Python-based geophysical data visualization platform and method

Abstract

The invention belongs to the field of visualization platforms, and particularly relates to a python-based geophysical data visualization platform and method.The python-based geophysical data visualization platform comprises a user interface module, a data management module and a visualization selection module, the data management module is connected with the user interface module, and the visualization selection module is connected with the data management module. The visual selection module is connected with the user interface module, the data management module comprises a data import module, a data export module, a data conversion module and a data preview module, the data import module is connected with the data management module, the data export module is connected with the data management module, and the data conversion module is connected with the data preview module. And the data conversion module is connected with the data management module. The method is reasonable in design, a user can modify the visualization effect in multiple links including colors, graph attributes, interpolation methods and the like, the user can obtain satisfactory visualization results conveniently, and analysis and explanation of geophysical data are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of visualization platforms, and in particular to a geophysical data visualization platform and method based on Python. Background Art

[0002] The existing geophysical visualization technologies mainly use OpenGL and VTK, and both are single visualizations of geophysical data, and cannot integrate geophysical data visualization into the same platform. The visualization methods based on OpenGL and VTK are relatively complex, involving some algorithms, etc., and are difficult to program. They do not directly provide data analysis or advanced graphics functions, and require users to build most functions from scratch, and cannot be used directly. The same VTK visualization method is more advanced than OpenGL, but still requires relatively more programming knowledge and a certain learning curve. It cannot be well integrated with the Python ecosystem (especially the scientific and data analysis stack), and the code is relatively complex. Therefore, we propose a geophysical data visualization platform and method based on Python to solve the above problems. Summary of the invention

[0003] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a geophysical data visualization platform and method based on Python.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A geophysical data visualization platform based on Python, comprising a user interface module, a data management module and a visualization selection module, wherein the data management module is connected to the user interface module, and the visualization selection module is connected to the user interface module;

[0006] The data management module includes a data import module, a data export module, a data conversion module and a data preview module, wherein the data import module is connected to the data management module, the data export module is connected to the data management module, the data conversion module is connected to the data management module, and the data preview module is connected to the data management module;

[0007] The visualization selection module includes a file selection submodule, a parameter corresponding submodule, an interpolation method selection module, a color selection module, a grid setting submodule, a grid parameter modification module and a graphic attribute modification module;

[0008] The file selection submodule is connected to the visualization selection module, the parameter corresponding submodule is connected to the visualization selection module, the interpolation method selection module is connected to the visualization selection module, the color selection module is connected to the visualization selection module, the grid setting submodule is connected to the visualization selection module, the grid parameter modification module is connected to the visualization selection module, and the graphic attribute modification module is connected to the visualization selection module.

[0009] Preferably, the user finds the data management button in the interface and clicks it, selects "Data Import" after a drop-down menu pops up, and during the data import process, the geophysical data file is read using Python's file reading function, and the user is asked to enter the row of the original data to start entering through a dialog box or the like, and the parameter name is re-filled using a text input box or the like.

[0010] Preferably, after the data is imported, the selected data is formatted by a Python data processing library. According to the format characteristics of the geophysical data, a corresponding conversion algorithm is written to convert the data into a .csv file format and store it in a temporary cache or a designated directory.

[0011] Preferably, the file selection submodule is used to select files to be visualized;

[0012] Parameter correspondence submodule: Users can determine the parameter names that need to be visualized for the selected file, and correspond the x, y, and z axes to the data parameters one by one;

[0013] Color selection submodule: supports users to select the fill color of the image in the visualization, and the fill color can be modified again in the subsequent simple human-computer interaction process.

[0014] Preferably, the interpolation method selection submodule provides a variety of interpolation methods such as linear interpolation (linear), cubic spline interpolation (spline), cubic interpolation (cubic) for users to choose from, and different interpolation methods will generate visualization images with different visual effects.

[0015] Preferably, in the grid setting submodule: the user can select whether a grid is required in the visualized image;

[0016] Grid parameter modification submodule: can modify the grid range and step size;

[0017] Graphics attribute modification submodule: For images that have generated visualization content, users can modify the image title and coordinate name on the page, and adjust the x-axis, y-axis, z-axis range, etc. In addition, the fill color can be modified again without returning to the original visualization parameter selection page.

[0018] A method for a geophysical data visualization platform based on Python, comprising the following steps:

[0019] S1: After opening the software, click Data Management, select "Data Import", and import the geophysical data to be processed into the platform; when importing data, you can choose the row of the original data to start entering, and you can re-fill in the selected parameter name;

[0020] S2: Since there are many types of geophysical data and different data formats, the imported geophysical data are converted into csv files to facilitate subsequent operations;

[0021] S3: After the format conversion is completed, the data can be viewed and edited;

[0022] S4: Save the confirmed geophysical data;

[0023] S5: Select the saved csv file and check the data content to be visualized on the page;

[0024] S6: Select the fill color of the visualization image;

[0025] S7: Select an available interpolation method to interpolate the data using the griddata() method. The optional interpolation methods are: linear interpolation (linear), nearest neighbor interpolation (nearest), and cubic spline interpolation (cubic);

[0026] S8: Select whether a grid is required in the visualization image;

[0027] S9: Use the meshgrid() method to perform default gridding on the selected visualization data in the csv file after format conversion;

[0028] S10: The range of data visualization and the step length of gridding can be adjusted to meet the needs of users;

[0029] S11: Generate a visualized picture (to achieve simple interaction) and check whether the picture is satisfactory. If not, you can readjust the step size. If you are satisfied, just save it.

[0030] S12: Export visual data images.

[0031] Beneficial effects of the present invention:

[0032] 1. Python language has the advantages of high developability and strong readability. With the Matplotlib visualization library, most of the data of gravity, magnetism, electricity and seismic in geophysics can be visualized. Matplotlib library is a visualization library based on Python language.

[0033] 2. Matplotlib's API is intuitive and easy to use, and can quickly generate charts from data. Matplotlib integrates very well with other Python scientific computing libraries (such as NumPy and Pandas), making it a standard charting tool in Python data analysis and scientific computing. This tight integration simplifies the workflow of data processing and visualization. At the same time, Matplotlib provides a wide range of drawing options, can generate high-quality graphics, and allows fine control over every element of the chart. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A block diagram of a geophysical data visualization platform based on Python proposed by the present invention;

[0035] Figure 2 A flowchart of a method for a geophysical data visualization platform based on Python proposed by the present invention;

[0036] Figure 3-14 This is a flow chart of three data parameters of a method for a Python-based geophysical data visualization platform proposed in the present invention. DETAILED DESCRIPTION

[0037] The technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Embodiment 1

[0039] Reference Figure 1A geophysical data visualization platform based on Python includes a user interface module, a data management module and a visualization selection module, the data management module is connected to the user interface module, and the visualization selection module is connected to the user interface module; the data management module includes a data import module, a data export module, a data conversion module and a data preview module, the data import module is connected to the data management module, the data export module is connected to the data management module, the data conversion module is connected to the data management module, and the data preview module is connected to the data management module; the visualization selection module includes a file selection submodule, a parameter corresponding submodule, an interpolation method selection module, a color selection module, a grid setting submodule, a grid parameter modification module and a graphic attribute modification module; the file selection submodule is connected to the visualization selection module, the parameter corresponding submodule is connected to the visualization selection module, the interpolation method selection module is connected to the visualization selection module, the color selection module is connected to the visualization selection module, the grid setting submodule is connected to the visualization selection module, the grid parameter modification module is connected to the visualization selection module, and the graphic attribute modification module is connected to the visualization selection module.

[0040] In this embodiment, the user finds the data management button in the interface and clicks it, and selects "Data Import" after a drop-down menu pops up. During the data import process, Python's file reading function is used to read the geophysical data file, and the user is asked to enter the row of the original data to start entering through a dialog box, and the parameter name is re-filled using a text input box or the like.

[0041] In this embodiment, after the data is imported, the selected data is converted into a format by the Python data processing library. According to the format characteristics of the geophysical data, a corresponding conversion algorithm is written to convert the data into a .csv file format and store it in a temporary cache or a specified directory.

[0042] In this embodiment, the file selection submodule is used to select the file to be visualized; the parameter corresponding submodule: the user can determine the parameter name corresponding to the selected file to be visualized, and correspond the x, y, and z axes to the data parameters one by one; the color selection submodule: supports the user to select the fill color of the image in the visualization, and the fill color can be modified again in the subsequent simple human-computer interaction process.

[0043] In this embodiment, the interpolation method selection submodule provides a variety of interpolation methods such as linear interpolation (linear), cubic spline interpolation (spline), and cubic interpolation (cubic) for users to choose from. Different interpolation methods will generate visualization images with different visual effects.

[0044] In this embodiment, the grid setting submodule: the user can choose whether to need a grid in the visualization picture; the grid parameter modification submodule: the grid range and step size can be modified; the graphic attribute modification submodule: for the picture that has generated visualization content, the user can modify the picture title and coordinate name on the page, and can also adjust the x-axis, y-axis, z-axis range, etc. In addition, the fill color can be modified again without returning to the initial visualization parameter selection page.

[0045] A method for a geophysical data visualization platform based on Python, comprising the following steps:

[0046] S1: After opening the software, click Data Management, select "Data Import", and import the geophysical data to be processed into the platform; when importing data, you can choose the row of the original data to start entering, and you can re-fill in the selected parameter name;

[0047] S2: Since there are many types of geophysical data and different data formats, the imported geophysical data are converted into csv files to facilitate subsequent operations;

[0048] S3: After the format conversion is completed, the data can be viewed and edited;

[0049] S4: Save the confirmed geophysical data;

[0050] S5: Select the saved csv file and check the data content to be visualized on the page;

[0051] S6: Select the fill color of the visualization image;

[0052] S7: Select an available interpolation method to interpolate the data using the griddata() method. The optional interpolation methods are: linear interpolation (linear), nearest neighbor interpolation (nearest), and cubic spline interpolation (cubic);

[0053] griddata is a function in Python for data interpolation, commonly used in scientific computing and data analysis. This function belongs to the scipy.interpolate module. griddata allows interpolation from scattered data points to a regular grid, which is very useful when you need to estimate the value of unknown data points on a uniform grid.

[0054] The main parameters are (1). points: a two-dimensional numpy array containing the coordinates of the input data points. Each row is the coordinates of a point.

[0055] (2).values: A one-dimensional array containing the values ​​corresponding to the coordinates in points.

[0056] (3).xi: An array of points representing the interpolation grid. This can be a list similar to points, or a multidimensional array generated by meshgrid to represent a grid in one or more dimensions.

[0057] (4).method: interpolation method. Common methods include: 'linear': linear interpolation, 'nearest': nearest neighbor interpolation, 'cubic': cubic spline interpolation (limited to two-dimensional input;)

[0058] (5).fill_value: The value used to fill the area outside the interpolation. The default value is NaN;

[0059] S8: Select whether a grid is required in the visualization image;

[0060] S9: Use the meshgrid() method to perform default gridding on the selected visualization data in the csv file after format conversion;

[0061] meshgrid is a very useful function in Python, which is often related to data analysis and graphical display. This function belongs to the numpy library and is used to generate a coordinate matrix from a one-dimensional array. This is very useful for grid-based data calculations and visualization, such as drawing three-dimensional surface plots or performing spatial interpolation. meshgrid can convert two one-dimensional arrays into two two-dimensional matrices corresponding to all (x, y) pairs, where x and y are taken from the original two one-dimensional arrays respectively. If higher dimensions are involved, meshgrid can also handle it and generate multiple grid arrays;

[0062] S10: The range of data visualization and the step length of gridding can be adjusted to meet the needs of users;

[0063] S11: Generate a visualized picture (to achieve simple interaction) and check whether the picture is satisfactory. If not, you can readjust the step size. If you are satisfied, just save it.

[0064] S12: Export visual data images.

[0065] Embodiment 2

[0066] Two data parameters

[0067] Reference Figure 2 , a method for a geophysical data visualization platform based on Python, comprising the following steps:

[0068] S1: After opening the software, click Data Management, select "Data Import", and import the geophysical data to be processed into the platform; when importing data, you can choose the row of the original data to start entering, and you can re-fill in the selected parameter name;

[0069] S2: Select the data to be processed and convert the format to convert the geophysical format into the required .csv file;

[0070] S3: You can preview or edit the converted data;

[0071] S4: Save the data after format conversion and export it to the project work area;

[0072] S5: Select the visualization file;

[0073] S6: Select the visualization method you need to use for the data; different visualization methods correspond to different types;

[0074] S7: Visualization into figures;

[0075] S8: When the mouse cursor moves to different positions, the horizontal and vertical coordinates of the point are displayed in the upper right corner of the page;

[0076] S9: Click the move icon on the page item bar to modify the top, bottom, left, and right proportions of the visualization page;

[0077] S10: You can modify the name of the visualization table, as well as the range and name of the x-axis and y-axis, etc. as needed;

[0078] S11: Select Save.

[0079] Embodiment 3

[0080] Three data parameters

[0081] Reference Figure 3-14 , a method for a geophysical data visualization platform based on Python, comprising the following steps:

[0082] S1: After opening the software, click Data Management, select "Data Import", and import the geophysical data to be processed into the platform; when importing data, you can choose the row of the original data to start entering, and you can re-fill in the selected parameter name;

[0083] S2: Select the data to be processed and convert the format to convert the geophysical format into the required .csv file;

[0084] S3: You can preview the converted data;

[0085] S4: Save the data after format conversion and export it to the project work area;

[0086] S5: Select the visualization file;

[0087] S6: For the selected file, select the parameter name corresponding to the one you want to visualize; match the x, y, and z axes with the data parameters one by one;

[0088] S7: Select the fill color of the image in the visualization; select the desired fill color; the fill color can be modified later in simple human-computer interaction;

[0089] S8: Select the interpolation method; linear interpolation (linear), cubic spline interpolation (spline), cubic interpolation (cubic), different interpolation methods correspond to different visualization pictures;

[0090] S9: Select whether a grid is required in the visualization image;

[0091] S10: The gridding range and step size can be modified;

[0092] S11: For the visualized content that has been mapped, you can modify the image title, coordinate name, etc. on the page, and you can also modify the x-axis, y-axis, z-axis range, etc.;

[0093] S12: Fill color can be modified without returning to the initial visualization parameter selection page;

[0094] S13: Save the visualized image.

[0095] In the present invention, Python language has advantages such as high developability, strong readability, etc. Under the Matplotlib visualization library, most of the data of gravity, magnetism, electricity, and seismic in geophysics can be visualized. Matplotlib library is a visualization library based on Python language. The API design of Matplotlib is intuitive and easy to use, and charts can be quickly generated from data. Matplotlib is well integrated with other scientific computing libraries of Python (such as NumPy and Pandas), which makes it a standard chart tool in the field of Python data analysis and scientific computing. This tight integration simplifies the workflow of data processing and visualization. At the same time, Matplotlib provides a wide range of drawing options, can generate high-quality graphics, and allows fine control of every element of the chart.

[0096] The above is a detailed introduction to a geophysical data visualization platform and method based on Python provided by the present invention. Specific embodiments are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core ideas of the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A geophysical data visualization platform based on Python, characterized in that: It includes a user interface module, a data management module and a visualization selection module, wherein the data management module is connected to the user interface module, and the visualization selection module is connected to the user interface module; The data management module includes a data import module, a data export module, a data conversion module and a data preview module, wherein the data import module is connected to the data management module, the data export module is connected to the data management module, the data conversion module is connected to the data management module, and the data preview module is connected to the data management module; The visualization selection module includes a file selection submodule, a parameter corresponding submodule, an interpolation method selection module, a color selection module, a grid setting submodule, a grid parameter modification module and a graphic attribute modification module; The file selection submodule is connected to the visualization selection module, the parameter corresponding submodule is connected to the visualization selection module, the interpolation method selection module is connected to the visualization selection module, the color selection module is connected to the visualization selection module, the grid setting submodule is connected to the visualization selection module, the grid parameter modification module is connected to the visualization selection module, and the graphic attribute modification module is connected to the visualization selection module.

2. A Python-based geophysical data visualization platform according to claim 1, characterized in that: The user finds the data management button in the interface and clicks it, and selects "Data Import" after a drop-down menu pops up. During the data import process, the geophysical data file is read using Python's file reading function, and the user is asked to enter the row of the original data to start entering through a dialog box or the like, and the parameter name is re-filled using a text input box or the like.

3. A geophysical data visualization platform based on Python according to claim 1, characterized in that: After the data is imported, the selected data is converted into a format using the Python data processing library. According to the format characteristics of the geophysical data, a corresponding conversion algorithm is written to convert the data into a .csv file format and store it in a temporary cache or a designated directory.

4. A Python-based geophysical data visualization platform and method according to claim 1, characterized in that: The file selection submodule is used to select the file to be visualized; Parameter correspondence submodule: Users can determine the parameter names that need to be visualized for the selected file, and correspond the x, y, and z axes to the data parameters one by one; Color selection submodule: supports users to select the fill color of the image in the visualization, and the fill color can be modified again in the subsequent simple human-computer interaction process.

5. A Python-based geophysical data visualization platform according to claim 1, characterized in that: The interpolation method selection submodule provides a variety of interpolation methods such as linear interpolation (linear), cubic spline interpolation (spline), and cubic interpolation (cubic) for users to choose from. Different interpolation methods will generate visualization images with different visual effects.

6. A Python-based geophysical data visualization platform according to claim 1, characterized in that: The grid setting submodule: the user can choose whether a grid is required in the visualization image.

7. A Python-based geophysical data visualization platform according to claim 1, characterized in that: The grid parameter modification submodule: can modify the grid range and step size; Graphics attribute modification submodule: For images that have generated visualization content, users can modify the image title and coordinate name on the page, and adjust the x-axis, y-axis, z-axis range, etc. In addition, they can modify the fill color again without returning to the original visualization parameter selection page.

8. The method of a Python-based geophysical data visualization platform according to claim 1, characterized in that: The following steps are involved: S1: After opening the software, click Data Management, select "Data Import", and import the geophysical data to be processed into the platform; when importing data, you can choose the row of the original data to start entering, and you can re-fill in the selected parameter name; S2: Since there are many types of geophysical data and different data formats, the imported geophysical data are converted into csv files to facilitate subsequent operations; S3: After the format conversion is completed, the data can be viewed and edited; S4: Save the confirmed geophysical data; S5: Select the saved csv file and check the data content to be visualized on the page; S6: Select the fill color of the visualization image; S7: Select an available interpolation method to interpolate the data using the griddata method. The optional interpolation methods are: linear interpolation (linear), nearest neighbor interpolation (nearest), and cubic spline interpolation (cubic); S8: Select whether a grid is required in the visualization image; S9: Use the meshgrid method to perform default gridding on the selected visualization data in the csv file after format conversion; S10: The range of data visualization and the step length of gridding can be adjusted to meet the needs of users; S11: Generate a visualized picture (to achieve simple interaction) and check whether the picture is satisfactory. If not, you can readjust the step size. If you are satisfied, just save it. S12: Export visual data images.

Images