[
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n# file that i ignore\n.DS_Store\n"
  },
  {
    "path": "README.md",
    "content": "[![ID](https://img.shields.io/badge/ID-strongnine-orange)](https://github.com/strongnine) [![](https://img.shields.io/badge/Series2Image-v0.4-orange)](https://github.com/strongnine/Series2Image)\n\n# Series2Image\n\n使用 Python 的 [pyts](https://pyts.readthedocs.io/en/stable/) 库将一维时间序列编码为二维图片，这样可以用于训练卷积神经网络（CNN）\n\n更多详细的内容参考 CSDN 博客：\n\n1. [将一维时间序列转化成二维图片](https://blog.csdn.net/weixin_39679367/article/details/86416439)\n2. [Python：使用 pyts 把一维时间序列转换成二维图片](https://blog.csdn.net/weixin_39679367/article/details/88653018)\n\n2022 年 05 月 14 号更新：\n\n1. `src/paa.py`：PAA 算法的代码；\n2. `src/gaf.py`：GAF 基础 demo；\n2. `src/generate_by_files.py`：将文件夹中的每个文件都生成一个 GAF 图；\n2. `src/generate_by_cols.py`：数据矩阵中的每一列生成对应的 GAF 图；\n3. `src/slide_window.py`：利用 滑动窗口 将一个长的序列信号生成多个 GAF 图片；\n4. `src/slide_window_multicol.py`：利用 滑动窗口 将多列长的序列信号生成多个 GAF 图片；\n\n\n\n---\n\n\n\nEncoding time series as images using GAF operation by `pyts`. \n\nsee more in CSDN Blogs: \n\n1. [将一维时间序列转化成二维图片](https://blog.csdn.net/weixin_39679367/article/details/86416439)\n2. [Python：使用 pyts 把一维时间序列转换成二维图片](https://blog.csdn.net/weixin_39679367/article/details/88653018)\n\n2022.05.14 update:\n\n1. `src/paa.py`：PAA algorithm code；\n2. `src/GAF.py`：GAF demo；\n2. `src/generate_by_files.py`：Generate GAF images in batches, each file in filedir generate a GAF image;\n2. `src/generate_by_cols.py`：Generate GAF images in batches, each column in the data file generate a GAF image;\n3. `src/slide_window.py`：Using a sliding window to generate GAF images from a long sequence of signals; \n4. `src/slide_window_multicol.py`：Using a sliding window to generate GAF images from multi long sequence of signals; \n\n\n\n"
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"0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n1.22E-16\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-2.45E-16\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n3.67E-16\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-4.90E-16\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n6.12E-16\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-7.35E-16\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n8.57E-16\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-9.80E-16\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n1.10E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-1.22E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n4.90E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-1.47E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n-1.96E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-1.71E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n5.39E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-1.96E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n-1.47E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-2.20E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n5.88E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-2.45E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n-9.81E-16\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-9.80E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n6.37E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-2.94E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n-4.91E-16\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n3.92E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n6.86E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-3.43E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n-1.24E-18\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-1.08E-14\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n7.35E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-3.92E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n4.89E-16\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n2.94E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n7.84E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-4.41E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n9.78E-16\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-1.18E-14\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n8.33E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-4.90E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n1.57E-14\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n1.96E-15\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n8.82E-15\r\n-0.382683432\r\n-0.707106781\r\n-0.923879533\r\n-1\r\n-0.923879533\r\n-0.707106781\r\n-0.382683432\r\n-1.96E-14\r\n0.382683432\r\n0.707106781\r\n0.923879533\r\n1\r\n0.923879533\r\n0.707106781\r\n0.382683432\r\n1.96E-15\r\n-0.382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  },
  {
    "path": "src/GAF.py",
    "content": "#!/usr/bin/python\r\n#copyright(c) strongnine\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import image\r\nfrom pyts.image import GramianAngularField\r\n\r\nsin_data = np.loadtxt('./data/sinx.csv', delimiter=\",\", skiprows=0).reshape(1, -1)  # 加载数据 (load the source data)\r\nimage_size = 28  # 生成的 GAF 图片的大小 (the size of each GAF image)\r\n\r\n# `method` 的可选参数有：`summation` and `difference`\r\n# The optional parameters of argument `method`: `summation` and `difference`\r\ngasf = GramianAngularField(image_size=image_size, method='summation')\r\nsin_gasf = gasf.fit_transform(sin_data)\r\n\r\ngadf = GramianAngularField(image_size=image_size, method='difference')\r\nsin_gadf = gadf.fit_transform(sin_data)\r\nimges = [sin_gasf[0], sin_gadf[0]]\r\ntitles = ['Summation', 'Difference']\r\n\r\n# 两种方法的可视化差异对比\r\n# Comparison of two different methods\r\nfig, axs = plt.subplots(1, 2, constrained_layout=True)\r\nfor img, title, ax in zip(imges, titles, axs):\r\n    ax.imshow(img)\r\n    ax.set_title(title)\r\nfig.suptitle('GramianAngularField', y=0.94, fontsize=16)\r\nplt.margins(0, 0)\r\nplt.savefig(\"./GramianAngularField.pdf\", pad_inches=0)\r\nplt.show()\r\n\r\nimage.imsave(\"./images/GAF_of_Sin.png\", sin_gasf[0])  # 保存图片 (save image)\r\nnp.savetxt(\"./images/GAF_of_Sin.csv\", sin_gasf[0], delimiter=',')  # 保存数据为 csv 文件\r\n"
  },
  {
    "path": "src/generate_by_cols.py",
    "content": "#!/usr/bin/python\n#copyright(c) strongnine\n# 批量生成 GAF 图片的代码 (Generate GAF images in batches)\n# 数据矩阵中的每一列生成对应的 GAF 图 (Each column in the data file generate a GAF image)\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import image\nfrom pyts.image import GramianAngularField\n\n# 根据自己的需求修改一下变量的参数\nfilename = \"./data/data_mat.csv\"  # 要处理的文件路径\nsavepath = \"./data\"               # GAF 图片保存的路径\nimg_sz = 28           # 确定生成的 GAF 图片的大小\nmethod = 'summation'  # GAF 图片的类型，可选 'summation'（默认）和 'difference'\n\n# 以下是 GAF 生成的代码\nprint(\"GAF 生成方法：%s，图片大小：%d * %d\" % (method, img_sz, img_sz))\nimg_path = \"%s/images\" % savepath  # 可视化图片保存的文件夹\ndata_path = \"%s/data\" % savepath   # 数据文件保存的文件夹\nif not os.path.exists(img_path):\n    os.makedirs(img_path)   # 如果文件夹不存在就创建一个\nif not os.path.exists(data_path):\n    os.makedirs(data_path)  # 如果文件夹不存在就创建一个\n\nprint(\"开始生成...\")\nprint(\"可视化图片保存在文件夹 %s 中，数据文件保存在文件夹 %s 中。\" % (img_path, data_path))\nsrc_data = np.loadtxt(filename, delimiter=\",\")  # 加载数据 (load the source data)\nimg_num = src_data.shape[1]  # 生成图片的总数为数据矩阵的列数 (the total numbers of GAF images)\ngaf = GramianAngularField(image_size=img_sz, method=method)\ngaf_images = gaf.fit_transform(src_data.T)\n\n# gaf_images 的 shape 为 （img_num, img_sz, img_sz)\nfor i in range(img_num):  # 把每个图片都保存起来\n    gaf_img = gaf_images[i, :, :]  # 得到第 i 个图片的数据\n    img_save_path = \"%s/%d.png\" % (img_path, i)\n    data_save_path = \"%s/%d.csv\" % (data_path, i)\n    image.imsave(img_save_path, gaf_img)                # 保存图片 (save image)\n    np.savetxt(data_save_path, gaf_img, delimiter=',')  # 保存数据为 csv 文件\n\nprint(\"生成完成，共处理 %d 个图片。\" % img_num)"
  },
  {
    "path": "src/generate_by_files.py",
    "content": "#!/usr/bin/python\n#copyright(c) strongnine\n# 批量生成 GAF 图片的代码 (Generate GAF images in batches)\n# 将文件夹中的每个文件都生成一个 GAF 图 (...)\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import image\nfrom pyts.image import GramianAngularField\n\n# 根据自己的需求修改一下变量的参数\ndirname = \"./data/dataset\"  # 要处理的文件夹路径\nsavepath = \"./data\"         # GAF 图片保存的路径\nimg_sz = 28           # 确定生成的 GAF 图片的大小\nmethod = 'summation'  # GAF 图片的类型，可选 'summation'（默认）和 'difference'\n\n\n# 以下是 GAF 生成的代码\nprint(\"GAF 生成方法：%s，图片大小：%d * %d\" % (method, img_sz, img_sz))\nimg_path = \"%s/images\" % savepath     # 可视化图片保存的文件夹\ndata_path = \"%s/data_mat\" % savepath  # 数据文件保存的文件夹\nif not os.path.exists(img_path):\n    os.makedirs(img_path)  # 如果文件夹不存在就创建一个\nif not os.path.exists(data_path):\n    os.makedirs(data_path)  # 如果文件夹不存在就创建一个\n\nprint(\"开始生成...\")\nprint(\"可视化图片保存在文件夹 %s 中，数据文件保存在文件夹 %s 中。\" % (img_path, data_path))\ngaf = GramianAngularField(image_size=img_sz, method=method)\nimg_num = 0  # 计算生成的图片个数\nfor fname in os.listdir(dirname):\n    filename, ext = os.path.splitext(fname)\n    if ext != '.csv': continue  # 如果不是 csv 文件则跳过\n    img_num += 1\n\n    src_data = np.loadtxt(\"{}/{}\".format(dirname, fname), delimiter=\",\")  # 加载数据 (load the source data)\n    x = src_data.reshape(1, -1)\n    img_gaf = gaf.fit_transform(x)\n\n    img_save_path = \"%s/%s.png\" % (img_path, filename)\n    image.imsave(img_save_path, img_gaf[0])  # 保存图片 (save image)\n\n    data_save_path = \"%s/%s.csv\" % (data_path, filename)\n    np.savetxt(data_save_path, img_gaf[0], delimiter=',')  # 保存数据为 csv 文件\n\nprint(\"生成完成，共处理 %d 个图片。\" % img_num)"
  },
  {
    "path": "src/paa.py",
    "content": "#!/usr/bin/python\n#copyright(c) strongnine\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pyts.approximation import PiecewiseAggregateApproximation\n\ndata = np.loadtxt(\"./data/data.csv\", delimiter=',')\ndata = data.reshape(1, -1)\nn_sampels, n_timestamps = data.shape\nn_paa = 128\nwindow_size = n_timestamps // n_paa\npaa = PiecewiseAggregateApproximation(window_size=window_size)\nX_paa = paa.transform(data)[:, :n_paa]\n# np.savetxt(\"./paa_result.csv\", X_paa.T, fmt='%.18f')\n\nplt.figure(figsize=(6, 4))\nplt.plot(data[0], 'o--', ms=0.1, label='Original', linewidth=1.)\nplt.plot(np.arange(window_size // 2,\n                   n_timestamps + window_size // 2,\n                   window_size)[:n_paa], X_paa[0], 'o--', ms=2.5, label='PAA', linewidth=1.)\nplt.show()"
  },
  {
    "path": "src/slide_window.py",
    "content": "#!/usr/bin/python\r\n#copyright(c) strongnine\r\n# 利用 滑动窗口 将一个长的序列信号生成多个 GAF 图片\r\n# Using a sliding window to generate GAF images from a long sequence of signals\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import image\r\nfrom pyts.image import GramianAngularField\r\n\r\n# 根据自己的需求修改一下变量的参数\r\nfilename = \"./data/data.csv\"  # 要处理的文件路径\r\nsavepath = \"./data\"           # GAF 图片保存的路径\r\nimg_sz = 128  # 生成的 GAF 图片的大小 (the size of each GAF image)\r\n# 如果 滑动窗口的大小 等于 滑动步长 则滑动窗口之间没有重叠\r\nwindow_sz = 128 # 滑动窗口的大小，需要满足 window_sz > img_sz\r\nstep = 16       # 滑动窗口的步长 (step of slide window)\r\nassert window_sz >= img_sz, \"window_sz < img_sz（滑动窗口大小 小于 GAF 图片尺寸）。\"\r\nmethod = 'summation'  # GAF 图片的类型，可选 'summation'（默认）和 'difference'\r\n\r\n# 以下是 GAF 生成的代码\r\nprint(\"GAF 生成方法：%s，图片大小：%d * %d\" % (method, img_sz, img_sz))\r\nimg_path = \"%s/images\" % savepath  # 可视化图片保存的文件夹\r\ndata_path = \"%s/data\" % savepath   # 数据文件保存的文件夹\r\nif not os.path.exists(img_path):\r\n    os.makedirs(img_path)   # 如果文件夹不存在就创建一个\r\nif not os.path.exists(data_path):\r\n    os.makedirs(data_path)  # 如果文件夹不存在就创建一个\r\n\r\nprint(\"开始生成...\")\r\nprint(\"可视化图片保存在文件夹 %s 中，数据文件保存在文件夹 %s 中。\" % (img_path, data_path))\r\ngaf = GramianAngularField(image_size=img_sz, method=method)\r\ndata = np.loadtxt(filename, delimiter=\",\").reshape(1, -1)\r\n_, n_sample = data.shape\r\n\r\nimg_num = 0  # 生成图片的总数 (the total numbers of GAF images)\r\nstart_index, end_index = 0, window_sz  # 序列开头以及末尾索引\r\n\r\nwhile end_index <= n_sample:\r\n    img_num += 1\r\n    sub_series = data[:, start_index:end_index]\r\n    gaf_img = gaf.fit_transform(sub_series)  # 转化为 GAF 图片\r\n\r\n    img_save_path = \"{}/{}.png\".format(img_path, img_num)\r\n    data_save_path = \"{}/{}.csv\".format(data_path, img_num)\r\n    image.imsave(img_save_path, gaf_img[0])                # 保存图片 (save image)\r\n    np.savetxt(data_save_path, gaf_img[0], delimiter=',')  # 保存数据为 csv 文件\r\n\r\n    # 滑动窗口向后移动\r\n    start_index += step\r\n    end_index += step\r\n\r\nprint(\"生成完成，共处理 {} 个图片。\".format(img_num))"
  },
  {
    "path": "src/slide_window_multicol.py",
    "content": "#!/usr/bin/python\r\n#copyright(c) strongnine\r\n# 利用 滑动窗口 将多列长的序列信号生成多个 GAF 图片\r\n# Using a sliding window to generate GAF images from a long sequence of signals\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import image\r\nfrom pyts.image import GramianAngularField\r\n\r\n# 根据自己的需求修改一下变量的参数\r\nfilename = \"./data/data_mat2.csv\"  # 要处理的文件路径\r\nsavepath = \"./data\"                # GAF 图片保存的路径\r\nimg_sz = 256  # 生成的 GAF 图片的大小 (the size of each GAF image)\r\n# 如果 滑动窗口的大小 等于 滑动步长 则滑动窗口之间没有重叠\r\nwindow_sz = 300 # 滑动窗口的大小，需要满足 window_sz > img_sz\r\nstep = 300      # 滑动窗口的步长 (step of slide window)\r\nassert window_sz >= img_sz, \"window_sz < img_sz（滑动窗口大小 小于 GAF 图片尺寸）。\"\r\nmethod = 'summation'  # GAF 图片的类型，可选 'summation'（默认）和 'difference'\r\n\r\n# 以下是 GAF 生成的代码\r\nprint(\"GAF 生成方法：%s，图片大小：%d * %d\" % (method, img_sz, img_sz))\r\nimg_path = \"%s/images\" % savepath  # 可视化图片保存的文件夹\r\ndata_path = \"%s/data\" % savepath   # 数据文件保存的文件夹\r\nif not os.path.exists(img_path):\r\n    os.makedirs(img_path)   # 如果文件夹不存在就创建一个\r\nif not os.path.exists(data_path):\r\n    os.makedirs(data_path)  # 如果文件夹不存在就创建一个\r\n\r\nprint(\"开始生成...\")\r\nprint(\"可视化图片保存在文件夹 %s 中，数据文件保存在文件夹 %s 中。\" % (img_path, data_path))\r\ngaf = GramianAngularField(image_size=img_sz, method=method)\r\ndata = np.loadtxt(filename, delimiter=\",\")\r\nn_sample, n_channels = data.shape\r\n\r\nimg_num = 0  # 生成图片的总数 (the total numbers of GAF images)\r\nstart_index, end_index = 0, window_sz  # 序列开头以及末尾索引\r\n\r\nwhile end_index <= n_sample:\r\n    img_num += 1\r\n    sub_series = data[start_index:end_index, :]   # 获得当前滑动窗口中的数据\r\n    gaf_images = gaf.fit_transform(sub_series.T)  # 转化为 GAF 图片\r\n    for c in range(n_channels):        # 保存每个 channel 的图片\r\n        gaf_img = gaf_images[c, :, :]  # 得到第 c 个 channel 的数据\r\n        \r\n        img_save_path = \"{}/{}_{}.png\".format(img_path, img_num, c)\r\n        data_save_path = \"{}/{}_{}.csv\".format(data_path, img_num, c)\r\n        image.imsave(img_save_path, gaf_img)                # 保存图片 (save image)\r\n        np.savetxt(data_save_path, gaf_img, delimiter=',')  # 保存数据为 csv 文件\r\n    \r\n    # 滑动窗口向后移动\r\n    start_index += step\r\n    end_index += step\r\n\r\nprint(\"生成完成，共处理 {} 个图片。\".format(img_num * n_channels))"
  }
]