Chapter 7 Multiple linear regression models (I)
7.1 Seminar
library(foreign)
library(texreg)
rm(list = ls())
7.1.1 Loading, Understanding and Cleaning our Data
Today, we load the full standard (cross-sectional) dataset from the Quality of Government Institute (this is a newer version that the one we used in week 3). This is a great data source for comparativist political science research. The codebook is available from their main website. You can also find time-series and cross-section data sets on this page.
The dataset is in stata format (.dta
). Loading it requires the foreign
library and the read.dta()
function which operates similar to read.csv()
. Download the data here
Let’s load the data set
# load dataset in Stata format from online source
world_data <- read.dta("qog_std_cs_jan15.dta")
# check the dimensions of the dataset
dim(world_data)
[1] 193 2037
The dataset contains many variables. We will select a subset of variables that we want to work with. We are interested in political stability. Specifically, we want to find out what predicts the level of political stability. Therefore, political_stability
is our dependent variable (also called response variable, left-hand-side variable, explained/predicted variable). We will also select a variable that identifies each row (observation) in the dataset uniquely: cname
which is the name of the country. Potential predictors (independent variables, right-hand-side variables, covariates) are:
lp_lat_abst
is the distance to the equator which we rename intolatitude
dr_ing
is an index for the level of globalization which we rename toglobalization
ti_cpi
is Transparency International’s Corruptions Perceptions Index, renamed toinstitutions_quality
(larger values mean better quality institutions, i.e. less corruption)br_dem
is a factor variable stating whether the relevant country is a democracy or not (with labels"1. Democracy"
and"0. Dictatorship"
)
Our dependent variable:
wbgi_pse
which we rename intopolitical_stability
(larger values mean more stability)
One approach of selecting a subset of variables we’re interested in, is to use the square bracket [ ]
operator. But first, we rename the variables we care about like we did last week.
names(world_data)[which(names(world_data) == "cname")] <- "country"
names(world_data)[which(names(world_data) == "wbgi_pse")] <- "political_stability"
names(world_data)[which(names(world_data) == "lp_lat_abst")] <- "latitude"
names(world_data)[which(names(world_data) == "dr_ig")] <- "globalization"
names(world_data)[which(names(world_data) == "chga_demo")] <- "democracy"
names(world_data)[which(names(world_data) == "ti_cpi")] <- "institutions_quality"
Now, we take our subset. We will create an object named keep
which is a vector of the variable names that we would like to keep for the analysis. We can then use this vector to subset our world_data
object by including it within the square parentheses:
keep <- c("country", "political_stability", "latitude", "globalization","democracy", "institutions_quality")
world_data <- world_data[, keep]
Let’s make sure we’ve got everything we need
head(world_data)
country political_stability latitude globalization
1 Afghanistan -2.5498192 0.3666667 31.46042
2 Albania -0.1913142 0.4555556 58.32265
3 Algeria -1.2624909 0.3111111 52.37114
4 Andorra 1.3064846 0.4700000 NA
5 Angola -0.2163249 0.1366667 44.73296
6 Antigua and Barbuda 0.9319394 0.1892222 48.15911
democracy institutions_quality
1 0. Dictatorship 1.4
2 1. Democracy 3.3
3 0. Dictatorship 2.9
4 1. Democracy NA
5 0. Dictatorship 1.9
6 1. Democracy NA
The function summary()
lets you summarize data sets. We will look at the dataset now. When the dataset is small in the sense that you have few variables (columns) then this is a very good way to get a good overview. It gives you an idea about the level of measurement of the variables and the scale. country
, for example, is a character variable as opposed to a number. Countries do not have any order, so the level of measurement is categorical.
If you think about the next variable, political stability, and how one could measure it you know there is an order implicit in the measurement: more or less stability. From there, what you need to know is whether the more or less is ordinal or interval scaled. Checking political_stability
you see a range from roughly -3 to 1.5. The variable is numerical and has decimal places. This tells you that the variable is at least interval scaled. You will not see ordinally scaled variables with decimal places. Examine the summaries of the other variables and determine their level of measurement.
summary(world_data)
country political_stability latitude globalization
Length:193 Min. :-3.10637 Min. :0.0000 Min. :24.35
Class :character 1st Qu.:-0.72686 1st Qu.:0.1444 1st Qu.:45.22
Mode :character Median :-0.01900 Median :0.2444 Median :54.99
Mean :-0.06079 Mean :0.2865 Mean :57.15
3rd Qu.: 0.78486 3rd Qu.:0.4444 3rd Qu.:68.34
Max. : 1.57240 Max. :0.7222 Max. :92.30
NA's :12 NA's :12
democracy institutions_quality
0. Dictatorship: 74 Min. :1.010
1. Democracy :118 1st Qu.:2.400
NA's : 1 Median :3.300
Mean :3.988
3rd Qu.:5.100
Max. :9.300
NA's :12
The variables latitude
, globalization
and inst_quality
have 12 missing values each marked as NA
. democracy
has 1 missing value. Missing values could cause trouble because operations including an NA
will produce NA
as a result (e.g.: 1 + NA = NA
). We will drop these missing values from our data set using the is.na()
function and square brackets. The exlamation mark in front of is.na()
means “not”. So, we keep all rows that are not NA’s on the variable latitude
.
world_data <- world_data[ !is.na(world_data$latitude) ,]
Generally, we want to make sure we drop missing values only from variables that we care about. Now that you have seen how to do this, drop missings from globalization
, institutions_quality
, and democracy
yourself.
world_data <- world_data[ !is.na(world_data$globalization) ,]
world_data <- world_data[ !is.na(world_data$institutions_quality) ,]
world_data <- world_data[ !is.na(world_data$democracy) ,]
summary(world_data)
country political_stability latitude globalization
Length:170 Min. :-2.67338 Min. :0.0000 Min. :25.46
Class :character 1st Qu.:-0.79223 1st Qu.:0.1386 1st Qu.:46.05
Mode :character Median :-0.03174 Median :0.2500 Median :55.87
Mean :-0.12018 Mean :0.2865 Mean :57.93
3rd Qu.: 0.66968 3rd Qu.:0.4444 3rd Qu.:69.02
Max. : 1.48047 Max. :0.7222 Max. :92.30
democracy institutions_quality
0. Dictatorship: 67 Min. :1.400
1. Democracy :103 1st Qu.:2.500
Median :3.300
Mean :4.050
3rd Qu.:5.175
Max. :9.300
Let’s look at the output of summary(world_data)
again and check the range of the variable latitude
. It is between 0
and 1
. The codebook clarifies that the latitude of a country’s capital has been divided by 90
to get a variable that ranges from 0
to 1
. This would make interpretation difficult. When interpreting the effect of such a variable a unit change (a change of 1
) covers the entire range or put differently, it is a change from a country at the equator to a country at one of the poles.
We therefore multiply by 90
again. This will turn the units of the latitude
variable into degrees again which makes interpretation easier.
# transform latitude variable
world_data$latitude <- world_data$latitude * 90
7.1.2 Estimating a Bivariate Regression
Is there a correlation between the distance of a country to the equator and the level of political stability? Both political stability (dependent variable) and distance to the equator (independent variable) are continuous. Therefore, we will get an idea about the relationship using a scatter plot.
plot(political_stability ~ latitude, data = world_data)
Looking at the cloud of points suggests that there might be a positive relationship: increases in our independent variable latitude
appear to be associated with increases in the dependent variable political_stability
(the further from the equator, the more stable).
We can fit a line of best fit through the points. To do this we must estimate the bivariate regression model with the lm()
function and then plot the line using the abline()
function.
latitude_model <- lm(political_stability ~ latitude, data = world_data)
# add the line
plot(political_stability ~ latitude, data = world_data)
abline(latitude_model)
We can also view a simple summary of the regression by using the screenreg
function:
# regression output
screenreg(latitude_model)
=======================
Model 1
-----------------------
(Intercept) -0.58 ***
(0.12)
latitude 0.02 ***
(0.00)
-----------------------
R^2 0.11
Adj. R^2 0.10
Num. obs. 170
RMSE 0.89
=======================
*** p < 0.001, ** p < 0.01, * p < 0.05
Thinking back to last week, how can we interpret this regression ouput?
- The coefficient for the variable
latitude
(\(\beta_1\)) indicates that a one-unit increase in a country’s latitude is associated with a0.02
increase in the measure of political stability, on average. Question: Is this association statistically significant at the 95% confidence level? - The coefficient for the
(intercept)
term (\(\beta_0\)) indicates that the average level of political stability for a country with a latitude of 0 is \(-0.58\) (wherelatitude = 0
is a country positioned at the equator) - The \(R^2\) of the model is 0.11. This implies that 11% of the variation in the dependent variable (political stability) is explained by the independent variable (latitude) in the model.
7.1.3 Multivariate Regression
The regression above suggests that there is a significant association between these variables However, as good social scientistis, we probably do not think that the distance of a country from the equator is a theoretically relevant variable for explaining political stability. This is because there is no plausible causal link between the two. We should therefore consider other variables to include in our model.
We will include the index of globalization (higher values mean more integration with the rest of the world), the quality of institutions, and the indicator for whether the country is a democracy. For all of these variables we can come up with a theoretical story for their effect on political stability.
To specify a multiple linear regression model, the only thing we need to change is what we pass to the formula
argument of the lm()
function. In particular, if we wish to add additional explanatory variables, the formula argument will take the following form:
dependent.variable ~ independent.variable.1 + independent.variable.2 ... independent.variable.k
where k
indicates the total number of independent variables we would like to include in the model. In the example here, our model would therefore look like the following:
# model with more explanatory variables
inst_model <- lm(political_stability ~ latitude + globalization + institutions_quality + democracy,
data = world_data)
Remember, political_stability
is our dependent variable, as before, and now we have four independent variables: latitude
, globalization
, democracy
and institutions_quality
. Again, just as with the bivariate model, we can view the summarised output of the regression by using screenreg()
. As we now have two models (a simple regression model, and a multiple regression model), we can join them together using the list()
function, and then put all of that inside screenreg()
.
screenreg(list(latitude_model, inst_model))
=============================================
Model 1 Model 2
---------------------------------------------
(Intercept) -0.58 *** -1.25 ***
(0.12) (0.20)
latitude 0.02 *** 0.00
(0.00) (0.00)
globalization -0.00
(0.01)
institutions_quality 0.34 ***
(0.04)
democracy1. Democracy 0.04
(0.11)
---------------------------------------------
R^2 0.11 0.50
Adj. R^2 0.10 0.49
Num. obs. 170 170
RMSE 0.89 0.67
=============================================
*** p < 0.001, ** p < 0.01, * p < 0.05
Including the two new predictors leads to substantial changes.
- First, we now explain 50% of the variance of our dependent variable instead of just 11%.
- Second, the effect of the distance to the equator is no longer significant.
- Third, better quality institutions are associated with more political stability. In particular, a one-unit increase in the measure of instituion quality (which ranges from 1 to 10) is associated with a 0.34 increase in the measure for political stability.
- Fourth, there is no significant relationship between globalization and political stability in this data.
- Fifth, there is no significant relationship between democracy and political stability in this data.
7.1.4 Joint Significance Test (F-statistic)
Whenever you add variables to your model, you will explain more of the variance in the dependent variable. That means, using your data, your model will better predict outcomes. We would like to know whether the difference (the added explanatory power) is statistically significant. The null hypothesis is that the added explanatory power is zero and the p-value gives us the probability of observing such a difference as the one we actually computed assuming that null hypothesis (no difference) is true.
The F-test is a joint hypothesis test that lets us compute that p-value. Two conditions must be fulfilled to run an F-test:
| Conditions for F-test model comparison |
|---|
Both models must be estimated from the same sample! If your added variables contain lots of missing values and therefore your n (number of observations) are reduced substantially, you are not estimating from the same sample. |
| The models must be nested. That means, the model with more variables must contain all of the variables that are also in the model with fewer variables. |
We specify two models: a restricted model and an unrestricted model. The restricted model is the one with fewer variables. The unrestricted model is the one including the extra variables. We say restricted model because we are “restricting” it to NOT depend on the extra variables. Once we estimated those two models we compare the residual sum of squares (RSS). The RSS is the sum over the squared deviations from the regression line and that is the unexplained error. The restricted model (fewer variables) is always expected to have a larger RSS than the unrestricted model. Notice that this is same as saying: the restricted model (fewer variables) has less explanatory power.
We test whether the reduction in the RSS is statistically significant using a distribution called “F distribution”. If it is, the added variables are jointly (but not necessarily individually) significant. You do not need to know how to calculate p-values from the F distribution, as we can use the anova()
function in R to do this for us.
anova(latitude_model, inst_model)
Analysis of Variance Table
Model 1: political_stability ~ latitude
Model 2: political_stability ~ latitude + globalization + institutions_quality +
democracy
Res.Df RSS Df Sum of Sq F Pr(>F)
1 168 133.121
2 165 74.229 3 58.892 43.636 < 0.00000000000000022 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As we can see from the output, the p-value here is very small, which means that we can reject the null hypothesis that the unrestricted model has no more explanatory power than the restricted model.
7.1.5 Predicting outcome conditional on institutional quality
Just as we did with the simple regression model last week, we can use the fitted model object to calculate the fitted values of our dependent variable for different values of our explanatory variables. To do so, we again use the predict()
function.
We proceed in three steps.
- We set the values of the covariates for which we would like to produce fitted values.
- You will need to set covariate values for every explanatory variable that you included in your model.
- As only one of our variables has a significant relationship with the outcome in the multiple regression model that we estimated above, we are really only interested in that variable (
institutions_quality
). - Therefore, we will calculate fitted values over the range of
institutions_quality
, while setting the values oflatitude
andglobalization
to their mean values. - As
democracy
is a factor variable, we cannot use the mean value. Instead, we will setdemocracy
to be equal to"1. Democracy"
which is the label for democratic countries
- We calculate the fitted values.
- We report the results (here we will produce a plot).
For step one, the following code produces a data.frame
of new covariate values for which we would like to calculate a fitted value from our model:
## Set the values for the explanatory variables
data_for_fitted_values <- data.frame(institutions_quality = seq(from = 1.4, to = 9.3, by = 1),
globalization = mean(world_data$globalization),
latitude = mean(world_data$latitude),
democracy = "1. Democracy"
)
Here, we have set the institutions_quality
variable to vary between 1.4 and 9.3, with increments of 1 unit. We have set globalization
to be equal to the mean value of globalization
in the world_data
object, and latitude
to be equal to the mean value of latitude
in the world_data
object. Finally, we have set democracy
to be equal to "1. Democracy"
(the value for democratic countries). We have then put all of these values into a new data.frame
called data_for_fitted_values
which we will pass to the predict()
function.
Before we do that, let’s just take a quick look at the data_for_fitted_values
object:
head(data_for_fitted_values)
institutions_quality globalization latitude democracy
1 1.4 57.93053 25.78218 1. Democracy
2 2.4 57.93053 25.78218 1. Democracy
3 3.4 57.93053 25.78218 1. Democracy
4 4.4 57.93053 25.78218 1. Democracy
5 5.4 57.93053 25.78218 1. Democracy
6 6.4 57.93053 25.78218 1. Democracy
As you can see, this has produced a data.frame in which every observation has a different value of institutions_quality
but the same value for latitude
, globalization
, and democracy
.
We can now calculate the fitted values for each of these combinations of our explanatory variables by passing the data_for_fitted_values
object to the newdata
argument of the predict()
function.
# Calculate the fitted values
fitted_values <- predict(inst_model, newdata = data_for_fitted_values)
## Save the fitted values as a new variable in the data_for_fitted_values object
data_for_fitted_values$fitted_values <- fitted_values
We can now look again at the data_for_fitted_values
object:
head(data_for_fitted_values)
institutions_quality globalization latitude democracy fitted_values
1 1.4 57.93053 25.78218 1. Democracy -1.00319887
2 2.4 57.93053 25.78218 1. Democracy -0.66431685
3 3.4 57.93053 25.78218 1. Democracy -0.32543483
4 4.4 57.93053 25.78218 1. Democracy 0.01344719
5 5.4 57.93053 25.78218 1. Democracy 0.35232921
6 6.4 57.93053 25.78218 1. Democracy 0.69121123
Hey presto! Now, for each of our explanatory variable combinations, we have the corresponding fitted values as calculated from our estimated regression.
Finally, we can plot these values:
plot(
fitted_values ~ institutions_quality, # Specify the formula for the plot (dependent.variable ~ independent.variable)
data = data_for_fitted_values, # Specify the data to use for the plot
xlab = "Institution Quality", # Specify the X-axis title
ylab = "Fitted value for political stability", # Specify the Y-axis title
frame.plot = FALSE, # The frame.plot = FALSE argument removes the box from around the plot
col = "blue", # The col argument specifies the color
type = "l", # type = "l" will produce a line plot, rather than the default scatter plot
lwd = 3 # lwd = 3 will increase the thinkness of the line on the plot
)
We could also use the output from our model to plot two separate lines of fitted values: one for democracies, and one for dictatorships. We have already done this for democracies, so the following code constructs a data.frame
of fitted values for dictatorships:
## Set the values for the explanatory variables
data_for_fitted_values_dictator <- data.frame(institutions_quality = seq(from = 1.4, to = 9.3, by = 1),
globalization = mean(world_data$globalization),
latitude = mean(world_data$latitude),
democracy = "0. Dictatorship"
)
# Calculate the fitted values
fitted_values_dictator <- predict(inst_model, newdata = data_for_fitted_values_dictator)
## Save the fitted values as a new variable in the data_for_fitted_values object
data_for_fitted_values_dictator$fitted_values <- fitted_values_dictator
## Take a look at the output
head(data_for_fitted_values_dictator)
institutions_quality globalization latitude democracy
1 1.4 57.93053 25.78218 0. Dictatorship
2 2.4 57.93053 25.78218 0. Dictatorship
3 3.4 57.93053 25.78218 0. Dictatorship
4 4.4 57.93053 25.78218 0. Dictatorship
5 5.4 57.93053 25.78218 0. Dictatorship
6 6.4 57.93053 25.78218 0. Dictatorship
fitted_values
1 -1.04148517
2 -0.70260315
3 -0.36372113
4 -0.02483911
5 0.31404291
6 0.65292493
Now that we have calculated these fitted values, we can add the line for dictatorships to the plot we created above using the lines()
function:
## Create the same plot as above for fitted values over the range of institution quality for democracies
plot(
fitted_values ~ institutions_quality, # Specify the formula for the plot (dependent.variable ~ independent.variable)
data = data_for_fitted_values, # Specify the data to use for the plot
xlab = "Institution Quality", # Specify the X-axis title
ylab = "Fitted value for political stability", # Specify the Y-axis title
frame.plot = FALSE, # The frame.plot = FALSE argument removes the box from around the plot
col = "blue", # The col argument specifies the color
type = "l", # type = "l" will produce a line plot, rather than the default scatter plot
lwd = 3 # lwd = 3 will increase the thinkness of the line on the plot
)
## Add an additional line of fitted values over the range of institution quality for dictatorships
lines(x = data_for_fitted_values_dictator$institutions_quality,
y = data_for_fitted_values_dictator$fitted_values,
col = "red",
lwd = 3)
We can see from the plot that the fitted values for democracies (blue line) are almost exactly the same as those for dictatorships (red line). This is reassuring, as the estimated coefficient on the democracy variable was very small (0.04) and was not statistically significantly different from 0. Often, however, it can be very illuminating to construct plots like this where we construct a line to indicate how our predicted values for Y vary across one of our explanatory variables (here, institution quality), and we create different lines for different values of another explanatory variable (here, democracy/dictatorship).
7.1.6 Additional Resources
7.1.7 Exercises
- Open a new script and save it as assignment5.
- Go to the QoG website and download the standard data and the accompanying codebook.
- Pretend that you are writing a research paper. Select one dependent variable that you want to explain with a statistical model.
- Run a simple regression (with one explanatory variable) on that dependent variable and justify your choice of explanatory variable. This will require you to think about the outcome that you are trying to explain, and what you think will be a good predictor for that outcome.
- Add some additional explanatory variables (no more than 5) to your model and justify the choice again.
- Carry out the F-test to check whether the new model explains more than the old model.
- Produce a regression table of the better model.
- Interpret the output of the better model. State your expectations and whether they were met.
- Calculate fitted values whilst varying at least one of your continuous explanatory variables.
- Plot the result.
- Interpret the plot.
- Source your entire script. If it produces errors, clean your script.
7.1.8 Midterm Preparation
We wrote a midterm preparation exercise for you. It envolves working a little bit with data (in section 3). You will obviously not get questions where you work with R, so you can skip that part of section 3 if you want.


Before Change
def shape_list(tensor):
Return a list of the tensor"s shape, and ensure no None values in list.
// Get statically known shape (may contain None"s for unknown dimensions)
shape = tensor.get_shape().as_list()
// Ensure that the shape values are not None
dynamic_shape = tf.shape(tensor)
for i in range(len(shape)): // pylint: disable=consider-using-enumerate
if shape[i] is None:
shape[i] = dynamic_shape[i]
return shape
def get_shape_keep_last_dim(tensor):
After Change
def shape_list(tensor):
Return a list of the tensor"s shape, and ensure no None values in list.
return tf_utils.get_shape_list(tensor)
def get_shape_keep_last_dim(tensor):
shape_list_obj = shape_list(tensor)


# Author: Kyohei Atarashi
# License: BSD-2-Clause
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_random_state, check_array
from sklearn.utils.validation import check_is_fitted
from . import RandomMaclaurin
[docs]class CompactRandomFeature(BaseEstimator, TransformerMixin):
"""Efficient random feature map by Compact Random Feature map.
You can construct (the simplest) CompactRandomFeature by combining random
features and sklearn.random_projection, e.g.,
>>> trans_up = RandomMaclaurin(n_components=n_components_up)
>>> trans_down = SparseRandomProjecion(n_components=n_components.
density=1)
>>> X_trans_up = trans_up.fit_transform(X)
>>> X_trans_down = trans_down.fit_transform(X_trans_down)
The advantages of this CompactRandomFeature is its memory efficiency.
Above-mentioned combinatorial approach might occur the memory error in
the up projection when the size of the original feature matrix is large.
CompactRandomFeature for a random feature map with a cython implementation
avoid the memory error because it does not compute all of up projection
random features at the same time.
Although you can avoid MemoryError by creating mini-batches of the training
instances, this CompactRandomFeature class save this step.
Parameters
----------
transformer_up : sklearn transformer object (default=None)
A random feature map object.
If None, RandomMaclaurin is used.
transformer_down: str or sklearn transformer object (default=None)
Transformer for down projection.
{"srht"} can be used.
If None, structured projection cannot be used. Standard RandomProjection
is used.
n_components : int (default=10)
Number of Monte Carlo samples per randomized features of transformer.
It corresponds to E in original paper [1].
Equals the dimensionality of the computed (mapped) feature space.
n_components_up : int (default=100)
Number of Monte Calro samples per original features.
It corresponds to D in original paper [1].
It is used when transformer = "random_maclaurin" or "tensor_sketch"
degree : int (default=2)
Degree of polynomial kernel.
This argument is used when transformer = None.
h01 : int (default=False)
Using h01 heuristics or not.
This argument is used when transformer = None.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If np.RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Attributes
----------
random_weights_ : array, shape (n_components, transformer.n_components)
or (transformer.n_components)
The sampled basis for down projection.
References
----------
[1] Compact Random Feature Maps.
Raffay Hamid, Ying Xiao, Alex Gittens, and Dennis DeCoste.
In ICML 2014.
(http://proceedings.mlr.press/v32/hamid14.pdf)
"""
def __init__(self, transformer_up=None, transformer_down=None,
n_components=10, n_components_up=100, degree=2, h01=False,
random_state=None):
self.transformer_up = transformer_up
self.transformer_down = transformer_down
self.n_components = n_components
self.n_components_up = n_components_up
self.degree = degree
self.h01 = h01
self.random_state = random_state
[docs] def fit(self, X, y=None):
"""Fit the transformers for up projection and down projection.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the transformer.
"""
random_state = check_random_state(self.random_state)
X = check_array(X, accept_sparse=True)
# fit up projection transformer
if self.transformer_up is None:
self.transformer_up = RandomMaclaurin(self.n_components_up,
degree=self.degree,
h01=self.h01,
random_state=random_state)
self.transformer_up.fit(X)
_, n_components_up = self.transformer_up.transform(X[:2]).shape
# fit down projection transformer
if not isinstance(self.transformer_down, TransformerMixin):
from . import DOWNSAMPLES
if self.transformer_down in DOWNSAMPLES.keys():
self.transformer_down = DOWNSAMPLES[self.transformer_down](
n_components=self.n_components, random_state=random_state
)
else:
raise ValueError("{0} not in {1}. Use {1} or transformer."
.format(self.transformer_down,
DOWNSAMPLES.keys()))
self.transformer_down.fit(self.transformer_up.transform(X[:2]))
self.n_components = self.transformer_down.n_components
self.random_weights_ = self.transformer_down.random_weights_
return self
def _remove_bases(self, indices):
if hasattr(self.transformer_down, "_remove_bases"):
ret = self.transformer_down._remove_bases(indices)
self.n_components_ = self.transformer_down.n_components
self.random_weights_ = self.transformer_down.random_weights_
return ret
else:
return False


Deuteronomy 33:18
And of Zebulun he said, Rejoice, Zebulun, in thy going out; and, Issachar, in thy tents.


Utilities# General Infrastructure# Default Settings Functional notation Random Number States Random Numbers with Python API The Unknown truth value Programming Utilities# Special Base Classes, Decorators, etc.# Abstract methods Bindable classes Decorators Constant functions Special Methods for Classes Metaclass for inheriting comparison functions Base Class to Support Method Decorators Multiplex calls to one object to calls to many objects Fast methods via Cython Attribute and method calling Lists and Iteration, etc.# Callable dictionaries Converting Dictionary Flatten nested lists Searching a sorted list Multidimensional enumeration multi_replace Threaded map function Ranges and the [1,2,..,n] notation Elements with labels. File and OS Access# Temporary file handling get_remote_file Message delivery Miscellaneous operating system functions Database Access# Relational (sqlite) Databases Module Media# Wrapper for Graphics Files Work with WAV files Warnings# Stopgaps Handling Superseded Functionality Miscellaneous Useful Functions# Miscellaneous functions Miscellaneous functions (Cython) Verbosity System and Logging in SageMath Laziness# Lazy attributes Lazy format strings Lazy imports Lazy import cache Lazy lists Lazy strings Caching# Cached Functions and Methods Fast and safe weak value dictionary Fast Expression Evaluation# Fast Expression Evaluation Fast Numerical Evaluation Code Evaluation# Cython support functions Fortran compiler A parser for symbolic equations and expressions Evaluating Python code without any preparsing Evaluating a String in Sage Evaluating shell scripts Formatted Output# Formatted Output# Symbols for Character Art Base Class for Character-Based Art Factory for Character-Based Art ASCII Art Unicode Art Unicode Characters Repr formatting support Sage Input Formatting Tables HTML and MathML# HTML Fragments MathML output support LaTeX# Installing and using SageTeX LaTeX printing support LaTeX macros Standalone LaTeX Document class and TikzPicture Saving and Loading Sage Objects# Support for persistent functions in .sage files Object persistence Function pickling A tool for inspecting Python pickles Fixing pickle for nested classes Loading and saving sessions and listing all variables Interactive Usage Support# Interactive Sage Sessions# SageMath version and banner info Interpreter reset Determination of programs for viewing web pages, etc. Pager for showing strings Format Sage documentation for viewing with IPython and the notebook Sphinx configuration shared by sage.misc.sphinxify and sage_docbuild Process docstrings with Sphinx Distribution# Listing Sage packages Installing shortcut scripts Credits# Dependency usage tracking for citations License Development Tools# Testing# Unit testing for Sage objects Random testing Test for nested class Parent Benchmarking and Profiling# Benchmarks Accurate timing information for Sage commands The timeit command Simple profiling tool C Function Profiler Using Google Perftools Miscellaneous Inspection and Development Tools# Dynamic documentation for instances of classes Inspect Python, Sage, and Cython objects Edit the source code of Sage interactively Class inheritance graphs Some tools for developers Function Mangling ReST index of functions Low-Level Utilities# Low-level memory allocation functions The C3 algorithm The C3 algorithm, under control of a total order Indices and Tables# Index Module Index Search Page


{% extends "_base.html" %} {% load static %} {% block title %} Vendor Dashboard {% endblock title %} {% block content %} {% if vendor.products.all|length > 0 %}
| # | Image | Title | Price | Quantity | Date Added | Last Modified | |
|---|---|---|---|---|---|---|---|
| {{forloop.counter}} | {{product.title}} | {{product.price}} | {{product.qty}} | {{product.add_date}} | {{product.last_modified}} | Update |
| # | Order | Product ID | Unit Price | Order Quantity | Total Price | Platform commisson | Stripe commisson | Total Fees |
|---|---|---|---|---|---|---|---|---|
| {{forloop.counter}} | {{order_product.order.tracking_code}} | {{order_product.product.id}} | {{order_product.purchase_price}} | {{order_product.product_qty}} | {{order_product.total_price}} | {{order_product.order.paltform_fees_percentage}} | 3% | {% widthratio order_product.total_price 100 order_product.order.paltform_fees_percentage as p_fees %} {% widthratio order_product.total_price 100 3 as stripe_fees%}{{p_fees|add:stripe_fees }} |
We are wating for Stripe approval to your account, stay tuned.
{% elif vendor.stripe_status == 2 %}Your Stripe account has been rejected.
{% elif vendor.stripe_status == 3 %}Stripe needs more Information to approve your account, please provide them when you ready.
Update my verification information” {% else %}Connect to stripe to be able to add products and, receive payments.
Connect to Stripe {% endif %}


Specification pattern is a particular software design pattern, whereby business rules can be recombined by chaining the business rules together using boolean logic (Wikipedia).
In pratical, it's mostly used to define reusable filters for entities or other business objects.
In this section, we will see the need for specification pattern. This section is generic and not related to ABP's implementation.
Assume that you have a service method that calculates total count of your customers as shown below:
public class CustomerManager
{
public int GetCustomerCount()
{
//TODO...
return 0;
}
}
You probably will want to get customer count by a filter. For example, you may have premium customers (which have balance more than $100,000) or you may want to filter customers just by registration year. Then you can create other methods like GetPremiumCustomerCount(), GetCustomerCountRegisteredInYear(int year), GetPremiumCustomerCountRegisteredInYear(int year) and more. As you have more criteria, it's not possible to create a combination for every possibility.
One solution to this problem is the specification pattern. We could create a single method that gets a parameter as the filter:
public class CustomerManager
{
private readonly IRepository<Customer> _customerRepository;
public CustomerManager(IRepository<Customer> customerRepository)
{
_customerRepository = customerRepository;
}
public int GetCustomerCount(ISpecification<Customer> spec)
{
var customers = _customerRepository.GetAllList();
var customerCount = 0;
foreach (var customer in customers)
{
if (spec.IsSatisfiedBy(customer))
{
customerCount++;
}
}
return customerCount;
}
}
Thus, we can get any object as parameter that implements ISpecification<Customer> interface which is defined as shown below:
public interface ISpecification<T>
{
bool IsSatisfiedBy(T obj);
}
And we can call IsSatisfiedBy with a customer to test if this customer is intented. Thus, we can use same GetCustomerCount with different filters without changing the method itself.
While this solution is pretty fine in theory, it should be improved to better work in C#. For instance, it's not efficient to get all customers from database to check if they satisfy the given specification/condition. In the next section, we will see ABP's implementation which overcome this problem.
ABP defines the ISpecification interface as shown below:
public interface ISpecification<T>
{
bool IsSatisfiedBy(T obj);
Expression<Func<T, bool>> ToExpression();
}
Adds a ToExpression() method which returns an expression and used to better integrate with IQueryable and Expression trees. Thus, we can easily pass a specification to a repository to apply a filter in the database level.
We generally inherit from Specification<T> class instead of directly implementing ISpecification<T> interface. Specification class automatically implements IsSatisfiedBy method. So, we only need to define ToExpression. Let's create some specification classes:
//Customers with $100,000+ balance are assumed as PREMIUM customers.
public class PremiumCustomerSpecification : Specification<Customer>
{
public override Expression<Func<Customer, bool>> ToExpression()
{
return (customer) => (customer.Balance >= 100000);
}
}
//A parametric specification example.
public class CustomerRegistrationYearSpecification : Specification<Customer>
{
public int Year { get; }
public CustomerRegistrationYearSpecification(int year)
{
Year = year;
}
public override Expression<Func<Customer, bool>> ToExpression()
{
return (customer) => (customer.CreationYear == Year);
}
}
As you see, we just implemented simple lambda expressions to define specifications. Let's use these specifications to get count of customers:
count = customerManager.GetCustomerCount(new PremiumCustomerSpecification());
count = customerManager.GetCustomerCount(new CustomerRegistrationYearSpecification(2017));
Now, we can optimize CustomerManager to apply filter in the database:
public class CustomerManager
{
private readonly IRepository<Customer> _customerRepository;
public CustomerManager(IRepository<Customer> customerRepository)
{
_customerRepository = customerRepository;
}
public int GetCustomerCount(ISpecification<Customer> spec)
{
return _customerRepository.Count(spec.ToExpression());
}
}
It's that simple. We can pass any specification to repositories since repositories can work with expressions as filters. In this example, CustomerManager is unnecessary since we could directly use repository with the specification to query database. But think that we want to execute a business operation on some customers. In that case, we could use specifications with a domain service to specify customers to work on.
One powerful feature of specifications is that they are composable with And, Or, Not and AndNot extension methods. Example:
var count = customerManager.GetCustomerCount(new PremiumCustomerSpecification().And(new CustomerRegistrationYearSpecification(2017)));
We can even create a new specification class from existing specifications:
public class NewPremiumCustomersSpecification : AndSpecification<Customer>
{
public NewPremiumCustomersSpecification()
: base(new PremiumCustomerSpecification(), new CustomerRegistrationYearSpecification(2017))
{
}
}
AndSpecification is a subclass of Specification class which satisfies only if both specifications are satisfied. Then we can use NewPremiumCustomersSpecification just like any other specification:
var count = customerManager.GetCustomerCount(new NewPremiumCustomersSpecification());
While specification pattern is older than C# lambda expressions, it's generally compared to expressions. Some developers may think it's not needed anymore and we can directly pass expressions to a repository or to a domain service as shown below:
var count = _customerRepository.Count(c => c.Balance > 100000 && c.CreationYear == 2017);
Since ABP's Repository supports expessions, this is completely a valid usage. You don't have to define or use any specification in your application and you can go with expressions. So, what's the point of specification? Why and when should we consider to use them?
Some benefits of using specifications:


Take a snapshot of the current video stream
Click on the Start WebCam button.
Start WebCam
Stop WebCam
Take Snapshot
Download
Post Request
Set Endpoint
Screenshots :


Force-Directed Graph of Global Contiguity
Hover Over Nodes For More Information / Click And Drag To Reposition
DECLUSTER DATA
CLUSTER DATA


|
| Emile Antoine Bourdelle art links |
| | |
Emile Antoine Bourdelle
[French Expressionist Sculptor, 1861-1929]
|
|
|
|
Emile Antoine Bourdelle Works Online
Categorized & Annotated
Commercial Galleries: Galleries: We invite you to register and list your site (no charge for this service)
Original works by Emile Antoine Bourdelle available for purchase at art galleries worldwide
Museums and Public Art Galleries Worldwide:
Art Institute of Chicago NEW!
Fine Arts Museums of San Francisco NEW!
Hermitage Museum, Saint Petersburg, Russia Eloquence, 1917 Large Tragic Mask of Beethoven, 1901 Portrait of the Artist Ingres, 1908
Louvre Museum Graphic Art Database, Paris (in French)
Musée Bourdelle, Paris
Musée d'Orsay, Paris
Museum of Modern Art, New York City
National Galleries of Scotland, Edinburgh NEW!
National Gallery of Art, Washington D.C. NEW! Most but not all artists in the NGA database have works online
National Gallery of Australia, Canberra 4 works by Bourdelle
Aichi Prefectural Museum of Art, Japan
Akron Art Museum, Ohio NEW! (Click on the "View objects by this artist" link)
Cleveland Museum of Art, Ohio
Courtauld Institute of Art, London, UK 54 works by or related to the artist
Harvard University Art Museums, Massachusetts Harvard have works in their collections, but unfortunately you have to type in the search yourself
Hirshhorn Museum and Sculpture Garden, Washington D.C. NEW!
Indianapolis Museum of Art, Indiana
Kimbell Art Museum, Fort Worth, Texas NEW!
Kröller-Müller Museum, Otterlo, Netherlands
Los Angeles County Museum of Art NEW!
Magyar Nemzeti Gal�ria (Hungarian National Gallery), Budapest Andromeda
Minneapolis Institute of Arts, Minnesota Door Knocker in the Form of Medusa, 1925
Musée des Beaux-Arts de Lyon, France (in French) Carpeaux au travail
Museo Nacional de Bellas Artes, Buenos Aires, Argentina (in Spanish)
National Museum of Art of Romania Icarus (illustration for "Le Mari� Magique" by J.C. Mardrus)
National Museum of Art of Romania Pomona
National Museum of Art of Romania Hercules Tensing his Arch
National Museum of Modern Art, Tokyo, Japan 2 works online
National Museum of Western Art, Tokyo Hercules the Archer, 1909
North Carolina Museum of Art, Raleigh Mask of Beethoven
Palais des Beaux-Arts de Lille, Lille, France (in French) Pénélope
Philadelphia Museum of Art NEW!
Princeton University Art Museum, New Jersey
Pulitzer Foundation for the Arts, Saint Louis, Missouri Mask of Beethoven, ca.1905
Royal Museums of Fine Arts of Belgium Catalogue (in French) Jeune Fille H�racl�s tue les oiseaux du Stymphale
San Diego Museum of Art, California NEW!
The Albertina Graphic Art Databank, Vienna (in German)
Valtion Taidemuseo (Finnish National Gallery), Helsinki, Finland
National Art Databases and Museum Inventories:
Art Fund for UK Museums Medusa's Head Doorknocker
Flemish Art Collection Database, Belgium (in Dutch)
Smithsonian American Art Museum National Art Inventories List of works nationwide from two sources: the Inventory of American Paintings Executed before 1914 and the Inventory of American Sculpture (only a few percent of listings have an accompanying image)
Art Market: (e.g. records of past sales at auction; sites providing examples of the artist's signature)
Art Signature Dictionary NEW!
Christie's Past Sale Archive (database goes as far back as 1991; images go about as far back as 1999)
Sotheby's Sold Lot Archive (database goes as far back as 1998; images where permitted by copyright go about as far back as 2001)
Pictures from Image Archives:
Wikimedia Commons Image Database
Bildindex der Kunst und Architektur (in German) (a welcome screen is displayed for a couple of seconds before the search results appear)
California State University WorldImages Database
Ciudad de la Pintura (in Spanish)
Digital Images � Photography by Ron Reznick Photographs of 3 works by Bourdelle from the Los Angeles County Museum of Art
Mary Ann Sullivan's Digital Imaging Project 3 works documented
New York Public Library Digital Gallery
PBase Photo Sharing (Tip: Set the image size to "original" to see photos at their highest resolution)
RKD Netherlands Imagebase NEW! Eclectic database of images that range from old black-and-white photos to superb, zoomable reproductions
Additional Image Search Tools:
(SafeSearch set to "strict"; go to Advanced Search (Flickr/Google) or Preferences (Bing) to change)
Miscellaneous Sites:
Galerie Nicolas Plescoff, Paris (in French) "Permanent Collection" - These works appear to not be for sale 3 works online (Note that the JPG images are much larger than displayed on the page)
Ohio Outdoor Sculpture Inventory
The Modernist Journals Project (Click "Retrieve Images" to see artworks)
Articles and Reference Sites:
Encyclopedia Britannica complete article on Emile Antoine Bourdelle Note: The full version of the article is available only if you follow this link. If you bookmark the article and return later, or if you navigate directly to the Britannica website, you will see a 100-word preview only. Troubleshooting
RKD Netherlands Institute for Art History
Union List of Artist Names (Getty Museum) Reference sheet with basic information about the artist and pointers to other references.
Wikipedia, the "Open Content" Encyclopedia Biographical info
|
|
|
|
|


Song Yoo Bin & Kim Kook Heon announce their new duo name
by beansssFormer 'Produce X 101' contestants Kim Kook Heon and Song Yoo Bin have announced their new duo name!
Up until now, the two former MYTEEN members have simply promoted as the duo Kim Kook Heon x Song Yoo Bin. But now, fans can begin calling them B.O.Y, or B.OF. YOU!
The duo name was selected after the boys received over 2,000 suggestions via their fans. The name symbolizes two phrases simultaneously - "Best of you" and "Both of you". B.O.Y's first official activity under their new name will be their year-end mini concert 'Dear, YOU', set to take place from December 24-25. Afterward, you can look forward to new music from B.O.Y early next year!


The Synthetics tests are designed to verify that the agent handles valid and invalid Synthetics requests.
Each test should run a simulated web transaction. A Synthetics HTTP request header is added to the incoming request at the beginning of a web transaction. During the course of the web transaction, an external request is made. And, at the completion of the web transaction, both a Transaction Trace and Transaction Event are recorded.
Each test then verifies that the correct attributes are added to the Transaction Trace and Transaction Event, and the proper request header is added to the external request when required. Or, in the case of an invalid Synthetics request, that the attributes and request header are not added.
| Name | Meaning |
|---|---|
name |
A human-meaningful name for the test case. |
The settings
hash contains a number of key-value pairs that
the agent will need to use for configuration for the test.
| Name | Meaning |
|---|---|
agentEncodingKey |
The encoding key used by the agent for deobfuscation of the Synthetics request header. |
syntheticsEncodingKey |
The encoding key used by Synthetics to obfuscate the Synthetics
request header. In most tests, agentEncodingKey and
syntheticsEncodingKey are the same.
|
transactionGuid |
The GUID of the simulated transaction. In a non-simulated
transaction, this will be randomly generated. But, for testing
purposes, you should assign this value as the GUID, since the tests
will check for this value to be set in the
nr.guid attribute of the Transaction Event.
|
trustedAccountIds |
A list of accounts ids that the agent trusts. If the Synthetics request contains a non-trusted account id, it is an invalid request. |
The input for each test is a Synthetics request header. The test fixture file shows both the de-obfuscated version of the payload, as well as the resulting obfuscated version.
| Name | Meaning |
|---|---|
inputHeaderPayload |
A decoded form of the contents of the
X-NewRelic-Synthetics request header.
|
inputObfuscatedHeader |
An obfuscated form of the X-NewRelic-Synthetics request
header. If you obfuscate inputHeaderPayload using the
syntheticsEncodingKey , this should be the output.
|
There are three different outputs that are tested for: Transaction Trace, Transaction Event, and External Request Header.
The outputTransactionTrace
hash contains three objects:
| Name | Meaning |
|---|---|
header |
The last field of the transaction sample array should be set to the
Synthetics Resource ID for a Synthetics request, and should be set
to null if it isn’t. (The last field in the array is
the 10th element in the header array, but is
header[9] in zero-based array notation, so the key name
is field_9 .)
|
expectedIntrinsics |
A set of key-value pairs that represent the attributes that should
be set in the intrinsics section of the Transaction Trace.
Note: If the agent has not implemented the Agent
Attributes spec, then the agent should save the attributes in the
Custom section, and the attribute names should have
‘nr.’ prepended to them. Read the spec for details. For agents in
this situation, they will need to adjust the expected output of the
tests accordingly.
|
nonExpectedIntrinsics |
An array of names that represent the attributes that should not be set in the intrinsics section of the Transaction Trace. |
The outputTransactionEvent
hash contains two objects:
| Name | Meaning |
|---|---|
expectedAttributes |
A set of key-value pairs that represent the attributes that should
be set in the Intrinsic hash of the Transaction Event.
|
nonExpectedAttributes |
An array of names that represent the attributes that should
not be set in the Intrinsic hash of
the Transaction Event.
|
The outputExternalRequestHeader
hash contains two objects:
| Name | Meaning |
|---|---|
expectedHeader |
The outbound header that should be added to external requests (similar to the CAT header), when the original request was made from a valid Synthetics request. |
nonExpectedHeader |
The outbound header that should not be added to external requests, when the original request was made from a non-Synthetics request. |


Manufacturers of Packaging Pouches, Packaging Bags
We have experience of 25 Plus Years
About Us
Co. was formerly known as “Shiva International” established in 1993, in Mumbai , India. The bigger platform is created so as to serve the customers with Reliable Products, good quality, reasonable prices, excellent service and unmatched back up.
The technocrat promoter are in flexible packaging business for almost 30 years and have enough experience and skills required to deliver full proof solutions to complex customer requirements.
Co. have carved a niche for itself by supplying industrial consumable, barrier packaging products to no. of industries in India. Follow good manufacturing practices.
Any fresh order is responsibility and one needs to stand to it.
The high standards of quality are applied throughout the manufacturing cycle. All aspects of quality sourcing from virgin and premium raw materials to final shipping are controlled to ensure that quality product reaches each time, every time.
OUR PROUCTS ARE OUR AMBASSADORS.
Co. products are US & Canadian FDA compliant.
Indian tropical conditions and prevailing material handling scenario enforces Package to be functionally ROBUST. INNOVATE, SURVICE AND STAY AHEAD is our mantra.
About Our Products
Co . is a Pioneer and premium producer of Al. foil packaging products. Al. foil packaging products is a segment of Flexible packaging . Al. foil packaging products are made out of Al. foil / plastic films like PET/ BOPA/ BOPP/LD/LLD/M-LLD, Multilayer of LD/LLD/HD/M-LLD. Al. foil packaging products could be printed / unprinted Barrier bags/pouches are tailor made and are available in different specifications, sizes, shapes and varying filling capacities.
Barrier foil bags/pouches prevent loss of aroma, weight, moisture due to climatic conditions. 30year experience and skill , service , backup , documentation in the field. Al. laminated bags/pouches enables business , creates awareness, reduces freight cost, retains customer – confidence, creates brand value, entails value for money, enables source reduction, above all product remains usable and fresh till consumption. Al. foil barrier packaging prevents packed goods from deterioration due to inward/ outward Ingres /loss of moisture , air, vv light, oxygen, humidity, extreme temperatures, (ice) rain sea humidity avoiding corrosion Al. foil barrier products are zero barrier and provides TOTAL PROTECTION.
Our Business
Co. is into business of manufacturing and supply of Barrier laminated bags/pouches/Rols for variety of consuming Industiries . Mainly for Dry powder protection as we as corrosion resistant levies for variety of produts .. from repractory brics to Engineering proucts to Electronic parts and so on.
- Fronline manufacturer and supplier last 25year to whos who of major Industries all over India.
- Co. having a 5000sq. ft. plant near mumbai – India, have installed prouct specific and dedicated Plant and machinery to produce premium quality speciality bags/pouches.
Co. product fulfills challing requirements of product specific consuming Industrial requirements.
Products Types
Quality is Essence of business
Flexible Packaging - Rolls / bags/pouches/ sachets
- Al. foil barrier foils/Rolls/bag/pouches (Triple Laminated Bags/pouches).
- 25kg. bags/ sacks
- 4side sealed barrier bags for cartoons
- Liners fir barrels / Fibre drums.
- Three Dimensional Barrier Bags – FIBC JUMBO Bags as inner barrier linder capacity 800kgs. – 1MT.
- AL. FOIL LAMINATED BAGS/POUCHES OR ROLLS
- PLASTIC LAMINATED BAGS/POUCHES OR ROLLS.
- 25KG. CAPACITY BAGS.
- 50KG. LINDERS FOR BARRELS/ FIBRE DRUMS
- 4SIDE SEALED BAGS FOR CARTOONS
- 800KGS- 1MT. CAPACITY JUMBO BAGS (BARRIER LINER) FOR PALLET LOAD ALUVAC BAGS ( BULD BAGS- LIINERS)
Manufacturing
Al. foil packaging products manufacturing is a premium segment of Flexible Packaging. Barrier foil production is a complex process and requires years of experience and skill. Climatic protection / barrier packaging production is very high demanding and performance oriented product modified atmosphere packaging solutions creation necessitates intellectual property development on continuous basis that too is a very complex manner.
Hermetically sealed containers for vaccum packaging /gas flushing requires custom made product specific machines to cater to highest standard of customer requirements / satisfaction.
Customer products are costly and any failure leads to return of whole consignment, erosion of credibility and loss of business which NOBODY CAN AFFORD.
CUSTOMER’S TOTAL SATISFACTION IS OUR MOTTO
The manufacturing processes involved are:
- Rotogravure printing
- Dry Bond Lamination
- Slitting
- Bag making * Automated / Semi Automatic, manual
MARKET SECTORS
Packaging solutions for a variety of market segments :
Aluminium foil / Barrier Foil Packaging products/ solutions provide total climatic protection for any product which may deteriorate due to atmospheric moisture, oxygon , air, water vapour, variation in temperature, humidity, rain , water and similar climate changes Co. provides tailor made solutions to suit customer specific requirements.
| PRODUCTS REQUIRING PROTECTION | CORROSION RESISTANCE |
|---|---|
| Food Powders/ Ingredients flavours | Automobiles spare parts |
| Pharmaceuticals – API, Bulk drug, drug intermediates / Nutraceuticals vitamins, capsules –empty [Secondary Packs] | Machine spare parts AQM |
| Pigments, Dyes & Paint powders, colours, master batches. | Electronics spare parts PCS |
| Chemicals, Hygroscopic powders | Engineering equipments. |
| Polymers, resins, Engineering plastics, XLPE compounds. | Defence equipments. |
| CTC, Darjeeling Tea. | Telecommunications |
| Dry fruits – Nuts, Almonds, Cashew, Walnut, Pistachio, Peanuts | Aerospace |
| Spices, savouries, dehydrated vegetables | Refractory Bricks |
| Dental formulation / powders. | Export Goods |
| Frozen – vegetables, IQF, Tomato, Purees | Metal Powders |
| View Detailsy to eat foots – RTE | Welding wire spools |
| Welding Electrodes, fluxes. |
Contact Us
Location:
Shop no 6,Sai Visweswar Apartment, Opposite Century Hospital Murbad Road, Post Shahad, Thane-421103, Maharashtra, India
Email:
<EMAIL_ADDRESS>Call: Sanjay Dagga
+91-7722087757
+91-8169412147


Accessible Tab Panels Demo
Work-related injury and illness
Causes of injury and illness
- Body stressing (strains, sprains, pulled muscles etc.) - 38%
- Falls, trips and slips - 24%
- Being hit by moving objects - 16%
- Hitting objects with a part of the body - 8%
- Mental stress - 6%
- Other - 8%
Types of injury and illness
- Traumatic joint/ligament and muscle/tendon injuries - 41%
- Wounds, lacerations, amputations and internal organ damage - 16%
- Musculoskeletal and connective tissue diseases - 16%
- Fractures - 11%
- Mental disorders - 7%
- Other - 9%
Injury and illness claims by occupation
- Labourers - 25%
- Technicians and trade workers - 19%
- Community and personal service workers - 17%
- Machinery operators and drivers - 14%
- Professionals - 10%
- Sales workers - 5%
- Managers - 5%
- Clerical and administrative workers - 4%
Work-related fatalities
Causes of fatalities
- Vehicle collision - 32%
- Being hit by moving objects - 18%
- Falls from heights - 15%
- Being hit by falling objects - 8%
- Rollover of non-road vehicle - 5%
- Other - 22%
Fatality claims by occupation
- Labourers - 29%
- Machinery operators and drivers - 29%
- Managers - 14%
- Professionals - 9%
- Technicians and trade workers - 9%
- Community and personal service workers - 7%
- Clerical and administrative workers - 2%
- Sales workers - 1%


AMAZING PEOPLE ARE MAKING AMAZING DESIGN IN A FUN ENVIRONMENT
when you work with us, you are part of a team of committed collaborators. All aspects pf our software development process are integrated, interactive and agile.
Our approach unifies, design, development and product management to create exceptional products.
We start each project by understanding your business goals, the user’s needs and the product requirements
Then we help translate them into effective and elegant solutions.
Before we design a contract or write a line of code our team will spend the necessary time needed to understand your product vision
The outcome of this scoping session is proposal or high-level statement of work.
based on your requirements, we can provide a complete offering or just the pieces you need.
DESIGN
DEVELOPMENT
PRODUCT MANAGEMENT


Represents a WebSocket binary message.
Represents a WebSocket message.
Represents a WebSocket message. A message can either be a binary message or a text message.
A PeerClosedConnectionException will be reported to the WebSocket handler if the peer has closed the connection.
A PeerClosedConnectionException will be reported to the WebSocket handler if the peer has closed the connection.
closeCode
and closeReason
contain close messages as reported by the peer.
Represents a WebSocket text message.
Represents a WebSocket text message. A text message can either be strict in which case the complete data is already available or it can be streamed in which case getStreamedText will return a Source streaming the data as it comes in.
A virtual header that WebSocket requests will contain.
A virtual header that WebSocket requests will contain. Use UpgradeToWebSocket.handleMessagesWith to create a WebSocket handshake response and handle the WebSocket message stream with the given handler.
Represents a WebSocket request.
Represents a WebSocket request. Use WebSocketRequest.create
to create a request
for a target URI and then use addHeader
or requestSubprotocol
to set optional
details.
Represents an upgrade response for a WebSocket upgrade request.
Represents an upgrade response for a WebSocket upgrade request. Can either be valid, in which
case the chosenSubprotocol
method is valid, or if invalid, the invalidationReason
method
can be used to find out why the upgrade failed.
Represents a WebSocket binary message. A binary message can either be strict in which case the complete data is already available or it can be streamed in which case getStreamedData will return a Source streaming the data as it comes in.


Hi, my name is Dan Heron. I am your Professional Development Instructor during the program and a Talent Manager you can utilize after the program. I created this professional development curriculum as a way to give bootcamp participants an introduction into what I believe it takes to succeed in the local tech industry. I have a variety of work experience from working with fast-paced tech startups to fortune 500 companies. You can get an idea of my background and connect with me via LinkedIn or Twitter @SuperHeronDan.
This is the home page for your professional development (aka Pro Dev) during the bootcamp. I maintain the website so if you see any errors or have any suggestions for improvement, don't hesitate to send me a message. It is a living document.
Before we get started, you need to pick a path to hyper focus on during the program. Do you want to get a job and work for someone or do you want to create you own job and get clients?
This module is about finding your passion and motivation for this line of work. Then, how do you quantify it into goals.
This module is about identify framing and how you see yourself now as a tech professional. Then, you will create a strategy for professional brand.
This module is about creating opportunities for yourself. Then, putting your initiatives into action.
This module is about collaboration and consultation. This is what really shines with our gradutes to employers.
What's Next? Congrats you graduated a training program. Now comes the real challenge applying your knowledge and skills.


Check your answers Directors' report - information
Principal activities of the company |
||
|---|---|---|
| Scaevola mnesarchum assueverit vis in. Unum novum sententiae cu duo, ex dico modus inimicus mel, justo appetere vel ex. At est oportere explicari rationibus, repudiare moderatius omittantur in cum. Pri nominavi comprehensam te, sint accusam incorrupte quo ea. | Change Principal activities of the company | |
Political and charitable donations |
||
| No information supplied. | Change Political and charitable donations | |
Company policy on disabled employees |
||
| No information supplied. | Change Company policy on disabled employees | |
Additional information |
||
| No information supplied. | Change Additional informatione |


title = "NotFound" url = "/404" layout = "default" == {% put title %} Page not found | Pinfort's blog {% endput %}
The page you requested is not found.


My name is Anand Dharne. I'm a Software Developer.
I build modern, simple, responsive cross browser supported applications.
As an experienced software engineer with knowledge in JavaScript ( React and Jquery), Node.js, CSS (Bootstrap), RESTful API development and familiarity with Agile software methodologies, JIRA and Scrum, I feel confident that I can prove to be an asset to the engineering team at a software firm. I have experience working in a start-up and learnt how to perform planning and development of Test Plans, Test Strategies and Test scenarios to meet product requirements. I also have experience in every phase of Quality assurance life cycle (QALC) and Software Development Life Cycle (SDLC). I offer 2 years of development and testing experience, a strong aptitude for learning new technologies, and a proven track record of meeting aggressive goals. My most notable skills include:
Software Development and Testing: I have experience working with Full Stack applications. Initially started out with building front-end applications and now gaining experience in working at the back-end. Experienced in testing Client/Server and Web based applications.
Fast Paced Environment: I love working in a start-up cultured environment. It has helped me learn a lot and also adapt to different changing environments.
Communication Skills: I am comfortable communicating with technical and non-technical employees throughout the company in order to complete projects and achieve organizational goals. I have won 2 awards for my previous start-up by pitching the business plan at different levels.
Github Profile: Github


Track company sentiment at the click of a button while keeping up with the latest news and company developments.
Access Babbl sentiment data from any device. Receive alerts about changes in social sentiment in the stocks/crypto you follow.
Customize your dashboard to find easy access to the data you care about. Personalize your alerts to stay up-to-date on the tickers you own.
Babbl was designed with retail in mind. We provide easy to understand data and investment tools for the everyday investor.
Check it out


If you are uploading a copy of a Notice of Assessment or Reassessment from the Canada Revenue Agency website, make sure the image contains:
-
Your name
-
The tax year
-
Your net income (line {{ benefitApp.taxYear >= 2019 ? 23600 : 236 }})
-
If you have a Registered Disability Savings Plan (line {{ benefitApp.taxYear >= 2019 ? 12500 : 125 }})


When and why people pay more attention on "Flight Crash"? In this visulization, the Google Trends on "Flight Crash" is shown along with all commercial flight crashes to help us explore more reason behind the scene. Do people care more about casualty? Do the causes of crash catch more attention?


FOR STACY
Balloon release planned for tragic Glasgow mum, 26, who died suddenly giving birth to baby boy
by Katy PaganA BALLOON release has been planned in tribute to a “beautiful” mum who tragically died after giving birth.
Stacy Louise Joyce, 26, from Drumchapel, Glasgow, passed away after giving birth to baby boy Hunter on Saturday.
Now, her loved ones are planning a lantern and balloon vigil in her memory.
The release is set to take place at Belsyde Park in Drumchapel at 7pm on Sunday, December 1.
Close friend Allyson Burrell, who has organised the vigil, said: "That's where we spend most of our days when we were growing up.
"Would be lovely if everyone who loved her could make it along."
Stacy's friends and family have vowed to make it a "beautiful night" for the tragic mum.
And they've asked heading along to make sure balloons or lanterns being released are environmentally friendly and biodegradable as Stacy "would have preferred that".
One said: "To think this is where all our childhood memories are.
"Breaks my heart that this will be the reason everyone will be getting together again but she deserves a beautiful night."
We told earlier how Stacy's heartbroken dad led tributes to his beautiful daughter after the childbirth tragedy.
Distraught John Joyce said: "Our world has come crashing down like we as a whole family could never imagine.
"Stacey Louise Joyce was sadly taken from us while giving birth to a perfect wee, over eight pounds, healthy baby boy.
"We promise you one thing, Stacy, that wee stunner will be looked after like you'll never know.
"Fly high my princess. Love you more than you'll ever know."


$[Delisting,T:QuantConnect.Data.Market.Delisting] events provide notice that an asset is no longer trading on the exchange. A delisting warning is issued on the final trading day for a stock delisting event to give your algorithm time to gracefully exit out of positions before forced termination.
def Initialize(self): self.SetStartDate(2007, 5, 16) self.SetEndDate(2007, 5, 25) self.SetCash(100000); equity = self.AddEquity("AAA", Resolution.Daily) def OnData(self, data): self.MarketOrder('AAA', 10) ## Print delisting warnings and noritifications if data.Delistings.ContainsKey('AAA'): delisting = data.Delistings['AAA'] ## Log the delisting warning type self.Log(delisting.ToString()) if delisting.Type == 0: self.Log('AAA will be delisted EOD') if delisting.Type == 1: self.Log('AAA delisted')


# What To Do For Collision Physics
* full blast one? Cannon.js ? Ammo.js ?
* maturity of those library ?
* hpw maintained they are?
* something simple ala babylon.js?
* something based on old collision.js from r40
* when to do that ?
* can we wait? Is that the most urgent now


| DevIL Documentation | Home | Previous | Next |
The iluEnlargeCanvas function enlarges the image while leaving the original data unchanged.
ILboolean iluEnlargeCanvas(
ILuint Width,
ILuint Height,
ILuint Depth
);
iluEnlargeCanvas enlarges the "canvas" of the current image, clearing the background to the colour specified in ilClearColour. To control the placement of the image, use iluImageParameter.
ILU_ILLEGAL_OPERATION - There is currently no image bound. Use ilGenImages and ilBindImage before calling this function. This error can also be used if any of the parameter dimensions are smaller than the original image's dimensions.
ilGenImages, ilBindImage, ilClearColour, iluImageParameter,


Wind turbine calculator
Inputs
Capacity
MW
Load factor
%
Capacity Growth
Outputs
Energy
Not known
Revenue
Not known
Capacity by year


species climate range shifts elevation change suitable Meconopsis RCP alpine distribution {border-style records mean px;} shift #ffffff;} predicted Himalaya–Hengduan data future Mountains plant region seven time Chinese Kunming Academy Wang, information models upward China current historical space Sciences area climatic global high potential ./j.-...x Institute Li, Table average habitat model Biology, Global Liu, Species higher mountain punicea size temperature using (.) Botany, Ecology Figure Supporting change. changes different elevational modeling plants projected px; scenarios {font-family Change Climate Diversity Gottfried, Grabherr, Pauli, Science, Yang, analysis collection conditions diversity font-size habitats increase latitude past species. specimens subnival warming year (−.) Ecology, Plant Program Yunnan Zhang, [], bio: biodiversity compared conservation decade double; impedita integrifolia period precipitation pre‐s projections racemosa rate results track used vegetation (Table Distributions, Evolution, Key National Occurrence Research Sciences, Trends Wiens, Wild analyzed and/or associated based border-color border-width collected contraction contractions cooler distributions experience field genetic line local longitude, new niche pessimistic predictions quintuplinervia response scenario solid; species, specimen van variables {font-style {font-weight (%) (C) (RCP (a) (b) ./s--- ./science. Anderson, Bank CAS Current Germplasm Hill, Historical John MaxEnt Proceedings Reiter, Roy, Shrestha, Thomas, Wiley Yang bold collab: combined consistently continue courier; ecological elevation, em; established impact lancifolia latitude, literature number optimistic post‐s pt;} punicea, rates relatively responses restricted scenarios, sensitive shifting showed significant species) subsequent suggest upwards warming. wide


Chitkara University in Punjab has been founded by Dr. Ashok Chitkara and Dr. Madhu Chitkara who have been passionate teachers for more than 40 years now. Since inception, Chitkara University has been different. ... So are our faculty, our academic strengths and our outlook on teaching and learning.


World wide health is a website which aims to raise awareness of the risks of smoking and drinking alcohol while also showing health tips about nutrition and exercise. It displays data about the level of smoking and alcohol consumption in different countries in a basic application. We advice that you fill out the questionnaire to have a better experience with our website.
Try out our Questionnaire today to improve your WWH experience. If you take the quick and easy questionnaire we will be able to provide information to suit you because we will be able to give the information about nutrition and exercise you need and want.
Start NowA game made in flash


40%
Got a tangible career benefit from this course
Your Courses > Operating System
This course teaches the basic operating system abstractions, mechanisms, and their implementations. All the features of this course are available for free. It does offer a certificate upon completion.
Continue WatchingInstructor - PQR STU
1,00,00+ already enrolled
Got a tangible career benefit from this course
started a new career after completing these courses


Previous Location
Next Location
Location
Maps
None
Temperature
Pressure
Humidity
Wind Speed
Radar (U.S.)
Infrared Satellite
Visible Satellite
Global Infrared Satellite
Detailed Observations
As reported at:
Pressure:
Monthly Rain:
Sunrise:
Sunset:
So Far Today
Daily Rain:
Rain Rate:
Wind Gust:
Weather Cameras
previous
next
Forecast
Summary Forecast
Detailed Forecast
Hourly Forecast
,
,
,
,
,
,
,
Time
Conditions
Chance Precip
Feels Like
Humidity
Dew Point
Wind
Sky Cover
Alerts
Detailed Observations
Forecasts
Weather Maps
Cameras
Refresh All Weather Data
Temperature Units
English
Metric
International Temperature Units
English
Metric
Window Options
The closest weather station camera image will display in the window (if available) instead of the weather animation.
WeatherBug
Version 2.2.7
Please email us your feedback! -<EMAIL_ADDRESS>Enter a Zip Code, City, or Address:
Select:
No Internet Connection Detected
The weather information displayed is from the last time WeatherBug had an internet connection; it will be updated when the connection is restored.
✖
?


{% extends "app/layout.html" %} {% block content %} {% load staticfiles %}
People that make the Alumni Association happen
President
Vice-President
Honorary General Secretary
Joint Secretary
Treasurer
Association Member
Developer


Hey. I kinda like writing stories. Dear Wenny. Google SWE Internship — The Why and The How At One's Own Pace Tale of a Wintern Soldier STOQO Internship Final Demo Internship at Blibli?


{% extends 'templates/nhs_transaction_layout.html' %}
{% import 'includes/form_macros.html' as form_macros %}
{% block afterHeader %}
{% include "includes/service-headers/health-costs-checker.html" %}
{% endblock %}
{% block content %}
Do you already have a Medical exemption card?
{% endblock %}


This section introduce some of the basic concepts used in HiTS.
The fundamental experimental unit, which can contain either raw or processed data. In this model it may contain
of experimental results.
The layout of wells should follow a (8 rows × 12 columns) [96] or a (16 rows × 24 columns) [384] grid, called physical plate.
For example:
| × | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| A | A01 | A02 | A03 | A04 | A05 | A06 | A07 | A08 | A09 | A10 | A11 | A12 |
| B | B01 | B02 | B03 | B04 | B05 | B06 | B07 | B08 | B09 | B10 | B11 | B12 |
| C | C01 | C02 | C03 | C04 | C05 | C06 | C07 | C08 | C09 | C10 | C11 | C12 |
| D | D01 | D02 | D03 | D04 | D05 | D06 | D07 | D08 | D09 | D10 | D11 | D12 |
| E | E01 | E02 | E03 | E04 | E05 | E06 | E07 | E08 | E09 | E10 | E11 | E12 |
| F | F01 | F02 | F03 | F04 | F05 | F06 | F07 | F08 | F09 | F10 | F11 | F12 |
| G | G01 | G02 | G03 | G04 | G05 | G06 | G07 | G08 | G09 | G10 | G11 | G12 |
| H | H01 | H02 | H03 | H04 | H05 | H06 | H07 | H08 | H09 | H10 | H11 | H12 |
| Replicate 1 | Replicate 2 | Replicate 3 | |
| Plate 1 | layout 1 | layout 1 | layout 1 |
| Plate 2 | layout 2 | layout 2 | layout 2 |
| Plate 3 | layout 3 | layout 3 | layout 3 |
| Plate 4 | layout 4 | layout 4 | layout 4 |
| Plate 5 | layout 5 | layout 5 | layout 5 |
| Plate 6 | layout 6 | layout 6 | layout 6 |
| Plate 7 | layout 7 | layout 7 | layout 7 |
| Plate 8 | layout 8 | layout 8 | layout 8 |
The plate is an abstract concept in cellHTS2. It represents a group of physical plates with the same well layout.
Also an abstract concept. It represents a group of physical plates which may have different layouts, but each group should have the same layouts in the same order.
For each well you may have different parameters describing their content (on each physical plate). These parameters are handled independently through the whole process.
This section shows some basic concepts and notations used in other parts of the program.
The pos, neg, sample in superscript means selection of the positive or negative controls, or the samples from a group of wells.
The p means a plate group, r means a replicate group, w means selection by well position.
When you see one of the aggregating functions having in subscript one or two of these notations it will mean that those are fixed for a value and only the others are aggregated (selected by that/those groups).
The log transform is performed on individual values.
The normalisation is done by physical plates.
The variance adjustment can be done by physical plates or by replicate groups, or it may be omitted.
Scoring is done by replicate groups by default, but you can choose per physical plates too for Z scores.
The summarisation of replicate values are computed by each plate groups of individual wells.
When log transform selected the additive normalisation will be used.
Simple 2 based logarithm.
In case of average like normalisation methods (mean, median, shorth, negatives) the aggregated value is subtracted from each value. Its natural point (no change value to the reference) is 0.
In case of average like normalisation methods (mean, median, shorth, negatives) each value is divided by the aggregated value. Its natural point (no change value to the reference) is 1.
Normalisation:
Scoring:
The robust version is the by plate variance adjustment with additive median normalisation:
When scoring:
Additive:
Multiplicative:
Additive:
Multiplicative:
1.4826*median(|{sample values}-median({sample values})|)
The multiplication with the 1.4826 value is necessary to get a standard deviation-like value.
| √ | μ((values-μ(values))2) |
The shorth is the shortest interval that covers half of the values in x. This function calculates the mean of the x values that lie in the shorth. (From R help for shorth.) For more information see: The Shorth Plot
For further information see: cellHTS2 complete manual's 11.2.3 paragraph (Appendix: Normalization methods implemented in cellHTS2 package/Non-controls-based normalization/B score method)
For more information see this wikipedia article: Local regression (stable)
For more information see the description of locfit R package: The locfit package
When computing the four quarters this value is between the lowest and the second lowest quarters.
When computing the four quarters this value is between the second highest and the highest quarters.


|
RL-Bin
2.0.0
Robust Low-overhead Binary Rewriter
|
RL-Bin differentiates itself from other binary rewriters by having the capability to robustly rewrite stripped binaries with very low overhead. The overhead added by RL-Bin itself is negligible (a few percent) and it is proportional to the added instrumentation; hence lightweight instrumentation can be added to applications that are deployed in live systems for monitoring and analysis purposes.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
First, you need to clone the repository to your system.
Then you need to install the prerequisites on your system.
For Building RL-Bin, you need to have Visual Studio 2019 installed on your machine. It can be downloaded from microsoft website. If you install it in any path other than the default location, modify make.bat
accordingly.
You need Python 2 to run some of the scripts. Make sure python
is in your system's path. For Windows, install package is available at Python website.
In order to generate graphs, you will also need to have Graphviz installed. CFGs and callgraph will use dot
program that comes with graphviz, so make sure it is in your system's path.
Change your directory to the root of cloned repository and run make.bat
batch file.
Also, for removing the binary files for a clean build, you can run clean.bat
command.
No installation is needed for RL-Bin. The software is portable and it can be executed from every path in the system provided that the bin
folder containing files inject.dll
and core.dll
, is in the same path.
Separate Tests are written for sample unit testing, and testing with SPEC CPU2017 benchamarks.
There are three categories of sample hand-written code to do unit testing. They are written in Assembly language, and C++. C++ Applications are either compiled by Visual Studio or GCC. Here is how to run these unit tests.
SPEC Benchmark integer or floating point can be executed with the following command.
Add additional notes about how to deploy this on a live system.
To run RL-Bin on any given program, it should be given as a command line parameter to RL-Bin. If the target application needs any input parameter, they need to be given after the name of the target application.
Another method to run RL-Bin is to drag and drop the target application over RL-Bin.exe
binary.
The outputs will be in analysis
folder created by RL-Bin and will be numbered and put in separate folder for each run.
RL-Bin can be customized by reading from the configuration file found at ./config/config.txt
. If the file is not present, default values will be used. In the configurateion file, you can customize the followings:
RL-Bin source code is fully documented and the documentation is generated by Doxygen and it can be found in ./documentaion/html/index.htm
.
Please follow the coding convention in `Coding Convention` to add code to the repo.
This project is licensed under the Creative Commons 4.0 Attribution-NonCommercial-NoDerivatives - see the LICENSE.md file for details


Psalm 139
For the chief musician. A psalm of David.
1 Yahweh, you have examined me, and you know me.
2 You know when I sit down and when I get up;
you understand my thoughts from far away.
3 You observe my path and when I lie down;
you are familiar with all my ways.
4 For there is not a word I speak
that you do not know completely, Yahweh.
5 Behind and before you surround me
and rest your hand upon me.
6 Such knowledge is too much for me;
it is too high, and I cannot understand it.
7 Where can I go to escape from your Spirit?
Where can I flee from your presence?
8 If I ascend up to the heavens, you are there;
if I make my bed in sheol, see, you are there.
9 If I fly away on the wings of the morning
and go to live in the uttermost parts across the sea,
10 even there your hand will lead me,
and your right hand will hold onto me.
11 If I say, "Surely the darkness will hide me,
and the night will be my light;"
12 even the darkness will not hide from you.

You know my life very well.
15 My body was not hidden from you
when I was made in private,
when I was intricately made in the depths of the earth.
16 You saw me inside the womb;
all the days ordained for me were recorded
in your book even before the first one happened.
17 How precious are your thoughts to me, God!
How vast is their sum!
18 If I tried to count them, they would be more in number than the sand.


WITNESS LIST
Education
February 21, 2013 9:00 AM
FOR:
Cervantes, Mary Jane Parent of students @ IDEA Allan (Self), Austin, TX
Del Bosque, Nora Parent HISD (Stand For Children), Houston, TX
DiBella, Mark Chief Operating Officer (YES Prep Public Schools), Houston, TX
Dunn, David Executive Director (Texas Charter Schools Association), Austin, TX
Fernandez, Adriana (Parent, KIPP SHINE Prep), Houston, TX
Fernandez, Martha Community Advocacy Coordinator (Uplift Education), Irving, TX
Golsan, James Education Policy Analyst (also providing written testimony) (Texas Public
Policy Foundation), Austin, TX
Goodman, Jennifer Superintendent (also providing written testimony) (Odyssey
Academy), Galveston, TX
Gregory, Gina Principal - Harmony (Public Charter Schools), Austin, TX
Guy, Shawana Parent - Waitlisted Charter School (Houston Metro Area), Cypress, TX
Heinz, Patricia Parent / Grandparent (Hampton Preparatory), Cedar Hill, TX
Larson, Mark CEO (also providing written testimony) (KIPP San Antonio), San Antonio,
TX
Ledbetter, Ron Superintendent - CFO (Texas Leadership Charter Academy), San Angelo,
TX
McClure, Debby Parent (Harmony - Harmony Political Science / North Austin),
Pflugerville, TX
McGriff, Deborah M. Board Chair (National Alliance for Public Charter Schools),
Washington, DC
Novelo, Sylvia Student (Responsive education Solution), Austin, TX
Pulliam, Charles Chief Development Officer (Life School of Dallas), Lancaster, TX
Ray, David Superintendent (St Anthony School), Dallas, TX
Richmond, Greg President (National Assoc. of Charter School Authorizers), Chicago, IL
Riggs IV, Joseph Regional Director of Academics (Responsive Education Solutions),
Austin, TX
Rodriguez, Ref President / CEO (Partners for Developing Futures), L.A., CA
Smith, Addie Director of Government Affairs, (TX Charter Management Organizations),
Austin, TX
Smith, Parc Executive Director (American YouthWorks - Drop out Recovery School),
Austin, TX
Spier, Alyson PTO Vice-President (Eden Park Academy), Austin, TX
Torkelson, Tom Founder, IDEA Public Schools (IDEA Public Schools), Weslaco, TX
Venable, Peggy Texas Director (Americans for Prosperity), Austin, TX
Walker, Jacqueline Translating For (Norah Del Bosque), Austin, TX
AGAINST:
Banks, Yannis Legislative Liaison (Texas NAACP), Austin, TX
Canaday, Jennifer M. Governmental Relations Manager (also providing written testimony)
(ATPE), Austin, TX
Canales, Ramiro Assistant Executive Director - Governmental Relations (Texas
Association of School Administrators), Austin, TX
WITNESS LIST
Education
February 21, 2013 9:00 AM
Cornelius-Fisher, Desiree Manor ISD Trustee (Texas Assoc. of School Boards), Austin, TX
Grey, John Government Relations Specialist (Texas State Teachers Association), Austin,
TX
Haenisch, Barry Consulting (TX Association of Community Schools, Self), Round Rock,
TX
Hantke, Jonathan General Counsel (Aldine ISD), Houston, TX
Hollingsworth, Lonnie Director of Governmental Relations (also providing written
testimony) (Texas Classroom Teachers Association), Austin, TX
Johnson, Lance Superintendent (Warren ISD, Region 5 Legislative Committee), Warren,
TX
McCraw, Ken Exec. Director (Tx Association of Community Schools (TACS)), Austin, TX
Quinzi, Patty Legislative Counsel (also providing written testimony) (TX - AFT), Austin,
TX
Wilson, Ben G. Asst. Supt. (Aldine ISD), Houston, TX
Woods, Charles Deputy Superintendent (Alief ISD), Houston, TX
ON:
Anderson, David General Counsel (Texas Education Agency), Austin, TX
Bahorich, Donna State Board of Education - Dist 6 (myself), Houston, TX
Berry, Traci VP Community Engagement (Goodwill Industries of Central Texas), Austin,
TX
Byer, Von Deputy General Counsel (Texas Education Agency)
Dawn-Fisher, Lisa Associate Commissioner / Chief School Finance Officer (Texas
Education Agency), Austin, TX
HEILIG, JULIAN Assoc Prof Ed Policy (Self), Austin, TX
Swanson, Steve Citizen (also providing written testimony) (-), Austin, TX
Van Beck, Scott A. Executive Director (Houston A+ Challenge), Houston, TX
Registering, but not testifying:
FOR:
Cherry, Wade Principal (John H. Wood Charter), New Braunfels, TX
Cotton, Inga (Self), San Antonio, TX
Erben, Andrew President (Texas Institute for Education Reform), Austin, TX
Gholsen, Bill President (Gulf Coast Trades Center), New Waverly, TX
Martin, Bill Board Member + Charter School Parent (IDEA Public School), Edinburg, TX
Ramirez, Patricia L. Asst. Supt. (John H. Wood, Jr Charter District), San Antonio, TX
Salinas, Nelson Governmental Affairs Manager (Texas Association of Business), Austin,
TX
AGAINST:
Arabie, Joseph Director (Texas AFL-CIO), Austin, TX
Figueroa, Luis Policy Analyst (Mexican American Legal Defense & Educational Fund
(M.A.L.D.E.F.)), San Antonio, TX
Lara, Rene Legislative Director (Texas AFL-CIO), Austin, TX
Pena, Martin Executive Director (South Texas Association of Schools), Laguna Vista, TX
Rogers, Don Executive Director (Texas Rural Education Association), Austin, TX
Villanueva, Chandra Policy Analyst (Center for Public Policy Priorities), Austin, TX
WITNESS LIST
Education
February 21, 2013 9:00 AM
Providing written testimony:
FOR:
Aquino Garza, Priscilla Deputy Director (Stand For Children - Texas), Austin, TX
Armbrust, John Executive Director (Austin Achieve), Austin, TX
McClure, Leroy CEO / Superintendent (Focus Learning Academy/ Triple A Academy),
Ovilla, TX
Prewett, Matt Volunteer (Texas Parents Union), Cedar Park, TX
AGAINST:
Anderson, David Consultant to Arlington ISD (Arlington ISD), Austin, TX
Anthony, David CEO (Raise Your Hand Texas), Houston, TX
Whitsett, Maria Consultant (Texas School Alliance), Austin, TX
FOR:
Rickey, Richard C.E.O. (Orenda Education), Georgetown, TX
Rockstroh, Bruce Superintendent (John H. Wood, Jr. Charter District), San Antonio, TX
Registering, but not testifying:
FOR:
Beneski, Amy Assoc. Executive Director (Texas Association of School Administrator),
Austin, TX
Canaday, Jennifer M. Governmental Relations Manager (ATPE), Austin, TX
Cherry, Wade Principal (John A. Wood Charter), New Braunfels, TX
Gholson, Bill President (Gulf Coast Trades Center), New Waverly, TX
Goodman, Jennifer Superintendent (TCSA), Galv., TX
Haenisch, Barry Consulting (Texas Association of Community Schools), Round Rock, TX
Johnson, Lance Superintendent (Warren ISD, Region 5 Legislative Committee), Warren,
TX
Ledbetter, Ron Superintendent - CFO (Texas Leadership Charter Academy), San Angelo,
TX
Lilly, Janna Director (TCASE Texas Council of Administrators of Special Education),
Austin, TX
Pulliam, Charles Chief Development Officer (Self / TCSA), Lancaster, TX
Ramirez, Patricia Asst. Superintendent (John H. Wood, Jr. Charter District), San Antonio,
TX
Rogers, Don Executive Director (Texas Rural Education Assn.), Austin, TX
Shields, Julie Asst. Dir. of Governmental Relations (Tx Assoc. of School Boards), Austin,
TX
ON:
Boyter, Dr. Gwyn Superintendent (University of Texas - University Charter School),
Austin, TX


|
Asteroid Destruction
1.0
Have fun !!!
|
The SoundBox class used to run sounds. More...
#include <SoundBox.h>
Public Member Functions | |
| SoundBox () | |
| Constructor. | |
| void | playExplosion () |
| playExplosion Method called when an explosion occures | |
| void | playShot () |
| playShot Method called when the player shoots | |
| void | playHit () |
| playHit Method called when the player takes some damage | |
| void | playClick () |
| playClick Method called when the player clicks on a button | |
| void | playPickup () |
| playPickup Method called when the player chooses is ship | |
| void | playMenuTheme () |
| playMenuTheme Method called when the main menu is displayed | |
| void | stopMenuTheme () |
| stopMenuTheme Method called when the player starts playing | |
| void | startLevelTheme () |
| startLevelTheme Method called when the player is playing | |
| void | stopLevelTheme () |
| startLevelTheme Method called when the player is back to the main menu | |
The SoundBox class used to run sounds.


(
no frames
)
Class List
Method List
File List
Documentation by YARD <IP_ADDRESS>
Alphabetic Index
File Listing
README
Namespace Listing A-Z
A
Application
(MarathonDeploy)
B
BadFormatError
(MarathonDeploy::Error)
BadURLError
(MarathonDeploy::Error)
D
Deployment
(MarathonDeploy)
DeploymentError
(MarathonDeploy::Error)
E
Environment
(MarathonDeploy)
Error
(MarathonDeploy)
H
HttpUtil
(MarathonDeploy)
I
IOError
(MarathonDeploy::Error)
M
Macro
(MarathonDeploy)
MarathonClient
(MarathonDeploy)
MarathonDefaults
(MarathonDeploy)
MarathonDeploy
MarathonError
(MarathonDeploy::Error)
MissingMarathonAttributesError
(MarathonDeploy::Error)
T
TimeoutError
(MarathonDeploy::Error)
U
UndefinedMacroError
(MarathonDeploy::Error)
UnsupportedFileExtension
(MarathonDeploy::Error)
Utils
(MarathonDeploy)
Y
YamlJson
(MarathonDeploy)


a02_example_highD_default.Rmd
In this article, we show the application of singleCellHaystack
on high-dimensional coordinates. Our method takes the coordinates of the cells in a >2-dimensional space (e.g. the first few principal components, etc), and predicts differentially expressed genes (DEGs) in that space. It does so by looking at the “expression distribution” of a gene (i.e. the distribution in the input space of cells that express a gene).
The data used in these examples can be found here. We recommend downloading the .rda file and loading it using the load()
function. You can also download the individual data files separately.
load(url("https://ndownloader.figshare.com/files/24593213")) ls() #> [1] "dat.expression" "dat.pca" "dat.tsne" "dat.umap"
This data should include the following objects:
dat.expression
: a matrix object with the expression of genes (rows) in each cell (columns).dat.pca
: the output of PCA. This data.frame contains the first 50 pricipal components (PCs).dat.tsne
: a data.frame with t-SNE coordinates (2D) based on the first 50 PCs.dat.umap
: a data.frame with UMAP coordinates (2D) based on the first 50 PCs.Let’s have a look at this example dataset:
# this data contains 12,030 genes and 1981 cells dim(dat.expression) #> [1] 12030 1981 # visualizing the cells in a t-SNE plot: ggplot(dat.tsne, aes(x = V1, y = V2)) + labs(x = "t-SNE1", y = "t-SNE2") + geom_point()
# the t-SNE coordinates are based on the 50 PCs dim(dat.pca) # the PCA data contains 1981 cells and 50 PCs #> [1] 1981 50
There are several groups of cells, although the borders between them are not clear and several might consist of additional subclusters. We can use singleCellHaystack
to predict DEGs without relying on grouping of the cells into arbitrary clusters.
singleCellHaystack
has two required input parameters: 1) the coordinates of cells in the input space, and 2) a table showing which genes are detected in which cells. The definition of detection is left to the user. In this example we will use the median normalized read count of each gene as threshold for defining detection. Alternatively, we could also define genes with counts > 0 as being detected, for example.
median.per.gene <- apply(dat.expression,1,median) # get the median read count per gene head(median.per.gene) # for many genes the median read count is 0 #> Mrpl15 Lypla1 Rb1cc1 Sgk3 Snhg6 Tram1 #> 0.000 0.000 0.000 0.000 0.000 136.243 dat.detection <- dat.expression > median.per.gene # use the medians as threshold for defining detection dat.detection[1:5,1:5] # TRUE means detected, FALSE means not detected #> AAACCTGCAGTAACGG.1 AAACGGGAGAAGAAGC.1 AAACGGGAGACCGGAT.1 #> Mrpl15 TRUE FALSE FALSE #> Lypla1 TRUE FALSE FALSE #> Rb1cc1 TRUE FALSE FALSE #> Sgk3 TRUE FALSE FALSE #> Snhg6 TRUE FALSE FALSE #> AAACGGGAGCGCTCCA.1 AAACGGGAGGACGAAA.1 #> Mrpl15 FALSE TRUE #> Lypla1 FALSE TRUE #> Rb1cc1 FALSE FALSE #> Sgk3 FALSE FALSE #> Snhg6 TRUE FALSE
haystack
on multi-dimensional coordinatesFirst, load the package.
library(singleCellHaystack)
Next, run haystack
on the first 50 principal components. Since the space is 50-dimensional, we set ‘method’ to ‘highD’. We also give the detection values as input to ‘detection’. This example dataset is relatively small, containing 1,981 cells, so running ‘haystack’ should take just 1 to 3 minutes to finish. We also set a random seed to ensure replicability.
set.seed(123) res.pc50 <- haystack(x = dat.pca[,1:50], detection = dat.detection, method = "highD") #> ### calling haystack_highD()... #> ### scaling input data... #> ### deciding grid points... #> ### calculating Kullback-Leibler divergences... #> ### performing randomizations... #> ### estimating p-values... #> ### returning result...
Let’s have a look at the most significant DEGs. The gene with the strongest differential expression is Trim10. We can plot the expression and detection of this gene using the plot_gene_haystack
function. From the t-SNE plots we can see that Trim10 is detected in the bottom right group of cells. In contrast, the expression of Eef1a1 is higher in cells of the top-right and bottom-left groups of cells.
show_result_haystack(res.haystack = res.pc50, n = 5) #> D_KL log.p.vals log.p.adj T.counts #> Trim10 0.011895191 -76.33235 -72.25208 368 #> Snca 0.009586202 -75.75859 -71.67832 474 #> Rpl10 0.004980578 -75.04406 -70.96380 990 #> Eef1a1 0.004977941 -75.02834 -70.94807 990 #> Rps11 0.004972334 -74.99488 -70.91462 990 # plotting detection of Trim10 plot_gene_haystack(x = dat.tsne, gene = "Trim10", expression = dat.detection)
# plotting log expression of Trim10 plot_gene_haystack(x = dat.tsne, gene = "Trim10", expression = log10(dat.expression))
# plotting detection of Eef1a1 plot_gene_haystack(x = dat.tsne, gene = "Eef1a1", expression = dat.detection)
# plotting log expression of Eef1a1 plot_gene_haystack(x = dat.tsne, gene = "Eef1a1", expression = log10(dat.expression))
Next, let’s take the top 1000 DEGs, and cluster them by their expression pattern in the input space (first 50 PCs). Here we use hclust_haystack
, which uses hierarchical clustering. Alternatively, we could use kmeans_haystack
for k-means clustering.
# get the top 1000 DEGs in the result res.top <- show_result_haystack(res.haystack = res.pc50, n = 1000) # cluster DEGs by their expression pattern in the 2D plot genes.top <- row.names(res.top) res.hc <- hclust_haystack(x = dat.tsne, genes = genes.top, detection = dat.detection)
hclust_haystack
returns as result a hclust
tree, which we can cut into clusters using the cutree
function. Here, we arbitrarily set the number of clusters to 5.
res.hc.clusters <- cutree(res.hc, k=5) table(res.hc.clusters) #> res.hc.clusters #> 1 2 3 4 5 #> 332 198 341 103 26
While some clusters contain hunderds of genes, others are quite small.
Let’s run through the 5 clusters and plot their averaged detection pattern using plot_gene_set_haystack
, which is similar to plot_gene_haystack
but uses a set of genes as input instead of just 1 gene.
pl <- lapply(1:5, function(cluster) { gene.set <- names(res.hc.clusters)[res.hc.clusters==cluster] plot.title <- paste0("Cluster ", cluster) p <- plot_gene_set_haystack(x = dat.tsne, genes = gene.set, detection = dat.detection) p + ggtitle(plot.title) + theme(legend.title = element_text(size = 8)) }) plot_grid(plotlist = pl, ncol = 2)
res.hc.clusters["Trim10"] # the top DEG is in cluster 5 #> Trim10 #> 5
The most significant DEG, Trim10, was clustered into cluster 5. Comparing its expression pattern (see above) with that of each cluster, we can indeed see that it fits most closely with that cluster.