A Plots Resolution
Plot refers to the storyline of the text. The plot is the sequence of events in the story or drama. There are several elements that are common to all plots: The introduction, or exposition, is the beginning of the story where the characters and the conflict are introduced. A plot device is a means of advancing the plot in a story. It is often used to motivate characters, create urgency, or resolve a difficulty. This can be contrasted with moving a story forward with dramatic technique; that is, by making things happen because characters take action for well-developed reasons. To save plots for including in documents, such as publications or slide presentations, use the exportgraphics function. This function enables you to save plots at the appropriate size, resolution, and background color for your document. The saved content is tightly cropped around the axes with minimal white space. A resolution can either highlight how truly enjoyable a story is or it can so ruin a good story that readers forget how much they enjoyed everything up to that point. At story’s end, the warrior protagonist has found victory or defeat, hero and heroine have declared their love, the detective has caught his murderer, and the lone wolf of an agent has saved the world. Elements of Drama: Characters, Plot, Setting & Symbolism 7:33 Plot Elements in Drama: From Exposition to Resolution 6:07 4:45.
- Resolution Plot Diagram Definition
- A Plots Resolution Meaning
- A Plots Resolution Background
- 7 Plot Point Story Structure
- A Plots Resolution Project
- Resolution In A Story
- The Resolution Of A Story Happens When
Stata has excellent graphic facilities, accessible through the graph command, see help graph for an overview. The most common graphs in statistics are X-Y plots showing points or lines. These are available in Stata through the twoway subcommand, which in turn has many sub-subcommands or plot types, the most important of which are scatter and line. I will also describe briefly bar plots, available through the bar subcommand, and other plot types.
Stata 10 introduced a graphics editor that can be used to modify a graph interactively. I do not recomment this practice, however, because it conflicts with the goals of documenting and ensuring reproducibility of all the steps in your research.
All the graphs in this section (except where noted) use a custom scheme with blue titles and a white background, but otherwise should look the same as your own graphs. I discuss schemes in Section 3.2.5.
3.1 Scatterplots
In this section I will illustrate a few plots using the data on fertility decline first used in Section 2.1. To read the data from net-aware Stata type
To whet your appetite, here’s the plot that we will produce in this section:
3.1.1 A Simple Scatterplot
To produce a simple scatterplot of fertility change by social setting you use the command
Note that you specify y first, then x. Stata labels the axes using the variable labels, if they are defined, or variable names if not. The command may be abbreviated to twoway scatter, or just scatter if that is the only plot on the graph. We will now add a few bells and whistles.
3.1.2 Fitted Lines
Suppose we want to show the fitted regression line as well. In some packages you would need to run a regression, compute the fitted line, and then plot it. Stata can do all that in one step using the lfit plot type. (There is also a qfit plot for quadratic fits.) This can be combined with the scatter plot by enclosing each sub-plot in parenthesis. (One can also combine plots using two horizontal bars to separate them.)
Now suppose we wanted to put confidence bands around the regression line. Stata can do this with the lfitci plot type, which draws the confidence region as a gray band. (There is also a qfitci band for quadratic fits.) Because the confidence band can obscure some points we draw the region first and the points later
Note that this command doesn’t label the y-axis but uses a legend instead. You could specify a label for the y-axis using the ytitle() option, and omit the (rather obvious) legend using legend(off). Here we specify both as options to the twoway command. To make the options more obvious to the reader, I put the comma at the start of a new line:
3.1.3 Labeling Points
There are many options that allow you to control the markers used for the points, including their shape and color, see help marker_options. It is also possible to label the points with the values of a variable, using the mlabel(varname) option. In the next step we add the country names to the plot:
One slight problem with the labels is the overlap of Costa Rica and Trinidad Tobago (and to a lesser extent Panama and Nicaragua). We can solve this problem by specifying the position of the label relative to the marker using a 12-hour clock (so 12 is above, 3 is to the right, 6 is below and 9 is to the left of the marker) and the mlabv() option. We create a variable to hold the position set by default to 3 o’clock and then move Costa Rica to 9 o’clock and Trinidad Tobago to just a bit above that at 11 o’clock (we can also move Nicaragua and Panama up a bit, say to 2 o’clock):
The command to generate this version of the graph is as follows
3.1.4 Titles, Legends and Captions
Resolution Plot Diagram Definition
There are options that apply to all two-way graphs, including titles, labels, and legends. Stata graphs can have a title() and subtitle(), usually at the top, and a legend(), note() and caption(), usually at the bottom, type help title_options to learn more. Usually a title is all you need. Stata 11 allows text in graphs to include bold, italics, greek letters, mathematical symbols, and a choice of fonts. Stata 14 introduced Unicode, greatly expanding what can be done. Type help graph text to learn more.
Our final tweak to the graph will be to add a legend to specify the linear fit and 95% confidence interval, but not fertility decline itself. We do this using the order(2 'linear fit' 1 '95% CI') option of the legend to label the second and first items in that order. We also use ring(0) to move the legend inside the plotting area, and pos(5) to place the legend box near the 5 o’clock position. Our complete command is then
The result is the graph shown at the beginning of this section.
3.1.5 Axis Scales and Labels
There are options that control the scaling and range of the axes, including xscale() and yscale(), which can be arithmetic, log, or reversed, type help axis_scale_options to learn more. Other options control the placing and labeling of major and minor ticks and labels, such as as xlabel(), xtick() and xmtick(), and similarly for the y-axis, see help axis_label_options. Usually the defaults are acceptable, but it’s nice to know that you can change them.
A Plots Resolution Meaning
3.2 Line Plots
I will illustrate line plots using data on U.S. life expectancy, available as one of the datasets shipped with Stata. (Try sysuse dir to see what else is available.)
The idea is to plot life expectancy for white and black males over the 20th century. Again, to whet your appetite I’ll start by showing you the final product, and then we will build the graph bit by bit.
3.2.1 A Simple Line Plot
The simplest plot uses all the defaults:
If you are puzzled by the dip before 1920, Google “US life expectancy 1918”. We could abbreviate the command to twoway line, or even line if that’s all we are plotting. (This shortcut only works for scatter and line.)
The line plot allows you to specify more than one “y” variable, the order is y1, y2, …, ym, x. In our example we specified two, corresponding to white and black life expectancy. Alternatively, we could have used two line plots: (line le_wmale year) (line le_bmale year).
3.2.2 Titles and Legends
The default graph is quite good, but the legend seems too wordy. We will move most of the information to the title and keep only ethnicity in the legend:
Here I used three options, which as usual in Stata go after a comma: title, subtitle and legend. The legend option has many sub options; I used order to list the keys and their labels, saying that the first line represented whites and the second blacks. To omit a key you just leave it out of the list. To add text without a matching key use a hyphen (or minus sign) for the key. There are many other legend options, see help legend_option to learn more.
I would like to use space a bit better by moving the legend inside the plot area, say around the 5 o’clock position, where improving life expectancy has left some spare room. As noted earlier we can move the legend inside the plotting area by using ring(0), the “inner circle”, and place it near the 5 o’clock position using pos(5). Because these are legend sub-options they have to go insidelegend():
3.2.3 Line Styles
I don’t know about you, but I find hard to distinguish the default lines on the plot. Stata lets you control the line style in different ways. The clstyle() option lets you use a named style, such as foreground, grid, yxline, or p1-p15 for the styles used by lines 1 to 15, see help linestyle. This is useful if you want to pick your style elements from a scheme, as noted further below.
Alternatively, you can specify the three components of a style: the line pattern, width and color:
- Patterns are specified using the
clpattern()option. The most common patterns aresolid,dash, anddot; seehelp linepatternstylefor more information.
- Line width is specified using
clwidth(); the available options includethin,mediumandthick, seehelp linewidthstylefor more.
- Colors can be specified using the
clcolor()option using color names (such asred,whiteandblue,teal,sienna, and many others) or RGB values, seehelp colorstyle.
Here’s how to specify blue for whites and red for blacks:
Note that clcolor() is an option of the line plot, so I put parentheses round the line command and inserted it there.
3.2.4 Scale Options
It looks as if improvements in life expectancy slowed down a bit in the second half of the century. This can be better appreciated using a log scale, where a straight line would indicate a constant percent improvement. This is easily done using the axis options of the two-way command, see help axis_options, and in particular yscale(), which lets you choose arithmetic, log, or reversed scales. There’s also a suboption range() to control the plotting range. Here I will specify the y-range as 25 to 80 to move the curves a bit up:
3.2.5 Graph Schemes
Stata uses schemes to control the appearance of graphs, see help scheme. You can set the default scheme to be used in all graphs with set scheme_name. You can also redisplay the (last) graph using a different scheme with graph display, scheme(scheme_name).
To see a list of available schemes type graph query, schemes. Try s2color for screen graphs, s1manual for the style used in the Stata manuals, and economist for the style used in The Economist. Using the latter we obtain the graph shown at the start of this section.
3.3 Other Graphs
I conclude the graphics section discussing bar graphs, box plots, and kernel density plots using area graphs with transparency.
3.3.1 Bar Graphs
Bar graphs may be used to plot the frequency distribution of a categorical variable, or to plot descriptive statistics of a continuous variable within groups defined by a categorical variables. For our examples we will use the city temperature data that ships with Stata.
If I was to just type graph bar, over(region) I would obtain the frequency distribution of the region variable. Let us show instead the average temperatures in January and July. To do this I could specify (mean) tempjan (mean) tempjuly, but because the default statistic is the mean I can use the shorter version below. I think the default legend is too long, so I also specified a custom one.
I use over() so the regions are overlaid in the same graph; using by() instead, would result in a graph with a separate panel for each region. The bargap() option controls the gap between bars for different statistics in the same over group; here I put a small space. The gap() option, not used here, controls the space between bars for different over groups. I also set the intensity of the color fill to 70%, which I think looks nicer.
Obviously the north-east and north-central regions are much colder in January than the south and west. There is less variation in July, but temperatures are higher in the south.
3.3.2 Box Plots
A quick summary of the distribution of a variable may be obtained using a “box-and-wiskers” plot, which draws a box ranging from the first to the third quartile, with a line at the median, and adds “wiskers” going out from the box to the adjacent values, defined as the highest and lowest values that are no farther from the median than 1.5 times the inter-quartile range. Values further out are outliers, indicated by circles.
Let us draw a box plot of January temperatures by region. I will use the over(region) option, so the boxes will be overlaid in the same graph, rather than by(region), which would produce a separate panel for each region. The option sort(1) arranges the boxes in order of the median of tempjan, the first (and in this case only) variable. I also set the box color to a nice blue by specifying the Red, Blue and Green (RGB) color components in a scale of 0 to 255:
We see that January temperatures are lower and less variable in the north-east and north-central regions, with quite a few cities with unusually cold averages.
3.3.3 Kernel Density Estimates
A more detailed view of the distribution of a variable may be obtained using a smooth histogram, calculated using a kernel density smoother using the kdensity command.
Let us run separate kernel density estimates for January temperatures in each region using all the defaults, and save the results.
Next we plot the density estimates using area plots with a floor at zero. Because the densities overlap, I use the new opacity option introduced in Stata 15 to make them 50% transparent. In this case I used color names, followed by a % symbol and the opacity. I also simplify the legend a bit, match the order of the densities, and put it in the top right corner of the plot.
The plot gives us a clear picture of regional differences in January temperatures, with colder and narrower distributions in the north-east and north-central regions, and warmer with quite a bit of overlap in the south and west.
3.4 Managing Graphs
Stata keeps track of the last graph you have drawn, which is stored in memory, and calls it “Graph”. You can actually keep more than one graph in memory if you use the name() option to name the graph when you create it. This is useful for combining graphs, type help graph combine to learn more. Note that graphs kept in memory disappear when you exit Stata, even if you save the data, unless you save the graph itself.
To save the current graph on disk using Stata’s own format, type graph save filename. This command has two options, replace, which you need to use if the file already exists, and asis, which freezes the graph (including its current style) and then saves it. The default is to save the graph in a live format that can be edited in future sessions, for example by changing the scheme. After saving a graph in Stata format you can load it from the disk with the command graph use filename. (Note that graph save and graph use are analogous to save and use for Stata files.) Any graph stored in memory can be displayed using graph display [name]. (You can also list, describe, rename, copy, or drop graphs stored in memory, type help graph_manipulation to learn more.)
If you plan to incorporate the graph in another document you will probably need to save it in a more portable format. Stata’s command graph export filename can export the graph using a wide variety of vector or raster formats, usually specified by the file extension. Vector formats such as Windows metafile (wmf or emf) or Adobe’s PostScript and its variants (ps, eps, pdf) contain essentially drawing instructions and are thus resolution independent, so they are best for inclusion in other documents where they may be resized. Raster formats such as Portable Network Graphics (png) save the image pixel by pixel using the current display resolution, and are best for inclusion in web pages. Stata 15 added Scalable Vector Graphics (SVG), a vector image format that is supported by all major modern web browsers.
You can also print a graph using graph print, or copy and paste it into a document using the Windows clipboard; to do this right click on the window containing the graph and then select copy from the context menu.
Continue with 4. Programming Stata
Matplotlib savefig
A path, or a Python file-like object, or possibly some backend-dependent object such as matplotlib.backends.backend_pdf.PdfPages. If format is not set, then the output format is inferred from the extension of fname , if any, and from rcParams['savefig.format'] = 'png' otherwise.
A path, or a Python file-like object, or possibly some backend-dependent object such as matplotlib.backends.backend_pdf.PdfPages. If format is set, it determines the output format, and the file is saved as fname .
quality int, default: rcParams['savefig.jpeg_quality'] (default: 95) Applicable only if format is 'jpg' or 'jpeg', ignored otherwise. The image quality, on a scale from 1 (worst) to 95 (best).
Matplotlib plot
matplotlib.pyplot.plot, When conflicting with fmt, keyword arguments take precedence. Plotting labelled data. There's a convenient way for plotting objects with labelled matplotlib.pyplot.plot¶ matplotlib.pyplot.plot (*args, scalex=True, scaley=True, data=None, **kwargs) [source] ¶ Plot y versus x as lines and/or markers. Call
Pyplot tutorial, phase_spectrum() plots the unwrapped version of this function. specgram(): specgram() can plot the angle spectrum of segments within the signal in a colormap. Matplotlib is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides Matplotlib with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit numfocus.org for more information. Donations to Matplotlib are managed by NumFOCUS.
pyplot, In Matplotlib, the figure (an instance of the class plt.Figure ) can be thought of as a single container that contains all the objects representing axes, graphics, text, The Matplotlib Object Hierarchy. One important big-picture matplotlib concept is its object hierarchy. If you’ve worked through any introductory matplotlib tutorial, you’ve probably called something like plt.plot([1, 2, 3]).
Matplotlib dpi
Relationship between dpi and figure size, Dots per inches (dpi) determines how many pixels the figure comprises. import matplotlib.pyplot as plt %matplotlib inline def plot(fs,dpi): fig, Dots per inches (dpi) determines how many pixels the figure comprises. The default dpi in matplotlib is 100. A figure of figsize=(w,h) will have . px, py = w*dpi, h*dpi # pixels # e.g. # 6.4 inches * 100 dpi = 640 pixels So in order to obtain a figure with a pixel size of e.g. (1200,600) you may chose several combinations of figure size and dpi
matplotlib.pyplot.figure, Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. · The dpi method of figure module of matplotlib The set_dpi() method figure module of matplotlib library is used to set the resolution of the figure in dots-per-inch. Syntax: set_dpi(self, val) Parameters: This method accept the following parameters that are discussed below: val : This parameter is the float value. Returns: This method does not returns any value.
matplotlib.pyplot.figure, dpi stands for dots per inch. It represents the number of pixels per inch in the figure. The default value for dpi in matplotlib.pyplot.figure() function dpi: [ None scalar > 0 'figure' ] The resolution in dots per inch. If None, defaults to rcParams['savefig.dpi'] = 'figure'. If 'figure', uses the figure's dpi value. quality: [ None 1 <= scalar <= 100 ] The image quality, on a scale from 1 (worst) to 95 (best). Applicable only if format is jpg or jpeg, ignored otherwise.
Matplotlib size of plot area
matplotlib chart area vs plot area, I think an example can help you. The figure size figsize can set the size of the window that your plot will inhabit. The axes list parameters [left, import matplotlib.pyplot as plt # here goes your code fig_size = plt.gcf().get_size_inches() #Get current size sizefactor = 0.8 #Set a zoom factor # Modify the current size by the factor plt.gcf().set_size_inches(sizefactor * fig_size) After changing the current size, it might occur that you have to fine tune the subplot layout.
Python Data Visualization with Matplotlib, Changing Default Plot Size. The first thing we will do is change the default plot size. By default, the size of the Matplotlib plots is 6 x 4 inches. So with matplotlib, the heart of it is to create a figure. On this figure, you can populate it with all different types of data, including axes, a graph plot, a geometric shape, etc. We may want to set the size of a figure to a certain size. You may want to make the figure wider in size, taller in height, etc.
A Plots Resolution Background
How to Set the Size of a Figure in Matplotlib with Python, So with matplotlib, the heart of it is to create a figure. On this figure, you can populate it with all different types of data, including axes, a graph plot, a geometric Matplotlib Series 4: Scatter plot; Matplotlib Series 5: Treemap; Matplotlib Series 6: Venn diagram; Matplotlib Series 7: Area chart (this blog) Matplotlib Series 8: Radar chart; Matplotlib Series 9: Word cloud; Matplotlib Series 10: Lollipop plot; Matplotlib Series 11: Histogram; Area chart. An area chart or area graph displays graphically
Python save figure as png high resolution
7 Plot Point Story Structure
Saving images in Python at a very high quality, If you are using Matplotlib and are trying to get good figures in a LaTeX document, save as an EPS. Specifically, try something like this after You can save to a figure that is 1920x1080 (or 1080p) using: fig = plt.figure(figsize=(19.20,10.80)) You can also go much higher or lower. The above solutions work well for printing, but these days you want the created image to go into a PNG/JPG or appear in a wide screen format.
Matplotlib - How to plot a high resolution graph?, You can use savefig() to export to an image file: plt.savefig('filename.png'). In addition, you can specify the dpi argument to some scalar value, To save a graph in high resolution in Matplotlib, we control various parameters of savefig() function. Similarly, we can plot graphs in high resolution by setting a high value of dpi parameter in figure() function. Plot Graph in High Resolution in Matplotlib. We can plot figures in high resolutions by setting high values of dpi in matplotlib
How to Plot and Save a Graph in High Resolution in Matplotlib, Plot Graph in High Resolution in Matplotlib; Save Figure in High Generally, pngs are better than jpeg for high-resolution plots as png is a The method to_png raised a PermissionError: [Errno 13] Permission. I am not familiar with selenium and how the web driver works. If it is screen shot, the resolution might still be well enough for pulishing.
Savefig matplotlib high resolution
Matplotlib - How to plot a high resolution graph?, You can use savefig() to export to an image file: plt.savefig('filename.png'). In addition, you can specify the dpi argument to some scalar value, Plot Graph in High Resolution in Matplotlib Save Figure in High Resolution in Matplotlib To save a graph in high resolution in Matplotlib, we control various parameters of savefig() function. Similarly, we can plot graphs in high resolution by setting a high value of dpi parameter in figure() function.
How to Plot and Save a Graph in High Resolution in Matplotlib, To save a graph in high resolution in Matplotlib, we control various parameters of savefig() function. Similarly, we can plot graphs in high Less successful test #1: plt.savefig('filename.png', dpi=300) This does save the image at a bit higher than the normal resolution, but it isn't high enough for publication or some presentations. Using a dpi value of up to 2000 still produced blurry images when viewed close up. Less successful test #2: plt.savefig('filename.pdf')
How to save images at very high quality in Python, savefig(filename, dpi=None) to save a matplotlib.pyplot figure as an image named filename with a resolution of dpi in dots per inch. A high dpi value such as plt.figure(figsize=(18, 12), dpi=400) plt.savefig('myplot.png', dpi = 400) However, this is not the solution that I'm looking for; as I want to be able to modify the plot using the dynamic pan and zoom features of the 'Figure' window before saving in a higher resolution than the default screen resolution. I'd be grateful for your help.
Matplotlib show high resolution
Matplotlib - How to plot a high resolution graph?, You can use savefig() to export to an image file: plt.savefig('filename.png'). In addition, you can specify the dpi argument to some scalar value, For future readers who found this question while trying to save high resolution images from matplotlib as I am, I have tried some of the answers above and elsewhere, and summed them up here. Best result: plt.savefig('filename.pdf') and then converting this pdf to a png on the command line so you can use it in powerpoint:
How to Plot and Save a Graph in High Resolution in Matplotlib, Save Figure in High Resolution in Matplotlib We can plot figures in high resolutions by setting high values of dpi in matplotlib. pyplot. savefig() function. We can control the resolution of the saved figure through dpi parameter in savefig() function. Plot Graph in High Resolution in Matplotlib Save Figure in High Resolution in Matplotlib To save a graph in high resolution in Matplotlib, we control various parameters of savefig() function. Similarly, we can plot graphs in high resolution by setting a high value of dpi parameter in figure() function.
Controlling the output resolution, By default, when using the output to a bitmap picture, matplotlib chooses the size and a high resolution, or, if we want to generate a thumbnail, then the resolution would 10, 1024) Y = np.sinc(X) plt.plot(X, Y) plt.savefig('sinc.png', dpi = 300). I recently purchased a new pc laptop with 4K resolution touch screen. when I run the following code while screen is at native max resolution of 4K. pyplot images and font sizes are displayed very tiny!!
A Plots Resolution Project
How to make matplotlib higher resolution
Plot Graph in High Resolution in Matplotlib Save Figure in High Resolution in Matplotlib To save a graph in high resolution in Matplotlib, we control various parameters of savefig() function. Similarly, we can plot graphs in high resolution by setting a high value of dpi parameter in figure() function.
Resolution In A Story
For future readers who found this question while trying to save high resolution images from matplotlib as I am, I have tried some of the answers above and elsewhere, and summed them up here. Best result: plt.savefig('filename.pdf') and then converting this pdf to a png on the command line so you can use it in powerpoint:
The Resolution Of A Story Happens When
I've read other posts on this forum which explain how to directly save a plot from Python in higher resolution by using the following commands to manipulate dpi and image size, e.g.: plt.figure(figsize=(18, 12), dpi=400) plt.savefig('myplot.png', dpi = 400)