The Game
You're miserable. Your life is a monotonous routine, the same thing, day after day. Your business provides you with more money than some small countries, yet day after day you experience the same living death that is characteristic of the post-modern rich boy. Douglas is a veteran at the role of the rich kid, having won a Best Actor Oscar in 1987 for his portrayal of Gordan Gekko in Oliver Stone's "Wall Street".
In this movie, however, he begins to recognize that Greed may not be as good as it was made out to be.
On his 38th birthday, business is as usual. It is a day that he dreads due to the suicide of his father thirty years earlier, in which his father leapt from the roof of the home in which Douglas now resides.
His brother, Conrad Van Orton, played by "Fast Times at Ridgemont High's" Sean Penn (ex husband of Madonna and current husband of Robin Wright of "Forest Gump"), is a perpetual child. Seemingly incapable of being mature, he makes a lunch date with his brother under the name of Seymour Butts. His gift is a certificate for an interesting game run by the company CRS (Consumer Recreation Services).
Staring at the card later at night, just off the phone with his ex-wife and watching cable he decides to get ready to play.
The next day he goes to CRS, is given a psychological test and a physical, taking up the entire day, and he is told that his game will begin within a few days.
But what is the Game?
That is the question in the back of your mind throughout the entirety of the movie, as you find yourself thinking for a few seconds that you know what it is and then contradicting yourself. Is it a con for money? A psychotic murder plot? A taunting reminder of times past? Or is it really a game?
Whatever it is you know that there is one simple rule for this movie – Trust No One. And, following that maxim or not following it, you won't know what's real and what's not until the very last frame.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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Match Play. Red leads 2-0 to 5. How should Red play 22?
pips Red=125 White=124
(CorrectAnswer= 0/0, ErrorScore= 0)
I had this position in the semi-final of a tournament at the RAC this week. I played 8/4(2). White rolled 62, pointing on my blot on his ace-point and I never entered, losing a gammon easily. Did I make the right play?
The answer is – just. For money the play is very clear but at this score the fact that a gammon lets White get to the Crawford game means Red should play with slightly more caution. However, the rollout gives the edge to 8/4(2) over 8/6(2), 8/4 by a whisker.
Scant consolation for losing the gammon and in the Crawford Game, the match!
The answer is – just. For money the play is very clear but at this score the fact that a gammon lets White get to the Crawford game means Red should play with slightly more caution. However, the rollout gives the edge to 8/4(2) over 8/6(2), 8/4 by a whisker.
Scant consolation for losing the gammon and in the Crawford Game, the match!
1. Cubeful 2-ply 8/4(2) Eq.: -0.232 0.409 0.105 0.003 - 0.591 0.136 0.003 2. Cubeful 2-ply 8/4 8/6(2) Eq.: -0.238 (-0.006) 0.396 0.083 0.001 - 0.604 0.113 0.003 3. Cubeful 2-ply 13/11(2) 8/4 Eq.: -0.272 (-0.040) 0.374 0.067 0.001 - 0.626 0.098 0.002 4. Cubeful 2-ply 8/4 3/1(2) Eq.: -0.277 (-0.045) 0.391 0.087 0.001 - 0.609 0.135 0.003 5. Cubeful 2-ply 8/6 5/1 3/1 Eq.: -0.278 (-0.047) 0.393 0.089 0.001 - 0.607 0.139 0.003


Springbank Half Marathon
London ON/CAN
Race Winners
(49 runnings)
09
Sep
2007
10 Sep
2006
11 Sep
2005
12 Sep
2004
07
Sep
2003
08
Sep
2002
09 Sep
2001
10 Sep
2000
09
Sep
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
23 Sep
1989
1988
1987
21
Sep 1986
22 Sep
1985
1984
25 Sep 1983
Sep
1982
Sep
1981
28 Sep
1980
Sep
1979
24 Sep
1978
25 Sep
1977
26 Sep
1976
28 Sep
1975
29 Sep
1974
23 Sep
1973
01 Oct
1972
25
Sep
1971
27
Sep
1970
13
Sep
1969
14
Sep
1968
Notes: 1968 was the inaugural year. Distances in the early years were reported
as 12 miles (19.31 km) but were clearly much less than advertised. The
course was four laps. Before 1973, a full lap was reported as 4666 meters
or 18.664 km for four laps. For 1973 and later, a new course was used with
a lap distance of 4622 meters or 18.488 km.
02 Oct
2016
Hf Mara
1:18:12
Paul Cochrane (ON/CAN)
1:17:24
Leslie Sexton-4
13 Sep
2015
Hf Mara
1:10:40
Brandon Laan-3
1:19:18
Leslie Sexton-3
07 Sep
2014
Hf Mara
1:08:27
Terence Attema (ON/CAN)
1:16:49
Lyudmila Korchagina-2
08
Sep 2013
Hf Mara
1:13:47
Matt Suda (ON/CAN)
1:18:46
Leslie Sexton-2
09 Sep
2012
Hf Mara
1:12:04
Adam Stacey (ON/CAN)
1:18:44
Leslie Sexton (ON/CAN)
11 Sep
2011
Hf Mara
1:07:43.7
Brandon Laan-2
1:17:48.2
Lucy Njeri Muhami (KEN)
12 Sep
2010
Hf Mara
1:09:09.7
Brandon Laan (ON/CAN)
1:16:08.8
Lanni Marchant (ON/CAN)
06 Sep
2009
Hf Mara
1:09:03.0
Adam Hortian (ON/CAN)
1:24:27.2
Grace (Pereira ) Bakker
(ON/CAN)
07 Sep
2008
Hf Mara
1:14:49
Zeljko Sabol-2
1:32:28
Shannyn Clancy (ON/CAN)
Hf Mara
1:11:12
Thomas Omwenga (KEN)
1:21:58
Lauren King-2
Hf Mara
1:13:03
Chris Smith (ON/CAN)
1:15:37
Lauren King (ON/CAN)
Hf Mara
1:11:49
Drew Macaulay (ON/CAN)
1:26:00
Adela
Flista-2
Hf Mara
1:09:08
Jason Weppler (ON/CAN)
1:24:32
Karen Cowling (ON/CAN)
Hf Mara
1:06:30
Simon Karanja Karuku (KEN)
1:23:08
Adela Flista (ON/CAN)
Hf Mara
1:13:13.6
Shingirai Badza (ZIM)
1:23:30.1
Teresa Duck-2
Hf Mara
1:13:10
Zeljko Sabol (ON/CAN)
1:19:47
Lyudmila Korchagina (RUS)
Hf Mara
1:08:09.1
Julius Githuka (KEN)
1:21:53.5
Patrice Kuntz (ON/CAN)
Hf Mara
1:10:35
John Cress (CAN)
1:23:16
Teresa Duck (ON/CAN)
---
---
n/a
---
n/a
---
---
n/a
---
n/a
---
---
n/a
---
n/a
---
---
n/a
---
n/a
---
---
n/a
---
n/a
---
---
n/a
---
n/a
---
---
n/a
---
n/a
---
---
n/a
---
n/a
---
---
n/a
---
n/a
---
---
n/a
---
n/a
15 km
47:41
David Edge (ON/CAN)
59:21
Laura Ouellette (ON/CAN)
---
---
n/a
---
n/a
15 km
45:35.8
Chris Lane (ON/CAN)
1:05:50.9
Wanda Cousineau (ON/CAN)
18.5 km
---
n/a
---
n/a
18.5 km
58:06.1
Paul Baldwin (MI/USA)
1:07:52
Brenda Thompson (ON/CAN)
18.5 km
56:49.5
Bill Weidenbach (MI/USA)
---
n/a
18.5 km
55:49.1
Mark Messler (MI/USA)
1:07:52
Janice Ettle-2
18.5 km
55:36.2
Mike McGuire (NY/USA)
---
n/a
18.5 km
55:09.9
Bruce Bickford (MA/USA)
1:10:26
Janice Ettle (MN/USA)
18.5 km
54:49.1
Kirk Pfeffer (CA/USA)
1:10:52
Cathy Good (ON/CAN)
18.5 km
56:28.2
Ric Rojas (CO/USA)
1:22:59
Colleen Connelly (ON/CAN)
18.5 km
54:31
Bill Rodgers (MA/USA)
18.5 km
55:47.6
Lionel Ortega (NM/USA)
18.5 km
54:52
Jerome Drayon-2
18.5 km
55:17
Miruts Yifter (ETH)
18.5 km
56:11
Neil
Cusack (IRL)
18.6 km
55:46.6
Frank Shorter (FL/USA)
18.6 km
55:33.8
Ken Moore (OR/USA)
18.6 km
55:34.6
Ronald Hill (ENG)
18.6 km
58:48.1
Jerome Drayon (ON/CAN)
18.6 km
58:23.0
Amby Burfoot (CT/USA)
Most Wins
(1968/date)
Men
3
Brandon Laan (ON/CAN)
2010-11,2015
Women
4
Leslie
Sexton (ON/CAN)
2012-13,2015-16
Prize Money
(ADR qualified runners)
Year Men Women Total 2011 $200 $350 $550 .... 2006 n/a 450 450 Sums $200 $800 $1,000 Race Time Bias (sec/km)
Year Men Women 1980 -2.4 1979 -2.4 1978 +0.2 1977 +4.3 1976 -2.7 .... 1974 -2.3 1973 -4.0 1972 -0.2 1971 -2.3 1970 -3.5 1969 +2.4 Avgs -1.2 Competitive Level (1990 to date) Insufficient data to calculate the competitive level
Quality Performances
Men
No men under 1:06
Women <1:16:00 1 <1:17:00 2
Springbank 4.5 mile
London ON/CAN
Race Winners
(?? runnings)
Sep
1984 25 Sep
1983
Sep
1982
Sep
1981
28 Sep
1980
Sep
1979
24 Sep
1978
25 Sep
1977
26 Sep
1976
28 Sep
1975
29 Sep
1974
23 Sep
1973
01 Oct
1972
25
Sep
1971
27
Sep
1970
13
Sep
1969
14
Sep
1968
Notes: 1968 is believed to have been the inaugural year. Distances in the
early years were reported as 4.5 miles but were clearly much less than
advertised. The actual distance seems to have been about 7.0 km (4.35
miles).
19:36.1
Ray Paulins (ON/CAN)
22:47.8
Sue (Lee) French (ON/CAN)
19:48.4
Paul McCloy (NF/CAN)
23:02.7
Elizabeth Natale (TN/USA)
19:51.2
Chris Brewster (CAN)
22:39
Nancy
(Tinari) Rooks-2
19:45.3
Mark
Scrutton-2
22:22.1
Nancy (Tinari) Rooks (CAN)
19:07.1
Mark Scrutton (ENG)
22:49.1
Carol Schenk (MI/USA)
19:42.8
Steve Crane (OH/USA)
22:55.4
Brenda Webb-3
19:31.9
John Treacy (IRL)
22:59.4
Brenda Webb-2
19:28
Duncan Macdonald (HI/USA)
22:43.2
Brenda Webb (TX/USA)
19:31.5
Nick Rose (ENG)
22:45
Donna Valatis (ON/CAN)
19:41.2
Karl Thornton (PA/USA)
23:53.4
Katy Laetsch (CA/USA)
19:46
Grant McLaren (ON/CAN)
23:16
Thelma Wright
19:59
Daniel Shaughnessy (CAN)
24:09
Francie Larrieu (TX/USA)
20:15.6
Barry Brown (FL/USA)
20:09.7
Steve Stageberg (DC/USA)
19:52.8
Frank
Shorter (FL/USA)
20:46.8
Robert Finlay (ON/CAN)
21:43.0
Ian Wainwright (ON/CAN)
Most Wins
(1968/??)
Men
2
Mark Scrutton (ENG)
1980-81
Women
3
Brenda Webb (TX/USA)
1977-79
Prize Money
(ADR qualified runners)
None
Race Time Bias (sec/km)
Year Men Women 1980 -2.0 n/c 1979 -1.8 n/c 1978 -1.8 n/c .... 1973 -0.5 n/c Avgs -1.5 n/c Competitive Level (1990 to date)
Insufficient data to calculate the competitive level
Last Updated on
12 Jul 2017
Contributors: Sherry Watts (50), Amby Burfoot (1)


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nativejson-benchmark
Benchmark
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Developed by
Milo Yip
<EMAIL_ADDRESS>Source CSV
Type,Library,Filename,Time (ms),Memory (byte),MemoryPeak (byte),AllocCount,FileSize (byte) 1. Parse,ArduinoJson (C++),canada.json,906.397996,10640116,10640116,20,0 1. Parse,ArduinoJson (C++),citm_catalog.json,13.389996,3875224,3875224,18,0 1. Parse,ArduinoJson (C++),twitter.json,4.853486,1171635,1171635,16,0 2. Stringify,ArduinoJson (C++),canada.json,206.310297,2251080,4672455,30,0 2. Stringify,ArduinoJson (C++),citm_catalog.json,27.682261,499624,1217069,27,0 2. Stringify,ArduinoJson (C++),twitter.json,22.280134,465384,943681,26,0 3. Prettify,ArduinoJson (C++),canada.json,384.865156,5596488,13768627,33,0 3. Prettify,ArduinoJson (C++),citm_catalog.json,32.793210,513272,1230717,27,0 3. Prettify,ArduinoJson (C++),twitter.json,32.363722,597176,1314621,27,0 4. Statistics,ArduinoJson (C++),canada.json,0.758213,0,0,0,0 4. Statistics,ArduinoJson (C++),citm_catalog.json,0.566649,0,0,0,0 4. Statistics,ArduinoJson (C++),twitter.json,0.678906,0,0,0,0 7. Code size,ArduinoJson (C++),jsonstat,0,0,0,0,20480 1. Parse,C++ REST SDK (C++11),canada.json,206.097660,5907688,5907768,393966,0 1. Parse,C++ REST SDK (C++11),citm_catalog.json,35.539100,2796192,2796368,86685,0 1. Parse,C++ REST SDK (C++11),twitter.json,21.339168,1697176,1697287,42580,0 2. Stringify,C++ REST SDK (C++11),canada.json,128.740734,2251080,6417783,58,0 2. Stringify,C++ REST SDK (C++11),citm_catalog.json,10.858275,500344,1824929,1398,0 2. Stringify,C++ REST SDK (C++11),twitter.json,8.862553,466888,1445965,6446,0 4. Statistics,C++ REST SDK (C++11),canada.json,2.619074,0,48,1,0 4. Statistics,C++ REST SDK (C++11),citm_catalog.json,0.777753,0,112,722,0 4. Statistics,C++ REST SDK (C++11),twitter.json,0.853996,0,368,2771,0 7. Code size,C++ REST SDK (C++11),jsonstat,0,0,0,0,127488 1. Parse,cJSON (C),canada.json,26.627889,10699574,10699574,167192,0 1. Parse,cJSON (C),citm_catalog.json,8.000512,2665791,2665791,64383,0 1. Parse,cJSON (C),twitter.json,4.246225,1276528,1276528,32014,0 2. Stringify,cJSON (C),canada.json,150.069174,1294697,2650673,223241,0 2. Stringify,cJSON (C),citm_catalog.json,22.970917,500321,1001081,87274,0 2. Stringify,cJSON (C),twitter.json,12.300375,466931,934592,30092,0 3. Prettify,cJSON (C),canada.json,152.356075,1405873,2872935,223241,0 3. Prettify,cJSON (C),citm_catalog.json,27.516366,789217,1578842,87274,0 3. Prettify,cJSON (C),twitter.json,13.367008,557961,1116464,30092,0 4. Statistics,cJSON (C),canada.json,2.801060,0,0,0,0 4. Statistics,cJSON (C),citm_catalog.json,0.661282,0,0,0,0 4. Statistics,cJSON (C),twitter.json,0.680438,0,0,0,0 7. Code size,cJSON (C),jsonstat,0,0,0,0,19456 1. Parse,dropbox/json11 (C++11),canada.json,145.826397,8922312,11262928,338942,0 1. Parse,dropbox/json11 (C++11),citm_catalog.json,29.834683,4623954,6402658,123093,0 1. Parse,dropbox/json11 (C++11),twitter.json,16.451201,1986648,2634834,54421,0 2. Stringify,dropbox/json11 (C++11),canada.json,122.657402,2965311,4942159,30,0 2. Stringify,dropbox/json11 (C++11),citm_catalog.json,11.968968,780120,1300174,27,0 2. Stringify,dropbox/json11 (C++11),twitter.json,8.727309,520094,866797,26,0 4. Statistics,dropbox/json11 (C++11),canada.json,3.217905,0,0,0,0 4. Statistics,dropbox/json11 (C++11),citm_catalog.json,0.818365,0,0,0,0 4. Statistics,dropbox/json11 (C++11),twitter.json,0.484659,0,0,0,0 7. Code size,dropbox/json11 (C++11),jsonstat,0,0,0,0,60416 1. Parse,mikeando/FastJson (C++),canada.json,118.293939,8715320,10967016,26,0 1. Parse,mikeando/FastJson (C++),citm_catalog.json,18.830053,2687370,4465674,26,0 1. Parse,mikeando/FastJson (C++),twitter.json,6.274130,1361504,2009444,30,0 2. Stringify,mikeando/FastJson (C++),canada.json,193.414539,2251080,2251080,2,0 2. Stringify,mikeando/FastJson (C++),citm_catalog.json,20.408930,501048,501048,2,0 2. Stringify,mikeando/FastJson (C++),twitter.json,3.689921,562456,562456,2,0 4. Statistics,mikeando/FastJson (C++),canada.json,0.747486,0,0,0,0 4. Statistics,mikeando/FastJson (C++),citm_catalog.json,0.253249,0,0,0,0 4. Statistics,mikeando/FastJson (C++),twitter.json,0.161298,0,0,0,0 7. Code size,mikeando/FastJson (C++),jsonstat,0,0,0,0,51712 1. Parse,gason (C++11),canada.json,10.770539,4938069,4938069,658,0 1. Parse,gason (C++11),citm_catalog.json,4.969574,2596905,2596905,202,0 1. Parse,gason (C++11),twitter.json,1.955876,978804,978804,83,0 2. Stringify,gason (C++11),canada.json,127.751493,1295048,2909298,29,0 2. Stringify,gason (C++11),citm_catalog.json,34.963256,601096,1318541,27,0 2. Stringify,gason (C++11),twitter.json,21.183234,481720,1199165,27,0 3. Prettify,gason (C++11),canada.json,187.181787,7316184,15488323,33,0 3. Prettify,gason (C++11),citm_catalog.json,47.463625,1802120,4223495,30,0 3. Prettify,gason (C++11),twitter.json,24.378535,768760,1844927,28,0 4. Statistics,gason (C++11),canada.json,0.840586,0,0,0,0 4. Statistics,gason (C++11),citm_catalog.json,0.617605,0,0,0,0 4. Statistics,gason (C++11),twitter.json,0.644807,0,0,0,0 7. Code size,gason (C++11),jsonstat,0,0,0,0,18432 1. Parse,Jansson (C),canada.json,153.048007,9574494,9574537,224392,0 1. Parse,Jansson (C),citm_catalog.json,37.746692,5087615,5087692,111230,0 1. Parse,Jansson (C),twitter.json,18.587532,1727520,1728050,43822,0 2. Stringify,Jansson (C),canada.json,127.005540,2362181,6556485,21,0 2. Stringify,Jansson (C),citm_catalog.json,10.261360,551271,1599847,19,0 2. Stringify,Jansson (C),twitter.json,5.015549,492628,1016916,18,0 3. Prettify,Jansson (C),canada.json,157.033704,8272183,16922935,22,0 3. Prettify,Jansson (C),citm_catalog.json,17.026278,1727221,3824373,20,0 3. Prettify,Jansson (C),twitter.json,6.523164,767328,1815904,19,0 4. Statistics,Jansson (C),canada.json,2.901057,0,0,0,0 4. Statistics,Jansson (C),citm_catalog.json,0.878516,0,0,0,0 4. Statistics,Jansson (C),twitter.json,0.535232,0,0,0,0 7. Code size,Jansson (C),jsonstat,0,0,0,0,28672 1. Parse,JsonCpp (C++),canada.json,194.596111,20540926,25043230,279307,0 1. Parse,JsonCpp (C++),citm_catalog.json,29.694458,5799699,9355219,134396,0 1. Parse,JsonCpp (C++),twitter.json,15.134001,1870080,3164295,53818,0 2. Stringify,JsonCpp (C++),canada.json,172.920555,2251080,4883846,108914,0 2. Stringify,JsonCpp (C++),citm_catalog.json,16.074585,500344,1200258,15302,0 2. Stringify,JsonCpp (C++),twitter.json,12.893843,466968,987022,13212,0 4. Statistics,JsonCpp (C++),canada.json,8.377128,0,32,1,0 4. Statistics,JsonCpp (C++),citm_catalog.json,1.807222,0,64,451,0 4. Statistics,JsonCpp (C++),twitter.json,1.312602,0,464,2336,0 7. Code size,JsonCpp (C++),jsonstat,0,0,0,0,65536 1. Parse,json-c (C),canada.json,211.215122,30420254,30421402,390651,0 1. Parse,json-c (C),citm_catalog.json,40.592962,12387831,12389013,131793,0 1. Parse,json-c (C),twitter.json,14.952015,2834918,2836550,49446,0 2. Stringify,json-c (C),canada.json,38.441689,6445364,6445364,21,0 2. Stringify,json-c (C),citm_catalog.json,22.171326,1025030,1025030,18,0 2. Stringify,json-c (C),twitter.json,9.357557,997271,997271,18,0 3. Prettify,json-c (C),canada.json,64.184549,13761847,13761847,22,0 3. Prettify,json-c (C),citm_catalog.json,29.814378,3353997,3353997,20,0 3. Prettify,json-c (C),twitter.json,11.204625,1680904,1680904,19,0 4. Statistics,json-c (C),canada.json,5.614764,0,0,0,0 4. Statistics,json-c (C),citm_catalog.json,2.686505,0,0,0,0 4. Statistics,json-c (C),twitter.json,0.581591,0,0,0,0 7. Code size,json-c (C),jsonstat,0,0,0,0,36864 1. Parse,JSON Spirit (C++),canada.json,238.182464,8053552,16797424,686313,0 1. Parse,JSON Spirit (C++),citm_catalog.json,157.270861,2900480,7577368,431416,0 1. Parse,JSON Spirit (C++),twitter.json,53.977594,1377592,3405224,118646,0 2. Stringify,JSON Spirit (C++),canada.json,211.621240,2251080,4672455,31,0 2. Stringify,JSON Spirit (C++),citm_catalog.json,24.230264,500392,1217837,1670,0 2. Stringify,JSON Spirit (C++),twitter.json,18.923153,693288,1410733,13057,0 3. Prettify,JSON Spirit (C++),canada.json,311.751006,8272216,20530424,35,0 3. Prettify,JSON Spirit (C++),citm_catalog.json,49.911440,2005176,4426551,1673,0 3. Prettify,JSON Spirit (C++),twitter.json,24.778905,1022440,2098607,13058,0 4. Statistics,JSON Spirit (C++),canada.json,3.444335,0,0,0,0 4. Statistics,JSON Spirit (C++),citm_catalog.json,0.710705,0,0,0,0 4. Statistics,JSON Spirit (C++),twitter.json,0.337154,0,0,0,0 7. Code size,JSON Spirit (C++),jsonstat,0,0,0,0,119808 1. Parse,hjiang/JSON++ (C++),canada.json,2262.750265,5467528,9969660,338938,0 1. Parse,hjiang/JSON++ (C++),citm_catalog.json,101.245822,4209242,7764594,107917,0 1. Parse,hjiang/JSON++ (C++),twitter.json,42.801704,1891496,3185499,51313,0 2. Stringify,hjiang/JSON++ (C++),canada.json,472.499150,3749704,18571796,489301,0 2. Stringify,hjiang/JSON++ (C++),citm_catalog.json,94.564043,946952,2970031,154965,0 2. Stringify,hjiang/JSON++ (C++),twitter.json,36.651325,576488,1870381,59943,0 4. Statistics,hjiang/JSON++ (C++),canada.json,2.102998,0,0,0,0 4. Statistics,hjiang/JSON++ (C++),citm_catalog.json,0.705342,0,0,0,0 4. Statistics,hjiang/JSON++ (C++),twitter.json,0.409566,0,0,0,0 7. Code size,hjiang/JSON++ (C++),jsonstat,0,0,0,0,38912 1. Parse,Jzon (C++),canada.json,285.400503,21415720,33798460,804292,0 1. Parse,Jzon (C++),citm_catalog.json,112.393361,4047178,10886154,151912,0 1. Parse,Jzon (C++),twitter.json,51.451237,1879200,4785971,88128,0 2. Stringify,Jzon (C++),canada.json,66.615123,2251080,4672455,108910,0 2. Stringify,Jzon (C++),citm_catalog.json,20.923473,500760,1218205,929,0 2. Stringify,Jzon (C++),twitter.json,11.954026,473000,951297,5467,0 3. Prettify,Jzon (C++),canada.json,114.998259,8272216,20530424,332130,0 3. Prettify,Jzon (C++),citm_catalog.json,31.424288,1988360,4409735,54574,0 3. Prettify,Jzon (C++),twitter.json,14.773093,789560,1865727,18721,0 4. Statistics,Jzon (C++),canada.json,6.371062,0,32,1,0 4. Statistics,Jzon (C++),citm_catalog.json,0.821430,0,64,451,0 4. Statistics,Jzon (C++),twitter.json,0.839054,0,464,2336,0 7. Code size,Jzon (C++),jsonstat,0,0,0,0,42496 1. Parse,Parson (C),canada.json,129.090148,5357678,5377614,336060,0 1. Parse,Parson (C),citm_catalog.json,32.721948,1962423,1962725,185585,0 1. Parse,Parson (C),twitter.json,16.926665,892368,892685,72477,0 4. Statistics,Parson (C),canada.json,2.362760,0,0,0,0 4. Statistics,Parson (C),citm_catalog.json,2.443984,0,0,0,0 4. Statistics,Parson (C),twitter.json,5.907475,0,0,0,0 7. Code size,Parson (C),jsonstat,0,0,0,0,22016 1. Parse,PicoJSON (C++),canada.json,244.480731,4029376,7236488,795175,0 1. Parse,PicoJSON (C++),citm_catalog.json,76.445079,3634986,5954522,437714,0 1. Parse,PicoJSON (C++),twitter.json,30.098277,1715660,3238960,127962,0 2. Stringify,PicoJSON (C++),canada.json,182.656260,2632806,4388016,108908,0 2. Stringify,PicoJSON (C++),citm_catalog.json,23.776255,520094,866797,26,0 2. Stringify,PicoJSON (C++),twitter.json,11.454042,520094,866797,224,0 4. Statistics,PicoJSON (C++),canada.json,0.798442,0,0,0,0 4. Statistics,PicoJSON (C++),citm_catalog.json,0.543278,0,0,0,0 4. Statistics,PicoJSON (C++),twitter.json,0.317614,0,0,0,0 7. Code size,PicoJSON (C++),jsonstat,0,0,0,0,37376 1. Parse,RapidJSON_AutoUTF (C++),canada.json,13.777340,4207842,4656249,77,0 1. Parse,RapidJSON_AutoUTF (C++),citm_catalog.json,9.772486,1573602,1585266,35,0 1. Parse,RapidJSON_AutoUTF (C++),twitter.json,5.812842,983562,991338,25,0 2. Stringify,RapidJSON_AutoUTF (C++),canada.json,26.883819,2873369,2873882,28,0 2. Stringify,RapidJSON_AutoUTF (C++),citm_catalog.json,3.527474,567624,568137,24,0 2. Stringify,RapidJSON_AutoUTF (C++),twitter.json,2.697998,567624,568137,24,0 3. Prettify,RapidJSON_AutoUTF (C++),canada.json,53.113253,9697485,9697998,31,0 3. Prettify,RapidJSON_AutoUTF (C++),citm_catalog.json,8.860255,1915598,1916111,27,0 3. Prettify,RapidJSON_AutoUTF (C++),twitter.json,3.930909,851408,851921,25,0 4. Statistics,RapidJSON_AutoUTF (C++),canada.json,0.891159,0,0,0,0 4. Statistics,RapidJSON_AutoUTF (C++),citm_catalog.json,0.401903,0,0,0,0 4. Statistics,RapidJSON_AutoUTF (C++),twitter.json,0.192714,0,0,0,0 5. Sax Round-trip,RapidJSON_AutoUTF (C++),canada.json,38.811793,56,2874139,30,0 5. Sax Round-trip,RapidJSON_AutoUTF (C++),citm_catalog.json,12.701512,56,568394,26,0 5. Sax Round-trip,RapidJSON_AutoUTF (C++),twitter.json,7.938828,56,568714,28,0 6. SaxStatistics,RapidJSON_AutoUTF (C++),canada.json,12.319148,0,257,2,0 6. SaxStatistics,RapidJSON_AutoUTF (C++),citm_catalog.json,9.368668,0,257,2,0 6. SaxStatistics,RapidJSON_AutoUTF (C++),twitter.json,5.467259,0,577,4,0 7. Code size,RapidJSON_AutoUTF (C++),jsonstat,0,0,0,0,31232 1. Parse,RapidJSON_FullPrec (C++),canada.json,22.298141,4207842,4656249,77,0 1. Parse,RapidJSON_FullPrec (C++),citm_catalog.json,5.282208,1573602,1585266,35,0 1. Parse,RapidJSON_FullPrec (C++),twitter.json,2.913317,983562,991338,25,0 2. Stringify,RapidJSON_FullPrec (C++),canada.json,17.029727,2873369,2873882,28,0 2. Stringify,RapidJSON_FullPrec (C++),citm_catalog.json,2.123304,567624,568137,24,0 2. Stringify,RapidJSON_FullPrec (C++),twitter.json,1.773124,567624,568137,24,0 3. Prettify,RapidJSON_FullPrec (C++),canada.json,26.220622,9697485,9697998,31,0 3. Prettify,RapidJSON_FullPrec (C++),citm_catalog.json,4.010984,1915598,1916111,27,0 3. Prettify,RapidJSON_FullPrec (C++),twitter.json,2.248971,851408,851921,25,0 4. Statistics,RapidJSON_FullPrec (C++),canada.json,0.888094,0,0,0,0 4. Statistics,RapidJSON_FullPrec (C++),citm_catalog.json,0.401903,0,0,0,0 4. Statistics,RapidJSON_FullPrec (C++),twitter.json,0.192331,0,0,0,0 5. Sax Round-trip,RapidJSON_FullPrec (C++),canada.json,36.823350,2873369,2874139,30,0 5. Sax Round-trip,RapidJSON_FullPrec (C++),citm_catalog.json,6.284858,567624,568394,26,0 5. Sax Round-trip,RapidJSON_FullPrec (C++),twitter.json,3.948150,567624,568714,28,0 6. SaxStatistics,RapidJSON_FullPrec (C++),canada.json,20.475594,0,257,2,0 6. SaxStatistics,RapidJSON_FullPrec (C++),citm_catalog.json,4.499857,0,257,2,0 6. SaxStatistics,RapidJSON_FullPrec (C++),twitter.json,2.367741,0,577,4,0 7. Code size,RapidJSON_FullPrec (C++),jsonstat,0,0,0,0,29184 1. Parse,RapidJSON_Insitu (C++),canada.json,12.922578,6458903,6907310,78,0 1. Parse,RapidJSON_Insitu (C++),citm_catalog.json,6.810511,3351275,3362939,36,0 1. Parse,RapidJSON_Insitu (C++),twitter.json,2.905272,1368318,1376094,22,0 2. Stringify,RapidJSON_Insitu (C++),canada.json,16.917470,2873369,2873882,28,0 2. Stringify,RapidJSON_Insitu (C++),citm_catalog.json,2.144376,567624,568137,24,0 2. Stringify,RapidJSON_Insitu (C++),twitter.json,1.734811,567624,568137,24,0 3. Prettify,RapidJSON_Insitu (C++),canada.json,26.267364,9697485,9697998,31,0 3. Prettify,RapidJSON_Insitu (C++),citm_catalog.json,4.044699,1915598,1916111,27,0 3. Prettify,RapidJSON_Insitu (C++),twitter.json,2.187670,851408,851921,25,0 4. Statistics,RapidJSON_Insitu (C++),canada.json,0.891159,0,0,0,0 4. Statistics,RapidJSON_Insitu (C++),citm_catalog.json,0.383513,0,0,0,0 4. Statistics,RapidJSON_Insitu (C++),twitter.json,0.180837,0,0,0,0 5. Sax Round-trip,RapidJSON_Insitu (C++),canada.json,27.863099,2873369,5124983,30,0 5. Sax Round-trip,RapidJSON_Insitu (C++),citm_catalog.json,8.174070,567624,2345850,26,0 5. Sax Round-trip,RapidJSON_Insitu (C++),twitter.json,4.514033,567624,1215173,26,0 6. SaxStatistics,RapidJSON_Insitu (C++),canada.json,10.873601,0,2251101,2,0 6. SaxStatistics,RapidJSON_Insitu (C++),citm_catalog.json,6.481403,0,1777713,2,0 6. SaxStatistics,RapidJSON_Insitu (C++),twitter.json,2.724818,0,647036,2,0 7. Code size,RapidJSON_Insitu (C++),jsonstat,0,0,0,0,29184 1. Parse,RapidJSON (C++),canada.json,10.092782,4207842,4656249,77,0 1. Parse,RapidJSON (C++),citm_catalog.json,5.059992,1573602,1585266,35,0 1. Parse,RapidJSON (C++),twitter.json,2.887265,983562,991338,25,0 2. Stringify,RapidJSON (C++),canada.json,17.021298,2873369,2873882,28,0 2. Stringify,RapidJSON (C++),citm_catalog.json,2.130200,567624,568137,24,0 2. Stringify,RapidJSON (C++),twitter.json,1.777338,567624,568137,24,0 3. Prettify,RapidJSON (C++),canada.json,26.180393,9697485,9697998,31,0 3. Prettify,RapidJSON (C++),citm_catalog.json,4.015964,1915598,1916111,27,0 3. Prettify,RapidJSON (C++),twitter.json,2.253185,851408,851921,25,0 4. Statistics,RapidJSON (C++),canada.json,0.890776,0,0,0,0 4. Statistics,RapidJSON (C++),citm_catalog.json,0.401903,0,0,0,0 4. Statistics,RapidJSON (C++),twitter.json,0.192714,0,0,0,0 5. Sax Round-trip,RapidJSON (C++),canada.json,24.968172,2873369,2874139,30,0 5. Sax Round-trip,RapidJSON (C++),citm_catalog.json,6.161490,567624,568394,26,0 5. Sax Round-trip,RapidJSON (C++),twitter.json,3.934741,567624,568714,28,0 6. SaxStatistics,RapidJSON (C++),canada.json,7.854156,0,257,2,0 6. SaxStatistics,RapidJSON (C++),citm_catalog.json,4.206380,0,257,2,0 6. SaxStatistics,RapidJSON (C++),twitter.json,2.366591,0,577,4,0 7. Code size,RapidJSON (C++),jsonstat,0,0,0,0,29184 1. Parse,sajson (C++),canada.json,13.071232,20259652,20259652,4,0 1. Parse,sajson (C++),citm_catalog.json,7.513937,15999160,15999160,4,0 1. Parse,sajson (C++),twitter.json,2.713707,5823067,5823067,4,0 4. Statistics,sajson (C++),canada.json,1.035982,0,0,0,0 4. Statistics,sajson (C++),citm_catalog.json,0.366272,0,0,0,0 4. Statistics,sajson (C++),twitter.json,0.195013,0,0,0,0 7. Code size,sajson (C++),jsonstat,0,0,0,0,33792 1. Parse,SimpleJSON (C++),canada.json,65.267656,29424200,33970482,506131,0 1. Parse,SimpleJSON (C++),citm_catalog.json,33.603146,8787112,12343722,191904,0 1. Parse,SimpleJSON (C++),twitter.json,22.378599,4118312,5413080,105306,0 2. Stringify,SimpleJSON (C++),canada.json,353.007165,3155281,7888103,337239,0 2. Stringify,SimpleJSON (C++),citm_catalog.json,65.884878,1004915,2499969,101834,0 2. Stringify,SimpleJSON (C++),twitter.json,31.906265,1901117,4330708,43475,0 4. Statistics,SimpleJSON (C++),canada.json,3.394145,0,0,0,0 4. Statistics,SimpleJSON (C++),citm_catalog.json,1.628684,0,0,0,0 4. Statistics,SimpleJSON (C++),twitter.json,0.400754,0,0,0,0 7. Code size,SimpleJSON (C++),jsonstat,0,0,0,0,33792 1. Parse,strdup (C),canada.json,0.616455,2251085,2251085,2,0 1. Parse,strdup (C),citm_catalog.json,0.519524,1777697,1777697,2,0 1. Parse,strdup (C),twitter.json,0.073561,647020,647020,2,0 2. Stringify,strdup (C),canada.json,0.616455,2251077,2251077,2,0 2. Stringify,strdup (C),citm_catalog.json,0.491172,1777689,1777689,2,0 2. Stringify,strdup (C),twitter.json,0.079308,647012,647012,2,0 1. Parse,udp/json-parser (C),canada.json,73.399582,12037126,12037126,223233,0 1. Parse,udp/json-parser (C),citm_catalog.json,33.909650,3381911,3381911,51205,0 1. Parse,udp/json-parser (C),twitter.json,11.886212,1601352,1601352,20237,0 2. Stringify,udp/json-parser (C),canada.json,189.820783,1043624,1043624,2,0 2. Stringify,udp/json-parser (C),citm_catalog.json,3.863096,500726,500726,2,0 2. Stringify,udp/json-parser (C),twitter.json,2.661601,472967,472967,2,0 3. Prettify,udp/json-parser (C),canada.json,199.524689,7957671,7957671,2,0 3. Prettify,udp/json-parser (C),citm_catalog.json,6.013602,1929503,1929503,2,0 3. Prettify,udp/json-parser (C),twitter.json,3.170014,835281,835281,2,0 4. Statistics,udp/json-parser (C),canada.json,3.108713,0,0,0,0 4. Statistics,udp/json-parser (C),citm_catalog.json,0.454392,0,0,0,0 4. Statistics,udp/json-parser (C),twitter.json,0.215319,0,0,0,0 7. Code size,udp/json-parser (C),jsonstat,0,0,0,0,23040 1. Parse,ujson4c (C),canada.json,9.558317,8372248,12874362,11,0 1. Parse,ujson4c (C),citm_catalog.json,5.858818,4177944,7733272,10,0 1. Parse,ujson4c (C),twitter.json,2.864660,2080792,3374770,9,0 4. Statistics,ujson4c (C),canada.json,0.854379,0,0,0,0 4. Statistics,ujson4c (C),citm_catalog.json,0.386961,0,0,0,0 4. Statistics,ujson4c (C),twitter.json,0.183136,0,0,0,0 7. Code size,ujson4c (C),jsonstat,0,0,0,0,25600 1. Parse,Vinenthz/libjson (C),canada.json,138.622028,4460990,4489927,334389,0 1. Parse,Vinenthz/libjson (C),citm_catalog.json,28.321322,1877047,1905986,153901,0 1. Parse,Vinenthz/libjson (C),twitter.json,12.740974,1052784,1081728,72620,0 2. Stringify,Vinenthz/libjson (C),canada.json,114.614746,4194336,4194336,13,0 2. Stringify,Vinenthz/libjson (C),citm_catalog.json,8.282112,524320,524320,10,0 2. Stringify,Vinenthz/libjson (C),twitter.json,5.424349,524320,524320,10,0 3. Prettify,Vinenthz/libjson (C),canada.json,114.008252,4194336,4194336,13,0 3. Prettify,Vinenthz/libjson (C),citm_catalog.json,8.140737,524320,524320,10,0 3. Prettify,Vinenthz/libjson (C),twitter.json,5.386419,524320,524320,10,0 4. Statistics,Vinenthz/libjson (C),canada.json,1.367389,0,0,0,0 4. Statistics,Vinenthz/libjson (C),citm_catalog.json,0.499601,0,0,0,0 4. Statistics,Vinenthz/libjson (C),twitter.json,0.455924,0,0,0,0 5. Sax Round-trip,Vinenthz/libjson (C),canada.json,30.402098,4194336,4198688,15,0 5. Sax Round-trip,Vinenthz/libjson (C),citm_catalog.json,17.100606,524320,528672,12,0 5. Sax Round-trip,Vinenthz/libjson (C),twitter.json,9.293191,524320,528672,12,0 6. SaxStatistics,Vinenthz/libjson (C),canada.json,21.038411,0,4352,2,0 6. SaxStatistics,Vinenthz/libjson (C),citm_catalog.json,12.783885,0,4352,2,0 6. SaxStatistics,Vinenthz/libjson (C),twitter.json,4.993328,0,4352,2,0 7. Code size,Vinenthz/libjson (C),jsonstat,0,0,0,0,28672 1. Parse,YAJL (C),canada.json,164.048423,10163570,10165994,501567,0 1. Parse,YAJL (C),citm_catalog.json,33.311967,2409606,2414078,189685,0 1. Parse,YAJL (C),twitter.json,12.546727,1172616,1177088,77047,0 2. Stringify,YAJL (C),canada.json,133.930607,4198520,4198520,15,0 2. Stringify,YAJL (C),citm_catalog.json,10.134160,528504,528504,12,0 2. Stringify,YAJL (C),twitter.json,4.614413,528504,528504,12,0 3. Prettify,YAJL (C),canada.json,163.722763,8392824,8392824,16,0 3. Prettify,YAJL (C),citm_catalog.json,18.531211,2101368,2101368,14,0 3. Prettify,YAJL (C),twitter.json,6.585614,1052792,1052792,13,0 4. Statistics,YAJL (C),canada.json,2.865809,0,0,0,0 4. Statistics,YAJL (C),citm_catalog.json,0.910699,0,0,0,0 4. Statistics,YAJL (C),twitter.json,0.665879,0,0,0,0 5. Sax Round-trip,YAJL (C),canada.json,235.833496,4198520,4202992,22,0 5. Sax Round-trip,YAJL (C),citm_catalog.json,17.907860,528504,532976,19,0 5. Sax Round-trip,YAJL (C),twitter.json,7.105138,528504,532976,19,0 6. SaxStatistics,YAJL (C),canada.json,99.789930,0,4472,7,0 6. SaxStatistics,YAJL (C),citm_catalog.json,8.937264,0,4472,7,0 6. SaxStatistics,YAJL (C),twitter.json,3.299512,0,4472,7,0 7. Code size,YAJL (C),jsonstat,0,0,0,0,29696
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Documentation¶
StruM: Structural Motifs¶
This package provides functionality for computing structural representations of DNA sequence motifs. Estimates for DNA structure comes from the DiNucleotide Property Database (http://diprodb.leibniz-fli.de/).
This version relies on the Cython framework for speed purposes. Improvements in speed are particularly seen with scoring longer sequences.
-
strum.
rev_comp
(str seq) → str¶ Reverse complement a DNA sequence.
Parameters: seq (str.) – A DNA sequence.
-
class
strum.
StruM
¶ StruM Object: Train and work with Structural Motifs
StruMs can be learned via maximum likelihood (
train()
) from a known set of aligned binding sites, or via expectation maximization (train_EM()
) from a set of broader regions.For speed, this package will trim the sequences to be the same length. This can be user specified, else the shortest sequence will be used as a guide.
Additional features other than those from DiProDB can be incorporated by using the
update()
method.-
__init__
(self, str mode='full', int n_process=1, custom_filter=[])¶ Create a FastStruM object.
Parameters: - mode (str.) –
Defines which subset of available features in the DiProDB table to use. Choose from: [‘basic’, ‘protein’, ‘groove’, ‘proteingroove’, ‘unique’, ‘full’, ‘nucs’, ‘custom’]
MODE Features basic Twist, Rise, Bend. protein (for DNA-protein complex) Roll, Twist, Tilt, Slide, Shift, Rise. groove Major Groove Width, Major Groove Depth, Major Groove Size, Major Groove Distance, Minor Groove Width, Minor Groove Depth, Minor Groove Size, Minor Groove Distance. proteingroove The union of the “protein” and “groove” filters. unique Filters the table for the first occurrence of each type of feature. full All available double stranded DNA features. nucs Adenine content, Guanine content, Cytosine content, Thymine content. custom Manually select desired features. - custom_filter (list of ints.) – Specifies the indices of desired features from the DiProDB table.
- n_process (int.) – Number of threads to use.
-1
uses all processers.
- mode (str.) –
-
define_PWM
(self, list seqs, weights=None)¶ Computes a position weight matrix from sequences used to train the StruM.
Parameters: - seqs (list of str.) – Training set, composed of gapless alignment of binding sites of equal length.
- weights (1D array of floats.) – Weights to associate with each of the sequences
in
seqs
to use in learning the motif.
Returns: None. Sets the position weight matrix
self.PWM
based on the weighted sequences.
-
filter
(self)¶ Update StruM to ignore uninformative position-specific features.
Features with a high variance, i.e. non-specific features, do not contribute to the specificity of the StruM model. Filtering them out may increase the signal-to-noise ratio. The position-specific- features are rank ordered by their variance, and a univariate spline is fit to the distribution. The point of inflection is used as the threshold for masking less specific features.
Once this method is run and the attribute self.filter_mask is generated, the method
score_seq_filt()
will become available.
-
plot
(self, save_path)¶ Save a graphical representation of the StruM.
Generates an image displaying the trends of each of the features in the StruM. The line indicates the average (scaled) value for that feature at each position. Shading represents +/- 1 standard deviation.
Note
This generates one row per feature. If you are including many features, your plot may be excessively tall.
Parameters: save_path (str.) – Filename to use when saving the image.
-
print_PWM
(self, labels=False)¶ Pretty prints the PWM to std_out.
Parameters: labels (bool.) – Flag indicating whether to print the PWM with labels indicating the position associated with each column, and the nucleotide associated with each row. Returns: Formatted position weight matrix suitable for display, or use in the MEME suite, e.g. Also prints the PWM to std_out
.Return type: str.
-
read_FASTA
(self, fasta_file)¶ Reads a FASTA formatted file for headers and sequences.
Parameters: fasta_file (file object.) – FASTA formatted file containing DNA sequences. Returns: The headers and sequences from the FASTA file, as two separate lists. Return type: (list, list)
-
score_seq
(self, seq, **kwargs)¶ Scores a sequence using pre-calculated StruM.
Note
This only scores the sequence in the given orientation. If you want to score both strands, use
rev_comp()
on seq, and runscore_seq()
again.Parameters: seq (str.) – DNA sequence, all uppercase characters, composed of letters from set ACGTN. Follows format of the appropriate translate function: ( translate_base()
,translate_func()
)Returns: Vector of scores for similarity of each kmer in seq
to the StruM.Return type: 1D array.
-
score_seq_filt
(self, seq, **kwargs)¶ A variation on
score_seq()
that masks non-specific features.Once the self.filter_mask is generated, this method becomes available. This scores a sequence with the precomputed StruM, masking non-specific features.
Refer to
score_seq()
for more information about the arguments.
-
text_strum
(self, avg, std, avg_range=None, std_range=None, colorbar=False)¶ Gerates a ANSI colored Unicode text representation of a StruM.
Parameters: - avg (Array of floats.) – The average values of the StruM to plot.
- std (Array of floats.) – The standard deviation values of the StruM to plot.
- avg_range (tuple
(low_value, high_value)
.) – Seven bins of even width will be generated across this range, and theavg
values will be assigned to one of the bins. - std_range (tuple
(low_value, high_value)
.) – Eight bins of even width will be generated across this range, and thestd
values will be assigned to one of the bins. - colorbar (bool.) – Whether to include a colorbar
Returns: Returns a tuple of strings. The first element is the formatted StruM representation. If
colorbar=True
, the second element is the colorbar string.Return type: tuple of strings.
-
train
(self, training_sequences, weights=None, lim=None, **kwargs)¶ Learn structural motif from a set of known binding sites.
Parameters: - training_sequences (list of str. Or if updated, list of tuples: (str, [args])) – Training set, composed of gapless alignment of binding sites of equal length.
- weights (1D array of floats.) – Weights to associate with each of the sequences
in
training_sequences
to use in learning the motif. - lim (float) – Minimum value allowed for variation in a given position-specific-feature. Useful to prevent any deviation at that position from resulting in a probability of 0.
Returns: None. Defines the structural motif
self.strum
and the corresponding position weight matrixself.PWM
, sets attributeself.fit = True
.
-
train_EM
(self, data, fasta=True, params=None, int k=10, int max_iter=1000, double convergence_criterion=0.001, random_seed=0, int n_init=1, lim=None, seqlength=None, background=None, seed_motif=None, bool verbose=False)¶ Performs Expectation-Maximization on a set of sequences to find motif.
Parameters: - data (list of str, open file object referring to a FASTA file.) – A set of sequences to use for training the model. Assumed to have one occurrence of the binding site per sequence.
- fasta (bool.) – Flag indicating whether
data
points to an open file object containing a FASTA formatted file with DNA sequences. - params (
*args
,**kwargs
.) – Additional parameters to pass toself.func
, if defined. - k (int.) – Size of binding site to consider. Since dinucleotides
are considered, in sequence-space the size of the binding
site will be
k + 1
. - max_iter (int.) – Maximum number of iterations of Expecation Maximization to perform if convergence is not attained.
- convergence_criterion (float.) – If the change in the likelihood between two iterations is less than this value, the model is considered to have converged.
- random_seed (int.) – Seed for the random number generator used in the EM algorithm for initialization.
- n_init (int.) – Number of random restarts of the EM algorithm to perform.
- lim (float) – Minimum value allowed for variation in a given position-specific-feature. Useful to prevent any deviation at that position from resulting in a probability of 0.
- seqlength (int.) – If set, the sequences in the training data will be trimmed symmetrically to this length. .. note:: This must be longer than the shortes sequence.
- background (str.) –
Method to use for computing the background model. Default is to assume equal probability of each dinucleotide. Passing
"compute"
adapts the background model to represent the dinucleotide frequencies in the training sequences. Otherwise this can the path to a tab delimited file specifying the representation of each dinucleotide. E.g.AA 0.0625 AC 0.0625 ...
- seed_motif (
StruM.strum
.) – Optional. A StruM.strum to use for initializing the Expectation-Maximization algorithm. If set,k
will be replaced by the corresponding value from theseed_motif
’s shape.n_init
will also be reset to 1. - verbose – Specifies whether to print a text version
of the converging StruM at each iteration. Default
False
.
Returns: None. Defines the structural motif
self.strum
and the corresponding position weight matrixself.PWM
.
-
translate
(self, seq, **kwargs)¶ Convert sequence from string to structural representation.
Based on the value of self.updated will either call the
translate_base()
function to use just the DiProDB values, or thetranslate_func()
to augment those with additional values.Parameters: seq – Necessary data for the sequence to be translated. See translate_base()
andtranslate_func()
for more information.Returns: Sequence in structural representation. Return type: 1D numpy array of floats.
-
translate_base
(self, str seq, **kwargs)¶ Convert sequence from string to structural representation.
Parameters: - seq (str.) – DNA sequence, all uppercase characters, composed of letters from set ACGTN.
- **kwargs – Ignored
Returns: Sequence in structural representation.
Return type: 1D numpy array of floats.
-
translate_func
(self, f_seq, **kwargs)¶ Convert sequence from string to structural representation, with additional features.
Parameters: - seq ((str, [args])) – DNA sequence, all uppercase characters, composed of letters from set ACGTN, with additional data for passing to the extra function, if necessary.
- **kwargs – Additional keyword arguments required
by
self.func
.
Returns: Sequence in structural representation.
Return type: 1D numpy array of floats.
-
update
(self, features, func, data=None)¶ Update the StruM to incorporate additional features.
Using this method will change the behavior of other methods, especially the
translate()
method is replaced bytranslate_func()
.Parameters: - features (list of str.) – Text description or label of the feature(s) being added into the model.
- func (function.) – The scoring function that produces the additional
features. These may be computed on sequence alone, or by
incorporating additional data. The output must be an array
of shape
[n, l-1]
, wheren
is the number of additional features (len(features)
) andl
is the length of the sequence being scored. The first argument of the function must be the sequence being scored. - data – Additional data that is used by the new function. May be a lookup table, for example, or a reference to an outside file.
-


Serves as a financial analyst and delivers advanced financial reporting for executives. On-going data visualization analysis and presentations of business trends for growth. Reporting and maintaining dashboards for department team leads. Streamlining revenue processes in excess of 8 million dollars. Reduced key report turnaround by 4 hours per week.
Developed and maintained department Key Performance Indicators. Coached and trained a team of 12 associates. Managed client and vendor service reporting and escalations. Implemented insurance compliance mandates within regulatory timelines.
Created product inventory management system using Google Docs to decrease waste by 10%. Managed vendor relationships and company resources for small and large meeting events. Trained new hires and developed on-going service based courses for client engagement.
Utilized web-scraping, natural language processing, and machine learning algorithm (Multinomial Naeve Bayes) to predict the decade a political speech originated. Visualized findings using amCharts, a d3 based Javascript library, on a bootstrapped HTML dashboard.
Using data from the United Nations, created visualizations using amCharts to show refugee movement overtime. The Flask API and site was then deployed on Heroku.


--- html_id: about title: about ---
AXE is a decentralized X11-based cryptocurrency. The foundation of AXE network is hybrid PoW/PoSe system. First-tier nodes provide hash power to move data and secure the blockchain, while the second tier of full nodes adds extra privacy and increases network performance.
AXE core clients are full nodes, upgradeable to Masternodes and the most secure way to access the network. Full node client downloads a complete copy of the blockchain and participating in transactions verification. Thin (mobile) clients are using SPV protocol to communicate with full nodes and perform operations without downloading a full copy of the blockchain.
The second tier of AXE network - masternodes. They sustain a certain minimum level of network performance and provide additional privacy (PoSe).
Allows for near-instant transactions. AXE InstantSend transactions are fully confirmed within four seconds.
Gives you financial privacy by obscuring the source of funds on the blockchain.
7mLZCucekeWCXYaiR93BThq9TdYc1ZS7Tw
32NDo6qd8KxvnDizseqBAAox5TM8rLmMiJ
LdihK8mesqJ1Pm11zBqtJW2SmksdgGMuNd


Born in secrecy, Kartchner Caverns State has 20th birthday
BENSON, Ariz. (AP) - Kartchner Caverns, the Arizona’s state parks system’s underground flagship attraction with formations that have been growing for thousands of years, is no longer a teenager.
Former state parks officials who helped develop it were among hundreds of visitors who attended a 20th anniversary celebration for the park on Nov. 16, the Herald-Review reported.
Those attending included Dick Ferdon, a retired park ranger who stood guard over the cave’s entrance to keep intruders out during its early development.
Ferdon also helped Randy Tufts and Gary Tenen, who had discovered the caverns in 1974, map the cave’s passages.
“I slept in a travel trailer a few hundred feet from the sinkhole (entrance) for 11 months,” said Ferdon, now 73. “Going into the cave at that stage of its development and working with Randy and Gary was an experience I’ll never forget.”
Tufts and Tenen were cave explorers who discovered the entrance in 1974 in rocky ranchland in the Whetstone Mountains near Benson in western Cochise County.
Tufts and Tenen told the Kartchner family — James and Lois Kartchner were ranchers who ran cattle on the property — several years later but, but the discoverers otherwise kept existence of the caverns a secret for 14 years for fear that its spectacular interior formations would be damaged by unregulated exploration.
The family was sworn to secrecy until a plan was in place for the cave’s development, which took years of work before the park was dedicated with a ribbon-cutting Nov. 5, 1999.
Ken Travous, the former State Parks executive director at the time, helped ramrod the state’s acquisition of the caverns through the Arizona Legislature in 1988. Because of the perceived need to keep the cave system a secret before word leaked out, the legislation to put the property in the state parks system was produced in secret and then considered and approved by both chambers ion one day (April 27, 1988).
“The Kartchners were wonderful to work with. The family is dedicated to education and public service,” said Travous, who attended the anniversary event.
Ferdon was one of the special guests invited to the celebration for a panel discussion of the cave’s development.
“I’m glad State Parks is keeping the cave’s history alive through events like this,” he said. “There are a lot of great stories tied to the cave’s discovery and development.”
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Please read our comment policy before commenting.


A class that holds a group of tiles (either fg or bg).
More...
#include <Tiles.h>
|
| | Tiles (std::string data) |
| | Constructs a set of tiles from string data. More...
|
| |
| | Tiles (Element *e) |
| | Constructs a set of tiles from an element. Constructs the tiles from the element's innertext string attribute. More...
|
| |
| | Tiles () |
| | Constructs an empty set of tiles. More...
|
| |
| bool | LoadFromElement (Element *e) |
| | Loads a set of tiles from an element. Loads as a string from the element's innerText attribute. More...
|
| |
| bool | LoadFromString (std::string data) |
| | Loads a set of tile data from a string. Fits the size to the tile map. More...
|
| |
| std::string | ToString (void) |
| | Encodes the tile data as a string. The string includes all trailing zeros. These zeroes are not necessary when saving (use CutTileString instead) More...
|
| |
| std::string | CutTileString (void) |
| | Encodes the tile data as a string. The string is shortened for file loading. Excess zeros are removed. More...
|
| |
| Element * | SaveToElement (const std::string &name) |
| | Saves the tile data to the elements innerText attribute. More...
|
| |
| char | GetTile (size_t x, size_t y) const |
| | Gets a char representing the tile at the given position. More...
|
| |
| void | SetTile (size_t x, size_t y, char in) |
| | Sets a tile in the tile data to the provided char. More...
|
| |
| char | GetTile (const Vector2< size_t > pos) const |
| | Gets a tile from the tiledata at the specified position and returns it as a char. More...
|
| |
| void | SetTile (const Vector2< size_t > pos, char in) |
| | Sets the tile at the specified position to the char provided. More...
|
| |
| Vector2< size_t > | GetSize (void) const |
| | Gets the size of the tile set as a vector,. More...
|
| |
| void | Resize (size_t newX, size_t newY) |
| | Resizes the tile map to fit the specified dimension. Cuts off excess characters (ones that aren't within the new size that were in before). Fills in excess characters with '0' air (ones that weren't within the size but are now) More...
|
| |
| void | Resize (const Vector2< size_t > &newSz) |
| | Resizes the tile map to fit the specified dimension. Cuts off excess characters (ones that aren't within the new size that were in before). Fills in excess characters with '0' air (ones that weren't within the size but are now) More...
|
| |
A class that holds a group of tiles (either fg or bg).
Tiles are stored as a multidimentional vector of char.
◆ Tiles() [1/3]
| Sakura::Tiles::Tiles |
( |
std::string |
data | ) |
|
Constructs a set of tiles from string data.
- Parameters
-
| data | The string to construct from. |
◆ Tiles() [2/3]
| Sakura::Tiles::Tiles |
( |
Element * |
e | ) |
|
Constructs a set of tiles from an element. Constructs the tiles from the element's innertext string attribute.
- Parameters
-
| e | The element to construct from. |
◆ Tiles() [3/3]
Constructs an empty set of tiles.
◆ CutTileString()
| std::string Sakura::Tiles::CutTileString |
( |
void |
| ) |
|
Encodes the tile data as a string. The string is shortened for file loading. Excess zeros are removed.
- Returns
- The tile data encoded as a string.
◆ GetSize()
| Vector2< size_t > Sakura::Tiles::GetSize |
( |
void |
| ) |
const |
Gets the size of the tile set as a vector,.
- Returns
- The size of the tile map.
◆ GetTile() [1/2]
| char Sakura::Tiles::GetTile |
( |
const Vector2< size_t > |
pos | ) |
const |
Gets a tile from the tiledata at the specified position and returns it as a char.
- Parameters
-
| pos | The position of the tile (as a vector) |
- Returns
- The tile at the position as a char.
◆ GetTile() [2/2]
| char Sakura::Tiles::GetTile |
( |
size_t |
x, |
|
|
size_t |
y |
|
) |
| const |
Gets a char representing the tile at the given position.
- Parameters
-
| x | The x position. |
| y | THe y position |
- Returns
- The char at that position.
◆ LoadFromElement()
| bool Sakura::Tiles::LoadFromElement |
( |
Element * |
e | ) |
|
Loads a set of tiles from an element. Loads as a string from the element's innerText attribute.
- Parameters
-
| e | The element to load from. |
- Returns
- True on success, false on failure.
◆ LoadFromString()
| bool Sakura::Tiles::LoadFromString |
( |
std::string |
data | ) |
|
Loads a set of tile data from a string. Fits the size to the tile map.
- Parameters
-
| data | The string to load from |
- Returns
- True on success, false on failure.
◆ Resize() [1/2]
| void Sakura::Tiles::Resize |
( |
const Vector2< size_t > & |
newSz | ) |
|
Resizes the tile map to fit the specified dimension. Cuts off excess characters (ones that aren't within the new size that were in before). Fills in excess characters with '0' air (ones that weren't within the size but are now)
- Parameters
-
| newSz | The new size of the tile map. |
◆ Resize() [2/2]
| void Sakura::Tiles::Resize |
( |
size_t |
newX, |
|
|
size_t |
newY |
|
) |
| |
Resizes the tile map to fit the specified dimension. Cuts off excess characters (ones that aren't within the new size that were in before). Fills in excess characters with '0' air (ones that weren't within the size but are now)
- Parameters
-
| newX | The new size of the tile map along the X axis. |
| newY | The new size of the tile map along the Y axis. |
◆ SaveToElement()
| Element * Sakura::Tiles::SaveToElement |
( |
const std::string & |
name | ) |
|
Saves the tile data to the elements innerText attribute.
- Parameters
-
| name | The name of the element. |
◆ SetTile() [1/2]
| void Sakura::Tiles::SetTile |
( |
const Vector2< size_t > |
pos, |
|
|
char |
in |
|
) |
| |
Sets the tile at the specified position to the char provided.
- Parameters
-
| pos | The position of the tile to set (as a vector) |
| in | The tile type to set it to. |
◆ SetTile() [2/2]
| void Sakura::Tiles::SetTile |
( |
size_t |
x, |
|
|
size_t |
y, |
|
|
char |
in |
|
) |
| |
Sets a tile in the tile data to the provided char.
- Parameters
-
| x | The x position |
| y | the y position |
| in | The char to set the tile to. |
◆ ToString()
| std::string Sakura::Tiles::ToString |
( |
void |
| ) |
|
Encodes the tile data as a string. The string includes all trailing zeros. These zeroes are not necessary when saving (use CutTileString instead)
- Returns
- The tile data encoded as a string.
The documentation for this class was generated from the following files:


D.A.R.C.
Distributed Asynchronous RGP Coordinator
Project DARC is a modular and mod-able platform that should allow groups of people to carry out a traditional table-top RPG campaign, despite having players and a DM (the person running the game) in different places (distributed) and not all online at the same time (asynchronous).
DARC is designed to be simple, secure, portable (mobile), and flexible/customizable.


@end
@end
@end
@if post['deleted'] == 0 and post['ptype'] == 3 and pollData['poll_open'] and (post['uid'] == current_user.uid or current_user.is_admin() or current_user.uid in subMods['all']):
@for ll in func.getSubFlairs(post['sid']):
@{ll.text!!e}
@end
@end
@if (post['ptype'] == 3) and (post['deleted'] == 0 or post['visibility'] == 'admin-self-del' or post['visibility'] == 'mod-del' or post['visibility'] == 'user-self-del'):
@{polls.renderPoll(pollData, postmeta, post)!!html}
@end
@if post['content']:
@if post['deleted'] == 0:
@{markdown(post['content'])!!html}
@{post['content']}
@else:
@if ((post['visibility'] == 'none') or (post['visibility'] == 'mod-self-del')):
@{_('[deleted]')}
@elif ((post['visibility'] == 'admin-self-del') or (post['visibility'] == 'mod-del') or (post['visibility'] == 'user-self-del')):
@{markdown(post['content'])!!html}
@{post['content']}
@end
@end
@elif not post['link']:
@end
@if content_history and post['content'] and (post['visibility'] != 'none' and post['visibility'] != 'mod-self-del'):
@for count, old_version in enumerate(content_history):
@if current_user.is_authenticated and post['deleted'] == 0 and not current_user.is_subban(sub) and not postmeta.get('lock-comments') and not post['is_archived']:
@elif current_user.is_authenticated and post['deleted'] == 0 and postmeta.get('lock-comments'):
@{_('This post is closed to new comments. It has been locked by the moderators.')}
@elif current_user.is_authenticated and post['deleted'] == 0 and post['is_archived']:
@{_('This post is closed to new comments. It has been automatically archived.')}
@{_('This post is closed to new comments. It has been locked by the moderators.')}


--- layout: page title: Homepage ---
Saturday, March 12, 2016 | 12:00 p.m. Akron, OH
Learn More
Saturday, May 2, 2016 | 12:00 p.m. Akron, OH
Deadline: May 3, 2017
Apply
Deadline: February 23, 2017


public interface NetworkServiceInterface
extends java.rmi.Remote
| Modifier and Type | Method and Description |
|---|---|
Network[] |
getAllNetworks()
Returns the list of
Network objects to which the current
login has
access. |
Network |
getCurrentNetwork()
Returns the current network for which requests are being made.
|
Network |
makeTestNetwork()
Creates a new blank network for testing purposes using the
current login.
|
Network |
updateNetwork(Network network)
Updates the specified network.
|
Network[] getAllNetworks() throws java.rmi.RemoteException, ApiException
Network
objects to which the current
login has
access.
Intended to be used without a network code in the SOAP header when the login may have more than one network associated with it.
java.rmi.RemoteException
ApiException
Network getCurrentNetwork() throws java.rmi.RemoteException, ApiException
java.rmi.RemoteException
ApiException
Network makeTestNetwork() throws java.rmi.RemoteException, ApiException
Each login(i.e. email address) can only have one test network.
Data from
any of your existing networks will not be transferred to the
new test network.
Once the test network is created, the test network can be
used in the API
by supplying the Network.networkCode
in the SOAP header
or by
logging into the DFP UI.
Test networks are limited in the following ways:
ForecastService
for more info.java.rmi.RemoteException
ApiException
Network updateNetwork(Network network) throws java.rmi.RemoteException, ApiException
network
- the network that needs to be updatedjava.rmi.RemoteException
ApiException


--- title: Images and Thumbnails section: Components ---
Use
.img-fluid
to make an image fit with its container.
Use
.rounded
,.rounded-circle
, or.img-thumbnail
on``````
to quickly style the borders.
.rounded-0
will remove theborder-radius
styles.


| native-encoding {farver} | R Documentation |
Colours in R are internally encoded as integers when they are passed around
to graphics devices. The encoding splits the 32 bit in the integer between
red, green, blue, and alpha, so that each get 8 bit, equivalent to 256
values. It is very seldom that an R user is subjected to this representation,
but it is present in the nativeRaster
format which can be obtained from
e.g. capturing the content of a graphic device (using dev.cap()
) or reading
in PNG files using png::readPNG(native = TRUE)
. It is very rare that you
might need to convert back and forth between this format, but it is provided
here for completeness.
encode_native(colour, ...) decode_native(colour)
colour |
For |
... |
Arguments passed on to |
encode_native()
returns an integer vector and decode_native()
returns a character vector, both matching the length of the input.
# Get native representation of navyblue and #228B22 native_col <- encode_native(c('navyblue', '#228B22')) native_col # Convert back decode_native(native_col)


Effectively Bridging Development and Design
Where are the cracks?
#1 (Company)
Build Smart Teams
#2 (Team)
What is good for experience is good for developers
DRY is a Design principle
#3 (Team)
Develop common vocabularies
Take an interest in each other's work
Strive for a universal lexicon across your organization
#4 (Career)
Be a squeaky wheel
#5 (Company)
Nurture squeaky wheels
#6 (Career)
Bring receipts
We're still, uh, figuring it out
@xiwcx


pizza like never before
mouth watering jalapeno madness
kes 1050
excuisite ham and salad
kes 1170
order now
piccanos amazing grille
kes 1080


Issue 55: Auto-completion for Command Editor
Steps to reproduce
- Open Firebug.
- Enable and switch to the the Console panel.
- Type:
function fun(anArgument1) { function fun2(anArgument2) {
} anArg
into the Command Editor.
- Press tab -> it should auto-complete to "anArgument1".
- Clear the editor, and type
myobj.prop
and press tab
-> an auto-completion popup should be displayed.
- Press down arrow and then tab -> it should auto-complete to "prop2".
- Clear the editor, type
document.getelementbyi
and put focus after "get".
- Press tab -> it should auto-complete to "getElementById".
- Clear the editor, type
document.gete
and put focus at the beginning of line.
- Press tab -> a tab should be entered.
- Clear the editor, type
document.get
and select the last character.
- Press tab -> the line should be indented with no change to the selection.


You can find the results of your search below. If you didn't find what you were looking for, you can create or edit the page named after your query with the appropriate tool.


Mum completely transforms kitchen into dream room for just £60
Hannah, who was pregnant at the time of her renovation, made the kitchen look completely different in just four days using products from Amazon, B&Q, and Wilko's
by Emma Munbodh, Niamh Shackleton, https://www.facebook.com/EmmaMunbodhA young mum has revealed how she transformed her entire kitchen into a dream family room, using just £60.
Hannah Williams, 25, from Grays, Essex, decided to bite the bullet and decorate her kitchen after being fed up of looking at her dull, brown cupboard doors.
Hannah, a junior project manager, said: "My partner Jordan, 28, and I moved into our flat in February 2018 and from day one I really wanted to redecorate the kitchen.
"I kept nagging Jordan about it but he kept pushing it back so, when he was away in Ireland one weekend, I decided to just do it myself as I was tired of it looking so dull.
"I'd seen articles online about people doing up their kitchens on a budget, so thought I'd give it a go.
"I did the majority of it by myself over the May bank holiday weekend - my dad helped a little bit here and there too."
Hannah, who was pregnant at the time, made the kitchen look completely different in just four days and kept the renovation cheap by getting items from places like Amazon, B&Q, and Wilko's.
The now mum of one added: "I didn't realise you could just paint over the cupboard doors, I thought you had to get new ones completely.
"Once I realised, I went to B&Q and bought some of their GoodHome multi surface paint for cupboard doors for just £20 - a hell of a lot cheaper than getting them refitted!"
Hannah's shopping list
B&Q - GoodHome multi surface paint for cupboard doors, £20
Amazon - door handles, £11.20
Vinyl Warehouse - worktop vinyl, £17.95
Wilko's - whispering sage wall paint 2.5L, £12
Total £61.15


Lorem Ipsum is simply dummy text of the printing and typesetting industry, New York Times. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry, New York Times. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). Google
The Catcher in the Rye
Ulysses
The Plague


I'm A Celebrity fans beg Emily Atack to go back blonde as she unveils new hair
She looks so different
by Richard BellITV2 viewers have called for presenter Emily Atack to go back to her old hairstyle after she showed off her new 'do on I'm A Celebrity... Get Me Out Of Here!.
The former campmate, 29, looked dramatically different last night (17.11.19) on the first episode of companion show Extra Camp, which she hosts alongside comedian Joel Dommett, 33, and former Emmerdale actor Adam Thomas, 31.
Read more: I'm A Celebrity viewers stunned as Ant McPartlin unveils new arm tattoos
But fans of the show didn't appear to be into the new brunette look, which she recently debuted at the Pride of Britain Awards.
One said on Twitter: "What has Emily done to her hair, it's a no from me #ImACeleb."
Another wrote: "Emily Atack dyed her hair?! #ImACeleb."
A third, who seemed to prefer Emily's blonde look, tweeted: "That is not the hair color or style for Emily Atack."
One added: "Don’t like Emily’s hair colour, back to blonde please."
Others loved her hair.
Emily Atack was among the celebs rumoured to be replacing former Extra Camp presenter Scarlett Moffatt, after the ex Gogglebox favourite decided to step away from the programme along with co-star Joe Swash.
In October, the Inbetweeners actress was confirmed for Extra Camp alongside Joel and Adam.
Emily said at the time: "I'm thrilled that I get to make more amazing memories in a place that means so much to me.
"I've loved working with Joel on Singletown, we're like brother and sister, and Adam has got such amazing energy. The three of us are going to have the best time ever!"
Read more: I'm A Celebrity fans divided over Caitlyn and Kate doing the bushtucker trial
I'm A Celeb returned on Sunday evening for a 19th series. It marked the first time presenter Ant McPartlin had hosted with his on-screen partner Dec Donnelly since 2017, as he missed last year's series following his drink-driving conviction and stint in rehab.
While his replacement Holly Willoughby was a hit with fans, viewers were nevertheless thrilled to have him back.
Within minutes of the opening title sequence, Ant and Dec were cracking jokes as though they had never been separated - and fans loved it! Some were even in tears that he was back and looking so well.
Leave us a comment on our Facebook page @EntertainmentDailyFix and let us know what you think of this story.


Asynchronous Server Technologies
Sergei Kudinov
Web Developer @ Adaltas
<EMAIL_ADDRESS>## About this course
* You will use / do :
* JavaScript / TypeScript & Markdown
* Node.JS & NPM or yarn
* Git & Github / GitLab
* Unit tests & Travis CI
* Use a framework & transpiler
* Use a storage (LevelDB)
* Read the doc ! Ask Google !
* You will apprehend tools of the Open Source
## About this course
* Evaluation :
* Continuous using Git
* Simple project at end of course
* Planning: [CALENDAR.md](https://github.com/adaltas/ece-nodejs/blob/2019/CALENDAR.md)
## Final project
* Based on code from class
* Using presented technologies
* Simple dashboard app :
* User login
* A user can insert simple metrics
* A user can retrieve his metrics displayed nicely in a graph
* A user can only access his own metrics
* See [PROJECT.md](https://github.com/adaltas/ece-nodejs/blob/2019/PROJECT.md)


--- layout: post title: Maze of Earphones date: 2009-02-14 21:08:08.000000000 +05:30 categories: - kiranoncemore - S3 IS tags: - B&W - earphones - experimental - tabletop status: publish type: post published: true meta: _edit_last: '2' author: login: kiranoncemore email<EMAIL_ADDRESS>display_name: kiranoncemore first_name: Kiran last_name: '' ---
Tv: 1/500s; Av: F/8.0; f: 6mm (35mm-equiv: 38mm); Flash used
Processed using GIMP2.
This was a totally experimental snap. I placed the the bunch of entangled and entwined earphones on a large white paper on the floor; and took the snap from right on top of the subject. I was experimenting with lighting conditions as has been suggested to me by many readers. I guess I still have a long way to go as far as getting the lighting and background right goes.
I tried several options while editing the image. My main intention was to get a raw B&W look. While I succeeded in that, I feel the image editing leaves a lot to be desired too. The intention of posting this image is to get advice from readers about how better this image could have been edited. On the lighting conditions front, I will continue experimenting till I get even the basics right.
What improvements would I like to see?
And thats just the beginning, I am sure!


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Check for Coding Errors¶
Datavyu scripts have the ability to check for coding errors to make up for potential human error. Inputting invalid codes is a common coding mistake. For example, a coder might accidentally input an “h”, when only “j” or “k” are acceptable values. Using scripts, coders can double-check their work for errors and fix them early on.
Datavyu API provides the checkValidCodes()
method to check for
coder errors. checkValidCodes()
requires at least three
arguments: the name of the column it will verify, the location that it
will output the results to, and at least one key-value pair.
Key-value pairs consist of a “key”, the name of a Datavyu code, and a
“value”, which is an array of valid values for that code.
checkValidCodes()
then checks each code (key) against its list
of valid values (the values).
The following examples check the look
column against its codes. You
can download the sample data used in this tutorial from here
.
Check Code Validity and Output to the Console¶
Basic Format¶
Example
require 'Datavyu_API.rb'
begin
# Check for errors. Notice the square brackets. These denote
# lists. The basic format is: "columnName", "dumpFile",
# "codename", ["validcode1", "validcode2", etc],
# "code2", [ "validcode1", "validcode2", etc], ...
checkValidCodes("look", "", "direction", ["l", "r"], "target", ["a", "b"])
end
Breaking down the function call makes it easier to follow what is
happening. Recall that checkValidCodes()
takes (at least) three
arguments: the name of the Datavyu variable it will be checking, the
location that it should direct the output to, and at least one key-value
pair.
In the example, we have:
checkValidCodes("look", "", "direction", ["l", "r"], "target", ["a", "b"])
"look"
is the name of the variable to check.""
is the location that we want to export the output to. Using""
indicates that we do not want to write the results to a file, and that it should instead display in the Datavyu Scripting Console."direction", ["l", "r"]
is the first key-value pair, which specifies that the “direction” Datavyu code should only have the values “l” or “r”."target" , ["a", "b"]
is the second key-value pair, which specifies that the “target” Datavyu code should only have the values “a” or “b”.
Advanced Format¶
You can perform the same verification by first assigning the valid codes to objects. This makes it is easier for human readers to parse the script, and makes it easy to modify or update in the future.
Set up the script:
require 'Datavyu_API.rb' begin
Assign each list of valid codes to a variable:
require 'Datavyu_API.rb' begin # Store each of the valid code arrays into a object first # so that it is easier to read directionCodes = ["l", "r"] targetCodes = ["a", "b"]
Check for coding errors using
checkValidCodes()
, replacing the lists with your newly-created objects, and end the script:require 'Datavyu_API.rb' begin directionCodes = ["l", "r"] targetCodes = ["h", "t"] # Check for typos, replacing the code arrays with your new variables: checkValidCodes("look", "", "direction", directionCodes, "target", targetCodes) end
Check Code Validity and Output to a File¶
checkValidCodes()
can write the results of its verification to a
“dump file”. Datavyu will create an output file if you specify a path
in the dumpFile
parameter.
Create an object that holds the path that it should output to using Ruby’s File.expand_path method, which converts a relative path, like
~/Desktop/file.txt
to an absolute path name, which contains the root directory, and all sub-directories, like/Users/alice/Desktop/file.txt
The following commands create a variable called
output
that holds the absolute path to a file on the Desktop calledoutput.txt
:require 'Datavyu_API.rb' begin output = File.expand_path("~/Desktop/output.txt")
Call
checkValidCodes()
on the “step” column, passingoutput
as the argument for thedumpFile
parameter:require 'Datavyu_API.rb' begin directionCodes = ["l", "r"] targetCodes = ["a", "b"] output = File.expand_path("~/Desktop/output.txt") # Check for errors, specifying the output variable as the dumpFile parameter checkValidCodes("look", output, "direction", directionCodes, "target", targetCodes) end
When the script ends, the output.txt file will be created on the
Desktop, containing the results of the code checking. For the
sample data
,
it should find one error, and the output should resemble:
Code ERROR: Var: look Ordinal: 2 Arg: direction Val: rj
Video Example of Checking for Errors¶
This video displays one way to check for errors (typos, impossible values, etc.) within a spreadsheet.


This is an example file. It represents the current (latest) version of eZ Platform, raw and unchanged from the main repo. This is specifically the latest tagged version, not the master branch's head.


Description: Each training will cover the same topics but will feature different local disability experts as guest speakers. Topics to be covered include: financing project for people with disabilities; legal and funding considerations; partnerships between CDFI and disability organizations; CDFI capital investments; and finance investments that support the strengthening of communities to be more accessible and inclusive for all. View the draft agenda here: [link to PDF].
Description: This 45-minute webinar is a pre-requisite to attending the Access for All in-person training. The purpose of the webinar is to prepare participants for the immediate learning objectives of the two-day training so that they may achieve the intended long-term outcomes upon returning to their communities. Participants will be oriented to the disability market and will be presented with tools that will help them get the maximum benefit from this learning experience. Those registering for the in-person trainings will automatically be registered for the corresponding pre-training webinar
Dates and Locations:
put in box
For the in-person trainings, BCT Partners will include the up to two nights of lodging for everyone attending the training from outside of a 75-mile radius. In addition, there will be scholarships available to fund the transportation to/ from the in-person meeting for qualified attendees. Upon registering for a training, registrants may request a transportation scholarship. Scholarships will be provided on a first-come-first-served basis for air, train or motor vehicle transportation.
Date & Time: December 9, 2019 (2pm EST)
Description: The focus of the webinar is to increase CDFI staff comfort, confidence and competence in working with individuals with disabilities seeking services. The training will offer tips and strategies for interacting with people with disabilities seeking economic growth and opportunity through CDFI services. Content includes basic etiquette for working with people with disabilities and information/tools for providing accommodations within CDFI services in the areas of interpersonal interactions, opening opportunity for services provision, and processing service coordination.
Registration: TBD
Date & Time: December 16, 2019 (2pm EST)
Description: Profile case scenarios and leveraging effects with CDFI products.
Registration: TBD
Date & Time: January 13, 2020 (Time TBD)
Description: This webinar will address the Workforce Innovations and Opportunities Act (WIOA) relevance and opportunities for the CDFI industry. Mr. Thelin will provide an overview of WIOA and Title IV of the Act (Rehabilitation Act of 1973, amended). These changes offer an opportunity for CDFIs to benefit from partnerships with WIOA entities (Workforce, Vocational Rehabilitation, Adult Education). Focused content will address how strategic partnerships between CDFI and WIOA entities can impact individuals in terms of career-oriented outcomes, salary increases, and career ladder opportunities that result in quality of life associated with fiscal security, savings, mortgage capacity, etc.
Registration: TBD
Date & Time: January 20, 2020 (2pm EST)
Description: Profile leveraging of service dollars and loan funds for inclusive/neighborhood-based housing development. Includes technical content on Medicaid funded long-term care certifications if required by resident.
Registration: TBD


Battlesquares Team Project:
As our first team project at Epicodus we used HTML CSS Javascript and JQuery to create a game similar to Battleship.
Rules:
- Determine with your opponent a set number of squares to pick.
- Don't look at the screen when your oponent is selecting their squares.
- Take turns selecting squares, first person to select allo their opponents squares wins.
Instructions to start:
- Click Player vs Player or VS Computer to begin.


On today's episode of Git it Gurl: the nerdy adventures of Kara Carrell; we're learning about the Cycle Method.
Huh? Method? What's that??
Let's step back and get some background info. In the Ruby programming language, everything is an object. when you want to do something to the object, you can use a "method" to do it. there are lots of methods that are defined in Ruby for anyone to use, and one of them is called the Cycle method.
the Cycle method is part of the enumerable methods, which allows you to use this method on Arrays and Hashes alike!. The way you call the Cycle method is to stick it onto an established array or hash, and give it a number of times you'd like the object to cycle through all it's elements. For example:
carrell_family = ["Carl Jr.", "Karla", "Kara", "Margie", "Carl Sr."] => Creates a new array of names as strings carrell_family.cycle(3) {|name| puts name} => Calls the cycle method to run each name and put it line by line until you have cycled through the list of names 3 times.
Why would I want to use this??
If I wanted to assign each person in my family a chore, for example, I could call Cycle to help run the list of family names and assign it an accompanying chore from another array, and make a chore chart hash.
When you utilize other related methods like .next or .rewind, you can do some pretty cool stuff. Check out this guy's super nerdy examples of how you can use .cycle.
This concludes another episode of Git it Gurl: the nerdy adventures of Kara Carrell. Stay tuned for next week, as we get more in-depth with the wonderful world of Ruby!


Control: Control the represented world with physical controllers.
Planned to make a controller looks like camera itself using LED strap, Potentiometer, accelerometer, but connection of the accelerometer was not stable enough without soldering, so I had to change the plan immediately.


I was privileged to have the chance to join a close-knit yet professional outfit for my operational posting on 1 Oct 2017. To some, it was an odd choice. It was too similar to my first stint, they said. Too little spotlight on the role, I was told.
Nonsense.
It was a perfect continuation of what I was doing. Policy IS implementation. So, it was a no-brainer to come over and implement what I pushed for previously. Yes, it was a niche role, without bells and whistles. But if one is in the service for the right reasons, this is precisely the kind of environment to thrive in and learn from.
I leave after the 6 months - a little older and, I believe, a much better listener. There is, unfortunately, some regret. I could not see out some of the things we were doing. But I know the team I leave behind has the right mentality and capacity to do what we set out to. And I would always be watching, scanning the papers, and cheering on, albeit at a distance. Thank you for the memories.
There are 3 takeaways I would like to share:
Listen
It is presumptuous to think that the authorities have the monopoly on knowledge, that Government knows best and knows it all. Only when one listens - intently, genuinely, does one understand. That is the first, crucial step.
Be Candid and Open
It turns out that candor, openness and transparency are even better than reason and logic in explaining a complex topic to an assumed-to-be hostile and unreceptive audience. In fact, only when we are humble and treat our people with respect, then may we expand our common space and enhance the maturity of the discourse. And if we must agree to disagree, then so be it. At least, we get across that our intentions are the same if not our views of how to achieve those intentions.
Start Small, Act Fast
Micro-innovations are better than circling the same ground over and over. And if some decisions are hard to take because we can disagree on the lesser of the evils, then perhaps we can just try one way or the other. And if we fail, at least we fail fast, and hopefully safely. For not trying, is failure at its grandest.


This post was written as a university assignment April 2014. I’ve been offered a 6 week internship working for the Green Brain Project in Summer 2014.
Why creating autonomous honey bee quad-copters is the next logical step.
From The Terminator’s cold hearted cyborg assassin, to the heart-throb operating system Samantha in Her, science fiction has long been predicting the rise of humanoid robots. The huge potential for AI and robotics to either improve or destroy our society has been illustrated countless times, including Isaac Asimov’s dramatic I-robot – with a utopian work-free society turning quickly to a robot-controlled utilitarian dictatorship. However, regardless of the disruption these technologies will bring, research and innovation into autonomous robotics will continue at an always increasing rate.
Sheffield’s Green Brain project aims to contribute to this revolution by reverse engineering the brain of a honey bee, and creating autonomous flying honey bee robots controlled by a simulation of the brain. Within the Kroto Innovation center at the University of Sheffield, their setup is impressive: A clinically white observation room, with a large window on one side, sits almost empty apart from three powerful, expensive gaming PCs stacked in the corner, and a grey metallic quad-copter sitting apprehensively in the center.
The Green Brain’s Quad-copter
The quad-copter is the honey bee – kitted up at every corner with sensors and receivers, mimicking accurately those of an actual bee. The gaming PCs are its brain – simulating a large and complex neural network on massively parallel GPUs.
This is the result of over two year’s hard work from the Green Brain Team – a small but impressive team of academics and engineers working at Sheffield. This work, they claim, is the next logical step in driving forward our understanding of our minds, and perhaps bringing us closer to the society abound with robots predicted unanimously by science fiction.
But why a bee?
You may have heard of Colony collapse disorder, it’s a name given to the very misunderstood mass-collapse of honey bee populations across the world. It’s due to many complex interacting factors that have unfortunately resisted fitting into any sound-bite sized headlines. Bees play a vital role in many ecological systems, so its consequences could be dire: disrupting our food supply and many delicately sustained wildlife habitats that depend on them.
A Honey Bee
Here, some of the goals of the Green Brain Project become justified: If they can simulate a bee from scratch with an accuracy sufficient to reproduce characteristics and behaviours of actual honey bees, they will have developed an invaluable tool to understanding and countering colony collapse disorder. As James Marshall – the leader of the Green Brain project – says: “robots give behavioural biologists the freedom to explore the mind of an animal in ways that would not be possible with living subjects.”
Replacing dying honey bees with ungainly flying quad-copters is farfetched at best. And although creating “artificial pollinators” has been mentioned as a potential direction for this technology, the act of researching and understanding the bee’s behaviours is fortunately a more valid and realistic goal in and of itself.
From worms to humans – choosing the scale of the next AI
The Green Brain Project is named after a more ambitious effort – The Blue Brain Project – which aims to simulate the entire human brain at a molecular level. The Blue Brain project is still in its early stages, having limited success in simulating a single cortical column of a rat. The Green and Blue brain projects are just two of many projects aiming to simulate brains at varying scales.
At the other extreme sits The Open Worm Project aiming to simulate the neural and muscular workings of a microscopic worm, containing only one-thousand cells and about three-hundred neurons. Although this organism appears simple, the individuals working on the team believe it’s the right scale to focus on given our current level of technology and biological understanding, and they might be right: even making quite basic predictions about the worm’s movement based on their simulation has been very difficult.
A Graphical representation of the Open Worm Simulation
Although these projects have different ambitions, working at dramatically different scales, they are producing new results and knowledge that will help each other progress faster than they ever could alone. There is no single correct scale that brain simulation research should focus; it is only by tackling the questions from every angle at once that we can understand the problem space deeply.
The reality is tough
Both the Blue Brain and Open Worm projects exist purely in a simulated world. This comes with a huge amount of freedom; it’s easier to measure, manipulate and control virtual worlds – the temptation is obvious. However, the Green Brain is built to control a physical quad-copter in the real world.
A well-known joke goes as follows:
The problem with engineers is that they tend to cheat in order to get results.
The problem with mathematicians is that they tend to work on toy problems in order to get results.
The problem with computer scientists is that they tend tocheat at toy problems in order to get results.
Simulating a brain should not be a toy problem. There’s no reason a realistic simulation of the honey bee brain, or any other brain, should be clean and mathematically elegant – it needs to work in real life. Ensuring this focus on results in the messy and complicated real world is the reason behind the Green Brain’s physical manifestation as a quad-copter.  The team believe, with good reason, that pushing their simulation to work in real environments will force them to solve difficult problems that might have been simplified away in a virtual world.
The Green Brain Project’s testing room
The rise of the quad
Although the first recorded quad-copter was built over 100 years ago, they have only recently become widely utilised. A number of popular videos and articles – including an awe-inspiring TED talk claiming over 2.5 million views – popularised the quad-copter and initialised a large boom in funding for multi-copter related projects. This sudden utilisation of multi-copters became possible due to a number of advances in technology, including wide availability of cheap, light and small computers, powerful and compact LiPo batteries, and efficient propeller motors.
Popularity of the word ‘quadcopter’ over time on Google Trends
Why are quad-copters so popular?
The answer is mechanical simplicity. In layman terms, a helicopter can control its acceleration by changing the pitch of its blades as they spin around. This is called a variable-pitch rotor. It allows for a large degree of control and efficiency, but the mechanism is complicated and prone to failure, particularly at small scales.
A variable-pitch rotor system on a helicopter; efficient but mechanically complex.
Due to having 4 blades, quad-copters can control themselves without the need for a variable-pitch rotor. However, contrary to popular belief, quad-copters are less efficient and less stable than a classical helicopter. Stabilising a quad-copter in-flight requires a low-latency central control system which feeds instructions rapidly back to the motors based on sensors and measurements from its accelerometer, gyroscope, and sometimes even GPS. However, the lack of a complicated variable-pitch rotor system and the incredible power and versatility gained from a computerised control system has led to quad and multi-copters gaining a dominance within the world of small-scale drones.
If you can’t beat them…
Robotics projects like the Green Brain will continue being funded, and general progress in robotics will increasingly influence our lives, but the transformations they bring will be complicated.
Richard Feynman described the limitations of science in his book The Meaning of it All:
Science and moral questions are independent. The common human problem, the big question, always is “Should I do this?” It is a question of action. “What should I do?” How can we answer such a question? We can divide it into two parts: We can say, “If I do this what will happen?” and “Well, do I want that to happen?” The first question is the typical scientific question. The method is “Try it and see.” But the question “Do I want this to happen?” is not scientific. Well, you say, if I do this, I see that everybody is killed, and, of course, I don’t want that! Well, how do you know you don’t want people killed? You see, at the end you must have some ultimate judgment.
Science and technology are hailed by some as an ultimate solution to many of life’s problems, but technology provides indiscriminate power – neither intrinsically positive nor negative. History has shown power only leads to prosperity when placed into the right hands; the internet has revolutionised the free press, but nuclear weapons are a constant ongoing threat to world peace.
The robots are coming, and although science will be responsible for their arrival, it will not guide us on how they should be used. Nobody can predict the eventual consequences of autonomous robotics spreading into our society, but as I long as I have the choice, I’d like to be on their side.


THE THEORY OF FLIGHT (Fine Line) Directed by Paul Greengrass There's a certain curiosity whenever real-life lovebirds team up onscreen, and inquiring minds will always want to know if the pairing will sizzle like Bogart and Bacall, or fizzle like Madonna and Sean Penn. In "The Theory of Flight," Kenneth Branagh and Helena Bonham Carter generate enough good will to keep aloft a charming bit of whimsy that probably would crash and burn with lesser talents at the controls. "Flight" is fluff but the actors, particularly the spellbinding Carter, give it an aura of elegance. Richard Hawkins' screenplay brings eccentric painter/inventor Richard (Branagh) and wheelchair-bound Jane (Carter) together when Richard is sentenced to do community service after trying to fly off the top of a London office building in a homemade glider. As it so happens, the generally bitter Jane only comes to life when she's on the flying swing ride at the local amusement park. Though both deny it, they know they're two of a kind. Though most of the people around her try to convince her otherwise, Jane knows she's in the last stages of a muscular deterioration disease, and she has one very specific last wish she'll need Richard's help to fulfill. Hawkins' writing is at its best when it addresses the condescending attitude the public tends to take towards the disabled, a stumbling block that threatens to keep Jane from achieving her goal. Jane acknowledges that "a long time ago my body and me separated," but she refuses to have anything to do with those folks who assume that her often garbled speech implies that she's losing her mind as well. Carter taps into Jane's rebellious passion for life and creates another in her gallery of striking portraits of unconventional women. This performance is easily as brave and incisive as her Oscar-nominated turn in last year's "The Wings of the Dove," as the actress undercuts any possible pathos with a sly naughtiness and dry wit. When Richard tells her he stopped painting because he thought he lacked the talent, Jane sneers, "Lack of talent never stopped anyone." "jasanfor"It's also pleasing to see Branagh, free of the tics that infested his performances in "Celebrity" and "Mary Shelley's Frankenstein," relax into a role as he does here. It's his most ingratiating work since "Much Ado About Nothing" and a reminder of what a refreshing presence he can be when he's not striving to hit some new histrionic high. James Sanford
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SIR, I AM HONOURABLY SOLICITING YOUR KIND ASSISTANCE AS FOLLOWS. : (1) TO PROVIDE A BANK ACCOUNT WHERE THIS MONEY WILL BE TRANSFERRED TO. (2) TO SERVE AS THE GUARDIAN OF THESE FUND, SINCE I AM A BOY OF 23 YEARS OLD. (3) TO MAKE ARRANGEMENT FOR ME IN YOUR COUNTRY TO CONTINUE MY EDUCATIONAL CAREER AND TO PROCURE ME A RESIDENTIAL PERMIT IN YOUR COUNTRY.  PLEASE, SIR I WILL BE VERY HAPPY IF THIS TANSACTION WILL BE CONCLUDED WITHIN FOURTEEN (14)WORKING DAYS FROM NOW.


130 Controls in the Sounds Tab There is no Sound Editor in Snap ! , and also no current sound the way there’s a current costume for each sprite. (The sprite always has an appearance unless hidden, but it doesn’t sing unless explicitly asked.) So the context menu for sounds has only rename, delete, and export options, and it has a clickable button labeled Play or Stop as appropriate. There is a sound recorder, which appears if you click the red record button ( ): The ﬁrst, round button starts recording. The second, square button stops recording. The third, triangular button plays back a recorded sound. If you don’t like the result, click the round button again to re-record. When you’re satisﬁed, push the Save button. If you need a sound editor, consider the free (both senses) http://audacity.sourceforge.net. D. Keyboard Editing An ongoing area of research is how to make visual programming languages usable by people with visual or motoric disabilities. As a ﬁrst step in this direction, we provide a keyboard editor, so that you can create and edit scripts without tracking the mouse. So far, not every user interface element is controllable by keyboard, and we haven’t even begun providing output support, such as interfacing with a speech synthesizer. This is an area in which we know we have a long way to go! But it’s a start. The keyboard editor may also be useful to anyone who can type faster than they can drag blocks. Starting and stopping the keyboard editor There are three ways to start the keyboard editor. Shift-clicking anywhere in the scripting area will start the editor at that point: either editing an existing script or, if you shift-click on the background of the scripting area, editing a new script at the mouse position. Alternatively, typing shift-enter will start the editor on an existing script, and you can use the tab key to switch to another script. Or you can click the keyboard button at the top of the scripting area. When the script editor is running, its position is represented by a blinking white bar: To leave the keyboard editor, type the escape key, or just click on the background of the scripting area. Navigating in the keyboard editor To move to a different script, type the tab key. Shift-tab to move through the scripts in reverse order.


the config of bot, please see readme.md
Sets or gets the default captureRejection value for all emitters.
This symbol shall be used to install a listener for only monitoring 'error'
events. Listeners installed using this symbol are called before the regular
'error'
listeners are called.
Installing a listener using this symbol does not change the behavior once an
'error'
event is emitted, therefore the process will still crash if no
regular 'error'
listener is installed.
Add alias for certain menu/app
instance of menu/app command
Add menu/app to this bot.
array of instances of menu/app command
Process the command.
Process the msg object and generate [command, ...args]
| void
Returns a list listener for a specific emitter event name.
Generated using TypeDoc
Creates an instance of KBotify.
KBotify


WITNESS LIST Pensions & Investments Committee March 10, 2003-8:00A HB 377 For: Durbin, William (Self and Police Officer) Prickett, Gregory (Combined Law Enforcement Associations of Texas) Against: Conradt, Trish (Texas Retired Teachers Association) On: Jung, Ronnie (Teacher Retirement System) HB 464 On: Johnston, Carol (Texas Department of Criminal Justice) Nail, William (Employees Retirement System) HB 752 For: Grace, Cary (City of Houston) Lawson, John (Houston Police Officers' Pension System) HB 1153 For: Campbell, Jim (City of San Antonio) Stewart, Teddy (San Antonio Fire & Police Pension Fund)