view.Base Class
Extends
Item Index
Methods
Methods
_init
-
callback
Parameters:
-
callback
Function
_isBound
-
checkId
has the data been bound
Parameters:
-
checkId
Boolean[default=false] optional - if true it will also check that the target it bound with the current view and not just generally bound to
Returns:
_render
-
those
Parameters:
-
those
Objectof Firebrick.views.renderTo
_renderHTML
-
html
Parameters:
-
html
String
Returns:
self
bind
()
Called by view.Base:render()
bindContent
()
detroy
()
Object
unbind and remove from DOM
Returns:
self
getData
()
Object
Returns data store data as object
Returns:
getEnclosedBindId
()
String
Returns:
getEnclosedTarget
()
JQuery Object | Null
Returns:
getStore
()
Returns the store linked to the view
getTarget
()
Object | Null
Returns:
jquery object
hide
()
hide target view.Base:getTarget
init
()
Called on creation
initStore
-
Firebrick.view.Base
Converts View data into a Store if not already done
Parameters:
-
Firebrick.view.Base
Objectobject
Returns:
self
initSubViews
()
private
initView
()
Object
Construct the view with template and data binding
Returns:
self
isTargetBound
()
Boolean
has data been bound to the target by THIS view
Returns:
isTargetBound
()
Boolean
has data been bound to the target by A|ANY view
Returns:
isVisible
()
prepHtml
()
String
prepare the HTML for rendering
Returns:
html
remove
()
Object
remove from dom
Returns:
self
render
()
Object
Calls renderTo without parameters
Returns:
self
setDisposeCallback
-
el
Parameters:
-
el
HTMLElement
show
()
show target view.Base:getTarget
startLoader
()
private
stopLoader
()
private
unbind
()
Object
unbind the data from this view
Returns:
self
unbound
()
private
called by view.Base:setDisposeCallback
update
-
data
update the view with new data
Parameters:
-
data
Objectextra data you wish to pass to the view
Returns:
self
Properties
_state
String
private
State the view is current in. "initial", "rendered", "unbound", "destroyed"
Default: "initial"
animations
$.show() Arguments
Default: null
appendTarget
Boolean
whether to append or overwrite the content of the target
Default: false
applyBindingsToDescendants
Boolean
bindings are applied to its decendants, not on the target itself
Default: false
async
Boolean
whether or not the template is to load asyncronously
Default: true
autoRender
Boolean
render the view on class creation
Default: true
bindAttribute
String
custom attribute to add to the element to mark as bound
Default: "fb-view-bind"
controller
String | Object name of the controller | | controller class itself
controller to bind to the view
Default: null
enclosedBind
Boolean
wrap the view inside its own div which gets bound separatly to its context
Default: false
enclosedBindIdPrefix
String
Default: "fb-enclosed-bind-"
html
String html
parsed html using the tpl and data
Default: ""
isView
Boolean
private
boolean whether class is view
loading
Boolean
private
whether the loader is being shown or not
Default: false
loadingTpl
String
loading template - loaded into target is showLoading == true
Default: Firebrick.templates:loadingTpl
showLoading
Boolean
whether to show that the view is loading
Default: true
store
String | Store Object
bind a store or plain data to the view
Default: null
subViews
String | Array of Strings | Object | Array of Objects
define subviews to load after creation of this view
Example:
subViews: MyApp.view.MyView
subViews: ["MyApp.view.MyView", "MyApp.view.MyView1"]
subViews: Firebrick.defineView(...)
subViews: [Firebrick.defineView(...), Firebrick.defineView(...)]
target
String | Object jquery selector | | jquery object
Target to which to render the html content
Default: null
tpl
String
set when the view is loaded by the ajax request
Default: ""


Refine by
Organization Type
Market Segment
Countries
| Name | Type | Sector | Website |
|---|---|---|---|
| Boston Dynamics | Company | Private | www.bostondynamics.com |
| Clearpath Robotics | Company | Private | www.clearpathrobotics.com |
| Kuka | Company | Public | www.kuka.com |
| Name | Type | Sector | Website |
|---|---|---|---|
| Boston Dynamics | Company | Private | www.bostondynamics.com |
| Clearpath Robotics | Company | Private | www.clearpathrobotics.com |
| Kuka | Company | Public | www.kuka.com |


{% extends "common/base.html" %} {% load base %} {% block html_body %} {% open_box %}
Your account {{ username }} has been logged out.
Return to Home Page


i
Reg
Chain
Phy
Ele
20%
Cor
Unident
Norm
Rare
Uniq
Map
Sac
Splint
Card
Gem
Essence
Foss
Oil
Sampl
Veil
$$$
Grab from stash.


mitoscripts package¶
Submodules¶
mitoscripts.mitodata module¶
-
mitoscripts.mitodata.
analyze_images
(data_dir, name_dict=None, data_name='index')¶ Runs image analysis on each sample within a data directory
- Parameters
data_dir (str) – path to where the data is.
name_dict (dict) – dict containing key:value pairs with keys of filename at time of acquisition and values of what they should be labeled (ex: KRAS_control: KRAS_ctrl)
data_name (str) – an optional name for your dataset, to designate that specific experiment (ex: compression_10_21_19)
- Returns
pandas df containing a summary of all pertinent data
-
mitoscripts.mitodata.
analyze_mitochondrial_length_distribution
(data_dir, name_dict=None, data_name='index')¶ Runs image analysis on each sample within a data directory
- Parameters
data_dir (str) – path to where the data is.
name_dict (dict) – dict containing key:value pairs with keys of filename at time of acquisition and values of what they should be labeled (ex: KRAS_control: KRAS_ctrl)
data_name (str) – an optional name for your dataset, to designate that specific experiment (ex: compression_10_21_19)
- Returns
pandas df containing data pretaining to the distribution of mitochondria within the cell
-
mitoscripts.mitodata.
append_conditions
(sheet, name_dict, is_for_edgedist=False)¶ Add row “Conditions” describing the treatment of each image (+/- drug, +/- compression, etc)
- Parameters
sheet (pandas df) – existing sheet with data on images from data_dir
name_dict (dict) –
dict containing key:value pairs with keys of filename at time of acquisition and values of what they should be labeled
(ex: KRAS_control: KRAS_ctrl, p53_aga: p53_aga) This will make anything labeled KRAS_control_001_041 etc named “KRAS_control”
is_for_edgelist (int) – this will be False unless set to true by proper function. Treats indices differently.
- Returns
updated sheet with appended conditions column
- Return type
sheet (pandas df)
-
mitoscripts.mitodata.
create_automated_mitograph_df
(mitograph_path)¶ Creates a cleanly-formatted df containing metrics that are automatic exports from the MitoGraph C code
- Parameters
mitograph_path (str) – path to a .mitograph file
- Returns
a cleanly-formatted df of MitoGraph automated outputs
- Return type
df (pandas df)
-
mitoscripts.mitodata.
create_degree_distribution_df
(big_tuple)¶ Creates a degree distribution df for processing in the analyze_image() func
- Parameters
big_tuple (tuple) –
containing: overall_network (igraph object): igraph object for a single image. Contains “length” data for each edge. edgelist_df (Pandas df): a pandas dataframe with the edgelist for the overall network combined_mito_dataframe (Pandas df): a pandas dataframe where every row is a single mitochondria in the overall
network and the columns describe how many nodes, edges, and how long the mitochondria is.
- Returns
dataframe containing data on degree distributions of an image
- Return type
degree_dist_df (pandas df)
-
mitoscripts.mitodata.
create_edgelist_df
(gnet_path)¶ Reads in a path to a .gnet file, produces a df of an edgelist for use in igraph
- Parameters
gnet_path (str) – This must be a path to a .gnet file
- Returns
- This is an edgelist to be then processed by igraph to create a graph
theory representation of the mitochondria within that .gnet file’s image
- Return type
df (pandas df)
-
mitoscripts.mitodata.
decompose_individual_mitochondria
(igraph_df_tuple)¶ Breaks down a whole-image igraph obj into its constituate mitochondria as separate igraph objs
- Parameters
igraph_df_tuple (tuple) – a tuple from initialize_network() containing: overall_network (igraph object): igraph object for a single image. Contains “length” data for each edge. edgelist_df (Pandas df): a pandas dataframe with the edgelist for the overall network
- Returns
- a tuple containing:
overall_network (igraph object): igraph object for a single image. Contains “length” data for each edge.
edgelist_df (Pandas df): edgelist for the overall network
- combined_mito_dataframe (Pandas df): a pandas dataframe where every row is a single mitochondria in the overall
network and the columns describe how many nodes, edges, and how long the mitochondria is
- Return type
tuple
-
mitoscripts.mitodata.
find_all_filetype
(data_dir, extension)¶ Finds all files in a directory, returns their path
- Parameters
data_dir (str) – path to where your MitoGraph output data are
extension (str) – the extension to search for (e.g. “.gnet”, “.mitograph”)
- Returns
- a list where each element is a string containing the path for desired files (with extension)
(e.g. path_list = [sample1.gnet, sample2.gnet])
- Return type
path_list (list of strings)
-
mitoscripts.mitodata.
initialize_network
(gnet_path)¶ Creates a graph theory network of all mitochondria in an image.
- Parameters
gnet_path (str) – path to a single gnet file, which contains connectivity data. One exists per image.
- Returns
- a tuple containing:
overall_network (igraph object): igraph object for a single image. Contains “length” data for each edge. edgelist_df (pandas df) the pandas df a pandas dataframe with the edgelist for the overall network
- Return type
(tuple)
-
mitoscripts.mitodata.
quant_graph_theory
(data_dir)¶ Maps out the analysis for a data_dir of images
- Parameters
data_dir (str) – path to where the data is
- Returns
a transformation from image file -> per graph per mito analysis
- Return type
map object
-
mitoscripts.mitodata.
remove_enclosing_dirs
(full_path)¶ Remove any directories in a filepath
If you pass a filename to this function, meaning it doesn’t contain the “/” string, it will simply return the input back to you.
- Parameters
full_path (str) – a Unix-based path to a file (with extension)
- Returns
just the filename (with extension) from the path
- Return type
filename (string)
-
mitoscripts.mitodata.
remove_extension
(string)¶ Removes the extention from a string, as well as the directories.
This function may fail if more than one . is in the file, such as “.tar.gz”
- Parameters
string – (string): either a path or a filename that for a specific file, with extension. (e.g. /usr/dir/sample.mitograph or sample.mitograph)
- Returns
just the filename without the extension (e.g. “sample”)
- Return type
filename_without_extension (str)
-
mitoscripts.mitodata.
summarize_image
(big_tuple)¶ Takes a big tuple filled with data about a specific image, then summarizes it into a single pandas df row
- Parameters
big_tuple (tuple) –
containing: overall_network (igraph object): igraph object for a single image. Contains “length” data for each edge. edgelist_df (Pandas df): a pandas dataframe with the edgelist for the overall network combined_mito_dataframe (Pandas df): a pandas dataframe where every row is a single mitochondria in the overall
network and the columns describe how many nodes, edges, and how long the mitochondria is.
- Returns
a single row to be concatonated later filled with metrics on a specific image
- Return type
summary_dataframe (pandas df)
-
mitoscripts.mitodata.
timefn
(fn)¶ wrapper to time the enclosed function
mitoscripts.mitographer module¶
-
mitoscripts.mitographer.
clean_df_and_get_corr
(df)¶
-
mitoscripts.mitographer.
clustermap
(df, title='', data_name='', savefig=False)¶
-
mitoscripts.mitographer.
create_graph_array
(cur_data, name_dict, savefigs=False)¶ currently not working
-
mitoscripts.mitographer.
create_graph_suite
(data_dir, data_name, name_dict, savefigs=False)¶ currently working
-
mitoscripts.mitographer.
get_default_col_to_drop
()¶
-
mitoscripts.mitographer.
heatmap
(df, title='', data_name='', savefig=False)¶
-
mitoscripts.mitographer.
make_centroid_plot
(df, to_drop=None, style='tableau-colorblind10')¶
-
mitoscripts.mitographer.
make_pca_plot
(df, to_drop=None, style='tableau-colorblind10')¶
-
mitoscripts.mitographer.
make_scree_plot
(df, to_drop=None, style='tableau-colorblind10', n_comp=None)¶
-
mitoscripts.mitographer.
pca_suite
(df, to_drop=None, style='tableau-colorblind10')¶
-
mitoscripts.mitographer.
run_pca
(df, to_drop=None)¶
-
mitoscripts.mitographer.
scatter_length_distribution
(data_dir, name_dict, data_name='', savefig=False)¶
-
mitoscripts.mitographer.
scattered_box_plot
(data, column, sample_order, data_name='', ax=None, x='Conditions', logscale=False)¶
mitoscripts.mitopca module¶
-
mitoscripts.mitopca.
force_50_50_axes
(df, min_cols=6)¶ Currently not working
-
mitoscripts.mitopca.
remove_dissimilarity
(df, threshold=0.95, min_cols=6)¶
-
mitoscripts.mitopca.
run_2_dim_pca
(df, take_sum=True)¶
-
mitoscripts.mitopca.
run_special_pca
(df, threshold=0.95, min_cols=6)¶


EXCLUSIVE: Azie Faison on Alpo Killing Rich Porter Over $300,000 (Flashback)
by VladTVIn this VladTV Flashback from 2016, Azie Faison talks about why he didn't like the ending of the film, denies cooperating with the police, admits Alpo killed Rich Porter over $300,000, and says he talked to Alpo while he served time in prison. Watch above.


kohls
Businessmodel of Kohl’s
Customer Segments
Kohl’s offers a broad range of apparel products and accessories, including for males and females of all ages. The Company serves a diverse range of customer demographics, however, recent reports state that is particularly popular among women, who enjoy the Company’s convenience and its frequent discounts on branded items. It is also popular with consumers aged between 35 and 54, an age range that, according to a recent survey, accounts for more than 40% of the Company’s customers.
Kohl’s serves an extensive domestic customer base in its native US, operating online and physical retail outlets across 49 of the country’s 50 states, with its largest presence being in California, Texas, and Illinois. The Company does not currently have any significant international operations.
Value Propositions
Kohl’s provides value to its customers in the following ways:
- Its reputation and track record, with the Company established as a reliable and efficient provider of branded and unbranded apparel, with a track record for providing high-quality products at reasonable prices;
- Its extensive product portfolio, with the Company offering a broad catalogue of products, including a full range of apparel products and accessories designed for men, women, and children of all ages;
- Its accessibility and reach, with the Company operating 1,164 physical retail outlets across 49 US states, serving a broad range of local communities, as well as operating an online sales channel that serves customers across the country quickly and efficiently; and
- Its brand strength, with the Company having developed a popular and well-known brand name that effectively serves its core customer demographic of women aged between 35 and 54. ### Channels
Kohl’s operates a corporate website at www.corporate.kohls.com, through which it provides information on its corporate activities, investors relations, and leadership. It also operates a separate consumer-facing website at www.kohls.com, through which it provides more specific detail on its products and services. This website also serves as an online sales channel, allowing customers to browse the Company’s products, make purchases, and organise deliveries independently. The Company additionally offers a mobile app, available on Android and iOS devices, that provides similar functionality.
Kohl’s principally makes its sales through its extensive network of physical retail outlets spread across 49 US states. This network comprises 1,164 outlets, through which the Company employs teams of sales personnel who directly serve the Company’s customers in person. The Company supports this sales infrastructure with its own distribution and logistics network, comprising nine retail distribution centres through which all of the Company’s merchandise is received.
Customer Relationships
Kohl’s offers a full range of products to its customers on a self-service basis through its online retail channel, which allows customers to browse products, make purchases, manage account details, and arrange product deliveries without interacting directly with the Company’s sales and support personnel.
Kohl’s provides personalised service to its customers through its in-store sales staff, who are available to provide assistance with products and customer enquiries, and handle complaints and product returns in person. The Company also operates a dedicated customer support organisation, the members of which customers are able to contact over the phone or online in order to receive tailored support.
Kohl’s also provides a range of online support resources to its customers through its website, including shipping and tracking information, guides, and FAQs, which can be accessed independently. The Company additionally operates a number of social media accounts – including with Facebook, Twitter, Pinterest, Instagram, Google+, and YouTube – through which it can interact directly with customers.
Key Activities
Kohl’s is an operator of department stores and online retailer of apparel and accessories. The Company operates more than 1,160 department stores across 49 of the US’s 50 states. The Company’s product portfolio includes private label, exclusive and national brand apparel, footwear, accessories, beauty and home products.
Its website also includes merchandise, which is available in its stores, as well as merchandise, which is available exclusively online. Kohl’s private brands include Apt. 9, Croft and Barrow, Jumping Beans, SO and Sonoma Goods for Life. Its exclusive brands include Food Network, Jennifer Lopez, Marc Anthony, Rock and Republic and Simply Vera Vera Wang.
Key Partners
Kohl’s works in close collaboration with a wide network of partner companies and organisations. These partners can be categorised broadly as follows:
- Supplier and Vendor Partners, including suppliers of products and merchandise that are resold by the Company online and across its retail outlets, as well as supplier of services, technologies, and equipment that are utilised more broadly across the enterprise;
- Brand Partners, comprising various high-profile brands with which the Company maintains exclusive and non-exclusive retail partnerships;
- Channel and Distribution Partners, comprising companies that assist in the distribution and delivery of the Company’s products, both to its stores and to its online customers;
- Hospital Partners, comprising various hospitals and healthcare facilities with which the Company collaborates in developing and providing effective and reliable care across communities in the US; and
- Strategic and Alliance Partners, comprising market-leading companies across multiple sectors with which the Company collaborates on various branding, marketing, and other projects. Kohl’s has agreed a number of partnerships in recent years. This includes a partnership with AwesomenessTV, a unit of Dreamworks, to market its products to teens, a retail tie-up with UnderArmour, and a fashion partnership with former Coach Inc executive creative director Reed Krakoff.
Key Resources
Kohl’s key resources are its intellectual properties and brands, its catalogue of products and supply chain, its online platform, its IT and communications infrastructure, its physical sales and distribution infrastructure, its partnerships, and its personnel.
Kohl’s owns and or leases a number of physical properties that are key to its ongoing activities. This notably includes the Company’s network of 1,164 stores with 83.8 million selling square feet across 49 states, as well as its nine distribution centres that manage all of the Company’s merchandise.
Kohl’s also owns a number of intellectual properties and brands. Searches of records published by the US Patent and Trademark Office identified no patent applications filed in the Company’s name.
Cost Structure
Kohl’s incurs costs in relation to the development of its intellectual properties, the procurement of products and merchandise, the procurement of professional services, the development of its online platform, the maintenance of its IT and communications infrastructure, the operation of its physical retail and distribution infrastructure, the management of its partnerships, the implementation of marketing and advertising campaigns, and the retention of its partnerships.
In 2015 the Company’s largest expense was its cost of merchandise sold, which totalled $12.27 billion for the year. The Company also recorded selling, general and administrative expenses – including include compensation and benefit costs and occupancy expenses – in the amount of $4.45 billion.
Revenue Streams
Kohl’s generates revenue through the sale of apparel and accessories, both online and through its chain of physical sales outlets. The Company derives its revenue principally in the form of general sales fees.
In 2015 Kohl’s recorded net sales for the year in the amount of $19.20 billion, up marginally on the $19.03 billion recorded by the Company in 2014.


william hayden
vcd
Email
368 Website
favorite foods
i lurve tacos
i love cheezburgers
i loves macaroni


A classifier satisfies this definition if all groups defined by the sensitive attribute have equal Positive Rate.
A classifier satisfies this definition if all groups defined by the sensitive attribute have equal True Positive Rate and equal False Positive Rate.
A classifier satisfies this definition if all groups defined by the sensitive attribute have equal True Positive Rate.
A classifier satisfies this definition if all groups defined by the sensitive attribute have equal Positive Predictive Value.
This metric measures whether the number of positive predictions (those with value "{{labelList[1]}}") is equal between the chosen group ("{{ currentReferenceGroup }}") and the other groups defined by the sensitive attribute.
The measure does not consider whether the positive predictions were true or false, only how many positive predictions appear in each group.
Disparity values close or equal to zero indicate equality or parity between the groups, while values close to one (or negative one) indicate imbalance in how many positive values are predicted in each group.
This metric measures whether the true positive rates and false positive rates are equal between the chosen group ("{{ currentReferenceGroup }}") and the other groups defined by the sensitive attribute.
It assesses the classifier’s ability to correctly predict positive values (those with value "{{labelList[1]}}") and the likelihood of incorrectly predicting positive values.
If the disparity value is low, it means that the classifier finds true positives at a similar rate across subgroups and incorrectly assigns the positive class at the same rate among groups.
This metric is a relaxed version of Equalized Odds that only measures the true positive rate between between the chosen group ("{{ currentReferenceGroup }}") and the other groups defined by the sensitive attribute.
When the disparity metric is close to zero, it means the classifier predicts true positive values (those with value "{{labelList[1]}}") at the same rate across groups.
A value farther from zero indicates that the classifier is better at predicting true positives for one group over another.
This metric measures the difference in precision between the chosen group ("{{ currentReferenceGroup }}") and the other groups defined by the sensitive attribute.
Precision is measured as the percentage of all predicted positives (those predicted with value "{{labelList[1]}}") that were actually true.
When the disparity metric is farther from zero, it indicates that the classifier predicts fewer false positives for one group than another.
Demographic Parity
Equalized Odds
Equality of Opportunity
Predictive Rate Parity
Fraction of positive cases ({{labelList[1]}}) predicted
Fraction of negative cases ({{labelList[0]}}) incorrectly predicted to be in the positive class out of all actual negative cases
Fraction of positive cases ({{labelList[1]}}) correctly predicted to be in the positive class out of all actual positive cases
Fraction of positive cases ({{labelList[1]}}) correctly predicted to be in the positive class out of all predicted positive cases


My name is Evan Druce, and I am in the Fiery Skippers cohort at Dev Bootcamp in New York City, class of 2016! This is where you will find professional information about me, including a resume and contact information, as well as links to projects I have completed and the blog I have been writing as part of Phase 0 of Dev Bootcamp.


mystateEngine.askOracle(reqName, val, myInstance)
reqName Name (string) of the request
valValue associated with the request (if any)
myInstance (optional) Instance of who is making the request. Only used if oracle uses it to determine how to answer.
Gives back the value that the oracle has returned, if any.
This method is used to request information from the engine's oracle. The oracle must be set up in advance to answer these types of requests. The oracle must have a method named irh()
to receive the request, and must return a value. This value is returned from askOracle()
.


Chinstrap Penguins (Pygoscelis papua) and Gentoo Penguins (P. papua), Danger Islands
Research
Population and individual level foraging variation among Pygoscelis penguins
While the diets and foraging ecology of Pygoscelis penguins have been well studied in the Antarctic Peninsula and Scotia Arc region, many studies overlook diet and niche variation among and within populations, as well as variation within individuals. One way to explore these questions is through stable isotope analysis (SIA), which provides an effective method to assess the diet and foraging niches of populations and individuals through time. Together, δ15N and δ13C values can be used to determine the 2-dimensional parameters of an isotopic niche space, a comparable measurement of an ecological or foraging niche. My Master’s research focused on investigating population and individual level foraging variation of Pygoscelis penguins through SIA. Quantifying the degree of niche variation and consistency at the population and individual level has the potential to aid in determining a species population level response to changes in food availability or other environmental change.
Phylogeography of North Pacific Procellariiformes
The genetic diversity and evolutionary histories of tubenosed seabirds (Procellariiformes) are poorly known, in large part because of the difficulty of obtaining samples from their remote breeding islands. Genetic data are lacking even from otherwise well-studied seabirds from the North Pacific Ocean, including those that breed in the Hawaiian Islands. New genomic techniques are now allowing us to retrieve data from new sources, such as specimens archived in natural history museums. I am using a technique called sequence capture to obtain DNA sequence data from across the genome from modern genetic samples and museum specimens in several seabirds distributed across the North Pacific. I will use these data to reconstruct the population history of each species and to assess population structure and divergence between breeding areas. These results might be used to revise species-level taxonomy and prioritize populations for conservation action.


THE CIDER HOUSE RULES *** (out of four stars) A review by Jamey Hughton
Starring-Tobey Maguire, Charlize Theron, Michael Caine, Delroy Lindo and Paul Rudd Director-Lasse Hallstrom Canadian Rating-PG
MOVIE VIEWS by Jamey Hughton http://Welcome.to/MovieViews
In anticipation of the John Irving adaptation `The Cider House Rules', I imagine audiences were more skeptical than optimistic. Although Irving's novels have been revised to big-screen format in such gems as `The World According to Garp', the most recent example was the sentimental 1998 drama `Simon Birch' - a film that Irving reportedly detested to such a degree that he wanted his name removed from the finished product. In addition, the production of `The Cider House Rules' represents a 13-year battle for theatrical redemption. But surely don't scoff and dismiss it as another boring sentimental weeper, because `Cider House' actually offers a touching human story with a finely textured dramatic impact. To pass it up would not be a substantial mistake, but the experience is light, moving and ultimately worth your while.
The story opens during the mid-1940's at an orphanage in St. Clouds, Maine, where Homer Wells (Tobey Maguire) has resided for his entire life. Homer was denied admittance into the army because of a heart defect, and so he has stayed to help the kindly old head physician Dr. Wilbur Larch (Michael Caine) conduct deliveries and illegal abortions. When young couple Candy Kendall (Charlize Theron) and Air Force pilot Lt. Wally Worthington (Paul Rudd) arrive at St. Clouds with a request for the latter, Homer spots an opportunity to depart and begin a life of his own. Once out in the world, he is given his first view of the rippling waves of the ocean and is graciously offered a job in an apple orchard owned by Wally and his mother. Here, living in the Cider House with Mr. Rose (Delroy Lindo) and his employees, Homer finds a comfortable niche away from the orphanage. And when Lt. Worthington is called into battle, a romantic subplot develops between Homer and Candy, who (as it turns out) are hopelessly in love with each other.
Quite simply, `The Cider House Rules' is a movie of subplots. Another particularly shocking one, involving Mr. Rose and his young daughter (Erykah Badu), also culminates as the film reaches the meat of the story during its second half. I suspect that, without these involving examples of threading the central coming-of-age story together, the film could have toppled altogether like a clumsy house of cards. The process of evaluating Irving's novel to the fullest and discovering the morals and values of Homer's journey is a somewhat overlong (and occasionally aimless) exercise for director Lasse Hallstrom and company. Ultimately though, the film peaks during the climactic moments in dramatic satisfaction without even beginning to get excessive, and the rough patches earlier are overcome by a string of marvelous performances from a top-notch ensemble cast.
Maguire (`Pleasantville') is totally believable as the chief protagonist in `The Cider House Rules', and his earlier scenes with Caine (shining brightly in an Oscar-nominated performance) are among the most effective in the film. Theron (the rising star of `Mighty Joe Young' and `The Astronaut's Wife') is superbly convincing as the character of Candy, who deals with grueling issues that will be easily related to. The remainder of the cast (including the always forceful Delroy Lindo) deliver uniform performances of greatness. Backed by a beautifully flowing musical score by Rachel Portman and gorgeous cinematography courtesy of Oliver Stapleton, `The Cider House Rules' moves melodiously until Homer's journey has come to a close, offering several Kodak moments of poignancy and good-natured humor along the journey.
Looking back on the film, I'm finding it difficult to properly interpret some of the underlying messages that Hallstrom was attempting to identify. At times, `The Cider House Rules' becomes a victim of its own leisurely agenda - in hindsight, the cream filling is more tasteless than it seemed as I was exiting the theater. But `Cider House' still manages to win you over with its sublimely moving story, even if the emotional depth you were hoping for never fully arises. John Irving should be pleased with this finished product, and for that matter, so should you. `The Cider House Rules' induces a rewarding smile as the curtain closes, and that seems sufficient enough.
(C) 2000, Jamey Hughton
MOVIE VIEWS by Jamey Hughton http://Welcome.to/MovieViews
Your Comments Appreciated<EMAIL_ADDRESS>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


Engineer Charged with Sending Trade Secrets to Iran
Federal prosecutors in Detroit have charged an engineer with sending corporate trade secrets to a brother in Iran.
Amin Hasanzadeh of Ypsilanti is also accused of lying on immigration forms by failing to disclose his service in the Iranian military.
He has permanent resident status in the US, commonly referred to as a green card.
The FBI said the 42-year-old Hasanzadeh sent confidential documents to Iran while working at a Michigan company that serves the auto and aerospace industries.
The government said his responsibilities included work on a supercomputer that had aerospace applications. The alleged scheme occurred in 2015 and 2016, reported The Associated Press.
Hasanzadeh appeared in court Wednesday and asked for an attorney. He stated he works at the University of Michigan. He is in custody until a Friday hearing.


An anology that I like to use to describe the relationship between HTML, CSS, and Javascript, is to think of a webpage as a coffee shop. The HTML of a web page can be compared to the base structure and architecture of the coffee shop, the walls, floors, windows, roof. The CSS is the outer style and interior design, such as the funiture, wall paper, carpet, wall decor, and the painting on the outside. With both the HTML & CSS, we have a nice looking coffee shop, but a coffee shop needs more to it in order to operate as a proper establishment. The JavaScript in this instance, covers all the inner working parts that help the coffee shop function, such as the barristas to take and serve orders, the coffee machines to make the orders, the register to keep track of sales and profits, the clocking system to record workers hours, the inventory management system to keep track of and make new orders, etc..
In JavaScript, loops are a method to perform functions repeatedly based on the type of loop it is (i.e. for, while, do/while). Control flow is the order that the computer reads the code in to determine how it will perform functions, by default it will be read from first line to last, however, there are keywords that can be used in order to change the structure that the computer reads in. To explain this in a more digestable way, let's write some psuedo-code based on the example of somebody who drinks 3 coffees a day and wants to track their daily intake in a journal.
Let's break this down. The loop here says that while the person has had more than 0 cups of coffee today, they want to add it to their daily total. Every time there is an addition to their daily total, they want to add an entry in their daily journal that documents how many cups of coffee they have had today. However, let's then say that this personal also wants to set a limit to how many cups of coffee they drink per day, and want to remind themselves to not go over the limit. This is when control flow comes it to add a break to our structure once that limit has been hit.
With this addition, the previous loop of drink coffee then add to journal, will function as long as the daily total is less than the persons daily limit. However, if the persons daily total is equal to their daily limit, then the control flow will shift to the next part of the loop, where they will document in their journal a remainder that their daily limit has been reached, and will stop drinking coffee for the day.
An analogy that could work for the DOM, is if we were to compare a web page to an apple tree. Much like the tree holds all of the branches and each of the branches holds apples, the DOM holds all of the objects on a webpage and seperates them based on identity.
The DOM, much like the apple tree allows us to visualize, interact with, and modify each of the objects that it contains. An example of how we may interact with the DOM, would be to use a JavaScript loop as previously discussed above, to check for any objects that were added under a date that we no longer want to display on the web page, and then make them invisible. Similiar to how with the apple tree, we want to pick any apples from it that have past their expirary date and dispose of them.
In JavaScript, an object is a piece of data that can store multiple unique properties, an Array is a type of object that has the capacity of storing other objects, including all of their individual properties. The properties of both objects and arrays can be accessed and modified. An anology that can be used here is if we think of the array as a binder, where we can classify the binder as an object that has to capacity to store many other objects within, which in this case would be documents. Let's also write up some pseudo code to show this in a code format.
Here, we see that in the code format, the binder object has been created, and within the square brackets, are each of the document objects. Now, let's say that we want to access a document from this binder. In the code example, we need to declare which specific document it is that we would like to access from the binder array. Because array objects are counted from 0 upwards, if we want to select the 1st document in the binder, in our code we need to specify the object in the array that is identified by 0.
After specifying the document that we want from our binder, we can inspect the specific attributes of it as we need. In code format, this could be the date that the document was published, in which we would not need to specify that property in the same way as the array object, we could specific it as "currentDocument.date". This is because there can only be one unique property called 'date' in the document object, whereas the binder array object could thearetically have multiple document objects that have the same name, so it relies on the numbered order that the document objects are organised in to identity them.
In JavaScript, a function is a piece of code that has been set up to perform a specific task that can retrieve and return values. An anology that I will use for this, is that of a balance scale. A balance scale has both a right side and a left side that are both purposed for holding items. The scale will then detect the weight of both items and will then showcase which panel has the heavier weight to it. Let's also take a look at this in psuedo-code to get an idea of what this would look like as a function.
If we break this down, we have essentially replicated the basic function of the balance scale in psuedo-code. The function takes in a left panel and a right panel, it checks if the weight property of the left panel is greater than the weight property, and it will then return the left panel as the heavier panel if that is indeed the case. If not, it will then perform the same calculation to the right panel and return that if it's the case. Otherwise, if both weigh the same, neither are the heavier panel and no value putting one over the other can be returned.


#http://nedbatchelder.com/blog/201302/finding_python_3_builtins.html
"""Look for builtins..."""
import types
def is_builtins(v):
"""Does v seem to be the builtins?"""
if hasattr(v, "open") and hasattr(v, "__import__"):
return True
if isinstance(v, dict):
return "open" in v and "__import__" in v
return False
def construct_some(cl):
"""Construct objects from class `cl`.
Yields (obj, description) tuples.
"""
# First yield the class itself
classname = "{}.{}".format(cl.__module__, cl.__name__)
yield cl, classname
made = False
for args in [
(), ("x",), ("x", "y"), ("x", "y", "z"),
("utf8",), ("os",), (1, 2, 3),
(0,0,0,0,0,b"KABOOM",(),(),(),"","",0,b""),
# Maybe there are other useful constructor args?
]:
try:
obj = cl(*args)
except:
continue
desc = "{}{}".format(classname, args)
yield obj, desc
made = True
if not made:
print("Couldn't make a {}".format(classname))
def attributes(obj):
"""Produce a sequence of (name, value), the attributes of `obj`."""
try:
for n in dir(obj):
if n in ('__dict__',):
continue
try:
yield n, getattr(obj, n)
except:
continue
except:
pass
def items(obj):
"""Produce a sequence of (key, value), the items of `obj`."""
try:
for k in obj.keys():
try:
yield k, obj[k]
except:
continue
except:
pass
def attrs_and_items(obj, desc):
"""Produce a sequence of (name, value, desc) for data from `obj`."""
for n, v in attributes(obj):
desc2 = "{}.{}".format(desc, n)
yield n, v, desc2
for k, v in items(obj):
desc2 = "{}[{!r}]".format(desc, k)
yield k, v, desc2
def examine(obj, desc, seen, depth):
"""Examine the data reachable from `obj`, looking for builtins."""
if depth > 10:
return
if id(obj) in seen:
return
if isinstance(obj, (type(""), type(b""), type(u""))):
return
seen.add(id(obj))
# Look at the attributes and items
for n, v, desc2 in attrs_and_items(obj, desc):
if is_builtins(v):
print("Looks like {} might be builtins".format(desc2))
else:
examine(v, desc2, seen, depth+1)
examined = 0
for cl in object.__subclasses__():
seen = set()
for obj, desc in construct_some(cl):
print("Examining {}".format(desc))
examine(obj, desc, seen, 0)
examined += len(seen)
print("Examined {} objects".format(examined))


Digital photography
Fred C. Billingsley, "Processing Ranger and Mariner Photography," in Computerized Imaging Techniques, Proceedings of SPIE , Vol. 0010, Jan. 1967 (Aug. 1965, San Francisco). "Mariner is unique in that the pictures were converted to 6-bit digital form in the spacecraft. The digital signals were transmitted at a very slow rate (8 1/3 bits/sec) and decoded and reformatted in the 7094 computer before being presented to the film-recording equipment on computer tape. Thus, there are no digitizing and synchronizing problems, and the operation consists merely of producing the digitally recorded film."
"Mariner to Mercury, Venus and Mars" (PDF) . NASA Facts . Retrieved 2 Aug 2012
"Digital Photography Milestones from Kodak" . Women in Photography International . Retrieved 17 September 2007.
"Kodak blog: We Had No Idea" .
Michael R. Peres (2007). The Focal Encyclopedia of Photography (4th ed.). Focal Press. ISBN 0-240-80740-5 .
"1990" . DigiCam History Dot Com . Retrieved 17 September 2007.
"Dycam Model 1: The world's first consumer digital still camera" . DigiBarn computer museum .
Carolyn Said, "DYCAM Model 1: The first portable Digital Still Camera", MacWeek , vol. 4, No. 35, 16 Oct. 1990, p. 34.
"How Digital Cameras Work" . HowStuffWorks . 2006-11-29. Retrieved 2016-10-11.
"PhotoAccess reveals 4:3 ratio prints" . Digital Photography Review . Jul 11, 2000. Retrieved Aug 22, 2014.
Foveon X3 Sensor Claims Put to the Test
Digital outsells film, but film still king to some | Macworld
Smith, Tony (2004-01-20). "Kodak to drop 35mm cameras in Europe, US" . The Register. Retrieved 2007-04-03.
"Nikon to End Many Film-Related Products" . 2006-01-11. Retrieved 2007-04-03.
Tomkins, Michael R. (2004-06-01). "Pentax plans to focus on digital" . The Imaging Resource. Retrieved 2007-04-03.
"Eastman Kodak Files for Bankruptcy" . The New York Times . 2012-01-19.
"Digital Camera Sales Trends A declining trend that's slowly stabilizing" . September 22, 2014.
Lombardi, Rosie (2006-12-20). "How long will my digital pictures last?" . PC World . Retrieved 2007-04-03.
"Photosynth" . Microsoft Research . Retrieved 2007-04-03.
Raskar, Ramesh; Amit Agrawal; Jack Tumblin. "Coded Exposure Photography: Motion Deblurring using Fluttered Shutter" . Retrieved 2007-04-03.
Lars Rehm (March 25, 2014). "HTC launches One M8 with new 'Duo Camera' " .
Reichmann, Michael. "The Ultimate Shoot-Out" . The Luminous Landscape. Retrieved 2007-04-03.
A 100 MP Digital Camera System for Under $2,000
"D90 from Nikon, Key Features" . Nikon Inc. Archived from the original on 9 September 2013. Retrieved 2009-09-03.
Canon EOS-1V HS Body with PB-E2 Power Drive Booster 2044A005 B&H


Retrieves the time between the two dates. ## Syntax
getTimeBetween(first: <#Variable#>, second: <#Variable#>, interval: <#Type (WFTimeUntilUnit)#>)
getTimeBetween(first: Current Date, second: Adjusted Date, interval: Minutes)


My name is Alistair Gillikin. I've lived in Los Lunas for several years. I am a strong reader and troubleshooter, and my hobbies include writing and Tabletop rpgs.
A goal I've had for a long time is to design and create a full game.
Facts about me:
Education: BA from New Mexico State University
Fandoms: Star Trek, Star Wars, and several others
Known Tabletop Game Systems: D&D 5e, Starfinder, Pathfinder, Hero 6e


https://github.com/udacity/Appify/
https://github.com/udacity/Sunflower/
https://github.com/udacity/Bokeh


Modules documentation¶
Filters:¶
Triple Moving Average¶
Here we take the average of 3 terms x0, A, B where, x0 = The point to be estimated A = weighted average of n terms previous to x0 B = weighted avreage of n terms ahead of x0 n = window size
-
orbitdeterminator.filters.triple_moving_average.
generate_filtered_data
(filename, window)[source]¶ Apply the filter and generate the filtered data
Parameters: - filename (string) – the name of the .csv file containing the positional data
- window (int) – window size applied into the filter
Returns: the final filtered array
Return type: numpy array
-
orbitdeterminator.filters.triple_moving_average.
triple_moving_average
(signal_array, window_size)[source]¶ Apply triple moving average to a signal
Parameters: - signal_array (numpy array) – the array of values on which the filter is to be applied
- window_size (int) – the no. of points before and after x0 which should be considered for calculating A and B
Returns: a filtered array of size same as that of signal_array
Return type: numpy array
Savintzky - Golay¶
Takes a positional data set (time, x, y, z) and applies the Savintzky Golay filter on it based on the polynomial and window parameters we input
Interpolation:¶
Lamberts-Kalman Method¶
Takes a positional data set and produces sets of six keplerian elements using Lambert’s solution for preliminary orbit determination and Kalman filters
-
orbitdeterminator.kep_determination.lamberts_kalman.
check_keplerian
(kep)[source]¶ Checks all the sets of keplerian elements to see if they have wrong values like eccentricity greater that 1 or a negative number for semi major axis
Parameters: kep (numpy array) – all the sets of keplerian elements in [semi major axis (a), eccentricity (e), inclination (i), argument of perigee (ω), right ascension of the ascending node (Ω), true anomaly (v)] format Returns: the final corrected set of keplerian elements that will be inputed in the kalman filter Return type: numpy array
-
orbitdeterminator.kep_determination.lamberts_kalman.
create_kep
(my_data)[source]¶ Computes all the keplerian elements for every point of the orbit you provide using Lambert’s solution It implements a tool for deleting all the points that give extremely jittery state vectors
Parameters: data (numpy array) – contains the positional data set in (Time, x, y, z) Format Returns: array containing all the keplerian elements computed for the orbit given in [semi major axis (a), eccentricity (e), inclination (i), argument of perigee (ω), right ascension of the ascending node (Ω), true anomaly (v)] format Return type: numpy array
-
orbitdeterminator.kep_determination.lamberts_kalman.
kalman
(kep, R)[source]¶ Takes as an input lots of sets of keplerian elements and produces the fitted value of them by applying kalman filters
Parameters: - kep (numpy array) – containing keplerian elements in this format (a, e, i, ω, Ω, v)
- R – estimate of measurement variance
Returns: final set of keplerian elements describing the orbit based on kalman filtering
Return type: numpy array
-
orbitdeterminator.kep_determination.lamberts_kalman.
lamberts
(x1, x2, traj)[source]¶ Takes two position points - numpy arrays with time,x,y,z as elements and produces two vectors with the state vector for both positions using Lamberts solution
Parameters: - x1 (numpy array) – time and position for point 1 [time1,x1,y1,z1]
- x2 (numpy array) – time and position for point 2 [time2,x2,y2,z2]
Returns: velocity vector for point 1 (vx, vy, vz)
Return type: numpy array
-
orbitdeterminator.kep_determination.lamberts_kalman.
orbit_trajectory
(x1_new, x2_new, time)[source]¶ Tool for checking if the motion of the sallite is retrogade or counter - clock wise
Parameters: - x1 (numpy array) – time and position for point 1 [time1,x1,y1,z1]
- x2 (numpy array) – time and position for point 2 [time2,x2,y2,z2]
- time (float) – time difference between the 2 points
Returns: true if we want to keep retrogade, False if we want counter-clock wise
Return type:
Gibb’s Method¶
-
class
orbitdeterminator.kep_determination.gibbsMethod.
Gibbs
[source]¶ -
classmethod
convert_list
(vec)[source]¶ Type casts the input data for the ease of use.
Converts the values of the input list with string datatype into float for the ease of furthur computation.
Parameters: vec (list) – input vector Returns: vector converted to float values Return type: list
-
classmethod
cross_product
(a, b)[source]¶ Computes cross product of the given vectors. Returns a vector.
Parameters: - a (list/array) – first vector
- b (list/array) – second vector
Returns: cross product of given vectors
Return type: list/array
-
classmethod
dot_product
(a, b)[source]¶ Computes dot product of two vectors. Multiplies corresponding axis with each other and then adds them. Returns a single value.
Parameters: - a (list/array) – first vector
- b (list/array) – second vector
Returns: dot product of given vectors
Return type:
-
classmethod
find_length
(path)[source]¶ Finds the length of the input file.
Calculates the length of the file with the given path containing all the position vectors. File should contain a header line describing all of its attributes in a single line only. This function removes the header line before it calculates file length. If the file does not contains the header line then the first line of data will get removed and you have to add 1 to the output after it returns the result.
Parameters: path (str) – file path Returns: length of file Return type: int
-
classmethod
gibbs
(r1, r2, r3)[source]¶ Computes state vector from the given set of three position vectors using Gibb’s implementation.
Computes velocity vector (part of state vector) using Gibb’s Method and takes r2 (input argument) as its position vector (part of state vector). Both combined forms state vector.
Parameters: Returns: velocity vector
Return type:
-
classmethod
magnitude
(vec)[source]¶ Computes magnitude of the input vector.
Parameters: vec (list) – vector Returns: magnitude of vector Return type: float
-
classmethod
operate_vector
(a, b, flag)[source]¶ Adds or subtracts input vectors based on the flag value.
If flag is 1 then add both vectors with corresponding values else if flag is 0 (zero) then subtract two vectors with corresponding values. Returns a vector.
Parameters: - a (list/array) – first vector
- b (list/array) – second vector
- flag (int) – checks for operation (addition/subtraction)
Returns: sum/difference of vector based on flag value
Return type: list/array
-
classmethod
orbital_elements
(r, v)[source]¶ Computes orbital elements from state vector.
Orbital elements is a set of six parameters which are semi-major axis, inclination, right ascension of the ascending node, eccentricity, argument of perigee and mean anomaly.
Parameters: Returns: set of six orbital elements
Return type:
-
read_file
(path)[source]¶ Invokes the Gibb’s implementation and stores the result in a list.
Read the file with the given path and forms a set of three position vectors then applies Gibb’s Method on that set and computes state vector for every set. After computing state vectors it stores the result into a list. Now, these state vectors can be used to find orbital elements.
Parameters: path (str) – path to input file Returns: list of all pair of position and velocity vector Return type: numpy.ndarray
-
classmethod
Spline Interpolation¶
Interpolation using splines for calculating velocity at a point and hence the orbital elements
-
orbitdeterminator.kep_determination.interpolation.
compute_velocity
(spline, point)[source]¶ Calculate the derivative of spline at the point(on the points the given spline corresponds to). This gives the velocity at that point.
Parameters: - spline (list) – component wise cubic splines of orbit data points of the format [spline_x, spline_y, spline_z].
- point (numpy array) – point at which velocity is to be calculated.
Returns: velocity vector at the given point
Return type: numpy array
-
orbitdeterminator.kep_determination.interpolation.
cubic_spline
(orbit_data)[source]¶ Compute component wise cubic spline of points of input data
Parameters: - orbit_data (numpy array) – array of orbit data points of the
- [time, x, y, z] (format) –
Returns: component wise cubic splines of orbit data points of the format [spline_x, spline_y, spline_z]
Return type:
-
orbitdeterminator.kep_determination.interpolation.
main
(data_points)[source]¶ Apply the whole process of interpolation for keplerian element computation
Parameters: data_points (numpy array) – positional data set in format of (x, y, z, time) Returns: computed keplerian elements for every point of the orbit Return type: numpy array
Ellipse Fit¶
Finds out the ellipse that best fits to a set of data points and calculates its keplerian elements.
-
orbitdeterminator.kep_determination.ellipse_fit.
determine_kep
(data)[source]¶ Determines keplerian elements that fit a set of points.
Parameters: data (nx3 numpy array) – A numpy array of points in the format [x y z]. Returns: (kep,res) - The keplerian elements and the residuals as a tuple. kep: 1x6 numpy array res: nx3 numpy array For the keplerian elements: kep[0] - semi-major axis (in whatever units the data was provided in) kep[1] - eccentricity kep[2] - inclination (in degrees) kep[3] - argument of periapsis (in degrees) kep[4] - right ascension of ascending node (in degrees) kep[5] - true anomaly of the first row in the data (in degrees)
For the residuals: (in whatever units the data was provided in) res[0] - residuals in x axis res[1] - residuals in y axis res[2] - residuals in z axis
Propagation:¶
Propagation Model¶
-
class
orbitdeterminator.propagation.sgp4.
SGP4
[source]¶ -
-
compute_necessary_kep
(kep, b_star=2.1109e-05)[source]¶ Initializes the necessary class variables which are needed in the computation of the propagation model.
Parameters: Returns: NIL
-
compute_necessary_tle
(line1, line2)[source]¶ Initializes the necessary class variables which are needed in the computation of the propagation model.
Parameters: Returns: NIL
-
propagate
(t1, t2)[source]¶ Invokes the function to compute state vectors and organises the final result.
The function extracts necessary information from the given TLE which the propagation model needs, then it computes the state vector for the next 8 hours (28800 seconds in 8 hours) at every time epoch (28800 time epcohs) using the sgp4 propagation model. The values of state vector is formatted upto five decimal points and then all the state vectors got appended in a list which stores the final output.
Parameters: Returns: vector containing all state vectorss
Return type: numpy.ndarray
-
propagation_model
(tsince)[source]¶ From the time epoch and information from TLE, applies SGP4 on it.
The function applies the Simplified General Perturbations algorithm SGP4 on the information extracted from the TLE at the given time epoch ‘tsince’ and computes the state vector from it.
Parameters: tsince (int) – time epoch Returns: position and velocity vector Return type: tuple
-
classmethod
recover_tle
(pos, vel)[source]¶ Recovers TLE back from state vector.
First of all, only necessary information (which are inclination, right ascension of the ascending node, eccentricity, argument of perigee, mean anomaly, mean motion and bstar) that are needed in the computation of SGP4 propagation model are recovered. It is using a general format of TLE. State vectors are used to find orbital elements which are then inserted into the TLE format at their respective positions. Mean motion and bstar is calculated separately as it is not a part of orbital elements. Format of TLE, x denotes that there is a digit, c denotes a character value, underscore(_) denotes a plus/minus(+/-) sign value and period(.) denotes a decimal point.
Parameters: Returns: line1 and line2 of TLE
Return type:
-
Cowell Method¶
Numerical orbit propagator based on RK4. Takes into account J2 and drag perturbations.
-
orbitdeterminator.propagation.cowell.
drag
(s)[source]¶ Returns the drag acceleration for a given state.
Parameters: s (1x6 numpy array) – the state vector [rx,ry,rz,vx,vy,vz] Returns: the drag acceleration [ax,ay,az] Return type: 1x3 numpy array
-
orbitdeterminator.propagation.cowell.
j2_pert
(s)[source]¶ Returns the J2 acceleration for a given state.
Parameters: s (1x6 numpy array) – the state vector [rx,ry,rz,vx,vy,vz] Returns: the J2 acceleration [ax,ay,az] Return type: 1x3 numpy array
-
orbitdeterminator.propagation.cowell.
propagate_state
(s, t0, tf)[source]¶ Equivalent to the rk4 function.
-
orbitdeterminator.propagation.cowell.
rk4
(s, t0, tf, h=30)[source]¶ Runge-Kutta 4th Order Numerical Integrator
- Args:
- s(1x6 numpy array): the state vector [rx,ry,rz,vx,vy,vz] t0(float) : initial time tf(float) : final time h(float) : step-size
Returns: the state at time tf Return type: 1x6 numpy array
-
orbitdeterminator.propagation.cowell.
rkf45
(s, t0, tf, h=10, tol=1e-06)[source]¶ Runge-Kutta Fehlberg 4(5) Numerical Integrator
- Args:
- s(1x6 numpy array): the state vector [rx,ry,rz,vx,vy,vz] t0(float) : initial time tf(float) : final time h(float) : step-size tol(float) : tolerance of error
Returns: the state at time tf Return type: 1x6 numpy array
Simulator¶
-
class
orbitdeterminator.propagation.simulator.
Simulator
(params)[source]¶ A class for the simulator.
-
__init__
(params)[source]¶ Initializes the simulator.
Parameters: params – A SimParams object containing kep,t0,t,period,speed, and op_writer Returns: nothing
-
calc
()[source]¶ Calculates the satellite state at current time and calls itself after a certain amount of time.
-
-
class
orbitdeterminator.propagation.simulator.
SimParams
[source]¶ SimParams class. This is just a container for all the parameters required to start the simulation.
kep(1x6 numpy array): the intial osculating keplerian elements epoch(float): the epoch of the above kep period(float): maximum time period between observations t0(float): starting time of the simulation speed(float): speed of the simulation op_writer(OpWriter): output handling object
-
class
orbitdeterminator.propagation.simulator.
OpWriter
[source]¶ Base output writer class. Inherit this class and override the methods.
-
close
()[source]¶ Anything that has to be executed after finishing writing the output. Runs once.
Example: Closing connection to a database
-
-
class
orbitdeterminator.propagation.simulator.
print_r
[source]¶ Bases:
orbitdeterminator.propagation.simulator.OpWriter
Prints the position vector
-
class
orbitdeterminator.propagation.simulator.
save_r
(name)[source]¶ Bases:
orbitdeterminator.propagation.simulator.OpWriter
Saves the position vector to a file
DGSN Simulator¶
-
class
orbitdeterminator.propagation.dgsn_simulator.
DGSNSimulator
(params)[source]¶ A class for the simulator.
-
__init__
(params)[source]¶ Initializes the simulator.
Parameters: params – A SimParams object containing kep,t0,t,period,speed, op_writer, dgsn_period, and dgsn_thresh. For a description of the parameters, look at the documentation of the SimParams class. Returns: nothing
-
calc
()[source]¶ Calculates the satellite state at current time and calls itself after a certain amount of time.
-
-
class
orbitdeterminator.propagation.dgsn_simulator.
SimParams
[source]¶ SimParams class. This is just a container for all the parameters required to start the simulation.
kep(1x6 numpy array): the intial osculating keplerian elements epoch(float): the epoch of the above kep period(float): maximum time period between observations t0(float): starting time of the simulation speed(float): speed of the simulation op_writer(OpWriter): output handling object
r_jit(float): std of Gaussian noise applied to observations dgsn_period(float): average time period between gaps dgsn_thresh(float): used to control the duration of the gap.
it is a number between 0 and 1. a higher number means a bigger gap.
-
class
orbitdeterminator.propagation.dgsn_simulator.
OpWriter
[source]¶ Base output writer class. Inherit this class and override the methods.
-
close
()[source]¶ Anything that has to be executed after finishing writing the output. Runs once.
Example: Closing connection to a database
-
-
class
orbitdeterminator.propagation.dgsn_simulator.
print_r
[source]¶ Bases:
orbitdeterminator.propagation.dgsn_simulator.OpWriter
Prints the position vector
-
class
orbitdeterminator.propagation.dgsn_simulator.
save_r
(name)[source]¶ Bases:
orbitdeterminator.propagation.dgsn_simulator.OpWriter
Saves the position vector to a file
Kalman Filter¶
Kalman Filter to smoothen observations. It continuously reads a file where observations are being written and updates its estimate based on the observations and the cowell model.
sgp4_prop¶
SGP4 propagator. This is a wrapper around the PyPI SGP4 propagator. However, this does not generate an artificial TLE. So there is no string manipulation involved. Hence this is faster than sgp4_prop_string.
-
orbitdeterminator.propagation.sgp4_prop.
kep_to_sat
(kep, epoch, bstar=0.21109E-4, whichconst=wgs72, afspc_mode=False)[source]¶ Converts a set of keplerian elements into a Satellite object.
- Args:
- kep(1x6 numpy array): the osculating keplerian elements at epoch epoch(float): the epoch bstar(float): bstar drag coefficient whichconst(float): gravity model. refer pypi sgp4 documentation afspc_mode(boolean): refer pypi sgp4 documentation
Returns: an sgp4 satellite object encapsulating the arguments Return type: Satellite object
-
orbitdeterminator.propagation.sgp4_prop.
propagate_kep
(kep, t0, tf, bstar=2.1109e-05)[source]¶ Propagates a set of keplerian elements.
Parameters: Returns: the position at tf vel(1x3 numpy array): the velocity at tf
Return type: pos(1x3 numpy array)
sgp4_prop_string¶
SGP4 propagator. This is a wrapper around PyPI SGP4 propagator. It constructs an artificial TLE and passes it to the PyPI module.
Utils:¶
kep_state¶
Takes a set of keplerian elements (a, e, i, ω, Ω, v) and transforms it into a state vector (x, y, z, vx, vy, vz) where v is the velocity of the satellite
-
orbitdeterminator.util.kep_state.
kep_state
(kep)[source]¶ Converts the keplerian elements to position and velocity vector
Parameters: kep (numpy array) – a 1x6 matrix which contains the following variables kep(0): semi major axis (km) kep(1): eccentricity (number) kep(2): inclination (degrees) kep(3): argument of perigee (degrees) kep(4): right ascension of the ascending node (degrees) kep(5): true anomaly (degrees) Returns: 1x6 matrix which contains the position and velocity vector r(0),r(1),r(2): position vector (x,y,z) km r(3),r(4),r(5): velocity vector (vx,vy,vz) km/s Return type: numpy array
read_data¶
Reads the positional data set from a .csv file
-
orbitdeterminator.util.read_data.
load_data
(filename)[source]¶ Loads the data in numpy array for further processing in tab delimiter format
Parameters: filename (string) – name of the csv file to be parsed Returns: array of the orbit positions, each point of the orbit is of the format (time, x, y, z) Return type: numpy array
state_kep¶
Takes a state vector (x, y, z, vx, vy, vz) where v is the velocity of the satellite and transforms it into a set of keplerian elements (a, e, i, ω, Ω, v)
-
orbitdeterminator.util.state_kep.
state_kep
(r, v)[source]¶ Converts state vector to orbital elements.
Parameters: - r (numpy array) – position vector
- v (numpy array) – velocity vector
Returns: array of the computed keplerian elements kep(0): semimajor axis (kilometers) kep(1): orbital eccentricity (non-dimensional)
(0 <= eccentricity < 1)
kep(2): orbital inclination (degrees) kep(3): argument of perigee (degress) kep(4): right ascension of ascending node (degrees) kep(5): true anomaly (degrees)
Return type: numpy array
input_transf¶
Converts cartesian co-ordinates to spherical co-ordinates and vice versa
-
orbitdeterminator.util.input_transf.
cart_to_spher
(data)[source]¶ Takes as an input a data set containing points in cartesian format (time, x, y, z) and returns the computed spherical coordinates (time, azimuth, elevation, r)
Parameters: data (numpy array) – containing the cartesian coordinates in format of (time, x, y, z) Returns: array of spherical coordinates in format of (time, azimuth, elevation, r) Return type: numpy array
-
orbitdeterminator.util.input_transf.
spher_to_cart
(data)[source]¶ Takes as an input a data set containing points in spherical format (time, azimuth, elevation, r) and returns the computed cartesian coordinates (time, x, y, z).
Parameters: data (numpy array) – containing the spherical coordinates in format of (time, azimuth, elevation, r) Returns: array of cartesian coordinates in format of (time, x, y, z) Return type: numpy array
rkf78¶
Uses Runge Kutta Fehlberg 7(8) numerical integration method to compute the state vector in a time interval tf
golay_window¶
-
orbitdeterminator.util.golay_window.
window
(error, data)[source]¶ Calculates the constant c which is needed to determine the savintzky - golay filter window window = len(data) / c ,where c is a constant strongly related to the error contained in the data set
Parameters: - error (float) – the a-priori error estimation for each measurment
- data (numpy array) – the positional data set
Returns: constant which describes the window that needs to be inputed to the savintzky - golay filter
Return type:
anom_conv¶
Vectorized anomaly conversion scripts
-
orbitdeterminator.util.anom_conv.
ecc_to_mean
(E, e)[source]¶ Converts eccentric anomaly to mean anomaly.
Parameters: - E (numpy array) – array of eccentric anomalies (in radians)
- e (float) – eccentricity
Returns: array of mean anomalies (in radians)
Return type: numpy array
new_tle_kep_state¶
This module computes the state vector from keplerian elements.
-
orbitdeterminator.util.new_tle_kep_state.
kep_to_state
(kep)[source]¶ This function converts from keplerian elements to the position and velocity vector
Parameters: kep (1x6 numpy array) – kep contains the following variables kep[0] = semi-major axis (kms) kep[1] = eccentricity (number) kep[2] = inclination (degrees) kep[3] = argument of perigee (degrees) kep[4] = right ascension of ascending node (degrees) kep[5] = true anomaly (degrees) Returns: r: 1x6 numpy array which contains the position and velocity vector
r[0],r[1],r[2] = position vector [rx,ry,rz] km r[3],r[4],r[5] = velocity vector [vx,vy,vz] km/s
-
orbitdeterminator.util.new_tle_kep_state.
tle_to_state
(tle)[source]¶ This function converts from TLE elements to position and velocity vector
Parameters: tle (1x6 numpy array) – tle contains the following variables tle[0] = inclination (degrees) tle[1] = right ascension of the ascending node (degrees) tle[2] = eccentricity (number) tle[3] = argument of perigee (degrees) tle[4] = mean anomaly (degrees) tle[5] = mean motion (revs per day) Returns: r: 1x6 numpy array which contains the position and velocity vector
r[0],r[1],r[2] = position vector [rx,ry,rz] km r[3],r[4],r[5] = velocity vector [vx,vy,vz] km/s
teme_to_ecef¶
Converts coordinates in TEME frame to ECEF frame.
-
orbitdeterminator.util.teme_to_ecef.
conv_to_ecef
(coords)[source]¶ Converts coordinates in TEME frame to ECEF frame.
Parameters: coords (nx4 numpy array) – list of coordinates in the format [t,x,y,z] Returns: - list of coordinates in the format
- [t, latitude, longitude, altitude]
Note that these coordinates are with respect to the surface of the Earth. Latitude, longitude are in degrees.
Return type: nx4 numpy array


USD longs slipped, EUR shorts increased – Rabobank
by Sandeep KanihamaRabobank analysts notes that the USD net longs slipped for a seventh consecutive week, as per the latest CFTC Commitment of Traders Report.
Key Quotes
“Net EUR short positions increased but there has been no strong direction in recent weeks.”
“For the first time since September net short GBP positions increased suggesting that confidence could be wavering as the December 12 election nears.”
“JPY net positions held in negative ground for a fifth consecutive week and shorts continued to rise.”
“CHF net shorts increased for a fourth week consistent with a drop in safe haven demand.”
“CAD net long positions dropped back sharply for a second week. BoC Governor Poloz this month has reiterated the case for keeping the door open for further policy easing.”
“AUD net shorts continued to rise as further monetary stimulus comes back into view.”


Compare
Don't know if you'll miss a feature from Nuke, Fusion or After Effects? Find out in this table.
License
Cost
OFX Support
Scripting Support
Hardware
Acceleration
Workflow Type
3D Workspace
32-Bit Support
Network/Headless
Rendering
1.
Natron
by the Natron Community
GNU GPL v2+
$0
Python
(PyPlug)
Node-based
Limited
2.
Nuke
by The Foundry
Proprietary
$500-10,000
Nuke
Internal/Blinkscript
Node-based
3.
After
Effects
by Adobe
Proprietary
$29.99/month
Adobe ExtendScript
Layer-based
Limited
Limited


The Terra Vista Ceremony thanks:
Jamie Steeg - for the great forms
Marcia Newcomb - for helping promote the event
Back to
[Award Ceremony]


Set and Setting:
A regional outdoor camping and dancing event. This group of people gathers every month or two to throw down a good party. The mindset was positive, fed by the good vibes projected by everyone present. The group of people was my burn family, and everyone knows everyone fairly well.
Experience level:
I regularly (once every month or two) participate in some form of psychedelic activity. Lately, I have been doing this mostly with MDMA (pure), but the effects have started to drop off just that little bit and I thought it was time to take a break of longer. I've done MDMA 18 times over the course of my three years of psychedelic use. LSD and DMT have been great to me the dozen or so times I have been able to get a hold of them. I have not had any notably positive experiences with mushrooms or heavy stimulants. Research chemicals remain mostly untouched.
The Trip:
At 20:20 on 18 April, I intended to take 250µg of LSD to trip into Bicycle Day, in typical fashion.
[Erowid Note: Claims of measured microgram dosages for LSD are usually unsupported. Quantitative measurements for LSD are very difficult to do and cannot be done casually. Without further detailed information about how the measurements were derived, it is reasonable to assume that most statements of microgram dosages of LSD on blotter or in microdots are either misinformed or overstated.] By 21:00 I felt ill to my stomach, coming up hard, but I knew I had nothing to vomit. I drank some water, trying to quell my stomach, but this was instantly rejected. I 10 minutes or so, I tried to get fluids into my body but this repeatedly failed over the course of two hours. The world was spinning and I could not tell which way was up. All I could think about was that I needed to get fluids and electrolytes into my body, to give myself something to burn.
The headspace of the trip was fairly interesting with heavy spinning patterns and thoughts about evolution, survival and how we've come to this point in our existence.
I was still trying to make this a good experience, but the physical symptoms overwhelmed me. Starting to think I might just be 'that kid at the rave that dies from dehydration', but I pushed through it, trying to figure out the easiest way to get what I needed into my body. Switching into survival mode, the journey began to find tolerable liquids, something with substance, but light. I decided to take a walk, because I knew this was not going to come to me.
At 23:00 I ran into the person who had given me the '250 mics'. He hesitantly asked:
'Hey man, did I accidentally give you the white ones?'
'Yes, why?'
'And you ate all three?'
'Yes, why?'
'Those were my personal ones, I dosed those at 300 a piece'
'That explains a lot.'
After learning of the actual dose, I needed to find something to level me off. As I did not want to go to sleep in such a state of dehydration, sedation by xanax or trazodone was out of the question. I tried some marijuana, as this generally works for nausea. I cough and gag, take a sip of water and vomit immediately.
Vomiting another time against a tree at around 00:10 on 19 April, I was approached by one of the long-time veterans of the group, and asked if I wanted just a little bit of ketamine to relax my body and stop the convulsions. I felt that this was the best option out of the few I was considering. Ketamine is something I had little experience with, but the few times had been completely relaxing, and I could see how this would help my situation greatly. She takes me to the magic bus and measures out a 50mg bump.
About five minutes after the drip passed, I was overwhelmed by relaxation and was offered a quart of orange gatorade. Sitting down, the gatorade slowly makes it into my system, and I feel better as the ketamine begins to take effect. I find some company, talk through my trip, working out a lot of existential issues. I give my thanks to the orange gatorade bottle, as it is now empty. This is a good thing and my fears of death-by-dehydration dissipate. My body is fueled. The water bottle needed refilling and my companion suggests that we talk a SLOW stroll around the beautiful landscape.
Standing on my own two feet again, I feel like I have made it through the storm, and explore the positive side of the night. The K has made the soreness and discomfort in my body vanish, and I can walk and dance again. We go to the bus again because a large party had built up around it. Dancing for the first time after feeling so much in agony was refreshing to say the least, and gaining a new perspective on space, time, and movement was a rewarding experience. I could feel the way my muscles were moving through the space and time. Visually, I cannot explain what was going on, but it was a beautiful fusion of hybridisation of space-time and emotions.
I was comfortable and finally managed to get more liquids in me with a tiny bit of solid food (bread, cream cheese, and jam). However, the K is beginning to fade, and the nausea is returning. Seeing me start to clench my stomach, my friend offers me another dose, and I take it, as it worked so well the first time. Almost instantly, I'm back on the way to comfort, and we decide to check in with each other every hour or so. The visuals at this point are almost continuously reminiscent of an Escher woodcut, drifting between full black and white and full neon colours. Staircases and columns are imposed over everything.
Each time I hit the peak, I had new insights, one of which was very useful to my physics thesis. I've been working on this one problem in quantum mechanics of how to tunnel through an infinitely tall yet narrow boundary. A fellow physics major, who was hit by a car in September, and was remarkably able to walk (with the aid of a cane) had a method which had worked for him. 'Baby steps' he says. I though of how he had overcome his 'infinite barrier' of a shattered pelvis, concussion, and a whole mess of related problems. This was ironically the same solution to my problem. Modeling the boundary as a step works, and solves the problem.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


To place a work order, please click the picture above to email the appropriate person. Please include a detailed description of the problem, as well as, the computer or equipment with a problem.


EVENTFD
Section: Linux Programmer's Manual (2)
Updated: 2014-07-08
Index
Return to Main Contents
NAME
eventfd - create a file descriptor for event notification
SYNOPSIS
#include <sys/eventfd.h>
int eventfd(unsigned int initval, int flags);
DESCRIPTION
eventfd()
creates an "eventfd object" that can be used as
an event wait/notify mechanism by user-space applications,
and by the kernel to notify user-space applications of events.
The object contains an unsigned 64-bit integer
(uint64_t)
counter that is maintained by the kernel.
This counter is initialized with the value specified in the argument
initval.
The following values may be bitwise ORed in
flags
to change the behavior of
eventfd():
- EFD_CLOEXEC (since Linux 2.6.27)
-
Set the close-on-exec
(FD_CLOEXEC)
flag on the new file descriptor.
See the description of the
O_CLOEXEC
flag in
open(2)
for reasons why this may be useful.
- EFD_NONBLOCK (since Linux 2.6.27)
-
Set the
O_NONBLOCK
file status flag on the new open file description.
Using this flag saves extra calls to
fcntl(2)
to achieve the same result.
- EFD_SEMAPHORE (since Linux 2.6.30)
-
Provide semaphore-like semantics for reads from the new file descriptor.
See below.
In Linux up to version 2.6.26, the
flags
argument is unused, and must be specified as zero.
As its return value,
eventfd()
returns a new file descriptor that can be used to refer to the
eventfd object.
The following operations can be performed on the file descriptor:
- read(2)
-
Each successful
read(2)
returns an 8-byte integer.
A
read(2)
will fail with the error
EINVAL
if the size of the supplied buffer is less than 8 bytes.
-
The value returned by
read(2)
is in host byte order---that is,
the native byte order for integers on the host machine.
-
The semantics of
read(2)
depend on whether the eventfd counter currently has a nonzero value
and whether the
EFD_SEMAPHORE
flag was specified when creating the eventfd file descriptor:
-
- *
-
If
EFD_SEMAPHORE
was not specified and the eventfd counter has a nonzero value, then a
read(2)
returns 8 bytes containing that value,
and the counter's value is reset to zero.
- *
-
If
EFD_SEMAPHORE
was specified and the eventfd counter has a nonzero value, then a
read(2)
returns 8 bytes containing the value 1,
and the counter's value is decremented by 1.
- *
-
If the eventfd counter is zero at the time of the call to
read(2),
then the call either blocks until the counter becomes nonzero
(at which time, the
read(2)
proceeds as described above)
or fails with the error
EAGAIN
if the file descriptor has been made nonblocking.
- write(2)
-
A
write(2)
call adds the 8-byte integer value supplied in its
buffer to the counter.
The maximum value that may be stored in the counter is the largest
unsigned 64-bit value minus 1 (i.e., 0xfffffffffffffffe).
If the addition would cause the counter's value to exceed
the maximum, then the
write(2)
either blocks until a
read(2)
is performed on the file descriptor,
or fails with the error
EAGAIN
if the file descriptor has been made nonblocking.
-
A
write(2)
will fail with the error
EINVAL
if the size of the supplied buffer is less than 8 bytes,
or if an attempt is made to write the value 0xffffffffffffffff.
- poll(2), select(2) (and similar)
-
The returned file descriptor supports
poll(2)
(and analogously
epoll(7))
and
select(2),
as follows:
-
- *
-
The file descriptor is readable
(the
select(2)
readfds
argument; the
poll(2)
POLLIN
flag)
if the counter has a value greater than 0.
- *
-
The file descriptor is writable
(the
select(2)
writefds
argument; the
poll(2)
POLLOUT
flag)
if it is possible to write a value of at least "1" without blocking.
- *
-
If an overflow of the counter value was detected,
then
select(2)
indicates the file descriptor as being both readable and writable, and
poll(2)
returns a
POLLERR
event.
As noted above,
write(2)
can never overflow the counter.
However an overflow can occur if 2^64
eventfd "signal posts" were performed by the KAIO
subsystem (theoretically possible, but practically unlikely).
If an overflow has occurred, then
read(2)
will return that maximum
uint64_t
value (i.e., 0xffffffffffffffff).
-
The eventfd file descriptor also supports the other file-descriptor
multiplexing APIs:
pselect(2)
and
ppoll(2).
- close(2)
-
When the file descriptor is no longer required it should be closed.
When all file descriptors associated with the same eventfd object
have been closed, the resources for object are freed by the kernel.
A copy of the file descriptor created by
eventfd()
is inherited by the child produced by
fork(2).
The duplicate file descriptor is associated with the same
eventfd object.
File descriptors created by
eventfd()
are preserved across
execve(2),
unless the close-on-exec flag has been set.
RETURN VALUE
On success,
eventfd()
returns a new eventfd file descriptor.
On error, -1 is returned and
errno
is set to indicate the error.
ERRORS
- EINVAL
-
An unsupported value was specified in
flags.
- EMFILE
-
The per-process limit on open file descriptors has been reached.
- ENFILE
-
The system-wide limit on the total number of open files has been
reached.
- ENODEV
-
Could not mount (internal) anonymous inode device.
- ENOMEM
-
There was insufficient memory to create a new
eventfd file descriptor.
VERSIONS
eventfd()
is available on Linux since kernel 2.6.22.
Working support is provided in glibc since version 2.8.
The
eventfd2()
system call (see NOTES) is available on Linux since kernel 2.6.27.
Since version 2.9, the glibc
eventfd()
wrapper will employ the
eventfd2()
system call, if it is supported by the kernel.
CONFORMING TO
eventfd()
and
eventfd2()
are Linux-specific.
NOTES
Applications can use an eventfd file descriptor instead of a pipe (see
pipe(2))
in all cases where a pipe is used simply to signal events.
The kernel overhead of an eventfd file descriptor
is much lower than that of a pipe,
and only one file descriptor is
required (versus the two required for a pipe).
When used in the kernel, an eventfd
file descriptor can provide a bridge from kernel to user space, allowing,
for example, functionalities like KAIO (kernel AIO)
to signal to a file descriptor that some operation is complete.
A key point about an eventfd file descriptor is that it can be
monitored just like any other file descriptor using
select(2),
poll(2),
or
epoll(7).
This means that an application can simultaneously monitor the
readiness of "traditional" files and the readiness of other
kernel mechanisms that support the eventfd interface.
(Without the
eventfd()
interface, these mechanisms could not be multiplexed via
select(2),
poll(2),
or
epoll(7).)
C library/kernel ABI differences
There are two underlying Linux system calls:
eventfd()
and the more recent
eventfd2().
The former system call does not implement a
flags
argument.
The latter system call implements the
flags
values described above.
The glibc wrapper function will use
eventfd2()
where it is available.
Additional glibc features
The GNU C library defines an additional type,
and two functions that attempt to abstract some of the details of
reading and writing on an eventfd file descriptor:
typedef uint64_t eventfd_t;
int eventfd_read(int fd, eventfd_t *value);
int eventfd_write(int fd, eventfd_t value);
The functions perform the read and write operations on an
eventfd file descriptor,
returning 0 if the correct number of bytes was transferred,
or -1 otherwise.
EXAMPLE
The following program creates an eventfd file descriptor
and then forks to create a child process.
While the parent briefly sleeps,
the child writes each of the integers supplied in the program's
command-line arguments to the eventfd file descriptor.
When the parent has finished sleeping,
it reads from the eventfd file descriptor.
The following shell session shows a sample run of the program:
$ ./a.out 1 2 4 7 14
Child writing 1 to efd
Child writing 2 to efd
Child writing 4 to efd
Child writing 7 to efd
Child writing 14 to efd
Child completed write loop
Parent about to read
Parent read 28 (0x1c) from efd
Program source
#include <sys/eventfd.h>
#include <unistd.h>
#include <stdlib.h>
#include <stdio.h>
#include <stdint.h> /* Definition of uint64_t */
#define handle_error(msg) \
do { perror(msg); exit(EXIT_FAILURE); } while (0)
int
main(int argc, char *argv[])
{
int efd, j;
uint64_t u;
ssize_t s;
if (argc < 2) {
fprintf(stderr, "Usage: %s <num>...\n", argv[0]);
exit(EXIT_FAILURE);
}
efd = eventfd(0, 0);
if (efd == -1)
handle_error("eventfd");
switch (fork()) {
case 0:
for (j = 1; j < argc; j++) {
printf("Child writing %s to efd\n", argv[j]);
u = strtoull(argv[j], NULL, 0);
/* strtoull() allows various bases */
s = write(efd, &u, sizeof(uint64_t));
if (s != sizeof(uint64_t))
handle_error("write");
}
printf("Child completed write loop\n");
exit(EXIT_SUCCESS);
default:
sleep(2);
printf("Parent about to read\n");
s = read(efd, &u, sizeof(uint64_t));
if (s != sizeof(uint64_t))
handle_error("read");
printf("Parent read %llu (0x%llx) from efd\n",
(unsigned long long) u, (unsigned long long) u);
exit(EXIT_SUCCESS);
case -1:
handle_error("fork");
}
}
SEE ALSO
futex(2),
pipe(2),
poll(2),
read(2),
select(2),
signalfd(2),
timerfd_create(2),
write(2),
epoll(7),
sem_overview(7)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- RETURN VALUE
-
- ERRORS
-
- VERSIONS
-
- CONFORMING TO
-
- NOTES
-
- C library/kernel ABI differences
-
- Additional glibc features
-
- EXAMPLE
-
- Program source
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:54:50 GMT, September 18, 2014


Opera Link Test
By Joel Spadin
Test Suite
Bookmarks
Notes
Search Engines
Speed Dial
URL Filter
Contacting the Opera Link server...
Your Opera Link authorization is missing, invalid, or expired.
Authorize
Bookmarks
Notes
Search Engines
Speed Dial
URL Filter


{% extends "base.html" %} {% block title %}Google App Engine implementation{% endblock %} {% block body %}
The Google App Engine implementation only supports one primary key for each class, and also
this primary key field must be of type long
or java.lang.Long
.
Any other annotation but @Id
is ignored.
The only thing you have to do to use the Google App Engine implementation is write the
siena.properties
file with the following content in the same directory than
your model classes:
implementation = siena.gae.GaePersistenceManager{% endblock %}


There are two folders within this mini site
Both sets of patters are evolving and changing and can be found in this repository
To use the code, including CSS and javascript, grab the code from the repository and open the index.html file. Or have a look at the following link which provides links to the various code patterns that you can explore.
In order to see the design patterns check out the repository or see the site at the link below. This will will provide you with a menu with which you can explore the patterns.


| Edition 2019-20 Edition 2020-21 |
A player has regret if it could have earned more by playing a certain action throughout. A player has conditional regret if it could have earned more by substituting a certain action in its past play by another action (throughout). An important general result in this area is that it is possible to define algorithms that eliminate conditional regret irrespective of the behaviour of opponents.
Presented byLecturer. |
SlidesFeb 19, 2015. |
ScreencastNot available. |
AssignmentNot this year. |
-


Show tracking web application with user-friendly interface.
Interaction design,
Full-stack development
Ruby on Rails, Rspec, HTML5,
SASS, SMACSS, OOCSS, BEM,
AngularJS, Heroku
One of my goals for this project was to improve my style of writing SASS. Before I was sticking to the SMACSS convention of code structuring (which I really like), but I started to notice that in different modules I use the same layout patterns, which ideally would be abstracted out into separate entities.
Another challenge was to maintain clear separation between different modules in order to reduce naming conflicts without increasing the depth of applicability of selectors.
Apart from that I wanted to learn how to work with Grunt task runner to automize the most common tasks: compilation of SASS, running local server, and autoreload.
One of my goals for this project was to improve my style of writing SASS. Before I was sticking to the SMACSS convention of code structuring (which I really like), but I started to notice that in different modules I use the same layout patterns, which ideally would be abstracted out into separate entities.
Another challenge was to maintain clear separation between different modules in order to reduce naming conflicts without increasing the depth of applicability of selectors.
Apart from that I wanted to learn how to work with Grunt task runner to automize the most common tasks: compilation of SASS, running local server, and autoreload.
Previous project: Studytube Website
Next project: Studytube CMS