Chapter 9 Quantitative Methods
Causal Inference
These notes are based on Professor Masten’s online course on Causal Inference at the Social Science Research Institute at Duke.
- Causal effect is often easy to detect with simple actions for which the effect immediately follows (e.g., you caused the alarm clock to stop ringing by pressing the snooze button)
With multiple causes and delayed effects, causality is much harder to detect.
- Measurement:
- Unit of analysis: countries, city blocks, people, firms, etc.
- Outcome variable: the characteristic of the unit of analysis that we want to affect
- Policy/treatment variable: the characteristic that we use to change the outcome varialbe
- A lot of characteristics cannot readily be quantified, so we often use proxy variables. For example, GDP could be a proxy for economic development.
Causality: how an intervention in the policy variable affects the outcome variable
- Data:
- The value of the policy variable has to vary in the dataset. Without this variation, you can’t analyze how changes in the policy variable might affect the outcome variable.
- Larger standard deviation = larger variation
- Correlation vs. Causation
- If the policy and outcome variables are correlated, this does not necessarily imply a casual relationship.
- Selection Problem: when units get to choose their policy variable, correlations between policy and outcome variables are unlikely to be causal.
- Example: Neighborhoods with a lot of trees tend to have less crime.
- If this were a casual relationship, then we could plant more trees in a neighborhood and expect crime to go down. However, this is unlikely.
- Average Treatment Effect
- Causal effects vary among people, so there is a distribution of causal effects in the population.
- Theoretical ideal: you would know the unit level of causal effect for each person and thus make individualized treatment decisions. This is impossible in practice. You can’t know the effect of receiving and not receiving treatment for an individual.
- Unit-level causal effect: difference in outcome between treatment & control, holding all other variables fixed
- Avg. treatment effect (ATE): avg. of all values for unit-level causal effects in a population
- Avg. outcome under the policy: avg. outcome when everyone is affected by the policy (i.e., receives treatment)
- Avg. outcome without the policy: avg. outcome when everyone is not affected by policy (i.e., does not receive treatment)
- ATE = Avg. outcome under policy - Avg. outcome w/o policy
9.0.1 Experiments
- Controlled Experiments
- Control group does not receive treatment
- Experimental group receives treatment
- All possible factors that could affect the outcome are identical for both groups, except for the treatment
- Difference in the outcome between the two groups is the treatment effect
- Typically used in hard sciences, but difficult to achieve in social sciences given the myriad of factors, many of which are difficult to measure and control
- Randomized Experiments
- Split units randomly into two large groups: treatment or control
- Right after randomization and before the experiment, both groups should be similar (i.e., avg. values of factors should be about the same), because the split was done randomly and the groups are very large
- Since the two groups are similar in all factors except treatment, changes in the average outcomes are due to treatment
- Complications:
- Noncompliance: Even when you randomly assign treatment, people in the treatment group may not all decide to take the treatment. Also, some people in the control group, who should not receive the treatment, might decide to get the treatment.
- Solution 1: Intent to Treat Analysis
- The intent to provide treatment is by design random regardless of treatment non-compliance.
- Thus, we can examine the causal effect of the option of providing treatment.
- Downside: cannot analyze causal effect of treatment itself
- For example, in the Oregon Health Experiment, while a lottery randomly selected people to receive free Medicaid, there was noncompliance in both the treatment/control groups. Original interpretation (effect of Medicaid on health outcomes) can be revised to effect of Medicaid lottery assignment on health outcomes.
- Solution 2: Instrumental Variables
- Advantage: We can analyze the causal effect of the treatment (not just the option of treatment) for a subset of the population.
- Downside: cannot analyze average treatment effect over the entire population
- Solution 3: Assume random compliance
- Assume people comply with their treatment assignment.
- Just drop the entries of the non-compliers.
- Advantage: We can analyze the causal effect of the treatment over the entire population.
- Downside: Decision to not comply is probably not random. We don’t observe the reasons for non-compliance.
- Solution 4: Bounds analysis
- Get lower/upper bounds of average treatment effects using extreme scenarios.
- Upper bounds: assume maximum value for outcome variable for noncompliers
- Lower bounds: assume minimum value for outcome variable for noncompliers
- Solution 1: Intent to Treat Analysis
- Survey nonresponse
- If nonresponse is not random, you cannot interpret the treatment effect as causal.
- Example: People in the treatment group with negative outcomes responded to surveys at higher rates than those with positive experiences. Data becomes biased toward negative outcomes for the treatment group.
- Sample Size: Even with a great research design, small sample size limits statistical inference.
- Control: You may not be able to control the assignment of treatment.
- Noncompliance: Even when you randomly assign treatment, people in the treatment group may not all decide to take the treatment. Also, some people in the control group, who should not receive the treatment, might decide to get the treatment.
- Issues:
- Ethics: Random assignment of treatment may have difficult ethical considerations (e.g., withholding a potentially life-saving drug to a terminally ill patient assigned to a control group in a randomized trial).
- Extrapolation: It may be hard to extrapolate the results of a randomized experiment to another study if the treatment conditions and features are different.
Natural Experiments
- Researchers not involved in the research design and data collection in natural experiments, unlike in randomized experiments.
- observational data used instead
- Example: charter school lotteries
- True Natural Experiments
- treatment was randomly assigned, just not by researcher
- As-If Natural Experiments
- treatment not actually randomly assigned, but the treatment/control groups appear randomized as though treatment assignment were random)
- treatment assignment not related to any variables that could affect outcome
- balance check: characteristics of all observed variables (other than outcome variable) need to be similar between the treatment/control groups
- There could still be differences between groups in unobserved variables.
- Thus, we cannot prove treatment assignment is truly random, but balanced observed variables between groups would be part of a convincingly arugment that the observational data represents an as-if natural experiment.
9.0.2 Regression
In general, regression analysis can show only correlations between variables but not causation. The following are some factors that would prevent you from making the leap from correlation to causation in regression analysis:
- Confounding Variables
- A confounding variable is a hidden variable that has an influence on both the dependent variable and independent (or outcome) variable.
- This distorts the asssocation between the dependent and independent variable.
- Example: Suppose we want to test the effect of the number of books at home (dependent variable) on a child’s reading proficiency (independent variable). A confounding factor could be the mother’s level of education, which could effect both the number of books at home and the child’s reading proficiency. Without accounting for the mother’s level of education, we could be overstating the effect of books on reading proficiency.
- Reverse Causality
- In your study of the effect of A on B, you spot an association between the two variables. However, the direction of causality acturally could be reversed (i.e., B has an effect on A).
- Example: Suppose you notice that as the debt of a country increases, its economic growth decreases. The cause-and-effect relationship could be the other way around. Maybe a faltering economy is the reason that countries have to accumulate more debt to meet their budgets.
- Simultaneity
- Similar to reverse causality, except that that direction of causality goes both ways at the same time. X causes Y, and Y causes X.
- Mediating Variable
- Example: Does consuming caffeine stunt children’s growth? We might question this causal link if we examine the mediating variable of sleep duration. Perhaps drinking coffee disrupts sleep patterns, which in turn affects growth.
Unconfoundedness Assumption
Under the unconfounded assumption (a.k.a. selection on observables assumption), we have controlled for enough variables that we assume no confounding variables exists. With this assumption, we can consider the association between the treatment and outcome variable to be causal, since conditioning on all the other variables makes the treatment assignment essentially random.
In practice, this assumption is difficult to make, because we cannot truly account for every possible variable relevant to the outcome variable. You would have to assume that all unobserved variables are not related to the treatment and do not contribute any effects to the outcome variable.
Ordinary Least Squares (OLS) Regression
- In OLS regressions, we want to draw a line through a set of points that represents the data.
- We calculate the distance between each data point and the line. We then square that distance. Do this for all data points and find the sum of all thes squared distances.
- The OLS regression line is the line that minimizes the sum of all these squared distances.
- Assumptions:
- sample is random
- outcome variable is continuous
Interaction Effects
If an independent variable \(z\) changes the effect of another independent variable \(x\) on the outcome variable \(y\), we have an interaction effect between \(x\) and \(z\). The original regression model \(y=\beta_0 +\beta_1 x + e\) turns into \(y=\beta_0 +\beta_1 x + \beta_2z + \beta_3~x*z + e\).
This concept is also called statistical moderation, since \(z\) moderates the effect of \(x\) on \(y\).
Calculating Average Treatment Effects
| Person | Gender | Treated | Outcome with Policy | Outcome without Policy | Unit-Level Causal Effect | Observed Outcome |
|---|---|---|---|---|---|---|
| 1 | M | Y | 80 | 60 | 20 | 80 |
| 2 | M | Y | 75 | 70 | 5 | 75 |
| 3 | M | Y | 85 | 80 | 5 | 85 |
| 4 | M | N | 70 | 60 | 10 | 60 |
| 5 | F | Y | 75 | 70 | 5 | 75 |
| 6 | F | N | 80 | 80 | 0 | 80 |
| 7 | F | N | 90 | 100 | -10 | 100 |
| 8 | F | N | 85 | 80 | 5 | 80 |
- In the example table above, we have two potential outcomes for each person:
Outcome with Policy
andOutcome without Policy
. In practice, you would never be able to observe both potential outcomes for each person. You either observeOutcome with Policy
if the person received treatment orOutcome without Policy
if the person did not receive treatment. In actual data, you would only obtainObserved Outcome
. - Unit-Level Causal Effect = Outcome with Policy - Outcome without Policy
- Average Treatment Effect (ATE) = average of all the unit-level causal effects
- Using the example data above, ATE \(= \frac{20+5+5+10+5+0-10+5}{8}= 5\).
- Conditional Avg. Treatment Effect (CATE) = average treatment effect for a subset of the units
- Example: CATE for men is \(\frac{20+5+5+10}{4} = 10\).
- Example: CATE for women is \(\frac{5+0-10+5}{4} = 0\).
- Average Treatment Effect on the Treated (ATT) = average treatment effect for only the people who received treatment
- In the table above, Persons 1, 2, 3, and 5 received treatment.
- To calculate ATT, we take the average of the unit-level causal effects of these treated people.
- ATT \(= \frac{20+5+5+5}{4} = 8.75\)
- Difference in Mean Outcomes b/w Treated and Untreated (Control) Groups = ATT + Bias
- left-hand side (LHS) = \(\frac{80+75+85+75}{4}-\frac{60+80+100+80}{4} =\) -1.25
- ATT = 8.75
- Bias = difference in the averages in
Outcome without Policy
between treated and untreated groups- If this bias term is nonzero, we can say that the data has selection bias, because the selection into treatment is associated with potential outcomes.
- For
Treated = Y
, averageOutcome without Policy
= \(\frac{60+70+80+70}{4}\) = 70. - For
Treated = N
, averageOutcome without Policy
= \(\frac{60+80+100+80}{4}\) = 80. - Bias = 70 - 80 = -10.
- In this example, people selected for treatment have a much lower potential outcome without treatment than that of people not selected for treatment.
- This negative selection bias will understate the actual impact of the treatment.
- Similarly, positive selection bias will overstate the actual impact of the treatment.
- right-hand side (RHS) = ATT + Bias = \(8.75 - 10 = -1.25\)
- Thus, LHS = RHS.
- In randomized experiments, the assignment of treatment is randomized, making the selection bias zero. Thus, the LHS (difference in the mean observed outcomes between the treated/untreated groups) equals ATT. Note that ATT is the average treatment effect (ATE) conditioned on being in the treatment group. Under randomization, we can assume that the potential outcomes are independent of the treatment assignment, so the average treatment effect is the same regardless of conditioning on treatment assignment. Thus, LHS = ATT + zero bias = ATE.
- In this example data, we know that treatment is not randomly assigned, since more men received treatment than women. This means that treatment is correlated with gender. Thus, the calculations done above do not represents estimates of the average treatment effect due to the selection bias.
- Instead, we assume that treatment assignment is randomized within each gender group. We calculate the estimated conditional average treatment effects \(\widehat{CATE}(m)\) and \(\widehat{CATE}(f)\) for males and females, respectively, by taking the difference between the average treatment group outcome and the average control (untreated) group outcome within each gender.
- \(\widehat{CATE}(m) = \frac{80+75+85}{3}-60 = 20\)
- \(\widehat{CATE}(f) = 75 - \frac{80+100+80}{3} = -11.67\)
- We add a hat on top of CATE to denote that the calculations are estimates. These estimates \(\widehat{CATE}(m)=20\) and \(\widehat{CATE}(f)=-11.67\) are different from \(CATE(m)=10\) and \(CATE(f)=0\) that we calculated earlier. The CATEs without the hats are the true conditional average effects based on the difference in the two potential outcomes (with/without treatment) for each person. In practice, we will never be able to calculate the true CATEs, because we can never know both potential outcomes for each person. We only have one observed outcome for each person that we use to estimate CATE with \(\widehat{CATE}\).
- To estimate ATE, we take the weighted average of the estimated CATEs. In this case, both gender groups are equally weighted, so \(\widehat{ATE} = \frac{1}{2}\widehat{CATE}(m)+\frac{1}{2}\widehat{CATE}(f) = 20 - 11.67 = 4.16\).
- Note that \(\widehat{ATE} = 4.16\) is positive, whereas earlier calculation of LHS (difference in the mean observed outcomes in the treated/untreated groups) is negative, which understates the average treatment effect due to negative selection bias arising from non-random assignment of treatment between gender.
9.0.3 Matching Methods
In this method, we want to find units with similar explanatory variables but assigned to different treatments. One way to do this is propensity score matching.
9.0.4 Instrumental Variables
9.1 Statistical Tests
- Choosing a Statistical Test
- Hypothesis Testing Roadmap
- Chosing the Correct Statistical Test in SAS, Stata, SPSS, and R
- Uses & Misuses of Statistics
- 1 group
- interval variables
- 1-sample t test for the mean
- chi-squared test for variance
- categorical variables
- z test for proportions (2 categories)
- chi-squared goodness-of-fit
- ordinal or interval
- one-sample median test
- interval variables
- 2 groups (independent groups)
- interval variables
- 2 independent sample t-test (equal variances)
- 2 independent sample t-test (unequal variances)
- F test for difference between 2 variances
- categorical variables
- z test for difference between 2 proportions
- chi-squared test for difference between 2 proportions
- Fisher’s exact test
- interval variables
- 2 groups (dependent or paired groups)
- paired t-test (interval variables)
- McNemar’s test (categorical variables)
- Wilcoxon signed ranks test (oridinal or interval variables)
- more than 2 groups (independent groups)
- one-way ANOVA (for interval variables)
- Kruskal Wallis (for ordinal or interval variables)
- chi-squared test (for categorical variables)
- more than 2 groups (dependent groups)
- one-way repeated measures ANOVA (for interval variables)
- repeated measures logistic regression (for categorical variables)
- Friedman test (for ordinal or interval)
9.1.1 1-sample t-test
- Assumptions:
- data is a simple random sample from population
- sample mean follows a normal distribution
- by the Central Limit Theorem, with sample size \(n>=30\), the sample mean is normally distributed regardless of the population distribution
Two-tailed Hypothesis: \[ H_0: \mu = \mu_0 \] \[ H_1: \mu \neq \mu_0 \]
Test Statistic: \[ T = \frac{\overline{X}-\mu_0}{\frac{S}{\sqrt{n}}} \sim t_{(n-1)} \]
- \(\overline{X}\) = sample mean
- \(\mu_0\) = hypothesized population mean
- \(S\) = sample standard deviation
- \(t_{(n-1)}\) = \(t\) distribution with \(n-1\) degrees of freedom
9.1.2 chi-squared test for variance
9.1.3 z test for proportions
- Assumptions:
- sample proportion \(p = \frac{X}{n}\) comes from random sample in population, where \(X\) is number of events of interest in sample size \(n\).
- \(p\) follows a binomial distribution, but we can assume normality when \(X\) and \(n-X\) are each at least 5 (old standards) or at least 15 (current standards)
Two-tailed Hypothesis: \[ H_0: \pi = \pi_0 \] \[ H_1: \pi \neq \pi_0 \]
Test Statistic: \[ z = \frac{p-\pi_0}{\sqrt{\frac{\pi_0 (1-\pi_0)}{n}}} \sim \mathcal{N}(0,1) \]
- \(\pi_0\) = hypothesized proportion
- \(p\) = sample proportion
9.1.4 t-test for 2 independent samples
- Assumptions:
- two independent samples are randomly selected from two populations with the same variance
- if you cannot use the assumption of same variance, use the Welch two-sample t-test
- test statistic is the same as below, but degrees of freedom are adjusted
- if populations are not normally distributed, the sample sizes \(n_1\) and \(n_2\) from the two populations needs to be at least 30 to ensure that the distribution of the sample means are normal by the Central Limit Theorem
Two-tailed Hypothesis: \[ H_0: \mu_1 = \mu_2 \] \[ H_1: \mu_1 \neq \mu_2 \]
- \(\mu_1\) = population mean of 1st sample
- \(\mu_2\) = population mean of 2nd sample
Test Statistic: \[ \frac{ (\overline{X}_1-\overline{X}_2) - (\mu_1 - \mu_2) }{ \sqrt{S_p^2 \left( \frac{1}{n_1} + \frac{1}{n_2} \right) } } \sim t_{(n_1 + n_2 -2)} \] \[ S_p^2 = \frac{(n_1-1)S_1^2 + (n_2-1)S_2^2}{(n_1-1)+(n_2-1)} \]
- \(S_p\) = pooled variance
- \(\overline{X}_1\) = mean of 1st sample
- \(\overline{X}_2\) = mean of 2nd sample
- \(S_1^2\) = variance of 1st sample
- \(S_2^2\) = variance of 2nd sample
- More Info
- R Example
9.1.5 paired t-test
Assumptions:
9.1.6 chi-squared test for proportions
- The chi-squared test for 2 x 2 frequency tables is equivalent to the square of the z-test for two proportions. See this link for detailed explanation.
9.1.7 chi-squared test for independence
- Explain connection between chi-squared test for independence and log-linear models, which are Poisson models for categorical data.
9.1.8 ANOVA
9.2 Regression
9.2.1 Simple Linear Regression
A simple linear regression has the form \(y=\beta_0 + \beta_1 x + e\), where
- \(y\) is the dependent (or outcome) variable
- \(x\) is the indepndent (or explanatory) variable
- \(\beta_0\), the regression intercept, represents the average value of the outcome variable \(y\) when \(x=0\)
- \(\beta_1\) is the regression coefficient of \(x\)
- for each 1-unit increase in \(x\), \(y\) changes by \(\beta_1\)
- \(\beta_1\) is the slope of this regression line
- \(e\) is the error term. Some textbooks will use the variable \(u\) instead of \(e\) to emphasize that this term includes all unobserved variables.
9.2.2 Multiple Linear Regression
Linear regressions with multiple explanatory variables are called multiple linear regressions. If we have \(n\) explanatory variables, the multiple linear regression will have the form \(y=\beta_0 + \beta_1 x_1 + \beta_2 x_2 + ... + \beta_n x_n + e\), where
- \(\beta_0\), the regression intercept, represents the average value of the outcome variable \(y\) when all the explanatory variables \(x_1, x_2,...,x_n\) are zero.
- \(\beta_1\) represents how much \(y\) changes when \(x_1\) increases by 1 unit, holding all other explanatory variables constant. The Latin phrase ceteris paribus, meaning “other things equal,” is often used to describe this concept. This does not mean the other explanatory variables do not change. Rather, we are isolating the effect of \(x_1\) on \(y\) under the theoretical condition that the other explanatory variables are held constant.
- This interpretation of \(\beta_1\) extends to all the other regresssion coeffients.
9.2.3 Logistic Regression
- outcome variable is binary (e.g., 0 or 1; yes or no)
9.3 Numerical Methods
In AP Calculus, you mostly encountered problems that can be solved analytically. However, in research, many differential equation models do not have analytical forms and must be solved numerically. Matlab is often used in applied math, engineering, and physical sciences for such cases as well as other modeling applications. Octave is an open-source alternative to Matlab. While R not the first language that comes to mind for numerical methods, many numerical R packages have been developed as well as integration with Matlab, Octave, and Julia.
- Numerical Computing with Matlab
- This site has PDF versions of Cleve Moler’s textbook on numerical computing alongside a video series with lectures on differential equations and linear algebra by Prof. Gilbert Strang and computational video tutorials by Moler.
- Numerically Solving Differential Euqations with R
9.3.1 Root-Finding Algorithms
9.3.2 Numerical Solutions to Differential Equations
- Integrating ODEs in R
- Euler Method Using Matlab
- Euler Method with Python
- Runge-Kutta Methods
9.4 Text Analysis
Mosteller and Wallace (1963) and the Federalist Papers


The 'lazy' vocabulary adds lazy lists to Factor. This provides the ability to describe infinite structures, and to delay execution of expressions until they are actually used.
Lazy lists, like normal lists, are composed of a head and tail.  To convert a 'promise' into its actual value a word called 'force' is used. To convert a value into a 'promise' the word to use is 'delay'.
| delay |
| force |
Many of the lazy list words are named similar to the standard list words but with an 'l' prefixed to it. Here are the commonly used words and their equivalent list operation:
| Lazy List | Normal List |
|---|---|
| lnil | [ ] |
| lnil? | Test for nil value |
| lcons | cons |
| lunit | unit |
| lcar | car |
| lcdr | cdr |
| lnth | nth |
| luncons | uncons |
| lmap | map |
| lsubset | subset |
| leach | each |
| lappend | append |
A few additional words specific to lazy lists are:
| ltake | Returns a normal list containing a specified number of items from the lazy list. |
| lappend* | Given a lazy list of lazy lists, concatenate them together in a lazy manner, returning a single lazy list. |
| list>llist | Given a normal list, return a lazy list that contains the same elements as the normal list. |
'delay' is used to convert a value or expression into a promise. The word 'force' is used to convert that promise back to its value, or to force evaluation of the expression to return a value.
The value on the stack that 'delay' expects must be quoted. This is a requirement to prevent it from being evaluated.
( 1 ) [ 42 ] delay dup . => << promise [ ] [ 42 ] [ ] [ ] >> ( 2 ) force . => 42
'force' will evaluate a promises original expression and leave the value of that expression on the stack.
A promise can be forced multiple times but the expression is only evaluated once. Future calls of 'force' on the promise will returned the cached value of the original force. If the expression contains side effects, such as i/o, then that i/o will only occur on the first 'force'. See below for an example (steps 3-5).
If a promise is itself delayed, a force will evaluate all promises until a value is returned. Due to this behaviour it is generally not possible to delay a promise. The example below shows what happens in this case.
( 1 ) [ 42 ] delay dup . => << promise [ ] [ 42 ] [ ] [ ] >> ( 2 ) force . => 42 #! Multiple forces on a promise returns cached value ( 3 ) [ "hello" print 42 ] delay dup . => << promise [ ] [ "hello" print 42 ] [ ] [ ] >> ( 4 ) dup force . => hello 42 ( 5 ) force . => 42 #! Forcing a delayed promise cascades up to return #! original value, rather than the promise. ( 6 ) [ [ 42 ] delay ] delay dup . => << promise [ ] [ [ 42 ] delay ] [ ] [ ] >> ( 7 ) force . => 42
Returns a value representing the empty lazy list.
( 1 ) lnil . => << promise [ ] [ [ ] ] t [ ] >>
Returns true if the given lazy cons is the value representing the empty lazy list.
( 1 ) lnil lnil? . => t ( 2 ) [ 1 ] list>llist dup lnil? . => [ ] ( 3 ) lcdr lnil? . => t
Provides the same effect as 'cons' does for normal lists. Both values provided must be promises (ie. expressions that have had delay called on them).
As the car and cdr passed on the stack are promises, they are not evaluated until lcar or lcdr are called on the lazy cons.
( 1 ) [ "car" ] delay [ "cdr" ] delay lcons dup . => << promise ... >> ( 2 ) dup lcar . => "car" ( 3 ) dup lcdr . => "cdr"
Provides the same effect as 'unit' does for normal lists. It creates a lazy list where the first element is the value given.
Like lcons, the value on the stack must be a promise and is not evaluated until the lcar of the list is requested.
( 1 ) [ 42 ] delay lunit dup . => << promise ... >> ( 2 ) dup lcar . => 42 ( 3 ) dup lcdr lnil? . => t ( 4 ) [ . ] leach => 42
Provides the same effect as 'car' does for normal lists. It returns the first element in a lazy cons cell. This will force the evaluation of that element.
( 1 ) [ 42 ] delay lunit dup . => << promise ... >> ( 2 ) lcar . => 42
Provides the same effect as 'cdr' does for normal lists. It returns the second element in a lazy cons cell and forces it. This causes that element to be evaluated immediately.
( 1 ) [ 1 ] delay [ 5 6 + ] delay lcons dup . => << promise ... >> ( 2 ) lcdr . => 11
( 1 ) 5 lfrom dup . => << promise ... >> ( 2 ) lcdr dup lcar . => 6 ( 3 ) lcdr dup lcar . => 7 ( 4 ) lcdr dup lcar . => 8
Provides the same effect as 'nth' does for normal lists. It returns the nth value in the lazy list. It causes all the values up to 'n' to be evaluated.
( 1 ) 1 lfrom dup . => << promise ... >> ( 2 ) 5 swap lnth . => 6
Provides the same effect as 'uncons' does for normal lists. It returns the car and cdr of the lazy list.
( 1 ) [ 5 ] delay [ 6 ] delay lcons dup . => << promise ... >> ( 2 ) luncons . . => 6 5
Lazily maps over a lazy list applying the quotation to each element. A new lazy list is returned which contains the results of the quotation.
When intially called nothing in the original lazy list is evaluated. Only when lcar is called will the item in the list be evaluated and applied to the quotation. Ditto with lcdr, thus allowing infinite lists to be mapped over.
( 1 ) 1 lfrom => < infinite list of incrementing numbers > ( 2 ) [ 2 * ] lmap => < infinite list of numbers incrementing by 2 > ( 3 ) 5 swap ltake llist>list . => [ 2 4 6 8 10 ]
Provides the same effect as 'subset' does for normal lists. It lazily iterates over a lazy list applying the predicate quotation to each element. If that quotation returns true, the element will be included in the resulting lazy list. If it is false, the element will be skipped. A new lazy list is returned which contains all elements where the predicate returned true.
Like lmap, when initially called no evaluation will occur. A lazy list is returned that when values are retrieved from in then items are evaluated and checked against the predicate.
( 1 ) 1 lfrom => < infinite list of incrementing numbers > ( 2 ) [ prime? ] lsubset => < infinite list of prime numbers > ( 3 ) 5 swap ltake llist>list . => [ 2 3 5 7 11 ]
Provides the same effect as 'each' does for normal lists. It lazily iterates over a lazy list applying the quotation to each element. If this operation is applied to an infinite list it will never return unless the quotation escapes out by calling a continuation.
( 1 ) 1 lfrom => < infinite list of incrementing numbers > ( 2 ) [ 2 mod 1 = ] lsubset => < infinite list of odd numbers > ( 3 ) [ . ] leach => 1 3 5 7 ... for ever ...
 This provides a convenient way of getting elements out of an infinite lazy list.
( 1 ) : ones [ 1 ] delay [ ones ] delay lcons ; ( 2 ) 5 ones ltake llist>list . => [ 1 1 1 1 1 ]
Lazily appends two lists together. The actual appending is done lazily on iteration rather than immediately so it works very fast no matter how large the list.
( 1 ) [ 1 2 3 ] list>llist [ 4 5 6 ] list>llist lappend ( 2 ) [ . ] leach => 1 2 3 4 5 6
Given a lazy list of lazy lists, concatenate them together in a lazy fashion. The actual appending is done lazily on iteration rather than immediately so it works very fast no matter how large the lists.
( 1 ) [ 1 2 3 ] list>llist ( 2 ) [ 4 5 6 ] list>llist ( 3 ) [ 7 8 9 ] list>llist ( 4 ) 3list list>llist lappend* ( 5 ) [ . ] leach => 1 2 3 4 5 6 7 8 9
Converts a normal list into a lazy list. This is done lazily so the initial list is not iterated through immediately.
( 1 ) [ 1 2 3 ] list>llist ( 2 ) [ . ] leach => 1 2 3
Copyright (c) 2004, Chris Double. All Rights Reserved.


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I'd like to see more people learn to code, so I co-founded Girl Develop It to provide judgment-free environments to learn about software development.
My love of computing began with databases. I previously led data and analytics for Paperless Post and wrangled data for TheLadders, Capital IQ, and WealthEngine.
I am interested in speaking to help make data, analytics, open source software, and technology in general more accessible. I'd also love to hear about anything you really, really appreciate in life.
Feel free to reach out via Ohours, Twitter @DBNess, or email.


BREAKING Scott McTominay taken off on stretcher after Man Utd win over Brighton
Manchester United got back to winning ways at Old Trafford, beating Brighton 3-1 with Marcus Rashford and Andreas Pereira on target
by Liam Prenderville, http://www.facebook.com/mirrorfootballManchester United midfielder Scott McTominay was taken off on a stretcher after the win over Brighton .
McTominay appeared in discomfort as he picked up an injury in the closing stages at Old Trafford.
The Scot was taken down the tunnel at full-time on a stretcher.
United returned to winning ways at Old Trafford with a 3-1 victory.
There will be more to follow. We'll bring you the very latest updates, pictures and video on this breaking news story.
For the latest news and breaking news visit Mirror.co.uk/sport . Get all the big headlines, pictures, analysis, opinion and video on the stories that matter to you.
Follow us on Twitter @MirrorFootball - the official Daily Mirror & Mirror Online Twitter account - for all the latest football news as it happens in real time.
For other sport news, follow us on @MirrorSport - the official Daily Mirror & Mirror Online account - for all the latest happenings in the sporting world.
We're also on Facebook /mirrorfootball for your must-see news, features, videos and pictures throughout the day from the Daily Mirror, Sunday Mirror, The People and Mirror Online


January 2022 Status Update
Well well… Happy New Year out there and hope everyone is doing well as they read this, considering the current circumstances and environment we find ourselves in this otherwise promising new year.
Let’s get on with the latest update here…
Playground Alpha Feature Complete
To start, I am pleased to announce that the playground alpha is feature complete and ready for deployment.
What that means is that the Playground Alpha, slated for availability next month, will allow you to use all the features of starbeam.one via fake deposits and withdrawals.
The idea here is that we want to see how many accounts we can create, along with sales, resales, and every other transactions made via those fake deposits and withdrawals. All of this is done while seeing what breaks. Yes, I am anticipating quite a lot of breaking. This application has, after all, been tested by me and me alone, so quality assurance is very little assurance, or the assurance only afforded by one person testing it.
Remaining Tasks
What tasks remain? Very simply, there are three stories that remain to achieve the elusive “Done” status:
- Deployment to Azure - get this bad boy to availability
- Marketing Videos - introduce you to me and the product
- Administration Application - for system maintenance/administration tasks
Deployment is estimated for two weeks currently, but it has the most risk as I am very unfamiliar with Azure and who knows what can happen.
The same goes for those videos. I have had my eye on those for a while now and I am honestly terrified with getting them done. I have to present myself to the world EEP! That means flipping my introvert into extravert. While I can do it, it takes a bit of adjustment (talking and acknowledging that here is a good first step).
I have three weeks scheduled for those videos. That will take us into next month and if everything goes well, the next status update.
Then it’s that administration application. That has another 2-3 weeks to it. However, this is not as important as the previous two tasks and I can work on this after the official announcement.
When Will This Happen?!
So it is 2022, and following in Microsoft’s footsteps, I look for Tuesday as the launch date for any endeavor that I do (e.g. the status update you are currently reading done on the first Tuesday of the month), and the date that caught my eye was the 22nd of February, 2022.
2022/02/22 - Yes please!
Schedule
So to summarize, the current schedule is as such:
- January 18th, 2022: Azure Deployment
- February 8th, 2022: Marketing Videos Complete
- February 22nd, 2022: Playground Alpha Announcement
There is so much that can happen between now and then, but that is what everything looks like at the moment. There is another status update on February 1st, so I will check in at that time and update here with the latest.
Until then, stay bright. ✌✨


I grind skills and knowledge then weld them with appropriate tool into beautiful and efficient e-learning material.
Hi, this is Ravi Gupta, an E-learning Content Developer, an Instructional Designer, a Freelancer, a Youtuber, a Tutor and always a learner. Graduated from Ramanujan College, University of Delhi in Bachelor of Vocational in Software Development. Along with general degree, Passed Junior Software Developer (Gold), Web Developer (Gold) and Software Developer (Gold), Junior Data Associate Job Packs of NASSCOM SSC IT-ITeS.
After passing class 6th, I started my journey as Home Tutor then teaching in school and Institutes till I passed my Class 12 th. During my graduation, I started working as Freelancer Auditor in Channelplay Limited meanwhile doing some Internships like in Digital Marketing (SEO), Manual Testing from Manacle Technologies Pvt. Ltd. and Internshala Student Partner as a Campus Ambassador.
After passing graduation, joined NIIT Limited as E-learning Content Developer. During the pandemic, our process ram-down and my contract with NIIT Limited ended. During my free time I started my Youtube Channel, OS Corporations, developed a project, DualSearch and started learning some new tools like Filmora and improved my previous knowledge. After some months I got placed as E-learning Developer in KontentEdge Pvt. Ltd., and after some months, I got promoted to E-learning Developer (Team Leader) here, side by side I co-founded the VAMM Academy with some of my friends.
To know me more get My CV
or Check my LinkedIn Profile
The Fundamental of Digital Marketing.
AWS Cloud Practitioner Essentials (Digital).
Introduction to Hyper-V Jumpstart, Introduction to ASP.NET MVC, DevOps with VSTS, DevOps Testing, DevOps Practices and Principles, Building Blocks - Game Development, ASP.NET Core - Beginner.
IBM Blockchain Essentials, Docker Essentials: A Developer Introduction.
Customer Relationship Management.
Articulate 360: First Look, Learning Articulate Storyline 3, Learning Lectora Inspire, Become an SEO Expert, International SEO, Local SEO, PowerPoint 2013 Essential Training, SEO Foundations, SEO: Keyword Strategy, SEO: Link Building in Depth, SEO: Videos, Advanced SEO: Developing an SEO-Friendly Website, Android Studio Essential Training, Creating Your First Android App with Kotlin, Learning Kotlin for Android Development, Unity 5: 2D Level Design.
Junior Software Developer (Gold), Web Developer (Gold), Software Developer (Gold), Junior Data Associate (Silver).
Internshala Graphic Design, Certificate of Appreciation, Internshala Student Partner 11 Completion.
Go Study 1, Command Line, Git, HTML & CSS, Java, JavaScript ES5, JavaScript ES6, PHP, Python, Ruby, Ruby on Rails5, SQL, jQuery, .
Would you like to know more, just check my LinkedIn Profile


The general philosophy of running a simulation is to configure the system of neurons, then observe their behavior for some time. To configure the system, however, is a complex task. The biological neurons are rather irregular (from the viewpoint of mathematics), so the simplest approach is taken: a set of (interdependent) configuration files (and subfile hierarchy) is used. When starting a simulation, the config files are read as described in section Settings. This chapter tells which configuration files are present and what it their expected contents.
As part of configuration, a list of ".ini" files is provided. Acually, the simulator uses two files: Simulator.ini and Neurons.ini.
The simulator internally works either with simple sequence number or ClusterAddress_t identifiers (say, in form of "Example.Proc.GP_XX", where "GP" means scGridPoint, and XX is is simple sequence number), which are not comfortable for working with neuronal simulations. Because of this, alias names can be defined. The file 'Neurons.ini' contains lines of form
NewName=OldName
That is, the user can define some alias that has for it some reasonable meaning, and it is mapped by the simulator to some internally used one. For example,
Neuron1=scExample.Proc.GP_07 Neuron2= Neuron3=Neuron1
define that out system comprises altogether two neurons. The simulator internally works with names such as "scExample.Proc.GP_07", which is essentially a simple sequence number (a unique ID), generated by the simulator. The first line means that the user wants to use a neuron with name "Neuron1", and that he wants to the neuron having ID 7.
The second line means that the user want to use a neuron with name "Neuron2", and does not care which sequence number the neuron has. The simulator will choose one of the still available ones.
The third line means that for some reason (say, in some configuration files the same neuron has another name) the user wants to introduce one more name for the same neuron. Both (or more) alias names will refer to the same physical neuron.
Take care, however, with those names. The simulator not necessarily executes the lines in the same sequence as you wrote the assignements. It may happen that the second line is executed first, and it may happen that the simulator maps "Neuron2" to the physical neuron "scExample.Proc.GP_07". After that, when the first line is executed, and you definitely named that you want to map "Neuron1" to ""scExample.Proc.GP_07", both "Neuron1" and "Neuron2" will refer to the same physical neuron. Even, because of the third line, the third neuron will refer to the same neuron. The best practice is to use either the explicite mapping, or implicite mapping, but not both. The simulator gives warnings, and it is the user's responsibility to decide if the noticed issue matters or not.
This file is a source of other ".ini" files. The entries found in the 'Neurons.ini' file mean that the simulator will look for settings file for those entries, i.e., a file with name "Neuron1.ini", where it looks for settings related to neuron aliased as "Neuron1'. Again, the simulator accesses those files in some arbitrary order, and sets the neuron's parameters both from file "Neuron1.ini' and "Neuron3.ini". It is the user's responsibility again, to provide (or not provide) the needed files.
Take also care that the ".ini" files have platform-independent contents and some systems do not distinguish lower/upper case letters. Because of this feature, on a different platform differently spelled ".ini" files shall be not found or duplicated.
The neurons may belong to one (or more) assemblies. One can define the possible assemblies in file "Assemblies.ini". This is a simple list, containg entries such as
Alias1 Alias2
The possible assemblies are simple lists of neurons. A neuron cand be member of several assemblies.
In the neuron initialization files different keyword/value pairs can be listed.
The neurons can be connected to partner neurons, and their connection parameters are listed in the ".ini" file of representing the neuron. For example, the "Neuron1.ini" file has the content
Works=3 [Neuron2] ConnectTime=100 [Neuron3] ConnectTime=90 [Group] Assembly1 Assembly2
Here, the words in square brackets "[]" represent groups of settings. In the example above, one keyword-value pair "Works=3" and three "child goups" are listed. They are describing the neuron as a single entity, such as threshold, mode, frequence, etc. for the detailed list of possible values see section Parameters.
The connection of the neurons can be described formally as a group, that is "[Neuron2]" means that the neuron "Neuron1" is connected to Neuron2, with the parameters listed in that group. Of course, only groups having name listed in "Neurons.ini" files are legal. See also section Connections.
In the example file above, "Neuron1" is connected to "Neuron2" and "Neuron3" (which, in this example, is mapped to "Neuron1") In this way a one-way connection can be created from "Neuron1" to "Neuron2". Our "Neuron1" can send messages to "Neuron2". To send Messages in the opposite direction, file "Neuron2.ini" must contain lines such as
[Neuron1] ConnectTime=100
A neuron can belong to multiple groups This feature can be used differently in different fields. For example, biological neurons can belong to different assemblies (maybe to more than one). Technical neurons can belong to various layers, with various connections. Because of this, using grouping depends on the user, it is not constrained at all.
The "file"NeuronX.ini" file can contain entries such as
[Group] Assembly1 Assembly2
that makes the neuron member of the listed assemblies, that is, it can be directed also by "collective" instructions. To belong to a group means that group-related operations are executed by all members of the assembly.
In all files (except "Neurons.ini") the key-value pairs without a group (at the top of the file) change some feature of the object (simulator or neuron). The feature has some default parameter (provided compile time), and it is overwritten by the value provided; maybe several times as decribed in section The "Hello World" of settings. Parameters related to neuron 'NeuronX' can be present, such as
Potential=65.4 Threshold=50
These settings replace the corresponding operating parameters of the neuron, provided that the keyword found, the value is of proper format and is in the allowed range.
Neural connection can be described simply by listing the partner neuron in the file of the sender neuron.
Parameter "ConnectTime" provides the time distance of the two partners. When needed, the neuron in question will send the timed message immediately, and the receiver neuron puts the message in a simlated time-ordered queue, and will delay its processing as the message requested. The time is of crucial importance in simulating both biological and technical neurons. This method of handling enables biological neurons to fire really "at the same time", and technical neurons can imitate different technical connection parameters.
Parameter "ConnectWeight" sets in the partner "Neuron2" to that value. The value is saturated at [-1024,1023]. It means that the neuron multiplies the received input by this weight
Parameter "ConnectStrength" is set in "Neuron1", for this connection. The value is saturated at [-1024,1023]. It is usually not changed during simulation, but it can be used to simulate special effects; such as aging).
The neurons communicate via using point-to-point, timed messages (scIGP_Message). The timing prescribes the expected arrival time
For biological neurons, the spikes are represented as timed messages, sent from a neuer to a neurer. The receiving neurer puts the message in its timed message queue, and will process the message exactly at the requested time.
For technical neurons, the messages are interpreted as a change in the corresponding level, and the time information can be used to describe time delay stemming out from the technical implementation of the network.


There are few things sweeter than seeing your hard work pay off in the form of a clean beautifly crafted web page come to life on the Web! My favorite language to code in is JavaScript.
I truely enjoy writing front-end code. This is what people see when they come to your site and without good front-end development, the first impression you will make or break the users experience.
The best way to reach me is through email. Or see some of my work on Github


{% extends 'disturbance/emails/base_email.html' %}
{% block content %}
Your Compliance {{ compliance.reference }} has been submitted.
Submitter: {{ compliance.submitter.get_full_name }}
Lodgement date: {{ compliance.lodgement_date }}
You can access this Compliance using the following link:
Access Compliance {% endblock %}


Incorporation of nanomaterials and nanostructures in polymers offer rich functional groups, large surface area, and unique properties (thermal or electrical conductivity and biological) that assist the healing process. Nanofillers can also promote self-healing properties. Some of the commonly used nanofillers are nano-silica, graphene, carbon nanotubes (CNTs), ceramic oxides, and nanocellulose.
Highlight
Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean. A small river named Duden flows by their place and supplies it with the necessary regelialia
“She packed her seven versalia, put her initial into the belt and made herself on the way. When she reached the first hills of the Italic Mountains, she had a last view back on the skyline of her hometown Bookmarksgrove ” — Jean Smith
Application of Self-Healing Nanoparticles
Self-healing DNA nanostructures
Scientists have recently designed DNA nanostructures that have self-healing properties. These nanostructures could be used for drug delivery and diagnostics. However, the first thing prior to the application of the DNA nanostructures is developing a strategy against nuclease attacks, i.e., finding a way to protect or repair the damaged DNA molecules. Nanostructures are typically destroyed within 24 hours in serum at body temperature. Researchers have created various strategies such as DNA-nanotubes to stabilize the nanostructures in serum. The addition of these smaller DNA tiles to serum containing the nanotubes can heal the damaged structures.
Self-repair of electrical damage in polymers
Scientists have demonstrated that the addition of superparamagnetic nanoparticles (less than 0.1 volume percent) to the thermoplastic polymer would help the healing of sections affected by electrical treeing. This measure would also ensure the restoration of the insulating properties. Under the influence of an oscillating magnetic field, the nanoparticles travel to the electrical trees and produce higher local temperatures. This results in the repairing of the electrical tree channels in the polymer. This method also increases the durability of power cables for electronics and energy applications.
Prevention of post-operative reappearance of breast cancer
Hydrogel was first reported in 1960. A hydrogel is a three-dimensional network of crosslinked hydrophilic polymers that swell in water. Due to the physical and chemical crosslinks of separate polymer chains, it can hold a large amount of water without disturbing the structure.
A hydrogel is an extremely important material, particularly for tumour therapy and regenerative medicine. This is because of its biomimetic ability to modulate tissue micro environment.
Using Schiff-base linkage, scientists have developed a novel self-healing hydrogel based on graphene nanoparticles. The graphene-nanoparticle-based self-healing hydrogel is composed of chondroitin sulphate multi-aldehyde and branched polyethylenimine conjugated graphene.
The graphene-nanoparticle-based self-healing hydrogel showed 100% self-healing property and improved mechanical properties. An in-vitro study of postoperative recurrence of breast cancer in mice models showed the graphene-nanoparticle-based self-healing hydrogel’s potential.
Self-healing batteries
Lithium-ion rechargeable batteries often engage a carbon-based negative electrode. These batteries are prone to the formation of dendrites, which are small metallic structures developed on one electrode and grow toward the other. They may cause a short-circuit or even a fire.
Even though silicon electrodes provide a higher energy per unit of volume, it often falls apart owing to the expansion and contraction with recharge cycles.
Researchers at the University of Illinois developed a self-healing electrode, utilizing a conductive substance embedded into microcapsules. The expansion of the electrode causes a rupture in the microcapsules, dispersing the crack-filling material.
Self-healing graphene-based composites biosensors
Wearable electronic sensors are powerful devices that help in the early diagnosis of diseases and aid in the constant monitoring of an individual’s health condition. However, these wearable sensing devices, when in contact with the human body, receive unavoidable scratches and mechanical cuts resulting in their malfunction.
In a proof-of-concept, researchers have revealed the development of a flexible, nanoparticle-based sensor with self-healing properties. They reported that the amendment of a self-repairing polymer with functionalized gold nanoparticle films increases the healing efficiency in the substrate and sensing films.


CCured works on Linux and MS Windows (Win95 operation is unreliable but Win98
and (highly recommended instead) Win2k or WinXP should work). CCured might
also work on other systems that use gcc, but we have not tried it.
CCured is somewhat sensitive to the version of the compiler that you are
using. More precisely, CCured is sensitive to the format of the system include
files that you are using. When you install CCured it will create slightly
modified copies of some of the system include files. These copies are created
based on some patches that we distribute with CCured. If your include files
are different from those that we used to create the patches then the CCured
installation might fail. At the moment we have tested CCured with the
following compilers:
-
On Windows, cl compiler version 12.00.8168, 13.00.9466, and
13.10.3077. Run cl with no arguments to get the compiler version.
- On Windows, using cygwin and gcc version 2.95.3, and
versions 3.0.4 through 3.4.4.
- On Linux, using gcc version 2.95.3, and versions 3.2 through 3.3.5.
Some of the system include files that CCured depends on are really part of
the standard library. CCured has been tried with the following versions of
glibc: glibc-2.2.3, glibc-2.2.5, glibc-2.2.93, and
glibc-2.3.2. To find out the version of your glibc you can run
/lib/libc.so.6 on Linux.
If you want to use CCured on Windows then you must get a complete
installation of cygwin (make sure you install the development tools such
as gcc and ld as well and the perl interpreter) and the source-code Ocaml distribution and compile it yourself using the cygwin tools
(as opposed to getting the Win32 native-code version of Ocaml). You will need
Ocaml release 3.08 or higher to build CCured. If you have not done this before
then take a look here. (Don't need to worry
about cvs and ssh unless you will need to use the master CVS
repository for CCured.)
2.1 Get the CCured sources
Download the CCured distribution (latest version is
distrib/ccured-1.3.5.tar.gz). See the Section 13 for recent changes to the CCured distribution.
2.2 Configure and Compile CCured
Run the following commands in the top level directory. If you are using
Windows then at least the configure command must be run from within
bash.
./configure
make
make quicktest (optional)
cd test
make testrun/hello INFERBOX=infer
The configure script tries to find appropriate defaults for your system.
You can control its actions by passing the following arguments:
-
CC=foo Specifies the path for the gcc executable. By default
whichever version is in the PATH is used.
The last line in the above sequence of commands will apply CCured (and then
run the result) on the file test/small1/hello.c. (See the
test/Makefile for the sequence of commands.)
It is possible that you get a configuration error saying that certain patterns
did not match. This means that your standard include files are different than
those that we have prepared the distribution for. (See the above discussion.)
Your recourse in this case is either to install one of the versions of the
compiler that we tested CCured for or to extend the patch files so that they
match your includes. It is not hard, and it is explained in the
patcher documentation.
After running make you have built a few executables (in the obj
directory) and have configured the bin/ccured Perl script. If you want to
move this script to another directory (e.g. to /usr/local/bin) make sure
to copy the CilConfig.pm file to the same directory.
Now you can continue with a tutorial (Chapter 3), or you can jump
ahead and find out how to run CCured (Chapter 4).
2.3 Test CCured
Once you have built CCured you can run
make quicktest
This will run a few small examples.


Here we go! Programming of Karel is quite easy challenge even for a kid! Karel can walk throught his own special worlds, turn around, look asides and enjoy the scenery. Also Karel adore to play with beepers. Put them to the ground, and pick up back to his bag. What are those beepers? No one knows, even Karel himself, but who cares? They are so shiny!
Let us start from simple moving. Karel lives in a strange isometric world, shrinked to an equal cells. He can move only to a cell in front of him. To move left, or right, he has to turn first. Try to use those commands and walk you karel.
// Tell Karel to move one step forward
move();
// To change Karel's fasing use following commands
turnLeft();
turnRight();
// You can't just tell Karel to move one cell left
// You have to do it in few steps
turnLeft();
move();
turnRight();
// Now Karel stands on a one cell to the left with same facing
But be aware! Karel will break if he stumble the wall or fall from the world edge, so be careful! That means, you have to forse Karel to look around before moving.
// There is how Karel can observe the world
frontIsClear(); // true if there is no any barrier in front of Karel
frontIsBlocked(); // true if the barrier blocks Karel in front
leftIsClear(); // true if there is no any barrier in the left side of Karel
leftIsBlocked(); // true if the barrier blocks Karel in left
rightIsClear(); // true if there is no any barrier in the right side of Karel
rightIsBlocked(); // true if the barrier blocks Karel in right
// Use conditional statement to check state of surrounding cells
// This construction allows Karel to move, only when the cell in front of Karel is not blocked
// with wall or the world's edge
if (frontIsClear()) {
move();
}
Don't you knew that Karel can perfectly orient in his world? Any time, he can tell you to wich side he is facing now. All you need -- is to ask him gently.
// Use following commands to find out Karel's facing
facingNorth();
facingEast();
facingSouth();
facingWest();
Now coming Karel's favourite part of his routine -- beepers!  Karel oftenly have some beepers in his bag. He really loves to drop beepers all over his small world and then to collect every las of them.
In the same time, Karel is very, very decent robot, so he will never drop any beeper or even pick it from the ground without your permission. You are the programmer, so rule!
One more. Remember, Karel can not pick nonexistent beepers, so if he coudn't find any on the ground while picking up, he will crush! Otherwise, Karel will allso stun if you ask him to drop the beeper while his bag is empty.
// Use commands to tell Karel put or pick beepers
putBeeper();
pickBeeper();
// To prevent crushes, you have to check bag and floor before doing something
if (beepersInBag()) {
putBeeper();
}
// Use following commands to manage beepers availability
beepersPresent(); // true if there is at least one beeper on the ground
noBeepersPresent(); // true if there are no beepers on the ground
beepersInBag(); // true if there is at least one beeper in Karel's bag
noBeepersInBag(); // true if there are no beepers in Karel's bag
Let me be fair. It is really annoying to tell Karel what to do step-by-step. Just try it, and you'll ensure this.  Your mission is to make Karel self-sufficient, teach him to solve different tasks.
You can do it subtle and honoreable by using cool programmer's hacks: loops and conditions.
// You've allready seen some conditions before
if (someStatement()) {
// this code will be executed
// if someStatement() is true
} else {
// otherwise code in else block
// will be executed
}
// You can combine if..else blocks in many ways
if (facingNorth()) {
if (frontIsClear()) {
move();
} else {
turnLeft();
}
} else if (facingEast()) {
if (beepersInBag()) {
putBeeper();
}
} else {
turnLeft();
}
// Loops allow you to do some actions repetedly
while (someStatement()) {
// This code will be executed many times repetedly
// until condition someStatement() in brackets becomes false
}
// If you want to execute your command fixed amound of times, you have to use scecial loop
for (var i=0; i<N; i++) {
// This code will be executed N times
}
And now, young padavan, when you reallised your real power, you can learn the greatest abilities of Karel and guide him throught the novel isometric worlds, right to his ultimate goal, the Knowledge! Teach Karel to do new things! Create new commands!
Step-by-step, day-by-day... Be patient and wise, young padavan, and you will reach the highest ranks of programming!
// Use functions to create new Karel commands
// It is usefull when you need to write same sequence of commands alot
function commandName() {
// some code here
}
// If you need to create command for checking some states
// just return true or false when is is necessary
function alwaysTrue() {
return true;
}
// Nou you can use your commads just as they would be Karel's native
function alwaysMove() {
if (alwaysTrue()) {
move();
}
}
alwaysMove();
// If you meet a sequence of conditions u can use logical operations
// Use logical AND (&&) to execute command only when all of the conditions are true
if (frontIsClear() && facingNorth()) {
move();
}
// Use logical OR (||) to execute command if there is at least one true condition
if (facingSouth() || facingNorth()) {
if (frontIsClear()) {
move();
}
}
I guess, now you are ready to try yourself in programming robots. Just press this big red button, start coding and gain new knowledge. Good luck!


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Who better than justice league europe's own stretchable sleuth to investigate one of the greatest mysteries of the decade? The elongated man foi criado pelo escritor john broome e o desenhista carmine infantino , com contribuições significativas do editor julius schwartz , que queria um novo personagem coadjuvante. Man logo free vector we have about (71,991 files) free vector in ai, eps, cdr, svg vector illustration graphic art design format.
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--- layout: default title: Bolanle Egbedokun, Data Scientist ---
A full-time Data Scientist/Machine Learning Engineer that utilizes machine learning and Business Intelligence skills for advance cutting-edge technologies, developing robust applications, Demonstrating expertise in handling unique data analysis requests using hot technologies and Offer assistance/guidance related to data analysis to the business and explains how it impacts the business and proffer guidance/advise on strategic ways to scaling a business.A seasoned Data Scientist Reliable anytime. Read more about my profile...


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Oculus Hospitality Training & Business Solutions Inc.
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Oculus Business Solutions
Mailing Address:
170-422 Richards StVANCOUVER, British Columbia
V6B 2Z4
Location Address:
170-422 Richards StVANCOUVER, British Columbia
V6B 2Z4
Tel.:<PHONE_NUMBER>
Tel.: (888) OCU-LUS4
Fax:<PHONE_NUMBER>
Logo
Company Profile
Oculus is a functional fusion of advertising sales, human resources and management consulting worlds, creating customized designer solutions that are results driven. By merging business operational fundamentals with creative and strategic execution, we provide up-to-date and a modernized approach to todays business operations. Our solutions are not from a box or book. Our customized consulting experience will be based on research, facts and insights regarding the business and area. No two clients are alike and Oculus will remain focused on your business, creating a personalized and private strategy to make a business competitive.
Full profile
Full profile
Contact information
Company description
611430 - Professional and Management Development Training
Product / Service / Licensing
CORPORATE TRAVEL
CUSTOMER SERVICE DEVELOPMENT
EVENT PLANNING
FITNESS ASSESSMENT
FITNESS TRAINING
HUMAN RESOURCES MANAGEMENT
LOCATION MYSTERY SHOP
LOYALTY PROGRAM DEVELOPMENT
MANGER & STAFF RECRUITMENT
MARKETING ASSESSMENT
MEETING MANAGEMENTMOTIVATIONAL SPEAKING
ONLINE SEMINARS
ONSITE EDUCATION SEMINARS
PHOTOGRAPHY & VIDEO PRODUCTION
PROMOTION & PACKAGE
QUALITY ONSITE ASSESSMENT
SALES ASSESSMENT
SALES MYSTERY SHOP
TELEPHONE TUTOR
TELPHONE MYSTERY SHOP
TRAINING & DEVELOPMENT
WEB DESIGN
These are just some of the areas that we can target from within your organization. As with other boutiques, anything that you require, we are here to provide. Oculus has partnered with an extensive list of professional and pre-approved consultants to produce enhanced consulting knowledge.
Mystery shopping is a perfect way to evaluate & facilitate positive changes from within your hotel or organization. Oculus is pleased to offer telephone, internet and live mystery assessment opportunities for retail, hotel or service based business or charities. Results will be presented based on a pre-set criteria and will include suggested personalized improvement tips. An Oculus Opportunity Map will guide your employees and staff through necessary steps on how to progress in their personal growth plan, ultimately encouraging revenue growth as a result of superior customer service and sales skills. Unlike our competition, our results are personalized for your team and provide individual feedback for increased return on your investment. You can choose between the Oculus IMPRESS training programs or we can design your own assessment and shopping program.
Telephone Mystery Shopping
We are pleased to offer recorded telephone mystery shopping both through traditional mystery shoppers and planned recordings from actual live incoming customers. Each result will receive a personalized opportunity map and can be customized to fit your business factors and regional exceptions. We are able to telephone mystery shop the following:
Assistant Living & Elderly Communities
Associations
Call & Appointment Centers
Car Dealerships
Charities
Concierge
Front Desk
Hotel Reservations
Hotel Housekeeping Departments
Information Centers and Services
Medical Centers
Real Estate
Restaurants
Retail
Sales & Marketing Departments
Ticket Centers
We can work within any organization to customize a mystery shopping experience.
Oculus Onsite Hotel Consulting & Reservations Training
How and when we provide service is a crucial element in all of our customer interactions. Both the timing and the relevance of that service will dictate how your customers perceive your business. At Oculus, we work with your entire team to ensure they have the most up-to-date and applicable service training tools to deliver and to exceed customer expectations. The IMPRESS program is designed to provide revenue generating training program ideas and to encourage employee development within your hotel or organization. We do not provide cookie-cutter programs. All of our training programs are designed for your specific hotel or organization. You place your companys signature to incorporate your unique brand and operating standards. Some of our customer service training programs include:
Customer Situation Management
Taking complaints, requests and challenges and making them revenue and loyalty opportunities. At the end of this training program your teams members will be able to:
* Effectively manage and solve incoming customer service complaints
* Provide cost effective customer centric solutions
* Create confidence and independence with operations staff
* Track and review opportunities for change management
* Ensure customer satisfaction to encourage positive reviews
Service Performance
Building service lasting memories. At the end of this training program your team members will be able to:
* Build lasting experience memories for customers
* Delivery superior customer service standards with ease
* Establish a personal connection and create the path to customer loyalty
* Increase customer service experience delivery
A Connection that is Personal
How to create loyalty through customer connection. In this training program, we will:
* Encourage customer interaction within a service setting
* Provide a customized and personalized service experience to impress customers
* Increase customer service experience delivery
Do First, Question Later
How to positively present a NO. In this training program, we will:
* Determine positive solutions to present in any challenging situation
* Take ownership of an opportunity
* Demonstrate leadership skills and an ability to serve without delay
* Showcase decision making confidence during customer service decision opportunities.
* Increase customer service experience delivery
Lets take a walk
How to move or relocate a customer. At the end of this training program your team members will be able to:
* Show empathy and care within a challenging opportunity
* Showcase a degree of resistance recovery
* Provide a satisfactory solution for incoming customers
* Establish positive follow up and guest experience
They come and they go
Understand how to deliver IMPRESSive check in and checkout guest experiences. In this training program, we will:
* Understand and meet requirements within a travel rating system
* Accurately receive all customer information and requests
* Encourage and identify revenue generating opportunities
* Identify potential service and experience challenges
* Follow up to ensure customer satisfaction
* Establish data collection process for change management
* Increase customer service experience delivery
The majority of the programs listed above would be suitable, and can be customized to fit the following industries:
- Hotels & Hospitality
- Reservations & Call-Centres
- Restaurants
- Casinos & Gambling Industries
- Charities and Associations
- Destination Training & Tourism
Oculus Onsite Hotel Consulting & Reservations Training
Although we have established that we are a customer centric organization, we still need to make money. At Oculus, we have taken proven sales strategy, scientific studies, and customer service knowledge to create a sales training program that is focused on both the customer experience, and revenue generation. All of our sales and reservations training programs are designed with you in mind and allow for complete customization. Using the results from our ongoing mystery shopping, we can target the static and weaker areas of your sales or reservation practices. Some of the sales and reservations training experiences that we can provide are:
IMPRESS the Guest Telephone Reservations Sales
How to generate additional revenue through maximizing sales and service opportunities. In this training program, we will:
* Present hotel or product value and describe accordingly
* Establish rapport and a connection with every experience
* Encourage additional revenue generating & sales opportunities
* Encourage and establish special requirements
* Maintain appropriate levels to establish hotel ratings
* Understand the need for cross selling
* Be able to present information and details applicable to customer experience
IMPRESS the Guest Sales Management
How to generate maximum revenue by using proven scientific and research sales strategies. In this training program, we will:
* Present hotel or product value customized to sales experience
* Collect appropriate company and caller data
* Measure and qualify a sales opportunity
* Understand competition value and present accordingly
* Establish rapport and a connection with every sales experience
* Encourage additional revenue generating & sales opportunities
* Encourage and establish special requirements
* Establish an advancement and follow up sales procedure
* Understand the need for cross selling
* Be able to present information and details applicable to customer experience
The Walk Around
Understand how to build an interesting site tour/inspection. In this training program, we will:
* Present hotel or product value customized to sales experience
* Set up a site experience to impress the incoming customer
* Collect appropriate company and caller data
* Measure and qualify a sales opportunity
* Establish rapport and a connection
* Establish an advancement and follow up sales procedure
* Be able to present information and details applicable to customer experience
Preparing for Take Off
Upselling to your current customers. In this training program, we will:
* Encourage selling options and packages
* Encourage creative thinking and pricing strategy
* Use qualifying techniques to establish customer needs
* Present differences between
IMPRESS for the YES
How to overcome opposition and resistance to product & pricing challenges. In this training program, we will:
* Showcase how to overcome price and product resistance through addressing customer areas of concern and subconscious thought.
* Use qualifying techniques to establish customer needs
Full and Happy Tummy, Empty Wallet
Maximizing the dining experience to increase average customer revenue. In this training program, we will:
* Present hotel or menu value and describe accordingly
* Establish personal level connection with every dining experience
* Encourage additional revenue generating & sales opportunities
* Encourage and establish special requirements
* Maintain appropriate standards levels to establish restaurant ratings
* Actual onsite practice and live demonstration
Sector information
Key / Major clients:
BC SPCA, Ramada Hotels, Best Western Hotels, Dodge, Holiday Inn, Fairmont Hotel & Resorts, Preferred Hotels and The Original CupcakesThird-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
611430 - Professional and Management Development Training
Products, services and licensing
Product / Service / Licensing
CORPORATE TRAVEL
CUSTOMER SERVICE DEVELOPMENT
EVENT PLANNING
FITNESS ASSESSMENT
FITNESS TRAINING
HUMAN RESOURCES MANAGEMENT
LOCATION MYSTERY SHOP
LOYALTY PROGRAM DEVELOPMENT
MANGER & STAFF RECRUITMENT
MARKETING ASSESSMENT
MEETING MANAGEMENTMOTIVATIONAL SPEAKING
ONLINE SEMINARS
ONSITE EDUCATION SEMINARS
PHOTOGRAPHY & VIDEO PRODUCTION
PROMOTION & PACKAGE
QUALITY ONSITE ASSESSMENT
SALES ASSESSMENT
SALES MYSTERY SHOP
TELEPHONE TUTOR
TELPHONE MYSTERY SHOP
TRAINING & DEVELOPMENT
WEB DESIGN
These are just some of the areas that we can target from within your organization. As with other boutiques, anything that you require, we are here to provide. Oculus has partnered with an extensive list of professional and pre-approved consultants to produce enhanced consulting knowledge.
Mystery shopping is a perfect way to evaluate & facilitate positive changes from within your hotel or organization. Oculus is pleased to offer telephone, internet and live mystery assessment opportunities for retail, hotel or service based business or charities. Results will be presented based on a pre-set criteria and will include suggested personalized improvement tips. An Oculus Opportunity Map will guide your employees and staff through necessary steps on how to progress in their personal growth plan, ultimately encouraging revenue growth as a result of superior customer service and sales skills. Unlike our competition, our results are personalized for your team and provide individual feedback for increased return on your investment. You can choose between the Oculus IMPRESS training programs or we can design your own assessment and shopping program.
Telephone Mystery Shopping
We are pleased to offer recorded telephone mystery shopping both through traditional mystery shoppers and planned recordings from actual live incoming customers. Each result will receive a personalized opportunity map and can be customized to fit your business factors and regional exceptions. We are able to telephone mystery shop the following:
Assistant Living & Elderly Communities
Associations
Call & Appointment Centers
Car Dealerships
Charities
Concierge
Front Desk
Hotel Reservations
Hotel Housekeeping Departments
Information Centers and Services
Medical Centers
Real Estate
Restaurants
Retail
Sales & Marketing Departments
Ticket Centers
We can work within any organization to customize a mystery shopping experience.
Oculus Onsite Hotel Consulting & Reservations Training
How and when we provide service is a crucial element in all of our customer interactions. Both the timing and the relevance of that service will dictate how your customers perceive your business. At Oculus, we work with your entire team to ensure they have the most up-to-date and applicable service training tools to deliver and to exceed customer expectations. The IMPRESS program is designed to provide revenue generating training program ideas and to encourage employee development within your hotel or organization. We do not provide cookie-cutter programs. All of our training programs are designed for your specific hotel or organization. You place your companys signature to incorporate your unique brand and operating standards. Some of our customer service training programs include:
Customer Situation Management
Taking complaints, requests and challenges and making them revenue and loyalty opportunities. At the end of this training program your teams members will be able to:
* Effectively manage and solve incoming customer service complaints
* Provide cost effective customer centric solutions
* Create confidence and independence with operations staff
* Track and review opportunities for change management
* Ensure customer satisfaction to encourage positive reviews
Service Performance
Building service lasting memories. At the end of this training program your team members will be able to:
* Build lasting experience memories for customers
* Delivery superior customer service standards with ease
* Establish a personal connection and create the path to customer loyalty
* Increase customer service experience delivery
A Connection that is Personal
How to create loyalty through customer connection. In this training program, we will:
* Encourage customer interaction within a service setting
* Provide a customized and personalized service experience to impress customers
* Increase customer service experience delivery
Do First, Question Later
How to positively present a NO. In this training program, we will:
* Determine positive solutions to present in any challenging situation
* Take ownership of an opportunity
* Demonstrate leadership skills and an ability to serve without delay
* Showcase decision making confidence during customer service decision opportunities.
* Increase customer service experience delivery
Lets take a walk
How to move or relocate a customer. At the end of this training program your team members will be able to:
* Show empathy and care within a challenging opportunity
* Showcase a degree of resistance recovery
* Provide a satisfactory solution for incoming customers
* Establish positive follow up and guest experience
They come and they go
Understand how to deliver IMPRESSive check in and checkout guest experiences. In this training program, we will:
* Understand and meet requirements within a travel rating system
* Accurately receive all customer information and requests
* Encourage and identify revenue generating opportunities
* Identify potential service and experience challenges
* Follow up to ensure customer satisfaction
* Establish data collection process for change management
* Increase customer service experience delivery
The majority of the programs listed above would be suitable, and can be customized to fit the following industries:
- Hotels & Hospitality
- Reservations & Call-Centres
- Restaurants
- Casinos & Gambling Industries
- Charities and Associations
- Destination Training & Tourism
Oculus Onsite Hotel Consulting & Reservations Training
Although we have established that we are a customer centric organization, we still need to make money. At Oculus, we have taken proven sales strategy, scientific studies, and customer service knowledge to create a sales training program that is focused on both the customer experience, and revenue generation. All of our sales and reservations training programs are designed with you in mind and allow for complete customization. Using the results from our ongoing mystery shopping, we can target the static and weaker areas of your sales or reservation practices. Some of the sales and reservations training experiences that we can provide are:
IMPRESS the Guest Telephone Reservations Sales
How to generate additional revenue through maximizing sales and service opportunities. In this training program, we will:
* Present hotel or product value and describe accordingly
* Establish rapport and a connection with every experience
* Encourage additional revenue generating & sales opportunities
* Encourage and establish special requirements
* Maintain appropriate levels to establish hotel ratings
* Understand the need for cross selling
* Be able to present information and details applicable to customer experience
IMPRESS the Guest Sales Management
How to generate maximum revenue by using proven scientific and research sales strategies. In this training program, we will:
* Present hotel or product value customized to sales experience
* Collect appropriate company and caller data
* Measure and qualify a sales opportunity
* Understand competition value and present accordingly
* Establish rapport and a connection with every sales experience
* Encourage additional revenue generating & sales opportunities
* Encourage and establish special requirements
* Establish an advancement and follow up sales procedure
* Understand the need for cross selling
* Be able to present information and details applicable to customer experience
The Walk Around
Understand how to build an interesting site tour/inspection. In this training program, we will:
* Present hotel or product value customized to sales experience
* Set up a site experience to impress the incoming customer
* Collect appropriate company and caller data
* Measure and qualify a sales opportunity
* Establish rapport and a connection
* Establish an advancement and follow up sales procedure
* Be able to present information and details applicable to customer experience
Preparing for Take Off
Upselling to your current customers. In this training program, we will:
* Encourage selling options and packages
* Encourage creative thinking and pricing strategy
* Use qualifying techniques to establish customer needs
* Present differences between
IMPRESS for the YES
How to overcome opposition and resistance to product & pricing challenges. In this training program, we will:
* Showcase how to overcome price and product resistance through addressing customer areas of concern and subconscious thought.
* Use qualifying techniques to establish customer needs
Full and Happy Tummy, Empty Wallet
Maximizing the dining experience to increase average customer revenue. In this training program, we will:
* Present hotel or menu value and describe accordingly
* Establish personal level connection with every dining experience
* Encourage additional revenue generating & sales opportunities
* Encourage and establish special requirements
* Maintain appropriate standards levels to establish restaurant ratings
* Actual onsite practice and live demonstration


Modal Window
An article from Wikipedia, the free encyclopedia
In user interface design, a modal window is a child window that requires users to interact with it before they can return to operating the parent application, thus preventing the workflow on the application main window. Modal windows are often called heavy windows or modal dialogs because the window is often used to display a dialog box.


Tests for metadata service.
Bases: nova.test.TestCase
Test that metadata is returning proper values.
Make sure that _format_instance_mappings works
The nova.tests.test_libvirt_vif Module
The nova.tests.test_migrations Module
Enter search terms or a module, class or function name.


200 OK
Engineers reporting everything is just fine!
POIntillisme web service is running on the server.


Hello ! I am a senior currently pursuing my undergrad in Chemical Engineering . I aspire to make novel discoveries and work towards betterment of technologies in the energy sector. Download my resume
Training Program on Geological Aspects involved in Exploration/Testing of Oil and Natural Gas.
Trained on safety, process, instrumentation, and control of petrochemical industrial systems.
Analyzed and quantified the deposition of zinc oxide on gold catalysts for solar photovoltaics systems and applications.
GPA: 9.47(10 point scale) | Recipient of the prestigious SSN scholastic scholarship (top 0.1%)
Completed several marathons to support social causes:
I also enjoy travelling and exploring, hiking, cooking and creating recipes


App design for the new Coach app featuring the Coach X Peanuts Collection. View the collection, get photobombed by the Peanuts, capture them at play and find your inner Peanut Realized at Coach


Welcome to my website! The menu is as follows:
Food is a symbol of love when words are inadequate.
One cannot think well, love well, sleep well, if one has not DINED well.


Toggle navigation
ESGF Console
Statistics overview
Cross-project statistics
Project-specific statistics
CMIP5 project
Obs4MIPs project
Federated data archive
IS-ENES2 KPIs
Statistics Overview
ESGF total number of datasets
ESGF total data volume
CMIP5 total number of datasets
CMIP5 total data volume
Obs4MIPs total number of datasets
Obs4MIPs total data volume
CORDEX total number of datasets
CORDEX total data volume


Welcome to zoho_crm_connector’s documentation¶
zoho_crm_connector¶
| copyright: |
|
|---|---|
| licence: | MIT, see LICENCE.txt for more details. |
This library is based on Zoho’s python sdk but is simplified, more pragmatic and modernised.
No database dependency is included. Short-lived access tokens are written to a text file with no alternative at present.
Multi-page requests are returned with yield (so they are generators).
pytest tests are included. You will need to provide authentication details; the tests assume these are in environment variables.
The Zoho licence is not specified at the time I referred to it, so I assumed public domain
Handy notes:
Search criteria does not work across modules (where the json returns is a {name,id} object) You will need to enumerate a super-set of candidate results and search, or use related_records (see test case) but you will still need to enumerate. This is too complicated to put in the API.
-
class
zoho_crm_connector.zoho_crm_api.
Zoho_crm
(refresh_token: str, client_id: str, client_secret: str, token_file_dir: pathlib.Path, base_url=None, default_zoho_user_name: str = None, default_zoho_user_id: str = None)[source]¶ An authenticated connection to zoho crm.
Initialise a Zoho CRM connection by providing authentication details including a refresh token.
Access tokens are obtained when needed.
- The base_url defaults to the live API for US usage;
- another base_url can be provided (for the sandbox API, for instance)
-
delete_from_module
(module_name: str, record_id: str) → Tuple[bool, dict][source]¶ deletes from a named Zoho CRM module
-
finduser_by_name
(full_name: str) → Tuple[str, str][source]¶ Tries to reutn the user as a tuple(full_name,Zoho user id), using the full full_name provided. The user must be active. If no such user is found, return the default user provided at initialisation of the Zoho_crm object.
-
get_users
(user_type: str = None) → dict[source]¶ Get zoho users, filtering by a Zoho CRM user type. The default value of None is mapped to ‘AllUsers’
-
upsert_zoho_module
(module_name: str, payload: Dict[str, List[Dict[KT, VT]]], criteria: str = None) → Tuple[bool, Dict[KT, VT]][source]¶ creation is done with the Record API and module “Accounts”. Zoho does not make mandatory fields such as Account_Name unique. But here, a criteria string can be passed to identify a ‘unique’ record: we will update the first record we find, and insert a new record without question if there is no match (critera is None reverts to standard Zoho behaviour: it will always insert)
For notes on criteria string see yield_page_from_module()
Note: payload looks like this: payload={‘data’: [zoho_account]} where zoho_account is a dictionary for one account.
Returns a tuple with a success boolean, and the entire record if successful. The Zoho API distinguishes between the record was already there and updated, or it was not there and it was inserted: here, both are True.
If unsuccessful, it returns the json result in the API reply. See https://www.zoho.com/crm/help/api/v2/#create-specify-records
-
yield_page_from_module
(module_name: str, criteria: str = None, parameters: dict = None, modified_since: datetime.datetime = None) → Generator[List[dict], None, None][source]¶ Yields a page of results. Usually called for you by a helper member function, such as get_users the API is different for module queries and User queries.
For use of the criteria parameter, please see search documentation: https://www.zoho.com/crm/help/api-diff/searchRecords.html ‘Performs search by the following shown criteria. (({apiname}:{starts_with|equals}:{value}) and ({apiname}:{starts_with|equals}:{value})) You can search a maximum of 10 criteria (with same or different columns) with equals and starts_with conditions as shown above.’


Ahora en el Cine
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
Aclamadas por la critica
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
No te debes perder
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes
The Secret Life of Pets
The quiet life of a terrier named Max is upended when his owner takes in Duke, a stray whom Max instantly dislikes


{% extends 'base.html' %} {% block content %}
Feedsearch provides a simple API for searching websites for RSS, Atom, and JSON feeds.
The long-term goal of Feedsearch is to provide a comprehensive, publicly accessible repository of feed information by saving the location and metadata of all crawled feeds.
Make a GET request to {{ url_for('search_api', _external=true) }} with a "url" value in the querystring containing the URL you'd like to search:
curl -X GET "{{ url_for('search_api', url='arstechnica.com', _external=true) }}"
The Feedsearch API accepts the following query parameters:
curl "{{ url_for('search_api', url='arstechnica.com', info='true', favicon='false', opml='false', skip_crawl='false', _external=true) }}"
The Feedsearch API returns a list of found feeds in JSON format, with attached metadata about the feed.
[
{
"bozo": 0,
"content_length": 82139,
"content_type": "text/xml; charset=UTF-8",
"description": "Serving the Technologist for more than a decade. IT news, reviews, and analysis.",
"favicon": "https://cdn.arstechnica.net/favicon.ico",
"favicon_data_uri": "data:image/png;base64,AAABAAMAIC...",
"hubs": [
"http://pubsubhubbub.appspot.com/"
],
"is_podcast": false,
"is_push": true,
"item_count": 20,
"last_seen": "2019-07-05T19:00:00+00:00",
"last_updated": "2019-07-05T16:00:30+00:00",
"score": 27,
"self_url": "http://feeds.arstechnica.com/arstechnica/index",
"site_name": "Ars Technica",
"site_url": "https://arstechnica.com/",
"title": "Ars Technica",
"url": "http://feeds.arstechnica.com/arstechnica/index",
"velocity": 7.827,
"version": "rss20"
}
]
If you provide results powered by Feedsearch, then you should provide an attribution link that is visible to your user on the search and results page.
powered by Feedsearch
Feedsearch extends the Feedsearch-Crawler library (available as a Python package on PyPI), by providing a public API and storing crawl results for public consumption.
Documentation and source code for the crawler can be found at the Feedsearch-Crawler GitHub repository.
Feedsearch acts as a Web crawler. It only crawls a site in response to a direct request, not as an automated crawler. It's designed to be as selective as possible in which URLs it crawls while looking for feeds, and stores information on crawled paths in order to reduce the load on crawled sites as much as possible. It does not attempt to bypass privacy pages, captchas, or any other anti-crawling measures.
Feedsearch was originally written to power the RSS feed search function at Auctorial.
If you have any issues with our crawling, or just wish to get in touch, please contact support@auctorial.com


How to Use
1. In the Heroes' grid view page, click icon to goto the Hero's detail page.
2. In the Hero's detail page, click the top left icon (Hero's icon) to go back to Heroes' grid view page, or you can use swipe gesture(from left to right) to go back.
Feedback


Data
What data is being collected?
Depending on the exact site different data may be collected. This data almost always includes Usage Data (IP Address, country of origin, User-Agent, time of access, the accessed URL). Additionally some API usage is logged. This includes any login attempts, account modifications, account deletions, and more. When using a service that requires a login the username / user id is also logged. Any errors that might occur during usage of a service may also be logged for the purpose of improving the service.
When registering the personally identifiable data that is being collected may include, but is not limited to
- Your Email address
- And your first & last name,
aswell as the previously mentioned Usage Data
Use of Data
The collected data is being used:
- To provide and maintain the Service
- To provide analysis or valuable information so that we can improve the Service
- To monitor the usage of the Service
- To detect, prevent and address technical issues
Security Of Data
The security of your data is important to us, but remember that no method of transmission over the Internet, or method of electronic storage is 100% secure. While we strive to use commercially acceptable means to protect your Personal Data, we cannot guarantee its absolute security.


Test purpose
- Create and animate a Wheel entity, using delta and deltaConstraint objects
- Check that reverse and loop actions work as expected
- Check that scaling animation can be added to and deleted from the delta object
setDeltaValues() actions not yet tested: 'newString', 'update', 'reverse', 'add', 'multiply'
Touch test: not required
Annotated code