Matplotlib Tutorial Python Plotting
This Matplotlib tutorial takes you through the basics Python data visualization: the anatomy of a plot, pyplot and pylab, and much more.
Humans are very visual creatures: we understand things better when we see things visualized. However, the step to presenting analyses, results or insights can be a bottleneck: you might not even know where to start or you might have already a right format in mind, but then questions like “Is this the right way to visualize the insights that I want to bring to my audience?” will have definitely come across your mind.
When you’re working with the Python plotting library Matplotlib, the first step to answering the above questions is by building up knowledge on topics like:
- The anatomy of a Matplotlib plot: what is a subplot? What are the Axes? What exactly is a figure?
- Plot creation, which could raise questions about what module you exactly need to import (pylab or pyplot?), how you exactly should go about initializing the figure and the Axes of your plot, how to use matplotlib in Jupyter notebooks, etc.
- Plotting routines, from simple ways to plot your data to more advanced ways of visualizing your data.
- Basic plot customizations, with a focus on plot legends and text, titles, axes labels and plot layout.
- Saving, showing, clearing, … your plots: show the plot, save one or more figures to, for example, pdf files, clear the axes, clear the figure or close the plot, etc.
- Lastly, you’ll briefly cover two ways in which you can customize Matplotlib: with style sheets and the rc settings.
What Does A Matplotlib Python Plot Look Like?
At first sight, it will seem that there are quite some components to consider when you start plotting with this Python data visualization library. You’ll probably agree with me that it’s confusing and sometimes even discouraging seeing the amount of code that is necessary for some plots, not knowing where to start yourself and which components you should use.
Luckily, this library is very flexible and has a lot of handy, built-in defaults that will help you out tremendously. As such, you don’t need much to get started: you need to make the necessary imports, prepare some data, and you can start plotting with the help of the plot()
function! When you’re ready, don’t forget to show your plot using the show()
function.
# Import the necessary packages and modules
import matplotlib.pyplot as plt
import numpy as np
# Prepare the data
x = np.linspace(0, 10, 100)
# Plot the data
plt.plot(x, x, label='linear')
# Add a legend
plt.legend()
# Show the plot
plt.show()
Note that you import the pyplot module of the matplotlib library under the alias plt.
Congrats, you have now successfully created your first plot! Now let’s take a look at the resulting plot in a little bit more detail.
What you can’t see on the surface is that you have maybe unconsciously made use of the built-in defaults that take care of the creation of the underlying components, such as the Figure and the Axes. You’ll read more about these defaults in the section that deals with the differences between pylab and pyplot.
For now, you’ll understand that working with matplotlib will already become a lot easier when you understand how the underlying components are instantiated. Or, in other words, what the anatomy of a matplotlib plot looks like:
In essence, there are two big components that you need to take into account:
- The Figure is the overall window or page that everything is drawn on. It’s the top-level component of all the ones that you will consider in the following points. You can create multiple independent Figures. A Figure can have several other things in it, such as a suptitle, which is a centered title to the figure. You’ll also find that you can add a legend and color bar, for example, to your Figure.
- To the figure you add Axes. The Axes is the area on which the data is plotted with functions such as
plot()
andscatter()
and that can have ticks, labels, etc. associated with it. This explains why Figures can contain multiple Axes.
Tip: when you see, for example, plt.xlim
, you’ll call ax.set_xlim()
behind the covers. All methods of an Axes object exist as a function in the pyplot
module and vice versa. Note that mostly, you’ll use the functions of the pyplot
module because they’re much cleaner, at least for simple plots!
You’ll see what “clean” means when you take a look at the following pieces of code.
Compare, for example, this piece of code:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1, 2, 3, 4], [10, 20, 25, 30], color='lightblue', linewidth=3)
ax.scatter([0.3, 3.8, 1.2, 2.5], [11, 25, 9, 26], color='darkgreen', marker='^')
ax.set_xlim(0.5, 4.5)
plt.show()
With the piece of code below:
plt.plot([1, 2, 3, 4], [10, 20, 25, 30], color='lightblue', linewidth=3)
plt.scatter([0.3, 3.8, 1.2, 2.5], [11, 25, 9, 26], color='darkgreen', marker='^')
plt.xlim(0.5, 4.5)
plt.show()
The second code chunk is definitely cleaner, isn’it it?
Note: that the above code examples come from the Anatomy of Matplotlib Tutorial by Benjamin Root.
However, if you have multiple axes, it’s still better to make use of the first code chunk because it’s always better to prefer explicit above implicit code! In such cases, you want to make use of the Axes object ax
.
Next to these two components, there are a couple more that you can keep in mind:
- Each Axes has an x-axis and a y-axis, which contain ticks, which have major and minor ticklines and ticklabels. There’s also the axis labels, title, and legend to consider when you want to customize your axes, but also taking into account the axis scales and gridlines might come in handy.
- Spines are lines that connect the axis tick marks and that designate the boundaries of the data area. In other words, they are the simple black square that you get to see when you don’t plot any data at all but when you have initialized the Axes, like in the picture below:
You see that the right and top spines are set to invisible.
Note that you’ll sometimes also read about Artist objects, which are virtually all objects that the package has to offers to users like yourself. Everything drawn using Matplotlib is part of the Artist module. The containers that you will use to plot your data, such as Axis, Axes and Figure, and other graphical objects such as text, patches, etc. are types of Artists.
For those who have already got some coding experience, it might be good to check out and study the code examples that you find in the Matplotlib gallery.
Matplotlib, pyplot and pylab: how are they related?
First off, you’ll already know Matplotlib by now. When you talk about “Matplotlib”, you talk about the whole Python data visualization package. This should not come to you as a big surprise :)
Secondly, pyplot
is a module in the matplotlib
package. That’s why you often see matplotlib.pyplot
in code. The module provides an interface that allows you to implicitly and automatically create figures and axes to achieve the desired plot.
This is especially handy when you want to quickly plot something without instantiating any Figures or Axes, as you saw in the example in the first section of this tutorial. You see, you haven’t explicitly specified these components, yet you manage to output a plot that you have even customized! The defaults are initialized and any customizations that you do, will be done with the current Figure and Axes in mind.
Lastly, pylab
is another module, but it gets installed alongside the matplotlib
package. It bulk imports pyplot
and the numpy
library and was generally recommended when you were working with arrays, doing mathematics interactively and wanted access to plotting features.
You might still see this popping up in older tutorials and examples of matplotlib
, but its use is no longer recommended, especially not when you’re using the IPython kernel in your Jupyter notebook. You can read more about this here.
As a solution, you can best use %matplotlib
magic in combination with the right backend, such as inline
, qt
, etc. Most of the times, you will want to use inline
, as this will make sure that the plots are embedded inside the notebook. Read more about that in DataCamp’s Definitive Guide to Jupyter Notebook.
Note that also when you’re not working in a Jupyter notebook, you’ll still need to choose a different backend, depending on your use case. In other words, if you don’t want to embed plots inside a notebook, but you rather want to embed them into graphical user interfaces, in batch scripts or web application servers, etc., you will need to specify the backend that you want to use. However, this topic is outside the scope of this tutorial; Instead, the tutorial assumes that you will be using Matplotlib to save your images to your local file system.
Data For Matplotlib Plots
As you have read in one of the previous sections, Matplotlib is often used to visualize analyses or calcuations. That’s why the first step that you have to take in order to start plotting in Python yourself is to consider revising NumPy, the Python library for scientific computing.
Scientific computing might not really seem of much interest, but when you’re doing data science you’ll find yourself working a lot with data that is stored in arrays. You’ll need to perform operations on them, inspect your arrays and manipulate them so that you’re working with the (subset of the) data that is interesting for your analysis and that is in the right format, etc.
In short, you’ll find NumPy extremely handy when you’re working with this data visualization library. If you’re interested in taking a NumPy tutorial to start well-prepared, go and take DataCamp’s tutorial and make sure to have your copy of their NumPy cheat sheet close!
Of course, arrays are not the only thing that you pass to your plotting functions; There’s also the possibility to, for example, pass Python lists. If you would like to know more about Python lists, consider checking out their Python list tutorial or the free Intro to Python for Data Science course.
Create Your Plot
Alright, you’re off to create your first plot yourself with Python! As you have read in one of the previous sections, the Figure is the first step and the key to unlocking the power of this package. Next, you see that you initialize the axes of the Figure in the code chunk above with fig.add_axes()
:
# Initialize a Figure
fig = plt.figure()
# Add Axes to the Figure
fig.add_axes([0,0,1,1])
plt.show()
What Is A Subplot?
You have seen all components of a plot and you have initialized your first figure and Axes, but to make things a bit more complicated, you’ll sometimes see subplots pop up in code.
You use subplots to set up and place your Axes on a regular grid. So that means that in most cases, Axes and subplot are synonymous, they will designate the same thing. When you do call subplot to add Axes to your figure, do so with the add_subplots()
function.
There is, however, a difference between the add_axes()
and the add_subplots()
function, but you’ll learn more about this later on in the tutorial.
Consider the following example:
import numpy as np
# Create a Figure
fig = plt.figure()
# Set up Axes
ax = fig.add_subplot(111)
# Scatter the data
ax.scatter(np.linspace(0, 1, 5), np.linspace(0, 5, 5))
# Show the plot
plt.show()
You see that the add_subplot()
function in itsef also poses you with a challenge, because you see add_subplots(111)
in the above code chunk.
What does 111
mean?
Well, 111
is equal to 1,1,1
, which means that you actually give three arguments to add_subplot()
.
The three arguments designate the:
- the number of rows (1),
- the number of columns (1)
- the plot number (1).
So you actually make one subplot.
Note: that you can really go bananas with this function when you are using this function, especially when you’re just starting out with this library and you keep on forgetting for what the three numbers stand.
Consider the following commands and try to envision what the plot will look like and how many Axes your Figure will have: ax = fig.add_subplot(2,2,1)
.
Your Figure will have four axes in total, arranged in a structure that has two rows and two columns. With the line of code that you have considered, you say that the variable ax is the first of the four axes to which you want to start plotting. The “first” in this case means that it will be the first axes on the left of the 2x2 structure that you have initialized.
What Is The Difference Between add_axes() and add_subplot()?
The difference between fig.add_axes()
and fig.add_subplot()
doesn’t lie in the result: they both return an Axes object. However, they do differ in the mechanism that is used to add the axes: you pass a list to add_axes()
which is the lower left point, the width and the height. This means that the axes object is positioned in absolute coordinates.
In contrast, the add_subplot()
function doesn’t provide the option to put the axes at a certain position: it does, however, allow the axes to be situated according to a subplot grid, as you have seen in the section above.
In most cases, you’ll use add_subplot()
to create axes; Only in cases where the positioning matters, you’ll resort to add_axes()
. Alternatively, you can also use subplots()
if you want to get one or more subplots at the same time. You’ll see an example of how this works in the next section.
How To Change The Size of Figures
Now that you have seen how to initialize a Figure and Axes from scratch, you will also want to know how you can change certain small details that the package sets up for you, such as the figure size.
Let’s say you don’t have the luxury to follow along with the defaults and you want to change this. How do you set the size of your figures manually?
Like everything with this package, it’s pretty easy, but you need to know first what to change.
Add an argument figsize
to your plt.figure()
function of the pyplot
module; You just have to specify a tuple with the width and hight of your figure in inches, just like this plt.figure(figsize=(3,4))
, for it to work.
Note that you can also pass figsize
to the the plt.subplots()
function of the same module; The inner workings are the same as the figure()
function that you’ve just seen.
See an example of how this would work here:
# Initialize the plot
fig = plt.figure(figsize=(20,10))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
# Plot the data
ax1.bar([1,2,3],[3,4,5])
ax2.barh([0.5,1,2.5],[0,1,2])
# Show the plot
plt.show()
# alternatively
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(20,10))
# or replace the three lines of code above by the following line:
#fig, (ax1, ax2) = plt.subplots(1,2, figsize=(20,10))
# Plot the data
ax1.bar([1,2,3],[3,4,5])
ax2.barh([0.5,1,2.5],[0,1,2])
# Show the plot
plt.show()
Working With Pyplot: Plotting Routines
Now that all is set for you to start plotting your data, it’s time to take a closer look at some plotting routines. You’ll often come across functions like plot()
and scatter()
, which either draw points with lines or markers connecting them, or draw unconnected points, which are scaled or colored.
But, as you have already seen in the example of the first section, you shouldn’t forget to pass the data that you want these functions to use!
These functions are only the bare basics. You will need some other functions to make sure your plots look awesome:
| Method | Description |
|---|---|
| ax.bar() | Vertical rectangles |
| ax.barh() | Horizontal rectangles |
| ax.axhline() | Horizontal line across axes |
| ax.vline() | Vertical line across axes |
| ax.fill() | Filled polygons |
| ax.fill_between() | Fill between y-values and 0 |
| ax.stackplot() | Stack plot |
If you’re curious how you can use these functions to plot your data, consider the following example.
data = [
{'x': [1,2,3],
'y':[3,4,5],
'horizontal': False},
{'x': [0.5,1,2.5],
'y':[0,1,2],
'horizontal': True}
]
# alternatively
fig, axes = plt.subplots(len(data), 1, figsize=(10,20))
# or replace the three lines of code above by the following line:
#fig, (ax1, ax2) = plt.subplots(1,2, figsize=(20,10))
for index, ax in enumerate(axes):
if data[index]['horizontal']:
ax.barh(data[index]['x'], data[index]['y'])
else:
ax.bar(data[index]['x'], data[index]['y'])
# Show the plot
plt.show()
x = np.array([ 0., 0.25, 0.5, 0.75, 1. ], dtype=np.float64)
y = np.array([ 0., 1.25, 2.5, 3.75, 5. ], dtype=np.float64)
# Initialize the plot
# fig = plt.figure(figsize=(15,10))
fig = plt.figure()
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
# Plot the data
ax1.bar([1,2,3],[3,4,5])
ax2.barh([0.5,1,2.5],[0,1,2])
ax2.axhline(0.45)
ax1.axvline(0.65)
ax3.scatter(x,y)
# Show the plot
plt.show()
Most functions speak for themselves because the names are quite clear. But that doesn’t mean that you need to limit yourself: for example, the fill_between()
function is perfect for those who want to create area plots, but they can also be used to create a stacked line graph; Just use the plotting function a couple of times to make sure that the areas overlap and give the illusion of being stacked.
Note that, of course, simply passing the data is not enough to create great plots. Make sure to manipulate your data in such a way that the visualization makes sense: don’t be afraid to change your array shape, combine arrays, etc.
When you move on and you start to work with vector fields or data distributions, you might want to check out the following functions:
| Method | Description |
|---|---|
| ax.arrow() | Arrow |
| ax.quiver() | 2D field of arrows |
| ax.streamplot() | 2D vector fields |
| ax.hist() | Histogram |
| ax.boxplot() | Boxplot |
| ax.violinplot() | Violinplot |
Note of course that you probably won’t use all of the functions listed in these tables; It really depends on your data and your use case. If you’re totally new to data science, you might want to check out the statistical plotting routines first!
On the other hand, when you work with 2-D or n-D data, you might also find yourself in need of some more advanced plotting routines, like these ones:
| Method | Description |
|---|---|
| ax.pcolor() | Pseudocolor plot |
| ax.pcolormesh() | Pseudocolor plot |
| ax.contour() | Contour plot |
| ax.contourf() | Filled contour plot |
| ax.clabel() | Labeled contour plot |
Note that contour plots are used to explore the potential relationship between three variables. Just like contour plots, also pseudocolor plots can be used for this purpose, since they are surface plot seen from above. Of course, this are not nearly all the functions that you can use to plot your data.
If you’re working with images or 2D data, for example, you might also want to check out imshow()
to show images in your subplots. For a practical example of how to use the imshow()
function, go to DataCamp’s scikit-learn tutorial.
The examples in the tutorial also make clear that this data visualization library is really the cherry on the pie in the data science workflow: you have to be quite well-versed in general Python concepts, such as lists and control flow, which can come especially handy if you want to automate the plotting for a great number of subplots. If you feel like revising these concepts, consider taking the free introduction to Python for data science course.
Customizing Your PyPlot
A lot of questions about this package come from the fact that there are a lot of things that you can do to personalize your plots and make sure that they are unique: besides adjusting the colors, you also have the option to change markers, linestyles and linewidths, add text, legend and annotations, and change the limits and layout of your plots.
It’s exactly the fact that there is an endless range of possibilities when it comes to these plots that makes it difficult to set out some things that you need to know when you start working on this topic.
Great tips that you should keep in the back of your mind are not only the gallery, which contains many real-life examples that are already coded for you and which you can use, but also the documentation, which can tell you more about the arguments that you can pass to certain functions to adjust visual features.
Also keep in mind that there are multiple solutions for one problem and that you learn most of this stuff when you’re getting your hands dirty with the package itself and when you run into troubles. You’ll see some of the most common questions and solutions in this section.
Deleting an Axis
If you ever want to remove an axes form your plot, you can use delaxes()
to remove and update the current axes.
Note: you can restore a deleted axes by adding fig.add_axes(ax)
right after fig.delaxes(ax3)
.
# Initialize the plot
fig = plt.figure()
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
# Plot the data
ax1.bar([1,2,3],[3,4,5])
ax2.barh([0.5,1,2.5],[0,1,2])
ax2.axhline(0.45)
ax1.axvline(0.65)
ax3.scatter(np.linspace(0, 1, 5), np.linspace(0, 5, 5))
# Delete `ax3`
fig.delaxes(ax3)
# Show the plot
plt.show()
How To Put The Legend Out of the Plot
There are a number of ways to address this question, but mostly all come back to the arguments that you can provide to legend()
:
- You can specify the
loc
orlocation
argument to something like center left or upper right, which ensures that your legend does not fall in the Axes or subplot area. - Alternatively, you can also add the
bbox_to_anchor
argument to your function and pass a tuple with the coordinates in which you want to put the legend. In this case, the box is put in the upper right corner of the plotting area:ax.legend(bbox_to_anchor=(1.1, 1.05))
.
How To Set Plot Title And Axes Labels
To change your plot title and axes labels, you can follow one of the following approaches, depending of which container of which you want to make use:
- The easiest way to set these things right is by using
ax.set(title="A title", xlabel="x", ylabel="y")
orax.set_xlim()
,ax.set_ylim()
orax.set_title()
. - If you want to work with the figure, you might also resort to
fig.suptitle()
to add a title to your plot. - If you’re making use of the default settings that the package has to offer, you might want to use
plt.title()
,plt.xlabel()
,plt.ylabel()
. - Define your own style sheet or change the default
matplotlibrc
settings. Read more about this here.
How To Fix The Plot Layout
A thing to consider when you’re using subplots to build up your plot is the tight_layout function, which will help you to make sure that the plots fit nicely in your figure.
Additionally, you might also be interested to use subplots_adjust()
, which allows you to manually set the width and height reserved for blank space between subplots, and also fix the left and right sides, and the top and bottom of the subplots.
Showing, Saving And Closing Your Plot
After you have done all the necessary customizations, you will want to show your plot because, as you will have noticed from working in the terminal, you just get to see that an object is made, but you never see the nice plot every time you make adjustments.
In the first example of this tutorial, this was implicitly done;
Do you remember? It’s this piece of code:
# Prepare the data
x = np.linspace(0, 10, 100)
# Plot the data
plt.plot(x, x, label='linear')
# Add a legend
plt.legend()
# Show the plot
plt.show()
The line plt.show()
says indeed that you want to see the plot. If you execute this line, you’ll see a window popping up. And you’ll see if it looks like what you had in your mind!
But this is where your questions start. How can I save this image and if it’s not to your liking, can you clear the image so that you can start anew? The following short sections will cover these questions.
How To Save A Plot To An Image File
You can easily save a figure to, for example, a png file by making use of plt.savefig()
. The only argument you need to pass to this function is the file name, just like in this example:
# Prepare the data
x = np.linspace(0, 10, 100)
# Plot the data
plt.plot(x, x, label='linear')
# Add a legend
plt.legend()
# Save Figure
plt.savefig("foo.png")
# Prepare the data
x = np.linspace(0, 10, 100)
# Plot the data
plt.plot(x, x, label='linear')
# Add a legend
plt.legend()
# Save Transparent Figure
plt.savefig("foo_transparent.png", transparent=True)
How To Save A Plot To A Pdf File
If you want to save multiple plots to a pdf file, you want to make use of the pdf backend, which you can easily import:
# Import PdfPages
from matplotlib.backends.backend_pdf import PdfPages
# Prepare the data
x = np.linspace(0, 10, 100)
# Plot the data
plt.plot(x, x, label='linear')
# Add a legend
plt.legend()
# Initialize the pdf file
pp = PdfPages('multipage.pdf')
# Save the figure to the file
pp.savefig()
# Close the file
pp.close()
When To Use cla(), clf() or close()?
When you’re finally ready with the inspection of your plot, it’s time to move on with something else (maybe with another plot!). When you’re working with this data visualization library for the first time, it might be weird at start because you can, of course, shut down the GUI window that appears, but that’s usually not the way you want to handle things, because it doesn’t always work as well when you’re working on several things at a time.
You have to explicitly tell Matplotlib to close down the plot that you’ve been working on so that you can move on. There are three functions that will come in handy once you’re at this point:
Use:
plt.cla()
to clear an axisplt.clf()
to clear the entire figureplt.close()
to close a window that has popped up to show you your plot
Customizing Matplotlib
By now, you’re already familiar with some basic options to customize your plots. But what if the customizations that you want to make situate more on a library level instead of a plot level? In such cases, also, you don’t need to panic: Matplotlib offers you several options to adjust some of the internal workings. This section will just cover two options, namely style sheets and rc settings.
If you want to know more, definitely check out this page.
How To Use A ggplot2 Style
For the R enthusiasts among you, Matplotlib also offers you the option to set the style of the plots to ggplot
. You can easily do this by running the following piece of code:
# Set the style to `ggplot`
plt.style.use("ggplot")
rc Settings
“rc” is common for configuration files: they usually end in rc
. It comes from the practice of having configs as executables: they are automatically run and configure settings, for example. You can read more about it here. Matplotlib has such an rc file to which you can make adjustments dynamically and statically.
To dynamically change default rc settings, you can use the rcParams variable:
# without rc Settings
# Prepare the data
x = np.linspace(0, 10, 100)
# Plot the data
plt.plot(x, x, label='linear')
# Add a legend
plt.legend()
# Show the plot
plt.show()
# with rc Settings
import matplotlib as mpl
mpl.rcParams['lines.linewidth'] = 5
# Prepare the data
x = np.linspace(0, 10, 100)
# Plot the data
plt.plot(x, x, label='linear')
# Add a legend
plt.legend()
# Show the plot
plt.show()
You just adjusted the line width in the example above, but you can also change figure size and dpi, line width, color and style, axes, axis and grid properties, text and font properties, …
If you want to work more statically, you should probably also know that you have a matplotlibrc
configuration file, which you can use to customize all kinds of properties (just like you did above with the line width parameter). If you want to find this specific file, you can just run the following:
import matplotlib
matplotlib.matplotlib_fname()
'/Users/kolobj/.matplotlib/matplotlibrc'
Continue Learning
Congratulations! You have gone through today’s Matplotlib tutorial successfully! There is still much to learn, but you’re definitely ready to go out on your own and create your own amazing plots. Don’t miss out on DataCamp’s Matplotlib cheat sheet that can help you to make plots in no time, step by step.
If you’re eager to discover more from Matplotlib, consider checking out DataCamp’s Viewing 3D Volumetric Data With Matplotlib tutorial to learn how to work with matplotlib’s event handler API or this tutorial, in which you’ll learn all about animating your plots.


Advisors
:
Organising Chairperson
:
Co-Chairperson
:
Organising Secretary
:
Joint Secretary
:
Treasurer
:
Scientific Committee Chairperson
:
Scientific Committee
:
Workshop Convener
:
Workshop Committee
:
Faculty Coordination Committee
:
Reception Committee
:
Finance Committee
:
Transport and Accomodation
:
Hall Management
:
Venue & Stalls
:
Audio - Visual Committee
:
Catering Committee
:


|
||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
See:
Description
Internal, messy, implementation-specific classes for the Baf intermediate representation. Baf is a low-level representation, similar to Java bytecode.
|
||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


Contents:
Conditional Statements
Looping
Other Statements That Affect Flow Control
Arrays
An Acronym Processor
System Variables That Are Arrays
This chapter covers some fundamental programming constructs. It covers all the control statements in the awk programming language. It also covers arrays, variables that allow you to store a series of values. If this is your first exposure to such constructs, you'll recognize that even sed provided conditional and looping capabilities. In awk, these capabilities are much more generalized and the syntax is much easier to use. In fact, the syntax of awk's conditional and looping constructs is borrowed from the C programming language. Thus, by learning awk and the constructs in this chapter, you are also on the way to learning the C language.
A conditional statement allows you to make a test before performing an action. In the previous chapter, we saw examples of pattern matching rules that were essentially conditional expressions affecting the main input loop. In this section, we look at conditional statements used primarily within actions.
A conditional statement is introduced by if and evaluates an expression placed in parentheses. The syntax is:
if (
expression
)
action1
[else
action2
]
If expression evaluates as true (non-zero or non-empty), action1 is performed. When an else clause is specified, action2 is performed if expression evaluates to false (zero or empty). An expression might contain the arithmetic, relational, or Boolean operators discussed in Chapter 7, Writing Scripts for awk.
Perhaps the simplest conditional expression that you could write is one that tests whether a variable contains a non-zero value.
if ( x ) print x
If x is zero, the print statement will not be executed. If x has a non-zero value, that value will be printed. You can also test whether x equals another value:
if ( x == y ) print x
Remember that "==" is a relational operator and "=" is an assignment operator. We can also test whether x matches a pattern using the pattern-matching operator "~":
if ( x ~ /[yY](es)?/ ) print x
Here are a few additional syntactical points:
If any action consists of more than one statement, the action is enclosed within a pair of braces.
if (
expression
) {
statement1
statement2
}
Awk is not very particular about the placement of braces and statements (unlike sed). The opening brace is placed after the conditional expression, either on the same line or on the next line. The first statement can follow the opening brace or be placed on the line following it. The closing brace is put after the last statement, either on the same line or after it. Spaces or tabs are allowed before or after the braces. The indentation of statements is not required but is recommended to improve readability.
A newline is optional after the close parenthesis, and after else.
if (
expression
)action1
[elseaction2
]
A newline is also optional after action1
, providing
that a semicolon ends action1.
if (
expression
)action1
; [elseaction2
]
You cannot avoid using braces by using semicolons to separate multiple statements on a single line.
In the previous chapter, we saw a script that averaged student grades. We could use a conditional statement to tell us whether the student passed or failed.
Presuming that an average of 65 or above is a passing grade, we could write the following conditional:
if ( avg >= 65 ) grade = "Pass" else grade = "Fail"
The value assigned to grade depends upon whether the expression "avg >= 65" evaluates to true or false.
Multiple conditional statements can be used to test whether one of several possible conditions is true. For example, perhaps the students are given a letter grade instead of a pass-fail mark. Here's a conditional that assigns a letter grade based on a student's average:
if (avg >= 90) grade = "A" else if (avg >= 80) grade = "B" else if (avg >= 70) grade = "C" else if (avg >= 60) grade = "D" else grade = "F"
The important thing to recognize is that successive conditionals like this are evaluated until one of them returns true; once that occurs, the rest of the conditionals are skipped. If none of the conditional expressions evaluates to true, the last else is accepted, constituting the default action; in this case, it assigns "F" to grade.
Awk provides a conditional operator that is found in the C programming language. Its form is:
expr
?action1
:action2
The previous simple if/else condition can be written using a conditional operator:
grade = (avg >= 65) ? "Pass" : "Fail"
This form has the advantage of brevity and is appropriate for simple conditionals such as the one shown here. While the ?: operator can be nested, doing so leads to programs that quickly become unreadable. For clarity, we recommend parenthesizing the conditional, as shown above.


Skeleton Free Closets
Designing internal APIs you can be proud of
@matt_mcclure
The Rise of the Internal API
Then: The Monolith
One application to rule them all.
Now: Microservices
A suite of small services that communicate.

What seems to happen
- There are two services (or an application is broken up)
- Need to glue these things together
- Slap together an API interface
- Functional? Done.
[actual] example interaction
- Me:
“Do you have any examples of a bad internal API?”
- Colleague:
“Well, the other day I saw...”
- Me:
“Perfect. Can you link me to the docs?”
- Colleague:
*blank stare*
- Colleague:
*laughter*
Design first
Every.
Single.
Time.
Get colleagues involved
Show design drafts to people who will have to use it
It takes an organization
Get * management involved as well
Kick bad habits
Your org is probably building bad internal APIs because it's a habit now.
Kill your darlings
It's good to have organization guidelines and all, but...
Don't cling for consistency sake
If patterns / best practices / dev preferences change enough, change with them
Aim for idiomatic
Internal APIs should feel "standard" within the broader context
Document everything
Even Especially if you're not proud of it.
Own up to shortcomings
Surprises are worse than idiosyncracies
Keep it up to date
This is harder than it seems. Admit it.
Be a good citizen
Update documentation when you notice problems.
TL;DR
Internal != cut corners
Think about future yous
These APIs will potentially be a communication layer for years.


Decision 2017
With the general elections only a couple of weeks away, below are
the names of those eyeing for the country's top office:
2017 Presidential Candidate
- Uhuru Kenyatta
- Raila Odinga
- Abduba Dida
- Shakhalaga Jirongo
- Japheth Kaluyu
- Michael Mwaura
- Joseph Nyagah


Make sure your photo meets these rules. If it doesn't, you may have problems with your application or when you travel.
How to get your digital photo
You can choose from 3 ways:
Get a friend to take one. They can use a phone, tablet or digital camera. We’ll show you how to take a good passport photo and upload it.
Go to a photo shop. You’ll get a digital copy of your photo. Some shops can also give you a photo code. We’ll show you how to use the code to retrieve your digital photo.
Go to a booth. From some booths in the UK and abroad, you'll be able to get a photo code with your printed photos. We’ll show you how to use the code to retrieve your digital photo.
What your digital photo must show
If you get someone to take your photo they need to fit your head, shoulders and upper body into the photo. Don't crop your photo – we'll do it for you.
 If you wear glasses that you can’t take off, your eyes must be visible without any glare or reflections.


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object uchicago.src.sim.analysis.ObjectDataRecorder
public class ObjectDataRecorder
Records Objects to files as Strings. ObjectDataRecorder will record Objects together with an optional String comment. Typically, this comment will be the tick count, but need not be.
In non-batch mode, a file header will be written that includes all the model's properties and the values of these properties. These values will be the value at the time the first call to write is made, allowing the user to manipulate the parameters after setting up but prior to running the model. Consequently, your initial model parameters should only be changeable via the user or through a parameter file. If not, the file header may be inaccurate. The actual data will be written as a block where the block includes the optional comment as a header, followed by the String representation of the Object itself.
In batch mode, any constant model parameters will be written in the file header. The data block will include the value of any dynamic parameters, the run number, as well as the optional comment (again, typically the tick count), together with the String representation of the Object to be recorded.
| Nested Class Summary | |
|---|---|
protected class |
ObjectDataRecorder.ObjectStringPair
|
| Field Summary | |
|---|---|
protected java.util.Vector |
data
|
protected DataFileHeader |
dfHeader
|
protected java.lang.String |
lineSep
|
protected DataFileWriter |
writer
|
| Constructor Summary | |
|---|---|
ObjectDataRecorder(java.lang.String fileName,
SimModel model)
Creates a new ObjectDataRecorder. |
|
ObjectDataRecorder(java.lang.String fileName,
java.lang.String modelHeader)
Creates a new ObjectDataRecorder. |
| Method Summary | |
|---|---|
void |
record(java.lang.Object o)
Records the specified object. |
void |
record(java.lang.Object o,
java.lang.String comment)
Records the specified Object together with the comment. |
void |
write()
Writes all the recorded data (ie Objects) to the file specified in the constructor. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
protected java.util.Vector data
protected DataFileHeader dfHeader
protected java.lang.String lineSep
protected DataFileWriter writer
| Constructor Detail |
|---|
public ObjectDataRecorder(java.lang.String fileName, SimModel model)
fileName
- the name of the file to record objects to.model
- the model associated with this ObjectDataRecorderpublic ObjectDataRecorder(java.lang.String fileName, java.lang.String modelHeader)
fileName
- the name of the file to record objects to.modelHeader
- user provided header| Method Detail |
|---|
public void record(java.lang.Object o, java.lang.String comment)
o
- the object to recordcomment
- a comment about the objectpublic void record(java.lang.Object o)
o
- the object to recordpublic void write()
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


{% extends 'admin/master.html' %}
{% block body %}
{{ super() }}
{% if current_user.is_authenticated %}
Patrol Deployment
Planning Dashboard
Crime Coverage Map View
Deployment Planning
Authentication
Welcome to the live demo of OptiPol. In this demo, editing of all data fields except number of patrols is disabled.
You have access to all the other functionality such as optimizing deployments and switching and training models.
OptiPol supports user authentication out-of-the-box. To use this demo, log in with the following credentials:
Email: admin
Password: admin


Static analysis
Report PE info, Strings, Hashes, VirusTotal, Behavior patterns
Upload file to analyse
Choose file
Automatic static analysis tool.
Author
@Zelvar


PIRCS Macintosh and PC computers
Both of these machines have Microsoft Office, telnet, WWW clients and ftp clients on them. X-servers will also be located on these machines eventually. The printers in room 3203 are already connected to these machines.
Please note that you should keep copies of important data on disket as we do not back-up the internal disk drives on either the Mac or PC.
If you have any software that you would like to see installed on these systems, please get in touch with Douglas Fils at 294-6196 or<EMAIL_ADDRESS>and I will do what I can to help you.
We have various video conferencing solutions available for both these machines, so if you are interested in these capabilities you sould just ask.


{% extends 'form/base.html' %} {% block title %}Feedback Form{% endblock %} {% block body %}
Welcome to the student-feedback-app the place where you can msg your professors about personal doubts or to give feedback on todays class anonymously


We provide unmatched quality, comfort, and style for property owners across the country. Our experts combine form and function in bringing your vision to life. Create a room in your own style with our collection and make your property a reflection of you and what you love.
Our multifunctional collection blends design and function to suit your individual taste. Make each room unique, or pick a cohesive theme that best express your interests and what inspires you. Find the furniture pieces you need, from traditional to contemporary styles or anything in between. Product specialists are available to help you create your dream space.


--- layout: post title: "Subfields of deterministic optimization with a single objective function" date: 2017-12-10 comments: true maths: true tags: maths excerpt: Subfields of deterministic optimization with a single objective function. ---


Oireachtas Joint Committee ramps up pressure on Sport Ireland and FAI
by Raf DialloThe Oireachtas Joint Committee on Transport, Tourism and Sport has called on Sport Ireland to clarify responses to six questions in regards to governance issues.
In email correspondence seen by Off The Ball that is dated November 20th 2019 and marked "Urgent", the committee states that it "wishes to put Sport Ireland, Minister [Shane] Ross, and the Football Association of Ireland, on urgent notice that it is ready to meet with" those stakeholders about governance issues as well as the potential "restoration of State funding to" the FAI, among other matters related to the Association.
The correspondence makes reference to a previous email dated October 7th which details six questions. The six queries in question are listed alphabetically from A to F. Those questions include expected dates for a number of reports into governance at the FAI. They also include a clarification of details regarding the "exit package" for ex-FAI chief executive John Delaney.
Six Questions
A) Expected date of receipt, by Sport Ireland of the forensic audit report being carried out into matters related to the FAI, by KOSI Corporation, and confirmation of the earliest date in which Sport Ireland will be available to discuss same with the Joint Committee thereafter.
B) The Committee notes the developments that follow receipt of that report by Sport Ireland (as per your letter) and the Committee requests that the final audit report be submitted to the Joint Committee, as soon as possible after it is presented to the Minister, bearing in mind that this forensic audit was requested by the Joint Committee at its hearings last April. An expected date of delivery of this report to the Joint Committee is requested.
C) The expected date of completion of the Mazars report into certain matters relating to the FAI, and if, and when that report will be presented to Sport Ireland and subsequently to the Joint Committee .
D) The expected date of completion of the Grant Thornton report into certain matters relating to the FAI, and if, and when that report will be presented to Sport Ireland and subsequently to the Joint Committee.
E) If details of the “exit package” agreed between the FAI and former Chief Executive/Executive Vice President following his recent resignation, have, or will be supplied to Sport Ireland, and if so the Joint Committee requests details of same.
F) Any other details of discussions/correspondence between Sport Ireland and the FAI, regarding governance issues at, and the restoration of State funding to that body which is currently suspended.
Delay
The forensic audit into the FAI's financing was delayed last month following Delaney's resignation from his role as FAI executive vice-president. On October 25th, the FAI subsequently stated that the pair of reports into the association's governance would be delivered "in the coming weeks".
The FAI-commissioned report by Mazars will be available in its initial form "towards the end of November".


+++ title = "Casa de Arena, Huacachina" slug = "casa-de-arena-huacachina" date = 2007-07-16T12:00:00+10:00 tags = ["accommodation", "barbeque", "pool"] locations = ["peru/huacachina"] draft = false summary = """
Like most of the places to stay in the oasis of Huacachina, Casa de Arena is a very chilled-out resort-slash-hostel affair, with a nice swimming pool (a bit cold this time of year), a poolside bar, and a buffet BBQ every night (not only with all-you-can-eat chicken and salad, but also all-you-can-drink Pisco Sours and Cuba Libres!). Pretty good value, too — as evidenced by the abundance of (stoned) Israelis that stay there. Not sure when I'm going to leave this place, but it ain't anytime soon. :P
""" +++Like most of the places to stay in the oasis of Huacachina, Casa de Arena is a very chilled-out resort-slash-hostel affair, with a nice swimming pool (a bit cold this time of year), a poolside bar, and a buffet BBQ every night (not only with all-you-can-eat chicken and salad, but also all-you-can-drink Pisco Sours and Cuba Libres!). Pretty good value, too — as evidenced by the abundance of (stoned) Israelis that stay there. Not sure when I'm going to leave this place, but it ain't anytime soon. :P
{{< galleryphoto "2007-07-16-12-00-00--pool-and-bar-area-of-casa-de-arena-oh-what-a-hard-life.jpg" >}}Rooms: 15-person dorms are spacious and airy, and the beds are comfy enough.
People: everyone's in resort mode here — as with everywhere in Huacachina, almost everyone is also stoned — so yeah, everyone's yer friend.
Staff: very friendly, very talkative, and most can speak good English, as well as Hebrew that's better than mine. However, nadie tiene nada cambio (lit: "none of them have any change").
Location: Huacachina's tiny, and it's inside it. 'Nuff said.
Food / Internet: no free food, but the s/5 breakfast is decent enough, and the s/20 buffet BBQ is shweetissimo. No Internet, but the only 'Net café in town (total ripoff — s/3 per hour) is across the road.
Hot water: reliable, but not enough showers (for those in the dorms — many rooms with private bathroom are available) — 6 more are currently under construction.


|
|
| New! |
|
To celebrate the 10-year anniversary of
The Opera House, we're now offering
baseball jerseys to go along with our T-shirts!
Stop by and pick one up--and if you're interested in other products,
let the Impresaria
know. (You can also show your appreciation the
old-fashioned way.) |
|
|
Come read our A to Z guide for more information
about the Opera House. Whether you're a first-time visitor or a regular
patron, you're sure to find useful tidbits within its pages.
Ah, hello there. Tickets, please. Number
...Here we are. Enjoy the show.
The last time the Impresaria was around to take inventory, polish the
seats, and pick up litter was 17 March 2009.
| New! | |
All lyrics from Final Fantasy Song Book: Mahoroba are now (finally) online!
Tsuki no Akari, a new version of
Rosa's Theme of Love from Final Fantasy IV on the DS.
Darkness and Starlight, a more thorough
Japanese version of The Opera from the Dark Mages' third album.
<plug>If you enjoy the music from the FF series, perhaps
you'd like to take a look at the lyricized songs of the Dragon
Quest (Dragon Warrior) series? It's
another site presented by our very own Impresaria.</plug>
|
We've made some room in our mess of a basement so our guests can go
there to find the lyrics to the final boss theme of FFVII. They do contain
a spoiler, and they're tucked away pretty well down there. If you think
you're ready, come right this way; we'll take the stairs.
I'll come with you.
Go back to my home page
Last edited on 17 March 2009 by N. S. Heath.
E-mail<EMAIL_ADDRESS>Border background by N. S. Heath; may be used with permission.
|


Heater Status
Environment
Temperature:
Humidtity:
Ambient Temperature:
Override Temperature
Temperature:
Expiry:
Hours:
Minutes:
Temperature:
Time left:
Target
Target Temperature:
Target Time:
Predicted Time:
Schedule JSON


--- layout: page title: Teaching ---
This page will be a record of my teaching experiences and materials that I provide to my students.


By @astronomyblog & @LoveGraphs. Stats last updated 28 December 2019.
It seems there may have been at least 142 shows and 711 listed appearances. With 5 people per show, iPlayer and @bbcbreakthenews have 0% more appearances listed than required (and probably a similar percent of people) perhaps due to listing special guests. The host/captains/regulars have included Des Clarke (Host × 136), Susan Calman (Host × 3, Panellist × 7), Chris Forbes (Host × 2, Panellist × 5), Kirsty Wark (Host × 1, Panellist × 3).
Total appearances:
Guest appearances i.e. ignoring the host/captains/regulars:
All people:
Guests i.e. ignoring the host/captains/regulars:
Last updated: 2019/12/28


---
layout: ds4/test
title: Icon - Misc
---
icon-chevron-up-disabled
icon-chevron-down-disabled
icon-chevron-left-disabled
icon-chevron-right-disabled
icon-checkbox-checked
icon-radio-unchecked
icon-checkbox-unchecked
icon-radio-checked
icon-check
icon-success
icon-follow
icon-sell
icon-sort
icon-fire
icon-box
icon-camera
icon-tag
icon-autocomplete
icon-call
icon-comments
icon-help
icon-save
icon-chat
icon-magnify
icon-window


WebPad
wiki, personal wiki.
textual
Price: $
0.00
unlimited* sync between devices forever*, well until this web-site is running.
Windows
Linux
MacOs
iOs
Android
https://webpad.com.ua/WebPad_logo_500.png
https://webpad.com.ua
https://webpad.com.ua/WebPad_logo_500.png
🇺🇦 Web
Pad
: personal wiki
: personal wiki
Support on Patreon
Join Discord
Android app
Desktop/Laptop releases
🇺🇦 Web
Pad
: options
: options
designed 🎨 and made 🏗 by Ukrainian 🇺🇦
Privacy policy
Support on Patreon
Join Discord
Android app
Join Discord
Background:
graphite
darkness
Demo notes:
show
hide
Export & import:
All data is stored only on your device, use export regularly to perform a backup:
export
Select a snapshot file to import:
import
Development is supported by:
This app is created by
Oleksandr Kryvonos
, a developer from Odesa, Ukraine
🇺🇦
.
It is an open-source project:
https://github.com/uprun/WebPad
if you have any suggestions feel free to e-mail me:
<EMAIL_ADDRESS>For more information you can visit a website of the project
https://webpad.com.ua
Version 1.9.* [2023-03-25]


https://specterops.io/assets/resources/an_ace_up_the_sleeve.pdf
We want to implement an AD DACL-based backdoor that:
Facilitates the regaining of elevated control in the AD environment
Blends in with normal ACL configurations ("hiding in plain sight"), or otherwise hidden from easy enumeration


{% extends 'tgrsite/main.html' %} {% block title %}Reset Password{% endblock %} {% block pagetitle %}Reset Password{% endblock %} {% block breadcrumbs_parents %}
A link to reset the password has been sent to the requested email.


lncRNAs drought cold stress expression cassava genes and/or transcripts Fig. RNAs RNA identified gene lincRNAs response target using lncNATs plant {border-style long RNA-seq miRNA protein-coding px;} #ffffff;} analysis noncoding Supplemental research stresses Plant identification lncRNAs, miR regulation S). expressed treatment used Data Genome-wide conditions pairs potential regulatory small Arabidopsis abiotic functions involved lincRNA lncRNA mimics number responses results transcriptome Genome coding data different function functional miRNAs non-coding reads responsive roles stress, biology cell control molecular novel pattern stress. total Wang cassava. compared important including known performed plants plants. protein sequencing up-regulated DE-lncRNAs Nucleic acids low prediction px; showed significantly stress-responsive transcription treatment, various {font-family h, adjacent conserved differentially font-size genomics indicated levels lncNAT lncRNAs. mRNAs network studies targets transcriptional BMC Cassava However, Nature act antisense based cassava, condition correlated discordant double; drought-responsive experimental generated identify length obtained pairs, pathways patterns predicted qRT-PCR regulate regulated reports respectively samples specifically suggesting transcribed ./s--- C). Zhang abundance according aligned border-color border-width chilling conducted differences endogenous enriched enrichment example, genes. genome level lower mapped mechanism metabolic miR-targeted miRNAs, million mimicry regions regulators relatively respectively. rice sense sequence siRNAs signal similar solid; study, targets, time transcript {font-style {font-weight ./--- ./srep Academy Characterization China Figure Functional Gene Interestingly, International KEGG Long National Sciences Transcriptome activity, analysis. analyzed analyzing apices approximately associated binding biological bold cDNA cleavage cold- computational conditions. conservation courier; crop em; employed evidence examples, explore features genome-wide growth hormone induced intergenic investigate investigation large leaves lincRNAs-neighbors lncNAT-siRNAs mRNA maize majority miR, networks new obtain pathways. play polymerase precursors processes provide pt;} reference resistance responses. revealed role scores secondary shoot ssRNA-seq strand-specific stress-induced stresses. study suggested systematically treatments work hour


Listen.
There's nothing here right now, but I'll be updating this as I go
along.
Every solo release so far weaves into another, it's all a big story.
I'll share how it's all connected one day, but for now sit back and
stream the backstory.
Easy,
-⚙


Flight statistics based on FIR/UIR
Usage
Click on any airspace (FIR/UIR) on the map to display the respective development of the
number of airline flights during the COVID-19 pandemic. You may select multiple airspaces
at the same time. Clicking a second time on the same airspace deselects
it. Switching between FIRs and UIRs on the map is done via the legend.
The view can be toggled between absolute numbers and a percentage
related to the average number of flights in the first ten days of the
year 2020.
Content
Some regions don't discriminate between FIR and UIR or
have been simplified due to a lack of reliable openly available shape
files and to save computation power in data processing.
Aircraft position data was consumed as state vectors from the
OpenSky Network API
as well as from the
OpenSky Network historical database
at intervals between 10 seconds and 5 minutes.
State vectors with incomplete positional information or on_ground state
were ignored.
This statistics is focused on airline flights and should ignore general
aviation. Only callsigns matching the following regular expression have
been processed:
^([A-Z]{3})[0-9](([0-9]{0,3})|([0-9]{0,2})([A-Z])|([0-9]?)([A-Z]{2}))$
World wide statistics
The flight statistics is based on
OpenSky Network
data and was processed as described above.
For simplicity, the COVID-19 related data is shown for the whole
world only. Much more information can be found at the source of the
dataset:
Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina and Joe Hasell
(2020) - "Coronavirus Pandemic (COVID-19)"


You are at a high table, like a standing desk type situation.
You see a Scanner attached to a computer with a monitor and mouse. On the screen it looks like the program that controls the scanner is up, so it’s ready to go.
You ponder, do I need to scan something?
| . . . . . | ||
| I have something I can scan. | No thanks. | |
| . . . . . |


GET STARTED TODAY
Lebowski ipsum when will you find these guys? I mean, do you have any promising leads? Dolor sit amet, consectetur adipiscing elit praesent ac magna justo pellentesque ac. Mr. Lebowski asked me to repeat that: Her life is in your hands. Lectus quis elit blandit fringilla a. One a those days, huh.  Ut turpis praesent felis ligula, malesuada suscipit malesuada non.
24/7 CUSTOMER SUPPORT
Lebowski ipsum when will you find these guys? I mean, do you have any promising leads? Dolor sit amet, consectetur adipiscing elit praesent ac magna justo pellentesque ac. Mr. Lebowski asked me to repeat that: Her life is in your hands. Lectus quis elit blandit fringilla a. One a those days, huh.  Ut turpis praesent felis ligula, malesuada suscipit malesuada non.
PAY MONTHLY OR ANNUALLY
Lebowski ipsum when will you find these guys? I mean, do you have any promising leads? Dolor sit amet, consectetur adipiscing elit praesent ac magna justo pellentesque ac. Mr. Lebowski asked me to repeat that: Her life is in your hands. Lectus quis elit blandit fringilla a. One a those days, huh.  Ut turpis praesent felis ligula, malesuada suscipit malesuada non.
CANCEL YOUR PLAN ANYTIME
Lebowski ipsum when will you find these guys? I mean, do you have any promising leads? Dolor sit amet, consectetur adipiscing elit praesent ac magna justo pellentesque ac. Mr. Lebowski asked me to repeat that: Her life is in your hands. Lectus quis elit blandit fringilla a. One a those days, huh.  Ut turpis praesent felis ligula, malesuada suscipit malesuada non.


Destroying a Cluster¶
Destruction (tearing down) a Cluster and its Bastion involves two separate steps.

If you destroy the bastion before destroying its clusters you are likely to leave a large number of cloud objects deployed that will be difficult and painstaking to remove manually.
Destroying a Cluster¶
From the Bastion’s okd-orchestrator
clone you can destroy a cluster
after providing its name. From a Bastion SSH session you can destroy the
simple AWS example with the following commands:
cd okd-orchestrator
./destroy.py simple-aws-frankfurt-3-9
Destruction is relatively fast. The utility simply relies on the underlying Terraform utility, which quickly deletes all of the objects that were originally created.
Destroying a Bastion¶
Once the Bastion’s clusters have been torn down you are free to delete it. But be sure that all the clusters managed by the bastion have been deleted before you consider deleting the Bastion.
Removal of the Bastion will remove all cluster information and the network created to manage them.
From the original host machine you used to create the Bastion, navigate to the
OKD project clone and run destroy.py
. For example, if you checked out the
project in your workstation’s home directory simply navigate there and run it:
cd $HOME/okd-orchestrator
./destroy.py --bastion
As it’s the Bastion you’re destroying you are presented with a cautionary reminder before you’re able to continue.


--- layout: home title: Austin Teer | Front-end Developer ---
I’m a front-end developer living in the Northwest. I build rich user experiences. Interested in engineering reusable and scalable components for the web. Currently am the UI Engineer at Upworthy. I’m the author of aphid: a Wordpress starter theme using sass, gulpjs, bower, and inuit.css. Visit my github page for more projects.


Rupesh Mayekar, Who create / (communicate with) internet related things like web application so that a user can see and interact with them directly. With symbols like "< />", "{}", etc. that means Front End developer :)
I believe it is essential in the development, not only to respect the client's vision but also to surpass it by bringing an interactive and lively personal touch. I worked the last seven years in the digital world whose the last four as Senior SDE.


{% extends "base.html" %} {% load i18n staticfiles cms_tags sekizai_tags %} {% block body_class %}tpl-home{% endblock %} {% block extrahead %} {% endblock %} {% block content %}
Your weekly schedule makes easy for others to know what do you need help with
Let Neural Networks suggest to your friends what to do.
An intuitive graphic interface easy to use as piece of cake.
What are we?
Being diagnosed with cancer brings great complications into everyday life, and repeatedly asking for help can become yet another heavy burden to overcome. Patients often don't know what to expect from their close relatives or how to express their needs efficiently. HelpingHand aims at providing an easy to use interface that enables sharing a weekly schedule that only inner circles of close friends or relatives have access to. This way, any of these circles members can volunteer to help in the tasks shared by the user. Moreover, an AI system provides insights on the state of the patient and suggests new tasks he might not come up with.
Sign up
Feedback


--- layout: default title: Home subtitle: Making meals tasty and keeping you healthy; Ottawa, Canada ---
Whether you are the herb enthusiest, adventurous home cook, green thumb or just curious to learn about herbs,here you will find plenty of inforamtive facts about herbs, their health benifits and many fun reciepes and DIY activities. Enjoy your stay and have fun discovering the exciting world of herbs.


Welcome to HTML5 Cafe!
Here you will find all sorts of delicious HTML5 and CSS3 treats. This is the sample site for Beginning HTML5 and CSS3 for Dummies, by Ed Tittel and Chris Minnick. To view all of the code samples from the book, visit the Menu.
Here you will find all sorts of delicious HTML5 and CSS3 treats. This is the sample site for Beginning HTML5 and CSS3 for Dummies, by Ed Tittel and Chris Minnick. To view all of the code samples from the book, visit the Menu.


Data Structures I: Homework submission rules
Basic rules
- The entire submission must be original code created by you without any additional "inspiration".
- Do not share your code, so that it does not become someone else's "inspiration".
- We recommend to implement given homeworks in low-level programming languages (e.g. C/C++, Pascal). Other languages can be used in exceptional cases, however this may lead to unexpected results. Please, discuss usage of other languages with your teacher.
- Given data structures and algorithms must be implemented efficiently, especially asymptotic complexity of your implementation must be as good as presented in lectures. On the other hand, a nice implementation may not be obtained by copying a pseudo-code from books or slides.
- The aim of this course is to understand the data structures and algorithms, and be able to implement them. Therefore it is not allowed to use standard nor third-party libraries in your solutions. Imagine that you have to implement given data structure in the model RAM or in assembly instructions, so use only pointers and arrays with sizes known in their allocation to access memory.
- Therefore, you are not allowed to used data structures vector, map, set, queue, deque and function std::sort in C++. Similar data structures cannot be used in other languages.
- On the other hand, you can use standard libraries to work with files, strings, time, etc.
- Solutions must be submitted through the submission server which will be available soon.
- Solutions that are not in compliance with these rules will not be evaluated.
Please, use your brain to implement given problems. It is impossible to specify all details of all homeworks and to forbid all crazy ideas some students may try to do. You can always ask if you are not sure about these rules. Furthermore, your reports should describe your approach and explain reasons to do so.
Some homeworks may require to process huge quantity of testing data. However, your program should be able to process them in few minutes. If your program is running many hours then your implementation is inefficient or something else is done in a wrong way. For example, it is not necessary to store testing data on hard disk. Do not search for some super computer. Computers available in u-lab are sufficient.
Evaluation
Each exercise is worth 100 body. The points will be distributed as follows:
- 20 points for the graphs
- 20 points for the description
- 20 points for the quality of the source code
- 40 points for successful completion of the posed task
Basic criteria
- Solutions must meet the specifications and the code must be functional (no bugs).
- You may resubmit a corrected solution, but this will incur a penalty of 10 points for the failed attempt.
- Solutions must be submitted within the deadline (penalties: 1 point for each hour of delay).
Graphs
The aim is to draw a graph from which the behaviour of the specified data structures can be read. Therefore the graph must be clearly visible and all curves and axes must be labelled appropriately. Use a logarithmic scale whenever this better displays the measured data. The output of your program must contain all measured values required in problem statements. You can use arbitrary tools to create plots from measured values.
Description of results
Explain the measured values, especially asymptotic trends and their multiplicative constants. Furthermore, compare the behaviour of different data structures, and the influences of parameters and data generators. Theoretical knowledge from lectures and exercises can be used without repeating proofs
Source codes
Your source code will be evaluated according to the usual criteria (readability, structure, commenting ...).


- madd(float, Color) - Method in class org.sunflow.image.Color
-
- madd(Color, Color) - Method in class org.sunflow.image.Color
-
- MAGENTA - Static variable in class org.sunflow.image.Color
-
- main(String[]) - Static method in class org.sunflow.Benchmark
-
- main(String[]) - Static method in class org.sunflow.core.tesselatable.Gumbo
-
- main(String[]) - Static method in class org.sunflow.image.RGBSpace
-
- main(String[]) - Static method in class org.sunflow.system.RenderGlobalsPanel
-
Auto-generated main method to display this JPanel inside a new JFrame.
- makeFromW(Vector3) - Static method in class org.sunflow.math.OrthoNormalBasis
-
- makeFromWV(Vector3, Vector3) - Static method in class org.sunflow.math.OrthoNormalBasis
-
- MathUtils - Class in org.sunflow.math
-
- Matrix4 - Class in org.sunflow.math
-
This class is used to represent general affine transformations in 3D.
- Matrix4(float, float, float, float, float, float, float, float, float, float, float, float) - Constructor for class org.sunflow.math.Matrix4
-
Creates a matrix with the specified elements
- Matrix4(float[], boolean) - Constructor for class org.sunflow.math.Matrix4
-
Initialize a matrix from the specified 16 element array.
- max(int, int, int) - Static method in class org.sunflow.math.MathUtils
-
- max(float, float, float) - Static method in class org.sunflow.math.MathUtils
-
- max(double, double, double) - Static method in class org.sunflow.math.MathUtils
-
- max(float, float, float, float) - Static method in class org.sunflow.math.MathUtils
-
- MAX_SIGMA_ORDER - Static variable in class org.sunflow.math.QMC
-
- Memory - Class in org.sunflow.system
-
- Memory() - Constructor for class org.sunflow.system.Memory
-
- mid(Point3, Point3, Point3) - Static method in class org.sunflow.math.Point3
-
- min(int, int, int) - Static method in class org.sunflow.math.MathUtils
-
- min(float, float, float) - Static method in class org.sunflow.math.MathUtils
-
- min(double, double, double) - Static method in class org.sunflow.math.MathUtils
-
- min(float, float, float, float) - Static method in class org.sunflow.math.MathUtils
-
- MirrorShader - Class in org.sunflow.core.shader
-
- MirrorShader() - Constructor for class org.sunflow.core.shader.MirrorShader
-
- MitchellFilter - Class in org.sunflow.core.filter
-
- MitchellFilter() - Constructor for class org.sunflow.core.filter.MitchellFilter
-
- mod1(double) - Static method in class org.sunflow.math.QMC
-
Compute mod(x,1), assuming that x is positive or 0.
- Modifier - Interface in org.sunflow.core
-
This represents a surface modifier.
- modifier(String, String) - Method in class org.sunflow.SunflowAPI
-
- modifier(String, String) - Method in interface org.sunflow.SunflowAPIInterface
-
Defines a modifier with a given name.
- modifierPlugins - Static variable in class org.sunflow.PluginRegistry
-
- modify(ShadingState) - Method in interface org.sunflow.core.Modifier
-
Modify the shading state for the point to be shaded.
- modify(ShadingState) - Method in class org.sunflow.core.modifiers.BumpMappingModifier
-
- modify(ShadingState) - Method in class org.sunflow.core.modifiers.NormalMapModifier
-
- modify(ShadingState) - Method in class org.sunflow.core.modifiers.PerlinModifier
-
- MovingMatrix4 - Class in org.sunflow.math
-
This class describes a transformation matrix that changes over time.
- MovingMatrix4(Matrix4) - Constructor for class org.sunflow.math.MovingMatrix4
-
Constructs a simple static matrix.
- mul(Color) - Method in class org.sunflow.image.Color
-
- mul(Color, Color) - Static method in class org.sunflow.image.Color
-
- mul(Color, Color, Color) - Static method in class org.sunflow.image.Color
-
- mul(float) - Method in class org.sunflow.image.Color
-
- mul(float, Color) - Static method in class org.sunflow.image.Color
-
- mul(float, Color, Color) - Static method in class org.sunflow.image.Color
-
- mul(float) - Method in class org.sunflow.image.XYZColor
-
- mul(float) - Method in class org.sunflow.math.Vector3
-
- mul(float, Vector3) - Method in class org.sunflow.math.Vector3
-
- MultipassRenderer - Class in org.sunflow.core.renderer
-
- MultipassRenderer() - Constructor for class org.sunflow.core.renderer.MultipassRenderer
-
- multiply(Matrix4) - Method in class org.sunflow.math.Matrix4
-
Computes this*m and return the result as a new Matrix4


1. Choose Voice: This allows you to select the kind of voice available on the system. You can select either a female voice or a male voice with multiple accents too.
2. Write Message: This allows you to input the text you want to covert into speech. .
3. : This icon serves as volume control. The slider in front of it allows you to control your volume level.
4. : This icon serves as rate control.
5. : This icon serves as pitch control. The slider in front of it allows you to regulate how deep you the sound of the audio output to be.
6. : This icon allows you to play your audio output. It can also be used after you might have paused or stop your audio output.
7. : This icon allows you to pause your audio output. After which, the Play icon can be used to continue the audio output.
8. : This icon allows you to stop or put an end to your audio output. After which, the Play icon can be used to restart the audio output all over again.
© Copyrights 2018, Final Year Project of Akinyemi Taiwo


- Login to post new content in the forum.
| Topic | Replies | Created | Last reply | |
|---|---|---|---|---|
| Is EmonTx Shield (no RF) compatible with Arduino Ethernet | 5 | 4 years 15 weeks ago by Guest |
4 years 5 weeks ago by Guest |
|
| testing rf12b rpi board | 4 | 4 years 15 weeks ago by Guest |
4 years 15 weeks ago by mharizanov |
|
| Where can I get a RFM12Pi | 3 | 4 years 16 weeks ago by Guest |
4 years 15 weeks ago by Guest |
|
| EmonGLCD Backlight | 6 | 4 years 16 weeks ago by divad |
4 years 15 weeks ago by Robert Wall |
|
| Newbe needs recommendation for power monitoring | 2 | 4 years 16 weeks ago by Guest |
4 years 16 weeks ago by Guest |
|
| SMA SB1600TL | 1 | 4 years 16 weeks ago by Guest |
4 years 16 weeks ago by alco |
|
| RESOLVED [emonGLCD] test sketch with partially scrambled text | 3 | 4 years 16 weeks ago by fluppie007 |
4 years 16 weeks ago by glyn.hudson |
|
| EmonGLCD Screen Stripes / Dark Spots (Solved) | 2 | 4 years 16 weeks ago by Guest |
4 years 16 weeks ago by Guest |
|
| Emontx Shield + Ethernet Shield | 18 | 4 years 16 weeks ago by Guest |
4 years 12 weeks ago by Guest |
|
| Emontx +relay module??? | 3 | 4 years 16 weeks ago by Guest |
4 years 16 weeks ago by glyn.hudson |
|
| EmonGLCD posts 16 nodes ??. | 0 | 4 years 17 weeks ago by Petrik |
n/a | |
| eMon running on the Dragino platform? | 4 | 4 years 17 weeks ago by fluppie007 |
4 years 5 weeks ago by Guest |
|
| Solar/Wind micro-generation monitoring with two emonTXs | 2 | 4 years 17 weeks ago by markp |
4 years 17 weeks ago by markp |
|
| RESOLVED: emonGLCD corruption on Adafruit ST7565 RGB LCD | 2 | 4 years 17 weeks ago by markp |
4 years 17 weeks ago by edllew |
|
| Measuring Export with only on AC/AC | 0 | 4 years 17 weeks ago by Guest |
n/a | |
| Two CT Current Sensors on the same Arduino board | 2 | 4 years 17 weeks ago by Guest |
4 years 3 weeks ago by Robert Wall |
|
| RESOLVED - EmonTX voltage error | 10 | 4 years 17 weeks ago by logic |
4 years 16 weeks ago by ianjm |
|
| Measuring Air-to-Air Heatpump RPM - pulse counting ... ??? | 2 | 4 years 17 weeks ago by Petrik |
4 years 15 weeks ago by Petrik |
|
| Incorrect time from emoncms.org....sometimes | 6 | 4 years 17 weeks ago by Guest |
4 years 16 weeks ago by Petrik |
|
| Is there a remote programmer interface ? | 4 | 4 years 17 weeks ago by Petrik |
4 years 15 weeks ago by mharizanov |
|
| emonTx + 2x Elster meters | 0 | 4 years 17 weeks ago by RobP |
n/a | |
| RESOLVED: Can't load sketch to new Emontx - not in sync | 15 | 4 years 18 weeks ago by Guest |
4 years 16 weeks ago by Petrik |
|
| estimates emonTX hardware evolutions | 0 | 4 years 18 weeks ago by mauromarson |
n/a | |
| Full-fat 3-phase monitor | 57 | 4 years 18 weeks ago by MartinR |
51 weeks 6 days ago by Robert Wall |
|
| emonBase sending no data with multiple emonTx | 0 | 4 years 18 weeks ago by Avontech |
n/a | |
| No output from CT2 from emonTX - RESOLVED | 2 | 4 years 19 weeks ago by Avontech |
4 years 19 weeks ago by Avontech |
|
| Fault tx PCB | 2 | 4 years 19 weeks ago by MartinR |
4 years 19 weeks ago by MartinR |
|
| Building an energy monitor | 1 | 4 years 19 weeks ago by Guest |
4 years 19 weeks ago by Robert Wall |
|
| Power Optimization for the Atmega328P | 1 | 4 years 19 weeks ago by Dawn |
4 years 19 weeks ago by glyn.hudson |
|
| NanodeRF ships with Uno or Duemilanove? | 2 | 4 years 19 weeks ago by edllew |
4 years 19 weeks ago by edllew |
|
| Cant connect to MySQL server | 7 | 4 years 19 weeks ago by boxer_ian |
4 years 17 weeks ago by boxer_ian |
|
| Dummy's guide to emonTx calibration | 1 | 4 years 19 weeks ago by Guest |
4 years 19 weeks ago by Robert Wall |
|
| Glcd no text on screen - RESOLVED | 2 | 4 years 20 weeks ago by Guest |
4 years 20 weeks ago by Guest |
|
| RESOLVED - Emonbase NanodeRF DHCP issues | 22 | 4 years 20 weeks ago by Guest |
4 years 18 weeks ago by Guest |
|
| DS18B20 temp spikes with emonTX | 8 | 4 years 20 weeks ago by Guest |
4 years 17 weeks ago by Petrik |
|
| decoding the rfm12 packets. | 3 | 4 years 20 weeks ago by robw |
4 years 20 weeks ago by Robert Wall |
|
| NanodeRF Voltage Check - How close is enough? | 1 | 4 years 20 weeks ago by Guest |
4 years 20 weeks ago by Robert Wall |
|
| emonBase (OKG Wiznet W5200) works for 1-10 minutes before stopping | 11 | 4 years 21 weeks ago by Guest |
4 years 19 weeks ago by Guest |
|
| DHCP fail with current NanodeRF sketch | 6 | 4 years 21 weeks ago by Guest |
3 years 34 weeks ago by TrystanLea |
|
| emonBase not connecting to emonCMS | 4 | 4 years 22 weeks ago by Guest |
3 years 35 weeks ago by Robert Wall |
|
| emonGLCD Clock not showing the correct time. | 1 | 4 years 22 weeks ago by Guest |
4 years 22 weeks ago by glyn.hudson |
|
| emonGLCD - Not responding. | 232 | 4 years 22 weeks ago by robw |
3 years 31 weeks ago by Robert Wall |
|
| Seperate monitoring of two inverters in a solar PV array | 1 | 4 years 22 weeks ago by Guest |
4 years 22 weeks ago by glyn.hudson |
|
| RESOLVED - emonGLCD Latest code | 22 | 4 years 23 weeks ago by robw |
4 years 22 weeks ago by robw |
|
| Battery life / stacking TX's | 2 | 4 years 23 weeks ago by Guest |
4 years 23 weeks ago by Guest |
|
| CT sensor reading different +/- values | 6 | 4 years 23 weeks ago by stuart |
4 years 23 weeks ago by Robert Wall |
|
| RESOLVED - emonGLCD clock error | 3 | 4 years 24 weeks ago by logic |
4 years 23 weeks ago by logic |
|
| EmonTX with only 1 CT - Should it still send CT2 and 3 to Base? | 7 | 4 years 24 weeks ago by Tailzer |
4 years 21 weeks ago by Robert Wall |
|
| the learning curve | 3 | 4 years 24 weeks ago by toby.cambray |
4 years 22 weeks ago by calypso_rae |
|
| Mk2 PV controller + emonTX + LCD display | 25 | 4 years 24 weeks ago by stuart |
4 years 18 weeks ago by stuart |


{% extends "forums/base.html" %} {% load thumbnail %} {% load bootstrap %} {% block body %} {% include "forums/_thread_breadcrumbs.html" with thread=thread %}


Explanation and instructions for the EPICS V4 project webspace on the SourceForge servers.
The EPICS V4 project webspace contains the EPICS V4 website.
Most of the manually created website content is kept in the repository pvDataWWW. The repository content is checked out and pushed to the SourceForge servers (using rsync over ssh) by a Jenkins CI job on CloudBees.
Binary content that is not expected to change (meeting slides, images) is kept in the same directory structure within the webspace, but is not part of the content repository. Such content has to be uploaded manually.
Automatically created content (Doxygen documentation, Javadoc) is pushed to the SourceForge servers (using rsync over ssh) by each software module's Jenkins CI job on CloudBees.
Some server-side applications and their data are also located within the project webspace, but outside of the website tree.
When using the SourceForge Shell Service, the webspace is mounted as
/home/project-web/epics-pvdata
.
It contains:
cgi-bin/ |
Server-side scripts, e.g., hgweb |
python/ |
Installation of mercurial and extensions, needed by hgweb |
python2.6/ |
|
repos/ |
Repository instances used by hgweb |
htdocs/ |
EPICS V4 website |
files/ |
Content not kept in the pvDataWWW repository |
docbuild/ |
Automatically created software documentation |
.../ |
Most of the remaining files and directories are content from the pvDataWWW repository, plus some old stuff that needs to be cleaned up |
... |
git clone https://github.com/epics-base/pvDataWWW.git
hg push
The Jenkins CI job on CloudBees will pick up your changes and publish them within a few minutes.
You can use any of the tools supported by SourceForge (rsync, scp, sftp) to access the webspace and upload your files.
When connecting to
<EMAIL_ADDRESS>, the home
directory will be the root directory of the webspace, so that you have full
access to the complete structure shown above.
Please keep manually uploaded files within the htdocs/files
directory, to avoid them being overwritten by the publishing Jenkins jobs.
Please don't upload random files to that directory, consider creating a
sensible subdirectory tree (e.g., htdocs/files/talks/<year>/
for group presentations).
Here is a commandline example using sftp:
> sftp<EMAIL_ADDRESS>Connected to web.sourceforge.net. sftp> cd htdocs/files sftp> pwd Remote working directory: /home/project-web/epics-pvdata/htdocs/files sftp> ls talks sftp>
Personally, I prefer using a GUI (e.g., FileZilla or FireFTP) that makes things like selectively transferring files very easy and - at least in my experience - less error prone.


Steve Jobs was born on February 24, 1955, in the city of San Francisco. His biological mother was an unwed graduate student named Joanne Simpson, and his biological father was either a political science or mathematics professor, a native Syrian named Abdulfattah John Jandali.
Being born out of wedlock in the puritan America of the 1950s, the baby was put up for adoption. Joanne had a college education, and she insisted that the future parents of her boy be just as well educated. Unfortunately, the candidates, Paul and Clara Jobs, did not meet her expectations: they were a lower-middle class couple that had settled in the Bay Area after the war. Paul was a machinist from the Midwest who had


Teledraw Canvas Example
Oh crap... your browser doesn't support canvas!
pencil
arrow
eraser
pan
black
red
blue
green
yellow
bigger
smaller
clear
undo
redo
zoom (+)
zoom (-)


The optimization package provides algorithms to optimize (i.e. either minimize or maximize) some objective or cost function. The package is split in several sub-packages dedicated to different kind of functions or algorithms.
The top level optimization package provides common interfaces for the optimization algorithms provided in sub-packages. The main interfaces defines defines optimizers and convergence checkers. The functions that are optimized by the algorithms provided by this package and its sub-packages are a subset of the one defined in the analysis package, namely the real and vector valued functions. These functions are called objective function here. When the goal is to minimize, the functions are often called cost function, this name is not used in this package.
The type of goal, i.e. minimization or maximization, is defined by the enumerated
Optimizers are the algorithms that will either minimize or maximize, the objective function by changing its input variables set until an optimal set is found. There are only four interfaces defining the common behavior of optimizers, one for each supported type of objective function:
Despite there are only four types of supported optimizers, it is possible to optimize a transform a non-differentiable multivariate vectorial function by converting it to a non-differentiable multivariate real function thanks to the
For each of the four types of supported optimizers, there is a special implementation which wraps a classical optimizer in order to add it a multi-start feature. This feature call the underlying optimizer several times in sequence with different starting points and returns the best optimum found or all optima if desired. This is a classical way to prevent being trapped into a local extremum when looking for a global one.
A
These algorithms usage is very similar to root-finding algorithms usage explained in the analysis package. The main difference is that the solve methods in root finding algorithms is replaced by
This package provides an implementation of George Dantzig's simplex algorithm for solving linear optimization problems with linear equality and inequality constraints.
Direct search methods only use cost function values, they don't need derivatives and don't either try to compute approximation of the derivatives. According to a 1996 paper by Margaret H. Wright (Direct Search Methods: Once Scorned, Now Respectable), they are used when either the computation of the derivative is impossible (noisy functions, unpredictable discontinuities) or difficult (complexity, computation cost). In the first cases, rather than an optimum, a not too bad point is desired. In the latter cases, an optimum is desired but cannot be reasonably found. In all cases direct search methods can be useful.
Simplex-based direct search methods are based on comparison of the cost function values at the vertices of a simplex (which is a set of n+1 points in dimension n) that is updated by the algorithms steps.
The instances can be built either in single-start or in multi-start mode. Multi-start is a traditional way to try to avoid being trapped in a local minimum and miss the global minimum of a function. It can also be used to verify the convergence of an algorithm. In multi-start mode, the minimizesmethod returns the best minimum found after all starts, and the etMinima method can be used to retrieve all minima from all starts (including the one already provided by the minimizes method).
The direct package provides four solvers:
The first two simplex-based methods do not handle simple bounds constraints by themselves. However there are two adapters(
The last methods do handle simple bounds constraints directly, so the adapters are not needed with them.
The general package deals with non-linear vectorial optimization problems when the partial derivatives of the objective function are available.
One important class of estimation problems is weighted least squares problems. They basically consist in finding the values for some parameters pk such that a cost function J = sum(wi(mesi - modi)2) is minimized. The various (targeti - modeli(pk)) terms are called residuals. They represent the deviation between a set of target values targeti and theoretical values computed from models modeli depending on free parameters pk. The wi factors are weights. One classical use case is when the target values are experimental observations or measurements.
Solving a least-squares problem is finding the free parameters pk of the theoretical models such that they are close to the target values, i.e. when the residual are small.
Two optimizers are available in the general package, both devoted to least-squares problems. The first one is based on the Gauss-Newton method. The second one is the Levenberg-Marquardt method.
In order to solve a vectorial optimization problem, the user must provide it as an object implementing the
| X | Y |
| 1 | 34.234064369 |
| 2 | 68.2681162306108 |
| 3 |<PHONE_NUMBER>84602 |
| 4 |<PHONE_NUMBER>38557 |
| 5 |<PHONE_NUMBER>16276 |
| 6 |<PHONE_NUMBER>51579 |
| 7 |<PHONE_NUMBER>91914 |
| 8 |<PHONE_NUMBER>70688 |
| 9 |<PHONE_NUMBER>67148 |
| 10 |<PHONE_NUMBER>85883 |
First we need to implement the interface
We'll tackle the implementation of the
For a quadratic which has three variables the Jacobian Matrix will have three columns, one for each variable, and the number of rows will equal the number of rows in our data set, which in this case is ten. So for example for [a = 1, b = 1, c = 1], the Jacobian Matrix is (excluding the first column which shows the value of x):
| x | d(ax2 + bx + c)/da | d(ax2 + bx + c)/db | d(ax2 + bx + c)/dc |
| 1 | 1 | 1 | 1 |
| 2 | 4 | 2 | 1 |
| 3 | 9 | 3 | 1 |
| 4 | 16 | 4 | 1 |
| 5 | 25 | 5 | 1 |
| 6 | 36 | 6 | 1 |
| 7 | 49 | 7 | 1 |
| 8 | 64 | 8 | 1 |
| 9 | 81 | 9 | 1 |
| 10 | 100 | 10 | 1 |
The implementation of the
private double[][] jacobian(double[] variables) { double[][] jacobian = new double[x.size()][3]; for (int i = 0; i < jacobian.length; ++i) { jacobian[i][0] = x.get(i) * x.get(i); jacobian[i][1] = x.get(i); jacobian[i][2] = 1.0; } return jacobian; } public MultivariateMatrixFunction jacobian() { return new MultivariateMatrixFunction() { private static final long serialVersionUID = -8673650298627399464L; public double[][] value(double[] point) { return jacobian(point); } }; }
Note that if for some reason the derivative of the objective function with respect to its variables is difficult to obtain, Numerical differentiation can be used.
The implementation of the
public double[] value(double[] variables) { double[] values = new double[x.size()]; for (int i = 0; i < values.length; ++i) { values[i] = (variables[0] * x.get(i) + variables[1]) * x.get(i) + variables[2]; } return values; }
Below is the the class containing all the implementation details (Taken from the Apache Commons Math org.apache.commons.math3.optimization.general.LevenbergMarquardtOptimizerTest):
private static class QuadraticProblem implements DifferentiableMultivariateVectorFunction, Serializable { private static final long serialVersionUID =<PHONE_NUMBER>052755854L; private List<Double> x; private List<Double> y; public QuadraticProblem() { x = new ArrayList<Double>(); y = new ArrayList<Double>(); } public void addPoint(double x, double y) { this.x.add(x); this.y.add(y); } public double[] calculateTarget() { double[] target = new double[y.size()]; for (int i = 0; i < y.size(); i++) { target[i] = y.get(i).doubleValue(); } return target; } private double[][] jacobian(double[] variables) { double[][] jacobian = new double[x.size()][3]; for (int i = 0; i < jacobian.length; ++i) { jacobian[i][0] = x.get(i) * x.get(i); jacobian[i][1] = x.get(i); jacobian[i][2] = 1.0; } return jacobian; } public double[] value(double[] variables) { double[] values = new double[x.size()]; for (int i = 0; i < values.length; ++i) { values[i] = (variables[0] * x.get(i) + variables[1]) * x.get(i) + variables[2]; } return values; } public MultivariateMatrixFunction jacobian() { return new MultivariateMatrixFunction() { private static final long serialVersionUID = -8673650298627399464L; public double[][] value(double[] point) { return jacobian(point); } }; } }
The below code shows how to go about using the above class and a LevenbergMarquardtOptimizer instance to produce an optimal set of quadratic curve fitting parameters:
QuadraticProblem problem = new QuadraticProblem(); problem.addPoint(1, 34.234064369); problem.addPoint(2, 68.2681162306); problem.addPoint(3,<PHONE_NUMBER>846); problem.addPoint(4,<PHONE_NUMBER>386); problem.addPoint(5,<PHONE_NUMBER>163); problem.addPoint(6,<PHONE_NUMBER>516); problem.addPoint(7,<PHONE_NUMBER>919); problem.addPoint(8,<PHONE_NUMBER>707); problem.addPoint(9,<PHONE_NUMBER>671); problem.addPoint(10,<PHONE_NUMBER>859); LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(); final double[] weights = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }; final double[] initialSolution = {1, 1, 1}; PointVectorValuePair optimum = optimizer.optimize(100, problem, problem.calculateTarget(), weights, initialSolution); final double[] optimalValues = optimum.getPoint(); System.out.println("A: " + optimalValues[0]); System.out.println("B: " + optimalValues[1]); System.out.println("C: " + optimalValues[2]);
If you run the above sample you will see the following printed by the console:
A: 7.998832172372726 B: 10.001841530162448 C: 16.324008168386605
In addition to least squares solving, the
The
The fitting package deals with curve fitting for univariate real functions. When a univariate real function y = f(x) does depend on some unknown parameters p0, p1 ... pn-1, curve fitting can be used to find these parameters. It does this by fitting the curve so it remains very close to a set of observed points (x0, y0), (x1, y1) ... (xk-1, yk-1). This fitting is done by finding the parameters values that minimizes the objective function sum(yi-f(xi))2. This is really a least squares problem.
For all provided curve fitters, the operating principle is the same. Users must first create an instance of the fitter, then add the observed points and once the complete sample of observed points has been added they must call the
The
The following example shows how to fit data with a polynomial function.
final CurveFitter fitter = new CurveFitter(new LevenbergMarquardtOptimizer()); fitter.addObservedPoint(-1.00, 2.021170021833143); fitter.addObservedPoint(-0.99, 2.221135431136975); fitter.addObservedPoint(-0.98, 2.09985277659314); fitter.addObservedPoint(-0.97, 2.0211192647627025); // ... Lots of lines omitted ... fitter.addObservedPoint( 0.99, -2.4345814727089854); // The degree of the polynomial is deduced from the length of the array containing // the initial guess for the coefficients of the polynomial. final double[] init = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2 // Compute optimal coefficients. final double[] best = fitter.fit(new PolynomialFunction.Parametric(), init); // Construct the polynomial that best fits the data. final PolynomialFunction fitted = new PolynomialFunction(best);
The more specialized


Defines configuration data for to be registered for a IMixedRealityService on startup.
More...
Defines configuration data for to be registered for a IMixedRealityService on startup.
Generally, used for configuring the extended interface, IMixedRealityDataProvider
◆ ComponentName
| string Microsoft.MixedReality.Toolkit.IMixedRealityServiceConfiguration.ComponentName |
|
get |
The name of the system, feature or manager.
◆ ComponentType
| SystemType Microsoft.MixedReality.Toolkit.IMixedRealityServiceConfiguration.ComponentType |
|
get |
The concrete type for the system, feature or manager.
◆ Priority
| uint Microsoft.MixedReality.Toolkit.IMixedRealityServiceConfiguration.Priority |
|
get |
The priority this system, feature or manager will be initialized in.
◆ Profile
Profile configuration associated with the service
◆ RuntimePlatform
| SupportedPlatforms Microsoft.MixedReality.Toolkit.IMixedRealityServiceConfiguration.RuntimePlatform |
|
get |
The runtime platform(s) to run this service.
The documentation for this interface was generated from the following file:


On the Table
by Matt
Earlier this year, Tasha and I decided we wanted to focus on building an outdoor dining area, since food and being in the outdoors amidst our creations are both very important to us. Some time in the spring, we began thinking about what we wanted in an outdoor table: big enough to seat at least 12 comfortably, charmingly rustic, yet simple enough in design that it could be built from several lengths of structural 3x10 and 2x10 rough hewn beams that we bought from Noonkester Lumber just up the road.
After giving it some thought, we decided on a design where four massive, 12-foot 3x10s that form the table surface would rest on three 3x10 trestles, themselves supported by five 6x6 legs. The fifth leg is in the middle of the table, connected to the other four legs by centerboards, which hold all the legs together. The challenge was in working with materials so big and heavy that I could barely move each individual piece myself, which made building the table in situ necessary. When I had finally assembled the table, upside down, Tasha and I had to ask our excellent and very gracious neighbors Donnie and Anita for help. All four of us were able to turn the table rightside up and nudge it into place on the patio. Since our patio isn’t excactly uniformly flat, made as it is from river rocks of various sizes, we had to shim a couple of the legs.
But back to the building process. As I mentioned, working with lumber that cumbersome and heavy made me think, and I thought of the old days, and of how people used to routinely build much larger things like houses, barns, and boats from beams heavier and more difficult to work with than the very forgiving locally sourced yellow pine we chose for our table. Not only did they lack flat bed trucks for delivery and band saws to mill the timber, they would have had to fell the tree with axes and saws, not chainsaws and skid-steers.
While I found it difficult enough to put the table together using engineered bolts, a worm-drive circular saw, and a power drill with specialty bores and mortise bits, I thought of how differently it would have been done in the days before electricity or mass-produced steel hardware. The mortise-and-tenon style of carpentry that I was faking (using dowel plugs to hide the countersunk bolt heads) would have been one of the only ways. A carpenter joining wood with mortise and tenon construction would have to turn the dowels (tenons) on a lathe, cut them to size, and then hammer them through a bore hole (the mortise) cut in exactly the right place at exactly the right dimension for a snug fit.
My friend Ted has made some beautiful benches in this style, but he’s an accomplished wood worker and carpenter, and the precision and attention to detail necessary to build a large table and benches this way are far in excess of my current skill level. Such a project would, I imagine, have taken rather longer than it took me, and required skills with hand tools and an understanding of wood that could only be acquired through a lengthy apprenticeship. In contrast, anyone with the money to buy the materials and power tools necessary to build a table can readily do so, with big box chain stores stocking everything from pre-milled lumber to build with, to electric saws and cordless drills, to the chemical varnishes and weather proof finishes one might wish to apply to the finished product.
We wanted to use rough-hewn, locally sourced timbers and to give the table and benches a rustic feel, rather than simply bolting together a bunch of pressure-treated lumber, and I’m happy the table and benches turned out as well as they did. It is truly humbling, though, to consider how differently and with how much greater skill a craftsman of even 150 years ago would have had to approach the task of something so seemingly mundane as building a dinner table.
Lately I find that I appreciate the outcome of a process in proportion to the amount of effort and thought that go into the process. Conversely, things that are built without thought or consideration are rarely worth caring about. The sourdough bread I spend a half hour working by hand just tastes that much better than what I might toss in a mixer. While people may think it’s strange that I prefer to cut the grass on our homestead using a scythe rather than a lawn mower, I get a great aerobic and core workout in the process and the satisfaction of using a tool that requires no external inputs besides the labor and the skill of the user.
I’m not about to grow a (much longer) beard or insist on traveling by horse or anything like that, but if I’ve learned anything from our homesteading experience so far, it’s that the closer I can get to the land and the work, the more fullfilling and rewarding the final outcome is. Technology and power tools are wonderful for saving time and getting a job done quickly, but they also separate us from our work: Artful things that once required skill and love to craft become just another product, just another process. Building this table was fun and I’m proud of the end result, but it’s truly humbling to consider how it would have been built in the days before electricity.
Subscribe via RSS