If you find this content useful, please consider supporting the work by buying the book! Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-coded regions. There are three Matplotlib functions that can be helpful for this task: plt.
This section looks at several examples of using these. We'll start by setting up the notebook for plotting and importing the functions we will use:. A contour plot can be created with the plt. It takes three arguments: a grid of x values, a grid of y values, and a grid of z values.
The x and y values represent positions on the plot, and the z values will be represented by the contour levels.
Perhaps the most straightforward way to prepare such data is to use the np. Notice that by default when a single color is used, negative values are represented by dashed lines, and positive values by solid lines. Alternatively, the lines can be color-coded by specifying a colormap with the cmap argument. Here, we'll also specify that we want more lines to be drawn—20 equally spaced intervals within the data range:. Here we chose the RdGy short for Red-Gray colormap, which is a good choice for centered data.
Matplotlib has a wide range of colormaps available, which you can easily browse in IPython by doing a tab completion on the plt. Our plot is looking nicer, but the spaces between the lines may be a bit distracting. We can change this by switching to a filled contour plot using the plt.
Additionally, we'll add a plt. The colorbar makes it clear that the black regions are "peaks," while the red regions are "valleys.
One potential issue with this plot is that it is a bit "splotchy. This could be remedied by setting the number of contours to a very high number, but this results in a rather inefficient plot: Matplotlib must render a new polygon for each step in the level. A better way to handle this is to use the plt. Finally, it can sometimes be useful to combine contour plots and image plots. For example, here we'll use a partially transparent background image with transparency set via the alpha parameter and overplot contours with labels on the contours themselves using the plt.
The combination of these three functions— plt. For more information on the options available in these functions, refer to their docstrings. If you are interested in three-dimensional visualizations of this type of data, see Three-dimensional Plotting in Matplotlib.
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I have a large binary file that contains all the information I want to plot. The data is ordered in such a way that its easiest to read into a 3D numpy array, this worked fine when I was using Mayavi to plot it using the contour3d function. Now I'm using Paraview and I can't find any examples of how I could accomplish the same thing.
It seems like the only way to get data in is to read it directly from a file in one of many formats and not a numpy array.
Any ideas? As far as I understand, mayavi is build on tvtka wrapper of vtk designed for Traits support and an easier handling of NumPy. ParaView on the other hand is based on pure vtkwhich makes it a tad less straightforward to manipulate ndarrays directly. However, some support functions are readily available:. Learn more. Creating a contour plot in paraview from a numpy array Ask Question.
Asked 7 years, 6 months ago. Active 7 years, 6 months ago. Viewed 2k times. Active Oldest Votes. In case anyone's interested there is a similar method: from paraview. As it turns out numpyTovtkDataArray only supports 2D arrays, which isn't very helpful for my situation. Sign up or log in Sign up using Google. Sign up using Facebook.Documentation Help Center.
The column and row indices of Z are the x and y coordinates in the plane, respectively. Specify levels as a scalar value n to display the contour lines at n automatically chosen levels heights. To draw the contour lines at specific heights, specify levels as a vector of monotonically increasing values.
To draw the contours at one height kspecify levels as a two-element row vector [k k].
ParaView/Users Guide/List of filters
Specify the options after all other input arguments. For a list of properties, see Contour Properties. Specify the axes as the first argument in any of the previous syntaxes.
Use c to set properties after displaying the contour plot. Define Z as a function of two variables. In this case, call the peaks function to create Z. Define Z as a function of two variables, X and Y. Then display contours at 10 levels of Z.
Define Z as a function of X and Y. In this case, call the peaks function to create XYand Z. Then display contours at levels 2 and 3. The white region corresponds to the heights less than 2. The purple region corresponds to heights between 2 and 3. And the yellow region corresponds to heights that are greater than 3. Create a filled contour plot. Make the contour lines thicker by setting the LineWidth property to 3. Insert NaN values wherever there are discontinuities on a surface.March 13,basic question contour plot in paraview.
Density and Contour Plots
Dear all, How can I plot a data as a contour plot with paraview, actually I was successful to upload my data, then what I need to do for the parameter to show the plot? March 14, Alan Russell. Kate, Since you didn't specify exactly what you're trying to do, here are some things to look try: 1.
In the Cavity tutorial, you look at pressure contours by moving to the time of interest then in the active variable controls, you select 'color by p'. There are two choices - the cube icon gives one value per cell and the circle icon interpolates pressure values. Make a slice through your domain and use the contour tool the half dome icon or Filters-Common-Contour from the drop down menus. You will need to click 'New Range' in the object inspector to define a range or set your own range by adding values.
The display tab will let you change the color scheme 'color by. There is a user guide for ParaView at their website www.
Density and Contour Plots
Some features are explained better than others - the tutorials at the end are well written. You need to download the dataset for the tutorials from the same website. Good luck, Alan. Thanks alot for your help, I will have a look at the attached file, actually my question seems to be basic, I want a contour plot for data formatted as a VTK format as in the attached file. I was successful to upload my data file in paraview, I pressed accept buttom, but it does not show any plot, how can I change the setting for the parameters to suit my data file.
Attached Files data. Last edited by Kate; March 14, at Sandeep Menon. I seemed to be able to open the file just fine. Attached Images data. Originally Posted by deepsterblue. Which version of paraview are you using? On ParaView 3. Nothing else to it. PHP Code:. Last edited by Kate; March 15, at Thread Tools.
BB code is On. Smilies are On. Trackbacks are Off. Pingbacks are On. Refbacks are On. Forum Rules.The main post-processing tool provided with OpenFOAM is a reader module to run with ParaViewan open-source, visualization application. It is executed like any of the OpenFOAM utilities either by the single command from within the case directory or with the -case option with the case path as an argument, e.
ParaView operates a tree-based structure in which data can be filtered from the top-level case module to create sets of sub-modules. For example, a contour plot of, say, pressure could be a sub-module of the case module which contains all the pressure data. The strength of ParaView is that the user can create a number of sub-modules and display whichever ones they feel to create the desired image or animation.
For example, they may add some solid geometry, mesh and velocity vectors, to a contour plot of pressure, switching any of the items on and off as necessary.
The general operation of the system is based on the user making a selection and then clicking the green Apply button in the Properties panel. The additional buttons are: the Reset button which can be used to reset the GUI if necessary; and, the Delete button that will delete the active module. The user can select mesh and field data which is loaded for all time directories into ParaView. As with any operation in paraFoamthe user must click Apply after making any changes to any selections.
The Apply button is highlighted in green to alert the user if changes have been made but not accepted.
Contour plot of a 2D Velocity Field in Paraview (Import .dat File)
This method of operation has the advantage of allowing the user to make a number of selections before accepting them, which is particularly useful in large cases where data processing is best kept to a minimum. If new data is written to time directories while the user is running ParaViewthe user must load the additional time directories by checking the Refresh Times button.
The Color Legend panel has a toggle switch for a colour bar legend and contains settings for the layout of the legend, e. The displayed toolbars can be selected from Toolbars in the main View menu. The function of many of the buttons is clear from their icon and, with tooltips enabled in the Help menu, the user is given a concise description of the function of any button.
Settings that are generally important only appear when the user checks the gearwheel button at the top of the Properties window, next to the search bar. These advanced properties include setting the background colour, where white is often a preferred choice for creating images for printed and website material. The Lights button opens detailed lighting controls within the Light Kit panel. A separate Headlight panel controls the direct lighting of the image.
Checking the Headlight button with white light colour of strength 1 seems to help produce images with strong bright colours, e. Other settings include Cube Axes which displays axes on the selected object to show the its orientation and geometric dimensions. The General panel controls some default behaviour of ParaView.
In particular, there is an Auto Accept button that enables ParaView to accept changes automatically without clicking the green Apply button in the Properties window. The General panel includes the level of detail LOD which controls the rendering of the image while it is being manipulated, e.
The Camera panel includes control settings for 3D and 2D movements. This presents the user with a map of rotation, translate and zoom controls using the mouse in combination with Shift- and Control-keys. The map can be edited to suit by the user. The filter acts on a given module so that, if the module is the 3D case module itself, the contours will be a set of 2D surfaces that represent a constant value, i. The Properties panel for contours contains an Isosurfaces list that the user can edit, most conveniently by the New Range window.
The chosen scalar field is selected from a pull down menu. To do so, the user must first use the Slice filter to create the cutting plane, on which the contours can be plotted.
The user can manipulate the cutting plane like any other using the mouse. The user can then run the Contour filter on the cut plane to generate contour lines. The filter reads the field selected in Vectors and offers a range of Glyph Types for which the Arrow provides a clear vector plot images.Set the x axis and y axis limits pylab.
Provide a title for the contour plot plot. Set x axis label for the contour plot plot. Set y axis label for the contour plot plot. Create contour lines or level curves using matplotlib. Display z values on contour lines plot. Display the contour plot plot.
Create contour lines for the Hyperbolic Paraboloid using matplotlib. Toggle navigation Pythontic. Each contour is a curve that is a resultant of cutting a surface by a plane.
Every contour need not form a curve. Some of the resultant contours can be a straight line as well. Here is the formal definition of a contour plot: A level curve of a function f x,y is the curve of points x,y where f x,y is some constant value, on every point of the curve.
Different level curves produced for the f x,y for different values of c - can be put together as a plot, which is called a level curve plot or a contour plot. Every contour line in a contour plot is drawn for different value of z, each value a constant. Applications of Contour Plots: A contour plot in cartography represents levels of equal elevation with respect to a base level. A contour line that connects places with the same temperature is called an isotherm.
Call the contour function of matplotlib. Example 1: import numpy as np import matplotlib.Recently we have added a new feature to GeoJS: 2D contour plots. The grid can be regular or irregular.
The colors can be stepped or smooth, and the scalar values can be mapped uniformly or in a non-linear manner. For example, here is a contour plot using the altitude z value values of Oahu, Hawaii. The colors are taken from one of the Paraview color maps. This demo will be on the geojs examples page after the next release. The code for it can be found on GitHub. There are many options for how the data is specified for the contour and how it is rendered.
Although the data must be arranged as a squarely-connected grid, the grid does not have to be even. Each point on the grid can have any latitude and longitude. Alternately, any corner of the grid can be specified along with the distance between rows and columns of the grid.
Each grid point can have its own opacity value. The scalar values used for the contour can be taken from the z value of each point or specified as a separate array. If a value is null, then all of the grid squares that use that point will not be rendered. For the example above, the ocean values were specified as null. The colors used for the contour can be specified completely with any number of discrete colors, each with its own opacity. Two separate colors can be used for values above or below a specified maximum and minimum the default is to use the entire range of the scalar value.
The mapping between scalar values and colors does not have to be linear — it will be piecewise linear between colors, but the colors can correspond to any monotonic sequence of values. This is a fast way to generate iso lines, but the line thickness will vary some. Each line can be distinctly colored. Lower left: the maximum and minimum values are cropped to alternate colors, and the contour is smoothly shaded within that range, rather than using discrete colors.
GeoJS is an open source geospatial analysis and visualization library hosted on Github.