end"]) + 1) + notes.append(note) + + # Add the group to the result if it contains more than one note + if len(simultaneous_group) > 1: + simultaneous_notes.append(notes) + + it += len(simultaneous_group) + + return simultaneous_notes + + +def get_chords(piece: ff.MidiPiece): + """ + Get the chords for simultaneous notes in a MIDI piece. + + Parameters: + - piece: ff.MidiPiece + The MIDI piece from which chord qualities are to be extracted. + """ + simultaneous_notes = get_simultaneous_notes(piece) + chords = [] + for group in simultaneous_notes: + chord = Dechorder.get_chord_quality(notes=group, start=group[0].start, end=group[-1].end) + if chord is not None: + chords.append(str(chord[0])) + + return chords + + +if __name__ == "__main__": + main()