Update tutorial and scatter example

chahak13 · chahak13 · commit a15d21259459 · 2022-10-29T16:18:44.000-05:00
diff --git a/tutorials/introductory/animation_tutorial.py b/tutorials/introductory/animation_tutorial.py
@@ -20,14 +20,16 @@
 #
 # The process of animation can be thought about in 2 different ways:
 #
-# 1. Generate data for first frame and then modify this data for each frame to
-# create an animated plot.
-# 2. Generate a list (iterable) of plots (/images) that will each be a frame in
-# the animation.
+# - :class:`~matplotlib.animation.FuncAnimation`: Generate data for first
+# frame and then modify this data for each frame to create an animated plot.
 #
-# Both of these thought processes can be translated into matplotlib using the
-# :class:`~matplotlib.animation`'s :class:`~matplotlib.animation.FuncAnimation`
-# and :class:`~matplotlib.animation.ArtistAnimation` classes respectively.
+# - :class:`~matplotlib.animation.FuncAnimation`: Generate a list (iterable)
+# of artists that will draw in each frame in the animation.
+#
+# :class:`~matplotlib.animation.FuncAnimation` is more efficient in terms of
+# speed and memory as it draws an artist once and then modifies it. On the
+# other hand :class:`~matplotlib.animation.ArtistAnimation` is flexible as it
+# allows any iterable of artists to be animated in a sequence.
 #
 # :class:`~matplotlib.animation.FuncAnimation`
 # --------------------------------------------
@@ -43,13 +45,12 @@
 # *func* that modifies the data plotted on the figure. It uses the *frames*
 # parameter to determine the length of the animation. The *interval* parameter
 # is used to determine time in milliseconds between drawing of two frames.
-# (This is different than *fps* in `.animation.save` that we will
-# cover later). We will now look at some examples of using
+# We will now look at some examples of using
 # :class:`~matplotlib.animation.FuncAnimation` with different artists.
 
 ###############################################################################
 # Animating :class:`~matplotlib.lines.Line2D`
-# ---------------------------------------------------------
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
 # `.pyplot.plot` returns a :class:`~matplotlib.lines.Line2D` collection. The
 # data on this collection can be modified by using the
@@ -60,62 +61,62 @@
 rng = np.random.default_rng()
 
 xdata, ydata = [], []
-line, = ax.plot(xdata, ydata, c='b')
+(line,) = ax.plot(xdata, ydata, c="b")
 ax.grid()
 ax.set_ylim(-1, 1)
+ax.set_xlim(0, 10)
 
 
 def update(frame):
-    xdata.append(frame)
+    # .set_data resets all the data for the line, so we add the new point to
+    # the existing line data and set that again.
+    xdata.append(frame / 30)
     ydata.append(np.sin(2 * np.pi * frame / 30))
-    xmin, xmax = ax.get_xlim()
 
-    if frame >= xmax:
-        ax.set_xlim(xmin, 2*xmax)
-        ax.figure.canvas.draw()
     line.set_data(xdata, ydata)
-    return line,
+    return (line,)
 
-ani = animation.FuncAnimation(
-    fig=fig, func=update, interval=30
-)
+
+ani = animation.FuncAnimation(fig=fig, func=update, interval=30)
 plt.show()
 
 ###############################################################################
 # Animating :class:`~matplotlib.collections.PathCollection`
-# ---------------------------------------------------------
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
 # `.pyplot.scatter` returns a :class:`~matplotlib.collections.PathCollection`
 # that can similarly be modified by using the
 # `.collections.PathCollection.set_offsets` function.
 
 fig, ax = plt.subplots()
 rng = np.random.default_rng()
+t = np.linspace(-4, 4, 1000)
+a, b = 3, 2
+delta = np.pi / 2
 
-scat = ax.scatter(
-    rng.uniform(low=0, high=1, size=100),
-    rng.uniform(low=0, high=1, size=100),
-    c='b'
-)
+scat = ax.scatter(np.sin(a * t[0] + delta), np.sin(b * t[0]), c="b", s=2)
 ax.grid()
+ax.set_xlim(-1, 1)
+ax.set_ylim(-1, 1)
 
 
 def update(frame):
-    x = rng.uniform(low=0, high=1, size=100)
-    y = rng.uniform(low=0, high=1, size=100)
+    # .set_offsets also resets the entire data for the collection.
+    # Therefore, we create the entire data in each frame to draw
+    x = np.sin(a * t[:frame] + delta)
+    y = np.sin(b * t[:frame])
     data = np.stack([x, y]).T
     scat.set_offsets(data)
-    return scat,
+    return (scat,)
 
-ani = animation.FuncAnimation(
-    fig=fig, func=update, frames=240, interval=300
-)
+
+ani = animation.FuncAnimation(fig=fig, func=update, interval=30)
 plt.show()
 
 
 ###############################################################################
 # Animating :class:`~matplotlib.image.AxesImage`
-# ---------------------------------------------------
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
 # When we plot an image using `.pyplot.imshow`, it returns an
 # :class:`~matplotlib.image.AxesImage` object. The data in this object can also
@@ -130,11 +131,10 @@ def update(frame):
 def update(frame):
     data = rng.uniform(low=0, high=1, size=(10, 10))
     aximg.set_data(data)
-    return aximg,
+    return (aximg,)
 
-ani = animation.FuncAnimation(
-    fig=fig, func=update, frames=None, interval=200
-)
+
+ani = animation.FuncAnimation(fig=fig, func=update, frames=None, interval=200)
 plt.show()
 
 ###############################################################################
@@ -153,49 +153,56 @@ def update(frame):
 x_frames = rng.uniform(low=0, high=1, size=(100, 120))
 y_frames = rng.uniform(low=0, high=1, size=(100, 120))
 artists = [
-    [
-        ax.scatter(x_frames[:, i], y_frames[:, i], c="b")
-    ]
+    [ax.scatter(x_frames[:, i], y_frames[:, i], c="b")]
     for i in range(x_frames.shape[-1])
 ]
 
 ani = animation.ArtistAnimation(fig=fig, artists=artists, repeat_delay=1000)
 plt.show()
 
-###############################################################################
-# :class:`~matplotlib.animation.FuncAnimation` is more efficient in terms of
-# speed and memory as it draws an artist once and then modifies it. On the
-# other hand :class:`~matplotlib.animation.ArtistAnimation` is flexible as it
-# allows any iterable of artists to be animated in a sequence.
-
 ###############################################################################
 # Animation Writers
-# -----------------
+# =================
 #
 # Animation objects can be saved to disk using various multimedia writers
 # (ex: Pillow, *ffpmeg*, *imagemagick*). Not all video formats are supported
-# by all writers. A list of supported formats for each writer can be found at
-# the end of this tutorial. There are 4 major types of writers:
+# by all writers. There are 4 major types of writers:
 #
-# 1. :class:`~matplotlib.animation.PillowWriter` - Uses the Pillow library to
+# - :class:`~matplotlib.animation.PillowWriter` - Uses the Pillow library to
 # create the animation.
 #
-# 2. :class:`~matplotlib.animation.HTMLWriter` - Used to create JS-based
+# - :class:`~matplotlib.animation.HTMLWriter` - Used to create JS-based
 # animations.
 #
-# 3. Pipe-based writers - :class:`~matplotlib.animation.FFMpegWriter` and
+# - Pipe-based writers - :class:`~matplotlib.animation.FFMpegWriter` and
 # :class:`~matplotlib.animation.ImageMagickWriter` are pipe based writers.
 # These writers pipe each frame to the utility (*ffmpeg* / *imagemagick*) which
 # then stitches all of them together to create the animation.
 #
-# 4. File-based writers - :class:`~matplotlib.animation.FFMpegFileWriter` and
+# - File-based writers - :class:`~matplotlib.animation.FFMpegFileWriter` and
 # :class:`~matplotlib.animation.ImageMagickFileWriter` are examples of
 # file-based writers. These writers are slower than their standard writers but
 # are more useful for debugging as they save each frame in a file before
 # stitching them together into an animation.
 #
-# Below we see examples for how to use different writers with
-# `.animation.Animation.save`
+# ================================================  ===========================
+# Writer                                            Supported Formats
+# ================================================  ===========================
+# :class:`~matplotlib.animation.PillowWriter`       .gif, .apng
+# :class:`~matplotlib.animation.HTMLWriter`         .htm, .html, .png
+# :class:`~matplotlib.animation.FFMpegWriter`       All formats supported by
+#                                                   *ffmpeg*
+# :class:`~matplotlib.animation.ImageMagickWriter`  .gif
+# ================================================  ===========================
+#
+# To save animations using any of the writers, we can use the
+# `.animation.Animation.save` method. It takes the *filename* that we want to
+# save the animation as and the *writer*, which is either a string or a writer
+# object. It also takes an *fps* argument. This argument is different than the
+# *interval* argument that `~.animation.FuncAnimation` or
+# `~.animation.ArtistAnimation` uses. *fps* determines the frame rate that the
+# **saved** animation uses, whereas *interval* determines the frame rate that
+# the **displayed** animation uses.
 
 fig, ax = plt.subplots()
 ax.grid()
@@ -204,7 +211,7 @@ def update(frame):
 scat = ax.scatter(
     rng.uniform(low=0, high=1, size=100),
     rng.uniform(low=0, high=1, size=100),
-    c='b'
+    c="b"
 )
 
 
@@ -213,11 +220,10 @@ def update(frame):
     y = rng.uniform(low=0, high=1, size=100)
     data = np.stack([x, y]).T
     scat.set_offsets(data)
-    return scat,
+    return (scat,)
 
-ani = animation.FuncAnimation(
-    fig=fig, func=update, frames=240, interval=200
-)
+
+ani = animation.FuncAnimation(fig=fig, func=update, frames=240, interval=200)
 # ani.save(filename="/tmp/pillow_example.gif", writer="pillow")
 # ani.save(filename="/tmp/pillow_example.apng", writer="pillow")
 
@@ -233,15 +239,3 @@ def update(frame):
 
 # Imagemagick
 # ani.save(filename="/tmp/imagemagick_example.gif", writer="imagemagick")
-
-###############################################################################
-#
-# ================================================  ===========================
-# Writer                                            Supported Formats
-# ================================================  ===========================
-# :class:`~matplotlib.animation.PillowWriter`       .gif, .apng
-# :class:`~matplotlib.animation.HTMLWriter`         .htm, .html, .png
-# :class:`~matplotlib.animation.FFMpegWriter`       All formats supported by
-#                                                   *ffmpeg*
-# :class:`~matplotlib.animation.ImageMagickWriter`  .gif
-# ================================================  ===========================
