CUC cuc carpel Arabidopsis stage STM expression CUCg-m formation Plant CMM (Figure type wild development shoot (%) meristem {border-style genes required px;} #ffffff;} gene CMMs (Figures gynoecia margins results cells mutant Aida early gynoecium ovule FIL Figure Tasaka meristematic previously primordia sections tissues activity detected factors plants protein CUCg-m, Development Expression Ishida described floral leaf px; regulatory thaliana {font-family Biol. Cell Scale abaxial bars developing developmental font-size indicate organ positioning septum structures μm. (Aida CMMs. COTYLEDON CUP-SHAPED KNOX double; formation. fused initiation late phenotype prepared regenerated region role roles transverse (compare ./dev. ./j.-X...x ./j.cub... CMMs, Curr. Figures MERISTEMLESS Mitsuhiro Nikovics SHOOT Table Takada activities adaxial apical background border-color border-width class critical cuc, cytokinin development. ectopic filamentous form formed fruit hybridization mRNA miR mid organs ovules pair patterning plant produce promote regulation replum reported septum. situ solid; transgenic valve white {font-style {font-weight (Ishida B). Col Early Furutani Galbiati RPL Science Sections Supplementary Yuri addition, affect article auxin bold carried central consistent corresponding courier; differentiation double em; forming function including indicating initiated involved meristems mutations number pattern peaks performed positions produced proteins pt;} reduced reduction stem strong used


Iran to unblock internet once 'abuse' stops – government
'The internet will come back gradually in some provinces where there are assurances the internet will not be abused,' government spokesman Ali Rabiei says
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You might have to start the questions again, or answer new questions about any new symptoms.
Choose the symptom that’s worrying you most and either use this service again or call 111.


CardsWe also make cards!If you are interesting in helping out but you don't have the time to call or video chat, you can contribute by making cards! Contact us down below if you are interested in sending a card to the residents! You do not have to be a registered volunteer to write a card. On the other hand, if you are a facility that would like us to send cards to your residents, contact us! |
Here are some cards we helped make


Mukarama Abdulai's BRACE helps Tyler Junior College to win National Championship
Mukarama Abdulai scored two goals as Tyler Junior College beat Monroe College 4-1 on Saturday to win the 2019 National Junior College Athletic Association (NJCAA) Championship.
The 2018 FIFA U17 World Cup star opened the scoring after eight minutes when she finished off an assist from Natalie Martinez.
But Tyler Junior College were pegged just before the break when Monroe College levelled through Luana Grabias.
Twelve minutes into the second half, Abdulai was at hand to restore their lead with another assist from Martinez.
On the hour mark, the former Black Maidens captain provided an assist for Martinez to score for the Lady Apaches.
With a minute to the end of regulation time Marta Estupinan scored the fourth goal to complete the drubbing.


org.tensorics.core.tree.domain
Interfaces
EditableResolvingContext
ExceptionHandlingNode
Expression
Node
RebuildableNode
RebuildingContext
ResolvingContext
Classes
AbstractDeferredExpression
Contexts
Path
RebuildingContextImpl
ResolvedExpression
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--- layout: default title: About ---
I am a PhD student at the intersection of geography and computer science. I am getting my degree in Urban and Regional Studies at the University of Birmingham. My supervisors are Emmanouil Tranos and Mirco Musolesi For my PhD thesis I am mainly interested in the interplay of geography, friendship networks, and communication patterns.
Besides that I am interested in:
Feel free to get in touch via {% include email.html %}


We have taken part in the East Lindsey Wolds Words Festival for many years. Last year, cultural
solutions uk took over the organisation, marketing and promotion of the festival, and immediately raised the
bar, with, for instance, a programme which had the look and feel of a trendy, happening event. In short, they
brought a touch of va-va-voom to the festival.
—Alan Mumby, Chairman, Far Welter'd (East Lindsey Dialect Society)
' ...FESTIVAL 800 was the pinnacle of the 8ooth anniversary celebrations... '
—Lydia Rusling - Tourism - Lincolnshire County Council
We'll wait before we go travelling until Darren finishes the learning in June. He'd never had done
any of this without you.
 They have a unique overview of the
cultural landscape in Lincolnshire and a fantastic capacity for strategic thinking and dynamic delivery.
—Simon Hollingworth - Freelance Arts consultant
The team are a highly professional and enthusiastic team to work with. Feedback from cultural
groups who they worked with on the Coastal Cultural Strategy, was that cultural solutions uk were approachable,
professional and connected with what was "needed" for culture to develop and be sustainable on the coast.
—Nicola Radford, Principal Development Officer, Economy and Culture, Lincolnshire County Council,
(Coastal Communities Alliance)
We have been working with cultural solutions uk since 2008 when we retained them as our cultural
advisers. Since then they have successfully secured funding for the past three annual beam! festivals before
going on to devise and deliver the festivals. They also researched and implemented a sustainable annual Artists
Market programme, now in its fifth year. What we like about them is they understand the needs of our 800
members' and how the arts can assist in realising their predominately economic aspirations.
—Michael Armstrong, Events and Promotions Manager Lincoln Business Improvement Group
The arts are capable of unlocking the potential of so-called marginalized young people who, for
various reasons, have lost out, cultural solutions uk enabled this to happen!
—Paul Boucher, Lincolnshire Traveller Initiative
We have enjoyed working with cultural solutions uk and think they make a real difference to the
community. They make things happen by giving young individuals a chance to achieve new skills, qualifications
and an enjoyable experience.
—Adele Dewell, Director, Streetz Ahead Theatre Arts Lincoln
We have taken part in the East Lindsey Wolds Words Festival for many years. Last year, cultural
solutions uk took over the organisation, marketing and promotion of the festival, and immediately raised the
bar, with, for instance, a programme which had the look and feel of a trendy, happening event. In short, they
brought a touch of va-va-voom to the festival.
—Alan Mumby, Chairman, Far Welter'd (East Lindsey Dialect Society)
' ...FESTIVAL 800 was the pinnacle of the 8ooth anniversary celebrations... '
—Lydia Rusling - Tourism - Lincolnshire County Council


Glow is a whole new product bringing a modern and intuitive way for households around the country to manage and save energy for heating.
We are a 7 student team studying at Imperial College London. Our objective is simple: taking our ideas further than a proof of concept creating a fully functional device.
Theo Franquet
Group Leading & Website Design
Alexandre Abou Chahine
3D Printing & Heat Transfer Research
Guillaume Ramé
Software, Calculation Back End & Database
XingZi Zhu
User Profiling & Behaviour Research
Yumeng Sun
Secretary & Graphical Design
Matthew Abdul-Rahim
Software, GUI Design and Back End
Ilaria Albanese
Treasurer & Thermometer Module


@***************************************************************************** * Page to display list of defined content formats * * Copyright (c) 2015 MIT Libraries * *****************************************************************************@ @(cformats: List[ContentFormat])(implicit request: play.api.mvc.RequestHeader) @layout.main("Content Formats - TopicHub") {
The hub is completely ignorant about how the content put in it is formatted, so the analyst must specify this information. You may install as many formats as needed, and then you may begin to add tools expecting these formats. For example, scholarly published articles in many disciplines are routinely produced with a standard XML schema (e.g. NLM-DTD/JATs), and this format is widely understood. To add a new content format, click the link below. Browse formats at left.


Introduction¶
SeisIO is a framework for working with geophysical time series data. The project is home to an expanding set of web clients, file format readers, and analysis utilities.
Overview¶
SeisIO stores data in minimalist data types that track record times and other necessary quantities for further processing. New data are easily merged into existing structures with basic commands like +
. Unwanted channels can be removed just as easily. Data can be saved to a native SeisIO format or written to SAC.
Installation¶
From the Julia prompt: press ]
to enter the Pkg environment, then type
add https://github.com/jpjones76/SeisIO.jl; build; precompile
Dependencies should be installed automatically. To run tests that verify functionality works correctly, type
test SeisIO
in the Pkg environment. Allow 10-20 minutes for all tests to complete.
To get started, exit the Pkg environment by pressing Control + C, then type
using SeisIO
Updating¶
From the Julia prompt: press ]
to enter the Pkg environment, then type update
. You may need to restart the Julia REPL afterward to use the updated version.


Go back to start
Filter:
Clear filter
Click on column to sort
We have {{len .}} TBs
Name
Languages
Client
Domain
Subject
Owner
LastModified
No. of entries
{{range .}}
{{.FriendlyName}}
{{.Languages}}
{{.Client}}
{{.Domain}}
{{.Subject}}
{{.TBOwner}}
{{.LastModified}}
{{.NumEntries}}
{{end}}
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The high technology that using in this game. Catch up with us and get more interest About AR Game
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|
Class Summary |
| CArrowButton |
A CArrowButton is a swing-compatible widget that
allows clients to display a semi-transpanrent arrow
in any of the four traditional directions:
NORTH, SOUTH, EAST, WEST. |
| CBarGraph |
|
| CBorderlessButton |
|
| CButton |
A CButton is a swing-compatible widget that allows clients
to display an oval button with an optional text. |
| CColorChooser |
|
| CFileHandler |
|
| CGlassScrollPane |
|
| CGraphite |
|
| CGraphiteButton |
A CButton is a swing-compatible widget that allows clients
to display an oval button with an optional text. |
| CGraphiteSquareButton |
|
| ChartData |
|
| CHeader |
|
| CHoverScrollPane |
The CHoverScrollPane is a swing-compatible widget that
allows clients of this CScrollPane to control the
width of the thumb, the color of the thumb, and
and the color of the track. |
| CIconButton |
A CIconButton is a swing-compatible widget that allows clients
to display a circular button with an optional icon. |
| CLabel |
|
| CLineGraph |
|
| CMenuItem |
|
| CPopupMenu |
|
| CProgressBar |
|
| CQueryField |
|
| CRadioactiveButton |
|
| CScrollPane |
The CScrollPane is a swing-compatible widget that
allows clients of this CScrollPane to control the
width of the thumb, the color of the thumb, and
and the color of the track. |
| CSignLabel |
it's like a neon sign, only cooler |
| CSlider |
|
| CSliderPane |
The CSliderPane is a swing-compatible widget that
allows users to interface with a slider through
the slider itself and three text fields. |
| CSVFilter |
|
| CTabButton |
|
| CTable |
CTable is a generic table with a look and feel that matches the
rest of codeblocks. |
| CTextField |
|
| CToolTip |
|
| CToolTipUI |
|
| CTracklessScrollPane |
The CScrollPane is a swing-compatible widget that
allows clients of this CScrollPane to control the
width of the thumb, the color of the thumb, and
and the color of the track. |
| CWheeler |
displays item by their preferred size |
| CWheelItem |
|
| DefaultCanvas |
A Canvas is a low-level, mutable, CSwing component. |
| GlassCard |
A GlassCard is used by glass explorers explorers as a
mediator to their canvases. |
| GlassExplorer |
See documentation for Explorer. |
| GlassExplorerEvent |
|
| GlassHorizontalBar |
A HorizontalBar is a vertical scroll bar that operates in conjuction with
the PARALLEL bouding model range passed as an argument in the
constructor. |
| GlassVerticalBar |
A VerticalBar is a vertical scroll bar that operates in conjuction with
the PARALLEL bouding model range passed as an argument in the
constructor. |
| GraphicsManager |
GraphicsManager maintains graphics context information that maybe useful for many aspects of
graphics rendering. |
| HorizontalBar |
A HorizontalBar is a vertical scroll bar that operates in conjuction with
the PARALLEL bouding model range passed as an argument in the
constructor. |
| HoverHorizontalBar |
A HorizontalBar is a vertical scroll bar that operates in conjuction with
the PARALLEL bouding model range passed as an argument in the
constructor. |
| HoverVerticalBar |
A VerticalBar is a vertical scroll bar that operates in conjuction with
the PARALLEL bouding model range passed as an argument in the
constructor. |
| ImageManager |
|
| LabelWidget |
|
| LowerRightCorner |
A Graphical Square Pane with the same same color as the thumb. |
| LRUCache<K,V> |
This class implements a Least Recently Used cache that stores arbitrary key/value pairs. |
| MagicExplorer |
See documentation for Explorer. |
| Navigator |
A navigator navigates between different Explorers. |
| NumberFormatter |
Class for displaying a number based on a specified
level of precision and other display parameters. |
| PopupExplorer |
See documentaton for Explorer. |
| SliderBlueprint |
Contains the location for the CSlider components
(in pixels) |
| Sound |
|
| SoundManager |
Manages the sounds for StarLogoBlocks |
| StackCard |
A StackCard is used by explorers to interface between
a CSwing Canvas and a CSwing explorer. |
| StackExplorer |
See documentation for Explorer. |
| TabbedExplorer |
See documentation for Explorer. |
| TabCard |
A Tab Card is used by glass explorers explorers as a
mediator to their canvases. |
| VerticalBar |
A VerticalBar is a vertical scroll bar that operates in conjuction with
the PARALLEL bouding model range passed as an argument in the
constructor. |
| WindowExplorer |
A WindowExplorer is an Explorer that explores its set of canvases by displaying a panel
of tabs on the top or bottom . |
| XMLStringWriter |
XMLStringWriter is a very simple and naive writer to create XML Strings that
are nicely formatted. |


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“Don't let the mistakes and disappointments of the past control and direct your future.”
Zig Ziglar


org.jclouds.aws.ec2.domain
Classes
AWSRunningInstance
Builder
IAMInstanceProfile
IAMInstanceProfileRequest
LaunchSpecification
MonitoringState
PlacementGroup
RegionNameAndPublicKeyMaterial
Spot
SpotInstanceRequest
State
Type


NEWS.md
line_via()
for identifying intermediary points on a transport networkSpatialLinesNetwork()
fixed (see #249)geo_length()
returns numeric vector of line lengths from sp or sf objects.?route_graphhopper
no longer mentions the depreciated ‘bike2’ profile - see #246
?route_osrm
mentions that the public API only routes for cars - see #246
introducing-stplanr
vignette to show new function and make more robustsf::st_length()
, used in SpatialLinesNetwork()
.sfNetwork
objects: now only plots the geometry by default.SpatialLinesNetwork()
and plot()
for spatial networks.sum_network_routes()
fixed (see #240).library(stplanr)
, making it less tied to sp. This is a continuation of the work to support sf and will make it easier for the package to work with alternative representations of geographic data.geo_select_aeq.sf()
was fixed by Jakub Nowosad in pull #238
od_aggregate.sf()
was fixed making it much fastergeo_bb()
supercedes bb2poly()
. The new function can return polygons, points and matrix objects determined by the output
argument. It also allows bounding boxes to be extended in metres, and scaled in x and y dimensions.geo_code()
now uses nominatim by default to find locations on the maps.od_coords()
takes a wide range of input data types to return a consistent output representing OD data as a data frame of origin and destination coordinates. This is used behind the scenes to make other functions more modular.Plans for the next release
route()
function for routing. This is more flexible and user-friendly than the existing line2route()
and route_*()
functions it enhances.route_cyclestreet()
now also called (correctly) route_cyclestreets()
geo_code()
function replaces dependency on RGoogleMapsgoogle_dist()
fixedcombinations
added to sum_network_routes()
so it runs quicker - see pull/177.sum_network_routes()
, weightfield()
and find_network_nodes()
- see e.g. example(sum_network_routes)
for details.l_poly
added.SpatialLinesNetwork()
fixed._each()
dplyr functions replaced with equivalent _at
or _all
functions. See here for more.vignette("stplanr-paper")
for details.introducing-stplanr
vignette has been updated. It now provides a more basic introduction for people new to R for spatial and transport data.line2route()
has been refactored to improve error detection and allow n_processes
arguments. Thanks @nikolai-b. See pull/151 for details.line_match()
function added, a wrapper around rgeos::gDistance()
, to find similar routes.od_aggregate()
and sp_aggregate()
have been added, to enable OD data to be aggregated to new geographic levels.#141
fixed: viaroute()
works again.bidirectional = TRUE
returns a different result in line_bearing()
now.onewayid()
is now a generic function, meaning it can handle spatial and non-spatial dataline2route()
allow you to specify variables to join-by - also has updated and more sensible defaultsod_id_order()
to put origin-destination ids in order, to identify 2 way duplicates (split out from onewayid()
)route_cyclestreet()
leading change_elev
and av_incline
being wrong now fixedNEW FEATURES
od2line()
. See example(od2line)
for an example.destinations
for showing how OD matrix with destinations can be converted to spatial datalist_output
allows the route information to be saved as a list, allowing save_raw = TRUE
(which does not return a Spatial
object) to be passed to the route_
function.BUG FIXES
line2route()
(#124) fixedNEW FEATURES
New function reproject()
is a simple wrapper around spTransform()
that uses crs_select_aeq()
to convert a spatial object in geographic (lat/lon) coordinates into on with projected coordinates, with units of 1 m. This is useful for various spatial operations, such as finding the length and area of an object.
Implement gprojected()
, a function for performing GIS operations on a temporary, projected, version of spatial objects.
Addition of line_bearing()
to return the bearing of lines based on start and end points.
Addition of angle_diff()
for finding the angular difference between lines: are they roughly parallel or perpendicular?
BUG FIXES
NEW FEATURES
Update to OSRM functions to support API v5.
New parameter byvars
in the overline()
function, to allow disaggregation of results by a grouping variable (see example(overline)
).
Faster implementation of od2line()
: od2line2()
. Plan is to replace the original if no issues are found with new implementation.
New function od2odf()
which converts OD data into a dataframe of origins and destinations (feeds od2line2()
but also useful as self-standing function).
New argument new_proj
in buff_geo()
allows the results to be exported to any coordinate reference system (CRS).
New function gprojected()
generalises concept of buff_geo()
, building on crs_select_aeq()
to allow any GIS query to be conducted on a temporary projected version of spatial objects with geographical CRSs.
New function od_dist()
can quickly calculate Euclidean distances of OD pairs without converting to spatial objects.
BUG FIXES
Bug fix in onewayid()
so it captures all lines.
Various improvements to documentation and code.
NEW FEATURES
Interface to the Google Distance Matrix API with dist_google
.
New transport planning API added, with route_transportapi_public
(for testing).
Update to line2route
, allowing it to accept different routing funtions via the new argument route_fun
(for testing - tested with route_fun = route_cyclestreet
).
New functions for creating origin-destination data frames (point2odf
) and SpatialLinesDataFrames (points2flow
).
Addition of n_vertices
and is_linepoint
for identifying the number of vertices in spatial objects and whether the ‘line’ is really a point.
BUG FIXES
line2route
refactored, with 10 fold speed increases on large (1000+) batches of lines.NEW FEATURES
Addition of new class definition SpatialLinesNetwork
, methods for plot
and summary
and functions calc_network_routes
and find_network_nodes
allowing fast route calculations via igraph and other network analysis functions.
Functions for removing beginning and end of lines: toptail
and toptailgs
. Helper functions buff_geo
, crs_select_aeq
and line2points
added.
Functionality for reading in the UK’s stats19 data: read_stats19_*
functions download, unzip and re-categorise the data.
read_table
functions added for reading Australian OD data.
decode_gl
added to decode Google polylines and other functions for querying and reading data from OSRM services.
gtfs2sldf
added to import GTFS routes as SpatialLinesDataFrames.


Using CartoDB 2.0 to geocode private prisons in comparison to state-by-state incarceration rates
16 Jul 2014The last post I wrote about CartoDB used the CartoDB API and Leaflet.js to produce a hover tooltip map of census data. Looking back, while it's quite useful to learn CartoDB's API, it's now possible to achieve those same visual cues and interactivity without spending time wading through complex, undocumented JavaScript. You can now do everything I did here using little more than CartoDB's frontend CMS.
So, when I ran across this database of all prisons and correctional facilities in the nation on Enigma.io, I used the dataset to put together this visualization displaying all 137 private prisons and correctional facilities – all of which are owned entirely by just five different private enterprises – and then added a simple chloropleth layer displaying incarceration rates by state underneath.
Can't see what's going on too well at the resolution below? Here's a standalone package:
[su_heading size="25"]Click here to view the project live[/su_heading]
(yes, it's responsive!)
This visualization is a case in which deeper statistical analysis and consideration of multiple variables would've bolstered the possible argument that private prisons lead to higher incarceration rates, if such a claim is true, that is (i.e., determining a statistically significant correlation between number of inmates in each state with the number of private prisons or correctional facilities located there).
Such a data-driven study of the private prison industry conducted by U.C. Berkely Ph.D. student Chris Petrella concluded that blacks are disproportionately incarcerated in private prisons as opposed to public jails.
A similar methodology could be applied to private prison populations in general, as many private prisons are required by state or local law to have a minimum quota of around 90 to 95 percent occupancy to keep their government contracts. It would be interesting, too, to visualize that data in a manner more easily accesible to the average user than an academic research journal.
Tools I would've used before new CartoDB:
- Batch geocode by findlatitudelongitude to geocode the address data into lat/long coordinates.
- ColorBrewer2.org to find equidistant color scales.
- Locating .shp, .kml or .geojson polygons for the U.S. states.
- Excel to merge tables and bind geographic with numeric data.
- A custom JavaScript powered control panel – like the one I used here – to categorize the locations by owner.
- Leaflet.js
- MapBox
- Who knows?


{% extends "cctbx/header.html" %} {% load static %} {% block content %}
This tutorial describes some non-obvious features of python and of the cctbx.
Whenever you say something like:
b = [1,2,3] # a list of values
a = b # does a get the value of b or is it a pointer
you want to ask yourself whether a is a pointer to b (that is, they are the same object with different names), or whether a is a new (separate) object. The reason this is important is that it affects what happens to b when you change something about a.
Let’s look at this example. We are going to change the second element of a and see what happens to b:
print(a) # prints out [1,2,3]
print(b) # prints out [1,2,3]
a[1] = 10 # set value of second element of a
print(a) # prints out [1,10,3]
print(b) # prints out [1,10,3]
So in this case, a is just a pointer to b. We change a or b and the other changes too. We could use the list() function to instead make a new object:
d = list(b) # make a new list from b
Now if we change b, we don’t change d:
print (d) # d looks like b: [1, 10, 3]
b[0] = 20 # set value of first element of b
print(d) # d is not a pointer to b: still prints [1, 10, 3]
What about a slightly more complicated case where we have an object (a list in this case) that contains other objects (another list):
f = [6,7,8] # f is an object (a list)
x = [1,2,f] # x is a list with some numeric values and an object (f)
print(x) # looks like [1, 2, [6, 7, 8]]
Let’s make a new object with the list() function:
y = list(x) # make a new list and call it y
The result is not exactly what you might expect. The object y is a new list, so as before if you change an element of x you do not change y:
print(y) # looks like [1, 2, [6, 7, 8]]
x[0]=7 # replace element 0 of x
x[2]=[3,4,5] # replace element 2 of x
print(y) # y is still [1, 2, [6, 7, 8]]
However if you change something about the object f (change a value in this object), you do change f within both x and y. Let’s run this from the beginning:
f = [6,7,8] # f is an object (a list)
x = [1,2,f] # a list with some numeric values and an object (f)
y = list(x) # make a new list and call it y
print(y) # looks like [1, 2, [6, 7, 8]]
x[0] = 100 # change element 0 of x
print(x) # changed:[100, 2, [6, 7, 8]]
print(y) # still looks like [1, 2, [6, 7, 8]]
f[0] = 32 # change the object f
print(x) # changed in x:[100, 2, [32, 7, 8]]
print(y) # the object f within y changes [1, 2, [32, 7, 8]]
Note (see below) that if we had instead made a deep copy of x, the object f would have been deep copied as well so that changing f would not have changed y at all.
Here is documentation on how all this works for Python: https://docs.python.org/3/reference/datamodel.html
In general when you are working with an object in Python or CCTBX and applying a member function of that object, you have to ask yourself whether you are changing the original object and whether the return value (if any) is a pointer to the original object or a copy that is separate. Some things to keep in mind:
The general rules in CCTBX for what to expect about something returned by a member function of some object are:
Note: If your goal is to change a specific part of a CCTBX object, usually the best approach is to use selections. You select part of the object (some elements), then you then set the values of those selected elements to values that you supply. See the sections on model and flex array selections for how to do this.
In general, you have to look at the documentation or the definition of the object or carry out some tests to find out for sure what is going to happen when you access a member function of a CCTBX object. Here are some examples.
Let’s set up a map_data object to work with:
from iotbx.map_model_manager import map_model_manager # load map_model_manager
mmm=map_model_manager() # get an initialized instance of the map_model_manager
mmm.generate_map()# get a model from a small library model and calculate a map
map_data = mmm.map_manager().map_data() # the map as flex.double() 3D array)
If we just make an assignment of map_data to a new array, these are the same object and changing one in any way changes the other:
map_data_copy = map_data # just a pointer to map_data
If we deep_copy() map_data, the new copy is totally separate, changing one has no effect on the other. The same holds for using the Python deepcopy() function:
map_data_deep_copy = map_data.deep_copy() # totally new array
from copy import deepcopy # import deepcopy
map_data_deepcopy = deepcopy(map_data) # totally new array
A similar situation occurs frequently in Python. If we have a list and use the sort() member function, this does not return anything; it just changes the list itself:
a = [5,3,8] # a list of numbers
a.sort() # sort the list. Nothing is returned
If we instead use the count() member function, a value is returned and the object is not changed:
n = a.count(3) # count values of 3 and return the number
print(n) # prints 1
For a CCTBX flex array, selecting a part of the array generally returns a new object. Let’s set up a little 1D array:
from scitbx.array_family import flex # import flex
array = flex.double() # set up a flex.double() array
array.append(100) # put in a value of 100
array.append(200) # and a value of 200
print(list(array)) # prints [100.0, 200.0]
Let’s select part of the array:
sel = (array == 100) # identify array elements equal to 100
selected_data = array.select(sel) # returns new object
print(list(selected_data)) # prints [100.0]
Now if we change the original array, the part that was selected and returned is not changed: array[0] = 23 # change original array
print(list(selected_data)) # prints [100.0]
Many CCTBX objects have member functions that return the result of some calculation. In these cases the object that is returned is a new object, and changes made to this new object do not affect the original one. For example, complex_double() objects have a function parts() that returns the real and imaginary parts of the object:
complex_array = flex.complex_double() # a complex double array
complex_array.append((1+2j)) # append the complex number (1+2i)
complex_array.append((23-6j)) # append the complex number (23-6i)
a,b = complex_array.parts() # pointers a and b to the real and imaginary parts
print(list(complex_array)) # print out the array: [(1+2j), (23-6j)]
print(list(a), list(b)) # prints ([1.0, 23.0], [2.0, -6.0])
The parts of the complex array are calculated with the parts() function. They are not existing attributes of the complex array. Now if we change a or b, the original is not changed:
a[1] = 99 # change pointer to a
print(list(complex_array)) # still prints out [(1+2j), (23-6j)]
Note that this result is not obvious. If the flex complex_double() type happened to store complex numbers as two lists, one of the real parts and one of the complex parts, then the parts() function would have returned pointers to each of these two lists and changing a or b would have changed the original.
Other CCTBX functions have member functions that return a pointer to an existing attribute. A map coefficients array has attributes of indices and data, and you can get a pointer to the data with the function data(). Let’s get a map coefficients object:
map_coeffs = mmm.map_manager().map_as_fourier_coefficients() # map coeffs
print(map_coeffs.data()[0]) # (22.1332152449-33.1246974818j)
And use the data() function to get a pointer to the data:
data = map_coeffs.data() # the map coefficients themselves
print(data[0]) # the first map coefficient ((22.1332152449-33.1246974818j))
If we change the value of data[0], it will change the map coefficients too:
data[0] = (10+6j) # set value of data[0]
print(map_coeffs.data()[0]) # prints (10+6j)
Note that the map coefficients object has another function called phases() that seems somewhat related but that performs a calculation instead of returning an existing attribute. Let’s get the return result from the phases() function:
phases = map_coeffs.phases() # new object with indices and phases only
The contents of phases.data()
have been calculated from map_coeffs.data() and changing the information in phases.data() has no effect on the values in map_coeffs.data().
A final example is a comparison of the member functions as_1d()
and as_float()
for a 3D flex array. The as_1d()
function returns a new object in which the data is just a pointer to the original. The as_float()
function that seems similar but which has a different behavior: it performs a calculation and returns a new object in which the data are completely separate from the original data:
map_data = mmm.map_manager().map_data() # 3D flex.double array
map_data_as_1d = map_data.as_1d() # new object, data are shared
map_data_as_float = map_data.as_float() # new object, new data
Let’s print out starting values:
print(map_data[0], map_data_as_1d[0], map_data_as_float[0]) #
Now set the value of map_data and see if the others change:
map_data[0] = 999. # set map_data
print(map_data[0], map_data_as_1d[0], map_data_as_float[0]) #
The map_data and map_data_as_1d both change, but the map_data_as_float is not changed.
One way to be sure that the object you are working with is a completely new one is to use the Python deepcopy() function or a member function of a cctbx class called deep_copy(). Each of these makes a new copy. Each of these also increases memory usage by the size of that new copy.
You use the Python deepcopy() function only when you really need it. It makes a completely new copy of whatever you specify:
from copy import deepcopy # import deepcopy
x = [1,2,[6,7,8]] # a list with some values and a list
y = deepcopy(x) # completely new copy of x. Change x; nothing happens to y
To see the use of deep_copy() in a CCTBX object, let’s set up a map_data object:
from iotbx.map_model_manager import map_model_manager # load map_model_manager
mmm=map_model_manager() # get an initialized instance of the map_model_manager
mmm.generate_map()# get a model from a small library model and calculate a map
map_data = mmm.map_manager().map_data() # the map as flex.double() 3D array)
print(map_data[27]) # prints original value of -0.0131240713008
We can create a pointer to map_data:
map_data_pointer = map_data # just points to map_data
or make a deep copy of map_data:
map_data_deep_copy = map_data.deep_copy() # completely new data
If we change map_data it changes map_data_pointer but not map_data_deep_copy:
map_data[27] = 100 # set value of map_data
print(map_data_pointer[27]) # prints 100
print(map_data_deep_copy[27]) # prints original value of -0.0131240713008
Note that making a deep_copy uses as much new memory as the original array, while using a pointer uses almost no memory. Therefore you want to make a deep_copy only when it is necessary.
if (not x)
or if (x)
expressions to test whether x is defined. Use if (x is not None)
In many cases you can get away with saying:
x = None
if (not x): # don't use this
print("""not x can be 0, None, False, "", [], {}, (),...""") # happens if x is (0, None, False, "", [], {}, (), ...)
but you should not use this construction because it is not clear whether you really meant to do something if x is zero, or if you meant to do it if x is None, or if you meant to do it if any of these occur.
Instead be clear and say:
if (x is None): # Use this instead
print("x is None") # happens if x is None (only)
Similarly be clear and say:
if (x is not None): # Use this too
print("x is not None") # happens unless x is None (only)
You might be tempted to make a function call with a default argument that is an empty list, like this:
def my_bad_function(value, current_list = []): # don't do this
current_list.append(value) # current_list from previous call
return current_list # returns current_list
This is supposed to use an empty list as the value of current_list if current_list is not supplied. This works the first time you do it:
print(my_bad_function(1)) # prints [1]...current_list was []
However the second time you call it without specifying the value of the default current_list, the value of current_list from the first call gets used:
print(my_bad_function(2)) # prints [1, 2] ...current_list was [1]
You can get the behavior that (presumably) you want by using None in the function call and setting the value of current_list to [] if its value is None:
def better_function(value, current_list = None): # ok way
if current_list is None: # catch uninitialized current_list
current_list = [] # set its value to []
current_list.append(value) # works
return current_list # returns current_list
print(better_function(1)) # prints [1]...current_list was []
print(better_function(2)) # prints [2]...current_list was []
Some CCTBX map functions assume that the map repeats indefinitely with a periodicity equal to the size of the map in each direction. This can sometimes result in these functions returning values that are not what you expect. An example is the tricubic_interpolation function.
You can access the value of a map at a grid point simply by referring to it: value = map_data[1,2,3] gives the value of the map at the grid point (1,2,3).
You can get interpolated values of a map with the tricubic_interpolation function, which works on fractional coordinates. If your map has a size of (10,10,10), then interpolated_value = map_data.tricubic_interpolation((0.1,0.2,0.3)) will give you the value at the same grid point (and these values will match).
Now what happens if we use grid points or fractional coordinates that are outside the bounds of the map? A CCTBX map with a size of (10,10,10) often would have gridding running from (0,0,0) to (9,9,9). The grid point (10,10,10) typically is assumed to have a value the same as the grid point at (0,0,0). This is referred to as "wrapping", as the map wraps around with a period equal to the size of the map.
If you try to access the value of the (10,10,10) grid point with value = map_data[10,10,10] you will get an error saying that an index is out of range (all three are out of range because the map only goes from 0 to 9 in each direction.)
On the other hand, if you try to access the value at the same place using interpolated_value = map_data.tricubic_interpolation((1.0,1.0,1.0)) you will get the value of the map at (0,0,0).
Why the difference? The tricubic_interpolation assumes that the map is wrapped. The interpolation process uses map values at grid points around the (x,y,z) coordinate to estimate the value at (x,y,z). If one of these grid points falls inside the bounds of the map, the value is taken directly from the map. Any grid points that do not fall inside the bounds of the map (0 to 9 in each direction in our example) are mapped inside the bounds by adding or subtracting multiples of 10 (or whatever the number of grid points along each direction).
Consequently the tricubic_interpolation function will give the same answers for (x,y,z) and (x+l, y+n, z+m) where l, n and m are any integers.
If your map is actually not wrapped, that is, your map does not repeat indefinitely, you need to make sure you do not use tricubic_interpolation to refer to any points outside the bounds of your map. Additionally, be aware that tricubic_interpolation can give incorrect values for points that are near the edges of your map because it may use values from the opposite face of the map when it is interpolating.


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Formerly a trade hub for spices, sandalwood and ivory, Trivandrum which was called the ‘evergreen city of India’ by Mahatma Gandhi is now an immense IT nest contributing to nearly 80% of the state’s software exports. Ranked as one of the best cities in Kerala to live in, it is mainly used by tourists as a springboard to visit nearby Kovalam and Varkala. Rookie mistake. Given a chance, this city will astound you on what all it has to offer. A zoo which houses a lion called Simba, who inspired Yann Martel’s ‘Life of Pi’, Shri Chitra Art Gallery showcasing the infamous paintings of Raja Ravi Varma, Napier Museum with its blend of Mughal artifacts with traditional Kerala architecture, and sunset cruises along pristine backwaters hugging rustic villages.
A famous port of international spice trade in ancient India, the present day Kollam is famous for its varied modern industries including cashew nut processing and research, fisheries, etc. Its association with the renowned Ashtamudi Lake makes it the gateway to the magnificent backwaters of Kerala.
Situated near the Western Ghats and bordered by the hills, Pathanamthitta district is a treat to eyes with its vast unending stretches of forests, rivers and rural landscapes. Blessed by nature, the district is famous for its scenic beauty, fairs and festivals.
Alappuzha, famous for its boat races, beaches, marine products and coir industry, is a world renowned backwater tourist destination of India. Kuttanad, Alappuzha backwaters and Alappuzha beach are the must-see tourist attractions in the district.
Kottayam is blessed with nature's bounty in abundance. It is the pride of God's own country with its beautiful churches, charming nearby hill stations, significant temples, and refreshing waterfalls. The Western Ghats and the backwaters add to the beauty to this undisturbed, rustic town of Kerala.
This rugged hill resort is known for its wildlife sanctuaries, beautiful bungalows, tea factories, rubber plantations and forests. Idukki's speciality lies in 650 feet long and 550 feet high arch dam across the Kuravan Kurathi mountain, which is the largest in the country.
Ernakulam in Kerala is considered as a major commercial area in Kochi that earned the name ‘Commercial Capital Of Kerala’. This ancient city grows with its electrifying ambience as time progresses. A rare yet beautiful combination of quaint charm and modern features makes Ernakulam in Kerala a must-visit place.
Thrissur is known as the cultural capital of Kerala, and the land of Poorams. The district is known for its ancient temples, churches, and mosques. Thrissur Pooram is the most colourful temple festival in Kerala.
Well, not all the tourist destinations in Kerala is all about scenic coastlines; Palakkad is just one of them! Though this city is deprived of any coastal line, it has something distinctive that makes it a must visit place! Palakkad is the ‘Gateway to Kerala’; it connects the India’s Spice Captial with other parts of the country.
A famous centre for Hindu-Vedic learning and Islamic philosophy, the temples and mosques of this region are well known for their spectacular festivals.
Today Kozhikode is one of the best cities in Kerala. Endowed with lush green countrysides, serene beaches, historic sites, wildlife sanctuaries, rivers, and hills, this city enchants both its inhabitants and its guests. With its unique culture and friendly ambiance, Kozhikode is a wondrous destination for all.
This popular town is famous for wildlife and its spice plantations. Wayanad is part of a forest reserve, located on the border of Tamil Nadu and Kerala. Because of it wild green coverage it offers great scenic beauty along with the experience of seeing wildlife.
Kannur is the birthplace of looms and immortal ritual folklore (team and folk music, etc.) and has always been a fascinating place. Land of looms and toys is also an attractive place in Kannur in Kerala. This historic area of Kerala has many attractions, including beaches, temples, hills, and other monuments.
The northernmost district of Kerala, Kasaragod is famous for its handlooms and its 293 kilometre long coastline. With its cultural uniqueness, the place is known as the land of gods. Rich in history, Kasaragod is home to the largest and best preserved fort in the State - Bekal.


In this workshop we covered the entire process from an idea through design to a finished gerber files necessary to create a copy of an Arduino.
If we could do it in an hour just imagine how straightforward it must be! No wonder there are so many copies on Amazon...
Apart from the Kicad part which is quite well covered here we also glimpsed over the design mindset necessary for success. In short: don't forget what you're trying to achieve.
Sorry, no presentation for this week


Kibra by-election: Victor Wanyama's message to brother Mariga
by Robert Abong'oTottenham Hotspurs midfielder and Harambee Stars captain Victor Mugubi Wanyama has taken to his social media to wish his brother McDonald Mariga all the best in the ongoing Kibra by-election.
Mariga is vying for the Member of Parliament seat under the Jubilee ticket and has expressed desire to change the face of Kibra once elected.
Through his Facebook, Wanyama wished Mariga luck, posting a picture of his brother donning the red Jubilee Party campaign uniform. Kibra residents in Nairobi County are today voting in a showdown pitting the Handshake/Kieleweke team against Deputy President William Ruto's team Tangatanga. The Handshake team comprises politicians brought together following a unity pact between Raila and President Uhuru Kenyatta in 2018. ODM - a subscriber of the handshake - has fielded Bernard Imran Okoth while Ruto’s Jubilee has ex-football sensation McDonald Mariga. The two are touted as the front runners based on the spirited campaigns that they have staged in the chase for the votes.
The constituency has five wards – Makina, Laini Saba, Sarang’ombe, Woodley/Kenyatta Golf Course and Lindi with 118,658 votes. Laini Saba 17,455 voters, Lindi 16,688, Makina 25,695, Woodley/ Kenyatta Golf Course 28,066 and Sarang’ombe 30,754. The by-election has attracted 24 candidates following the death of MP Ken Okoth in July after a protracted battle with cancer.
Do not miss out on the latest news. Join the Standard Digital Telegram channel HERE.


Main aerials in use are now a 80/40/20 Trapped Dipole, with a 20m end fed Wire in an inverted L configureation, tuned against ground. Obviously it is quite noisy at times living in suburbia.


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High Performance
JSON Parsing
Haskell Library
Goals
Small memory footprint
Traditional DOM
Succinct data structures
Document navigation and queries
Streaming
Cursors
Fast performance
Traditional DOM
Laziness
& SIMD
Succinct Data Structures
A data structure which uses an amount of space that is "close" to the information-theoretic lower bound,
but still allows for efficient query operations
Rank
\[(Q, Σ, δ, q_{0}, F)\]
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If you know as a minimum the date of birth AND gender AND surname, you can check against NHS records to find the right individual. Optionally, you can also add postcode and/or first name into your search. Enter the details you know and click the 'Lookup NHS Number' button below.
Once you get a match you can click it to copy the NHS number. Don't forget to Save the record afterwards.


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Each score choise gives neo if completed or takes if not.


Startingkdbplus/tables
Contents |
4. Kdb+ Tables
4.1 Overview
A basic understanding of the internal structure of tables is needed to work with kdb+. The structure is actually quite simple, but very different from conventional databases.
This section gives a quick overview, followed by an explanation of the sp.q script, and then a typical table for stock data. After completing this, you should read the page kdbplus database, which has a detailed comparison of kdb+ and conventional RDBMS.
Kdb+ tables are created out of lists. A table with no key columns is essentially a list of column names associated with a list of corresponding column values, each of which is a list. A table with key columns is internally built from a pair of tables - the key columns associated with the non-key columns.
Kdb+ tables are created in-memory, and then written to disk if required. When written to disk, smaller tables can be stored in a single file, while larger tables are usually partitioned in some way. The partitioning can be seen when viewing the file directories, but the table is treated as a single object within a q process.
4.2 Creating Tables
There are two ways of creating a table. One way explicitly associates lists of column names and data; the other uses a q expression that specifies the column names and initial values. The second method also permits the each column's datatype to be given, and so is particularly useful when a table is created with no data.
- create table by association:
q)tab:flip `items`sales`prices!(`nut`bolt`cam`cog;6 8 0 3;10 20 15 20) q)tab items sales prices ------------------ nut 6 10 bolt 8 20 cam 0 15 cog 3 20
- create table by specifying column names and initial values:
q)tab2:([]items:`nut`bolt`cam`cog;sales:6 8 0 3;prices:10 20 15 20) q)tab~tab2 / tab and tab2 are identical 1b
The form for the second method, for a table with j primary keys and n columns in total, is:
t:([c1:v1;...;cj:vj]cj+1:vj+1;...;cn:vn)
Here table t is defined with column names ci, and corresponding values vi. The square brackets are for primary keys, and are required even if there are no primary keys.
4.3 Suppliers and Parts
The script sp.q defines C.J. Date's Suppliers and Parts database. You can view this script in an editor to see the definitions. Load the script with:
q)\l sp.q
Table s
Table s has a primary key column, also called s, given as a list of symbols which should be unique. Note that in this example, the name "s" is used both for the table and the primary key column, but this is not required.
The remaining columns are of type symbol, integer, symbol.
s:([s:`s1`s2`s3`s4`s5] name:`smith`jones`blake`clark`adams; status:20 10 30 20 30; city:`london`paris`paris`london`athens)
Display in q:
q)s s | name status city --| ------------------- s1| smith 20 london s2| jones 10 paris s3| blake 30 paris s4| clark 20 london s5| adams 30 athens
Note that the column types are set from the data given. If this were first created as an empty table, say table "t", then the column types could be defined explicitly as follows:
q)t:([s:`$()]name:`$();status:"I"$();city:`$())
Insert a row:
q)`t insert (`s1;`smith;20;`london) ,0 q)t s | name status city --| ------------------- s1| smith 20 london
Table p
Table p is created much like table s. As before, the table name and primary key name are both the same:
p:([p:`p1`p2`p3`p4`p5`p6] name:`nut`bolt`screw`screw`cam`cog; color:`red`green`blue`red`blue`red; weight:12 17 17 14 12 19; city:`london`paris`rome`london`paris`london)
Display in q:
q)p p | name color weight city --| ------------------------- p1| nut red 12 london p2| bolt green 17 paris p3| screw blue 17 rome p4| screw red 14 london p5| cam blue 12 paris p6| cog red 19 london
Table sp
Table sp is defined with no primary key. Columns s and p reference tables s and p respectively as foreign keys. The syntax for specifying another table's primary key as a foreign key is:
`tablename$data
The definition of sp is:
sp:([] s:`s$`s1`s1`s1`s1`s4`s1`s2`s2`s3`s4`s4`s1; p:`p$`p1`p2`p3`p4`p5`p6`p1`p2`p2`p2`p4`p5; qty:300 200 400 200 100 100 300 400 200 200 300 400)
Display in q:
q)sp s p qty --------- s1 p1 300 s1 p2 200 s1 p3 400 s1 p4 200 s4 p5 100 ...
4.4 Stock Data
The following is a typical layout populated with random data. The definitions are in script trades.q in the start directory.
Load as:
q)\l start/trades.q
A trade table might include: date, time, symbol, price, size, condition code:
q)trades:([]date:`date$();time:`time$();sym:`symbol$(); price:`real$();size:`int$(); cond:`char$()) q)`trades insert (2010.02.21;10:03:54.347;`IBM;20.83e;40000;"N") q)`trades insert (2010.02.21;10:04:05.827;`MSFT;88.75e;2000;"B") q)trades date time sym price size cond --------------------------------------------- 2010.02.21 10:03:54.347 IBM 20.83 40000 N 2010.02.21 10:04:05.827 MSFT 88.75 2000 B
The ? verb will generate random data:
q)syms:`IBM`MSFT`UPS`BAC`AAPL q)tpd:100 / trades per day q)day:5 / number of days q)cnt:count syms / number of syms q)len:tpd*cnt*day / total number of trades q)date:2010.02.21+len?day q)time:"t"$raze (cnt*day)#enlist 09:30:00+15*til tpd q)time+:len?1000 q)sym:len?syms q)price:len?100e q)size:100*len?1000 q)cond:len?" ABCDENZ" q)`trades:0#trades / empty trades table q)`trades insert (date;time;sym;price;size;cond) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 .. q)trades:`date`time xasc trades / sort on time within date q)5#trades date time sym price size cond ------------------------------------------------ 2010.02.21 09:30:00.766 UPS 70.38 46900 A 2010.02.21 09:30:00.801 IBM 89.24799 58600 N 2010.02.21 09:30:00.942 UPS 38.4812 54600 A 2010.02.21 09:30:15.116 IBM 25.56824 55700 A 2010.02.21 09:30:15.224 MSFT 75.97006 23800 E
-- Prev: 3. Q IPC Next: 5. Historical Database


Drainiac! (2000) Rating: 3.0 stars out of 5.0 stars
Cast: Georgia Hatzis, Alexandra Boylan, Ethan Krasnoo, Samara Doucette, Philip (Phip) Barbour, Robert Gorden, Steven Bornstein, Elizabeth Hurley Written and Directed by: Brett Piper Running Time: 81 minutes
"What's going on?" "A freaking exorcism!  --- A high school student (Ethan Krasnoo) questions some strange goings on and exorcist Leon Plummer (Phip Barbour) answers directly.
 Julie has been having nightmares ever since her mother committed suicide and her father isn't helping matters any. Unbeknownst to Julie and her father, an evil water spirit resides in the pipes of the house and it's out for blood. When Julie's friends Lisa, Jake, and Tanya (Alexandra Boylan, Ethan Krasnoo, and Samara Doucette respectively) come to visit, the evil force decides to make itself known and the four teens must do everything they can to stop it.
Drainiac! is a moderately entertaining low budget horror film from Brett Piper, the director of the Troma classic A Nymphoid Barbarian in Dinosaur Hell (originally titled Dark Fortress and available on DVD with a great commentary track from Piper himself), and it feels like an Evil Dead film in spirit. In fact, many of the same type of special effects seen in Evil Dead II are similar to the ones seen in this film. I've always been against rampant computer generated effects and I find it refreshing to see good old fashioned stop motion animation and puppetry, and Drainiac! certainly doesn't disappoint in this regard.
The gore effects are done fairly well for a low budget film and some of them even caught me by surprise.  One particularly well made sequence involves Julie taking a bath when blood starts to pour into the water, completely enveloping her while she relaxes. Eventually what looks like uncooked chicken and umbilical cords start to wrap around her body and pull her into the drain. A very creepy and interesting scene.
 As for the rest of the cast, they perform well and at least two of them are very pleasing to the eye. Georgia Hatzis is pretty attractive and she even does a nude scene before she is covered by the uncooked chicken creature. The most attractive cast member though is Alexandra Boylan who plays Lisa. She has the distinct look of a future Sherilyn Fenn and I hope she continues to work in film. For fans of Elizabeth Hurley, guess what... she's not in this film, but an actress with the same name as her appears as Julie's deceased mother.
Although there are some lapses in logic (such as the character of Leon the exorcist, showing up at the haunted house for seemingly no reason), Drainiac! is still a decently entertaining way to kill an hour and twenty minutes. Spectrum Films is handling the foreign distribution of the film, but unfortunately there is currently no US distributor. If there happens to be a US distributor out there that is looking for a solidly acted, well made horror film that could be a decent repeat renter at the video store I suggest they look towards Drainiac!. I know I'd give this film a try if I saw it on the shelf of my local video store.
For more information about the film, visit Kinetic Image, the web site of the creators of Drainiac! at www.kineticimage.com
Reviewed by Brian Matherly -<EMAIL_ADDRESS>AOL Instant Messenger: Widescreen25 The Jacksonville Film Journal - http://www.jaxfilmjournal.com/
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Related links: index of all rec.arts.movies.reviews reviews


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The large circle containes all the tweets that had replies (tweets without replies are not included in this graph)
The text specifies the tweet (by id) and the inner circles are the replies.
Out of 100,000 tweets received 1,144 had replies.
The large circle containes all the users that had posted more than one tweet. The name of each user is followed by the number of tweets they have done.
Inside each circle are smaller circles with the id of the tweets posted by the user.
Here are only the users that posted more than 3 tweet.
The graph shows how many unique screen names there were per letter of the alphabet.
The graph shows the keyword count that tweets tended to have.
No retweets were found in the data.


Choosing a typeface is the most vexing, infuriating, time-consuming, and pleasurable part.
— Richard Hendel, On Book Design
Similarly, designing a personal site is both frustrating and satisfying — you know what you want. Intimate familiarity with our own personality and style demand precision in how we choose to represent ourselves.
How did I get to Jos Buivenga’s Calluna?
Personality
As an amateur typographer (which is a very generous title), I wanted to select a typeface that I felt represented me well, and conveyed my personality.
What resonated with me the most about Calluna is how, despite maintaining the appearance of a standard humanist serif, certain aspects of it have been modified to give it a subtle, unique feel. I find it important to embrace change and difference, while still remaining grounded.
I grew up partially in Japan. My greatest takeaway was undoubtedly the unique Japanese culture — a dichotomy of tradition and progress. While seemingly incompatible, this unyielding connection to ancient culture complements their achievements in technological innovation. In design, I maintain deep respect and recognition for tried-and-tested usability principles and patterns. In fact, progress is born not from neglect of these principles, but from understanding of them. By understanding the origins and contexts of these principles, we can learn to adapt or build off of them, creating value and exploring new ideas.
Calluna’s asymmetrical characters create a unique and fresh new feel, for an otherwise traditional humanist serif. Despite being gently rooted in old-style aesthetic, several aspects of the characters, such as the serifs and the shoulders, slope towards the upper-right corner, creating a forward flow. These details give it a crisp perkiness uncommon in old-style typefaces, while maintaining the formality, credibility, and robustness.
Style
Having written much of the content before the design process, I knew there was an abundance of italics throughout the writing — in an attempt to capture the many stresses and intonations I use colloquially. The italic style of Calluna is elegant and fluid, but more importantly, maintains the confidence and density of the regular characters. While other italics often crumple under the stature of their normal-weighted neighbors, Calluna’s proudly stand among them, fitting in, while still occasionally flaunting their magnificence.
Apart from the em dash, my most commonly used form of punctuation is probably the question mark — often closing rhetorical questions in my writing. Calluna’s question marks are less formal and decorated than those of other fonts — the sleek curvature helps the sentence main its flow and direction. Instead of ending the sentence with a flourish and waiting for a response, Calluna’s uncomplicated question marks preserve the flow of the sentence, making for a frank and slightly sassy transition into the next thought.
That’s it
Basically, I went through dozens of typefaces, looking for the perfect body font, landing on regular 20-point Calluna. It was selected for its synthesis of traditional humanist features, along with unique asymmetry and flow. Whether it accurately represents me is up for contention, as most areas of subjectivity are. I can’t convince you that Calluna is a typographic representation of me in my purest form, but I can explain my process and thinking, and let you decide for yourself.
Humbly,
Wilbur


|
YAGP 0.2
|
Represents an individual's name. More...
Inherits SharpGEDParser.Model.StructCommon, SharpGEDParser.Model.SourceCitHold, and SharpGEDParser.Model.NoteHold.
Properties | |
| List< SourceCit > | Cits [get] |
| Any Source citations associated with the record. More... | |
| string | Names [get, set] |
| The person's given name(s) - from the NAME line. More... | |
| List< Note > | Notes [get] |
| Any NOTEs associated with the record. More... | |
| List< Tuple< string, string > > | Parts [get] |
| The person's name data, as represented with extra sub-records. More... | |
| string | Suffix [get, set] |
| Any name suffix for the person - from the NAME line. More... | |
| string | Surname [get, set] |
| The person's surname - from the NAME line. More... | |
| Properties inherited from SharpGEDParser.Model.StructCommon | |
| List< UnkRec > | Errors [get] |
| Problems encountered at this level. More... | |
| List< LineSet > | OtherLines [get] |
| Any other lines encountered in the record. More... | |
| Properties inherited from SharpGEDParser.Model.SourceCitHold | |
| List< SourceCit > | Cits [get] |
| Any source citations associated to a record. More... | |
| Properties inherited from SharpGEDParser.Model.NoteHold | |
| List< Note > | Notes [get] |
| Any NOTE references associated with a record. More... | |
Represents an individual's name.
A NAME entry in the GEDCOM is of the form:
The Names property will contain "given1 given2" (any text before the first slash). The Surname property will contain "surname" (any text within the slashes). The Suffix property will contain "suffix" (any text after the slashes).
Additional name data is contained in the Parts structure.
|
get |
Any Source citations associated with the record.
An empty list if none.
|
getset |
The person's given name(s) - from the NAME line.
|
get |
Any NOTEs associated with the record.
An empty list if none.
|
get |
The person's name data, as represented with extra sub-records.
|
getset |
Any name suffix for the person - from the NAME line.
|
getset |
The person's surname - from the NAME line.


This project aims to classify whether or not a given patient has Parkinson's disease based on various variables derived from their speech sounds. We used a KNN classification algorithm with six predictors to create a model with an accuracy of 80%. We concluded it's accuracy is satisfactory for the scope of DSCI 100, working with the tools we had, but not accurate enough for commercial use. We discussed how this study may be extended to other degenerative diseases. Finally, we elaborated on how one would make such a model usable by the public by providing a website frontend for collecting new data points to test.
Parkinson’s disease (PD) is a degenerative disorder of the central nervous system (CNS) which severely impacts motor control. Parkinson’s has neither a cure nor a known cause. Speech, however, is widely known to play a significant role in the detection of PD. According to Sakar1 , vocal disorder exists in 90% of patients at an early stage.
[1] Here's the link to the original data set, and corresponding paper. Note that the paper used the data set with a similar goal but with very different predictor variables than the ones we select here.
To what extent can we use elements of speech sounds to predict whether or not a patient has Parkinson’s disease?
Our data set is from UCI’s Machine Learning repository. This data set contains numerous variables derived from speech sounds gathered by Sakar using the Praat acoustic analysis software. Each participant in the study was asked to repeat their pronunciations of the /a/ phoneme three times; each entry is thus associated with an ID parameter ranging from 0 to 251. We chose to focus on the following six predictor parameters:
| Speech Sound Component | Variable Name in Data Set | Description |
|---|---|---|
| Pitch Period Entropy (PPE) | PPE | measures the impaired control of someone’s pitch |
| Detrended Fluctuation Analysis (DFA) | DFA | a measure of the ‘noisiness’ of speech waves |
| Recurrence Period Density Entropy (RPDE) | RPDE | repetitiveness of speech waves |
| Jitter | locPctJitter | a measure of frequency instability |
| Shimmer | locShimmer | a measure of amplitude instability |
| Fundamental Frequency | meanPeriodPulses | someone’s pitch (calculated from the inverse of the mean period) |
There’s a lot of complicated mathematics associated with each of these parameters (including Fourier transforms, dense equations, etc.) so the interested reader is encouraged to reference Sakar directly.
Many researchers have attempted to use speech signal processing for Parkinson’s disease classification. Based on Sakar and other widely supported literature, the predictors identified above are the most popular speech features used in PD studies and should prove effective. They are also all related to motor control, which is what the early stages of Parkinson’s directly impacts.
Each participant’s data (i.e., their pronunciation of /a/) was collected three times; to analyze this data set, each of these three observations was merged by taking the mean of each of our predictor variables in question.
The data was not completely tidy, but the columns that we used were, so the untidy columns were neglected. Next, to be able to conduct classification, our predictor variables were standardized and centered. Each of our variables had a unique distribution; PPE, DFA, and RPDE were on a scale of 0 to 1 and the pitch variable was on an order of magnitude to 10-3, for example, so the entire data set was scaled so that we did not have to worry about each variable’s individual distribution.
Since our question is a binary classification problem, we used the K-nearest neighbours classification algorithm (KNN).  We then determined its accuracy using the test set.
To visualize the effectiveness of our KNN model, we were not able to draw a scatter plot directly, as we worked with more than 2-dimensions. We had a line plot showing the trend of increasing values of k on the accuracy of our model for many folds to assist in determining which k we should use for our final model. Then, we created a bar plot to show the number of correct predictions, false positives, and false negatives.
# Reinstall packages (if needed)
install.packages("tidyverse")
install.packages("caret")
install.packages("repr")
install.packages("GGally")
install.packages("formula.tools")
# Fix for strange error message
install.packages("e1071")
# Load in the necessary libraries
library(caret)
library(repr)
library(GGally)
library(tidyverse)
library(formula.tools)
# Make all of the following results reproducible, use this value across the analysis
set.seed(28)
To begin, we read in the data from the UCI repository. We did not read it directly from UCI's URL, as it contained a zip, and trying to deal with that in R is tedious. Rather, we uploaded the file to Google Drive and we accessed it from there.
# Reads the dataset from the web into R; the Drive URL is self-made
pd_data <- read_csv("https://drive.google.com/uc?export=download&id=1p9JuoTRM_-t56x7gptZU2TRNue8IHFHc", skip = 1)
#narrows down the dataset to the variables that we will use
baseline_data <- pd_data %>%
select(id:meanHarmToNoiseHarmonicity, class)
head(baseline_data)
As was mentioned in the methods section, each participant was represented three times (e.g., see three rows with id == 0
above). We merged these by taking the mean of each of the predictor columns, after grouping by id
.
# Averages the values of each subject's three trials so that each subject is represented by one row
project_data <- baseline_data %>%
group_by(id) %>%
summarize(PPE = mean(PPE),
DFA = mean(DFA),
RPDE = mean(RPDE),
meanPeriodPulses = mean(meanPeriodPulses),
locPctJitter = mean(locPctJitter),
locShimmer = mean(locShimmer),
# meanAutoCorrHarmonicity = mean(meanAutoCorrHarmonicity),--legacy from project proposal
class = mean(class)) %>%
mutate(class = as.factor(class)) %>%
mutate(has_pd = (class == 1))
head(project_data)
Below we created the training and test sets using createDataPartition()
from the caret
package.
# Determines which percentage of rows will be used in the training set and testing set (75%/25% split)
set.seed(28)
training_rows <- project_data %>%
select(has_pd) %>%
unlist() %>%
createDataPartition(p = 0.75, list = FALSE)
# Splits the dataset into a training set and testing set
training_set <- project_data %>% slice(training_rows)
testing_set <- project_data %>% slice(-training_rows)
head(training_set)
As mentioned in the data wrangling section of "Methods," we eventually scaled our data. Scaling and other pre-processing was done in the analysis section.
Here we looked at the testing set in more detail, exploring the balance and spread of our selected columns.
# Reports the number of counts per class
class_counts <- training_set %>%
group_by(has_pd) %>%
summarize(n = n())
class_counts
options(repr.plot.width=4,repr.plot.height=4)
class_counts_plot <- ggplot(class_counts, aes(x = has_pd, y = n, fill = has_pd)) +
geom_bar(stat="identity") +
labs(x = 'Has PD?', y = 'Number', fill = "Has PD") +
ggtitle("Balance in PD Data Set") +
theme(text = element_text(size = 18), legend.position = "none")
class_counts_plot
We had many more—almost three times as many—patients with PD than without in this data set. Therefore, we could conclude our training set was somewhat imbalanced (in fact, it was the same imbalance as the original data set thanks to createDataPartition()
handling stratification for us); however, it was not severe enough to warrant use of upScale()
. This limitation is further discussed at the end of our analysis.
# Reports the means, maxes, and mins of each predictor variable used
predictor_max <- training_set %>%
select(PPE:locShimmer) %>%
map_df(~ max(., na.rm = TRUE))
predictor_min <- training_set %>%
select(PPE:locShimmer) %>%
map_df(~ min(., na.rm = TRUE))
predictor_mean <- training_set %>%
select(PPE:locShimmer) %>%
map_df(~ mean(., na.rm = TRUE))
stats_merged <- rbind(predictor_max, predictor_min, predictor_mean)
stat <- c('max','mean','min')
stats_w_names <- data.frame(stat, stats_merged)
predictor_stats <- gather(stats_w_names,
key = variable,
value = value,
PPE:locShimmer)
predictor_stats
# Visualizes and compares the distributions of each of the predictor variables
plot_pairs <- training_set %>%
select(PPE:locShimmer) %>%
ggpairs(title = "PD speech predictor variable correlations")
# plot_pairs
plot_pairs_by_class <- training_set %>%
ggpairs(.,
legend = 9,
columns = 2:8,
mapping = ggplot2::aes(colour=has_pd),
lower = list(continuous = wrap("smooth", alpha = 0.3, size=0.1)),
title = "PD speech predictor variable correlations by class") +
theme(legend.position = "bottom")
# plot_pairs_by_class
The following two plots were created using the GGPairs
library. The first, without color, strictly provides detail about the distribution and correlation between each pair created from our six predictor variables. Three of our predictors, DFA, RPDE, and meanPeriodPulses take on a much wider range of values than PPE, jitter, and shimmer. Many of our variables exhibit somewhat positive correlations on the scatterplot, though some have an entirely fuzzy distribution. For example, compare the plots in the PPE column to those in the RPDE column. This likely comes as a result of the spread of the predictors.
options(repr.plot.width=10,repr.plot.height=10, repr.plot.pointsize=20)
plot_pairs
With this understanding, we used a second plot, grouped and colored by has_pd
, to assist in anticipating what the impact of these predictors would be. We noted that for every individual distribution, there is a marked difference between the red and blue groupings, which boded well for our analysis. On average, the healthy patients (i.e., has_pd == FALSE
) fell on the lower end of the spectrum for our predictors, apart from PPE, where healthy patients exhibited higher values on average. Though we weren’t able to visualize our final model directly (as it was in six dimensions), we predicted from these plots that the new patients which fell on the "lower end" for most of these variables would be healthy. This also made intuitive sense; Parkinson’s is a degenerative disease for the muscles, so unhealthy patients would likely experience more rapid change in various speech variables due to tremors.
options(repr.plot.width=10,repr.plot.height=10, repr.plot.pointsize=20)
plot_pairs_by_class
From our analysis, we expected to find that the six variables of speech we identified could form an effective model for determining whether or not a patient has Parkinson’s. We anticipated our findings would allow us to make reasonable predictions of whether or not a new patient has PD given their speech data.
# Scale the data set (pre-processing)
scale_transformer <- preProcess(training_set, method = c("center", "scale"))
training_set <- predict(scale_transformer, training_set)
testing_set <- predict(scale_transformer, testing_set)
# head(training_set)
# head(testing_set)
X_train <- training_set %>%
select(-class, -has_pd) %>%
data.frame()
X_test <- testing_set %>%
select(-class, -has_pd) %>%
data.frame()
Y_train <- training_set %>%
select(class) %>%
unlist()
Y_test <- testing_set %>%
select(class) %>%
unlist()
# head(X_train)
# head(Y_train)
train_control <- trainControl(method="cv", number = 10)
k <- data.frame(k = seq(from = 1, to = 51, by = 2))
knn_model_cv_10fold <- train(x = X_train, y = Y_train, method = "knn", tuneGrid = k, trControl = train_control)
# knn_model_cv_10fold
accuracies <- knn_model_cv_10fold$results
head(accuracies)
options(repr.plot.height = 5, repr.plot.width = 5)
accuracy_vs_k <- ggplot(accuracies, aes(x = k, y = Accuracy)) +
geom_point() +
geom_line() +
ggtitle("Graphing Accuracy Against K") +
labs(subtitle = "Initial attempt—all predictors used")
accuracy_vs_k
k <- accuracies %>%
arrange(desc(Accuracy)) %>%
head(n = 1) %>%
select(k)
k
It looks like a choice of 5 here yields the high accuracy value. After approximately a k value of 40, our accuracy plateaus at just above 0.75. We will now retrain our model using this choice of k.
k = data.frame(k = 5)
model_knn <- train(x = X_train, y = Y_train, method = "knn", tuneGrid = k)
# model_knn
Y_test_predicted <- predict(object = model_knn, X_test)
# head(Y_test_predicted)
model_quality <- confusionMatrix(data = Y_test_predicted, reference = Y_test)
model_quality
Our final model had an accuracy of 79.4%, which is pretty good! Give our model is in six dimensions, there is no simple visualization for it. However, we can visualize whether or not our model had more false positives or false negatives, and which it was better are predicting: sick or healthy. The confusion matrix gives us all of these values in a 2x2 grid.
# Create a dataset
matrix_table <- as.data.frame(model_quality$table) %>%
mutate(isCorrect = (Prediction == Reference))
# matrix_table
matrix_plot <- ggplot(matrix_table, aes(fill = isCorrect, x = Reference, y = Freq)) +
geom_bar(position="stack", stat="identity", width = 0.7) +
labs(title = "Confusion Matrix Bar Chart", y = "Frequency", x = "Has PD?") +
scale_fill_discrete(name="Correction Prediction?")
matrix_plot
We noted from the above bar chart that our model was fairly accurate at predicting whether or not a patient did have Parkinson's, but were very inaccurate when working with healthy patients. This was likely a result of our imbalanced training set, though we did still end up with an 80% accuracy.
Being able to predict Parkinson’s accurately has significant impact alone; doctors often struggle to make accurate, timely predictions as Parkinson’s is a long-term degenerative disease. Additionally, there are no specific tests for determining whether or not someone has Parkinson’s. Currently a neurologist must use a combination of other factors like medical history, signs and symptoms, and a physical examination to make a prediction. Speech sounds could be another cheap, fast tool for a neurologist to employ to help them make a more accurate prediction.
From this analysis, I could see us moving to three questions for further study:
Beyond these further questions, it would also be interesting to see the use of this model in a practical setting. For example, this model could be used by doctors in combination with the patient's other symptoms to make a more accurate prediction. In fact, Google has a similar system for breast cancer screening currently under international review. Given countless electronic devices we interact with on a day-to-day basis possess a microphone, this could even be done without the doctor's help. The only catch would be the consumer-facing product would need to give ample warnings about a potential false-negative or false-positive, and would additionally need to be robust enough to collect the relevant variables from a speech sound, like Praat did in collecting the data set used here.
Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Apaydin, H. (2019). A comparative analysis of speech signal processing algorithms for parkinson's disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing Journal, 74, 255. doi:10.1016/j.asoc.2018.10.022.


Tasks
Write a function, times10, that takes an argument, n, and multiply n times 10
Use an object to cache the results of your times10 function.
Tip 1: Create a function that checks if the value for n has been calculated before.
Tip 2: If the value for n has not been calculated, calculate and then save the result in the cache object.
const times10 = () => {};
console.log('~~~~~~~~~~~~~~TASK 1~~~~~~~~~~~~~~');
console.log('times10 returns:', times10(9));
// Task 2: Use an object to cache the results of your times10 function.
// protip 1: Create a function that checks if the value for n has been calculated before.
// protip 2: If the value for n has not been calculated, calculate and then save the result in the cache object.
const cache = {};
const memoTimes10 = (n) => {
}
console.log('~~~~~~~~~~~~~~TASK 2~~~~~~~~~~~~~~');
console.log('Task 2 calculated value:', memoTimes10(9)); // calculated
console.log('Task 2 cached value:', memoTimes10(9)); // cached


Persia has been a series of empires that roughly have encompassed the same geographical area since 500 BCE. As different dynasties/ethnic groups took control of the area the empire is classified under different names.
During the time of the Roman Empire, Persia was ruled by the Parthians. The Parthians had restored Persian control after the Greek descendants of Alexander ruled the area, although culturally Parthia was a mix of Greek and Archaic Persian
Parthia was also the only power bordering Rome that proved a substantial threat, but between wars the central location of Parthia between Rome and China as well as India made it an important trade center.
The grandson of a warrior named Sasan (from where the name of the Sassanid dynasty comes), Ardashir was the founder of the new Persian dynasty through means that are contested in various sources. The Parthian Empire was weakend by the Roman invasions of Septimius Severus in 198, and by another war in 216, leaving Parthia weakend militarily. Ardashir from Pars rose in rebellion, although the reasons are unclear; in any case the Battle of Hormozdgan on April 28, 224, Artabanus was killed by Ardashir and the Parthian dynasty was overthrown with his death. Ardashir became the new shah, founding the Sassanid dynasty.
After taking control Ardashir had to spend several years establishing his control of the old borders of the Parthian Empire. By 230 this brought him to the Roman border with Mesopotamia, which apparently didn't have enough signs posted because he kept invading, eventually fighting Severus Alexander to a stalemate in Armenia. Then Severus Alexander got stabbed by his own soldiers and Ardashir, smelling blood, invaded again in 237, culminating in the taking of the city of Hatra. The city had sort of become independent from the Parthians before Ardashir. It had successfully resisted Roman and Persian attempts to capture it before this attack.
Ardashir seemed to harshly repudiate the Parthian style of rule, and instead attempted to emulate the older Achaemenids.
Lorem ipsum dolor sit amet. Consectetur adipisicing elit Eaque commodi.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Beatae voluptatem mollitia obcaecati quod maxime. Soluta libero eligendi voluptatum, natus debitis.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Rerum, quasi!
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Beatae voluptatem mollitia obcaecati quod maxime. Soluta libero eligendi voluptatum, natus debitis.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Rerum, quasi!


Messi was born on June 24, 1987, in Rosario, Argentina. As a young boy, Messi tagged along when his two older brothers played soccer with their friends, unintimidated by the bigger boys. At the age of 8, he was recruited to join the youth system of Newell's Old Boys, a Rosario-based club.
Recognizably smaller than most of the kids in his age group, Messi was eventually diagnosed by doctors as suffering from a hormone deficiency that restricted his growth. Messi's parents, Jorge and Ceclia, decided on a regimen of nightly growth-hormone injections for their son, though it soon proved impossible to pay several hundred dollars per month for the medication.
So, at the age of 13, when Messi was offered the chance to train at soccer powerhouse FC Barcelona's youth academy, La Masia, and have his medical bills covered by the team, Messi's family picked up and moved across the Atlantic to make a new home in Spain. Although he was often homesick in his new country, Messi moved quickly through the junior system ranks.
Ultimately, Messi's short stature (5 feet, 7 inches), combined with his speed and relentless attacking style, has drawn comparisons to another famous Argentinian footballer, Diego Maradona.


---
layout: game-page
redirect_from: /game/rocker-madness
title: Rocker Madness
year: 2005
semester: Fall
description: "A rocking good time
Can you feel the music? Good. Now try it upside down."
tags: Windows Singleplayer
# tag options: "Windows", "Mac", "Linux",
# "Singleplayer", "Multiplayer",
# "Controller-Compatible", "VR",
# "Game-Jam"
project_members: {
Bhargav Bhat: Writing,
Chris DeLeon: "Development Directing, Programming, Art",
David Hsu: Art,
Eric Barndollar: "SFX/Music Lead",
Greg Hallenbeck: Level Designer,
Greg Peng: "Level Design Lead, Sprite Art",
Hamza Taha: Level Design,
Henry Wladkowski: Level Design,
Joe Laquinte: Art,
Joshua Jelin: Level Design,
Kwasi Mensah: "Project Lead, Programming Lead",
Matthew Siffert: Music,
Michael Menchaca: Art Lead,
Raihan Islam: Writing,
Robert Strickland: Level Design,
Tarun Agarwal: Level Design,
Theresa Chen: Art
}
stage_game_path: rockermadness/
builds: {
Windows: rocker-madness.win32.zip
}
thumbnail: rockermadness-1.jpg
top-img: rockermadness-1.jpg
screenshots: [
rockermadness-1.jpg,
rockermadness-2.jpg
]
# Optional
top-img-fit: cover
# top-img-fit options:
# "cover" - Cover the entire top area with the image, without squashing/stretching
# "fill" - Fit the FULL image into the top area, squashing/stretching as necessary
---


biotea-ontololgy
An ontology to support the RDFization of scientific documents in the Biotea project. Specifically, it supports group-based distribution and semantic similarity.
Table of Contents
Background
Semantic similarity
Similarity between a query document and a related document taking into account semantic annotations identified in the text. A semantic annotation is a word or set of words associated to an ontological term. The similarity can be restricted to some particular groups defined in a model.
Group-based distribution
Distribution of all the terms in a document according to a model describing a set of groups. Every group gets a score [0.0, 1.0] and the summation of all scores is 1.0.
Semantic groups model
Both group-based distribution and semantic similarty refers to a model of semantic groups such as UMLS or Biolinks. Biolinks is a customization of UMLS semantic groups aiming for more granular groups. The groups consider in Biolinks are: ACTI (Activities & Behaviors), ANAT (Anatomy), CHEM (Chemical entities), CONC (Concepts & Ideas), DEVI (Devices), DISO (Disorders), DRUG (Drugs), GENE (Genes & Molecular Sequences), GEOG (Geographic Areas), GNPT (DNA & Protein molecules), OBJC (Objects), OBSV (Physiology attributes & processes), OCCU (Occupations), ORGA (Organizations), PEOP (People and population groups), PHEN (Phenomena), PHYS (Physiological functions), PROC (Procedures), SYMP (Disorder symptoms) and TAXA (Taxonomic terms).
Other ontologies
Our model references terms in the following ontologies:
Ontology at a glance
Semantic similarity model
Group-based distribution model
Classes
Biolink
ElementSelector
| Selector used in annotations to point to elements within an RDF file. In Biotea this selector is used to link an annotation to the section or paragraph where the corresponding term was identified. |
| used as domain in object properties |
| ao:onResource |
| used as range in object properties |
| ao:context |
Model
| A model of semantic groups, e.g., UMLS or Biolinks semantic groups. A set of concepts is associated to one group and only one in the model. |
| used as domain in object properties |
| dcterms:subject |
| used as domain in data properties |
| biotea:group, rdfs:label |
| used as range in object properties |
| biotea:hasModel |
SemanticAnnotation
| A portion of a document (i.e., word or sequence of words) associated to a semantic entity (e.g., CUI in UMLS). |
| used as domain in object properties |
| dcterms:references |
| used as domain in data properties |
| biotea:idf, biotea:tf |
| used as range in object properties |
| biotea:link |
Topic
| Group name and distribution score calculated for such a group. |
| used as domain in data properties |
| biotea:score, rdfs:label |
| used as range in object properties |
| biotea:hasTopic |
TopicDistribution
| A class representing a Biolinks group-based distribution. Such a distribution is defined on a document and all Biolinks group. A score is associated to each group, representing the weight of that group in the document. |
| used as domain in object properties |
| biotea:annotator, biotea:hasModel, biotea:onDocument, biotea:hasTopic, pav:createdBy |
| used as domain in data properties |
| biotea:totalTF, pav:createdOn |
Object properties
annotator
hasModel
hasTopic
link
| Link between a biotea:Biolink and a semantic annotation. Used to record the semantic annotations participating in the similarity between two documents. |
| declared domains |
| biotea:Biolink |
| suggested ranges |
| biotea:SemanticAnnotation |
onDocument
| Points to the document for which the group-based distribution has been calculated. |
| declared domains |
| biotea:TopicDistribution |
onQueryDocument
| Points to the query document used to calculate a semantic similarity. |
| declared domains |
| biotea:Biolink |
| Points to the compared document used to calculate a semantic similarity. |
| declared domains |
| biotea:Biolink |
paragraphList
| Points to a list of paragraphs in an RDFized article. |
| suggested domains |
| doco:Section |
| suggested ranges |
| rdf:Seq |
sectionList
| Points to a list of sections in an RDFized article. |
| suggested domains |
| bibo:Document, doco:Section |
| suggested ranges |
| rdf:Seq |
Data type properties
group
idf
| Inverse document frequency of a term in a collection of documents. |
| suggested domains |
| biote:SemanticAnnotation |
| declared type |
| xsd:Double |
occurrences
score
tf
totalTF
| Total term frequency of all the terms used to calculate a TopicDistribution. |
| suggested domains |
| biotea:TopicDistribution |
| declared type |
| xsd:Integer |


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| 017:064 Khan | : |
"And Istafziz [literally means: befool them gradually] those whom you can among them with your voice (i.e. songs, music, and any other call for Allah's disobedience), make assaults on them with your cavalry and your infantry, mutually share with them wealth and children (by tempting them to earn money by illegal ways usury, etc., or by committing illegal sexual intercourse, etc.), and make promises to them." But Satan promises them nothing but deceit. |
| 017:064 Maulana | : |
And incite whom thou canst of them with thy voice, and collect against them thy horse and thy foot, and share with them in wealth and children, and promise them. And the devil promises them only to deceive. |
| 017:064 Pickthal | : |
And excite any of them whom thou canst with thy voice, and urge thy horse and foot against them, and be a partner in their wealth and children, and promise them. Satan promiseth them only to deceive. |
| 017:064 Rashad | : |
"You may entice them with your voice, and mobilize all your forces and all your men against them, and share in their money and children, and promise them. Anything the devil promises is no more than an illusion. |
| 017:064 Sarwar | : |
Draw anyone of them you can into sin by your voice and by your cavalry and infantry, share their property and children with them and make promises to them. Your promises are all lies. |
| 017:064 Shakir | : |
And beguile whomsoever of them you can with your voice, and collect against them your forces riding and on foot, and share with them in wealth and children, and hold out promises to them; and the Shaitan makes not promises to them but to deceive: |
| 017:064 Sherali | : |
And entice whomsoever of them thou canst with thy voice, and urge against them thy horsemen and thy footmen and share with them in wealth, and their children, and make promises to them.' And Satan promises them naught but deceit. |
| 017:064 Yusufali | : |
"Lead to destruction those whom thou canst among them, with thy (seductive) voice; make assaults on them with thy cavalry and thy infantry; mutually share with them wealth and children; and make promises to them." But Satan promises them nothing but deceit. |
Concepts/Themes Covered: الإيمان > الغيب > الشيطان > وسوسته Faith> The Unseen World> Satan, the Devil> Satanic Temptations الجهاد > أدوات الجهاد > الخيل Jihad>The Holy War Tools>Horses الإنسان والعلاقات الأخلاقية > الأخلاق الذميمة > الغرور Man and The Moral Relations>The Dispraised Morals>Pride تنظيم العلاقات المالية > الأموال Organizing Financial Relationships>Wealth Property
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


%rebase('osnova.html')
Hello! This is an application to help you find the right drink to match your meal. You enter information about your food in the search form and get a plethora of drink suggestions that would go well alongside it.
The search can be found here and you can find more general information about the app and food/drink pairing here.


Low-level protocol classes used by the org.globus.io.gass.client package.
Package Specification
(none)
Related Documentation
For overviews, tutorials, examples, guides, and tool documentation, please see:
Globus