service
Elementservice
Elements - Serving Datasets with Multiple MethodsDatasets can be made available through more than one access method by defining and then referencing a compound service
element. The
following:
<service name="all" base="" serviceType="Compound" > <service name="odap" serviceType="OPeNDAP" base="/thredds/dodsC/" /> <service name="http" serviceType="FileServer" base="/thredds/fileServer/" /> </service>
defines a compound service named "all" which contains two nested services. Any dataset that reference the compound service will have two access methods. For instance:
<datasetRoot path="precip" location="/machine/tds/data/precip" /> <dataset name="Precip Data" ID="precipdata" urlPath="precip/nws_precip_conus_20130909.nc" dataType="Grid" serviceName="all" />
would result in these two access URLs:
/thredds/dodsC/precip/nws_precip_conus_20130909.nc
/thredds/fileServer/precip/nws_precip_conus_20130909.nc
Note: The contained services can still be referenced independently. For instance:
<dataset name="More Precip Data" ID="precipdata2" urlPath="precip/nws_precip_conus_20130910.nc" dataType="Grid" serviceName="odap" />
results in a single access URL:
/thredds/dodsC/precip/nws_precip_conus_20130910.nc
Using compound services allow maintaining groups of services across datasets, rather than needing to maintain individual lists with each dataset. For instance,
we can add the DAP4 service to the above all
service:
... <service name="all" base="" serviceType="Compound" > <service name="odap" serviceType="OpenDAP" base="/thredds/dodsC/" /> <service name="dap4" serviceType="DAP4" base="/thredds/dap4/" /> <service name="http" serviceType="FileServer" base="/thredds/fileServer/" /> </service> ...
This would then add an additional access URL for DAP4:
/thredds/dap4/precip/nws_precip_conus_20130909.nc
service
Element in a CatalogService names are used by datasets to reference the service
element that represents the available service(s) for that dataset. So that the
service reference resolves to a unique service
element, all service names within a given catalog must be unique.
Here's an example:
... <service name="any" serviceType="Compound" base="" > <service name="service1" serviceType="OpenDAP" base="/thredds/dodsC/" /> <service name="service2" serviceType="HTTPServer" base="/thredds/fileServer/" /> </service> <service name="grid" serviceType="Compound" base="" > <service name="service1" serviceType="OpenDAP" base="/thredds/dodsC/" /> <service name="service2" serviceType="WCS" base="/thredds/wcs/" /> <service name="service3" serviceType="WMS" base="/thredds/wms/" /> <service name="service4" serviceType="HTTPServer" base="/thredds/fileServer/" /> </service> ...
service1
" will be fine. But only because both "service1
" instances have the same serviceType
and base
URL.
service2
" may find either the HTTP file service or the WCS service.service
ElementsThe TDS provides data access services at predefined URL base paths. Therefore, it is required that service base URLs must exactly match the values given here according to service type:
<service name="odap" serviceType="OPeNDAP" base="/thredds/dodsC/" />
<service name="ncss" serviceType="NetcdfSubset" base="/thredds/ncss/" />
<service name="wcs" serviceType="WCS" base="/thredds/wcs/" />
<service name="wms" serviceType="WMS" base="/thredds/wms/" />
<service name="fileServer" serviceType="HTTPServer" base="/thredds/fileServer/" />
Notes: These base URLs are relative to the server so your catalogs are independent of your servers hostname or port.
You can check that a data file is recognized as "gridded" with netCDF-Java ToolsUI (available for download from the netCDF-Java home page or you can use webstart).
/machine/tds/data/nam_12km
directory and open a dataset file.Note: While you have ToolsUI open, take a look at the "Viewer" tab and the "THREDDS" tab
The "Viewer" tab supports inspection of the dataset at the netCDF/CDM Data Access level (i.e., dimensions, variables, and attributes).
The "THREDDS" tab supports browsing of THREDDS catalogs and selection of datasets.
<metadata xlink:title="some good metadata" xlink:href="http://my.server/md/data1.xml" />
<documentation xlink:title="My Data" xlink:href="http://my.server/md/data1.html" />
... <dataset name="Precip Set"> <metadata inherited="true"> <serviceName>all</serviceName> <description>Multi-sensor precipitation estimates</description> <keyword>Precipitation</keyword> <creator> <name>National Weather Service</name> <contact url="http://water.weather.gov/precip/"<EMAIL_ADDRESS>/> </creator> <dataType>Grid</dataType> </metadata> <dataset name="Precip Data" ID="precip1" urlPath="precip/nws_precip_conus_20130909.nc"> <date type="created">2013-09-09</date> </dataset> <dataset name="More Precip Data" ID="precip2" urlPath="precip/nws_precip_conus_20130910.nc"> <metadata> <serviceName>odap</serviceName> <date type="created">2013-09-10</date> </metadata> </dataset> </dataset> ...
1) Child datasets inherit the netCDF dataFormat element
2) This child's inherited metadata is overridden.
The datasetScan element is an extension of the dataset element and so can contain metadata.
... <datasetScan name="Precip Set" ID="precipset" path="precip" location="/machine/tds/data/precip/"> <metadata inherited="true"> <serviceName>all</serviceName> <documentation>Multi-sensor precipitation estimates</documentation> <keyword>Precipitation</keyword> <creator> <name>National Weather Service</name> <contact url="http://water.weather.gov/precip/"<EMAIL_ADDRESS>/> </creator> <dataType>Grid</dataType> <date type="created">2013</date> </metadata> </datasetScan> ...
The client view of the above datasetScan element will be a catalogRef element which will also contain any metadata contained in the datasetScan element. It will look something like:
<catalogRef xlink:href="/thredds/catalog/precipscan/catalog.xml" xlink:title="Precip Set" ID="precipset" name=""> <metadata inherited="true"> ... </metadata> </catalogRef >
All generated catalogs that are descendants of this datasetScan will contain all inherit-able (inherited="true") metadata contained in the datasetScan
element. For instance, given that the precip
directory contained five files,
the resulting child catalog will look like:
<service name="all" serviceType="Compound" base=""> <service name="odap" serviceType="OPENDAP" base="/thredds/dodsC/"/> <service name="dap4" serviceType="DAP4" base="/thredds/dap4/"/> <service name="http" serviceType="HTTPServer" base="/thredds/fileServer/"/> </service> <dataset name="Precip Set" ID="precipset"> <metadata inherited="true"> <serviceName>all</serviceName> <dataType>GRID</dataType> <documentation>Multi-sensor precipitation estimates</documentation> <creator> <name>National Weather Service</name> <contact url="http://water.weather.gov/precip/"<EMAIL_ADDRESS></creator> <keyword>Precipitation</keyword> <date type="created">2013</date> </metadata> <dataset name="nws_precip_conus_20130913.nc" ID="precipset/nws_precip_conus_20130913.nc" urlPath="precipscan/nws_precip_conus_20130913.nc"> <dataSize units="Mbytes">1.710</dataSize> <date type="modified">2014-10-16T16:19:53Z</date> </dataset> <dataset name="nws_precip_conus_20130912.nc" ID="precipset/nws_precip_conus_20130912.nc" urlPath="precipscan/nws_precip_conus_20130912.nc"> <dataSize units="Mbytes">1.710</dataSize> <date type="modified">2014-10-16T16:19:53Z</date> </dataset> <dataset name="nws_precip_conus_20130911.nc" ID="precipset/nws_precip_conus_20130911.nc" urlPath="precipscan/nws_precip_conus_20130911.nc"> <dataSize units="Mbytes">1.710</dataSize> <date type="modified">2014-10-16T16:19:53Z</date> </dataset> <dataset name="nws_precip_conus_20130910.nc" ID="precipset/nws_precip_conus_20130910.nc" urlPath="precipscan/nws_precip_conus_20130910.nc"> <dataSize units="Mbytes">1.710</dataSize> <date type="modified">2014-10-16T16:19:53Z</date> </dataset> <dataset name="nws_precip_conus_20130909.nc" ID="precipset/nws_precip_conus_20130909.nc" urlPath="precipscan/nws_precip_conus_20130909.nc"> <dataSize units="Mbytes">1.710</dataSize> <date type="modified">2014-10-16T16:19:53Z</date> </dataset> </dataset>
First, the TDS catalog initialization log
<tds.content.root.path>/thredds/logs/catalogInit.log
contains all warning and error messages from parsing the configuration catalogs. As such, it is a great place to look for information if you run into problems with your TDS configuration catalogs.
Second, the TDS Remote Management page provides access to a list of all the configuration catalogs the TDS has successfully read:
datasetRoot
and datasetScan
ElementsYou can have as many datasetRoot and datasetScan elements as you want, for example
<datasetRoot path="model" location="/data/ncep" /> <datasetRoot path="obs" location="/data/raw/metars" /> <datasetRoot path="cases/001" location="C:/casestudy/data/001" /> <datasetScan path="myData" location="/data/ncep/run0023" name="NCEP/RUN 23" serviceName="myserver" /> <datasetScan path="myData/gfs" location="/pub/ldm/gfs" name="NCEP/GFS" serviceName="myserver" />
The datasetRoot and datasetScan are said to define a data root.
path
for all catalogs used by the TDS.Note: Because the TDS uses the set of all given path values to map URLs to datasets, each path value MUST be unique across all config catalogs on a given
TDS installation. Duplicates will cause warning messages in the catalogInit.log
file.
For example, using the above data roots, the following matches would be made:
| urlPath | file |
|---|---|
model/run0023/mydata.nc |
/data/ncep/run0023/mydata.nc |
obs/test.nc |
/data/raw/metars/test.nc |
myData/mydata.nc |
/data/ncep/run0023/mydata.nc |
myData/gfs/mydata.nc |
/pub/ldm/gfs/mydata.nc |
cases/001/test/area/two |
C:/casestudy/data/001/test/area/two |
The structure of a full OPeNDAP URL for the first urlPath above would look like:
http://hostname:port/thredds/dodsC/model/run0023/mydata.nc |<--- server --->|<----->|<--->|<--->|<- filename ->| | | | webapp name -| | |- data root | service -|
where:
The TDS Remote Management page has a link to list all known dataset roots:
Add a few more datasetScan elements:
$ ls /machine/tds/data/
fc/ gfs/ nam_12km/ ocean/ precip/
$ ls /machine/tds/data/gfs GFS_CONUS_95km_20141010_0000.grib2 GFS_CONUS_95km_20141011_1800.grib2 GFS_CONUS_95km_20141013_1200.grib2 GFS_CONUS_95km_20141010_0600.grib2 GFS_CONUS_95km_20141012_0000.grib2 GFS_CONUS_95km_20141013_1800.grib2 GFS_CONUS_95km_20141010_1200.grib2 GFS_CONUS_95km_20141012_0600.grib2 GFS_CONUS_95km_20141014_0000.grib2 GFS_CONUS_95km_20141010_1800.grib2 GFS_CONUS_95km_20141012_1200.grib2 GFS_CONUS_95km_20141014_0600.grib2 GFS_CONUS_95km_20141011_0000.grib2 GFS_CONUS_95km_20141012_1800.grib2 GFS_CONUS_95km_20141014_1200.grib2 GFS_CONUS_95km_20141011_0600.grib2 GFS_CONUS_95km_20141013_0000.grib2 GFS_CONUS_95km_20141014_1800.grib2 GFS_CONUS_96km_20141011_1200.grib2 GFS_CONUS_95km_20141013_0600.grib2
$ ls /machine/tds/data/nam_12km NAM_CONUS_12km_20141010_0000.grib2 NAM_CONUS_12km_20141011_1800.grib2 NAM_CONUS_12km_20141013_1200.grib2 NAM_CONUS_12km_20141010_0600.grib2 NAM_CONUS_12km_20141012_0000.grib2 NAM_CONUS_12km_20141013_1800.grib2 NAM_CONUS_12km_20141010_1200.grib2 NAM_CONUS_12km_20141012_0600.grib2 NAM_CONUS_12km_20141014_0000.grib2 NAM_CONUS_12km_20141010_1800.grib2 NAM_CONUS_12km_20141012_1200.grib2 NAM_CONUS_12km_20141014_0600.grib2 NAM_CONUS_12km_20141011_0000.grib2 NAM_CONUS_12km_20141012_1800.grib2 NAM_CONUS_12km_20141014_1200.grib2 NAM_CONUS_12km_20141011_0600.grib2 NAM_CONUS_12km_20141013_0000.grib2 NAM_CONUS_12km_20141011_1200.grib2 NAM_CONUS_12km_20141013_0600.grib2
$ ls /machine/tds/data/ocean OCEAN_Global_5x2p5deg_20141010_0000.nc OCEAN_Global_5x2p5deg_20141012_1200.nc OCEAN_Global_5x2p5deg_20141010_1200.nc OCEAN_Global_5x2p5deg_20141013_0000.nc OCEAN_Global_5x2p5deg_20141011_0000.nc OCEAN_Global_5x2p5deg_20141013_1200.nc OCEAN_Global_5x2p5deg_20141011_1200.nc OCEAN_Global_5x2p5deg_20141014_0000.nc OCEAN_Global_5x2p5deg_20141012_0000.nc OCEAN_Global_5x2p5deg_20141014_1200.nc
$ cd ${tomcat_home}/content/thredds $ vi catalog.xml // Use the editor of your choice
datasetScan
element for the GFS output:
<datasetScan name="GFS" ID="GFS" path="gfs" location="/machine/tds/data/gfs"> <metadata inherited="true"> <serviceName>all</serviceName> </metadata>
</datasetScan>
$ cd ${tomcat_home}/bin $ ./shutdown.sh $ ./startup.sh
datasetScan
elements are working:
http://localhost:8080/thredds/catalog.html
Now that we have multiple dataset roots ...
http://localhost:8080/thredds/admin/debug
datasetScan
element so that the value of the
path attribute matches the one for the NAM (12km) datasetScan
element.
$ cd ${tomcat_home}/content/thredds $ vi catalog.xml // Use the editor of your choice
$ cd ${tomcat_home}/bin $ ./shutdown.sh $ ./startup.sh
datasetScan
ElementA datasetScan
element can specify which files and directories it will include with a filter
element (see
spec for more details). When no filter
element is given, all files
and directories are included in the generated catalog(s). Adding a filter
element to your datasetScan
element allows you to
include (and/or exclude) the files and directories as desired. The datasetScan
element for the NAM (12km) example included the following:
<filter>
<include wildcard="*.grib2" />
</filter>
To exclude the 00Z runs, the filter could be modified to:
<filter>
<include wildcard="*.grib2" />
<exclude wildcard="*0000.grib2" />
</filter>
The include
and exclude
elements both determine which datasets they match on whether their wildcard pattern (given by the wildcard
attribute) or regular expression (given by the regExp
attribute) match the dataset name. By
default, includes and excludes apply only to regular files (atomic datasets). You can specify that they apply to directories (collection datasets) as well
by using the atomic
and collection
attributes. For example, if the nam_12km directory contained a badData directory, I could
exclude it by adding the following to the filter:
<exclude wildcard="badData" atomic="false" collection="true" />
Error { code = 500; message = "Cant read /machine/tds/data/gfs/.scour: not a valid NetCDF file."; };
filter
element to the datasetScan
elements. Something like:
<filter> <include wildcard="*.nc" /> <include wildcard="*.grib1" /> <include wildcard="*.grib2" /> </filter>
$ cd ${tomcat_home}/bin $ ./shutdown.sh $ ./startup.sh
filter
element to the "ocean" datasetScan
element to exclude the "2013" directory. Something like:
<exclude wildcard="2013" atomic="false" collection="true" />
$ cd ${tomcat_home}/bin $ ./shutdown.sh $ ./startup.sh
By default, datasets are listed in decreasing lexigraphic order by the dataset name. A sort
element can be added to a datasetScan
element to specify an increasing lexigraphic order:
<sort>
<lexigraphicByName increasing="true" />
</sort>
Currently, the lexigraphic increasing or decreasing sort algorithm is the only one supported.
All generated datasets are given an ID. The IDs are simply the path of the dataset appended to the datasetScan path value or, if one exists, the ID of the
datasetScan element. So, for the nam_12km
directory and our current configuration:
<datasetScan name="NCEP NAM 12km" ID="nam" path="nam" location="/machine/tds/data/nam_12km">
and for the data file NAM_CONUS_12km_20141010_0000.grib2, the value of the dataset ID would be "NAM_12km/NAM_CONUS_12km_20141010_0000.grib2".
By default, all datasets are named with the name of their underlying file. By adding a namer
element, you can specify more human readable
dataset names. For instance, the following namer
element causes any dataset named "NCEP NAM_12km" to be
renamed with the value of replaceString
:
<namer> <regExpOnName regExp="NCEP NAM 12km" replaceString="NCEP NAM 12km model output" /> </namer>
While renaming datasets can be used to make the resulting dataset name more human readable, the renaming does not affect the behavior of any of the other datasetScan operations (filtering, sorting, etc.). All datasetScan operations that use or modify the dataset name use the name of the underlying dataset, e.g., the data file on disk.
More complex renaming is possible as well. The namer uses a regular expression match on the dataset name. If the match succeeds, any regular expression capturing groups are used in the replacement string.
A capturing group is a part of a regular expression enclosed in parenthesis. When a regular expression with a capturing group is applied to a string, the substring that matches the capturing group is saved for later use. The captured strings can then be substituted into another string in place of capturing group references,"$n", where "n" is an integer indicating a particular capturing group. (The capturing groups are numbered according to the order in which they appear in the match string.) For example, the regular expression "Hi (.*), how are (.*)?" when applied to the string "Hi Fred, how are you?" would capture the strings "Fred" and "you". Following that with a capturing group replacement in the string "$2 are $1." would result in the string "you are Fred."
Here's an example namer:
<namer> <regExpOnName regExp="nws_precip_conus_(\d{4})(\d{2})(\d{2}).nc" replaceString="NWS CONUS Precipitation for $2-$3-$1"/> </namer>
the regular expression has 3 capturing groups
When applied to the dataset name "nws_precip_conus_20130910.nc", the strings "2013", "09", and "10" are captured. After replacing the capturing group
references in the replaceString
attribute value, we get the name "NWS CONUS Precipitation 2013-09-10".
Add a namer
element to the nam_12km datasetScan
element that extracts the date/time from the file name and uses the date/time in
generating a new name (similar to above) from the value of the path attribute matches.
timeCoverage
ElementsA datasetScan element may contain an addTimeCoverage
element. The addTimeCoverage
element indicates that a
timeCoverage
metadata element should be added to each dataset in the collection and describes how to determine the time coverage for each
datasets in the collection.
Currently, the addTimeCoverage
element can only construct start/duration timeCoverage
elements and uses
the dataset name to determine the start time. As described in the "Naming Datasets" section above, the addTimeCoverage
element applies a regular expression match to the dataset name. If the match succeeds, any regular
expression capturing groups are used in the start time replacement
string to build the start time string. The values of the following attributes are used to determine the time coverage:
datasetNameMatchPattern
or the datasetPathMatchPattern
attribute gives a regular expression used to match on the
dataset name or path, respectively. If a match is found, a timeCoverage element is added to the dataset. The match pattern should include
capturing groups which allow the match to save substrings from the
dataset name.
startTimeSubstitutionPattern
attribute value has all capture group references ("$n") replaced by the corresponding substring that was
captured during the match. Theresulting string is used as the start value of the resulting timeCoverage
element.
duration
attribute value is used as the duration value of the resulting timeCoverage
element.For instance, adding
<addTimeCoverage datasetNameMatchPattern="nws_precip_conus_(\d{4})(\d{2})(\d{2}).nc" startTimeSubstitutionPattern="$1-$2-$3T00:00:00" duration="24 hours" />
to a datasetScan
element and given a data file named
nws_precip_conus_20130910.nc
results in the following timeCoverage
element:
<timeCoverage> <start>2013-09-10T00:00:00</start> <duration>24 hours</duration> </timeCoverage>
Add an addTimeCoverage
element to the GFS datasetScan
element that extracts the date/time from the file name and uses the
date/time to generate the timeCoverage
element (similar to above).
With a real-time archive, it is convenient to define a "proxy" dataset that always points to the most recent dataset in a collection. Other types of proxy
datasets may be useful as well and the addProxies
element provides a place for describing proxy datasets. Currently, only two
addProxies
child elements are defined. They are both "Latest" proxy elements. The simpleLatest
element adds a proxy dataset which
proxies the existing dataset whose name is lexigraphically greatest (which finds the latest dataset assuming a timestamp is part of the dataset name). The
latestComplete
element behaves similarly to simpleLatest
except that the proxied dataset does not include any datasets that have
been modified more recently than a given time limit, e.g., you could specify you want the most recent (lexigraphically) dataset that hasn't been modified
for 60 minutes. Both the simpleLatest
and latestComplete
elements must point to an existing service
element.
To add a "Latest" dataset to our "nam" dataset, we could add:
<service name="latest" serviceType="Resolver" base="" />
to our catalog and
<addProxies>
<latestComplete name="latestComplete.xml" top="true" serviceName="latest" lastModifiedLimit="60" />
</addProxies>
to our "nam" datasetScan
element. This would result in the following dataset being at the top of the "nam" collection of datasets:
<dataset name="latestComplete.xml" serviceName="latest" urlPath="latestComplete.xml" />
The latestComplete
element includes a name
attribute which provides the name of the proxy dataset, the serviceName
attribute that references the service used by the proxy dataset, the top
attribute which indicates if the proxy dataset should appear at the
top or bottom of the list of datasets in this collection, and the lastModifiedLimit
which feeds into the algorithm which determines which
dataset is being proxied.
The simpleLatest
element allows for the same attributes as the latestComplete
element minus the lastModifiedLimit
attribute. In this case, all the attributes have default values: the name
attribute defaults to "latest.xml", the top
attribute
defaults to "true", and the serviceName
attribute defaults to "latest".


NHS COVID-19 app statistics
This dashboard presents public data from the NHS COVID-19 app and UKHSA dashboard data to help understand the performance of the NHS COVID-19 app for England and Wales. Updated every other Thursday, in line with the app data.


jQuery is a fast, small, and feature-rich JavaScript library.
For information on how to get started and how to use jQuery, please see jQuery’s documentation. For source files and issues, please visit the jQuery repo.
If upgrading, please see the blog post for 3.6.0. This includes notable differences from the previous version and a more readable changelog.
Below are some of the most common ways to include jQuery.
Babel is a next generation JavaScript compiler. One of the features is the ability to use ES6/ES2015 modules now, even though browsers do not yet support this feature natively.
There are several ways to use Browserify and Webpack. For more information on using these tools, please refer to the corresponding project’s documentation. In the script, including jQuery will usually look like this…
AMD is a module format built for the browser. For more information, we recommend require.js’ documentation.
To include jQuery in Node, first install with npm.
For jQuery to work in Node, a window with a document is required. Since no such window exists natively in Node, one can be mocked by tools such as jsdom. This can be useful for testing purposes.


WITNESS LIST Finance Committee April 12, 1999-9:00A SB 5 ON: Daugherty, Craig (Comptroller's Office), Austin Lavine, Richard (Center For Public Policy Priorities), Austin Registering, but not testifying: For: Golden, Cathy (Greater Dallas Chamber), Dallas Kowalski, Tom (Texas Healthcare and Bioscience Institute), Austin Pitstick, David (Texas Economic Development Council), Richardson Wise,Andrew (American Electronics Association), Austin On: Barnes, James (Texas Workforce Commission), Austin LeBas, James (Comptroller's Office), Austin SB 1056 THIS LIST HAS NO WITNESSES. SB 1695 THIS LIST HAS NO WITNESSES. SJR 42 THIS LIST HAS NO WITNESSES.


One of the Ruby-specific Stapler extension is to delegate the HTTP request dispatching process completely to a Rack application. This allows your plugin to define a subtree of the URL space by using a familiar Ruby-based web framework, such as Sinatra and Ruby on Rails.
To do this, simply include Jenkins::RackSupport
to your model class, then implement the call method that follows the Rack specification. Most typically, you'll simply delegate to a Rack application created elsewhere. The following example shows an implementation of RootAction that uses a sinatra app to define its URL subspace. In this case, an instance of TestRootAction
is bound to /root_action
, so /root_action/hi
would be processed by this Sinatra app to say hello world.
require 'sinatra/base' require 'jenkins/rack' class SomeSinatraApp < Sinatra::Base get '/hi' do 'Hello world from Sinatra!' end end class TestRootAction < Jenkins::Actions::RootAction include Jenkins::RackSupport def call(env) SomeSinatraApp.new.call(env) end display_name "Test Root Action" def icon "gear.png" end def url_name "root_action" end end


Internal reflection templates for implementing mocking behaviour.
MIT. See LICENSE for full details.
Iterate through the methods of T generating code using the generator.
| T | The class to inspect. |
| generator | The template to use to generate code for each method. |
| index | The beginning index of the members. |
Iterate through the constructors of T generating code using the generator.
| T | The class to inspect. |
| generator | The template to use to generate code for each constructor. |


A good friend of mine had recently become interested in processing morning glory seeds after he, his brother, and I tripped on them late February. Being as close as we were, he'd offered me a free dose if I'd just come and hang out with him. Being that I'm in college, I'm a ghost around my hometown for the most part. I stayed at his house for a while, got the seeds, and drove back to the college.
A little background information: I'm an average build 19-year-old male with some experience with psychotropic substances (alcohol, weed, DXM, diphenhydramine HCl, MDA, salvia divinorum, nitrous, tobacco). I have a very high tolerance level for drugs, i.e. it takes a lot to get me where I'm going. I'm extremely open-minded and seek out hallucinogenic drugs for shamanic, revelatory purposes and tend to enjoy all aspects of a trip, barring nausea.
Mindset: Calm, somewhat excited about experiencing LSA without the horrific nausea the seeds bring on.
At around 9 pm, I got back to my dorm and spread the seed powder I'd been given out on a sheet of tissue paper. I took the time to ready and completely calm myself before taking the seeds instead of gulping the matter down in excitement. Leaned over and smelled the pile of fine dust laying on my desk. No chemical smell - good. He'd finished up to the extraction before alcohol steeping, so I figured it'd be okay. I trust my friend with my life, as he's less foolhardy and easy going than I am, so I didn't have any second thoughts about taking the seeds with the toxins extracted.
I got a glass of water to down the seed dust and started on the task. When I say task, I DO MEAN task. The first time I'd taken it, I chewed the seeds, leaving a indescribably bad feeling from my mouth to my stomach. Taking the powder is absolutely no different whatsoever. You can't possibly imagine how bad it is until you try it. In fact, sitting here typing this report up, my throat and mouth are filling with mucus as a precautionary measure and I'm physically gagging at the thought of undergoing the experience again. My roommate walked in with his girlfriend and asked me what I was doing, and I excitedly told them about what I thought was going to be a great experience (oh, how wrong I was). After I finished the seeds, they left me to my own devices and I put on some music to lead the trip.
After 30 uneventful minutes, I got the sudden feeling that I had to get in bed and go to sleep before I got sick. I knew it was coming and I had to avoid it at any cost. I hurriedly turned off the music, hit the light switch, jumped into the top bunk, and instantaneously began to writhe in agony as the seeds' poison began to take its effects on my body. I fought against it, trying to keep my eyes closed, but my consciousness wouldn't drift away. I periodically blanked out and came to, to varying degrees of whole-body pain.
This wasn't a simple 'Oh, haha, I can vomit and make things better' nausea, or a body load, or any other kind of pain I'd ever felt. It was like having the entire body expanded and compressed against its limits and feeling hot and cold at the same time. This was, I was certain, death. I remember thinking at one point. 'This is it. This is death. I've been poisoned and I'm going to die.' I oddly felt no remorse from this realization, but in fact felt completely neutral. Truth be told, if anything, I felt a little bit of relief thinking that I would soon be released from this pain.
This continued for what seemed like an eternity until, at some point, my mind and body shut down and granted me a few moments of numb, dreamless, deathlike sleep. I came to with my eyes open, my entire body vibrating as if the building were being torn apart by some massive machine. I looked towards the window. The sun was just beginning to start its trek across the sky. A new start perhaps, or just a signal to resume my suffering?
I closed my eyes against the light and time stopped. A massive fractal book ripples with an alien language, words continually disappearing and appearing elsewhere, its history being constantly rewritten. Colors explode like flying through fireworks as they explode, bright and vibrant but simultaneously dull and sickening.
 A voice screams all of the atrocities committed by every single person, all the rapes and murders and wars that ever happened being forced through a loud speaker in another room. That same voice then tells all of the secrets of the universe in excruciating detail, and I begin to realize what a beautiful abomination all of reality truly is. All of this was going on in this sequence, yet happening at the exact same time, something amazingly hard to comprehend now.
Suddenly, instantly, everything became still. I cautiously opened my eyes to my room, streaked pale orange with a morning's light I made tentative movements to slip out of my bed and managed to do so without any unexpected shocks to my system. I threw off my shirt because of the heat and lay on the floor for a while, coping with the slowly disintegrating pain centered on my stomach and almost began to weep. I forced myself to my feet and grabbed a cigarette from my desk. I went outside and fell backwards into a sitting position and lit my cigarette. The nicotine calmed my still-aching body.
'I cheated death... again.' I threw my head back and laughed.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Running multiple instances of an app
In the previous modules we created a Deployment, and then exposed it publicly via a Service . The Deployment created only one Pod for running our application. When traffic increases, we will need to scale the application to keep up with user demand.
Scaling is accomplished by changing the number of replicas in a Deployment
Summary:
- Scaling a Deployment
You can create from the start a Deployment with multiple instances using the --replicas parameter for the kubectl run command
Scaling overview
Scaling up a Deployment will ensure new Pods are created and scheduled to Nodes with available resources. Scaling down will reduce the number of Pods to the new desired state. Kubernetes also supports autoscaling of Pods, but it is outside of the scope of this tutorial. Scaling to zero is also possible, and it will terminate all Pods of the specified Deployment.
Running multiple instances of an application will require a way to distribute the traffic to all of them. Services have an integrated load-balancer that will distribute network traffic to all Pods of an exposed Deployment. Services will monitor continuously the running Pods using endpoints, to ensure the traffic is sent only to available Pods.
Scaling is accomplished by changing the number of replicas in a Deployment.
Once you have multiple instances of an Application running, you would be able to do Rolling updates without downtime. We’ll cover that in the next module. Now, let’s go to the online terminal and scale our application.


Summary: A simple web challenge requiring basic knowledge of web technologies and basic penetration testing skills.
This was a CTF hosted by Flawcon and Manchester Grey Hats in my hometown of Manchester!Starting off we are presented with a standard looking website. We quickly notice a login page and try a few common attacks (intercepting and replaying traffic, SQLi, XSS etc.) but to no avail.
After some digging through the Javascript files we came across a script called profile.js, in this script on the 4th line we can see a http GET request being sent to retrieve information about a particular user.
As this is being handled within the Javascript code this means that the request is being made by the client instead of the server allowing us the ability to interract with the API ourselves, and sure enough by sending a GET request to '/api/users' we are able to dump all of the user data from the site like so.
As you can see highlighted above we have found the admin account with a hashed password, let's run the hash through hash-identifier and see what we get.
Now we know which algorithm has been most likely used we can crack the password using John The Ripper, if you look at the help for john the -w flag is used to specify a wordlist and the --format= flag is used to specify the hash type. For me the command format was john -w /usr/share/wordlists/rockyou.txt pass.hash --format=Raw-SHA256
which gave us the following output.
Now we have the password 'manchester' all that's left to do is login to the admin account...
Download the backup file from the site...
And voila, we have our flag!


Dear sir , I am Mrs. Mrs. Grace Abba-Ganna, personal accountant to Late Chief Bola Ige(SAN) the Attorney General of the Federation and Minister of Justice, who wasrecently assassinated at his country home on the 23rd December, 2001 by assassins (May his gentle soul rest in peace) Amen. Before his death,he entrusted a certificate of deposit valued US$10,000,000 (United States Ten Million Dollars), with a security company in Europe.He told me that this money was deposited for him by the Former Military Head of State Rtd. General Ibrahim Babangida to protect his interest not to appear at the Oputa panel for re-conciliation and human rights violation investigation commission headed by Rtd. Justice Chukwudife Oputa. My reason of contacting you is for both of us to join heads and hands together to make claim over the US$10,000,000, as this money was deposited on my favour at the security company as instructed by the late Attorney General, to avoid trace of his name over accepting such gift or offer. His intention before his death was to contact a foreign partner who is trust worthy, capable of handling such huge amount of money and also operate a foreign account where the money will be deposited or paid into waiting for his retirement. The reason is because our country law states that civil servants are not entitled to operate foreign accounts. As it is now, with your full co-operation both of us can make claim over the US$10,000,000, what is expected of you is: (a) Your full name and address (b) Your phone and fax number (c) Your International Passport or Drivers license and your readiness to appear at the security company to sign off the money to your personal account. As soon as I confirm your interest, I will forward to you the certificate of deposit, power of attorney/transfer which will give you legal backing to make claim over the money at the security company. I will also forward to you the contact particulars of the Security Company and letter of agreement between both of us over the security of my fund that will be in your custody. For accepting to help out 30% of the US$10,000,000 will be for you, 60% for me and 10% set aside for re-imbursement of expenses incurred by both parties during the transaction. Awaiting your urgent response. Regards, Mrs. Grace Abba-Ganna alternate email<EMAIL_ADDRESS>-----------------------------------------------------------------------------------This E-mail was sent through Albawaba E-mail. Get your own at mail.albawaba.com ----------------------------------------------------------------------------------- This E-mail was sent through Albawaba E-mail. Get your own at mail.albawaba.com


--- layout: post title: Ultra-low input ribosome profiling for characterizing translation in development, cancer and immunology html: True summary: A fundamental goal of molecular biology is to characterize the dynamics of gene expression from individual cells. This knowledge will ultimately reveal the diversity of cell types that make up healthy tissues as well as changes that lead to disease states. Furthermore, mechanistic and quantitative understanding of cellular decisions will require the ability to measure analytes in individual cells with high precision. ---
A fundamental goal of molecular biology is to characterize the dynamics of gene expression from individual cells. This knowledge will ultimately reveal the diversity of cell types that make up healthy tissues as well as changes that lead to disease states. Furthermore, mechanistic and quantitative understanding of cellular decisions will require the ability to measure analytes in individual cells with high precision.
Towards this goal, recent years have seen an explosion of methods that can measure RNA expression and many DNA-level attributes from single cells. However, we currently lack methods for measuring protein synthesis rates in single cells. mRNA translation can be measured transcriptome-wide by sequencing of mRNA fragments protected by ribosomes from RNase digestion. Despite ongoing efforts over the last ten years, this approach (ribosome profiling) still requires approximately one million cells. Reducing the sample input requirements for ribosome profiling remains a formidable challenge that must be overcome to make progress on understanding the role of translation.
To address this limitation, we pioneered an on-chip isotachophoresis (ITP) method that enables simultaneous isolation and size selection of RNA fragments protected by ribosomes from complex lysates. This has been a wonderful collaboration with engineers and colleagues from the National Institute of Standards and Technology over the last 5 years. We coupled this innovation with the latest advances in small RNA sequencing library preparation and optimization of RNase digestion conditions. We established the feasibility of generating high quality ribosome profiling data from as few as 100 cells. Our laboratory will further develop this approach to yield a groundbreaking technology that will reveal the translation landscape of individual cells from a wide range of biologically important samples.


The top level Geolocation module. The Geolocation module is used for accessing device location based information.
| Name | Description |
|---|---|
| addEventListener | add an event listener for the instance to receive view triggered events |
| fireEvent | fire a synthesized event to the views listener |
| removeEventListener | remove a previously added event listener |
| setShowCalibration | configure the calibration UI. set the false to disable the calibration display. |
| Name | Type | Description |
|---|---|---|
| ACCURACY_BEST | int | accuracy constant |
| ACCURACY_HUNDRED_METERS | int | accuracy constant |
| ACCURACY_KILOMETER | int | accuracy constant |
| ACCURACY_NEAREST_TEN_METERS | int | accuracy constant |
| ACCURACY_THREE_KILOMETERS | int | accuracy constant |
| locationServicesEnabled | boolean | returns true if the user has enabled or disable location services for the device (not the application). This is an iPhone only property. |
| showCalibration | boolean | returns true if the calibration UI can show |
| Name | Description | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| calibration |
fired only on iPhone/iPad when the device detects interface and requires calibration. when this event is fired, the calibration UI is being displayed to the end user.
Event properties
|
||||||||||
| heading |
fired when a heading event is received
Event properties
|
||||||||||
| location |
fired when a location event is received
Event properties
|


Before you click "Select multiple", you cannot select more than one option (by holding down the Shift or Ctrl key).
After you have clicked "Select multiple", you can.


--- layout: post title: "Week 6 Write Up" subtitle: "Network Security" date: 2019-08-03 14:28:10 -0800 background: '' ---
Network, is always the most effetive way to spread the malware, contaminate a thousands or or millsions of machine. Therefore, to prevent the infection, the enhance the network security plays an really important role to deal with the dark arts.
To get started, we first introduce few protection strategies.
Postive Policy - Which means a network is designed for specific purpose. Only administrator or authorized person knows what the network is used for. Other activilites will not be allowed. For example, a network is designed for mail server, if a hacker trying to sneak in the network, it will be spotted immediately.
Firewall / Security Zones - To devide the network into a series of security zones. Then define the rules that to determeine the interactions of the protocols. For example, if there are some third party coporation trying to connect to your network, you can sepecific a secuirty zone to allowd only speicial protocols to communicate.
Defense in Depth - Like a castle, mutiple layering defense, if one defense down, the others can be retreated to.
Intrusion Protection - Looking for things that you don't want to happend. Using signatures to determine you are being attacked or not.
Honey Nets - To trick the attacker thought he is attacking the right place.
Quarantine - A seperate layer that prevent the suspicious host to attack the main network.
Reputation - A big data strategy to collect the specific host / ip address reputation. The data will be share between the hosts and server. For example, if a ip address have been mark as suspicious a serveral times. Then other machine's firewall can use this information to block the connections for this ip address. Furthermore, we can bind the real world location to the MAC address. Which means if a range of the ip address or ip sets are located n the same area, then it will be also marked a suspicious.
Then, let's move to the next topic - MITM attack.
MITM - Which means man in the middle. MITM doesn't mean is always the bad thing. For example, proxy is actually is one of the example of the MITM. HTTP Proxy, allows to intercept the http traffic to monitor the red flegs. Terminating TCP proxy, for example to used to reduce the load on the main servers by offloading the complex calculation to the other server or machine.
When it comes to bad side, the MITM attack will become a very dangerous but very effetive way to attack patinet. With MITM, the attack can inspect all the traffic from the attack patient. For example, when you using online baking service, the attacker use MITM to inspect the traffic, the attacker possibliy will be able to obtain the banking login cookie. Then the attacker will be able use the cookie to login the banking service. If the website is using the http protocol, then the attacker will be really easy extract to information such as username and password since the http protocol is not encrypted.
So, how the MITM attack happen? How to start a MITM attack? The typical example are ARP Poisoning, which tricks the host thought the attacker machine is the actual machine. TCP Hijacking manipulating the TCP Data by adding or editing what's in it.
How to prevent it? Using https protocol is a very effetive way to prevent such situation. HTTPS protocol encypted the bandwidth, and only valid certificate can be able to decrypted it. However, in realitiy, there is also chances that the attacker add custom certificate to the machine, then force to add the custom certificate to the trust zones. Then, the https will be meanless.


Legendre polynomials and associated Legendre polynomials.
v = plegendre(L,z) v = plegendre(L,M,z) v = plegendre(L,M,z,CSphase)
With two input arguments, plegendre
returns the value(s) of the Legendre polynomal of degree L (with L ≥ 0).
With three or four input arguments, plegendre
calculates the value(s) of the associated Legendre polynomal of degree L (with L ≥ 0) and order M (with -L ≤ M ≤ L).
z
are the values for which to evaluate the polynomial. z
must satisfy -1≤ z ≤ +1 and can be a scalar or an array.
The optional input CSphase
(true
or false
) specifies whether the Condon-Shortley phase (-1)M should be included (true
) or not (false
). The default is true
.
plegendre
uses recurrence relations to compute the values, as described e.g. in Press et al, Numerical Recipes in C++ (2002).
clebschgordan, spherharm, wigner3j, wigner6j, wignerd


{loader} {config}
Tests the interaction between discarded Browser Interactions and Errors. Interaction is discarded when there is no change in URL hash.


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You are viewing an historical archive of past issues. Please report new issues to the appropriate project issue tracker on GitHub.
Comments
-
Greg Hurrell
Vim can get confused if documentation files move or change names. This happened recently in these two commits:
- http://git.wincent.com/command-t.git/commit/2328b949f17cdc617248b0882f5ca4d60d10f8cc
- http://git.wincent.com/command-t.git/commit/15c2f61f40e0ad12fb78c3a5423726fbc4142076
The solution seems to be to blow away your tags file and then get Vim to regenerate the helptags (see
:h :helptags
). I've found that Vim doesn't cope well if you simply blow the tags file away; you have to replace it with an empty file (eg.rm ~/.vim/doc/tags && touch ~/.vim/doc/tags
). At least, that's what I've found when using Pathogen to manage my Vim plug-ins. -
Greg Hurrell
Status changed:
- From: open
- To: closed
Add a comment
Comments are now closed for this issue.


The perfect personal assistant. Relaxr does your job for you so you can enjoy life as it is meant to be.
“Relaxr changed my life. I’ve been able to travel the world, spend limited time working and my boss keeps thanking me for crushing work.”
- Amanda, Intuit
Copyright 2015. Relaxr.


--- title: Emacs Web Server layout: post tags: [emacs, elisp] uuid: 1e0a3639-6df2-3c5b-3b16-bb29ac300602 ---
As part of my quest of developing solid knowledge of GNU Emacs lisp, I have implemented a pseudo-HTTP/1.0 web server within Emacs. Behold,
git clone git://github.com/skeeto/emacs-http-server.git
To all other non-emacsen text editors, can your text editor do that?! Ha!
To fire it up, load it into Emacs and run the extended command
(M-x
) httpd-start
. By default it will serve
files from "~/public_html
". To change this, change the
variable httpd-root
to the desired web root. You can stop
the server with httpd-stop
.
It's about 200 lines of code and can serve static websites made of small, static files. I say small files because it serves files from buffers, meaning it has to read the entire file in first.
For a simple, text editor based server it can hold up to a pretty
decent load. At one point I hit it with 8 wget
instances all making
rapid recursive downloads and my manual navigation wasn't slowed down
noticeably. Despite running in the slow elisp interpreter, I think it
can have much better performance by caching commonly served files in
buffers.
It should run, unmodified, anywhere a modern Emacs can run, so I expect that it's already very portable. I can imagine it being useful in a situation where someone needs to temporarily host some files but there isn't a web server on the machine. Just grab this script and throw it at Emacs.
Well, it only does IPv4 right now, though I expect IPv6 only requires changing one number (namely, 4 to 6). I don't have any IPv6 systems to test it on.
When writing it I also had security in mind so, as far as I know, it
should be safe to use. It cleans up the GET
from the
client so that no files underneath the serving root can be accessed.
The server log is lisp itself. Here is an example log starting the server, serving one request, and halting,
'(log (start "Wed May 13 23:33:34 2009") (connection (date "Wed May 13 23:36:25 2009") (address "<IP_ADDRESS>") (get "/0001.html") (req ("Referer" "http://<IP_ADDRESS>:8080/") ("Connection" "keep-alive") ("Keep-Alive" "300") ("Accept-Charset" "ISO-8859-1,utf-8;q=0.7,*;q=0.7") ("Accept-Encoding" "gzip,deflate") ("Accept-Language" "en-us,en;q=0.5") ("Accept" "image/png,image/*;q=0.8,*/*;q=0.5") ("User-Agent" "Mozilla/5.0 [...] Iceweasel/3.0.9 (Debian-3.0.9-1)") ("Host" "<IP_ADDRESS>:8080") ("GET" "/0001.html" "HTTP/1.1")) (path "~/public_html/0001.html") (status 200)) (stop "Wed May 13 23:38:17 2009"))
The log is alists of alists, making a hierarchical tree structure that can be explored with some simple lisp functions. Normally this sort of thing is done with XML, but lisp already has its own structured format: lists!
When GET
is a directory, it looks for
"index.html
" and serves that if it exists. More indexes
can be added to the variable httpd-indexes
. This can
actually be done in a special ".htaccess.el
" file.
If a ".htaccess.el
" exists in the directory from which a
file is being served, Emacs will first load/execute it. You see, it's
just a lisp program. If you wanted to add a new index file name, the
hypertext access file could contain this,
It's a bit like a .emacs
file.
But I think one of the coolest things about having a lisp-based server is that the server can be modified in place without disrupting or restarting it. In my Emacs web server, the only change that requires a restart is changing the server port. In fact, I wrote most of it while the server was running and tested my changes from a browser right as I made them — all on the same instance of the server.
If you want to look into the AI side of this, the server could modify its own code in response to its use.
I also had the idea of creating dynamic websites with elisp, in the
same way PHP or Perl does. If a .el
file (or
.elc
) is accessed, the server would pass the
GET
/POST
arguments as an alist to a function
in the elisp file. The server would also provide some nifty HTML
generation macros. A dynamic script might look like this,
However, this is not (yet?) implemented. Just an idea.
I will continue to work on it, though I don't expect to add much more to it. I will mostly improve the code and documentation.


Definition at line 33 of file CBsp.h.
Public Attributes | |
| CPlane | plane |
| Splitting plane. | |
| int | planeFlipped |
| Set to 1 if the plane derived from a polygon was flipped when performing the test. | |
| int | fc |
| Number of polygons in front of plane, after split. | |
| int | bc |
| Number of polygons behind plane, after split. | |
| int | dc |
| Difference of front and back polys. | |
| int | sc |
| Number of polygons spanning plane. |


The information contained in this website is for general information purposes only. We endeavour to keep the information up to date and correct, we make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability or availability with respect to the website or the information. Any reliance you place on such information is therefore strictly at your own risk.
The data which is visible to the user are accessed from the application’s database. The application cannot display any data which is not available in its database. Every effort is made to keep the website up and running smoothly. However, we are not responsible or liable for, the website being temporarily unavailable due to technical issues beyond our control.


Sod turned for Evergreen Acres expansion project in Clearwater - Barriere Star Journal
by Jaime PolmateerProject will add 20 more affordable units for seniors
The Evergreen Acres Senior Housing Society (EASHS) held the sod turning for its expansion project, with representatives from EASHS, local dignitaries and Selina Robinson, Minister for Municipal Affairs and Housing, all picking up shovels for the occasion.
Despite cold rain, a large crowd showed up for the Nov. 15 event, which began with a blessing of the land from Mona Jules and Margaret Joseph of Simpcw First Nation.
Upon completion, the expanded facility will offer 20 new rental homes for seniors in need of housing that will be close to public transit, the nearby medical clinic, and the shopping centre.
Robinson touched on the importance of seniors being able to afford to stay in their home communities.
“We all need to do our part to make sure people can find a home that meets their needs, and that they can afford, in the community where they have connections, relationships and where they’ve had a life,” she said.
“With these new homes, seniors in Clearwater will have the opportunity that everyone deserves — to stay in their community, close to their friends and family, close to the community that makes this a home.”
The Province’s Building BC: Community Housing Fund is pitching in $2 million toward the expansion.
Other major funding partners include the Wells Gray Community Forest Society, which donated $1,050,000, and the District of Clearwater, which contributed about $80,000 through in-kind donations for development fees.


Copyright OpenFX and contributors to the OpenFX project.
SPDX-License-Identifier: BSD-3-Clause
Copying and redistribution with or without
modification, is permitted provided that the following conditions are met:
- Redistributions of the document must retain the above copyright notice
and this list of conditions.
- Neither the name of The Open Effects Association Ltd nor names of its
contributors may be used to
endorse or promote products derived from this software without specific
prior written permission.
Automatic documentation generated by Doxygen.


4chan is an English imageboard-type website infamous for doing bad things and slightly illegal activities, it's personally my favourite site because there is almost never a dull moment. *wink wonk* The website is in an "imageboard" format, where people create "threads" for various topics, to ask for help, or just for fun.
You never need to sign up for 4chan, everyone is anonymous and anyone can post whatever they want with only remote threat of accountability for what they have said. Everyone being anonymous also includes you cannot "message" other users or establish any kind of social relationship without one of you publicly displaying information. Being a social network, this is weird to a lot of people and even sociologists have spent time just attempting to figure out how it works.
Christopher Poole, also known by his alias "Moot" created 4chan in his bedroom with the means to be an American counterpart to Futaba Channel as a place to discuss Japanese comics and Japanese TV shows known as Anime. The website quickly gained popularity and other boards were created to accomodate the diversity of the new members
Some of the things 4chan has done in the past doesn't really need to be said but a simple google search for 4chan you get results with peoples who's lives were ruined because they posted something, 4chan made fake advertising for the "Apple Wave" where people microwaved their iPhones, they started and hosted pretty much all of that one time iCloud was cracked into and many celebrities nude photos were leaked, as well as many cases of the anonymous users "raiding" websites which basically means a bunch of users posting things users from the website they are raiding don't want, and even sometimes hacking into the servers to freeze the domain or remove things. Most of the time when they raid, it's generally because they don't like that group because they quarrel with 4chan because of the nature of the website.
Most of the time all of this "bad" stuff happens because it gains popularity on the /b/ board (random board) where there is nearly no rules and moderators are pretty lenient and it just takes off.


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Generalized Linear Models¶
Generalized linear models currently supports estimation using the one-parameter exponential families.
See Module Reference for commands and arguments.
Examples¶
# Load modules and data
In [1]: import statsmodels.api as sm
In [2]: data = sm.datasets.scotland.load()
In [3]: data.exog = sm.add_constant(data.exog)
# Instantiate a gamma family model with the default link function.
In [4]: gamma_model = sm.GLM(data.endog, data.exog, family=sm.families.Gamma())
In [5]: gamma_results = gamma_model.fit()
In [6]: print(gamma_results.summary())
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: y No. Observations: 32
Model: GLM Df Residuals: 24
Model Family: Gamma Df Model: 7
Link Function: inverse_power Scale: 0.0035843
Method: IRLS Log-Likelihood: -83.017
Date: Mon, 14 May 2018 Deviance: 0.087389
Time: 21:48:07 Pearson chi2: 0.0860
No. Iterations: 6 Covariance Type: nonrobust
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const -0.0178 0.011 -1.548 0.122 -0.040 0.005
x1 4.962e-05 1.62e-05 3.060 0.002 1.78e-05 8.14e-05
x2 0.0020 0.001 3.824 0.000 0.001 0.003
x3 -7.181e-05 2.71e-05 -2.648 0.008 -0.000 -1.87e-05
x4 0.0001 4.06e-05 2.757 0.006 3.23e-05 0.000
x5 -1.468e-07 1.24e-07 -1.187 0.235 -3.89e-07 9.56e-08
x6 -0.0005 0.000 -2.159 0.031 -0.001 -4.78e-05
x7 -2.427e-06 7.46e-07 -3.253 0.001 -3.89e-06 -9.65e-07
==============================================================================
Detailed examples can be found here:
Technical Documentation¶
The statistical model for each observation \(i\) is assumed to be
\(Y_i \sim F_{EDM}(\cdot|\theta,\phi,w_i)\) and \(\mu_i = E[Y_i|x_i] = g^{-1}(x_i^\prime\beta)\).
where \(g\) is the link function and \(F_{EDM}(\cdot|\theta,\phi,w)\) is a distribution of the family of exponential dispersion models (EDM) with natural parameter \(\theta\), scale parameter \(\phi\) and weight \(w\). Its density is given by
\(f_{EDM}(y|\theta,\phi,w) = c(y,\phi,w) \exp\left(\frac{y\theta-b(\theta)}{\phi}w\right)\,.\)
It follows that \(\mu = b'(\theta)\) and \(Var[Y|x]=\frac{\phi}{w}b''(\theta)\). The inverse of the first equation gives the natural parameter as a function of the expected value \(\theta(\mu)\) such that
\(Var[Y_i|x_i] = \frac{\phi}{w_i} v(\mu_i)\)
with \(v(\mu) = b''(\theta(\mu))\). Therefore it is said that a GLM is determined by link function \(g\) and variance function \(v(\mu)\) alone (and \(x\) of course).
Note that while \(\phi\) is the same for every observation \(y_i\) and therefore does not influence the estimation of \(\beta\), the weights \(w_i\) might be different for every \(y_i\) such that the estimation of \(\beta\) depends on them.
| Distribution | Domain | \(\mu=E[Y|x]\) | \(v(\mu)\) | \(\theta(\mu)\) | \(b(\theta)\) | \(\phi\) |
|---|---|---|---|---|---|---|
| Binomial \(B(n,p)\) | \(0,1,\ldots,n\) | \(np\) | \(\mu-\frac{\mu^2}{n}\) | \(\log\frac{p}{1-p}\) | \(n\log(1+e^\theta)\) | 1 |
| Poisson \(P(\mu)\) | \(0,1,\ldots,\infty\) | \(\mu\) | \(\mu\) | \(\log(\mu)\) | \(e^\theta\) | 1 |
| Neg. Binom. \(NB(\mu,\alpha)\) | \(0,1,\ldots,\infty\) | \(\mu\) | \(\mu+\alpha\mu^2\) | \(\log(\frac{\alpha\mu}{1+\alpha\mu})\) | \(-\frac{1}{\alpha}\log(1-\alpha e^\theta)\) | 1 |
| Gaussian/Normal \(N(\mu,\sigma^2)\) | \((-\infty,\infty)\) | \(\mu\) | \(1\) | \(\mu\) | \(\frac{1}{2}\theta^2\) | \(\sigma^2\) |
| Gamma \(N(\mu,\nu)\) | \((0,\infty)\) | \(\mu\) | \(\mu^2\) | \(-\frac{1}{\mu}\) | \(-\log(-\theta)\) | \(\frac{1}{\nu}\) |
| Inv. Gauss. \(IG(\mu,\sigma^2)\) | \((0,\infty)\) | \(\mu\) | \(\mu^3\) | \(-\frac{1}{2\mu^2}\) | \(-\sqrt{-2\theta}\) | \(\sigma^2\) |
| Tweedie \(p\geq 1\) | depends on \(p\) | \(\mu\) | \(\mu^p\) | \(\frac{\mu^{1-p}}{1-p}\) | \(\frac{\alpha-1}{\alpha}\left(\frac{\theta}{\alpha-1}\right)^{\alpha}\) | \(\phi\) |
The Tweedie distribution has special cases for \(p=0,1,2\) not listed in the table and uses \(\alpha=\frac{p-2}{p-1}\).
Correspondence of mathematical variables to code:
- \(Y\) and \(y\) are coded as
endog
, the variable one wants to model - \(x\) is coded as
exog
, the covariates alias explanatory variables - \(\beta\) is coded as
params
, the parameters one wants to estimate - \(\mu\) is coded as
mu
, the expectation (conditional on \(x\)) of \(Y\) - \(g\) is coded as
link
argument to theclass Family
- \(\phi\) is coded as
scale
, the dispersion parameter of the EDM - \(w\) is not yet supported (i.e. \(w=1\)), in the future it might be
var_weights
- \(p\) is coded as
var_power
for the power of the variance function \(v(\mu)\) of the Tweedie distribution, see table - \(\alpha\) is either
- Negative Binomial: the ancillary parameter
alpha
, see table - Tweedie: an abbreviation for \(\frac{p-2}{p-1}\) of the power \(p\) of the variance function, see table
- Negative Binomial: the ancillary parameter
References¶
- Gill, Jeff. 2000. Generalized Linear Models: A Unified Approach. SAGE QASS Series.
- Green, PJ. 1984. “Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives.” Journal of the Royal Statistical Society, Series B, 46, 149-192.
- Hardin, J.W. and Hilbe, J.M. 2007. “Generalized Linear Models and Extensions.” 2nd ed. Stata Press, College Station, TX.
- McCullagh, P. and Nelder, J.A. 1989. “Generalized Linear Models.” 2nd ed. Chapman & Hall, Boca Rotan.
Module Reference¶
Results Class¶
GLMResults (model, params, …[, cov_type, …]) |
Class to contain GLM results. |
PredictionResults (predicted_mean, var_pred_mean) |
Families¶
The distribution families currently implemented are
Family (link, variance) |
The parent class for one-parameter exponential families. |
Binomial ([link]) |
Binomial exponential family distribution. |
Gamma ([link]) |
Gamma exponential family distribution. |
Gaussian ([link]) |
Gaussian exponential family distribution. |
InverseGaussian ([link]) |
InverseGaussian exponential family. |
NegativeBinomial ([link, alpha]) |
Negative Binomial exponential family. |
Poisson ([link]) |
Poisson exponential family. |
Tweedie ([link, var_power]) |
Tweedie family. |
Link Functions¶
The link functions currently implemented are the following. Not all link functions are available for each distribution family. The list of available link functions can be obtained by
>>> sm.families.family.<familyname>.links
Link |
A generic link function for one-parameter exponential family. |
CDFLink ([dbn]) |
The use the CDF of a scipy.stats distribution |
CLogLog |
The complementary log-log transform |
Log |
The log transform |
Logit |
The logit transform |
NegativeBinomial ([alpha]) |
The negative binomial link function |
Power ([power]) |
The power transform |
cauchy () |
The Cauchy (standard Cauchy CDF) transform |
cloglog |
The CLogLog transform link function. |
identity () |
The identity transform |
inverse_power () |
The inverse transform |
inverse_squared () |
The inverse squared transform |
log |
The log transform |
logit |
|
nbinom ([alpha]) |
The negative binomial link function. |
probit ([dbn]) |
The probit (standard normal CDF) transform |


Testing the
strong
element
Published:
July 27, 2019
ARIA labelled by
Description
strong
Strong
strong
with
title
Strong
strong
with
aria-label
Strong
strong
with
aria-label
and
title
Strong
strong
with
tabindex=0
Strong
strong
with
tabindex=0
and
title
Strong
strong
with
tabindex=0
,
aria-label
and
title
Strong


[1]
Good screen & voice prompts; Long sat acquisition time. Almost no customisation possible.
[2]
Wide screen, software; Lacks traffic, real time information


for i : 0 .. 7
parallelput (2 ** i) %Set data bit i on the parallel port
put "Data bit ", i, " or Pin ", i + 2, "has just been set"
end for
| 1 | Data bit 0 | 16 | Data bit 4 | ||||
| 2 | Data bit 1 | 32 | Data bit 5 | ||||
| 4 | Data bit 2 | 64 | Data bit 6 | ||||
| 8 | Data bit 3 | 128 | Data bit 7 |
For example, the command parallelput (97) sets data bits 0, 5 and 6 high (97 = 1 + 32 + 64) and sets the other data pins low. Because there are only 8 data pins in the parallel port, the value passed to parallelput must be from 0 to 255.
The parallelput procedure is not meant for sending a stream of characters to the parallel port (for example, if you want to send the string "Hello" to the printer). If you want to do this, open the file "LPT1" using the open statement and put to the file.


Enter10ment Anime Section
One stop addicion to anime
Reason why Anime is Love
One of the key points that made anime different from a handful of the Western cartoons is the potential for amazing content and the zeal to show point blank reality.
Top Animes on the basis of Popularity
Dragon Ball Super
Death Note
Naruto:Shipbudden
1
Website
Website
2
Creators
Creators
10 GB
Of Fun Content
Of Fun Content
All In One
Enter10ment.com
Enter10ment.com
Anime v/s the Ordinary Cartoons
- Anime
- Distinct facial expressions
- Concentrates mostly on life issues
- Originated from Japan
- Full of action and real life content
-
Imagination
meets life
- Cartoons
- Further from reality than anime
- Made to make people laugh
- Associated with caricatures for humor and satire
- Cartoons originated from the US.
-
Still Love It?
Really?
Quick Snippets of the Best Moments
Contact Us
Sparsh Garg
Chitkara University
Rajpura,Punjab,IN
Lets get in touch. Drop us a message:


I am Software Professional with around 5 Years experience in IT Industry. I have expertise in different areas of Web and Mobile Application Development from Analysing, Designing, Coding and Testing. However, I specialize in Web design and development.
Front End :
I have skills and experience with HTML5, CSS3, JQuery, Angular JS, JavaScript, Kendo UI, JSON, LESS, Responsive design and Flat Design. Furthermore, I take pride in developing code which fulfil the standards of WCAG2.0 AA and W3C as well as cross-browser requirements and accessibility requirements.
Back End :
Likewise, I have back-end skills in web technologies such as PHP, C#, ASP.Net, MVC(Razor), JSP, Magneto(CMS), Web services (SOAP & REST), WCF, Agile metrologies, MVC Frameworks and SQL & MySQL databases. Moreover, I’m certified by ISTQB Tester. As a add-on, I have adequate knowledge and skills in of Android and iOS application.
For More Information download my Resume
Download Resume


| DOSE: |
|
smoked |
Cannabis |
(plant material) |
| |
10 mg |
oral |
AMT |
(powder / crystals) |
It is very important to understand before reading this that I am a very lightweight to most chemicals other than (meth)amphetamines. I was visiting a friend in a collage town a few states away from home and had not had the opportunity to fry (other then mushrooms) in a few months. After dosing on about 220 micrograms of LSD the night before I was sitting in a co-op room with a couple friends smoking weed and discussing my trip from the previous night. After getting really stoned we started talking about chemistry and such with one of the people in the co-op. he thought he might have some foxy and looked for it. Not finding it he found his last bit of AMT and offered it to me.
I took it and continued smoking some very good weed for about another half-hour before leaving. Two friends and I walked from the co-op to the library on campus to use the computers. While sitting in the library I had completely forgotten that I took the AMT and was noticing that I was VERY VERY high off what I thought was just the weed. I kept thinking that I was higher then usual and much higher then my friends. Talking on the internet with a friend from back home for about 20 minutes I realized that I was to the point of being so high it was difficult to keep up with what was going on. We ended up leaving the library and my friends had also forgotten that I was on AMT.
We walked down one of the busy streets and spoke to a few people on the streets. I was feeling very empathic and didn't understand why I was connection with people so well. We ended up going into another co-op housing project and sitting down talking to a few people in one of the rooms. I was feeling very awkward yet welcomed by one particular person and kept staring at them feeling that they wanted to be understood so I read them some bits out of the book T.A.Z. that I was carrying with me. After a while I looked at my friend and he asked 'woah. . . are you frying on the AMT?' and it hit me. I had completely forgotten that I had taken it. And I started to peak as soon as I realized it.
Very slight visuals began. Watching colors stick out more then usual and lights being almost unbearably bright then decided to take me through the house to look at all the art and graffiti on the walls. I was in awe the whole time. Admiring the creative and brilliance of the colors I walked around saying 'holy shit' and 'what the fuck' whenever I saw something so intense that it melted into a pattern. I then began noticing that my extreme empathic feelings were controlling my thoughts. I couldn't rationalize or reason I could only concentrate or tweak on the emotions I was feeling that were flowing with the visual patterns I saw. It was frustrating a bit to not be able to concentrate or recognize reality very well. I would have to think hard to interpret into empathic patterns when somebody tried to speak to me.
After I stopped peaking about an hour later I was just very very empathic and ran into a character who was very hateful. I could feel unfocused hate just oozing from him and even see patterns in those feelings around him. I told my friend I had to get out of the area and started walking back to the place we were when I took the AMT. On the way there I began coming down or at least I thought I was. I could concentrate a bit better but was still a little empathic and trying to rationalize and explain the trip to my friend. That was about 6 hours after I originally took the AMT. for the next 4 hours I sat around tweaking on lights a bit but mostly being very introverted and ripping apart things about myself and cried a lot.
Overall I loved the experience and will try a 30mg to 50mg dose next time as I love intensity.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


1st Workshop on Autonomy, Robotics and Cognition
University of Maryland, College Park
Homepage
Workshop Details
Workshop Schedule
Speakers
Location
Registration
Contact


Output stream for event logging.
This is a simple logging stream. It doesn't interface to syslog() and expects good co-operation with the caller.
| LogStream() | Constructors. |
| close() | Closes open logging stream. |
| open() | Open logging stream. |
| reopen() | Close and then re-open logging stream. |
| ~LogStream() | Destructor. |
LogStream::LogStream()
LogStream::LogStream(const char* ident)
LogStream::LogStream(const char* ident, const char* filename)
Constructors.
A LogStream must be 'open'ed before use if the default constructor is used.
If no filename is given, logging is to clog (same as cerr).
void LogStream::close()
Closes open logging stream.
void LogStream::open(const char* ident, const char* filename="")
Open logging stream.
If a filename is not given, then output goes to cerr.
The logging stream must have been previously closed with close() or created with the default constructor.
void LogStream::reopen()
Close and then re-open logging stream.
This routine is provided for log rotation.
LogStream::~LogStream()
Destructor.


Form Fields
New Field
Label
Wrap Field?
Label Classes (separate by commas)
Field Type
Model Property (ng-model to bind back to)
Required
Placeholder Text
Group ID
Field Classes (separate by commas)
Options
Add Option
Option Label
Wrap Option?
Option Label Classes (separate by commas)
Option Value
Option Group ID
Current Field Options
Label
Value
Actions
{{option.label.name}}
{{option.value}}
Edit
Delete
Current Form Fields
Label
Type
Actions
{{field.label.name}}
{{field.type}}
Edit
Delete
Form Properties
Form Classes (separate by commas)
Form Groups
Add Group
ID (integer to be referenced by field's group property)
Name
Group Classes (separate by commas)
Current Form Groups
Name
ID
Actions
{{group.name}}
{{group.id}}
Edit
Delete
Resulting Form
Form Model
{{myFormModel | json}}


Enter your email to get updates.
Most truck owners play catch up with multiple online calendars, and that makes their schedules messy and hard for customers to follow. If that sounds like you, this tool will automatically sync your schedules across all platforms and profiles.
Want people to plan around your schedule? Email is the best way to turn people into loyal fans, and build a base that will follow you around town - and we can show you how to do it.
We’d love to help you build a real marketing strategy, not just a bunch of hopeful hacks. For the rest of 2019, we’re offering food truck owners a totally free strategy call.


Heres a complete example build in a bot-like app. Type help to be able to receive different message types. Enjoy the conversation and the beautiful UI.
help
Main features:


{include file="public/headeren"} {include file="public/naven"} {include file="public/mobileheaderen"}
{$page}


Examples examples
Example application to learn from. Here is the list of the example JavaScriptMVC applications. They cover specific JavaScriptMVC features and are ordered by complexity. If you need an introduction to the JavaScriptMVC framework it is best to read them in order.
TodoMVC JavaScriptMVC - open application ⇗
In this guide, we're going to be installing and walking through the simplest JavaScriptMVC application imaginable — a TODO list manager.
This article covers:
- Separation of application logic from user interface
- Using model lists to manage collection of models
Srchr - open application ⇗
Srchr searches multiple services (like Flickr, Upcoming, and Twitter) and saves the results between page requests.
This article covers:
- The ideas behind JavaScriptMVC
- How JavaScriptMVC enables code separation
- Event oriented architecture
Contacts - open application ⇗
The contacts example is a lightweight application that allows users to add and organize their friend's contact information.
This article covers:
- Installing and running the application
- The application's structure and organization
- How the application's widgets were designed
- The anatomy of the application's widgets
- How we glued the application's widgets together using event-oriented-architecture


It's ready — Daemo workers have submitted all tasks for your project, {{ project_name }}.
Now you can go review their work and approve it. As a reminder, each submission is automatically approved and paid after
two business days if you don't take any action. Before then, you can ask workers to revise their submission if you'd like them to iterate based on your feedback.
- The Daemo Team


--- title: Israel shot 146 date: 2004-05-26T00:00:00.000Z filepathOriginal: photos/IMG0217.jpg filepathThumbnail: photos/IMG0217.thumbnail.jpg filepathPreview: photos/IMG0217.preview.jpg ---
Academy 2's attempt at popping out from behind the trees, sound-of-music-style (a bit more teamwork was required)


{% extends 'layout.html' %} {% load tags %} {% block content %} This feature is currently under development. Please check back soon. {% endblock %}


Let’s imagine that a contagious person wears a 50% effective mask.
By "50% effective" we mean that wearing this mask cuts in half the chance that this person will infect a nearby susceptible person.
Interaction Type 1: The unmasked contagious person is near an unmasked susceptible person. Disease transmission does not drop (0%).Interaction Type 2: The contagious person wears a 50% effective mask.
What if the susceptible person wears the mask instead?
The effectiveness of a mask usually depends on whether you’re inhaling or exhaling through it.
Let’s keep things simple and assume that masks are equally effective when we breathe in or breathe out through them.
Interaction Type 1: The unmasked contagious person is near an unmasked susceptible person. Disease transmission does not drop (0%).Interaction Type 3: The susceptible person wears a 50% effective mask.
What if both people wear 50% effective masks?
Will the disease not be transmitted to the susceptible person?
What evidence do you have?
Interaction Type 1: The unmasked contagious person is near an unmasked susceptible person. Disease transmission does not drop (0%).Interaction Type 4: Both people wear 50% effective masks.


import warnings
from typing import Iterable, Optional
from pandas import Series
import numpy as np
import pandas as pd
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from scipy.optimize import minimize
[docs]def norm_line_to_residuals(
ph_line: Iterable,
prot_line: Iterable,
regularization_values: Optional[Iterable] = None,
cv: Optional[int] = None,
**ridgecv_kwargs
) -> Series:
"""
Args:
ph_line:
prot_line:
regularization_values:
cv:
**ridgecv_kwargs:
Returns:
"""
if regularization_values is None:
regularization_values = [2 ** i for i in range(-10, 10, 1)]
if cv is None:
cv = 5
warnings.filterwarnings("ignore", category=DeprecationWarning)
nonull = (~np.isnan(ph_line) & ~np.isnan(prot_line))
if sum(nonull) < cv:
return np.empty(len(ph_line))
features = prot_line[nonull].values.reshape(-1, 1)
labels = ph_line[nonull].values
ridgecv_kwargs['alphas'] = regularization_values
ridgecv_kwargs['cv'] = cv
model = RidgeCV(**ridgecv_kwargs).fit(features, labels)
prediction = model.predict(features)
residuals = labels - prediction
return pd.Series(residuals, index=ph_line[nonull].index)
# Adapted from https://github.com/alexmill/alexmill.github.io/tree/91be34d6fafa90cf5d78e7f934328a060c8a70c0/_site/posts/linear-model-custom-loss-function-regularization-python
[docs]class SigmoidRegression:
"""
Linear model: Y = B*(1/1+e**X), fit by minimizing the provided loss_function
with L2 regularization
"""
def __init__(self, loss_function=mean_squared_error,
X=None, Y=None, sample_weight=None, coef_init=None,
regularization=0.00012, cv=5, alphas=None):
if alphas is None:
alphas = [2 ** i for i in range(-10, 10, 1)]
self.alphas = alphas
self.cv = cv
self.regularization = regularization
self.coef_ = None
self.loss_function = loss_function
self.sample_weight = sample_weight
self.coef_init = coef_init
self.X = X
self.Y = Y
[docs] def predict(self, X):
"""
Args:
X:
Returns:
"""
prediction = np.matmul((1/np.exp(1+X)), self.coef_)
return (prediction)
[docs] def model_error(self):
"""
Returns:
"""
error = self.loss_function(
self.Y, self.predict(self.X), sample_weight=self.sample_weight
)
return error
[docs] def l2_regularized_loss(self, coef_):
"""
Args:
coef_:
Returns:
"""
self.coef_ = coef_
return self.model_error() + sum(self.regularization * np.array(self.coef_) ** 2)
[docs] def fit_no_cv(self, maxiter=250):
"""
Args:
maxiter:
Returns:
"""
# Initialize coef estimates (you may need to normalize
# your data and choose smarter initialization values
# depending on the shape of your loss function)
if type(self.coef_init) == type(None):
# set coef_init = 1 for every feature
self.coef_init = np.array([1] * self.X.shape[1])
else:
# Use provided initial values
pass
if self.coef_ is not None and all(self.coef_init == self.coef_):
print("Model already fit once; continuing fit with more itrations.")
res = minimize(self.l2_regularized_loss, self.coef_init,
method='BFGS', options={'maxiter': maxiter})
self.coef_ = res.x
self.coef_init = self.coef_
[docs]class CustomCrossValidator:
"""Cross validates arbitrary model using MAPE criterion on list of alphas.
Args:
X:
Y:
model_class:
sample_weight:
loss_function:
"""
def __init__(self, X, Y, model_class,
sample_weight=None,
loss_function=mean_squared_error):
self.X = X
self.Y = Y
self.model_class = model_class
self.loss_function = loss_function
self.sample_weight = sample_weight
[docs] def cross_validate(self, alphas, cv=5):
"""
alphas: set of regularization parameters to try
cv: number of folds to cross-validate against
"""
self.alphas = alphas
self.cv_scores = []
X = self.X
Y = self.Y
# coef_ values are not likely to differ dramatically
# between differnt folds. Keeping track of the estimated
# coef_ coefficients and passing them as starting values
# to the .fit() operator on our model class can significantly
# lower the time it takes for the minimize() function to run
coef_init = None
for lam in self.alphas:
# Split data into training/holdout sets
kf = KFold(n_splits=cv, shuffle=True)
kf.get_n_splits(X)
# Keep track of the error for each holdout fold
k_fold_scores = []
# Iterate over folds, using k-1 folds for training
# and the k-th fold for validation
f = 1
for train_index, test_index in kf.split(X):
# Training data
CV_X = X[train_index, :]
CV_Y = Y[train_index]
CV_weights = None
if type(self.sample_weight) != type(None):
CV_weights = self.sample_weight[train_index]
# Holdout data
holdout_X = X[test_index, :]
holdout_Y = Y[test_index]
holdout_weights = None
if type(self.sample_weight) != type(None):
holdout_weights = self.sample_weight[test_index]
# Fit model to training sample
alpha_fold_model = self.model_class(
regularization=lam,
X=CV_X,
Y=CV_Y,
sample_weight=CV_weights,
coef_init=coef_init,
loss_function=self.loss_function
)
alpha_fold_model.fit_no_cv()
# Extract coef values to pass as coef_init
# to speed up estimation of the next fold
coef_init = alpha_fold_model.coef_
# Calculate holdout error
fold_preds = alpha_fold_model.predict(holdout_X)
fold_mape = self.loss_function(
holdout_Y, fold_preds, sample_weight=holdout_weights
)
k_fold_scores.append(fold_mape)
f += 1
# Error associated with each alpha is the average
# of the errors across the k folds
alpha_scores = np.mean(k_fold_scores)
self.cv_scores.append(alpha_scores)
# Optimal alpha is that which minimizes the cross-validation error
self.alpha_star_index = np.argmin(self.cv_scores)
self.alpha_star = self.alphas[self.alpha_star_index]
return self
[docs]class SigmoidCV (SigmoidRegression):
def __init__(
self,
loss_function=mean_squared_error,
sample_weight=None,
coef_init=None,
regularization=0.00012,
cv=5,
alphas=None
):
"""
Args:
loss_function:
sample_weight:
coef_init:
regularization:
cv:
alphas:
"""
super().__init__(loss_function, sample_weight, coef_init, regularization, cv, alphas)
if alphas is None:
alphas = [2 ** i for i in range(-10, 10, 1)]
self.alphas = alphas
self.cv = cv
self.regularization = regularization
self.coef_ = None
self.loss_function = loss_function
self.sample_weight = sample_weight
self.coef_init = coef_init
[docs] def fit(self, X, Y):
self.X = X
self.Y = Y
cross_validator = CustomCrossValidator(
X, Y, SigmoidRegression,
loss_function=self.loss_function
)
cross_validator.cross_validate(self.alphas, cv=self.cv)
alpha_star = cross_validator.alpha_star
self.regularization = alpha_star
self.fit_no_cv()


- Project collaborators
- Granted by
- German Research Foundation (DFG)
- Duration of the Project
-
- 2011 - 2013 (first phase)
- 2014 - 2016 (second phase)
In the first project phase we developed heuristical algorithms to extract references to research datasets from publications in the field of Social Science and to integrate those links into the Primo Search Portal of Mannheim University Library and the Datenbestandskatalog of GESIS.
In the current phase of the project, the goal is to extend the results of the previous project, in terms of quality, quantity and supported languages. Furthermore, the technical infrastructure will be developed further to provide a solid and vendor-independent foundation for the algorithms. Last but not least, the nature of publication-dataset links will be thoroughly examined and formalized to allow more fine-grained description of these links.
The current main goals are:
1. Expansion to other disciplines and languages
During the first project phase, we concentrated mostly on German sources from the social sciences domain. InFoLiS II expands this focus on additional scientific and scholarly domains and languages beyond German. In concrete terms, the InfoLink toolkit will be used with various publications and datasets from the fields of social and economic studies and related disciplines in both English and German language. To massively expand the corpus of texts and datasets, we are cooperating with national and international research institutions, repository administrators and publishers.
2. Linked Data based infrastructure
We aim to build a flexible, and long-term sustainable infrastructure to house the algorithms developed in the precursor project for finding links between publications and datasets and embedding them into existing systems.
Based on the paradigms of Linked Data and RESTful web services, all the steps of the InfoLink toolkit are being implemented as self-contained components and workflows are being set into place to automatically update our knowledge base with new data on a regular basis.
We embrace wide-spread usage of the links detected by InfoLink. To work towards wider acceptance, we are developing software to integrate those links into existing research platforms such as publication databases, research data repositories, research infrastructure databases and discovery platforms.
To demonstrate how thusly integrated data improves Information Retrieval, we will build a research prototype that uses the full power of the graph spanned between publications, research data and authors.
3. Improve the reusability of generated links
It is a truth universally acknowledged that the trend to describing, archiving and making available of research data is gaining traction. However, this is a very heterogenous process.
On the one hand, the granularity (what is the smallest element of research data in need of description?) and the possible, aggregating intermediary steps vary widely. On the other hand, the nature of the relation between publication and dataset varies widely as well.
To formalize this, we will develop an ontology, reusing the DDI-RDF Discovery Vocabulary developed by the Data Documentation Initiative. The resulting improvements of the search process and the reusability of the links in general are also a focus of the project.


Application code can _issue_ a control request using the _CommandProcessor_ interface provided by the Master interface. You can learn about the different control types and operation modes [[here | Control types and modes]].
// Java
public interface CommandProcessor {
ListenableFuture<CommandStatus> selectAndOperate(ControlRelayOutputBlock command, long index);
....
}
This interface has a method signature for every control and operating mode pair. In C++ you provide a callback lambda for the result, and in C# and Java a _future_ of type CommandStatus is returned. A future is an interface that represents the eventual value of the operation. You may either synchronously block for the result of the operation or asynchronously listen. The C# example below issues a CROB using the SBO operation mode and then calls Await() on the future to block the current thread until the control requests is complete:
// C#
var crob = new ControlRelayOutputBlock(ControlCode.PULSE, 1, 100, 100), index);
var future = master.GetCommandProcessor().SelectAndOperate(crob);
CommandStatus result = future.Await();
Console.WriteLine("Result: " + result);
Check out the master demo for your language to see the idiomatic way to issue a control request.


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I grew up in a small town called Oxnard, just about 80 miles north of Los Angeles. I have spent a majority of my professional career in the Construction Management & Accounting fields, but I have always had a passion for art and nature.I started Serenity CMD initially as a Construction/Bookkeeping service, and have begun Hemp Jewelery. I plan on incorporating my Chalk Pastel Art, and Holistic Butters & Sprays.
I am NOT licensed nor do I hold any degrees in any areas. However, life has taught me that living experiences trump book knowledge 80% of the time. So all the information, items and anything I do on here has been researched and/or tested by myself. I feel that sharing my knowledge and wisdom with all those I encounter helps them but also helps me in my personal growth and life journey.Thank You For Your Time & I Hope To Work With You Soon!!


μ = 0; σ = 1;
ggplot(data=data.frame(x=c(μ - σ*4, μ + σ*4)), aes(x)) +
stat_function(fun=dnorm, args=list(mean=μ, sd=σ), size=1.05) +
labs(x=expression(theta), y='Density')
α <- .5; β <- 5;
ggplot(data=data.frame(x=c(0, 1)), aes(x)) +
stat_function(fun=dbeta, args=list(shape1=α, shape2=β), n=1001, size=1.05) +
labs(x=expression(theta), y='Density', title='Beta Distribution')
bball1970[,c('Player', 'Hits', 'AB')]
| Player | Hits | AB |
|---|---|---|
| Clemente | 18 | 45 |
| Robinson | 17 | 45 |
| Howard | 16 | 45 |
| Johnstone | 15 | 45 |
| Berry | 14 | 45 |
| Spencer | 14 | 45 |
| Kessinger | 13 | 45 |
| Alvarado | 12 | 45 |
| Santo | 11 | 45 |
| Swaboda | 11 | 45 |
| Petrocelli | 10 | 45 |
| Rodriguez | 10 | 45 |
| Scott | 10 | 45 |
| Unser | 10 | 45 |
| Williams | 10 | 45 |
| Campaneris | 9 | 45 |
| Munson | 8 | 45 |
| Alvis | 7 | 45 |
sample_mean
priors
ggplot(data=data.frame(x=c(0, 1)), aes(x)) +
stat_function(fun=baseball_model, n=1001, size=1.05) +
labs(x=expression(theta), y='Density', title='Model')
posterior
Shouldn't baseball players have individual batting averages?
First attempt, unpooled:
unpooled
Bayesian attempt, partially pooled:
partially_pooled
Let's judge these three models relative to future data
bball1970[,c("Player", "RemainingHits", "RemainingAB")]
| Player | RemainingHits | RemainingAB |
|---|---|---|
| Clemente | 127 | 367 |
| Robinson | 127 | 426 |
| Howard | 144 | 521 |
| Johnstone | 61 | 275 |
| Berry | 114 | 418 |
| Spencer | 126 | 466 |
| Kessinger | 155 | 586 |
| Alvarado | 29 | 138 |
| Santo | 137 | 510 |
| Swaboda | 46 | 200 |
| Petrocelli | 142 | 538 |
| Rodriguez | 42 | 186 |
| Scott | 132 | 435 |
| Unser | 73 | 277 |
| Williams | 195 | 591 |
| Campaneris | 159 | 558 |
| Munson | 129 | 408 |
| Alvis | 14 | 70 |
round(future <- bball1970$RemainingHits / bball1970$RemainingAB, 2)
round(unpooled_predicted, 2)
Prediction Error, unpooled model
round(mean((unpooled_predicted - future)^2), 3) # mean square error
Prediction Error
round(df_mse, 3)
| partially pooled | unpooled | pooled | |
|---|---|---|---|
| present | 0.003 | 0.000 | 0.005 |
| future | 0.001 | 0.005 | 0.002 |
Dr. Banet designed an experiment to directly test the impact of maternal stress on the next generation of Pacific salmonids. Stress was simulated via cortisol baths that were randomly applied to sibling pairs at fertilization.
salmonids


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#include <largeobject.h>
Inheritance diagram for pqxx::largeobjectaccess:
Public Types | |
| typedef long | off_type |
| typedef size_type | pos_type |
| typedef PGSTD::ios::openmode | openmode |
| Open mode: in, out (can be combined with the "or" operator). | |
| typedef PGSTD::ios::seekdir | seekdir |
| Seek direction: beg, cur, end. | |
| typedef long | size_type |
Public Member Functions | |
| largeobjectaccess (dbtransaction &T, openmode mode=PGSTD::ios::in|PGSTD::ios::out) | |
| Create new large object and open it. | |
| largeobjectaccess (dbtransaction &T, oid O, openmode mode=PGSTD::ios::in|PGSTD::ios::out) | |
| Open large object with given oid. | |
| largeobjectaccess (dbtransaction &T, largeobject O, openmode mode=PGSTD::ios::in|PGSTD::ios::out) | |
| Open given large object. | |
| largeobjectaccess (dbtransaction &T, const PGSTD::string &File, openmode mode=PGSTD::ios::in|PGSTD::ios::out) | |
| Import large object from a local file and open it. | |
| ~largeobjectaccess () | |
| void | to_file (const PGSTD::string &File) const |
| Export large object's contents to a local file. | |
| void | write (const char Buf[], size_type Len) |
| Write data to large object. | |
| void | write (const PGSTD::string &Buf) |
| Write string to large object. | |
| size_type | read (char Buf[], size_type Len) |
| Read data from large object. | |
| size_type | seek (size_type dest, seekdir dir) |
| Seek in large object's data stream. | |
| pos_type | cseek (off_type dest, seekdir dir) throw () |
| Seek in large object's data stream. | |
| off_type | cwrite (const char Buf[], size_type Len) throw () |
| Write to large object's data stream. | |
| off_type | cread (char Buf[], size_type Len) throw () |
| Read from large object's data stream. | |
| oid | id () const throw () |
| Object identifier. |
|
|
|
|
|
Open mode: in, out (can be combined with the "or" operator). According to the C++ standard, these should be in std::ios_base. We take them from std::ios instead, which should be safe because it inherits the same definition, to accomodate gcc 2.95 & 2.96. |
|
|
|
|
|
Seek direction: beg, cur, end. According to the C++ standard, these should be in std::ios_base. We take them from std::ios instead, which should be safe because it inherits the same definition, to accomodate gcc 2.95 & 2.96. |
|
|
|
|
|
Create new large object and open it.
|
|
|
Open large object with given oid. Convert combination of a transaction and object identifier into a large object identity. Does not affect the database.
|
|
|
Open given large object. Open a large object with the given identity for reading and/or writing
|
|
|
Import large object from a local file and open it. Creates a large object containing the data found in the given file.
|
|
|
|
|
|
Read from large object's data stream. Does not throw exception in case of error; inspect return value and errno instead. Returns number of bytes actually read, or -1 if an error occurred.
|
|
|
Seek in large object's data stream. Does not throw exception in case of error; inspect return value and errno instead. Returns new position in large object, or -1 if an error occurred.
|
|
|
Write to large object's data stream. Does not throw exception in case of error; inspect return value and errno instead. Returns number of bytes actually written, or -1 if an error occurred.
|
|
|
Object identifier. The number returned by this function identifies the large object in the database we're connected to. |
|
|
Read data from large object. Returns the number of bytes read, which may be less than the number of bytes requested if the end of the large object is reached. Throws an exception if an error occurs while reading.
|
|
|
Seek in large object's data stream. Returns the new position in the large object. Throws an exception if an error occurs. |
|
|
Export large object's contents to a local file. Writes the data stored in the large object to the given file.
|
|
|
Write string to large object. If not all bytes could be written, an exception is thrown.
|
|
|
Write data to large object. If not all bytes could be written, an exception is thrown.
|


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