차이

문서의 선택한 두 판 사이의 차이를 보여줍니다.

--- matplotlib [2018/12/06 02:48] – [한글 출력] rex8312
+++ matplotlib [2024/03/23 02:38] (현재) – 바깥 편집 127.0.0.1
@@ 줄 3: / 줄 3: @@
 ===== Animation =====
-  - http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/
+  * http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/
+  * [[https://towardsdatascience.com/animate-your-graphs-in-python-in-4-easy-steps-243dccad9a7|Animate your Graphs in Python in 4 Easy Steps! (GIF 파일 만들기)]]
@@ 줄 48: / 줄 49: @@
   - https://matplotlib.org/examples/pylab_examples/custom_ticker1.html
+===== 예제 =====
+<code python>
+plt.rcParams.update({'font.family': "NanumMyeongjo"})
+# or "Noto Sans CJK KR", "Noto Sans Mono CJK KR"
+plt.rcParams.update({'font.size': 22})
+plt.rcParams.update({'xtick.labelsize': 22, 'ytick.labelsize': 22})
+fig, ax = plt.subplots(figsize=(8, 4), clear=True)
+ax.set_xlabel('Generation')
+ax.set_ylabel(f'{args.tag}')
+for cond in dfs:
+  line, = ax.plot(dfs[cond].index, dfs[cond]['50%'], '-', label=cond)
+  ax.fill_between(dfs[cond].index, dfs[cond]['25%'], dfs[cond]['75%'], color=line.get_color(), alpha=0.3)
+  ax.legend()
+  plt.show()
+</code>
+===== Seaborn =====
+  * https://towardsdatascience.com/everything-you-need-to-know-about-scatter-plots-for-data-visualisation-924144c0bc5
+===== imshow =====
+<code python>
+fig, (ax1, ax2) = plt.subplots(1, 2)
+cs = list()
+min_diff = 0.5
+xs = np.arange(0, 10).reshape((-1, 1))
+ax1.imshow(xs, cmap='gray')
+for x in xs.reshape(-1):
+    b = x / 10
+    w = 1 - b
+    if b < 0.5:
+        c = (1 - min_diff) * w + min_diff
+    else:
+        c = (1 - min_diff) * w
+    c = np.clip(c, 0, 1)
+    cs.append(c)
+    ax1.text(0,
+             x,
+             f'{x}',
+             color=(c, c, c),
+             horizontalalignment='center',
+             verticalalignment='center')
+ax2.scatter(xs, cs)
+plt.show()
+</code>
+===== Radar chart =====
+  * https://matplotlib.org/3.1.1/gallery/specialty_plots/radar_chart.html#sphx-glr-gallery-specialty-plots-radar-chart-py
+<code python>
+kvs = dict(a=1.0, b=0.2, c=0.4, d=0.5, e=0.7)
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='polar')
+ax.set_title(f'Radar chart')
+ax.set_ylim([0, 1.0])
+theta = np.linspace(0.0, 2 * np.pi, len(kvs) + 1, endpoint=True)
+ax.set_xticks(theta)
+ax.set_xticklabels([f'{k}' for k in kvs])
+vs = np.hstack([list(kvs.values()), [list(kvs.values())[0]]])
+ax.plot(theta, vs)
+ax.fill_between(theta, 0, vs)
+plt.show()
+plt.close()
+</code>
+===== Subplot =====
+  * https://matplotlib.org/3.1.1/gallery/recipes/create_subplots.html#sphx-glr-gallery-recipes-create-subplots-py
+===== Embedded plot =====
+<code python>
+fig, ax = plt.subplots(1, 1, figsize=(12.8, 9.6))
+data = np.array([
+  np.random.random(2),
+  np.random.random(2),
+  np.random.random(2),
+])
+subdata = np.array([
+  np.random.random(3),
+  np.random.random(3),
+  np.random.random(3),
+])
+scatter = ax.scatter(data[:, 0], data[:, 1], cmap='jet')
+ax.set_xlim([-0.2, 1.2])
+ax.set_ylim([-0.2, 1.2])
+for idx, (x, y) in enumerate(data):
+    subdata_ = subdata[idx]
+    box = ax.get_position()
+    width = box.width * 0.2
+    height = box.height * 0.2
+    xmin, xmax, ymin, ymax = ax.axis()
+    nx = (x - xmin) / (xmax - xmin) - width / 2
+    ny = (y - ymin) / (ymax - ymin) - height / 2
+    inax_position  = ax.transAxes.transform([nx, ny])
+    transFigure = fig.transFigure.inverted()
+    infig_position = transFigure.transform(inax_position)
+    x = infig_position[0]
+    y = infig_position[1]
+    subax = fig.add_axes([x, y, width, height])
+    subax.get_xaxis().set_visible(False)
+    subax.get_yaxis().set_visible(False)
+    subax.plot(subdata_)
+plt.show()
+</code>
+===== Neural Net Weights/Activation =====
+  * [[https://matplotlib.org/3.1.1/gallery/specialty_plots/hinton_demo.html#sphx-glr-gallery-specialty-plots-hinton-demo-py|hinton diagram]]