The normal distribution has a uniquely important place in probability theory and data science. In this chapter we will study a variety of ways in which it is used in inference.
You started working with the normal distribution in Data 8 long before you understood much probability theory. So you have taken several basic facts for granted without proof. We will start by establishing those facts.


Traditional and spatial capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive “marks”, such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks.
| Version: | 2.0.1 |
| Depends: | R (≥ 3.2.1) |
| Imports: | parallel, Matrix, coda, statmod, RMark, Brobdingnag, mvtnorm, graphics, methods, stats, utils, prodlim, sp, raster |
| Published: | 2017-06-30 |
| Author: | Brett T. McClintock [aut, cre], Acho Arnold [ctb, cph] (C original matrix library, https://github.com/najela/matrix.h), Barry Brown [ctb] (Fortran original ranlib library), James Lovato [ctb] (Fortran original ranlib library), John Burkardt [ctb] (C original ranlib library, http://people.sc.fsu.edu/~jburkardt/c_src/ranlib), Cleve Moler [ctb] (C original linpack library, http://www.kkant.net/geist/ranlib/), Arjun Gopalaswamy [ctb] (modified snippets of R package SPACECAP code) |
| Maintainer: | Brett T. McClintock <brett.mcclintock at noaa.gov> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Citation: | multimark citation info |
| Materials: | NEWS |
| CRAN checks: | multimark results |
| Reference manual: | multimark.pdf |
| Package source: | multimark_2.0.1.tar.gz |
| Windows binaries: | r-devel: multimark_2.0.1.zip, r-release: multimark_2.0.1.zip, r-oldrel: multimark_2.0.1.zip |
| OS X binaries: | r-release: multimark_2.0.1.tgz, r-oldrel: multimark_2.0.1.tgz |
| Old sources: | multimark archive |
Please use the canonical form https://CRAN.R-project.org/package=multimark to link to this page.


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Bootstrap Modern is a theme for Bootstrap4
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/s - prints/shows content of a embedded sample script
The command above produces the following output:
| using System; using System.Windows.Forms; class Script { static public void Main(string[] args) { MessageBox.Show("Just a test!"); for (int i = 0; i < args.Length; i++) { Console.WriteLine(args[i]); } } } |
The sample script can be used as a starting script (skeleton code) for further development. The easiest way to create your initial script (eg. test.cs) is to execute the following command in command prompt:


Insta supports interface of both bi-directional and uni-directional lab equipments for automatic upload of lab results into the test results in Insta HMS. Insta HMS generates bar codes for samples for usage in this machines. We have many clients who have used this feature that reduces human errors in manual entry of results. Over 30 different types of lab equipment have interfaced with Insta HMS.
Today many hospitals and clinics use variety of imaging equipment like cameras, digital x-ray machines, ultra sonography, endoscopy and various scopy instruments. If these images can be automatically uploaded into Insta HMS, then doctors can see the images wherever they are and enhances EMR records of patients. Insta has implemented this solution for both Dicom and non-Dicom based systems.
Nowadays, many hospitals record surgeries, patient counseling sessions through video and keep them in the video server. In Insta HMS, these videos can be linked to EMR data of patient through a link, instead of copying the video file. This will reduce the database load of video files, but the at the same time, the videos can be viewed from EMR View of the patient from Insta HMS.
Many hospitals use PACS when they procure advanced radiology imaging systems like CT, MRI, etc. Insta HMS provides following interfaces with PACS systems. These interfaces are provided using HL7 and the PACS should support standard HL7 messages, which most of the standard PACS systems do.
Insta HMS does not include the modules for - Financial Accounting, Human Resource Management and Payroll since there are several excellent systems available in the market that service these needs.
Insta HMS is well integrated with Focus ERP which has the modules such as – Financial Accounting, Human Resource Management, Payroll, Procurement, Inventory Management and Fixed Assets. The integration done by Insta HMS includes generating the vouchers for posting into the FA (Financial Accounting) system in a batch mode. Insta HMS has the facility of online integration with Tally and Focus ERP. For other FA systems like SAP, Oracle, MS Dynamics or MS Navision, we provide access to our database for online integration.
There are many specialty healthcare systems that hospitals and clinics may want to interface with Insta HMS. Insta is open to provide such interface using HL7 interface standards.
Insta HMS has implemented the option of bar code printing as well as usage of bar code scanners for quicker data entry. The facilities provided for use of bar code in Insta HMS are as follows:
Hospitals and clinics take signatures of patients on papers like consent forms, etc. This adds to the paper load of hospitals. Insta has implemented digital capture of patient signatures and implemented in some hospitals. Here, the patients see the consent forms on the computer monitors and sign their acceptance on digital signature machines (as they do in many retail shops for credit card signatures). Insta HMS stores the digital signature, date, time and IP address of machine from where digital signature is taken along with the consent form. The print of the signed form can be taken and given to patient.
In many hospitals doctors use voice recording to dictate the notes. It is possible to connect voice dictation systems to Insta HMS and use the powerful features provided by these systems for voice to text conversion.


Data available from 13-Oct-2016 to 27-Oct-2017.
Updated on: 27-Oct-2017.
GitHub repository for this project.


The buttons to select or deselect the checklist may lie outside of the .pat-checklist element. However, to avoid mismatches, the selectors then need to be disambiguated by starting with an object id. In other words, the selector needs to start with #elementId.
Clicking the toggle checkbox to change the states.
Clicking select all / deselect all only affects parent of buttons and its children.


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{{- define "content" }}
| Sid | {{- if .Conference.CanViewProperty "Sid" }} {{- template "sid" .Conference }} {{- else }}hidden | {{- end }}|
|---|---|---|
| Friendly Name | {{- if .Conference.CanViewProperty "FriendlyName" }}{{ .Conference.FriendlyName }} | {{- else }}hidden | {{- end }}
| Region | {{- if .Conference.CanViewProperty "Region" }}{{ .Conference.Region }} | {{- else }}hidden | {{- end }}
| Date Created | {{- if .Conference.CanViewProperty "DateCreated" }}{{ friendly_date (.Conference.DateCreated.Time.In $.Loc) }} | {{- else }}hidden | {{- end }}
| Status | {{- if .Conference.CanViewProperty "Status" }}{{ .Conference.Status }} | {{- else }}hidden | {{- end }}
Despite offering this functionality in the Dashboard, Twilio does not offer an API to view conference participants, or a way to figure out which call sids are involved in a call. Please contact Customer Support and ask for the ability to retrieve call SIDs for a Conference via the API.
Auto-scraping this data from the Twilio Dashboard would be a
violation of the Acceptable Use Policy. We cannot embed it
via an <iframe>
, because Twilio sets a
X-Frame-Options: SAMEORIGIN framebusting header. Please contact Support to ask for the ability
to view conference Participants after a conference has finished.
If you have access to the Twilio Dashboard, you can click here to view the Participants for this conference.


Interpreting the output¶
Demo data¶
The following examples are produced by fivepseq v1.0b0 taking as input alignment and genome files from yeast of three strains: wild type (BY4741) and translation elongation factor eIF5A mutants (hyp2-3 and hyp2-1). You may find description of the dataset in the _README.txt file.
Download and navigate to the yest_eIF5A folder and run the following command to produce fivepseq output:
fivepseq \
-g genome/* \
-a gff/* \
-b "bam/*.bam" \
-o fivepseq \
-t yeast_eIF5A \
-gs genesets/genesets_eIF5A.txt
This will produce a filetree that looks like this:
fivepseq
├── fivepseq_counts
├── fivepseq_plots
│ ├── main
│ ├── png
│ ├── svg
│ ├── yeast_eIF5A_combined.html
│ ├── yeast_eIF5A_main.html
│ ├── supplement
│ ├── png
│ ├── svg
│ ├── yeast_eIF5A_amino_acid_linecharts.html
│ ├── yeast_eIF5A_codon_heatmaps.html
│ ├── yeast_eIF5A_codon_linecharts.html
│ ├── yeast_eIF5A_dicodon_linecharts.html
│ ├── yeast_eIF5A_dipeptide_linecharts.html
│ ├── yeast_eIF5A_tricodon_linecharts.html
│ ├── yeast_eIF5A_tripeptide_linecharts.html
│ ├── comparison
│ ├── png
│ ├── svg
│ ├── yeast_eIF5A_differential_heatmaps.html
│ ├── genesets
│ ├── png
│ ├── svg
│ ├── yeast_eIF5A_samples_per_geneset.html
│ ├── yeast_eIF5A_genesets_per_sample.html
│ ├── yeast_eIF5A.html # <<-- this is the main entry point for all the reports
├── log
├── count_summary.txt
You may directly download the report files from here.
Entry point: fivepseq_plots/RUNNAME.html¶
This file contains a summary of your run, and links to variuos reports.
Input and run configurations¶
This paragraph stores absolute paths to your input files and options provided.
Summary statistics¶
For now, the number of reads in the coding regions is provided for each sample, in millions (M).
Report files¶
This section contains hyperlinks to various report files. Note that your files should be in the same subfolders to be found and opened with your browser. If you’ve made rearrangements, you should find and open the files directly.
General notes on interacting with html report files¶
Be patient when loading, please¶
The report files are heavy due to the large number of points contained in them. Please, wait for a couple of minutes until the white screen is replaced with charts.
Selecting samples¶
Sometimes plotting many samples may produce messy graphs that are hard to make sense of. You may use the legend box to filter the samples you’d like to focus on. The legend box is located at the top of each graph in the report file. Clicking on each of the samples in the box will make it disappear or appear back.
Hovering¶
To get exact count information for each sample at the point of interest, simply hover with the mouse over the point and pick your desired sample. A tooltip will appear to provide the necessary information on your selection.
Main report: fivepseq_plots/main/RUNNAME_main.html¶
This report contains general information on metagene count distribution, gene-specific and genome wide 3-nucleotide (3nt) periodicity patterns and frame preferences, and count heatmaps relative to amino-acids.
Metagene counts (START and STOP)¶
Metagenes counts are produced by aligning the transcripts at the translation start/stop sites and summing up the counts in each position across the transcripts. The x axis plots the base positions relative to translation start/stop, with the first nucleotide of the first/last codon corresponding to the 0 start/stop positions. By default, the counts are spanned 100bp around the start/stop site, but this value may be changed with the –span option. The charts with _raw suffix contain raw counts along the y axis, while those with _scaled suffix represent reads per million of mapped reads in the coding regions (RPM) along the y axis. Usually you may want to focus your attention on peaks relative to start/stop positions. For yeast, the peaks 14 nt and 17 nt upstream from start and stop (-14/-17) respectively, indicate on ribosome stalling at translation initiation/termination. These numbers may differ depending on the species, strain or experimental conditions. You may also focus on additional peaks/valleys of possible importance. The count files fivepseq_counts/meta_count_peaks_START.txt and fivepseq_counts/meta_count_peaks_TERM.txt also show the significance of observed peaks (see the section “Count files” for details).
Global frame preference¶
For each sample, the total (cumulative) number of read 5’ endpoints within each frame of coding transcripts is depicted with frame barplots. The x axis shows the frame (F0, F1 and F2), while the y axis – the total counts across all the transcripts (or the percentage in the plots below). At the top of each chart, are the statistics for frame preferences. For reach frame i, the frame protection index (FPI) is computed with the formula: log2(2Fi/(Ftotal- Fi)). An FPI greater than 0 indicates that the frame is preferred and vice versa. The preference p values are computed for each pair of frames with a t test, and for each frame, the maximum p-value between two possible comparisons is displayed.
Gene frame preference¶
The read 5’ endpoints in each frame can be counted for each gene transcript separately. These counts can be converted to two-dimensional coordinates with a so-called triangle transformation:
x = 1/2 * (f0+ 2f1) / (f0+ f1+ f2))
y = (sqrt(3) / 2) * (f0/ (f0+ f1+ f2))
Using these transformations, each gene is represented as a point within a triangle, where it is placed near its frame of preference. If all the frames are equally preferred the point will be closer to the center, while if either of the frames is preferred, it will lean towards its tip.
Note that only the genes having counts greater than 10 are displayed by default. This threshold may be changed at input.
Fourier transform (START and STOP)¶
The presence of 3-nt periodicity pattern, which may indicate on co-translational generation of the 5’ mRNA intermediates, is best demonstrated with fast Fourier transformations. For this, the transcripts are aligned to the start or to the stop, and are trimmed from end/beginning to the 0.75 percentile of lengths. Transcripts shorter than this are padded with 0s. The counts at each position are then summed to produce a one-dimensional vector of metacounts. This vector is then subjected to fast Fourier transformation, which de-convolutes it into waves of varying frequencies. These frequencies are converted to periodicity values and displayed on the x axis, while the intensity of each wave is displayed on the y axis. When hovering over with the mouse at the toll peak in our example, you may see that it is close to the periodicity value of 3. The clearer the 3-nt pattern, the higher the intensity will be. Most of the times the patterns will not differ much whether the transcripts are aligned to the start or to the stop. However, when certain periodicity patterns prevail closer to the termination or to the initiation, you may as well observe a difference between the two charts.
Amino acid relative count heatmaps¶
The amino acid heatmaps highlight the cumulative counts in the gene body (discarding the start and termination regions) relative to the distance from each codon representing an amino acid or termination. These are computed by summing up the counts at certain distance from a certain amino acid across all the transcripts. In these heatmaps, the columns correspond to the relative distance from each amino acid, while the rows represent each amino acid. The values in the heatmaps are either the raw cumulative counts (top charts) or those scaled in each row (bottom charts). The values are then colored on the blue-to-yellow scale, with yellow corresponding to higher values.
Combined report: fivepseq_plots/main/RUNNAME_combined.html¶
This report contains the same set of charts, as those found in main, but it merges or combines all the samples. There are two ways to combine the samples: either sum the reads without accounting for the relative library sizes (top chart), or assign weights to each sample according to the library size and sum afterwards (bottom chart). Library sizes are the number of reads in the coding regions.
Supplement: fivepseq_plots/supplement/RUNNAME_amino_acid_linecharts.html¶
This report contains linecharts for cumulative counts in the gene body (discarding the start and termination regions) relative to the distance from each codon representing an amino acid or termination for individual samples (left) or the samples combined (right). Samples are combined by accounting for their library size. The read counts are normalized to reads per million (RPM). In essence, these are the same data seen in amino acid heatmaps in the main report, in a different format.
Supplement: fivepseq_plots/supplement/RUNNAME_codon_linecharts.html¶
This report contains the same charts as the one for amino acids, but for each codon separately. Note: as there are 64 charts in this report, loading the file in your browser may take long.
Supplement: fivepseq_plots/supplement/RUNNAME_codon_heatmaps.html¶
The heatmaps in this report are the same as amino acid heatmaps in the main report, but are expanded for each codon individually and also expanded to the distance from -30 to +6. The top charts contain raw counts, the second line is scaled for each row. The third and the forth lines contain raw and scaled heatmaps too, but there the rows are sorted alphabetically, based on codons.
Then follow heatmaps for counts relative to dicodons, dipeptides, tricodons and tripeptides. For each, first the raw counts are shown, then those scaled by row. Note that each sample may differ by the codon motifs presented and their order, because only top 50 motifs sorted by relative counts in position (-14 nt) and (-11 nt) are included, unless these positions are modified at input.
Supplement: fivepseq_plots/supplement/RUNNAME_dicodon_linecharts.html¶
This report contains overlaid linecharts for top 20 dicodon motifs for each sample. Note, as top 20 motifs may not be the same for each sample, some of the linecharts may not include all of the samples and the number of linecharts will usually exceed 20.
Supplement: fivepseq_plots/supplement/RUNNAME_dipeptide_linecharts.html¶
The same for dipeptide motifs.
Supplement: fivepseq_plots/supplement/RUNNAME_tricodon_linecharts.html¶
The same for tricodon motifs.
Supplement: fivepseq_plots/supplement/RUNNAME_tripeptide_linecharts.html¶
The same for tripeptide motifs.
Genesets: fivepseq_plots/genesets/RUNNAME_genesets_per_sample.html¶
These reports are generated only if the option -gs is provided, with a few sets of genes annotated to different categories. This report compares the charts described for the main report across different genesets. These comparisons are made for each sample.
For layouting purposes, the genesets provided by the user are converted to short names, described in the top of the report.
The charts contain tabs, which allow to switch from sample to sample, and compare the geneset-specific patterns for each sample individually.
Genesets: fivepseq_plots/genesets/RUNNAME_samples_per_geneset.html¶
This report is structured in the same way, but here, instead of comparing genesets for each sample, samples are compared with each other - for each geneset separately.
Text output¶
Aside from the HTML formatted report files, you may also want to observe and use the count distribution patterns elsewhere. For this reason, fivepseq also stores a wide variety of text files in the fivepseq_counts folder of the output path. The folder contains text formatted files for each sample and each gene set individually. By default, only the protein_coding geneset is provided, which refers to all the mRNA transcripts. If the -gs option is provided, text files for each gene set are stored in respective subfolders. The text files contain transcript tables, count vectors, count distribution, amino acid relative counts, fourier transformation vectors, frame-specific counts, etc.


Hello, my name is Brian, and I’m a web developer. Born, raised, and currently located in Central New Jersey, I bring that tenacious Jersey attitude to all my work. Put my head down, and just do the work.
 I see a problem set before me, and my first instinct is to break it down into its constituent parts and just tackle the problem. I want to create; to look at something and say “I MADE THIS.”
My inspiration for programming comes from my major hobbies:
With a keen interest in Full Stack development, and a concentration in JavaScript and the plethora of tools that it brings to the table for web application development, I am always pushing myself to learn new concepts, new technologies, and new methodologies to broaden my programming skills.


PERF analysis report
PERF
Summary
Sequence Info
Homo sapiens.fna
File Name
3.24Gb
Total reads
45.4
Read length range
545
Total bases
Repeat Info
4,631,324
Total repeats
69.09 Mb
Reads with repeats
2.15 %
Percent reads with repeats
501/501
Repeat classes
1428
Repeats per million reads
21467
Percent repeat bases
Summary Table
Basic charts
Options
Show
Order
Plot
{repeat_options}
Plot
Data:
Bases
Frequency
Reads
Options
Kmer grouping
Select Repeats:
{repeat_options}
Plot
Data:
Bases
Frequency
Reads
Options
to
length
range
{repeat_options}
Plot
Annotation charts
Options
Group
{repeat_options}
Plot
Options
Bin size:
Bin size
{repeat_options}
Plot


Specify a path to your groovy-eclipse-batch jar location. Click Select Path dialog, select the location of
the jar file and click OK.
to open
Note that it is necessary to enter the location of the groovy-eclipse-batch jar file
for the compiler to work correctly.


All endpoints use POST method, you can use them to retrive, update, add, delete my portfolio details stored in MongoDB, You can also send message to me using the last endpoint.
https://st-portfolio-on.herokuapp.com/all_work
https://st-portfolio-on.herokuapp.com/update_work
https://st-portfolio-on.herokuapp.com/delete_work
https://st-portfolio-on.herokuapp.com/add_work
https://st-portfolio-on.herokuapp.com/message
Mothod: POST
Mothod: POST Requst: title, imgUrl
Mothod: POST Request: _id, title, imgUrl
Mothod: POST Request: _id
Mothod: POST Request: message


Add user, ch9_tt
Login:
Password:
Repeat password:
Name:
Country:
Choose country
City:
Choose city
Street:
Home:
Apart:
Role:
Music:


Twiage is a Boston based digital health startup that builds disruptive communication technology used between hospitals and ambulances to accelerate emergency care. For the summer of 2017, I had the opportunity to work as the company’s first design intern.
Field Research
Web Design
Usability Testing
Marketing + Visual Branding
Web Dashboard for Emergency Room Hospitals
Responsive Marketing Landing Page
As the first thing that potential clients will see, the landing page is a crucial entry point for explaining the product, communicating the company's brand mesage, and hopefully garner enough interest to compel customers to sign up for a demo. With a designer now on board, the team decided that it was about time the site had a redesign.
Upon first glance, the old site looked flat and outdated.
Stylistic improvements aside, the main problems of the old site was that it was failing to get signups. This could be due to a variety of reasons but the ones I suspected was its lack of call to actions, confusion as to who the product was for and how it could help.
So, the three main questions I set out to answer before designing:
Who are the user groups that are coming to the site and what are they looking for?
I had the opportunity to sit in on a few focus groups with paramedics who use the app as well as sales pitches to hospital ED directors. While listening in and feverishly taking notes and asking questions throughout, I was able to get a better picture of who I was designing for and what their needs might be when looking for a product.
The three main user groups I was designing for:
Are the user groups able to understand the product and see how they might use the product in their own workflows after reading through the site?
In order to identify any usability issues with the old site and subsequently evaluate the future design iterations, I used a heuristic analysis / question list. From my observations from people who have not seen the site or know what Twiage is, users generally have difficult understanding what the product is and who the product is for. This is most likely due to there being multiple components in the Twiage product eco system (mobile app used by paramedics, desktop web app used by ER nurse, separate mobile app used by doctors).
Landing page will have to breakdown the eco system into three separate products for three separate users and explain how the different parts work together.
Question List:
First Impression
Why are they here? How did they get here?
What do they already know? What don’t they know?
What makes me want to trust a site/business I’ve never heard of before?
After reading main flow
After reading the site, do they fully understand what the app does?
What do I still not understand? Where can I get this information?
Layout
Does the layout make sense?
Does the flow line up with my thought process?
Can I access the information I need easily?
Product Reflection
Why would they want to use Twiage?
What do I like about the app?
What would make me impressed?
Are other people using this? Even if I think it’s good, have other people thought the same?
If I thought it was bad, am I wrong or did I just not understand the product?
 Do I even need some of the features? I would like this feature, but I don’t remember it being there, do they have this feature or is it too much of a hassle to find it again?
Am I curious to find out how it works on the EMS side?
In order to ensure that what I was creating was what the team was envisioning, I organized a series of website critques/UX copywriting/style brainstorming sessions with the entire team.
Hearing everyone's feedback was immensely helpful for my work moving forward. The team eventually settled on: BOLD, Powerful, Simple, Hassle free, Speed, Efficient, Privacy, Trust, Secure as the most important themes
After many, many hours of iterating, implementing, and figuring out Flexbox, the site was ready to be deployed. The project was my first real go at web design so I’m thankful for the time, support, and patience from my supervisor YiDing and the Twiage team.
I performed a lot of initial research on the target users before designing the site but wasn’t able to get data on how users currently think of the website. With more time and resources, I would have liked to perform some usability testing.
Because I had to work around the Wordpress template theme, I spent a lot of time working around the template and Wordpress content editor, when I could’ve spent more time iterating and improving upon my designs. I also could not figure out a way to incorporate javascript which could have added more snazz and interactivity to the site.
© 2019 Dennis Kim


CUT
Section: User Commands (1)Updated: June 2012
Index Return to Main Contents
NAME
cut - remove sections from each line of filesSYNOPSIS
cut OPTION... [FILE]...DESCRIPTION
Print selected parts of lines from each FILE to standard output.
Mandatory arguments to long options are mandatory for short options too.
- -b, --bytes=LIST
- select only these bytes
- -c, --characters=LIST
- select only these characters
- -d, --delimiter=DELIM
- use DELIM instead of TAB for field delimiter
- -f, --fields=LIST
- select only these fields; also print any line that contains no delimiter character, unless the -s option is specified
- -n
- with -b: don't split multibyte characters
- --complement
- complement the set of selected bytes, characters or fields
- -s, --only-delimited
- do not print lines not containing delimiters
- --output-delimiter=STRING
- use STRING as the output delimiter the default is to use the input delimiter
- --help
- display this help and exit
- --version
- output version information and exit
Use one, and only one of -b, -c or -f. Each LIST is made up of one range, or many ranges separated by commas. Selected input is written in the same order that it is read, and is written exactly once. Each range is one of:
- N
- N'th byte, character or field, counted from 1
- N-
- from N'th byte, character or field, to end of line
- N-M
- from N'th to M'th (included) byte, character or field
- -M
- from first to M'th (included) byte, character or field
With no FILE, or when FILE is -, read standard input.
AUTHOR
Written by David M. Ihnat, David MacKenzie, and Jim Meyering.REPORTING BUGS
Report cut bugs to<EMAIL_ADDRESS>coreutils home page: <http://www.gnu.org/software/coreutils/>
General help using GNU software: <http://www.gnu.org/gethelp/>
Report cut translation bugs to <http://translationproject.org/team/>
COPYRIGHT
Copyright © 2010 Free Software Foundation, Inc. License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>.This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law.
SEE ALSO
The full documentation for cut is maintained as a Texinfo manual. If the info and cut programs are properly installed at your site, the command- info coreutils aqcut invocationaq
should give you access to the complete manual.
Index
This document was created by man2html, using the manual pages.
Time: 05:29:02 GMT, December 24, 2015


CORDOVA
Name: Marilou
Address: Pangasinan
Age: 20 yrs old
Birthday: November 1996
Sex: Female
Course: Bs Information Technology
This is our final project in Cordova namely "The book of Bible-Stories". This is the link of our application. Bible Stories You can download it.


Pricing
Personal
Licensing
- ETLBox Core (without Connectors) under MIT license
- Connectors for personal use only
- Only 10.000 rows for each source/destination
Benefits
- Open source for core components and transformations
- GitHub Issues
Free
Ideal for testing
Startup
Licensing
- ETLBox Core under MIT license
- All connectors (all databases and other types) under Standard EULA
- Annual license
- One developer license
Dedicated Support
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399€
per developer / year
Business
Licensing
- ETLBox Core under MIT license
- All connectors (All databases and other types) under Standard EULA
- Annual license
- Up to 5 developers
- Access to private NuGet feed
Dedicated Support
- Technical support
- Remote training session
Purchasing
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799€
< 5 developer / year
Enterprise
Licensing
- ETLBox Core under MIT license
- All connectors (all databases and other types) under Standard EULA
- Annual license
- Unlimited developers
- Access to private NuGet feed
- Access to source code
Dedicated Support
- Technical support
- Remote training session
- Remote support
Purchasing
- PayPal
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Request a quote
per organization / year
Frequently asked questions
What if I am not satisfied?
If you are not 100% satisfied with your purchase, we will refund the full purchase price within 30 days of the purchase date.
Is there a trial version?
If you download the official nuget package of ETLBox, you can already set it up and use it for testing purposes. The database and flat file connectors are limited to 10.000 rows. For further testing, you can request a trial valid for 30 days. If you need a renewal of the trial period, no problem, just contact us.OEM, Educational, Non-Profit, and Custom Licenses
If you are interested in other licensing options, please contact us describing your specific needs.
How many licenses do I need to buy?
Licensing is based on the amount of developer that are developing or working with ETLBox.Are there perpetual licenses
We want to make sure that you always have access to the latest version and features of ETLBox. To guarantee continued support and development, we only offer annual licenses.What if I need to change a subscription?
Not a problem. Just let us know, and we'll create a custom invoice so that you only pay the difference. You'll receive a confirmation email upon purchase which can be used as an invoice/receipt.


Nowhere in manufacturing has the technology moved faster than in the world of CNC Machining. As computing power has increased so has the machines ability to produce more complex geometries!
Open die forging actually serves two purposes, hammering the metal into a desired shape while at the same time refining the grain structure which improves the strength properties of the material.
We find metal castings fascinating because there are often opportunities to save cost by moving to a different metal casting technology than perhaps a client had originally envisioned.
Metal fabrication can mean a lot of things to a lot of people. We define it as meaning changing an existing form of material into something else. This can mean welding, punching, bending, forming, any of a myriad of processes.
Metal stamping involves cutting and forming sheet metal with the aid of a punch press. Punch presses vary extensively in bed size, speed and tonnage.
Extruded aluminum is perhaps the most versatile and cost effective metalworking process available to designers and engineers. The aluminum extrusion process has been compared to the play dough toy you had as a kid that would make the girders and tubes.
Here you can request a quote in 3 easy steps!


Checkboxes allow the user to select multiple options from a set.
If you have multiple options appearing in a list, you can preserve space by using checkboxes instead of on/off switches.
If you have a single option, avoid using a checkbox and use an on/off switch instead.
|
hover |
focused |
pressed |
disabled |
|
|
On |
|
|
|
|
|
Off |
|
|
|
|
Light theme for check box in hover, focused, pressed, disabled states.


Front End Development Quiz
Question 3
3) What is the name of the library we use to help with our designs?
Answer
Previous
1
2
3
Next


Domain
Site Noop
Site Noop
Total Website Url's:
New Website Url
Website
×
Name *
Url *
for path params: https://boodskap.io/{path}
Tags
Basic Auth
Headers
Path
Params
Form Data
Body
Username
Password
Key
Value
Action
Key
Value
Action
Key
Value
Action
Key
Type
Value
Action
Body Content
Method *
GET
POST
PUT
DELETE
PATCH
Response Code *
200
201
202
400
401
402
403
404
405
500
501
502
503
504
Response should match
(Regular expression)
Response should contains
Timezone *
Email *
multiple email id's with comma
Phone Number
multiple phone number's with comma. country code is must with + sign


Mike Posthumus
Choose one:
A Just a guy learning how to code
B: Vice President of the Grand Rapids Public Museum
C: Education Innovation Expert
D: All of the above
A Just a guy learning how to code
B: Vice President of the Grand Rapids Public Museum
C: Education Innovation Expert
D: All of the above


By Phil Curtolo
Recently, disaster has been fresh on the minds of many movie director's. For instance, just last year, director Jan De Bot released the blockbuster hit Twister. It's success surprised no one, from esteemed critics to ordinary fans. It toppled the box office charts, remaining in the number one spot until the next disaster flick was released. Director Roland Emmerich's Independence Day, the 6th highest grossing film in movie history, again, took critics and fans to the edge of the seats in suspense and awe. It's astounding special effects astonished sell-out crowds in every theater throughout the nation.
As of today, just about every natural disaster has been covered in movies. Earthquakes, typhoons, hurricanes, alien invasions, towering infernos and whatever else picks at your brain has been covered by movie directors, each one having it's own high points of success. Yet the only natural disasters yet to be focused on and to become successful are the terror-wrenching volcanoes. On February 7 of this year, this fact ceased to exist, as director Roger Donaldson let loose on of the most death-defying, special effects enhanced films ever made. Dante's Peak had finally arrived with much acclaim.
Dante's Peak, Washington has just been named the second most desirable town in America, population under 20,000, in which to reside. It seems all is perfect for Mayor Rachel Wando (Linda Hamilton, The Shadow Conspiracy) and the town's people, as the huge celebration kicks off with a mammoth, possible multi-million dollar investment by a large corporation. However, little do they know the danger they are actually in, due to their dormant, fire-breathing mountain.
In Vancouver, Washington, word spreads of reading from the near extinct Dante's Peak. Head of Operations Paul Dreyfus (Charles Hallahan, Executive Decision) sends his best volcanologist, Harry Dalton (Pierce Brosnan, Goldeneye) to examine the napping beast.
Dalton has quite a past with volcanoes. As it turns out, he lost his only love and fiancée to a volcano in Columbia.
Now for the main event. When Dalton investigates the mammoth creature of destruction, he becomes suspicious of his scientific evidence, revealing a possible eruption. However, his boss, Dreyfus, doesn't want to alarm the people and scare off the investor over something that had no hard-core proof. When Dalton presents the evidence needed, it's too late, and the evacuation is on. In a mad panic, the people of the town rush to escape without thinking of the consequences. In an extremely well done scene, citizens are trampled, cars are smashed, and roads turn to ruins, as the volcano spits out it's fiery flem.
But this is just the beginning, as the party has just begun and Dante's Peak has only just begun to begin it's reign of terror. In a desperate attempt to escape, Dalton's team of experts barely make it alive. However, he, Mayor Wando, her children, their grandmother, and her dog are stranded on the mainland, as the town's only escape point, a rickety old bridge, has already collapsed. So it's survival of the fittest between man and nature.
The movie is filled with many strong points. It's release date was it's only weak point. Why would someone release this big-budget flick one measly week after the much anticipated re-releasing of the first episode of the critically acclaimed Star Wars Trilogy. There is simply no logical answer to this question, however, no movie could go flawless, even though this one came pretty darn close. It's astonishing special effects, were obviously the strong point of this movie. With the creeping lava, the incredibly detailed, exploding buildings, throwing each brick through the air, the blinding ash storms, and the acid-filled lake, Dante's Peak's effects were twenty times more powerful than Twister's and ten times more captivating than those of Independence Day.
Although a poor release date may hamper this movie's success, any movie fan would do anything it takes to see this movie. Beat up your grandma and steal her Social Security check, or rob a homeless man of his pencil money and see one of the best special effects movie's of our time, in our opinions. Dante's Peak is a must see. Grade: A+, ***** out of *****
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Binning and plotting functions for hexagonal bins.
| Version: | 1.27.2 |
| Depends: | R (≥ 2.0.1), methods |
| Imports: | lattice, grid, graphics, grDevices, stats, utils |
| Suggests: | marray, affy, Biobase, limma |
| Published: | 2018-01-15 |
| Author: | Dan Carr, ported by Nicholas Lewin-Koh and Martin Maechler, contains copies of lattice functions written by Deepayan Sarkar |
| Maintainer: | Edzer Pebesma <edzer.pebesma at uni-muenster.de> |
| License: | GPL-2 |
| URL: | http://github.com/edzer/hexbin |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| In views: | Graphics |
| CRAN checks: | hexbin results |
| Reference manual: | hexbin.pdf |
| Vignettes: |
Hexagon Binning |
| Package source: | hexbin_1.27.2.tar.gz |
| Windows binaries: | r-devel: hexbin_1.27.2.zip, r-release: hexbin_1.27.2.zip, r-oldrel: hexbin_1.27.2.zip |
| OS X binaries: | r-release: hexbin_1.27.2.tgz, r-oldrel: hexbin_1.27.2.tgz |
| Old sources: | hexbin archive |
| Reverse depends: | ASPBay, audiolyzR, BoutrosLab.plotting.general, somplot, tiger |
| Reverse imports: | autocogs, BayLum, datadr, extracat, hexSticker, hextri, mbgraphic, NEArender, openair, PhyInformR, pitchRx, plotluck, plotly, Radviz, rasterVis, rbokeh, som.nn, SpatialBall |
| Reverse suggests: | beadplexr, ggmap, ggplot2, harrypotter, lidR, poliscidata, RGraphics, survey, viridis, viridisLite, WVPlots |
Please use the canonical form https://CRAN.R-project.org/package=hexbin to link to this page.


|
Walls And Holes
1
|
Functions | |
| void | findBounds (const QPoint &start, const QPoint &end, QPoint &topLeft, QPoint &bottomRight) |
| QRegion | rect (QPoint start, QPoint end) |
| QRegion | rectOutline (QPoint start, QPoint end) |
| QRegion | ellipseOutline (QPoint start, QPoint end) |
| QRegion | line (QPoint start, QPoint end) |
| QRegion ShapeRegion::ellipseOutline | ( | QPoint | start, |
| QPoint | end | ||
| ) |
| void ShapeRegion::findBounds | ( | const QPoint & | start, |
| const QPoint & | end, | ||
| QPoint & | topLeft, | ||
| QPoint & | bottomRight | ||
| ) |
| QRegion ShapeRegion::line | ( | QPoint | start, |
| QPoint | end | ||
| ) |
| QRegion ShapeRegion::rect | ( | QPoint | start, |
| QPoint | end | ||
| ) |
| QRegion ShapeRegion::rectOutline | ( | QPoint | start, |
| QPoint | end | ||
| ) |


Abdul Rauf<EMAIL_ADDRESS>Abdul Rauf [:haseeb]
@haseeb_offline
www.webcompat.com
Web Compatibility?
Why webcompat.com?
How it works
Mozilla effort only?
How can i get involved?
Reporting issues
Reporting issues
Stages of Bug
Help diagnose issues
Help contact sites
Development
planet.webcompat.com
Lots of broken sites out there. We need your help.
Thanks Mozilla
Abdul Rauf [:haseeb]
<EMAIL_ADDRESS>@haseeb_offline
www.webcompat.com
https://miketaylr.com/pres/jquery-to/
lmandel.github.io/buildmobileweb/
https://hacks.mozilla.org/2014/06/introducing-webcompat-com/


Key Word(s): ??
CS109B Data Science 2: Advanced Topics in Data Science
Lecture 10 - Bayesian Analysis and Introduction to pyMC3¶
Harvard University
Spring 2021
Instructors: Pavlos Protopapas, Mark Glickman, and Chris Tanner
Additional Instructor and content: Eleni Angelaki Kaxiras
## RUN THIS CELL TO PROPERLY HIGHLIGHT THE EXERCISES
import requests
from IPython.core.display import HTML
styles = requests.get("https://raw.githubusercontent.com/Harvard-IACS/2019-CS109B/master/content/styles/cs109.css").text
HTML(styles)
import pymc3 as pm
# from pymc3 import summary
import arviz as az
from matplotlib import gridspec
# Ignore a common pymc3 warning that comes from library functions, not our code.
# Pymc3 may throw additional warnings, but other warnings should be manageable
# by following the instructions included within the warning messages.
import warnings
messages=[
"Using `from_pymc3` without the model will be deprecated in a future release",
]
# or silence all warnings (not recommended)
# warnings.filterwarnings('ignore')
for m in messages:
warnings.filterwarnings("ignore", message=m)
print(f"Using PyMC3 version: {pm.__version__}")
print(f"Using ArviZ version: {az.__version__}")
import pymc3 as pm
from scipy import optimize
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
import pandas as pd
import seaborn as sns
%matplotlib inline
%%javascript
IPython.OutputArea.auto_scroll_threshold = 20000;
# pandas trick
pd.options.display.max_columns = 50 # or =None -> No Restrictions
pd.options.display.max_rows = 200 # or =None -> *Be careful with this*
pd.options.display.max_colwidth = 100
pd.options.display.precision = 3
Learning Objectives¶
By the end of this lab, you should be able to:
- identify and describe some of the most important probability distributions.
- apply Bayes Rule in calculating probabilities (and would have had fun in the process).
- create probabilistic models in the PyMC3 library.
1. Some common families of distributions¶
Statistical distributions are characterized by one of more parameters, such as $\mu$ and $\sigma^2$ for a Gaussian distribution.
\begin{equation} Y \sim \mathcal{N}(\mu,\,\sigma^{2}) \end{equation}Discrete Distributions¶
The probability mass function (pmf) of a discrete random variable $Y$ is equal to the probability that our random variable will take a specific value $y$: $f_y=P(Y=y)$ for all $y$. The variable, most of the times, assumes integer values. Plots for pmfs are better understood as stem plots since we have discrete values for the probabilities and not a curve. Probabilities should add up to 1 for proper distributions.
- Bernoulli for a binary outcome, success has probability $\theta$, and we only
have $one$ trial:
- Binomial for a binary outcome, success has probability $\theta$, $k$ successes in $n$ trials:
Our random variable is $Y$= number of successes in $n$ trials.
\begin{equation} P(Y=k|n,\theta) = {{ n}\choose{k}} \cdot \theta^k(1-\theta)^{n-k} \quad k=0,1,2,...,n \end{equation}
As a reminder ${{ n}\choose{k}}$ is "from $n$ choose $k$":
\begin{equation}
{{ n}\choose{k}} = \frac{n!}{k!(n-k)!}
\end{equation}
$EY=n\theta$, $VarY = np(1-p)$
Note : Binomial (1,$p$) = Bernouli ($p$)
# probabity of success
theta = 0.5
n = 5
k = np.arange(0, n+1)
print(k)
pmf = stats.binom.pmf(k, n, theta)
plt.style.use('seaborn-darkgrid')
plt.stem(k, pmf, label=r'n = {}, $\theta$ = {}'.format(n, theta))
plt.xlabel('Y', fontsize=14)
plt.ylabel('P(Y)', fontsize=14)
plt.legend()
plt.show()
- Negative Binomial for a binary outcome, success has probability $\theta$, we have $r$ successes in $x$ trials:
Our random variable is $X$= number of trials to get to $r$ successes.
\begin{equation} P(X=x|r,\theta) = {{ x-1}\choose{r-1}} \cdot \theta^r(1-\theta)^{x-r} \quad x=r,r+1,... \end{equation}- Poisson (counts independent events and has a single parameter $\lambda$)
plt.style.use('seaborn-darkgrid')
x = np.arange(0, 10)
lam = 4
pmf = stats.poisson.pmf(x, lam)
plt.stem(x, pmf, label='$\lambda$ = {}'.format(lam))
plt.xlabel('Y', fontsize=12)
plt.ylabel('P(Y)', fontsize=12)
plt.legend(loc=1)
plt.ylim=(-0.1)
plt.show()
- Discrete Uniform for a random variable $X\in(1,N)$:
# boring but useful as a prior
plt.style.use('seaborn-darkgrid')
N = 40
x = np.arange(0, N)
pmf = stats.randint.pmf(x, 0, N)
plt.plot(x, pmf, label='$N$ = {}'.format(N))
fontsize=14
plt.xlabel('X', fontsize=fontsize)
plt.ylabel(f'P(X|{N})', fontsize=fontsize)
plt.legend()
plt.show()
- Categorical, or Multinulli (random variables can take any of K possible categories, each having its own probability; this is a generalization of the Bernoulli distribution for a discrete variable with more than two possible outcomes, such as the roll of a die)
Continuous Distributions¶
The random variable has a probability density function (pdf) whose area under the curve equals to 1.
- Uniform (variable equally likely to be near each value in interval $(a,b)$) \begin{equation} f(x|a,b) = \frac{1}{b - a} \quad x\in [a,b] \quad \text{and 0 elsewhere}. \end{equation}
Remember that the y-axis in a continuous probability distribution does not shows the actual probability of the random variable having a specific value in the x-axis because that probability is zero!. Instead, to see the probability that the variable is within a small margin we look at the integral below the curve of the PDF.
The uniform is often used as a noninformative prior.
Uniform - numpy.random.uniform(a=0.0, b=1.0, size)
$\alpha$ and $\beta$ are our parameters. size
is how many tries to perform.
The mean is $\mu = \frac{(a+b)}{2}$
from scipy.stats import uniform
a = 0
b = 1
r = uniform.rvs(loc=a, scale=b-a, size=1000)
pdf = uniform.pdf(r,loc=a, scale=b-a)
plt.plot(r, pdf,'b-', lw=3, alpha=0.6, label='uniform pdf')
plt.hist(r, density=True, histtype='stepfilled', alpha=0.2)
plt.ylabel(r'pdf')
plt.xlabel(f'x')
plt.legend(loc='best', frameon=False)
plt.show()
Normal (a.k.a. Gaussian) \begin{equation} X \sim \mathcal{N}(\mu,\,\sigma^{2}) \end{equation}
A Normal distribution can be parameterized either in terms of precision $\tau$ or variance $\sigma^{2}$. The link between the two is given by \begin{equation} \tau = \frac{1}{\sigma^{2}} \end{equation}
- Expected value (mean) $\mu$
- Variance $\frac{1}{\tau}$ or $\sigma^{2}$
- Parameters:
mu: float
,sigma: float
ortau: float
- Range of values (-$\infty$, $\infty$)
plt.style.use('seaborn-darkgrid')
x = np.linspace(-5, 5, 1000)
mus = [0., 0., 0., -2.]
sigmas = [0.4, 1., 2., 0.4]
for mu, sigma in zip(mus, sigmas):
pdf = stats.norm.pdf(x, mu, sigma)
plt.plot(x, pdf, label=r'$\mu$ = '+ f'{mu},' + r'$\sigma$ = ' + f'{sigma}')
plt.xlabel('random variable', fontsize=12)
plt.ylabel('probability density', fontsize=12)
plt.legend(loc=1)
plt.show()
- Beta (where the variable ($\theta$) takes on values in the interval $(0,1)$, and is parametrized by two positive parameters, $\alpha$ and $\beta$ that control the shape of the distribution. Note that Beta is a good distribution to use for priors (beliefs) because its range is $[0,1]$ which is the natural range for a probability and because we can model a wide range of functions by changing the $\alpha$ and $\beta$ parameters. Its density (pdf) is:
where the normalisation constant, $B$, is a beta function of $\alpha$ and $\beta$,
\begin{equation} B(\alpha, \beta) = \int_{x=0}^1 x^{\alpha - 1} (1 - x)^{\beta - 1} dx. \end{equation}- 'Nice', unimodal distribution
- Range of values $[0, 1]$
$EX = \frac{a}{a+b}$, $VarX = \frac{ab}{(a+b)^2(a+b+1)}$
from scipy.stats import beta
fontsize = 15
alphas = [0.5] #, 0.5, 1., 3., 6.]
betas = [0.5] #, 1., 1., 3., 6.]
x = np.linspace(0, 1, 1000)
colors = ['red', 'green', 'blue', 'black', 'pink']
fig, ax = plt.subplots(figsize=(8, 5))
for a, b, colors in zip(alphas, betas, colors):
plt.plot(x, beta.pdf(x,a,b), c=colors,
label=f'a={a}, b={b}')
ax.set_ylim(0, 3)
ax.set_xlabel(r'$\theta$', fontsize=fontsize)
ax.set_ylabel(r'P ($\theta|\alpha,\beta)$', fontsize=fontsize)
ax.set_title('Beta Distribution', fontsize=fontsize*1.2)
ax.legend(loc='best')
fig.show();
$p(\theta|2,5) = 30 \cdot \theta(1 - \theta)^4$
Code Resources:¶
- Statistical Distributions in numpy/scipy: scipy.stats
Bayes Rule¶
\begin{equation} \label{eq:bayes} P(A|\textbf{B}) = \frac{P(\textbf{B} |A) P(A) }{P(\textbf{B})} \end{equation}$P(A|\textbf{B})$ is the posterior distribution, probability(parameters| data).
$P(\textbf{B} |A)$ is the likelihood function, how probable is my data for different values of the parameters.
$P(A)$ is the marginal probability to observe the data, called the prior, this captures our belief about the data before observing it.
$P(\textbf{B})$ is the marginal distribution (sometimes called marginal likelihood). This serves a normalization purpose.
Let's Make a Deal¶
The problem we are about to solve gained fame as part of a game show "Let's Make A Deal" hosted by Monty Hall, hence its name. It was first raised by Steve Selvin in American Statistician in 1975.
The game is as follows: there are 3 doors behind one of which are the keys to a new car. There is a goat behind each of the other two doors. Let's assume your goal is to get the car and not a goat.
You are asked to pick one door, and let's say you pick Door1. The host knows where
the keys are. Of the two remaining closed doors, he will always open the door that has a goat behind
it. He'll say "I will do you a favor and open Door2". So he opens Door2 inside
which there is, of course, a goat. He now asks you, do you want to open the initial Door1 you chose
or change to Door3? Generally, in this game, when you are presented with this
choice should you swap the doors?
Hint:
Start by defining the
events
of this probabilities game. One definition is:$A_i$: car is behind door $i$
$B_i$ host opens door $i$
$i\in[1,2,3]$
- In more math terms, the question is: is the probability of the price is behind Door 1 higher than the probability of the price is behind Door2, given that an event has occured?
Bayes rule revisited¶
We have data that we believe come from an underlying distribution of unknown parameters. If we find those parameters, we know everything about the process that generated this data and we can make inferences (create new data).
\begin{equation} P(\theta|\textbf{D}) = \frac{P(\textbf{D} |\theta) P(\theta) }{P(\textbf{D})} \end{equation}But what is $\theta \;$?¶
$\theta$ is an unknown yet fixed set of parameters. In Bayesian inference we express our belief about what $\theta$ might be and instead of trying to guess $\theta$ exactly, we look for its probability distribution. What that means is that we are looking for the parameters of that distribution. For example, for a Poisson distribution our $\theta$ is only $\lambda$. In a normal distribution, our $\theta$ is often just $\mu$ and $\sigma$.
3. Bayesian Regression with pyMC3
¶
PyMC3 is a Python library for programming Bayesian analysis, and more specifically, data creation,
model definition, model fitting, and posterior analysis. It uses the concept of a model
which contains assigned parametric statistical distributions to unknown quantities in the model.
Within models we define random variables and their distributions. A distribution requires at least a
name
argument, and other parameters
that define it. You may also use the
logp()
method in the model to build the model log-likelihood function. We define and
fit the model.
PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model
building blocks. They are not meant to be used outside of a model
, and you can invoke
them by using the prefix pm
, as in pm.Normal
.
For more see: PyMC3 Quickstart
Distributions in PyMC3
:¶
- Statistical Distributions in pyMC3.
Information about PyMC3 functions including descriptions of distributions, sampling methods, and
other functions, is available via the help
command.
#help(pm.Beta)
Defining a Model in PyMC3¶
Our problem is the following: we want to perform multiple linear regression to predict an outcome variable $Y$ which depends on variables $\bf{x}_1$ and $\bf{x}_2$.
We will model $Y$ as normally distributed observations with an expected value $mu$ that is a linear function of the two predictor variables, $\bf{x}_1$ and $\bf{x}_2$.
\begin{equation} Y \sim \mathcal{N}(\mu,\,\sigma^{2}) \end{equation} \begin{equation} \mu = \beta_0 + \beta_1 \bf{x}_1 + \beta_2 x_2 \end{equation}where $\sigma^2$ represents the measurement error (in this example, we will use $\sigma^2 = 10$)
We also choose the parameters to have normal distributions with those parameters set by us.
\begin{eqnarray} \beta_i \sim \mathcal{N}(0,\,10) \\ \sigma^2 \sim |\mathcal{N}(0,\,10)| \end{eqnarray}
We will artificially create the data to predict on. We will then see if our model predicts them
correctly.
Let's create some synthetic data¶
np.random.seed(123)
###################################
## Hidden true parameter values
sigma = 1
beta0 = 1
beta = [1, 2.5]
####################################
# Size of dataset
size = 100
# Predictor variable
x1 = np.linspace(0, 1., size)
x2 = np.linspace(0,2., size)
# Simulate outcome variable
Y = beta0 + beta[0]*x1 + beta[1]*x2 + np.random.randn(size)*sigma
Y.shape, x1.shape, x2.shape
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
fontsize=14
labelsize=8
title='Observed Data (created artificially by ' + r'$Y(x_1,x_2)$)'
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x1, x2, Y)
ax.set_xlabel(r'$x_1$', fontsize=fontsize)
ax.set_ylabel(r'$x_2$', fontsize=fontsize)
ax.set_zlabel(r'$Y$', fontsize=fontsize)
ax.tick_params(labelsize=labelsize)
fig.suptitle(title, fontsize=fontsize)
fig.tight_layout(pad=.1, w_pad=10.1, h_pad=2.)
fig.subplots_adjust(); #top=0.5
plt.tight_layout
plt.show()
Building the model¶
Step1: Formulate the probability model for our data: $Y \sim \mathcal{N}(\mu,\,\sigma^{2})$
Y_obs = pm.Normal('Y_obs', mu=mu, sd=sigma, observed=Y)
Step2: Choose a prior distribution for our unknown parameters.
beta0 = Normal('beta0', mu=0, sd=10)
# Note: betas is a vector of two variables, b1 and b2, (denoted by shape=2)
# so, in array notation, our beta1 = betas[0], and beta2=betas[1]
betas = Normal('betas', mu=0, sd=10, shape=2)
sigma = HalfNormal('sigma', sd=1)
Step3: Construct the likelihood function.
Step4: Determine the posterior distribution, this is our main goal.
Step5: Summarize important features of the posterior and/or plot the parameters.
with pm.Model() as my_linear_model:
# Priors for unknown model parameters, specifically created stochastic random variables
# with Normal prior distributions for the regression coefficients,
# and a half-normal distribution for the standard deviation of the observations.
# These are our parameters.
# I need to give the prior some initial values for the parameters
mu0 = 0
sd0 = 10
beta0 = pm.Normal('beta0', mu=mu0, sd=sd0)
# Note: betas is a vector of two variables, b1 and b2, (denoted by shape=2)
# so, in array notation, our beta1 = betas[0], and beta2=betas[1]
mub = 0
sdb = 10
betas = pm.Normal('betas', mu=mub, sd=sdb, shape=2)
sds = 1
sigma = pm.HalfNormal('sigma', sd=sds)
# mu is what is called a deterministic random variable, which implies that
# its value is completely determined by its parents’ values
# (betas and sigma in our case). There is no uncertainty in the
# variable beyond that which is inherent in the parents’ values
mu = beta0 + betas[0]*x1 + betas[1]*x2
# Likelihood function = how probable is my observed data?
# This is an observed variable; it is identical to a standard
# stochastic variable, except that its observed argument,
# which passes the data to the variable, indicates that the values for this
# variable were observed, and should not be changed by any
# fitting algorithm applied to the model.
# The data can be passed in the form of a numpy.ndarray or pandas.DataFrame object.
Y_obs = pm.Normal('Y_obs', mu=mu, sd=sigma, observed=Y)
my_linear_model.basic_RVs
my_linear_model.free_RVs
Note: If our problem was a classification for which we would use Logistic regression.
## do not worry if this does not work, it's just a nice graph to have
## you need to install python-graphviz first
# conda install -c conda-forge python-graphviz
pm.model_to_graphviz(my_linear_model)
Now all we need to do is sample our model.
... to be continued¶
Appendix A: Bayesian Logistic Regression
with pyMC3
¶
If the problem above was a classification that required a Logistic Regression, we would use the logistic function ( where $\beta_0$ is the intercept, and $\beta_i$ (i=1, 2, 3) determines the shape of the logistic function).
\begin{equation} Pr(Y=1|X_1,X_2,X3) = {\frac{1}{1 + exp^{-(\beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_3)}}} \end{equation}Since both $\beta_0$ and the $\beta_i$s can be any possitive or negative number, we can model them as gaussian random variables.
\begin{eqnarray} \beta_0 \sim \mathcal{N}(\mu,\,\sigma^2) \\ \beta_i \sim \mathcal{N}(\mu_i,\,\sigma_i^2) \end{eqnarray}In PyMC3 we can model those as:
pm.Normal('beta_0', mu=0, sigma=100)
(where $\mu$ and $\sigma^2$ can have some initial values that we assign them, e.g. 0 and 100)
The dererministic variable would be:
logitp = beta0 + beta_1 * X_1 + beta_2 * X_2 + beta_3 * X_3
To connect this variable (logit-p) with our observed data, we would use a Bernoulli as our likelihood.
our_likelihood = pm.Bernoulli('our_likelihood', logit_p=logitp, observed=our_data)
Notice that the main difference with Linear Regression is the use of a Bernoulli distribution instead of a Gaussian distribution, and the use of the logistic function instead of the identity function.
# A reminder of what the logistic function looks like.
# Change parameters a and b to see the shape of the curve change
b = 5.
x = np.linspace(-8, 8, 100)
plt.plot(x, 1 / (1 + np.exp(-b*x)))
plt.xlabel('y')
plt.ylabel('y=logistic(x)')
Appendix B: Is this a fair coin?¶
Is this a fair coin?¶
Let's say you visit the casino in Monte Carlo. You want to test your theory that
casinos are dubious places where coins have been manipulated to have a larger probability for tails.
So you will try to estimate how fair a coin is based on a certain amount of flips.
You have no prior opinion on the coin's fairness (i.e. what $p$ might be), and begin flipping the
coin. You get either Heads ($H$) or Tails ($T$) as our observed data and want to see if your
posterior probabilities change as you obtain more data, that is, more coin flips. A nice way to
visualize this is to plot the posterior probabilities as we observe more flips (data).
We will be using Bayes rule. $\textbf{D}$ is our data.
\begin{equation} P(\theta|\textbf{D}) = \frac{P(\textbf{D} |\theta) P(\theta) }{P(\textbf{D})} \end{equation}We start with a non-informative prior, a Beta distribution with (a=b=1.)
\begin{equation} P(\theta|\textbf{k=0}) = Beta(1., 1.) \end{equation}Then, as we get new data (say, we observe $k$ heads in $n$ tosses), we update our Beta with new a,b as follows:
\begin{equation} P(\theta|\textbf{k}) = Beta(\alpha + \textbf{k}, \beta + (n - \textbf{k})) \end{equation}(the proof is beyond our scope, if interested, see this Wikipedia article)
we can say that $\alpha$ and $\beta$ play the roles of a "prior number of heads" and "prior number of tails".
# play with the priors - here we manually set them but we could be sampling from a separate Beta
trials = np.array([0, 1, 3, 5, 10, 15, 20, 100, 200, 300])
heads = np.array([0, 1, 2, 4, 8, 10, 10, 50, 180, 150])
x = np.linspace(0, 1, 100)
# for simplicity we set a,b=1
plt.figure(figsize=(10,8))
for k, N in enumerate(trials):
sx = plt.subplot(len(trials)/2, 2, k+1)
posterior = stats.beta.pdf(x, 1 + heads[k], 1 + trials[k] - heads[k])
plt.plot(x, posterior, alpha = 0.5, label=f'{trials[k]} tosses\n {heads[k]} heads');
plt.fill_between(x, 0, posterior, color="#348ABD", alpha=0.4)
plt.legend(loc='upper left', fontsize=10)
plt.legend()
plt.autoscale(tight=True)
plt.suptitle("Posterior probabilities for coin flips", fontsize=15);
plt.tight_layout()
plt.subplots_adjust(top=0.88)
References¶
- Salvatier J, Wiecki TV, Fonnesbeck C. 2016. Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 (https://doi.org/10.7717/peerj-cs.55)
- Distributions in PyMC3
- More Details on Distributions
This linear regression example is from the original paper on PyMC3: Salvatier J, Wiecki TV, Fonnesbeck C. 2016. Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 https://doi.org/10.7717/peerj-cs.55
Cool Reading¶
- How Bayesian Analysis and Lawrence D. Stone found the Wreckage of Air France Flight AF 447.
- Search for the gold on the sunken SS Central America.


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dbm.FluidMixture¶
-
class
dbm.
FluidMixture
(composition, delta=None, user_data={}, delta_groups=None, isair=False, sigma_correction=array([[1.], [1.]]))[source]¶ Class object for a fluid mixture
This object defines the behavior of a fluid mixture defined as a standard thermodynamic system. The mixture may contain just liquid phase, a gas and liquid phase together, or a pure gas phase. The Peng-Robinson equation of state returns the properties of each phase in the mixture. If the mixture is pure liquid or pure gas, the properties of each phase will be the same; otherwise, the gas properties will be in the first row of all two-dimensional return variables and the liquid properties in the second row.
- Parameters
- compositionstring list, length nc
Contains the names of the chemical components in the mixture using the same key names as in ./data/ChemData.csv
- deltandarray, size (nc, nc)
Binary interaction coefficients for the Peng-Robinson equation of state. If not passed at instantiation, Python will assume a full matrix of zeros.
- user_datadict
A dictionary of chemical property data. If not specified, the data loaded from /tamoc/data/ChemData.csv by
chemical_properties
will be used. To load a different properties database, use thechemical_properties.load_data
function to load in a custom database, and pass that data to this object as user_data.- delta_groupsndarray (nc, 15)
Provides the group contribution numbers (normalized) for each component in the mixture for the 15 groups used by the Privat and Jaubert (2012) group contribution method for binary interaction coefficients. Default is None, in which case the values in delta will be used.
- airbool
Flag indicating whether or not fluid is air. The methods for viscosity and interfacial tension below use correlations developed for hydocarbons. If air is False (default value), these built in methods are used. If air is True, then these methods are replaced with correlations between air and seawater.
- sigma_correctionndarray, default = np.array([[1], [1]])
Correction factor to adjust the interfacial tension value supplied by the default model to a value measured for the mixture of interest. The correction factor should be computed as sigma_measured / sigma_model at a single P and T value. sigma_correction should be a two-element array, the first element is for the gas phase and the second element is for the liquid phase.
See also
Examples
>>> air = FluidMixture(['nitrogen', 'oxygen']) >>> yk = np.array([0.79, 0.21]) >>> m = air.masses(yk) >>> air.density(m, 273.15+10., 101325.) array([[ 1.24260911], [ 1.24260911]])
- Attributes
- ncinteger
Number of chemical components in the mixture
- issolublelogical, True
Indicates the object contents are soluble
- Mndarray, size (nc)
Molecular weights (kg/mol)
- Pcndarray, size (nc)
Critical pressures (Pa)
- Tcndarray, size (nc)
Critical temperatures (K)
- omegandarray, size (nc)
Acentric factors (–)
- kh_0ndarray, size (nc)
Henry’s law constants at 298.15 K and 101325 Pa (kg/(m^3 Pa))
- neg_dH_solRndarray, size (nc)
The negative of the enthalpies of solution / Ru (K).
- nu_barndarray, size (nc)
Partial molar volumes at infinite dilution (m^3/mol)
- Bndarray, size (nc)
White and Houghton (1966) pre-exponential factor (m^2/s)
- dEndarray, size (nc)
Activation energy (J/mol)
- K_saltSetschenow constants (m^3/mol)
Methods
biodegradation_rate
(self, t[, lag_time])Determine the biodegradation rate constant
density
(self, m, T, P)Compute the gas and liquid density (kg/m^3) of a fluid mixture at the given state.
diffusivity
(self, Ta, Sa, P)Compute the diffusivity (m^2/s) of each component of a mixture into seawater at the given temperature.
equilibrium
(self, m, T, P[, K])Computes the equilibrium composition of a gas/liquid mixture.
fugacity
(self, m, T, P)Compute the gas and liquid fugacity (Pa) of a fluid mixture at the given state.
hydrate_stability
(self, m, P)Compute the hydrate formation temperature at the given pressure
interface_tension
(self, m, T, S, P)Computes the interfacial tension between gas/liquid and water
mass_frac
(self, n)Calculate the mass fraction (–) from the number of moles of each component in a mixture.
masses
(self, n)Convert the moles of each component in a mixture to their masses (kg).
mol_frac
(self, m)Calcualte the mole fraction (–) from the masses of each component in a mixture.
moles
(self, m)Convert the masses of each component in a mixture to their moles (mol).
partial_pressures
(self, m, P)Compute the partial pressure (Pa) of each component in a mixture.
solubility
(self, m, T, P, Sa)Compute the solubility (kg/m^3) of each component of a mixture in both gas and liquid dissolving into seawater.
viscosity
(self, m, T, P)Computes the dynamic viscosity of the gas/liquid mixture.
-
FluidMixture.__init__(self, composition, delta=None, user_data={}, delta_groups=None, isair=False, sigma_correction=array([[1.],
-
[1.]]))
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
())Initialize self.
biodegradation_rate
(self, t[, lag_time])Determine the biodegradation rate constant
density
(self, m, T, P)Compute the gas and liquid density (kg/m^3) of a fluid mixture at the given state.
diffusivity
(self, Ta, Sa, P)Compute the diffusivity (m^2/s) of each component of a mixture into seawater at the given temperature.
equilibrium
(self, m, T, P[, K])Computes the equilibrium composition of a gas/liquid mixture.
fugacity
(self, m, T, P)Compute the gas and liquid fugacity (Pa) of a fluid mixture at the given state.
hydrate_stability
(self, m, P)Compute the hydrate formation temperature at the given pressure
interface_tension
(self, m, T, S, P)Computes the interfacial tension between gas/liquid and water
mass_frac
(self, n)Calculate the mass fraction (–) from the number of moles of each component in a mixture.
masses
(self, n)Convert the moles of each component in a mixture to their masses (kg).
mol_frac
(self, m)Calcualte the mole fraction (–) from the masses of each component in a mixture.
moles
(self, m)Convert the masses of each component in a mixture to their moles (mol).
partial_pressures
(self, m, P)Compute the partial pressure (Pa) of each component in a mixture.
solubility
(self, m, T, P, Sa)Compute the solubility (kg/m^3) of each component of a mixture in both gas and liquid dissolving into seawater.
viscosity
(self, m, T, P)Computes the dynamic viscosity of the gas/liquid mixture.


[1]
very efficient and quiet aircon , hot water shower , but a bit slow in operation .
[2]
a blessing to some as the noise level in the resort was nice and low .


{% extends "layout.html" %} {% block body %} {% include "forms/csv_upload.html" %}
Wikimetrics expects either a file or pasted text in comma separated format, as shown below. Project is the langauge or project code in which that username or userid exists. Project is optional and will default to the cohort's default project.
user_name,project
user_id,project
Evan (WMF),en Milimetric,en Without project is ok too User Names can even contain commas, but these need a project at the end,enOR User Ids:
123,en 234,en 456


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