scatter_frequency.ipynb

{"cells":[{"cell_type":"code","source":["! sudo apt-get install texlive-latex-recommended\n","! sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended\n","! wget http://mirrors.ctan.org/macros/latex/contrib/type1cm.zip\n","! unzip type1cm.zip -d /tmp/type1cm\n","! cd /tmp/type1cm/type1cm/ && sudo latex type1cm.ins\n","! sudo mkdir /usr/share/texmf/tex/latex/type1cm\n","! sudo cp /tmp/type1cm/type1cm/type1cm.sty /usr/share/texmf/tex/latex/type1cm\n","! sudo texhash\n","!apt install cm-super"],"metadata":{"id":"vYWkg9yxjVQC"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[],"metadata":{"id":"LruPwfwlYb1m"}},{"cell_type":"code","source":["# coding=utf-8\n","from copy import copy\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","import matplotlib as mtp\n","import numpy as np\n","from random import randint\n","\n","df = pd.read_csv(\"data_count.csv\")\n","\n","y = df[\"exp\"]\n","\n","x = df[\"score\"]*4\n","\n","s = df[\"count\"]\n","# print(len(x.unique()))\n","# x.hist()\n","\n","print(len(x))\n","s= [v*100 for v in s]\n","plt.scatter(x,y, s=s, alpha=1)\n","\n","plt.yticks([0,1,2,3,4], labels=[\"Low\",\"\",\"\",\"\",\"High\"])\n","\n","plt.xlabel(\"Score (S)\")\n","\n","plt.ylabel(\"Prior experience with robots\")\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"Z15AIAI5b4zA","executionInfo":{"status":"ok","timestamp":1688633511790,"user_tz":-120,"elapsed":382,"user":{"displayName":"Francesco Vigni","userId":"16400058101304334372"}},"outputId":"ae43b1bd-9fe2-479e-c28f-092eea5d60e4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["66\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["lst = np.array([x,y]).T\n","\n","np.vstack({tuple(e) for e in lst})\n","\n","\n","d = pd.DataFrame([x,y]).T\n","a = d.drop_duplicates()\n","# print(a)\n","# print(len(a))\n","\n","c = lst.flatten().tolist()\n","\n","import collections\n","\n","clt = collections.Counter(c)\n","\n","mst = clt.most_common()\n","\n","sorted(mst)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hhWMBANWK9Ue","executionInfo":{"status":"ok","timestamp":1688631515979,"user_tz":-120,"elapsed":4,"user":{"displayName":"Francesco Vigni","userId":"16400058101304334372"}},"outputId":"4de35b8b-91ad-4043-b6bc-1abd7ac7eeb5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-44-cd35b8505a11>:3: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n","  np.vstack({tuple(e) for e in lst})\n"]},{"output_type":"execute_result","data":{"text/plain":["[(-0.05, 1),\n"," (0.0, 22),\n"," (0.15, 1),\n"," (0.2, 5),\n"," (0.25, 8),\n"," (0.45, 6),\n"," (0.5, 20),\n"," (0.65, 2),\n"," (0.7, 5),\n"," (0.75, 8),\n"," (0.9, 1),\n"," (0.95, 1),\n"," (1.0, 33),\n"," (2.0, 9),\n"," (3.0, 5),\n"," (4.0, 5)]"]},"metadata":{},"execution_count":44}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z6gSDNihoFfg"},"outputs":[],"source":["\n","\n","mtp.rcParams['pdf.fonttype'] = 42\n","plt.rcParams.update({\n","    \"text.usetex\": True,\n","    \"font.family\": \"Helvetica\"\n","})\n","\n","def annotate_barplot_dataframe(bar0, bar1, text, patches, dh=0.2, c='black'):\n","    \"\"\"annotate a grouped barplot from a pandas dataframe\n","    An annotation is built from bar0 to bar1\n","\n","    Args:\n","        bar0 (int): index of first bar\n","        bar1 (int): index of second bar\n","        text (string): what to write on the annotation\n","        patches (matplotlib.patches): data source\n","    \"\"\"\n","    patches.sort(key=lambda x: x.xy[0])\n","    left = patches[bar0]\n","    right = patches[bar1]\n","    y = max(left._height, right._height) + dh\n","\n","    l_bbox = left.get_bbox()\n","    l_mid = l_bbox.x1 - left._width / 2\n","\n","    r_bbox = right.get_bbox()\n","    r_mid = r_bbox.x1 - right._width / 2\n","    barh = 0.07\n","    barx = [l_mid, l_mid, r_mid, r_mid]\n","    bary = [\n","        y,\n","        y + barh,\n","        y + barh,\n","        y,\n","    ]  # lower-left, upper-left, upper-right, lower-right\n","    plt.plot(barx, bary, c=c)\n","    kwargs = dict(ha=\"center\", va=\"bottom\")\n","    # if fs is not None:\n","    # kwargs['fontsize'] = fs\n","    mid = ((l_mid + r_mid) / 2, y + 0.01)\n","    plt.text(*mid, text, **kwargs)\n","\n","\n","def annotate_errors(l, r, text, dh=0.2):\n","    \"\"\"annotate a grouped barplot from a pandas dataframe\n","    An annotation is built from bar0 to bar1\n","\n","    Args:\n","        bar0 (int): index of first bar\n","        bar1 (int): index of second bar\n","        text (string): what to write on the annotation\n","        patches (matplotlib.patches): data source\n","    \"\"\"\n","    # patches.sort(key=lambda x: x.xy[0])\n","    left = l\n","    right = r\n","    y = max(left._height, right._height) + dh\n","\n","    l_bbox = left.get_bbox()\n","    l_mid = l_bbox.x1 - left._width / 2\n","\n","    r_bbox = right.get_bbox()\n","    r_mid = r_bbox.x1 - right._width / 2\n","    barh = 0.07\n","    barx = [l_mid, l_mid, r_mid, r_mid]\n","    bary = [\n","        y,\n","        y + barh,\n","        y + barh,\n","        y,\n","    ]  # lower-left, upper-left, upper-right, lower-right\n","    plt.plot(barx, bary, c=\"black\")\n","    kwargs = dict(ha=\"center\", va=\"bottom\")\n","    # if fs is not None:\n","    # kwargs['fontsize'] = fs\n","    mid = ((l_mid + r_mid) / 2, y + 0.01)\n","    plt.text(*mid, text, **kwargs)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wwTxqzV7yaPG","colab":{"base_uri":"https://localhost:8080/","height":226},"executionInfo":{"status":"ok","timestamp":1688562873557,"user_tz":-120,"elapsed":2228,"user":{"displayName":"Francesco Vigni","userId":"16400058101304334372"}},"outputId":"dc63a637-9a71-43df-94e5-4423d68c5389"},"outputs":[{"output_type":"stream","name":"stdout","text":["[5.78 6.09 5.3  5.45 1.87 1.13 4.85 5.43]\n","[5.78 6.09 5.3  5.45 1.87 1.13 4.85 5.43]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        }\n","      }\n","      const blob = new Blob(buffers, {type: 'application/binary'});\n","      const a = document.createElement('a');\n","      a.href = window.URL.createObjectURL(blob);\n","      a.download = filename;\n","      div.appendChild(a);\n","      a.click();\n","      div.remove();\n","    }\n","  "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_66c452e6-c19b-4e24-9f81-60bf37cb7d4e\", \"barplot.png\", 8185)"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 370x130 with 1 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\n"},"metadata":{}}],"source":["\n","# c1 = {\"ease\":30.06,\"enjoy\":32.67,\"reactance\":42.85,\"beliefs\":29.21}\n","# c2 = {\"ease\":36.94,\"enjoy\":34.33,\"reactance\":24.15,\"beliefs\":37.79}\n","\n","c1 = {\"ease\":5.78,\"enjoy\":5.3,\"reactance\":1.87,\"beliefs\":4.85}\n","c2 = {\"ease\":6.09,\"enjoy\":5.45,\"reactance\":1.13,\"beliefs\":5.43}\n","\n","\n","# df = pd.DataFrame.from_dict({c1,c2},orient='index')\n","df = pd.DataFrame([c1,c2]).T\n","\n","fig, ax = plt.subplots(figsize=(3.7, 1.3))\n","width = 0.30\n","x = np.arange(df.shape[0])\n","# print(df.shape[0])\n","# fdfcdc\n","# plot data in grouped manner of bar type\n","plt.bar(x - width * 2,\n","        df.iloc[:,0],\n","        width,\n","        color='#f07167',\n","        edgecolor='black',\n","        hatch=\"\",\n","        label=\"C1\",\n","        zorder=3)\n","plt.bar(x - width,\n","        df.iloc[:,1],\n","        width,\n","        color='#eff6ee',\n","        edgecolor='black',\n","        hatch=\"\",\n","        label=\"C2\",\n","        zorder=3)\n","\n","ax.set_ylim(0, 7)\n","\n","patches = []\n","for p in ax.patches._axes.axes.containers:\n","    patches.extend(p.patches)\n","labels = [\"ease\",\"enjoy\",\"reactance\",\"beliefs\"]\n","plt.xticks(x, labels)\n","\n","\n","ax.set_xticklabels(labels, rotation=15)\n","import matplotlib\n","dx =-0.3; dy = 0/72.\n","offset = matplotlib.transforms.ScaledTranslation(dx, dy, fig.dpi_scale_trans)\n","ax.tick_params(axis='both', which='both', length=0, labelsize=8)\n","\n","# apply offset transform to all x ticklabels.\n","for label in ax.xaxis.get_majorticklabels():\n","    label.set_transform(label.get_transform() + offset)\n","\n","annotate_barplot_dataframe(4, 5, \"*\", patches, 1)  # *\n","ax.yaxis.grid(zorder=0)\n","\n","\n","handles, labels = ax.get_legend_handles_labels()\n","labels = [\"Antagonistic\", \"Agreeable\"]\n","lgd = ax.legend(handles,\n","                labels,\n","                bbox_to_anchor=(0.86, 1.02),\n","                loc=\"lower right\",\n","                ncol=2,\n","                fancybox=True,\n","                prop={'size': 8})  # (x,y)\n","\n","\n","rects = ax.patches\n","print(df.values.flatten())\n","print(df.stack().values)\n","# Make some labels.\n","labels = df.values.flatten()\n","\n","for rect, label in zip(rects, labels):\n","    height = rect.get_height()\n","    ax.text(\n","        rect.get_x() + rect.get_width() / 2, height + 0.05, height, ha=\"center\", va=\"bottom\", size=6\n","    )\n","\n","fn = \"barplot.png\"\n","plt.savefig(fname=fn, transparent = True, bbox_inches = 'tight', pad_inches = 0.05)\n","\n","from google.colab import files\n","\n","files.download(fn)\n"]}],"metadata":{"colab":{"provenance":[{"file_id":"1JEFLdtWjaYndkQ82GyukSiF7y9l0pp69","timestamp":1688566958106}],"authorship_tag":"ABX9TyNpgb/MqROSV1u/0nqWoDlv"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}