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Derek Eder
@derekeder
Thank you @ChicagoOIG @BetterGov and @invinst for demanding no less than actual transparency from our city and police department. Let's make sure they get it right the next time around.
https://www.thedailyline.com/police-transparency-ordinance-hits-another-wall-as-watchdog-sponsors-remain-at-odds
5/25/2021, 9:42:30 AM
Favs: 2
Retweets: 2
link


meteoJS/modelviewer
Source: Modelviewer.
Child class
Abstract types
Class
Modelviewer
Abstract types
makeContainerNode(container) → (HTMLElement or jQuery)
Creates and returns a HTMLElement or jQuery object for a container. The content of the container is appended to this element. This function has to append the element to the modelviewer's containersNode.
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
container |
|
Container to append. |
- Returns
-
(HTMLElement or jQuery)
Top node of the appended container.
options Object
Options for constructor.
Parameters
| Name | Type | Optional | Description |
|---|---|---|---|
|
resources |
|
Available resources. |
|
|
containersNode |
(HTMLElement or jQuery) |
|
Node to append the containers. |
|
timeline |
Yes |
Shared Timeline between containers. |
|
|
makeContainerNode |
Yes |
Function to create the top node of each container. |
|
|
firstTimeOnInit |
boolean |
Yes |
Selects the first time in timeline once for the first time times are added to the timeline. Defaults to |
|
lastTimeOnInit |
boolean |
Yes |
Selects the last time in timeline once for the first time times are added to the timeline. Ignored if firstTimeOnInit is true. Defaults to |


|
Steam Audio API
2.0-beta.18
|
Functions for controlling an OpenCL compute device. More...
Data Structures | |
| struct | IPLComputeDeviceFilter |
| Specifies constraints on the type of OpenCL device to create. More... | |
Enumerations | |
| enum | IPLComputeDeviceType { IPL_COMPUTEDEVICE_CPU, IPL_COMPUTEDEVICE_GPU, IPL_COMPUTEDEVICE_ANY } |
| The type of device to use with OpenCL. More... | |
Functions | |
| IPLAPI IPLerror | iplCreateComputeDevice (IPLhandle context, IPLComputeDeviceFilter deviceFilter, IPLhandle *device) |
| Creates a Compute Device object. More... | |
| IPLAPI IPLvoid | iplDestroyComputeDevice (IPLhandle *device) |
| Destroys a Compute Device object. More... | |
Functions for controlling an OpenCL compute device.
Phonon requires OpenCL when used with the following third-party technologies:
If you are not using one of the above technologies, you do not need to call any of the Phonon API functions that deal with OpenCL Compute Device objects.
| enum IPLComputeDeviceType |
The type of device to use with OpenCL.
The appropriate OpenCL drivers must be installed on the user's system. Multiple OpenCL drivers may be installed on the same system; in this case the first available driver that exposes the specified kind of device will be used.
| Enumerator | |
|---|---|
| IPL_COMPUTEDEVICE_CPU | Use a CPU device only. |
| IPL_COMPUTEDEVICE_GPU | Use a GPU device only. |
| IPL_COMPUTEDEVICE_ANY | Use either a CPU or GPU device, whichever is listed first by the driver. |
| IPLAPI IPLerror iplCreateComputeDevice | ( | IPLhandle | context, |
| IPLComputeDeviceFilter | deviceFilter, | ||
| IPLhandle * | device | ||
| ) |
Creates a Compute Device object.
The same Compute Device must be used by the game engine and audio engine parts of the Phonon integration. Depending on the OpenCL driver and device, this function may take some time to execute, so do not call it from performance-sensitive code.
| context | The Context object used by the game engine. |
| deviceFilter | Constraints on the type of device to create. |
| device | [out] Handle to the created Compute Device object. |
Destroys a Compute Device object.
If any other API objects are still referencing the Compute Device object, it will not be destroyed; destruction occurs when the object's reference count reaches zero.
| device | [in, out] Address of a handle to the Compute Device object to destroy. |


We are open for everyone: groups, professional developers, hackers, experts, students, all are welcome!


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Arrays and Hashes
by Jim O'Neal on Friday, Dec 4, 2015
In this blog I will discuss Arrays and Hashes in Ruby, what they are, what are the differences, and will give some examples of both.
Arrays and hashes have important similarities and important differences. Both arrays and hashes are like storage bins that can hold pieces of information (elements) in specific slots. Both arrays and hashes can be assigned a name, so that the array or hash can be utilized in other parts of the program, and the elements can be accessed individually. Both arrays and hashes can contain strings, numbers, or even other arrays or hashes.
The important difference between an array and hash is how the 'slots' are named. In an array, each slot has an index number, starting at 0 and then increasing by 1's for each element. The elements in an array are accessed by refering to this index number. Hashes on the other hand index by using a 'key' which can be assigned a specific name. This allows the program to access the value in the hash by refering to a name rather than just an index number.
Below are some examples showing the format of an array and how to access elements:
sample_array = ["this", "is", "an", "array"]
return sample_array[0] # "this"
return sample_array[1] # "is"
return sample_array[2] # "an"
return sample_array[3] # "array"
Below is an example of a hash and how to access the values in a hash using the keys:
sample_hash = {
favorite_number: 13,
favorite_color: "blue",
favorite_animal: "elephant",
}
return sample_hash[:favorite_number] # 13
return sample_hash[:favorite_color] # "blue"
return sample_hash[:favorite_animal] # "elephant"


# File lib/rake.rb, line 511 511: def initialize(task_name, app) 512: @name = task_name.to_s 513: @prerequisites = [] 514: @actions = [] 515: @already_invoked = false 516: @full_comment = nil 517: @comment = nil 518: @lock = Monitor.new 519: @application = app 520: @scope = app.current_scope 521: @arg_names = nil 522: end


down
and
SQUARES 003
nearly-humorous visual punnery
Stumbled across "vertical-align" to get the boxes to go where I wanted. Still not sure why it was needed.


clickhouse_ecto v0.2.4 ClickhouseEcto.Connection View Source
Link to this section Summary
Functions
Receives options and returns DBConnection
supervisor child specification
Executes the given prepared query with DBConnection
Prepares and executes the given query with DBConnection
Returns a stream that prepares and executes the given query with DBConnection
Link to this section Types
The cache query which is a DBConnection Query
The prepared query which is an SQL command
Link to this section Functions
Receives options and returns DBConnection
supervisor child specification.
Link to this function
execute(conn, query, params, options)
View Source
execute(connection :: DBConnection.t(), prepared_query :: cached(), params :: [term()], options :: Keyword.t()) :: {:ok, term()} | {:error | :reset, Exception.t()}
execute(connection :: DBConnection.t(), prepared_query :: prepared(), params :: [term()], options :: Keyword.t()) :: {:ok, term()} | {:error, Exception.t()}
Executes the given prepared query with DBConnection
.
Link to this function
prepare_execute(conn, name, prepared_query, params, options)
View Source
prepare_execute(connection :: DBConnection.t(), name :: String.t(), prepared(), params :: [term()], options :: Keyword.t()) :: {:ok, query :: map(), term()} | {:error, Exception.t()}
Prepares and executes the given query with DBConnection
.
Link to this function
stream(conn, prepared, params, options)
View Source
stream(connection :: DBConnection.conn(), prepared_query :: prepared(), params :: [term()], options :: Keyword.t()) :: Enum.t()
Returns a stream that prepares and executes the given query with DBConnection
.


Learn the basics about funflow: write your first pipeline and run it in the default environment.
A simple example to illustrate the basics.
Compile and execute a simple C program using funflow.
Handle and throw errors in your pipelines.
Learn how to configure tasks at load time.
Create your own effects and interpreters for tailor-made pipelines.
An example demonstrating some usage of funflow for a data science project.
An example per feature to illustrate how to use funflow.


Supervising the quality of data collection is not straightforward. Survey questionnaires are often quite long and have systematic control is worth automatizing.
HighFrequencyCheck
can be used to detect programming errors, surveyor errors, data fabrication, poorly understood questions, and other issues. The results of these checks can also be useful in improving your survey, identifying enumerator effects, and assessing the reliability of your outcome measures. It allows teams to catch survey issues and collection mistakes in time to correct them and ensure high-quality data
The HighFrequencyCheck
package is a translation in in R of the Stata package based on best practices from Innovations for Poverty Action. High Frequency Checks are also recommend by the World Bank. It can be installed from github with devtools::install_github("unhcr/HighFrequencyChecks")
.
The package brings a series of convenience functions to monitor data quality during the data collection when running a survey with KoboToolbox (or any xlsform
compatible platform).
Those are the basis of a feedback mechanism with enumerators and can be performed periodically during the data collection process to check for possible errors and provide meaningful inputs to the enumerators. All these functions do not have to be ran at the same period of time. They are provided there to help data supervisor to build reports:
A wrapper
function is included to generate directly an final data quality assessment Rmd Report
A ShinyApp
Interface is also included to provide a live monitoring dashboard to be run locally.
Data collection quality monitoring includes 4 different dimensions
Ideally the data collection monitoring dashboard should be known to all enumerators so that they are aware of the data quality metrics that will be used to assess the quality of their work and potentially some incentive can be offered for the enumerators performing on the quality metrics (It is good to recall that each records in household survey cost between 15 to 50 USD). Some of those indicators can support some remedial supervision interventions, such as calling individually the enumerator and point some specific issues that were detected.
It is important to prepare high frequency checks (code and instructions), as part of the data quality assurance plan, before before starting with field data collection.
Below are the required configuration and an illustration of those indicators based on a demo dataset included in the package.
When it comes to back checks, variables can be divided into the following different categories:
Type 1 variables are based on straightforward questions where there is very little possibility of error. For example, age and education. If there is an error in these variables, it means there is a serious problem with enumerator, or with the questions.
Type 2 variables are based on questions where a risk of error is possible. For example, questions based on sensitive topics, or questions that involve calculations. If there is an error in these variables, there might be a need to provide further training for enumerators.
Type 3 variables are based on questions about the survey instrument, and errors in this case provide feedback which can help improve the survey instrument itself. This is often the case for questions including or other, please specify question type.
These checks are designed to ensure that responses are consistent for a particular survey instrument, and that the responses fall within a particular range. For example, checks to ensure that all variables are standardized, and there are no outliers in the data. Share a daily log of such errors, and check if it is an issue with enumerators, in which case there might be a need to re-train enumerators.
Missing data: Are some questions skipped more than others? Are there questions that no respondents answered? This may indicate a programming error.
Categorical variables: Are respondents selecting the given categories or are many respondents selecting “None of the above”, or “other”? If conducting a survey, you may want to add categories or modify your existing ones.
Too many similar responses: Is there a question where all respondents answer in the same way?
Respondent IDs: Are there duplicates of your unique identifiers? If so, does the reason why make sense? (e.g., one circumstance in which there may be duplicates of unique IDs is when surveyors have to end and restart an interview.) Are there blank or invalid IDs? This might be a sign your surveyors are not interviewing the correct respondent.
These are designed to check if data shared by any particular enumerator is significantly different from the data shared by other enumerators. Some parameters to check enumerator performance include percentage of “Don’t know” responses, or average interview duration. In the first case, there might be a need to re-draft the questions, while in the second case, there might be a need to re-train enumerators.
Outliers can be observed when some respondents reporting values drastically higher or lower than the average response. This may require to have these variables to be top or bottom coded. Many outlier checks can be directly programmed into the survey, either to flag responses or bar responses that are outside the acceptable range.


Extending Comprior - How-Tos¶
Add New Preprocessing Functionality¶
1. Implement a new Preprocessor¶
Every preprocessing functionality must be implemented in its own class.
To achieve that, create a new class that inherits from preprocessing.Preprocessor
and implements preprocessing.Preprocessor.preprocess()
method, e.g.
class ExamplePreprocessor(Preprocessor):
"""Does some example preprocessing
:param input: absolute path to the input file to preprocess.
:type input: str
:param output: absolute path to the output directory where to store the preprocessing results.
:type output: str
:param whatever_you_need: whatever other parameters your preprocessor needs.
"""
def __init__(self, input, output, whatever_you_need):
self.whatever_you_need = whatever_you_need
super().__init__(input, None, output)
def preprocess(self):
"""Does some example preprocessing
:return: absolute path to the new file location.
:rtype: str
"""
#implement your preprocessing here...
return self.output
2. Update the Config File (optional)¶
If your preprocessing functionality requires that the user sets any specific parameters, adapt the preprocessing section of config.ini and include these parameters. Do not forget to provide this config to your preprocessor class then.
3. Include the Preprocessor in the Execution Pipeline¶
Add your preprocessor to pipeline.Pipeline.preprocessData()
method.
Make sure that a) preprocessing.DataTransformationPreprocessor
is always the first preprocessor (it transforms the file to use pandas’ default separator so that we do not need to handle different separators anymore), b) all preprocessores write their outputs to the same intermediate directory, and c) set the correct inputs: your preprocessor’s input is the output of the preceding preprocessor, your preprocessor’s output will be the input of the subsequent preprocessor:
dataFormatter = preprocessing.DataTransformationPreprocessor(input, input_metadata, intermediate_output, sep)
transposed_input = dataFormatter.preprocess()
#here comes your new preprocessor
examplePreprocessor = preprocessing.ExamplePreprocessor(transposed_input, intermediate_output, whatever_you_need)
exampled_input = examplePreprocessor.preprocess()
mappingPreprocessor = preprocessing.MappingPreprocessor(exampled_input, intermediate_output, currentIDFormat, desiredIDFormat, False)
mapped_input = mappingPreprocessor.preprocess()
Add a New Knowledge Base¶
Every knowledge base needs to classes: One class inheriting from knowledgebases.KnowledgeBase
and implementing the interface methods and a second class inheriting from bioservice’s REST class for web service access.
1 Implement KnowledgeBase Class¶
The class inheriting from knowledgebase.KnowledgeBase
is accessed from any other class within Comprior that wants to retrieve prior knowledge.
It must implement the methods knowledgebases.KnowledgeBase.getRelevantGenes()
, knowledgebases.KnowledgeBase.getGeneScores()
, and knowledgebases.KnowledgeBase.getRelevantPathways()
(depending on the type of knowledge base, raise a NotImplementedError).
class ExampleKB(KnowledgeBase):
def __init__(self):
#pass the knowledge base name, its config, the web service accessing class, and booleans indicating what type of knowledge is provided by that knowledge base
geneInfo = True
pathwayInfo = False
super().__init__("ExampleKB", util.getConfig("ExampleKB"), ExampleKBWS(), geneInfo, pathwayInfo)
def getRelevantGenes(self, labels):
"""Get all genes that are somehow associated to the given labels, e.g. disease names.
:param labels: list of identifiers, e.g. disease names, for which to find associated genes.
:type labels: list of str
:return: list of associated genes.
:rtype: list of str
"""
#implement the gene retrieval strategy here...
return genes
def getGeneScores(self, labels):
"""Get all genes and their association scores that are related to the given labels, e.g. disease names.
:param labels: list of identifiers, e.g. disease names, for which to find associated genes.
:type labels: list of str
:return: DataFrame of associated genes and their association scores, in descending order.
:rtype: :class:`pandas.DataFrame`
"""
#implement the gene score retrieval strategy here...
#if your knowledge base provides pathways only, you can implement an own strategy or raise a NotImplementedError
return geneScores
def getRelevantPathways(self, labels):
"""As this knowledge base currently does not provide pathway information, this feature is not implemented.
:param labels: list of labels for which to find related pathways.
:type labels: list of str
:return: :class:`NotImplementedError` as this knowledge base is not intended to be used for such analyses.
:rtype: :class:`NotImplementedError`
"""
#if the knowledge base does not provide pathway information, raise a NotImplementedError
raise NotImplementedError()
2 Implement a Pathway Parser (optional)¶
If the knowledge base provides pathway or network information, parse the pathway information into a pypath pypath.Network
(for an example, see knowledgebases.KEGGPathwayParser
).
Inherit from knowledgebases.PathwayParser
to do that and implement your own knowledgebases.PathwayParser.parsePathway()
method:
class ExamplePathwayParser(PathwayParser):
"""Parses a pathway from a custom format to :class:`pypath.Network`.
"""
def parsePathway(self, pathway, pathwayID):
"""Parse pathway to the internally used format of :class:`pypath.Network`.
:param pathway: pathway string to parse
:type pathway: str
:param pathwayID: name of the pathway
:type pathwayID: str
:return: pathway in the internally used format..
:rtype: :class:`pypath.Network`
"""
#implement your pathway parsing strategy here...
return pathway
3 Implement Web Service Accessing Class¶
First of all, check if bioservices already provides a class for accessing the knowledge base web service: https://bioservices.readthedocs.io/en/master/references.html#services If your knowledge base is not available, implement your own class by inheriting from bioservice’s REST or WSDL classes. Optimally, your REST class provides the API endpoints as methods, so if there is an endpoint search, implement a corresponding method named search. To send the actual request, construct a query string that specifies our endpoint (without the general API url) and provide that string to self.http_get(your_string).
class ExampleKBWS(REST):
"""Queries the web service of ExampleKB for a given set of labels and retrieves association scores for all genes related to the query labels.
"""
def __init__(self):
super().__init__("ExampleKBWS", url=util.config["ExampleKB"]["webservice_url"])
def getVersion(self):
"""Get the current version of the API endpoint.
:return: web service version infos.
:rtype: json dict
"""
ret = self.http_get("/version")
return ret
4 Update the Config File¶
Create a new section in config.ini for your knowledge base. It must contain an output directory (where intermediate results are written to), optionally the web service API URL (if you had to implement your own REST class), and any other parameter you need for your knowledge base class to function.
[ExampleKB]
webservice_url = https://www.myexamplekbwebservice.org/api
outputDir = ${General:externalKbDir}ExampleKB/
5 Register the Knowledge Base at KnowledgeBaseFactory¶
Register your new knowledge base at knowledgebases.KnowledgeBaseFactory.createKnowledgeBase()
.
If your knowledge base is o not forget to provide the pathway parser to your knowledge base when creating
def createKnowledgeBase(self, knowledgebase):
"""Creates knowledge base based on a given name.
:param knowledgebase: name of the knowledge base to be created.
:type knowledgebase: str
:return: knowledge base object.
:rtype: :class:`KnowledgeBase` or inheriting classes
"""
if knowledgebase == "ExampleKB":
return ExampleKB()
if knowledgebase == "ExamplePathwayKB":
#create a pathway parser if your knowledge base requires that
pathwayparser = ExamplePathwayParser()
return ExamplePathwayKB(pathwayparser)
Add a new Feature Selector Approach¶
1a. Implement a Feature Selector¶
Feature selection approaches are implemented in separate classes for each approach.
Inherit from one (or multiple) of the many abstract classes that are available for feature selectors, e.g. featureselection.RSelector
when invoking R code or featureselection.PriorKnowledgeSelector
when implementing a prior knowledge approach that uses a knowledge base.
See all the class hierarchy HERE.
class ExampleSelector(PriorKnowledgeSelector):
def __init__(self, knowledgebase, whatever_you_need):
self.whatever_you_need = whatever_you_need
super().__init__("YourSelectorName", knowledgebase)
def selectFeatures(self):
"""Your feature selection strategy.
:return: absolute path to the output ranking file.
:rtype: str
"""
#define the name of the output file name (must follow this schema!)
outputFilename = self.output + self.getName() + ".csv"
#implement your feature selection procedure here...
return outputFilename
1b. Implement a Network Selector¶
If you want to implement a network approach that maps the original feature space of the data to (sub-)networks, e.g. pathways, inherit from featureselection.NetworkSelector
and implement its featureselection.NetworkSelector.selectPathways()
instead:
class ExampleNetworkSelector(NetworkSelector):
def __init__(self, knowledgebase, featuremapper):
super().__init__("YourNetworkSelector", knowledgebase, featuremapper)
def selectPathways(self, pathways):
"""Your pathway selection strategy
:param pathways: selector name
:type pathways: str
:returns: pathway ranking with pathway scores
:rtype: :class:`pandas.DataFrame`
"""
#implement your pathway/subnetwork selection strategy here...
return pathwayRanking
Classes inheriting from featureselection.NetworkSelector
additionally require a feature mapper that, once the (sub-) networks were selected as new features by your new network selector, creates new feature values for every selected (sub-) network.
To do that, either use an existing feature mapper or implement a new one that inherits from featureselection.FeatureMapper
and implements featureselection.FeatureMapper.mapFeatures()
:
class ExampleFeatureMapper(FeatureMapper):
def __init__(self, ):
super().__init__()
def mapFeatures(self, original_data, subnetworkNames, subnetworks):
"""Your feature mapping strategy
:param original_data: the original data set of which to map the feature space.
:type original_data: :class:`pandas.DataFrame`
:param pathways: dict of pathway names as keys and corresponding pathway :class:`pypath.Network` objects as values
:type pathways: dict
:returns: the transformed data set with new feature values
:rtype: :class:`pandas.DataFrame`
"""
#implement your feature mapping strategy here...
return mappedDataset
2. Update the Config File¶
List the new feature selection approach in the comments of the config.ini file and preferably, this Wiki ;). When providing a name to your feature selection approach, follow this naming schema (YourSelectorName MUST be the same as the name provided in the selector’s init method):
- YourSelectorName for a traditional approach without any knowledge base
- YourSelectorName_kbName for a selector that uses a knowledge base
- YourSelectorName_tradName_kbName for a selector that uses a knowledge base and another selector.
3. Register Feature/Network Selection Approach to the FeatureSelectorFactory¶
Register your new feature selector class at featureselection.FeatureSelectorFactory
in one of the following methods, depending on the type of selector you implemented (see the sources as the methods are encapsulated in a singleton construct):
featureselection.FeatureSelectorFactory.createTraditionalSelector()
if it is a traditional selector not using a knowledge basefeatureselection.FeatureSelectorFactory.createCombinedSelector()
if it uses a traditional approach and a knowledge base in a combined mannerfeatureselection.FeatureSelectorFactory.createIntegrativeSelector()
if it is a selector that only uses a knowledge base
When registering, you need to specify the first part (=YourSelectorName) of the overall name as an if-statement
def createIntegrativeSelector(self, selectorName, kb):
"""Creates a feature selector using a knowledge base from the given selector and knowledge base names.
Register new implementations of a prior knowledge selector here that does not requires a (traditional) selector.
Stops processing if the selector could not be found.
:param selectorName: selector name
:type selectorName: str
:param kb: knowledge base name
:type kb: str
:return: instance of a feature selector implementation.
:rtype: :class:`FeatureSelector` or inheriting class
"""
kbfactory = knowledgebases.KnowledgeBaseFactory()
knowledgebase = kbfactory.createKnowledgeBase(kb)
if selectorName == "YourSelectorName":
return ExampleSelector(knowledgebase)
if selectorName == "YourNetworkSelectorName":
featureMapper = ExampleFeatureMapper()
return ExampleNetworkSelector(knowledgebase, featureMapper)
Add Custom Code from R/Java/another Programming Languages¶
Invoking R or Java Code¶
The benchutils package provides methods for invoking R or Java code (
benchutils.runRCommand()
andbenchutils.runJavaCommand()
, respectively). These methods are already used, e.g. byfeatureselection.RSelector.selectFeatures()
andfeatureselection.JavaSelector.selectFeatures()
. If you have R or Java code that you want to invoke, use these methods and provide them with the R/Java config parameters, the name of the script/jar to execute, and a list of parameters. The example below runs an R script called “FS_LassoPenalty.R” that expects three parameters providing file names to the input, output, and external score files.params = [input_filename, output_filename, externalscores_filename] benchutils.runRCommand(self.rConfig, "FS_LassoPenalty.R" , params)
Invoking Code from Another Programming Language than R or Java¶
Currently, Comprior supports to run Python, R, and Java code. If you want to integrate custom code from another programming language, you can implement a corresponding function like
benchutils.runRCommand()
andbenchutils.runJavaCommand()
. Such a function constructs an execution string that is then forwarded to the command line. To do that,
- create a new folder for your programming language in Comprior’s code directory (next to the Python, R, and Java directories), e.g. Cpp for adding C++ code
- place your executable files or script(s) into the new directory
- adapt the config.ini file and add a new segment for the programming language that contains the path to your executable source code (e.g. the compiled files of your C++ code) and to the programming language interpreter (for C++, however, code is invoked by just typing ./filename on the command line)
[C++] code = ${General:homePath}code/Cpp CppLocation=./
- adapt benchutils.py and implement an additional function (do not forget to add code documentation!)
def runCppCommand(cppConfig, filename, params): """Run external C++ code. :param cppConfig: C++ config parameters (store paths to C++ and the C++ code). :type cppConfig: dict :param filename: name of the C++ file to be executed. :type filename: str :param params: list of parameters that will be forwarded to the C++ file. :type params: list of str """ args = [cppConfig["C++"], filename] args.extend(params) print(args) p = subprocess.Popen(args, cwd=cppConfig["code"]) p.wait()
- invoke your code from within Python as described above in Invoking R or Java Code.


5 Part 3. Statistical Analysis
You will need to install and load package vegan
.
5.1 Univariate Analysis
We will be doing a simple Generalized Linear Model (GLM). You could easily do an entire course on GLMs, so this is not even going to begin to scratch the surface of what there is to learn about this type of statistical analysis.
In the paper by McMullin et al. (2019), the authors examined which lichen sites and species might be most threatened by sea level rise. They did not look for other patterns of species distribution. Here we will test whether lichen richness follows the pattern predicted by the latitudinal species gradient that has been observed for other species.
Here is the code to create a GLM called lat_test
(short for “latitude test”). We’re simply testing whether latitude (“Lat”) explains species richness with a linear function.
<- glm(sppRich ~ Lat, data = MACPall) lat_test
To see the model output, type summary(lat_test)
.
It’s always important to check that your model doesn’t violate assumptions of normality. You can assess this by inspecting the ratio of the null deviance to residual deviance in the model summary.
You can do this by plotting the model fits. Do this with the code plot(lat_test)
. You will see plots of 1) residuals vs. fitted data; 2) a Q-Q plot; 3) Scale-location plot; 4) Residuals vs. Leverage.
Click the links here to read about how to interpret the Residuals vs Fitted plot; the Q-Q plot. If your model does not meet the assumptions of normality, you may need to use a different error structure (e.g., Poisson, logNormal). Going into the whys and hows of that is beyond the scope of this course. The Q-Q plot fit here for lat_test
is slightly skewed but we’re going to accept it.
To make a scatterplot of the data with the line of best fit included, use the code below. Note the new function predict.glm
which calls the model we built (lat-test
) and builds a predictive line across the range of x-values in our plot. The code chunk below also demonstrates how to add customized x and y axis labels (third “layer” in the ggplot2 code). In the first line (geom_point
function), there is a demonstration of how to colour code the points by one of the attributes in the data base (in this case ER
for Ecoregion) AND how to use position=jitter
to add some slight noise to the data that allows you to see more points by minimizing overlap between points. This makes your graph slightly less accurate on a very fine scale, but enables you to see broader scale patterns. Both of these can be very useful functions.
ggplot(data = MACPall) +
geom_point(mapping = aes(x = Lat, y = sppRich, colour = ER), position = "jitter") +
geom_line(mapping = aes(x = Lat, y = predict.glm(lat_test)), size = 1) +
labs(x = "latitude", y = "lichen species richness")
5.2 Multivariate Analysis
Multivariate analysis refers to statistical analyses of more than one variable at a time. Again, one could easily fill an entire course on the topic of multivariate statistics, so we’re only going to demonstrate what one kind of multivariate analysis looks like using R. We’re going to do a type of ordination, called a Non-metric multidimensional scaling (or NMDS). There are many types of ordination analysis out there - you may have heard of some of them, some of these are Principal Components Analysis (PCA), Canonical Components Analysis (CCA).
An ordination simply summarizes multivariate data and projects into a lower dimensional space - usually across two axes. It can be a useful way to see intrinsic patterns in complex data sets, or to simplify data before doing different analyses.
You will need to load the vegan
package to do this analysis.
To do the NMDS ordination, we use the function metaMDS
. We’ll apply it to the species data set. Recall that this is a dataset with 215 rows (one row per site) and 599 species. This is a fairly large data set. The ordination will allow us to see whether certain species are closer together in data space, and if this corresponds to some of the site-level attributes.
Before we do this, we need to manipulate the MACPspp
data frame so that it only includes the species data. You could use a tool from the dplyr
package, or since we know the sites are in the first column and there are 600 columns total in MACPspp
we can use a simple baseR
funcction:
<- MACPspp[,2:600] spp
Here is the code to do the ordination of spp
as well as plot it. The default in the plot settings is to use black circles for sites and red +
signs for species.
<- metaMDS(spp) ordination_spp
## Run 0 stress 0.2011619
## Run 1 stress 0.2011618
## ... New best solution
## ... Procrustes: rmse 0.0001526031 max resid 0.001597708
## ... Similar to previous best
## Run 2 stress 0.2140844
## Run 3 stress 0.2051861
## Run 4 stress 0.2027495
## Run 5 stress 0.2025941
## Run 6 stress 0.2015875
## ... Procrustes: rmse 0.01716666 max resid 0.2302712
## Run 7 stress 0.2066741
## Run 8 stress 0.205073
## Run 9 stress 0.2065044
## Run 10 stress 0.2033103
## Run 11 stress 0.2010589
## ... New best solution
## ... Procrustes: rmse 0.005617899 max resid 0.07903478
## Run 12 stress 0.2055039
## Run 13 stress 0.2080301
## Run 14 stress 0.2010597
## ... Procrustes: rmse 0.000268988 max resid 0.003849411
## ... Similar to previous best
## Run 15 stress 0.2011619
## ... Procrustes: rmse 0.005585886 max resid 0.07858387
## Run 16 stress 0.2447931
## Run 17 stress 0.2065044
## Run 18 stress 0.2064097
## Run 19 stress 0.2650103
## Run 20 stress 0.2120983
## *** Solution reached
plot(ordination_spp)
We can adjust this using the function display
. Play around with the code below to see how you can change the plot.
plot(ordination_spp, display = "sites")
text(ordination_spp, display = "spec", cex = 0.7, col = "blue")
The text
function adds the species names. There are so many that they become illegible. You could try adjusting the font size, or if this were your own data, you might apply 2-letter species codes, if possible.
Often we want to layer other information on an ordination to better see pattern. For example, we might want to add convex hulls around the site points that are from the same ecoregion. This will let us see whether some ecoregions have species that are unique to them (the hull will be isolated from other hulls) or if two or more ecoregions have similar species composition (the hulls will have a lot of overlap).
Before we do that, we need to strip the “sites” column from the MACPsites
dataframe so we have just the environmental variables. We could use the baseR method we did above to create spp
, or you could use the select
function from the dplyr
package.
<- select(MACPsites, ER, VEG, PPT, TEMP, ELEV) env
Then specify to create convex hulls using the ecoregion field (ER
) from env
:
ordihull(ordination_spp, env$ER, col = 1:4, label = TRUE)
Try the above code, and then see if you can modify it to create convex hulls by vegetation type.
If you can’t see the hulls because the text labels are too big, change label = TRUE
to label = FALSE
and run the code again.
Another data visualization might be to use ellipses instead of convex hulls. You can try it with this code:
ordiellipse(ordination_spp, env$ER, col = 1:4, draw = "polygon")
Finally, we might want to do a test to see how different environmental variables at the sites load onto to the two axis of our ordination. This can start to tell us how what’s happening at the sites might be influencing the species pattern(s).
ord.fit <- envfit(ordination_spp ~ ELEV + PPT + TEMP, data = env, perm = 999)
To see the test output, type ord.fit
To plot the fits of elevation, temperature and precipitation on the ordination, use these to lines of code.
plot(ordination_spp)
plot(ord.fit, col = "black")
5.3 Your assignment for Part 3
Describe what you can conclude about the effect of latitude on species richness for the mid-Atlantic Coastal Plain. Is the fit significant? Are there issues with the data that might affect the inferences drawn from the statistical analysis?
What interpretation can you make from the final ordination plot we made here?
Think about a possible hypothesis that McMullin RT (2019) could have tested with these data, but did not.
- Write out the hypothesis in words and include a prediction
- Sketch a “toy graph” of how you expect the data to look if the hypothesis is supported
- Identify what kind of statistical test you will apply
- Describe how what steps you need to take with these data to execute the steps (i.e., do you need to do any data wrangling first?). A flow chart or bullet points would be a good way to do this.
- Execute the test. Include a copy of the code, the statistical output and a short interpretation of the hypothesis test.
For graduate students: Do the above step twice (i.e., test two different hypotheses). Challenge yourself to try a new test that you may not have done before.


Version: 1.0.0
Release date: 30/05/2018
Publisher: IOT Builders
Download
You can now download the application.


BlueHarmony enables IBM's "Globally Integrated Enterprise" strategy. It is IBM's next major transformation program to radically simplify the design and operation of three Globally Integrated Support Processes - Finance, Opportunity-to-Order, and Order-to-Cash.
I led the requirements gathering, data modeling, solution architecture, and solution design of the integrated BMSIW - BlueHarmony Release through multiple iterations. Led an extended team of 12 business analysts and data modelers to develop and enhance a subject area model around customer information, customer contracts, billings, project management, project & cost accounting, and materials. This model enables integrated reporting during the BH transition, and contains significant enhancements and new data feeds that will to be used well after the transition. The new model contains 115 new data entities and 5 new feeds from both BH and non-BH systems and will be the starting point for the Services IW ODS’s ADL (Operational Data Store’s Atomic Data Layer).
I lead the 2,104 member BH – BMSIW Integration Community and conduct a weekly Q&A session based on discussion threads entered within the forum portlet of the community.
Redesigned Work Breakdown Structure feed from BH after deployment when real volumes exceeded estimated volume by a 10x order of magnitude. New design increased throughput from 32K to 975K/minute and reduced delta processing from 12 to 1.5 hours.


Fun with CTools Plugins
September 17, 2014
About Me
Will Long
Objectives
- Cover what the CTools module provides
- Understand the concept of CTools plugins
What is CTools?
CTools Provides
- Page Manager
- Understand the concept of CTools plugins
Questions
Thanks for attending.


Andy Ruiz Jr makes Deontay Wilder vs Luis Ortiz rematch prediction
Ortiz is out to shock Wilder in their rematch on November 23 - but Ruiz Jr reckons it doesn't end well for the Cuban
by Gareth BicknellAndy Ruiz Jr has revealed his prediction for this month's rematch between Deontay Wilder vs Luis Ortiz - and it's not good news for the Cuban.
Ortiz gave Wilder a huge test in their WBC heavyweight title bout last March before gassing out in the latter stages and being knocked out in the 10th round.
He will again look to cause an upset in their rematch on November 23, with Wilder the overwhelming favourite to maintain his unbeaten record.
But Ruiz Jr - who is involved in a rematch himself when he defends his heavyweight titles against Anthony Joshua on December 7 - reckons it will be the outcome between Wilder and Ortiz.
The WBA, IBF, WBO and IBO champion was asked for his prediction on the Wilder vs Ortiz while being interviewed by iFLTV.
He said: "I think it's going to be the same outcome.
"Wilder's the younger man, the stronger man.
"Plus he's a good friend of mine, so I'm going to be cheering for him."
Ortiz has warned Wilder he has learnt lessons from his defeat in their first fight - and is heading into their rematch confident of becoming world champion .
"Wilder knows and if he’s not prepared and taking me seriously, he’s in big trouble," Ortiz told Fighthub.
"I saw there were mistakes I made in the fight, and what I've done is strengthen my preparation so that I don’t repeat that.
After The Bell: Watch Mirror Fighting's new boxing show
Mirror Fighting has launched a new weekly boxing show in association with Betfair in which we analyse the latest big fights, play matchmaker, and preview forthcoming bouts.
Former super-middleweight world champion George Groves provides expert analysis.
To watch the latest show in full, click here.
"I await November 23 to be world champion, and realize my dream. Everything that happened, it just wasn’t my night.
“I just want to show the world that I’m better, and the best. I’m going to fight this fight differently. When the bell sounds on November 23, I will be the difference, and do everything I have done in my whole life and I will do that night."


A modern HTML development framework packed with the most critical commonly used features in today's modern workflows.


--- layout: page title: Neo4J date: 2015-07-30 22:22:13.000000000 -04:00 type: page parent_id: '' published: true status: publish categories: [] tags: [] meta: {} author: ---
[caption id="" align="alignnone" width="1181.0"] [/caption]
XN Logic is a Neo Technology partner. We offers services, licensing, an application framework, and visualization to drive value off the relationships in your data, using the power of the Neo4j graph database.
XN Logic offers end to end Neo4j advice and support. We’ll consult on data modelling and query design, run graph training focused on the needs of your team, assist with complex cluster design and deployment, and offer full stack graph application development to accelerate your team’s delivery. While we prefer to be in the room while you’re deciding on going with a graph to solve your business problem, we’re not averse to grabbing the next flight across continents or oceans to ensure that your Neo4j project hits its deadlines and key deliverables.
XN Logic resells and provides advice on licenses for Neo4j Enterprise Edition. Whether you’re a startup or a Fortune-10 firm, XN Logic will help you decide on the right license for your use case and business and get you running quickly.
The XN Logic framework is a perfect complement to your Neo4j database. The framework offers the security, permissions, and auditing that your enterprise application need while also providing a domain-specific API and HTML 5 front end. Our domain specific language reduces the complexity of developing and delivering graph applications, giving more leverage to your resources. The framework reduces the time to value of your projects and dramatically cuts project risk.
Ask us about doing a proof-of-concept today!
XN Logic delivers Graph Academy training courses on Neo4J as well as custom packages for your team, in your office.
See here for a list of upcoming Neo4j training events.
The XN Logic framework integrates with a number of visualization tools to give you a better understanding of your data. KeyLines, Linkurious, and D3.js are our core tools for representing the relationships within your data. Our standards compliant foundations allow for rapid integration of other tools for our clients’ custom applications.


This page is used to navigate the website. It contains links to the report page and the first assignment.
To get to the my introduction page click here.
To get to the first assignment (S01) click here.


No. It cannot be the final destination of data, so it should be connected to some other stream.
The updated example program uses a BufferedWriter
and a PrintWriter
.
Look at the file it creates with NotePad (or other text editor).
The lines of text should appear correctly separated
(with the earlier examples they might not).
import java.io.*; class WriteTextFile3 { public static void main ( String[] args ) { String fileName = "reaper.txt" ; PrintWriter print = null; try { print = new PrintWriter( new BufferedWriter( new FileWriter( fileName ) ) ); } catch ( IOException iox ) { System.out.println("Problem writing " + fileName ); } print.println( "No Nightingale did ever chaunt" ); print.println( "More welcome notes to weary bands" ); print.println( "Of travellers in some shady haunt," ); print.println( "Among Arabian sands." ); print.close(); } }
Opening the file might throw an exception, so a try/catch
structure
is needed for the constructor.
However the println()
statements can be moved outside of the
structure.


---
authors:
- brad-czerniak
#categories: ["one", "two"]
#date: 2020-03-03 02:02:02
front: true
#hero_classes: "background-color--main-dark color--white"
menus:
# copyright: 1
footer: 1
main: 1
# utility: 1
meta:
description: "Brad Czerniak is a Drupal and JAMstack developer in Michigan."
# image:
# alt: "Default social image"
# src: required/meta-image--default.jpg
# robots: "index,follow"
# title: "Overrides the tab title and social titles"
#permalink: /index.html
#published: true
redirect_from:
- /home
#schema:
# type: "Thing"
sections:
- freshness
- cta
- mailchimp
#sitemap: true
#tags: ["three", "four"]
title: "Home"
---
{% include atoms/heading.html
children="Brad would rather be at trivia night"
classes=""
level="1"
%}
{% include atoms/spacer.html size="4" %}
{% include atoms/heading.html
children="Biography"
classes="text-align--center"
level="2"
%}
{% include atoms/spacer.html size="4" %}
{% include atoms/heading.html
children="The answer is yes"
classes="font-weight--100"
level="2"
%}
{% include atoms/spacer.html size="2" %}
Brad loves doing presentations/talks and writing in-depth articles, and is interested in your next event or guest
writing on your blog. Similarly, if you'd like to do a talk or write a guest post, Brad has this site,
Solve it once, Lower barriers,
and organizes a few Michigan technologymeetupgroups.


Celtic to face Partick Thistle in Scottish Cup fourth round as Rangers drawn against Stranraer
The Hoops will travel to Firhill as they take on Ian McCall's side for the second time this season
by Graeme Young, https://www.dailyrecord.co.uk/authors/graeme-young/Holders Celtic will face Partick Thistle in an all-Glasgow clash after being drawn against their city rivals in the Scottish Cup fourth round.
The Hoops will travel to Firhill as they take on Ian McCall's side for the second time this season after sweeping them aside 5-0 in the Betfred Cup in October.
Neil Lennon's Celtic side are eyeing a fourth consecutive Scottish Cup triumph after coming out on top in each of the last three seasons.
Title rivals Rangers will take on Stranraer at Ibrox as the Steven Gerrard looks to make inroads in the tournament which came to a shuddering halt last season against Aberdeen at the quarter-final stage.
Derek McInnes' side, on the back of Dave Cormack being confirmed as chairman, will take on League One side Dumbarton at Pittodrie.
Lowland League side East Kilbride missed out on a money-spinning clash as they take on BSC Glasgow who are eyeing progression to the next round after stunning East Fife.
Managerless Hearts could face a tricky task against League One leaders Airdrie.
The fourth round ties will be played on the weekend of January 18/19 when the Premiership teams come back from their winter break.
Scottish Cup fourth round draw in full:
Rangers vs Stranraer
Dundee vs Motherwell
East Kilbride vs BSC Glasgow
Partick Thistle vs Celtic
Hamilton vs Edinburgh City
St Johnstone vs Morton/Brora Rangers
Bonnyrigg Rose vs Clyde
Aberdeen vs Dumbarton
Ayr United vs Ross County
Dundee United vs Hibs
Auchinleck Talbot/Arbroath vs Falkirk
Livingston vs Raith Rovers
Alloa vs Inverness
Hearts vs Aidrieonians
Kilmarnock vs Queen’s Park
St Mirren vs Broxburn


# standard Python imports
from pathlib import Path
# scientific package imports
import numpy as np
from numpy.linalg import norm
import astropy.units as u
from galaxy.galaxies import Galaxies
[docs]class Approaches(Galaxies):
"""
A class to work with all 3 galaxies when in close proximity.
Args:
snap (int):
Snap number, equivalent to time elapsed.
Defaults to the last timepoint.
datadir (str):
Directory to search first for the required file. Optional, and a
default list of locations will be searched.
usesql (bool):
If True, data will be taken from a PostgreSQL database instead of
text files.
stride (int):
Optional. For stride=n, get every nth row in the table.
Only valid with usesql=True.
ptype (str):
can be 'lum' for disk+bulge, 'dm' for halo
Class attributes:
data (np.ndarray):
type, mass, position_xyz, velocity_xyz for each particle
"""
def __init__(self, snap=801, datadir=None, usesql=False, stride=1, ptype='lum'):
"Initial setup. Currently it calls read_file(), but this may change."
self.snap = snap
self.ptype = ptype
if usesql:
self.read_db(stride)
else:
raise NotImplementedError
[docs] def read_db(self, stride):
"""
Get relevant data from a PostgreSQL database and format it to be
identical to that read from test files.
Args:
stride (int):
Optional. For stride=n, get every nth row in the table.
Changes:
`self.time`, `self.particle_count` and `self.data` are set.
Returns: nothing
"""
from galaxy.db import DB
db = DB()
cur = db.get_cursor()
# set the elapsed time
sql_t = f"SELECT time FROM simdata WHERE galname in ('MW', 'M31')"
sql_t += f" and snap={self.snap} LIMIT 1"
cur.execute(sql_t)
time = cur.fetchone()
try:
self.time = time[0] * u.Myr
except TypeError:
print(self.name, self.snap, ptype)
# set the bulk of the data
colheads = ','.join(['galname','type','m','x','y','z','vx','vy','vz'])
if stride > 1:
sql_d = f"SELECT {colheads}, ROW_NUMBER() OVER () as rn from simdata"
else:
sql_d = f"SELECT {colheads} FROM simdata WHERE snap={self.snap}"
if self.ptype == 'lum':
sql_d += f" and type in (2,3)"
elif self.ptype == 'dm':
sql_d += f" and type=1"
else:
raise ValueError("Valid ptype is 'lum' or 'dm'")
sql_d += " ORDER BY galname, pnum"
if stride > 1:
sql_d = f"SELECT {colheads} from ( {sql_d} ) as t where rn % {stride} = 0"
dtype=[('galname', 'U3'), ('type', 'uint8'), ('m', '<f4'),
('x', '<f4'), ('y', '<f4'), ('z', '<f4'),
('vx', '<f4'), ('vy', '<f4'), ('vz', '<f4')]
cur.execute(sql_d)
self.data = np.array(cur.fetchall(), dtype=dtype)
self.particle_count = len(self.data)
[docs] def xyz(self):
"""
Convenience method to get positions as a np.array of shape (3,N)
"""
return np.array([self.data[xi] for xi in ('x','y','z')])
[docs] def vxyz(self):
"""
Convenience method to get velocities as a np.array of shape (3,N)
"""
return np.array([self.data[vxi] for vxi in ('vx','vy','vz')])


+++
title = "PiWeatherRock"
linkTitle = "PiWeatherRock"
+++
{{< blocks/cover title="Display local weather on a Raspberry Pi" image_anchor="top" height="full" color="orange" >}}
{{< /blocks/cover >}}
{{% blocks/lead color="red" %}}
On March 30, 2020 an announcement was posted to the Dark Sky Blog stating that they have
been acquired by Apple. The announcement went on to say that they are no longer accepting new requests for API access and that they
will be shutting the API down at the end of 2021.
This unfortunate bit of news isn't the end of this project... I've been here before. This just means that I will have to do a third
API migration. I have decided to use ClimaCell as the new backend. This is actually
going to enable some new features. Work related to this, and suggestions on what API to use, are being tracked in
issue 48 of the project's GitHub repo.
{{% /blocks/lead %}}
{{% blocks/lead color="primary" %}}
PiWeatherRock displays local weather from Dark Sky on (almost) any screen you connect to a Raspberry Pi.
{{% /blocks/lead %}}
{{< blocks/section color="white" >}}
PiWeatherRock has three screens that it cycles through to show you a daily forecast, a hourly forecast, and an information screen.
The weather show is from Dark Sky and is hyper-local which makes it much more accurate than many other sources. Data is refereshed
every five minutes so that you'll always have up-to-date information before heading outside.
{{< /blocks/section >}}
{{< blocks/section color="dark" >}}
{{% blocks/feature icon="fab fa-gitter" title="Come chat on Gitter" url="https://gitter.im/PiWeatherRock/community" %}}
PiWeatherRock has a community chat on Gitter where you can ask questions and give feedback
{{% /blocks/feature %}}
{{% blocks/feature icon="fab fa-github" title="Contributions welcome!" url="https://github.com/genebean/PiWeatherRock" %}}
We do a [Pull Request](https://github.com/genebean/PiWeatherRock/pulls) contributions workflow on **GitHub**. New users are always welcome!
{{% /blocks/feature %}}
{{% blocks/feature icon="fab fa-twitter" title="Follow me on Twitter!" url="https://twitter.com/technicalissues" %}}
I'll announce new features here.
{{% /blocks/feature %}}
{{< /blocks/section >}}


‘Harry Potter’ star Tom Felton wants to play Draco Malfoy again
Star also thinks the franchise will "definitely" be rebooted
Harry Potter star Tom Felton has admitted he would like to play the role of Draco Malfoy again.
The actor portrayed Potter’s school nemesis over franchise’s eight movies but has revealed he’s not done yet; he’d take the opportunity to play Malfoy again in the future.
“Go back to Hogwarts one more time? Yeah, I’m endlessly proud and affectionate of young Draco, so any chance to reprise that role would be great,” Felton told MailOnline.
However, Felton joked that if the series’ original stars return they should swap roles, adding: “I think we should come back and do the opposite, Rupert [Grint] should play Draco, I’ll play Hermione, Dan can play Dobby, and Emma can be Hagrid, it’s the perfect re-casting!
“It’ll be an interesting spin [on it]. But, seriously, if there was another chance to be in another movie then I’m sure all of us would jump at the chance to be part of it.”
Tom Felton as Draco Malfoy
Felton also admitted he thinks the franchise will “definitely” be rebooted in the future, suggesting he could play Draco’s father Lucius if that were to happen.
“I don’t think Jo [Rowling] would want her films, her books rather and her films, to be remade anytime soon, but I’m sure in 20 years when they’ve got 4D, scratch-and-sniff cinemas, or whatever, then they’ll want to do it all over again,” he said.
“My hope is that when they do it I’ll be old enough to play Lucius! Give it another 15 years and I’ll come back and play Lucius. I would do it in a heartbeat.”
Meanwhile, Felton recently shared a throwback video of himself with co-stars Emma Watson and Daniel Radcliffe during the filming of Harry Potter and the Philosopher’s Stone.


Simple functions to deflate nominal Brazilian Reais using several popular price indexes downloaded from the Brazilian Institute for Applied Economic Research.
| Version: | 1.1.2 |
| Depends: | R (≥ 3.1.2) |
| Imports: | dplyr (≥ 0.5.0), lubridate, httr |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-09-28 |
| Author: | Fernando Meireles [aut, cre] |
| Maintainer: | Fernando Meireles <fmeireles at ufmg.br> |
| BugReports: | https://github.com/meirelesff/deflatebr/issues/ |
| License: | MIT + file LICENSE |
| URL: | https://github.com/meirelesff/deflatebr/ |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | deflateBR results |
| Reference manual: | deflateBR.pdf |
| Package source: | deflateBR_1.1.2.tar.gz |
| Windows binaries: | r-devel: deflateBR_1.1.2.zip, r-release: deflateBR_1.1.2.zip, r-oldrel: deflateBR_1.1.2.zip |
| OS X binaries: | r-release: deflateBR_1.1.2.tgz, r-oldrel: deflateBR_1.1.2.tgz |
Please use the canonical form https://CRAN.R-project.org/package=deflateBR to link to this page.


Annotation Type RestInject
Used on methods of Rest
-annotated classes to denote methods and fields that override and customize beans
used by the REST framework.
Example
The name()
/value()
attributes are used to differentiate between named beans.
Example
The methodScope()
attribute is used to define beans in the scope of specific RestOp
-annotated methods.
Example
This annotation can also be used to inject arbitrary beans into the bean store which allows them to be
passed as resolved parameters on RestOp
-annotated methods.
Example
This annotation can also be used on uninitialized fields. When fields are uninitialized, they will be set during initialization based on beans found in the bean store.
Example
Notes:
- Methods and fields can be static or non-static.
- Any injectable beans (including spring beans) can be passed as arguments into methods.
- Bean names are required when multiple beans of the same type exist in the bean store.
- By default, the injected bean scope is class-level (applies to the entire class). The
methodScope()
annotation can be used to apply to method-level only (when applicable).
Any of the following types can be customized via injection:
-
Optional Element Summary
-
Element Details
-
name
The bean name to use to distinguish beans of the same type for different purposes.For example, there are two
HeaderList
beans:"defaultRequestHeaders" and"defaultResponseHeaders" . This annotation would be used to differentiate between them.- Returns:
- The bean name to use to distinguish beans of the same type for different purposes, or blank if bean type is unique.
- Default:
- ""
-
value
Same asname()
.- Returns:
- The bean name to use to distinguish beans of the same type for different purposes, or blank if bean type is unique.
- Default:
- ""
-
methodScope
The short names of the methods that this annotation applies to.Can use
"*" to apply to all methods.Ignored for class-level scope.
- Returns:
- The short names of the methods that this annotation applies to, or empty if class-scope.
- Default:
- {}
-


Walt Ribeiro
Software Engineer
Resume
Projects
Work
Certifications
Github Repo
Length:
Uppercase
Lowercase
Numbers
Symbols
<EMAIL_ADDRESS>(240) 487-9258
Philadelphia
Made by Walt Ribeiro 2020


This scenario demonstrates how to format a number as a decimal. An application using this approach to format a number as a decimal will be returned a string that is formatted to match the user’s preferences. An application can also choose to override the user’s language preferences and will be returned a string properly formatted for that override.
The various options available for this formatting option are illustrated below including specifying the number of integer or fractional digits and with or without grouping separators. Numbers will be formatted using the current user’s preference and overrides.


We are currently developing a prototype of our eventual cloud robotics gaming platform. Play multiplayer games using physical robot avatars with people across the world. Be one of the first to try out our robots.
See your surroundings through a low-latency HD stream. With pan and tilt controls, you have full freedom on where you are looking.
Navigate your surroundings with intuitive and easy to use controls. Combining precision with power, you are free to go where you wish.
Interact with your surroundings by picking up objects with your gripper arm. Expect additional toys and features in the near future.
Hello!
Feel free to drive around our lab. This is a prototype so the robot may be unstable. If there's a lineup to use the robot, your turn will end in 60 seconds.
Thumper is undergoing repairs. We are trying to get it working ASAP!
Queue Length: N/A
Your Position: N/A
Robot speed is currently: 0


Check your network connection - Windows Vista
To check the status of your local area connection, follow the steps below.
-
From your desktop, click on Start and select Control Panel.
-
From the Control Panel window, select Network and Internet.
-
Select Network and Sharing Center.
-
In the Network Sharing Center window, click on Manage network connections on the left. A yellow triangle displayed on this screen indicates that there is no connectivity.
-
In the Network Connections window, look for the Local Area Connection icon.
-
If the Local Area Connection icon shows 'Identifying', as shown in the screen above, continue to step 7 to change your IP settings to a manual configuration.
- If the Local Area Connection icon does not show 'Identifying', this means you are successfully connected to the Sky wireless router. If you are still experiencing connection problems, check your web browser's connection settings.
-
To manually configure your IP settings, right-click on the Local Area Connection icon in the Network Connections window, then click on Properties at the bottom of the menu.
-
In the Local Area Connection Properties window, scroll down in the window 'This connection uses the following items' until you see Internet Protocol Version 4 (TCP/IPv4) - click this once, then select Properties.
-
In the Internet Protocol Version 4 (TCP/IPv4) window:
- Select Use the following IP address
- Set the IP address to: 192.168.0.X (where X must be a number of your choice between 2 and 254 and should be unique for each PC connecting to the Sky wireless router)
- Set the Subnet mask to <IP_ADDRESS>
- Set the Default gateway as <IP_ADDRESS>
- Select Use the following DNS server addresses
- Set the Preferred DNS server to <IP_ADDRESS>
- Leave the Alternate DNS server blank
- Once completed, the details should look similar to the image below. Click OK
-
Click OK again in the Local Area Connection Properties window.
-
Restart your computer and open a web browser to check if you can now browse the web using your broadband service.


Female MP Desperately Crosses Flooded River After Abandoning Car [VIDEO]
by Michael MusyokaA female MP was over the weekend forced to abandon her car and wade through a flooded river.
In a video and photos on her Facebook account on Sunday, November 24, Nyeri Woman Representative Rahab Mukami found herself in a desperate situation where she had to walk against the river's raging water.
The legislator and her entourage had to leave their vehicles as the bridge was completely engulfed by the river's water.
Acknowledging that it was a difficult task to complete following the heavy rains experienced in her area, The MP was thankful for being able to cross over safely.
"Finally, we made it. Glory and honour to our almighty God. Thank God for Journey mercies.
"It’s a long journey but our strong faith and trust in God we made it. Always follow your dream without turning back, with God everything is possible. Faith can move mountains. Always count your blessings one by one. It’s an experience and a half," she wrote on her FaceBook.
It wasn't established which river had flooded and the exact location of the bridge, but other road users appeared to struggle to get across the river.
A passenger bus had to be pulled by a tractor as other vehicles carefully manuevered to make it across avoiding being swept away by the river's persisent current.
Here is the video of the Woman Representative's Vehicle crossing the flooded bridge:


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Gardens
Garden
Soil Fertility
Starting a Garden
Creative Raised Beds
Starting Seeds
Planting
Chickens
Chickens
Chicken Homes
Chicks
Chickens for Meat
Geese
Geese
Goslings
Older Goslings
Geese in Winter
Sheep
Sheep
Sheep Winter
Sheep Shearing
Sheep Wool
Sheep Lambs
Sheep Graze
Sheep Fall, Coats, Lambs
Starting with Sheep
Starting with Sheep
Grazing, Equipment, Handling
Nourishment
Nourishment
Farm Stories
Friends
New Chickens
Sweet Flying Friends
Sherlock's Adventure
Chicken Moms
In Praise of Beer
Fun at the Ocean
Momma Hen
Solar Power
Barra the Lamb
Visitors
Where did they all go?
Ms. Goldie
Beginnings
Mr. Mac
Inspiration
Changes
Resources
Resources
Contact
Contact


Winds battered the side of my van through the night, perched atop a hill in Monterey County. I awoke to a rainy windshield, a crisp, dewey morning with a glowing potential to blossom into a beautiful sunny afternoon.
I spent Thursday and Friday working and cycling. The Laguna Seca Recreation Area is home to the Mazda Laguna Seca raceway, which served as a veritable road cyclist's utopia with its maze of paved and barely-traveled access roads around the campus. For two days, I cheerfully alternated between cycling and working every few hours. I hardly spoke to a soul, but this solidarity gave me new perspective.


Chris Sean is one of the most inspiriting web deveolopers out there, and hundreds of fans are thankful for the opportunity to learn from him.
He gives those of us starting out from scratch pure hope that we too can achieve any dream, if we just try hard enough. Doesn't matter if you've had a late start or not, it's determination in the end.


THE HIGH COURT OF MADHYA PRADESH MCRC-13321-2018 (M.P. POLLUTION CONTROL BOARD Vs CENTRAL INDIA PAPER AND BOARD MILL LTD.) 1 Jabalpur, Dated : 24-04-2018 Mr.Sourabh Soni, learned counsel for the petitioner. The complainant of the petitioner under section 24 & 25 of the Water (Pollution & Control) Act, 1974 has been dismissed vide judgment judgment dated 27.2.2017 by Judicial Magistrate First Class, sh Sagar in Criminal Case No.557/2008. Subsequently, the e petitioner/M.P. Pollution Control Board preferred criminal appeal ad which was registered as Criminal AppealNo.122/2017. The same was Pr dismissed by order dated 19.12.2017 by 2nd Additional Sessions Judge, Sagar on the ground that it is not maintainable as the judgment a hy of acquittal is appealable before the High Court only. Therefore, the petitioner has preferred this leave to appeal under section 378(4) of the ad Code of Criminal Procedure alongwith an application under section 14 M of the Limitation Act for condonation of delay in filing the petition.
Considering the above to be bonafide, the default pointed out by of the office is ignored.
rt Let the case be listed for consideration of I.A.No.5382/2018 ou after a week.
C (SUSHIL KUMAR PALO) h JUDGE ig H Digitally signed by RAJESH T MAMTANI Date: 2018.04.28 18:03:54 +05'30' RM


Definition at line 24 of file Graph.hpp.
|
|
| DataSet () |
| | Construct an empy data set.
|
| |
|
| DataSet (float *data, size_t n_data) |
| | Construct a data set with data and n_data points.
|
| |
|
virtual float | at (size_t i) const |
| | Returns the data at point i.
|
| |
|
virtual size_t | size () const |
| | Returns the number of data points in the set.
|
| |
| void | set (const float *data, size_t n) |
| | Set the data pointer and number of points. More...
|
| |
|
float | max () const |
| | Returns the maximum value in the set.
|
| |
|
float | min () const |
| | Retuns the minimum value in the set.
|
| |
| void Graph::DataSet::set |
( |
const float * |
data, |
|
|
size_t |
n |
|
) |
| |
|
inline |
This sets the data to use in the graph. The caller needs to manage the memory for the data points. This object simply has a pointer to the external data.
- Parameters
-
| data | A pointer to the data |
| n | The number of data points |
Definition at line 53 of file Graph.hpp.


AWS::RDS::DBSecurityGroup
The AWS::RDS::DBSecurityGroup type is used to create or update an Amazon RDS DB Security Group. For more information about DB security groups, see Working with DB Security Groups in the Amazon Relational Database Service Developer Guide. For details on the settings for DB security groups, see CreateDBSecurityGroup.
Note
If you use DB security groups, the settings that you can specify for your DB instances are limited. For more information, see the DBSecurityGroups property.
When you specify an AWS::RDS::DBSecurityGroup as an argument to the Ref
function, AWS CloudFormation returns
the value of the DBSecurityGroupName
.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type" : "AWS::RDS::DBSecurityGroup", "Properties" : { "EC2VpcId" : { "Ref" :
"myVPC"
}, "DBSecurityGroupIngress" : [RDS Security Group Rule object 1, ...
], "GroupDescription" :String
, "Tags" : [Resource Tag, ...
] } }
YAML
Type: AWS::RDS::DBSecurityGroup Properties: EC2VpcId:
String
DBSecurityGroupIngress: -RDS Security Group Rule
GroupDescription:String
Tags: -Resource Tag
Properties
EC2VpcId
-
The Id of the VPC. Indicates which VPC this DB Security Group should belong to.
Important
The
EC2VpcId
property exists only for backwards compatibility with older regions and is no longer recommended for providing security information to an RDS DB instance. Instead, useVPCSecurityGroups
.Type: String
Required: Conditional. Must be specified to create a DB Security Group for a VPC; may not be specified otherwise.
Update requires: Replacement
DBSecurityGroupIngress
-
Network ingress authorization for an Amazon EC2 security group or an IP address range.
Type: List of RDS Security Group Rules.
Required: Yes
Update requires: No interruption
GroupDescription
-
Description of the security group.
Type: String
Required: Yes
Update requires: Replacement
Tags
-
The tags that you want to attach to the Amazon RDS DB security group.
Required: No
Type: A list of resource tags.
Update requires: No interruption
Template Examples
Tip
For more RDS template examples, see Amazon RDS Template Snippets.
Single VPC security group
This template snippet creates/updates a single VPC security group, referred to by EC2SecurityGroupName.
JSON
"DBSecurityGroup": { "Type": "AWS::RDS::DBSecurityGroup", "Properties": { "EC2VpcId" : { "Ref" : "VpcId" }, "DBSecurityGroupIngress": [ {"EC2SecurityGroupName": { "Ref": "WebServerSecurityGroup"}} ], "GroupDescription": "Frontend Access" } }
YAML
DBSecurityGroup: Type: AWS::RDS::DBSecurityGroup Properties: EC2VpcId: Ref: "VpcId" DBSecurityGroupIngress: - EC2SecurityGroupName: Ref: "WebServerSecurityGroup" GroupDescription: "Frontend Access"
Multiple VPC security groups
This template snippet creates/updates multiple VPC security groups.
JSON
{ "Resources" : { "DBinstance" : { "Type" : "AWS::RDS::DBInstance", "Properties" : { "DBSecurityGroups" : [ {"Ref" : "DbSecurityByEC2SecurityGroup"} ], "AllocatedStorage" : "5", "DBInstanceClass" : "db.m1.small", "Engine" : "MySQL", "MasterUsername" : "YourName", "MasterUserPassword" : "YourPassword" }, "DeletionPolicy" : "Snapshot" }, "DbSecurityByEC2SecurityGroup" : { "Type" : "AWS::RDS::DBSecurityGroup", "Properties" : { "GroupDescription" : "Ingress for Amazon EC2 security group", "DBSecurityGroupIngress" : [ { "EC2SecurityGroupId" : "sg-b0ff1111", "EC2SecurityGroupOwnerId" : "111122223333" }, { "EC2SecurityGroupId" : "sg-ffd722222", "EC2SecurityGroupOwnerId" : "111122223333" } ] } } } }
YAML
Resources: DBinstance: Type: AWS::RDS::DBInstance Properties: DBSecurityGroups: - Ref: "DbSecurityByEC2SecurityGroup" AllocatedStorage: "5" DBInstanceClass: "db.m1.small" Engine: "MySQL" MasterUsername: "YourName" MasterUserPassword: "YourPassword" DeletionPolicy: "Snapshot" DbSecurityByEC2SecurityGroup: Type: AWS::RDS::DBSecurityGroup Properties: GroupDescription: "Ingress for Amazon EC2 security group" DBSecurityGroupIngress: - EC2SecurityGroupId: "sg-b0ff1111" EC2SecurityGroupOwnerId: "111122223333" - EC2SecurityGroupId: "sg-ffd722222" EC2SecurityGroupOwnerId: "111122223333"


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Never Been Kissed **1/2
rated PG-13
starring Drew Barrymore, David Arquette, Michael Vartan, Molly Shannon, John C. Reilly, Leelee Sobieski
written by Abby Kohn and Marc Silverstein directed by Raja Gosnell
>From E.T. to THE WEDDING SINGER, Drew Barrymore always seems to shine. That is probably the main reason why she was chosen to play the lead role in Raja Gosnell's NEVER BEEN KISSED. Barrymore turns in a fine comic performance and steals the show from a spectacular supporting cast, proving that she deserves to play the protagonist more often. It's too bad that Abby Kohn and Marc Silverstein couldn't give her a better script to work with, because NEVER BEEN KISSED ends up playing out as nothing more than contrived fun
Barrymore plays Josie Geller, a copy editor for the Chicago Sun-Times who is picked to do her first piece as a reporter. She must go back to high school and write a story about any specific problem she finds within it. But her brother, Rob(David Arquette), reminds her of just how bad her high school years were. She wore huge braces, never washed her hair, was asked out to the prom as a joke, and was called "Josie Grossie." But she still decides to give high school a second shot. But instead of hanging out with the kids who cause trouble worth writing about, she befriends a nerdy mathematician(Leelee Sobieski) and crushes on her English teacher(Michael Vartan). When her boss(John C. Reilly) demands that she somehow become popular so that she can get her story, her brother enrolls in the school, becomes popular in a day, and starts to hint to people that Josie does have a cooler side.
NEVER BEEN KISSED has some very funny scenes(the prom theme is "Great Couples in History" and two people dress up as the Doctor and Gonzo from FEAR AND LOATHING IN LAS VEGAS), but the story seems forced, as if a Fox executive demanded that the writers find some way to combine Drew Barrymore with high school, so that everyone in the world will want to see it. As I wrote earlier, Barrymore makes a great leading lady, and makes it seem that the top-notch supporting cast was wasted. But even a charming Drew can't change the fact that NEVER BEEN KISSED is predictable and insignificant. But if you end up seeing the film, stay for the credits, where every actor and crew member's high school photo is shown.
a review by Akiva Gottlieb, The Teenage Movie Critic<EMAIL_ADDRESS>http://www.angelfire.com/mo/film watch me on TBS' "Dinner And A Movie" May 21, 8:05pm EST
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