{"id":115070,"date":"2026-10-11T04:16:57","date_gmt":"2026-10-10T20:16:57","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/115070.html"},"modified":"2026-10-11T04:16:57","modified_gmt":"2026-10-10T20:16:57","slug":"%e7%a7%91%e7%a0%94%e7%bb%98%e5%9b%be%e6%95%88%e7%8e%87%e9%9d%a9%e5%91%bd%ef%bc%9a%e4%bd%bf%e7%94%a8%e5%a4%a7%e6%a8%a1%e5%9e%8b%e4%b8%80%e9%94%ae%e7%94%9f%e6%88%90-matplotlib-%e9%a1%b6%e4%bc%9a","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/115070.html","title":{"rendered":"\u79d1\u7814\u7ed8\u56fe\u6548\u7387\u9769\u547d\uff1a\u4f7f\u7528\u5927\u6a21\u578b\u4e00\u952e\u751f\u6210 Matplotlib \u9876\u4f1a\u7ea7\u53cc\u8f74\u6298\u7ebf\u56fe\u4e0e\u70ed\u529b\u56fe"},"content":{"rendered":"

\"\u5c01\u9762\u4fe1\u606f\u56fe\"<\/p>\n

\u6bcf\u5e74\u9876\u4f1a\u622a\u7a3f\u524d\u7684\u6700\u540e\u4e24\u5468，\u5b9e\u9a8c\u5ba4\u91cc\u7684\u7a7a\u6c14\u603b\u662f\u683c\u5916\u7d27\u5f20\u3002\u8d70\u5eca\u5c3d\u5934\u7684\u6253\u5370\u673a\u4e0d\u77e5\u75b2\u5026\u5730\u5410\u7740\u53cc\u680f\u6392\u7248\u7684\u8349\u7a3f，\u8367\u5149\u7b14\u5212\u8fc7\u6700\u591a\u7684\u5730\u65b9\u4e0d\u662f\u7b97\u6cd5\u516c\u5f0f，\u800c\u662f\u5b9e\u9a8c\u7ae0\u8282\u91cc\u7684\u56fe\u8868\u3002<\/p>\n

\u5f88\u591a\u8bfb\u7814\u7684\u540c\u5b66\u90fd\u6709\u8fc7\u7c7b\u4f3c\u7684\u6298\u78e8\u7ecf\u5386：\u597d\u4e0d\u5bb9\u6613\u628a\u5206\u5e03\u5f0f\u8c03\u5ea6\u7684\u6027\u80fd\u6570\u636e\u8dd1\u51fa\u6765\u4e86，\u6253\u5f00 Python \u8c03 Matplotlib \u7ed8\u56fe，\u7ed3\u679c\u6298\u817e\u4e86\u6574\u6574\u4e00\u4e2a\u4e0b\u5348\u2014\u2014\u53cc Y \u8f74\u7684\u523b\u5ea6\u7f51\u683c\u7ebf\u5728\u80cc\u666f\u91cc\u80e1\u4e71\u4ea4\u9519\u6253\u67b6；\u5bfc\u51fa\u7684 PDF \u77e2\u91cf\u56fe\u653e\u8fdb Overleaf \u7f16\u8bd1\u65f6\u75af\u72c2\u62a5\u5b57\u4f53\u672a\u5d4c\u5165\u9519\u8bef（Font type 3）；\u6216\u8005\u9ed8\u8ba4\u7684\u56fe\u4f8b\u76f4\u63a5\u628a\u5173\u952e\u62d0\u70b9\u6321\u5f97\u4e25\u4e25\u5b9e\u5b9e，\u7ea2\u7ea2\u7eff\u7eff\u7684\u539f\u751f\u914d\u8272\u900f\u51fa\u4e00\u80a1\u6d53\u90c1\u7684\u8349\u53f0\u73ed\u5b50\u6c14\u606f\u3002<\/p>\n

\u5b9e\u9645\u4e0a，\u5b66\u672f\u8bba\u6587（\u65e0\u8bba\u662f IEEE\/ACM \u4f1a\u8bae\u8fd8\u662f CCF-A \u7c7b\u671f\u520a）\u5bf9\u914d\u56fe\u6709\u6781\u4e25\u82db\u7684\u5de5\u4e1a\u7ea7\u6807\u51c6：\u5355\u680f\u5bbd\u5ea6\u5fc5\u987b\u7cbe\u786e\u5728 3.3 \u81f3 3.5 \u82f1\u5bf8\u4e4b\u95f4，\u5b57\u4f53\u8981\u4e0e\u6b63\u6587\u4e25\u683c\u540c\u6e90（\u5982 Times New Roman），\u7070\u9636\u6253\u5370\u65f6\u5fc5\u987b\u4f9d\u7136\u5177\u5907\u9ad8\u8fa8\u8bc6\u5ea6\u3002\u4ee5\u5f80\u8fd9\u4e9b\u5fae\u8c03\u9700\u8981\u7ffb\u70c2 StackOverflow，\u800c\u73b0\u5728\u901a\u8fc7\u7cbe\u51c6\u7684\u7ed3\u6784\u5316\u7ea6\u675f Prompt，\u6211\u4eec\u53ef\u4ee5\u8ba9\u5927\u6a21\u578b\u5728\u51e0\u79d2\u5185\u76f4\u63a5\u751f\u6210\u5177\u5907\u9876\u4f1a\u5ba1\u7f8e\u7684 Matplotlib \u7ed8\u56fe\u811a\u672c\u3002<\/p>\n

\n

\u4e00\u3001\u9876\u4f1a\u914d\u56fe\u7684\u6838\u5fc3\u5de5\u4e1a\u6807\u51c6<\/h3>\n

\u5728\u8c03\u6559\u5927\u6a21\u578b\u5199\u7ed8\u56fe\u811a\u672c\u524d，\u5fc5\u987b\u628a\u9876\u4f1a\u5ba1\u7f8e\u7684\u9690\u6027\u89c4\u5219\u663e\u5f0f\u5316\u3002\u4e00\u4efd\u5408\u683c\u7684\u9876\u4f1a\u56fe\u8868\u5fc5\u987b\u6ee1\u8db3\u4ee5\u4e0b\u56db\u6761\u94c1\u5f8b：<\/p>\n

  • \u5c3a\u5bf8\u4e0e\u5b57\u4f53\u7269\u7406\u6bd4\u4f8b\u5bf9\u9f50：\u8bba\u6587\u5355\u680f\u5bbd\u5ea6\u56fa\u5b9a\u4e3a 3.3-3.5 \u82f1\u5bf8（\u7ea6 8.5cm）\u3002\u5982\u679c\u753b\u5e03\u8bbe\u5f97\u592a\u5927（\u5982\u9ed8\u8ba4\u7684 $10 \\\\times 8$ \u82f1\u5bf8），\u7f29\u653e\u5230\u8bba\u6587\u5355\u680f\u540e，10pt \u7684\u5b57\u4f53\u4f1a\u7f29\u5c0f\u5230\u8089\u773c\u4e0d\u53ef\u89c1\u7684 3pt\u3002\u56e0\u6b64\u5fc5\u987b\u5728\u539f\u59cb\u5c3a\u5bf8（figsize）\u8bbe\u4e3a $(3.5, 2.6)$ \u82f1\u5bf8\u7684\u524d\u63d0\u4e0b\u8bbe\u5b9a\u5b57\u4f53，\u4fdd\u6301\u5b57\u53f7\u4e0e\u6b63\u6587\u534f\u8c03（\u6807\u9898 9-10pt，\u523b\u5ea6 8pt）\u3002<\/li>\n
  • \u77e2\u91cf\u56fe\u5b57\u4f53\u5d4c\u5165（TrueType\/Type 42）：IEEE\/ACM \u63d0\u4ea4\u7cfb\u7edf\u4f1a\u5f3a\u5236\u626b\u63cf PDF \u5b57\u4f53\u3002\u5982\u679c Matplotlib \u9ed8\u8ba4\u4f7f\u7528\u4e86 Type 3 \u4f4d\u56fe\u5b57\u4f53，\u4f1a\u88ab\u7cfb\u7edf\u76f4\u63a5\u9000\u7a3f\u3002\u5fc5\u987b\u663e\u5f0f\u914d\u7f6e pdf.fonttype = 42\u3002<\/li>\n
  • \u53cc\u8f74\u56fe\u7684\u7f51\u683c\u7ebf\u7edd\u4e0d\u6253\u67b6：\u5de6\u8f74\u5c55\u793a\u541e\u5410\u91cf（Throughput），\u53f3\u8f74\u5c55\u793a P99 \u5ef6\u8fdf（Latency）\u3002\u5de6\u8f74\u6709 5 \u4e2a\u523b\u5ea6，\u53f3\u8f74\u5982\u679c\u81ea\u53d1\u51fa\u73b0 7 \u4e2a\u523b\u5ea6，\u80cc\u666f\u7f51\u683c\u7ebf\u5c31\u4f1a\u91cd\u53e0\u6210\u6805\u680f\u3002\u5fc5\u987b\u901a\u8fc7\u7ebf\u6027\u6620\u5c04\u5f3a\u5236\u53cc\u8f74\u7684\u4e3b\u523b\u5ea6（Tick Locators）\u6570\u91cf\u5b8c\u5168\u76f8\u7b49，\u4e14\u53ea\u4fdd\u7559\u5355\u4fa7\u7f51\u683c\u3002<\/li>\n
  • \u8272\u76f2\u53cb\u597d\u4e0e\u660e\u5ea6\u5dee：\u4e25\u7981\u76f4\u63a5\u4f7f\u7528\u7eaf\u7ea2（#FF0000）\u548c\u7eaf\u7eff（#00FF00）\u3002\u91c7\u7528\u5982 Paul Tol \u6216 Seaborn-deep \u8fd9\u7c7b\u7ecf\u8fc7\u611f\u77e5\u5747\u5300\u5ea6\u6821\u51c6\u7684\u8c03\u8272\u76d8，\u786e\u4fdd\u9ed1\u767d\u6253\u5370\u65f6\u4ec5\u9760\u7070\u9636\u548c\u70b9\u578b（Marker）\u5c31\u80fd\u533a\u5206\u66f2\u7ebf\u3002<\/li>\n
    \n

    \u4e8c\u3001\u573a\u666f\u4e00：\u53cc\u8f74\u6298\u7ebf\u56fe（\u541e\u5410\u91cf vs P99 \u5ef6\u8fdf）<\/h3>\n

    \u5728\u5206\u5e03\u5f0f\u7cfb\u7edf\u3001\u6570\u636e\u5e93\u4e0e\u7f51\u7edc\u9876\u4f1a\u4e2d，\u8bc4\u4ef7\u7cfb\u7edf\u8d1f\u8f7d\u6700\u7ecf\u5178\u7684\u5448\u73b0\u5f62\u5f0f\u5c31\u662f\u541e\u5410\u91cf\u4e0e\u5ef6\u8fdf\u7684\u53cc\u8f74\u66f2\u7ebf，\u5f80\u5f80\u8fd8\u4f34\u968f\u7f6e\u4fe1\u533a\u95f4（Confidence Interval）\u3002<\/p>\n

    1. \u751f\u4ea7\u7ea7 Python \u7ed8\u56fe\u4ee3\u7801<\/h4>\n

    import matplotlib.pyplot as plt
    \nimport numpy as np<\/p>\n

    # 1. \u5168\u5c40\u6837\u5f0f\u6ce8\u5165：\u4e25\u683c\u5bf9\u9f50 ACM\/IEEE \u89c4\u8303
    \nplt.rcParams.update({
    \n "font.family": "serif",
    \n "font.serif": ["Times New Roman", "DejaVu Serif"],
    \n "font.size": 9,
    \n "axes.labelsize": 9.5,
    \n "axes.titlesize": 10,
    \n "xtick.labelsize": 8,
    \n "ytick.labelsize": 8,
    \n "legend.fontsize": 7.5,
    \n "figure.titlesize": 10,
    \n "pdf.fonttype": 42, # \u5f3a\u5236\u8f93\u51fa Type 42 \u77e2\u91cf\u5b57\u4f53
    \n "ps.fonttype": 42,
    \n "axes.linewidth": 0.8,
    \n "grid.linewidth": 0.5,
    \n "grid.alpha": 0.4,
    \n "grid.linestyle": "–"
    \n})<\/p>\n

    # 2. \u6a21\u62df\u771f\u5b9e\u538b\u6d4b\u5b9e\u9a8c\u6570\u636e
    \nconcurrency = np.array([10, 50, 100, 200, 400, 800, 1600])
    \n# \u541e\u5410\u91cf (kops) \u4e0e\u6807\u51c6\u5dee
    \nthroughput_mean = np.array([12.5, 48.2, 92.4, 165.0, 240.5, 275.2, 280.1])
    \nthroughput_std = np.array([0.8, 1.5, 3.2, 5.1, 7.8, 9.2, 10.5])
    \n# P99 \u5ef6\u8fdf (ms) \u4e0e\u6807\u51c6\u5dee
    \np99_latency_mean = np.array([1.2, 1.8, 2.5, 4.2, 8.9, 24.5, 68.2])
    \np99_latency_std = np.array([0.1, 0.2, 0.3, 0.6, 1.2, 3.8, 8.5])<\/p>\n

    # 3. \u521b\u5efa\u7cbe\u786e\u5355\u680f\u5c3a\u5bf8\u7684\u753b\u5e03
    \nfig, ax1 = plt.subplots(figsize=(3.5, 2.5), dpi=300)<\/p>\n

    # \u5b66\u672f\u51b7\u8272\u7cfb：\u84dd（\u541e\u5410）\u4e0e\u9152\u7ea2（\u5ef6\u8fdf）
    \ncolor_tp = "#1f77b4"
    \ncolor_lat = "#d62728"<\/p>\n

    # \u7ed8\u5236\u5de6\u8f74：\u541e\u5410\u91cf
    \nline1 = ax1.plot(concurrency, throughput_mean, color=color_tp, marker='o',
    \n markersize=4, linewidth=1.2, label='Throughput')
    \nax1.fill_between(concurrency, throughput_mean – throughput_std,
    \n throughput_mean + throughput_std, color=color_tp, alpha=0.15)
    \nax1.set_xlabel("Client Concurrency", fontweight='medium')
    \nax1.set_ylabel("Throughput (kops)", color=color_tp, fontweight='medium')
    \nax1.tick_params(axis='y', labelcolor=color_tp, color=color_tp)
    \nax1.set_xscale('log')
    \nax1.set_xticks(concurrency)
    \nax1.set_xticklabels([str(c) for c in concurrency])
    \nax1.grid(True, which='both', axis='x', linestyle=':', alpha=0.3)
    \nax1.grid(True, which='major', axis='y', linestyle='–', alpha=0.4)<\/p>\n

    # 4. \u884d\u751f\u53f3\u8f74：P99 \u5ef6\u8fdf
    \nax2 = ax1.twinx()
    \nline2 = ax2.plot(concurrency, p99_latency_mean, color=color_lat, marker='s',
    \n markersize=4, linewidth=1.2, linestyle='-.', label='P99 Latency')
    \nax2.fill_between(concurrency, p99_latency_mean – p99_latency_std,
    \n p99_latency_mean + p99_latency_std, color=color_lat, alpha=0.15)
    \nax2.set_ylabel("P99 Latency (ms)", color=color_lat, fontweight='medium')
    \nax2.tick_params(axis='y', labelcolor=color_lat, color=color_lat)
    \nax2.set_yscale('log')
    \n# \u4e25\u7981\u5f00\u542f\u53f3\u8f74\u7684 Y \u7f51\u683c，\u9632\u6b62\u4e0e\u5de6\u8f74\u6253\u67b6
    \nax2.grid(False)<\/p>\n

    # 5. \u53cc\u8f74\u56fe\u4f8b\u4f18\u96c5\u5408\u5e76\u81f3\u540c\u4e00\u6846\u5185
    \nlines = line1 + line2
    \nlabels = [l.get_label() for l in lines]
    \nax1.legend(lines, labels, loc='upper left', frameon=True,
    \n facecolor='white', framealpha=0.9, edgecolor='#cccccc')<\/p>\n

    plt.tight_layout(pad=0.2)
    \n# plt.savefig("fig_throughput_latency.pdf", format="pdf", bbox_inches="tight")<\/p>\n

    \u8fd9\u6bb5\u4ee3\u7801\u901a\u8fc7\u8bbe\u5b9a ax1.twinx() \u5e76\u5173\u95ed ax2.grid(False)，\u5f7b\u5e95\u6839\u6cbb\u4e86\u53cc\u8f74\u7f51\u683c\u7ebf\u4ea4\u53c9\u95ea\u70c1\u7684\u95ee\u9898；\u540c\u65f6\u5229\u7528 ax1.legend(lines, labels) \u5c06\u4e24\u6839\u8f74\u7684\u56fe\u4f8b\u5408\u5e76\u5230\u4e00\u4e2a\u534a\u900f\u660e\u7684\u5706\u89d2\u77e9\u5f62\u5185，\u7d27\u51d1\u4e14\u4e0d\u906e\u6321\u5173\u952e\u7684\u6570\u636e\u53d1\u6563\u62d0\u70b9\u3002<\/p>\n

    \n

    \u4e09\u3001\u573a\u666f\u4e8c：\u9876\u4f1a\u7ea7\u7279\u5f81\u5173\u8054\u4e0e\u6ce8\u610f\u529b\u6743\u91cd\u70ed\u529b\u56fe<\/h3>\n

    \u70ed\u529b\u56fe\u5728 AI \u9876\u4f1a（\u5982 Transformer \u6ce8\u610f\u529b\u77e9\u9635\u3001\u6df7\u6dc6\u77e9\u9635）\u53ca\u7cfb\u7edf\u74f6\u9888\u5206\u6790（\u5982\u4e0d\u540c\u5e76\u53d1\u4e0e\u6570\u636e\u5206\u7247\u4e0b\u7684\u7f13\u5b58\u547d\u4e2d\u7387）\u4e2d\u6781\u5176\u9ad8\u9891\u3002\u75db\u70b9\u5728\u4e8e：\u5982\u4f55\u6839\u636e\u80cc\u666f\u8272\u6df1\u6d45，\u81ea\u52a8\u53cd\u8f6c\u5355\u5143\u683c\u5185\u6587\u5b57\u7684\u989c\u8272（\u6df1\u8272\u80cc\u666f\u5199\u767d\u5b57，\u6d45\u8272\u80cc\u666f\u5199\u9ed1\u5b57），\u4ee5\u53ca\u4fdd\u8bc1\u8272\u6807（Colorbar）\u4e0e\u4e3b\u56fe\u7b49\u9ad8\u3002<\/p>\n

    1. \u751f\u4ea7\u7ea7\u70ed\u529b\u56fe\u5b9e\u73b0\u4ee3\u7801<\/h4>\n

    import matplotlib.pyplot as plt
    \nimport numpy as np<\/p>\n

    # \u6570\u636e\u51c6\u5907：\u6a21\u62df 6×6 \u6a21\u5757\u95f4\u7684\u4ea4\u53c9\u5e72\u6270\u5ef6\u8fdf\u52a0\u6743\u77e9\u9635
    \nlabels = [f"M_{i}" for i in range(1, 7)]
    \ndata = np.array([
    \n [0.92, 0.12, 0.08, 0.05, 0.15, 0.02],
    \n [0.10, 0.88, 0.25, 0.03, 0.08, 0.04],
    \n [0.05, 0.18, 0.95, 0.12, 0.06, 0.01],
    \n [0.08, 0.04, 0.14, 0.89, 0.21, 0.05],
    \n [0.12, 0.09, 0.07, 0.18, 0.91, 0.11],
    \n [0.01, 0.03, 0.02, 0.06, 0.10, 0.97]
    \n])<\/p>\n

    fig, ax = plt.subplots(figsize=(3.4, 2.8), dpi=300)<\/p>\n

    # \u4f7f\u7528\u611f\u77e5\u5747\u5300\u7684\u9ad8\u8d28\u611f\u5355\u8272\u6e10\u53d8\u8272\u677f (\u5982 Blues \u6216 Viridis)
    \ncmap = plt.cm.Blues
    \nim = ax.imshow(data, cmap=cmap, vmin=0, vmax=1.0)<\/p>\n

    # \u8f74\u523b\u5ea6\u914d\u7f6e
    \nax.set_xticks(np.arange(len(labels)))
    \nax.set_yticks(np.arange(len(labels)))
    \nax.set_xticklabels(labels, fontsize=8)
    \nax.set_yticklabels(labels, fontsize=8)
    \nax.tick_params(top=False, bottom=True, labeltop=False, labelbottom=True)<\/p>\n

    # \u52a8\u6001\u6587\u672c\u53cd\u8272\u903b\u8f91：\u8d85\u8fc7\u4eae\u5ea6\u9608\u503c\u65f6，\u81ea\u52a8\u7531\u9ed1\u5b57\u5207\u6362\u4e3a\u767d\u5b57
    \nthreshold = (data.max() + data.min()) \/ 2.0
    \nfor i in range(len(labels)):
    \n for j in range(len(labels)):
    \n val = data[i, j]
    \n text_color = "white" if val > threshold else "black"
    \n ax.text(j, i, f"{val:.2f}", ha="center", va="center",
    \n color=text_color, fontsize=6.8, fontfamily='serif')<\/p>\n

    # \u7cbe\u7ec6\u5316\u8272\u6807 (Colorbar)：\u4fdd\u8bc1\u8272\u6807\u9ad8\u5ea6\u4e0e\u56fe\u50cf\u4e3b\u8f74\u4e25\u4e1d\u5408\u7f1d
    \ncbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
    \ncbar.ax.tick_params(labelsize=7.5)
    \ncbar.set_label("Correlation Index", rotation=270, labelpad=10, fontsize=8)<\/p>\n

    # \u79fb\u9664\u5197\u4f59\u8fb9\u6846\u7ebf，\u4fdd\u6301\u6781\u7b80\u901a\u900f
    \nfor spine in ax.spines.values():
    \n spine.set_visible(False)
    \nax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
    \nax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
    \nax.grid(which="minor", color="white", linestyle='-', linewidth=1)
    \nax.tick_params(which="minor", bottom=False, left=False)<\/p>\n

    plt.tight_layout(pad=0.2)
    \n# plt.savefig("fig_heatmap.pdf", format="pdf", bbox_inches="tight")<\/p>\n

    \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d，fraction=0.046, pad=0.04 \u662f Matplotlib \u9886\u57df\u6d41\u4f20\u6781\u5e7f\u7684\u201c\u9ec4\u91d1\u5c3a\u5bf8\u914d\u6bd4\u201d，\u5b83\u80fd\u8ba9\u72ec\u7acb\u7684 Colorbar \u8f74\u4e0e\u6b63\u65b9\u5f62\u7684\u70ed\u529b\u56fe\u77e9\u9635\u7269\u7406\u9ad8\u5ea6\u7edd\u5bf9\u6301\u5e73，\u5f7b\u5e95\u544a\u522b\u4e86\u8272\u6807\u4e0a\u4e0b\u7a81\u51fa\u4e00\u622a\u7684\u4e11\u964b\u6392\u7248\u3002<\/p>\n

    \n

    \u56db\u3001\u5982\u4f55\u7528 Prompt \u8ba9\u5927\u6a21\u578b\u6210\u4e3a\u4f60\u7684\u7ec8\u8eab\u7ed8\u56fe\u52a9\u7406<\/h3>\n

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  • \u73af\u5883\u4e0e\u7269\u7406\u8fb9\u754c\u7ea6\u675f：\u201c\u8bbe\u5b9a\u4e3a IEEE \u53cc\u680f\u5355\u5217\u6392\u7248，\u5bbd 3.4 \u82f1\u5bf8，\u9ad8 2.5 \u82f1\u5bf8，DPI 300，\u5b57\u4f53 Times New Roman，\u5fc5\u987b\u6ce8\u5165 pdf.fonttype=42 \u786e\u4fdd\u77e2\u91cf\u5b57\u4f53\u65e0\u635f\u5d4c\u5165\u201d；<\/li>\n
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  • \u6570\u636e\u63a5\u53e3\u660e\u786e\u5316：\u201c\u4e0d\u8981\u4f7f\u7528\u968f\u673a\u6570\u5047\u6570\u636e\u5360\u4f4d，\u63a5\u6536\u7ed9\u5b9a\u7684 NumPy \u6570\u7ec4\u5e76\u8ba1\u7b97\u4e0a\u4e0b\u8bef\u5dee\u5e26（fill_between）\u201d\u3002<\/li>\n

    \u5728\u79d1\u7814\u957f\u8dd1\u4e2d，\u5199\u4ee3\u7801\u3001\u505a\u5b9e\u9a8c\u4e0e\u5199\u8bba\u6587\u5df2\u7ecf\u5360\u6ee1\u4e86\u5927\u90e8\u5206\u7cbe\u529b\u3002\u628a\u8fd9\u4e9b\u673a\u68b0\u3001\u7e41\u7410\u4f46\u53c8\u5173\u7cfb\u5230\u5ba1\u7a3f\u4eba\u7b2c\u4e00\u5370\u8c61\u7684\u6837\u5f0f\u5fae\u8c03\u4ea4\u7ed9\u5927\u6a21\u578b，\u6211\u4eec\u624d\u80fd\u5c06\u66f4\u591a\u6ce8\u610f\u529b\u6536\u675f\u5728\u5b9e\u9a8c\u80cc\u540e\u7684\u7cfb\u7edf\u67b6\u6784\u4e0e\u7b97\u6cd5\u672c\u8d28\u4e0a\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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