Research Analyst Intern
Jun 2021 - Aug 2021
YMY Solutions | Chicago, Illinois
- Conducted market study, research, and analysis with the financials statements of companies and monitored world markets trends.
- Assisted in developing business strategies and contributed to the business development of the enterprise.
Application Development Analyst
Dec 2018 - Nov 2019
Accenture Solutions Pvt. Ltd. | Hyderabad, India
- Engineered automations across applications and business processes saving ~70 hrs./year for client including use of Automation Anywhere.
- Enhanced team’s productivity and dynamic by supporting reporting tool called Time Tracker, hosted on AWS server, whose data was leveraged to provide insights to senior management on resource utilization for efficient project management.
- Supported OSI PI and its features, an application to capture, process, analyze and store real-time data; Resolved issues occurring with the visualization trends in PI Process Book.
- Presented concepts on automating reports and analytical capabilities replacing excel-based tasks to amplify managerial efficiency by 40%.
- Delivered numerous analytics intensive dashboards and reports focused on project lifecycles, financial insights and optimizing operations.
- Worked on OSIsoft's features such as PI Data Archive, PI Asset Framework, PI processbook and others to efficiently record data from process control systems into a compressed time series database.
Application Development Associate
Jul 2017 - Nov 2018
Accenture Solutions Pvt. Ltd. | Hyderabad, India
- Consulted Oil & Gas client to support real-time data integrations, applications for their upstream business operations.
- Resolved 80+ incidents of which 30+ were high priority SLAs disrupting client’s application performance.
- Delivered 40+ enhancements in collaboration with other business units for desktop and web apps using SQL, Oracle by understanding requirement and creating BRDs.
- Presented aforementioned features on test servers before deployment on production servers.
- Performed 30+ upgrades of upstream business operation applications such as CMG, AutoCAD, HOG among others.
- Executed the ITIL framework, SDLC lifestyle and Agile methodology on features with empirical and quality documentation.
Summer Intern - Industrial Project
Nov 2016 - Apr 2017
Bharat Electronics Limited | Hyderabad, India
- Part of missile tracking and analysis group working on detecting low probability of Intercept Radars with high ptobability of intercept.
- Involved in actions taken to search for, intercept, locate, record and analyze radiated electromagnetic energy.
- Drafted the final submission report with conclusion based on analysis of results obtained.


Joy Behar to Audience: 'This Isn’t a MAGA Rally!' | Breitbart
by Pam KeyOn Thursday’s broadcast of ABC’s “The View,” co-host Joy Behar yelled at the audience saying, “listen this isn’t a MAGA rally, OK?” during an interview with Donald Trump Jr. and Kimberly Guilfoyle, while they battled over the outing the whistleblower.
Partial transcript as follows:
HOSTIN: Do you regret doing that? TRUMP JR.: I don’t regret doing it. I don’t think I should have to forego my First Amendment rights. It’s out there. I read it in an article. I’ve been reading it for a week. I saw it on the Drudge Report. This is not some secret. But to then say, because it’s Donald Trump Jr., and he’s an outspoken guy — BEHAR: Listen This isn’t a MAGA rally, okay? Clam it down. TRUMP JR.: Why was there no outrage when the Drudge Report, which is a major website, probably the largest aggregator of media news in America, he puts it out there a few days ago and crickets. No problem with that.
Follow Pam Key on Twitter @pamkeyNEN


Mecanum Drive
What is it?
Mecanum drive is a method of driving using specially designed
wheels with rollers at 45 degree angles that allow the robot to
drive in any direction without changing the orientation of the
robot. A robot with a conventional drivetrain (four or six
wheels) must turn in the direction it needs to drive. A mecanum
robot can move in any direction without first turning and is also
called a holonomic drive.
Properties
- Left Front Motor
- The motor to use as the front left motor for robot drive
actions.
- Left Rear Motor
- The motor to use as the rear left motor for robot drive
actions.
- Right Front Motor
- The motor to use as the front right motor for robot drive
actions.
- Right Rear Motor
- The motor to use as the rear right motor for robot drive
actions.
- Maximum Output
- Maximum allowable output to the motors, generally this
should be one.
- Safety Enabled
- Whether or not motor safety should be in effect
- Safety Expiration Time
- How long before motor safety kicks in.
See Also


I learned the major pieces of the web; Me, my computer running a browser, the Internet, and Servers.
We learned that the language used to communiate with the computer is called Hypertext Markup language.
It is made up of text content (what you see), markings (what it looks like), and the reference. We also learned tags.
There are different types of tags; opening and closing tags. There are also void tags. When you have
an opening tag with content
and a closing tag, these items together are called an element. If I wanted to make an item italicized, the tag makes them
italicized, but once I insert the closing tag, everything after the closing tag goes back to regular type.
I also learned that I can make words bold by using the "b" tag. We also learned about attributes. That
means
I can use an attribute name like href and insert a website. One example we learned in class
is href = "www.reddit.com"> derp"
We learned that we can insert an image by using the tag This is blind text because images
don't have
a closing tag.' They are called Void tags. An Anchor tag uses href attributes while image tags use src attributes.
We also learned about inline and block tags.
We learned hat a web page is a text document written in a language called HTML.
Web
browserss read these documents, and then interpret and display them.
Coding happens when programmers write text in a language a computer can understand. The computer
which is a dumb machine, can follow the instructions the programmer wrote
That being said, programmers need to write
"exactly" the way a computer understands. This is
also known as writing with correct syntax. "


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import models.ui.{TextInput, CurrencyInput, CurrencyPoundsOnlyInput, NumberInput} @(input: models.ui.Input, name: String, form: Form[_])


| gam.fit3 {mgcv} | R Documentation |
Estimation of GAM smoothing parameters is most stable if optimization of the UBRE/AIC, GCV, GACV, REML or ML score is outer to the penalized iteratively re-weighted least squares scheme used to estimate the model given smoothing parameters.
This routine estimates a GAM (any quadratically penalized GLM) given log smoothing paramaters, and evaluates derivatives of the smoothness selection scores of the model with respect to the log smoothing parameters. Calculation of exact derivatives is generally faster than approximating them by finite differencing, as well as generally improving the reliability of GCV/UBRE/AIC/REML score minimization.
The approach is to run the P-IRLS to convergence, and only then to iterate for first and second derivatives.
Not normally called directly, but rather service routines for gam
.
gam.fit3(x, y, sp, Eb ,UrS=list(), weights = rep(1, nobs), start = NULL, etastart = NULL, mustart = NULL, offset = rep(0, nobs), U1 = diag(ncol(x)), Mp = -1, family = gaussian(), control = gam.control(), intercept = TRUE,deriv=2,gamma=1,scale=1, printWarn=TRUE,scoreType="REML",null.coef=rep(0,ncol(x)), pearson.extra=0,dev.extra=0,n.true=-1,Sl=NULL,...)
x |
The model matrix for the GAM (or any penalized GLM). |
y |
The response variable. |
sp |
The log smoothing parameters. |
Eb |
A balanced version of the total penalty matrix: usd for numerical rank determination. |
UrS |
List of square root penalties premultiplied by transpose of orthogonal basis for the total penalty. |
weights |
prior weights for fitting. |
start |
optional starting parameter guesses. |
etastart |
optional starting values for the linear predictor. |
mustart |
optional starting values for the mean. |
offset |
the model offset |
U1 |
An orthogonal basis for the range space of the penalty — required for ML smoothness estimation only. |
Mp |
The dimension of the total penalty null space — required for ML smoothness estimation only. |
family |
the family - actually this routine would never be called with |
control |
control list as returned from |
intercept |
does the model have and intercept, |
deriv |
Should derivatives of the GCV and UBRE/AIC scores be calculated? 0, 1 or 2, indicating the maximum order of differentiation to apply. |
gamma |
The weight given to each degree of freedom in the GCV and UBRE scores can be varied (usually increased) using this parameter. |
scale |
The scale parameter - needed for the UBRE/AIC score. |
printWarn |
Set to |
scoreType |
specifies smoothing parameter selection criterion to use. |
null.coef |
coefficients for a model which gives some sort of upper bound on deviance. This allows immediate divergence problems to be controlled. |
pearson.extra |
Extra component to add to numerator of pearson statistic in P-REML/P-ML smoothness selection criteria. |
dev.extra |
Extra component to add to deviance for REML/ML type smoothness selection criteria. |
n.true |
Number of data to assume in smoothness selection criteria. <=0 indicates that it should be the
number of rows of |
Sl |
A smooth list suitable for passing to gam.fit5. |
... |
Other arguments: ignored. |
This routine is basically glm.fit
with some
modifications to allow (i) for quadratic penalties on the log likelihood;
(ii) derivatives of the model coefficients with respect to
log smoothing parameters to be obtained by use of the implicit function theorem and
(iii) derivatives of the GAM GCV, UBRE/AIC, REML or ML scores to be
evaluated at convergence.
In addition the routines apply step halving to any step that increases the penalized deviance substantially.
The most costly parts of the calculations are performed by calls to compiled C code (which in turn calls LAPACK routines) in place of the compiled code that would usually perform least squares estimation on the working model in the IRLS iteration.
Estimation of smoothing parameters by optimizing GCV scores obtained at convergence of the P-IRLS iteration was proposed by O'Sullivan et al. (1986), and is here termed ‘outer’ iteration.
Note that use of non-standard families with this routine requires modification
of the families as described in fix.family.link
.
Simon N. Wood<EMAIL_ADDRESS>The routine has been modified from glm.fit
in R 2.0.1, written
by the R core (see glm.fit
for further credits).
Wood, S.N. (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36
O 'Sullivan, Yandall & Raynor (1986) Automatic smoothing of regression functions in generalized linear models. J. Amer. Statist. Assoc. 81:96-103.
http://www.maths.bris.ac.uk/~sw15190/


Written statement to Parliament
David Laws, the Minister of State for Schools, laid this written ministerial statement on school funding reforms before Parliament.
Schools across the country are unfairly funded as a result of a historic and out-of-date system. In March last year, the Secretary of State confirmed that we would rectify these injustices with a new national funding formula. It will be introduced during the next spending review period. The Secretary of State also announced a number of changes to the school funding system to pave the way for a national funding formula. These changes took effect from April 2013.
This started a welcome journey towards a fairer and more transparent funding system, but following consultation with the sector a number of improvements to the initial arrangements need to be made.
The department carried out a review to understand how the changes introduced in April 2013 have been implemented. We published a document on 12 February seeking views from our partners, and officials also undertook fieldwork in 11 local authorities spread across the country.
We are grateful to the many MPs, headteachers, governors, local authority officers and parents who have taken the time to contribute as part of the review.
Today we are publishing a document which sets out the changes we will be making to school funding arrangements in light of the findings from the review.
In order to maintain momentum towards a national funding formula, we will ensure that more money is targeted to pupils. We will require local authorities to allocate a minimum of 80% of their funding on the basis of pupil characteristics and we will also be setting a minimum amount that each pupil should receive.
In our consultation there was concern about the ability of local authorities to support small schools in rural areas. From April 2014, therefore, we will enable local authorities to provide additional funding for schools in sparsely populated areas.
The document also sets out new flexibilities to provide different amounts of funding to cover the fixed costs of primary and secondary (as well as middle and all through) schools. These new flexibilities will ensure local authorities can act to take account of varying fixed costs between different types of school. Schools that merge will also be able to keep some of their funding for fixed costs for at least the first year.
We will continue to target support on deprived and vulnerable pupils. Local authorities will be required to target additional funding to deprived pupils in addition to the pupil premium. We are also making changes to ensure that those pupils who are less likely to attain well at the end of the primary or secondary phase are identified and attract additional funding.
We also want to encourage local authorities to provide the right level of additional funding for schools to enable them to support looked-after children, regardless of how long the child has been in care.
We made significant changes to the funding arrangements for pupils with special educational needs last year so we are not making any further substantial changes in 2014. We are, though, intending to require all local authorities to provide notional SEN budgets to their schools on the basis that the school will meet the costs of the first £6,000 of additional support required by a pupil with SEN .
In the document we are publishing today, we are providing the detailed findings from the review, the approach which will be put in place from April 2014 and technical guidance on this for local authorities. Copies of these documents will be placed on the House libraries.
Taken together, these changes will further strengthen our funding reforms and will help us move towards our aim of ensuring that pupils attract a more consistent amount of funding wherever they go to school in the country.
Published: 4 June 2013


Parameter fitting
The performance of distfit
can be examined by various aspects. In this section we will evaluate the detected parameters, and the goodness of fit of the detected probability density function (pdf).
Lets evaluate the performance of distfit
of the detected parameters when we draw random samples from a normal (Gaussian) distribution with mu*=0 and *std*=2. We would expect to find *mu and std very close to the input values.
from distfit import distfit
import numpy as np
# Initialize model and specify distribution to be normal
X = np.random.normal(0, 2, 5000)
For demonstration puprposes we pre-specify the normal
distribution to find the best parameters. When we do that, as shown below, a mean or loc of 0.004 is detected and a standard deviation (scale) with 2.02 which is very close to our input parameters.
dfit = distfit(distr='norm')
dfit.fit_transform(X)
print(dfit.model)
# {'name': 'norm',
# 'score': 0.0008652960233748114,
# 'loc': -0.03151369033439687,
# 'scale': 2.0045971659158206,
# 'arg': (),
# 'params': (-0.03151369033439687, 2.0045971659158206),
# 'model': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x1dbd0819450>,
# 'bootstrap_score': 0,
# 'bootstrap_pass': None,
# 'color': '#e41a1c',
# 'CII_min_alpha': -3.3287826092676776,
# 'CII_max_alpha': 3.265755228598883}
dfit.plot(chart='pdf')
Probability Density Function fitting
To measure the goodness of fit of PDFs, we will evaluate multiple PDFs using RSS. The goodness of fit scores are stored in dfit.summary
. In this example, we will not specify any distribution but only provide the empirical data and compute the fits for the different PDFs.
An example of the results are shown in the code section.
dfit = distfit()
dfit.fit_transform(X)
print(dfit.summary)
# distr score ... scale arg
# 0 norm 0.00215419 ... 2.021 ()
# 1 t 0.00215429 ... 2.02105 (2734197.302263666,)
# 2 gamma 0.00216592 ... 0.00599666<PHONE_NUMBER>7029496,)
# 3 beta 0.00220002 ... 39.4803 (46.39522231565038, 47.98055489856441)
# 4 lognorm 0.00226011 ... 139.173 (0.014515926633415211,)
# 5 genextreme 0.00370334 ... 2.01326 (0.2516817342848604,)
# 6 dweibull 0.00617939 ... 1.732 (1.2681369071313497,)
# 7 uniform 0.244839 ... 14.3579 ()
# 8 pareto 0.358765 ... 2.40844e+08 (31772216.567824945,)
# 9 expon 0.360553 ... 7.51848 ()
If we repeat the experiment again, teh results will change slightly due to the stochastic element. See figure below.
dfit.plot_summary()
The model detected beta
as the best PDF but a good RSS score is also detected for other distributions.
But why did the normal distribution not have the lowest Residual Sum of Squares despite we generated random normal data?
Well, first of all, our input data set will always be a finite list that is bound within a (narrow) range. In contradition, the theoretical (normal) distribution goes to infinity in both directions.
Secondly, all statistical analyses are based on models, and all models are merely simplifications of the real world. Or in other words, to approximate the theoretical distributions, we need to use multiple statistical tests, each with its own (dis)advantages.
Finally, some distributions have a very flexible character for which the (log)gamma is a clear example. For a large k, the gamma distribution converges to normal distribution.
The result is that the top 7 distributions have a similar and low RSS score, among them the normal distribution. We can see in the summary statistics that the estimated parameters for the normal distribution are loc=~0 and scale=~2, which is very close to our initially generated random sample population (mean=0, std=2). All things considered, A very good result.
Bootstrapping
We can further validate our fitted model using a bootstrapping approach and the Kolmogorov-Smirnov (KS) test to assess the goodness of fit. If the model is overfitting, the KS test will reveal a significant difference between the bootstrapped samples and the original data, indicating that the model is not representative of the underlying distribution. In distfit, the n_bootst parameter can be set during initialization or afterward (see code section). Note that bootstrapping is computationally intensive.
The goal here is to estimate the KS statistic of the fitted distribution when the params are estimated from data.
Resample using fitted distribution.
Use the resampled data to fit the distribution.
Compare the resampled data vs. fitted PDF.
Repeat 1000 times the steps 1-3
return score=ratio succes / n_boots
return whether the 95% CII for the KS-test statistic is valid.
# Import library
from distfit import distfit
# Initialize with 100 permutations
dfit = distfit(n_boots=100)
# Generate data from PDF
# X = np.random.exponential(0.5, 10000)
# X = np.random.uniform(0, 1000, 10000)
X = np.random.normal(163, 10, 10000)
# Fit model
results = dfit.fit_transform(X)
# Results are stored in summary
dfit.summary[['name', 'score', 'bootstrap_score', 'bootstrap_pass']]
# Create summary plot
dfit.plot_summary()
The plot contains on the left axes the goodness of fit test and on the right axes (orange line) the bootstrap result. The bootstrap score is a value between [0, 1] and depicts the fit-success ratio for the number of bootstraps and the PDF. In addition, the green and red dots depict whether there was a significant difference between the bootstrapped samples and the original data. The bootstrap test now excludes a few more PDFs that showed no stable results. The combined information demonstrates that the top 6 distributions are stable, whereas the t-distribution and the dweibull-distribution are not representative for the Emperical data.
Varying sample sizes
The goodness of fit will change according the number of samples that is provided. In the example above we specified 5000 samples which gave good results. However, with a relative low number of samples, a poor fit can occur. For demonstration purposes we will vary the number of samples and store the mu, std. In this experiment we are generating random continuous values from a normal distribution. We will fixate fitting normal distribution and examine the loc, and scale parameters.
# Initialize without verbose.
dfit = distfit(verbose=0)
# Create random data with varying number of samples
samples = np.arange(250, 10000, 250)
# Initialize model
distr='norm'
# Estimate parameters for the number of samples
for s in samples:
print(s)
X = np.random.normal(0, 2, s)
dfit.fit_transform(X)
out.append([dfit.model['loc'], dfit.model['scale'], dfit.model['name'], s])
When we plot the results, distfit
nicely shows that by increasing the number of samples results in a better fit of the parameters. A convergence towards mu=2 and std=0 is clearly seen.
Smoothing
If the number of samples is very low, it can be difficult to get a good fit on your data.
A solution is to play with the bin
size, eg. increase bin size.
Another manner is by smoothing the histogram with the smooth
parameter. The default is set to None
.
Lets evaluate the effect of this parameter.
# Generate data
X = np.random.normal(0, 2, 100)
# Fit model without smoothing
model = distfit()
model.fit_transform(X)
model.plot()
# Fit model with heavy smoothing
model = distfit(smooth=10)
model.fit_transform(X)
model.plot()
Here we are going to combine the number of samples with the smoothing parameter. It is interesting to see that there is no clear contribution of the smoothing. The legends depicts the smoothing window with the average mu. We see that all smooting windows jump up-and-down the mu=2. However, the more samples, the smaller the variation becomes. The smooting parameter seems to be only effective in very low sample sizes.
Lets analyze the RSS score acorss the varying sample sizes and smooting windows. The figure below depicts number of samples on the x-axis, and the RSS score on the y-axis. The lower the RSS score (towards zero) the better the fit. What we clearly see is that not smooting shows the best fit by an increasing number of samples (blue line). As an example, from 7000 samples, the smooting window does not improve the fitting at all anymore. The conlusion is that smooting seems only be effective for samples sizes lower then approximately 5000 samples. Note that this number may be different across data sets.
Integer fitting
It is recommend to fit integers using the method=discrete
.
Here, I will demonstrate the effect of fitting a uniform distribution on integer values. lets generate random integer values from a uniform distribution, and examine the RSS scores. We will iterate over sample sizes and smoothing windows to analyze the performance.
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
import numpy as np
from distfit import distfit
# Sample sizes
samples = np.arange(250, 20000, 250)
# Smooting windows
smooth_window=[None, 2, 4, 6, 8, 10]
# Figure
plt.figure(figsize=(15,10))
# Iterate over smooting window
for smooth in tqdm(smooth_window):
# Fit only for the uniform distribution
dfit = distfit(distr='uniform', smooth=smooth, stats='RSS', verbose=0)
# Estimate paramters for the number of samples
out = []
# Iterate over sample sizes
for s in samples:
X = np.random.randint(0, 100, s)
dfit.fit_transform(X)
out.append([dfit.model['score'], dfit.model['name'], s])
df = pd.DataFrame(out, columns=[dfit.stats,'name','samples'])
ax=df[dfit.stats].plot(grid=True, label='smooth: '+str(smooth) + ' - RSS: ' + str(df[dfit.stats].mean()))
ax.set_xlabel('Nr.Samples')
ax.set_ylabel('RSS')
ax.set_xticks(np.arange(0,len(samples)))
ax.set_xticklabels(samples.astype(str))
ax.set_ylim([0, 0.001])
ax.legend()
The code above results in the underneath figure, where we have varying sample sizes on the x-axis, and the RSS score on the y-axis. The lower the RSS score (towards zero) the better the fit. What we clearly see is that orange is jumping up-and-down. This is smooting window=2. Tip: do not use this. Interesting to see is that not smooting shows the best fit by an increasing number of samples. Smooting does not improve the fitting anymore in case of more then 7000 samples. Note that this number may be different across data sets.
From these results we can conclude that smooting seems only usefull for small(er) samples sizes.


{% extends "base.html" %} {% import 'bootstrap/wtf.html' as wtf %} {% block app_content %} {% if help %} {% set help_temporal_coverage_id, help_temporal_coverage_title, help_temporal_coverage_content = help[0] %} {% set help_temporal_coverage_btn = help_temporal_coverage_id ~ '_btn' %} {% set help_temporal_coverage_dialog = help_temporal_coverage_id ~ '_dialog' %} {% endif %}
Temporal Coverage{% if entity_name %} for Data Entity: {{ entity_name }}{% endif %} |
{{ macros.help_button(help_temporal_coverage_btn) }} |


perl - <IP_ADDRESS>3 docs
Directory list for docs
Index made on Saturday, November 12 2005 at 02:58
Contents
Nr
Name
Size
Contents
Type
1.
perl.<IP_ADDRESS>3
4.0K
Unknown contents
Directory
Package assembled by
UNI
·
C
Saturday, November 12 2005 at 02:58


{{userName}} offered to help on {{formatDate madeOn}}
Email this solver: {{userEmail}}
Message:
{{message}}


TVWise is an accredited installer of various satilite television brands in South Africa
DSTV is South Africa's leading satalite television company. TVWise is an offical accredited DSTV installer
Single view installation to one point...
Single view installation to two points...
Explora installation to one point...
Explora installation to two points...


Welcome to I'm On A Call
I'm On A Call was created in Go using
Goji web microframework,
Gorilla web toolkit sessions .
It provides essentials that most web applications need - MVC pattern and user authorisation that can be easily extended.
Project's source code can be found here:
{{end}}


Using the Criteria API and Metamodel API to Create Basic Typesafe Queries
The basic semantics of a Criteria query consists of a SELECT clause, a FROM clause, and an optional WHERE clause, similar to a JPQL query. Criteria queries set these clauses by using Java programming language objects, so the query can be created in a typesafe manner.
Creating a Criteria Query
The javax.persistence.criteria.CriteriaBuilder interface is used to construct
Criteria queries
Selections
Expressions
Predicates
Ordering
To obtain an instance of the CriteriaBuilder interface, call the getCriteriaBuilder method on either an EntityManager or an EntityManagerFactory instance.
The following code shows how to obtain a CriteriaBuilder instance by using the EntityManager.getCriteriaBuilder method.
EntityManager em = ...; CriteriaBuilder cb = em.getCriteriaBuilder();
Criteria queries are constructed by obtaining an instance of the following interface:
javax.persistence.criteria.CriteriaQuery
CriteriaQuery objects define a particular query that will navigate over one or more entities. Obtain CriteriaQuery instances by calling one of the CriteriaBuilder.createQuery methods. For creating typesafe queries, call the CriteriaBuilder.createQuery method as follows:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class);
The CriteriaQuery object’s type should be set to the expected result type of the query. In the preceding code, the object’s type is set to CriteriaQuery<Pet> for a query that will find instances of the Pet entity.
In the following code snippet, a CriteriaQuery object is created for a query that returns a String:
CriteriaQuery<String> cq = cb.createQuery(String.class);
Query Roots
For a particular CriteriaQueryobject, the root entity of the query, from which all navigation originates, is called the query root. It is similar to the FROM clause in a JPQL query.
Create the query root by calling the from method on the CriteriaQuery instance. The argument to the from method is either the entity class or an EntityType<T> instance for the entity.
The following code sets the query root to the Pet entity:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Root<Pet> pet = cq.from(Pet.class);
The following code sets the query root to the Pet class by using an EntityType<T> instance:
EntityManager em = ...; Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet_);
Criteria queries may have more than one query root. This usually occurs when the query navigates from several entities.
The following code has two Root instances:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Root<Pet> pet1 = cq.from(Pet.class); Root<Pet> pet2 = cq.from(Pet.class);
Querying Relationships Using Joins
For queries that navigate to related entity classes, the query must define a join to the related entity by calling one of the From.join methods on the query root object or another join object. The join methods are similar to the JOIN keyword in JPQL.
The target of the join uses the Metamodel class of type EntityType<T> to specify the persistent field or property of the joined entity.
The join methods return an object of type Join<X, Y>, where X is the source entity and Y is the target of the join. In the following code snippet, Pet is the source entity, and Owner is the target:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); Join<Pet, Owner> owner = pet.join(Pet_.owners);
Joins can be chained together to navigate to related entities of the target entity without having to create a Join<X, Y> instance for each join:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); EntityType<Owner> Owner_ = m.entity(Owner.class); Root<Pet> pet = cq.from(Pet.class); Join<Owner, Address> address = cq.join(Pet_.owners).join(Owner_.addresses);
Path Navigation in Criteria Queries
Path objects are used in the SELECT and WHERE clauses of a Criteria query and can be query root entities, join entities, or other Path objects. The Path.get method is used to navigate to attributes of the entities of a query.
The argument to the get method is the corresponding attribute of the entity’s Metamodel class. The attribute can either be a single-valued attribute, specified by @SingularAttribute in the Metamodel class, or a collection-valued attribute, specified by one of @CollectionAttribute, @SetAttribute, @ListAttribute, or @MapAttribute.
The following query returns the names of all the pets in the data store. The get method is called on the query root, pet, with the name attribute of the Pet entity’s Metamodel class, Pet_ as the argument:
CriteriaQuery<String> cq = cb.createQuery(String.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.select(pet.get(Pet_.name));
Restricting Criteria Query Results
The results of a query can be restricted on the CriteriaQuery object according to conditions set by calling the CriteriaQuery.where method. Calling the where method is analogous to setting the WHERE clause in a JPQL query.
The where method evaluates instances of the Expression interface to restrict the results according to the conditions of the expressions. Expression instances are created by using methods defined in the Expression and CriteriaBuilder interfaces.
The Expression Interface Methods
An Expression object is used in a query's SELECT, WHERE, or HAVING clause. Table 35-1 shows conditional methods you can use with Expression objects.
Method |
Description |
|---|---|
isNull |
Tests whether an expression is null |
isNotNull |
Tests whether an expression is not null |
in |
Tests whether an expression is within a list of values |
The following query uses the Expression.isNull method to find all pets where the color attribute is null:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.where(pet.get(Pet_.color).isNull());
The following query uses the Expression.in method to find all brown and black pets:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.where(pet.get(Pet_.color).in("brown", "black"));
The in method also can check whether an attribute is a member of a collection.
Expression Methods in the CriteriaBuilder Interface
The CriteriaBuilder interface defines additional methods for creating expressions. These methods correspond to the arithmetic, string, date, time, and case operators and functions of JPQL. Table 35-2 shows conditional methods you can use with CriteriaBuilder objects.
Conditional Method |
Description |
|---|---|
equal |
Tests whether two expressions are equal |
notEqual |
Tests whether two expressions are not equal |
gt |
Tests whether the first numeric expression is greater than the second numeric expression |
ge |
Tests whether the first numeric expression is greater than or equal to the second numeric expression |
lt |
Tests whether the first numeric expression is less than the second numeric expression |
le |
Tests whether the first numeric expression is less than or equal to the second numeric expression |
between |
Tests whether the first expression is between the second and third expression in value |
like |
Tests whether the expression matches a given pattern |
The following code uses the CriteriaBuilder.equal method:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.where(cb.equal(pet.get(Pet_.name), "Fido")); ...
The following code uses the CriteriaBuilder.gt method:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); Date someDate = new Date(...); cq.where(cb.gt(pet.get(Pet_.birthday), date));
The following code uses the CriteriaBuilder.between method:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); Date firstDate = new Date(...); Date secondDate = new Date(...); cq.where(cb.between(pet.get(Pet_.birthday), firstDate, secondDate));
The following code uses the CriteriaBuilder.like method:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.where(cb.like(pet.get(Pet_.name), "*do"));
Multiple conditional predicates can be specified by using the compound predicate methods of the CriteriaBuilder interface, as shown in Table 35-3.
Method |
Description |
|---|---|
and |
A logical conjunction of two Boolean expressions |
or |
A logical disjunction of two Boolean expressions |
not |
A logical negation of the given Boolean expression |
The following code shows the use of compound predicates in queries:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Metamodel m = em.getMetamodel(); EntityType<Pet> Pet_ = m.entity(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.where(cb.equal(pet.get(Pet_.name), "Fido") .and(cb.equal(pet.get(Pet_.color), "brown")));
Managing Criteria Query Results
For queries that return more than one result, it’s often helpful to organize those results. The CriteriaQuery interface defines the orderBy method to order query results according to attributes of an entity. The CriteriaQuery interface also defines the groupBy method to group the results of a query together according to attributes of an entity, and the having method to restrict those groups according to a condition.
Ordering Results
The order of the results of a query can be set by calling the CriteriaQuery.orderBy method and passing in an Order object. Order objects are created by calling either the CriteriaBuilder.asc or the CriteriaBuilder.desc method. The asc method is used to order the results by ascending value of the passed expression parameter. The desc method is used to order the results by descending value of the passed expression parameter. The following query shows the use of the desc method:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.select(pet); cq.orderBy(cb.desc(pet.get(Pet_.birthday)));
In this query, the results will be ordered by the pet’s birthday from highest to lowest. That is, pets born in December will appear before pets born in May.
The following query shows the use of the asc method:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Root<Pet> pet = cq.from(Pet.class); Join<Owner, Address> address = cq.join(Pet_.owners).join(Owner_.address); cq.select(pet); cq.orderBy(cb.asc(address.get(Address_.postalCode)));
In this query, the results will be ordered by the pet owner’s postal code from lowest to highest. That is, pets whose owner lives in the 10001 zip code will appear before pets whose owner lives in the 91000 zip code.
If more than one Order object is passed to orderBy, the precedence is determined by the order in which they appear in the argument list of orderBy. The first Order object has precedence.
The following code orders results by multiple criteria:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Root<Pet> pet = cq.from(Pet.class); Join<Pet, Owner> owner = cq.join(Pet_.owners); cq.select(pet); cq.orderBy(cb.asc(owner.get(Owner_.lastName), owner.get(Owner_.firstName)));
The results of this query will be ordered alphabetically by the pet owner’s last name, then first name.
Grouping Results
The CriteriaQuery.groupBy method partitions the query results into groups. These groups are set by passing an expression to groupBy:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.groupBy(pet.get(Pet_.color));
This query returns all Pet entities and groups the results by the pet’s color.
The CriteriaQuery.having method is used in conjunction with groupBy to filter over the groups. The having method takes a conditional expression as a parameter. By calling the having method, the query result is restricted according to the conditional expression:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); Root<Pet> pet = cq.from(Pet.class); cq.groupBy(pet.get(Pet_.color)); cq.having(cb.in(pet.get(Pet_.color)).value("brown").value("blonde"));
In this example, the query groups the returned Pet entities by color, as in the preceding example. However, the only returned groups will be Pet entities where the color attribute is set to brown or blonde. That is, no gray-colored pets will be returned in this query.
Executing Queries
To prepare a query for execution, create a TypedQuery<T> object with the type of the query result by passing the CriteriaQuery object to EntityManager.createQuery.
Queries are executed by calling either getSingleResult or getResultList on the TypedQuery<T> object.
Single-Valued Query Results
The TypedQuery<T>.getSingleResult method is used for executing queries that return a single result:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); ... TypedQuery<Pet> q = em.createQuery(cq); Pet result = q.getSingleResult();
Collection-Valued Query Results
The TypedQuery<T>.getResultList method is used for executing queries that return a collection of objects:
CriteriaQuery<Pet> cq = cb.createQuery(Pet.class); ... TypedQuery<Pet> q = em.createQuery(cq); List<Pet> results = q.getResultList();


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Memory Buffers¶
wgrib2 C API has an unorthodox design. Usually, libraries are written
first, executables call functions from those libraries. In case of wgrib2,
the C main
functions was made a library function. As a consequence,
all arguments must be strings. Data returned by wgrib2
is written to
memory buffers. The API provides methods to read those buffers. wgrib2
can also process data written to memory buffers by the client application.
There can be up to 30 distinct memory buffers (and 20 RPN registers, used
to pass calculated data, those are also chunks of memory). The buffers are
identifiable by a number.
In order to free the library user from keeping track of those numbers,
pywgrib2_xr
has its own classes MemoryBuffer
and RPNRegister
that shadow their wgrib2
counterparts.
Instantiating a class retrieves available number from a pool, calling
close()
returns buffer number
to the pool. It does not free allocated memory. Closing the buffer can
also be achieved by using the with
construct.
Examples:
Create a short inventory of a GRIB2 file in memory, then print it.
import pywgrib2_xr as pywgrib2 from pywgrib2_xr.utils import localpath file = localpath('CMC_glb_TMP_ISBL_850_ps30km_2020012500_P000.grib2') with pywgrib2.MemoryBuffer() as buf: args = [file, '-inv', buf] pywgrib2.wgrib(*args) inv = buf.get('s') print(inv)
1:0:d=2020012500:TMP:850 mb:anl:
Here, the argument 's'
in a call to buf.get()
means the returned data should
be a string.
Read values and geolocation data into
numpy
arrays.
import pywgrib2_xr as pywgrib2 from pywgrib2_xr.utils import localpath from contextlib import ExitStack file = localpath('CMC_glb_ps30km_2020012512.grib2') with ExitStack() as stack: data_reg = stack.enter_context(pywgrib2.RPNRegister()) lon_reg = stack.enter_context(pywgrib2.RPNRegister()) lat_reg = stack.enter_context(pywgrib2.RPNRegister()) inv_buf = stack.enter_context(pywgrib2.MemoryBuffer()) args = [file, '-rewind_init', file, '-d', 3, '-inv', inv_buf, '-ftn_api_fn0', '-rpn_sto', data_reg, '-rpn', 'rcl_lon:sto_{}'.format(lon_reg), '-rpn', 'rcl_lat:sto_{}'.format(lat_reg)] pywgrib2.wgrib(*args) npts, nx, ny = [int(i) for i in buf.get('s').split()[2:5]] tmp = data_reg.get().reshape((ny, nx)) lon = lon_reg.get().reshape((ny, nx)) lat = lat_reg.get().reshape((ny, nx)) print(lon[:3,:3]) print(lat[:3,:3]) print(tmp[0,0], tmp[-1, -1])
[[225.38573 225.62788 225.87093]
[225.2796 225.5226 225.76648]
[225.17259 225.41641 225.66115]]
[[32.549114 32.637794 32.725685]
[32.752975 32.842205 32.93064 ]
[32.957066 33.04685 33.135838]]
288.38113 285.1811
All three registers and the memory buffers are available for reuse.


You can specify multiple symbols for a given state by adding a new entry under the first state entry and leaving the "State Name" column blank.
The first state in the table will be considered the initial state.
Valid symbols are any single character, "any" for any non-blank character or "none" for a blank square.
The "any" symbol is considered a fallback symbol if the state has any other symbols associated with it.
Valid operations are a comma-separated list of the following items (e.g. "R, R, P1"):
L: Shift head left, R: Shift head right, Px: Print x, where x can be any character, E: Erase character
| State Name | Symbol | Operations | Result State |
|---|


Samsung Galaxy S10 Lite Will Give the S10 a Run for Its Money Based on Leaked Specs
by Shabana ArifWeirdly, the specs for the S10 Lite are significantly better than we'd expect.

He tweeted that the S10 Lite will house the Snapdragon 855 processor, and will sport a 48MP main camera, 5MP depth, and 12MP ultra-wide, as well as a 32MP front-facing camera.
The S10 contains an Exynos 9820 chipset here in the UK, and the Snapdragon 855 in the US, so there's no huge difference there. The cameras are another story, with a much lower pixelcount than the S10 Lite. The S10 features just a 1oMP main camera, and 16MP + 12MP + 12MP rear camera.
It seems odd that the specs for a 'lite' version of anything should be better than the original, but the leaker is generally spot on, so if they say it's the S10 Lite and not some version of the S11, than that's what it is. For now. [Trusted Reviews]


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The LOOP package provides a library of classes implemented using its models. All classes included in its library were created or derived from actual implementations used in either academic or commercial applications, and many are reused in more than one application. Therefore, even though the classes presented here are provided mainly as use examples of LOOP models they are very useful for construction of software applications.
Most of these classes were initially created as Lua conventional modules and were later converted to classes so it is straightforward to convert them back to modules that don't lay on LOOP. Some of these classes can even be used as an instance of itself thus avoiding the creation of singleton objects. However, in some scenarios, it is interesting to be able to have multiple instances. The LOOP Class Library is organized in six groups as presented below.
This group contains classes that implement different data structures that are particularly efficient in Lua.
Some of these classes were specifically devised for the implementation of a particular class of the LOOP library and therefore are not very general.
However, classes like ObjectCache
, UnorderedArray
and OrderedSet
are very general and may be used in many different situations.
Class of objects that store a sequence of values as a Lua array but removes the elements from any position without shifting the following elements. However it does not guarantee the order the values are stored in the array. This class is useful for storing efficiently a sequence of values that may be iterated in any order and the number of elements varies constantly during interation.
Class of objects that store a sequence of values as a Lua array like class UnorderedArray. However it additionally maps values to its current position in the array allowing efficient containment checks and location of a value in the array for removal. This class is useful when it is necessary to store a set elements (no duplicates) as an array (e.g. to comply with some library API) but such elements are removed and inserted frequently or it is necessary to perform quick containment checks.
Class of objects that store a set of values arranged in a particular order. As a usual set, it does not allow duplicates and checks for containment efficiently. The values are stored like a linked list, therefore the sequence can only be iterated in a single direction, i.e. cannot get the previous value. Insertions and removals at any point of the sequence are done efficiently. This class is useful for storing a large set of values without repetitions that are organized in a particular order and accepts frequent insertions and removals at any point of the sequence during interations.
Class of objects that store a set of values with associated weight values so that these values are arranged in the descending order of weight.
The values are stored like in the OrderedSet
class, but the values are sorted in descending order of weight.
This class is useful for implementing priority queues with no repeated values and which priorities are comparable values like numbers or strings.
Class of objects that store in a single table a mapping of non-integer key values to arbitrary values and a sequence of all the key values currently mapped.
The array of keys is stored just like the sequence stored by UnorderedArray
instances.
This class is useful for storing a map of non-integer values and an array of its keys in a single table.


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STATION 4 of 4
SDE4’s landscape seamlessly integrates greenery, water, and community through spaces that support everyday activities. For instance, this water feature is only active when it rains - adding sound, texture, and movement to the landscape while also becoming a pedagogical tool for teaching and research.


The Dark Forest lurks with friends and foes, thieves and martians...tames
the monkeys and whispers to sapplings, raging its arms in swirls.
Mom and Timmie were strolling in the Dark Forest to spend quality time.
Enchanted by the pink worms in the loops of ground, Timmie sees his mom disappear,
swallowed by the dark night.
Timmie needs to find his mom before the forest rests to black.
Help Timmie find his mom!


This year, as freshman students in Epita we had to do a six months computer project in groups of four. Capz was created in these circumstances. the other point that created the group was the motivation. Indeed, we are a group of motivated students, especially in programmation and in fact, what we love. Maining that a choosen project should be a point of motivation for all of us. Finally we had quielty the same point of view on videos games, so we had the same expectations for this project. All of theses circumstances, motivated us to create this group.
-
The History page is available if you want more details


3 references to Read_CodeReference
System.Management.Automation (3)
engine\TypeTable.cs (3)
940
codeReference =
Read_CodeReference
();
1182
getter =
Read_CodeReference
();
1196
setter =
Read_CodeReference
();


class: center, middle, inverse, title-slide # Quantitative and qualitative characters ## Quantitative inheritance
Qualitative inheritance
Threshold characters
Multiple factor hypothesis
### Deependra Dhakal ### 2019/04/13 (updated: 2020-11-06) --- class: inverse, center, middle # Inheritance of traits --- # Background - The character may be simply inherited or complex inherited with effect of many genes at different loci, each contributing a small effect to phenotypic expression of the character 1. Qualitative characters 2. Quantitative characters - Study of inheritance of most characters/phenotypes can be classified into: 1. Easily distinguished into discrete classes - barley plants may be - black or white hulled - two or six rowed - rough or smooth awned - rust resistanct or rust susceptible 2. Cannot be easily classified into discrete classes - for grain yield `\(kg~ha^{-1}\)` - thousand grain weight (gram), - plant height (cm) variation may be differing by small units --- background-image: url(./images/Pea_lathyrus_odoratus_flower.jpg) background-position: 50% 50% background-size: 70% class: inverse, middle, center # Qualitative and quantitative inheritance --- ## Quantitative inheritance - Most of the important variation displayed by nearly all plant traits affecting growth, development and reproduction, is quantitative. - Also called: *Continuous*, *Polygenic variation*, *Multiple gene controlled traits* - Demonstrate same basic Mendelian properties for a gene, and also the Hardy-Weinberg equilibrium. - Quantitative characters are governed by several genes; each gene has a small effect, which is usually cumulative. - The environments considerably affect these characters. - Quantitative characters often show continuous variation with normal distribution --- ## Qualitative inheritance - Mendel purposed the law of inheritance based on his studies with qualitative characters. - In the studies of qualitative inheritance, we study phenomena such as: 1. Dominance, 2. Segregation and independent assortment, 3. Gene action and interactions (Epistatis, Masking gene action, Duplicate gene action, Complementary gene action, Additive gene action, Inhibiting gene action, Modifying gene action and Pleiotropy). 4. Penetrance and expressivity 5. Linkage ---

Qualitative inheritance (incomplete dominance); In snapdragons, a heterozygote is pink, intermediate between the two homozygotes red and white. The pink heterozygote demonstrates incomplete dominance.

 --- ## Difference between quantitative and qualitative traits

Difference between qualitative and quantitative traits

  Character 	  Qualitative 	  Quantitative

  Number of gene and loci controlling the character 	  Few, one (mongenic) or few major genes (oligogenic) 	  Many, polygenes (polygeneic)

  Effect of individual gene 	  Large 	  Small

  Follow mendel's law 	  Yes 	  Yes

  Classification on few discrete classes that are easily distinguished 	  Yes 	  No

  Measurement of the character 	  Observation 	  Measured in metric units

  Frequency distribution 	  Discrete 	  Continuous and often normal

  Effect of environment on expression of character 	  No or little affected 	  Largely affected

  Examples 	  Disease resistance, Presence of awns, Seed color 	  Grain yield (kg/ha), Thousand kernel weight (gram)

 --- - Nature of traits: Qualitative genetics is concerned with traits that have Mendelian inheritance and can be described according to kind and, as previously discussed, can be unambiguously categorized. Quantitative genetic traits are described in terms of the degree of expression of the trait, rather than the kind. - Scale of variability: Qualitative genetic traits provide discrete (discontinuous) phenotypic variation, whereas quantitative genetic traits produce phenotypic variation that spans the full spectrum (continuous). - Number of genes: In qualitative genetics, the effects of single genes are readily detectable, while in quantitative genetics single gene effects are not discernible. Rather, traits are under polygenic control (genes with small indistinguishable effects). --- - Mating pattern: Qualitative genetics is concerned with individual matings and their progenies. Quantitative genetics is concerned with a population of individuals that may comprise of a diversity of mating kinds. - Statistical analysis: Qualitative genetic analysis is quite straight forward; it is based on counts and ratios. On the other hand, quantitative analysis provides estimates of population parameters (attributes of the population from which the sample was obtained). --- class: inverse, center, middle # Penetrance and expressivity --- ## Penetrance - The ability of a gene to express itself in the individuals carrying it in the appropriate genotype. - Generally, oligogenes express themselves in all individuals that carry them and their expression is fairly uniform. - But some oligogenes fail to express themselves in some individuals carrying them and are said to have incomplete penetrance. - Gene expressing itself in every individuals that carries it is said to have complete penetrance. --- - In the analysis of single-gene inheritance, there is a natural tendency to choose mutants that produce clear Mendelian ratios. In such cases, we can use the phenotype to distinguish mutant and wild-type genotypes with almost 100 percent certainty. In these cases, we say that the mutation is 100 percent penetrant into the phenotype. However, many mutations show incomplete penetrance: that is, not every individual with the genotype expresses the corresponding phenotype. Thus, penetrance is defined as the percentage of individuals with a given allele who exhibit the phenotype associated with that allele. - Reasons for incomplete penetrance: 1. The influence of the environment 2. The influence of other interacting genes 3. The subtlety of the mutant phenotype ---

In this human pedigree of a dominant allele that is not fully penetrant, person Q does not display the phenotype but passed the dominant allele to at least two progeny. Because the allele is not fully penetrant, the other progeny (for example, R) may or may not have inherited the dominant allele.

 --- ## Expressivity - The ability of a gene to express itself uniformly in all individuals that carry it in the appropriate genotype. - A gene that expresses itself uniformly in all individuals has uniform expressivity while those that are unable to do so have variable expressivity. - Expressivity measures the degree to which a given allele is expressed at the phenotypic level; that is, expressivity measures the intensity of the phenotype. For example, "brown" animals (genotype b/b) from different stocks might show very different intensities of brown pigment from light to dark. As for penetrance, variable expressivity may be due to variation in the allelic constitution of the rest of the genome or to environmental factors. - Expressivity is indicated as variable or uniform. ---

Assume that all the individuals shown have the same pigment allele (P) and possess the same potential to produce pigment. Effects from the rest of the genome and the environment may suppress or modify pigment production in any one individual. The color indicates the level of expression.

 --- ## Effect in selection - Incomplete penetrance and variable expressivity confuse the relation between genotype and phenotype. - Consequently such genes pose difficulty in selection of desired types. --- class: inverse, center, middle # Threshold character --- - Some genes require specific environment, e.g a particular temperature, for expression. - Traits governed by such genes are known as threshold characters. - For example, a mutant gene in barley produces albino seedlings at temperatures below `\(8^\circ C\)`. But when the seedling-carrying gene is grown at `\(19^\circ C\)` or above, they develop into normal green seedlings. - Incomplete penetrance of some genes may be due to a threshold requirement. --- class: inverse, center, middle # Multiple factor hypothesis --- ## Multiple factor hypothesis1 - Many genes with small cumulative effect for any one quantitative character account for bell-shaped normal curve appearance of common traits. The genetic factors responsible for this type of effects were termed as multiple factors. - Now multiple factor has been replaced by polygene or multiple genes (purposed by Mather). - In 1908, Nilsson-Ehle presented experiment evidence to support this hypothesis by studying seed color in wheat and oats (demonstrated an actual segregation and assortment of genes with quantitative effect). - Nilsson-Ehle crossed two varieties of wheat, one with deep red grain of genotype R1R1R2R2, and the other white grain of genotype r1r1r2r2. - The distribution of resulting individuals from cross was explained on the basis of two pairs of genes segregating independently, which each dominant allele adding to the intensity of the red color. - Two or more non allelic genes that affect in similar way of development of a quantitative character are called multiple genes or polygenes. .footnote[ Refer to Chapter 14: Quantitative inheritance, Genetics, Monroe. M. Strickberger, Page 245 ] --- - According to Nilsson-ehle, there were three individual gene pairs involved in determination of grain color in wheat, i.e., Aa, Bb, Cc, with genes for red (ABC) dominant over genes for white (abc). Each of these three gene pairs segregated in predictable mendelian fashion, so that the products of heterozygotes for any one pair, i.e. Aa x Ax, produced offspring in the ratio 3 red (A\_):1 white (aa). When two gene-pair differences were segregating at the same time in Nilsson-Ehle's experiments, i.e., AaBb x AaBb, the results also followed mendelian principles, producing a ratio 15 red (A\_B\_, A\_bb, aaB\_): 1 white (aabb). Similarly a cross between heterozygotes for three gene paris produced a close fit to the predicted ratio 63 red: 1 white. ---

Wheat seed color variation; An example case of multiple factor trait expression

  Number of genotypes 	  F2 genotypes 	  Color 	  Number of plants (Phenotype) 	  Number of dominant genes

  1 	  R1R1R2R2 	  Very dark red 	  1 	  4

  2 	  R1R1R2r2 	  Dark red 	  4 	  3

  2 	  R1r2R2R2 	  Dark red 	  	  3

  1 	  R1R1r2r2 	  Medium red 	  6 	  2

  4 	  R1r1R2r2 	  Medium red 	  	  2

  1 	  R1R1R2R2 	  Medium red 	  	  2

  2 	  R1r1r2r2 	  Light red 	  4 	  1

  2 	  r1r1R2r2 	  Light red 	  	  1

  1 	  r1r1r2r2 	  White 	  1 	  0

 --- class: inverse, center, middle # Transgressive segregation --- - Appearance of features that are unlike either parent. - Quantitative character of progeny may fall outside the range of parents is called transgressive segregation. - Transgressive segregation is used extensively by breeders to obtain segregates following a cross that are superior to the parental strains for traits inherited in a quantitative manner.  --- ## Problem 1 The leaves of pineapples can be classified into three types: spiny (S), spiny tip (ST), and piping (nonspiny; P). In crosses between pure strains followed by intercrosses of the F1, the following results appeared:  a. Assign gene symbols. Explain these results in regard to the genotypes produced and their ratios. b. Using the model from part a, give the phenotypic ratios that you would expect if you crossed 1. the F1 progeny from piping x spiny with the spiny parental stock, and 2. the F1 progeny of piping x spiny with the F1 progeny of spiny x spiny tip. --- ## Solution 1 **a**. First, let's look at the F2 ratios. We have clear 3 : 1 ratios in crosses 1 and 2, indicating single-gene segregations. Cross 3, however, shows a ratio that is almost certainly a 12 : 3 : 1 ratio. How do we know this ratio? Well, there are simply not that many complex ratios in genetics, and trial and error brings us to the 12 : 3 : 1 quite quickly. In the 128 progeny total, the numbers of 96 : 24 : 8 are expected, but the actual numbers fit these expectations remarkably well. One of the principles of this chapter is that modified Mendelian ratios reveal gene interactions. Cross 3 gives F2 numbers appropriate for a modified dihybrid Mendelian ratio, and so it looks as if we are dealing with a two-gene interaction. It seems the most promising place to start; we can return to crosses 1 and 2 and try to fit them in later. Any dihybrid ratio is based on the phenotypic proportions 9 : 3 : 3 : 1. Our observed modification groups them as follows: $$ `\begin{aligned} 9A/\_;B/\_ &~& 9~piping \\ 3A/\_;b/b &~& 3~piping \\ 3a/a;B/\_ &~& 3~spiny~tip \\ 1a/a;b/b &~& 1~spiny \end{aligned}` $$ --- So, without worrying about the name of the type of gene interaction (we are not asked to supply this anyway), we can already define our three pineapple-leaf phenotypes in relation to the proposed allelic pairs A/a and B/b: $$ `\begin{aligned} piping &=& A/\_ (B/b~irrelevant) \\ spiny tip &=& a/a; B/\_ \\ spiny &=& a/a; b/b \end{aligned}` $$ What about the parents of cross 3? The spiny parent must be a/a ; b/b, and, because the B gene is needed to produce F2 spiny-tip leaves, the piping parent must be A/A ; B/B. (Note that we are told that all parents are pure, or homozygous.) The F1 must therefore be A/a ; B/b. Without further thought, we can write out cross 1 as follows: ---   --- We know that the F2 of cross 2 shows single-gene segregation, and it seems certain now that the A/a allelic pair has a role. But the B allele is needed to produce the spiny-tip phenotype, and so all plants must be homozygous B/B:  Notice that the two single-gene segregations in crosses 1 and 2 do not show that the genes are not interacting. What is shown is that the two-gene interaction is not revealed by these crosses - but only by cross 3, in which the F1 is heterozygous for both genes. --- **b**. Now it is simply a matter of using Mendel's laws to predict cross outcomes:  --- class: inverse, middle, center # Gene action and interaction --- ## Gene action2 ### Dominance ### Overdominance ### Additive ## Gene interaction3 .footnote[ - Refer to Lecture 10 (Heterosis and inbreeding depression) of Introductory Plant Breeding Course. - Refer to Lecture 7 (Gene Action and Interaction) of Introductory Genetics course. ] --- class: center, middle, inverse # Measurement of continuous variation --- ## Number of genes contributing to a quantitative character A formula has been purposed for estimating the number of genes n involved in the inheritance of a quantitative character. `$$n = \frac{(\bar{P_1} -\bar{P_2})^2}{8[\sigma_{F_2}^2-\sigma_{F_1}^2]}$$` Where, `\(\bar{P_1}\)` and `\(\bar{P_2}\)` are trait means of inbred parents `\(P_1\)` and `\(P_2\)`, respectively. `\(\sigma_{F_2}^2\)` and `\(\sigma_{F_1}^2\)` are standard deviation of `\(F_2\)` and `\(F_1\)`, respectively. --- ## Components of variation of a quantitative character - Quantitative characters are more affected by environments. Thus phenotypic mean may be described according to a mathematical model to facilitate statistical analysis and interpretation: `$$V_{P} = V_{G} + V_{E} + V_{GE}$$` Where, `\(V_P\)` = Observed phenotypic variance `\(V_G\)` = Genotypic variance `\(V_E\)` = Environmental variance `\(V_{GE}\)` = Variance due to interaction of Genotype and Environment --- ## Heritability - To make economically meaningful progress in an organized programme of selective breeding, two conditions must be met; - There must be some observable phenotypic variation within the crop. This would normally be expected, even if it were due entirely to the effects of a variable environment. - At least some of this phenotypic variation must have a genetic basis. - This leads to the concept of heritability ( `\(h^2\)`), which is the proportion of phenotypic variance that is genetic in origin. - The values of `\(h^2\)` can range from 0 to 1. If `\(h^2\)` is close to zero, there will be little scope for advancement and there would be little point in trying to improve this character in a plant breeding program. --- - There are three main ways of estimating heritability: 1. Carrying out particular genetic crosses and observing the performance of their progeny so that the resulting data can be partitioned into genetic and environmental components. 2. Based on the direct measurement of the degree of resemblance between offspring and one, or both, of their parents. This is achieved by regression of the former onto the latter in the absence of selection. 3. Measuring the response of a population to given levels of selection. - Dominance model of quantitative inheritance dictates that total genetic variance will contain dominance genetic variance (denoted by `\(V_D\)`) and additive genetic variance (denoted by `\(V_A\)`). - Dominance genetic variance is variation caused by heterozygotes loci in the individuals in the population, whereas additive genetic variance is the variation existing between homozygous loci in the segregating population. --- ## Broad sense heritability - The total genetic varinace divided by the total phenotypic variance is Broad-sense heritability ( `\(h_b^2\)`). - This estimation uses the total genetic variance in a additive-dominance model, while the total phenotypic variance is obtained by adding environmental variance to this genetic variance. $$ h_b^2 = \frac{V_A + V_D}{V_A + V_D + V_E} \tag{i} $$ - Dominant genetic variance will be dependent upon the degree of heterozygosity in the population and will differ between fillial generations. --- ## Narrow sense heritability - A more useful form of heritability for plant breeders, therefore, is *narrow-sense heritability* ( `\(h_n^2\)`), which is: $$ h_n^2 = \frac{V_A}{V_A+V_D+V_E} \tag{ii} $$ --- ## Variance partitioning of filial generation - In the first filial generation ( `\(F_1\)`), after hybridization between two homozygous parents, there is not genetic variance between individuals of a progeny (they will be genetically alike) and all the variation observed between `\(F_1\)` plants will be entirely environmental. - In the generation following ( `\(F_2\)` and forth) there are both genetic and environmental components of phenotypic variance. - The genetic variance of the `\(F_2\)` generation is: $$ \sigma_{\bar{F_2}}^2 = \frac{1}{2}V_A + \frac{1}{4}V_D + \sigma_E^2 $$ - Thus broad sense heritability of the `\(F_2\)` generation is: $$ \label{eqn:hbs-filial} h_b^2 = \frac{\frac{1}{2}V_A + \frac{1}{4}V_D}{\frac{1}{2}V_A + \frac{1}{4}V_D + \sigma_E^2} \tag{iii} $$ - In simple terms, to estimate the `\(h_b^2\)` of `\(F_2\)` family (or any other segregating family), only following estimates are required: 1. Total phenotypic variance (Obtained from measurement on plants within `\(F_2\)` families) 2. Environmental variance (Obtained from measurement on `\(F_1\)` families) ??? 1. Note that denominator has variation of simple `\(V_P = V_A + V_D + V_E\)`. This is because total phenotypic variation of `\(F_2\)` has larger contribution of environmental and additive components than dominance component. --- ## Partitioning environmental variance The other generation in which the phenotypic variance is also entirely attributable to environmental effects is the `\(F_1\)`. On the other hand, if the phenotypic variances of all these three generations were available, the environmental component of the phenotypic variance of the `\(F_2\)` generation could be estimated as follows (in a simplified way): `$$\sigma_E = \frac{\sigma_{\bar{P_1}}^2 + 2\sigma_{\bar{F_1}}^2 + \sigma_{\bar{P_2}}^2}{4}$$` --- ## Partitioning genetic variance The ratio of additive genetic variance to total phenotypic variance is called the narrow-sense heritability. $$ h_n^2 = \frac{\frac{1}{2}V_A}{\frac{1}{2}V_A + \frac{1}{4}V_D + V_E} \tag{v} $$ In order to estimate `\(h_n^2\)`, it is therefore necessary to partition the genetic variance into its two components ( `\(V_A\)` and `\(V_D\)`). This is done by considering the phenotypic variance of the two backcross families ( `\(\sigma_{\bar{B_1}}^2\)` and `\(\sigma_{\bar{B_2}}^2\)` ). Following known relation can be used for estimating genetic components of `\(F_2\)` generation: `$$V_D = 4\left(V_{\bar{B_1}} + V_{\bar{B_2}} - V_{\bar{F_2}} - V_E \right)$$` `$$V_A = 2V_{\bar{F_2}} - (V_{B_1} + V_{B_2})$$` ??? 1. Expressions for both `\(V_D\)` and `\(V_A\)` are derived from known relations: `$$V_{\bar{F_2}} = \frac{1}{2}A + \frac{1}{4}D + V_E$$` and `$$V_{B_1} + V_{B_2} = \frac{1}{2}A + \frac{1}{2}D + 2V_E$$` --- ## Estimates of heritability - Variance based approaches - ratio of variance estimates `$$h_{bs}^2 = \frac{V_G}{V_P} \\ h_{ns}^2 = \frac{V_A}{V_P}$$` - Mean based approaches: - realized heritability: If divergence (R) has been estimated and the accumulated effects of selection (S) are known, an estimate for the trait heritability can be calculated. This is known as the realized heritability $$ h^2 = \frac{R}{S} $$ - Six generation mean analysis method --- ## Parent offspring regression - This method is based on how much does the resemblance parents and offspring exist. - If there is perfect resemblance between parents and offspring, then, `\(b = 1\)` and there is perfect heritable genetic effect. - On contrary if there is no resemblance between parents and offspring `\(b = 0\)`, and there is no heritable effect but variation is only due to environment. - Therefore, narrow sense heritability ( `\(h_{ns}\)` ) `$$b= h_{ns}^2 = \frac{V_A}{V_P}$$` - If only one parent is known (animal experiments or polycrosses) `$$\begin{eqnarray} b &=& \frac{1}{2}.\frac{V_A}{V_P} \\ h_{ns}^2 &=& 2b \end{eqnarray}$$` `\(b\)` = slope of parent offspring regression line ---

Heritability estimates of some common plant architectural traits; Source: Acquaah, 2014

  Trait 	  Heritability

  Plant height 	  45

  Hypocotyl diameter 	  38

  Number of branches/plant 	  56

  Nodes in lower third 	  36

  Nodes in mid section 	  45

  Nodes in upper third 	  46

  Pods in lower third 	  62

  Pods in mid section 	  85

  Pods in upper third 	  80

  Pod width 	  81

  Pod length 	  67

  Seed number per pod 	  30

  100 seed weight 	  77

 --- ## Variance component estimation summary Variances from additive, dominant and environmental effects may be obtained as follows: `\(V_{P1} = E; \quad V_{P2} = E; \quad V_{F1} = E; \quad \frac{V_{P1} + V_{P2} + 2V_{F1}}{4} \sim \frac{V_{P1} + V_{P2} + V_{F1}}{3} = E\)` `\(V_{F2} = \frac{1}{2}A + \frac{1}{4}D + E\)` `\(V_{B1} = \frac{1}{4}A + \frac{1}{4}D + E\)` `\(V_{B2} = \frac{1}{4}A + \frac{1}{4}D + E\)` `\(V_{B1} + V_{B2} = \frac{1}{2}A + \frac{1}{2}D + 2E\)` --- class: inverse, middle, center # Questions --- ## Problem Consider a field experiment with an inbreeding species such as wheat or barley. Parent 1 included 20 plants, Parent 2 included 20 plants and `\(F_2\)` family derived from selfing of `\(F_1\)` generation, which was obtained by intercrossing the two parents (i.e. Parent 1 x Parent 2), consisted of 100 individuals. These 140 plants were completely randomized within the experiment, and at harvest the weight of seeds from each plant was recorded. The variances in seed weight of the two parents were `\(\sigma_{\bar{P_1}}^2 = 16.8~kg^2\)` and `\(\sigma_{\bar{P_2}}^2 = 18.4~kg^2\)`. The phenotypic variance (which included both genetic and environmental variation) of the `\(F_2\)` was `\(\sigma_{\bar{F_2}}^2 = 56.9~kg^2\)`. The total phenotypic varinace of the `\(F_2\)` generation is represented by denominator term of `\(h_b^2\)` is estimated to be `\(56.9 kg^2\)`. But the problem is what is the value of environmental component of the phenotypic variance `\(\sigma_E^2\)`? --- ## Solution It now follows that the `\(h_b^2\)`, from given inform is: $$ h_b^2 = \frac{56.9-\sigma_E^2}{56.9} $$ Since, both parents are homozygous inbreds, any variance displayed by either must be attributable exclusively to the environment. The best esimate of the `\(\sigma_E^2\)` is therefore: $$ `\begin{aligned} \sigma_E^2 &= \frac{\sigma_{\bar{P_1}}^2 + \sigma_{\bar{P_2}}^2}{2} \\ &= \frac{16.8 + 18.4}{2} = 17.6~kg^2 \end{aligned}` $$ --- And, therefore, $$ h_b^2=\frac{56.9-17.6}{56.9} = 0.691 $$ Thus 69.1% of the phenotypic variance of the `\(F_2\)` generation is estimated to be genetic in origin. --- ## Problem 1. Two pure lines of rice are crossed. In the `\(F_1\)` the variance in spike length is 1.5. The F1 is selfed. In the `\(F_2\)` the variance in spike length is 4.5. Estimate broad sense heritability of spike length in rice 2. The phenotypic variance of yield in maize 200 `\(kg^2\)` per acre. The variance within an inbred line is 80. The regression of offspring phenotype on mid parent values is 0.32. Find additive variance, genetic variance, environmental variance, narrow sense heritability and broad sense heritability. --- 3. Estimate heritability (Broad sense and narrow sense) from given information.

  Population 	  Variance

  P1 	  11.00

  P2 	  10.32

  F1 	  5.23

  F2 	  90.35

  BC1 	  47.35

  BC2 	  54.29

 Elaborate solution: `\(h^2_{bs}\)` = 0.844 `\(h^2_{ns}\)` = 0.778 Simplified solution (refer to Acquaah, P 86.): `\(h^2_{bs}\)` = 0.912 `\(h^2_{ns}\)` = 0.764 ??? 1. To choose any one method suggest the simpler method to students. --- 3. Estimate heritability through parent offspring regression method from the following available data.

  Mid parent value (X) 	  Individual offspring (Y)

  20 	  25

  18 	  21

  15 	  20

  17 	  20

  21 	  26

  22 	  25

 --- ## Solution  First calculate Mean of X and Mean of Y;

  	  x 	  y

  	  20.0 	  25.0

  	  18.0 	  21.0

  	  15.0 	  20.0

  	  17.0 	  20.0

  	  21.0 	  26.0

  	  22.0 	  25.0

  Total 	  113.0 	  137.0

  Average 	  18.8 	  22.8

 --- Add SD and Variance;

  	  x 	  y

  	  20.00 	  25.00

  	  18.00 	  21.00

  	  15.00 	  20.00

  	  17.00 	  20.00

  	  21.00 	  26.00

  	  22.00 	  25.00

  Total 	  113.00 	  137.00

  Average 	  18.83 	  22.83

  SD 	  2.64 	  2.79

  Variance 	  6.97 	  7.77

 SD (also denoted as `\(\sigma\)`) is given by: $$ \sigma = {\sqrt \frac{\sum _{i=1}^{n}(x-\bar{x})^2}{n-1}}\text{ and, variance} = \sigma^2 $$ Where, `\(n\)` = 6 --- Now add deviance and squared deviance as well as product of deviances of x and y;

  x 	  y 	  `\(x-\bar{x}\)` 	  `\(y-\bar{y}\)` 	  `\((x-\bar{x})^2\)` 	  `\((y-\bar{y})^2\)` 	  `\(x-\bar{x}\times y-\bar{y}\)`

  20 	  25 	  1.167 	  2.17 	  1.361 	  4.69 	  2.53

  18 	  21 	  -0.833 	  -1.83 	  0.694 	  3.36 	  1.53

  15 	  20 	  -3.833 	  -2.83 	  14.694 	  8.03 	  10.86

  17 	  20 	  -1.833 	  -2.83 	  3.361 	  8.03 	  5.19

  21 	  26 	  2.167 	  3.17 	  4.694 	  10.03 	  6.86

  22 	  25 	  3.167 	  2.17 	  10.028 	  4.69 	  6.86

 --- Then find sum of squared deviances of x and y and sum of product of deviances of x and y;

  	  x 	  y 	  `\(x-\bar{x}\)` 	  `\(y-\bar{y}\)` 	  `\((x-\bar{x})^2\)` 	  `\((y-\bar{y})^2\)` 	  `\(x-\bar{x}\times y-\bar{y}\)`

  	  20.0 	  25.0 	  1.167 	  2.17 	  1.361 	  4.69 	  2.53

  	  18.0 	  21.0 	  -0.833 	  -1.83 	  0.694 	  3.36 	  1.53

  	  15.0 	  20.0 	  -3.833 	  -2.83 	  14.694 	  8.03 	  10.86

  	  17.0 	  20.0 	  -1.833 	  -2.83 	  3.361 	  8.03 	  5.19

  	  21.0 	  26.0 	  2.167 	  3.17 	  4.694 	  10.03 	  6.86

  	  22.0 	  25.0 	  3.167 	  2.17 	  10.028 	  4.69 	  6.86

  Total 	  113.0 	  137.0 	  0.000 	  0.00 	  34.833 	  38.83 	  33.83

  Average 	  32.3 	  39.1 	  0.000 	  0.00 	  9.952 	  11.10 	  9.67

 --- Then calculate correlation coefficient; `$$r_{xy}={\frac {\sum _{i=1}^{n}(x_{i}-{\bar {x}})(y_{i}-{\bar {y}})}{{\sqrt {\sum _{i=1}^{n}(x_{i}-{\bar {x}})^{2}}}{\sqrt {\sum _{i=1}^{n}(y_{i}-{\bar {y}})^{2}}}}}$$` Mathematically, the correlation coefficient is unit free and always between -1 and 1. The sign of the correlation indicates the sign of the relationship and matches the sign of the slope of the regression line: positive (increasing) or negative (decreasing). Since we have, `\({\sum _{i=1}^{n}(x_{i}-{\bar {x}})(y_{i}-{\bar {y}})}\)` = 33.833 `\({\sum _{i=1}^{n}(x_{i}-{\bar {x}})^{2}}\)` = 34.833 `\({\sum _{i=1}^{n}(y_{i}-{\bar {y}})^{2}}\)` = 38.833 `\(r_{xy}={\frac {...}{...}}\)` = 0.92 --- Now, the coefficient of regression of `\(y\)` on `\(x\)` ( `\(b\)`) can be calculated from Pearson's correlation coefficient ( `\(r\)`) as: `\(b_{yx} = r_{xy}\frac{\sigma_y}{\sigma_x}\)` = 0.971 ??? The sample correlation coefficient between `\(Y\)` and `\(X\)` is `$$r_{yx} = \frac { \operatorname{\hat Cov}(Y,X)}{\hat \sigma_y\hat \sigma_x}$$` while in a simple regression `\(Y = \beta_0 + \beta_1X+ u\)`, the OLS estimator for the slope coefficient is `$$\hat \beta_1 = \frac { \operatorname{\hat Cov}(Y,X)}{\hat \sigma_x^2}$$` Combining, we have the relation `$$\hat \beta_1 = \frac {\hat \sigma_y}{\hat \sigma_x}r$$` --- 4. Suppose 2 inbred lines A and B are crossed to produce hybrid. The genotype of A is AAbbccDDEEFF and the genotype of B is aaBBCCddeeFF. A, B, C, D, E and F are the dominant genes. If dominant homozygote contributes 2 `\(ton~ha^{-1}\)`, recessive homozygote contributes 1 `\(ton~ha^{-1}\)` and heterozygote contributes 2.5 `\(ton~ha^{-1}\)`, Find the grain yields of Parent A, Parent B and `\(F_1\)` hybrid on the basis of dominance and over-dominance hypothesis.


This popular town lies in the centre of Costa Rica's Pacific coast. It is an attractive resort with excellent beaches and good facilities for visiting yachts.
Position 9°25.5′N, 84°9.4′W.
See Costa Rica Fomalities for full details on clearing into and out of Costa Rica.
Quepos is now a port of entry. Clearance is available at the Marina Pez Vela but the officials are not based at the port and have to travel from Puntarenas at additional cost.
To enable the marina to arrange clearance a number of documents need to be sent in advance. See http://www.marinapezvela.com/en/clearing-information/ for full details.
Last updated September 2016.
The marina is not cheap, so many cruisers choose to anchor long term here. They go to Playa Biesanz, just SE of the marina. Take care as there are a number of rocks visible at low tide.
A steep path from shore will take you up to the road where you can catch a taxi in to town. Call from the beach (or ask one of the vendors to do so for you) and the taxi will be waiting for you at the top.
There is good snorkeling around the reef here.
Last updated September 2016.


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My culture has influenced me both in terms of who I am but also who I am not. In terms of my identity, from a young age never really connected with the idea of New Zealand culture and always felt inspiration and association more-so while consuming media from countries far abroad. This was furthered ramified by my mum and step-dad who were vocal about wanting to leave New Zealand for a bigger and "better" country elsewhere. Spending unquantifiable amounts of time on the internet as a pre-teen, I further develop an identity based around alternative American, European and Japanese cultural exports and would partake in seeking out emerging media, fashion and design from a range of blogging networks. Although I still carry these influences with me and continue to seek out emerging cultural exports from around the world, I now see how my values around racial issue, idea of self and empathy are more closely linked to my upbringing In New Zealand.
Looking back at the scope of my time spent studying or lack thereof, I can say that not holding any form of tertiary classification has made seeking employment very challenging. Having left high school seven years ago, I have spent the majority of this time jumping between various entry-level jobs. Without holding any current tertiary classification, I am left with all but a few employment options. While hopping between workplaces and the lack of expectation from these roles has been great for funding personal creative projects and travel, now that I am approaching my mid-twenties, I feel it is the right time to pursue new avenues of employment. Although working in these low-paying and high-stress jobs has been challenging. The degree of understanding that comes from interacting with people from different backgrounds, has strengthened my ability to acknowledge other peoples circumstances.
One core strength for me while approaching this learning journey is my ability to break challenges and create a smaller set of manageable tasks. Coincidentally this is one of the skills outlined by EDA and it felt good to see something I had been doing for sometime outlined as the ‘right’ way to approach tasks and challenges. Another strength I possess is the ability to solve problems, as a problem solver, I can think from a multitude of angles when approaching new tasks. Having found some difficulties while undertaking this first sprint, I was able to reevaluate my approach and fathom different approaches to rectify the problem.
An example of tension I experienced while approaching a team-based activity would be conflicts that arose while playing club football as a child. On various occasions, my team-mates would miss the chance to score, because they took it upon themselves to shoot and not pass to an available teammate. These situations would cause tension between my teammates and further affect our ability to play well. To prevent this from happening again, I approached my teammates and coaches to discuss the said problem and see what we could do to rectify it. In discussing this problem, my coaches were able to focus our practice on better team communication. Looking back at this situation, I feel like what I did was the best way to approach the challenge and its ensuing tension. Because this problem came down to team communication, the best way to treat was through additional team communication.


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This page will be removed in future. If you are bookmarking this page, I'd recommend to change the URL on the link below.
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This notification was written in 2023-07-31.


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In the previous post, I had discussed regarding the EM algorithm from a programmer's perspective. That blog post sparked an interesting discussion on Twitter which has led to the current post.
Thus, I started to investiage how good would MCMC methods perform in comparison to EM.
For a detailed understanding, refer to the previous post. In short, the problem is as follows.
You have two coins - A and B. Both of them have their own biases (probability of obtaining a head (or a tail )). We pick a coin at random and toss it up 10 times. We repeat this procedure 5 times, totaling in 5 observation sets of 10 observations each. However, we are not told which coin was tossed. So, looking at the data and some rough initial guess about the respective coin biases, can we tell something about the likely biases? Let us work it up using PyMC.
import pymc as pm
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
import seaborn as sns
%matplotlib inline
We use 1 for Heads and 0 for tails.
# 5 observation sets of 10 observations each
observations = np.array([[1,0,0,0,1,1,0,1,0,1],
[1,1,1,1,0,1,1,1,1,1],
[1,0,1,1,1,1,1,0,1,1],
[1,0,1,0,0,0,1,1,0,0],
[0,1,1,1,0,1,1,1,0,1]])
observations_flattened = observations.flatten()
n_heads_array = np.sum(observations, axis=1)
plt.bar(range(len(n_heads_array)),n_heads_array.tolist());
plt.title("Number of heads vs Observation set")
plt.xlabel("Observation set")
plt.ylabel("#Heads observed");
The true sequence of coin tosses which was hidden from us. True
indicated coin A and False
indicates coin B.
# Ground truth
coins_id = np.array([False,True,True,False,True])
Number of observation sets and number of observations in each set. This allows us to modify data easily (as opposed to hard coding stuff).
n_observation_sets = observations.shape[0]
n_observation_per_set = observations.shape[1]
We pick up a simple prior on the bias of coin A.
$\theta_a$ ~ $\beta(h_a,t_a)$
Similarly, for coin B.
$\theta_b$ ~ $\beta(h_b,t_b)$
For any given observation set, we assign equal probability to it coming from coin A or B.
$coin choice$ ~ DiscreteUniform(0,1)
Thus, if coin choice is known, then the associated bias term is fixed.
$\theta$ = $\theta_a$ if $coin choice$ =1 else $\theta_b$
Like, we did in the previous post, we use Binomial distribution with the bias as $\theta$. For each observation set, we calculate the number of heads observed and model it as our observed variable obs
.
$obs$ = Binomial(n_tosses_per_observation_set, p = $\theta$ )
Let us draw this model using daft
.
import daft
pgm = daft.PGM([3.6, 2.7], origin=[1, 0.65])
pgm.add_node(daft.Node("theta_a", r"$\theta_a$", 4, 3, aspect=1.8))
pgm.add_node(daft.Node("theta_b", r"$\theta_b$", 3, 3, aspect=1.2))
pgm.add_node(daft.Node("theta", r"$\theta$", 3.5, 2, aspect=1.8))
pgm.add_node(daft.Node("coin_choice", r"coin_choice", 2, 2, aspect=1.8))
pgm.add_node(daft.Node("obs", r"obs", 3.5, 1, aspect=1.2, observed=True))
pgm.add_edge("theta_a", "theta")
pgm.add_edge("theta_b", "theta")
pgm.add_edge("coin_choice", "theta")
pgm.add_edge("theta", "obs")
pgm.render();
The following segment codes the above model.
# Prior on coin A (For now we choose 2 H, 2 T)
theta_a = pm.Beta('theta_a',2,2)
# Prior on coin B (For now we choose 2 H, 2 T)
theta_b = pm.Beta('theta_b',2,2)
# Choosing either A or B
coin_choice_array = pm.DiscreteUniform('coin_choice',0,1, size = n_observation_sets)
# Creating a theta (theta_a if A is tossed or theta_b if B is tossed)
@pm.deterministic
def theta(theta_a = theta_a, theta_b=theta_b, coin_choice_array=coin_choice_array):
#print coin_choice_array
out = np.zeros(n_observation_sets)
for i, coin_choice in enumerate(coin_choice_array):
if coin_choice:
out[i] = theta_a
else:
out[i] = theta_b
return out
Let us examine how theta would be related to coin choice and other variables we have defined.
theta_a.value
theta_b.value
coin_choice_array.value
theta.value
So, whenever we see coin A, we put it's bias in theta and likewise if we observe coin B. Now, let us create a model for our observations which is binomial as discussed above.
observation_model = pm.Binomial("obs", n=n_observation_per_set, p=theta, value = n_heads_array, observed=True)
model = pm.Model([observation_model, theta_a, theta_b, coin_choice_array])
Let us view a few samples from the observation_model
which returns the number of heads in 5 sets of 10 tosses.
observation_model.random()
observation_model.random()
mcmc = pm.MCMC(model)
mcmc.sample(40000, 10000, 1)
Let us have a look at our posteriors for $\theta_a$ and $\theta_b$
plt.hist(mcmc.trace('theta_a')[:], bins=20,alpha=0.7,label = r"$\theta_a$");
plt.hist(mcmc.trace('theta_b')[:], bins=20,alpha=0.4,label= r"$\theta_b$");
plt.legend();
Looks like both $\theta_a$ and $\theta_b$ peak around the same value. But, wasn't it expected? We gave both of them the same priors. This was also the case when we initialed EM with same values for both $\theta_a$ and $\theta_b$. So, let us add some informative priors an see how we do. Like in EM experiment, we knew that one of the coin was more biased than the other. So, let us make that the case and rerun the experiment.
# Prior on coin A (more likely to have heads)
theta_a = pm.Beta('theta_a',4,2)
# Prior on coin B (more likely to have tails)
theta_b = pm.Beta('theta_b',2,4)
# Choosing either A or B (for 5 observations)
coin_choice_array = pm.DiscreteUniform('coin_choice',0,1, size = 5)
# Creating a theta (theta_a if A is tossed or theta_b if B is tossed)
@pm.deterministic
def theta(theta_a = theta_a, theta_b=theta_b, coin_choice_array=coin_choice_array):
#print coin_choice_array
out = np.zeros(n_observation_sets)
for i, coin_choice in enumerate(coin_choice_array):
if coin_choice:
out[i] = theta_a
else:
out[i] = theta_b
return out
observation_model = pm.Binomial("obs", n=10, p=theta, value = n_heads_array, observed=True)
model = pm.Model([observation_model, theta_a, theta_b, coin_choice_array])
mcmc = pm.MCMC(model)
mcmc.sample(40000, 10000, 1)
plt.hist(mcmc.trace('theta_a')[:], bins=20,alpha=0.7,label = r"$\theta_a$");
plt.hist(mcmc.trace('theta_b')[:], bins=20,alpha=0.4,label= r"$\theta_b$");
plt.legend();
Quiet a clear distinction now! $\theta_a$ seems to peak around 0.8 and $\theta_b$ around 0.5. This matches our results from EM. However, unlike EM, we have much more than point estimates.
Feel free to leave your comments and suggestions by.


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Before I tell you a bit more about myself, I would like to thank you for being here and reading this. Whether you are coming from a referral, from social media or even through a Google search, I assume you have a vision for your current business and want to see it come to life.
I am a freelance web designer and developer in Melbourne. I make logos, mobile app designs, animations videos and website design and development for small business in Australia, and all around the world.
I built the brand identity and websites for different companies in Australia. I also took part in several Hackathons and won most of them.
I learned how to develop solutions to business problems and add more value through beautifully crafted designs.
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Donald John Trump
born June 14, 1946
Businessman
Producer
Actor
Cartoon Actor
45th president of the United States from 2017 to 2021
I'm rich. And I will do the America Great again!
Let`s do it with me or get out!
Let`s do it with me or get out!
I'm hack Americans, I'm hack Republicans,
I was humiliate Democratic Party and dishonor Hillary
All bankers, media and internet tycoons, masons and globalists had to unite to win against me.
I was humiliate Democratic Party and dishonor Hillary
All bankers, media and internet tycoons, masons and globalists had to unite to win against me.
I have a lot of Experience:
- Building
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- Money
- A lot of Money
- Girls
- A lot of Girls
Languages: Americanian (i don`t have any idea why other people name it english)
Skills: Sh-sh-sh!.... This page can be read by a federal judge
Preferred phrases: Mexica must pay for illegal mexicans
Europe must pay for protect
Canada must pay, I don`t know why... But he must!
You Fired!
And you!...
And you!...
Skills: Sh-sh-sh!.... This page can be read by a federal judge
Preferred phrases: Mexica must pay for illegal mexicans
Europe must pay for protect
Canada must pay, I don`t know why... But he must!
You Fired!
And you!...
And you!...


Hi there! I am a recent Public Health-Global Health grad who is interested in coding, design, and understanding how institutional, structural, inter-personal, and individual racism is so heavily ingrained in our society, culture, and politics which then causes poor health in the communities we live in.
Click on my face to bring up my resume:
p.s. you need to try to find the link hidden in my face
p.p.s it's an eye booger
But also, if you don't want to play the game, click here: Resume
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