adamspy.postprocess package¶
Subpackages¶
Submodules¶
adamspy.postprocess.animation module¶
Code for generating animations automatically in Adams View
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adamspy.postprocess.animation.
create_animation
(model_file: str, res_file: str, model_name: str, analysis_name: str, timeout=60)[source]¶ Creates an animation from the given model.
Parameters: - model_file (str) – Adams View model (.cmd) file
- res_file (str) – Adams results (.res) file
- model_name (str) – Name of the model
- analysis_name (str) – Name of the analysis
- wait (bool, optional) – If True, code execution will freeze until the animation script completes and the postprocessor closes. (the default is True)
adamspy.postprocess.exported module¶
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adamspy.postprocess.exported.
import_spreadsheet
(filename, output_type='dict')[source]¶ Reads data from a file generated in Adams View using File>Export>Spreadsheet.
Parameters: Returns: Pandas :obj:Dataframe if output_type is ‘DataFrame’ or :obj:dict if output_type is ‘dict’
Return type: DataFrame or dict
adamspy.postprocess.mode_tables module¶
-
class
adamspy.postprocess.mode_tables.
Modal
(modes: Dict[int, adamspy.postprocess.mode_tables.Mode])[source]¶ Bases:
object
-
get_modes_where_parts_participate
(part_suffix, table, column, threshold=0.9, sum_or_max='max')[source]¶ Returns a dictionary where the keys are mode numbers and the values are :obj:Mode objects in which any part with the suffix part_suffix has at least threshold percent of the total.
Parameters: - part_suffix (str) – Suffix of parts to check for
- table (str) – Table type. Options are ‘coords’, ‘kinetic_energy’, ‘strain_energy’, ‘dissipative_energy’
- column (str) – Column name from the table
- threshold (float) – Part’s value must be greater than threshold multiplied the sum or max (depending on sum_or_max value) of the column in the table to be considered participating
- sum_or_max (str) – Must be ‘sum’ or ‘max’
Returns: Dictionary where the keys are mode numbers and the values are :obj:Mode objects
Return type:
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adamspy.postprocess.msg module¶
This module contains tools for extracing data from the message file.
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adamspy.postprocess.msg.
get_modes
(filename, output_type='dict', i_analysis=0, underdamped_only=True, sort_by_wn=True)[source]¶ Gets the modes from an Adams Solver message file.
Parameters: - filename (str) – Name of Adams Solver message (.msg) file.
- output_type (str, optional) – If ‘DataFrame’, returns a pandas :obj:pd.DataFrame if ‘dict’, returns a :obj:dict. (Default is ‘dict’)
- i_analysis (int, (optional)) – If the Adams Solver message (.msg) file contains multiple eigensolutions, this specifies which one to get. (Default is 0 which takes the first analysis)
Returns: ` – Table of modes
Return type: obj:pd.DataFrame` or :obj:dict
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adamspy.postprocess.msg.
get_process_id
(filename: pathlib.Path)[source]¶ Returns the process ID of the Adams job that generated the given message file.
Parameters: filename (Path) – Path to the message file. Returns: Process ID of the Adams job that generated the given message file. Return type: int
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adamspy.postprocess.msg.
get_runtime_summary
(filename)[source]¶ Returns a list of timestamps from the Adams message (.msg) file. Each timestamp in the list is a list as follows [elapsed time, cpu time, percent speedup]
Parameters: filename (str) – Filename of message file Returns: List of timestamps where each timestamp in the list is a list as follows [elapsed time, cpu time, percent speedup]. Return type: list
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adamspy.postprocess.msg.
get_timestamps
(filename)[source]¶ Returns a list of timestamps from the Adams message (.msg) file. Each timestamp in the list is a list as follows [simulation time, step size, Function evaluations, cumulative steps taken, integration order, simulation time]
Parameters: filename (str) – Filename of message file Returns: List of timestamps where each timestamp in the list is a list as follows [simulation time, step size, Function evaluations, cumulative steps taken, integration order, simulation time]. Return type: list
adamspy.postprocess.ppt module¶
This module is for sending scripts through Adams/PPT
-
adamspy.postprocess.ppt.
edit_results
(res_file, input_dict, new_res_file=None, _just_write_script=False, timeout=300)[source]¶
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adamspy.postprocess.ppt.
filter_results
(res_file, reqs_to_clean, freq_cutoff, N_filter=5, reqs_to_check=None, t_min=None, t_max=None, _just_write_script=False, timeout=300, _inplace=False, return_raw=False)[source]¶ Similar to manually_remove_spikes, but allows user to plot the signals in batches. Instead of passing a dictionary for the reqs_to_check argument, pass a list of dictionaries and the results in each dictionary in the list will be plotted together.
Parameters: - res_file (str) – Adams Results (.res) filename
- reqs_to_clean (dict) – Nested dictionary of result sets and result components to clean
- freq_cutoff (float) – Cutoff freqency of filter in Hz
- N_filter (int) – Order of filter
- reqs_to_check (list of dicts) – list of nested dictionary of result sets and result components to check for spikes, by default same as reqs_to_clean
- t_min (float, optional) – Minumum simulation time to clean, by default None
- t_max (float, optional) – Maximum simulation time to clean, by default None
- timeout (float, optional) – Number of seconds to wait for results to load before timing out, by default _TIMEOUT
Returns: Nested dictionary of cleaned results
Return type:
-
adamspy.postprocess.ppt.
get_lunar_results
(res_files, reqs_to_get, t_min, t_max, output_file, _just_write_script=False, timeout=300)[source]¶
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adamspy.postprocess.ppt.
get_results
(res_file, reqs_to_get, t_min=None, t_max=None, _just_write_script=False, timeout=300)[source]¶ Gets results from an Adams results (.res) file.
Example
>>> result_file = 'example.res' >>> t_min = 70 >>> t_max = 80 >>> reqs_to_get = {} >>> reqs_to_get['MSE'] = ['Instantaneous_Bottom_MSE', 'Filtered_Surface_MSE'] >>> reqs_to_get['ROP_controls'] = ['Command_ROP', 'True_WOB'] >>> requests, units = get_results(result_file, reqs_to_get, t_min, t_max)
Note
This funciton only works with Requests. It does not work with Result Sets.
Note
This function only works with xml results files.
Parameters: - result_file (str) – Filename of an Adams results (.res) file
- reqs_to_get (dict) – Dictionary of requests to extract (the default is None, which gets all results)
- t_min (float, optional) – Minimum time for which to extract results (the default is None)
- t_max (float, optional) – Maximum time for which to extract results (the default is None)
Returns: Dictionary of request data
Return type:
-
adamspy.postprocess.ppt.
manually_remove_spikes
(res_file, reqs_to_clean, reqs_to_check=None, t_min=None, t_max=None, _just_write_script=False, timeout=300, _inplace=False)[source]¶ Allows the user to manually scan through the result sets to pick out points to eliminate.
Parameters: - res_file (str) – Adams Results (.res) filename
- reqs_to_clean (dict, optional) – Nested dictionary of result sets and result components to clean
- reqs_to_check (dict) – Nested dictionary of result sets and result components to check for spikes, by default same as reqs_to_clean
- t_min (float, optional) – Minumum simulation time to clean, by default None
- t_max (float, optional) – Maximum simulation time to clean, by default None
- timeout (float, optional) – Number of seconds to wait for results to load before timing out, by default _TIMEOUT
Returns: Nested dictionary of cleaned results
Return type:
-
adamspy.postprocess.ppt.
manually_remove_spikes_batch
(res_file, reqs_to_clean, reqs_to_check=None, t_min=None, t_max=None, _just_write_script=False, timeout=300, _inplace=False)[source]¶ Similar to manually_remove_spikes, but allows user to plot the signals in batches. Instead of passing a dictionary for the reqs_to_check argument, pass a list of dictionaries and the results in each dictionary in the list will be plotted together.
Parameters: - res_file (str) – Adams Results (.res) filename
- reqs_to_clean (dict) – Nested dictionary of result sets and result components to clean
- reqs_to_check (list of dicts) – list of nested dictionary of result sets and result components to check for spikes, by default same as reqs_to_clean
- t_min (float, optional) – Minumum simulation time to clean, by default None
- t_max (float, optional) – Maximum simulation time to clean, by default None
- timeout (float, optional) – Number of seconds to wait for results to load before timing out, by default _TIMEOUT
Returns: Nested dictionary of cleaned results
Return type:
adamspy.postprocess.xml module¶
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adamspy.postprocess.xml.
get_component
(res_file, comp_name)[source]¶ Returns the numeric data in res_file associated with the result component comp_name.
Parameters: Returns: List of values from the result (.res) file.
Return type: Raises: ValueError
– Raised if comp_name not found in res_file.
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adamspy.postprocess.xml.
get_results
(result_file, reqs_to_get=None, t_min=None, t_max=None, return_units=False, overwrite_pickle=True, use_iterparse=False)[source]¶ Gets results from an Adams results (.res) file.
Example
>>> result_file = 'example.res' >>> t_min = 70 >>> t_max = 80 >>> reqs_to_get = {} >>> reqs_to_get['MSE'] = ['Instantaneous_Bottom_MSE', 'Filtered_Surface_MSE'] >>> reqs_to_get['ROP_controls'] = ['Command_ROP', 'True_WOB'] >>> requests, units = get_results(result_file, reqs_to_get, t_min, t_max)
Note
This funciton only works with Requests. It does not work with Result Sets.
Note
This function only works with xml results files.
Parameters: - result_file (str) – Filename of an Adams results (.res) file
- reqs_to_get (dict) – Dictionary of requests to extract (the default is None, which gets all results)
- t_min (float, optional) – Minimum time for which to extract results (the default is None)
- t_max (float, optional) – Maximum time for which to extract results (the default is None)
Returns: - dict – Dictionary of request data
- dict – Dictionary defining units for each request. NOTE: This is only returned if return_units=True
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adamspy.postprocess.xml.
get_stepmap_dictionary
(file_name)[source]¶ Create dictionary where keys are result names and values are indices of components.
Note
Res names must be full, like: * time.time * part_2_xform.fx * part_3_xform.accy
Parameters: file_name (str) – Results (.res) file Returns: Step map Return type: dict
-
adamspy.postprocess.xml.
shrink_results
(result_file, reqs_to_keep=None, t_min=None, t_max=None, new_result_file=None, in_place=False)[source]¶ Shrinks a results file by eliminating unwanted data.
Example
>>> result_file = 'example.res' >>> t_min = 70 >>> t_max = 80 >>> reqs_to_keep = {} >>> reqs_to_keep['MSE'] = ['Instantaneous_Bottom_MSE', 'Filtered_Surface_MSE'] >>> reqs_to_keep['ROP_controls'] = ['Command_ROP', 'True_WOB'] >>> shrink_results(result_file, reqs_to_keep, t_min, t_max)
Note
This funciton only works with Requests. It does not work with Result Sets.
Parameters: - result_file (str) – Filename of the results file
- reqs_to_keep (dict) – Dictionary of requests and request components that should be saved.
- t_min (float, optional) – Start time of period of interest (default is None which uses the first time step)
- t_max (float, optional) – End time of period of interest (default is None which uses the last time step)
- new_result_file (str) – Name that will be given to the new results file. (Default is None which adds a ‘_shrunk’ suffix to result_file if in_place=False or uses result_file if in_place=True.)
- in_place (bool, optional) – If True, existing file will be deleted.
Module contents¶
Package for working with Adams Results
-
adamspy.postprocess.
get_log_errors
(log_file: pathlib.Path)[source]¶ Checks the log file for errors of the type AviewError.
Parameters: log_file (str) – Filename of aview log file (usulally aview.log)
-
adamspy.postprocess.
launch_ppt
(res_file, cmd_file=None, wait=False, timeout=30, _terminate=False)[source]¶ Launches the Adams PostProcessor and reads in the specified results file.
Parameters: - res_file (str) – Filepath to an Adams Results file.
- cmd_file (str, optional) – Adams View command (.cmd) file. If given, this will be loaded before the results file. This is necessary to view animations.
- wait (bool, optional) – If True, code execution will freeze until the postprocessor is closed. (the default is False)
- timeout (float, optional) – If wait`=`False, code execution will freeze until the postprocessor has started loading the results file or until timeout secodns have elapsed.
- _terminate (bool, optional) – If True, the PostProcessor will close immediately. This is for testing purposes.


class DvsMenuItem extends Eloquent
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player: A software module in the Digital Chroma that plays notes on a virtual {_instrument}. The keyboard is connected to two players, one for the main sound and the other for a {_link_program link} sound. There are also 64 players, one for each MIDI channel on the {_USB_device_port}, that are intended for sequence playback, when connected to a DAW.
When a {_program} is selected, the player creates a new "active" instrument, and sends all subsequent notes to it, but the previous instrument, now "inactive", continues to exist as long as any if its notes are held. {_parameter Parameter} changes are sent to the active instrument, so have no effect on inactive instruments. However, the player is where the {_performance_control} values are recorded, so they apply to both active and inactive instruments.


@(message: String) @main(message) {
This form let's you upload an image directly and receive its feature vector with the resulting classification. A typical client application would first call /identifications using POST receiving a result containing the upload url which can then be used to transfer the image data in a seperate POST request.
@helper.form(action = routes.IdentificationController.uploadImageDirectly(), 'enctype -> "multipart/form-data") {} }


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I’m sorry, but we couldn't find anything matching "{SearchQuery}". Suggestions:
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| 695 | (A) | and some down for dole daschede to the grownde |
| 698 | (C) | And some down for sorowe . dassched to þe ground |
| 690 | (D) | And some doun for dole dasshid to the ground |
| 672 | (E) | & summe doun for dool . dassched to þe grounde . |
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| 651 | (P) | And somm doune for dole daschen to grounde |
| 680 | (U) | And som doun for dole / dasshed to grounde |
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You may still choose to change the defaults or to add additional profiles. This can be done via the Service Configuration dialog. This dialog can also be used to set up a multi-machine node, where the services are run on distributed machines to reduce the load on any one machine.


This is an auto-generated email to inform you that the daily import of opportunities Failed. You'll find the log file in attachment.
Problem: unable to connect to server 'localhost':JSchException: UnknownHostKey: localhost.


Driver for MySQL.
Almost every method in this class will throw 503 error on connection problems.
Located in /app/databaseMysql.php (line 10)
appCore | --appDatabase | --databaseMysql
| Class | Description |
|---|---|
| This is an example model used with MySQL. To use the example for user's login add the following data to your MySQL: |
Whether or not the data should be cached.
Can be changed per model.
Default cache namespace. It's set in __construct().
Whether or not the data should be encrypted.
Can be changed per model.
Database name to use - should be set for each model.
Database table to use - should be set for each model.
Default id field name.
Can be changed per model.
Inherited from appCore
appCore::$_cookies
appCore::$_view
Selects appropriate database table and sets $_cacheKey.
Encrypts data in model if encryption is set.
If encryption is off, this will simply return the original data.
Decrypts data in model if encryption is set.
If encryption is off, this will simply return the original data.
Shortcut method for deleting rows by id (so it doesn't have to be added to each model).
Shortcut method for getting all rows in table (so it doesn't have to be added to each model).
Results can be returned by "pages".
Shortcut method for getting rows by id (so it doesn't have to be added to each model).
Returns the name of current table.
Executes given SQL query and returnes the result.
Inherited From appDatabase
appDatabase::__construct()
appDatabase::crypt()
appDatabase::decrypt()
appDatabase::getModel()
Inherited From appCore
appCore::cache()
appCore::cookies()
appCore::db()
appCore::redirect()
appCore::request()
appCore::start()
appCore::sthrowError()
appCore::throw404()
appCore::throwError()
appCore::user()
appCore::view()
Documentation generated on Thu, 05 Jan 2012 09:39:04 +0100 by phpDocumentor 1.4.3


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From last article, I have showed how to decide the value for each tree node in order to obtain the minimum loss. The techniques for solving that problem are Tyler Formula and transformation from sample to node.
But still, that optimal solution is under the assumption that the tree structure is fixed, meaning for each data sample, we know which leave node it belongs to. So the question is: how to split the tree?
1. Tree Splitting Algorithm
1.1 Splitting Gain
To know how to split the tree, first we need to set up a rule to decide the goodness of this split. Remember from last post we get the structure score: \[obj = -\frac{1}{2}\sum_{j=1}^{T}\frac{G^2_j}{H_j+\lambda}+\gamma T\] Which basically tells us under this tree structure, what is the total loss. And if we ignore \(T\) which tells us the depth of the tree, actually this term \(-\frac{G^2_j}{H_j+\lambda}\) describes how the samples in each node contribute to the total loss, the less, the better. (remember \(G_j = \sum_{i\in I_j}g_i\), \(H_j =\sum_{i\in I_j}h_i\)). In turn, \(\frac{G^2_j}{H_j+\lambda}\) describes how the samples in each node contribute to the total gain, the larger, the better.
So by using this information, we can summarize an expression for measuring the gain of the split:
\[Gain = \frac{1}{2}[\frac{G^2_L}{H_L+\lambda}+\frac{G^2_R}{H_R+\lambda} - \frac{(G_L+G_R)^2}{H_L+H_R+\lambda}]-\gamma\] Where \(\frac{G^2_L}{H_L+\lambda}\) tells the gain for the left subtree, \(\frac{G^2_R}{H_R+\lambda}\) tells the gain for the right subtree. And \(\frac{(G_L+G_R)^2}{H_L+H_R+\lambda}\) tells the gain if we don't split tree. And eventually \(\gamma\) is the price for adding a new tree. So to sum them up, The total gain is the combination of left gain plus right gain minus no splitting gain and tree complication penalty.
Normally we will set up a threshold, if the gain smaller the threshold, we will not allow this split.
After knowing how to decide whether this split is good or not by looking at the gain value, then let's look at the algorithm we will use to split the tree.
1.2 Tree Splitting Algorithm
We use greedy method for tree splitting: start from the root tree, we traverse all the features and values and find out the feature value which has the maximum gain.
1.2.1 Exact Greedy Algorithm
1.2.2 Approximate Algorithm
2. Others
There are several more innovations related to XGBoost which I will talk in this part
2.1 Sparsity-aware Split Finding
XGBoost introduces default direction to deal with the sparse or missing values. What it does is to let the model itself to learn the optimal default directions from data.
Specifically, for every split the system will calculate the Splitting Gain for both putting all the sparse value in the left tree and right tree. Then choose the direction which has larger gains.
2.2 System Design
One of the great innovation of XGBoost is it uses parallel learning and cache-aware access to speed up the calculation.
3. Parameter Explanation
Finally, let's look at how we can leverage XGBoost in Python:
Jupyter Notebook For XGBoost Implementation
We can visualize the tree structure to get the insights as well as find the feature importance for further selection:
Reference:
https://arxiv.org/abs/1603.02754
https://www.datacamp.com/community/tutorials/xgboost-in-python
https://blog.csdn.net/wzmsltw/article/details/50994481
https://xgboost.readthedocs.io/en/latest/python/python_api.html


C/C++ editor preferences
This section describes how to set preferences for the C/C++ editor.
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Typedefs | |
| typedef signed char | int8 |
| typedef unsigned char | uint8 |
| typedef unsigned char | uchar |
| typedef signed short | int16 |
| typedef unsigned short | uint16 |
| typedef signed int | int32 |
| typedef unsigned int | uint32 |
| typedef const char | cchar |
| typedef unsigned char | bool |
Definition in file TeleoTypes.h.
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Definition at line 18 of file TeleoTypes.h. Referenced by buttonPress(), capacityWarning(), dataReceive(), main(), moduleBound(), resetReceive(), TeleoEasy_Init(), and TeleoModule_Init(). |
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Definition at line 12 of file TeleoTypes.h. Referenced by doInput(), doTimer(), main(), moduleBound(), TeleoEasy_Init(), and TeleoModule_Init(). |
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copyright © 2002, 2003 MakingThings LLC |


--- layout: post tags: [Miscellaneous, Services] date: 2009-10-24 01:52:18 UTC title: "WCF Podcast with me" --- {% include JB/setup %}
Daniel Frost has published a podcast where he discusses WCF with me. It's about half an hour and in Danish. Hear it here.


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We built this tool because we didn't want to use a bloated CRM for our own profitability tracking. We hope you find it useful as well.
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Your new password has been set. You may go ahead and log in now.


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Election (1999) Reviewed by Eugene Novikov http://www.ultimate-movie.com Member: Online Film Critics Society
**** out of ****
Starring Matthew Broderick, Reese Witherspoon, Chris Klein. Rated R.
It comes as a walloping disappointment to moi that Election is being actively marketed as the latest derivative teen flick. Derivative it ain't, and it isn't a teen flick either, although mature teenagers are likely to enjoy it. Instead it is a brilliant, biting satire about crossing the very boundaries of dignity and sacrificing one's career for something one believes in -- or thinks he believes in.
Matthey Broderick, whom I have never really appreciated as an actor (not even in Ferris Bueller's Day Off, which I think deserves to be a classic) finally takes a step in the direction of thespianism. He plays Jim McCallister, an extraordinary teacher who truly loves his job, loves his profession, loves his life. His legacy and image are virtually unscathed, until the latest Student Council Election comes along. You see, there's this girl named Tracy Flick.  Tracy is a member of every committee imaginable; she spends long hours and weekends working on school projects.
McCallister loathes her with all his heart. Perhaps he is jealous, perhaps he is simply annoyed by her, but either way when she runs unopposed for Student Council President, he can't stand to see her win. He dreads that someday someone like her will rule the country. He is afraid to imagine what it will be like then. The only thing to do for him, it seems, is to launch a counter campaign. He singles out Paul Metzler, a good, popular, albeit slightly slow guy to run against Tracy. Unfortunately his anger soon leads him to go much farther than that to prevent Tracy Flick from becoming the next Student Council Prez.
If satire is simply a witty exaggeration of ordinary events, then this is satire at its brightest. Besides being an effective political satire, this can be viewed as an affectionate take on what can happen when a student becomes the pet peeve of an important teacher. But underneath this is also a serious movie, one about sacrifice and revenge and it works like a gem, emotionally sincere as well as scathingly funny.
Broderick is brilliant; his character is unabashedly subversive but he makes him touchingly and unsentimentally sympathetic as well. I look forward to Broderick doing more movies like this one (unfortunately, his next project is Inspector Gadget). Reese Witherspoon (Pleasantville) is not quite as effective, but she does a decent job as well, although realism isn't one of her strong points in this one.
Those who argue that Election is morally depraved because it puts the perfectionist Tracy in a bad light are missing the point by a mile. That's not what this movie is about. It's about anger, it's about revenge, it's about obsession. Director Alexander Payne's movie is about important and significant things, and is above and beyond caring whether it offends a select group of people who have no reason to be offended.
I love this movie. It's a satire that blends hilarity with resonant (but not overwhelming) moral statements. It's brutal, it's funny, it's brilliant. ©1999 Eugene Novikov
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Related links: index of all rec.arts.movies.reviews reviews


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TerminusDB 4.0 - The Star's End Release
TerminusDB 4.0 - The Star’s End Release
Version 4.0 of TerminusDB - the Star’s End Release * - is a big step forward in solidifying and extending the functionality of TerminusDB and TerminusHub.
TerminusDB is an open source, revision control graph database designed for distributed collaboration. TerminusDB allows you to push, pull, time-travel and merge data, much in the way that is possible with code in Git.
With this release TerminusDB takes a significant step towards our vision of a general-purpose tool for data & content management and collaboration.
TerminusDB 4.0 extends the revision control features to allow greater visibility and reproducability by allowing you to look inside individual commits to see what changes were made. Provenance, lineage, and compliance are increasingly essential in the management of data and content.
The major additional features are:
Model Building Tool
The database ships with an integrated visual schema building tool. It allows users to build complex data models using point and click tooling. This makes building models significantly quicker, more efficient, and inclusive of a broader range of people by lowering the technical bar for the application of business rules to your data.
It lets you visually design knowledge graphs in the same way they are presented to business users. We have tried to make the tool easy to use, while preserving the ability to model the most complex domains.
Like everything in TerminusDB, the models are versioned so you can make changes in testing and if it breaks your database, you can easily roll back to an earlier working version. This collaborative knowledge graph design and implementation functionality does not exist elsewhere.
You can currently share your models/schemas through TerminusHub - the next step is to make modelling and collaboration even easier by providing data-libraries on TerminusHub, including schema.org but also basic standard models for common domains such as CRM, accounting, and inventory. Users can then generate and share models with collaborators or the broader public.
Document Editor
TerminusDB 4.0 has full surfability, clickability and editability of database documents through the console. It is a wiki or catalogue of all your data that you can edit in place. You don’t have to write code or execute a query, just edit the document directly. If you have a collaborative project, this is a useful approach to building and curating data assets.
The documents link to associated documents delivering a linked data application. You can filter documents, you can find the document you want using search, you can create a new document in the interface - it is a general-purpose tool for managing your data.
This is the beginning of the TerminusDB content management system, which we will be expanding over the coming months - next step is to provide web accessibility to TerminusHub databases and then a publisher API to provide the ability to publish the results of any query as a static content set.
CSV Manager
With a single click, you can now build a database from a CSV or a group of CSVs. You can easily use TerminusDB and TerminusHub as a place to version and collaborate when working with CSV data. This will be especially useful with fast changing data as frequently used in machine learning and data science projects.
We have tried to make it as simple as possible for users to interact with the CSV tooling. You can view and edit CSVs directly in the document viewer. You can also add CSVs from this interface. No need to write code.
The database automatically spots if the CSV is already in the database and will just add the delta. There is an append mode that just adds the data that is not there and an update mode that updates the entire CSV.
Manage your CSVs and other data via TerminusDB and Hub.
Command Line Interface
We are excited to launch the TerminusDB CLI. You can connect to TerminusDB with a Git-like CLI to run queries or use the revision control features.
You can create databases and branches, you can list databases/branches, you can query in the command line and you can load CSVs. You also get full error reporting in the CLI. Use the CLI to import a CSV, commit changes and push the changes to TerminusHub.
The CLI will continue to develop and add new operations. We will make it easier to execute queries directly from the CLI. We’re also planning to launch CLIs for the JavaScript and Python clients.
TerminusDB is a big step towards our ambition to be a complete decentralized data and content management system that is accessible to all.
END
* Star’s End is a reference to the mysterious location of the Second Foundation in the Asimov series of novels. The home of the First Foundation was Terminus.
Building the database of the future.


Examples: gene symbol (SCN1A),
genomic range (1:65000-70000),
genomic position (1:7737651),
dbSNP ID (rs35735053)
or mixed.
| Chr | Pos | Ref | Alt | dbSNP | Freq | Genes | HGVS | Clinical | Coverage | Quality | Dataset | Samples | Assembly |
|---|


Autonomous Power Racing
Home
What's the cost?
$65.00/team per year
The maximum for a team is 4 students. Your school, however, can have as many teams as they’d like.
Team dashboard to share code and progress
Participate in post season championship
Access to leaderboard
sign up


org.drip.spaces.metric
Interfaces
GeneralizedMetricVectorSpace
R1Normed
RdNormed
Classes
R1Combinatorial
R1CombinatorialBall
R1Continuous
R1ContinuousBall
RdCombinatorialBall
RdCombinatorialBanach
RdCombinatorialHilbert
RdContinuousBall
RdContinuousBanach
RdContinuousHilbert


--- layout: post title: "Types + Properties = Software: initial state" description: "How to define the initial state in a tennis game, using F#." date: 2016-02-17 08:51 UTC tags: [Software Design, F#] --- {% include JB/setup %}
{{ page.description }}
This article is the sixth in a series of articles that demonstrate how to develop software using types and properties. In the previous article, you saw how to compose a function that returns a new score based on a previous score, and information about which player won the ball. In this article, you'll see how to define the initial state of a tennis game.
The source code for this article series is available on GitHub.
You may recall from the article on designing with types that a Score is a discriminated union. One of the union cases is the Points case, which you can use to model the case where both players have either Love, Fifteen, or Thirty points.
The game starts with both players at love. You can define this as a value:
let newGame = Points { PlayerOnePoint = Love; PlayerTwoPoint = Love }
Since this is a value (that is: not a function), it would make no sense to attempt to test it. Thus, you can simply add it, and move on.
You can use this value to calculate ad-hoc scores from the beginning of a game, like this:
> let firstBall = score newGame PlayerTwo;; val firstBall : Score = Points {PlayerOnePoint = Love; PlayerTwoPoint = Fifteen;} > let secondBall = score firstBall PlayerOne;; val secondBall : Score = Points {PlayerOnePoint = Fifteen; PlayerTwoPoint = Fifteen;}
You'll soon get tired of defining firstBall
, secondBall
, thirdBall
, and so on, so a more general way to handle and calculate scores is warranted.
In this article, you saw how to define the initial state for a tennis game. There's nothing to it, but armed with this value, you now have half of the requirements needed to turn the tennis score function into a finite state machine. You'll see how to do that in the next article.


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
Dietary
Preheat the oven to 180C/365F/Gas 4.
Place the golden syrup, molasses sugar and butter into a heavy-based pan over a medium heat and bring to the boil. Reduce the heat and simmer gently for 5-6 minutes, or until the sugar has completely dissolved.
Remove the pan from the heat, add the flour and ground ginger and mix well.
Add the brandy and stir to combine.
Divide the mixture into four equal portions and place onto a greased and lined baking tray (leave enough space between the portions on the tray to account for spreading as they cook).
Transfer to the oven to bake for six minutes, then remove and allow to cool slightly, until just setting but still pliable.
Place two tins a little distance apart and balance (bridge) a wooden spoon across the tins. Gently wrap the pliable brandy snap around the spoon to make a hollow cigar shape and set onto a cool plate. Repeat with the remaining brandy snaps.
For the filling, place the double cream, vanilla seeds, icing sugar, kirsch and 50g/2oz of the blackberries into a large bowl and whip together until thickened and purple from the blackberry juice.
Spoon the blackberry cream into a piping bag, then pipe into each brandy snap to fill up.
Place a blackberry at the end of each brandy snap opening and serve on plates with a spoonful of blackberries alongside.


By logging in to and using the Yuri Web Interface, you accept the use of cookies for saving login credential infomation. Here you can rad more on how Yuri is using cookies and local storage.


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Hi there, Sometimes all you want is to send a simple HTML email with a basic design. Really simple HTML email templateThis is a really simple email template. It's sole purpose is to get you to click the button below. How do I use it?All the information you need is on GitHub. Feel free to use, copy, modify this email template as you wish. Thanks, have a lovely day. |
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And this is the last tab. TOYYYYS!


front-end dev
This is my service 01This is my service 01This is my service 01This is my service 01This is my service 01This is my service 01
This is my service 02This is my service 02This is my service 02This is my service 02This is my service 02This is my service 02
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coooooooooooooooooooool
This is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about meThis is about me
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Tink is heavily inspired on the Bootstrap framework. We decided to use a lot of its functionality and to improve it where we thought we could.
Normally, a minimum of effort is needed to Tinkify your work. Most projects made with Bootstrap can be ported to Tink easily.
Tink makes use of the HTML5 doctype. Include it at the beginning of all your projects.
To ensure proper rendering and touch zooming, add the viewport meta tag to your <head>
:
If you would inspect the Tink bower package you will also find the complete Sass version of Tink inside. You can use these files to compile Tink yourself or you can use some of the files to just use our variables, functions and mixins:


Next: abs, Previous: /, Up: Numbers Dictionary
1
+ ⇒ number
successor
1
- ⇒ number
predecessor
number—a number.
successor, predecessor—a number.
1+ returns a number that is one more than its argument number. 1- returns a number that is one less than its argument number.
(1+ 99) ⇒ 100 (1- 100) ⇒ 99 (1+ (complex 0.0)) ⇒ #C(1.0 0.0) (1- 5/3) ⇒ 2/3
Might signal type-error if its argument is not a number. Might signal arithmetic-error.
(1+ number) ≡ (+ number 1) (1- number) ≡ (- number 1)
Implementors are encouraged to make the performance of both the previous expressions be the same.


Layout
Item sizing
All sizing and styling of items is handled by your own CSS.

...

.grid-item {
float: left;
width: 80px;
height: 60px;
border: 2px solid hsla(0, 0%, 0%, 0.5);
}
.grid-item--width2 { width: 160px; }
.grid-item--height2 { height: 140px; }
Responsive layouts
Item sizes can be set with percentages for responsive layouts. With the masonry
layout mode, set percentage-width columnWidth
with element sizing. Set percentPosition: true
so item positions are likewise set with percentages to reduce adjustment transitions on window resize.

...

/* fluid 5 columns */
.grid-sizer,
.grid-item { width: 20%; }
/* 2 columns */
.grid-item--width2 { width: 40%; }
$('.grid').masonry({
// set itemSelector so .grid-sizer is not used in layout
itemSelector: '.grid-item',
// use element for option
columnWidth: '.grid-sizer',
percentPosition: true
})
imagesLoaded
Unloaded images can throw off Masonry layouts and cause item elements to overlap. imagesLoaded resolves this issue. imagesLoaded is a separate script you can download at imagesloaded.desandro.com.
Initialize Masonry, then trigger layout
after each image loads.
// init Masonry
var $grid = $('.grid').masonry({
// options...
});
// layout Masonry after each image loads
$grid.imagesLoaded().progress( function() {
$grid.masonry('layout');
});
Edit this demo or vanilla JS demo on CodePen
Or, initialize Masonry after all images have been loaded.
var $grid = $('.grid').imagesLoaded( function() {
// init Masonry after all images have loaded
$grid.masonry({
// options...
});
});
Edit this demo or vanilla JS demo on CodePen


Kevin Mitnick
Hackers have been around since the dawn of computers, and they've wreaked more havoc than many people realize. Law enforcement has yet to catch up with many of their antics. Of these one of the most influential is Kevin Mitnick. Mitnick became the world’s most notorious black hat hacker, breaking into the networks of companies like IBM, Nokia, Motorola, and other targets. After a stint in prison, he reinvented himself as a white hat hacker, selling his skills as a penetration tester and security consultant. With his latest business venture, Mitnick has switched hats again: This time to an ambiguous shade of grey.
Black Hat
At age 15, Mitnick used social engineering and dumpster diving[9] to bypass the punch card system used in the Los Angeles bus system. After a friendly bus driver told him where he could buy his own ticket punch, he could ride any bus in the greater LA area using unused transfer slips he found in the trash. Social engineering later became his primary method of obtaining information, including user-names and passwords and modem phone numbers.
According to the U.S. Department of Justice, Mitnick gained unauthorized access to dozens of computer networks while he was a fugitive. He used cloned cellular phones to hide his location and, among other things, copied valuable proprietary software from some of the country's largest cellular telephone and computer companies. Mitnick also intercepted and stole computer passwords, altered computer networks, and broke into and read private e-mail.
After a well-publicized pursuit, the FBI arrested Mitnick on February 15,1995, at his apartment in Raleigh, North Carolina, on federal offenses related to a 2½-year period of computer hacking which included computer and wire fraud.
White Hat
In his 2002 book, The Art of Deception, Mitnick states that he compromised computers solely by using passwords and codes that he gained by social engineering. He claims he did not use software programs or hacking tools for cracking passwords or otherwise exploiting computer or phone security.
Since 2000, Mitnick has been a paid security consultant, public speaker and author. He does security consulting for Fortune 500 companies, performs penetration testing services for the world's largest companies and teaches Social Engineering classes to dozens of companies and government agencies.
Grey Hat
Mitnick revealed a new branch of his security consultancy business he calls Mitnick’s Absolute Zero Day Exploit Exchange. Since its quiet inception, he says the service has offered to sell corporate and government clients high-end “zero-day” exploits, hacking tools that take advantage of secret bugs in software for which no patch yet exists. Mitnick says he’s offering exploits developed both by his own in-house researchers and by outside hackers, guaranteed to be exclusive and priced at no less than $100,000 each, including his own fee. These Customers are not required to share what the purpose of these exploits are, furthurmore Mitnick's company doesn't want to know.


{% extends 'base.html' %} {% block content %} Let's fight corruption Corruption has become the cancer of this country and the main hindrance to progress
Posted by Albert on 21/2/2019


Variant Comparer
In this window, you can compare a set of selected variants and perform tradeoff analysis.
Select and unselect variants by clicking on bubbles in the graph or column headers in the table.
Press
to remove a variant from the view.
Save the selected variants into a
.data
file using
button.
Return to Welcome page


On the boat
Ready to be part of a group, learn and grow with them
Feb 26, 2015
By now, I’m getting to the end of DBC Phase 0’s first week. And I must say this is pretty exciting! Since application, DBC has been creating a friendly and engaging environment. I’m usually the type of person that takes a time to feel comfortable in a new group. And what I’ve been seeing - and it’s been just a few days - is that I am part of a receptive and eager to learn cohort, who are willing to learn by helping others learn. I feel more and more motivated each day, I feel that I fit, that I belong.
I can’t wait to dig in new challenges, concepts, tools, knowledge, pairing. It is a little scary, I won’t lie, once I see in DBV very high standards, and I want to be able to correspond to it. Because I know that, if I do, then I’ll reach my personal goal.
The most important thing to keep in mind is that all along this journey, we need to be open to try, to experiment and… to fail! Over and over, because this is what learning is about. I want to be 100% in this challenge, and put my best energies on it to learn how to learn, after all this is what I’ll take home and what will make me succeed from that point on.


Chase ends in Highland park, 3 suspects in custody after after running into occupied home
HIGHLAND PARK, LOS ANGELES (KABC) -- Three suspects were taken into custody Friday night after a bailing out of a chase in Highland Park and running into a home with a large family inside.
The chase ended before 9 p.m. near the home on Avenue 41. Police said the three suspects ran out of a red sedan and into the home, prompting authorities to swarm the area.
More than a dozen people were evacuated from inside the home, including several small children. All of them came out of the home with their hands raised.
Once the home was cleared, heavily armed officers entered and the suspects were taken into custody.


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3mdeb Embedded Systems Consulting maintains https://pcengines.github.io and https://github.com/pcengines on behalf of PC Engines.
Those services are provided as part of our coreboot maintainership package.
If you are interested in similar support please
CONTACT US


Browsers
Sad but a fact, July 14th, 2021 browsers supporting recognition.
Languages
The language set is limited by the recognition engine.
I only provide samples for these languages
English works sad (for me), French is better (for me), ... yes I am French
When I spoke 'fill' the recognition find : Phil, feel, ...
Thankfully we started the POC with French and it works well at begining


{% extends "allmychanges/base.html" %} {% block header %}{% endblock %} {% block footer %}{% endblock %} {% block content %}
Are you tired of sorting out errors after dependencies update?
AllMyChanges will help to track changes.
Subscribe to become one of the first users and get a 3-month subscription for free!
Email:
How does it work? -----------------
* You choose which libraries to track. * Our robot regularly checks updates. * It sends a digest to your email together with news.
Advantages: ------------
* The digest contains not only the list of the new versions but also their description. * You are always aware of latest features and critical updates. * You get information quickly and in a single format.
Become one of the first users and get a 3-month subscription for free!


Challenge 17
The final coded used for the function is listed below you can review chg17-switchFunction.js for all the code iterations.
some code
The final coded used for the function is listed below you can review chg17-switchFunction.js for all the code iterations.
some code


SWCF - Simple Web Contact Form
Your Name :
Your e-mail :
Your issue :
...
Query
Feedback
Complaint
Other
Content Here :
Submit


SHE'S ALL THAT A film review by Christopher Null Copyright 1999 Christopher Null filmcritic.com
Well, as it turns out, she's *not* all that. She's like half that. Maybe less.
The way I ended up at SHE'S ALL THAT is because my wife refused to go see 8MM, thus sticking us with this little flick, which she figured I wouldn't mind seeing on account of the girls with large bazongas. And she is right: In point of fact, I do not mind seeing girls with large bazongas, especially when they shake 'em on the dance floor.
Said shaking is done on Prom Night, six weeks after Zach (Prinze) is poised to win a bet in which he is tasked with turning uber-dork Laney (Cook) into a teenage goddess. And Zach does some damn fine work. You see, Laney's mom has died, and she's a bit of a nut job on account of being the only woman in the family and having Kevin Pollack as a dad. Zach's got his own troubles, too: He can't choose between attending Dartmouth, Yale, or Harvard... *or* he might wanna be a hacky-sack performance artist! (This is not a joke.)
*Of course* Laney and Zach help each other through all this rabid insanity! But Laney finds out about the bet and - the horror - she gets mad at Zach and goes to the prom with someone else! Could their blossoming relationship be doomed? Well, you'll just have to get yourself to the theater for that answer!
Okay, there are a few chuckles, but I can count them all on one hand. That Prom Night shaking is also the best-choreographed prom I've ever seen, and that includes all the ones on 90210.  As far as teen comedy/romances go... well, Molly Ringwald is probably turning over in her grave.
Er, wait a second.
RATING: **
|------------------------------| \ ***** Perfection \ \ **** Good, memorable film \ \ *** Average, hits and misses \ \ ** Sub-par on many levels \ \ * Unquestionably awful \ |------------------------------|
MPAA Rating: PG-13
Director: Robert Iscove Producer: Peter Abrams, Richard N. Gladstein, Robert L. Levy Writer: R. Lee Fleming Jr. Starring: Freddie Prinze Jr., Rachael Leigh Cook, Matthew Lillard, Paul Walker, Jodi Lyn O'Keefe, Kevin Pollack, Anna Paquin
http://www.miramax.com:8888/ows-doc/shesallthat/index.html
Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% extends "layout_unbranded.html" %} {% block page_title %} Getting started at DWP {% endblock %} {% block main_content %}
Gmail
The user experience team use gmail as well as the official DWP email.
Set up a gmail account in this format<EMAIL_ADDRESS>Trello
Most projects and the user experience team use Trello to track work and tasks.
Set up an account at www.trello.com, using your gmail account. You'll be invited or added to Trello boards.
Design community
The DWP user experience team is part of a wider design community in government, centred around GDS.
Your line manager will get you added to the digital service designers email list.
Slack
The DWP user experience community and the Government-wide design community use slack instant messaging to ask questions and share ideas.
Email<EMAIL_ADDRESS>from your gmail and ask him to add you to the digitalDWP slack channel.
There are particular channels for each area - join at least the content and general channels.
You can also join the ukgovernmentdigital.slack.com channel using your DWP email when you have it.
Your timesheet
Make your own copy of this DWP timesheet and add it to your google drive - update this at least weekly, ideally every day.
Your bank details
You need to put your own bank details into 'RM' - the internal staff system. To do this you'll need a DWP machine and your smartcard. You can't be paid until you've done this so don't forget.
A business travel account
Email<EMAIL_ADDRESS>from your gsi email (you'll need a DWP machine). Include your staff number, and ask them to set up a www.trips.uk.com account - this is where you'll book work travel and accommodation
Social media
Many of the team tweet and blog about the work we do. You may like to follow these twitter accounts for a start:


--- layout: page title: About Me ---
As a faculty, mentor, advisor, and researcher I work with a lot of students. I find most students have similar sets of questions, struggles, and skills to master. This blog is an attempt to collate all my advice into a single archive for potential graduate students in engineering disciplines to consider.
Note that my thoughts are really only relevant to engineering and computer science disciplines, and perhaps even more narrowly focused on my particular research and don't necessarily represent my employer or the NIMBUS lab.
A site focused on my research can be found here.
Contact me here: admin(at)robotprofessor(dot)org
I am an Assistant Professor in the Computer Science and Engineering Department at the University of Nebraska - Lincoln. I am also a co-director of the NIMBUS lab where my research focuses on the intersection of control and computer science with applications in aerospace vehicles.
My background is fairly broad and spans several disciplines. I have a BS in Computer Engineering from BYU, a MS in Electrical Engineering also from BYU where I was in the MAGICC lab. I then worked for a time as a software engineer at Lawrence Livermore National Laboratory on the integrated computer control system for the National Ignition Facility. I then matriculated at the University of Michigan Aerospace Engineering Department where I earned MS and PhD degrees in Aerospace Engineering under the advisement of Dr. Ella M. Atkins. I have worked with UAS for roughly a decade, and can fly quadcopters, fixed wing, and helicopter model aircraft.


tiger team
`Prev `__
T
`Next `__
--------------
**tiger team**: n.
[U.S. military jargon]
1. Originally, a team (of `*sneaker* <../S/sneaker.html>`__\ s) whose
purpose is to penetrate security, and thus test security measures. These
people are paid professionals who do hacker-type tricks, e.g., leave
cardboard signs saying “bomb” in critical defense installations,
hand-lettered notes saying “Your codebooks have been stolen” (they
usually haven't been) inside safes, etc.  Serious successes of tiger teams sometimes lead to early
retirement for base commanders and security officers (see the
`*patch* <../P/patch.html>`__ entry for an example).
2. Recently, and more generally, any official inspection team or special
`*firefighting* <../F/firefighting.html>`__ group called in to look at a
problem.
A subset of tiger teams are professional
`*cracker* <../C/cracker.html>`__\ s, testing the security of military
computer installations by attempting remote attacks via networks or
supposedly ‘secure’ comm channels. Some of their escapades, if
declassified, would probably rank among the greatest hacks of all times.
The term has been adopted in commercial computer-security circles in
this more specific sense.
--------------
+---------------------------------+----------------------------+------------------------------+
| `Prev `__ | `Up <../T.html>`__ | `Next `__ |
+---------------------------------+----------------------------+------------------------------+
| tickle a bug | `Home <../index.html>`__ | time bomb |
+---------------------------------+----------------------------+------------------------------+


Here’s how to get your free year of Apple TV+
by Michael PotuckAfter years in the making, Apple TV+ with new original content has launched. Ready to check it out? Read on for how to get your free year of Apple TV+ on iPhone, Mac, Apple TV, and more.
Apple TV+ has officially arrived with The Morning Show and SEE as the two headlining original shows at launch. We’ve already got a breakdown of all of the shows that are available on the streaming service.
Any Apple customers who have purchased a new iPhone, iPad, iPod touch, Mac, or Apple TV since September 10, 2019, that can run the latest operating systems will get a free year of Apple TV+. The deal will automatically show up for those eligible (check out the full terms and conditions here).
If you’re not eligible for the free year trial, you’ll still get Apple TV+ free for 7 days.
Here’s how to get your free year of Apple TV+
- Open the TV app on your iPhone, iPad, iPod touch, Mac, or Apple TV (running the latest software)
- Tap or click on a new Apple original like The Morning Show or SEE
- Choose Enjoy 1 Year Free (7-day free trial for those who haven’t purchased a new device)
- Tap or Click Continue, then Confirm the free year
- Alternately, head to tv.apple.com on a Mac or PC, sign in in the top right corner, then activate your free trial
If you have a discounted Apple Music student plan, you won’t see a button for the free trial, Apple TV+ will just be unlocked.
Note: If you’re not seeing the free year offer and you think you should, check back a bit later, it’s still rolling out around the world. Also, double-check that you’re signed in with your Apple ID, have purchased the new device from Apple or an authorized reseller, and if you use Family Sharing, your family organizer can claim it, which will auto share it with others. Family sharing for Apple TV+ is automatically turned on after the family organizer signs up.
Here’s how the process looks:
Now you should see a confirmation. Swipe down to see more Apple TV+ shows.
Here’s how the process looks on Mac:
Read more 9to5Mac tutorials:
AirPods Pro:
- AirPods Pro: How to use Ear Tip Fit Test and change tips
- AirPods Pro: How to use noise cancellation and Transparency mode
- AirPods Pro vs Powerbeats Pro: How specs, size, price, and more compare
- What’s the difference in AirPods and AirPods Pro? Here’s how size, price, and features compare
iPhone & iPad:
- How to use the Deep Fusion iPhone 11 and iPhone 11 Pro camera feature
- iOS 13: How to use custom fonts on iPhone and iPad
- How to edit messages on iPhone and iPad with two-finger tap
- How to use the ‘Remind when messaging’ feature on iPhone, iPad, and Mac
- iOS 13: How to use Reader View on iPhone and iPad
- How to change icons and colors for Reminders Lists on iPhone, iPad, and Mac
- How to make subtasks and sublists with Reminders in iOS 13 and macOS Catalina
- How to get started with and use ‘Sign in with Apple’ on iPhone, iPad, and Mac
- iPadOS 13: How to make iPad app icons and text bigger
- How to use the ultra wide camera on iPhone 11 and iPhone 11 Pro
- How to use iCloud Keychain to manage and store your passwords
- How to use Night mode on iPhone 11 and iPhone 11 Pro
macOS Catalina:
- How to get started with and use Voice Control in macOS Catalina
- How to check iPad and Mac Sidecar requirements
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- How to create a bootable macOS Catalina 10.15 USB install drive [Video]
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Our application process can be completed in minutes not hours. Don't get stuck filling in tedious forms.
Humanizing your insurance.
Get your life insurance coverage easier and faster. We blend our expertise and technology to help you find the plan that's right for you. Ensure you and your loved ones are protected.


Barcode Scanner continuously scans a square region shown on your screen -- just line up the phone so the barcode is completely inside the viewfinder rectangle:
1D barcodes like those found on products require a phone with autofocus. Without it, only 2D formats like QR Codes and Data Matrix codes will be scannable.
When a barcode is read, a beep sound will play and you'll see the results of the scan, a description of what the barcode contains, and options to take action on the contents.
If you're having trouble scanning, make sure to hold the phone steady. If the camera is unable to focus, try moving the phone further or closer from the barcode.


Plugin Information |
|---|
View People Redirector on the plugin site for more information. |
Allows you to integrate with an external user management system.
HTTP requests which go to the Jenkins-internal "people" page are redirected to the external site.
Configuration
Global configuration
Changelog
Version 1.3 (May 11, 2012)
- Fixed JENKINS-12224
Version 1.2 (Oct 12, 2011)
- Redirects now also when the URL has a single trailing slash.
Version 1.1 (Oct 11, 2011)
- Fixed JENKINS-11058
- Added unit test
Version 1.0 (Sep 4, 2011)
- First release
Attachments:
people-redirector-global-config.png (image/png)


The GPU solution is slightly different than the CPU, because the initialisation is also done on the GPU on a much finer grid.


As the president of the Novice-to-Expert (N2E) Coding Club, I organized MIT App Inventor workshops on January 14-15, 2017 followed by a competition on February 4, 2017. These events were sponsored by a Google outreach grant.
MIT App Inventor is a web application for developing apps for android devices. The simple drag and drop operations make it fun to learn basics of programming. I like it as one of the first learning tool for introduction to programming because it focuses on developing logical reasoning and critical thinking rather than the the syntax. Kids love playing games on mobile devices. Why not make apps instead of just playing them and learn a powerful skill while having fun?
The workshops took place in ECE Computer Lab and were led by me and Umama Ahmed (sophomore, ECE). Hsiu-fen Lin, an apartment assistant with Rutgers Residence Life, coordinated the event. They were attended by 15 students (ages 9-14) and their parents who learned basics of programming and developed three fun apps along with the instructors. The students were given 3 weeks to develop their own apps, which they showcased in a competition held on February 4, 2017. Besides the instructors, Parul Pandey (graduate student, ECE) was invited as an additional judge to select the winners. All the students impressed the judges with their apps and they enjoyed presenting their own apps to friends and family.
Three students with most creative apps were selected as winners. The three winners are:
- First Place: Dima-Al Quzwini (4th grade), App: Learning for Kids
- Second Place:Younis R. Mazin (7th grade), App: Shoot the Bird
- Third Place: Namisha Shrvastava (9th grade), App: General Knowledge Quiz
Congratulations to the winners!
If you would like to learn more about MIT App Inventor, here are a few resources:


{{ head() | safe}}
Oh My Word!
{{ nav("primary") | safe}}
{% block content %} Nothing to see here {% endblock %}
{% include "partials/footer.html" %}


TRIUMPH OF THE WILL A film review by Steve Rhodes Copyright 1995 Steve Rhodes
RATING (0 TO ****): ****
He who does not really know the devil, can not defeat him. Ostrich like ignorance provides no power. Only those who understand at a deep level the evil they are dealing have any hope of victory.
-Richard Sinclair
Leni Riefenstahl's most remembered and most vilified film is TRIUMPH OF THE WILL. It is a documentary of the 1935 Nazi party congress. It has been called the most effective propaganda film of all time. It is now playing in revival at some of the art house around the US, and it is available on video.
I found the movie to be a religious film, and, in fact, Hitler himself talks about National Socialism as being the equivalent of a religious order. It has some of the same feel of the movies about religious cults except this time it is a true life cult, and these are not actors. Another difference is that cults usually harm only themselves whereas as this one caused the death of tens of millions of people.
Make no mistake about it, the people in this film were worshipping someone they thought was their messiah. Leni captures this with chilling accuracy. Her fault, if one wants to ascribe fault, is that she was so good at it.
The print I saw was in poor quality. I once saw parts of a video of this movie that was in much worse shape. Nevertheless, you could see the zealotry and feel the intense emotions. It is easy to dismiss all of this with - well, I would never have been a Nazi.
Watching the expressions of the people there, you could see how many of them did believe that National Socialism was their future and Hitler was their salvation. Hitler could really fire up their passions. He made you feel that the government can do everything for you, and socialism is the only answer. Sure there were some bad eggs he said, but he had gotten rid of them. All very scary stuff.
You could easily close you ears and just watch the master of Leni's camerawork. If you are careful you can even see one of her many cameras going up in an elevator. Opening back up your ears, you get the music and sound that evoke the power of the this self-proclaimed "master race". Interestingly, of the many speakers, with the exception of Hitler and a few others, most of the leaders were crude and coarse looking. Leni has them looking like that just finished a big meal at the local beer hall and you are waiting for the big burp into the camera.
If you have any doubts that this is a religious picture, wait for the scene where Hitler goes on a long slow walk through 200,000 party members standing in attention. He and two of his aids walk down a wide road and at the end they pay homage to the party martyrs. It is clearly evocative of Moses parting the Red Sea and leading his people across toward the promised land.
When you leave the theater, you know you have seen the power of true evil, and as the quote at the start said, knowledge of evil is a precondition to its defeat. For this reason and for the brilliance of the film making, I recommend this to everyone old enough to truly understand the difference between good and evil. It has no MPAA rating. It is clearly a **** show. TRIUMPH OF THE WILL runs 1:54, has some spoken German in it along with English subtitles, but it is essentially a montage of extremely effective imagery.
Finally, anyone interest in Leni should be sure to see the recent documentary THE WONDERFUL, HORRIBLE LIFE OF LENI RIEFENSTAHL about her life and her films. It is absolutely fascinating.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEWED WRITTEN ON: September 23, 1994
Opinions expressed are mine and not meant to reflect my employer's.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


1. Plug in your Jewelbot to start charging
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4. Complete the Prep Work section
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3 Circles
1 Circles
2 Circles
3 Circles
4 Circles
5 Circles
6 Circles
7 Circles
8 Circles
Grid
Load
Request
Area:
0
Polygon Area:
0
Arc Area:
0


The class Level defines the level of a logging event.
More...
#include <level.h>
List of all members.
Public Types |
| enum | Value {
NULL_INT = 0,
ALL_INT = 32,
TRACE_INT = 64,
DEBUG_INT = 96,
INFO_INT = 128,
WARN_INT = 150,
ERROR_INT = 182,
FATAL_INT = 214,
OFF_INT = 255
} |
Public Member Functions |
| | Level (Value value=NULL_INT) |
| int | syslogEquivalent () const |
| int | toInt () const |
| bool | operator== (const Level &rOther) const |
| bool | operator!= (const Level &rOther) const |
| bool | operator< (const Level &rOther) const |
| bool | operator<= (const Level &rOther) const |
| bool | operator> (const Level &rOther) const |
| bool | operator>= (const Level &rOther) const |
| QString | toString () const |
Static Public Member Functions |
| static Level | fromString (const QString &rName, bool *pOk=0) |
Friends |
| QDataStream & | operator<< (QDataStream &rStream, const Level &rLevel) |
| QDataStream & | operator>> (QDataStream &rStream, Level &rLevel) |
Related Functions |
(Note that these are not member functions.)
|
| QDebug | operator<< (QDebug debug, const Level &rLevel) |
Detailed Description
The class Level defines the level of a logging event.
- Note:
- All the functions declared in this class are thread-safe.
Member Enumeration Documentation
The enumeration Value contains all possible Level values.
- Enumerator:
| NULL_INT |
NULL_INT is used for no level has been specified
|
| ALL_INT |
|
| TRACE_INT |
|
| DEBUG_INT |
|
| INFO_INT |
|
| WARN_INT |
|
| ERROR_INT |
|
| FATAL_INT |
|
| OFF_INT |
|
Constructor & Destructor Documentation
| Log4Qt::Level::Level |
( |
Value |
value = NULL_INT |
) |
[inline] |
Member Function Documentation
| static Level Log4Qt::Level::fromString |
( |
const QString & |
rName, |
|
|
bool * |
pOk = 0 | |
|
) |
| | [static] |
| bool Log4Qt::Level::operator!= |
( |
const Level & |
rOther |
) |
const [inline] |
| bool Log4Qt::Level::operator< |
( |
const Level & |
rOther |
) |
const [inline] |
| bool Log4Qt::Level::operator<= |
( |
const Level & |
rOther |
) |
const [inline] |
| bool Log4Qt::Level::operator== |
( |
const Level & |
rOther |
) |
const [inline] |
| bool Log4Qt::Level::operator> |
( |
const Level & |
rOther |
) |
const [inline] |
| bool Log4Qt::Level::operator>= |
( |
const Level & |
rOther |
) |
const [inline] |
| int Log4Qt::Level::syslogEquivalent |
( |
|
) |
const |
| int Log4Qt::Level::toInt |
( |
|
) |
const [inline] |
| QString Log4Qt::Level::toString |
( |
|
) |
const |
Friends And Related Function Documentation
| QDataStream & operator<< |
( |
QDataStream & |
rStream, |
|
|
const Level & |
rLevel | |
|
) |
| | [friend] |
Writes the given error rLevel to the given stream rStream, and returns a reference to the stream.
| QDebug operator<< |
( |
QDebug |
debug, |
|
|
const Level & |
rLevel | |
|
) |
| | [related] |
Writes all object member variables to the given debug stream rDebug and returns the stream.
Level("ERROR")
- See also:
- QDebug
| QDataStream & operator>> |
( |
QDataStream & |
rStream, |
|
|
Level & |
rLevel | |
|
) |
| | [friend] |
Reads an error from the given stream rStream into the given error rLevel, and returns a reference to the stream.
The documentation for this class was generated from the following file:


Every person has their own unique story. Here is a glimpse into mine.
I am Douglas a student at moringa school currently studying I.T computer programming and coding, i have managed to gain skills like web developmentusing languages like HTML(also using css to style the html),JAVASCRIPT and also to interact with alot of websites and computer languages.
My passion for it begun immidietly when i started doing computer packages i found it more interesting and enguaging since i like things to do with compurets and technology.
I am the second born in the family of three children, raised by a single mother in Nairobi.I have attended school from primary to adiploma level which i amcurrently doing I.T (coding and programming),i have learned computer languages like,
1:HTML and using CSS cascading styles sheet
2:JAVASCRIPT
3:BOOTSTRAP
4:PYTHON
5:ANGULAR
this journey has not been easy becouse i can remember there are many times i was sent home for schoolfees especially when i was in high school, my mother couldn't afford to pay schoolfees for the two of us.
It reached a point when i was in form two i thought of dropping out becouse of beeing sent home numerouse times ,like every time people had tobe sent home i knew that i wount survive so i had to prepare myself when i hered about fee balance ,it was an embaressment to me whe people saw that every term i am home doing nothing ,sometimes i pretended tht i am sick just to evade the gossiping of people, staying at home really draged me behind i even started performing poorly in school.
I love playig football, and creative arts drawing the most also i love technology most thing to do with gadgets and elecronics.When i was young i used to connect wires, batterys, motors, and bulbs my joy was to see them working as i expected. I am antisocial sometimes and thatswhat i am trying to work on,i am noteasy to make friends it takes timeor me but i seepeopleare somehoweasy to make friendship in thefirstmeeting, i am in a certain organisation wherewe train the kids on socil abehaviours, that confidence of talking to many people is what i am gaining and ibelievethat soon enough i will be making friends easily like other people.
On my free time i listen to music, watch movies, draw pictures, or play soccer,i also like making box houses becouse they look good and it can also be sold for decoration in the house
I have done ict , i am a community coach i have also attended lifeskills courses and now i am working on being a professional in I.T websitedeveloping and codding, i can work under minimum supervision becouse i know good work ethics that are expected from the empoyee.
In 2019, i attended ict course at Daystar University for computer packages
I also worked in a certain bakery in Kariobani in the packaging sector for five months and even before that i used to volunteer in an non-govermental organisation as a youth leader which i gained skill in coaching, comming up with activational games for both elderly people and kids


Before Change
self.mlBatchInfo[batch] = {}
// specializing the self.localInitialize()
if solutionExport != None:
self.localInitialize(solutionExport=solutionExport)
else:
self.localInitialize()
def localInitialize(self,solutionExport=None):
Use this function to add initialization features to the derived class
it is call at the beginning of each step
After Change
for entry in self.assemblerDict.get("Preconditioner",[]):
cls,typ,name,model = entry
if cls != "Models" or typ != "ExternalModel":
self.raiseAnError(IOError,"Currently only "ExternalModel" models can be used as preconditioners! Got "{}.{}" for "{}".".format(cls,typ,name))
self.preconditioners[name] = model
self.mdlEvalHist = self.assemblerDict["TargetEvaluation"][0][3]
self.objSearchingROM = SupervisedLearning.returnInstance("SciKitLearn", self, **{"SKLtype":"neighbors|KNeighborsRegressor", "Features":",".join(list(self.fullOptVars)), "Target":self.objVar, "n_neighbors":1,"weights":"distance"})


Battery Status API
Your browser doesn't support the Battery Status API :(
Battery Status
Power Source
---
Level percentage
---
Fully charged in
---
Remaining time
---


Phillip is a girl who grew up in a single parent house hold. Her mom took care of her. When Phillip was twelve she have a baby brother. She made bad choices and picked the wrong group of friends before her brother was born. However even though Phillip avoided trouble it alway found her. So one day a group of girls that phillip used to bully. The girls thought it would be a good idea to get revenge on Phillip now that she had learn to control herself.
Picks A Fight Doesn't Pick The Fight


The type of file (or directory) being made TouchTestable.
- Project instruments the source code of an iOS or Android project.
- APK instruments the binary content of a compiled Android .apk file.
- IPA instruments the binary content of a compiled iOS .ipa file.
- iOS App Bundle instruments the binary content of an iOS application bundle (.app directory).
More information can be found here.


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Error!
{{ error_text }}
Please let us know how you encountered this error using the form below.
{% if compensate == True %}This HIT will be marked as completed and you should be compensated automatically once the above form is submitted.
To enquire about compensation, please contact the researcher at {{contact_address}} and quote the following information.
Error type: {{ error_type }}
HIT id: {{ hit_id }}
Assignment id: {{ assignment_id }}
Worker id: {{ worker_id }}


Two yards doesn't sound like much in 2D. Upgrade it to 3D, add a shovel, and suddenly it seems a lot bigger.
3/6/2010, 11:07:30 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Two yards doesn't sound like much in 2D. Upgrade it to 3D, add a shovel, and suddenly it seems a lot bigger.
3/6/2010, 11:07:30 PM
Favs: 0
Retweets: 0
link


Chapter 4 Linear regression: Predicting course evaluation scores
# import packages used in this notebook
suppressPackageStartupMessages({
suppressWarnings({
# core libraries
library(tidyverse)
# helper libraries
library(janitor) # for cleaning names
library(skimr) # for summary statistic reports
library(glue) # for string formattin
}) })
# global setting for notebook
theme_set(theme_light())
# import my own plotting functions for styling plots
source("plotting_functions.R")
4.1 Data importing and cleaning
4.1.1 Importing the data
The data I am using in this project is available in the github repository for the book ‘Regression and Other Stories.’ So, I can read in the csv directly from the repository.
# set url for dataset
<- "https://raw.githubusercontent.com/avehtari/ROS-Examples/master/Beauty/data/ProfEvaltnsBeautyPublic.csv"
data_url
# read data from github
<- read_csv(data_url) %>%
teaching_eval ::clean_names() # try to clean up names
janitor
# display dataframe for initial inspection
glimpse(teaching_eval)
## Rows: 463
## Columns: 64
## $ tenured <dbl> 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0~
## $ profnumber <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1~
## $ minority <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0~
## $ age <dbl> 36, 59, 51, 40, 31, 62, 33, 51, 33, 47, 35, 37, 42, ~
## $ beautyf2upper <dbl> 6, 2, 5, 4, 9, 5, 5, 6, 5, 6, 4, 5, 5, 5, 7, 3, 1, 6~
## $ beautyflowerdiv <dbl> 5, 4, 5, 2, 7, 6, 4, 4, 3, 5, 5, 4, 4, 3, 6, 2, 3, 3~
## $ beautyfupperdiv <dbl> 7, 4, 2, 5, 9, 6, 4, 6, 7, 7, 7, 5, 7, 3, 7, 4, 4, 5~
## $ beautym2upper <dbl> 6, 3, 3, 2, 6, 6, 4, 3, 5, 6, 7, 4, 5, 5, 4, 7, 3, 4~
## $ beautymlowerdiv <dbl> 2, 2, 2, 3, 7, 5, 4, 2, 5, 3, 2, 3, 4, 1, 5, 5, 2, 4~
## $ beautymupperdiv <dbl> 4, 3, 3, 3, 6, 5, 4, 3, 3, 6, 4, 5, 4, 7, 4, 4, 2, 4~
## $ btystdave <dbl> 0.2015666, -0.8260813, -0.6603327, -0.7663125, 1.421~
## $ btystdf2u <dbl> 0.2893519, -1.6193560, -0.1878249, -0.6650018, 1.720~
## $ btystdfl <dbl> 0.4580018, -0.0735065, 0.4580018, -1.1365230, 1.5210~
## $ btystdfu <dbl> 0.8758139, -0.5770065, -1.5455530, -0.0927330, 1.844~
## $ btystdm2u <dbl> 0.6817153, -1.1319040, -1.1319040, -1.7364440, 0.681~
## $ btystdml <dbl> -0.9000649, -0.9000649, -0.9000649, -0.3125226, 2.03~
## $ btystdmu <dbl> -0.1954181, -0.6546507, -0.6546507, -0.6546507, 0.72~
## $ class1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class2 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class3 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class4 <dbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0~
## $ class5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0~
## $ class10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0~
## $ class11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class13 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class14 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class15 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class16 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class17 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class18 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class19 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class20 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class21 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class22 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class23 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class24 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class25 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class26 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class27 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class28 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class29 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ class30 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ courseevaluation <dbl> 4.3, 4.5, 3.7, 4.3, 4.4, 4.2, 4.0, 3.4, 4.5, 3.9, 3.~
## $ didevaluation <dbl> 24, 17, 55, 40, 42, 182, 33, 25, 48, 16, 18, 30, 28,~
## $ female <dbl> 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0~
## $ formal <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ fulldept <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
## $ lower <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1~
## $ multipleclass <dbl> 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0~
## $ nonenglish <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0~
## $ onecredit <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ percentevaluating <dbl> 55.81395, 85.00000, 100.00000, 86.95652, 87.50000, 6~
## $ profevaluation <dbl> 4.7, 4.6, 4.1, 4.5, 4.8, 4.4, 4.4, 3.4, 4.8, 4.0, 3.~
## $ students <dbl> 43, 20, 55, 46, 48, 282, 41, 41, 60, 19, 25, 34, 40,~
## $ tenuretrack <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0~
## $ blkandwhite <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ btystdvariance <dbl> 2.1298060, 1.3860810, 2.5374350, 1.7605770, 1.693100~
## $ btystdavepos <dbl> 0.201567, 0.000000, 0.000000, 0.000000, 1.421450, 0.~
## $ btystdaveneg <dbl> 0.000000, -0.826081, -0.660333, -0.766312, 0.000000,~
Next I focused down on variables within the dataset to be used in the exploratory data analysis and modeling below. I reviewed the data code book to identify the outcome variable and promising potential predictor variables. A number of variables have been dropped in the process of focusing down the dataset , as I thought these dropped variable were unlikely to important predictors of course_evaluation
scores. This was a judgment based on my experiences of working in various universities over a decade or so.
Due to the nature of the variable names in the dataset, automatically cleaning all variables names to make readable and easy to interpret was not possible. So, in the process of focusing down the dataset I renamed various variables to make them easier to remember, interpret and work with. I also reorder the variables to group them in a more logical order: an outcome variable, followed by potential predictors relating to the professor, and then potential predictors relating to the class.
# focus down on features in the dataset with a good potential to act as predictors
<- teaching_eval %>%
teaching_eval_focus
select(
# select and rename outcome variable
course_evaluation = courseevaluation,
# select and rename potential predictor variables relating to the professor
:age,
tenuredprof_ave_rating = profevaluation,
prof_ave_beauty_rating = btystdave,
female, formal, non_native_english = nonenglish,
tenure_track = tenuretrack,
# select and rename potential predictor relating relating to the course
lower,multiple_prof = multipleclass,
one_credit = onecredit,
eval_response_rate = percentevaluating,
students,:class30 # dummy variables
class1
%>%
)
# Move a potential indentifer variable to the front of the dataframe
# and rename for consistency
rename(prof_number = profnumber) %>%
relocate(prof_number, .before = course_evaluation)
teaching_eval_focus
## # A tibble: 463 x 46
## prof_number course_evaluation tenured minority age prof_ave_rating
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 4.3 0 1 36 4.7
## 2 2 4.5 1 0 59 4.6
## 3 3 3.7 1 0 51 4.1
## 4 4 4.3 1 0 40 4.5
## 5 5 4.4 0 0 31 4.8
## 6 6 4.2 1 0 62 4.4
## 7 7 4 0 0 33 4.4
## 8 8 3.4 1 0 51 3.4
## 9 9 4.5 0 0 33 4.8
## 10 10 3.9 0 0 47 4
## # ... with 453 more rows, and 40 more variables: prof_ave_beauty_rating <dbl>,
## # female <dbl>, formal <dbl>, non_native_english <dbl>, tenure_track <dbl>,
## # lower <dbl>, multiple_prof <dbl>, one_credit <dbl>,
## # eval_response_rate <dbl>, students <dbl>, class1 <dbl>, class2 <dbl>,
## # class3 <dbl>, class4 <dbl>, class5 <dbl>, class6 <dbl>, class7 <dbl>,
## # class8 <dbl>, class9 <dbl>, class10 <dbl>, class11 <dbl>, class12 <dbl>,
## # class13 <dbl>, class14 <dbl>, class15 <dbl>, class16 <dbl>, class17 <dbl>,
## # class18 <dbl>, class19 <dbl>, class20 <dbl>, class21 <dbl>, class22 <dbl>,
## # class23 <dbl>, class24 <dbl>, class25 <dbl>, class26 <dbl>, class27 <dbl>,
## # class28 <dbl>, class29 <dbl>, class30 <dbl>
4.1.2 Checking for missing values
Next I checked for missing data. While importing the data I notice there were 30 dummy variables used to identify the class being evaluated. I wanted to look in a bit more detail at potential missing values across the dummy variables. So, first I removed the dummy variables and looked at all the other variables. From the missing values check below it looks like this dataset has already to processed as not NA
s have been identified.
::vis_miss(teaching_eval_focus %>%
visdatselect(-starts_with("class")))
At first glance the there are no NA
s identified for the dummy variables, for all pairs of observations of observations and dummy variables either a 1
or a 0
is recorded.
::vis_miss(teaching_eval_focus %>%
visdatselect(starts_with("class")))
However, if looking at these dummy variables in more detail it is clear that a lot of data is missing. By summing the all the dummy variables for a given observation, we can see if any classes are recorded for each observation. I would expect this to be 1
where a class is recorded and 0
where a class is not recorded. Hence, in this context a 0
actually corresponds to a missing value and in the plot below we can see that for approximately two thirds of observations a class is not recorded.
# check if each evaluation has a class (1-30) associated with it
# by processing the dummy variables
<- teaching_eval_focus %>%
dummy_var_row_sum select(class1:class30) %>%
rowSums() %>%
as_tibble() %>%
rename(class_indentified = value) # not all evaluations have a class # associated with them
::vis_miss(dummy_var_row_sum %>%
visdatna_if(0))
Working with the dummy variables in the analysis and modeling below would entail dropping two thirds observations and n
= 463. So, although I recognized the course identifier dummy variables as potentially important predictors of course_evaluation
I dropped them from the analysis, rather than loose so many observations from the dataset.
<- teaching_eval_focus %>%
teaching_eval_clean select(-starts_with("class"))
glimpse(teaching_eval_clean)
## Rows: 463
## Columns: 16
## $ prof_number <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, ~
## $ course_evaluation <dbl> 4.3, 4.5, 3.7, 4.3, 4.4, 4.2, 4.0, 3.4, 4.5, 3.~
## $ tenured <dbl> 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,~
## $ minority <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,~
## $ age <dbl> 36, 59, 51, 40, 31, 62, 33, 51, 33, 47, 35, 37,~
## $ prof_ave_rating <dbl> 4.7, 4.6, 4.1, 4.5, 4.8, 4.4, 4.4, 3.4, 4.8, 4.~
## $ prof_ave_beauty_rating <dbl> 0.2015666, -0.8260813, -0.6603327, -0.7663125, ~
## $ female <dbl> 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0,~
## $ formal <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
## $ non_native_english <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,~
## $ tenure_track <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,~
## $ lower <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
## $ multiple_prof <dbl> 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,~
## $ one_credit <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
## $ eval_response_rate <dbl> 55.81395, 85.00000, 100.00000, 86.95652, 87.500~
## $ students <dbl> 43, 20, 55, 46, 48, 282, 41, 41, 60, 19, 25, 34~
4.2 Exploratory data analysis
4.2.1 The response variable
So, I started the exploratory data analysis quickly looking at summary statistics for course_evaluation
, which is the response variable in this exercise. This first things I noted was that the both the mean and median course_evaluation
score is 4, and the mini-histogram indicates a potential left skew of the data. So, it appears that the course_evaluation
scores tend to be towards the higher end of the 5 point scale. I want to look at this in a little more detail, so moved on to plotting a full histogram.
::skim(teaching_eval_clean, course_evaluation) skimr
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| course_evaluation | 0 | 1 | 4 | 0.55 | 2.1 | 3.6 | 4 | 4.4 | 5 | ▁▂▆▇▅ |
<- nrow(teaching_eval_clean)
num_evals <- nrow(
num_evals_three_plus $course_evaluation >= 3,]
teaching_eval_clean[teaching_eval_clean
)
<- round((num_evals_three_plus / num_evals) * 100, 0)
perc_evals_three_plus
<- glue::glue("{perc_evals_three_plus}% of courses have
anno_text an average evaluation
score of 3 or above")
# create plot base
<- ggplot(data = teaching_eval_clean,
p mapping = aes(x = course_evaluation)) +
# add a reference line
geom_vline(xintercept = 3, colour = "grey70", size = 1.5, linetype = "longdash") +
geom_rect(xmin = 3, xmax = 5.05, ymin = 0 , ymax = 40, fill = "grey92", alpha = 0.05) +
# add annotation
geom_label(x = 3.2, y = 35,
label = anno_text, size = 3, colour = "grey20") +
# create histogram with minimal styling
geom_minimal_hist(ylim=c(0,40),
binwidth = 0.1) +
# adjust x axis labels for readability
scale_x_continuous(breaks = seq(0,5,0.5)) +
expand_limits(x = 0) +
# adjust y axis labels for readability
scale_y_continuous(breaks = seq(0,40,10)) +
# add plot labels
labs(title = "Course evaluation scores tend to be towards\nthe top end of the five point scale\n",
x = "\nAverage course evaluation score\n(given by students)",
y = "Number of courses\n")
So, it full plot there is some indication of left skew, in the form of the longer tail to the left of the plot. Also, evident is the fact there is a hard limit (of 5) on the maximum course_evaluation
score, which accentuates the impression of the skew.
p
So, I took a quick look at course_evaluation
centered around the mean. This looks like a close enough approximation to the normal distribution to me. So, ahead of modeling I don’t need to think about transforming course_evaluation
scores to address the limited amount of skew evident in the plot. However, it might be worth centering the course_evaluation
scores as this might help make the regression coefficients easier to interpret. But I’ll come back to that later on.
= mean(teaching_eval_clean$course_evaluation)
course_evaluation_mean
<- ggplot(data = teaching_eval_clean,
p mapping = aes(x = course_evaluation - course_evaluation_mean)) +
geom_minimal_hist(ylim=c(0,40),
binwidth = 0.1)
+
p labs(title = "Centering course evaluation scores makes it\neasier to see the extent of skew\n",
x = "\nCentred average course evaluation score",
y = "Number of courses\n")
4.2.2 Potential explanatory variables
So, I moved on to looking at the potential explanatory variables within the dataset. Remembering that when I imported the data I had thought about the potential explanatory variables as belonging to two distinct groups: (1) Professor related variables; and, (2) course related variables. So, below I take at look at these two groups of variables in turn.
4.2.3 Relationships between variables
<IP_ADDRESS> Relationships between continuous variables
Look for colinearity of predictors
<IP_ADDRESS> Relationships between discrete variables and continuous variables
Looking for interactions
4.3 Modelling
Remember to split into test and training sets


Gillespie Cemetery
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
This old Cemetery is located (to be completed soon)
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Table of Contents of
Photographs
1.
2.
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Gillespie Cemetery
Arrow Points
Approximately North
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Approximate length of
cemetery (east to west) is ?? feet
Approximate width of
cemetery (north to south) is ?? feet
Top of above chart is North
The cemetery is oriented
almost exactly on North/South (length) and East/West (width) axes
Names listed by Stone Number
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Can t
read |
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In
Memory of Sarah (can t read the rest of the inscription) |
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KSusannah
Jefferies d May 22, 1884 aged 55 yr, 5 mo, 28 da |
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4 |
Markie
dau of HP & KG Gillespie 1895 1896 |
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John
Gillespie 1844 1905 and Harriet C. his wife 1845 1929 |
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Annkie
Gillespie Norris 1881 1951 |
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John
H. Gillespie 1872 1934 and Amanda M. 1880 - 1960 |
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Mary
J. wife of (can t read) d 1887 |
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Can t
read |
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Readable
but in a bush |
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Annie
Jeffries d May 29, 1885 aged 85 yr, 1 mo, 25 da and Joseph Jeffries d May 3,
1851 aged 54 yr, 8 mo, 22 da |
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Eliza
dau of J & A Jefferies d May 15, 1846 |
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Infant
dau of ? & R Jefferies d 1851 |
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Sarah
wife of C. Hart b. Feb 22, 1820 d.
July 2, 1904 |
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Cephag
Hart d. Sept 29, 1885 aged 70 yr & 27 da |
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Can t
read |
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Brandy
Jo Thompson Dec 29, 1977 Sept 14, 1978 |
Inventoried, Measured, and
Charted September 12, 2003 by R.E. Harrington
Two
Latitude/Longitude/elevation measurements were made for the Giillespie
Cemetery. One GPS reading was made on
the headstone of Sarah wife of C. Hart (Feb 22, 1820 - July 2, 1904). Accuracy = +/- 41 feet; elevation corrected to ground level = 1349
feet; latitude = N39o 45.190 ; longitude = W081o
03.451 . A second reading was made on
the headstone of John H. & Amanda M Gillespie s tombstone: Accuracy = +/- 16 feet; elevation corrected to ground level = 1321
feet; latitude = N39o 45.197 ; longitude = W081o 03.444 .
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