├── .gitignore ├── GramianAngularField.pdf ├── README.md ├── data ├── data.csv ├── data_mat.csv ├── data_mat2.csv ├── dataset │ ├── 0.csv │ ├── 1.csv │ ├── 2.csv │ ├── 3.csv │ └── 4.csv └── sinx.csv ├── images └── GAF_of_Sin.png └── src ├── GAF.py ├── generate_by_cols.py ├── generate_by_files.py ├── paa.py ├── slide_window.py └── slide_window_multicol.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | # file that i ignore 107 | .DS_Store 108 | -------------------------------------------------------------------------------- /GramianAngularField.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/strongnine/Series2Image/189453a9f410ee628f019fee0a17c7995240667a/GramianAngularField.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![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) 2 | 3 | # Series2Image 4 | 5 | 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） 6 | 7 | 更多详细的内容参考 CSDN 博客： 8 | 9 | 1. [将一维时间序列转化成二维图片](https://blog.csdn.net/weixin_39679367/article/details/86416439) 10 | 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) 11 | 12 | 2022 年 05 月 14 号更新： 13 | 14 | 1. `src/paa.py`：PAA 算法的代码； 15 | 2. `src/gaf.py`：GAF 基础 demo； 16 | 2. `src/generate_by_files.py`：将文件夹中的每个文件都生成一个 GAF 图； 17 | 2. `src/generate_by_cols.py`：数据矩阵中的每一列生成对应的 GAF 图； 18 | 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； 19 | 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； 20 | 21 | 22 | 23 | --- 24 | 25 | 26 | 27 | Encoding time series as images using GAF operation by `pyts`. 28 | 29 | see more in CSDN Blogs: 30 | 31 | 1. [将一维时间序列转化成二维图片](https://blog.csdn.net/weixin_39679367/article/details/86416439) 32 | 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) 33 | 34 | 2022.05.14 update: 35 | 36 | 1. `src/paa.py`：PAA algorithm code； 37 | 2. `src/GAF.py`：GAF demo； 38 | 2. `src/generate_by_files.py`：Generate GAF images in batches, each file in filedir generate a GAF image; 39 | 2. `src/generate_by_cols.py`：Generate GAF images in batches, each column in the data file generate a GAF image; 40 | 3. `src/slide_window.py`：Using a sliding window to generate GAF images from a long sequence of signals; 41 | 4. `src/slide_window_multicol.py`：Using a sliding window to generate GAF images from multi long sequence of signals; 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /data/data.csv: -------------------------------------------------------------------------------- 1 | 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439 | 0.382683432 440 | -3.92E-15 441 | -0.382683432 442 | -0.707106781 443 | -0.923879533 444 | -1 445 | -0.923879533 446 | -0.707106781 447 | -0.382683432 448 | -6.86E-15 449 | 0.382683432 450 | 0.707106781 451 | 0.923879533 452 | 1 453 | 0.923879533 454 | 0.707106781 455 | 0.382683432 456 | 1.76E-14 457 | -0.382683432 458 | -0.707106781 459 | -0.923879533 460 | -1 461 | -0.923879533 462 | -0.707106781 463 | -0.382683432 464 | 2.48E-18 465 | 0.382683432 466 | 0.707106781 467 | 0.923879533 468 | 1 469 | 0.923879533 470 | 0.707106781 471 | 0.382683432 472 | 1.08E-14 473 | -0.382683432 474 | -0.707106781 475 | -0.923879533 476 | -1 477 | -0.923879533 478 | -0.707106781 479 | -0.382683432 480 | -2.16E-14 481 | 0.382683432 482 | 0.707106781 483 | 0.923879533 484 | 1 485 | 0.923879533 486 | 0.707106781 487 | 0.382683432 488 | 3.92E-15 489 | -0.382683432 490 | -0.707106781 491 | -0.923879533 492 | -1 493 | -0.923879533 494 | -0.707106781 495 | -0.382683432 496 | -1.47E-14 497 | 0.382683432 498 | 0.707106781 499 | 0.923879533 500 | 1 501 | 0.923879533 502 | 0.707106781 503 | 0.382683432 504 | -2.94E-15 505 | -0.382683432 506 | -0.707106781 507 | -0.923879533 508 | -1 509 | -0.923879533 510 | -0.707106781 511 | -0.382683432 512 | -7.84E-15 -------------------------------------------------------------------------------- /images/GAF_of_Sin.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/strongnine/Series2Image/189453a9f410ee628f019fee0a17c7995240667a/images/GAF_of_Sin.png -------------------------------------------------------------------------------- /src/GAF.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | #copyright(c) strongnine 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | from matplotlib import image 6 | from pyts.image import GramianAngularField 7 | 8 | sin_data = np.loadtxt('./data/sinx.csv', delimiter=",", skiprows=0).reshape(1, -1) # 加载数据 (load the source data) 9 | image_size = 28 # 生成的 GAF 图片的大小 (the size of each GAF image) 10 | 11 | # `method` 的可选参数有：`summation` and `difference` 12 | # The optional parameters of argument `method`: `summation` and `difference` 13 | gasf = GramianAngularField(image_size=image_size, method='summation') 14 | sin_gasf = gasf.fit_transform(sin_data) 15 | 16 | gadf = GramianAngularField(image_size=image_size, method='difference') 17 | sin_gadf = gadf.fit_transform(sin_data) 18 | imges = [sin_gasf[0], sin_gadf[0]] 19 | titles = ['Summation', 'Difference'] 20 | 21 | # 两种方法的可视化差异对比 22 | # Comparison of two different methods 23 | fig, axs = plt.subplots(1, 2, constrained_layout=True) 24 | for img, title, ax in zip(imges, titles, axs): 25 | ax.imshow(img) 26 | ax.set_title(title) 27 | fig.suptitle('GramianAngularField', y=0.94, fontsize=16) 28 | plt.margins(0, 0) 29 | plt.savefig("./GramianAngularField.pdf", pad_inches=0) 30 | plt.show() 31 | 32 | image.imsave("./images/GAF_of_Sin.png", sin_gasf[0]) # 保存图片 (save image) 33 | np.savetxt("./images/GAF_of_Sin.csv", sin_gasf[0], delimiter=',') # 保存数据为 csv 文件 34 | -------------------------------------------------------------------------------- /src/generate_by_cols.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | #copyright(c) strongnine 3 | # 批量生成 GAF 图片的代码 (Generate GAF images in batches) 4 | # 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) 5 | import os 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | from matplotlib import image 9 | from pyts.image import GramianAngularField 10 | 11 | # 根据自己的需求修改一下变量的参数 12 | filename = "./data/data_mat.csv" # 要处理的文件路径 13 | savepath = "./data" # GAF 图片保存的路径 14 | img_sz = 28 # 确定生成的 GAF 图片的大小 15 | method = 'summation' # GAF 图片的类型，可选 'summation'（默认）和 'difference' 16 | 17 | # 以下是 GAF 生成的代码 18 | print("GAF 生成方法：%s，图片大小：%d * %d" % (method, img_sz, img_sz)) 19 | img_path = "%s/images" % savepath # 可视化图片保存的文件夹 20 | data_path = "%s/data" % savepath # 数据文件保存的文件夹 21 | if not os.path.exists(img_path): 22 | os.makedirs(img_path) # 如果文件夹不存在就创建一个 23 | if not os.path.exists(data_path): 24 | os.makedirs(data_path) # 如果文件夹不存在就创建一个 25 | 26 | print("开始生成...") 27 | 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)) 28 | src_data = np.loadtxt(filename, delimiter=",") # 加载数据 (load the source data) 29 | 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) 30 | gaf = GramianAngularField(image_size=img_sz, method=method) 31 | gaf_images = gaf.fit_transform(src_data.T) 32 | 33 | # gaf_images 的 shape 为 （img_num, img_sz, img_sz) 34 | for i in range(img_num): # 把每个图片都保存起来 35 | gaf_img = gaf_images[i, :, :] # 得到第 i 个图片的数据 36 | img_save_path = "%s/%d.png" % (img_path, i) 37 | data_save_path = "%s/%d.csv" % (data_path, i) 38 | image.imsave(img_save_path, gaf_img) # 保存图片 (save image) 39 | np.savetxt(data_save_path, gaf_img, delimiter=',') # 保存数据为 csv 文件 40 | 41 | print("生成完成，共处理 %d 个图片。" % img_num) -------------------------------------------------------------------------------- /src/generate_by_files.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | #copyright(c) strongnine 3 | # 批量生成 GAF 图片的代码 (Generate GAF images in batches) 4 | # 将文件夹中的每个文件都生成一个 GAF 图 (...) 5 | import os 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | from matplotlib import image 9 | from pyts.image import GramianAngularField 10 | 11 | # 根据自己的需求修改一下变量的参数 12 | dirname = "./data/dataset" # 要处理的文件夹路径 13 | savepath = "./data" # GAF 图片保存的路径 14 | img_sz = 28 # 确定生成的 GAF 图片的大小 15 | method = 'summation' # GAF 图片的类型，可选 'summation'（默认）和 'difference' 16 | 17 | 18 | # 以下是 GAF 生成的代码 19 | print("GAF 生成方法：%s，图片大小：%d * %d" % (method, img_sz, img_sz)) 20 | img_path = "%s/images" % savepath # 可视化图片保存的文件夹 21 | data_path = "%s/data_mat" % savepath # 数据文件保存的文件夹 22 | if not os.path.exists(img_path): 23 | os.makedirs(img_path) # 如果文件夹不存在就创建一个 24 | if not os.path.exists(data_path): 25 | os.makedirs(data_path) # 如果文件夹不存在就创建一个 26 | 27 | print("开始生成...") 28 | 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)) 29 | gaf = GramianAngularField(image_size=img_sz, method=method) 30 | img_num = 0 # 计算生成的图片个数 31 | for fname in os.listdir(dirname): 32 | filename, ext = os.path.splitext(fname) 33 | if ext != '.csv': continue # 如果不是 csv 文件则跳过 34 | img_num += 1 35 | 36 | src_data = np.loadtxt("{}/{}".format(dirname, fname), delimiter=",") # 加载数据 (load the source data) 37 | x = src_data.reshape(1, -1) 38 | img_gaf = gaf.fit_transform(x) 39 | 40 | img_save_path = "%s/%s.png" % (img_path, filename) 41 | image.imsave(img_save_path, img_gaf[0]) # 保存图片 (save image) 42 | 43 | data_save_path = "%s/%s.csv" % (data_path, filename) 44 | np.savetxt(data_save_path, img_gaf[0], delimiter=',') # 保存数据为 csv 文件 45 | 46 | print("生成完成，共处理 %d 个图片。" % img_num) -------------------------------------------------------------------------------- /src/paa.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | #copyright(c) strongnine 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | from pyts.approximation import PiecewiseAggregateApproximation 6 | 7 | data = np.loadtxt("./data/data.csv", delimiter=',') 8 | data = data.reshape(1, -1) 9 | n_sampels, n_timestamps = data.shape 10 | n_paa = 128 11 | window_size = n_timestamps // n_paa 12 | paa = PiecewiseAggregateApproximation(window_size=window_size) 13 | X_paa = paa.transform(data)[:, :n_paa] 14 | # np.savetxt("./paa_result.csv", X_paa.T, fmt='%.18f') 15 | 16 | plt.figure(figsize=(6, 4)) 17 | plt.plot(data[0], 'o--', ms=0.1, label='Original', linewidth=1.) 18 | plt.plot(np.arange(window_size // 2, 19 | n_timestamps + window_size // 2, 20 | window_size)[:n_paa], X_paa[0], 'o--', ms=2.5, label='PAA', linewidth=1.) 21 | plt.show() -------------------------------------------------------------------------------- /src/slide_window.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | #copyright(c) strongnine 3 | # 利用 滑动窗口 将一个长的序列信号生成多个 GAF 图片 4 | # Using a sliding window to generate GAF images from a long sequence of signals 5 | import os 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | from matplotlib import image 9 | from pyts.image import GramianAngularField 10 | 11 | # 根据自己的需求修改一下变量的参数 12 | filename = "./data/data.csv" # 要处理的文件路径 13 | savepath = "./data" # GAF 图片保存的路径 14 | img_sz = 128 # 生成的 GAF 图片的大小 (the size of each GAF image) 15 | # 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 16 | window_sz = 128 # 滑动窗口的大小，需要满足 window_sz > img_sz 17 | step = 16 # 滑动窗口的步长 (step of slide window) 18 | assert window_sz >= img_sz, "window_sz < img_sz（滑动窗口大小 小于 GAF 图片尺寸）。" 19 | method = 'summation' # GAF 图片的类型，可选 'summation'（默认）和 'difference' 20 | 21 | # 以下是 GAF 生成的代码 22 | print("GAF 生成方法：%s，图片大小：%d * %d" % (method, img_sz, img_sz)) 23 | img_path = "%s/images" % savepath # 可视化图片保存的文件夹 24 | data_path = "%s/data" % savepath # 数据文件保存的文件夹 25 | if not os.path.exists(img_path): 26 | os.makedirs(img_path) # 如果文件夹不存在就创建一个 27 | if not os.path.exists(data_path): 28 | os.makedirs(data_path) # 如果文件夹不存在就创建一个 29 | 30 | print("开始生成...") 31 | 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)) 32 | gaf = GramianAngularField(image_size=img_sz, method=method) 33 | data = np.loadtxt(filename, delimiter=",").reshape(1, -1) 34 | _, n_sample = data.shape 35 | 36 | img_num = 0 # 生成图片的总数 (the total numbers of GAF images) 37 | start_index, end_index = 0, window_sz # 序列开头以及末尾索引 38 | 39 | while end_index <= n_sample: 40 | img_num += 1 41 | sub_series = data[:, start_index:end_index] 42 | gaf_img = gaf.fit_transform(sub_series) # 转化为 GAF 图片 43 | 44 | img_save_path = "{}/{}.png".format(img_path, img_num) 45 | data_save_path = "{}/{}.csv".format(data_path, img_num) 46 | image.imsave(img_save_path, gaf_img[0]) # 保存图片 (save image) 47 | np.savetxt(data_save_path, gaf_img[0], delimiter=',') # 保存数据为 csv 文件 48 | 49 | # 滑动窗口向后移动 50 | start_index += step 51 | end_index += step 52 | 53 | print("生成完成，共处理 {} 个图片。".format(img_num)) -------------------------------------------------------------------------------- /src/slide_window_multicol.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | #copyright(c) strongnine 3 | # 利用 滑动窗口 将多列长的序列信号生成多个 GAF 图片 4 | # Using a sliding window to generate GAF images from a long sequence of signals 5 | import os 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | from matplotlib import image 9 | from pyts.image import GramianAngularField 10 | 11 | # 根据自己的需求修改一下变量的参数 12 | filename = "./data/data_mat2.csv" # 要处理的文件路径 13 | savepath = "./data" # GAF 图片保存的路径 14 | img_sz = 256 # 生成的 GAF 图片的大小 (the size of each GAF image) 15 | # 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 16 | window_sz = 300 # 滑动窗口的大小，需要满足 window_sz > img_sz 17 | step = 300 # 滑动窗口的步长 (step of slide window) 18 | assert window_sz >= img_sz, "window_sz < img_sz（滑动窗口大小 小于 GAF 图片尺寸）。" 19 | method = 'summation' # GAF 图片的类型，可选 'summation'（默认）和 'difference' 20 | 21 | # 以下是 GAF 生成的代码 22 | print("GAF 生成方法：%s，图片大小：%d * %d" % (method, img_sz, img_sz)) 23 | img_path = "%s/images" % savepath # 可视化图片保存的文件夹 24 | data_path = "%s/data" % savepath # 数据文件保存的文件夹 25 | if not os.path.exists(img_path): 26 | os.makedirs(img_path) # 如果文件夹不存在就创建一个 27 | if not os.path.exists(data_path): 28 | os.makedirs(data_path) # 如果文件夹不存在就创建一个 29 | 30 | print("开始生成...") 31 | 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)) 32 | gaf = GramianAngularField(image_size=img_sz, method=method) 33 | data = np.loadtxt(filename, delimiter=",") 34 | n_sample, n_channels = data.shape 35 | 36 | img_num = 0 # 生成图片的总数 (the total numbers of GAF images) 37 | start_index, end_index = 0, window_sz # 序列开头以及末尾索引 38 | 39 | while end_index <= n_sample: 40 | img_num += 1 41 | sub_series = data[start_index:end_index, :] # 获得当前滑动窗口中的数据 42 | gaf_images = gaf.fit_transform(sub_series.T) # 转化为 GAF 图片 43 | for c in range(n_channels): # 保存每个 channel 的图片 44 | gaf_img = gaf_images[c, :, :] # 得到第 c 个 channel 的数据 45 | 46 | img_save_path = "{}/{}_{}.png".format(img_path, img_num, c) 47 | data_save_path = "{}/{}_{}.csv".format(data_path, img_num, c) 48 | image.imsave(img_save_path, gaf_img) # 保存图片 (save image) 49 | np.savetxt(data_save_path, gaf_img, delimiter=',') # 保存数据为 csv 文件 50 | 51 | # 滑动窗口向后移动 52 | start_index += step 53 | end_index += step 54 | 55 | print("生成完成，共处理 {} 个图片。".format(img_num * n_channels)) --------------------------------------------------------------------------------