Save plt as pdf in python

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Save plt as pdf in python

In this article, we show how to save a figure in MatplotLib with Python. So with Matplotlib, the heart of it is to create a figure. On this figure, it is possible to populate it with all the different data types, including axes, a graphics, a geometric shape, etc. How can we save this figure after creating it? And we can do it with the Savefig () function. With the SAVEFIG () function, we can save the figure in the current work directory. We can save this figure as a name and any type of image file, such as PNG, JPG, etc. This is shown in the following code below. Import matplotlib.pyplot as PLT fig = ptt.figure () axis = fig.add_axes ([0,1,0,1,08,0.8]) x = [1,2,3,4,5,6, 7,89, 10,11,12] y = [x ** 2 for x in x] axes.plot (x, y) fig.savefig ('graph1.png') ptt.show () then the first thing that We must do is Import Matplotlib. We do it with the line, IMPORT MATPLOTLIB.PYPLOT as PLT, so we create a variable fig and set it equal to, plt.figure (). This creates an empty figure object. Then add axles to the object. We therefore have the X coordinates. The y coordinates are the same as the square of each of the X coordinates. Then we treat the X and Y coordinates. So we save the figure as 'graph1.png'. The file name is 'Graph1' and file format is PNG. They then show the figure using the PLT.Show () function. The PNG file will then be saved in the current working directory. You can discover the current working current by importing the operating system. And then call the function, os.getcwd (). It is here that the figure will be saved. And this is how to save a figure in Matplotlib with Python. Related Resources How to randomly select or mix a list in Python This article will show you how to view, edit and save images in Python, more specifically in matplotlib. In the following sections, we will see how to upload images, how to change some of its properties and finally as salvarle.long Story Short-shortful you want to import an image and display it in a window matplotlib, the matplotlib imereci function () works perfectly. After importing the image file as an array, you can create a matplotlib window and axes in which we can view the image using imshow (). By changing some of the properties available within imshow () we can change the color, size and even decide to crop the displayed image. Once we are satisfied with the result of our modifications, the SaveFig () function allows you to save the figure as input parameters specifying the location where you want the image to be stored. Taking advantage of some additional parameters of the latter function You can decide whether to make the image transparent borders or even eliminarli.Main used functions: imShowsyNtax: imshow () Parameters: x (a similar image array) The image DATE FileCMAP ( STR) COLORMAPALPHA (Float) Transparency of the ImageAeeSpect {? ? "? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? "Aspect ratio of Axesvmin, VMAX (float) minimum and maximum values for the color Rangereturn Valuenonemore Information: .html.savefigshow.html.savefigsyntax: Savefig () Parameters: FName (STR or path) path where you want the file is salvatoBox_inches (STR) in inches. If ? ? ? ? ? ? ? ? tighta, try to understand the BBox Tight Bbox the amount of figure_inches (float) amount of padding around the figure when Bbox_inches is ? ? ? ? ? ~ ? ~ ? ? ? .. .Return Valuenonemore information here: Stable / API / _S_GEN / MATPLOTLIB.PYPLOT.SAVEFIG.HTML.Importing an image in your script, start the tutorial by importing the image into our script, in this way we will be able to take further action and then change its appearance. To do so, The Matplotlib imread () function, which reads an image from a file in an array (official documentation: . The function takes the path of the image file that we want to import as the only input parameter. The image file is converted into M m N X 3 (in the case of an RGB image) Matrix with M and N stands for the number of pixels for width and height, respectively. In the case of an RGB image, each minj sub-machine will contain 3 numbers corresponding to the combination of blue green values that identify that specific color; In the case of RGBA images, we would have a M x N x 4 array.In the following code lines, we import the MatplotLib library and Assig to the variable a pica matrix point that describes the image file we wanted to import.import matplotlib.pyplot As PLT URL = R "File_Path" Pic = PLT.imread (URL) Printing (Photo) The result of the print command will be displayed in the terminal a 3D matrix that describes the image file that you uploaded.Obtaining the image Sizeit You can know how many pixels compose our image and what kind of color code that uses, simply by applying the .Shape () method for the variable in which we have stored the matrix describing the image file. In fact, obtaining the form of a matrix, we will get the different dimensions of the matrix, since each subarray will represent a pixel with the color values, we will obtain an indication of the exact number of pixels that make up our image (ie each element corresponds to a Pixel). # Get pixels of the image to be printed (pic.Shape) >> (2559, 4550, 3) in our case, we have an image that is 2559 pixels high, width 4550 pixels and is defined according to an RGB color Scheme (from 3 values in the last dimension of the array) The images show in Matplotlotlibin This section we will see how to view an image file in a MatplotLib window; The procedure is extremely simple and very similar to what is used to trace a normal chart. We begin by creating Matplotlib figure and the axes. # Display the image in a MPL figure fig = plt.figure () = ax fig.subplots () At this point, the IMSHOW MATPLOTLIB () function is used to view the image within the newly created axes. The IMSHOW () function accepts as the main variable parameter input which refers to the image file, which in our case is a pica point. The result from the following code lines is then displayed in Figure 1.ax.imshow (PIC) plt.show () Figure 1: After creating a MatplotLib figure and the axes, IMSHOW () allows you to view the image file by Figure.Changing axes The imageby owner expressed by specifying other input parameters within the IMShow function () You can change some of the properties of the displayed image, such as size, color and its transparency. I can be able to modify your own owner, we have to indicate the pixels on which we want to apply any changes, if we want to change the entire image, we only need to indicate all the pixels (in this case, it means PIC writing [:::, 1]) . The first property we will change is the transparency of the image; We can make the value of the alpha parameter (from 0 to 1) .we can also change the color map used in the image, for example using a color map ? ? Gray? ? turning color image in a black and white photo. This thing can be made by changing the CMAP parameter (here you can find all the maps available for Matplotlib the possible color: .if We want to change the color interval used to 'Interior of the selected color map, you could change the values of the VMIN and VMAX parameters to grant the highest and lower values of the color range. If you don? ? t specify these two parameters, the full color range will be used to display the image.In addition to change the color properties, we can also change the aspect ratio of the pixels of the image; This is an important theme to keep in mind, especially if Change the dimensions of the axes of the figure. This can be done by exploiting the parameter appearance; We can choose between two different or equal car options. Using cars will keep fixed axes and the aspect ratio of the pixels is adjusted accordingly, generally consequent non-square pixels. On Hand, the same guarantees a proportion of 1 for pixels in the image, ie square pixels.You can find more information about the IMSHOW () function and all its properties on the official documentation page: / API / _AS_GEN / MATPLOTLIB.PYPLOT.IMSHOW.HTML. In the following line code, it is assigned to the V variable the image file and change all the aforementioned properties.f = ax.imshow (PIC [::, 1], alpha = 0.5, cmap = 'gray', vmin = 0, Vmax = 500, aspect = 'car') It is also possible to add a color bar next to the image by applying the colorbar () method for figure.f = ax.imshow (PIC [:::, 1], alpha = 0.5, CMAP = 'Gray', VMIN = 0, VMAX = 500, Aspect = 'Auto') The final result is shown in Figure 2; Note that since I wait for it = ? ? Auto? ?, the proportions of the pixels has changed thus producing an image whose shape is slightly different (see the video for a better example) from that of Figure 2 1.Figure: Changing the properties within the Imshow () function allowed to change the scale appearance of colors and pixels ratio.Changing Range and Visibilityas can be seen from previous figures, all axles and notch labels are still present and visible In the window track. If we would like to remove them and then only have the image traced inside the Matplotlib window, you can deactivate the axes using .asse (? ? offa). If you are also interested in tracing only one part of the image, we can do it by adjusting the value of the maximum XEY values to be displayed on each axis. All these commands are displayed in the following code lines: # Change the ax ax.set_xlim axes (0, 2500) ax.set_ylim (2500, 0) ax.axis ('OFF') You can notice that we have chosen to view the Image for all its height, but with a width ranging from 0 to 2500 value. Also note that the coordinates of the axis are expressed in the inverted order; This is because the y axis of the image goes from the upper part to the lower part of the figure. If you gave you the limits of y in reverse order, the imaginated will be turned upside down. The final result is illustrated in Figure 3.Figure 3: it is possible to crop the image simply by changing the values of the maximum XEY coordinates along the axes. To view only the image, you can then set to ? ? Offa axis visibility.Saving The image file We ended up modifying our image, we can save it using the Savefig Matplotlib () function. The mandatory unique input parameter is the path where you want to save the image. In the event that we want to save the image without white board, we can specify the two optional BBox_inches parameters (to set it to a Tighta) and pad_inches (setting equal to zero). With the first option we do the boundary of the figure to adapt to the size of the image, with the second we set to zero the quantity of the border around the figure (which must first series of ? ? tighta propriet? bbox_inches) .plt.savefig (r " C: Users Andrea Desktopewimage.jpg ", BBox_Inches = 'Narrow', Pad_Inches = 0) Conclusion, ?, in this article, we have just learned how to import and modify an image file using Matplotlib. First of all, we imported the image file inside a Matplotlib window; Then based on the appropriate features, it was possible to view the image, change its color, its size and proportion. In the end we also seen how to save the modified image in a new image file. All these procedures represent a very simple and fast solution for rapid image adjustment within a Python session. session.

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