Merge pull request #17 from ywatanabe1989/develop

v1.1.0

requirements.txt

@@ -28,3 +28,10 @@ wheel
 pybids
 scikit-image
 icecream
+Pyarrow
+ruamel.yaml
+pytest
+pytest-cov
+pytest-xdist
+pytest-env
+umap-learn

src/mngs/__init__.py

@@ -1,14 +1,14 @@
 #!/usr/bin/env python3
-# Time-stamp: "2024-02-03 16:06:22 (ywatanabe)"
+# Time-stamp: "2024-03-08 20:48:06 (ywatanabe)"
 
 from . import dsp
 from . import general
 from . import general as gen
-from . import gists, io, linalg, ml, nn, plt, resource, stats
+from . import gists, io, linalg, ml, nn, os, plt, resource, stats
 from .general.debug import *
 
-__copyright__ = "Copyright (C) 2021 Yusuke Watanabe"
-__version__ = "1.0.1"
+__copyright__ = "Copyright (C) 2024 Yusuke Watanabe"
+__version__ = "1.1.0"
 __license__ = "GPL3.0"
 __author__ = "ywatanabe1989"
 __author_email__ = "[email protected]"

src/mngs/dsp/.#fft.py

@@ -0,0 +1 @@
+ywatanabe@ywata-note-win.2006005053169024153

src/mngs/dsp/__init__.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 # from .wavelet import wavelet
+from ._psd import psd_torch
 from .demo_sig import demo_sig_np, demo_sig_torch
 from .feature_extractors import (
     FeatureExtractorTorch,

src/mngs/dsp/_psd.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Time-stamp: "2024-02-21 08:58:35 (ywatanabe)"
+
+import torch
+
+
+def psd_torch(signal, samp_rate):
+    """
+    # Example usage:
+    samp_rate = 480  # Sampling rate in Hz
+    signal = torch.randn(480)  # Example signal with 480 samples
+    freqs, psd = calculate_psd(signal, samp_rate)
+
+    # Plot the PSD (if you have matplotlib installed)
+    import matplotlib.pyplot as plt
+
+    plt.plot(freqs.numpy(), psd.numpy())
+    plt.xlabel('Frequency (Hz)')
+    plt.ylabel('Power/Frequency (V^2/Hz)')
+    plt.title('Power Spectral Density')
+    plt.show()
+    """
+    # Apply window function to the signal (e.g., Hanning window)
+    window = torch.hann_window(signal.size(-1))
+    signal = signal * window
+
+    # Perform the FFT
+    fft_output = torch.fft.fft(signal)
+
+    # Compute the power spectrum (magnitude squared of the FFT output)
+    power_spectrum = torch.abs(fft_output) ** 2
+
+    # Normalize the power spectrum to get the PSD
+    # Usually, we divide by the length of the signal and the sum of the window squared
+    # to get the power in terms of physical units (e.g., V^2/Hz)
+    psd = power_spectrum / (samp_rate * (window**2).sum())
+
+    # Since the signal is real, we only need the positive half of the FFT output
+    # The factor of 2 accounts for the energy in the negative frequencies that we're discarding
+    psd = psd[: len(psd) // 2] * 2
+
+    # Adjust the DC component (0 Hz) and Nyquist component (if applicable)
+    psd[0] /= 2
+    if len(psd) % 2 == 0:  # Even length, Nyquist freq component is included
+        psd[-1] /= 2
+
+    # Frequency axis
+    freqs = torch.fft.fftfreq(signal.size(-1), 1 / samp_rate)[: len(psd)]
+
+    return freqs, psd

src/mngs/dsp/demo_sig.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-# Time-stamp: "2024-01-20 13:58:43 (ywatanabe)"#!/usr/bin/env python3
+# Time-stamp: "2024-02-14 20:04:18 (ywatanabe)"#!/usr/bin/env python3
 
 import numpy as np
 import torch
@@ -53,14 +53,59 @@ def demo_sig_np(
     return data
 
 
+# def demo_sig_torch(
+#     batch_size=64, n_chs=19, samp_rate=1000, len_sec=10, freqs_hz=[2, 5, 10]
+# ):
+#     time = torch.arange(0, len_sec, 1 / samp_rate)
+#     sig = torch.vstack(
+#         [torch.sin(f * 2 * torch.pi * time) for f in freqs_hz]
+#     ).sum(dim=0)
+#     sig = sig.unsqueeze(0).unsqueeze(0).repeat(batch_size, n_chs, 1)
+#     return sig
+
+
 def demo_sig_torch(
-    batch_size=64, n_chs=19, samp_rate=1000, len_sec=10, freqs_hz=[2, 5, 10]
+    batch_size=64, n_chs=19, samp_rate=128, len_sec=1, freqs_hz=[2, 5, 10]
 ):
+    """
+    Generate a batch of demo signals with varying phase shifts and amplitudes,
+    and add some noise to simulate more realistic data.
+
+    Parameters:
+    - batch_size: Number of samples in the batch.
+    - n_chs: Number of channels per sample.
+    - samp_rate: Sampling rate in Hz.
+    - len_sec: Length of the signal in seconds.
+    - freqs_hz: List of frequencies in Hz to include in the signal.
+
+    Returns:
+    - sig: Tensor of shape (batch_size, n_chs, samp_rate * len_sec) containing the generated signals.
+    """
     time = torch.arange(0, len_sec, 1 / samp_rate)
-    sig = torch.vstack(
-        [torch.sin(f * 2 * torch.pi * time) for f in freqs_hz]
-    ).sum(dim=0)
-    sig = sig.unsqueeze(0).unsqueeze(0).repeat(batch_size, n_chs, 1)
+    # Initialize an empty signal tensor
+    sig = torch.zeros(batch_size, n_chs, len(time))
+
+    # Loop over each frequency and add a sinusoid with a random phase shift and amplitude
+    for f in freqs_hz:
+        # Generate a random phase shift for each sample in the batch
+        phase_shifts = torch.rand(batch_size, 1, 1) * 2 * torch.pi
+        # Generate a random amplitude for each sample in the batch
+        amplitudes = (
+            torch.rand(batch_size, 1, 1) * 0.5 + 0.5
+        )  # Amplitudes between 0.5 and 1.0
+        # Create the sinusoid with the phase shift and amplitude
+        sinusoid = amplitudes * torch.sin(
+            f * 2 * torch.pi * time + phase_shifts
+        )
+        # Repeat the sinusoid across channels
+        sinusoid = sinusoid.repeat(1, n_chs, 1)
+        # Add the sinusoid to the signal
+        sig += sinusoid
+
+    # Add some Gaussian noise to the signal
+    noise = torch.randn_like(sig) * 0.1  # Noise level can be adjusted
+    sig += noise
+
     return sig
 
 

src/mngs/general/__init__.py

@@ -39,10 +39,13 @@
     merge_dicts_wo_overlaps,
     partial_at,
     pop_keys,
+    print_block,
     search,
     squeeze_spaces,
     suppress_output,
     take_the_closest,
+    unique,
+    uq,
     wait_key,
 )
 from .pandas import (

src/mngs/general/_close.py

@@ -1,7 +1,9 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-# Time-stamp: "2024-01-29 15:44:06 (ywatanabe)"
+# Time-stamp: "2024-03-08 20:13:59 (ywatanabe)"
 
+import os
+import shutil
 from datetime import datetime, timedelta
 from glob import glob
 from time import sleep
@@ -23,27 +25,43 @@ def format_diff_time(diff_time):
     return diff_time_str
 
 
-def close(CONFIG, message=":)", show=True):
+def close(CONFIG, message=":)", notify=True, show=True):
     try:
-        end_time = datetime.now()
-        diff_time = format_diff_time(end_time - CONFIG["START_TIME"])
-        CONFIG["TimeSpent"] = diff_time
-        del CONFIG["START_TIME"]
+        CONFIG["END_TIME"] = datetime.now()
+        CONFIG["SPENT_TIME"] = format_diff_time(
+            CONFIG["END_TIME"] - CONFIG["START_TIME"]
+        )
+        if show:
+            print(f"\nEND TIME: {CONFIG['END_TIME']}")
+            print(f"\nSPENT TIME: {CONFIG['SPENT_TIME']}")
+
     except Exception as e:
         print(e)
 
     mngs.io.save(CONFIG, CONFIG["SDIR"] + "CONFIG.pkl")
+    mngs.io.save(CONFIG, CONFIG["SDIR"] + "CONFIG.yaml")
 
     try:
         if CONFIG.get("DEBUG", False):
             message = f"[DEBUG]\n" + message
         sleep(3)
-        mngs.gen.notify(
-            message=message,
-            ID=CONFIG["ID"],
-            log_paths=glob(CONFIG["SDIR"] + "*.log"),
-            show=show,
-        )
+        if notify:
+            mngs.gen.notify(
+                message=message,
+                ID=CONFIG["ID"],
+                log_paths=glob(CONFIG["SDIR"] + "*.log"),
+                show=show,
+            )
+    except Exception as e:
+        print(e)
+
+    # RUNNING to FINISHED
+    src_dir = CONFIG["SDIR"]
+    dest_dir = src_dir.replace("RUNNING", "FINISHED")
+    os.makedirs(dest_dir, exist_ok=True)
+    try:
+        os.rename(src_dir, dest_dir)
+        print(f"\nRenamed from: {src_dir} to {dest_dir}")
     except Exception as e:
         print(e)
 
@@ -53,8 +71,11 @@ def close(CONFIG, message=":)", show=True):
 
     import matplotlib.pyplot as plt
     import mngs
+    from icecream import ic
 
-    CONFIG, sys.stdout, sys.stderr, plt = mngs.gen.start(sys, plt, show=False)
+    CONFIG, sys.stdout, sys.stderr, plt, CC = mngs.gen.start(
+        sys, plt, show=False
+    )
 
     ic("aaa")
     ic("bbb")

src/mngs/general/_start.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-# Time-stamp: "2024-01-29 15:41:58 (ywatanabe)"
+# Time-stamp: "2024-03-08 19:55:48 (ywatanabe)"
 
 import inspect
 import os as _os
@@ -25,23 +25,24 @@ def start(
     tf=None,
     seed=42,
     # matplotlib
-    dpi=100,
-    save_dpi=300,
-    figsize=(16.2, 10),
-    figscale=1.0,
-    fontsize=16,
-    labelsize="same",
-    legendfontsize="xx-small",
-    tick_size="auto",
-    tick_width="auto",
-    hide_spines=False,
+    fig_size_mm=(160, 100),
+    fig_scale=1.0,
+    dpi_display=100,
+    dpi_save=300,
+    font_size_base=8,
+    font_size_title=8,
+    font_size_axis_label=8,
+    font_size_tick_label=7,
+    font_size_legend=6,
+    hide_top_right_spines=True,
+    alpha=0.75,
 ):
     """
     import sys
     import matplotlib.pyplot as plt
     import mngs
 
-    CONFIG, sys.stdout, sys.stderr, plt = mngs.gen.start(sys, plt)
+    CONFIG, sys.stdout, sys.stderr, plt, cc = mngs.gen.start(sys, plt)
 
     # YOUR CODE HERE
 
@@ -52,13 +53,15 @@ def start(
 
     # Debug mode check
     try:
-        is_debug = mngs.io.load("./config/is_debug.yaml").get("DEBUG", False)
+        IS_DEBUG = mngs.io.load("./config/IS_DEBUG.yaml").get(
+            "IS_DEBUG", False
+        )
     except Exception as e:
-        is_debug = False
+        IS_DEBUG = False
 
     # ID
-    ID = mngs.gen.gen_ID()
-    ID = ID if not is_debug else "[DEBUG] " + ID
+    ID = mngs.gen.gen_ID(N=4)
+    ID = ID if not IS_DEBUG else "DEBUG_" + ID
     print(f"\n{'#'*40}\n## {ID}\n{'#'*40}\n")
     sleep(1)
 
@@ -69,20 +72,22 @@ def start(
             __file__ = "/tmp/fake.py"
         spath = __file__
         _sdir, sfname, _ = mngs.general.split_fpath(spath)
-        sdir = _sdir + sfname + "/" + ID + "/"  # " # + "/log/"
+        sdir = (
+            _sdir + sfname + "/" + "RUNNING" + "/" + ID + "/"
+        )  # " # + "/log/"
     _os.makedirs(sdir, exist_ok=True)
 
     # CONFIGs
-    CONFIGS = mngs.io.load_configs(is_debug)
+    CONFIGS = mngs.io.load_configs(IS_DEBUG)
     CONFIGS["ID"] = ID
     CONFIGS["START_TIME"] = start_time
-    CONFIGS["SDIR"] = sdir
+    CONFIGS["SDIR"] = sdir.replace("/./", "/")
     if show:
         print(f"\n{'-'*40}\n")
         print(f"CONFIG:")
         for k, v in CONFIGS.items():
             print(f"\n{k}:\n{v}\n")
-            sleep(0.1)
+            # sleep(0.1)
         print(f"\n{'-'*40}\n")
 
     # Logging (tee)
@@ -109,22 +114,23 @@ def start(
 
     # Matplotlib configuration
     if plt is not None:
-        plt = mngs.plt.configure_mpl(
+        plt, cc = mngs.plt.configure_mpl(
             plt,
-            dpi=dpi,
-            save_dpi=save_dpi,
-            figsize=figsize,
-            figscale=figscale,
-            fontsize=fontsize,
-            labelsize=labelsize,
-            legendfontsize=legendfontsize,
-            tick_size=tick_size,
-            tick_width=tick_width,
-            hide_spines=hide_spines,
+            fig_size_mm=(160, 100),
+            fig_scale=fig_scale,
+            dpi_display=dpi_display,
+            dpi_save=dpi_save,
+            font_size_base=font_size_base,
+            font_size_title=font_size_title,
+            font_size_axis_label=font_size_axis_label,
+            font_size_tick_label=font_size_tick_label,
+            font_size_legend=font_size_legend,
+            hide_top_right_spines=hide_top_right_spines,
+            alpha=alpha,
             show=show,
         )
 
-    return CONFIGS, sys.stdout, sys.stderr, plt
+    return CONFIGS, sys.stdout, sys.stderr, plt, cc
 
 
 if __name__ == "__main__":
@@ -134,7 +140,7 @@ def start(
     import mngs
 
     # --------------------------------------------------------------------------- #
-    CONFIG, sys.stdout, sys.stderr, plt = mngs.gen.start(sys, plt)
+    CONFIG, sys.stdout, sys.stderr, plt, cc = mngs.gen.start(sys, plt)
     # --------------------------------------------------------------------------- #
 
     # YOUR CODE HERE