Custom partition key
CREATE TABLE ... ENGINE = MergeTree
ORDER BY CounterID, Date
PARTITION BY toStartOfWeek(Date)
SETTINGS index_granularity = 8192
Geospatial functions:
— pointInPolygon;
— pointInEllipses;
— greatCircleDistance;
SELECT pointInPolygon((lat, lon),
[(6, 0), (8, 4), (5, 8), (0, 2), ...])
Integration with CatBoost machine learning models
SELECT modelEvaluate('purchase', f1, ... fn)
Working with date and time intervals:
SELECT date + INTERVAL 1 MONTH
— support for time zones with fractional offset from UTC;
— extended supported time range up to year 2105.
— timeDiff, add/subtractInterval functions.
— EXTRACT(... FROM ...) operator.
Proper support for timezones
+ toTimeZone function
Q: What is the result of this query?
SELECT toStartOfHour(
toDateTime('2017-12-12 01:02:03'),
'Asia/Shanghai') AS t
Distributed DDL queries
CREATE TABLE ... ON CLUSTER cluster
* doesn't work with cross-over replication;
* doesn't work properly with NAT;
Support for storing multidimensional arrays in tables.
CREATE TABLE t
(
x Array(Array(String)),
z Nested(x Array(String), ...)
)
ENGINE = MergeTree ORDER BY x
Extended array functions:
— arrayConcat, arraySlice;
— arrayPushFront, arrayPushBack, arrayPopFront, arrayPopBack;
— arrayReduce, arraySort, arrayReverseSort, arrayStringConcat;
— arrayHasAll, arrayHasAny, arrayIntersect, arrayCumSum;
Improvements in dictionaries
— introspection of dictionaries:
CREATE DATABASE dictionaries ENGINE = Dictionary
— invalidate_query for updating dictionaries;
— update_field for partial dictionary reload;
— SYSTEM RELOAD DICTIONARY;
Sessions in HTTP interface:
curl 'http://host:port/?session_id=123&session_timeout=60' \
-d 'CREATE TEMPORARY TABLE t'
Native support for SSL in HTTP and TCP servers.
Node to node traffic encryption.
Sending progress info in HTTP headers.
Distributed cluster copier
— for copying between different clusters
or resharding within single cluster.
Using index for IN (subquery).
SELECT ...
WHERE key IN
(
SELECT ...
)
Using index for IN with tuples.
SELECT ...
WHERE (key1, key2) IN
(
SELECT ...
)
Querying foreign databases: MySQL, ODBC.
Through table functions mysql, odbc:
SELECT ...
FROM mysql(
'host:port',
'db', 'table',
'user', 'password')
Or through CREATE TABLE ... ENGINE = MySQL(...)
Improvements in SQL compatibility:
— Don't return empty resultset in case of aggregating by empty data.
— limited support for table aliases and qualified names;
— name quoting in ANSI style;
— type aliases for compatibility;
— INSERT SELECT match columns by positions;
— type conversions in INSERT SELECT;
— type conversions in UNION ALL;
— NULLS FIRST/LAST for ORDER BY.
ODBC driver:
+ Tableau basically works;
− but we still have many obstacles;
Support for NULLs.
Remains:
— NULLs in external dictionaries;
— NULLs in "transform" function;
O_DIRECT for large background merges.
Support for complex DEFAULT expressions for semistructured formats JSONEachRow, TSKV.
Support for Protobuf and Parquet formats for I/O.
Predicate push down from WHERE into subqueries.
Support for subqueries in VIEWs.
Support for short-circuit evaluation of expressions.
SELECT number != 0
? intDiv(10, number)
: 0
FROM system.numbers LIMIT 10
WITH ROLLUP and WITH CUBE modifiers for GROUP BY.
Taking advantage of primary key for ORDER BY and GROUP BY.
Allow to use custom compression algorithms
for each column.
Secondary index data structures for data skipping.
Allow to store data in different block devices on single server.
Allow to create external dictionaries through DDL query.
LDAP integration for managing user access rights.
Support for UPDATE/DELETE.
Resource pools; resource sharing while executing multiple concurrent queries.
Proper (SQL compatible) JOIN syntax.
"Cloud" tables.
Web site: https://clickhouse.com/
Google groups: https://groups.google.com/forum/#!forum/clickhouse
Maillist<EMAIL_ADDRESS>Telegram chat: https://telegram.me/clickhouse_en and https://telegram.me/clickhouse_ru (now with over 1500 members)
GitHub: https://github.com/ClickHouse/ClickHouse/
Twitter: https://twitter.com/ClickHouseDB
+ meetups. Moscow, Saint-Petersburg, Novosibirsk, Ekaterinburg, Minsk, Nizhny Novgorod, Berlin, Palo Alto, Beijing.


Definition 6.2.1. Interest.
An amount of money, usually a percentage of the amount owed, which is added on to a debt which has not been paid. Usually interest is added on once a month or once a year. The percentage is called the interest rate.
An amount of money, usually a percentage of the amount owed, which is added on to a debt which has not been paid. Usually interest is added on once a month or once a year. The percentage is called the interest rate.


Hi,
We have to develop a setup to monitor energy-, heating- and water-consumption of a large university-building on a fancy dashboard in real-time, so I came to OEM, and have the following questions:
-Honestly, what do you think about OEM's long-term operation, is it as stable as usual industrial data-loggers, or does it require regular resets / new installations / maintenance? Could this kind of maintenance be done easily by the usual IT-guys, or do you think an "industrial" of-the-shelf solution would be the better choice here? (I personally have not much IT-knowledge, and will be there only for installation of the system, but not afterwards.)
-as there are distances of about 20m and concrete walls between the different meters, I doubt RF is a good idea (same for serial connection). Did I get it right, that I could use one EmonTx-device for every of the 3 meters (probably all connectet via impulse-output), and connect these 3 EmonTX-devices directly over Ethernet to the webserver with EMONCMS, without an additional RaspberryPI? Again, focus is on stable and reliable operation.
-do you have examples for nice dashboards, so I can see what is possible? As a focus of the project is on communication-design, it should be quite freely configurable.
-last but not least, do you think it is a good idea to use OEM if we have only very basic linux-skills and IT-knowledge, and can't write any code ourselfs but just follow instructions? Or do you know any of-the-shelf software-solutions that can do nice dashboards?
Thanks!
Re: OEM in big building: RF reach? long-term maintainability? etc.
You may find example dashboards here:
http://openenergymonitor.org/emon/forum/7
http://openenergymonitor.blogspot.fr/2012/01/openenergymonitor-in-france...
http://openenergymonitor.org/emon/sites/default/files/emoncms%20dashboar...
http://openenergymonitor.blogspot.fr/2012/09/display-live-energy-data-on...
http://harizanov.com/2012/04/attiny84-remote-temperature-sensor-with-rfm...
Re: OEM in big building: RF reach? long-term maintainability? etc.
Thanks for the examples!
Does anyone have information on my other questions?


SHAPEES, A complicated name for an easy-to-use system...
Early Years Programming Design and Scheduler Tool
In order to solve the Kids’ Uni’s issues, all operations and data collected in the process of following child’s learning development within Kids’ Uni Child Centre will be managed with a centralized system, which will improve the workflow, enable easier access to data, and simplify the process of creating the documentation and reporting.
Benefits of our solution include, but are not limited to:
- A streamlined alternative to excessive paperwork and documentation.
- Productive scheduling system
- Consistency amongst children programming and documentation
- Creates a standard through templates of reporting and documentation
- Best suited to the Kids’ Uni’s requirements and needs
- Providing Director’s of childcare a more effective and efficient method
- A solution that allows educators to focus more on educating children, rather than filling out paperwork
- Bringing the childcare industry into the digital age


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viu path/to/file- Render an image or GIF from the internet using `curl`:
curl -s https://example.com/image.png | viu -- Render an image with a transparent background:
viu -t path/to/file- Render an image with a specific width and height in pixels:
viu -w width -h height path/to/file- Render an image or GIF and display its file name:
viu -n path/to/file


Marc is a Full Stack Web Developer who is skilled in building RESTful APIs.
Alex is a Full Stack Web Developer who designed and helped build the front-end app
Michael is a Full Stack Web Developer who helped build the front-end app.
Chris is Web Developer who created the marketing page for the DevDesk app.


Hey Username! You have no projects,create some !😀
Hey Username! This is the list of your projects
Keep up the good work 🚀
Keep up the good work 🚀
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Admin :


Contents
- Load dataset with images and print descriptives
- Collect some variables we need
- Plot the ratings
- Prediction
- Run the Base model
- Plot the results
- Summarize within-person classification accuracy
- Visualize weight map
- Bootstrap weights: Get most reliable weights and p-values for voxels
- Try normalizing/scaling data
- Try selecting an a priori 'network of interest'
- re-run prediction and plot
- Other ideas
Dependencies:
% Matlab statistics toolbox % Matlab signal processing toolbox % Statistical Parametric Mapping (SPM) software https://www.fil.ion.ucl.ac.uk/spm/ % For full functionality, the full suite of CANlab toolboxes is recommended. See here: Installing Tools
% About this dataset % ------------------------------------------------------------------------- % % This dataset includes 33 participants, with brain responses to six levels % of heat (non-painful and painful). % % Aspects of this data appear in these papers: % Wager, T.D., Atlas, L.T., Lindquist, M.A., Roy, M., Choong-Wan, W., Kross, E. (2013). % An fMRI-Based Neurologic Signature of Physical Pain. The New England Journal of Medicine. 368:1388-1397 % (Study 2) % % Woo, C. -W., Roy, M., Buhle, J. T. & Wager, T. D. (2015). Distinct brain systems % mediate the effects of nociceptive input and self-regulation on pain. PLOS Biology. 13(1): % e1002036. doi:10.1371/journal.pbio.1002036 % % Lindquist, Martin A., Anjali Krishnan, Marina López-Solà, Marieke Jepma, Choong-Wan Woo, % Leonie Koban, Mathieu Roy, et al. 2015. ?Group-Regularized Individual Prediction: % Theory and Application to Pain.? NeuroImage. % http://www.sciencedirect.com/science/article/pii/S1053811915009982. %
Load dataset with images and print descriptives
This dataset is shared on figshare.com, under this link: https://figshare.com/s/ca23e5974a310c44ca93
Here is a direct link to the dataset file with the fmri_data object: https://ndownloader.figshare.com/files/12708989
The key variable is image_obj This is an fmri_data object from the CANlab Core Tools repository for neuroimaging data analysis. See https://canlab.github.io/
image_obj.dat contains brain data for each image (average across trials) image_obj.Y contains pain ratings (one average rating per image)
image_obj.additional_info.subject_id contains integers coding for which Load the data file, downloading from figshare if needed
fmri_data_file = which('bmrk3_6levels_pain_dataset.mat'); if isempty(fmri_data_file) % attempt to download disp('Did not find data locally...downloading data file from figshare.com') fmri_data_file = websave('bmrk3_6levels_pain_dataset.mat', 'https://ndownloader.figshare.com/files/12708989'); end load(fmri_data_file); descriptives(image_obj);
Source: BMRK3 dataset from CANlab, PI Tor Wager Data: .dat contains 6 images per participant, activation estimates during heat on L arm from level 1(44.3 degrees C) to level 6(49.3), in 1 degree increments. ____________________________________________________________________________________________________________________________________________ Wager, T.D., Atlas, L.T., Lindquist, M.A., Roy, M., Choong-Wan, W., Kross, E. (2013). An fMRI-Based Neurologic Signature of Physical Pain. The New England Journal of Medicine. 368:1388-1397 (Study 2) Woo, C. -W., Roy, M., Buhle, J. T. & Wager, T. D. (2015). Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain. PLOS Biology. 13(1): e1002036. doi:10.1371/journal.pbio.1002036 Lindquist, Martin A., Anjali Krishnan, Marina López-Solà, Marieke Jepma, Choong-Wan Woo, Leonie Koban, Mathieu Roy, et al. 2015. “Group-Regularized Individual Prediction: Theory and Application to Pain.” NeuroImage. http://www.sciencedirect.com/science/article/pii/S1053811915009982. ____________________________________________________________________________________________________________________________________________ Summary of dataset ______________________________________________________ Images: 198 Nonempty: 198 Complete: 198 Voxels: 223707 Nonempty: 223707 Complete: 223707 Unique data values: 35583281 Min: -11.529 Max: 7.862 Mean: -0.004 Std: 0.227 Percentiles Values ___________ __________ 0.1 -1.529 0.5 -0.88055 1 -0.67632 5 -0.31891 25 -0.087519 50 0.00034864 75 0.087461 95 0.29757 99 0.60449 99.5 0.77676 99.9 1.3177
Collect some variables we need
% subject_id is useful for cross-validation % % this used as a custom holdout set in fmri_data.predict() below will % implement leave-one-subject-out cross-validation subject_id = image_obj.additional_info.subject_id; % ratings: reconstruct a subjects x temperatures matrix % so we can plot it % % The command below does this only because we have exactly 6 conditions % nested within 33 subjects and no missing data. ratings = reshape(image_obj.Y, 6, 33)'; % temperatures are 44 - 49 (actually 44.3 - 49.3) in order for each person. temperatures = image_obj.additional_info.temperatures;
Plot the ratings
-------------------------------------------------------------------------
create_figure('ratings'); lineplot_columns(ratings, 'color', [.7 .3 .3], 'markerfacecolor', [1 .5 0]); xlabel('Temperature'); ylabel('Rating');
Prediction
There are many options
% Base model: Linear, whole brain prediction % % Outcome distribution: % Continuous: Regression (LASSO-PCR) Categorical/2 choice: SVM, logistic regression % % Model Options: % - Feature selection (part of the brain) % - Feature integration (new features) % % Testing options: % - Input data (what level) % - Cross-validation % - What testing metric (classification accuracy? RMSE?) and at what level % (single-trial, condition averages, one-map-per-subject) % % Inference options: % - Component maps, voxel-wise maps % - Thresholding (Bootstrapping, Permutation) % - Global null hypothesis testing / sanity checks (Permutation)
Run the Base model
% relevant functions: % predict method (fmri_data) % predict_test_suite method (fmri_data) % Define custom holdout set. If we use subject_id, which is a vector of % integers with a unique integer per subject, then we are doing % leave-one-subject-out cross-validation. % let's build five-fold cross-validation set that leaves out ALL the images % from a subject together. That way, we are always predicting out-of-sample % (new individuals). If not, dependence across images from the same % subjects may invalidate the estimate of predictive accuracy. holdout_set = zeros(size(subject_id)); % holdout set membership for each image n_subjects = length(unique(subject_id)); C = cvpartition(n_subjects, 'KFold', 5); for i = 1:5 teidx = test(C, i); % which subjects to leave out imgidx = ismember(subject_id, find(teidx)); % all images for these subjects holdout_set(imgidx) = i; end algoname = 'cv_lassopcr'; % cross-validated penalized regression. Predict pain ratings [cverr, stats, optout] = predict(image_obj, 'algorithm_name', algoname, 'nfolds', holdout_set);
Cross-validated prediction with algorithm cv_lassopcr, 5 folds Completed fit for all data in: 0 hours 0 min 5 secs Fold 1/5 done in: 0 hours 0 min 4 sec Fold 2/5 done in: 0 hours 0 min 4 sec Fold 3/5 done in: 0 hours 0 min 3 sec Fold 4/5 done in: 0 hours 0 min 3 sec Fold 5/5 done in: 0 hours 0 min 4 sec Total Elapsed Time = 0 hours 0 min 22 sec
Plot the results
% Plot cross-validated predicted outcomes vs. actual % Critical fields in stats output structure: % stats.Y = actual outcomes % stats.yfit = cross-val predicted outcomes % pred_outcome_r: Correlation between .yfit and .Y % weight_obj: Weight map used for prediction (across all subjects). % Continuous outcomes: create_figure('scatterplots', 1, 2); plot(stats.yfit, stats.Y, 'o') axis tight refline xlabel('Predicted pain'); ylabel('Observed pain'); % Consider that each subject has their own line: for i = 1:n_subjects YY{i} = stats.Y(subject_id == i); Yfit{i} = stats.yfit(subject_id == i); end subplot(1, 2, 2); line_plot_multisubject(Yfit, YY, 'nofigure'); xlabel('Predicted pain'); ylabel('Observed pain'); axis tight % Yfit is a cell array, one cell per subject, with fitted values for that subject
average within-person r = 0.60, t( 32) = 7.93, p = 0.000000, num. missing: 0
Summarize within-person classification accuracy
Turn into classification:
% If the stim intensity increases by 1 degree, how often do we correctly % predict an increase? diffs = cellfun(@diff, Yfit, 'UniformOutput', false); % successive increases in predicted values with increases in stim intensity % Subjects (rows) x comparisons (cols), each comparison 1 degree apart diffs = cat(2, diffs{:})'; acc_per_subject = sum(diffs > 0, 2) ./ size(diffs, 2); acc_per_comparison = (sum(diffs > 0) ./ size(diffs, 1))'; cohens_d_per_comparison = (mean(diffs) ./ std(diffs))'; fprintf('Mean acc is : %3.2f%% across all successive 1 degree increments\n', 100*mean(acc_per_subject)); % Create and display a table: comparison = {'45-44' '46-45' '47-46' '48-47' '49-48'}'; results_table = table(comparison, acc_per_comparison, cohens_d_per_comparison); disp(results_table) % 45-44 degrees, 46-45, 47-46, etc.
Mean acc is : 73.94% across all successive 1 degree increments comparison acc_per_comparison cohens_d_per_comparison __________ __________________ _______________________ '45-44' 0.48485 -0.0587 '46-45' 0.81818 0.76512 '47-46' 0.84848 0.93114 '48-47' 0.9697 1.432 '49-48' 0.57576 0.37981
Visualize weight map
w = stats.weight_obj; % This is an image object that we can view, etc. orthviews(w) create_figure('montage'); o2 = montage(w); o2 = title_montage(o2, 5, 'Predictive model weights');
SPM12: spm_check_registration (v6245) 17:48:58 - 03/08/2018 ======================================================================== Display /Users/torwager/Documents/GitHub/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 Grouping contiguous voxels: 3 regions ans = 1×1 cell array {1×3 region} Setting up fmridisplay objects This takes a lot of memory, and can hang if you have too little. Grouping contiguous voxels: 3 regions sagittal montage: 4350 voxels displayed, 219357 not displayed on these slices sagittal montage: 4292 voxels displayed, 219415 not displayed on these slices sagittal montage: 3990 voxels displayed, 219717 not displayed on these slices axial montage: 32223 voxels displayed, 191484 not displayed on these slices axial montage: 34538 voxels displayed, 189169 not displayed on these slices
Bootstrap weights: Get most reliable weights and p-values for voxels
% Re-run prediction, adding a 'bootstrap' flag. % For a final analysis, at least 5K bootstrap samples is a good idea % (This is not run in the example; see help fmri_data.predict for how to % run bootstrapping)
Try normalizing/scaling data
% z-score every image, removing the mean and dividing by the std. % Remove some scaling noise, but also some signal... image_obj = rescale(image_obj, 'zscoreimages'); % re-run prediction and plot [cverr, stats, optout] = predict(image_obj, 'algorithm_name', algoname, 'nfolds', holdout_set); create_figure('scatterplots'); for i = 1:n_subjects YY{i} = stats.Y(subject_id == i); Yfit{i} = stats.yfit(subject_id == i); end line_plot_multisubject(Yfit, YY, 'nofigure'); xlabel('Predicted pain'); ylabel('Observed pain'); axis tight fprintf('Overall prediction-outcome correlation is %3.2f\n', stats.pred_outcome_r) figure; o2 = montage(stats.weight_obj); o2 = title_montage(o2, 5, 'Predictive model weights');
Cross-validated prediction with algorithm cv_lassopcr, 5 folds Completed fit for all data in: 0 hours 0 min 5 secs Fold 1/5 done in: 0 hours 0 min 4 sec Fold 2/5 done in: 0 hours 0 min 3 sec Fold 3/5 done in: 0 hours 0 min 3 sec Fold 4/5 done in: 0 hours 0 min 3 sec Fold 5/5 done in: 0 hours 0 min 4 sec Total Elapsed Time = 0 hours 0 min 22 sec average within-person r = 0.59, t( 32) = 13.18, p = 0.000000, num. missing: 0 Overall prediction-outcome correlation is 0.59 Setting up fmridisplay objects This takes a lot of memory, and can hang if you have too little. Grouping contiguous voxels: 3 regions sagittal montage: 4350 voxels displayed, 219357 not displayed on these slices sagittal montage: 4292 voxels displayed, 219415 not displayed on these slices sagittal montage: 3990 voxels displayed, 219717 not displayed on these slices axial montage: 32223 voxels displayed, 191484 not displayed on these slices axial montage: 34538 voxels displayed, 189169 not displayed on these slices
Try selecting an a priori 'network of interest'
% re-load load('bmrk3_6levels_pain_dataset.mat', 'image_obj') % load an a priori pain-related mask and apply it mask = fmri_data(which('pain_2s_z_val_FDR_05.img.gz')); % in Neuroimaging_Pattern_Masks repository image_obj = apply_mask(image_obj, mask); % show mean data with mask m = mean(image_obj); figure; o2 = montage(m); o2 = title_montage(o2, 5, 'Mean image data, masked');
Using default mask: /Users/torwager/Documents/GitHub/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask.nii loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 1409312 bytes Loading image number: 1 Elapsed time is 0.019081 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Setting up fmridisplay objects This takes a lot of memory, and can hang if you have too little. Grouping contiguous voxels: 27 regions sagittal montage: 1088 voxels displayed, 47895 not displayed on these slices sagittal montage: 1171 voxels displayed, 47812 not displayed on these slices sagittal montage: 980 voxels displayed, 48003 not displayed on these slices axial montage: 7496 voxels displayed, 41487 not displayed on these slices axial montage: 7950 voxels displayed, 41033 not displayed on these slices
re-run prediction and plot
[cverr, stats, optout] = predict(image_obj, 'algorithm_name', algoname, 'nfolds', holdout_set); create_figure('scatterplots'); for i = 1:n YY{i} = stats.Y(subject_id == i); Yfit{i} = stats.yfit(subject_id == i); end line_plot_multisubject(Yfit, YY, 'nofigure'); xlabel('Predicted pain'); ylabel('Observed pain'); axis tight fprintf('Overall prediction-outcome correlation is %3.2f\n', stats.pred_outcome_r) figure; o2 = montage(stats.weight_obj); o2 = title_montage(o2, 5, 'Predictive model weights');
Cross-validated prediction with algorithm cv_lassopcr, 5 folds Completed fit for all data in: 0 hours 0 min 1 secs Fold 1/5 done in: 0 hours 0 min 1 sec Fold 2/5 done in: 0 hours 0 min 1 sec Fold 3/5 done in: 0 hours 0 min 1 sec Fold 4/5 done in: 0 hours 0 min 1 sec Fold 5/5 done in: 0 hours 0 min 1 sec Total Elapsed Time = 0 hours 0 min 4 sec average within-person r = 0.60, t( 32) = 6.59, p = 0.000000, num. missing: 0 Overall prediction-outcome correlation is 0.60 Setting up fmridisplay objects This takes a lot of memory, and can hang if you have too little. Grouping contiguous voxels: 27 regions sagittal montage: 1088 voxels displayed, 47895 not displayed on these slices sagittal montage: 1171 voxels displayed, 47812 not displayed on these slices sagittal montage: 980 voxels displayed, 48003 not displayed on these slices axial montage: 7496 voxels displayed, 41487 not displayed on these slices axial montage: 7950 voxels displayed, 41033 not displayed on these slices
Other ideas
There are many ways to build from this basic analysis.
% Data % ------------ % Examine/clean outliers % Transformations of data: Scaling/centering to remove global mean, % ventricle mean % Identify unaccounted sources of variation/subgroups (experimenter sex, % etc.) % Features % ------------ % A priori regions / meta-analysis % Feature selection in cross-val loop: Univariate, recursive feature elim


Bayelsa election: Jonathan’s kinsmen complain of late arrival of INEC workers, materials
by Tony OkaforTony Okafor, Awka
The kinsmen of a former president, Good luck Jonathan in Otueke, Ogbai Local Government Area of Bayelsa State have decried the late arrival of the officials of the Independent National Electoral Commission.
As of the time of sending report no officials of the commission could be sited Otasi ward 13 voting centre.
A voter, Cyril Nemete, who spoke with our correspondent expressed dissatisfaction over the development.
“This is a rigging strategy, what will say was the reason for this, our people who came out to cast their votes, including the former president Jonathan, had gone back to their houses,” he states.
Another resident of the area, who gave his name as Charles, described the act as marginalisation.
“They are doing us this just because our son is no longer in power. Yenagoa, where they are distributing the materials from, is just about 30 minutes drive; why can’t these INEC people be here before 8 am?
Generally, the people of the area conducted themselves peacefully.
Heavy security presence was noticed in the area, but they were not molesting citizens.


Please adjust your screen until the red rectangle is fully in view.
To zoom in/out
Shortcut on Mac: command + '+'/'-'
Shortcut on Windows: Ctrl + '+'/'-'
Detailed help page http://learnhowtozoom.com/


Screenshot Codes
How can I use?
Codes on this repository are for taking screenshots of code images.
Just clone the repository, or download and open with your favourite editor.
How can I take screenshot?
-
Windows(10+):Windows+Shift+s
-
Mac:shift + command + 5
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Linux&FreeBSD and variants: You know your tools.
What they look like
Here are some sample screenshots:
(Visual studio Code "Night Cruise" theme)


{{ tracks.artist }}
{% if tracks.album and tracks.album != '' %} - {{ tracks.album }}{% endif %}
{% endfor %}
What is this?
Every month I make a playlist full of the music I've really enjoyed that month. I try and not repeat songs month to month, but sometimes it can't be helped. Credit to Katie DeCorah's Spotify-to-Jekyll scraper for making this so much easier!
{% endif %}
{% endfor %}


from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from numpy import asarray
from numpy import meshgrid
from numpy import linspace
from numpy import amax
from numpy import amin
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
from compas.geometry import scalarfield_contours_numpy
__all__ = [
'mesh_isolines_numpy',
'mesh_contours_numpy',
]
[docs]def mesh_isolines_numpy(mesh, attr_name, N=50):
"""Compute the isolines of a specified attribute of the vertices of a mesh.
Parameters
----------
mesh : Mesh
A mesh object.
attr_name : str
The name of the vertex attribute.
N : int (50)
The density of the isolines.
Default is ``50``.
Returns
-------
tuple
A tuple of a list of levels and a list of isolines.
The list of levels contains the z-values at each of the isolines.
Each isoline is a list of paths, and each path is a list polygons.
"""
xy = [mesh.vertex_coordinates(key, 'xy') for key in mesh.vertices()]
s = [mesh.vertex[key][attr_name] for key in mesh.vertices()]
return scalarfield_contours_numpy(xy, s, N)
[docs]def mesh_contours_numpy(mesh, levels=None, density=100):
"""Compute the contours of the mesh.
Parameters
----------
mesh : Mesh
The mesh object.
N : int, optional
The density of the contours.
Default is ``50``.
Returns
-------
tuple
A tuple of a list of levels and a list of contours.
The list of levels contains the z-values at each of the contours.
Each contour is a list of paths, and each path is a list polygons.
Notes
-----
The contours are defined as the isolines of the z-coordinates of the vertices
of the mesh.
"""
xy = [mesh.vertex_attributes(key, 'xy') for key in mesh.vertices()]
z = [mesh.vertex_attribute(key, 'z') for key in mesh.vertices()]
xy = asarray(xy)
z = asarray(z)
x = xy[:, 0]
y = xy[:, 1]
if not levels:
levels = 50
X, Y = meshgrid(linspace(amin(x), amax(x), 2 * density),
linspace(amin(y), amax(y), 2 * density))
Z = griddata((x, y), z, (X, Y), method='cubic')
fig = plt.figure()
ax = fig.add_subplot(111, aspect='equal')
c = ax.contour(X, Y, Z, levels)
contours = [0] * len(c.collections)
levels = c.levels
for i, coll in enumerate(iter(c.collections)):
paths = coll.get_paths()
contours[i] = [0] * len(paths)
for j, path in enumerate(iter(paths)):
polygons = path.to_polygons()
contours[i][j] = [0] * len(polygons)
for k, polygon in enumerate(iter(polygons)):
contours[i][j][k] = polygon
plt.close(fig)
return levels, contours
# ==============================================================================
# Main
# ==============================================================================
if __name__ == "__main__":
pass


Reports the usage of a package object.
Package objects are deprecated in Scala 3 and are going to be phased out. Consider using top level definitions or regular objects instead.
See Package objects in Dotty.


This paragraph will be in red and center-aligned.
This paragraph will be in red, center-aligned and large font size


Projects The following are projects that have and am working on.


fun evaluate(rule: AssociationRule<*>): Double
Overrides Operator.evaluate
Calculates the heuristic value of a specific rule.
Return The heuristic value, which has been calculated


- occ - Variable in class com.wsfmn.habit.Habit
-
- OfflineController - Class in com.wsfmn.controller
-
Created by Fredric on 2017-10-16.
- OfflineController() - Constructor for class com.wsfmn.habitcontroller.OfflineController
-
- OfflineController.GetHabitHistory - Class in com.wsfmn.controller
-
Getting Habit events for Habit History in HabitHistory.sav
- OfflineController.GetHabitList - Class in com.wsfmn.controller
-
Retrieve the Habit list Locally from HabitList.sav
- OfflineController.GetUserProfile - Class in com.wsfmn.controller
-
Getting the user's profilename from Profile.sav
- OfflineController.StoreHabitHistory - Class in com.wsfmn.controller
-
Storing habit events from Habit History to HabitHistory.sav
- OfflineController.StoreHabitList - Class in com.wsfmn.controller
-
Store Habit list locally to HabitList.sav
- OfflineController.StoreUserProfile - Class in com.wsfmn.controller
-
Storing the user's profilename locally in Profile.sav
- OfflineControllerTest - Class in com.wsfmn.view.controller
-
Created by Fredric on 2017-10-21.
- OfflineControllerTest() - Constructor for class com.wsfmn.habittracker.habitcontrollers.OfflineControllerTest
-
- oldOcc - Variable in class com.wsfmn.habit.Habit
-
- onActivityResult(int, int, Intent) - Method in class com.wsfmn.habittracker.HabitEventActivity
-
- onActivityResult(int, int, Intent) - Method in class com.wsfmn.habittracker.habitHistoryDetailActivity
-
- onActivityResult(int, int, Intent) - Method in class com.wsfmn.habittracker.ProfileActivity
-
Method to retrieve results from another activity.
- onConnected(Bundle) - Method in class com.wsfmn.habittracker.MapsActivity
-
- onConnectionFailed(ConnectionResult) - Method in class com.wsfmn.habittracker.MapsActivity
-
- onConnectionSuspended(int) - Method in class com.wsfmn.habittracker.MapsActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.AddLocationActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.AddNewHabitActivity
-
- onCreate() - Method in class com.wsfmn.habittracker.App
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.ChangeLocationActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.FriendActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.HabitEventActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.HabitHistoryActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.habitHistoryDetailActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.HabitListViewActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.HabitListViewDetailActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.HabitsForTodayActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.imageActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.MainActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.MapsActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.ProfileActivity
-
Creates variables and activities
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.selectHabitActivity
-
- onCreate(Bundle) - Method in class com.wsfmn.habittracker.UserName_Activity
-
Create the Activity, set and create some variables.
- OnlineController - Class in com.wsfmn.controller
-
A controller for managing Habit and HabitEvent model data stored on an ElasticSearch server.
- OnlineController() - Constructor for class com.wsfmn.habitcontroller.OnlineController
-
- OnlineController.CheckUnique - Class in com.wsfmn.controller
-
Checking ElasticSearch if a name exists in DB.
- OnlineController.DeleteHabitEvents - Class in com.wsfmn.controller
-
When GetHabits.execute(String...
- OnlineController.DeleteHabits - Class in com.wsfmn.controller
-
When DeleteHabits.execute(Habit...
- OnlineController.DeleteProfileName - Class in com.wsfmn.controller
-
Deletes a profilename in ElasticSearch, Main use for intent testing.
- OnlineController.DeleteRequest - Class in com.wsfmn.controller
-
Delete a specific Request from ElasticSearch based on give search_parameters
- OnlineController.GetHabitEvents - Class in com.wsfmn.controller
-
Created by romansky on 10/20/16.
- OnlineController.GetHabits - Class in com.wsfmn.controller
-
When GetHabits.execute(String...
- OnlineController.GetRequest - Class in com.wsfmn.controller
-
Retrieve requests from ElasticSearch, Get back at most 10 Requests from Elastic Search with
given query search based on search_parameters.
- OnlineController.RequestExist - Class in com.wsfmn.controller
-
Check for a certain Request matching name, searchName, and requestType.
- OnlineController.SendRequest - Class in com.wsfmn.controller
-
Send Requests to ElasticSearch proceed if the device is connected to the internet and will store the
given Request on an ElasticSearch DB.
- OnlineController.StoreHabitEvents - Class in com.wsfmn.controller
-
When StoreHabitEvents.execute(HabitEvent...
- OnlineController.StoreHabits - Class in com.wsfmn.controller
-
When StoreHabits.execute(Habit...
- OnlineController.StoreNameInDataBase - Class in com.wsfmn.controller
-
Storing the user's ProfileName in ElasticSearch DB
- OnlineControllerTest - Class in com.wsfmn.view.controller
-
Created by nicholasmayne on 2017-10-17.
- OnlineControllerTest() - Constructor for class com.wsfmn.habittracker.habitcontrollers.OnlineControllerTest
-
- onLocationChanged(Location) - Method in class com.wsfmn.habittracker.MapsActivity
-
- onMapReady(GoogleMap) - Method in class com.wsfmn.habittracker.MapsActivity
-
Manipulates the map once available.
- onRequestPermissionsResult(int, String[], int[]) - Method in class com.wsfmn.habittracker.AddLocationActivity
-
- onRequestPermissionsResult(int, String[], int[]) - Method in class com.wsfmn.habittracker.ChangeLocationActivity
-
- onRequestPermissionsResult(int, String[], int[]) - Method in class com.wsfmn.habittracker.MapsActivity
-
- onStart() - Method in class com.wsfmn.habittracker.FriendActivity
-
- onStart() - Method in class com.wsfmn.habittracker.HabitHistoryActivity
-
- onStart() - Method in class com.wsfmn.habittracker.HabitListViewActivity
-
- onStart() - Method in class com.wsfmn.habittracker.HabitsForTodayActivity
-
- onStart() - Method in class com.wsfmn.habittracker.MainActivity
-
- onStart() - Method in class com.wsfmn.habittracker.ProfileActivity
-
Starts the Activity with certain conditions.
- onStart() - Method in class com.wsfmn.habittracker.selectHabitActivity
-
- onStart() - Method in class com.wsfmn.habittracker.UserName_Activity
-
Starts and checks if user is connected to internet.
- overlapAnchor - Static variable in class com.wsfmn.habittracker.R.attr
-
May be a boolean value, such as "true
" or
"false
".


{% extends 'layout.html' %} {% set version = "v3" %} {% set backLink = "ethnic-group" %} {% set heading = "Does your work mean you have face to face contact with different people every day?" %} {% set mainClasses = "nhsuk-main-wrapper--s" %} {% block content %}


100,000 Maranao sign petition to halt planned military camps in Marawi
Increased military presence in the war-ravaged city may rile up residents' cultural and religious sensitivities, and take up land that has historic meaning to the Maranao people, the petitioners say
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Position Absolute: element comes out of the flow. The top, bottom, right, left properties work. They alter the position of element relative to itself.
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Through my years I've created several recipes, and what follows are my earlier favourite brews together with the upcoming one/s.


Use reactive forms, input binding and event emitters to keep all models in sync.
Parent Value: {{parentModel}}


2nd year Computer Science Student at the University of Toronto.
Experience in coding: 1.5 year
2nd year Computer Science Student at the University of Toronto. Getting new girlfriend from instagram soon
Microsoft intern
2nd year Computer Science Student at the University of Toronto. 4.0GPA guy.
Experience in coding: 1.5 year
2nd year Computer Science Student at the University of Toronto.
Experience in coding: 1.5 year. Experience in relationship: 0
3rd year at the University of Waterloo. Correct me if I'm wrong
Experience in coding: ?? year


Features
Fill inputs, then press enter or add button to register an item
All inputs are displayed
Double click for editing
Click on the cross to delete
{{ item.input1 }}-{{ item.input2 }}
X


ION-Roller - Immutable software deployment service for AWS
Gilt has been at the forefront of the wave of microservice architecture. With the advantage of many individual services that do one thing well comes increased complexity of service management.
Our previous system was based on a reasonably traditional model of deployment scripts that simply replaced software on machines in order, and performed health checks before proceeding to the next server. A number of issues caused friction in attempts to release software. One was that in spite of services being run as microservices, many deployment decisions had to be done in lockstep, due to the shared platform. Another was that services that took a long time to start up (cache loads, etc.) had a large cost when the service was stopped; if a new release had to be rolled back, this could take more time than people might like.
Two ideas for software deployment arose from this. One was to separate the environments for each piece of software, and due to this, Gilt became an early leader in trials of the Docker deployment platform. Another idea was inspired by the principles of functional programming, where data was not changed in-place, but was modified by creating a new copy with the changes applied. This idea, “immutable infrastructure”, could allow new software releases, without the risk attached to shutting down the previous software version, as rollbacks would be quick and painless.
A third constraint appeared, which was not core to the concept, but based on the realities of Gilt’s deployment environments, which was that the AWS services, and EC2 computing platform, were going to be central to any service we created.
ION-Roller took these ideas, to create a deployment tool for immutable releases of software, using Docker environments, on top of the AWS infrastructure.
Following our article about deploying microservices published in InfoQ, now we made our work publicly available: https://github.com/gilt/ionroller. Check out a short demo!
Since development of the product started, Amazon has released a number of features in their own products (including the release of, and then enhancements to, the CodeDeploy service for deploying applications with EC2). In many cases, this is sufficient, especially for small companies which do not want to run their own deployment services. The major difference between the approaches is that CodeDeploy is not currently “immutable”, that is, it replaces the software as it installs new versions. We expect future changes to AWS services to further close the gap in functionality.


Speech
Parliamentary Under Secretary of State for Children and Families Edward Timpson speaks about special educational needs policy reform in the Pears Annual Lecture, organised by Ambitious about Autism.
Thanks, Robert (Buckland, MP, Chair) for the introduction - and the valuable contribution you and your colleagues on the All Party Parliamentary Group on Autism have made to our reforms to special educational needs (SEN). I’m grateful for the opportunity to discuss them with you here today.
Thanks too to Ambitious about Autism for your positive and constructive engagement with these important changes.
These, as you know, are currently going through Parliament as part of the Children and Families Bill. This has just been through the committee stage in the House of Commons, with the report stage and third reading to come.
As your outstanding work at TreeHouse School shows, you know better than most what it takes to help children and young people with autism fulfil their potential. Which is why I so greatly value your input and that of other support groups, young people with autism and parents in shaping our reforms so that they really deliver for children and families living with the condition.
But our reforms are as much about a change in culture as a change in law. The 20 local pathfinders we’ve set up are pivotal to this change. Over 700 families are involved in these across 31 local authorities. Helping not only test the reforms, so we get the law and regulations right, but, as your conference title says, turning policy into practice.
Hearing what David (Nicholson, Youth Patron, Ambitious about Autism) and what the young people in the film had to say now, offers us a strong reminder of just what this means for children with autism.
It’s clear that in cherishing hopes and dreams, they are no different from their peers.
This sentiment also came across loud and clear when I met a very passionate group of parent bloggers, who have children with special educational needs, a few weeks ago.
They put it well, when they said they only wanted for their children what we want for our own children – support to help them develop and thrive, so they can make the most of their talents - whatever that might be. Surely the least these families deserve, given the huge challenges they already face.
Instead, too many found themselves, to quote one mother, in “an unending battle” with the system that’s supposed to help them.
Too many found that their children’s special educational needs were picked up late.
Too many had to fight to get different services to work together – services that focused too much on the SEN label rather than on meeting the child’s needs and supporting their life outcomes.
And too many found that young people lost the protections and rights they had at school when they went on to further education.
So, it’s little wonder that young people with SEN often have such poor prospects, lagging behind their peers at school and college and being more likely to be out of education, training and employment at 18.
This is clearly unacceptable – a terrible waste of untapped potential and lives peppered by missed opportunities.
Which is why we must work together to fundamentally transform the system, so that it fits in with the needs of children and young people with SEN rather than the other way around.
The reforms in the Children and Families Bill aim to do just this – to create a clearer, more joined-up approach that’s more focused on outcomes and better supports transition to adulthood.
Crucially, they’ll also do much more to involve children, young people and their families:
In doing so, we recognise that children and young people with autism can have particularly complex needs demanding intervention from a number of different agencies. So they should particularly benefit from the more joined-up approach in the Education, Health and Care Plan.
Many children with autism also stand to gain from a stronger push for early identification of SEN through initiatives such as the two to two-and-a-half year health visitor review. We’re committed to creating an integrated review from 2015 combining the health visitor review and the two year old Early Years Foundation progress review.
In addition, young people and parents will be able to opt for a personal budget, so they can have more choice and control over the support they or their child receives.
Now, as many of you will know, we’ve made a number of changes to the bill, having drawn on the views of children and young people with autism, parents and others in the field.
Most, notably, we’ve clarified our commitment to maintaining and, indeed, extending, existing rights and protections. I hope this reassures parents, who’ve fought so hard for their children, about what these changes mean. The truth is that their children will be better off because, for the first time, comparable protections will be in place from birth right through school into further education and training.
I know that one of the biggest frustrations young people with SEN and their families face is having to fight to get good information about what support’s available.
So, we’ve made some changes to the local offer to make this easier. These will require local authorities to involve children, young people and parents in developing and reviewing the local offer. The local authority will also have to publish and respond to their comments about the local offer.
It’s this direct input from families and local flexibility that will drive services to be truly responsive to needs on the ground.
We’re also developing proposals to ensure families get coordinated information across education, health and social care as part of the joint commissioning of services.
I’m conscious that transition from school to adulthood and independence also presents a huge challenge for young people with autism and other special educational needs.
Which is why we’ve made progress in this area a priority through the new 0 to 25 system, which stretches support further than ever before.
The focus on outcomes in the education, health and care plan, on what the child, young person and their family want to achieve in education, work and other areas, includes planning around key transition points.
Provisions in the bill further strengthen this support after the age of 16 in a number of ways - by, for example, for the first time, giving young people up to the age of 25 the right to request an assessment for an education, health and care plan.
Young people will also have the right to request that a particular college is named in their plan and to appeal against a decision made by the local authority at the tribunal.
In addition, we amended the legislation – following representations from Ambitious About Autism and others during the pre-legislative scrutiny stage – to ensure that local authorities maintain plans for young people who are not in education, employment or training up until age 18.
And for young people planning to leave education within 2 years, local authorities must ensure that the education, health and care plan looks ahead; that they set out clear transition plans, focused on outcomes, that help the young person progress successfully to adulthood.
We’re keen to ensure that all children get the right support they need at the right time.
Better identification of their needs is key. Which is why we recently contracted the Autism Education Trust to continue and expand its important work in this area; so the trust’s work to train staff in schools will now be extended to early years and further education.
Better identification of need is also the spur for our proposals to replace the current school action/school action plus system with a new single SEN category.
This is a simplified, rigorous approach that aims to focus the system on the impact of the support provided to the individual child rather than on how child accesses support.
The changes, which will be set out in the new SEN code of practice, don’t change the legislative duties on schools. But they do challenge schools to improve the quality of teaching and learning for all pupils, rather than inappropriately and inaccurately labelling some pupils as having SEN.
This drive to raise education standards and better prepare all children for adult life is underlined by our commitment to raise the participation age in education or training to 18.
However, we know that young people with autism can find it especially hard to find work. Which is why we’re trialling supported internships for young people with complex learning difficulties or disabilities, at 15 colleges around England. These are further education courses, based mainly with an employer, that provide young people with SEN with on-the-job training and support from expert job coaches.
Similar programmes have already yielded some success stories. One young man called David, who is on the autistic spectrum, found a job in the catering department of the University of Surrey after taking part in an internship supported by the university, Guildford College and EmployAbility, a not-for-profit body that helps disabled students find work. The confidence he gained led him to taking on a role as a voluntary caterer at the Olympics.
A fantastic example of how targeted support can go a long way towards helping young people with SEN become more independent.
These are just the sort of outcomes that our reforms aim to achieve.
But, as I’ve said before, it’s a shift in culture, above all, that’s needed to really transform support and aspiration for children with SEN and their families.
Getting different services to work together and put the child and their family in the driving seat isn’t easy. But pathfinders, across the country are developing new systems with families at their heart, proving it can be done.
In West Sussex, the SE7 has developed new arrangements based on early years multi-agency planning meetings. The 30 families who’ve been involved report that the new approach is proving much more supportive and responsive to their needs.
Elsewhere, Medway has carried out single coordinated assessment for a small number of younger children and used a video play tool to bring parents and professionals together as part of the assessment process. They have also acted to make sure assessment reports and education, health and care plans are clear and jargon-free.
As with those in West Sussex, parents and professionals are said to be delighted with this new approach which, they feel, truly puts the child at the heart of the process.
Over 600 families now have a new education, health and care plan. These seem to be going down well - of the 65 families surveyed for an evaluation, the vast majority said they felt positive about the pathfinder process.
I’ve seen for myself the difference the new plans are making on recent visits to pathfinders in Surrey and Bromley. I heard some really encouraging stories there from young people and their parents who said they felt much more involved in drawing up their package of support through the education, health and care plans.
I also heard about some of the challenges we face in making sure elements of the reforms, like personal budgets and transitions into adulthood, are working as well as we would want.
It seems, from what I’ve seen, that one of keys to success of the pathfinder work, is collaboration done in positive spirit - individuals and organisations being willing to work with others they haven’t worked with before, with a focus on what needs to be achieved and a plan, broken down into small steps, of how to get there.
As I said last year, the pathfinders will continue until September 2014 so we can make the most of their experience
Their insights, along with the views of other interested parties, will inform a new shorter, clearer code of practice. This will provide practical guidance to professionals and families and will include, for the first time, information on post-16 and the further education sector.
As you may know, we published an indicative draft of the code in March to support the committee’s scrutiny of the SEN clauses of the bill.
As the name suggests, the draft is indicative of the government’s thinking and is not, I should stress, a final document. So there will be plenty of time to have a say on this during the passage of the Bill and when we consult publicly on a full draft of the code this autumn.
Learning from the pathfinders and others will also feed into our approach to transition as we move from the old to the new system.
We want children and young people with SEN and their families to benefit from the improved arrangements as soon as possible, while at the same time ensuring that that the best possible service is maintained through transition.
Our ambition, once the legislation comes into force, is to see all new assessments undertaken to be for new education, health and care plans.
To ensure a smooth changeover, we believe a phased approach will be needed to transfer children and young people with existing statements and learning difficulty assessments to education, health and care plans.
These changes will need very careful consideration and we’re gathering views from interested parties to find the best way forward. We’ll set out details of our preferred approach in a draft order which will be subject to formal consultation later this year.
I’d like to reassure those worried that existing statements could lose their legal status during transition. I’m clear that existing statements and learning difficulty assessments will retain current legal rights until they’re replaced by education, health and care plans.
I want to work with you, as we’ve done on the bill, to make sure this transition period runs smoothly for all concerned, so that children and young people with SEN and their families can benefit from our reforms as soon as possible - and when I say our reforms, I hope, very much, that you will also see them as your reforms.
Because you all have a significant role to play in making them a reality on the ground.
Now, I know that change can be daunting, especially when it involves vulnerable children and change to a system that’s gone largely untouched for three decades.
But we have a real opportunity here to make a difference.
By extending support into adulthood, getting services working together and providing better information, these reforms have the potential to transform thousands of young lives and those of their families.
Indeed, given the complex nature of autism, our success in improving support and outcomes for children with the condition can be seen as a test of the reforms.
But to make them a reality, we need you, as trusted groups, individuals and leaders, to help explain these changes and ensure children with SEN and their families fully benefit from them.
We’re keen to support you in this, which is why I’m pleased that we’ll be continuing to support Ambitious about Autism over the next two years through our grants programme.
I know that you are planning to work with the Association of Colleges and some mainstream colleges on innovative ways of helping young people with autism make the transition from school to college. I’ll be very interested to hear about the progress you make.
I’m optimistic that, by working together in this way, we can build a better system that’s truly on the side of those depend on it.
A system that, as one of the parent bloggers put it, “sees my son as the funny, smart little boy I see. The little boy who deserves to have people who are there to help him driven to do their very best so he has as many opportunities as other children.”
That’s what I want to see. And I know it’s what you want. Let’s work together to make it happen.
Thank you.
Published: 29 April 2013


Image set: RGA variations Rainbow Bubbles (solid) Rainbow Bubbles (80%) normal screen multiply difference src-in exclusion add lighten darken overlay hardlight colordodge colorburn blend over Page Background: Black 25% Grey 50% Grey 75% Grey White Grey Checkered Pink Checkered Red Green Blue
yields should be


Glance数据库架构¶
Glance数据库公共API¶
Glance数据库API包含几个方法来初级给持久存储发送和接受的消息。在下面你可以找到一个列表按目录划分了公共方法。
镜像方法通用参数¶
- 以下的参数可以用在下面所有的镜像方法中：
context
对应glance.context.RequestContext对象的一个值，它存储了用户如何访问系统的信息和请求的额外信息；image_id
— 对应镜像标识（identifier）的一个字符串；memb_id
— 对应镜像的成员（member）标识的一个字符串。
镜像基本方法¶
镜像处理方法：
image_create(context, values)
- 使用 values 字典（Dictionary）的参数创建一个新的镜像记录。返回一个 glance.db.sqlalchemy.models.Image 对象的字典表达式（Dictionary Representation）。image_update(context, image_id, values, purge_props=False, from_state=None)
- updates the existing image with an identifier image_id with values listed in the values dictionary. Returns a dictionary representation of a newly created Image object.
- 可选参数有：
purge_props
- a flag indicating that all the existing properties not listed in the values[‘properties’] should be deleted;from_state
- a string filter indicating that the updated image must be in the specified state.
image_destroy(context, image_id)
- deletes all the database record of an image with an identifier image_id, like tags, properties, and members, and sets a ‘deleted’ status to all the image locations.image_get(context, image_id, force_show_deleted=False)
- gets an image with an identifier image_id and returns its dictionary representation. A parameter force_show_deleted is a flag that indicates to show image info even if it was ‘deleted’, or its ‘pending_delete’ statuses.image_get_all(context, filters=None, marker=None, limit=None, sort_key=None, sort_dir=None, member_status='accepted', is_public=None, admin_as_user=False, return_tag=False)
- gets all the images that match zero or more filters.
- 可选参数有：
filters
- dict of filter keys and values. If a ‘properties’ key is present, it is treated as a dict of key/value filters in the attribute of the image properties.marker
- image id after which a page should start;limit
- maximum number of images to return;sort_key
- list of image attributes by which results should be sorted;sort_dir
- directions in which results should be sorted (asc, desc);member_status
- only returns shared images that have this membership status;is_public
- if true, returns only public images. If false, returns only private and shared images.admin_as_user
- for backwards compatibility. If true, admin receives an equivalent set of images that he would see if he was a regular user.return_tag
- indicates whether an image entry in the result includes its relevant tag entries. This can improve upper-layer query performance and prevent using separated calls.
镜像位置方法¶
镜像位置处理方法：
image_location_add(context, image_id, location)
- adds a new location to an image with an identifier image_id. This location contains values listed in the dictionary location.image_location_update(context, image_id, location)
- updates an existing location with an identifier location[‘id’] for an image with an identifier image_id with values listed in the dictionary location.image_location_delete(context, image_id, location_id, status, delete_time=None)
- sets a ‘deleted’ or ‘pending_delete’ status to an existing location record with an identifier location_id for an image with an identifier image_id.
镜像属性方法¶
所以如果你想修改它，你必须先删除它然后重新创建新的。
镜像属性处理方法：
image_property_create(context, values)
- creates a property record with parameters listed in the values dictionary for an image with values[‘id’]. Returns a dictionary representation of a newly created ImageProperty object.image_property_delete(context, prop_ref, image_ref)
- deletes an existing property record with a name prop_ref for an image with an identifier image_ref.
镜像成员方法¶
处理镜像成员关系方法：
image_member_create(context, values)
- creates a member record with properties listed in the values dictionary for an image with values[‘id’]. Returns a dictionary representation of a newly created ImageMember object.image_member_update(context, memb_id, values)
- updates an existing member record with properties listed in the values dictionary for an image with values[‘id’]. Returns a dictionary representation of an updated member record.image_member_delete(context, memb_id)
- deletes an existing member record with memb_id.image_member_find(context, image_id=None, member=None, status=None)
- returns all members for a given context with optional image identifier (image_id), member name (member), and member status (status) parameters.image_member_count(context, image_id)
- returns a number of image members for an image with image_id.
镜像标签（Tag）方法¶
处理镜像标签方法：
image_tag_set_all(context, image_id, tags)
- changes all the existing tags for an image with image_id to the tags listed in the tags param. To remove all tags, a user just should provide an empty list.image_tag_create(context, image_id, value)
- adds a value to tags for an image with image_id. Returns the value of a newly created tag.image_tag_delete(context, image_id, value)
- removes a value from tags for an image with image_id.image_tag_get_all(context, image_id)
- returns a list of tags for a specific image.
镜像信息方法¶
下面两个方法告诉用户他修改和查看镜像的能力。这里的*image*参数是*Image*对象的字典表达式（Dictionary Representation）。
is_image_mutable(context, image)
- informs a user about the possibility to modify an image with a given context. Returns True if the image is mutable in this context.is_image_visible(context, image, status=None)
- informs about the possibility to observe the image details with a given context and optionally with a status. Returns True if the image is visible in this context.
Glance数据库Schema
| figwidth: | 100% :align: center
|
|---|
Image 1. Glance镜像数据库Schema


My first post on this forum, therefore I'm obligated to first congratulate you guys on this fine job you're doing with BB and expending original one with all the relevant data. Well done!!!
Back to the topic, firstly I search this forum and couldn't find (maybe I'm wrong) anyone registering the problem regarding the counting grenade kills in "Dude, I'm a Frag Master" assignment. Prior the latest patch everything was fine, but after, instead of count , statement undefined/500 is shown.
http://battlelog.battlefield.com/bf3/soldier/WooIfson/assignments/349458157/pc/
I fulfill this assignment before the mentioned update, but situation is the same with my friends, who didn't reach 500 kills.
Thanks in advance!
Regards,


Intersection graphs#
The methods defined here appear in sage.graphs.graph_generators
.
- sage.graphs.generators.intersection.IntersectionGraph(S)#
Return the intersection graph of the family \(S\)
The intersection graph of a family \(S\) is a graph \(G\) with \(V(G)=S\) such that two elements \(s_1,s_2\in S\) are adjacent in \(G\) if and only if \(s_1\cap s_2\neq \emptyset\).
INPUT:
S
– a list of sets/tuples/iterablesNote
The elements of \(S\) must be finite, hashable, and the elements of any \(s\in S\) must be hashable too.
EXAMPLES:
sage: graphs.IntersectionGraph([(1,2,3),(3,4,5),(5,6,7)]) Intersection Graph: Graph on 3 vertices
- sage.graphs.generators.intersection.IntervalGraph(intervals, points_ordered=False)#
Return the graph corresponding to the given intervals.
An interval graph is built from a list \((a_i,b_i)_{1\leq i \leq n}\) of intervals : to each interval of the list is associated one vertex, two vertices being adjacent if the two corresponding (closed) intervals intersect.
INPUT:
intervals
– the list of pairs \((a_i,b_i)\) defining the graph.points_ordered
– states whether every interval \((a_i,b_i)\) of \(intervals\) satisfies \(a_i<b_i\). If satisfied then settingpoints_ordered
toTrue
will speed up the creation of the graph.
Note
The vertices are named 0, 1, 2, and so on. The intervals used to create the graph are saved with the graph and can be recovered using
get_vertex()
orget_vertices()
.
EXAMPLES:
The following line creates the sequence of intervals \((i, i+2)\) for i in \([0, ..., 8]\):
sage: intervals = [(i,i+2) for i in range(9)]
In the corresponding graph
sage: g = graphs.IntervalGraph(intervals) sage: g.get_vertex(3) (3, 5) sage: neigh = g.neighbors(3) sage: for v in neigh: print(g.get_vertex(v)) (1, 3) (2, 4) (4, 6) (5, 7)
The is_interval() method verifies that this graph is an interval graph.
sage: g.is_interval() True
The intervals in the list need not be distinct.
sage: intervals = [ (1,2), (1,2), (1,2), (2,3), (3,4) ] sage: g = graphs.IntervalGraph(intervals,True) sage: g.clique_maximum() [0, 1, 2, 3] sage: g.get_vertices() {0: (1, 2), 1: (1, 2), 2: (1, 2), 3: (2, 3), 4: (3, 4)}
The endpoints of the intervals are not ordered we get the same graph (except for the vertex labels).
sage: rev_intervals = [ (2,1), (2,1), (2,1), (3,2), (4,3) ] sage: h = graphs.IntervalGraph(rev_intervals,False) sage: h.get_vertices() {0: (2, 1), 1: (2, 1), 2: (2, 1), 3: (3, 2), 4: (4, 3)} sage: g.edges(sort=True) == h.edges(sort=True) True
- sage.graphs.generators.intersection.OrthogonalArrayBlockGraph(k, n, OA=None)#
Return the graph of an \(OA(k,n)\).
The intersection graph of the blocks of a transversal design with parameters \((k,n)\), or \(TD(k,n)\) for short, is a strongly regular graph (unless it is a complete graph). Its parameters \((v,k',\lambda,\mu)\) are determined by the parameters \(k,n\) via:
\[v=n^2, k'=k(n-1), \lambda=(k-1)(k-2)+n-2, \mu=k(k-1)\]As transversal designs and orthogonal arrays (OA for short) are equivalent objects, this graph can also be built from the blocks of an \(OA(k,n)\), two of them being adjacent if one of their coordinates match.
For more information on these graphs, see Andries Brouwer’s page on Orthogonal Array graphs.
Warning
Brouwer’s website uses the notation \(OA(n,k)\) instead of \(OA(k,n)\)
For given parameters \(k\) and \(n\) there can be many \(OA(k,n)\) : the graphs returned are not uniquely defined by their parameters (see the examples below).
If the function is called only with the parameter
k
andn
the results might be different with two versions of Sage, or even worse : some could not be available anymore.
INPUT:
k,n
(integers)OA
– An orthogonal array. If set toNone
(default) thenorthogonal_array()
is called to compute an \(OA(k,n)\).
EXAMPLES:
sage: # needs sage.modules sage: G = graphs.OrthogonalArrayBlockGraph(5,5); G # needs sage.schemes OA(5,5): Graph on 25 vertices sage: G.is_strongly_regular(parameters=True) # needs sage.schemes (25, 20, 15, 20) sage: G = graphs.OrthogonalArrayBlockGraph(4,10); G OA(4,10): Graph on 100 vertices sage: G.is_strongly_regular(parameters=True) (100, 36, 14, 12)
Two graphs built from different orthogonal arrays are also different:
sage: # needs sage.modules sage: k = 4; n = 10 sage: OAa = designs.orthogonal_arrays.build(k,n) sage: OAb = [[(x+1)%n for x in R] for R in OAa] sage: set(map(tuple,OAa)) == set(map(tuple,OAb)) False sage: Ga = graphs.OrthogonalArrayBlockGraph(k, n, OAa) sage: Gb = graphs.OrthogonalArrayBlockGraph(k, n, OAb) sage: Ga == Gb False
As
OAb
was obtained fromOAa
by a relabelling the two graphs are isomorphic:sage: Ga.is_isomorphic(Gb) # needs sage.modules True
But there are examples of \(OA(k,n)\) for which the resulting graphs are not isomorphic:
sage: oa0 = [[0, 0, 1], [0, 1, 3], [0, 2, 0], [0, 3, 2], ....: [1, 0, 3], [1, 1, 1], [1, 2, 2], [1, 3, 0], ....: [2, 0, 0], [2, 1, 2], [2, 2, 1], [2, 3, 3], ....: [3, 0, 2], [3, 1, 0], [3, 2, 3], [3, 3, 1]] sage: oa1 = [[0, 0, 1], [0, 1, 0], [0, 2, 3], [0, 3, 2], ....: [1, 0, 3], [1, 1, 2], [1, 2, 0], [1, 3, 1], ....: [2, 0, 0], [2, 1, 1], [2, 2, 2], [2, 3, 3], ....: [3, 0, 2], [3, 1, 3], [3, 2, 1], [3, 3, 0]] sage: g0 = graphs.OrthogonalArrayBlockGraph(3, 4, oa0) # needs sage.modules sage: g1 = graphs.OrthogonalArrayBlockGraph(3, 4, oa1) # needs sage.modules sage: g0.is_isomorphic(g1) # needs sage.modules False
But nevertheless isospectral:
sage: g0.spectrum() # needs sage.modules [9, 1, 1, 1, 1, 1, 1, 1, 1, 1, -3, -3, -3, -3, -3, -3] sage: g1.spectrum() # needs sage.modules [9, 1, 1, 1, 1, 1, 1, 1, 1, 1, -3, -3, -3, -3, -3, -3]
Note that the graph
g0
is actually isomorphic to the affine polar graph \(VO^+(4,2)\):sage: graphs.AffineOrthogonalPolarGraph(4,2,'+').is_isomorphic(g0) # needs sage.modules True
- sage.graphs.generators.intersection.PermutationGraph(second_permutation, first_permutation=None)#
Build a permutation graph from one permutation or from two lists.
Definition:
If \(\sigma\) is a permutation of \(\{ 1, 2, \ldots, n \}\), then the permutation graph of \(\sigma\) is the graph on vertex set \(\{ 1, 2, \ldots, n \}\) in which two vertices \(i\) and \(j\) satisfying \(i < j\) are connected by an edge if and only if \(\sigma^{-1}(i) > \sigma^{-1}(j)\). A visual way to construct this graph is as follows:
Take two horizontal lines in the euclidean plane, and mark points \(1, ..., n\) from left to right on the first of them. On the second one, still from left to right, mark \(n\) points \(\sigma(1), \sigma(2), \ldots, \sigma(n)\). Now, link by a segment the two points marked with \(1\), then link together the points marked with \(2\), and so on. The permutation graph of \(\sigma\) is the intersection graph of those segments: there exists a vertex in this graph for each element from \(1\) to \(n\), two vertices \(i, j\) being adjacent if the segments \(i\) and \(j\) cross each other.
The set of edges of the permutation graph can thus be identified with the set of inversions of the inverse of the given permutation \(\sigma\).
A more general notion of permutation graph can be defined as follows: If \(S\) is a set, and \((a_1, a_2, \ldots, a_n)\) and \((b_1, b_2, \ldots, b_n)\) are two lists of elements of \(S\), each of which lists contains every element of \(S\) exactly once, then the permutation graph defined by these two lists is the graph on the vertex set \(S\) in which two vertices \(i\) and \(j\) are connected by an edge if and only if the order in which these vertices appear in the list \((a_1, a_2, \ldots, a_n)\) is the opposite of the order in which they appear in the list \((b_1, b_2, \ldots, b_n)\). When \((a_1, a_2, \ldots, a_n) = (1, 2, \ldots, n)\), this graph is the permutation graph of the permutation \((b_1, b_2, \ldots, b_n) \in S_n\). Notice that \(S\) does not have to be a set of integers here, but can be a set of strings, tuples, or anything else. We can still use the above visual description to construct the permutation graph, but now we have to mark points \(a_1, a_2, \ldots, a_n\) from left to right on the first horizontal line and points \(b_1, b_2, \ldots, b_n\) from left to right on the second horizontal line.
INPUT:
second_permutation
– the unique permutation/list defining the graph, or the second of the two (if the graph is to be built from two permutations/lists).first_permutation
(optional) – the first of the two permutations/lists from which the graph should be built, if it is to be built from two permutations/lists.When
first_permutation is None
(default), it is set to be equal tosorted(second_permutation)
, which yields the expected ordering when the elements of the graph are integers.
See also
Recognition of Permutation graphs in the
comparability module
.Drawings of permutation graphs as intersection graphs of segments is possible through the
show()
method ofPermutation
objects.The correct argument to use in this case is
show(representation = "braid")
.
EXAMPLES:
sage: p = Permutations(5).random_element() sage: PG = graphs.PermutationGraph(p) sage: edges = PG.edges(sort=True, labels=False) sage: set(edges) == set(p.inverse().inversions()) True sage: PG = graphs.PermutationGraph([3,4,5,1,2]) sage: sorted(PG.edges(sort=True)) [(1, 3, None), (1, 4, None), (1, 5, None), (2, 3, None), (2, 4, None), (2, 5, None)] sage: PG = graphs.PermutationGraph([3,4,5,1,2], [1,4,2,5,3]) sage: sorted(PG.edges(sort=True)) [(1, 3, None), (1, 4, None), (1, 5, None), (2, 3, None), (2, 5, None), (3, 4, None), (3, 5, None)] sage: PG = graphs.PermutationGraph([1,4,2,5,3], [3,4,5,1,2]) sage: sorted(PG.edges(sort=True)) [(1, 3, None), (1, 4, None), (1, 5, None), (2, 3, None), (2, 5, None), (3, 4, None), (3, 5, None)] sage: PG = graphs.PermutationGraph(Permutation([1,3,2]), Permutation([1,2,3])) sage: sorted(PG.edges(sort=True)) [(2, 3, None)] sage: graphs.PermutationGraph([]).edges(sort=True) [] sage: graphs.PermutationGraph([], []).edges(sort=True) [] sage: PG = graphs.PermutationGraph("graph", "phrag") sage: sorted(PG.edges(sort=True)) [('a', 'g', None), ('a', 'h', None), ('a', 'p', None), ('g', 'h', None), ('g', 'p', None), ('g', 'r', None), ('h', 'r', None), ('p', 'r', None)]
- sage.graphs.generators.intersection.ToleranceGraph(tolrep)#
Return the graph generated by the tolerance representation
tolrep
.The tolerance representation
tolrep
is described by the list \(((l_0,r_0,t_0), (l_1,r_1,t_1), \ldots, (l_k,r_k,t_k))\) where \(I_i = (l_i,r_i)\) denotes a closed interval on the real line with \(l_i < r_i\) and \(t_i\) a strictly positive value, called tolerance. This representation generates the tolerance graph with the vertex set \(\{0,1, \ldots, k\}\) and the edge set \(\{(i,j): |I_i \cap I_j| \ge \min\{t_i, t_j\}\}\) where \(|I_i \cap I_j|\) denotes the length of the intersection of \(I_i\) and \(I_j\).INPUT:
tolrep
– list of triples \((l_i,r_i,t_i)\) where \((l_i,r_i)\) denotes a closed interval on the real line and \(t_i\) a positive value.
Note
The vertices are named \(0, 1, \ldots, k\). The tolerance representation used to create the graph is saved with the graph and can be recovered using
get_vertex()
orget_vertices()
.EXAMPLES:
The following code creates a tolerance representation
tolrep
, generates its tolerance graphg
, and applies some checks:sage: tolrep = [(1,4,3),(1,2,1),(2,3,1),(0,3,3)] sage: g = graphs.ToleranceGraph(tolrep) sage: g.get_vertex(3) (0, 3, 3) sage: neigh = g.neighbors(3) sage: for v in neigh: print(g.get_vertex(v)) (1, 2, 1) (2, 3, 1) sage: g.is_interval() False sage: g.is_weakly_chordal() True
The intervals in the list need not be distinct
sage: tolrep2 = [(0,4,5),(1,2,1),(2,3,1),(0,4,5)] sage: g2 = graphs.ToleranceGraph(tolrep2) sage: g2.get_vertices() {0: (0, 4, 5), 1: (1, 2, 1), 2: (2, 3, 1), 3: (0, 4, 5)} sage: g2.is_isomorphic(g) True
Real values are also allowed
sage: tolrep = [(0.1,3.3,4.4),(1.1,2.5,1.1),(1.4,4.4,3.3)] sage: g = graphs.ToleranceGraph(tolrep) sage: g.is_isomorphic(graphs.PathGraph(3)) True


We will review the pull request ASAP and update your contribution to WTF! If you're not familiar with Github, you can also send us the JSON in an email to: wtf(at)valispace(dot)com Thank you for being an awesome contributor!


| US Personal Income - MoM (%) | ||||||
| Release Date | For Month | Market Expectation | Kshitij Expectation | Previous | Released | Revised |
| 22-Dec-17 | Nov-17 | 0.40 | - | 0.39 | 0.33 | 0.34 |
| 29-Jan-18 | Dec-17 | 0.30 | 0.08 | 0.34 | 0.35 | 0.38 |
| 1-Mar-18 | Jan-18 | 0.20 | 0.58 | 0.38 | 0.39 | - |
| 30-Apr-18 | Feb-18 | - | - | 0.39 | - | - |
| Copyright: Kshitij Consultancy Services |


Understanding the principles of neuroplasticity increases your capacity to learn and increases performance as a result. A study in the journal Child Development in 2007 ‘found that both morale and grade points took a leap when students understood the idea that intelligence is malleable’.
I think completing these reflections on the subject should give me some benefit, if the source material is correct then even understanding the subject should provide tangible benefits. The idea that practice makes perfect isn’t a new one, but for my own process I know that it is extremely helpful in learning new skills so that is something I will try to replicate myself even if the curriculum doesn’t. Putting information in context is a big one for me as well, I feel like understanding why you are doing something is what gives the information context for me. However, this seems to appose the ‘just in time’ style of learning which is being pushed for some information. I guess I must trust in the process and see how I get on.
Awareness of the concepts, practice, practice and practice and putting information in context.
This Ted talk on feats of memory was really interesting.
This seems to link to neuroplasticity in the sense that having an awareness of how intelligence is malleable is part of having a growth mindset. For me the difference between the two is that growth mindset seems to take into account the understanding of others intelligence and how that can affect your empathy and ability to work as part of a team. With growth mindset there is a focus on happiness and positive emotional responses rather than more tangible results like increased learning and intelligence.
Not really, this seems to be a bit more of a deep dive that was covered in the pre-curriculum work. I do find it interesting though and I guess going over these topics again is only improving my awareness of it and will hopefully benefit me as a result.
I will try and incorporate the concept into my group work when working with others who maybe aren’t understanding concepts as quickly as others. Also using it in self-reflection in the same way.
I enjoyed this article as it gave examples that resonate with me like basketball and the gym which I have both enjoyed.
To be honest I’m not sure. Practicing and context are both things that I would be incorporating already. I guess having awareness can be linked with the mindfulness part of my learning plan. Making sure that I am practicing mindfulness could help me take a step back and be more aware of myself and how I am interpreting the curriculum throughout the process, this in turn could help with increasing my awareness of neuroplasticity and help reap the benefits.


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Class KJUR.jws.JWS
JSON Web Signature(JWS) class
Defined in: jws-3.0.js.
| Constructor Attributes | Constructor Name and Description |
|---|---|
|
JSON Web Signature(JWS) class.
|
| Field Attributes | Field Name and Description |
|---|---|
|
This property is set after JWS signature verification.
|
|
|
string of Encrypted JWS Header
|
|
|
string of decoded JWS Header
|
|
|
string of Encrypted JWS Payload
|
|
|
string of Signature Input
|
|
|
string of Encrypted JWS signature value
|
|
|
BigInteger(defined in jsbn.js) object of JWS signature value
|
|
|
hexadecimal string of JWS signature value
|
| Method Attributes | Method Name and Description |
|---|---|
| <static> |
KJUR.jws.JWS.generateJWSByKey(sHead, sPayload, RSA)
generate JWS signature by Header, Payload and a RSA private key.
|
| <static> |
KJUR.jws.JWS.generateJWSByNED(sHead, sPayload, hN, hE, hD)
generate JWS signature by Header, Payload and a naked RSA private key.
|
| <static> |
KJUR.jws.JWS.generateJWSByP1PrvKey(sHead, sPayload, string)
generate JWS signature by Header, Payload and a PEM formatted PKCS#1 RSA private key.
|
| <static> |
KJUR.jws.JWS.getEncodedSignatureValueFromJWS(sJWS)
get Encoed Signature Value from JWS string.
|
| <static> |
KJUR.jws.JWS.isSafeJSONString(s)
check whether a String "s" is a safe JSON string or not.
|
| <static> |
KJUR.jws.JWS.parseJWS(sJWS)
parse JWS string and set public property 'parsedJWS' dictionary.
|
| <static> |
KJUR.jws.JWS.readSafeJSONString(s)
read a String "s" as JSON object if it is safe.
|
| <static> |
KJUR.jws.JWS.sign(alg, sHead, sPayload, key, pass)
generate JWS signature by specified key
This method supports following algorithms. |
| <static> |
KJUR.jws.JWS.verify(sJWS, key)
verify JWS signature by specified key or certificate
|
| <static> |
KJUR.jws.JWS.verifyJWSByKey(sJWS, key)
verify JWS signature with RSA public key.
|
| <static> |
KJUR.jws.JWS.verifyJWSByNE(sJWS, hN, hE)
verify JWS signature with naked RSA public key.
|
| <static> |
KJUR.jws.JWS.verifyJWSByPemX509Cert(sJWS, sPemX509Cert)
verify JWS signature by PEM formatted X.509 certificate.
|
Class Detail
KJUR.jws.JWS()
JSON Web Signature(JWS) class.
Supported Algorithms
Here is supported algorithm names for KJUR.jws.JWS.sign and KJUR.jws.JWS.verify methods.| alg value | spec requirement | jsjws support |
|---|---|---|
| HS256 | REQUIRED | SUPPORTED |
| HS384 | OPTIONAL | - |
| HS512 | OPTIONAL | SUPPORTED |
| RS256 | RECOMMENDED | SUPPORTED |
| RS384 | OPTIONAL | SUPPORTED |
| RS512 | OPTIONAL | SUPPORTED |
| ES256 | RECOMMENDED+ | SUPPORTED |
| ES384 | OPTIONAL | SUPPORTED |
| ES512 | OPTIONAL | - |
| PS256 | OPTIONAL | SUPPORTED |
| PS384 | OPTIONAL | SUPPORTED |
| PS512 | OPTIONAL | SUPPORTED |
| none | REQUIRED | SUPPORTED |
- Since:
- jsjws 1.0
- Requires:
- base64x.js, json-sans-eval.js and jsrsasign library
Field Detail
{Dictionary}
parsedJWS
This property is set after JWS signature verification.
Following "parsedJWS_*" properties can be accessed as "parsedJWS.*" because of JsDoc restriction.
Following "parsedJWS_*" properties can be accessed as "parsedJWS.*" because of JsDoc restriction.
{String}
parsedJWS_headB64U
string of Encrypted JWS Header
{String}
parsedJWS_headS
string of decoded JWS Header
{String}
parsedJWS_payloadB64U
string of Encrypted JWS Payload
{String}
parsedJWS_si
string of Signature Input
{String}
parsedJWS_sigvalB64U
string of Encrypted JWS signature value
{String}
parsedJWS_sigvalBI
BigInteger(defined in jsbn.js) object of JWS signature value
{String}
parsedJWS_sigvalH
hexadecimal string of JWS signature value
Method Detail
<static>
{String}
KJUR.jws.JWS.generateJWSByKey(sHead, sPayload, RSA)
generate JWS signature by Header, Payload and a RSA private key.
This only supports "RS256", "RS512", "PS256" and "PS512" algorithms.
This only supports "RS256", "RS512", "PS256" and "PS512" algorithms.
- Parameters:
- {String} sHead
- string of JWS Header
- {String} sPayload
- string of JWS Payload
- {RSAKey} RSA
- private key
- Deprecated:
- from 3.0.0 please move to KJUR.jws.JWS.sign
- Throws:
- if sHead is a malformed JSON string.
- if supported signature algorithm was not specified in JSON Header.
- Returns:
- {String} JWS signature string
<static>
{String}
KJUR.jws.JWS.generateJWSByNED(sHead, sPayload, hN, hE, hD)
generate JWS signature by Header, Payload and a naked RSA private key.
This only supports "RS256" and "RS512" algorithm.
This only supports "RS256" and "RS512" algorithm.
- Parameters:
- {String} sHead
- string of JWS Header
- {String} sPayload
- string of JWS Payload
- {String} hN
- hexadecimal string for modulus of RSA public key
- {String} hE
- hexadecimal string for public exponent of RSA public key
- {String} hD
- hexadecimal string for private exponent of RSA private key
- Deprecated:
- from 3.0.0 please move to KJUR.jws.JWS.sign
- Throws:
- if sHead is a malformed JSON string.
- if supported signature algorithm was not specified in JSON Header.
- Returns:
- {String} JWS signature string
<static>
{String}
KJUR.jws.JWS.generateJWSByP1PrvKey(sHead, sPayload, string)
generate JWS signature by Header, Payload and a PEM formatted PKCS#1 RSA private key.
This only supports "RS256" and "RS512" algorithm.
This only supports "RS256" and "RS512" algorithm.
- Parameters:
- {String} sHead
- string of JWS Header
- {String} sPayload
- string of JWS Payload
- {String} string
- for sPemPrvKey PEM formatted PKCS#1 RSA private key
Heading and trailing space characters in PEM key will be ignored.
- Deprecated:
- from 3.0.0 please move to KJUR.jws.JWS.sign
- Since:
- 1.1
- Throws:
- if sHead is a malformed JSON string.
- if supported signature algorithm was not specified in JSON Header.
- Returns:
- {String} JWS signature string
<static>
{String}
KJUR.jws.JWS.getEncodedSignatureValueFromJWS(sJWS)
get Encoed Signature Value from JWS string.
- Parameters:
- {String} sJWS
- JWS signature string to be verified
- Throws:
- if sJWS is not comma separated string such like "Header.Payload.Signature".
- Returns:
- {String} string of Encoded Signature Value
<static>
{Number}
KJUR.jws.JWS.isSafeJSONString(s)
check whether a String "s" is a safe JSON string or not.
If a String "s" is a malformed JSON string or an other object type this returns 0, otherwise this returns 1.
If a String "s" is a malformed JSON string or an other object type this returns 0, otherwise this returns 1.
- Parameters:
- {String} s
- JSON string
- Returns:
- {Number} 1 or 0
<static>
KJUR.jws.JWS.parseJWS(sJWS)
parse JWS string and set public property 'parsedJWS' dictionary.
- Parameters:
- {String} sJWS
- JWS signature string to be parsed.
- Since:
- jws 1.1
- Throws:
- if sJWS is not comma separated string such like "Header.Payload.Signature".
- if JWS Header is a malformed JSON string.
<static>
{Object}
KJUR.jws.JWS.readSafeJSONString(s)
read a String "s" as JSON object if it is safe.
If a String "s" is a malformed JSON string or not JSON string, this returns null, otherwise returns JSON object.
If a String "s" is a malformed JSON string or not JSON string, this returns null, otherwise returns JSON object.
- Parameters:
- {String} s
- JSON string
- Since:
- 1.1.1
- Returns:
- {Object} JSON object or null
<static>
{String}
KJUR.jws.JWS.sign(alg, sHead, sPayload, key, pass)
generate JWS signature by specified key
This method supports following algorithms.
This method supports following algorithms.
| alg value | spec requirement | jsjws support |
|---|---|---|
| HS256 | REQUIRED | SUPPORTED |
| HS384 | OPTIONAL | - |
| HS512 | OPTIONAL | SUPPORTED |
| RS256 | RECOMMENDED | SUPPORTED |
| RS384 | OPTIONAL | SUPPORTED |
| RS512 | OPTIONAL | SUPPORTED |
| ES256 | RECOMMENDED+ | SUPPORTED |
| ES384 | OPTIONAL | SUPPORTED |
| ES512 | OPTIONAL | - |
| PS256 | OPTIONAL | SUPPORTED |
| PS384 | OPTIONAL | SUPPORTED |
| PS512 | OPTIONAL | SUPPORTED |
| none | REQUIRED | SUPPORTED |
- NOTE1:
- salt length of RSAPSS signature is the same as the hash algorithm length because of IETF JOSE ML discussion.
- NOTE2:
- The reason of HS384 unsupport is CryptoJS HmacSHA384 bug.
- Parameters:
- {String} alg
- JWS algorithm name to sign and force set to sHead or null
- {String} sHead
- string of JWS Header
- {String} sPayload
- string of JWS Payload
- {String} key
- string of private key or key object to sign
- {String} pass
- (OPTION)passcode to use encrypted private key
- Since:
- jws 3.0.0
- Returns:
- {String} JWS signature string
<static>
{Boolean}
KJUR.jws.JWS.verify(sJWS, key)
verify JWS signature by specified key or certificate
- Parameters:
- {String} sJWS
- string of JWS signature to verify
- {String} key
- string of public key, certificate or key object to verify
- Since:
- jws 3.0.0
- Returns:
- {Boolean} true if the signature is valid otherwise false
<static>
{Boolean}
KJUR.jws.JWS.verifyJWSByKey(sJWS, key)
verify JWS signature with RSA public key.
This only supports "RS256", "RS512", "PS256" and "PS512" algorithms.
This only supports "RS256", "RS512", "PS256" and "PS512" algorithms.
- Parameters:
- {String} sJWS
- JWS signature string to be verified
- {RSAKey} key
- RSA public key
- Deprecated:
- from 3.0.0 please move to KJUR.jws.JWS.verify
- Throws:
- if sJWS is not comma separated string such like "Header.Payload.Signature".
- if JWS Header is a malformed JSON string.
- Returns:
- {Boolean} returns true when JWS signature is valid, otherwise returns false
<static>
{String}
KJUR.jws.JWS.verifyJWSByNE(sJWS, hN, hE)
verify JWS signature with naked RSA public key.
This only supports "RS256" and "RS512" algorithm.
This only supports "RS256" and "RS512" algorithm.
- Parameters:
- {String} sJWS
- JWS signature string to be verified
- {String} hN
- hexadecimal string for modulus of RSA public key
- {String} hE
- hexadecimal string for public exponent of RSA public key
- Deprecated:
- from 3.0.0 please move to KJUR.jws.JWS.verify
- Throws:
- if sJWS is not comma separated string such like "Header.Payload.Signature".
- if JWS Header is a malformed JSON string.
- Returns:
- {String} returns 1 when JWS signature is valid, otherwise returns 0
<static>
{String}
KJUR.jws.JWS.verifyJWSByPemX509Cert(sJWS, sPemX509Cert)
verify JWS signature by PEM formatted X.509 certificate.
This only supports "RS256" and "RS512" algorithm.
This only supports "RS256" and "RS512" algorithm.
- Parameters:
- {String} sJWS
- JWS signature string to be verified
- {String} sPemX509Cert
- string of PEM formatted X.509 certificate
- Deprecated:
- from 3.0.0 please move to KJUR.jws.JWS.verify
- Since:
- 1.1
- Throws:
- if sJWS is not comma separated string such like "Header.Payload.Signature".
- if JWS Header is a malformed JSON string.
- Returns:
- {String} returns 1 when JWS signature is valid, otherwise returns 0


The Story of Doctor Dolittle
by Hugh Lofting
Chapter 18: Smells
Additional Information
- Year Published: 1920
- Language: English
- Country of Origin: England
- Source: Lofting, H. (1920). The Story of Doctor Dolittle . New York, NY: Frederick A. Stokes
-
Readability:
- Flesch–Kincaid Level: 4.2
- Word Count: 1,942
- Genre: Fantasy
- Keywords: adventure, helping others
- ✎ Cite This
Downloads
"Your uncle must now be FOUND," said the Doctor—"that is the next thing—now that we know he wasn't thrown into the sea."
Then Dab–Dab came up to him again and whispered,
"Ask the eagles to look for the man. No living creature can see better than an eagle. When they are miles high in the air they can count the ants crawling on the ground. Ask the eagles."
So the Doctor sent one of the swallows off to get some eagles.
And in about an hour the little bird came back with six different kinds of eagles: a Black Eagle, a Bald Eagle, a Fish Eagle, a Golden Eagle, an Eagle–Vulture, and a White–tailed Sea Eagle. Twice as high as the boy they were, each one of them.
Gub–Gub was scared of them and got behind a barrel. He said he felt as though those terrible eyes were looking right inside of him to see what he had stolen for lunch.
And the Doctor said to the eagles,
"A man has been lost—a fisherman with red hair and an anchor marked on his arm. Would you be so kind as to see if you can find him for us? This boy is the man's nephew."
Eagles do not talk very much. And all they answered in their husky voices was,
"You may be sure that we will do our best—for John Dolittle."
Then they flew off—and Gub–Gub came out from behind his barrel to see them go. Up and up and up they went—higher and higher and higher still. Then, when the Doctor could only just see them, they parted company and started going off all different ways—North, East, South and West, looking like tiny grains of black sand creeping across the wide, blue sky.
"My gracious!" said Gub–Gub in a hushed voice. "What a height! I wonder they don't scorch their feathers—so near the sun!"
They were gone a long time. And when they came back it was almost night.
And the eagles said to the Doctor,
"We have searched all the seas and all the countries and all the islands and all the cities and all the villages in this half of the world. But we have failed. In the main street of Gibraltar we saw three red hairs lying on a wheel–barrow before a baker's door. But they were not the hairs of a man—they were the hairs out of a fur–coat. Nowhere, on land or water, could we see any sign of this boy's uncle. And if WE could not see him, then he is not to be seen.... For John Dolittle—we have done our best."
Then the six great birds flapped their big wings and flew back to their homes in the mountains and the rocks.
"Well," said Dab–Dab, after they had gone, "what are we going to do now? The boy's uncle MUST be found—there's no two ways about that. The lad isn't old enough to be knocking around the world by himself. Boys aren't like ducklings—they have to be taken care of till they're quite old.... I wish Chee–Chee were here. He would soon find the man. Good old Chee–Chee! I wonder how he's getting on!"
"If we only had Polynesia with us," said the white mouse. "SHE would soon think of some way. Do you remember how she got us all out of prison—the second time? My, but she was a clever one!"
"I don't think so much of those eagle–fellows," said Jip. "They're just conceited. They may have very good eyesight and all that; but when you ask them to find a man for you, they can't do it—and they have the cheek to come back and say that nobody else could do it. They're just conceited—like that collie in Puddleby. And I don't think a whole lot of those gossipy old porpoises either. All they could tell us was that the man isn't in the sea. We don't want to know where he ISN'T—we want to know where he IS."
"Oh, don't talk so much," said Gub–Gub. "It's easy to talk; but it isn't so easy to find a man when you have got the whole world to hunt him in. Maybe the fisherman's hair has turned white, worrying about the boy; and that was why the eagles didn't find him. You don't know everything. You're just talking. You are not doing anything to help. You couldn't find the boy's uncle any more than the eagles could—you couldn't do as well."
"Couldn't I?" said the dog.  I haven't begun to try yet, have I? You wait and see!"
Then Jip went to the Doctor and said,
"Ask the boy if he has anything in his pockets that belonged to his uncle, will you, please?"
So the Doctor asked him. And the boy showed them a gold ring which he wore on a piece of string around his neck because it was too big for his finger. He said his uncle gave it to him when they saw the pirates coming.
Jip smelt the ring and said,
"That's no good. Ask him if he has anything else that belonged to his uncle."
Then the boy took from his pocket a great, big red handkerchief and said, "This was my uncle's too."
As soon as the boy pulled it out, Jip shouted,
"SNUFF, by Jingo!—Black Rappee snuff.  His uncle took snuff— Ask him, Doctor."
The Doctor questioned the boy again; and he said, "Yes. My uncle took a lot of snuff."
"Fine!" said Jip. "The man's as good as found. 'Twill be as easy as stealing milk from a kitten. Tell the boy I'll find his uncle for him in less than a week. Let us go upstairs and see which way the wind is blowing."
"But it is dark now," said the Doctor. "You can't find him in the dark!"
"I don't need any light to look for a man who smells of Black Rappee snuff," said Jip as he climbed the stairs. "If the man had a hard smell, like string, now—or hot water, it would be different. But SNUFF!—Tut, tut!"
"Does hot water have a smell?" asked the Doctor.
"Certainly it has," said Jip. "Hot water smells quite different from cold water. It is warm water—or ice—that has the really difficult smell. Why, I once followed a man for ten miles on a dark night by the smell of the hot water he had used to shave with—for the poor fellow had no soap.... Now then, let us see which way the wind is blowing. Wind is very important in long–distance smelling. It mustn't be too fierce a wind—and of course it must blow the right way. A nice, steady, damp breeze is the best of all.... Ha!—This wind is from the North."
Then Jip went up to the front of the ship and smelt the wind; and he started muttering to himself,
"Tar; Spanish onions; kerosene oil; wet raincoats; crushed laurel–leaves; rubber burning; lace–curtains being washed—No, my mistake, lace–curtains hanging out to dry; and foxes—hundreds of 'em—cubs; and—"
"Can you really smell all those different things in this one wind?" asked the Doctor.
"Why, of course!" said Jip. "And those are only a few of the easy smells—the strong ones. Any mongrel could smell those with a cold in the head. Wait now, and I'll tell you some of the harder scents that are coming on this wind—a few of the dainty ones."

For a long time he said nothing. He kept as still as a stone. He hardly seemed to be breathing at all. When at last he began to speak, it sounded almost as though he were singing, sadly, in a dream.
"Bricks," he whispered, very low—"old yellow bricks, crumbling with age in a garden–wall; the sweet breath of young cows standing in a mountain–stream; the lead roof of a dove–cote—or perhaps a granary—with the mid–day sun on it; black kid gloves lying in a bureau–drawer of walnut–wood; a dusty road with a horses' drinking–trough beneath the sycamores; little mushrooms bursting through the rotting leaves; and—and—and—"
"Any parsnips?" asked Gub–Gub.
"No," said Jip. "You always think of things to eat. No parsnips whatever. And no snuff—plenty of pipes and cigarettes, and a few cigars. But no snuff. We must wait till the wind changes to the South."
"Yes, it's a poor wind, that," said Gub–Gub.  Who ever heard of finding a man in the middle of the ocean just by smell! I told you you couldn't do it."
"Look here," said Jip, getting really angry. "You're going to get a bite on the nose in a minute! You needn't think that just because the Doctor won't let us give you what you deserve, that you can be as cheeky as you like!"
"Stop quarreling!" said the Doctor—"Stop it! Life's too short. Tell me, Jip, where do you think those smells are coming from?"
"From Devon and Wales—most of them," said Jip—"The wind is coming that way."
"Well, well!" said the Doctor. "You know that's really quite remarkable—quite. I must make a note of that for my new book. I wonder if you could train me to smell as well as that.... But no—perhaps I'm better off the way I am. 'Enough is as good as a feast,' they say. Let's go down to supper. I'm quite hungry."
"So am I," said Gub–Gub.


Today, 5:38 PM
Hello, can you hear me?
I'm in California dreaming
... about who we used to be.

When we were younger and free...
I've forgotten how it felt before
I've forgotten how it felt before
When we were younger and free...
I've forgotten how it felt before
I've forgotten how it felt before
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