Repository: strongnine/Series2Image Branch: master Commit: 189453a9f410 Files: 17 Total size: 534.7 KB Directory structure: gitextract_5g6hcw7r/ ├── .gitignore ├── README.md ├── data/ │ ├── data.csv │ ├── data_mat.csv │ ├── data_mat2.csv │ ├── dataset/ │ │ ├── 0.csv │ │ ├── 1.csv │ │ ├── 2.csv │ │ ├── 3.csv │ │ └── 4.csv │ └── sinx.csv └── src/ ├── GAF.py ├── generate_by_cols.py ├── generate_by_files.py ├── paa.py ├── slide_window.py └── slide_window_multicol.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # file that i ignore .DS_Store ================================================ FILE: README.md ================================================ [![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) # Series2Image 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） 更多详细的内容参考 CSDN 博客： 1. [将一维时间序列转化成二维图片](https://blog.csdn.net/weixin_39679367/article/details/86416439) 2. [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) 2022 年 05 月 14 号更新： 1. `src/paa.py`：PAA 算法的代码； 2. `src/gaf.py`：GAF 基础 demo； 2. `src/generate_by_files.py`：将文件夹中的每个文件都生成一个 GAF 图； 2. `src/generate_by_cols.py`：数据矩阵中的每一列生成对应的 GAF 图； 3. `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； 4. `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； --- Encoding time series as images using GAF operation by `pyts`. see more in CSDN Blogs: 1. [将一维时间序列转化成二维图片](https://blog.csdn.net/weixin_39679367/article/details/86416439) 2. [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) 2022.05.14 update: 1. `src/paa.py`：PAA algorithm code； 2. `src/GAF.py`：GAF demo； 2. `src/generate_by_files.py`：Generate GAF images in batches, each file in filedir generate a GAF image; 2. `src/generate_by_cols.py`：Generate GAF images in batches, each column in the data file generate a GAF image; 3. `src/slide_window.py`：Using a sliding window to generate GAF images from a long sequence of signals; 4. `src/slide_window_multicol.py`：Using a sliding window to generate GAF images from multi long sequence of signals; ================================================ FILE: data/data.csv ================================================ 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-0.707106781 -0.923879533 -1 -0.923879533 -0.707106781 -0.382683432 -7.84E-15 ================================================ FILE: src/GAF.py ================================================ #!/usr/bin/python #copyright(c) strongnine import numpy as np import matplotlib.pyplot as plt from matplotlib import image from pyts.image import GramianAngularField sin_data = np.loadtxt('./data/sinx.csv', delimiter=",", skiprows=0).reshape(1, -1) # 加载数据 (load the source data) image_size = 28 # 生成的 GAF 图片的大小 (the size of each GAF image) # `method` 的可选参数有：`summation` and `difference` # The optional parameters of argument `method`: `summation` and `difference` gasf = GramianAngularField(image_size=image_size, method='summation') sin_gasf = gasf.fit_transform(sin_data) gadf = GramianAngularField(image_size=image_size, method='difference') sin_gadf = gadf.fit_transform(sin_data) imges = [sin_gasf[0], sin_gadf[0]] titles = ['Summation', 'Difference'] # 两种方法的可视化差异对比 # Comparison of two different methods fig, axs = plt.subplots(1, 2, constrained_layout=True) for img, title, ax in zip(imges, titles, axs): ax.imshow(img) ax.set_title(title) fig.suptitle('GramianAngularField', y=0.94, fontsize=16) plt.margins(0, 0) plt.savefig("./GramianAngularField.pdf", pad_inches=0) plt.show() image.imsave("./images/GAF_of_Sin.png", sin_gasf[0]) # 保存图片 (save image) np.savetxt("./images/GAF_of_Sin.csv", sin_gasf[0], delimiter=',') # 保存数据为 csv 文件 ================================================ FILE: src/generate_by_cols.py ================================================ #!/usr/bin/python #copyright(c) strongnine # 批量生成 GAF 图片的代码 (Generate GAF images in batches) # 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) import os import numpy as np import matplotlib.pyplot as plt from matplotlib import image from pyts.image import GramianAngularField # 根据自己的需求修改一下变量的参数 filename = "./data/data_mat.csv" # 要处理的文件路径 savepath = "./data" # GAF 图片保存的路径 img_sz = 28 # 确定生成的 GAF 图片的大小 method = 'summation' # GAF 图片的类型，可选 'summation'（默认）和 'difference' # 以下是 GAF 生成的代码 print("GAF 生成方法：%s，图片大小：%d * %d" % (method, img_sz, img_sz)) img_path = "%s/images" % savepath # 可视化图片保存的文件夹 data_path = "%s/data" % savepath # 数据文件保存的文件夹 if not os.path.exists(img_path): os.makedirs(img_path) # 如果文件夹不存在就创建一个 if not os.path.exists(data_path): os.makedirs(data_path) # 如果文件夹不存在就创建一个 print("开始生成...") print("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)) src_data = np.loadtxt(filename, delimiter=",") # 加载数据 (load the source data) img_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) gaf = GramianAngularField(image_size=img_sz, method=method) gaf_images = gaf.fit_transform(src_data.T) # gaf_images 的 shape 为 （img_num, img_sz, img_sz) for i in range(img_num): # 把每个图片都保存起来 gaf_img = gaf_images[i, :, :] # 得到第 i 个图片的数据 img_save_path = "%s/%d.png" % (img_path, i) data_save_path = "%s/%d.csv" % (data_path, i) image.imsave(img_save_path, gaf_img) # 保存图片 (save image) np.savetxt(data_save_path, gaf_img, delimiter=',') # 保存数据为 csv 文件 print("生成完成，共处理 %d 个图片。" % img_num) ================================================ FILE: src/generate_by_files.py ================================================ #!/usr/bin/python #copyright(c) strongnine # 批量生成 GAF 图片的代码 (Generate GAF images in batches) # 将文件夹中的每个文件都生成一个 GAF 图 (...) import os import numpy as np import matplotlib.pyplot as plt from matplotlib import image from pyts.image import GramianAngularField # 根据自己的需求修改一下变量的参数 dirname = "./data/dataset" # 要处理的文件夹路径 savepath = "./data" # GAF 图片保存的路径 img_sz = 28 # 确定生成的 GAF 图片的大小 method = 'summation' # GAF 图片的类型，可选 'summation'（默认）和 'difference' # 以下是 GAF 生成的代码 print("GAF 生成方法：%s，图片大小：%d * %d" % (method, img_sz, img_sz)) img_path = "%s/images" % savepath # 可视化图片保存的文件夹 data_path = "%s/data_mat" % savepath # 数据文件保存的文件夹 if not os.path.exists(img_path): os.makedirs(img_path) # 如果文件夹不存在就创建一个 if not os.path.exists(data_path): os.makedirs(data_path) # 如果文件夹不存在就创建一个 print("开始生成...") print("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)) gaf = GramianAngularField(image_size=img_sz, method=method) img_num = 0 # 计算生成的图片个数 for fname in os.listdir(dirname): filename, ext = os.path.splitext(fname) if ext != '.csv': continue # 如果不是 csv 文件则跳过 img_num += 1 src_data = np.loadtxt("{}/{}".format(dirname, fname), delimiter=",") # 加载数据 (load the source data) x = src_data.reshape(1, -1) img_gaf = gaf.fit_transform(x) img_save_path = "%s/%s.png" % (img_path, filename) image.imsave(img_save_path, img_gaf[0]) # 保存图片 (save image) data_save_path = "%s/%s.csv" % (data_path, filename) np.savetxt(data_save_path, img_gaf[0], delimiter=',') # 保存数据为 csv 文件 print("生成完成，共处理 %d 个图片。" % img_num) ================================================ FILE: src/paa.py ================================================ #!/usr/bin/python #copyright(c) strongnine import numpy as np import matplotlib.pyplot as plt from pyts.approximation import PiecewiseAggregateApproximation data = np.loadtxt("./data/data.csv", delimiter=',') data = data.reshape(1, -1) n_sampels, n_timestamps = data.shape n_paa = 128 window_size = n_timestamps // n_paa paa = PiecewiseAggregateApproximation(window_size=window_size) X_paa = paa.transform(data)[:, :n_paa] # np.savetxt("./paa_result.csv", X_paa.T, fmt='%.18f') plt.figure(figsize=(6, 4)) plt.plot(data[0], 'o--', ms=0.1, label='Original', linewidth=1.) plt.plot(np.arange(window_size // 2, n_timestamps + window_size // 2, window_size)[:n_paa], X_paa[0], 'o--', ms=2.5, label='PAA', linewidth=1.) plt.show() ================================================ FILE: src/slide_window.py ================================================ #!/usr/bin/python #copyright(c) strongnine # 利用 滑动窗口 将一个长的序列信号生成多个 GAF 图片 # Using a sliding window to generate GAF images from a long sequence of signals import os import numpy as np import matplotlib.pyplot as plt from matplotlib import image from pyts.image import GramianAngularField # 根据自己的需求修改一下变量的参数 filename = "./data/data.csv" # 要处理的文件路径 savepath = "./data" # GAF 图片保存的路径 img_sz = 128 # 生成的 GAF 图片的大小 (the size of each GAF image) # 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 window_sz = 128 # 滑动窗口的大小，需要满足 window_sz > img_sz step = 16 # 滑动窗口的步长 (step of slide window) assert window_sz >= img_sz, "window_sz < img_sz（滑动窗口大小 小于 GAF 图片尺寸）。" method = 'summation' # GAF 图片的类型，可选 'summation'（默认）和 'difference' # 以下是 GAF 生成的代码 print("GAF 生成方法：%s，图片大小：%d * %d" % (method, img_sz, img_sz)) img_path = "%s/images" % savepath # 可视化图片保存的文件夹 data_path = "%s/data" % savepath # 数据文件保存的文件夹 if not os.path.exists(img_path): os.makedirs(img_path) # 如果文件夹不存在就创建一个 if not os.path.exists(data_path): os.makedirs(data_path) # 如果文件夹不存在就创建一个 print("开始生成...") print("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)) gaf = GramianAngularField(image_size=img_sz, method=method) data = np.loadtxt(filename, delimiter=",").reshape(1, -1) _, n_sample = data.shape img_num = 0 # 生成图片的总数 (the total numbers of GAF images) start_index, end_index = 0, window_sz # 序列开头以及末尾索引 while end_index <= n_sample: img_num += 1 sub_series = data[:, start_index:end_index] gaf_img = gaf.fit_transform(sub_series) # 转化为 GAF 图片 img_save_path = "{}/{}.png".format(img_path, img_num) data_save_path = "{}/{}.csv".format(data_path, img_num) image.imsave(img_save_path, gaf_img[0]) # 保存图片 (save image) np.savetxt(data_save_path, gaf_img[0], delimiter=',') # 保存数据为 csv 文件 # 滑动窗口向后移动 start_index += step end_index += step print("生成完成，共处理 {} 个图片。".format(img_num)) ================================================ FILE: src/slide_window_multicol.py ================================================ #!/usr/bin/python #copyright(c) strongnine # 利用 滑动窗口 将多列长的序列信号生成多个 GAF 图片 # Using a sliding window to generate GAF images from a long sequence of signals import os import numpy as np import matplotlib.pyplot as plt from matplotlib import image from pyts.image import GramianAngularField # 根据自己的需求修改一下变量的参数 filename = "./data/data_mat2.csv" # 要处理的文件路径 savepath = "./data" # GAF 图片保存的路径 img_sz = 256 # 生成的 GAF 图片的大小 (the size of each GAF image) # 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 window_sz = 300 # 滑动窗口的大小，需要满足 window_sz > img_sz step = 300 # 滑动窗口的步长 (step of slide window) assert window_sz >= img_sz, "window_sz < img_sz（滑动窗口大小 小于 GAF 图片尺寸）。" method = 'summation' # GAF 图片的类型，可选 'summation'（默认）和 'difference' # 以下是 GAF 生成的代码 print("GAF 生成方法：%s，图片大小：%d * %d" % (method, img_sz, img_sz)) img_path = "%s/images" % savepath # 可视化图片保存的文件夹 data_path = "%s/data" % savepath # 数据文件保存的文件夹 if not os.path.exists(img_path): os.makedirs(img_path) # 如果文件夹不存在就创建一个 if not os.path.exists(data_path): os.makedirs(data_path) # 如果文件夹不存在就创建一个 print("开始生成...") print("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)) gaf = GramianAngularField(image_size=img_sz, method=method) data = np.loadtxt(filename, delimiter=",") n_sample, n_channels = data.shape img_num = 0 # 生成图片的总数 (the total numbers of GAF images) start_index, end_index = 0, window_sz # 序列开头以及末尾索引 while end_index <= n_sample: img_num += 1 sub_series = data[start_index:end_index, :] # 获得当前滑动窗口中的数据 gaf_images = gaf.fit_transform(sub_series.T) # 转化为 GAF 图片 for c in range(n_channels): # 保存每个 channel 的图片 gaf_img = gaf_images[c, :, :] # 得到第 c 个 channel 的数据 img_save_path = "{}/{}_{}.png".format(img_path, img_num, c) data_save_path = "{}/{}_{}.csv".format(data_path, img_num, c) image.imsave(img_save_path, gaf_img) # 保存图片 (save image) np.savetxt(data_save_path, gaf_img, delimiter=',') # 保存数据为 csv 文件 # 滑动窗口向后移动 start_index += step end_index += step print("生成完成，共处理 {} 个图片。".format(img_num * n_channels))