2 Week 2
Week 2 covers calculating confidence intervals. Below is the activity.
Complete all Exercises, and submit answers to Questions on the Coursera platform.
If you have access to data on an entire population, say the size of every house in Ames, Iowa, it’s straight forward to answer questions like, “How big is the typical house in Ames?” and “How much variation is there in sizes of houses?”. If you have access to only a sample of the population, as is often the case, the task becomes more complicated. What is your best guess for the typical size if you only know the sizes of several dozen houses? This sort of situation requires that you use your sample to make inference on what your population looks like.
Setting a seed: We will take some random samples and calculate confidence based on these samples in this lab, which means you should set a seed on top of your lab. If this concept is new to you, review the previous lab and ask your TA.
Setting a seed will cause R to sample the same sample each time you knit your document. This will make sure your results don’t change each time you knit, and it will also ensure reproducibility of your work (by setting the same seed it will be possible to reproduce your results). You can set a seed like this:
set.seed(19122018) # my seed
The number above is completely arbitraty. If you need inspiration, you can use your ID, birthday, or just a random string of numbers. The important thing is that you use each seed only once. You only need to do this once in your R Markdown document, but make sure it comes before sampling.
2.1 Getting Started
2.1.1 Load packages
In this lab we will explore the data using the dplyr
package and visualize it using the ggplot2
package for data visualization. The data can be found in the companion package for this course, statsr
.
Let’s load the packages.
library(statsr)
library(dplyr)
library(ggplot2)
2.1.2 The data
We consider real estate data from the city of Ames, Iowa. This is the same dataset used in the previous lab. The details of every real estate transaction in Ames is recorded by the City Assessor’s office. Our particular focus for this lab will be all residential home sales in Ames between 2006 and 2010. This collection represents our population of interest. In this lab we would like to learn about these home sales by taking smaller samples from the full population. Let’s load the data.
data(ames)
In this lab we’ll start with a simple random sample of size 60 from the population. Specifically, this is a simple random sample of size 60. Note that the data set has information on many housing variables, but for the first portion of the lab we’ll focus on the size of the house, represented by the variable area
.
n <- 60
samp <- sample_n(ames, n)
Exercise: Describe the distribution of homes in your sample. What would you say is the “typical” size within your sample? Also state precisely what you interpreted “typical” to mean.
Answer: Typical size between 1000 and 1500 as it seems to be a bi-modal distribution.‘Typical’ meaning size which is more frequent.
# type your code for the Exercise here, and Knit
ggplot(samp, aes(x = area)) + geom_histogram(binwidth = 320)
- True or False: My distribution should be similar to others’ distributions who also collect random samples from this population, but it is likely not exactly the same since it’s a random sample.
- True.
- False.
2.2 Confidence intervals
Return for a moment to the question that first motivated this lab: based on this sample, what can we infer about the population? Based only on this single sample, the best estimate of the average living area of houses sold in Ames would be the sample mean, usually denoted as \(\bar{x}\) (here we’re calling it x_bar
). That serves as a good point estimate but it would be useful to also communicate how uncertain we are of that estimate. This uncertainty can be quantified using a confidence interval.
A confidence interval for a population mean is of the following form \[ \bar{x} \pm z^\star \frac{s}{\sqrt{n}} \]
You should by now be comfortable with calculating the mean and standard deviation of a sample in R. And we know that the sample size is 60. So the only remaining building block is finding the appropriate critical value for a given confidence level. We can use the qnorm
function for this task, which will give the critical value associated with a given percentile under the normal distribution. Remember that confidence levels and percentiles are not equivalent. For example, a 95% confidence level refers to the middle 95% of the distribution, and the critical value associated with this area will correspond to the 97.5th percentile.
We can find the critical value for a 95% confidence interal using
z_star_95 <- qnorm(0.975)
z_star_95
## [1] 1.959964
which is roughly equal to the value critical value 1.96 that you’re likely familiar with by now.
Let’s finally calculate the confidence interval:
samp %>%
summarise(lower = mean(area) - z_star_95 * (sd(area) / sqrt(n)),
upper = mean(area) + z_star_95 * (sd(area) / sqrt(n)))
## # A tibble: 1 x 2
## lower upper
## <dbl> <dbl>
## 1 1403. 1631.
To recap: even though we don’t know what the full population looks like, we’re 95% confident that the true average size of houses in Ames lies between the values lower and upper. There are a few conditions that must be met for this interval to be valid.
- For the confidence interval to be valid, the sample mean must be normally distributed and have standard error \(s / \sqrt{n}\). Which of the following is not a condition needed for this to be true?
- The sample is random.
- The sample size, 60, is less than 10% of all houses.
- The sample distribution must be nearly normal.
2.3 Confidence levels
- What does “95% confidence” mean?
- 95% of the time the true average area of houses in Ames, Iowa, will be in this interval.
- 95% of random samples of size 60 will yield confidence intervals that contain the true average area of houses in Ames, Iowa.
- 95% of the houses in Ames have an area in this interval.
- 95% confident that the sample mean is in this interval.
In this case we have the rare luxury of knowing the true population mean since we have data on the entire population. Let’s calculate this value so that we can determine if our confidence intervals actually capture it. We’ll store it in a data frame called params
(short for population parameters), and name it mu
.
params <- ames %>%
summarise(mu = mean(area))
Exercise: Does your confidence interval capture the true average size of houses in Ames?
# type your code for the Exercise here, and Knit
# Yes, the true mean is contained within the confidence interval.
- What proportion of 95% confidence intervals would you expect to capture the true population mean?
- 1%
- 5%
- 95%
- 99%
Using R, we’re going to collect many samples to learn more about how sample means and confidence intervals vary from one sample to another.
Here is the rough outline:
- Obtain a random sample.
- Calculate the sample’s mean and standard deviation, and use these to calculate and store the lower and upper bounds of the confidence intervals.
- Repeat these steps 50 times.
We can accomplish this using the rep_sample_n
function. The following lines of code takes 50 random samples of size n
from population (and remember we defined \(n = 60\) earlier), and computes the upper and lower bounds of the confidence intervals based on these samples.
set.seed(19122018);
ci <- ames %>%
rep_sample_n(size = n, reps = 50, replace = TRUE) %>%
summarise(lower = mean(area) - z_star_95 * (sd(area) / sqrt(n)),
upper = mean(area) + z_star_95 * (sd(area) / sqrt(n)), samplemeans.95 = mean(area))
Let’s view the first five intervals:
#set.seed(19122018);
ci %>%
slice(1:5)
## # A tibble: 5 x 4
## replicate lower upper samplemeans.95
## <int> <dbl> <dbl> <dbl>
## 1 1 1382. 1608. 1495.
## 2 2 1341. 1605. 1473.
## 3 3 1221. 1439. 1330.
## 4 4 1417. 1682. 1549.
## 5 5 1300. 1574. 1437.
Next we’ll create a plot similar to Figure 4.8 on page 175 of OpenIntro Statistics, 3rd Edition. First step will be to create a new variable in the ci
data frame that indicates whether the interval does or does not capture the true population mean. Note that capturing this value would mean the lower bound of the confidence interval is below the value and upper bound of the confidence interval is above the value. Remember that we create new variables using the mutate
function.
#set.seed(19122018)
ci <- ci %>%
mutate(capture_mu = ifelse(lower < params$mu & upper > params$mu, "yes", "no"))
The ifelse
function is new. It takes three arguments: first is a logical statement, second is the value we want if the logical statement yields a true result, and the third is the value we want if the logical statement yields a false result.
We now have all the information we need to create the plot, but we need to re-organize our data a bit for easy plotting. Specifically, we need to organize the data in a new data frame where each row represents one bound, as opposed to one interval. So this
lower upper capture_mu
1 1350.540 1544.360 yes
2 1333.441 1584.425 yes
3 1412.133 1663.801 yes
...
should instead look like
ci_id ci_bounds capture_mu
1 1 1350.540 yes
2 2 1333.441 yes
3 3 1412.133 yes
4 1 1544.360 yes
5 2 1584.425 yes
6 3 1663.801 yes
...
We can accomplish this using the following:
set.seed(19122018)
ci_data <- data.frame(ci_id = c(1:50, 1:50),
ci_bounds = c(ci$lower, ci$upper),
capture_mu = c(ci$capture_mu, ci$capture_mu))
And finally we can create the plot using the following:
#set.seed(19122018)
ggplot(data = ci_data, aes(x = ci_bounds, y = ci_id,
group = ci_id, color = capture_mu)) +
geom_point(size = 2) + # add points at the ends, size = 2
geom_line() + # connect with lines
geom_vline(xintercept = params$mu, color = "darkgray") # draw vertical line
2.4 How set.seed() works - testing?
set.seed(111112)
ci.95 <- ames %>%
rep_sample_n(size = n, reps = 50, replace = TRUE) %>%
summarise(samplemeans.95 = mean(area))
head(ci.95)
## # A tibble: 6 x 2
## replicate samplemeans.95
## <int> <dbl>
## 1 1 1526.
## 2 2 1474.
## 3 3 1492.
## 4 4 1473.
## 5 5 1446.
## 6 6 1483.
set.seed(111112)
ci.99 <- ames %>%
rep_sample_n(size = n, reps = 50, replace = TRUE) %>%
summarise(samplemeans.99 = mean(area))
head(ci.99)
## # A tibble: 6 x 2
## replicate samplemeans.99
## <int> <dbl>
## 1 1 1526.
## 2 2 1474.
## 3 3 1492.
## 4 4 1473.
## 5 5 1446.
## 6 6 1483.
Exercise: What proportion of your confidence intervals include the true population mean? Is this proportion exactly equal to the confidence level? If not, explain why.
Answer: It is almost all of the CIs = 98% include the true population mean except 1 sample. The proportion is not exactly equal to the confidence level because the confidence interval only provides a plausible range within which the mean is captured. BY definition, we are 95% confident, but results might vary based on how the samples are randonly selected. This is an overestimation of the 95% CI sampling.
- What is the appropriate critical value for a 99% confidence level?
- 0.01
- 0.99
- 1.96
- 2.33
- 2.58
# type your code for the Question 5 here, and Knit
set.seed(19122018)
z_star_99 <- qnorm(0.995)
z_star_99
## [1] 2.575829
Exercise: Calculate 50 confidence intervals at the 99% confidence level. You do not need to obtain new samples, simply calculate new intervals based on the 95% confidence interval endpoints you had already collected. Plot all intervals and calculate the proportion of intervals that include the true population mean.
# type your code for the Exercise here, and Knit
set.seed(19122018)
#samples for 99% confidence interval
ci_99 <- ames %>%
rep_sample_n(size = n ,reps = 50, replace = TRUE) %>%
summarise(lower = mean(area) - z_star_99 * (sd(area) / sqrt(n)),
upper = mean(area) + z_star_99 * (sd(area) / sqrt(n)), samplemeans.99 = mean(area))
#assignment of capture
ci_99 <- ci_99 %>%
mutate(capture_mu_99 = ifelse(lower < params$mu & upper > params$mu, "yes", "no"))
#datagrame for plot
ci_data_99 <- data.frame(ci_id_99 = c(1:50, 1:50),
ci_bounds_99 = c(ci_99$lower, ci_99$upper),
capture_mu_99 = c(ci_99$capture_mu_99, ci_99$capture_mu_99))
#plot
ggplot(data = ci_data_99, aes(x = ci_bounds_99, y = ci_id_99,
group = ci_id_99, color = capture_mu_99)) +
geom_point(size = 2) + # add points at the ends, size = 2
geom_line() + # connect with lines
geom_vline(xintercept = params$mu, color = "darkblue") # draw vertical line
ci_99 %>%
slice(1:5)
## # A tibble: 5 x 5
## replicate lower upper samplemeans.99 capture_mu_99
## <int> <dbl> <dbl> <dbl> <chr>
## 1 1 1347. 1644. 1495. yes
## 2 2 1300. 1647. 1473. yes
## 3 3 1187. 1473. 1330. no
## 4 4 1375. 1723. 1549. yes
## 5 5 1257. 1617. 1437. yes
```
- We would expect 99% of the intervals to contain the true population mean.
- True
- False
This is a product of OpenIntro that is released under a Creative Commons Attribution-ShareAlike 3.0 Unported. This lab was written for OpenIntro by Andrew Bray and Mine Çetinkaya-Rundel.


[gecode-users] variable selection heuristics
Guido Tack
tack at ps.uni-sb.de
Fri Nov 6 15:48:25 CET 2009
Dear Alejandro,
there's no direct support for restart-based search, but it's easy to
do with the search engines we provide. Just use a FailStop object
with a DFS engine, and when next() returns NULL, check whether the
engine was stopped or search is exhausted. If it was stopped, reset
the engine and restart, setting a new limit on your FailStop object.
We've been looking into IBS as well, but, at least currently, I can't
see a way to implement it in a non-intrusive way. It's slightly
difficult to do IBS in combination with recomputation - you have to
measure the impact of a single choice, but with recomputation, you
most of the time compute a fixpoint of more than one choice. Also,
keeping track of domain sizes is more intrusive than a simple failure
count. Anyway, we'll think some more about it, maybe we can come up
with a solution that is general and elegant enough to be included in
Gecode.
Cheers,
Guido
Alejandro Arbelaez wrote:
> Dear Gecode developers,
>
> I've seen that the last gecode version includes variables' weighted
> degree information which i think is a great new feature of gecode,
> because now it will be possible to use the current state-of-the-art
> variable selection heuristics (dom/wdeg) in gecode. I don't have all
> the details about this new release, but I'd like know if this
> release also includes a restart-base search engine. (i.e, restarting
> the search engine when some cutoff limit in the number of failures
> is met). I know this is not difficult to implement but would like to
> know if this is already available.
>
> And my last question, are you considering to include Impacts [1]
> into gecode (I mean in the near future)?
>
>
> [1] P. Refalo. Impact-based search strategies for Constraint
> Programming, In CP 2004.
>
>
> Cheers,
> Alejandro
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
More information about the gecode-users
mailing list


SCYTERA (COAL TAR) FOAM, 2%- coal tar aerosol, foam
DPT Laboratories, Ltd.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Scytera™ Foam is an emollient foam containing 10% coal tar topical
solution (Liquor Carbonis Detergens) equivalent to 2% coal tar.
Active Ingredient - Purpose
Active
ingredient: Coal Tar 2% (from Coal Tar Topical Solution USP,
10%)
Purpose: Psoriasis
Use
For relief of the symptoms of psoriasis.
Warnings
For
external use only.
Flammable
Avoid fire, flame, or smoking during and immediately following application.
Do not use
- for prolonged periods
without consulting a doctor
- this product in or around
the rectum or in the genital area or groin except on the advice of a doctor
Ask a doctor before use if you have
a
condition that covers a large area of your body.
Ask a doctor or pharmacist before use if you are
using
the product with other forms of psoriasis therapy such as ultraviolet radiation
or prescription drugs.
When using this product
-
avoid contact with the eyes. If contact occurs, rinse eyes thoroughly with
water.
- use caution in exposing skin to sunlight after applying this product. It
may increase your tendency to sunburn for up to 24 hours after application.
- product can stain clothing, contact lenses, and hair. To minimize
staining, allow product to dry before contact.
Stop use and ask doctor if
condition worsens or does not improve after regular use of this product as directed.
If pregnant or breastfeeding
ask a health professional before use.
Keep out of reach of children.
If swallowed, get medical help or contact a poison control center right
away.
Directions
- shake the can well before use.
- invert the can to dispense. Upright dispensing will cause loss of
propellant.
- apply to affected areas one to four times daily or as directed by a doctor.
Other information
- contents under pressure. Do not puncture or incinerate.
- store at 68°F to 77°F (20°C to 25°C)
- do not expose to heat or store at temperatures above 120°F (49°C)
Inactive Ingredients
Caprylic/capric triglyceride, ceteareth 20, citric acid, cyclomethicone,
dimethicone, EDTA disodium, glycerin, hydroxypropyl methyl cellulose, isopropyl
myristate, peppermint oil, phenoxyethanol, potassium sorbate, purified water,
sodium citrate, stearic acid, xanthan gum, hydrocarbon (propane/butane)
propellant.
Questions?
1-888-966-8766
Warning
This product contains chemicals known to the state of California to cause cancer.
Patent Pending
Made in USA.
Marketed by:
Promius Pharma, LLC
Bridgewater,
NJ 08807
Under license from
Foamix LTD, Israel
Scytera™ (Coal Tar)Foam 2%, 100 g
Carton and Can Label
NDC 67857-801-52
Psoriasis Foam
Scytera™
(Coal Tar) Foam 2%
Promius Pharma™NET WT. 3.3 oz (100 g)
Carton (California)
NDC 67857-801-26
Psoriasis Foam
Scytera™
(Coal Tar) Foam 2%
Promius Pharma™NET WT. 3.3 oz (100 g)
Scytera™ (Coal Tar)Foam 2%, 12 g Sample Size
Packer (Box)
Available in 100 g Cans
Scytera™
(Coal Tar) Foam 2%
Promius Pharma™
Professional Samples
Net Quantity 12 x 12 g Cans
NDC 67857-801-12
Packer (Box) (California)
Available in 100 g Cans
Scytera™
(Coal Tar) Foam 2%
Promius Pharma™
Professional Samples
Net Quantity 12 x 12 g Cans
NDC 67857-801-19
Can Label
NDC 67857-801-12
Psoriasis Foam
Scytera™
(Coal Tar) Foam 2%
SAMPLE, NOT FOR SALE
NET WT. 0.4 oz (12 g)
DPT Laboratories, Ltd.


Editing a Product
How to edit a product as a Catalog Manager using the Store module.
- Go to the Store page.
- Using the Store Menu module, navigate to and select the category where the product is located.
- In the Store Catalog module, locate the required product.
- Click the Edit button beside the title of the required product.
- Edit one or more product details. See "Adding a Product Category" for details of available fields.
-
Click the Update button.


|
Ext.NET
5.3.0
.NET Component Framework for Enterprise Level Apps
|
Public Member Functions | |
| Builder (TMultiSelectorSearch component) | |
| virtual TBuilder | Border (bool? border) |
| Specify false to turn off the border for this component. More... | |
| virtual TBuilder | Floating (bool floating) |
| Specify as true to float the AbstractComponent outside of the document flow using CSS absolute positioning. More... | |
| virtual TBuilder | SearchText (string searchText) |
| This text is displayed as the "emptyText" of the search textfield. Defaults to: 'Search...' More... | |
| virtual TBuilder | Field (string field) |
| virtual TBuilder | GetSearchStore (JFunction getSearchStore) |
| virtual TBuilder | StoreID (string storeID) |
| The data store to use. More... | |
| virtual TBuilder | Store (Action< StoreCollection< Store >> action) |
| virtual TBuilder | SearchGridConfig (GridPanel searchGridConfig) |
| virtual TBuilder | SearchFieldConfig (TextField searchFieldConfig) |
| Builder () | |
| Builder (MultiSelectorSearch component) | |
| Builder (MultiSelectorSearch.Config config) | |
| Public Member Functions inherited from Ext.Net.Panel.Builder< TMultiSelectorSearch, TBuilder > | |
| Builder (TPanel component) | |
| Builder () | |
| Builder (Panel component) | |
| Builder (Panel.Config config) | |
| virtual TBuilder | Listeners (Action< PanelListeners > action) |
| Client-side JavaScript Event Handlers More... | |
| virtual TBuilder | DirectEvents (Action< PanelDirectEvents > action) |
| Server-side Ajax Event Handlers More... | |
Static Public Member Functions | |
| static implicit | operator Builder (MultiSelectorSearch component) |
| Static Public Member Functions inherited from Ext.Net.Panel.Builder< TMultiSelectorSearch, TBuilder > | |
| static implicit | operator Builder (Panel component) |
| TMultiSelectorSearch | : | MultiSelectorSearch | |
| TBuilder | : | Builder | |
| TBuilder | : | TMultiSelectorSearch | |
| TBuilder | : | TBuilder |
|
inline |
|
inline |
|
inline |
|
inline |
|
inlinevirtual |
Specify false to turn off the border for this component.
|
inlinevirtual |
|
inlinevirtual |
Specify as true to float the AbstractComponent outside of the document flow using CSS absolute positioning.
|
inlinevirtual |
|
inlinestatic |
|
inlinevirtual |
|
inlinevirtual |
|
inlinevirtual |
This text is displayed as the "emptyText" of the search textfield. Defaults to: 'Search...'
|
inlinevirtual |
| action | The action delegate |
|
inlinevirtual |
The data store to use.


pymatgen.analysis.local_env module¶
-
class
JMolNN
(tol=0.001, el_radius_updates=None)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Determine near-neighbor sites and coordination number using an emulation of JMol’s default autoBond() algorithm. This version of the algorithm does not take into account any information regarding known charge states.
Parameters: - tol (float) – tolerance parameter for bond determination (default: 1E-3).
- el_radius_updates – (dict) symbol->float to override default atomic radii table values
-
get_max_bond_distance
(el1_sym, el2_sym, constant=0.56)[source]¶ Use JMol algorithm to determine bond length from atomic parameters :param el1_sym: (str) symbol of atom 1 :param el2_sym: (str) symbol of atom 2 :param constant: (float) factor to tune model
Returns: (float) max bond length
-
get_nn_info
(structure, n)[source]¶ Get all near-neighbor sites as well as the associated image locations and weights of the site with index n using the bond identification algorithm underlying JMol.
Parameters: Returns: - tuples, each one
of which represents a neighbor site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))
-
class
MinimumDistanceNN
(tol=0.1, cutoff=10.0)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Determine near-neighbor sites and coordination number using the nearest neighbor(s) at distance, d_min, plus all neighbors within a distance (1 + delta) * d_min, where delta is a (relative) distance tolerance parameter.
Parameters: - tol (float) – tolerance parameter for neighbor identification (default: 0.1).
- cutoff (float) – cutoff radius in Angstrom to look for trial near-neighbor sites (default: 10.0).
-
get_nn_info
(structure, n)[source]¶ Get all near-neighbor sites as well as the associated image locations and weights of the site with index n using the closest neighbor distance-based method.
Parameters: Returns: - tuples, each one
of which represents a neighbor site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))
-
class
MinimumOKeeffeNN
(tol=0.1, cutoff=10.0)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Determine near-neighbor sites and coordination number using the neighbor(s) at closest relative distance, d_min_OKeffee, plus some relative tolerance, where bond valence parameters from O’Keeffe’s bond valence method (J. Am. Chem. Soc. 1991, 3226-3229) are used to calculate relative distances.
Parameters: - tol (float) – tolerance parameter for neighbor identification (default: 0.1).
- cutoff (float) – cutoff radius in Angstrom to look for trial near-neighbor sites (default: 10.0).
-
get_nn_info
(structure, n)[source]¶ Get all near-neighbor sites as well as the associated image locations and weights of the site with index n using the closest relative neighbor distance-based method with O’Keeffe parameters.
Parameters: Returns: - tuples, each one
of which represents a neighbor site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))
-
class
MinimumVIRENN
(tol=0.1, cutoff=10.0)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Determine near-neighbor sites and coordination number using the neighbor(s) at closest relative distance, d_min_VIRE, plus some relative tolerance, where atom radii from the ValenceIonicRadiusEvaluator (VIRE) are used to calculate relative distances.
Parameters: - tol (float) – tolerance parameter for neighbor identification (default: 0.1).
- cutoff (float) – cutoff radius in Angstrom to look for trial near-neighbor sites (default: 10.0).
-
get_nn_info
(structure, n)[source]¶ Get all near-neighbor sites as well as the associated image locations and weights of the site with index n using the closest relative neighbor distance-based method with VIRE atomic/ionic radii.
Parameters: Returns: - tuples, each one
of which represents a neighbor site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))
-
class
NearNeighbors
[source]¶ Bases:
object
Base class to determine near neighbors that typically include nearest neighbors and others that are within some tolerable distance.
-
get_cn
(structure, n, use_weights=False)[source]¶ Get coordination number, CN, of site with index n in structure.
Parameters: Returns: coordination number.
Return type: cn (integer or float)
-
get_nn
(structure, n)[source]¶ Get near neighbors of site with index n in structure.
Parameters: Returns: near neighbors.
Return type: sites (list of Site objects)
-
get_nn_images
(structure, n)[source]¶ Get image location of all near neighbors of site with index n in structure.
Parameters: Returns: - image locations of
near neighbors.
Return type: images (list of 3D integer array)
-
get_nn_info
(structure, n)[source]¶ Get all near-neighbor sites as well as the associated image locations and weights of the site with index n.
Parameters: Returns: - each dictionary provides information
about a single near neighbor, where key ‘site’ gives access to the corresponding Site object, ‘image’ gives the image location, and ‘weight’ provides the weight that a given near-neighbor site contributes to the coordination number (1 or smaller).
Return type: siw (list of dicts)
-
-
class
ValenceIonicRadiusEvaluator
(structure)[source]¶ Bases:
object
Computes site valences and ionic radii for a structure using bond valence analyzer
Parameters: structure – pymatgen.core.structure.Structure -
radii
¶ List of ionic radii of elements in the order of sites.
-
structure
¶ Returns oxidation state decorated structure.
-
valences
¶ List of oxidation states of elements in the order of sites.
-
-
class
VoronoiNN
(tol=0, targets=None, cutoff=10.0, allow_pathological=False)[source]¶ Bases:
pymatgen.analysis.local_env.NearNeighbors
Uses a Voronoi algorithm to determine near neighbors for each site in a structure.
Parameters: - tol (float) – tolerance parameter for near-neighbor finding (default: 0).
- targets (Element or list of Elements) – target element(s).
- cutoff (float) – cutoff radius in Angstrom to look for near-neighbor atoms. Defaults to 10.0.
- allow_pathological (bool) – whether to allow infinite vertices in determination of Voronoi coordination.
-
get_nn_info
(structure, n)[source]¶ ” Get all near-neighbor sites as well as the associated image locations and weights of the site with index n in structure using Voronoi decomposition.
Parameters: Returns: - tuples, each one
of which represents a coordinated site, its image location, and its weight.
Return type: siw (list of tuples (Site, array, float))
-
get_voronoi_polyhedra
(structure, n)[source]¶ Gives a weighted polyhedra around a site. This uses the Voronoi construction with solid angle weights. See ref: A Proposed Rigorous Definition of Coordination Number, M. O’Keeffe, Acta Cryst. (1979). A35, 772-775
Parameters: Returns: A dict of sites sharing a common Voronoi facet with the site n and their solid angle weights
-
get_neighbors_of_site_with_index
(struct, n, approach='min_dist', delta=0.1, cutoff=10.0)[source]¶ Returns the neighbors of a given site using a specific neighbor-finding method.
Parameters: - struct (Structure) – input structure.
- n (int) – index of site in Structure object for which motif type is to be determined.
- approach (str) – type of neighbor-finding approach, where “min_dist” will use the MinimumDistanceNN class, “voronoi” the VoronoiNN class, “min_OKeeffe” the MinimumOKeeffe class, and “min_VIRE” the MinimumVIRENN class.
- delta (float) – tolerance involved in neighbor finding.
- cutoff (float) – (large) radius to find tentative neighbors.
Returns: neighbor sites.
-
get_okeeffe_distance_prediction
(el1, el2)[source]¶ Returns an estimate of the bond valence parameter (bond length) using the derived parameters from ‘Atoms Sizes and Bond Lengths in Molecules and Crystals’ (O’Keeffe & Brese, 1991). The estimate is based on two experimental parameters: r and c. The value for r is based off radius, while c is (usually) the Allred-Rochow electronegativity. Values used are not generated from pymatgen, and are found in ‘okeeffe_params.json’.
Parameters: el2 (el1,) – two Element objects Returns: a float value of the predicted bond length
-
get_okeeffe_params
(el_symbol)[source]¶ Returns the elemental parameters related to atom size and electronegativity which are used for estimating bond-valence parameters (bond length) of pairs of atoms on the basis of data provided in ‘Atoms Sizes and Bond Lengths in Molecules and Crystals’ (O’Keeffe & Brese, 1991).
Parameters: el_symbol (str) – element symbol. Returns: - atom-size (‘r’) and electronegativity-related (‘c’)
- parameter.
Return type: (dict)
-
site_is_of_motif_type
(struct, n, approach='min_dist', delta=0.1, cutoff=10.0, thresh=None)[source]¶ Returns the motif type of the site with index n in structure struct; currently featuring “tetrahedral”, “octahedral”, “bcc”, and “cp” (close-packed: fcc and hcp) as well as “square pyramidal” and “trigonal bipyramidal”. If the site is not recognized, “unrecognized” is returned. If a site should be assigned to two different motifs, “multiple assignments” is returned.
Parameters: - struct (Structure) – input structure.
- n (int) – index of site in Structure object for which motif type is to be determined.
- approach (str) – type of neighbor-finding approach, where “min_dist” will use the MinimumDistanceNN class, “voronoi” the VoronoiNN class, “min_OKeeffe” the MinimumOKeeffe class, and “min_VIRE” the MinimumVIRENN class.
- delta (float) – tolerance involved in neighbor finding.
- cutoff (float) – (large) radius to find tentative neighbors.
- thresh (dict) – thresholds for motif criteria (currently, required keys and their default values are “qtet”: 0.5, “qoct”: 0.5, “qbcc”: 0.5, “q6”: 0.4).
Returns: motif type (str).


--- layout: archive title: Zulip Chat Archive permalink: /stream/144837-PR-reviews/topic/.23951.20inj_iff_trivial_ker.html ---
@Ramon Fernandez Mir Thanks for this PR! I think it is looking pretty good. I have a few simple questions for people like @Mario Carneiro and/or @Chris Hughes. Should lemmas like
@[to_additive is_add_group_hom.trivial_ker_iff_eq_zero] lemma trivial_ker_iff_eq_one (f : α → β) [is_group_hom f] : ker f = trivial α ↔ ∀ x, f x = 1 → x = 1 :=
be in the is_group_hom
namespace, or should we put them in the root namespace? Currently we have a similar lemma for ring homs in the is_ring_hom
namespace. Should the latter just be removed?
It should be in the is_group_hom
namespace, because there are several theorems like that
isn't there one for linear maps too?
Hmm... this wasn't a good example. Because actually ker f
is defined differently for is_group_hom
and is_ring_hom
. Anyway, my question did apply to
lemma inj_iff_eq_zero : function.injective f ↔ ∀ x, f x = 0 → x = 0 :=
@Ramon Fernandez Mir Would you mind making these update? (When you have time...)
is_ring_hom.ker
sounds like it should should be is_add_group_hom.ker
. I think we need some better names than having to remember the weakest conditions for everything all the time.
I must confess I sometimes setup aliases when I'm tired of this problem
how about add_ker
and mul_ker
?
is_ring_hom.ker
sounds like it should should beis_add_group_hom.ker
. I think we need some better names than having to remember the weakest conditions for everything all the time.
No, there is a real difference between the two. One is an ideal
(which is bundled) and the other is an is_add_subgroup
(which is unbundled).
So, (as long as we use bundled stuff) there is a legitimate reason to have both.
Ramon Fernandez Mir Would you mind making these update? (When you have time...)
So I just need to move this lemma out of the is_group_hom
namespace and remove it from is_ring_hom
?
@Ramon Fernandez Mir Yes, please make inj_iff_eq_one
a lemma in the root namespace (in an appropriate file in the hierarchy). And include the additive version.
For some reason AppVeyor is failing for this PR. I don't understand the error.
It's because the branch is located in another repo. We can't test the scripts well when that happens.
Since he doesn't change the scripts, you can ignore the failure and merge by hand if nothing is building
#931 has the same problem, and it's on a branch of the community repo
I'll see what I can do
Thanks!
Restarting the build was sufficient. It looks pretty fishy too me. I'll have to look at that script again
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Resources
Recommended Reading
•Concurrent and Real-time Programming in Java, Andy Wellings, Wiley 2004, (www)
Links
•RTEMS operating system
•LEON (Gaisler Research)
•Real-time Specification for Java
•fVM
•Rapita Systems
•LEON3
•LEON3 manual
•Grmon manual
Grmon Download: ftp
Tsim Download: ftp
RTEMS Download: ftp


| DATE | TYPE | SENDER | RECIPIENT | FEE | UNITS |
PORTFOLIO
The Portfolio area serves both as an analytic tool and a summary overview of a user's holdings. It is the perfect place to draft market strategies and plan future investments.
The Portfolio tab will start operating as Waves platform incorporates additional markets, meanwhile the transactions can be tracked via History tab.
DECENTRALIZED EXCHANGE
Waves' features include a decentralized exchange in which users will be able to trade tokens of all kinds. There will be no AML/KYC requirements in order to use this service.
The Decentralized Exchange is scheduled for launch in the last quarter of 2016.
DECENTRALIZED VOTING
With a fair and automated system, Waves' voting area enables communities and projects to achieve consensus for critical decision-making that might otherwise prove difficult or controversial. A full set of analytic tools will also be available to follow up the voting process in real time.
The Decentralized Voting feature is scheduled for launch in the final quarter of 2016.
| DATE | TYPE | SENDER | RECIPIENT | FEE | UNITS |
ASSET CREATION
The Asset Creation area is utilized to build custom Assets composed by Tokens. It is the first step in order to initialize a project in the platform.
The Asset Creation tab will start being operative in the fall of 2016, shortly after the Full Node code is released publicly or even at the same time.
MESSAGING CENTER
Soon to be renamed/rebranded as CONTACTS, this is an area in which users will be able to add other individuals or groups to several list types, providing an easy way to keep track of friends, execute regular, automated payments, and much more.
The Contacts area services is scheduled for launch in the third quarter of 2016.
DISCLAIMER: The Community tab will temporarily hold block information for testing purposes. After alpha phase this data will be relocated inside the Preferences - Advanced area.
LATEST BLOCKS INFORMATION
| HEIGHT | DATE | TRANSACTIONS | GENERATOR |


Use this tool to generate computerized art. Choose the number of points, dimensions for your image, and pick a few colors. Then hit "Generate Triangles" and save your unique image.
Control + click and "Save Image As..." to save the image below.


May 19
Wherefore be ye not unwise, but understanding what the will of the Lord is. Ephesians 5:17
Sometimes, believers wring their hands and worry about whether or not they're praying according to God's will. "Oh, dear me," they say, "I certainly can't expect God to do something for me that's outside His will." And they're absolutely right. But they don't need to waste time standing around looking puzzled. They need to grab their Bibles and find out what the will of God is.
God has made some very specific promises in His Word and it's His will to fulfill every one of them in your life. In fact, those promises are divinely guaranteed by a contract that's been signed in the blood of Jesus. Think of it this way. Your Bible is the last will and testament of Jesus Christ. It is a record of your inheritance. Everything that belongs to you has been written down in that book and the smartest thing you can do is to find out what is in it. Don't leave it lying on the coffee table and hit the floor crying, "Oh, God! Oh, God! I'm searching for the truth."
 Find out how you need to change to get in line with God's will. Some people don't do that. They try to change God instead.  They'll beg and plead and wheedle, all the while acting as though they had to somehow change God's mind about the situation. Those folks are in for a long wait.
God is never going to change. The Bible says He's the same yesterday, today, and forever. His will is too. So don't waste your time wondering if your prayers are in line with it. Get the Book and base your prayers on it. Then you can rest assured the answer is on the way!
Scripture Reading: Proverbs 2:1-12


The NovoRender cloud makes it possible to visualize endless amounts of 3D data using any browser on any device. First, NovoRender combines 3D models in different formats to create the full scene. Next, NovoRender splits the combined 3D model into small chunks. Finally, the small chunks are streamed on demand to the viewer, dependent on which part of the scene is being displayed.
This is different from the traditional way of viewing 3D models which is based on sending the entire 3D file to the viewer before rendering the scene. By splitting large models into small chunks and stremaing them on an as-needed basis, any 3D model can be visualized on any device with a web-browser. In addition, access to metadata is enabled, allowing the user to click on and inspect any element in the scene. No plugins or downloads are required.


To Do List
Alert!
Things I have to do:
Fill in the
js/index.js
file and complete the exercise
Add task to list
Am I done?


Welcome new board members Brian Kesler and Kyle Kirchhoefer and returning board members Greg Sopa, and Tim Yardley.
Thank you for your service Rachel Shekar!
Where: Prairie Fields Neighborhood
When: Sunday, December 18th 5:30PM to 7:30PM
Planning
We will be planning at the next HOA Board meeting at Carrie Busey Elementary on Monday, November 14th at 8pm. If you'd like to help out in any capacity of the planning please attend.
Treat Tables - If you'd like to host, please e-mail<EMAIL_ADDRESS>will be looking for folks to provide and monitor the treat tables/voting stations. Each table will be given $30 to purchase the treats. We would like to focus on having these tables stationed at corners throughout the neighborhood to make it easy for visitors to find. This year we are looking to have ballot style voting sheets that people can carry with them as they look at the houses.
PRIZES!
As was the case last year, there will be trophies for 1st, 2nd, and 3rd Place as well as honorable mention ribbons. We will also have a fun prize for one random participating house!
It's time to cast your vote for the four open positions on the board for 2017/18! Voting will close November 30th, 2016.
You can access the electronic ballot here.
To opt for a paper ballot, please drop a note in the HOA dropbox.
One vote per household. Homeowners must be a member in good standing to vote in the election, and not owe the association more than $10. You can access the past due list by clicking here.
If you are interested in running for a seat on the PF HOA Board of Directors, you can download an application form here.
The day to day operations of the PF HOA are run by an all-volunteer Board of Directors, along with help from many other committee volunteers.The Board establishes the annual dues assessment and is responsible for using the money assessed to maintain and improve HOA property and offer programs which are of value to Prairie Fields residents, thereby increasing the value of the neighborhood. If you have 1-2 hours a month to donate to the neighborhood, you are encouraged to run one of the 3 upcoming vacancies on the Board for a term which begins January 1, 2017 and runs through December 31, 2017. Applications must be submitted by October 21.
This year, voting will be conducted electronically. You can opt for paper ballots by dropping a note in the HOA dropbox. Homeowners must be a member in good standing to vote in the election, and not owe the association more than $10. You can access the past due list by clicking here.
For more information, please email<EMAIL_ADDRESS>Calling all kids and those young at heart! The Annual Halloween Costume parade will be held October 31st at 5:30 pm. Participants should meet at the parking lot at Prairie Fields Park at 5:15 for the Costume Contest.
Prizes will be rewarded before the parade. There will be awards for best:
The parade route will leave the park and go west on Gentian St., north on Tickseed Ave., east on Dropseed Dr., and south at Prairie Rose Lane where they will return to the Park. Residents are also encouraged to line the parade route and cheer the participants along.
Construction has now begun on Phase 14 of Prairie Fields. The layout for the new the new housing phase being constructed on the west of the PF subdivision is available here.
As with previous phases, phase will not be part of the HOA until it is completed. Until then, it is under the control of Signature Homes.
School has started and there are children walking to Carrie Busey and to the neighborhood bus stops. Please drive carefully!
You can help by slowing down, being sure to come to a complete stop and avoiding distractions. If possible, avoid the roads by Carrie Busey before and after school. Children should walk on the sidewalks as much as possible, and only cross on a crosswalk or when a guard is present
If you see speeding in our neighborhood contact the Sheriff's Department patrol commander, Lt. Brian Mennenga with your observations including specific information that may better engage in enforcement (eg more specific times, directions, days of week that are worse, particular vehicle, etc.).
Meet at Prairie Fields park shortly before 9am with your bikes/strollers/wagons/children and pets decorated for the festivities! We will depart the park at 9am and return around 9:15am.
The route will be the usual "Prairie Fields Parade Route" starting on Gentian, heading North on Tickseed, and East on Dropseed back to the park. As we did last year, the Savoy Fire Department will be on hand to celebrate with us as well.
Please join your neighbors in celebrating!
Saturday, June 11th from 2PM-5PM we will be hosting our first neighborhood block party!
Food vouchers for first 100 PF visitors!
The Prairie Fields Neighborhood Watch is up and running! If you would like to be added to the Neighborhood Watch email list, please add your contact information here: https://groups.google.com/a/prairiefieldshoa.com/forum/#!forum/watch
Remember, if you see anything suspicious or out of the ordinary, call 911 first before spreading the word through the Neighborhood Watch email list or on Nextdoor.
With collective effort, Prairie Fields will remain a safe place to live, work, play, or attend school!
This year's neighborhood sale will be advertised for Saturday, June 4 from 8-2.
Deputy Oliger will be discussing how we can keep Prairie Fields a safe place to live.
Friday, January 22 7:30-8:30
Carrie Busey
The HOA Annual Meeting will be held on Thursday. January 21st at 7pm in the Carrie Busey library.
This is your chance to:
All residents of Prairie Fields are encouraged to attend. Annual Meeting Agenda (subject to change)
Welcome new board members MariAnne Schmidt and Dan Harmon and returning board member PJ Trautman.
Thank you for your service Tyler Kuntz and Eric Ray!
Thank you EVERYONE who participated and/or went around the neighborhood to view the lights. It was a great turnout and the weather actually held up for the event. In all we had 85 votes for 21 different houses! Next year we plan to make it even better! As it gets closer we will probably look to create a committee to help planning and make it even better for everyone.
First Place - 303 Trefoil
Second Place - 900 Phlox
Third Place - 501 Trefoil
Honorable Mention - 414 Gentian
Honorable Mention - 208 Goldenrod
Honorable Mention - 603 Indigo
Street with the most votes - Trefoil - 31 votes
We also have trophies and ribbons for the top three houses and ribbons for the honorable mentions. Please feel free to display those proudly somewhere on your house.
Also, the board drew one of the houses that received votes for a 20" Leg Lamp Award. And the winner is 900 Phlox!
We raised $59 and some change for the Crisis Nursery and also 2 bags of food and toy donations!
If you have any ideas for the 2016 competition, please email the Board of Directors!


Calibration of every computational code. It uses a Bayesian framework to rule the estimation. With a new data set, the prediction will create a prevision set taking into account the new calibrated parameters. The choices between several models is also available. The methods are described in the paper Carmassi et al. (2018) <arXiv:1801.01810>.
| Version: | 0.1.1 |
| Imports: | R6, ggplot2, DiceKriging, DiceDesign, MASS, coda, parallel, gridExtra, gtools |
| LinkingTo: | Rcpp, RcppArmadillo, Matrix |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-07-24 |
| Author: | Mathieu Carmassi [aut, cre] |
| Maintainer: | Mathieu Carmassi <mathieu.carmassi at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | CaliCo results |
| Reference manual: | CaliCo.pdf |
| Vignettes: |
Introduction to CaliCo |
| Package source: | CaliCo_0.1.1.tar.gz |
| Windows binaries: | r-devel: CaliCo_0.1.1.zip, r-release: CaliCo_0.1.1.zip, r-oldrel: CaliCo_0.1.1.zip |
| OS X binaries: | r-release: CaliCo_0.1.1.tgz, r-oldrel: CaliCo_0.1.1.tgz |
| Old sources: | CaliCo archive |
Please use the canonical form https://CRAN.R-project.org/package=CaliCo to link to this page.


let name = "John"
echo "Hello, " . name
You can either put this in a script (script.vim
) and run it (:source script.vim
), or you can type the commands individually in normal mode as :let
and :echo
.
function! SuperTab()
let l:part = strpart(getline('.'),col('.')-2,1)
if (l:part =~ '^\W\?$')
return "\<Tab>"
else
return "\<C-n>"
endif
endfunction
imap <Tab> <C-R>=SuperTab()<CR>
Here's another example with functions, variables and mapping.
{: .-prime}
let var = "hello"
let g:ack_options = '-s -H' " g: global
let s:ack_program = 'ack' " s: local (to script)
let l:foo = 'bar' " l: local (to function)
The s:
prefix is also available in function names. See :help local-variables
let w:foo = 'bar' " w: window
let b:state = 'on' " b: buffer
let t:state = 'off' " t: tab
echo v:var " v: vim special
let @/ = '' " @ register (this clears last search pattern)
echo $PATH " $ env
echo 'tabstop is ' . &tabstop
if &insertmode
echo &g:option
echo &l:option
Prefix Vim options with &
a + b " numbers only!
'hello ' . name " concat
let var -= 2
let var += 5
let var .= 'string' " concat
let str = "String"
let str = "String with \n newline"
let literal = 'literal, no \ escaping'
let literal = 'that''s enough' " double '' => '
echo "result = " . re " concatenation
Also see :help literal-string
and :help expr-quote
. See: Strings
strlen(str) " length
len(str) " same
strchars(str) " character length
split("one two three") "=> ['one', 'two', 'three']
split("one.two.three", '.') "=> ['one', 'two', 'three']
join(['a', 'b'], ',') "=> 'a,b'
tolower('Hello')
toupper('Hello')
Also see :help functions
See: String functions
{: .-prime}
" prefix with s: for local script-only functions
function! s:Initialize(cmd, args)
" a: prefix for arguments
echo "Command: " . a:cmd
return true
endfunction
See: Functions
function! myplugin#hello()
call s:Initialize()
call s:Initialize("hello")
echo "Result: " . s:Initialize()
function! myfunction() abort
endfunction
Aborts when an error occurs.
function! infect(...)
echo a:0 "=> 2
echo a:1 "=> jake
echo a:2 "=> bella
for s in a:000 " a list
echon ' ' . s
endfor
endfunction
infect('jake', 'bella')
See :help function-argument
. See: Var arguments
for s in list
echo s
continue " jump to start of loop
break " breaks out of a loop
endfor
while x < 5
endwhile
{: .-prime}
command! Save :set fo=want tw=80 nowrap
Custom commands start with uppercase letters. The !
redefines a command if it already exists.
command! Save call <SID>foo()
{: .-setup}
function! s:foo()
...
endfunction
command! -nargs=? Save call script#foo(<args>)
{: .-setup}
| What | What |
|---|---|
-nargs=0 |
0 arguments, default |
-nargs=1 |
1 argument, includes spaces |
-nargs=? |
0 or 1 argument |
-nargs=* |
0+ arguments, space separated |
-nargs=+ |
1+ arguments, space reparated |
let char = getchar()
if char == "\<LeftMouse>"
" ...
elseif char == "\<RightMouse>"
" ...
else
" ...
endif
if 1 | echo "true" | endif
if 0 | echo "false" | endif
if 1 "=> 1 (true)
if 0 "=> 0 (false)
if "1" "=> 1 (true)
if "456" "=> 1 (true)
if "xfz" "=> 0 (false)
No booleans. 0
is false, 1
is true. See: Truthiness
if 3 > 2
if a && b
if (a && b) || (c && d)
if !c
See :help expression-syntax
. See: Operators
if name ==# 'John' " case-sensitive
if name ==? 'John' " case-insensitive
if name == 'John' " depends on :set ignorecase
" also: is#, is?, >=#, >=?, and so on
a is b
a isnot b
Checks if it's the same instance object.
"hello" =~ 'xx*'
"hello" !~ 'xx*'
"hello" =~ '\v<\d+>'
\v
enables “extended” regex mode which allows word boundary (<>
), +
, and more.
if empty(a:path) | return [] | endif
a ? b : c
Use |
to join lines together.
if g:use_dispatch && s:has_dispatch
···
endif
let mylist = [1, two, 3, "four"]
let first = mylist[0]
let last = mylist[-1]
" Suppresses errors
let second = get(mylist, 1)
let second = get(mylist, 1, "NONE")
len(mylist)
empty(mylist)
sort(list)
let sortedlist = sort(copy(list))
split('hello there world', ' ')
let longlist = mylist + [5, 6]
let mylist += [7, 8]
let shortlist = mylist[2:-1]
let shortlist = mylist[2:] " same
let shortlist = mylist[2:2] " one item
let alist = [1, 2, 3]
let alist = add(alist, 4)
call map(files, "bufname(v:val)") " use v:val for value
call filter(files, 'v:val != ""')
let colors = {
\ "apple": "red",
\ "banana": "yellow"
}
echo colors["a"]
echo get(colors, "apple") " suppress error
See :help dict
remove(colors, "apple")
" :help E715
if has_key(dict, 'foo')
if empty(dict)
keys(dict)
len(dict)
max(dict)
min(dict)
count(dict, 'x')
string(dict)
map(dict, '<>> " . v:val')
for key in keys(mydict)
echo key . ': ' . mydict(key)
endfor
keys(s:)
Prefixes (s:
, g:
, l:
, etc) are actually dictionaries.
" Extending with more
let extend(s:fruits, { ... })
str2float("2.3")
str2nr("3")
float2nr("3.14")
{: .-prime}
let int = 1000
let int = 0xff
let int = 0755 " octal
See :help Number
. See: Numbers
let fl = 100.1
let fl = 5.4e4
See :help Float
3 / 2 "=> 1, integer division
3 / 2.0 "=> 1.5
3 * 2.0 "=> 6.0
sqrt(100)
floor(3.5)
ceil(3.3)
abs(-3.4)
sin() cos() tan()
sinh() cosh() tanh()
asin() acos() atan()
execute "vsplit"
execute "e " . fnameescape(filename)
Runs an ex command you typically run with :
. Also see :help execute
. See: Execute a command
normal G
normal! G " skips key mappings
execute "normal! gg/foo\<cr>dd"
Use :normal
to execute keystrokes as if you're typing them in normal mode. Combine with :execute
for special keystrokes. See: Running keystrokes
echo expand("%") " path/file.txt
echo expand("%:t") " file.txt
echo expand("%:p:h") " /home/you/path/file.txt
echo expand("%:r") " path/file
echo expand("%:e") " txt
See :help expand
silent g/Aap/p
Suppresses output. See :help silent
echoerr 'oh it failed'
echomsg 'hello there'
echo 'hello'
echohl WarningMsg | echomsg "=> " . a:msg | echohl None
set number
set nonumber
set number! " toggle
set numberwidth=5
set guioptions+=e
let result = confirm("Sure?")
execute "confirm q"
has("feature") " :h feature-list
executable("python")
globpath(&rtp, "syntax/c.vim")
exists("$ENV")
exists(":command")
exists("variable")
exists("+option")
exists("g:...")
{: .-three-column}
nmap
vmap
imap
xmap
nnoremap
vnoremap
inoremap
xnoremap
...
[nvixso](nore)map
│ └ don't recurse
│
└ normal, visual, insert,
eX mode, select, operator-pending
{: .-setup}
<buffer>
| only in current buffer |<silent>
| no echo |<nowait>
| |hi Comment
term=bold,underline
gui=bold
ctermfg=4
guifg=#80a0ff
augroup filetypedetect
au! BufNewFile,BufRead *.json setf javascript
augroup END
au Filetype markdown setlocal spell
set conceallevel=2
syn match newLine "
" conceal cchar=}
hi newLine guifg=green
syn region inBold concealends matchgroup=bTag start="" end=""
hi inBold gui=bold
hi bTag guifg=blue
syn match :name ":regex" :flags
syn region Comment start="/\*" end="\*/"
syn region String start=+"+ end=+"+ skip=+\\"+
syn cluster :name contains=:n1,:n2,:n3...
flags:
keepend
oneline
nextgroup=
contains=
contained
hi def link markdownH1 htmlH1
if exists('g:loaded_myplugin')
finish
endif
" ...
let g:loaded_myplugin = 1


Outcome: Luminance contrast between background and text
Provides adequate luminance contrast (lightness/darkness difference) between background and text colors in order to read the text easily.
This outcome relates to guideline Visual contrast of text.
Methods:


{% load staticfiles %} {% load add_class %}
The goal of Tark is to create an archive of all transcript sequences from Ensembl, RefSeq and other sources, including historical gene sets. To ensure robust tracking of exact changes between gene and transcript sets, checksums are included at a fine grained level.
The data is accessible via an API to explore and determine the exact changes between sequences and other feature attributes from one release to the other or between two different sources. In the longterm, Tark will become the primary repository for all sequences in all Ensembl releases.
This beta is provided to give the community an opportunity to explore Tark's functionality. Please provide feedback on possible improvements to help meet future needs. Note, this is a beta site and should not be counted upon to be stable, the API may change and this server may be unavailable at times.


This application uses Google OAuth 2.0 to authenticate the user and grant permission
to use the Google Plus API to retrieve your name and public profile URL.
You have been authenticated!


Number of systematic models: {{ nsys }}
Wavelength mid point = {{ wavelength_mid_point }}
Wavelength half width = {{ wavelength_half_width }}
C1 = {{ c1 }}
C2 = {{ c2 }}
C3 = {{ c3 }}
C4 = {{ c4 }}
Check the chi-squared values and the AIC evidence for reasonable fits.
If the chi-squared values far exceed the DOF then it is likely that the input data contains additional noise, double check the spectral extraction.
Model numbers = {{ top_five_numbers }}
DOF = {{ top_five_dof }}
Chi-squared = {{ top_five_chisq }}
AIC evidence = {{ top_five_aicevidence}}
Weights = {{ top_five_weights }}
SDNR = {{ top_five_sdnr }}
If None, parameter was not fit for.
Rp/R* = {{ rl_marg }} +/- {{ rl_marg_err }}
Epoch (MJD) = {{ epoch_marg }} +/- {{ epoch_marg_err }}
Inclination (rad) = {{ inclin_rad_marg }} +/- {{ inclin_rad_marg_err }}
Inclination (deg) = {{ inclin_deg_marg }} +/- {{ inclin_deg_marg_err }}
System density (Ms+Mp/R^3) = {{ msmpr_marg }} +/- {{ msmpr_marg_err }}
a/R* = {{ aor_marg }} +/- {{ aor_marg_err }}
Top: Evidence-based weight associated with each systematic model when fit with the data.
Middle: Standard deviation of the residuals after correcting for each systematic model.
Bottom: Radius ratio measured from the transit depth when the light curve has been corrected
using each systematic model. If present, grey crosses mark discarded systematic models (poor AIC evidence).
Top: Input lightcurve with no systematic model correction applied.
Middle: Lightcurve corrected by highest weight systematic model plotted with the smooth
planetary transit model centred on the mid-transit time.
Bottom: Residuals and uncertainties associated with the middle panel lightcurve. The upper and
lower standard deviation bounds are shown in dotted lines relative to zero.


$BASHPID
Process ID of the current instance of Bash. This is not the same as the $$ variable, but it often gives the same result.
bash4$ echo $$
11015
bash4$ echo $BASHPID
11015
bash4$ ps ax | grep bash4
11015 pts/2 R 0:00 bash4
But ...
#!/bin/bash4
echo "\$\$ outside of subshell = $$" # 9602
echo "\$BASH_SUBSHELL outside of subshell = $BASH_SUBSHELL" # 0
echo "\$BASHPID outside of subshell = $BASHPID" # 9602
echo
( echo "\$\$ inside of subshell = $$" # 9602
echo "\$BASH_SUBSHELL inside of subshell = $BASH_SUBSHELL" # 1
echo "\$BASHPID inside of subshell = $BASHPID" ) # 9603
# Note that $$ returns PID of parent process.


---
sort_order: 12
tei_id: rdx_b5j2m.p.idp147537904
annotation_count: 0
images:
small-thumbnail: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p73qc/mini-thumbnail/
json: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p73qc/info/
full: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p73qc/fs/
page: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p73qc/single-page/
thumbnail: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p73qc/thumbnail/
title: Page 12
number: 12
---
{% raw %}y{% endraw %}
{% raw %}PREFACE.{% endraw %}
{% raw %}The African race appear as yet to have been com¬{% endraw %}
{% raw %}panions onlv of the sufferings of Christ. In the{% endraw %}
{% raw %}melancholy scene of His death—while Europe in the{% endraw %}
{% raw %}person of the Roman delivered Him unto death, and{% endraw %}
{% raw %}Asia in the person of the Jew clamoured for His{% endraw %}
{% raw %}execution—Africa was represented in the person of{% endraw %}
{% raw %}Simon the Cyrenean, who came patiently bearing{% endraw %}
{% raw %}after Him. the load of the cross; and ever since then{% endraw %}
{% raw %}poor Africa has been toiling on, bearing the weary{% endraw %}
{% raw %}cross of contempt and oppression after Jesus.  Verily a day{% endraw %}
{% raw %}shall come when He shall appear for these His{% endraw %}
{% raw %}hidden ones, and then " many that are last shall be{% endraw %}
{% raw %}first, and the first shall be last."{% endraw %}
{% raw %}HARRIET BEECHER STOWE.{% endraw %}
{% raw %}Anclover, Mass.{% endraw %}
{% raw %}INTRODUCTORY NOTE.{% endraw %}
{% raw %}By George Sturge, and S. Morley, Esq., M.P{% endraw %}
{% raw %}ON Rev. J. Hcnson's visit to England, Samuel{% endraw %}
{% raw %}Morley, Esq., M.P., and George Sturge,{% endraw %}
{% raw %}kindly undertook to be the treasurers of the{% endraw %}
{% raw %}fund to liquidate the claims of his mortgagees.{% endraw %}


We run multiple events throughout the year, including several coding challenges and an annual hackathon. We invite notable tech speakers, connect our students with different opportunities and run a number of social events to bring together the community of students who are interested in Computing.
This is a wider card with supporting text below as a natural lead-in to additional content. This content is a little bit longer.
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This is a wider card with supporting text below as a natural lead-in to additional content. This content is a little bit longer.
Last updated 3 mins ago
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Class a
Class defined in:..\framework\appstorm\a.js:31
Examples: here
clone
( obj
)
..\framework\appstorm\a.js:68
Duplicate a state (used internally) FROM : http://www.xenoveritas.org/blog/xeno/the-correct-way-to-clone-javascript-arrays Credits to them ! Little bug corrected :p
Parameters:-
obj
<Object>A state object
A new state object
contains
( obj
)
..\framework\appstorm\a.js:46
The value exist in given object/array
Parameters:-
obj
<Array | Object>A collection to search in
getDefaultAjaxOptions
(
)
..\framework\appstorm\a.js:206
Get the default ajax options currently stored (and used by every ajax request)
The default ajax options setted
isArray
( ar
)
..\framework\appstorm\a.js:180
Test a variable is an array
Parameters:-
ar
<Mixed>The variable to check
True if it's an array, false in other cases
isBoolean
( u
)
..\framework\appstorm\a.js:168
Test a variable is a boolean
Parameters:-
u
<Mixed>The variable to check
True if it's a boolean, false in other cases
isFunction
( s
)
..\framework\appstorm\a.js:144
Check a variable is a function
Parameters:-
s
<Mixed>The variable to check
True if it's a function, false in other cases
isNull
( u
)
..\framework\appstorm\a.js:156
Test a variable is undefined or null
Parameters:-
u
<Mixed>The variable to check
True if it's a undefined variable, false in other cases
isNumber
( o
)
..\framework\appstorm\a.js:108
Check a variable is a number
Parameters:-
o
<Mixed>The variable to check
True if it's a number, false in other cases
isObject
( o
)
..\framework\appstorm\a.js:120
Check a variable is an object
Parameters:-
o
<Mixed>The variable to check
True if it's an object, false in other cases
isString
( s
)
..\framework\appstorm\a.js:132
Check a variable is a string
Parameters:-
s
<Mixed>The variable to check
True if it's a string, false in other cases
setDefaultAjaxOptions
( options
)
..\framework\appstorm\a.js:192
Define the default ajax options to send on every request. At any time, by providing good options, you can override this content on a single ajax request.
Parameters:-
options
<Object>The default options to set


I'll begin this recounting by stating that I smoke 'spice', or various other brand name (i.e. K2) or nameless herbal incense blends, on a regular basis. I also smoke cannabis on a regular basis; I usually buy spice when I can't afford high-grade cannabis due to the extremely cheap price of the herbal blends in my area.
T 11:30PM - I am extremely stoned off of several bowls of some great weed. I purchased a new piece earlier in the day. I'm smoking out of it as much as possible to make its color change more quickly. More bowls are to be packed, as my friends are eager to smoke more.
T 12:00AM - I am slightly intoxicated off of a malt liquor beverage and even more stoned than before, as more bowls have been packed. I decide to call it a night and depart from my friend's house and make my way back home, which is just next door.
T 12:30AM - I am laying in bed, feeling a wonderful mix of drunken stupor and stoned euphoria. I fall fast asleep.
T 6:30AM - I receive a text from a close friend telling me to wake up because he is on his way to my house to smoke a bowl of spice with me. I still feel the effects from before I fell asleep earlier.
T 6:45AM - My friend arrives. We sit in his truck and smoke three bowls of spice quickly and consecutively. I am expecting to get as high as I usually get, but soon after we finish the bag I start to feel unlike I ever have.
T 6:50AM - My vision is skewed and everything looks crooked and significantly more blurry and fuzzy than I've ever experienced. I start to laugh hysterically and uncontrollably. Nothing around me seems as real as it did moments ago, but I am still able to grasp reality as I keep repeating aloud, 'I'm so stoned!' or other variant phrases. It feels like I keep getting more and more high, and I don't want to. I am confused as I've smoked considerably more spice than this at one time and felt nothing like this.
T 7:00AM - My friend and I chat; he points out how obvious it is that I'm high. I say, 'How are you not as stoned as I am?!' hysterically, and he says that he is. I look at the clock and exit the vehicle at my friend's request because he has to go to school. My thoughts are going all over the place and my stream of consciousness doesn't seem to make sense. I feel like my body and mind are not connected. I make my way back into my house, finding it difficult to walk. My heart feels like it is beating out of my chest. I think to myself over and over again 'I need to quit smoking.' I stumble my way into my room and lay in my bed. My eyes feel like they are vibrating violently, as I can't focus on anything with them, and my body feels like it won't stop shaking or twitching. Nothing feels real.
T 7:15AM - I get out of my bed to get a glass of water, hoping that will somehow calm me down. It doesn't, and so I sit up and zone out for what feels like an hour or more. I look at the clock and only a few minutes have passed. I feel aware of my body and can control my movements fine, but I feel very unaware of my thoughts, or rather lacking control of them. I think about what death feels like, and if this was it. My vision is stroboscopic. Life feels like it's in stop-motion.
I passed out at some time, probably around 7:40AM. I woke up a few hours later and felt fine. Overall, the experience lasted for less than an hour but it felt like it lasted several hours. I'm not sure if the intensity of the experience has anything to do with how high I was before I smoked the spice or if it's because I wake-and-baked with it or anything, but it was a terrifying trip. I asked myself and my friend if it was salvia that we smoked, as the trip was similar to that of a salvia divinorum high. It was not salvia; it was the same spice that I've been buying and smoking regularly. I don't know why I had such an experience, but henceforth I'll look at JWH-018 with much more caution. Don't overdo it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


{% extends "base.html" %} {% block content %}
No worries if you are new, we will automatically create an account!


Other allocators may be defined. Any class having the same members as this default allocator and following its minimum requirements can be used as an allocator with the standard containers.
Other allocators may be defined. Any class
Alloc for which
allocator_traits<Alloc> produces a valid instantiation with the appropriate members defined can be used as an allocator on standard containers (
Alloc may or may not implement the functionality through member functions).
Except for its destructor, no member of the standard default
allocator class template shall introduce data races. Calls to member functions that allocate or deallocate storage shall occur in a single total order, and each such deallocation shall happen before the next allocation (if any) in this order.


Two way data binding allows us to start with a model, most likely requested from the server and bind it to a page. The page can then make changes to the model and send it back to the server.
| Text value: | |
| Password: | |
| Bool value: | |
| Selected option: | |
| Multi-selected options: | |
| Radio button selection: |


Chapter 1 Basics
1.1 Logit
\[f(x)=log(\frac{p(y=1)}{1-p(y=1)})\] The basic idea of logistic regression: \[p(y=1)=\frac{1}{1+e^{-(\beta_0+\beta_1x_1+...+\beta_nx_n)}}=\frac{e^{\beta_0+\beta_1x_1+...+\beta_nx_n}}{1+e^{\beta_0+\beta_1x_1+...+\beta_nx_n}}\] Thus, \(e^{\beta_0+\beta_1x_1+...+\beta_nx_n}\) can be from \(-\infty\) to \(+\infty\), and \(p(y=1)\) will be always within the range of \((0,1)\).
f<-function(x){exp(x)/(1+exp(x))}
data<-seq(-10,10,1)
plot(data,f(data),type = "b")
We can also write the function into another format as follows: \[log \frac{p(y=1)}{1-p(y=1)}= \beta_0+\beta_1x_1+...+\beta_nx_n\] Thus, we know that the regression coeficients of \(\beta_i\) actually change the “log-odds” of the event. Of course, note that the magnitude of \(\beta_i\) is dependent upon the units of \(x_i\).
The following is an example testing whether that home teams are more likely to win in NFL games. The results show that the odd of winning is the same for both home and away teams.
mydata = read.csv(url('https://raw.githubusercontent.com/nfl-football-ops/Big-Data-Bowl/master/Data/games.csv'))
mydata$result_new<-ifelse(mydata$HomeScore>mydata$VisitorScore,1,0)
summary(mydata$result_new)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4945 1.0000 1.0000
mylogit1 = glm(result_new~1, family=binomial, data=mydata)
summary(mylogit1)
##
## Call:
## glm(formula = result_new ~ 1, family = binomial, data = mydata)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.168 -1.168 -1.168 1.187 1.187
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02198 0.20967 -0.105 0.917
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 126.14 on 90 degrees of freedom
## Residual deviance: 126.14 on 90 degrees of freedom
## AIC: 128.14
##
## Number of Fisher Scoring iterations: 3
1.2 Probit
As noted above, logit \(f(x)=log(\frac{p(y=1)}{1-p(y=1)})\) provides the resulting range of \((0,1)\). Another way to provide the same rage is through the cdf of normal distribution.The following R code is used to illusrate this process.
data2<-seq(-5,5,1)
plot(data2,pnorm(data2),type = "b")
Thus, the cdf of normal distribution can be used to indicate the probability of \(p(y=1)\).
\[\Phi(\beta_0+\beta_1x_1+...+\beta_nx_n )= p(y=1)\]
Similar to logit model, we can also write the inverse function of the cdf to get the function that can be from \(-\infty\) to \(+\infty\).
\[\beta_0+\beta_1x_1+...+\beta_nx_n =\Phi^{-1}(p(y=1))\]
Thus, for example, if \(X\beta\) = -2, based on \(\Phi(\beta_0+\beta_1x_1+...+\beta_nx_n )= p(y=1)\) we can get that the \(p(y=1)=0.023\).
In contrast, if \(X\beta\) = 3, the \(p(y=1)=0.999\).
pnorm(-2)
## [1] 0.02275013
pnorm(3)
## [1] 0.9986501
Let’s assume that there is a latent variable called \(Y^*\) such that
\[Y^*=X\beta+\epsilon, \epsilon \sim N(0,\sigma^2)\] You could think of \(Y^*\) as a kind of “proxy” between \(X\beta+\epsilon\) and the observed \(Y (1 or 0)\). Thus, we can get the following. Note that, it does not have to be zero, and can be any constant.
\[ Y^*=\begin{cases} 0 \;\;\: if \; y_i^* \leq 0 \\ 1 \;\;\: if \; y_i^* > 0 \end{cases} \]
Thus,
\[y_i^* > 0 \Rightarrow \beta^{'}X_i + \epsilon_i >0 \Rightarrow \epsilon_i > -\beta^{'}X_i\]
Thus, we can write it as follows. Note that \(\frac{ \epsilon_i}{\sigma} \sim N(0,1)\)
\[p(y=1|x_i)= p(y_i^* >0|x_i)=p(\epsilon_i > -\beta^{'}X_i)= p(\frac{ \epsilon_i}{\sigma}>\frac{-\beta^{'}X_i}{\sigma})=\Phi(\frac{\beta^{'}X_i}{\sigma}) \] We thus can get:
\[p(y=0|x_i)=1-\Phi(\frac{\beta^{'}X_i}{\sigma})\]
For \(p(y=1|x_i)=\Phi(\frac{\beta^{'}X_i}{\sigma})\), we can not really estimate both \(\beta\) and \(\sigma\) as they are in a ratio. We can assume \(\sigma =1\), then \(\epsilon \sim N(0,1)\). We know \(y_i\) and \(x_i\) since we observe them. Thus, we can write it as follows.
\[p(y=1|x_i)=\Phi(\beta^{'}X_i)\]


This inspection reports redundant enclosing blocks around expressions (including ones within interpolated strings).


Compiler properties
Not all compilers and platforms are alike. Therefore Meson provides the tools to detect properties of the system during configure time. To get most of this information, you first need to extract the compiler object from the main meson variable.
compiler = meson.get_compiler('c')
Here we extract the C compiler. We could also have given the argument cpp
to get the C++ compiler, objc
to get the objective C compiler and so on. The call is valid for all languages specified in the project declaration. Trying to obtain some other compiler will lead to an unrecoverable error.
System information
This is a bit complex and more throughly explained on the page on [cross compilation](Cross compilation). But if you just want to know the operating system your code will run on, issue this command:
host_machine.system()
Compiler id
The compiler object has a method called get_id
, which returns a lower case string describing the "family" of the compiler. It has one of the following values.
| Value | Compiler family |
|---|---|
| gcc | The GNU Compiler Collection |
| clang | The Clang compiler |
| msvc | Microsoft Visual Studio |
| intel | Intel compiler |
| pathscale | The Pathscale Fortran compiler |
| pgi | The Portland Fortran compiler |
| sun | Sun Fortran compiler |
| g95 | The G95 Fortran compiler |
| open64 | The Open64 Fortran Compiler |
| nagfor | The NAG Fortran compiler |
Does code compile?
Sometimes the only way to test the system is to try to compile some sample code and see if it works. This is a two-phase operation. First we define some code using the multiline string operator:
code = '''#include<stdio.h>
void func() { printf("Compile me.\n"); }
'''
Then we can run the test.
result = compiler.compiles(code, name : 'basic check')
The variable result will now contain either true
or false
depending on whether the compilation succeeded or not. The keyword argument name
is optional. If it is specified, Meson will write the result of the check to its log.
Does code compile and link?
Sometimes it is necessary to check whether a certain code fragment not only compiles, but also links successfully, e.g. to check if a symbol is actually present in a library. This can be done using the '''.links()''' method on a compiler object like this:
code = '''#include<stdio.h>
void func() { printf("Compile me.\n"); }
'''
Then we can run the test.
result = compiler.links(code, args : '-lfoo', name : 'link check')
The variable result will now contain either true
or false
depending on whether the compilation and linking succeeded or not. The keyword
argument name
is optional. If it is specified, Meson will write the
result of the check to its log.
Compile and run test application
Here is how you would compile and run a small test application.
code = '''#include<stdio.h>
int main(int argc, char **argv) {
printf("%s\n", "stdout");
fprintf(stderr, "%s\n", "stderr");
return 0;
}
'''
result = compiler.run(code, name : 'basic check')
The result
variable encapsulates the state of the test, which can be extracted with the following methods. The name
keyword argument works the same as with compiles
.
Method | Return value
-------|----------------
compiled | True
if compilation succeeded. If false
then all other methods return undefined values.
returncode | The return code of the application as an integer
stdout | Program's standard out as text.
stderr | Program's standard error as text.
Here is an example usage:
if result.stdout().strip() == 'some_value'
# do something
endif
Does a header exist?
Header files provided by different platforms vary quite a lot. Meson has functionality to detect whether a given header file is available on the system. The test is done by trying to compile a simple test program that includes the specified header. The following snippet describes how this feature can be used.
if compiler.has_header('sys/fstat.h')
# header exists, do something
endif
Expression size
Often you need to determine the size of a particular element (such as int
, wchar_t
or char*
). Using the compiler
variable mentioned above, the check can be done like this.
wcharsize = compiler.sizeof('wchar_t', prefix : '#include<wchar.h>')
This will put the size of wchar_t
as reported by sizeof into variable wcharsize
. The keyword argument prefix
is optional. If specified its contents is put at the top of the source file. This argument is typically used for setting #include
directives in configuration files.
In older versions (<= 0.30) meson would error out if the size could not be determined. Since version 0.31 it returns -1 if the size could not be determined.
Does a function exist?
Just having a header does say anything about its contents. Sometimes you need to explicitly check if some function exists. This is how we would check whether the function somefunc
exists in header someheader.h
if compiler.has_function('somefunc', prefix : '#include<someheader.h>')
# function exists, do whatever is required.
endif
Does a structure contain a member?
Some platforms have different standard structures. Here's how one would check if a struct called mystruct
from header myheader.h</hh> contains a member called
some_member`.
if compiler.has_member('struct mystruct', 'some_member', prefix : '#include<myheader.h>')
# member exists, do whatever is required
endif
Type alignment
Most platforms can't access some data types at any address. For example it is common that a char
can be at any address but a 32 bit integer only at locations which are divisible by four. Determining the alignment of data types is simple.
int_alignment = compiler.alignment('int') # Will most likely contain the value 4.
Has argument
This method tests if the compiler supports a given command line argument. This is implemented by compiling a small file with the given argument.
has_special_flags = compiler.has_argument('-Wspecialthing')
Note: some compilers silently swallow command line arguments they do not understand. Thus this test can not be made 100% reliable.
The results of the search are


Radio Button
Radio Button
Option one is this and that—be sure to include why it's great
Option one is this and that—be sure to include why it's great
Option three is disabled
Inline Radio buttons
1
2
3
Radio Button
Radio Button
Option one is this and that—be sure
to include why it's great
Option one is this and that—be sure to include why it's great
Option three is disabled
Inline Radio buttons
1
2
3


Name: Owiti Charles
Profile: Full Stack Developer
Email<EMAIL_ADDRESS>Phone: +254704-220-133
I’m a fullstack web designer and developer with 2 years of working experience, both as a juniour developer and as a freelance developer. I have gained the following skills over the time.


Models for BRE4393 Temporary work design 150 mm Slab (S1) by yidizhu on Sketchfab 200 mm Slab (S2) by yidizhu on Sketchfab 750 x 500 Beam (B1) by yidizhu on Sketchfab 1500 x 500 Beam (B2) by yidizhu on Sketchfab 250 mm Wall (W1) by yidizhu on Sketchfab 375 mm Wall (W2) by yidizhu on Sketchfab 500 x 500 x 3150 Column (C1) by yidizhu on Sketchfab 1200 x 500 x 3900 Column (C2) by yidizhu on Sketchfab 1700 x 500 x 3900 Column (C3) by yidizhu on Sketchfab


Chapter 6 Exporting data
Now that you have learned how to use dplyr
to extract information from or summarize your raw data, you may want to export these new data sets to share them with your collaborators or for archival.
Similar to the read_csv()
function used for reading CSV files into R, there is a write_csv()
function that generates CSV files from data frames.
write_csv(surveys_spread, "data/surveys_spread.csv")
6.1 Factors
This section is from Jenny Bryan’s course at UBC: Stat 545
Factors are the variable type that useRs love to hate. It is how we store truly categorical information in R. The values a factor can take on are called the levels. In general, the levels are friendly human-readable character strings, like “male/female” and “control/treated”. But never ever ever forget that, under the hood, R is really storing integer codes 1, 2, 3, etc.
This Janus-like nature of factors means they are rich with booby traps for the unsuspecting but they are a necessary evil. I recommend you learn how to be the boss of your factors. The pros far outweigh the cons. Specifically in modelling and figure-making, factors are anticipated and accommodated by the functions and packages you will want to exploit.
The worst kind of factor is the stealth factor. The variable that you think of as character, but that is actually a factor (numeric!!). This is a classic R gotcha. Check your variable types explicitly when things seem weird. It happens to the best of us.
Where do stealth factors come from? Base R has a burning desire to turn character information into factor. The happens most commonly at data import via read.csv() and friends. But data.frame() and other functions are also eager to convert character to factor. To shut this down, use stringsAsFactors = FALSE in read.csv() and data.frame() or – even better – use the tidyverse! For data import, use readr::read_csv(), readr::read_tsv(), etc. For data frame creation, use tibble::tibble(). And so on.
See for yourself the difference in how strings are treated with the two different methods of reading a csv file.
surveys. <- read.csv("data/surveys.csv")
surveys_ <- read_csv("data/surveys.csv")
#> Parsed with column specification:
#> cols(
#> record_id = col_integer(),
#> month = col_integer(),
#> day = col_integer(),
#> year = col_integer(),
#> plot_id = col_integer(),
#> species_id = col_character(),
#> sex = col_character(),
#> hindfoot_length = col_integer(),
#> weight = col_integer()
#> )
str(surveys.)
str(surveys_)
6.1.1 The forcats Package
forcats is a core package in the tidyverse. It is installed via install.packages("tidyverse")
and attached via library(tidyverse)
. You can always load it individually via library(forcats)
. Main functions start with fct_. There really is no coherent family of base functions that forcats replaces – that’s why it’s such a welcome addition.
library(tidyverse)
library(forcats)
library(gapminder)
Get to know your factor before you start touching it! It’s polite. Let’s use gapminder$continent as our example.
str(gapminder$continent)
levels(gapminder$continent)
nlevels(gapminder$continent)
class(gapminder$continent)
To get a frequency table as a tibble, from a tibble, use dplyr::count(). To get similar from a free-range factor, use forcats::fct_count().
gapminder %>%
count(continent)
fct_count(gapminder$continent)
<IP_ADDRESS> Dropping unused levels:
Just because you drop all the rows corresponding to a specific factor level, the levels of the factor itself do not change. Sometimes all these unused levels can come back to haunt you later, e.g., in figure legends.
Watch what happens to the levels of country (= nothing) when we filter Gapminder to a handful of countries.
nlevels(gapminder$country)
h_countries <- c("Egypt", "Haiti", "Romania", "Thailand", "Venezuela")
h_gap <- gapminder %>%
filter(country %in% h_countries)
nlevels(h_gap$country)
Even though h_gap
only has data for a handful of countries, we are still schlepping around all 142 levels from the original gapminder tibble.
How can you get rid of them? The base function droplevels()
operates on all the factors in a data frame or on a single factor. The function forcats::fct_drop()
operates on a factor.
h_gap_dropped <- h_gap %>%
droplevels()
nlevels(h_gap_dropped$country)
h_gap$country %>%
fct_drop() %>%
levels()
<IP_ADDRESS> Change order of the levels, principled
By default, factor levels are ordered alphabetically. Which might as well be random, when you think about it! It is preferable to order the levels according to some principle:
- Frequency.Make the most common level the first and so on.
- Another variable. Order factor levels according to a summary statistic for another variable. Example: order Gapminder countries by life expectancy.
First, let’s order continent by frequency, forwards and backwards. This is often a great idea for tables and figures, esp. frequency barplots.
## default order is alphabetical
gapminder$continent %>%
levels()
## order by frequency
gapminder$continent %>%
fct_infreq() %>%
levels()
## backwards!
gapminder$continent %>%
fct_infreq() %>%
fct_rev() %>%
levels()
## order countries by median life expectancy
fct_reorder(gapminder$country, gapminder$lifeExp) %>%
levels() %>% head()
<IP_ADDRESS> Change order of the levels, “because I said so”
Sometimes you just want to hoist one or more levels to the front. Why? Because I said so.
h_gap$country %>% levels()
h_gap$country %>% fct_relevel("Romania", "Haiti") %>% levels()
This might be useful if you are preparing a report for, say, the Romanian government. The reason for always putting Romania first has nothing to do with the data, it is important for external reasons and you need a way to express this.
<IP_ADDRESS> Recode the levels
Sometimes you have better ideas about what certain levels should be. This is called recoding.
i_gap <- gapminder %>%
filter(country %in% c("United States", "Sweden", "Australia")) %>%
droplevels()
i_gap$country %>% levels()
i_gap$country %>%
fct_recode("USA" = "United States", "Oz" = "Australia") %>% levels()
Exercise: Isolate the data for “Australia”, “Korea, Dem. Rep.”, and “Korea, Rep.” in the 2000x. Revalue the country factor levels to “Oz”, “North Korea”, and “South Korea”.
<IP_ADDRESS> Learning Objectives
- Produce scatter plots, boxplots, and time series plots using ggplot.
- Set universal plot settings.
- Describe what faceting is and apply faceting in ggplot.
- Modify the aesthetics of an existing ggplot plot (including axis labels and color).
- Build complex and customized plots from data in a data frame.


Data category
ITS defines
data category
as an abstract concept for a particular type of information for internationalization and localization of XML schemas and documents.
For the implementation of ITS, apply the rules in the order:
- Default
- Rules in the schema
- Rules in the document instance
- Local attributes
Et voila !
The last rule wins.


William Fearing Gill's book Edgar Allan Poe—After Fifty Years begins with a supremely bad-tempered paragraph:
“When Rufus W. Griswold, ”the pedagogue vampire,“ as he was aptly termed by one of his contemporaries, committed the immortal infamy of blighting a collection of Edgar Allan Poe's works, which he found ready at hand, by supplementing his perfunctory labors with a calumniating memoir of the poet, nearly fifty years ago, there were many protests uttered by the poet's contemporaries at home and abroad. Charles Baudelaire, the Poe of French literature, in his tribute to the dead poet, indignantly wrote: ”What is the matter with America? Are there, then, no regulations there to keep the curs out of the cemeteries?“ In view of the fact that the Griswold biography of Poe has been incontestably discredited, and proved to be merely a scaffolding of malevolent falsehoods—the outcome of malice and mendacity—the deference paid to Griswold and his baleful work in the memoir accompanying the latest publication of Poe's writings seems well-nigh incomprehensible.”