Merge branch 'update-0.1.2' into 74_reduce_poetry_redundancy

miv/mea/__init__.py

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+from miv.mea.grid import *

miv/mea/grid.py

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+__all__ = ["GridMEA"]
+
+from typing import Tuple
+
+import matplotlib
+import numpy as np
+
+from miv.mea.protocol import MEAGeometryProtocol
+
+
+class GridMEA:
+    def __init__(self, nrow: int, ncol: int, xid: np.ndarray, yid: np.ndarray):
+        self.nrow = nrow
+        self.ncol = ncol
+        self.xid = xid
+        self.yid = yid
+
+    def get_closest_node(self, x: float, y: float) -> int:
+        """Given xy coordinate, return closest node idx"""
+        raise NotImplementedError
+
+    def get_xy(self, idx: int) -> Tuple[float, float]:
+        """Given node index, return xy coordinate"""
+        raise NotImplementedError
+
+    def view(self) -> matplotlib.pyplot.Figure:
+        """Simplified view of MEA orientation"""
+        raise NotImplementedError
+
+    def save(self, path: str) -> None:
+        """Export MEA information"""
+        raise NotImplementedError
+
+    def load(self, path: str) -> None:
+        """Import MEA from external source"""
+        raise NotImplementedError

miv/mea/protocol.py

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+__all__ = ["MEAGeometryProtocol"]
+
+import typing
+from typing import Any, Iterable, Protocol, Tuple
+
+import matplotlib
+
+
+class MEAGeometryProtocol(Protocol):
+    def get_closest_node(self, x: float, y: float) -> int:
+        """Given xy coordinate, return closest node idx"""
+        ...
+
+    def get_xy(self, idx: int) -> Tuple[float, float]:
+        """Given node index, return xy coordinate"""
+        ...
+
+    def save(self, path: str) -> None:
+        ...
+
+    def load(self, path: str) -> None:
+        ...
+
+    def view(self) -> matplotlib.pyplot.Figure:
+        """Simplified view of MEA orientation"""
+        ...

miv/visualization/raw_signal.py

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+__doc__ = """Multi-channel signal plotting for MEA channels"""
+__all__ = ["multi_channel_signal_plot"]
+
+from typing import Any, List, Optional
+
+import matplotlib
+import matplotlib.animation as manimation
+import numpy as np
+from matplotlib import pyplot as plt
+from tqdm import tqdm
+
+from miv.mea.protocol import MEAGeometryProtocol
+from miv.typing import SignalType
+
+matplotlib.use("Agg")  # Must be before importing matplotlib.pyplot or pylab!
+
+
+def multi_channel_signal_plot(
+    signal_list: SignalType,
+    mea_geometry: MEAGeometryProtocol,
+    start_step: int,
+    end_step: int,
+    n_steps_in_window: int,
+    rendering_fps: int,
+    video_name: str,
+    **kwargs,
+):
+    """
+    Plotting recorded neuron signals from each channel of MEA. Subplots for each channel are aligned with position of
+    electrical probes on MEA.
+
+    Parameters
+    ----------
+    signal_list : list
+        Contains list  2D numpy.ndarray
+        List of channel recordings in time.
+    mea_geometry : list
+        Contains list of tuples.
+        Each tuple contains, channel id, channel y position id and channel x position id on MEA grid.
+    start_step : int
+        Start step for plotting.
+    end_step : int
+        End step for plotting.
+    n_steps_in_window : int
+        Window length for plotting channel signal.
+    rendering_fps : int
+        Video frame rate
+    video_name : str
+        Video name
+    kwargs
+
+    Returns
+    -------
+
+    """
+    total_steps = end_step - start_step
+
+    channel_id = []
+    xid = []
+    yid = []
+    for i, channel_info in enumerate(mea_geometry):
+        channel_id.append(channel_info[0])
+        yid.append(channel_info[1])
+        xid.append(channel_info[2])
+
+    n_channels = len(channel_id)
+    max_subplots_in_x = kwargs.get("n_subplot_in_x", 8)
+    max_subplots_in_y = kwargs.get("n_subplot_in_y", 8)
+    dpi = kwargs.get("dpi", 100)
+
+    FFMpegWriter = manimation.writers["ffmpeg"]
+    metadata = dict(title="Movie Test", artist="Matplotlib", comment="Movie support!")
+    writer = FFMpegWriter(fps=rendering_fps, metadata=metadata)
+    fig = plt.figure(2, figsize=(20, 12), frameon=True, dpi=dpi)
+    plt.rcParams.update({"font.size": 10})
+    axs = []
+
+    for i, channel_info in enumerate(mea_geometry):
+        channel_id = channel_info[0]
+        yid = channel_info[1]
+        xid = channel_info[2]
+        axs.append(plt.subplot2grid((max_subplots_in_y, max_subplots_in_x), (yid, xid)))
+
+    signal_line_list = [None for _ in range(n_channels)]
+
+    for signal_id in range(n_channels):
+        signal = signal_list[
+            signal_id
+        ]  # Signal list contains 2D np.arrays for each channel
+
+        x_value = signal[start_step : start_step + n_steps_in_window, 0]
+        y_value = signal[start_step : start_step + n_steps_in_window, 1]
+        signal_line_list[signal_id] = axs[signal_id].plot(
+            x_value, y_value, "-", linewidth=3
+        )[0]
+
+        y_min = np.min(signal[start_step:end_step, 1])
+        y_max = np.max(signal[start_step:end_step, 1])
+
+        axs[signal_id].set_ylim(y_min, y_max)
+
+    plt.tight_layout()
+    fig.align_ylabels()
+
+    with writer.saving(fig, video_name, dpi):
+        for step in tqdm(range(1, total_steps - 1, int(1))):
+            current_step = start_step + step
+            for signal_id in range(n_channels):
+                signal = signal_list[signal_id]
+                x_value = signal[current_step : current_step + n_steps_in_window, 0]
+                y_value = signal[current_step : current_step + n_steps_in_window, 1]
+
+                signal_line_list[signal_id].set_xdata(x_value)
+                signal_line_list[signal_id].set_ydata(y_value)
+
+                # X limits should move together with window
+                axs[signal_id].set_xlim(x_value[0], x_value[-1])
+
+            writer.grab_frame()
+
+    # Be a good boy and close figures
+    # https://stackoverflow.com/a/37451036
+    # plt.close(fig) alone does not suffice
+    # See https://github.com/matplotlib/matplotlib/issues/8560/
+    plt.close(plt.gcf())