pymatgen.analysis.surface_analysis module¶
-
class
SurfaceEnergyAnalyzer
(material_id, vasprun_dict, ref_element, exclude_ids=[], custom_entries=[], mapi_key=None)[source]¶ Bases:
object
- A class used for analyzing the surface energies of a material of a given
material_id. By default, this will use entries calculated from the Materials Project to obtain chemical potential and bulk energy. As a result, the difference in VASP parameters between the user’s entry (vasprun_dict) and the parameters used by Materials Project, may lead to a rough estimate of the surface energy. For best results, it is recommend that the user calculates all decomposition components first, and insert the results into their own database as a pymatgen-db entry and use those entries instead (custom_entries). In addition, this code will only use one bulk entry to calculate surface energy. Ideally, to get the most accurate surface energy, the user should compare their slab energy to the energy of the oriented unit cell with both calculations containing consistent k-points to avoid converegence problems as the slab size is varied. See:
- Sun, W.; Ceder, G. Efficient creation and convergence of surface slabs,
- Surface Science, 2013, 617, 53–59, doi:10.1016/j.susc.2013.05.016.
- and
- Rogal, J., & Reuter, K. (2007). Ab Initio Atomistic Thermodynamics for
- Surfaces : A Primer. Experiment, Modeling and Simulation of Gas-Surface Interactions for Reactive Flows in Hypersonic Flights, 2–1 – 2–18.
-
ref_element
¶ - All chemical potentials cna be written in terms of the range of chemical
- potential of this element which will be used to calculate surface energy.
-
mprester
¶ - Materials project rester for querying entries from the materials project.
- Requires user MAPIKEY.
-
ucell_entry
¶ Materials Project entry of the material of the slab.
-
x
¶ Reduced amount composition of decomposed compound A in the bulk.
-
y
¶ Reduced amount composition of ref_element in the bulk.
-
gbulk
¶ Gibbs free energy of the bulk per formula unit
-
chempot_range
¶ List of the min and max chemical potential of ref_element.
-
e_of_element
¶ - Energy per atom of ground state ref_element, eg. if ref_element=O,
- than e_of_element=1/2*E_O2.
-
vasprun_dict
¶ - Dictionary containing a list of Vaspruns for slab calculations as
- items and the corresponding Miller index of the slab as the key
- Analyzes surface energies and Wulff shape of a particular
- material using the chemical potential.
Parameters: - material_id (str) – Materials Project material_id (a string, e.g., mp-1234).
- vasprun_dict (dict) – Dictionary containing a list of Vaspruns for slab calculations as items and the corresponding Miller index of the slab as the key. eg. vasprun_dict = {(1,1,1): [vasprun_111_1, vasprun_111_2, vasprun_111_3], (1,1,0): [vasprun_111_1, vasprun_111_2], …}
- element – element to be considered as independent variables. E.g., if you want to show the stability ranges of all Li-Co-O phases wrt to uLi
- exclude_ids (list of material_ids) – List of material_ids to exclude when obtaining the decomposition components to calculate the chemical potential
- custom_entries (list of pymatgen-db type entries) – List of user specified pymatgen-db type entries to use in finding decomposition components for the chemical potential
- mapi_key (str) – Materials Project API key for accessing the MP database via MPRester
-
area_frac_vs_chempot_plot
(cmap=<matplotlib.colors.LinearSegmentedColormap object>, at_intersections=False, increments=10)[source]¶ Plots the change in the area contribution of each facet as a function of chemical potential. :param cmap: A matplotlib colormap object, defaults to jet. :type cmap: cm :param at_intersections: Whether to generate a Wulff shape for each
intersection of surface energy for a specific facet (eg. at the point where a (111) stoichiometric surface energy plot intersects with the (111) nonstoichiometric plot) or to just generate two Wulff shapes, one at the min and max chemical potential.Parameters: increments (bool) – Number of data points between min/max or point of intersection. Defaults to 5 points.
-
calculate_gamma
(vasprun)[source]¶ Calculates the surface energy for a single slab. :param vasprun: A Vasprun object :type vasprun: Vasprun
- Returns (list): The surface energy for the minimum/maximun
- chemical potential and the second list gives the range of the chemical potential
-
chempot_vs_gamma_plot
(cmap=<matplotlib.colors.LinearSegmentedColormap object>, show_unstable_points=False)[source]¶ - Plots the surface energy of all facets as a function of chemical potential.
- Each facet will be associated with its own distinct colors. Dashed lines will represent stoichiometries different from that of the mpid’s compound.
Parameters: - cmap (cm) – A matplotlib colormap object, defaults to jet.
- show_unstable_points (bool) – For each facet, there may be various terminations or stoichiometries and the relative stability of these different slabs may change with chemical potential. This option will only plot the most stable surface energy for a given chemical potential.
-
get_intersections
(miller_index)[source]¶ - Returns a all intersections for a specific facet. Useful for
- finding when the configuration of a particular facet changes.
Parameters: miller_index ((h, k, l)) – Miller index of the facet we are interested in
-
get_slope_and_intercept
(surf_e_pair)[source]¶ - Returns the slope and intercept of the surface
- energy vs chemical potential line
Parameters: surf_e_pair ([e_at_min_u, e_at_max_u]) – The surface energy at the minimum chemical potential and maximum chemical potential
-
wulff_shape_dict
(symprec=1e-05, at_intersections=False)[source]¶ - As the surface energy is a function of chemical potential, so too is the
- Wulff shape. This methods generates a dictionary of Wulff shapes at certain chemical potentials where a facet goes through a transition. Returns a dict, eg. {chempot1: WulffShape1, chempot2: WulffShape2}
Parameters: - symprec (float) – for recp_operation, default is 1e-5.
- at_intersections (bool) – Whether to generate a Wulff shape for each intersection of surface energy for a specific facet (eg. at the point where a (111) stoichiometric surface energy plot intersects with the (111) nonstoichiometric plot) or to just generate two Wulff shapes, one at the min and max chemical potential.


PSALMS 108
A psalm written by David
1 God, I am very confident in you.
I will sing to praise you.
It is an honor to wake up and praise you.
2 I will arise before the sun rises,
and I will praise you while I play my harp and my lyre.
3 I prayed, "Yahweh, I will thank you among all the people groups;
I will sing to praise you among the nations
4 because your faithful love for us reaches up to the heavens,
and you are as faithful in keeping your promises as the clouds are high above the earth.
5 Yahweh, show in the heavens that you are very great!
Show your glory to people all over the earth!
6 Answer our prayers and, by your powe,r help us to defeat our enemies
so that we, the people whom you love, may be saved."
7 Yahweh answered our prayers and spoke from his temple, saying, "Because I have conquered your enemies, I will joyfully divide the city of Shechem,
and I will distribute among my people the land in the Valley of Succoth.
8 The region of Gilead is mine;
the people of the tribe of Manasseh are mine;
and the tribe of Judah is like my scepter.
9 The region of Moab is like my washbasin;
I throw my sandal over the region of Edom to show that it belongs to me;
I shout triumphantly because I have defeated the people of the region of Philistia."
10 Because we want to attack the people of Edom,
who will lead my army to their capital city that has strong walls around it?
11 God, we do not want you to abandon us;
we want you to go with us when our army marches out to fight our enemies.
12 We need you to help us when we fight against our enemies
because the help that humans can give us is worthless.
13 But with you helping us, we shall win;
you will enable us to defeat our enemies.


Mr Robogers' Number Bumbler
Input the number you want Mr. Robogers to count up to:
Your bumbled numbers:
Now that he has counted up, have Mr. Robogers count down.
Countdown!
Your countdown:
Wind Mr. Robogers up for another go!


Request Unauthorized
This is a custom error page for 401 status. This page will refresh to the Jetty Demo in 2 seconds.


Ecclesiastes 8:3
Be not hasty to go out of his sight: stand not in an evil thing; for he doeth whatsoever pleaseth him.


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Serves 12
Dietary
Baked cheesecake with vanilla and strawberries is a perfect pudding with a light and creamy texture.
Melt the butter in a medium saucepan over a gentle heat. Brush a little of the melted butter around the base and sides of a 23cm/9in springform tin, then line the base with a circle of baking parchment.
 Use a rolling pin, or a bottle, to crush the biscuits to fine crumbs.
Stir the crumbs into the melted butter until completely combined. Press the crumbs into the tin. Use the back of the spoon to compact the crumbs as much as you can in an even layer. Put into the fridge to firm up while you make the cheesecake, roughly 30 mins - 1 hour.
Preheat the oven to 180C/350F/Gas 4.
To separate the eggs, have three small bowls ready, preferably glass or ceramic. Gently crack the shell against the side of a small bowl. Slowly pull the shell apart as cleanly as possible along the crack, tipping the yolk into one half of the shell. Let the white drain away into the bowl below. Drop the yolk into another small bowl. Separate the next egg over a third bowl - that way if you accidentally break the yolk, it won’t contaminate the clean whites. (The egg whites will keep for a week in the fridge, and can be whipped up to lighten pancakes.)
Slit the vanilla pod lengthwise, and scrape the seeds out with the blunt side of your knife. Scrape the seeds from your knife (or spoon the vanilla paste) into a large mixing bowl with the cream cheese. Beat the cheese and vanilla together with a wooden spoon or a hand-held mixer until smooth.
Add the sugar, then beat again till smooth. Now add the eggs and yolks, one at a time, followed by half of the crème fraîche.
When the base is well chilled, wrap the outside of the tin in two sheets of wide foil. Ensure that the foil covers the sides of the tin more than halfway, then sit the wrapped tin in a deep roasting tin. Spoon in the cheesecake filling and smooth the top.
 Then place the roasting tin with the cheesecake on the top shelf of the oven. Pour the hot water from the kettle into the roasting tin around the cheesecake so that it comes halfway up the sides and carefully slide the tin into the oven. Bake for 10 minutes at 180C/350F/Gas 4, then turn the oven down to 140C/275F/Gas 1 and cook for another 40-50 minutes until the cheesecake is set, with a slight wobble in the middle.
Turn the oven off, and let the cheesecake cool in the oven, with the door ajar. When cool, remove from the oven and chill thoroughly. Cooling the cheesecake slowly will help to prevent the top from cracking.
For strawberry sauce, put half of the strawberries into a food processor or blender, add the icing sugar then pulse until smooth. Slice the remaining strawberries and set aside.
About half an hour before you want to eat the cheesecake, remove it from the fridge. Sweep a palette knife between the cake and the side of the tin, then carefully unclip the springform. Transfer the cheesecake to a serving plate. Spread the remaining crème fraîche over the surface of the cheesecake - this will cover any cracks that might have appeared. Decorate the top with the sliced strawberries - then cut into wedges and serve with the strawberry sauce poured over.


Lagrange comes out of the box with MathJax and syntax highlighting through fenced code blocks. MathJax allows you to display mathematical equations in your posts through the use of LaTeX. Syntax highlighting allows you to display source code in different colors and fonts depending on what programming language is being displayed.
As always, Jekyll offers support for GitHub Flavored Markdown, which allows you to format your posts using the Markdown syntax. Examples of these text formatting features can be seen below. You can find this post in the _posts
directory.
The Schrödinger equation is a partial differential equation that describes how the quantum state of a quantum system changes with time:
Joseph-Louis Millennial was an Italian mathematician and astronomer who was responsible for the formulation of Lagrangian mechanics, which is a reformulation of Newtonian mechanics.
You can find the full list of supported programming languages here.
#container {
float: left;
margin: 0 -240px 0 0;
width: 100%;
}
def print_hi(name)
puts "Hi, #{name}"
end
print_hi('Tom')
#=> prints 'Hi, Tom' to STDOUT.
Another option is to embed your code through Gist.
Written on August 8th, 2015 by Paul Le


Exploring the parameters of convolutional neural networks to create an accurate image classifier.
Final GitHub Repo: EECS349_Project
Contributors: Adam Pollack, Chainatee Tanakulrungson, Nate Kaiser
Summary
The objective of this project was to predict the presence of lung cancer given a 40×40 pixel image snippet extracted from the LUNA2016 medical image database. This problem is unique and exciting in that it has impactful and direct implications for the future of healthcare, machine learning applications affecting personal decisions, and computer vision in general. The medical field is a likely place for machine learning to thrive, as medical regulations continue to allow increased sharing of anonymized data for the sake of better care. Not only that, but the field is still new enough that our project implements methods at the forefront of technology.
Approach
Due to the complex nature of our task, most machine learning algorithms are not well-posed for this project. There are currently two prominent approaches for machine learning image data: either extract features using conventional computer vision techniques and learn the feature sets, or apply convolution directly using a CNN. In the past few years, however, CNNs have far outpaced traditional computer vision methods for difficult, enigmatic tasks such as cancer detection. We decided to implement a CNN in TensorFlow, Google’s machine learning framework.
Figure 1: Examples of cancerous images
Figure 2: Examples of non-cancerous images
Because we collectively had limited experience with convolutional neural networks, we decided to first explore the hyperparameters of a CNN. We did so by creating an experiment in which we varied the kernel size and number of filters of each convolutional layer and the dropout rate for a total of 108 models. For this study, we kept a constant network architecture.
| Attribute | Values Tested |
|---|---|
| Kernel Size (Convolutional Layer 1) | 3x3, 5x5, 7x7 |
| Kernel Size (Convolutional Layer 2) | 3x3, 5x5, 7x7 |
| Number of Filters (Convolutional Layer 1) | 16, 32 |
| Number of Filters (Convolutional Layer 2) | 32, 64 |
| Dropout Rate | 0.1, 0.2, 0.3 |
Each model was trained on 2,064 images (batch size of 104), validation was run every 10 epochs on another 442 images, and a final test was run after 500 epochs on another 442 images.
Results
After determining the best set of hyperparameters based on average peak validation accuracy, we then tested six new architectures based on these hyperparameters. The structure of each of these architectures was decided based on the principles described in the Stanford CS231n course notes[1]. After running the final six architectures at 500 epochs, we found the inflection point of the loss to be around 250 epochs. We then ran each of the six architectures for 250 epochs and recorded the final test accuracy. The best network architecture of these six achieved a test accuracy of 96.38%.
Figure 3: Tensorboard Graph of Accuracy for Final Model at 500 epochs (Orange Line = Training Dataset, Blue Line = Validation Dataset)
Figure 4: Tensorboard Graph of Loss for Final Model at 500 epochs (Orange Line = Training Dataset, Blue Line = Validation Dataset)
Conclusion
After finding our best model, we ran further analysis to extract a confusion matrix and misclassified images of the final test results to determine why this number was not closer to 100%.
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | 226 | 12 |
| Actual Negative | 4 | 200 |
Figure 5: Examples of misclassified images from the test dataset
Our model classified more examples as negative when they should have been positive than vice versa. We believe this is because of the nature of some of the positive examples. For example, the first four misclassified images above are all positive examples of cancer even though two of them have almost no distinct features. It is likely that it would be just as difficult for a human to classify those images as a doctor. We also can’t guarantee that the data we used is completely correctly classified; it is possible there are some mislabeled images.
Future Work
We plan to test our model on entire scans of a lung by extracting 40x40 images from each image slice of the lung. Sliding a window with a stride of around 20 would give us a large set of images to test for cancer but with a pre-trained model, this would be relatively easy to do. Our hope is that this method would allow us to determine whether or not cancer is present in an entire lung instead of a predetermined section. We would also like to try implementing one or more named convolutional neural networks such as AlexNet[2] or Inception[3].
Final Report
If you are intersted in learning more about the details of this project, please read our report.
References
[1] Stanford Course Notes on CNNs: http://cs231n.github.io/convolutional-networks/
[2] AlexNet: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
[3] Inception (by Google): https://arxiv.org/abs/1409.4842


Metadata
none
minimal
minimal to full
full
Recognize Dates
yes/no
MetaData
POST food entry
POST category entry


Super_Search:Cloud template tag
This function allows you to display a list or "cloud" of all terms searched upon over time. It will show all popular search terms and if desired, let you show a weighted cloud, where more popular search terms appear larger than less popular search terms.
If you wish to use the weighted cloud method: the number of times the most popular search term is used establishes the top of the scale, with 0 as the bottom. You can specify the number of groups into which your search terms will be sorted. Each of your search terms will be evaluated as to which group they belong in. They will be given a numerical ranking based on this. CSS styling is then used to convert that number value into a font size.
For example, imagine you have 3 terms that were searched upon on your site: apple, orange and pie. You specify that you want to show a cloud with 3 groups. If apple was searched 12 times, orange 7 times, and pie 1 time, the top of your scale will be 12. apple will get the highest rank and the largest font size. pie will get the smallest ranking and font size. orange will get the middle font because it falls into the middle grouping. The range for falling into the lowest group would be 0-4. For the middle, 5-8. And for the top, 9-12.
{exp:super_search:cloud} content {/exp:super_search:cloud}
Parameters
The following parameters are available for use:
- groups
- start
- step
- day_limit
- start_on
- stop_on
- site_id
- fixed_order
- term
- term_id
- exclude_term
- exclude_term_id
- limit
- most_popular
- paginate
- orderby
- sort
- backspace
- searched_only
groups=
groups="5"
This parameter tells the search cloud how many groups to divide your search terms into. For example, if you specify 5, it will create 5 groups and place the search terms into the appropriate group based on their popularity. This will determine the font size for each tag. Defaults to 5.
start=
start="10"
This parameter controls which integer of font size to start counting. This affects the size variable which sets the font size of each search term in your cloud. Defaults to 10.
step=
step="2"
This parameter controls the amount to increment the size variable. This allows you to create greater differences in font sizes between search term popularity groups. Defaults to 2.
day_limit=
day_limit="5"
Using this parameter, you can filter your list of search terms by those which have been searched upon within the last number of days. Defaults to none.
start_on=
start_on="2011-12-20 14:00"
Using this parameter, you can filter your list of search terms by those which have been searched upon within the specified date range. The date/time must use this format: YYYY-MM-DD HH:MM. Used in conjunction with the stop_on parameter. Defaults to none.
stop_on=
stop_on="2012-01-20 14:00"
Using this parameter, you can filter your list of search terms by those which have been searched upon within the specified date range. The date/time must use this format: YYYY-MM-DD HH:MM. Used in conjunction with the start_on parameter. Defaults to none.
site_id=
site_id="3"
The parameter allows you to filter the list of search terms and their popularity by MSM sites the terms were searched upon. Separate multiples with the pipe character (|), or specify all to look across all sites. Defaults to current site.
fixed_order=
fixed_order="81|17|26|43"
This parameter allows you to optionally override the results and set them into a fixed order by specifying term ID's. Separate multiples with the pipe character (|).
term=
term="cheese|pie|corn"
This parameter allows you to filter down your list of search terms results by specifying search terms. Separate multiples with the pipe character (|).
term_id=
term_id="12|82|48"
This parameter allows you to filter down your list of search terms results by specifying ID's of search terms. Separate multiples with the pipe character (|).
exclude_term=
exclude_term="burger|hammy|pimpkins"
This parameter allows you to exclude specific search terms from your results, using the search term. Separate multiples with the pipe character (|).
exclude_term_id=
exclude_term_id="34|234|89"
This parameter allows you to exclude specific search terms from your results, using the search term ID. Separate multiples with the pipe character (|).
limit=
limit="20"
This parameter is available to limit the amount of results in the cloud. Defaults to 20.
most_popular=
most_popular="no"
By default, the results in the cloud/list are filtered to contain the most popular search terms, when used in conjunction with the limit parameter. So for example, if you have 50 search terms, and you have a limit set to 20, the top 20 most popular search terms will be displayed, and then ordered by the value in the orderby parameter. This is more beneficial when ordering your results by anything other than count. To disable this behaviour, call this parameter and specify a value of no. Defaults to yes.
paginate=
paginate="both"
Pagination works just like it does in the Channel:Entries tag. When specified, and used in conjunction with the super_search_paginate variable pair, pagination will display. Options are top, bottom, or both. Refer to Channel:Entries tag documentation for more information.
orderby=
orderby="term"
This parameter allows you to order your search terms by the method you wish. Default method is count. Available methods are:
- term - alphabetical
- count - number of times the term has been searched for
- first_seen - the date which the term was first searched upon
- last_seen - the date which the term was most recently searched upon
- entry_count - the number of times the term appears in the site(s) data
- random - randomly selects one of the above methods
sort=
sort="asc"
This parameter allows you to sort search terms by ascending (asc) or descending (desc) order. Default is desc.
backspace=
backspace="2"
In the event you wish to trim the last few characters from the results, you would specify this parameter. For example, if you have a comma-seperated list of search terms, you would have ,[space] formatting within the template tag. If you specify 2 in the backspace parameter, it will trim off the last ,[space] when the template parses.
searched_only=
searched_only="no"
This parameter lets you choose whether you want words that have NOT been searched upon yet show up in the results. By default, the Cloud tag will not show words not searched on. To display these words, specify this parameter with a value of no.
Variables
The following variables are available for use:
- super_search_term
- super_search_term_id
- super_search_total_searches
- super_search_first_seen
- super_search_last_seen
- super_search_site_id
- super_search_size
- super_search_step
- super_search_count
- super_search_absolute_count
- super_search_total_results
- super_search_absolute_results
- super_search_switch
super_search_term
{super_search_term}
This variable returns the search term.
super_search_term_id
{super_search_term_id}
This variable returns the ID of the search term.
super_search_total_searches
{super_search_total_searches}
This variable will return the total number of times the given term has been searched upon.
super_search_first_seen
{super_search_first_seen format="%F %j, %Y"}
This variable returns the date of when the given term was first searched by someone. Use standard date variable formatting for this variable.
super_search_last_seen
{super_search_last_seen format="%F %j, %Y"}
This variable returns the date of when the given term was most recently searched by someone. Use standard date variable formatting for this variable.
super_search_site_id
{super_search_site_id}
This variable will display the MSM site(s) that had this term searched upon.
super_search_size
{super_search_size}
This variable will return the group number the search term is part on, based on the popularity of the search term (determined by the groups parameter).
super_search_step
{super_search_step}
This variable relates to the step parameter. It helps you represent the breakpoints between groups of search terms by their popularity. The usual usage for this is to set different font sizes of the search terms to create the "cloud" effect.
super_search_count
{super_search_count}
This variable will return the relative position number of the search term within the list of results.
super_search_absolute_count
{super_search_absolute_count}
This variable will return the absolute position number of the search term across all pages of results.
super_search_total_results
{super_search_total_results}
This variable will return the total amount of search term results being displayed in the results of the cloud on the current page. You would typically use this in a conditional statement.
super_search_absolute_results
{super_search_absolute_results}
This variable will return the total amount of search term results across all pages in the results of the cloud on the current page.
super_search_switch
{super_search_switch="style_1|style_2"}
This variable will alternately output each of the multiple values you provide. You would typically use this with CSS.
Variable Pairs
The following variable pairs are available for use:
super_search_paginate
{super_search_paginate}
 Page {super_search_current_page} of {super_search_total_pages} pages {super_search_pagination_links}
 {/super_search_paginate}
OR
{super_search_paginate}
 {super_search_pagination_links} {first_page}
First
 {/first_page} {previous_page}
« Previous
 {/previous_page} {page}
{pagination_page_number}
 {/page} {next_page}
Next »
 {/next_page} {last_page}
Last
 {/last_page} {/super_search_pagination_links}
 {/super_search_paginate}
Pagination is available for this tag and works just like regular EE pagination, but each variable/variable pair is prepended with super_search_ (except for any variables/variable pairs inside of the super_search_pagination_links variable pair). Used in conjunction with the paginate parameter.
Conditionals
Most of the variables above are available for use as conditionals. Additionally, the following conditional is available for use:
super_search_no_results
{if super_search_no_results} Sorry, no results were found. {/if}
This conditional displays it's contents when there are no results found.
Examples
The following are a few different examples of how you can use this tag:
Popular Search Terms Cloud
Below is a simple typical usage of the search cloud in "cloud" format:

 {exp:super_search:cloud start="15" groups="10" step="3" limit="25" orderby="term" sort="asc" }  {super_search_term}  {/exp:super_search:cloud}

Popular Search Terms List
You could also display your search term results in list form if you wish. You can also keep font size weighting in place, but is removed from the example below:

 {exp:super_search:cloud limit="25" paginate="both" }
  {super_search_term}  ({super_search_total_searches})
 {if super_search_no_results}
Sorry, no results were found.
 {/if} {super_search_paginate}

 {super_search_pagination_links} {first_page}
First
 {/first_page} {previous_page}
« Previous
 {/previous_page} {page}
{pagination_page_number}
 {/page} {next_page}
Next »
 {/next_page} {last_page}
Last
 {/last_page} {/super_search_pagination_links}

 {/super_search_paginate} {/exp:super_search:cloud}


Fight Club (1999)
Cast: Brad Pitt, Edward Norton, Helena Bonham Carter, Meat Loaf, Jared Leto Director: David Fincher Screenplay: Jim Uhls, based on the novel by Chuck Palahniuk 139 minutes Rated R
Review by Donlee Brussel
David Fincher's latest cold, bleak, depressing masterpiece is a 139- min. adrenaline rush. It's a film that stabs you in the heart with a rusty knife and then sprays it with 100% proof alcohol. It's one that goes straight for the jugular and leaves you gasping for its entire run. And did I mention how funny this macabre comedy is?
Based on Chuck Palahniuk's intensely provocative first novel of the same name and armed with innovative visual techniques, jaw-droppingly superb performances from its stars, and a darkly satirical & original screenplay, `Fight Club' proves that it's one of the best films of the decade.
Hype for this film has been incredibly high and so have the stakes for everyone involved. The book has become a cult classic and rightfully so. Having read the novel myself, I can honestly say the movie is as good, if not better than the book. The brilliant wordplay, the monologues, its originality, all has been brought to life and Fincher has taken them to the next level after that. David Fincher's career hits a peak with this film, with `Fight Club' he becomes the best director at combining style and substance and never has it been done this well. Fincher has also gotten the best performances out of all his players.
Edward Norton gives another superior performance as the everyman Narrator and once again proves he is one of the most versatile actors working in Hollywood today. Not only that, but Brad Pitt is back, going all out in anti-mainstream mode as Tyler Durden, delivering his best performance to date. And Helena Bonham Carter portrays her sardonic character with such deadpan panache that if she doesn't get an Oscar nomination in February; I'll be bitching for a very long time.
`Fight Club' starts out about a group of yuppies that start a club where each week they take their aggressions out on each other by fighting. However, that's only the beginning. Later on, Tyler takes Fight Club up a notch with Project Mayhem, a terrorist group that rebels against society in many ways. Their antics get more and more extreme, right up until its tremendous first-rate climax. Unlike other recent releases, the film's ending is very satisfying. `Fight Club' doesn't end abruptly with no conclusion, it doesn't end with a predictable twist, it ends with a nice and twisted dark ending.
To tell you any more about the plot would be wrong of me, so stop reading any reviews and go see `Fight Club', the best film I've seen all year.
- © 1999 by Donlee Brussel
Sent via Deja.com http://www.deja.com/ Before you buy.
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Hey!
I'm Colin, a Computer Science student and freelance web developer and IT solutions manager, and you've stumbled onto the internets equivilent of an answering machine message.
I host things I've worked on here such as 'blog with words' and 'projects with fewer words'.
Please scroll down to see said stuff.
Here are some of the projects that you can visit
Most have a write up on the blog to go with them
"Having worked closely with me to set up a website I was happy with, I can firmly say Colin is a pleasure to work with." - Owner of zaya.gallery
"I had little experience with designing a website and didn't know what I wanted, Colin
turned out an end product that I loved in a short time frame." - Owner of GCP QA Solutions
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Feel free to email me or drop a comment on the blog, or to keep up to date with goings on around here follow me on twitter, github, or visit the blog.


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Data Connection Builder Help
What is Data Connection Builder?
Data Connection Builder is used to build connection strings for use with Microsoft's ActiveX Data Objects .NET (ADO.NET) Framework Data Provider for Object Linking and Embedding, Database (OLE DB) technology. Such a connection string contains the information needed to establish a connection to an OLE DB data source through ADO.NET. This information typically includes, at minimum, the name of the data source and the provider.
Data Connection Builder supports the three Microsoft OLE DB providers that have been tested with ADO.NET: SQL Server, Oracle, and ACE (used with Access and Excel). A connection string might also include security information, such as user ID and password. For more information, see Connection String Syntax (OleDb Connection Strings). ConnectionStrings.com also provides information on connection strings for many additional OLE DB providers.
Building a new connection string
-
Specify a Provider and a Data Source. These properties are always required.
-
Specify other properties as appropriate. Certain properties may be required depending on the provider. Properties that do not apply to the specified provider are hidden.
-
It is always a good idea to make sure that the connection string is valid once it is built. Test the connection string by clicking the Test Connection button.
Troubleshooting
If a data provider is not installed, an error will occur when the connection is tested. Below are the errors that typically occur and links for downloading each data provider.
| Provider |
Error |
Link(s) |
| ACE (Access/Excel) |
The 'Microsoft.ACE.OLEDB.12.0' provider is not registered on the local machine. |
|
| Oracle |
The 'MSDAORA.1' provider is not registered on the local machine. |
|
| SQL Server |
[DBNETLIB][ConnectionOpen (Connect()).]SQL Server does not exist or access denied. |
|
© 2017-8 Jeffrey Paul Bourdier


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Streaming Topic Model Training and Inference
Suneel Marthi
March 21, 2019
DataWorks Summit 2019 - Barcelona
$WhoAreWe
-
Suneel Marthi
@suneelmarthi
- Member of Apache Software Foundation
- Committer and PMC on Apache Mahout, Apache OpenNLP, Apache Streams
Agenda
- Motivation for Topic Modeling
- Existing Approaches for Topic Modeling
- Topic Modeling on Streams
Motivation for Topic Modeling
Topic Models
- Automatically discovering main topics in collections of documents
- Overall
- What are the main themes discussed on a mailing list or a discussion forum ?
-
What are the most recurring topics discussed in research papers ?
Topic Models (Contd)
- Per Document
- What are the main topics discussed in a certain (single) newspaper article ?
- What is the most distinctive topic that makes a certain research article more interesting than the others ?
Uses of Topic Models
- Search Engines to organize text
- Annotating Documents according to these topics
- Uncovering hidden topical patterns across a document collection
Common Approaches to Topic Modeling
- Latent Dirichlet Allocation (LDA)
- Topics are composed by probability distributions over words
- Documents are composed by probability distributions over Topics
- Batch Oriented approach
LDA Model
|
- N: vocab size
- M/D: # of documents
- alpha: prior on per-document topic dist.
- beta: prior on topic-word dist.
- theta: per-document topic dist.
- phi: per-topic word dist.
- z: topic for word w
|
Intuition: Documents are a mixture of topics, topics are a mixture of words. So documents can be generated by drawing a sample of topics, and then drawing a sample of words. |
- Latent Semantic Analysis (LSA)
- SVDed TF-IDF Document-Term Matrix
- Batch Oriented approach
- Drawbacks of Traditional Topic Modeling Approach
- Problem : labelling topics is often a manual task
E.g. LDA topic:30% basket,15% ball,10% drunk,…
can be tagged as basketball
Common Approaches Deep Learning (of course)
- Learn to perform LDA: LDA supervised DNN training (inference speed up)
https://arxiv.org/abs/1508.01011
- Deep Belief Networks for Topic Modelling
- https://arxiv.org/abs/1501.04325
What are Embeddings ?
Represent a document as a point in space, semantically similar docs are close together when plotted
Such a representation is learned via a (shallow) neural network algorithm
Embeddings for Topic Modeling
- LDA2Vec: Mixing LDA and word2vec word embeddings
https://arxiv.org/abs/1605.02019
- Navigating embeddings: geometric navigation in word
and doc embeddings space looking for topics
Static vs Dynamic Corpora
- Learning Topic Models over a fixed set of Documents
- Fetch the Corpus
- Train and Fit a topic model
- Extract topics
- ...other downstream tasks
Static vs Dynamic Corpora (Contd)
- What to do with Corpus updates?
- Document collections are not static in real world
- There may be no document collections to begin with
- Memory limits with large corpora
- Reprocess entire corpora when new documents are added
Static vs Dynamic Corpora (Contd)
- Streams, Streams, Streams
- Twitter stream
- Reddit posts
- Papers on ResearchGate and Arxiv
- ....
Questions for Audience
-
Who’s doing inference or scoring on streaming data?
-
Who’s doing online learning and optimization on streaming data?
(2) is hard, sometimes because the algorithms aren’t there.
But, also because people don’t know the algorithms are there.
Topic Modeling on Streams
2 Streaming Approaches
- Learning Topics from Jira Issues
- Online LDA
Learning Topics on Jira Issues
Workflow
FLINK-9963: Add a single value table format factory
Topics extracted for Flink-9963
- format factory
- table schema
- table source
- sink factory
- table sink
FLINK-8286: Fix Flink-Yarn-Kerberos integration for FLIP-6
Topics extracted for Flink-8286
- yarn kerberos integration
- kerberos integrations
- yarn kerberos
Bayesian Inference
Goal: Calculate the posterior distribution of a probabilistic model given data
- Gibbs sampling/MCMC: Repeatedly sample from conditional distribution(s) to approximate the posterior of the joint distribution;
typically a batch process
-
Variational Bayes: Optimize the parameters of a function that approximates joint distribution
Variational Bayes
- Expectation Maximization-like optimization
- Faster and more accurate
- Can often be computed online (on windows over streams)
Online LDA (2/2)
|
- job-constant
- K: number of topics
- alpha: topic-document dist. prior
- eta: topic-word dist. prior
- tau0: learning rate
- kappa: decay rate
- (mini)batch-local
- gamma: theta prior
- theta: param. for per-document topic dist
- phi: param. for per-topic word dist.
- rho: update weight
- job-global
- lambda: parameter for topic-word dist.
- t: documents/batches seen (index)
|
|
Goal: Optimize the topic-word dist. parameter lambda; it tells us what words belong to the topics.
[Image source: Hoffman, Blei & Bach, 2010.]
|
Job-global parameters can be tracked in a state store. Batch-local parameters can be discarded. |
Online LDA on Beam
- job-constant
- K: number of topics
- alpha: topic-document dist. prior
- eta: topic-word dist. prior
- tau0: learning rate
- kappa: decay rate
|
Set job parameters globally with setGlobalJobParameters() on execution environment, or pass as arguments to operators |
- (mini)batch-local
- gamma: theta prior
- theta: param. for per-document topic dist
- phi: param. for per-topic word dist.
- rho: update weight
|
Operator/task-internal and don't need to be accessible by other tasks |
- job-global
- lambda: parameter for topic-word dist.
- t: documents/batches seen (index)
|
- Several options:
- External key-value store
- Accumulators: lambda update as increment, accumulator + broadcast variables
- Broadcast state
|
And, (mini)batches selected by Window size |
OnlineLDA Pipeline
- For many tasks, the default is batch training/optimization
- Often, even if good online training and optimization algorithms exist (why?)
- So if your algorithms only require 1-step of parameter state, why not just do streaming?
- You can get instant, always up-to-date results by putting the effort into using a different class of optimization algorithms an using a stream processing engine, so just do that
Links
- http://papers.nips.cc/paper/3902-online-learning-for-latentdirichlet-allocation
- Learning from LDA using Deep Neural Networks - https://arxiv.org/pdf/1508.01011.pdf
- Deep Belief Nets for Topic Modeling - https://arxiv.org/pdf/1501.04325.pdf
- Topic Modeling on Jira issues: https://github.com/tteofili/jtm
Credits
- Tommaso Teofili, Simone Tripodi (Adobe - Rome)
- Joern Kottmann, Bruno Kinoshita (Apache OpenNLP)
- Fabian Hueske (Apache Flink, Data Artisans)


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HSJ Health Check: The pensions crisis latest
Listen to the HSJ Health Check podcast — this week we examine the tentative solution to the NHS pensions crisis, maternity failures in Shropshire, and the mysterious departure of a London finance director.
Each week, HSJ Health Check gathers a cast from our team of expert journalists, to explain and debate the most important news issues right now in NHS policy and leadership.
You can listen above, follow us on Spotify, listen on Apple Podcasts, or other podcast platforms.
The latest episode features HSJ deputy editor Dave West, workforce correspondent Annabelle Collins, bureau chief for finance and policy Lawrence Dunhill, and mental health correspondent Rebecca Thomas.
We discuss: Whatever has happened to the solution, leaked early this week, to the NHS pensions crisis? Would it work? And what are the long-term consequences of doctors having now discovered a work-life balance?
We also look at the Shrewsbury and Telford maternity care scandal, and the curious departure of an ex-private sector finance director from a long-strained London hospital trust.
The podcast will be available every Friday morning here and across the major platforms including Spotify, Apple Podcasts, and others.
Got feedback or something you would like us to discuss? Contact<EMAIL_ADDRESS>or @davewwest.


Slave Recovery in Apache Mesos
High availability is one of the key features of Mesos. For example, a typical Mesos cluster in production involves 3-5 masters with one acting as leader and the rest on standby. When a leading master fails due to a crash or goes offline for an upgrade, a standby master automatically becomes the leader without causing any disruption to running services. Leader election is currently performed by using ZooKeeper.
With the latest Mesos release, 0.14.1, we are bringing high availability to the slaves by introducing a new feature called Slave Recovery. In a nutshell, slave recovery enables:
Executors/tasks to keep running when the slave process is down.
A restarted slave process to reconnect with running executors/tasks on the slave.
Why it matters
A recoverable slave is critical for running services in production on Mesos for several reasons:
Stateful services
In a typical production environment there are stateful services (e.g., caches) running in the cluster. It is not uncommon for these services to have a high startup time (e.g., cache warm up time of a few hours). Even in the analytics world, there are cases where a single task is responsible for doing work that takes hours to complete. In such cases a restart of the slave (e.g, crash or upgrade) will have a huge impact on the service. While sharding the service wider mitigates this impact it is not always possible to do so (e.g, legacy services, data locality, application semantics). Such stateful applications would benefit immensely from running under a more resilient slave.
Faster cluster upgrades
It is important for clusters to frequently upgrade their infrastructure to stay up-to-date with the latest features and bug fixes.  In production environments there is always tension between upgrading the infrastructure frequently and the need to not impact long running services as much as possible. With respect to Mesos upgrades, if upgrading the slave binary has no impact on the underlying services, then it is a win for both the cluster operators and the service owners.
Resilience against slave failures
While upgrading the slaves is most often the reason for restarting slaves, there might be other causes for a slave to fail. A slave crash could happen due to a bug in the slave code or due to external factors like a bug in the kernel or ZooKeeper client library. Typically such crashes are temporary and a restart of the slave is enough to correct the situation. If such slave restarts do not affect applications running on the slave it is a huge win for the applications.
How it works
Checkpointing
Slave recovery works by having the slave checkpoint enough information (e.g., task information, executor information, status updates) about the running tasks and executors to local disk. Once the slave and the framework(s) enable checkpointing, any subsequent slave restarts would recover the checkpointed information and reconnect with the executors. When a checkpointing slave process goes down, both the leading master and the executors running on the slave host wait for the slave to come back up and reconnect. A nice thing about slave recovery is that frameworks and their executors/tasks are oblivious to a slave restart.
Executor Driver Caching
As part of this feature, the executor driver has also been improved to make it more resilient in the face of a slave failure. As an example, status updates sent by the executor while the slave is down are cached by the driver and sent to the slave when it reconnects with the restarted slave. Since this is all handled by the executor driver, framework/executor writers do not have to worry about it! The executors can keep sending status updates for their tasks while remaining oblivious to the slave being up or down.
Reliable status updates
Another benefit of slave checkpointing the status updates is that now updates are more reliably delivered to frameworks in the face of failures. Before slave recovery if the slave fails at the same time that a master is failing over, no TASK_LOST updates for tasks running on the slave were sent to the framework. This is partly because the Mesos master is stateless. A failed over master reconstructs the cluster state from the information given to it by re-registering slaves and frameworks. With slave recovery, status updates and tasks are no longer lost when slaves fail. Rather, the slave recovers tasks, status updates and reconnects with the running executors. Even if an executor does terminate when the slave is down, a recovered slave knows about it from its checkpointed state and reliably sends TASK_LOST updates to the framework.
For more information about how to enable slave recovery in your cluster, please refer to the documentation.
Looking ahead
Easy Executor/Task Upgrades
In a similar vein to how slave recovery makes upgrading a Mesos cluster easy, we would like to enable frameworks to upgrade their executors/tasks as well. Currently the only way to upgrade an executor/task is to kill the old executor/task and launch the new upgraded executor/task. For the same reasons as we have discussed earlier this is not ideal for stateful services. We are currently investigating proper primitives to provide to frameworks to do such upgrades, so that not every framework have to (re-)implement that logic.
State Reconciliation
While slave recovery greatly improves the reliability of delivering status updates there are still some rare cases where updates could be lost. For example if a slave crashes when a master is failing over and never comes back then the new leading master doesn’t know about the lost slave and executors/tasks running on it. In addition to status updates, any driver methods (e.g., launchTasks, killTask) invoked when the master is failing over are silently dropped. Currently, frameworks are responsible for reconciling their own task state using the reconciliation API. We are currently investigating ways to provide even better guarantees around reconciliation.
Self Updates of Mesos
Currently, updating Mesos involves a cluster operator to manually upgrade the master and slave binaries and roll them in a specific manner (e.g., masters before slaves). But what if Mesos can update itself! It is not hard to imagine a future where Mesos masters can orchestrate the upgrade of slaves and maybe also upgrade one another! This would also help making rollbacks easy incase an upgrade doesn’t work because it would be much easier Mesos to check if various components are working as expected.
So what are you waiting for? Go ahead and give Mesos a whirl and let us know what you think.


ax5dialog themes using six colors defined by the bootstrap is provided. If you want to change the theme to the dialog you can define the theme attribute at the time of method calls, you can use to define in advance setConfig.
method
setConfig


The WallStreetBets dataset provides QuiverWallStreetBets and QuiverWallStreetBetsUniverse objects.
QuiverWallStreetBets objects have the following attributes:
QuiverWallStreetBetsUniverse objects have the following attributes:


THE CORRUPTOR
As far as I know, Chow Yun Fat is the most charismatic man alive. If I was smart when I was a kid, I would have trained to become Chow Yun Fat as an adult. If there were some way I could pay money to actually become Chow Yun Fat, I'd probably be saving up. There is something powerful about his presence that I couldn't really describe with words. So insert you're cliche here - he's The Man, he's the Master, he's God. I haven't seen all of his work, but I've seen enough of the basics (The Killer, Hard Boiled, A Better Tomorrow) to see that he's a lot more than a gunslinging badass. He's a great actor. Unfortunately, his American debut The Replacement Killers was only interested in the gunslinging badass side of his persona. He pulled off the role (with minimal English) and his charisma was powerful enough to hold the movie. But fortunately The Corruptor - while no The Killer - is a much better vehicle for his talents.
The new film reminds me more of Hard Boiled than anything else - sort of an American take on that brand of HK crime epic. There's a gang war, rival cops, confusion about who's on which side. There's no shootout in a teahouse, but there's one in a lamp store. There's even a scene where Wahlberg walks around with a toothpick hanging out of his mouth, which may or may not be an homage to Tequila's habit in Hard Boiled. Of course, The Corruptor's gun battles and miscellaneous mayhem, while pretty extravagant by American standards, don't have the same balletic grace and adrenaline pumping power of Woo's films. And I would understand if someone found the portrayal of Chinese gangs, prostitutes and crooked cops offensive in the context of an American movie (although I think the movie -- perhaps due to the influence of Woo's films -- goes to unusual length to add ambiguity and dimension to the white-cop-comes-into-Chinatown scenario). I'm also a fan of Mark Wahlberg. I think his performance in Boogie Nights should have won him an Oscar.  But like I told a co-worker before the screening, "He's not gonna rap, is he? Then there's nothing to worry about." Outside of Boogie Nights, Wahlberg mostly exudes a boyish innocence that makes him very likable. I enjoyed his work in Traveller and The Big Hit (which had some extremely bad comic moments but went far to establish Wahlberg as a likable and agile action hero). His role here isn't quite as consistent, but it works, and I'm not sure there's anyone else I would have rather seen in the role. His best moments are on the too rare occasions when he gets a real chemistry going with Chow, like in the scene where they're staking out a drug house and he teases Chow by pretending to misinterpret his comment as a confession of deep love. The shootouts are pretty long, but they never give you the same "Oh my god, am I really seeing this?" shock of, say, the hospital shootout in Hard Boiled. Still, they have more punch than standard Hollywood fare. I particularly liked the drawn out gunbattle/car chase, even if the style is somewhere in between the kind of fluid editing I prefer and the incomprehensible Bruckheimer style that I usually despise. The storyline is fairly standard but it's more quick paced and complicated than your usual cop movie. I'm embarrassed to admit that I couldn't even keep up with a few of the story points. I don't think it's a particularly well written film but it's better than I expected, improving as it builds. What struck me is that by the end of the movie I really did care about Chow's character and wasn't sure how I wanted the film to end. In the last act he has become, by the usual action movie standards, "the bad guy," but Wahlberg and the audience (me, anyway) still like him. In real life, I don't think crooked cops - hell, straight cops - are exactly cool. (Sure, a society may need rules, but you gotta wonder about someone who actually wants to be the guy to enforce them. And then when they don't even want to follow the rules themselves, is there any arguing that they are soulless bastards?) So I found myself torn wondering if I'd rather see Wahlberg and internal affairs bust a corrupt cop who had it coming, or see Chow - the man, the master, God - get away scott free. And while I was struggling with this, I realized that the movie was working on me. The Corruptor isn't the masterpiece we're all hoping King's Ransom (Chow's long rumored reunion with Woo) will be, but it's light years ahead of the could-be-straight-to-video formulaic junk of The Replacement Killers. It's worth it just for the joy of Chow's acting - he's not just the cocky badass, he's also the jokester, the brooder, the guy who's been betrayed, the guy who cooks. It's a real role, and he eats it up. It's also notable that his English skills have improved immensely - I think he speaks it better than Jackie Chan, who's been trying for much longer. I wish The Corruptor had opened big so that our man could be considered "bankable."
--Bryan Frankenseuss Theiss
"I write rhymes so fresh I try to bite my own verses." --Tash
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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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.
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R interface to 'suncalc.js' library, part of the 'SunCalc.net' project <http://suncalc.net>, for calculating sun position, sunlight phases (times for sunrise, sunset, dusk, etc.), moon position and lunar phase for the given location and time.
| Version: | 0.4 |
| Imports: | V8 (≥ 1.5) |
| Published: | 2018-02-22 |
| Author: | Vladimir Agafonkin [aut, cph] (suncalc.js library/lib, https://github.com/mourner), Benoit Thieurmel [aut, cre] (R interface) |
| Maintainer: | Benoit Thieurmel <benoit.thieurmel at datastorm.fr> |
| License: | GPL-2 | file LICENSE |
| URL: | https://github.com/datastorm-open/suncalc, https://github.com/mourner/suncalc |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | suncalc results |
| Reference manual: | suncalc.pdf |
| Package source: | suncalc_0.4.tar.gz |
| Windows binaries: | r-devel: suncalc_0.4.zip, r-release: suncalc_0.4.zip, r-oldrel: suncalc_0.4.zip |
| OS X binaries: | r-release: suncalc_0.4.tgz, r-oldrel: suncalc_0.4.tgz |
| Old sources: | suncalc archive |
Please use the canonical form https://CRAN.R-project.org/package=suncalc to link to this page.


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Frequently Asked Questions
Answers to common questions can be found here, but for anything else you need to know, please get in touch!
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If the device is beyond repair, we may still be able to recover data for you, though again this is not guaranteed.
However, some newer models are harder to fix, because some Apple computers have parts that are normally removable fixed in place, or do not have access to them. We would advise if this is the case.
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Trade-in and Recycling
Working or Faulty:
- Desktop Computers (Towers)
- Laptops
- Components and Parts
We will also accept the following, providing they are in working order:
- Computer Mice and Keyboards
- Computer Speaker Systems
- Flat-screen Screens and Monitors
- Old phones and tablets
- Cables and Chargers
We cannot accept the following:
- CRT 'Bulky'/'Old-style' Computer Monitors
The offer we are able to make represents the value of the device to us (and of course - you are not obliged to accept it).
Please be aware that we are not obliged to accept a trade-in instead of payment.
Trade-in and payment for services are viewed as 2 separate transactions even if of equal value.


package main
import (
"bufio"
"fmt"
"os"
"strconv"
"strings"
)
func main() {
reader := bufio.NewReader(os.Stdin)
fmt.Println("Enter a number1:")
number1, _ := reader.ReadString('\n')
number1 = strings.TrimSpace(number1)
intNumber1, _ := strconv.Atoi(number1)
fmt.Println("Enter number2:")
number2, _ := reader.ReadString('\n')
number2 = strings.TrimSpace(number2)
intNumber2, _ := strconv.Atoi(number2)
fmt.Println("Numbers Entered by user:", intNumber1, intNumber2)
fmt.Println("Press 1 for addition")
fmt.Println("Press 2 for subtraction")
fmt.Println("Press 3 for multiply")
fmt.Println("Press 4 for division")
press, _ := reader.ReadString('\n')
press = strings.TrimSpace(press)
intPressnumber, _ := strconv.Atoi(press)
fmt.Println("Number press by user:", intPressnumber)
switch intPressnumber {
case 1:
fmt.Println("addition of two number:", intNumber1+intNumber2)
case 2:
fmt.Println("subtraction of two number:", intNumber1-intNumber2)
case 3:
fmt.Println("multiplication of two number:", intNumber1*intNumber2)
case 4:
fmt.Println("division of two number:", intNumber1/intNumber2)
default:
fmt.Println("please press valid number from 1-4!")
}
}
//Output:
Enter a number1:
2
Enter number2:
4
Numbers Entered by user: 2 4
Press 1 for addition
Press 2 for subtraction
Press 3 for multiply
Press 4 for division
1
Number press by user: 1
addition of two number: 6
In this we take two input number from user i.e. number1 , number2.
After that based on the user choice we have to perform addition, subtraction, multiplication, division on the two numbers i.e.
number1, number2 using switch case.
Switch expression {
Case label 1:
Statement
Case label 2:
Statement
……
……….
Case label n:
Statement
default: statements
}


gplgt - Logtalk integration script for GNU Prolog
gplgt [--quiet] [--query-goal goal]
gplgt is a Logtalk integration script for GNU Prolog. It automatically loads the GNU Prolog adapter file, the Logtalk compiler and runtime, the Logtalk default table of library names and paths, and any existing settings file found on the lookup directories. It uses the gprolog executable --entry-goal option to load the Logtalk files. It passes any options to the gprolog executable; see the GNU Prolog documention for the available options.
--quiet Silence Logtalk default startup messages and compilation messages by setting the report flag to warnings (requires GNU Prolog 1.5.1 or a later version).
--query-goal goal Initialization goal, written between double quotes, to be executed after Logtalk startup.
GNU Prolog 1.4.5 or a later version is required. This integration script looks for a gprolog executable in the system path.
LOGTALKHOME Specifies the location of the Logtalk installation.
LOGTALKUSER Specifies the location of the Logtalk user directory.
$LOGTALKHOME/integration/logtalk_gp.pl Prolog file used for loading the necessary files for starting Logtalk using GNU Prolog as the backend compiler.
$LOGTALKHOME/adapters/gnu.pl The GNU Prolog adapter file.
$LOGTALKHOME/core/core.pl The Logtalk core compiler and runtime file.
$LOGTALKHOME/paths/paths.pl The default table of library names and paths.
./settings.lgt Logtalk settings file (for overriding default settings in the adapter files). May not exist.
$LOGTALKUSER/settings.lgt Logtalk settings file (for overriding default settings in the adapter files). May not exist.
$HOME/settings.lgt Logtalk settings file (for overriding default settings in the adapter files). May not exist.
$XDG_CONFIG_HOME/logtalk/settings.lgt Logtalk settings file (for overriding default settings in the adapter files). May not exist.
$HOME/.config/logtalk/settings.lgt Logtalk settings file (for overriding default settings in the adapter files). May not exist.
The Logtalk website at https://logtalk.org/
The Logtalk User and Reference Manuals at $LOGTALKHOME/manuals/index.html
The GNU Prolog website at http://www.gprolog.org/
logtalk_user_setup(1), logtalk_version_select(1), logtalk_backend_select(1)
Paulo Moura<EMAIL_ADDRESS>Copyright (c) 1998-2023 Paulo Moura.
This HTML page was made with roffit.
Description
Options
Requirements
Environment
Files
See also
Author
Copyright


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Data Storage
S3 Storage Features
Synapse can use a variety of storage mechanisms to store content, however the most common storage solution is AWS S3. This article illustrates some special features that can be used with S3 storage and how they interact with the Python client.
External storage locations
Synapse folders can be configured to use custom implementations for their underlying data storage. More information on this feature can be found here. The most common implementation of this is to configure a folder to store data in a user controlled AWS S3 bucket rather than Synapse’s default internal S3 storage.
The following illustrates creating a new folder backed by a user specified S3 bucket.
Ensure that the bucket is properly configured.
Create a folder and configure it to use external S3 storage:
# create a new folder to use with external S3 storage folder = syn.store(Folder(name=folder_name, parent=parent)) folder, storage_location, project_setting = syn.create_s3_storage_location( folder=folder, bucket_name='my-external-synapse-bucket', base_key='path/within/bucket', ) # if needed the unique storage location identifier can be obtained e.g. storage_location_id = storage_location['storageLocationId']
Once an external S3 storage folder exists, you can interact with it as you would any other folder using Synapse tools. If you wish to add an object that is stored within the bucket to Synapse you can do that by adding a file handle for that object using the Python client and then storing the file to that handle.
parent_synapse_folder_id = 'syn123' local_file_path = '/path/to/local/file' bucket = 'my-external-synapse-bucket' s3_key = 'path/within/bucket/file' # in this example we use boto to create a file independently of Synapse s3_client = boto3.client('s3') s3_client.upload_file( Filename=local_file_path, Bucket=bucket, Key=s3_key ) # now we add a file handle for that file and store the file to that handle file_handle = syn.create_external_s3_file_handle( bucket, s3_key, local_file_path, parent=parent_synapse_folder_id, ) file = File(parentId=folder['id'], dataFileHandleId=file_handle['id']) file_entity = syn.store(file)
STS Storage Locations
Create an STS enabled folder to use AWS Security Token Service credentials with S3 storage locations. These credentials can be scoped to access individual Synapse files or folders and can be used with external S3 tools such as the awscli and the boto3 library separately from Synapse to read and write files to and from Synapse storage. At this time read and write capabilities are supported for external storage locations, while default Synapse storage is limited to read only. Please read the linked documentation for a complete understanding of the capabilities and restrictions of STS enabled folders.
Creating an STS enabled folder
Creating an STS enabled folder is similar to creating an external storage folder as described above, but this time passing an additional sts_enabled=True keyword parameter. The bucket_name and base_key parameters apply to external storage locations and can be omitted to use Synapse internal storage. Note also that STS can only be enabled on an empty folder.
# create a new folder to use with STS and external S3 storage folder = syn.store(Folder(name=folder_name, parent=parent)) folder, storage_location, project_setting = syn.create_s3_storage_location( folder=folder, bucket_name='my-external-synapse-bucket', base_key='path/within/bucket', sts_enabled=True, )
Using credentials with the awscli
This example illustrates obtaining STS credentials and using them with the awscli command line tool. The first command outputs the credentials as shell commands to execute which will then be picked up by subsequent aws cli commands. Note that the bucket-owner-full-control ACL is required when putting an object via STS credentials. This ensures that the object ownership will be transferred to the owner of the AWS bucket.
$ synapse get-sts-token -o shell syn123 read_write export SYNAPSE_STS_S3_LOCATION="s3://my-external-synapse-bucket/path/within/bucket" export AWS_ACCESS_KEY_ID="<access_key_id>" export AWS_SECRET_ACCESS_KEY="<secret_access_key>" export AWS_SESSION_TOKEN="<session_token> # if the above are executed in the shell, the awscli will automatically apply them # e.g. copy a file directly to the bucket using the exported credentials $ aws s3 cp /path/to/local/file $SYNAPSE_STS_S3_LOCATION --acl bucket-owner-full-control
Using credentials with boto3 in python
This example illustrates retrieving STS credentials and using them with boto3 within python code, in this case to upload a file. Note that the bucket-owner-full-control ACL is required when putting an object via STS credentials. This ensures that the object ownership will be transferred to the owner of the AWS bucket.
# the boto output_format is compatible with the boto3 session api. credentials = syn.get_sts_storage_token('syn123', 'read_write', output_format='boto') s3_client = boto3.client('s3', **credentials) s3_client.upload_file( Filename='/path/to/local/file, Bucket='my-external-synapse-bucket', Key='path/within/bucket/file', ExtraArgs={'ACL': 'bucket-owner-full-control'}, )
Automatic transfers to/from STS storage locations using boto3 with synapseclient
The Python Synapse client can be configured to automatically use STS tokens to perform uploads and downloads to enabled storage locations using an installed boto3 library rather than through the traditional Synapse client APIs. This can improve performance in certain situations, particularly uploads of large files, as the data transfer itself can be conducted purely against the AWS S3 APIs, only invoking the Synapse APIs to retrieve the necessary token and to update Synapse metadata in the case of an upload. Once configured to do so, retrieval of STS tokens for supported operations occurs automatically without any change in synapseclient usage.
To enable STS/boto3 transfers on all get and store operations, do the following:
Ensure that boto3 is installed in the same Python installation as synapseclient.
pip install boto3
To enable automatic transfers on all uploads and downloads, update your Synapse client configuration file (typically “.synapseConfig” in your $HOME directory, unless otherwise configured) with the [transfer] section, if it is not already present. To leverage STS/boto3 transfers on a per Synapse client object basis, set the use_boto_sts_transfers property.
# add to .synapseConfig to automatically apply as default for all synapse client instances [transfer] use_boto_sts=true # alternatively set on a per instance basis within python code syn.use_boto_sts_transfers = True
Note that if boto3 is not installed, then these settings will have no effect.
Storage location migration
There are circumstances where it can be useful to move the files underlying Synapse entities from one storage location to another without impacting the structure or identifiers of the Synapse entities themselves. An example scenario is needing to use STS features with an existing Synapse Project that was not initially configured with an STS enabled custom storage location.
The Synapse client has utilities for migrating entities to a new storage location without having to download the content locally and re-uploading it which can be slow, and may alter the meta data associated with the entities in undesirable ways.
Migrating programmatically
Migrating a Synapse project or folder programatically is a two step process.
First ensure that you know the id of the storage location you want to migrate to. More info on storage locations can be found above and here.
Once the storage location is known, the first step to migrate an entity is create a migratable index of its contents using the index_files_for_migration function, e.g.
import synapseutils entity_id = 'syn123' # a Synapse entity whose contents need to be migrated, e.g. a Project or Folder dest_storage_location_id = '12345' # the id of the destination storage location being migrated to # a path on disk where this utility can create a sqlite database to store its index. # nothing needs to exist at this path, but it must be a valid path on a volume with sufficient # disk space to store a meta data listing of all the contents in the indexed entity. # a rough rule of thumb is 100kB per 1000 entities indexed. db_path = '/tmp/foo/bar.db' result = synapseutils.index_files_for_migration( syn, entity_id, dest_storage_location_id, db_path, # optional args, see function documentation linked above for a description of these parameters source_storage_location_ids=['54321', '98765'], file_version_strategy='new', include_table_files=False, continue_on_error=True )
If called on a container (e.g. a Project or Folder) the index_files_for_migration function will recursively index all of the children of that container (including its subfolders). Once the entity has been indexed you can optionally programmatically inspect the the contents of the index or output its contents to a csv file in order to manually inspect it using the available methods on the returned result object.
The next step to trigger the migration from the indexed files is using the migrate_indexed_files function, e.g.
result = synapseutils.migrate_indexed_files( syn, db_path, # optional args, see function documentation linked above for a description of these parameters create_table_snapshots=True, continue_on_error=False, force=True )
The result can be again be inspected as above to see the results of the migration.
Note that above the force parameter is necessary if running from a non-interactive shell. Proceeding with a migration requires confirmation in the form of user prompt. If running programatically this parameter instead confirms your intention to proceed with the migration.
Migrating from the command line
Synapse entities can also be migrated from the command line. The options are similar to above. Whereas migrating programatically involves two separate function calls, from the command line there is a single migrate command with the dryRun argument providing the option to generate the index only without proceeding onto the migration.
Note that as above, confirmation is required before a migration starts. As above, this must either be in the form of confirming via a prompt if running the command from an interactive shell, or using the force command.
The optional csv_log_path argument will output the results to a csv file for record keeping, and is recommended.
synapse migrate syn123 54321 /tmp/migrate.db --csv_log_path /tmp/migrate.csv
- Sample output:
Indexing Project syn123 Indexing file entity syn888 Indexing file entity syn999 Indexed 2 items, 2 needing migration, 0 already stored in destination storage location (54321). Encountered 0 errors. 21 items for migration to 54321. Proceed? (y/n)? y Creating new version for file entity syn888 Creating new version for file entity syn999 Completed migration of syn123. 2 files migrated. 0 errors encountered Writing csv log to /tmp/migrate.csv
SFTP
Installation
Installing the extra libraries that the Python client uses to communication with SFTP servers may add a few steps to the installation process.
Installing on Unix variants
Building these libraries on Unix OS’s is straight forward, but you need the Python development headers and libraries. For example, in Debian or Ubuntu distributions:
sudo apt-get install python-dev
Once this requirement is met, sudo pip install synapseclient
should be able
to build pycrypto.
Installing on Windows
Binary distributions of pycrypto built for Windows is available from Michael Foord at Voidspace. Install this before installing the Python client.
After running the pycrypto installer, sudo pip install synapseclient
should work.
Another option is to build your own binary with either the free developer tools from Microsoft or the MinGW compiler.
Configure your client
Make sure you configure your ~/.synapseConfig file to connect to your SFTP server.


The report also indicates word-level and nibble-level error probabilities. The two sets of error probabilities differ from each other for absolute values since they are calculated using separate word and nibble-level PeMMs respectively. It is noted that the errors are expanded into adjacent nibbles with reduced error probabilities due to the inter-nibble masking effects of algorithmic operations.


Pipelines are great tools for duplicating efforts you’ve already made against a dataset, making them available to new information. It wraps up your data cleaning, your model, and any other intermediary steps all in one ordered operation.
Let's start with a very simple data frame to work on.
import pandas as pd
import numpy as np
df = pd.DataFrame({
'age': [25,22,26,np.nan,30,35,40,42,43,44],
'income': [40,37,42,60,58,70,62,85,120,95],
'owns_car': [0,0,1,0,0,1,1,0,1,1]
})
df
| age | income | owns_car | |
|---|---|---|---|
| 0 | 25.0 | 40 | 0 |
| 1 | 22.0 | 37 | 0 |
| 2 | 26.0 | 42 | 1 |
| 3 | NaN | 60 | 0 |
| 4 | 30.0 | 58 | 0 |
| 5 | 35.0 | 70 | 1 |
| 6 | 40.0 | 62 | 1 |
| 7 | 42.0 | 85 | 0 |
| 8 | 43.0 | 120 | 1 |
| 9 | 44.0 | 95 | 1 |
We will be predicting car ownership so let's split the features from the target now.
features = ['age','income']
X = df[features]
y = df['owns_car']
To begin lest go through some of the usual cleaning suspects to get this ready for a logistic regression. Specifically lets bring in SimpleImputer and StandardScaler.
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
To start let's deal with the NaN value in Age. SimpleImputer will handle imputing the mean age for that value. We are then stuffing that all back into a DataFrame so we can see what we've done.
imp = SimpleImputer(missing_values=np.nan,strategy='mean', copy=True)
imputed = pd.DataFrame(imp.fit_transform(X))
imputed.columns = X.columns
X = imputed
X
| age | income | |
|---|---|---|
| 0 | 25.000000 | 40.0 |
| 1 | 22.000000 | 37.0 |
| 2 | 26.000000 | 42.0 |
| 3 | 34.111111 | 60.0 |
| 4 | 30.000000 | 58.0 |
| 5 | 35.000000 | 70.0 |
| 6 | 40.000000 | 62.0 |
| 7 | 42.000000 | 85.0 |
| 8 | 43.000000 | 120.0 |
| 9 | 44.000000 | 95.0 |
Let's bring in StandardScaler to scale our features. Again we stuff it back into a DataFrame for ease of viewing.
scal = StandardScaler()
scaled = pd.DataFrame(scal.fit_transform(X))
scaled.columns = X.columns
X = scaled
X
| age | income | |
|---|---|---|
| 0 | -1.189305e+00 | -1.068765 |
| 1 | -1.580906e+00 | -1.187959 |
| 2 | -1.058772e+00 | -0.989303 |
| 3 | 9.274965e-16 | -0.274144 |
| 4 | -5.366378e-01 | -0.353606 |
| 5 | 1.160298e-01 | 0.123166 |
| 6 | 7.686974e-01 | -0.194682 |
| 7 | 1.029764e+00 | 0.719132 |
| 8 | 1.160298e+00 | 2.109719 |
| 9 | 1.290831e+00 | 1.116443 |
We now have happy data prepared for the vast majority of algorithms you may want to throw at it. We are going to throw a default logistic regression on it, using the features in X to predict car ownership.
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(random_state=42, solver='lbfgs')
lr.fit(X,y)
lr.score(X,y)
0.8
Now we have a working model, maybe not the most accurate, but it does run. If we wanted to apply this model to a new piece of data we would need to run through each of those steps, making transformations against the new data. That seems like a hassle. That is a hassle. Instead below I am going to bring in pipeline to solve this. First I am going to re-instantiate our original feature data as bf.
bf = pd.DataFrame({
'age': [25,22,26,np.nan,30,35,40,42,43,44],
'income': [40,37,42,60,58,70,62,85,120,95]
})
Now that we have some "New" raw data to have the model predict against we will create the Pipeline. It consists of a list of tuples it will run in order. The first half of the tuple is always the name of the step, the second is the function it will execute.
from sklearn.pipeline import Pipeline
pipe = Pipeline([
('simple_imputer', SimpleImputer(missing_values=np.nan,strategy='mean')),
('scale', StandardScaler())
])
With pipe now instantiated we can use it to transform bf to align with what our model needs. Make sure the fit happens against original data and not against the new data, as this will alter how the scaling and average used in our cleaning.
pipe.fit(df[features])
piped_bf = pipe.transform(bf)
piped_bf = pd.DataFrame(piped_bf)
piped_bf.columns = bf.columns
piped_bf
| age | income | |
|---|---|---|
| 0 | -1.189305e+00 | -1.068765 |
| 1 | -1.580906e+00 | -1.187959 |
| 2 | -1.058772e+00 | -0.989303 |
| 3 | 9.274965e-16 | -0.274144 |
| 4 | -5.366378e-01 | -0.353606 |
| 5 | 1.160298e-01 | 0.123166 |
| 6 | 7.686974e-01 | -0.194682 |
| 7 | 1.029764e+00 | 0.719132 |
| 8 | 1.160298e+00 | 2.109719 |
| 9 | 1.290831e+00 | 1.116443 |
Now we can use our original logistic regression to predict against the new data.
lr.predict(piped_bf)
array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
Now what has been done above works, but it's not aligned with how we would actually get this going in production. Train-test-split was not really executed, and a sample size of 10 is easy to demonstrate on but very unrealistic. As well Pipeline gives us the ability to wrap the model within the pipeline and we should look at that.
Below I am going to lay out how I actually implement Pipeline. To do this I will be bringing in DataFrameMapper from sklearn_pandas, I will not get verbose on how to use mapper, but I would highly suggest you take a look at its documentation as it is a great tool. I am also going to sample our data out to get up to a number that seems a bit more realistic for running this sort of prediction.
from sklearn.utils import resample
cf = resample(df,n_samples=500_000,random_state=42)
cf = cf.reset_index()
cf.tail()
| index | age | income | owns_car | |
|---|---|---|---|---|
| 499995 | 3 | NaN | 60 | 0 |
| 499996 | 0 | 25.0 | 40 | 0 |
| 499997 | 8 | 43.0 | 120 | 1 |
| 499998 | 7 | 42.0 | 85 | 0 |
| 499999 | 7 | 42.0 | 85 | 0 |
Now that we have it sampled out to 500,000 we will train test split, features is still defined from much earlier in the code, and standard train test split parameters will be used.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(cf[features], cf['owns_car'], random_state=42)
X_train.head()
| age | income | |
|---|---|---|
| 359342 | 22.0 | 37 |
| 236051 | 30.0 | 58 |
| 452617 | 43.0 | 120 |
| 34245 | 25.0 | 40 |
| 373935 | 43.0 | 120 |
from sklearn_pandas import DataFrameMapper
mapper = DataFrameMapper([
(['age'], [SimpleImputer(missing_values=np.nan,strategy='mean'),StandardScaler()]),
(['income'], StandardScaler())
])
Now that we have mapper set up we will make a new pipeline, pipe_2, and stuff it with the mapper and the logistic regression. Pipeline always executes in order, so the mapper will clean our data up and the regression will then be applied. If we needed extra steps, say some PCA, we can just slot it in after the cleaning, because again it always executes in order.
pipe_2 = Pipeline([
('map', mapper),
('log', LogisticRegression(random_state=42, solver='lbfgs'))
])
Now it's time to execute pipe_2. First we fit it against the training set, this will establish the cleaning parameters and create the logistic model for us to predict off of. Next we have pipe_2 predict against X_test and then compare these predictions to y_test.
pipe_2.fit(X_train, y_train)
y_pred = pipe_2.predict(X_test)
comp = pd.DataFrame({
'y_pred': y_pred,
'y_test': y_test
})
comp.head(15)
| y_pred | y_test | |
|---|---|---|
| 104241 | 1 | 1 |
| 199676 | 1 | 1 |
| 140199 | 1 | 1 |
| 132814 | 0 | 0 |
| 408697 | 1 | 1 |
| 163280 | 0 | 0 |
| 215758 | 0 | 0 |
| 442316 | 1 | 0 |
| 6940 | 1 | 1 |
| 382310 | 1 | 1 |
| 472236 | 0 | 0 |
| 309086 | 0 | 0 |
| 230672 | 0 | 0 |
| 209236 | 1 | 0 |
| 102953 | 1 | 1 |
Not perfect, but it's clearly working. Pipeline makes your flow much more modular. If you need more steps you slot them in the pipeline where you want them to happen. If you need a new model you swap it out. Better yet Pipeline can take pipelines within them. Have a weird replace-NA to Lambda to regularization function to fix a feature before it hits mapper? No problem, wrap it up and throw it in the pipeline before mapper.
As stated at the top Pipeline helps wrap your process all together. It's the box you can throw everything into. Hopefully it will let you be a bit more poetic. Throughout this I have shown you Pipeline, which requires a tuple of a title and a function. There is another option in make_pipeline, which doesn't require a title, just the function, which it arbitrarily names for you. Most days you will end up using make_pipeline, as it is faster. But when you need more specific documenting, Pipeline will be the ticket.


From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
About HARD-UI
- Web ui framework
- without Javascript
- responsive
- 24 grid
Status
Under construction
Get started
- Download hard-ui.css or hard-ui.min.css.
- Add html tag.
<link rel="stylesheet" href="/css/hard-ui.css">
Compornents
Nav horizontal

HARD-UI

Menu1

Menu2

Menu3

Menu4

Text

Nav vertical

HARD-UI

Menu1

Menu2

Menu3

Menu4

Text

HARD-UI

Menu1

Menu2

Menu3

Menu4

Text

HARD-UI

Menu1

Menu2

Menu3

Menu4

Text

Columns(equally divided)
2colmns
col
col
col
col
col
col

col

col

col

col

col

col

3colmns
col
col
col
col
col
col
col
col
col

col

col

col

col

col

col

col

col

col

4colmns
col
col
col
col
col
col
col
col
col
col
col
col

col

col

col

col

col

col

col

col

col

col

col

col

to max 24colmns
col
col
col
col
col
col
col
col
col
col
col
col
col
col
col
col
col
col
col
col
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col
col
col
col
col
col
col
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col
col
col
col
col
col

col

col
 : :
col

Grid
24 divided grid with media query by screen size
| default | small(≤600px) | middle(≤960px) | large(>960px) |
|---|---|---|---|
| col-* | col-s-* | col-m-* | col-l-* |
col-*, middle *cols, small:1col
col-1
col-1
col-1
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col-1

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large:6colmns, middle:4colmns, small:3colmns
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)
col-(l,m,s)-(4,6,8)

Content

Content

Content

Content

Content

Content

Content

Content

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Content

Content

Content

12:12
col-12
col-12

col-12

col-12

column multiple width
col-2
col-4
col-6
col-12

col-2

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offset
col-2
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Form
Form Block
<form class="hd-form hd-form-block">
 Text <input type="text" name="box" id="box" value="" placeholder="Text box">

 Textarea <textarea name="txtara" id="txtara" value="" placeholder="Input text"></textarea>

 Column layout <textarea name="opttxt" id="opttxt" value="" placeholder="Input text"></textarea>

 Select <select class="hd-form-select" id="slct1" name="slct2"> <option value="1">First</option> <option value="2">Second</option> <option value="3">Third</option> <option value="4">Forth</option> <option value="5">Fifth</option> </select>

 Select <select class="hd-form-select" id="slct2" name="slct2"> <option value="1">First</option> <option value="2">Second</option> <option value="3">Third</option> <option value="4">Forth</option> <option value="5">Fifth</option> </select>

 Checkbox  <input type="checkbox" name="chk" id="chk11" value="1"> check1   <input type="checkbox" name="cnk" id="chk12" value="2"> check2

 Radio  <input type="radio" name="rdo" id="rdo11" value="1"> opt1   <input type="radio" name="rdo" id="rdo12" value="2"> opt2

 <input type="submit" name="btn" value="Default Button"> <input type="submit" class="hd-btn-default" name="btn" value="hd-btn-default"> <input type="submit" class="hd-btn-gray" name="btn" value="hd-btn-gray"> <input type="submit" class="hd-btn-white" name="btn" value="hd-btn-white">
 </form>
Form Inline
<form class="hd-form hd-form-inline">
 Search <input type="text" name="search" value="" placeholder="Search">

 <select class="hd-form-select" id="slct2" name="slct"> <option value="1">First</option> <option value="2">Second</option> <option value="3">Third</option> <option value="4">Forth</option> <option value="5">Fifth</option> </select>

  <input type="checkbox" name="chk" id="chk21" value="1"> check1   <input type="checkbox" name="cnk" id="chk22" value="2"> check2

  <input type="radio" name="rdo" id="rdo21" value="1"> opt1   <input type="radio" name="rdo" id="rdo22" value="2"> opt2

 <input type="submit" name="btn" value="Default Button"> <input type="submit" class="hd-btn-default" name="btn" value="hd-btn-default"> <input type="submit" class="hd-btn-gray" name="btn" value="hd-btn-gray"> <input type="submit" class="hd-btn-white" name="btn" value="hd-btn-white">
 </form>
Table
Default html table
| name | column1 | column2 | column3 |
|---|---|---|---|
| name1 | data11 | data12 | data13 |
| name2 | data21 | data22 | data23 |
| name3 | data31 | data32 | data33 |

 name	 column1	 column2	 column3

 name1	 data11	 data12	 data13

 name2	 data21	 data22	 data23

 name3	 data31	 data32	 data33

Contents
row display
-
Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness.  Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness.
 From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second. Grass a their behold fish first their. Seas thing deep without whales. Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping. From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second. Grass a their behold fish first their. Seas thing deep without whales. Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.

 footer, date, etc.

  short title

 footer, date, etc.

card display
Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness. Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness. Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness. Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness. Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.
From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second.
Grass a their behold fish first their. Seas thing deep without whales.
Meat signs herb seas Third heaven two.  Years divide fruit creature creature land saying you said meat yielding fill him whales whose gathering he us place likeness.
 From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second. Grass a their behold fish first their. Seas thing deep without whales. Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping. From so, him were. Herb together first kind. Fourth seed open he whose great image good after saying own beast you she'd life upon wherein second. Grass a their behold fish first their. Seas thing deep without whales. Meat signs herb seas Third heaven two. Fourth darkness you'll bring creeping.

 footer, date, etc.