nflgame module
nflgame is an API to retrieve and read NFL Game Center JSON data. It can work with real-time data, which can be used for fantasy football.
nflgame works by parsing the same JSON data that powers NFL.com's live GameCenter. Therefore, nflgame can be used to report game statistics while a game is being played.
The package comes pre-loaded with game data from every pre- and regular season game from 2009 up until the present (I try to update it every week). Therefore, querying such data does not actually ping NFL.com.
However, if you try to search for data in a game that is being currently played, the JSON data will be downloaded from NFL.com at each request (so be careful not to inspect for data too many times while a game is being played). If you ask for data for a particular game that hasn't been cached to disk but is no longer being played, it will be automatically cached to disk so that no further downloads are required.
Here's a quick teaser to find the top 5 running backs by rushing yards in the first week of the 2013 season:
import nflgame games = nflgame.games(2013, week=1) players = nflgame.combine_game_stats(games) for p in players.rushing().sort('rushing_yds').limit(5): msg = '%s %d carries for %d yards and %d TDs' print msg % (p, p.rushing_att, p.rushing_yds, p.rushing_tds)
And the output is:
L.McCoy 31 carries for 184 yards and 1 TDs T.Pryor 13 carries for 112 yards and 0 TDs S.Vereen 14 carries for 101 yards and 0 TDs A.Peterson 18 carries for 93 yards and 2 TDs R.Bush 21 carries for 90 yards and 0 TDs
Or you could find the top 5 passing plays in the same time period:
import nflgame games = nflgame.games(2013, week=1) plays = nflgame.combine_plays(games) for p in plays.sort('passing_yds').limit(5): print p
And the output is:
(DEN, DEN 22, Q4, 3 and 8) (4:42) (Shotgun) P.Manning pass short left to D.Thomas for 78 yards, TOUCHDOWN. Penalty on BAL-E.Dumervil, Defensive Offside, declined. (DET, DET 23, Q3, 3 and 7) (5:58) (Shotgun) M.Stafford pass short middle to R.Bush for 77 yards, TOUCHDOWN. (NYG, NYG 30, Q2, 1 and 10) (2:01) (No Huddle, Shotgun) E.Manning pass deep left to V.Cruz for 70 yards, TOUCHDOWN. Pass complete on a fly pattern. (NO, NO 24, Q2, 2 and 6) (5:11) (Shotgun) D.Brees pass deep left to K.Stills to ATL 9 for 67 yards (R.McClain; R.Alford). Pass 24, YAC 43 (NYG, NYG 20, Q1, 1 and 10) (13:04) E.Manning pass short middle to H.Nicks pushed ob at DAL 23 for 57 yards (M.Claiborne). Pass complete on a slant pattern.
If you aren't a programmer, then the tutorial for non programmers is for you.
If you need help, please come visit us at IRC/FreeNode on channel #nflgame
.
If you've never used IRC before, then you can
use a web client.
(Enter any nickname you like, make sure the channel is #nflgame
, fill in
the captcha and hit connect.)
Failing IRC, the second fastest way to get help is to open a new issue on the tracker. There are several active contributors to nflgame that watch the issue tracker. We tend to respond fairly quickly!
""" nflgame is an API to retrieve and read NFL Game Center JSON data. It can work with real-time data, which can be used for fantasy football. nflgame works by parsing the same JSON data that powers NFL.com's live GameCenter. Therefore, nflgame can be used to report game statistics while a game is being played. The package comes pre-loaded with game data from every pre- and regular season game from 2009 up until the present (I try to update it every week). Therefore, querying such data does not actually ping NFL.com. However, if you try to search for data in a game that is being currently played, the JSON data will be downloaded from NFL.com at each request (so be careful not to inspect for data too many times while a game is being played). If you ask for data for a particular game that hasn't been cached to disk but is no longer being played, it will be automatically cached to disk so that no further downloads are required. Here's a quick teaser to find the top 5 running backs by rushing yards in the first week of the 2013 season: #!python import nflgame games = nflgame.games(2013, week=1) players = nflgame.combine_game_stats(games) for p in players.rushing().sort('rushing_yds').limit(5): msg = '%s %d carries for %d yards and %d TDs' print msg % (p, p.rushing_att, p.rushing_yds, p.rushing_tds) And the output is: L.McCoy 31 carries for 184 yards and 1 TDs T.Pryor 13 carries for 112 yards and 0 TDs S.Vereen 14 carries for 101 yards and 0 TDs A.Peterson 18 carries for 93 yards and 2 TDs R.Bush 21 carries for 90 yards and 0 TDs Or you could find the top 5 passing plays in the same time period: #!python import nflgame games = nflgame.games(2013, week=1) plays = nflgame.combine_plays(games) for p in plays.sort('passing_yds').limit(5): print p And the output is: (DEN, DEN 22, Q4, 3 and 8) (4:42) (Shotgun) P.Manning pass short left to D.Thomas for 78 yards, TOUCHDOWN. Penalty on BAL-E.Dumervil, Defensive Offside, declined. (DET, DET 23, Q3, 3 and 7) (5:58) (Shotgun) M.Stafford pass short middle to R.Bush for 77 yards, TOUCHDOWN. (NYG, NYG 30, Q2, 1 and 10) (2:01) (No Huddle, Shotgun) E.Manning pass deep left to V.Cruz for 70 yards, TOUCHDOWN. Pass complete on a fly pattern. (NO, NO 24, Q2, 2 and 6) (5:11) (Shotgun) D.Brees pass deep left to K.Stills to ATL 9 for 67 yards (R.McClain; R.Alford). Pass 24, YAC 43 (NYG, NYG 20, Q1, 1 and 10) (13:04) E.Manning pass short middle to H.Nicks pushed ob at DAL 23 for 57 yards (M.Claiborne). Pass complete on a slant pattern. If you aren't a programmer, then the [tutorial for non programmers](http://goo.gl/y05fVj) is for you. If you need help, please come visit us at IRC/FreeNode on channel `#nflgame`. If you've never used IRC before, then you can [use a web client](http://webchat.freenode.net/?channels=%23nflgame). (Enter any nickname you like, make sure the channel is `#nflgame`, fill in the captcha and hit connect.) Failing IRC, the second fastest way to get help is to [open a new issue on the tracker](https://github.com/BurntSushi/nflgame/issues/new). There are several active contributors to nflgame that watch the issue tracker. We tend to respond fairly quickly! """ try: from collections import OrderedDict except: from ordereddict import OrderedDict # from PyPI import itertools import sys if sys.version_info[0] != 2 or sys.version_info[1] < 6: print("nflgame requires Python 2.6+ and does not yet work with Python 3") print("You are running Python version {}.{}".format( sys.version_info.major, sys.version_info.minor)) sys.exit(1) import nflgame.game import nflgame.live import nflgame.player import nflgame.sched import nflgame.seq from nflgame.version import __version__ assert OrderedDict # Asserting the import for static analysis. VERSION = __version__ # Deprecated. Backwards compatibility. NoPlayers = nflgame.seq.GenPlayerStats(None) """ NoPlayers corresponds to the identity element of a Players sequences. Namely, adding it to any other Players sequence has no effect. """ players = nflgame.player._create_players() """ A dict of all players and meta information about each player keyed by GSIS ID. (The identifiers used by NFL.com GameCenter.) """ teams = [ ['ARI', 'Arizona', 'Cardinals', 'Arizona Cardinals'], ['ATL', 'Atlanta', 'Falcons', 'Atlanta Falcons'], ['BAL', 'Baltimore', 'Ravens', 'Baltimore Ravens'], ['BUF', 'Buffalo', 'Bills', 'Buffalo Bills'], ['CAR', 'Carolina', 'Panthers', 'Carolina Panthers'], ['CHI', 'Chicago', 'Bears', 'Chicago Bears'], ['CIN', 'Cincinnati', 'Bengals', 'Cincinnati Bengals'], ['CLE', 'Cleveland', 'Browns', 'Cleveland Browns'], ['DAL', 'Dallas', 'Cowboys', 'Dallas Cowboys'], ['DEN', 'Denver', 'Broncos', 'Denver Broncos'], ['DET', 'Detroit', 'Lions', 'Detroit Lions'], ['GB', 'Green Bay', 'Packers', 'Green Bay Packers', 'G.B.', 'GNB'], ['HOU', 'Houston', 'Texans', 'Houston Texans'], ['IND', 'Indianapolis', 'Colts', 'Indianapolis Colts'], ['JAC', 'Jacksonville', 'Jaguars', 'Jacksonville Jaguars', 'JAX'], ['KC', 'Kansas City', 'Chiefs', 'Kansas City Chiefs', 'K.C.', 'KAN'], ['MIA', 'Miami', 'Dolphins', 'Miami Dolphins'], ['MIN', 'Minnesota', 'Vikings', 'Minnesota Vikings'], ['NE', 'New England', 'Patriots', 'New England Patriots', 'N.E.', 'NWE'], ['NO', 'New Orleans', 'Saints', 'New Orleans Saints', 'N.O.', 'NOR'], ['NYG', 'Giants', 'New York Giants', 'N.Y.G.'], ['NYJ', 'Jets', 'New York Jets', 'N.Y.J.'], ['OAK', 'Oakland', 'Raiders', 'Oakland Raiders'], ['PHI', 'Philadelphia', 'Eagles', 'Philadelphia Eagles'], ['PIT', 'Pittsburgh', 'Steelers', 'Pittsburgh Steelers'], ['SD', 'San Diego', 'Chargers', 'San Diego Chargers', 'S.D.', 'SDG'], ['SEA', 'Seattle', 'Seahawks', 'Seattle Seahawks'], ['SF', 'San Francisco', '49ers', 'San Francisco 49ers', 'S.F.', 'SFO'], ['STL', 'St. Louis', 'Rams', 'St. Louis Rams', 'S.T.L.'], ['TB', 'Tampa Bay', 'Buccaneers', 'Tampa Bay Buccaneers', 'T.B.', 'TAM'], ['TEN', 'Tennessee', 'Titans', 'Tennessee Titans'], ['WAS', 'Washington', 'Redskins', 'Washington Redskins', 'WSH'], ] """ A list of all teams. Each item is a list of different ways to describe a team. (i.e., JAC, JAX, Jacksonville, Jaguars, etc.). The first item in each list is always the standard NFL.com team abbreviation (two or three letters). """ def find(name, team=None): """ Finds a player (or players) with a name matching (case insensitive) name and returns them as a list. If team is not None, it is used as an additional search constraint. """ hits = [] for player in players.itervalues(): if player.name.lower() == name.lower(): if team is None or team.lower() == player.team.lower(): hits.append(player) return hits def standard_team(team): """ Returns a standard abbreviation when team corresponds to a team in nflgame.teams (case insensitive). All known variants of a team name are searched. If no team is found, None is returned. """ team = team.lower() for variants in teams: for variant in variants: if team == variant.lower(): return variants[0] return None def games(year, week=None, home=None, away=None, kind='REG', started=False): """ games returns a list of all games matching the given criteria. Each game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.). As a special case, if the home and away teams are set to the same team, then all games where that team played are returned. The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST. The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4. The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter. Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site.  If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com). """ return list(games_gen(year, week, home, away, kind, started)) def games_gen(year, week=None, home=None, away=None, kind='REG', started=False): """ games returns a generator of all games matching the given criteria. Each game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.). As a special case, if the home and away teams are set to the same team, then all games where that team played are returned. The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST. The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4. The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter. Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site.  If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com). """ infos = _search_schedule(year, week, home, away, kind, started) if not infos: return None def gen(): for info in infos: g = nflgame.game.Game(info['eid']) if g is None: continue yield g return gen() def one(year, week, home, away, kind='REG', started=False): """ one returns a single game matching the given criteria. The game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.). one returns either a single game or no games. If there are multiple games matching the given criteria, an assertion is raised. The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST. The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4. The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter. Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site.  If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com). """ infos = _search_schedule(year, week, home, away, kind, started) if not infos: return None assert len(infos) == 1, 'More than one game matches the given criteria.' return nflgame.game.Game(infos[0]['eid']) def combine(games, plays=False): """ DEPRECATED. Please use one of nflgame.combine_{game,play,max}_stats instead. Combines a list of games into one big player sequence containing game level statistics. This can be used, for example, to get PlayerStat objects corresponding to statistics across an entire week, some number of weeks or an entire season. If the plays parameter is True, then statistics will be dervied from play by play data. This mechanism is slower but will contain more detailed statistics like receiver targets, yards after the catch, punt and field goal blocks, etc. """ if plays: return combine_play_stats(games) else: return combine_game_stats(games) def combine_game_stats(games): """ Combines a list of games into one big player sequence containing game level statistics. This can be used, for example, to get GamePlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season. """ return reduce(lambda ps1, ps2: ps1 + ps2, [g.players for g in games if g is not None]) def combine_play_stats(games): """ Combines a list of games into one big player sequence containing play level statistics. This can be used, for example, to get PlayPlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season. This function should be used in lieu of combine_game_stats when more detailed statistics such as receiver targets, yards after the catch and punt/FG blocks are needed. N.B. Since this combines *all* play data, this function may take a while to complete depending on the number of games passed in. """ return reduce(lambda p1, p2: p1 + p2, [g.drives.players() for g in games if g is not None]) def combine_max_stats(games): """ Combines a list of games into one big player sequence containing maximum statistics based on game and play level statistics. This can be used, for example, to get GamePlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season. This function should be used in lieu of combine_game_stats or combine_play_stats when the best possible accuracy is desired. """ return reduce(lambda a, b: a + b, [g.max_player_stats() for g in games if g is not None]) def combine_plays(games): """ Combines a list of games into one big play generator that can be searched as if it were a single game. """ chain = itertools.chain(*[g.drives.plays() for g in games]) return nflgame.seq.GenPlays(chain) def _search_schedule(year, week=None, home=None, away=None, kind='REG', started=False): """ Searches the schedule to find the game identifiers matching the criteria given. The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST. The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4. The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter. If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com). """ infos = [] for info in nflgame.sched.games.itervalues(): y, t, w = info['year'], info['season_type'], info['week'] h, a = info['home'], info['away'] if year is not None: if isinstance(year, list) and y not in year: continue if not isinstance(year, list) and y != year: continue if week is not None: if isinstance(week, list) and w not in week: continue if not isinstance(week, list) and w != week: continue if home is not None and away is not None and home == away: if h != home and a != home: continue else: if home is not None and h != home: continue if away is not None and a != away: continue if t != kind: continue if started: gametime = nflgame.live._game_datetime(info) now = nflgame.live._now() if gametime > now and (gametime - now).total_seconds() > 300: continue infos.append(info) return infos
Module variables
var VERSION
var players
A dict of all players and meta information about each player keyed by GSIS ID. (The identifiers used by NFL.com GameCenter.)
var teams
A list of all teams. Each item is a list of different ways to describe a team. (i.e., JAC, JAX, Jacksonville, Jaguars, etc.). The first item in each list is always the standard NFL.com team abbreviation (two or three letters).
Functions
def combine(
games, plays=False)
DEPRECATED. Please use one of nflgame.combine_{game,play,max}_stats instead.
Combines a list of games into one big player sequence containing game level statistics.
This can be used, for example, to get PlayerStat objects corresponding to statistics across an entire week, some number of weeks or an entire season.
If the plays parameter is True, then statistics will be dervied from play by play data. This mechanism is slower but will contain more detailed statistics like receiver targets, yards after the catch, punt and field goal blocks, etc.
def combine(games, plays=False): """ DEPRECATED. Please use one of nflgame.combine_{game,play,max}_stats instead. Combines a list of games into one big player sequence containing game level statistics. This can be used, for example, to get PlayerStat objects corresponding to statistics across an entire week, some number of weeks or an entire season. If the plays parameter is True, then statistics will be dervied from play by play data. This mechanism is slower but will contain more detailed statistics like receiver targets, yards after the catch, punt and field goal blocks, etc. """ if plays: return combine_play_stats(games) else: return combine_game_stats(games)
def combine_game_stats(
games)
Combines a list of games into one big player sequence containing game level statistics.
This can be used, for example, to get GamePlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season.
def combine_game_stats(games): """ Combines a list of games into one big player sequence containing game level statistics. This can be used, for example, to get GamePlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season. """ return reduce(lambda ps1, ps2: ps1 + ps2, [g.players for g in games if g is not None])
def combine_max_stats(
games)
Combines a list of games into one big player sequence containing maximum statistics based on game and play level statistics.
This can be used, for example, to get GamePlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season.
This function should be used in lieu of combine_game_stats or combine_play_stats when the best possible accuracy is desired.
def combine_max_stats(games): """ Combines a list of games into one big player sequence containing maximum statistics based on game and play level statistics. This can be used, for example, to get GamePlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season. This function should be used in lieu of combine_game_stats or combine_play_stats when the best possible accuracy is desired. """ return reduce(lambda a, b: a + b, [g.max_player_stats() for g in games if g is not None])
def combine_play_stats(
games)
Combines a list of games into one big player sequence containing play level statistics.
This can be used, for example, to get PlayPlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season.
This function should be used in lieu of combine_game_stats when more detailed statistics such as receiver targets, yards after the catch and punt/FG blocks are needed.
N.B. Since this combines all play data, this function may take a while to complete depending on the number of games passed in.
def combine_play_stats(games): """ Combines a list of games into one big player sequence containing play level statistics. This can be used, for example, to get PlayPlayerStats objects corresponding to statistics across an entire week, some number of weeks or an entire season. This function should be used in lieu of combine_game_stats when more detailed statistics such as receiver targets, yards after the catch and punt/FG blocks are needed. N.B. Since this combines *all* play data, this function may take a while to complete depending on the number of games passed in. """ return reduce(lambda p1, p2: p1 + p2, [g.drives.players() for g in games if g is not None])
def combine_plays(
games)
Combines a list of games into one big play generator that can be searched as if it were a single game.
def combine_plays(games): """ Combines a list of games into one big play generator that can be searched as if it were a single game. """ chain = itertools.chain(*[g.drives.plays() for g in games]) return nflgame.seq.GenPlays(chain)
def find(
name, team=None)
Finds a player (or players) with a name matching (case insensitive) name and returns them as a list.
If team is not None, it is used as an additional search constraint.
def find(name, team=None): """ Finds a player (or players) with a name matching (case insensitive) name and returns them as a list. If team is not None, it is used as an additional search constraint. """ hits = [] for player in players.itervalues(): if player.name.lower() == name.lower(): if team is None or team.lower() == player.team.lower(): hits.append(player) return hits
def games(
year, week=None, home=None, away=None, kind='REG', started=False)
games returns a list of all games matching the given criteria. Each game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.).
As a special case, if the home and away teams are set to the same team, then all games where that team played are returned.
The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST.
The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4.
The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter.
Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site. A game's JSON data is only cached to disk once the game is over, so be careful with the number of times you call this while a game is going on. (i.e., don't piss off NFL.com.)
If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com).
def games(year, week=None, home=None, away=None, kind='REG', started=False): """ games returns a list of all games matching the given criteria. Each game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.). As a special case, if the home and away teams are set to the same team, then all games where that team played are returned. The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST. The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4. The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter. Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site.  If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com). """ return list(games_gen(year, week, home, away, kind, started))
def games_gen(
year, week=None, home=None, away=None, kind='REG', started=False)
games returns a generator of all games matching the given criteria. Each game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.).
As a special case, if the home and away teams are set to the same team, then all games where that team played are returned.
The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST.
The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4.
The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter.
Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site. A game's JSON data is only cached to disk once the game is over, so be careful with the number of times you call this while a game is going on. (i.e., don't piss off NFL.com.)
If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com).
def games_gen(year, week=None, home=None, away=None, kind='REG', started=False): """ games returns a generator of all games matching the given criteria. Each game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.). As a special case, if the home and away teams are set to the same team, then all games where that team played are returned. The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST. The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4. The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter. Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site.  If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com). """ infos = _search_schedule(year, week, home, away, kind, started) if not infos: return None def gen(): for info in infos: g = nflgame.game.Game(info['eid']) if g is None: continue yield g return gen()
def one(
year, week, home, away, kind='REG', started=False)
one returns a single game matching the given criteria. The game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.).
one returns either a single game or no games. If there are multiple games matching the given criteria, an assertion is raised.
The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST.
The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4.
The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter.
Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site. A game's JSON data is only cached to disk once the game is over, so be careful with the number of times you call this while a game is going on. (i.e., don't piss off NFL.com.)
If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com).
def one(year, week, home, away, kind='REG', started=False): """ one returns a single game matching the given criteria. The game can then be queried for player statistics and information about the game itself (score, winner, scoring plays, etc.). one returns either a single game or no games. If there are multiple games matching the given criteria, an assertion is raised. The kind parameter specifies whether to fetch preseason, regular season or postseason games. Valid values are PRE, REG and POST. The week parameter is relative to the value of the kind parameter, and may be set to a list of week numbers. In the regular season, the week parameter corresponds to the normal week numbers 1 through 17. Similarly in the preseason, valid week numbers are 1 through 4. In the post season, the week number corresponds to the numerical round of the playoffs. So the wild card round is week 1, the divisional round is week 2, the conference round is week 3 and the Super Bowl is week 4. The year parameter specifies the season, and not necessarily the actual year that a game was played in. For example, a Super Bowl taking place in the year 2011 actually belongs to the 2010 season. Also, the year parameter may be set to a list of seasons just like the week parameter. Note that if a game's JSON data is not cached to disk, it is retrieved from the NFL web site.  If started is True, then only games that have already started (or are about to start in less than 5 minutes) will be returned. Note that the started parameter requires pytz to be installed. This is useful when you only want to collect stats from games that have JSON data available (as opposed to waiting for a 404 error from NFL.com). """ infos = _search_schedule(year, week, home, away, kind, started) if not infos: return None assert len(infos) == 1, 'More than one game matches the given criteria.' return nflgame.game.Game(infos[0]['eid'])
def standard_team(
team)
Returns a standard abbreviation when team corresponds to a team in nflgame.teams (case insensitive). All known variants of a team name are searched. If no team is found, None is returned.
def standard_team(team): """ Returns a standard abbreviation when team corresponds to a team in nflgame.teams (case insensitive). All known variants of a team name are searched. If no team is found, None is returned. """ team = team.lower() for variants in teams: for variant in variants: if team == variant.lower(): return variants[0] return None
Sub-modules
A module that provides convenience functions for sending alerts. Currently this includes sending text (SMS) messages or emails.
This is accomplished through two mechanisms: Google Voice B{or} SMTP.
Using Google Voice
To use Google Voice, this module requires a Google Voice accou...
The live module provides a mechanism of periodically checking which games are being actively played.
It requires the third party library pytz to be installed, which makes sure game times are compared properly with respect to time zones. pytz can be downloaded from PyPI: http://pypi.python.org/pypi/...
The stats module maps statistical category identifiers from NFL.com's GameCenter JSON feed to a representation of what we believe that statistical category means. This mapping has been reverse engineered with a lot of help from reddit users rasherdk and curien.
B{Note}: We now have a data dictionar...


public class FrontEnd extends BaseDataProcessor
The front end is modeled as a series of data processors, each of which performs a specific signal processing
function. For example, a processor performs Fast-Fourier Transform (FFT) on input data, another processor performs
high-pass filtering. Figure 1 below describes how the front end looks like:
Figure 1: The Sphinx4 front end.
Each such data processor implements the DataProcessor
interface. Objects that
implements the Data
interface enters and exits the front end, and go between the
processors in the front end. The input data to the front end is typically audio data, but this front end allows any
input type. Similarly, the output data is typically features, but this front end allows any output type. You can
configure the front end to accept any input type and return any output type. We will describe the configuration of
the front end in more detail below.
The Pull Model of the Front End
The front end uses a pull model. To obtain output from the front end, one would call the method:
FrontEnd frontend = ... // see how to obtain the front end below
Data output = frontend.getData();
Calling getData
on the front end would in turn call the getData() method on the last
DataProcessor, which in turn calls the getData() method on the second last DataProcessor, and so on, until the
getData() method on the first DataProcessor is called, which reads Data objects from the input. The input to the
front end is actually another DataProcessor, and is usually (though not necessarily) part of the front end and is not
shown in the figure above. If you want to maintain some control of the input DataProcessor, you can create it
separately, and use the setDataSource
method to set it
as the input DataProcessor. In that case, the input DataProcessor will be prepended to the existing chain of
DataProcessors. One common input DataProcessor is the Microphone
, which
implements the DataProcessor interface.
DataProcessor microphone = new Microphone();
microphone.initialize(...);
frontend.setDataSource(microphone);
Another common input DataProcessor is the StreamDataSource
. It turns a Java
InputStream
into Data objects. It is usually used in batch mode decoding.
Configuring the front end
The front end must be configured through the Sphinx properties file. For details about configuring the front end, refer to the document Configuring the Front End.
Current state-of-the-art front ends generate features that contain Mel-frequency cepstral coefficients (MFCC). To specify such a front end (called a 'pipeline') in Sphinx-4, insert the following lines in the Sphinx-4 configuration file:
<component name="mfcFrontEnd" type="edu.cmu.sphinx.frontend.FrontEnd"> <propertylist name="pipeline"> <item>preemphasizer</item> <item>windower</item> <item>dft</item> <item>melFilterBank</item> <item>dct</item> <item>batchCMN</item> <item>featureExtractor</item> </propertylist> </component> <component name="preemphasizer" type="Note: In this example, 'mfcFrontEnd' becomes the name of the front end.edu.cmu.sphinx.frontend.filter.Preemphasizer
"/> <component name="windower" type="edu.cmu.sphinx.frontend.window.RaisedCosineWindower
"/> <component name="dft" type="edu.cmu.sphinx.frontend.transform.DiscreteFourierTransform
"/> <component name="melFilterBank" type="edu.cmu.sphinx.frontend.frequencywarp.MelFrequencyFilterBank
"/> <component name="dct" type="edu.cmu.sphinx.frontend.transform.DiscreteCosineTransform
"/> <component name="batchCMN" type="edu.cmu.sphinx.frontend.feature.BatchCMN
"/> <component name="featureExtractor" type="edu.cmu.sphinx.frontend.feature.DeltasFeatureExtractor
"/>
Sphinx-4 also allows you to:
For details on how to do this, refer to the document Configuring the Front End.
Obtaining a Front End
In order to obtain a front end, it must be specified in the configuration file. The Sphinx-4 front end is connected to the rest of the system via the scorer. We will continue with the above example to show how the scorer will obtain the front end. In the configuration file, the scorer should be specified as follows:
<component name="scorer" type="edu.cmu.sphinx.decoder.scorer.SimpleAcousticScorer"> <property name="frontend" value="mfcFrontEnd"/> </component>In the SimpleAcousticScorer, the front end is obtained in the
newProperties
method as follows:
public void newProperties(PropertySheet ps) throws PropertyException { FrontEnd frontend = (FrontEnd) ps.getComponent("frontend", FrontEnd.class); }
| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
PROP_PIPELINE
the name of the property list of all the components of the frontend pipe line
|
logger
| Constructor and Description |
|---|
FrontEnd() |
FrontEnd(java.util.List<DataProcessor> frontEndList) |
| Modifier and Type | Method and Description |
|---|---|
void |
addSignalListener(SignalListener listener)
Add a listener to be called when a signal is detected.
|
protected void |
fireSignalListeners(Signal signal)
Fire all listeners for signals.
|
Data |
getData()
Returns the processed Data output, basically calls
getData() on the last processor. |
java.util.List<DataProcessor> |
getElements()
Returns the collection of
DataProcessor s of this FrontEnd . |
DataProcessor |
getLastDataProcessor()
Returns the last data processor within the
DataProcessor chain of this FrontEnd . |
void |
initialize()
Initializes this DataProcessor.
|
void |
newProperties(PropertySheet ps)
This method is called when this configurable component needs to be reconfigured.
|
void |
removeSignalListener(SignalListener listener)
Removes a listener for signals.
|
void |
setDataSource(DataProcessor dataSource)
Sets the source of data for this front end.
|
void |
setPredecessor(DataProcessor dataSource)
Sets the source of data for this front end.
|
java.lang.String |
toString()
Returns a description of this FrontEnd in the format: <front end name> {<DataProcessor1>, <DataProcessor2> ...
|
getPredecessor
getName, initLogger
@S4ComponentList(type=DataProcessor.class) public static final java.lang.String PROP_PIPELINE
public FrontEnd(java.util.List<DataProcessor> frontEndList)
public FrontEnd()
public void newProperties(PropertySheet ps) throws PropertyException
Configurable
newProperties
in interface Configurable
newProperties
in class ConfigurableAdapter
ps
- a property sheet holding the new dataPropertyException
- if there is a problem with the properties.public void initialize()
BaseDataProcessor
initialize
in interface DataProcessor
initialize
in class BaseDataProcessor
public void setDataSource(DataProcessor dataSource)
dataSource
- the source of datapublic java.util.List<DataProcessor> getElements()
DataProcessor
s of this FrontEnd
.public Data getData() throws DataProcessingException
getData()
on the last processor.getData
in interface DataProcessor
getData
in class BaseDataProcessor
DataProcessingException
- if a data processor error occurspublic void setPredecessor(DataProcessor dataSource)
setDataSource(dataSource)
.setPredecessor
in interface DataProcessor
setPredecessor
in class BaseDataProcessor
dataSource
- the source of datapublic void addSignalListener(SignalListener listener)
listener
- the listener to be addedpublic void removeSignalListener(SignalListener listener)
listener
- the listener to be removedprotected void fireSignalListeners(Signal signal)
signal
- the signal that occurredpublic DataProcessor getLastDataProcessor()
DataProcessor
chain of this FrontEnd
.public java.lang.String toString()
toString
in class ConfigurableAdapter


Create a translation for RoyaleBot
Please don't google translate
You
So that I can credit you if your translation is used
Name
Discord Client ID
Language
Name (in language)
Name (in english)
ISO-639-1 Code (
lookup here
)
Messages
Translate the messages, please maintain anything inside {} however you can move them around to suit the new language.
Translate from


Dine In Time
Order
Profile
Logout
Manager's Dashboard:
Restaurant Name
Get Started
Table
Search:
NAME
Time
Price
Check Order
After Ready
Jeanna Schmal
44
2016-10-17
$317.000
$317.000
Caren Rials
35
2013-04-12
$445.500
$445.500
Leon Rogol
66
2016-05-22
$152.558
$152.558
Shala Barrera
70
2016-05-15
$459.146
$459.146
Showing
entries
(filtered from
total entries)


| Name: MAST ADRITH | Reg No: 4-12SMMHC18 |
|---|---|
| Age / Sex:2.8YEARS | Contact No:9740009257 |
| Marital Status: | Date:8/12/18 |
| Occupation:PLAY SCHOOL SINCE JUNE | Dr. PJFP |
Address: NATIVE OF TAMIL NADU – COIMBATORE IN BLORE SINCE BIRTH. BEML LAYOUT LAKSHMI NIVAS APT. |
DIAGNOSIS- |
SEIZURE ON FEB 7 AND 14TH 2018. HE WILL BE PLAYIN AND SUDDENLY WILL FALL BACK AND EYES WILL ROLL UP HANDS BECOME STIFF. IN NOVEMBER AGAIN GOT. TODAY MNG HE GOT 2 SEIZURES.
VERY ACTIVE ALWAYS ON THE MOVE. SHOUTS IF NOT GIVEN WHAT HE WANTS. HE WANTED MOBILE AND WAS STUBBORN IN WANTING IT. SCREAMING NOT DIVERTED EASILY.
F/H:
MGF: SEIZURES,
MOM: SEIZURES.
PGF: DM, HTN,
PGM: SKIN ALLERGIES.
p/H:
FTND. 2.94KG BWT.

PARENTS HISTORY:
LOVE MARRIAGE. 2005-2009 TILL MARRIAGE PROBLEMS WERE THERE. FIRST CHILD 2011. SECOND CHILD 2016.
MOM: BSC MATHS. TILL 2015 WAS WORKING.
DAD: MBA.
BOTH DAD AND MGF ARE ACTIVE.
WT: 12.5 KG.
 LIKES LADDOO.
WONT TRY ANYTHNG NEW. SAME THINGS HE WANTS. LIKES BEATS AND MUSIC.
HE WANTS TO BE COOL. NO SWEATERS. LIKES VEHICLE RHYMES. SOUND OF AEROPLANES.
RX:
KALI PHOS 6X 4-4-4-4.
TUB 1M – BOIRON 2 DOSES – WEEKLY.
TARENT HISPANIA 1M – 4 DOSES – WEEKLY TWICE.MNG B/BURSH.
RUB 15 1 DOSE NIGHT B/FOOD.
CAMPHOR 200 – 4-0-4 DISC.
22/12/18: GOT ON SATURDAY MNG. HAD FEVER ON FRIDAY NIGHT. TOOK CROCIN . GAVE BATH SAT MNG 9 AM AFTER THT SHIVERING. GOT ONCE THE EP. AFTER THT. NO APP. DROWSY AND SLEEPING. RECOVERED FASTER THAN BEFORE.
RX:
TUB/ALFALFA/AVENA 200 – 4-4-4-4.
TARENT HISPANIA 1M – 4 DOSES WEEKLY.
RUB 30 – NIGHT B/FOOD.
SULPHUR 200 – 1 DOSE STAT.
HYDR.ACI 200 – 4-0-4.
12/1/19: appetite inc a bit. Taking levi pill also.
Rx:
TUB/ALFALFA/AVENA 200 – 4-4-4-4. 2 dram
TARENT HISPANIA 1M – 2 alternate. DOSES WEEKLY.
RUB 21 – NIGHT B/FOOD.
Five phos 4-4-4.
4/3/19: no fever cold no episodes also. Wt: 12.8kg.
Rx:
TUB/ALFALFA/AVENA 1m – 4-4-4-4. 2 dram
Ars alb 1M – 4 alternate. DOSES WEEKLY.
RUB 60 – NIGHT B/FOOD.
Five phos 4-4-4.
6/5/19: got twice after stopping the convulsant med. In sleep he is getting. Duriation is less thn before and conscious is coming back imm. He use to be drowsy before now he is able to get back normally. Food is bit better. No weight gain but.
Rx:
Sil 200 /tuberculinum1m/bell1m/cham200 – 4-4-4-4
Ginseng Q
Rub 30
Kali phos 6x qid.
18/5/19: ad 2 episodes in last night n this mng. Ight played and danced well very active while watching some English movie first scne wr there was blood and some violence imm got the ep after which he said he wants to sleep and then agn in the mng.
Rc:
Coffea1m – tid fr 3 days.


Which declension is
?
Declension
Number
Nominative
Accusative
Instrumental
Dative
Ablative
Genative
Locative
Vocative
Singular
Plural
Check
Show
New
Include:
Regular Nouns
Irregular Nouns


The most important functions for GUI programming are
and less frequently usedScripts are embedded with the #TCL[...] syntax. At the dots any literal Tcl script can occur. For example
(cltcl:event-loop #TCL[message .m -text "Your message here" button .b -text "Close" -command exit pack .m pack .b])
A script is read as a quoted list of strings with each string a command from the script. The previous example could also have been written as
(let ((script #TCL[message .m -text "Your message here" button .b -text "Close" -command exit pack .m pack .b])) (cltcl:event-loop script))or even as
(let ((partA #TCL[message .m -text "Your message here" button .b -text "Close" -command exit]) (partB #TCL[pack .m pack .b])) (cltcl:event-loop (append partA partB)))The next repl interaction shows how #TCL reads a script
CL-USER> (dolist (x #TCL[message .m -text "Your message here here" button .b -text "Close" -command exit pack .m pack .b]) (print x)) "message .m -text \"Your message here\"" "button .b -text \"Close\" -command exit" "pack .m" "pack .b" NIL CL-USER>
Use function event-loop to set up an event loop for your application. It starts the Tcl/Tk interpreter, executes the given script and starts listening for events to dispatch.
A typical application event loop might look like the following fragment
(let ((script #TCL[proc main {} { wm title . "Your appname here" # Add a menu bar menu .mbar . config -menu .mbar # Add a file menu menu .mbar.file -tearoff 0 .mbar add cascade -label "File" -underline 0 -menu .mbar.file .mbar.file add command \ -label "Open..." \ -accelerator "Ctrl+O" \ -underline 0 \ -command {cltcl::callLisp on-file-open} .mbar.file add separator .mbar.file add command -label "Exit..." -command exit # etc # Widgets text .t # etc # Let a window manager display the widgets. grid .t -sticky nsew grid rowconfigure . 0 -weight 1 grid columnconfigure . 0 -weight 1 #etc } main])) (handler-bind ((condition (lambda (error) (ignore-errors (cltcl:call "tk_messageBox" :message (format nil "Error:~2%~A" error))) (cltcl:keep-listening)))) (cltcl:event-loop script)))The Tcl/Tk script defines proc main and ends with a call to it (it is usually a good idea to define a proc to avoid polluting Tcl/Tk's namespace for variables). The script is run by event-loop. The condition handlers catches errors that occur in event handlers. Restart keep-listening prevents abortion of the even loop.
During development you might want to remove the condition handler and let the debugger catch the error. In that case the debugger shows the restart as an option.
During the event loop's lifetime variable *stream* is bound to the stream that connects to the interpreter to make it accessable from event handlers. This stream is used by functions call, post and run.
The event loop also binds variable *interpreter* to the interpreter that was given to make it the default for nested event loops. Therefore you have to give the path to the interpreter only once at the top level of you application. This variable's value is the default for the :interpreter keyword in other functions
User events in Tcl/Tk are handled by scripts that are bound to a widget via the bind command or via the -command option. Use function cltcl::callLisp in a Tck/Tk event to make a call back to Lisp. The event loop listener calls the Lisp event handler. For example
(cltcl:event-loop #TCL[set counter 0 button .b -text "Click me and watch your repl" -command { cltcl::callLisp print $counter set counter [expr $counter+1] } button .c -text Close -command exit pack .b pack .c])When the button is clicked a call is make to Lisp's print function.
Function cltcl::callLisp expects a command and a number of arguments:
cltcl::callLisp function ?arg0 ?arg1 ...?The listener applies the Lisp function to the arguments, sends the result back to Tcl/Tk as a properly escaped string, and resumes listening.
Use function call to call a Tcl/Tk command from an event handler. For example a handler on-file-open for the example application top loop could start as follows:
(defun on-file-open () "Called when the user selects the 'File Open menu." (let ((file (cltcl:call "tk_getOpenFile" :defaultextension ".lisp" :filetypes '(("Lisp" ".lisp") ("All files" ".*"))))) ...))The call corresponds to Tcl/Tk call
tk_getOpenFile -defaultextension ".lisp" \ -filetypes {{"Lisp" ".lisp"} {"All files" ".*"}}
Function call expects a command and a number of arguments and lets Tcl/Tk interpret the command with all arguments properly escaped. For convenience keyword symbols are written with a hyphen prefixed, the Tcl convention for options. All other values are written to string and escaped.
Function post is similar to function call except it doesn't wait for a reply. Since this doesn't disrupt the event loop's listener this can for example be useful to update the gui from a seperate thread.
Calling commands with function call or post is a good way to exchange information with Tcl/TK. It takes care of all necessary string escaping. If you inject them in a script with function format-script you have to take care of the escaping yourself.
Run scripts from an event handler with function run. The arguments are bound to Tcl variable argv. This variable normally contains a Tcl script's command line arguments.
Although the arguments of run are properly escaped, often it is more convenient to simply wrap the script in a Tcl proc. The script containing this proc needs to be run only once and the proc can be called safely with function call or post.
Any peace of Tcl data is a string so that is easy. The only difficult part is handling Tcl lists.
Tcl has a peculiar way of writing lists but as long as you know the list structure this is no problem. A list is written as a whitespace seperated string. The problem is that not every whitespace seperated list was necessarily intended to be a list. For that reason there are no inverse conversion functions between Tcl lists and Lisp lists.
Use function write-list-to-Tcl-string to write a Lisp list to a Tcl string. For example
CL-USER> (cltcl:write-list-to-Tcl-string (list "foo" "foo bar" (list "foo" "foo bar"))) "foo foo\\ bar {foo foo\\ bar}"All list items are properly escaped and nested lists are written recursively.
Use function read-tcl-list-from-string1 to read a Tcl list from a string. Continuing from the previous example
CL-USER> (cltcl:read-tcl-list-from-string1 *) ("foo" "foo bar" "foo foo\\ bar") CL-USER> (cltcl:read-tcl-list-from-string1 (third *)) ("foo" "foo bar")Nested lists are left intact, just the top level of the list is read.
The next example show why reading is not recursive. The string "foo bar" would be seen as a list.
CL-USER> (cltcl:read-tcl-list-from-string1 (second *)) ("foo" "bar")You have make the proper calls depending on the contents of a list.
Functions call, run and write-list-to-Tcl-string escape their arguments and are always safe to use. For other functions you have to ensure yourself that values are properly escaped.
Use function escape to escape strings. String that are not properly escaped might contain Tcl commands that could be evaluated unintenionally.
Function repl start a read-eval-print loop. Be careful not to send commands that write output because that will be picked up by the listener. You might want to run the event loop in a seperate thread to have access to the Lisp repl. Then you can start the clTcl repl from there.
Functions for reading and manipulation Tcl/Tk scripts and data are
Functions to connect to a Tcl/Tk interpreter areUse function format-script to inject Lisp values into a Tcl script. All formatter commands can be used but usually ~A is sufficient. Use [list ~A] for lists, as in the following example.
(cltcl:event-loop (cltcl:format-script #TCL[set l [list ~A] tk_messageBox -message ~A button .b -text ~A -command { foreach x $l { tk_messageBox -message $x } exit } pack .b] (cltcl:write-list-to-Tcl-string (list "[expr 1 + 1]" "[list foo bar]")) "[expr 1 + 1]" (cltcl:escape "[exit]")))All strings contain Tcl command, but only one of them gets evaluated, the "[expr 1 + 1]" that is not escaped.
Be careful and always use escape to prevent code injection.
See the specification for with-Tcl/Tk, open-Tcl/Tk-stream, close-Tcl/Tk-stream, send-script and receive-line. These functions hide the implementation dependent details behind an abstraction layer.
See the specification for functions read-script, read-list and read-word. Tcl's syntax rules are described here.


| pgpool-II 4.0.3 Documentation | |||
|---|---|---|---|
| Prev | Up | Chapter 7. Configuration Examples | Next |
Amazon Aurora for PostgreSQL Compatibility (Aurora) is a managed service for PostgreSQL. From user's point of view, Aurora can be regarded as a streaming replication cluster with some exceptions. First, fail over and online recovery are managed by Aurora. So you don't need to set failover_command, follow_master_command, and recovery related parameters. In this section we explain how to set up Pgpool-II for Aurora.
Create pgpool.conf from pgpool.conf.sample-stream.
Set sr_check_period to 0 to disable streaming replication delay checking. This is because Aurora does not provide neccessary functions to check the replication delay.
Enable enable_pool_hba to on so that md5 authentication is enabled (Aurora always use md5 authentication).
Create pool_password. See Section 6.2.2 for more details.
Set backend_hostname0 for the Aurora writer node. Set other backend_hostname for the Aurora reader node. Set appropreate backend_weight as usual. You don't need to set backend_data_directory
Set ALWAYS_MASTER flag to the backend_flag for backend_hostname0.
Enable health checking. Set health_check_period to 5. Set health_check_user, health_check_password, health_check_user and health_check_database to appropriate values. Enable health check retry. Aurora shutdowns all DB nodes while switching over or failover. If the retry is not performed, Pgpool-II thinks that all DB nodes are in down status so that it is required to restart Pgpool-II. Set health_check_max_retries to 20. Set health_check_retry_delay to 1 to avoid the problem.
Disable failover_on_backend_error to avoid failover when connecting to the backend or detecting errors on backend side while executing queries for the same reasons above.


| Developer Spotlight |
Athomas Goldberg of New York is the President and CTO of Improv Technologies, Inc. and Chief Architect of Improv's Cirquet Platform, a component-based framework for developing, deploying and managing large-scale distributed applications based on JXTA. He has been a member of the Project JXTA community since April 2001 and dedicates 45-55% of his time to Project JXTA. Athomas is the Project Owner for many projects on jxta.org: jaas-membership, jnlp-loader, lrmp-endpoint, and validation-agent. In addition, he's a committer on the platform, soap and gameplatform modules. His principal contributions to Project JXTA have been in trying to provide enterprise-level services on top of the core platform, as well as trying to ensure that the platform itself meets the needs of enterprise scale applications. In addition, Athomas is active in the Jini developer community as well as various groups devoted to intelligent agent development. Athomas sees the P2P network embodied by Project JXTA to be the next real evolutionary step for the Internet and the Web. He says that the client-server model that dominates the Internet can never match the flexibility and scalability an architecture like JXTA's provides. As JXTA matures he believes that we will begin to see the distributed applications deployed on a global-scale, connecting everything from the largest super-computers to the smallest hand-held devices and appliances. Athomas doesn't think the challenge for JXTA is finding the killer-app, because he thinks the world is full of distributed applications inefficiently deployed on out-dated centralized architectures. When you compare the cost and effort of deploying and maintaining applications in a traditional manner versus a peer-to-peer approach like JXTA, the benefits are obvious. He says that the real challenge for JXTA will be in making these applications easily accessible to the average user in the same way that J2EE made client-server applications easily accessible to anyone with a computer, an internet connection and a web browser. You can reach Athomas on the Project JXTA discuss mailing list or at<EMAIL_ADDRESS>|
Back to the Developer Spotlight Archives.


BBNaija’s Thelma receives car gift from fans during meet and greet (video & photos)
by Ifeanyi NgeremFormer housemate and reality star of the recently concluded reality TV show Big Brother Naija season 4, Thelma has just been gifted a Mercedes car gift by fans during her meet and greet.
According to reports, fans of the reality star presented her the Benz gift during her meet and greet session with well wishers.
In the video shared online, the reality star was utterly speechless as she became emotional and enjoyed the paparazzi as light, camera and cheering adorned the moment.
She took to her Instagram page some moments ago to share photos and videos of her new car gift.
See photos below:
Watch the video below;


The classic basil, tomato, and mozzarella. This is the way it was oringinally intended.
This is a desc of this pizza


restore_matrix.Rd
Restore the index vector to index matrix in layer_fun
restore_matrix(j, i, x, y)
| j | Column indices directly from |
|---|---|
| i | Row indices directly from |
| x | Position on x-direction directly from |
| y | Position on y-direction directly from |
The values that are sent to layer_fun
are all vectors (for the vectorization
of the grid graphic functions), however, the heatmap slice where
layer_fun
is applied to, is still represented by a matrix, thus, it would be
very convinient if all the arguments in layer_fun
can be converted to the
sub-matrix for the current slice. Here, as shown in above example,
restore_matrix
does the job. restore_matrix
directly accepts the first
four argument in layer_fun
and returns an index matrix, where rows and
columns correspond to the rows and columns in the current slice, from top to
bottom and from left to right. The values in the matrix are the natural order
of e.g. vector j
in current slice.
For following code:
Heatmap(small_mat, name = "mat", col = col_fun, row_km = 2, column_km = 2, layer_fun = function(j, i, x, y, w, h, fill) { ind_mat = restore_matrix(j, i, x, y) print(ind_mat) } )
The first output which is for the top-left slice:
[,1] [,2] [,3] [,4] [,5] [1,] 1 4 7 10 13 [2,] 2 5 8 11 14 [3,] 3 6 9 12 15
As you see, this is a three-row and five-column index matrix where the first
row corresponds to the top row in the slice. The values in the matrix
correspond to the natural index (i.e. 1, 2, ...) in j
, i
, x
, y
,
... in layer_fun
. Now, if we want to add values on the second column in the
top-left slice, the code which is put inside layer_fun
would look like:
for(ind in ind_mat[, 2]) { grid.text(small_mat[i[ind], j[ind]], x[ind], y[ind], ...) }
set.seed(123) mat = matrix(rnorm(81), nr = 9) Heatmap(mat, row_km = 2, column_km = 2, layer_fun = function(j, i, x, y, width, height, fill) { ind_mat = restore_matrix(j, i, x, y) print(ind_mat) })#> [,1] [,2] [,3] [,4] [,5] #> [1,] 1 4 7 10 13 #> [2,] 2 5 8 11 14 #> [3,] 3 6 9 12 15 #> [,1] [,2] [,3] [,4] #> [1,] 1 4 7 10 #> [2,] 2 5 8 11 #> [3,] 3 6 9 12 #> [,1] [,2] [,3] [,4] [,5] #> [1,] 1 7 13 19 25 #> [2,] 2 8 14 20 26 #> [3,] 3 9 15 21 27 #> [4,] 4 10 16 22 28 #> [5,] 5 11 17 23 29 #> [6,] 6 12 18 24 30 #> [,1] [,2] [,3] [,4] #> [1,] 1 7 13 19 #> [2,] 2 8 14 20 #> [3,] 3 9 15 21 #> [4,] 4 10 16 22 #> [5,] 5 11 17 23 #> [6,] 6 12 18 24


TSW Mapper
Home
Parser
About
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Input:
Sorted:
Filtered:
Output:


Drag and Drop
HTML5 introduces a new feature, called drag and drop, in Web pages. The drag and drop feature allows you to drag a textual data or an image from the source area to target on a Web page, where source and target area can be two containers or text areas. You can perform drag and drop operation by creating a drag data store, which specifies the data that is used for drag and drop operation. It contains a drag data store item list, which is a list of items representing the dragged data, such as text or files. You can create a draggable element by using the draggable attribute and the dragstart event to store the data that has to be dragged. The event handler checks whether the selected text is draggable or not and store the data in the DataTransfer object.
The DragEvent interface is used to create objects of the DataTransfer interface and initialize the dragging operation. It provides the dataTransfer attribute and initDragEvent() method for this purpose. The dataTransfer attribute of the DragEvent interface returns an object of the DataTransfer interface. The initDragEvent() method initializes an event to drag the data.
You can make a draggable HTML element by using the draggable attribute. The syntax to use the draggable attribute is given as follows:
element . draggable [ = value ]
In the preceding syntax, the value can be true, false, or auto. The true value indicates that the element is draggable; the false value indicates that the element is not draggable; and the auto value specifies the default behavior of the element.
The following code snippet shows an example of using the draggable attribute:
< BODY > < p draggable="true" > You can drag this paragraph </p > < p draggable="false" > You cannot drag this paragraph < /p > < /BODY >
The HTML elements may specify the dropzone attribute to drop the data. This attribute is specified by using any one of the following values:
- copy: Creates a copy of the draggable data from source location to a target location
- move: Moves the draggable data from the source location and place it to a target location
- link: Creates a link of the draggable data from source location to a target location
Click here for demos to see how to implement drag and drop feature:
demo


Contents
Short Descriptions
|
is the dd command for SMB. |
|
|
is used to read and manipulate TDB/CTDB databases using the dbwrap interface. |
|
|
is used to write records to eventlogs from STDIN, add the
specified source and DLL eventlog registry entries and
display the active eventlog names (from |
|
|
is used to list info about machines that respond to SMB name queries on a subnet. |
|
|
is used to run random generic SMB operations against two SMB servers and show the differences in behavior. |
|
|
is a command-line utility for adding records to an LDB database. |
|
|
is a command-line program for deleting LDB database records. |
|
|
allows you to edit LDB databases using your preferred editor. |
|
|
allows you to modify records in an LDB database. |
|
|
allows you to rename LDB databases. |
|
|
searches an LDB database for records matching a specified expression. |
|
|
is used to find differences in locking between two SMB servers. |
|
|
is used to find differences in wildcard matching between Samba's implementation and that of a remote server. |
|
|
runs Spotlight searches against a SMB server. |
|
|
is used to recursively rename extended attributes. |
|
|
is a DCE/RPC Packet Parser and Dumper. |
|
|
is a tool for administration of Samba and remote CIFS servers, similar to the net utility for DOS/Windows. |
|
|
is the Samba NetBIOS name server. |
|
|
is used to query NetBIOS names and map them to IP addresses. |
|
|
is a tool to allow external access to Winbind's NTLM authentication function. |
|
|
converts LDAP schema's to LDB-compatible LDIF. |
|
|
is a tool used to manage the SAM database. |
|
|
is a utility that reports and changes SIDs in Windows registry files. It currently only supports Windows NT. |
|
|
is a Diff program for Windows registry files. |
|
|
applies registry patches to registry files. |
|
|
is a Windows registry file browser using readline. |
|
|
is a text-mode registry viewer. |
|
|
is used to execute MS-RPC client side functions. |
|
|
is a server to provide AD and SMB/CIFS services to clients. |
|
|
is used to update our DNS names using TSIG-GSS. |
|
|
downgrades the Samba AD database to be compatible with a previous version of Samba. |
|
|
is a script used to compute your KCC (Knowledge Consistency Checker) topology. |
|
|
allows you to edit Microsoft Group Policy Objects (GPOs). |
|
|
is a ncurses based tool to manage the Samba registry. |
|
|
is a script to update the servicePrincipalName names from spn_update_list. |
|
|
is the main Samba administration tool. |
|
|
is a Unix SMB/CIFS implementation. |
|
|
manipulates share ACL permissions on SMB file shares. |
|
|
is used to manipulate Windows NT access control lists. |
|
|
is a SMB/CIFS access utility, similar to FTP. |
|
|
is used to control running smbd, nmbd and winbindd daemons. |
|
|
is used to manipulate Windows NT quotas on SMB file shares. |
|
|
is the main Samba daemon which provides SMB/CIFS services to clients. |
|
|
is a simple utility with wget-like semantics, that can download files from SMB servers. You can specify the files you would like to download on the command-line. |
|
|
changes a user's Samba password. |
|
|
sends a print job to an SMB printer. |
|
|
reports current Samba connections. |
|
|
is a shell script used for backing up SMB/CIFS shares directly to Linux tape drives or a file. |
|
|
is a testsuite that runs several tests against a SMB server. |
|
|
is a text-based SMB network browser. |
|
|
is a tool for backing up or validating the integrity of
Samba |
|
|
is a tool used to print the contents of a Samba |
|
|
is a tool for creating a Samba |
|
|
is a tool which allows simple database manipulation from the command line. |
|
|
checks an |
|
|
queries a running winbindd daemon. |
|
|
resolves names from Windows NT servers. |
|
|
provides Name Service Switch API functions for resolving names from NT servers. |
|
|
provides API functions for Samba's implementation of the Windows Internet Naming Service. |
|
|
provides the API functions for the administration tools used for Samba and remote CIFS servers. |
|
|
provides the API functions for the Samba SMB client tools. |
|
|
provides API functions for Windows domain client services. |


My_html
I choose to use bold and italic fonts!
I can even combine them together !
title
So you wanna do this as well?
Follow these instructions!
- open MS Edge
- visit the website baidu
- attention:
- do not ask me how to use baidu!
- search for whatever you want
the end
www
aa
aa
aa


Assessments
Employee Login
ContactUs
Services
About
Home
Call Now:<PHONE_NUMBER>
24 Hr | Certified Nurses | Hourly
Serving the Minneapolis area for over 20 years.
Learning Objectives
This where you will learn about client illnesses, diseases, health issues and how to treat them. Take a look at each of the learning objectives below.
You must complete every objective that relates to your client.


nips nips2007 nips2007-162 nips2007-162-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Chris Atkeson, Benjamin Stephens
Abstract: We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using local trajectory optimizers to globally optimize a policy and associated value function. Our focus is on ﬁnding steady state policies for deterministic time invariant discrete time control problems with continuous states and actions often found in robotics. In this paper we show that we can now solve problems we couldn’t solve previously. 1
[1] J. Rust. Using randomization to break the curse of dimensionality. Econometrica, 65(3):487– 516, 1997.
[2] M. Hauskrecht. Incremental methods for computing bounds in partially observable Markov decision processes. In Proceedings of the 14th National Conference on Artiﬁcial Intelligence (AAAI-97), pages 734–739, Providence, Rhode Island, 1997. AAAI Press / MIT Press.
[3] N.L. Zhang and W. Zhang. Speeding up the convergence of value iteration in partially observable Markov decision processes. JAIR, 14:29–51, 2001.
[4] J. Pineau, G. Gordon, and S. Thrun. Point-based value iteration: An anytime algorithm for POMDPs. In International Joint Conference on Artiﬁcial Intelligence (IJCAI), 2003.
[5] T. Smith and R. Simmons. Heuristic search value iteration for POMDPs. In Uncertainty in Artiﬁcial Intelligence, 2004.
[6] M.T.J. Spaan and Nikos V. A point-based POMDP algorithm for robot planning. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 2399–2404, New Orleans, Louisiana, April 2004.
[7] S. Thrun. Monte Carlo POMDPs. In S.A. Solla, T.K. Leen, and K.-R. M¨ ller, editors, Advances u in Neural Information Processing 12, pages 1064–1070. MIT Press, 2000.
[8] C. G. Atkeson. Using local trajectory optimizers to speed up global optimization in dynamic programming. In Jack D. Cowan, Gerald Tesauro, and Joshua Alspector, editors, Advances in Neural Information Processing Systems, volume 6, pages 663–670. Morgan Kaufmann Publishers, Inc., 1994.
[9] F. L. Lewis and V. L. Syrmos. Optimal Control, 2nd Edition. Wiley-Interscience, 1995.
[10] C. Szepesv´ ri. Efﬁcient approximate planning in continuous space Markovian decision proba lems. AI Communications, 13(3):163–176, 2001.
[11] J. N. Tsitsiklis and Van B. Roy. Regression methods for pricing complex American-style options. IEEE-NN, 12:694–703, July 2001.
[12] V. D. Blondel and J. N. Tsitsiklis. A survey of computational complexity results in systems and control, 2000.
[13] R. Munos and A. W. Moore. Variable resolution discretization in optimal control. Machine Learning Journal, 49:291–323, 2002.
[14] S. M. LaValle. Planning Algorithms. Cambridge University Press, 2006.
[15] C. G. Atkeson. Randomly sampling actions in dynamic programming. In 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL), 2007.
[16] C. G. Atkeson and J. Morimoto. Nonparametric representation of a policies and value functions: A trajectory based approach. In Advances in Neural Information Processing Systems 15. MIT Press, 2003.
[17] P. Dyer and S. R. McReynolds. The Computation and Theory of Optimal Control. Academic Press, New York, NY, 1970.
[18] D. H. Jacobson and D. Q. Mayne. Differential Dynamic Programming. Elsevier, New York, NY, 1970.
[19] C. G. Atkeson and B. Stephens. Multiple balance strategies from one optimization criterion. In Humanoids, 2007. 8


ASSERT
Section: Linux Programmer's Manual (3)
Updated: 2013-09-26
Index
Return to Main Contents
NAME
assert - abort the program if assertion is false
SYNOPSIS
#include <assert.h>
void assert(scalar expression);
DESCRIPTION
If the macro
NDEBUG
was defined at the moment
<assert.h>
was last included, the macro
assert()
generates no code, and hence does nothing at all.
Otherwise, the macro
assert()
prints an error message to standard error and terminates the program
by calling
abort(3)
if
expression
is false (i.e., compares equal to zero).
The purpose of this macro is to help programmers find bugs in
their programs.
The message "assertion failed in file foo.c, function
do_bar(), line 1287" is of no help at all to a user.
RETURN VALUE
No value is returned.
CONFORMING TO
POSIX.1-2001, C89, C99.
In C89,
expression
is required to be of type
int
and undefined behavior results if it is not, but in C99
it may have any scalar type.
BUGS
assert()
is implemented as a macro; if the expression tested has side-effects,
program behavior will be different depending on whether
NDEBUG
is defined.
This may create Heisenbugs which go away when debugging
is turned on.
SEE ALSO
abort(3),
assert_perror(3),
exit(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- RETURN VALUE
-
- CONFORMING TO
-
- BUGS
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:55:18 GMT, September 18, 2014


{% extends "base.html" %} {% block content %}
If you save a Django user with an e-mail address, a multimail EmailAddress instance will be created for the user's e-mail. That address will be marked as the user's primary e-mail address.
You can also create e-mail addresses directly for a user. Whenever a new EmailAddress object is created, a verification link is sent to that address.
In order to create a verified e-mail address associated with your account, you will need to enter a valid address where you can receive a verification link.
I do not spam and I do not share these addresses with anyone. You will only receive verification e-mails for the sake of this demo. You may delete e-mail addresses or your full account at any time. As part of routine maintenance, accounts and related information are occasionally deleted. Feel free to re-create your account at any time.
{{ addr.email }} {% if addr.is_verified %}verified{% else %}unverified {% endif %}{% if addr.is_primary %} primary{% endif %} delete {% if not addr.is_primary %}set as primary{% endif %}
{% if not addr.is_verified %} Re-send verification link for {{ addr.email }} {% endif %} {% empty %}None yet. Submit the user form above with an email address, or create a multimail address directly with the form on the right.
{% endfor %}You may leave your account for future use, or delete it at any time.
Delete Account


Keplerian system¶
-
class
starry.
System
¶ A system of bodies in Keplerian orbits about a central primary body.
- Parameters
primary (
Primary
) – The central body.secondaries (
Secondary
) – One or more secondary bodies in orbit about the primary.time_unit (optional) – An
astropy.units
unit defining the time metric for this object. Defaults toastropy.units.day.
light_delay (bool, optional) – Account for the light travel time delay to the barycenter of the system? Default is False.
texp (scalar) – The exposure time of each observation. This can be a scalar or a tensor with the same shape as
t
. Iftexp
is provided,t
is assumed to indicate the timestamp at the middle of an exposure of lengthtexp
.oversample (int) – The number of function evaluations to use when numerically integrating the exposure time.
order (int) – The order of the numerical integration scheme. This must be one of the following:
0
for a centered Riemann sum (equivalent to the “resampling” procedure suggested by Kipping 2010),1
for the trapezoid rule, or2
for Simpson’s rule.
-
property
bodies
¶ A list of all objects in the Keplerian system.
-
design_matrix
(t)¶ Compute the system flux design matrix at times
t
.Note
This is the unweighted design matrix, i.e., it does not include the scaling by the amplitude of each body’s map. To perform this weighting, do
X = sys.design_matrix(**kwargs) for i, body in zip(sys.map_indices, sys.bodies): X[:, i] *= body.map.amp
- Parameters
t (scalar or vector) – An array of times at which to evaluate the design matrix in units of
time_unit
.
-
draw
()¶ Draw a map from the posterior distribution and set the
y
map vector of each body.Users should call
solve()
to enable this attribute.
-
flux
(t, total=True)¶ Compute the system flux at times
t
.- Parameters
t (scalar or vector) – An array of times at which to evaluate the flux in units of
time_unit
.total (bool, optional) – Return the total system flux? Defaults to True. If False, returns arrays corresponding to the flux from each body.
-
property
light_delay
¶ Account for the light travel time delay? Read-only
-
lnlike
(*, design_matrix=None, t=None, woodbury=True)¶ Returns the log marginal likelihood of the data given a design matrix.
This method computes the marginal likelihood (marginalized over the spherical harmonic coefficients of all bodies) given a system light curve and its covariance (set via the
set_data()
method) and a Gaussian prior on the spherical harmonic coefficients (set via theset_prior()
method).- Parameters
design_matrix (matrix, optional) – The flux design matrix, the quantity returned by
design_matrix()
. Default is None, in which case this is computed based onkwargs
.t (vector, optional) – The vector of times at which to evaluate
design_matrix()
, if a design matrix is not provided. Default is None.woodbury (bool, optional) – Solve the linear problem using the Woodbury identity? Default is True. The Woodbury identity is used to speed up matrix operations in the case that the number of data points is much larger than the number of spherical harmonic coefficients. In this limit, it can speed up the code by more than an order of magnitude. Keep in mind that the numerical stability of the Woodbury identity is not great, so if you’re getting strange results try disabling this. It’s also a good idea to disable this in the limit of few data points and large spherical harmonic degree.
- Returns
The log marginal likelihood.
- Return type
lnlike
-
property
map_indices
¶ A list of the indices corresponding to each body in the design matrix.
-
property
order
¶ The order of the numerical integration scheme. Read-only
0
: a centered Riemann sum1
: trapezoid rule2
: Simpson’s rule
-
property
oversample
¶ Oversample factor when integrating over exposure time. Read-only
-
position
(t)¶ Compute the Cartesian positions of all bodies at times
t
.- Parameters
t (scalar or vector) – An array of times at which to evaluate the position in units of
time_unit
.
-
property
primary
¶ The primary (central) object in the Keplerian system.
-
rv
(t, keplerian=True, total=True)¶ Compute the observed radial velocity of the system at times
t
.- Parameters
t (scalar or vector) – An array of times at which to evaluate the radial velocity in units of
time_unit
.keplerian (bool) – Include the Keplerian component of the radial velocity of the primary? Default is True. If False, this method returns a model for only the radial velocity anomaly due to transits (the Rossiter-McLaughlin effect) and time-variable surface features (Doppler tomography) for all bodies in the system.
total (bool, optional) – Return the total system RV? Defaults to True. If False, returns arrays corresponding to the RV contribution from each body.
-
property
secondaries
¶ A list of the secondary (orbiting) object(s) in the Keplerian system.
-
set_data
(flux, C=None, cho_C=None)¶ Set the data vector and covariance matrix.
This method is required by the
solve()
method, which analytically computes the posterior over surface maps for all bodies in the system given a dataset and a prior, provided both are described as multivariate Gaussians.- Parameters
flux (vector) – The observed system light curve.
C (scalar, vector, or matrix) – The data covariance. This may be a scalar, in which case the noise is assumed to be homoscedastic, a vector, in which case the covariance is assumed to be diagonal, or a matrix specifying the full covariance of the dataset. Default is None. Either C or cho_C must be provided.
cho_C (matrix) – The lower Cholesky factorization of the data covariance matrix. Defaults to None. Either C or cho_C must be provided.
-
show
(t, cmap='plasma', res=300, interval=75, file=None, figsize=(3, 3), html5_video=True, window_pad=1.0)¶ Visualize the Keplerian system.
- Parameters
t (scalar or vector) – The time(s) at which to evaluate the orbit and the map in units of
time_unit
.cmap (string or colormap instance, optional) – The matplotlib colormap to use. Defaults to
plasma
.res (int, optional) – The resolution of the map in pixels on a side. Defaults to 300.
figsize (tuple, optional) – Figure size in inches. Default is (3, 3) for orthographic maps and (7, 3.5) for rectangular maps.
interval (int, optional) – Interval between frames in milliseconds (animated maps only). Defaults to 75.
file (string, optional) – The file name (including the extension) to save the animation to (animated maps only). Defaults to None.
html5_video (bool, optional) – If rendering in a Jupyter notebook, display as an HTML5 video? Default is True. If False, displays the animation using Javascript (file size will be larger.)
window_pad (float, optional) – Padding around the primary in units of the primary radius. Bodies outside of this window will be cropped. Default is 1.0.
-
property
solution
¶ The posterior probability distribution for the maps in the system.
This is a tuple containing the mean and lower Cholesky factorization of the covariance of the amplitude-weighted spherical harmonic coefficient vectors, obtained by solving the regularized least-squares problem via the
solve()
method.Note that to obtain the actual covariance matrix from the lower Cholesky factorization \(L\), simply compute \(L L^\top\).
Note also that this is the posterior for the amplitude-weighted map vectors. Under this convention, the map amplitude is equal to the first term of the vector of each body and the spherical harmonic coefficients are equal to the vector normalized by the first term.
-
solve
(*, design_matrix=None, t=None)¶ Solve the least-squares problem for the posterior over maps for all bodies.
This method solves the generalized least squares problem given a system light curve and its covariance (set via the
set_data()
method) and a Gaussian prior on the spherical harmonic coefficients (set via theset_prior()
method). The map amplitudes and coefficients of each of the bodies in the system are then set to the maximum a posteriori (MAP) solution.- Parameters
design_matrix (matrix, optional) – The flux design matrix, the quantity returned by
design_matrix()
. Default is None, in which case this is computed based onkwargs
.t (vector, optional) – The vector of times at which to evaluate
design_matrix()
, if a design matrix is not provided. Default is None.
- Returns
The posterior mean for the spherical harmonic coefficients l > 0 and the Cholesky factorization of the posterior covariance of all of the bodies in the system, stacked in order (primary, followed by each of the secondaries in the order they were provided.)
-
property
time_unit
¶ An
astropy.units
unit defining the time metric for the system.


cin·u·os·i·ty
noun
the ability to curve or bend easily and flexibly.
a deviation, especially in a Spotify stream.
I wanted a way to discover new music in a way that was cool and interesting. Cinuosity generates pseudorandom playlists for you automatically and helps you to get out of the rut that many music recommendation systems leave you in. With Cinuosity you will be continually exploring new genres and music that spans all cultures.
Cinousity is open source. Check out the code on GitHub.


Explore more than 2,000 Ted talks to learn about the topics. If any spark your curiousity you can go ahead and watch them too.


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--- layout: post status: publish published: true title: using notepad++ update asap author: display_name: vishvendra login: vishvendra email<EMAIL_ADDRESS>url: '' author_login: vishvendra author_email<EMAIL_ADDRESS>wordpress_id: 499 wordpress_url: https://vishvendrasingh.com/?p=499 date: '2017-03-09 06:54:26 +0530' date_gmt: '2017-03-09 06:54:26 +0530' categories: - Hacking tags: [] comments: [] ---
Are you using notepadd++ or any of the listed tool in the below article by wikileaks.
https://wikileaks.org/ciav7p1/
You must update as soon as possible. It may be act as an virus for you personal computer. Ohh you still feel like you don't need to worry, you have no secrets in you computer. I think you must read the below article by notepad plus plus.
https://notepad-plus-plus.org/news/notepad-7.3.3-fix-cia-hacking-issue.html
You can comment if you feel security conscious of you system and need advice.
Title : using notepad++ update asap


Bucket Ball is a new tailgating sport that can virtually be played anywhere. As an extension from the popular game "Beer Pong", Bucket Ball has become most popular becasue of its compact design.
Bucket Ball is a new tailgating sport that can virtually be played anywhere. As an extension from the popular game "Beer Pong", Bucket Ball has become most popular becasue of its compact design.
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Check out our KickStarter Campaign and take advantage of the early bird special deals on BucketBall!
Bucket Ball is a new tailgating sport that can virtually be played anywhere. As an extension from the popular game "Beer Pong", Bucket Ball has become most popular becasue of its compact design.


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{% extends "base.html" %} {% block title %}Download{% endblock %} {% block content %}
Two versions of libmodbus are currently maintained:
Download the tarball of the version you want and run ./configure; make; make install;
to compile and install libmodbus on your system.
The latest stable release is also available in many Linux distributions:
You can also browse the source code on stephane/libmodbus or clone with the command:
$ git clone git://github.com/stephane/libmodbusto compile and test the latest version. {% endblock %}


(Windows) Changes to DPI scaling settings are now automatically applied to dred dynamically at run-time:
The "ui-scale" setting is now applied immediately without the need for a restart. This setting stacks with the system DPI setting.
Added tooltips to the tab bar showing the full path of the file when the mouse is hovered over a tab.
Added support for running dred in portable mode in response to detecting a "portable.dred" config file in the same directory as the exe.
Fixed a bug with autocomplete where the popup box would not be filled correctly after executing a command.
Fixed a bug where the command bar would not be relieved of the keyboard focus when opening any kind of dialog box via a command.
Fixed a possible crash when opening a dialog box.
Fixed a bug where favourites do not show correctly.
Fixed a printing bug where an excess page is printed.
Windows Only
N/A
Linux and GTK Only
N/A
Source Code Improvements
Improved the font scaling system. The new system removes the old acquire/unacquire system for sub-fonts and replace it with a system
where each of the relevant font and text painting APIs take a scale. The handling of font scaling is now done internally by DTK rather
than by dred. The advantage to this new system is that it greatly simplifies font management.
Improved image scaling system. The new system adds support for SVG images and introduces the notion of stock images.


Software Engineer
Bring to the table win-win survival strategies to ensure proactive domination. At the end of the day, going forward, a new normal that has evolved from generation X is on the runway heading towards a streamlined cloud solution. User generated content in real-time will have multiple touchpoints for offshoring.
Learned about network engineering and other aspects of hardware in the buisness world. Fixed malfunctioning hardware. Also, learned how to deal with customer relations.
Organize and help plan meetings where members could get assit on building their computers. Members would also be advised on parts to buy.
Arranged Practices and weekly team building exercises to a bunch of distracted teenagers. Employed social media to send important information out to teammates
GPA: 3.193
August 2017 - Present
GPA: 3.9
August 2013 - May 2017
Hi, my name is Noah. I am currently a computer science major at University of Maryland. I have loved working with computer ever since a young age and have been programming for several years. I am a fun-loving guy who is always willing to learn.
My desire for knowledge has accelerated my path to success. As a result I have started several of my own projects to expand my horizons. With my experience in the restaurant industry I believe my ability to communicate and work with others will help kick start any enterprise.


AUD/USD Technical Analysis: Aussie holds above the 0.6800 handle
by Flavio Tosti- The Aussie is trading quietly just above the 0.6800 handle as the London session comes to an end.
- The level to beat for bears is the 0.6794 support level.
AUD/USD daily chart
AUD/USD is trading in a downtrend below the 50, 100 and 200-day simple moving averages (DMAs) on the daily chart. This Monday the Aussie is trapped in a tight range above the 0.6800 handle.
AUD/USD 4-hour chart
The market is trading off the November low just above the 0.6800 handle and below its main SMAs. To resume the bear trend, bears need to overcome the 0.6809 and 0.6794 support levels. Further down, support is seen at the 0.6773 and 0.6738 levels, according to the Technical Confluences Indicator.
AUD/USD 30-minute chart
AUD/USD is trading above its main SMAs, suggesting a bullish momentum in the near term. Resistances are seen at the 0.6834 and 0.6868 price levels, according to the Technical Confluences Indicator.
Additional key levels
AUD/USD
| Overview | |
|---|---|
| Today last price | 0.6816 |
| Today Daily Change | 0.0000 |
| Today Daily Change % | 0.00 |
| Today daily open | 0.6816 |
| Trends | |
|---|---|
| Daily SMA20 | 0.6859 |
| Daily SMA50 | 0.6817 |
| Daily SMA100 | 0.6842 |
| Daily SMA200 | 0.6939 |
| Levels | |
|---|---|
| Previous Daily High | 0.6818 |
| Previous Daily Low | 0.678 |
| Previous Weekly High | 0.6866 |
| Previous Weekly Low | 0.6769 |
| Previous Monthly High | 0.693 |
| Previous Monthly Low | 0.667 |
| Daily Fibonacci 38.2% | 0.6803 |
| Daily Fibonacci 61.8% | 0.6795 |
| Daily Pivot Point S1 | 0.6791 |
| Daily Pivot Point S2 | 0.6767 |
| Daily Pivot Point S3 | 0.6753 |
| Daily Pivot Point R1 | 0.6829 |
| Daily Pivot Point R2 | 0.6843 |
| Daily Pivot Point R3 | 0.6867 |


Performs (exact) hypergeometric tests on IxJ and 2x2x2 contingency tables using parallelized C code.
| Version: | 1.3.6 |
| Depends: | R (≥ 3.0.0) |
| Suggests: | RUnit |
| Published: | 2018-01-26 |
| Author: | Markus Boenn |
| Maintainer: | Markus Boenn <markus.boenn.sf at googlemail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | OpenMP (>=3.0) |
| Citation: | hypergea citation info |
| Materials: | NEWS |
| CRAN checks: | hypergea results |
| Reference manual: | hypergea.pdf |
| Package source: | hypergea_1.3.6.tar.gz |
| Windows binaries: | r-devel: hypergea_1.3.6.zip, r-release: hypergea_1.3.6.zip, r-oldrel: hypergea_1.3.6.zip |
| OS X binaries: | r-release: hypergea_1.3.6.tgz, r-oldrel: hypergea_1.3.6.tgz |
| Old sources: | hypergea archive |
Please use the canonical form https://CRAN.R-project.org/package=hypergea to link to this page.


#assign eventlistening = "true">
Since version 4.0.1, Ebean supports JPA EntityListener Annotations:
To listen for events on entity, you can also use the BeanPersistController
BeanPersistController is used to enhance or override the default bean persistence mechanism.
Note that if want to totally change the finding, you need to use a BeanQueryAdapter rather than using postLoad().
Note that getTransaction() on the PersistRequest returns the transaction used for the insert, update, delete or fetch. To explicitly use this same transaction you should use this transaction via methods on EbeanServer.
```java Object extaBeanToSave = ...; Transaction t = request.getTransaction(); EbeanServer server = request.getEbeanServer(); server.save(extraBeanToSave, t); ```It is worth noting that BeanPersistListener is different in three main ways from BeanPersistController postXXX methods.
BeanPersistListener only sees successfully committed events. BeanController pre and post methods occur before the commit or a rollback and will see events that are later rolled back
BeanPersistListener runs in a background thread and will not effect the response time of the actual persist where as BeanController code will BeanPersistListener can be notified of events from other servers in a cluster.
A BeanPersistController is either found automatically via class path search or can be added programmatically via ServerConfiguration.addEntity().


Please select atleast one algorithm
The Gaze Plot illustrates the movement of ones eyes from one place to the next
The Fixation Plot illustrates time spent starring at a location
The Heat map is useful to find most dense fixation regions
One can you use this time series plot highligth portion of the Gaze, Fixation and Density Graphs by the means of Linked Brushing !