We often discuss dynamic documents such as Sweave
and knitr
in reference to final products such as publications or software package vignettes. In this case, all the elements involved are already fixed: external functions, code, text, and so forth. The dynamic documentation engine is really just a tool to combine them (knit them together). Using dynamic documentation on a day-to-day basis on ongoing research presents a compelling opportunity but a rather more complex challenge as well. The code base grows, some of it gets turned into external custom functions where it continues to change. One analysis script branches into multiple that vary this or that. The text and figures are likewise subject to the same revision as the code, expanding and contracting, or being removed or shunted off into an appendix.
Structuring a dynamic document when all the parts are morphing and moving is one of the major opportunities for the dynamic approach, but also the most challenging. Here I describe some of those challenges along with various tricks I have adopted to deal with them, mostly in hopes that someone with a better strategy might be inspired to fill me in.
The old way
For a while now I have been using the knitr dynamic documentation/reproducible research software for my project workflow. Most discussion of dynamic documentation focuses on ‘finished’ products such as journal articles or reports. Over the past year, I have found the dynamic documentation framework to be particularly useful as I develop ideas, and remarkably more challenging to then integrate into a final paper in a way that really takes advantage of its features. I explain both in some detail here.
My former workflow followed a pattern no doubt familiar to many:
- Bash away in an R terminal, paste useful bits into an R script…
- Write manuscript separately, pasting in figures, tables, and in-line values returned from R.
This doesn’t leave much of a record of what I did or why, which is particularly frustrating when some discussion reminds me of an earlier idea.
Dynamic docs: .Rmd
files
When I begin a new project, I now start off writing a .Rmd
file, intermixing notes to myself and code chunks. Chunks break up the code into conceptual elements, markdown gives me a more expressive way to write notes than comment lines do. Output figures, tables, and in-line values inserted. So far so good. I version manage this creature in git/Github. Great, now I have a trackable history of what is going on, and all is well:
Document my thinking and code as I go along on a single file scratch-pad
Version-stamped history of what I put in and what I got out on each step of the way
Rich markup with equations, figures, tables, embedded.
Caching of script chunks, allowing me to tweak and rerun an analysis without having to execute the whole script. While we can of course duplicate that behavior with careful save and load commands in a script, in knitr this comes for free.
Limitations to .Rmd alone
As I go along, the
.Rmd
files starts getting too big and cluttered to easily follow the big picture of what I’m trying to do.Before long, my investigation branches. Having followed one
.Rmd
script to some interesting results, I start a new.Rmd
script representing a new line of investigation. This new direction will nevertheless want to re-use large amounts of code from the first file.
A solution? The R package “research compendium” approach
I start abstracting tasks performed in chunks into functions, so I can re-use these things elsewhere, loop over them, and document them carefully somewhere I can reference that won’t be in the way of what I’m thinking. I start to move these functions into R/
directory of an R package structure, documenting with Roxygen
. I write unit tests for these functions (in inst/tests
) to have quick tests to check their sanity without running my big scripts (recent habit). The package structure helps me:
- Reuse the same code between two analyses without copy-paste or getting our of sync
- Document complicated algorithms outside of my working scripts
- Test complicated algorithms outside of my working scripts (
devtools::check
and/or unit tests) - Manage dependencies on other packages (DESCRIPTION, NAMESPACE), including other projects of mine
This runs into trouble in several ways.
Problem 1: Reuse of code chunks
What to do with code I want to reuse across blocks but do not want to write as a function, document, or test?
Perhaps this category of problem doesn’t exist, except in my laziness.
This situation arises all the time, usually through the following mechanism: almost any script performs several steps that are best represented as chunks calling different functions, such as load_data
, set_fixed_parameters
, fit_model
, plot_fits
, etc. I then want to re-run almost the same script, but with a slightly different configuration (such as a different data set or extra iterations in the fixed parameters). For just a few such cases, it doesn’t make sense to write these into a single function,1 instead, I copy this script to a new file and make the changes there.
This is great until I want to change something in about the way both scripts behave that cannot be handled just by changing the R/
functions they share. Plotting options are a good example of this (I tend to avoid wrapping ggplot
calls as separate functions, as it seems to obfuscate what is otherwise a rather semantic and widely recognized, if sometimes verbose, function call).
I have explored using knitr
’s support for external chunk inclusion, which allows me to maintain a single R script with all commonly used chunks, and then import these chunks into multiple .Rmd
files. An example of this can be seen in my nonparametric-bayes
repo, where several files (in the same directory) draw most of their code from external-chunks.R.
Problem 2: package-level reproducibility
Minor/relatively easy to fix.
Separate files can frustrate reproducibility of a given commit. As I change the functions in R/
, the .Rmd
file can give different results despite being unchanged. (Or fail to reflect changes because it is caching chunks and does not recognize the function definitions have changed underneath it). Git provides a solution to this: since the .Rmd
file lives in the same git repository (inst/examples
) as the package, I can make sure the whole repository matches the hash of the .Rmd
file: install_github("packagename", "cboettig", "hash")
.
This solution is not fail-safe: the installed version, the potentially uncommitted (but possibly installed) version of the R functions in the working directory, and the R functions present at the commit of the .Rmd
file (and thus matching the hash) could all be different. If we commit and install before every knit
, we can avoid these potential errors (at the cost of some computational overhead), restoring reproducibility to the chain.
Problem 3: Synthesizing results into a manuscript
In some ways this is the easiest part, since the code-base is relatively static and it is just a matter of selecting which results and figures to include and what code is necessary to generate it. A few organizational challenges remain:
While we generally want knitr
code chunks for the figures and tables that will appear, we usually aren’t interested in displaying much, if any, of the actual code in the document text (unlike the examples until this point, where this was a major advantage of the knitr approach). In principle, this is as simple as setting echo=FALSE
in the global chunk options. In practice, it means there is little benefit to having the chunks interwoven in the document. What I tend to want is having all the chunks run at the beginning, such that any variables or results can easily be added (and their appearance tweaked by editing the code) as figure chunks or in-line expressions. The only purpose of maintaining chunks instead of a simple script is the piecewise caching of chunk dependencies which can help debugging.
Since displaying the code is suppressed, we are then left with the somewhat ironic challenge of how best to present code as a supplement. One option is simply to point to the source .Rmd
, another is to use the tangle()
option to extract all the code as a separate .R
file. In either case, the user must also identify the correct version of the R package itself for the external R/
functions.
Problem 4: Branching into other projects
Things get most complicated when projects begin to branch into other projects. In an ideal world this is simple: a new idea can be explored on a new branch of the version control system and merged back in when necessary, and an entirely new project can be built as a new R package in a different repo that depends on the existing project. After several examples of each, I have learned that it is not so simple. Despite the nice tools, I’ve learned I still need to be careful in managing my workflows in order to leave behind material that is understandable, reproducible, and reflects clear provenance. So far, I’ve learned this the hard way. I use this last section of the post to reflect on two of my own examples, as writing this helps me work through what I should have done differently.
example: warning-signals project
For instance, my work on early warning signals dates back to the start of my open notebook on openwetware, when my code lived on a Google code page which seems to have disappeared. (At the time it was part of my ‘stochastic population dynamics’ project). When I moved to Github, this project got it’s own repository, warningsignals, though after a major re-factorization of the code I moved to a new repository, earlywarning. Okay, so far that was due to me not really knowing what I was doing.
My first paper on this topic was based on the master branch of that repository, which still contains the code required. When one of the R dependencies was moved from CRAN I was able to update the codebase to reflect the replacement package (see issue #10). Even before that paper appeared I started exploring other issues on different branches, with the prosecutor
branch eventually becoming it’s own paper, and then it’s own repository.
That paper sparked a comment letter in response to it, and the analysis involved in my reply piece was just developed on the same master branch of the prosecutor-fallacy repository. This leaves me with a total of three repositories across four branches, with one repo that corresponds more-or-less directly to a paper, one to two papers, and one to no papers.
All four branches have diverged and unmerge-able code. Despite sharing and reusing functions across these projects, I often found it better to simply change the function on the new branch or new repo as I desired for the new work. These changes could not be easily merged back as they broke the original function calls of the earlier work.
Hindsight being 20-20, it would have been preferable that I had maintained one repository, perhaps developed each paper on a different branch and clearly tagged the commit corresponding to the submission of each publication. Ideally these could be merged back where possible to a master branch. Tagged commits provide a more natural solution than unmerged branches to deal with changes to the package that would break methods from earlier publications.
example: optimal control projects
A different line of research began through a NIMBioS working group called “Pretty Darn Good Control”, beginning it’s digital life in my pdg_control repository. Working in different break-out groups as well as further investigation on my own soon created several different projects. Some of these have continue running towards publication, others terminating in dead ends, and still others becoming completely separate lines of work. Later work I have done in optimal control, such nonparametric-bayes and multiple_uncertainty depend on this package for certain basic functions, though both also contain their own diverged versions of functions that first appeared in pdg_control.
Because the topics are rather different and the shared code footprint is quite small, separate repositories probably makes more sense here. Still, managing the code dependencies in separate repositories requires extra care, as checking out the right version of the focal repository does not guarantee that one will also have the right version of the [pdg_control] repository. Ideally I should note the hash of [pdg_control] on which I depend, and preferably install that package at that hash (easy enough thanks to devtools
), since depending on a separate project that is also still changing can be troublesome. Alternatively it might make more sense to just duplicate the original code and remove this potentially frail dependency. After all, documenting the provenance need not rely on the dependency, and it is more natural to think of these separate repos as divergent forks.
If I have a lot of different configurations, it may make sense to wrap up all these steps into a single function that takes input data and/or parameters as it’s argument and outputs a data frame with the results and inputs.↩


| TIFFReadRGBATile - read and decode an image tile into a fixed-format raster |
#include <tiffio.h> #define TIFFGetR(abgr) ((abgr) & 0xff) #define TIFFGetG(abgr) (((abgr) >> 8) & 0xff) #define TIFFGetB(abgr) (((abgr) >> 16) & 0xff) #define TIFFGetA(abgr) (((abgr) >> 24) & 0xff) int TIFFReadRGBATile(TIFF* tif, uint32 x, uint32 y, uint32 * raster ) |
| TIFFReadRGBATile reads a single tile of a tile-based image into memory, storing the result in the user supplied RGBA raster. The raster is assumed to be an array of width times length 32-bit entries, where width is the width of a tile (TIFFTAG_TILEWIDTH) and length is the height of a tile (TIFFTAG_TILELENGTH). |
| The x and y values are the offsets from the top left corner to the top left corner of the tile to be read. They must be an exact multiple of the tile width and length. |
| Note that the raster is assume to be organized such that the pixel at location (x,y) is raster[y*width+x]; with the raster origin in the lower-left hand corner of the tile. That is bottom to top organization. Edge tiles which partly fall off the image will be filled out with appropriate zeroed areas. |
| Raster pixels are 8-bit packed red, green, blue, alpha samples. The macros TIFFGetR, TIFFGetG, TIFFGetB, and TIFFGetA should be used to access individual samples. Images without Associated Alpha matting information have a constant Alpha of 1.0 (255). |
| See the TIFFRGBAImage(3T) page for more details on how various image types are converted to RGBA values. |
| Samples must be either 1, 2, 4, 8, or 16 bits. Colorimetric samples/pixel must be either 1, 3, or 4 (i.e. SamplesPerPixel minus ExtraSamples). |
| Palette image colormaps that appear to be incorrectly written as 8-bit values are automatically scaled to 16-bits. |
| TIFFReadRGBATile is just a wrapper around the more general TIFFRGBAImage(3T) facilities. It's main advantage over the similar TIFFReadRGBAImage() function is that for large images a single buffer capable of holding the whole image doesn't need to be allocated, only enough for one tile. The TIFFReadRGBAStrip() function does a similar operation for stripped images. |
| 1 is returned if the image was successfully read and converted. Otherwise, 0 is returned if an error was encountered. |
| All error messages are directed to the TIFFError(3T) routine. |
| Sorry, can not handle %d-bit pictures. The image had BitsPerSample other than 1, 2, 4, 8, or 16. |
| Sorry, can not handle %d-channel images. The image had SamplesPerPixel other than 1, 3, or 4. |
| Missing needed "PhotometricInterpretation" tag. The image did not have a tag that describes how to display the data. |
| No "PhotometricInterpretation" tag, assuming RGB. The image was missing a tag that describes how to display it, but because it has 3 or 4 samples/pixel, it is assumed to be RGB. |
| No "PhotometricInterpretation" tag, assuming min-is-black. The image was missing a tag that describes how to display it, but because it has 1 sample/pixel, it is assumed to be a grayscale or bilevel image. |
| No space for photometric conversion table. There was insufficient memory for a table used to convert image samples to 8-bit RGB. |
| Missing required "Colormap" tag. A Palette image did not have a required Colormap tag. |
| No space for tile buffer. There was insufficient memory to allocate an i/o buffer. |
| No space for strip buffer. There was insufficient memory to allocate an i/o buffer. |
| Can not handle format. The image has a format (combination of BitsPerSample, SamplesPerPixel, and PhotometricInterpretation) that TIFFReadRGBAImage can not handle. |
| No space for B&W mapping table. There was insufficient memory to allocate a table used to map grayscale data to RGB. |
| No space for Palette mapping table. There was insufficient memory to allocate a table used to map data to 8-bit RGB. |
| libtiff(3T), TIFFOpen(3T), TIFFRGBAImage(3T), TIFFReadRGBAImage(3T), TIFFReadRGBAStrip(3T) |


Money Play. How should Red play 54?
pips Red=157 White=150
(CorrectAnswer= 0/0, ErrorScore= 0)
When 64 is played 8/2, 6/2 in the opening many people don’t know the correct responses. This also applies to first few moves of the game.
This game started with both players moving 8/2, 6/2 and then White rolling 61. It looks intuitive to play 24/15 or 24/20, 13/8 with Red’s 54 but this allows White to employ the tactic that he wants to use after making his 2-pt. That tactic is to attack as the priming option is an unlikely game plan.
Very clear here is 13/9, 13/8 which thwarts White’s plan and develops Red’s position.
Note that if both players had opened with 53 and not 64 then the right move would be 24/20, 13/8. Interesting ne c’est pas?
This game started with both players moving 8/2, 6/2 and then White rolling 61. It looks intuitive to play 24/15 or 24/20, 13/8 with Red’s 54 but this allows White to employ the tactic that he wants to use after making his 2-pt. That tactic is to attack as the priming option is an unlikely game plan.
Very clear here is 13/9, 13/8 which thwarts White’s plan and develops Red’s position.
Note that if both players had opened with 53 and not 64 then the right move would be 24/20, 13/8. Interesting ne c’est pas?
1. Cubeful 2-ply 13/9 13/8 Eq.: -0.139 0.460 0.154 0.007 - 0.540 0.170 0.009 2. Cubeful 2-ply 24/20 13/8 Eq.: -0.184 (-0.046) 0.462 0.136 0.006 - 0.538 0.189 0.007 3. Cubeful 2-ply 24/15 Eq.: -0.186 (-0.047) 0.463 0.129 0.005 - 0.537 0.196 0.006 4. Cubeful 2-ply 13/4 Eq.: -0.249 (-0.110) 0.438 0.134 0.006 - 0.562 0.185 0.012 5. Cubeful 0-ply 13/8 6/2 Eq.: -0.329 (-0.190) 0.410 0.118 0.006 - 0.590 0.190 0.009


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Overview of Ambient Module¶
| Release: | 1.1 |
|---|---|
| Date: | Nov 08, 2017 |
Ambient Module¶
Define functions, classes, and methods to handle ambient sewater data
This module defines functions to read in arbitrary format ambient data files
(e.g., CTD profiles), manipulate the data to extract profiles with monotonic
depth coordinate values, and adjust parameter units to standard units. It also
defines functions and classes to store the measured CTD data in
netCDF4-classic format following NODC guidelines and to manipulate the data
for use by simulation models in TAMOC
. These manipulations include
interpolation methods, the ability to add or remove data from the database,
including the addition of synthetic data when needed, and to extend a profile
to deeper depths using a rational means of maintaining the stratification
structure.
These methods are particularly useful to rapidly create ambient profile
databases for use by TAMOC
and for archiving data obtained from arbitrary
formats in standard format netCDF4-classic files. These methods also allow
seemless coupling of TAMOC
simulation modules with general ocean
circulation models or Lagrangian particle tracking models that store their
seawater properties data in netCDF format.
See Also¶
netCDF4
:- Package for creating and manipulating netCDF datasets
datetime
:- Package to create and manipute dates
numpy.fromfile
:- Read data from a simple text file in single table format. Similar to the Matlab load function. Does not support comments or skipped lines. Works for text or binary files.
numpy.loadtxt
:- A more sophisticated method to read data from a text file. This method can handle multiple data types (e.g., strings, floats, and integers together), can perform transformation on the data (e.g., date format to a date object), and can unpack to a tuple of variables. One limitation is that all rows must have the same number of columns and there is no method to handle missing data.
numpy.genfromtxt
:- The most advanced numpy method to read data from a file. Includes the
capabilities in
numpy.loadtxt
, but also allows for missing data, data flags, and multiple methods to replace missing data or flags.
Class Objects¶
-
class
ambient.
Profile
(nc, ztsp=['z', 'temperature', 'salinity', 'pressure'], chem_names=None, err=0.01)[source][source]¶ Class object for ambient seawater profiles
This object collects the data describing the ambient seawater (e.g., CTD data, currents, etc.) and provides efficient access to interpolated results at arbitrary depths. All of the raw data are stored in a netCDF4-classic format dataset, which resides on the hard-drive. Profile objects can be initiated either from an open netCDF dataset object or from a file path to the desired object. The netCDF dataset is expected to have variables for ‘z’, ‘temperature’, ‘salinity’, and ‘pressure’ and any other variables requested at instantiation through the chem_names variable.
Parameters: nc : netCDF dataset object, path, or str
Provides the information necessary to access the netCDF dataset. If a file path or string is provided, then the netCDF file is opened and the resulting dataset object is stored in self.nc.
ztsp : str list
String list containing the variables names for depth, temperature, salinity, and pressure that are used in the netCDF dataset.
chem_names : str list, optional
Names of the chemicals (e.g., those constituents in addition to z, T, S, P) in the netCDF dataset that should be accessible through the self.get_values interpolation method or the self.get_units interrogator. If chem_names = ‘all’, then all variables in the netCDF file except for ‘time’, ‘lat’, ‘lon’, and the strings in ztsp will be loaded as ambient chemical data.
err : float
The interpolation dataset is a subset of the complete raw dataset stored in the netCDF file. err sets the acceptable level of relative error using linear interpolation expected of the self.get_values method. This value is passed to the
coarsen
function to provide an optimal interpolation dataset.See also
netCDF4
,create_nc_db
,fill_nc_db
,coarsen
,chemical_properties
Examples
>>> bm54 = Profile('./test/output/test_BM54.nc', chem_names='all') >>> print bm54.nc.variables.keys() ['time', 'lat', 'lon', 'z', 'temperature', 'salinity', 'pressure', 'T', 'wetlab_fluorescence', 'S', 'density', 'oxygen'] >>> bm54.get_values(1000.0, ['temperature', 'salinity', 'pressure']) array([ 2.78274540e+02, 3.49278396e+01, 1.01933088e+07]) >>> bm54.get_units('oxygen') ['kg/m^3'] >>> bm54.buoyancy_frequency(1500.) 0.00081815 >>> bm54.nc_close()
Attributes
nc_open (bool) Flag stating whether or not the netCDF dataset is open or closed nchems (int) Number of chemicals in chem_names z (ndarray) Array containing the complete raw dataset of depths y (ndarray) Array containing the complete raw dataset for T, S, P, and chemicals in chem_names. f_names (str list) concatenated string list containing ztsp and chem_names f_units (str list) List of units associated with the variables stored in f_names. f (object) scipy.interpolate.interp1d
object containing z and y.Methods
append
(data, var_symbols, var_units, comments)Add data to the netCDF dataset and update the object attributes build_interpolator
()Build the interpolator function from the netCDF dataset buoyancy_frequency
(z[, h])Calculate the local buoyancy frequency close_nc
()Close the netCDF dataset extend_profile_deeper
(z_new, nc_name[, h_N, …])Extend the CTD profile to the depth z_new using a fixed buoyancy get_units
(names)Return a list of units for the variables in names get_values
(z, names)Return values for the variables listed in names interpolated to the
Module Functions¶
create_nc_db (nc_file, summary, source, …) |
Create an empty netCDF4-classic dataset to store ambient data |
fill_nc_db (nc, data, var_symbols, var_units, …) |
Add data to a netCDF4-classic ambient profile dataset |
get_nc_db_from_roms (nc_roms, nc_file, t_idx, …) |
Create a netCDF4-classic dataset ready to use with TAMOC simulation models from ROMS netCDF output. |
extract_profile (data[, z_col, z_start, …]) |
Function to extract a CTD profile with monotonically increasing depth |
coarsen (raw[, err]) |
Reduce the size of a raw database for interpolation |
stabilize (raw) |
Force the density profile to be stable or neutral |
compute_pressure (z, T, S, fs_loc) |
Compute the pressure profile by integrating the density |
convert_units (data, units) |
Convert the values in data to standard units |
load_raw (fname) |
Read data from a text file. |


Deprecated: This plugin has been removed from the Jenkins Update Centre
This is built in to Jenkins core as of version 1.416 — CAPTCHA support is now optional.
Archived versions of this plugin remain available for download.
Plugin Information |
|---|
No information for the plugin 'security-no-captcha' is available. It may have been removed from distribution. |
Simply extends Jenkins' simple security to remove the CAPTCHA check.
Roadmap
Version 1.x
- Probably should deprecate this plugin and roll into the core security, but I didn't want to try and touch the core since it's "security".
- If this can't be rolled into the core security, then at least the core should be tweaked to be more extensible - e.g. make about 5 methods protected instead of private. The problem with that is then this plugin would depend on the very latest core, which is okay since Jenkins warns you not to upgrade.


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We make a living by what we get, but we make a life by what we give. "Winston Churchill"
Am currently in Moringa school pursuing a professional certificate in Software Development.
Am also a
student at Meru University of Science And Technology pursuing a degree in Bachelor of Science in Information
Technology.
I love using my computer a lot and I take it as my best friend.Coding is the most part of my passion.
Am
also good in the field,I swim,play football and badminton.
In the website we offer towing services,mechanical and passengers safety travel whenever a
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Visit my social media
“Life is trying things to see if they work.”
– Ray Bradbury


I am currently a PhD student with Dr. Robin Murphy at Texas A&M University.
My interests lay the intersection of machine learning and disaster response. Specifically, how to improve robustness and usability of automated systems in extreme environments.
I grew up in Las Vegas and I enjoy running, hiking, spending time outdoors and in my spare time I am also an Instrument Rated Private Pilot, Advanced EMT, and a Firefighter.
I graduated with my Masters from Carnegie Mellon's Language Technologies Insitutute and my Bachelors from Rensselaer Polytechnic Institute. My educational background is in NLP and ML and my graduate research included collaborations with Bosch GmbH, DARPA, and Boeing.
After graduate school I joined Microsoft where I split my time between product focused ML teams and collaborations with groups like the CDC and the WHO in development of machine learning and data management systems to respond to disasters like the COVID-19 pandemic.
Download CVExamination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation
Shao, Mantravadi, Manzini, Buendia, Knoertzer, Srinivasan, Quirk
Workshop on Natural Language Interfaces, Association for Computational Linguistics 2020
Seattle, Washington, USA
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings
Manzini, Lim, Tsvetkov, Black
NAACL-HLT ’19. The Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Minneapolis, Minnesota, USA
Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities
Pham, Liang, Manzini, Morency, Póczos
AAAI ’19. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
Honolulu, Hawaii, USA
Paper Presentation Poster Repository


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Nicklas Backstrom out vs. Rangers; Capitals do salary-cap gymnastics w
by Adam ZielonkaNicklas Backstrom is day-to-day with an upper-body injury and will miss the Washington Capitals’ game Wednesday night at the New York Rangers.
Forwards Carl Hagelin and Nic Dowd also will miss Wednesday’s game, and with a suspension likely looming for fellow forward Garnet Hathaway, the Capitals were forced to make a slew of roster moves to stay under the salary cap.
They called up four players from AHL Hershey: forwards Beck Malenstyn and Mike Sgarbossa, defenseman Tyler Lewington and goalie Ilya Samsonov. They returned goalie Vitek Vanecek to Hershey.
Washington also retroactively placed Hagelin on long-term injured reserve, meaning he must sit out a total of 10 games. The Capitals said he is eligible to be activated in time for their Dec. 3 game at the San Jose Sharks.
Dowd was placed on standard IR. He’s also considered day-to-day, so it likely signals that the Capitals feel he’ll be ready to play in their next game Saturday against Vancouver. Dowd has been sidelined by a cut on his hand.
Hathaway has a hearing Wednesday morning for spitting in an opponent’s face during a brawl in Monday’s game against the Ducks. His salary will still count against Washington’s cap if he is suspended, so the front office responded by making these moves, which will only affect the roster for a few games.
Malenstyn has yet to play in an NHL game, while Sgarbossa last appeared at the NHL level for the Florida Panthers in the 2016-17 season.
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 Here is some more text to increase the height of the column.
Barometer
Measure height above sea level.
Accelerometer
Measure linear accelerations.
Gyroscope
Measure 3D orientation.
Hygrometer
Measure humidity.
Compass
Measure magnetic north.


001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 package org.apache.commons.configuration.beanutils; 018 019 /** 020 *
 021 * The default implementation of the {@code BeanFactory} interface. 022 *
 023 *
 024 * This class creates beans of arbitrary types using reflection. Each time the 025 * {@code createBean()} method is invoked, a new bean instance is 026 * created. A default bean class is not supported. 027 *
 028 *
 029 * An instance of this factory class will be set as the default bean factory for 030 * the {@link BeanHelper} class. This means that if not bean 031 * factory is specified in a {@link BeanDeclaration}, this 032 * default instance will be used. 033 *
 034 * 035 * @since 1.3 036 * @author Commons 038 * Configuration team 039 * @version $Id: DefaultBeanFactory.java 1208758 2011-11-30 20:38:59Z oheger $ 040 */ 041 public class DefaultBeanFactory implements BeanFactory 042 { 043 /** Stores the default instance of this class. */ 044 public static final DefaultBeanFactory INSTANCE = new DefaultBeanFactory(); 045 046 /** 047 * Creates a new bean instance. This implementation delegates to the 048 * protected methods {@code createBeanInstance()} and 049 * {@code initBeanInstance()} for creating and initializing the bean. 050 * This makes it easier for derived classes that need to change specific 051 * functionality of the base class. 052 * 053 * @param beanClass the class of the bean, from which an instance is to be 054 * created 055 * @param data the bean declaration object 056 * @param parameter an additional parameter (ignored by this implementation) 057 * @return the new bean instance 058 * @throws Exception if an error occurs 059 */ 060 public Object createBean(Class<?> beanClass, BeanDeclaration data, 061 Object parameter) throws Exception 062 { 063 Object result = createBeanInstance(beanClass, data); 064 initBeanInstance(result, data); 065 return result; 066 } 067 068 /** 069 * Returns the default bean class used by this factory. This is always 070 * null for this implementation. 071 * 072 * @return the default bean class 073 */ 074 public Class<?> getDefaultBeanClass() 075 { 076 return null; 077 } 078 079 /** 080 * Creates the bean instance. This method is called by 081 * {@code createBean()}. It uses reflection to create a new instance 082 * of the specified class. 083 * 084 * @param beanClass the class of the bean to be created 085 * @param data the bean declaration 086 * @return the new bean instance 087 * @throws Exception if an error occurs 088 */ 089 protected Object createBeanInstance(Class<?> beanClass, BeanDeclaration data) 090 throws Exception 091 { 092 return beanClass.newInstance(); 093 } 094 095 /** 096 * Initializes the newly created bean instance. This method is called by 097 * {@code createBean()}. It calls the 098 * {@link BeanHelper#initBean(Object, BeanDeclaration) initBean()} 099 * of {@link BeanHelper} for performing the initialization. 100 * 101 * @param bean the newly created bean instance 102 * @param data the bean declaration object 103 * @throws Exception if an error occurs 104 */ 105 protected void initBeanInstance(Object bean, BeanDeclaration data) 106 throws Exception 107 { 108 BeanHelper.initBean(bean, data); 109 } 110 }


For internal reasons, Boost.Build sorts all the properties alphabetically. This means that if you write:
exe a : a.cpp : <include>b <include>a ;
then the command line with first mention the "a" include directory, and then "b", even though they are specified in the opposite order. In most cases, the user doesn't care. But sometimes the order of includes, or other properties, is important. For example, if one uses both the C++ Boost library and the "boost-sandbox" (libraries in development), then include path for boost-sandbox must come first, because some headers may override ones in C++ Boost. For such cases, a special syntax is provided:
exe a : a.cpp : <include>a&&b ;
The &&
symbols separate values of an
property, and specify that the order of the values should be preserved. You
are advised to use this feature only when the order of properties really
matters, and not as a convenient shortcut. Using it everywhere might
negatively affect performance.


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$100 Google Store credit for pre-ordering Pixel 4 now available
by Abner LiThis year’s pre-order promotion for ordering the Pixel 4 and Pixel 4 XL before launch from the Google Store was a $100 credit. That amount has quietly been applied this evening and is available for use ahead of Black Friday.
That straightforward promo effectively reduced the Pixel 4’s starting price to $699 and was available until October 26th. Google positioned it as a way to purchase accessories for your phone, especially given the lack of included USB-C headphones or adapter.
Coincidentally, Made by Google just posted a Pixel 4 Cases ad yesterday. Delivery of the credit was promised within 6-8 weeks after the deal’s end, so the company is early by about two weeks.
[Update 11/22: Google communicated the credit’s arrival on Friday morning to customers via email.] You can manually check by visiting the Google Store account page or the “Payment” tab in Google Pay on Android. There are no steps for recipients to redeem. The promotional balance expires on 01/31/20, so it’s unlikely the new Pixel Buds — arriving this spring — can be purchased with the credit.
The Pixel 4 pre-order credit follows Google yesterday delivering the Cash Back from being a higher-tier Google One subscriber. Meanwhile, like last year, early adopters will likely be peeved about the deeper $200 Black Friday and Cyber Monday discount that starts November 24th.
More about Pixel 4:
- JerryRig’s latest teardown shows what a clear-backed Pixel 4 XL would look like
- Bank of America updates Android app to support Pixel 4 face unlock
- Pixel 4’s Motion Sense feature ran a musical performance w/ Karol G
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Main Ingredients
-
{{recipe.ingredients}}
Main Ingredients
Method
Preheat oven to 160°C
Slice raisin loaf, then butter and quarter slices
Layer raisin bread into a medium-sized ovenproof dish or 4-6 smaller dishes for individual portions
Mix custard ingredients together and pour over raisin bread
Bake for about 35-40 minutes for larger puddings or 25-30 minutes for smaller ones
Dust with icing sugar and serve
More ideas
Serve with custard, cream or icing sugar
Cut costs by using stale sliced bread or buns in place of raisin bread, and margarine in place of butter


How do I even Iron Yard?


| Path: | README |
| Last Update: | Tue Nov 24 14:05:52 -0800 2009 |
WordPairs outputs pairs of words with alternate spellings. This example produces word alternates by reversing a variable-length ending word segment.
output:
agonies, agonise # trim = 2 analog, angola # trim = 4 animal, lamina # trim = 6
WordPairs outputs comma-separated pairs to STDOUT.
WordPairs.run(words, dictionary, trim) * The words argument is an Enumerable object that responds to #each containing instances of String. * The dictionary argument is a plaintext dictionary instance of String. * The trim argument is any positive integer.
See bin/wordpairs, or bin/wordpairs -h, for details.
* In order to provide complete output, matched words are not removed from the dictionary. * Word alternates must be unique and longer than the provided trim length to be eligible for matching. * Compound hyphenated words, abbreviations and contractions are eligible for matching. * Terms containing multiple words (eg. "St. Tropez") are eligible for matching if provided, but are broken down into individual words in the default word list. Author: Scott Burton Copyright: Copyright 2009, Burton Sound LLC License: Distributed under the MIT license, http://www.opensource.org/licenses/mit-license.php


Learning from samples¶
[1]:
import tensorflow as tf
from tf_agents.agents.dqn import dqn_agent
from tf_agents.drivers import dynamic_episode_driver, dynamic_step_driver
from tf_agents.environments import suite_gym
from tf_agents.environments.tf_py_environment import TFPyEnvironment
from tf_agents.networks import q_network
from tf_agents.replay_buffers import tf_uniform_replay_buffer
from tf_agents.utils import common
from bellman.environments.environment_model import EnvironmentModel
from bellman.environments.tf_wrappers import TFTimeLimit
from bellman.environments.transition_model.keras_model.keras import (
KerasTrainingSpec,
KerasTransitionModel,
)
from bellman.environments.transition_model.keras_model.linear import LinearTransitionNetwork
from bellman.environments.transition_model.keras_model.trajectory_sampling import (
InfiniteHorizonTrajectorySampling,
)
from examples.utils.classic_control import (
MountainCarInitialState,
MountainCarReward,
MountainCarTermination,
)
from examples.utils.mountain_car import (
plot_mountain_car_policy_decisions,
plot_mountain_car_transitions,
)
from examples.utils.policies import sample_uniformly_distributed_observations_and_get_actions
from examples.utils.trajectories import sample_uniformly_distributed_transitions
from tests.tools.bellman.environments.reward_model import ConstantReward
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
/tmp/ipykernel_2478/3602052060.py in <module>
----> 1 import tensorflow as tf
2 from tf_agents.agents.dqn import dqn_agent
3 from tf_agents.drivers import dynamic_episode_driver, dynamic_step_driver
4 from tf_agents.environments import suite_gym
5 from tf_agents.environments.tf_py_environment import TFPyEnvironment
ModuleNotFoundError: No module named 'tensorflow'
“”” This notebook demonstrates how to assemble a batch model based reinforcement learning system. In this example we generate trajectories from the Mountain Car gym environment using a random policy, use that data to train a linear model of the transition function, and use sampled transitions from the model to train a DQN agent. “””
%matplotlib inline
This example is based on Mountain Car because it is straightforward to visualise the state space and the linearised dynamics contain enough information to learn a controller using reinforcement learning.
[2]:
global_step = tf.compat.v1.train.get_or_create_global_step()
tf_env = TFPyEnvironment(suite_gym.load("MountainCar-v0"))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/2824442390.py in <module>
----> 1 global_step = tf.compat.v1.train.get_or_create_global_step()
2 tf_env = TFPyEnvironment(suite_gym.load("MountainCar-v0"))
NameError: name 'tf' is not defined
Dynamics model¶
We define the linear model that is going to be used to model the transition function of this environment. We plot the predicted dynamics of the untrained model. The action space of the mountain car environment consists of three discrete elements. These are represented in all subsequent plots with three colours: - left impulse: blue - no impule: red - right impulse: green
In these state-space plots, the x-axis is the agents position and the y-axis is the velocity.
[3]:
batch_size = 64
training_spec = KerasTrainingSpec(
epochs=5000,
training_batch_size=256,
callbacks=[tf.keras.callbacks.EarlyStopping(monitor="loss", patience=3)],
verbose=0,
)
linear_transition_network = LinearTransitionNetwork(tf_env.observation_spec())
trajectory_sampling_strategy = InfiniteHorizonTrajectorySampling(batch_size, 1)
transition_model = KerasTransitionModel(
[linear_transition_network],
tf_env.observation_spec(),
tf_env.action_spec(),
)
reward_model = ConstantReward(tf_env.observation_spec(), tf_env.action_spec())
sample_transitions = sample_uniformly_distributed_transitions(
transition_model, 1000, reward_model
)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/3286094786.py in <module>
1 batch_size = 64
2
----> 3 training_spec = KerasTrainingSpec(
4 epochs=5000,
5 training_batch_size=256,
NameError: name 'KerasTrainingSpec' is not defined
[4]:
plot_mountain_car_transitions(
sample_transitions.observation.numpy(),
sample_transitions.action.numpy(),
sample_transitions.next_observation.numpy(),
)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/3989627936.py in <module>
----> 1 plot_mountain_car_transitions(
2 sample_transitions.observation.numpy(),
3 sample_transitions.action.numpy(),
4 sample_transitions.next_observation.numpy(),
5 )
NameError: name 'plot_mountain_car_transitions' is not defined
TF-Agents Agent¶
In this notebook we train a TF-Agents DQN agent on samples from the dynamics model. The TF-Agents agents define two policies: a collect policy and a training policy. For this DQN agent, the training policy is a greedy policy parametrised by a Q value neural network, and the collect policy is the associated epsilon greedy policy.
We use the collect policy from the untrained DQN agent to generate trajectories from the real Mountain Car environment in order to train the dynamics model.
[5]:
train_sequence_length = 1
fc_layer_params = (100,)
collect_model_training_episodes = 100
model_training_buffer_capacity = 25000
collect_steps_per_iteration = 10000
epsilon_greedy = 0.1
replay_buffer_capacity = 1000000
target_update_tau = 0.05
target_update_period = 5
learning_rate = 1e-3
n_step_update = 1
gamma = 0.99
gradient_clipping = None
reward_scale_factor = 1.0
debug_summaries = False
summarize_grads_and_vars = False
q_net = q_network.QNetwork(
tf_env.observation_spec(), tf_env.action_spec(), fc_layer_params=fc_layer_params
)
tf_agent = dqn_agent.DqnAgent(
tf_env.time_step_spec(),
tf_env.action_spec(),
q_network=q_net,
epsilon_greedy=epsilon_greedy,
n_step_update=n_step_update,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate),
td_errors_loss_fn=common.element_wise_squared_loss,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=global_step,
)
tf_agent.initialize()
eval_policy = tf_agent.policy
collect_policy = tf_agent.collect_policy
model_training_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
collect_policy.trajectory_spec,
batch_size=tf_env.batch_size,
max_length=model_training_buffer_capacity,
)
model_collect_driver = dynamic_episode_driver.DynamicEpisodeDriver(
tf_env,
collect_policy,
observers=[model_training_buffer.add_batch],
num_episodes=collect_model_training_episodes,
)
model_collect_driver.run()
def train_model_step():
trajectories = model_training_buffer.gather_all()
return transition_model.train(trajectories, training_spec)
train_model_step()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/4200779551.py in <module>
16 summarize_grads_and_vars = False
17
---> 18 q_net = q_network.QNetwork(
19 tf_env.observation_spec(), tf_env.action_spec(), fc_layer_params=fc_layer_params
20 )
NameError: name 'q_network' is not defined
This plot was produced in the same manner as the previous one, but the model has now been trained on data from the real environment. The dynamics of the environment have been captured by the model.
[6]:
sample_transitions = sample_uniformly_distributed_transitions(
transition_model, 1000, reward_model
)
plot_mountain_car_transitions(
sample_transitions.observation.numpy(),
sample_transitions.action.numpy(),
sample_transitions.next_observation.numpy(),
)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/4204429102.py in <module>
----> 1 sample_transitions = sample_uniformly_distributed_transitions(
2 transition_model, 1000, reward_model
3 )
4
5 plot_mountain_car_transitions(
NameError: name 'sample_uniformly_distributed_transitions' is not defined
Training on samples¶
We define an environment which uses the trained transition model for the dynamics, along with a reward function, episode termination condition, initial state distributions and bound on episode length.
[7]:
reward = MountainCarReward(tf_env.observation_spec(), tf_env.action_spec())
terminates = MountainCarTermination(tf_env.observation_spec())
initial_state_distribution = MountainCarInitialState(tf_env.observation_spec())
environment_model = TFTimeLimit(
EnvironmentModel(transition_model, reward, terminates, initial_state_distribution),
duration=200,
)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/2607481859.py in <module>
----> 1 reward = MountainCarReward(tf_env.observation_spec(), tf_env.action_spec())
2 terminates = MountainCarTermination(tf_env.observation_spec())
3 initial_state_distribution = MountainCarInitialState(tf_env.observation_spec())
4 environment_model = TFTimeLimit(
5 EnvironmentModel(transition_model, reward, terminates, initial_state_distribution),
NameError: name 'MountainCarReward' is not defined
The agent is trained on data gathered from the environment model. Using the environment interface means the TF-Agents drivers can be used to generate rollouts.
[8]:
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_capacity,
)
collect_driver = dynamic_step_driver.DynamicStepDriver(
environment_model,
collect_policy,
observers=[replay_buffer.add_batch],
num_steps=collect_steps_per_iteration,
)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/3903815623.py in <module>
----> 1 replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
2 data_spec=tf_agent.collect_data_spec,
3 batch_size=tf_env.batch_size,
4 max_length=replay_buffer_capacity,
5 )
NameError: name 'tf_uniform_replay_buffer' is not defined
Before training the policy we plot the decisions made at random locations in the state space.
[9]:
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
collect_driver.run(time_step=time_step, policy_state=policy_state)
sample_trajectories = replay_buffer.gather_all()
samples = sample_uniformly_distributed_observations_and_get_actions(tf_agent.policy, 1000)
plot_mountain_car_policy_decisions(samples["observations"].numpy(), samples["actions"].numpy())
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/493832024.py in <module>
1 time_step = None
----> 2 policy_state = collect_policy.get_initial_state(tf_env.batch_size)
3 collect_driver.run(time_step=time_step, policy_state=policy_state)
4
5 sample_trajectories = replay_buffer.gather_all()
NameError: name 'collect_policy' is not defined
After a single training iteration, we again plot the decisions made by the policy.
[10]:
def train_step():
dataset = replay_buffer.as_dataset(
num_parallel_calls=3, sample_batch_size=batch_size, num_steps=train_sequence_length + 1
).prefetch(3)
iterator = iter(dataset)
experience, _ = next(iterator)
return tf_agent.train(experience)
time_step, policy_state = collect_driver.run(time_step=time_step, policy_state=policy_state)
train_loss = train_step()
samples = sample_uniformly_distributed_observations_and_get_actions(tf_agent.policy, 1000)
plot_mountain_car_policy_decisions(samples["observations"].numpy(), samples["actions"].numpy())
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/1814195236.py in <module>
8
9
---> 10 time_step, policy_state = collect_driver.run(time_step=time_step, policy_state=policy_state)
11 train_loss = train_step()
12
NameError: name 'collect_driver' is not defined
Having trained the policy for a small number of iterations it is clear that the policy is improving.
[11]:
for _ in range(5):
time_step, policy_state = collect_driver.run(
time_step=time_step, policy_state=policy_state
)
train_loss = train_step()
samples = sample_uniformly_distributed_observations_and_get_actions(tf_agent.policy, 1000)
plot_mountain_car_policy_decisions(samples["observations"].numpy(), samples["actions"].numpy())
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_2478/258358337.py in <module>
1 for _ in range(5):
----> 2 time_step, policy_state = collect_driver.run(
3 time_step=time_step, policy_state=policy_state
4 )
5 train_loss = train_step()
NameError: name 'collect_driver' is not defined


#include "../thirdparty/include/opencv2/opencv.hpp"
#include "../thirdparty/include/opencv2/stitching/stitcher.hpp"
#include <stdio.h>
#include <vector>
Go to the source code of this file.
Classes | |
| struct | AGError |
| struct | AGParameters |
| Brief description. Brief description continued. More... | |
| struct | AGImage |
Enumerations | |
| enum | ImageDirection { Down = 0, Up = 1, Left = 2, Right = 3 } |
Variables | |
| const int | WHITE_PIXEL = 0xFF |
| const int | FIRST_ROW = 0 |
| const double | PATH_RANGE = 20.0 |
| const double | POINT_Y_OFFSET = 0.1 |
| const double | POINT_X_OFFSET = 0.1 |
| enum ImageDirection |
| Enumerator | |
|---|---|
| Down | |
| Up | |
| Left | |
| Right |
Definition at line 27 of file AGDataStructures.h.
| const int FIRST_ROW = 0 |
Definition at line 17 of file AGDataStructures.h.
| const double PATH_RANGE = 20.0 |
Definition at line 20 of file AGDataStructures.h.
| const double POINT_X_OFFSET = 0.1 |
Definition at line 24 of file AGDataStructures.h.
| const double POINT_Y_OFFSET = 0.1 |
Definition at line 23 of file AGDataStructures.h.
| const int WHITE_PIXEL = 0xFF |
Definition at line 16 of file AGDataStructures.h.


version = "1.0.1"
try:
sha = subprocess.check_output(["git", "rev-parse", "HEAD"],
cwd=cwd).decode("ascii").strip()
version += "+" + sha[:7]
except Exception:
pass
version = "1.1.1"
try:
from datetime import date
today = date.today()
day = today.strftime("b%d%m%Y")
version += day
except Exception:
pass
def create_version_file():


|
|
| server (upper_layer_ptr up) |
| |
| error | start (octet_stream::lower_layer *down) override |
| | Initializes the upper layer.
|
| |
| void | abort (const error &reason) override |
| | Called by the lower layer for cleaning up any state in case of an error or when disposed.
|
| |
| ptrdiff_t | consume (byte_span input, byte_span) override |
| | Consumes bytes from the lower layer.
|
| |
| void | prepare_send () override |
| | Gives the upper layer an opportunity to add additional data to the output buffer.
|
| |
| bool | done_sending () override |
| | Queries whether all pending data has been sent.
|
| |
| virtual error | start (lower_layer *down)=0 |
| | Initializes the upper layer.
|
| |
| virtual ptrdiff_t | consume (byte_span buffer, byte_span delta)=0 |
| | Consumes bytes from the lower layer.
|
| |
| virtual void | prepare_send ()=0 |
| | Gives the upper layer an opportunity to add additional data to the output buffer.
|
| |
| virtual bool | done_sending ()=0 |
| | Queries whether all pending data has been sent.
|
| |
| virtual void | abort (const error &reason)=0 |
| | Called by the lower layer for cleaning up any state in case of an error or when disposed.
|
| |
Implements the server part for the WebSocket Protocol as defined in RFC.
- Initially, the layer performs the WebSocket handshake. Once completed, this layer decodes RFC 6455 frames and forwards binary and text messages to
UpperLayer
.


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Fierce Creatures John Cleese, Jamie Lee Curtis, Kevin Kline, Michael Palin Rating: 3 out of 5 stars
Review by Ed Johnson-Ott, NUVO Newsweekly For more reviews, go to www.nuvo-online.com and click on "film."
"Fierce Creatures," which reunites John Cleese, Jamie Lee Curtis, Kevin Kline and Michael Palin from "A Fish Called Wanda," is a pleasant, funny little film. It's not anywhere near as good as "Wanda," which is a shame, but even a half-baked offering from this cast is much more entertaining than what usually passes for comedy.
Kline plays Rod McCain, a blustering Rupert Murdoch-like billionaire mogul who buys and sells just for the power rush. Management of one of his recent acquisitions, a British zoo, is delegated to Rollo Lee (Cleese), whose duty is to wring a 20% profit from the place, or else. Lee, operating on the theory that violence is the only thing that sells, declares that the zoo must be purged of all but the dangerous animals, hence the "fierce creatures" of the title. His efforts are complicated by the arrival of two Americans; newly hired marketing expert Willa Weston (Curtis) and McCain's loutish son Vince (Kline again,) who aspires to transform the zoo into a corporate and celebrity sponsored theme park.
The multi-layered story line is promising, and delivers some juicy moments. The zoo keepers try to save their furry friends by convincing Lee that their animals are dangerous, leading to some very creative lying. One staff member gestures towards a group of meek animals and exclaims "the meercat is known as the piranha of the desert. It can strip a corpse clean in three minutes!"
Despite the many juicy moments in "Fierce Creatures," the film has an uneven and occassionally forced feel. This isn't surprising, as the production endured a reworked script, two directors, and several reshot scenes. In "A Fish Called Wanda," the story and the character development flowed smoothly. Things are more herky-jerky in this film.
Michael Palin is essentially wasted as the talky zoo-keeper. His character is wafer-thin – he exists only to annoy. Palin often did this sort of thing in Monty Python skits, but those sketches were just a few minutes long. Why a performer of his caliber chose to play a one-note bit part is mystifying. Jamie Lee Curtis fares better. Her character, an extremely bright corporate climber, has an abrupt change in personality mid way into the movie, going from ruthless to sweet in seconds. It's an awkward change, but Curtis handles it well. Despite the frantic goings-on, she radiates an air of confidence and serenity. The combination of intelligence, drop dead good looks and that wicked twinkle in her eye make Curtis a compelling screen presence.
In his dual roles as father and son, Kevin Kline is clearly having a whale of a good time. As the tyrant Rod McCain, Kline is suitably pompous and self-absorbed, punctuating his statements with belches and flatulence. The character is appropriately overbearing, but lacks flair. However, Kline's turn as Vince, McCain's whacked-out son, is much more rewarding.  While his performance fails to hit the manic peak he reached in "Wanda," his entertaining work here shows he hasn't lost his wonderful comic edge.
The center of "Fierce Creatures" is, of course, the magnificent John Cleese. As Rollo Lee, he plays a variation of his classic Basil Fawlty character. Lee wants to be wicked, he tries to be wicked, but underneath it all he's a softy. Cleese sputters better than anybody, and he provides some of the film's biggest laughs.
"Fierce Creatures" isn't a sequel to "A Fish Called Wanda," although, late in the film, there is a very funny one-word nod to the earlier movie. Because of the huge critical and financial success of "Wanda," it's easy to go into the theater with unreasonably high expectations. However, if you enter the theater with an open mind and just relax, you're in for some real fun. "Fierce Creatures" may lack bite, but it's still a bloody good time.
copyright 1997, Ed Johnson-Ott
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


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FORTYSEVEN MEDIA
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tooz.dev
Fri, 24 May 2019 16:20:00 GMT
Learning Rust
The last week or so I've made a commitment to myself to learn Rust. I've made a couple attempts over the last five years or so and have made progress here and there, but never had a project to work on. I'd make my way through the book, and write a couple little things and that was it. There was huge gaps where I wouldn't write any Rust. Then I would go back and it was just like learning all over again.
This time I am going to keep it up. I am making my way through the book again, I've gone through all of Rustlings, listening to The New Rustacean Podcast, and I've started working on a CLI project that will help me use the things I am learning from all the resources.
Rust is not easy to learn for me. I've been programming for over twenty years now, and have picked up multiple languages along the way, but Rust is very different (in a great way) than what I am used to. The community is great though and it's pretty fun to try something different.
The goal of this is to know Rust as well as I know javascript and node. It took years and years to get to that point. I don't expect it to be any different with Rust. I'm not doing this only for fun either. The ultimate goal is to position my career so that I am writing Rust full time.
This place is going to be used as my scratch pad. Sometimes I will write about what I am working on, or write out notes for things I don't quite understand. This type of note taking, writing, and editing helps me learn. Throughout the process I am going to keep a list of all the resources that I go through. I've already linked some here, but I am going to have a separate page of resources.


My Adventure
Dear community,
There are moments in our life that define the tipping points in our mental capacity. For a lot of people, this can be moving out for the first time, getting into a serious relationship, getting children of our own. All of these events cause us to look at the same things differently. Let’s take a lollipop for example, when we were young, lollipops were a source of joy. When we get into university, the lollipop is a statement of old fashion. When we have our children, a lollipop may mean a couple of minutes of silence in the house or an educational motivator for our children. The same object takes on different meanings. This sets the tone of my return.
I am back. After going into full-on momentum with my Master's degree, I have graduated. September 2019, I walked on stage with my neatly dressed suit and proud smile. The two minutes of glory on stage never felt so amazing before. A well-deserved breakthrough.
Lots of people ask me how I feel graduating Master’s and I must say, It changed my perspective about how I approach a lot of questions that I have already answered in the past. The conclusion of graduating Master’s is:
“I feel a lot more stupid.”
My Thoughts
Now I want to come back and re-look this question I have posed as “what is the meaning of life?”
I mean, one of the biggest paradigm shifts I gain from the degree is to learn:
“Do not assume anything until I have proven it or I can refer to someone else’s work that has proved my concept”
Some questions that my blogs never answered was things like:
1. What do I perceive as “life”?
2. What kinds of life am I referring to?
3. What do I see as “meaning”?
4. Who has studied this topic before?
5. Etc.
So I decided to come back to this blog and continue with my adventure on establishing the meaning of life.
See you all in the later blogs where I discuss these question further 😊
Follow me on social media so that you won't miss an update from me.


Everyday we present one featured menu
Allowing you to choose from the different meal options and order
With the Available Meal Options
Cinamon flavour lorem ipsum
UGX: 20,000
Potatoes with chicken stew flavour
UGX: 12,000
English Coffee with cinamon flavour
UGX: 10,200
Burgers mixed with lorem flavour
UGX 20000


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Serves 1
This cheffy, spring-y recipe is a wild, seasonal food lover’s delight. Multiply the quantities by the number of guests for a dinner party to impress.
For the guinea fowl, preheat the grill to high. Remove the skin from the guinea fowl breast. Set aside the guinea fowl breast.
For the brioche crumb, preheat the oven to 180C/350F/Gas 4.
Place the guinea fowl skin flat onto a non-stick baking tray and place another tray on top (this is to cook the skin evenly).
Put the trays into the oven and cook for 5-8 minutes, or until crisp. Remove from the oven and take the skin off the tray to cool.
When the skin is cool, thoroughly chop the skin to get a rough crumb.
Place into a bowl with the brioche crumbs and toasted sunflower seeds. Lightly season with a little salt.
For the guinea fowl, place the guinea fowl breast between two sheets of cling film and bat the breast with a heavy knife to even the thickness – try to retain the shape.
Preheat the grill to hot. Lightly season the guinea fowl breast with salt and freshly ground black pepper and place onto a roasting tray. Drizzle with a little olive oil and cook under the grill for five minutes, or until the breast is just cooked.
Once cooked, evenly coat the top of the guinea fowl with the brioche crumb and place back under the grill for 1-2 minutes, or until the brioche crumb is lightly golden-brown.
For the sorrel sauce, heat the chicken consommé or stock in a pan. Remove from the stove and blend in the soft herb purée, butter and crème fraîche using a hand-blender, until thoroughly dissolved. Taste and add salt and freshly ground black pepper if necessary. Add the shredded wild sorrel.
For the St George mushrooms, heat the oil in a small pan and fry the shallot until softened but without colour.
Add the mushrooms and garlic and season with a pinch of salt
Cook for 2-3 minutes, or until the mushrooms are tender. Remove from the pan, check the seasoning, and add salt or pepper if necessary, and finish with the snipped chives.
For the asparagus, bend each stick of asparagus near the end. They will break naturally where the spear is no longer tough. For an elegant presentation, evenly shave the end of each stick of asparagus to make a spear.
Bring a pan of water to the boil and season with salt and sugar
Place the asparagus into the water and cook for 1-2 minutes, or until the asparagus is just cooked. You should be able to just slip a small knife through the stem.
Remove from the water with a slotted spoon, brush with the melted butter and season with a little salt.
To serve, cut the guinea fowl breast in half diagonally. Place in the centre of the plate slightly overlapping the two pieces (which gives the dish a bit of height)
Arrange the mushrooms and asparagus tips around the breast. Drizzle the sauce around delicately and, sprinkle the shredded sorrel over the vegetables. Arrange the red vein sorrel on top and serve.


Part of elita.models View In Hierarchy
| Method | GetApplications | Get all applications. Pull from in-memory root_tree rather than mongo for speed. |
| Method | GetApplication | Get application document |
| Method | NewApplication | Create new application and all subcontainers and root_tree sub-references |
| Method | DeleteApplication | Delete application and all root_tree references and sub-objects. |
| Method | GetApplicationCensus | No summary |
| Method | UpdateApplication | Update application with keys in doc |
Inherited from GenericChildDataService:
| Method | __init__ | No summary |
| Method | populate_dependencies | Child dataservice classes may need to access methods of siblings. This allows parent dataservice to inject cross dependencies as needed without generating a big reference cycle. |
| Method | NewContainer | Create new container object suitable for a root_tree reference |
| Method | UpdateObject | Generic method to update a particular object (document) with the data in doc |
| Method | AddThreadLocalRootTree | Add new node to thread-local root_tree (copy from mongo's root_tree) |
| Method | RmThreadLocalRootTree | Remove deleted node from thread-local root_tree |
Get all applications. Pull from in-memory root_tree rather than mongo for speed.
Create new application and all subcontainers and root_tree sub-references
Delete application and all root_tree references and sub-objects.


Color legend for SNV posterior probability
Color legend for the lollipop circles that appear on the plot. Blue color indicates a posterior probability of 1, while red indicates probability 0.
Show on the plot only the SNVs with posterior probability greater than the threshold.


Twilio Client for Android HowTo's
We've provided a few complete example apps to give you an idea of what you can do with the SDK.
However, each app requires a publicly-accessible web server that Twilio's services can contact to set up calls. Because of this, there are a few things you'll need to do to get the apps running.
BasicPhone
BasicPhone is an application that can accept an incoming call from someone dialing from a regular phone. It can also dial out, but you'll only hear a pre-recorded message.
Stuff you need to do to get it running:
- Copy the stuff in the
server
subfolder to a publicly-accessible web server. - Create a new Twilo Application
and set the VoiceUrl to the URL on your server where you put
basic-call.php
. - In
auth.php
, replace$accountSid
and$authToken
with your Twilio account credentials, and replace$appSid
with the SID for the application you created above. - Purchase a phone number
(or use the Twilio Sandbox Number) and set the VoiceUrl to the URL on
your server where you put
basic-call.php
. - In the Andrdoid application itself, open
BasicPhone.java
, and edit the constantAUTH_PHP_SCRIPT
to point to yourauth.php
URL.
After you build an install the app on your Android device, you can call the number you purchased (or the Sandbox Number if you used that instead), and you'll be connected to BasicPhone!
ConferencePhone
ConferencePhone lets you create a multi-caller conference call using the
<Conference>
TwiML verb.
You can create a new named conference, join an existing conference, or
select people to add to the conference, whether they're also using
ConferencePhone or a regular phone.
Stuff you need to do to get it running:
- Copy the stuff in the
server
subfolder to a publicly-accessible web server. - Create a new Twilo Application
and set the VoiceUrl to the URL on your server where you put
join-conference.php
. - In
auth.php
, replace$accountSid
and$authToken
with your Twilio account credentials, and replace$appSid
with the SID for the application you created above. - In
make-call.php
, replace$twilioPhoneNumber
with a number you've verified with Twilio. Also replace$accountSid
and$authToken
as before. Finally, change$url
to point to where yourjoin-conference.php
script lives on your server. - In the Andrdoid application itself, open
ConferencePhone.java
, and edit the constantsAUTH_PHP_SCRIPT
to point to yourauth.php
URL, andMAKE_CALL_PHP_SCRIPT
to point to yourmake-call.php
URL.
Now you can build and install the Android app on your Android device and start conferencing!


Carlo Santos
CIS155 19056 - Web Development I
-
01-21-2021: Created html and css files and pushed to github
-
01-22-2021: Created navigation bar, chose color pallete, began creating css styles for nav
and
background colors.
-
02-02-2021: Created new wireframe
Whimsical
wireframe
-
02-04-2021: Change index page styling to three column design. Added css reset file.
-
02-05-2021:
-
Changed color pallet
-
Added background image to index
-
Changed lead image on
index
-
Created information and statistics html pages
-
Added stats to statistics page
-
Changed about page to two column design and changed font styling
-
Added articles to Info section, added new picture to info, organized css styling.
-
02-06-21:
-
Added favicon
-
Fixed z-index of nav bar
-
Updated index covid data


Package com.jme.math
Class Easing
- java.lang.Object
-
- com.jme.math.Easing
-
public class Easing extends java.lang.Object
Expose several Easing function from Robert Penner Created by Nehon on 26/03/2017.
-
-
Nested Class Summary
Nested Classes Modifier and Type Class Description static class
Easing.InOut
An Ease function composed of 2 sb function for custom in and out easing
-
Field Summary
Fields Modifier and Type Field Description static EaseFunction
constant
static EaseFunction
inBounce
static EaseFunction
inCubic
static EaseFunction
inElastic
In Elastic and bouncestatic EaseFunction
inOutBounce
static EaseFunction
inOutCubic
static EaseFunction
inOutElastic
static EaseFunction
inOutQuad
inOutstatic EaseFunction
inOutQuart
static EaseFunction
inOutQuint
static EaseFunction
inQuad
static EaseFunction
inQuart
static EaseFunction
inQuint
static EaseFunction
linear
Instatic EaseFunction
outBounce
static EaseFunction
outCubic
static EaseFunction
outElastic
Out Elastic and bouncestatic EaseFunction
outQuad
Outstatic EaseFunction
outQuart
static EaseFunction
outQuint
static EaseFunction
smootherStep
static EaseFunction
smoothStep
Extra functions
-
Constructor Summary
Constructors Constructor Description Easing()
-
-
-
Field Detail
-
constant
public static EaseFunction constant
-
linear
public static EaseFunction linear
In
-
inQuad
public static EaseFunction inQuad
-
inCubic
public static EaseFunction inCubic
-
inQuart
public static EaseFunction inQuart
-
inQuint
public static EaseFunction inQuint
-
outElastic
public static EaseFunction outElastic
Out Elastic and bounce
-
outBounce
public static EaseFunction outBounce
-
inElastic
public static EaseFunction inElastic
In Elastic and bounce
-
inBounce
public static EaseFunction inBounce
-
outQuad
public static EaseFunction outQuad
Out
-
outCubic
public static EaseFunction outCubic
-
outQuart
public static EaseFunction outQuart
-
outQuint
public static EaseFunction outQuint
-
inOutQuad
public static EaseFunction inOutQuad
inOut
-
inOutCubic
public static EaseFunction inOutCubic
-
inOutQuart
public static EaseFunction inOutQuart
-
inOutQuint
public static EaseFunction inOutQuint
-
inOutElastic
public static EaseFunction inOutElastic
-
inOutBounce
public static EaseFunction inOutBounce
-
smoothStep
public static EaseFunction smoothStep
Extra functions
-
smootherStep
public static EaseFunction smootherStep
-
-


Toggle Mode
Text Field
Html Field
Video Field
Empty Video Field
Image Field
Empty Image Field
Background Image Field
HOOOLA
Empty Background Image Field
HOOOLA
Link Field
AAA
BB
Media Field Image
Media Field Video


Allow your students or employees to check-in automatically using their smartphones
How Proximate Works
link your calendar and iBeacons
attendees install the iOS/Android app
reports are saved to the cloud
effortless
get started in one sitting
powerful
notification, report and more
precise
accurate to one meter
secure
easily spot fakers
flexible
export your data anytime
free
no limits on time or features
"With Proximate, I don't have to waste time scanning the room. I get a record of who's there and who's not with zero effort. I feel more like a class lead and less like a babysitter."


person( 1 ) drops an object which weighs WEIGHT_OBJ kilograms into a fluid with a density of DENSE_FLUID \frac{\text{kilograms}}{\text{meter}^3}
. If the density of the object is DENSE_OBJ \frac{\text{kilograms}}{\text{meter}^3}
, what is the weight of the displaced fluid (in kilograms)? (Round to the nearest tenth)
Any object, wholly or partially immersed in a fluid, is buoyed up by a force equal to the weight of the fluid displaced by the object.
\frac{\text{density of object}}{\text{density of fluid}}=\frac{\text{weight of object}}{\text{weight of displaced fluid}}
\text{weight of displaced fluid}=\frac{\text{density of fluid}\cdot\text{weight of object}}{\text{density of object}}=\frac{DENSE_OBJ\frac{\text{kilograms}}{\text{meter}^3}\cdotWEIGHT_FLUID\text{ kilograms}}{DENSE_FLUID\frac{\text{kilograms}}{\text{meter}^3}}
\text{The answer is } WEIGHT_FLUID\text{ kilograms}
person( 1 ) drops an object into a fluid with a density of DENSE_FLUID \frac{\text{kilograms}}{\text{meter}^3}
and WEIGHT_FLUID kilograms of fluid is displaced. If the density of the object is DENSE_OBJ \frac{\text{kilograms}}{\text{meter}^3}
, what is the weight of the object (in kilograms)? (Round to the nearest tenth)
Any object, wholly or partially immersed in a fluid, is buoyed up by a force equal to the weight of the fluid displaced by the object.
\frac{\text{density of object}}{\text{density of fluid}}=\frac{\text{weight of object}}{\text{weight of displaced fluid}}
\text{weight of object}=\frac{\text{density of object}\cdot\text{weight of displaced fluid}}{\text{density of fluid}}=\frac{DENSE_OBJ\frac{\text{kilograms}}{\text{meter}^3}\cdotWEIGHT_FLUID\text{ kilograms}}{DENSE_FLUID\frac{\text{kilograms}}{\text{meter}^3}}
\text{The answer is } WEIGHT_OBJ\text{ kilograms}
person( 1 ) drops an object with a weight of WEIGHT_OBJ kilograms and a density of DENSE_OBJ \frac{\text{kilograms}}{\text{meter}^3}
into a fluid with an unknown density. If the weight of the displaced fluid is WEIGHT_FLUID kilograms, what is the density of the fluid in \frac{\text{kilograms}}{\text{meter}^3}
? (Round to the nearest tenth)
Any object, wholly or partially immersed in a fluid, is buoyed up by a force equal to the weight of the fluid displaced by the object.
\frac{\text{density of object}}{\text{density of fluid}}=\frac{\text{weight of object}}{\text{weight of displaced fluid}}
\text{density of fluid}=\frac{\text{weight of displaced fluid}\cdot\text{density of object}}{\text{weight of object}}=\frac{DENSE_OBJ\frac{\text{kilograms}}{\text{meter}^3}\cdotWEIGHT_FLUID\text{ kilograms}}{WEIGHT_OBJ\text{ kilograms}
\text{The answer is } DENSE_FLUID\frac{\text{kilograms}}{\text{meter}^3}
person( 1 ) drops an object with a weight of WEIGHT_OBJ kilograms into a fluid with a density DENSE_FLUID \frac{\text{kilograms}}{\text{meter}^3}
. If the weight of the displaced fluid is WEIGHT_FLUID kilograms, what is the density of the object in \frac{\text{kilograms}}{\text{meter}^3}
? (Round to the nearest tenth)
Any object, wholly or partially immersed in a fluid, is buoyed up by a force equal to the weight of the fluid displaced by the object.
\frac{\text{density of object}}{\text{density of fluid}}=\frac{\text{weight of object}}{\text{weight of displaced fluid}}
\text{density of object}=\frac{\text{weight of object}\cdot\text{density of fluid}}{\text{weight of displace fluid}}=\frac{DENSE_FLUID\frac{\text{kilograms}}{\text{meter}^3}\cdotWEIGHT_OBJ\text{ kilogram}}{WEIGHT_FLUID\text{ kilograms}}
\text{The answer is } DENSE_OBJ\frac{\text{kilograms}}{\text{meter}^3}


Prevents long running commands from timing out. A 'waiting' message is sent to the log after the interval period (ms). Default value is 0, which disables the tick message.


@main("Welcome to Moot") {
Moot
The way to Happiness
Have fun discovering a new kind of discussion. Share your life, experiences, feelings, it only just take one Moot. Share, Learn, Educate... It's simple. You just need to knock the door.
Moot is like a magic portal. Cross the threshold ! Enlighten your day with delightful members. Follow, Moot, Discuss... Imagine a world of greatness. You just need to sign-in and open the door.
Moot needs you !

Join our #octohappycat team.


{% extends 'layouts/ecommerce_base.html' %} <<<<<<< HEAD {% block content %} Sign in {{ form.csrf_token }} {% for error in form.email.errors %} {{ error }} {% endfor %} {% for error in form.password.errors %} {{ error }} {% endfor %} Forgot password? Remember Login Don't have account? Sign up ======= {% import 'macros/form_macros.html' as f %} {% block content %}
Don't have account? Sign up


plasma COVID- patients use COVID {border-style donors px;} #ffffff;} convalescent antibodies plasmapheresis clinical blood therapeutic transfusion infection targeted health risk based benefit collection current fully present process px; recipients recovered {font-family derived donor font-size high used Moreover, apheresis disease double; effective essential line need population potential research safety scientific strategy testing using viral Elsevier FFP a.] affinity available b.] border-color border-width care considered d.] individuals is, laboratory make neutralizing pandemic. patients, preventative small solid; standard step treatment vaccine validated {font-style {font-weight CCP Convalescent Transfusion able abnormalities absence additional approach beneficial benefits bold c.] centre collected column courier; critically em; exchange ill immunoglobulins important inactivation incidence including information pandemic pathogen patient possible practice pt;} resource right risks severe state sterilization taken thrombosis trained well-established


@import models.users._
@*
* This template takes a single argument, a String containing a
* message to display.
*@
@* Param List *@
@(message: String, user: User)
@main("Home Page", user) {
Products catalogue with public and admin site
The default, public site
- Managed by HomeController
- Public visitors and customers see site with read only product catalogue
- Login option in navigation bar
Login and access control Process
- User class (models.users.User) defines a user
- The Administrator, Customer, and Manager user types all inherit from user
- Login checks if a user with matching email+password is in the DB
- If found, log in by storing email in a session cookie
- After login, replace login link with logout
- All controller methods pass a user object to the views
by calling getCurrentUser() which checks if a user is logged in
- All views accept user: User as a parameter and pass to main(user)
- Main uses an if statement to show login if no user (null) or else logout
Admin Site
- Managed by AdminController
- Admin views in views\productAdmin
- Admin product page allows add, edit, and delete products
- Secured using the Secured class
note: @@Security.Authenticated(Secured.class) at top of controller
CheckIfAdmin class @@With(CheckIfAdmin.class) verifies user role before granting access
Very Important!!
- Paramater list in controller action methods must match views
- The number of parameters, type, and order is importand, example:
- HomeController products(): return ok(products.render(productList, categoryList, getCurrentUser()));
- products,scala.html: @@(productList: List[Product], categoryList: List[Category], user: User)
- The view may not need user but main() will still require it!
}


|
SlowDown Algorithmic Regularization (SDAR)
Algorithmic Regularization with slowdown method for integrating few-body motions
|
Slow-down parameter control class. More...
#include <slow_down.h>
Public Member Functions | |
| SlowDown () | |
| defaulted constructor More... | |
| void | clear () |
| clear function More... | |
| void | initialSlowDownReference (const Float _kappa_ref, const Float _timescale_max) |
| initialize slow-down parameters More... | |
| void | setRealTime (const Float _time_real) |
| set real time value More... | |
| Float | getRealTime () const |
| get real time value More... | |
| void | driftRealTime (const Float _dt_int) |
| drift real time More... | |
| void | setUpdateTime (const Float _time) |
| set update time for new slowdown factor More... | |
| Float | getUpdateTime () const |
| get update time for new slowdown factor More... | |
| void | increaseUpdateTimeOnePeriod () |
| advance update time by one period More... | |
| void | setSlowDownFactor (const Float _kappa) |
| manually set kappa More... | |
| Float | calcSlowDownFactor () |
| calculate slowdown factor based on perturbation and inner acceleration More... | |
| Float | getSlowDownFactor () const |
| Get slow-down factor. More... | |
| Float | getSlowDownFactorOrigin () const |
| Get original slow-down factor. More... | |
| Float | getSlowDownFactorReference () const |
| Get sd reference factor. More... | |
| Float | getSlowDownFactorMax () const |
| Get slow-down fact maximum. More... | |
| Float | getPertIn () const |
| Float | getPertOut () const |
| Float | getTimescaleMax () const |
| void | writeBinary (FILE *_fout) |
| write class data with BINARY format More... | |
| void | readBinary (FILE *_fin) |
| read class data with BINARY format More... | |
| int | getBackupDataSize () const |
| get backup data size More... | |
| int | backupSlowDownFactorAndTimeReal (Float *_bk) |
| backup real time and force ratio More... | |
| int | restoreSlowDownFactorAndTimeReal (Float *_bk) |
| restore real time and force ratio More... | |
| void | print (std::ostream &fout, const int precision=15, const int width=23) |
| print slowdown data More... | |
| void | printColumnTitle (std::ostream &_fout, const int _width=20) |
| print titles of class members using column style More... | |
| void | printColumn (std::ostream &_fout, const int _width=20) |
| print data of class members using column style More... | |
Public Attributes | |
| Float | pert_in |
| Float | pert_out |
| Float | timescale |
| Float | period |
Slow-down parameter control class.
Determine the slow-down factor due to the perturbation and internal force \( \kappa = k_0 / [F_{pert,max}/F_{inner}] \)
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defaulted constructor
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backup real time and force ratio
| [in] | _bk | backup data array, should be size of getBackupDataSize() (2) |
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calculate slowdown factor based on perturbation and inner acceleration
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clear function
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drift real time
drift real time with integrated time step x kappa
| [in] | _dt_int | integrated time step |
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get backup data size
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get real time value
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Get slow-down factor.
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Get slow-down fact maximum.
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Get original slow-down factor.
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Get sd reference factor.
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get update time for new slowdown factor
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advance update time by one period
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initialize slow-down parameters
Set slow-down parameters, slow-down method will be switched on
| [in] | _kappa_ref | reference kappa factor; slow-down factor kappa = max(1,kref/perturbation_factor) |
| [in] | _timescale_max | maximum timescale |
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print slowdown data
Print slowdown data
| [in] | fout | ofstream for printing |
| [in] | precision | printed precision for one variable |
| [in] | width | printing width for one variable |
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print data of class members using column style
print data of class members in one line for column style. Notice no newline is printed at the end
| [out] | _fout | std::ostream output object |
| [in] | _width | print width (defaulted 20) |
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print titles of class members using column style
print titles of class members in one line for column style
| [out] | _fout | std::ostream output object |
| [in] | _width | print width (defaulted 20) |
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read class data with BINARY format
| [in] | _fin | file IO for read |
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restore real time and force ratio
| [in] | _bk | restore data array[3] |
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set real time value
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manually set kappa
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set update time for new slowdown factor
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write class data with BINARY format
| [in] | _fout | file IO for write |
| Float AR::SlowDown::period |
| Float AR::SlowDown::pert_in |
| Float AR::SlowDown::pert_out |
| Float AR::SlowDown::timescale |


Iran Rocked By Deadly Fuel Protests
by RFE/RL's Radio FardaSeveral people have been killed in Iran after four days of violent protests over a spike in gasoline prices.
Several people have been killed in Iran after four days of violent protests over a spike in gasoline prices.


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