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Tax Analysis. Simplified.
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| 8 Dec 1925 |
Sammy Davis, Jr. born to Sam Davis (dancer) and Elvera Sanchez (Puerto Rican chorus girl), Harlem NY. |
| 9 Sep 1933 |
Stars in Rufus Jones for President. Yes, it's as racist as it sounds. |
| 1941 |
Meets Frank Sinatra while in Detroit. |
| 1943 |
Drafted into the U.S. Army. |
| 19 Nov 1954 |
Sammy Davis, Jr. loses his eye in an automobile accident. |
| 12 Jan 1955 |
Telegram from Marlon Brando: "'Never dug you before.  You the man. Marlon." |
| 1956 |
Converts to Judaism. |
| 1957 |
Dates actress Kim Novak. |
| 1 Jan 1958 |
Because of the shitstorm caused by black Sammy dating white Kim Novak, a mixture of Mafia pressure and that of Harry Cohn (in charge of Novak's studio, Columbia) forces Sammy to break up the romance. He quickly marries black chorus girl Loray White, for which she was paid $25,000. Harry Belafonte was best man. Sammy attempts suicide on his marriage night, according to former personal assistant Arthur Silber, Jr.: "Sammy was putting a gun to his head. I jumped on top of him and pinned his arms as I wrestled the gun out of his hand. It was loaded. I twisted his arm and the gun fell to the floor." |
| 13 Nov 1960 |
Marries Swedish actress May Britt (born Maybritt Wilkens). Lasts until 1968. |
| 1962 |
What Kind of Fool Am I sells one million records. |
| 1965 |
Autobiography Yes I Can published. |
| 7 Jan 1966 |
Sammy Davis, Jr. Show. |
| 12 Oct 1966 |
Sammy Davis, Jr. makes a cameo appearance on the ABC television series Batman, during one of their legendary Batclimbs. |
| 28 May 1971 |
Files of Richard M. Nixon, in meeting notes by Egil Krogh: "It is suggested that the President consent to an interview with Sammy Davis, Jr. on the subject of drug abuse as an introduction to a ninety minute drug abuse television special M.C.'d by Sammy Davis, Jr. and
participated in by well known musicians and actors." |
| 1 Jul 1971 |
Sammy Davis, Jr. meets personally with Richard M. Nixon. Sammy was a Tricky Dick supporter. |
| 1972 |
The Candy Man hits #1 (from the Willie Wonka soundtrack.) |
| 13 Apr 1973 |
Sammy Davis, Jr. inducted into the Church of Satan by Anton LaVey's wife Karla, and Michael Aquino. |
| 1978 |
Does a television commercial for Alka Seltzer. |
| 19 Jun 1981 |
Cannonball Run, as Morris Fenderbaum. |
| 1989 |
Autobiography Why Me?: The Sammy Davis Jr. Story published. |
| 16 May 1990 |
Sammy Davis, Jr. dies from throat cancer, Beverly Hills CA. |


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Your new flier!
| Name: | your-name |
|---|---|
| Email: |<EMAIL_ADDRESS>|
| Your other website: | your-other-website |
| Site title: | your-title |
| Site tagline: | Go blue! |
If the above values aren’t right, you can change them in _config.yml
. Head here for more instructions.


nflgame.player module
from __future__ import division
import json
import os.path
from nflgame import OrderedDict
import nflgame.seq
import nflgame.statmap
_player_json_file = os.path.join(os.path.dirname(__file__), 'players.json')
def _create_players(jsonf=None):
"""
Creates a dict of Player objects from the players.json file, keyed
by GSIS ids.
"""
if jsonf is None:
jsonf = _player_json_file
try:
data = json.loads(open(jsonf).read())
except IOError:
return {}
players = {}
for playerid in data:
players[playerid] = Player(data[playerid])
return players
class Player (object):
"""
Player instances represent meta information about a single player.
This information includes name, team, position, status, height,
weight, college, jersey number, birth date, years, pro, etc.
Player information is populated from NFL.com profile pages.
"""
def __init__(self, data):
self.player_id = data['gsis_id']
self.gsis_name = data.get('gsis_name', '')
self.full_name = data.get('full_name', '')
self.first_name = data.get('first_name', '')
self.last_name = data.get('last_name', '')
self.team = data.get('team', '')
self.position = data.get('position', '')
self.profile_id = data.get('profile_id', 0)
self.profile_url = data.get('profile_url', '')
self.uniform_number = data.get('number', 0)
self.birthdate = data.get('birthdate', '')
self.college = data.get('college', '')
self.height = data.get('height', '')
self.weight = data.get('weight', '')
self.years_pro = data.get('years_pro', 0)
self.status = data.get('status', '')
# API backwards compatibility.
self.gsis_id = self.player_id
self.playerid = self.player_id
self.name = self.full_name
self.number = self.uniform_number
def stats(self, year, week=None):
games = nflgame.games(year, week)
players = list(nflgame.combine(games).filter(playerid=self.playerid))
if len(players) == 0:
return GamePlayerStats(self.player_id, self.gsis_name,
None, self.team)
return players[0]
def plays(self, year, week=None):
plays = []
games = nflgame.games(year, week)
for g in games:
plays += filter(lambda p: p.has_player(self.playerid),
list(g.drives.plays()))
return nflgame.seq.GenPlays(plays)
def __str__(self):
return '%s (%s, %s)' % (self.name, self.position, self.team)
class PlayerDefense (Player):
def __init__(self, team):
self.playerid = None
self.name = team
self.team = team
self.position = 'DEF'
def stats(self, year, week=None):
assert False, 'Cannot be called on a defense.'
def plays(self, year, week=None):
assert False, 'Cannot be called on a defense.'
def __str__(self):
return '%s Defense' % self.team
class PlayerStats (object):
"""
Player represents a single player and all of his statistical categories.
Every player has 'playerid', 'name' and 'home' fields.
Additionally, depending upon which statistical categories that player
was involved in for the game, he'll have properties such as 'passing_tds',
'rushing_yds', 'defense_int' and 'kicking_fgm'.
In order to know whether a paricular player belongs to a statical category,
you may use the filtering methods of a player sequence or alternatively,
use the has_cat method with arguments like 'passing', 'rushing', 'kicking',
etc. (A player sequence in this case would be an instance of
GenPlayerStats.)
You may also inspect whether a player has a certain property by using
the special __dict__ attribute. For example::
if 'passing_yds' in player.__dict__:
# Do something with player.passing_yds
"""
def __init__(self, playerid, name, home, team):
"""
Create a new Player instance with the player id (from NFL.com's
GameCenter), the player's name (e.g., "T.Brady") and whether the
player is playing in a home game or not.
"""
self.playerid = playerid
self.name = name
self.home = home
self.team = team
self._stats = OrderedDict()
self.player = None
if self.playerid in nflgame.players:
self.player = nflgame.players[self.playerid]
def has_cat(self, cat):
for f in self._stats:
if f.startswith(cat):
return True
return False
@property
def guess_position(self):
"""
Guesses the position of this player based on the statistical
categories present in this object when player meta is not
present.
Note that if this resorts to a guess, then it will be more
effective on aggregate data rather than data from just a
single play. (e.g., if a QB runs the ball, and that's the
only data available, the position returned will be RB.)
When a position is guessed, only the following positions will
be returned: QB, RB, WR, DEF, K and P.
"""
# Look for the player meta first. Duh.
if self.player is not None:
return self.player.position
stats = [
(self.passing_att, 'QB'),
(self.rushing_att, 'RB'),
(self.receiving_tar, 'WR'),
(self.defense_tkl, 'DEF'),
(self.defense_ast, 'DEF'),
(self.kicking_tot, 'K'),
(self.kicking_fga, 'K'),
(self.punting_tot, 'P'),
]
return sorted(stats, reverse=True)[0][1]
@property
def tds(self):
"""
Returns the total number of touchdowns credited to this player across
all statistical categories.
"""
n = 0
for f, v in self.__dict__.iteritems():
if f.endswith('tds'):
n += v
return n
@property
def twopta(self):
"""
Returns the total number of two point conversion attempts for
the passing, rushing and receiving categories.
"""
return (self.passing_twopta
+ self.rushing_twopta
+ self.receiving_twopta)
@property
def twoptm(self):
"""
Returns the total number of two point conversions for
the passing, rushing and receiving categories.
"""
return (self.passing_twoptm
+ self.rushing_twoptm
+ self.receiving_twoptm)
@property
def twoptmissed(self):
"""
Returns the total number of two point conversion failures for
the passing, rushing and receiving categories.
"""
return (self.passing_twoptmissed
+ self.rushing_twoptmissed
+ self.receiving_twoptmissed)
@property
def stats(self):
"""
Returns a dict of all stats for the player.
"""
return self._stats
def formatted_stats(self):
"""
Returns a roughly-formatted string of all statistics for this player.
"""
s = []
for stat, val in self._stats.iteritems():
s.append('%s: %s' % (stat, val))
return ', '.join(s)
def _add_stats(self, stats):
for k, v in stats.iteritems():
self.__dict__[k] = self.__dict__.get(k, 0) + v
self._stats[k] = self.__dict__[k]
def _overwrite_stats(self, stats):
for k, v in stats.iteritems():
self.__dict__[k] = v
self._stats[k] = self.__dict__[k]
def __str__(self):
"""
Simply returns the player's name, e.g., "T.Brady".
"""
return self.name
def __add__(self, other):
"""
Adds two players together. Only two player objects that correspond
to the same human (i.e., GameCenter identifier) can be added together.
If two different players are added together, an assertion will
be raised.
The effect of adding two player objects simply corresponds to the
sums of all statistical values.
Note that as soon as two players have been added, the 'home' property
becomes undefined if the two operands have different values of 'home'.
"""
assert self.playerid == other.playerid
assert type(self) == type(other)
if self.home != other.home:
home = None
else:
home = self.home
new_player = self.__class__(self.playerid, self.name, home, self.team)
new_player._add_stats(self._stats)
new_player._add_stats(other._stats)
return new_player
def __sub__(self, other):
assert self.playerid == other.playerid
assert type(self) == type(other)
new_player = GamePlayerStats(self.playerid,
self.name, self.home, self.team)
new_player._add_stats(self._stats)
for bk, bv in other._stats.iteritems():
if bk not in new_player._stats: # stat was taken away? ignore.
continue
new_player._stats[bk] -= bv
if new_player._stats[bk] == 0:
del new_player._stats[bk]
else:
new_player.__dict__[bk] = new_player._stats[bk]
anydiffs = False
for k, v in new_player._stats.iteritems():
if v > 0:
anydiffs = True
break
if not anydiffs:
return None
return new_player
def __getattr__(self, name):
# If name has one of the categories as a prefix, then return
# a default value of zero
for cat in nflgame.statmap.categories:
if name.startswith(cat):
return 0
raise AttributeError
def passer_rating(self):
"""
Calculate and return the passer rating using the NFL formula. Passer
rating is calculated using a player's passing attempts, completions,
yards, touchdowns, and interceptions. Passer rating in the NFL is on a
scale from 0 to 158.3.
"""
l = [((self.passing_cmp / self.passing_att) - .3) * 5]
l.append(((self.passing_yds / self.passing_att) - 3) * .25)
l.append((self.tds / self.passing_att) * 20)
l.append(2.375 - (self.passing_ints / self.passing_att * 25))
m = []
for a in l:
if a < 0:
a = 0
m.append(a)
elif a > 2.375:
a = 2.375
m.append(a)
else:
m.append(a)
rating = round((sum(m) / 6) * 100, 1)
return rating
class GamePlayerStats (PlayerStats):
def __init__(self, playerid, name, home, team):
super(GamePlayerStats, self).__init__(playerid, name, home, team)
self.games = 1
def __add__(self, other):
new_player = super(GamePlayerStats, self).__add__(other)
new_player.games = self.games + other.games
return new_player
class PlayPlayerStats (PlayerStats):
pass
Classes
class GamePlayerStats
class GamePlayerStats (PlayerStats):
def __init__(self, playerid, name, home, team):
super(GamePlayerStats, self).__init__(playerid, name, home, team)
self.games = 1
def __add__(self, other):
new_player = super(GamePlayerStats, self).__add__(other)
new_player.games = self.games + other.games
return new_player
Ancestors (in MRO)
- GamePlayerStats
- PlayerStats
- __builtin__.object
Instance variables
var games
var guess_position
Inheritance:
PlayerStats
.guess_position
Guesses the position of this player based on the statistical categories present in this object when player meta is not present.
Note that if this resorts to a guess, then it will be more effective on aggregate data rather than data from just a single play. (e.g., if a QB runs the ball, and that's the only data available, the position returned will be RB.)
When a position is guessed, only the following positions will be returned: QB, RB, WR, DEF, K and P.
var tds
Inheritance:
PlayerStats
.tds
Returns the total number of touchdowns credited to this player across all statistical categories.
var twopta
Inheritance:
PlayerStats
.twopta
Returns the total number of two point conversion attempts for the passing, rushing and receiving categories.
var twoptm
Inheritance:
PlayerStats
.twoptm
Returns the total number of two point conversions for the passing, rushing and receiving categories.
var twoptmissed
Inheritance:
PlayerStats
.twoptmissed
Returns the total number of two point conversion failures for the passing, rushing and receiving categories.
Methods
def __init__(
self, playerid, name, home, team)
Inheritance:
PlayerStats
.__init__
Create a new Player instance with the player id (from NFL.com's GameCenter), the player's name (e.g., "T.Brady") and whether the player is playing in a home game or not.
def __init__(self, playerid, name, home, team):
super(GamePlayerStats, self).__init__(playerid, name, home, team)
self.games = 1
def formatted_stats(
self)
Inheritance:
PlayerStats
.formatted_stats
Returns a roughly-formatted string of all statistics for this player.
def formatted_stats(self):
"""
Returns a roughly-formatted string of all statistics for this player.
"""
s = []
for stat, val in self._stats.iteritems():
s.append('%s: %s' % (stat, val))
return ', '.join(s)
def has_cat(
self, cat)
Inheritance:
PlayerStats
.has_cat
def has_cat(self, cat):
for f in self._stats:
if f.startswith(cat):
return True
return False
def passer_rating(
self)
Inheritance:
PlayerStats
.passer_rating
Calculate and return the passer rating using the NFL formula. Passer rating is calculated using a player's passing attempts, completions, yards, touchdowns, and interceptions. Passer rating in the NFL is on a scale from 0 to 158.3.
def passer_rating(self):
"""
Calculate and return the passer rating using the NFL formula. Passer
rating is calculated using a player's passing attempts, completions,
yards, touchdowns, and interceptions. Passer rating in the NFL is on a
scale from 0 to 158.3.
"""
l = [((self.passing_cmp / self.passing_att) - .3) * 5]
l.append(((self.passing_yds / self.passing_att) - 3) * .25)
l.append((self.tds / self.passing_att) * 20)
l.append(2.375 - (self.passing_ints / self.passing_att * 25))
m = []
for a in l:
if a < 0:
a = 0
m.append(a)
elif a > 2.375:
a = 2.375
m.append(a)
else:
m.append(a)
rating = round((sum(m) / 6) * 100, 1)
return rating
class PlayPlayerStats
class PlayPlayerStats (PlayerStats):
pass
Ancestors (in MRO)
- PlayPlayerStats
- PlayerStats
- __builtin__.object
Instance variables
var guess_position
Inheritance:
PlayerStats
.guess_position
Guesses the position of this player based on the statistical categories present in this object when player meta is not present.
Note that if this resorts to a guess, then it will be more effective on aggregate data rather than data from just a single play. (e.g., if a QB runs the ball, and that's the only data available, the position returned will be RB.)
When a position is guessed, only the following positions will be returned: QB, RB, WR, DEF, K and P.
var tds
Inheritance:
PlayerStats
.tds
Returns the total number of touchdowns credited to this player across all statistical categories.
var twopta
Inheritance:
PlayerStats
.twopta
Returns the total number of two point conversion attempts for the passing, rushing and receiving categories.
var twoptm
Inheritance:
PlayerStats
.twoptm
Returns the total number of two point conversions for the passing, rushing and receiving categories.
var twoptmissed
Inheritance:
PlayerStats
.twoptmissed
Returns the total number of two point conversion failures for the passing, rushing and receiving categories.
Methods
def __init__(
self, playerid, name, home, team)
Inheritance:
PlayerStats
.__init__
Create a new Player instance with the player id (from NFL.com's GameCenter), the player's name (e.g., "T.Brady") and whether the player is playing in a home game or not.
def __init__(self, playerid, name, home, team):
"""
Create a new Player instance with the player id (from NFL.com's
GameCenter), the player's name (e.g., "T.Brady") and whether the
player is playing in a home game or not.
"""
self.playerid = playerid
self.name = name
self.home = home
self.team = team
self._stats = OrderedDict()
self.player = None
if self.playerid in nflgame.players:
self.player = nflgame.players[self.playerid]
def formatted_stats(
self)
Inheritance:
PlayerStats
.formatted_stats
Returns a roughly-formatted string of all statistics for this player.
def formatted_stats(self):
"""
Returns a roughly-formatted string of all statistics for this player.
"""
s = []
for stat, val in self._stats.iteritems():
s.append('%s: %s' % (stat, val))
return ', '.join(s)
def has_cat(
self, cat)
Inheritance:
PlayerStats
.has_cat
def has_cat(self, cat):
for f in self._stats:
if f.startswith(cat):
return True
return False
def passer_rating(
self)
Inheritance:
PlayerStats
.passer_rating
Calculate and return the passer rating using the NFL formula. Passer rating is calculated using a player's passing attempts, completions, yards, touchdowns, and interceptions. Passer rating in the NFL is on a scale from 0 to 158.3.
def passer_rating(self):
"""
Calculate and return the passer rating using the NFL formula. Passer
rating is calculated using a player's passing attempts, completions,
yards, touchdowns, and interceptions. Passer rating in the NFL is on a
scale from 0 to 158.3.
"""
l = [((self.passing_cmp / self.passing_att) - .3) * 5]
l.append(((self.passing_yds / self.passing_att) - 3) * .25)
l.append((self.tds / self.passing_att) * 20)
l.append(2.375 - (self.passing_ints / self.passing_att * 25))
m = []
for a in l:
if a < 0:
a = 0
m.append(a)
elif a > 2.375:
a = 2.375
m.append(a)
else:
m.append(a)
rating = round((sum(m) / 6) * 100, 1)
return rating
class Player
Player instances represent meta information about a single player. This information includes name, team, position, status, height, weight, college, jersey number, birth date, years, pro, etc.
Player information is populated from NFL.com profile pages.
class Player (object):
"""
Player instances represent meta information about a single player.
This information includes name, team, position, status, height,
weight, college, jersey number, birth date, years, pro, etc.
Player information is populated from NFL.com profile pages.
"""
def __init__(self, data):
self.player_id = data['gsis_id']
self.gsis_name = data.get('gsis_name', '')
self.full_name = data.get('full_name', '')
self.first_name = data.get('first_name', '')
self.last_name = data.get('last_name', '')
self.team = data.get('team', '')
self.position = data.get('position', '')
self.profile_id = data.get('profile_id', 0)
self.profile_url = data.get('profile_url', '')
self.uniform_number = data.get('number', 0)
self.birthdate = data.get('birthdate', '')
self.college = data.get('college', '')
self.height = data.get('height', '')
self.weight = data.get('weight', '')
self.years_pro = data.get('years_pro', 0)
self.status = data.get('status', '')
# API backwards compatibility.
self.gsis_id = self.player_id
self.playerid = self.player_id
self.name = self.full_name
self.number = self.uniform_number
def stats(self, year, week=None):
games = nflgame.games(year, week)
players = list(nflgame.combine(games).filter(playerid=self.playerid))
if len(players) == 0:
return GamePlayerStats(self.player_id, self.gsis_name,
None, self.team)
return players[0]
def plays(self, year, week=None):
plays = []
games = nflgame.games(year, week)
for g in games:
plays += filter(lambda p: p.has_player(self.playerid),
list(g.drives.plays()))
return nflgame.seq.GenPlays(plays)
def __str__(self):
return '%s (%s, %s)' % (self.name, self.position, self.team)
Ancestors (in MRO)
- Player
- __builtin__.object
Instance variables
var birthdate
var college
var first_name
var full_name
var gsis_id
var gsis_name
var height
var last_name
var name
var number
var player_id
var playerid
var position
var profile_id
var profile_url
var status
var team
var uniform_number
var weight
var years_pro
Methods
def __init__(
self, data)
def __init__(self, data):
self.player_id = data['gsis_id']
self.gsis_name = data.get('gsis_name', '')
self.full_name = data.get('full_name', '')
self.first_name = data.get('first_name', '')
self.last_name = data.get('last_name', '')
self.team = data.get('team', '')
self.position = data.get('position', '')
self.profile_id = data.get('profile_id', 0)
self.profile_url = data.get('profile_url', '')
self.uniform_number = data.get('number', 0)
self.birthdate = data.get('birthdate', '')
self.college = data.get('college', '')
self.height = data.get('height', '')
self.weight = data.get('weight', '')
self.years_pro = data.get('years_pro', 0)
self.status = data.get('status', '')
# API backwards compatibility.
self.gsis_id = self.player_id
self.playerid = self.player_id
self.name = self.full_name
self.number = self.uniform_number
def plays(
self, year, week=None)
def plays(self, year, week=None):
plays = []
games = nflgame.games(year, week)
for g in games:
plays += filter(lambda p: p.has_player(self.playerid),
list(g.drives.plays()))
return nflgame.seq.GenPlays(plays)
def stats(
self, year, week=None)
def stats(self, year, week=None):
games = nflgame.games(year, week)
players = list(nflgame.combine(games).filter(playerid=self.playerid))
if len(players) == 0:
return GamePlayerStats(self.player_id, self.gsis_name,
None, self.team)
return players[0]
class PlayerDefense
class PlayerDefense (Player):
def __init__(self, team):
self.playerid = None
self.name = team
self.team = team
self.position = 'DEF'
def stats(self, year, week=None):
assert False, 'Cannot be called on a defense.'
def plays(self, year, week=None):
assert False, 'Cannot be called on a defense.'
def __str__(self):
return '%s Defense' % self.team
Ancestors (in MRO)
- PlayerDefense
- Player
- __builtin__.object
Instance variables
Methods
def __init__(
self, team)
def __init__(self, team):
self.playerid = None
self.name = team
self.team = team
self.position = 'DEF'
def plays(
self, year, week=None)
def plays(self, year, week=None):
assert False, 'Cannot be called on a defense.'
def stats(
self, year, week=None)
def stats(self, year, week=None):
assert False, 'Cannot be called on a defense.'
class PlayerStats
Player represents a single player and all of his statistical categories. Every player has 'playerid', 'name' and 'home' fields. Additionally, depending upon which statistical categories that player was involved in for the game, he'll have properties such as 'passing_tds', 'rushing_yds', 'defense_int' and 'kicking_fgm'.
In order to know whether a paricular player belongs to a statical category, you may use the filtering methods of a player sequence or alternatively, use the has_cat method with arguments like 'passing', 'rushing', 'kicking', etc. (A player sequence in this case would be an instance of GenPlayerStats.)
You may also inspect whether a player has a certain property by using the special dict attribute. For example::
if 'passing_yds' in player.__dict__:
# Do something with player.passing_yds
class PlayerStats (object):
"""
Player represents a single player and all of his statistical categories.
Every player has 'playerid', 'name' and 'home' fields.
Additionally, depending upon which statistical categories that player
was involved in for the game, he'll have properties such as 'passing_tds',
'rushing_yds', 'defense_int' and 'kicking_fgm'.
In order to know whether a paricular player belongs to a statical category,
you may use the filtering methods of a player sequence or alternatively,
use the has_cat method with arguments like 'passing', 'rushing', 'kicking',
etc. (A player sequence in this case would be an instance of
GenPlayerStats.)
You may also inspect whether a player has a certain property by using
the special __dict__ attribute. For example::
if 'passing_yds' in player.__dict__:
# Do something with player.passing_yds
"""
def __init__(self, playerid, name, home, team):
"""
Create a new Player instance with the player id (from NFL.com's
GameCenter), the player's name (e.g., "T.Brady") and whether the
player is playing in a home game or not.
"""
self.playerid = playerid
self.name = name
self.home = home
self.team = team
self._stats = OrderedDict()
self.player = None
if self.playerid in nflgame.players:
self.player = nflgame.players[self.playerid]
def has_cat(self, cat):
for f in self._stats:
if f.startswith(cat):
return True
return False
@property
def guess_position(self):
"""
Guesses the position of this player based on the statistical
categories present in this object when player meta is not
present.
Note that if this resorts to a guess, then it will be more
effective on aggregate data rather than data from just a
single play. (e.g., if a QB runs the ball, and that's the
only data available, the position returned will be RB.)
When a position is guessed, only the following positions will
be returned: QB, RB, WR, DEF, K and P.
"""
# Look for the player meta first. Duh.
if self.player is not None:
return self.player.position
stats = [
(self.passing_att, 'QB'),
(self.rushing_att, 'RB'),
(self.receiving_tar, 'WR'),
(self.defense_tkl, 'DEF'),
(self.defense_ast, 'DEF'),
(self.kicking_tot, 'K'),
(self.kicking_fga, 'K'),
(self.punting_tot, 'P'),
]
return sorted(stats, reverse=True)[0][1]
@property
def tds(self):
"""
Returns the total number of touchdowns credited to this player across
all statistical categories.
"""
n = 0
for f, v in self.__dict__.iteritems():
if f.endswith('tds'):
n += v
return n
@property
def twopta(self):
"""
Returns the total number of two point conversion attempts for
the passing, rushing and receiving categories.
"""
return (self.passing_twopta
+ self.rushing_twopta
+ self.receiving_twopta)
@property
def twoptm(self):
"""
Returns the total number of two point conversions for
the passing, rushing and receiving categories.
"""
return (self.passing_twoptm
+ self.rushing_twoptm
+ self.receiving_twoptm)
@property
def twoptmissed(self):
"""
Returns the total number of two point conversion failures for
the passing, rushing and receiving categories.
"""
return (self.passing_twoptmissed
+ self.rushing_twoptmissed
+ self.receiving_twoptmissed)
@property
def stats(self):
"""
Returns a dict of all stats for the player.
"""
return self._stats
def formatted_stats(self):
"""
Returns a roughly-formatted string of all statistics for this player.
"""
s = []
for stat, val in self._stats.iteritems():
s.append('%s: %s' % (stat, val))
return ', '.join(s)
def _add_stats(self, stats):
for k, v in stats.iteritems():
self.__dict__[k] = self.__dict__.get(k, 0) + v
self._stats[k] = self.__dict__[k]
def _overwrite_stats(self, stats):
for k, v in stats.iteritems():
self.__dict__[k] = v
self._stats[k] = self.__dict__[k]
def __str__(self):
"""
Simply returns the player's name, e.g., "T.Brady".
"""
return self.name
def __add__(self, other):
"""
Adds two players together. Only two player objects that correspond
to the same human (i.e., GameCenter identifier) can be added together.
If two different players are added together, an assertion will
be raised.
The effect of adding two player objects simply corresponds to the
sums of all statistical values.
Note that as soon as two players have been added, the 'home' property
becomes undefined if the two operands have different values of 'home'.
"""
assert self.playerid == other.playerid
assert type(self) == type(other)
if self.home != other.home:
home = None
else:
home = self.home
new_player = self.__class__(self.playerid, self.name, home, self.team)
new_player._add_stats(self._stats)
new_player._add_stats(other._stats)
return new_player
def __sub__(self, other):
assert self.playerid == other.playerid
assert type(self) == type(other)
new_player = GamePlayerStats(self.playerid,
self.name, self.home, self.team)
new_player._add_stats(self._stats)
for bk, bv in other._stats.iteritems():
if bk not in new_player._stats: # stat was taken away? ignore.
continue
new_player._stats[bk] -= bv
if new_player._stats[bk] == 0:
del new_player._stats[bk]
else:
new_player.__dict__[bk] = new_player._stats[bk]
anydiffs = False
for k, v in new_player._stats.iteritems():
if v > 0:
anydiffs = True
break
if not anydiffs:
return None
return new_player
def __getattr__(self, name):
# If name has one of the categories as a prefix, then return
# a default value of zero
for cat in nflgame.statmap.categories:
if name.startswith(cat):
return 0
raise AttributeError
def passer_rating(self):
"""
Calculate and return the passer rating using the NFL formula. Passer
rating is calculated using a player's passing attempts, completions,
yards, touchdowns, and interceptions. Passer rating in the NFL is on a
scale from 0 to 158.3.
"""
l = [((self.passing_cmp / self.passing_att) - .3) * 5]
l.append(((self.passing_yds / self.passing_att) - 3) * .25)
l.append((self.tds / self.passing_att) * 20)
l.append(2.375 - (self.passing_ints / self.passing_att * 25))
m = []
for a in l:
if a < 0:
a = 0
m.append(a)
elif a > 2.375:
a = 2.375
m.append(a)
else:
m.append(a)
rating = round((sum(m) / 6) * 100, 1)
return rating
Ancestors (in MRO)
- PlayerStats
- __builtin__.object
Instance variables
var guess_position
Guesses the position of this player based on the statistical categories present in this object when player meta is not present.
Note that if this resorts to a guess, then it will be more effective on aggregate data rather than data from just a single play. (e.g., if a QB runs the ball, and that's the only data available, the position returned will be RB.)
When a position is guessed, only the following positions will be returned: QB, RB, WR, DEF, K and P.
var home
var name
var player
var playerid
var stats
Returns a dict of all stats for the player.
var tds
Returns the total number of touchdowns credited to this player across all statistical categories.
var team
var twopta
Returns the total number of two point conversion attempts for the passing, rushing and receiving categories.
var twoptm
Returns the total number of two point conversions for the passing, rushing and receiving categories.
var twoptmissed
Returns the total number of two point conversion failures for the passing, rushing and receiving categories.
Methods
def __init__(
self, playerid, name, home, team)
Create a new Player instance with the player id (from NFL.com's GameCenter), the player's name (e.g., "T.Brady") and whether the player is playing in a home game or not.
def __init__(self, playerid, name, home, team):
"""
Create a new Player instance with the player id (from NFL.com's
GameCenter), the player's name (e.g., "T.Brady") and whether the
player is playing in a home game or not.
"""
self.playerid = playerid
self.name = name
self.home = home
self.team = team
self._stats = OrderedDict()
self.player = None
if self.playerid in nflgame.players:
self.player = nflgame.players[self.playerid]
def formatted_stats(
self)
Returns a roughly-formatted string of all statistics for this player.
def formatted_stats(self):
"""
Returns a roughly-formatted string of all statistics for this player.
"""
s = []
for stat, val in self._stats.iteritems():
s.append('%s: %s' % (stat, val))
return ', '.join(s)
def has_cat(
self, cat)
def has_cat(self, cat):
for f in self._stats:
if f.startswith(cat):
return True
return False
def passer_rating(
self)
Calculate and return the passer rating using the NFL formula. Passer rating is calculated using a player's passing attempts, completions, yards, touchdowns, and interceptions. Passer rating in the NFL is on a scale from 0 to 158.3.
def passer_rating(self):
"""
Calculate and return the passer rating using the NFL formula. Passer
rating is calculated using a player's passing attempts, completions,
yards, touchdowns, and interceptions. Passer rating in the NFL is on a
scale from 0 to 158.3.
"""
l = [((self.passing_cmp / self.passing_att) - .3) * 5]
l.append(((self.passing_yds / self.passing_att) - 3) * .25)
l.append((self.tds / self.passing_att) * 20)
l.append(2.375 - (self.passing_ints / self.passing_att * 25))
m = []
for a in l:
if a < 0:
a = 0
m.append(a)
elif a > 2.375:
a = 2.375
m.append(a)
else:
m.append(a)
rating = round((sum(m) / 6) * 100, 1)
return rating


{% import "bootstrap/wtf.html" as wtf %} {% import "utils/_my_wtf.html" as mywtf %} {% macro render_simple_errors(form) -%} {% if form.errors %}


gsecdump.exe was developed by Johannes Gumbel (Truesec, 2009). It is used to
dump hashes from active logon sessions and from SAM and AD (among others).


Learning with Different Output Space
- Binary Classification
- credit approve/disapprove
- email spam/non-spam
- patient sick/not sick
- ad profitable/not profitable
- answer correct/incorrect
- Muticlass Classification
- written digits: 0, 1, …, 9
- pictures: apple, orange, strawberry
- emails: spam, primary, social, promotion, update(Google)
- recognition
- Regression
- Structured Learning
- natural language process (NLP)
- protein data
- speech data
Learning with Different Data Label
- Supervised
- Unsupervised
- articles to topics
- consumer profiles to consumer groups
- density estimation
- outlier detection
- Semi-supervised
- Reinforcement Learning
- training ad system
- black jack agent
Learning with Different Protocol
- Batch Learning (duck feeding)
- Online Learning (passive sequdential)
- Active Learning (question asking)
- concrete
- Raw Features
- Abstract Features


PERF\-REPORT
Section: Misc. Reference Manual Pages (1)Updated: 10/16/2012
Index Return to Main Contents
NAME
perf-report - Read perf.data (created by perf record) and display the profileSYNOPSIS
perf report [-i <file> | --input=file]
DESCRIPTION
This command displays the performance counter profile information recorded via perf record.
OPTIONS
-i, --input=
- Input file name. (default: perf.data unless stdin is a fifo)
-v, --verbose
- Be more verbose. (show symbol address, etc)
-d, --dsos=
- Only consider symbols in these dsos. CSV that understands m[blue]file://filenamem[] entries.
-n, --show-nr-samples
- Show the number of samples for each symbol
--showcpuutilization
- Show sample percentage for different cpu modes.
-T, --threads
- Show per-thread event counters
-c, --comms=
- Only consider symbols in these comms. CSV that understands m[blue]file://filenamem[] entries.
-S, --symbols=
- Only consider these symbols. CSV that understands m[blue]file://filenamem[] entries.
-U, --hide-unresolved
- Only display entries resolved to a symbol.
-s, --sort=
- Sort by key(s): pid, comm, dso, symbol, parent.
-p, --parent=<regex>
- regex filter to identify parent, see: --sort parent
-x, --exclude-other
- Only display entries with parent-match.
-w, --column-widths=<width[,width...]>
- Force each column width to the provided list, for large terminal readability.
-t, --field-separator=
- Use a special separator character and doncqt pad with spaces, replacing all occurrences of this separator in symbol names (and other output) with a . character, that thus itcqs the only non valid separator.
-D, --dump-raw-trace
- Dump raw trace in ASCII.
-g [type,min[,limit],order], --call-graph
-
Display call chains using type, min percent threshold, optional print limit and order. type can be either:
- * flat: single column, linear exposure of call chains.
- * graph: use a graph tree, displaying absolute overhead rates.
-
*
fractal: like graph, but displays relative rates. Each branch of the tree is considered as a new profiled object.
-
order can be either: - callee: callee based call graph. - caller: inverted caller based call graph.
-
Default: fractal,0.5,callee.
-
-G, --inverted
- alias for inverted caller based call graph.
--pretty=<key>
- Pretty printing style. key: normal, raw
--stdio
- Use the stdio interface.
--tui
- Use the TUI interface, that is integrated with annotate and allows zooming into DSOs or threads, among other features. Use of --tui requires a tty, if one is not present, as when piping to other commands, the stdio interface is used.
-k, --vmlinux=<file>
- vmlinux pathname
--kallsyms=<file>
- kallsyms pathname
-m, --modules
- Load module symbols. WARNING: This should only be used with -k and a LIVE kernel.
-f, --force
- Doncqt complain, do it.
--symfs=<directory>
- Look for files with symbols relative to this directory.
-C, --cpu
- Only report samples for the list of CPUs provided. Multiple CPUs can be provided as a comma-separated list with no space: 0,1. Ranges of CPUs are specified with -: 0-2. Default is to report samples on all CPUs.
-M, --disassembler-style=
- Set disassembler style for objdump.
--source
- Interleave source code with assembly code. Enabled by default, disable with --no-source.
--asm-raw
- Show raw instruction encoding of assembly instructions.
--show-total-period
- Show a column with the sum of periods.
-I, --show-info
- Display extended information about the perf.data file. This adds information which may be very large and thus may clutter the display. It currently includes: cpu and numa topology of the host system.
SEE ALSO
perf-stat(1), perf-annotate(1)
Index
This document was created by man2html, using the manual pages.
Time: 05:29:07 GMT, December 24, 2015


suitcss
widths
.Grid-cell-1
.Grid-cell-11
.Grid-cell-2
.Grid-cell-10
.Grid-cell-3
.Grid-cell-9
.Grid-cell-4
.Grid-cell-8
.Grid-cell-5
.Grid-cell-7
.Grid-cell-6
.Grid-cell-6
.Grid-cell-7
.Grid-cell-5
.Grid-cell-8
.Grid-cell-4
.Grid-cell-9
.Grid-cell-3
.Grid-cell-10
.Grid-cell-2
.Grid-cell-11
.Grid-cell-1
wrapping
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
.Grid-cell-4
nesting
.Grid-cell-6
.Grid-cell-6
.Grid-cell-6
.Grid-cell-6
.Grid-cell-6
.Grid-cell-6
flush
.Grid-cell-3
.Grid-cell-3
.Grid-cell-3
.Grid-cell-3
heights
.Grid-cell-3
.Grid-cell-3
tall column
.Grid-cell-3
.Grid-cell-3
tall column


This field is required.
This field is required.
This field is required.
Not a valid number.
Minimum duration must be at least one day.
This field is required.
Not a valid number.
Minimum Time Period must be at least not negative.
This passing percentage not negative.


--- layout: post authors: ["Greg Wilson"] title: Code of Conduct date: 2012-12-21 time: "09:00:00" tags: ["Community"] ---
Software Carpentry is dedicated to providing a harassment-free learning experience for everyone, regardless of gender, sexual orientation, disability, physical appearance, body size, race, religion, or choice of text editor. Following the example of the Python Software Foundation, PyCon Canada, and other community members, we have therefore decided to adopt a code of conduct to make it clear what kinds of language and behavior are and are not acceptable at our events. If you'd like to know more, we couldn't possibly write anything better than this blog post by the Python Software Foundation's Jesse Noller.


ADMINISTRATION
This School Is Administered / Managed In The Following Way
the school is been headed by a Principal whom is in charge of the running of the School, assisted by the
Dean and the Deputy Dean, these are followed by Head of Sections (Head Of Departments) then the Tutors/Teachers, non teaching staffs,
students representatives.
for a list of our staffs and the current form Masters and Assistants of the various classes click here
the chart below shows the organogram of the schools management
SCHOOLS ADMINISTRATIVE ORGANOGRAM


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Manage your account and access personalized content.
Access your cloud dashboard, manage orders, and more.
Posted 6/08/17 // Tags: Java, Development Tools, DevOps // Headlines Archive
Released 2017/09/21
Released 2017/07/26


This is a fancy name for what is actually a very simple thing: combining variables and constants inside a string.
Clear out all the code you just wrote and leave only this:
var name = "Tim McGraw"
If we wanted to print out a message to the user that included their name, string interpolation is what makes that easy: you just write a backslash, then an open parenthesis, then your code, then a close parenthesis, like this:
var name = "Tim McGraw"
"Your name is \(name)"
The results pane will now show "Your name is Tim McGraw" all as one string, because string interpolation combined the two for us.
Now, we could have written that using the +
operator, like this:
var name = "Tim McGraw"
"Your name is " + name
but that's not as efficient, particularly if you're combining multiple variables together. In addition, string interpolation in Swift is smart enough to be able to handle a variety of different data types automatically. For example:
var name = "Tim McGraw"
var age = 25
var latitude = 36.166667
"Your name is \(name), your age is \(age), and your latitude is \(latitude)"
Doing that using +
is much more difficult, because Swift doesn't let you add integers and doubles to a string.
At this point your result may no longer fit in the results pane, so either resize your window or hover over the result and click the + button that appears to have it shown inline.
One of the powerful features of string interpolation is that everything between \(
and )
can actually be a full Swift expression. For example, you can do mathematics in there using operators, like this:
var age = 25
"You are \(age) years old. In another \(age) years you will be \(age * 2)."


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/le_nhds_of_cauchy_adhp.html ---
What does adhp
stand for in this name?
lemma le_nhds_of_cauchy_adhp {f : filter α} {x : α} (hf : cauchy f) (adhs : f ⊓ 𝓝 x ≠ ⊥) : f ≤ 𝓝 x
adherence is French for closure I think? Did Patrick write the lemma?
adherent point, I would guess
I've also heard this called a cluster point, if I'm not mistaken
https://en.wikipedia.org/wiki/Filter_(mathematics)#Clustering
Moreover, every cluster point of a Cauchy filter is a limit point.
I think this is the lemma in question
That is the kind of sentence I would like to see as leandoc on the theorem
Thank you for the explanation. I'll PR a docstring together with my simplification of the proof.
I didn't write that lemma, but the name definitely stands for "adherent point". I don't think anyone cares strongly about that name. Yury's struggle proves that we need at least a good docstring, maybe a better name if someone finds one.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


{% extends "base.html" %} {% block content %}
Sourcing, normalizing and quality checking a set of key reference and indicator datasets such as country codes, currencies, GDP and population.
All datasets provided in a standardized form and can be accessed in bulk as CSV together with a simple JSON schema.
All data is in data packages and is versioned using git so all changes are visible and data can be collaboratively maintained.
We need help suggesting, preparing and maintaining a set of "core" datasets as Data Packages. Note that:


Yeshivat Simchat HaTorah of Beit Shemesh offers a comprehensive and inspiring curriculum that provides a serious Limudei Kodesh education coupled with an emphasis on middot and derech eretz. In addition, our students will receive a superb education that will allow them to master the bagruyot. Our goal is to provide a well-rounded high school education that is easily accessible and affordable, while also broadening the boys’ personal perspectives and sense of obligation towards their own community and to the greater Klal Yisrael.
Our Limudei Kodesh curriculum, which includes Gemara, Chumash, Navi and Halacha, will instill within our students a love for Hashem, an enthusiasm for the study and practice of Torah and a deep appreciation for עם ישראל and ארץ ישראל.
Our Limudei Chol curriculum is in accordance with the standards set forth by the Education Ministry of Israel. Our curriculum includes English, Mathematics, History, Science, and life skills classes. In addition, the students will be required to take the bagruyot. Our vocational training will provide the boys with hands-on experience, enabling them to learn a skilled trade and expose them to the rapidly expanding hi-tech field.
View Sample Schedule
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|---|
| 7:50 | Shacharit | Shacharit | Shacharit | Shacharit | Shacharit | Shacharit |
| 9:30-10:30 | Gemara Be'iyun | Gemara Be'iyun | Gemara Be'iyun | Gemara Be'iyun | Gemara Be'iyun | Parsha Shiur |
| 10:50-11:40 | Halacha | Halacha | Halacha | Halacha | Halacha | Chessed |
| 12:00-13:00 | Chavruta Seder | Chavruta Seder | Chavruta Seder | Chavruta Seder | Chavruta Seder | |
| 13:00 | Lunch | Lunch | Lunch | Lunch | Lunch | |
| 13:20 | Mincha | Mincha | Mincha | Mincha | Mincha | |
| 14:00-14:50 | Mussar | Gemara Bekiyut | Gemara Bekiyut | Gemara Bekiyut | Physical Education | |
| 15:00-15:30 | Preparative Seder | Preparative Seder | Preparative Seder | Preparative Seder | Preparative Seder | |
| 15:40-17:15 | Geography | Electives | English | Civics | Computers | |
| 17:20-18:55 | English | Math | Hebrew | Math | Science |


Viewpoint
Who do they think they are? Wnt-responsive cells reveal their family trees
Breast Cancer Research 2012, 14:327 (10 December 2012)
Subscription required
All research articles in Breast Cancer Research are open access. The journal also publishes a range of other articles that are available to subscribers.


1 IN DREAMS
Reviewed by Harvey Karten, Ph.D. Dreamworks Director: Neil Jordan Writer: Bruce Robinson & Neil Jordan Cast: Annette Bening, Aidan Quinn, Stephen Rea, Robert Downey Jr., Paul Guilfoyle
Hamlet contemplates suicide but gives pause when he questions "what dreams may come when we have shuffled off this mortal coil." For him, the Long Sleep might not end "the heartache and the thousand natural shocks that flesh is heir to." What happens when the decision to live does not solve the problem--when the ills we bear include the most dreadful nightmares that flow on unceasingly? Claire Cooper (Annette Bening) faces this plight in Neil Jordan's Hitchcockian, adult horror tale, "In Dreams," a piece that is edited (by Tony Lawson) is such an abrupt and choppy style that the audience cannot avoid being bewildered by the narrative line. Strange to say, this disjointed mess was directed by a man whose credits include the sensational "The Butcher Boy."
"In Dreams" borrows from the usual conventions of the horror tale--the individual with psychic powers, the concerned spouse, disbelieving authorities, the deranged killer whose loneliness leads him to serial murder. Writer-director Jordan winnows a measure of innovation but lets it float away like the detritus of a flood which destroys his New England town during the 1960s. The screenplay, which borders on incoherence, occasionally draws inappropriate laughter from the audience. Still, Annette Bening's fleshed-out performance dares to give some integrity to the film, as she carries virtually the full load of the fable in the role of a woman whose recurring nightmares cause disbelief from the police, skepticism from the medical profession, and an unhealthy instability to her marriage.
The fragmented nature of the film begins with the opening scene, with its images of divers searching the wreckage of a village which had been evacuated, rendered into a ghost town by a flood which buried everything in its path. Neil Jordan cuts to a present-day vista, a spacious New England home at the foot of a forest, unnecessarily concealing its connection to the flood until midway into the movie. Until then we wonder whether he has patched together two altogether distinct tales whose only commonality is its Massachusetts whereabouts.
The camera hones in on Claire Cooper whose idyllic life as the wife of 747 pilot Paul (Aidan Quinn) and loving relationship with her nine-year-old daughter Rebecca (Katie Sagona) are sullied by a succession of disturbing dreams. She observes an innocent little girl walking hand-in-hand to an unknown destination with a man whose intentions appear to Claire to be malevolent. Her husband, who is home only sporadically between flights to Australia and other parts, is annoyed with Claire's obsessiveness, complains that she is "not there" when he returns, and who feels justified in having a near-affair a woman he has met Down Under. As Claire's recurrent nightmares convince her that her daughter is in mortal danger and that some lunatic killer is feeding her his own fantasies, she goes off the deep end, and is hospitalized. Soon she receives counseling from an incredulous neurosurgeon, Dr. Stevens (Dennis Boutskikaris), a psychiatrist, Dr. Silverman (Stephen Rea), and police detective Jack Kay (Paul Guilfoyle). The diabolical killer Vivian Thompson, a character perhaps inspired by Alfred Hitchcock's Norman Bates, does not appear until the final one-third of the film, giving cinematographer Darius Khondji the chance to portray some startling images of hospital rooms, foggy groves, and a haunted house, as Claire migrates through a sequence of nightmares so steadily that we scarcely know when she is awake and when she is unanchored.
If Bruce Robinson and Neil Jordan's screenplay were as carefully structured as the movie's visuals are astonishing, "In Dreams" would have been that rare find: a genuinely adult contribution to the genre of horror now dominated by adolescent piffles like "I Still Know What You Did Last Summer." Bening's performance as a woman driven to psychosis by a madman's exploits and Robert Downey Jr.'s achievement as a Norman Bates without that psychopath's charming, other self would have carried the show. But Rosco, a burly nurse whose job consists primarily in restraining raving patients at the psychiatric facility, mirrors the feelings of the audience. When the authorities explain the whole situation to him, he looks puzzled and replies, "I'm lost."
Rated R. Running Time: 112 minutes. (C) 1999 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


| Year | school | school/University | Major | |
|---|---|---|---|---|
| 2002 | SLC | Adarsh secondary school | Opt Math | |
| 2006 | 10+2 | Adarsh Multiple Campus | Account | |
| 2011 | Bacher Degree | RR Campus | Sociyology | |
| 2017 | Master Degree | Sindhuli Multiple | Sociyology |
| period | Office>/th> | position |
|---|---|---|
| June 2017-Oct 2018 | SREE Sindhuli NGO | Program Coordinator |


SapphireDb includes methods to get information about the collection.
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Check access
ng-sapphiredb provides multiple methods to check if the user can access the collection


Module: external.tifffile
¶
|
Return image data from TIFF file(s) as numpy array. |
|
Write image data to TIFF file. |
|
Plot n-dimensional images using matplotlib.pyplot. |
Return dict with keys from keys|keyvals and values from kwargs|keyvals. |
|
|
Read image and metadata from TIFF, STK, LSM, and FluoView files. |
Sequence of image files. |
|
|
Write image data to TIFF file. |
imread¶
-
skimage.external.tifffile.
imread
(files, **kwargs)[source]¶ Return image data from TIFF file(s) as numpy array.
Refer to the TiffFile class and member functions for documentation.
- Parameters
- filesstr, binary stream, or sequence
File name, seekable binary stream, glob pattern, or sequence of file names.
- kwargsdict
Parameters ‘multifile’, ‘multifile_close’, ‘pages’, ‘fastij’, and ‘is_ome’ are passed to the TiffFile class. The ‘pattern’ parameter is passed to the TiffSequence class. Other parameters are passed to the asarray functions. The first image series is returned if no arguments are provided.
Examples
>>> imsave('temp.tif', numpy.random.rand(3, 4, 301, 219)) >>> im = imread('temp.tif', key=0) >>> im.shape (4, 301, 219) >>> ims = imread(['temp.tif', 'temp.tif']) >>> ims.shape (2, 3, 4, 301, 219)
imsave¶
-
skimage.external.tifffile.
imsave
(file, data, **kwargs)[source]¶ Write image data to TIFF file.
Refer to the TiffWriter class and member functions for documentation.
- Parameters
- filestr or binary stream
File name or writable binary stream, such as a open file or BytesIO.
- dataarray_like
Input image. The last dimensions are assumed to be image depth, height, width, and samples.
- kwargsdict
Parameters ‘append’, ‘byteorder’, ‘bigtiff’, ‘software’, and ‘imagej’, are passed to the TiffWriter class. Parameters ‘photometric’, ‘planarconfig’, ‘resolution’, ‘compress’, ‘colormap’, ‘tile’, ‘description’, ‘datetime’, ‘metadata’, ‘contiguous’ and ‘extratags’ are passed to the TiffWriter.save function.
Examples
>>> data = numpy.random.rand(2, 5, 3, 301, 219) >>> imsave('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})
imshow¶
-
skimage.external.tifffile.
imshow
(data, title=None, vmin=0, vmax=None, cmap=None, bitspersample=None, photometric='rgb', interpolation=None, dpi=96, figure=None, subplot=111, maxdim=8192, **kwargs)[source]¶ Plot n-dimensional images using matplotlib.pyplot.
Return figure, subplot and plot axis. Requires pyplot already imported from matplotlib import pyplot.
- Parameters
- bitspersampleint or None
Number of bits per channel in integer RGB images.
- photometric{‘miniswhite’, ‘minisblack’, ‘rgb’, or ‘palette’}
The color space of the image data.
- titlestr
Window and subplot title.
- figurematplotlib.figure.Figure (optional).
Matplotlib to use for plotting.
- subplotint
A matplotlib.pyplot.subplot axis.
- maxdimint
maximum image width and length.
- kwargsoptional
Arguments for matplotlib.pyplot.imshow.
parse_kwargs¶
-
skimage.external.tifffile.
parse_kwargs
(kwargs, *keys, **keyvalues)[source]¶ Return dict with keys from keys|keyvals and values from kwargs|keyvals.
Existing keys are deleted from kwargs.
>>> kwargs = {'one': 1, 'two': 2, 'four': 4} >>> kwargs2 = parse_kwargs(kwargs, 'two', 'three', four=None, five=5) >>> kwargs == {'one': 1} True >>> kwargs2 == {'two': 2, 'four': 4, 'five': 5} True
TiffFile
¶
-
class
skimage.external.tifffile.
TiffFile
(arg, name=None, offset=None, size=None, multifile=True, multifile_close=True, pages=None, fastij=True, is_ome=None)[source]¶ Bases:
object
Read image and metadata from TIFF, STK, LSM, and FluoView files.
TiffFile instances must be closed using the ‘close’ method, which is automatically called when using the ‘with’ context manager.
Examples
>>> with TiffFile('temp.tif') as tif: ... data = tif.asarray() ... data.shape (5, 301, 219)
- Attributes
- pageslist of TiffPage
All TIFF pages in file.
- serieslist of TiffPageSeries
TIFF pages with compatible shapes and types.
- micromanager_metadata: dict
Extra MicroManager non-TIFF metadata in the file, if exists.
- All attributes are read-only.
-
__init__
(self, arg, name=None, offset=None, size=None, multifile=True, multifile_close=True, pages=None, fastij=True, is_ome=None)[source]¶ Initialize instance from file.
- Parameters
- argstr or open file
Name of file or open file object. The file objects are closed in TiffFile.close().
- namestr
Optional name of file in case ‘arg’ is a file handle.
- offsetint
Optional start position of embedded file. By default this is the current file position.
- sizeint
Optional size of embedded file. By default this is the number of bytes from the ‘offset’ to the end of the file.
- multifilebool
If True (default), series may include pages from multiple files. Currently applies to OME-TIFF only.
- multifile_closebool
If True (default), keep the handles of other files in multifile series closed. This is inefficient when few files refer to many pages. If False, the C runtime may run out of resources.
- pagessequence of int
Indices of the pages to read. If None (default) all pages are read. Can be used to read only the first page with pages=[0]. Specifying pages might invalidate series based on metadata.
- fastijbool
If True (default), try to use only the metadata from the first page of ImageJ files. Significantly speeds up loading movies with thousands of pages.
- is_omebool
If False, disable processing of OME-XML metadata.
-
asarray
(self, key=None, series=None, memmap=False, tempdir=None)[source]¶ Return image data from multiple TIFF pages as numpy array.
By default the first image series is returned.
- Parameters
- keyint, slice, or sequence of page indices
Defines which pages to return as array.
- seriesint or TiffPageSeries
Defines which series of pages to return as array.
- memmapbool
If True, return an read-only array stored in a binary file on disk if possible. The TIFF file is used if possible, else a temporary file is created.
- tempdirstr
The directory where the memory-mapped file will be created.
-
property
filehandle
¶ Return file handle.
-
property
filename
¶ Return name of file handle.
-
fstat
¶ Lazy object attribute whose value is computed on first access.
-
is_bigtiff
¶ Lazy object attribute whose value is computed on first access.
-
is_fei
¶ Lazy object attribute whose value is computed on first access.
-
is_fluoview
¶ Lazy object attribute whose value is computed on first access.
-
is_imagej
¶ Lazy object attribute whose value is computed on first access.
-
is_indexed
¶ Lazy object attribute whose value is computed on first access.
-
is_lsm
¶ Lazy object attribute whose value is computed on first access.
-
is_mdgel
¶ Lazy object attribute whose value is computed on first access.
-
is_mediacy
¶ Lazy object attribute whose value is computed on first access.
-
is_micromanager
¶ Lazy object attribute whose value is computed on first access.
-
is_nih
¶ Lazy object attribute whose value is computed on first access.
-
is_ome
¶ Lazy object attribute whose value is computed on first access.
-
is_rgb
¶ Lazy object attribute whose value is computed on first access.
-
is_scn
¶ Lazy object attribute whose value is computed on first access.
-
is_sem
¶ Lazy object attribute whose value is computed on first access.
-
is_stk
¶ Lazy object attribute whose value is computed on first access.
-
is_tvips
¶ Lazy object attribute whose value is computed on first access.
-
is_vista
¶ Lazy object attribute whose value is computed on first access.
-
series
¶ Lazy object attribute whose value is computed on first access.
TiffSequence
¶
-
class
skimage.external.tifffile.
TiffSequence
(files, imread=<class 'skimage.external.tifffile.tifffile.TiffFile'>, pattern='axes', *args, **kwargs)[source]¶ Bases:
object
Sequence of image files.
The data shape and dtype of all files must match.
- Attributes
- fileslist
List of file names.
- shapetuple
Shape of image sequence.
- axesstr
Labels of axes in shape.
-
__init__
(self, files, imread=<class 'skimage.external.tifffile.tifffile.TiffFile'>, pattern='axes', *args, **kwargs)[source]¶ Initialize instance from multiple files.
- Parameters
- filesstr, or sequence of str
Glob pattern or sequence of file names. Binary streams are not supported.
- imreadfunction or class
Image read function or class with asarray function returning numpy array from single file.
- patternstr
Regular expression pattern that matches axes names and sequence indices in file names. By default this matches Olympus OIF and Leica TIFF series.
-
asarray
(self, memmap=False, tempdir=None, *args, **kwargs)[source]¶ Read image data from all files and return as single numpy array.
If memmap is True, return an array stored in a binary file on disk. The args and kwargs parameters are passed to the imread function.
Raise IndexError or ValueError if image shapes do not match.
TiffWriter
¶
-
class
skimage.external.tifffile.
TiffWriter
(file, append=False, bigtiff=False, byteorder=None, software='tifffile.py', imagej=False)[source]¶ Bases:
object
Write image data to TIFF file.
TiffWriter instances must be closed using the ‘close’ method, which is automatically called when using the ‘with’ context manager.
Examples
>>> data = numpy.random.rand(2, 5, 3, 301, 219) >>> with TiffWriter('temp.tif', bigtiff=True) as tif: ... for i in range(data.shape[0]): ... tif.save(data[i], compress=6)
-
__init__
(self, file, append=False, bigtiff=False, byteorder=None, software='tifffile.py', imagej=False)[source]¶ Open a TIFF file for writing.
Existing files are overwritten by default. Use bigtiff=True when creating files larger than 2 GB.
- Parameters
- filestr, binary stream, or FileHandle
File name or writable binary stream, such as a open file or BytesIO. The file is created if it does not exist.
- appendbool
If True and ‘file’ is an existing standard TIFF file, image data and tags are appended to the file. Appending data may corrupt specifically formatted TIFF files such as LSM, STK, ImageJ, NIH, or FluoView.
- bigtiffbool
If True, the BigTIFF format is used.
- byteorder{‘<’, ‘>’}
The endianness of the data in the file. By default this is the system’s native byte order.
- softwarestr
Name of the software used to create the file. Saved with the first page in the file only.
- imagejbool
If True, write an ImageJ hyperstack compatible file. This format can handle data types uint8, uint16, or float32 and data shapes up to 6 dimensions in TZCYXS order. RGB images (S=3 or S=4) must be uint8. ImageJ’s default byte order is big endian but this implementation uses the system’s native byte order by default. ImageJ does not support BigTIFF format or LZMA compression. The ImageJ file format is undocumented.
-
TAGS
= {'bits_per_sample': 258, 'color_map': 320, 'compression': 259, 'datetime': 306, 'document_name': 269, 'extra_samples': 338, 'image_depth': 32997, 'image_description': 270, 'image_length': 257, 'image_width': 256, 'new_subfile_type': 254, 'orientation': 274, 'page_name': 285, 'photometric': 262, 'planar_configuration': 284, 'predictor': 317, 'resolution_unit': 296, 'rows_per_strip': 278, 'sample_format': 339, 'samples_per_pixel': 277, 'smax_sample_value': 341, 'smin_sample_value': 340, 'software': 305, 'strip_byte_counts': 279, 'strip_offsets': 273, 'subfile_type': 255, 'tile_byte_counts': 325, 'tile_depth': 32998, 'tile_length': 323, 'tile_offsets': 324, 'tile_width': 322, 'x_resolution': 282, 'y_resolution': 283}¶
-
TYPES
= {'2I': 5, '2i': 10, 'B': 1, 'H': 3, 'I': 4, 'Q': 16, 'b': 6, 'd': 12, 'f': 11, 'h': 8, 'i': 9, 'q': 17, 's': 2}¶
-
save
(self, data, photometric=None, planarconfig=None, tile=None, contiguous=True, compress=0, colormap=None, description=None, datetime=None, resolution=None, metadata={}, extratags=())[source]¶ Write image data and tags to TIFF file.
Image data are written in one stripe per plane by default. Dimensions larger than 2 to 4 (depending on photometric mode, planar configuration, and SGI mode) are flattened and saved as separate pages. The ‘sample_format’ and ‘bits_per_sample’ tags are derived from the data type.
- Parameters
- datanumpy.ndarray
Input image. The last dimensions are assumed to be image depth, height (length), width, and samples. If a colormap is provided, the dtype must be uint8 or uint16 and the data values are indices into the last dimension of the colormap.
- photometric{‘minisblack’, ‘miniswhite’, ‘rgb’, ‘palette’, ‘cfa’}
The color space of the image data. By default this setting is inferred from the data shape and the value of colormap. For CFA images, DNG tags must be specified in extratags.
- planarconfig{‘contig’, ‘planar’}
Specifies if samples are stored contiguous or in separate planes. By default this setting is inferred from the data shape. If this parameter is set, extra samples are used to store grayscale images. ‘contig’: last dimension contains samples. ‘planar’: third last dimension contains samples.
- tiletuple of int
The shape (depth, length, width) of image tiles to write. If None (default), image data are written in one stripe per plane. The tile length and width must be a multiple of 16. If the tile depth is provided, the SGI image_depth and tile_depth tags are used to save volume data. Few software can read the SGI format, e.g. MeVisLab.
- contiguousbool
If True (default) and the data and parameters are compatible with previous ones, if any, the data are stored contiguously after the previous one. Parameters ‘photometric’ and ‘planarconfig’ are ignored.
- compressint or ‘lzma’
Values from 0 to 9 controlling the level of zlib compression. If 0, data are written uncompressed (default). Compression cannot be used to write contiguous files. If ‘lzma’, LZMA compression is used, which is not available on all platforms.
- colormapnumpy.ndarray
RGB color values for the corresponding data value. Must be of shape (3, 2**(data.itemsize*8)) and dtype uint16.
- descriptionstr
The subject of the image. Saved with the first page only. Cannot be used with the ImageJ format.
- datetimedatetime
Date and time of image creation. Saved with the first page only. If None (default), the current date and time is used.
- resolution(float, float[, str]) or ((int, int), (int, int)[, str])
X and Y resolutions in pixels per resolution unit as float or rational numbers. A third, optional parameter specifies the resolution unit, which must be None (default for ImageJ), ‘inch’ (default), or ‘cm’.
- metadatadict
Additional meta data to be saved along with shape information in JSON or ImageJ formats in an image_description tag. If None, do not write a second image_description tag.
- extratagssequence of tuples
Additional tags as [(code, dtype, count, value, writeonce)].
- codeint
The TIFF tag Id.
- dtypestr
Data type of items in ‘value’ in Python struct format. One of B, s, H, I, 2I, b, h, i, 2i, f, d, Q, or q.
- countint
Number of data values. Not used for string values.
- valuesequence
‘Count’ values compatible with ‘dtype’.
- writeoncebool
If True, the tag is written to the first page only.
-


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Solar Panels
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Heavy Cloud
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Humidity 66%
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11.9 - 22.7° C
Humidity 69%
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| survdiff {survival} | R Documentation |
Tests if there is a difference between two or more survival curves using the G-rho family of tests, or for a single curve against a known alternative.
survdiff(formula, data, subset, na.action, rho=0)
formula |
a formula expression as for other survival models, of the form
|
data |
an optional data frame in which to interpret the variables occurring in the formula. |
subset |
expression indicating which subset of the rows of data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), a numeric vector indicating which observation numbers are to be included (or excluded if negative), or a character vector of row names to be included. All observations are included by default. |
na.action |
a missing-data filter function. This is applied to the |
rho |
a scalar parameter that controls the type of test. |
a list with components:
n |
the number of subjects in each group. |
obs |
the weighted observed number of events in each group. If there are strata, this will be a matrix with one column per stratum. |
exp |
the weighted expected number of events in each group. If there are strata, this will be a matrix with one column per stratum. |
chisq |
the chisquare statistic for a test of equality. |
var |
the variance matrix of the test. |
strata |
optionally, the number of subjects contained in each stratum. |
This function implements the G-rho family of
Harrington and Fleming (1982), with weights on each death of S(t)^rho,
where S is the Kaplan-Meier estimate of survival.
With rho = 0
this is the log-rank or Mantel-Haenszel test,
and with rho = 1
it is equivalent to the Peto & Peto modification
of the Gehan-Wilcoxon test.
If the right hand side of the formula consists only of an offset term,
then a one sample test is done.
To cause missing values in the predictors to be treated as a separate
group, rather than being omitted, use the factor
function with its
exclude
argument.
Harrington, D. P. and Fleming, T. R. (1982). A class of rank test procedures for censored survival data. Biometrika 69, 553-566.
## Two-sample test survdiff(Surv(futime, fustat) ~ rx,data=ovarian) ## Stratified 7-sample test survdiff(Surv(time, status) ~ pat.karno + strata(inst), data=lung) ## Expected survival for heart transplant patients based on ## US mortality tables expect <- survexp(futime ~ ratetable(age=(accept.dt - birth.dt), sex=1,year=accept.dt,race="white"), jasa, cohort=FALSE, ratetable=survexp.usr) ## actual survival is much worse (no surprise) survdiff(Surv(jasa$futime, jasa$fustat) ~ offset(expect))


The following positions for the C major scale are generated by positionScale
using chooseNotesWithinRange(-1, 3)
. Only five positions are shown here, and they are shown out-of-order to align them with the positions of the “CAGED” framework.
Made with Picker.js.


Category Definitions
These are the descriptions for each of the Topic Categories for Outbreak.info.
There are 9 broad categories, and 24 specific subcategories.
Whenever possible, please select the most appropriate subcategories in lieu of the broad category.
If you find that a specific subcategory is appropriate, there is NO need to select the corresponding broad category.
- Clinical - Clinical presentations of COVID-19. This encompasses work related to the observation and treatment of actual patients.
- Case Descriptions - Descriptions of specific patient cases. e.g.- Clinical Case Report.
- Risk Factors - Risk factors associated with case presentation, severity, or mortality
- Diagnosis - Disease assessment through symptoms, test results, and radiological features
- Symptoms - Diagnosis based off clinical symptoms
- Rapid Diagnostics - Diagnosis based off of rapid testing procedures and generally enable point of care testing.
- Antibody Detection - Antibody-based detection. Enables determination of past history of infection by looking at an aspect of the body's immunological response to the virus.
- Virus Detection - RT-PCR detection. Enables determination of active infection by looking for the presence of viral genetic material.
- Testing Prevalence - Extent of testing
- Pathology/Radiology - Diagnosis based off pathology and/or radiology. This often involves some sort of patient sampling (e.g.- like a biopsy or dissection), and some sort of imaging technology (e.g.- microscopy, x-rays, ct scans, etc.)
- Forecasting - Modelling and estimating the trend of COVID-19 spread
- Mechanism - Underlying cause(s) of COVID-19 infections and transmission and possible drug mechanism of action
- Virus Factors - How virus factors influence infection and transmission. This includes viral genes and proteins.
- Host Factors - How host genetic factors influence infection and transmission. This includes variant genes and proteins in the host.
- Immunological Response - Description of immunological response upon virus exposure.
- Mechanism of Infection - Description of how the virus enters and infects human cells
- Mechanism of Transmission - Description of how the virus transmits between hosts
- Prevention - Prevention, control, and management strategies either at the individual or societal level
- Public Health Interventions - Descriptions of public health interventions and/or evaluations of their efficacy
- Individual Prevention - Descriptions of individual transmission prevention methods and/or their efficacy, including behavior change campaigns
- Transmission - Characteristics and modes of COVID-19 transmissions, such as human-to-human
- Host/Intermediate Reservoirs - Descriptions of the host source of SARS-CoV-2 and its spread through intermediate reservoirs
- Viral Shedding / Persistence - How much the virus sheds from host and how long it persists in environments
- Treatment - Treatment strategies, therapeutic procedures, and vaccine development
- Vaccines - Vaccine development and immune response
- Pharmaceutical Treatments - Development of new pharmaceutical treatments of COVID-19, especially small molecule development
- Repurposing - Repurposing candidates of approved drugs
- Biologics - Development of biologically based treatments of COVID-19, including the use of convalescent plasma
- Medical Care - Medical interventions or procedures to treat COVID-19 such as dialysis, ventilators, etc.
- Epidemiology - Description of the spread of cases
- Molecular epidemiology - Description of chains of transmission, R0, doubling time, dating outbreak in specific regions/overall based on molecular/genetic assays
- Classical epidemiology - Description of cases, chains of transmission, broader scale dynamics (R0, doubling time, duration of infectiousness,...) based on classical epidemiological methods like case counts and contact tracing
- Behavioral Research - Mental health, behavioral, or social science research directly or indirectly related to COVID-19 such as studies on depression or social isolation, incidence of domestic violence, protests against public health directives, and more.


Here's a simple activity that will get your child enthused about reading and writing: Grab a blank notebook and write the first three (gripping!) sentences of a story on the first page. Then, hide the notebook under your child’s pillow with an invitation for them to continue the story, and then hide it again for you.


Oh no, I am a bad advertisement, disguised as a download button so that you will me!
Let's turn off the lights so we can find a better place to
Adding some light can make it so that even the sneakiest of foes will
Let's just this light switch on and off a couple of times to make sure it is in working order!
Anything that can
Must also be able to
Ninja's have perfected the long lost technique of the . This is why they appear as quickly as they disappear!
They are masters of the shadows. Allowing them to easily ...
...and at will!
They can blend into any or dojo as if they were always there!
Ninja's tools add to their deceptions. They can make things appear right or them.
They can other shadows to their side to confuse their enemies!
The old Switcheroo, brought to you by
A ninja can even shrink himself! What??
Through the path of the ninja, one learns the of things.
may appear out of thin air!
A ninja is also a professional at gathering
and


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--- layout: default title: Ocean Care ---
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mountpoint - see if a directory is a mountpoint
/bin/mountpoint
/path/to/directory
/bin/mountpoint
Mountpoint checks if the directory is a mountpoint.
Be quiet - don't print anything.
Print major/minor device number of the filesystem on stdout.
Print major/minor device number of the blockdevice on stdout.
Zero if the directory is a mountpoint, non-zero if not.
Symbolic links are not followed, except when the -x option is used. To force following symlinks, add a trailing slash to the path of the directory.
The name of the command is misleading when the -x option is used, but the option is useful for comparing if a directory and a device match up, and there is no other command that can print the info easily.
Miquel van Smoorenburg<EMAIL_ADDRESS>stat (1)


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The Carl Compass maps your political orientation the traditional left/right axis, as well as a new red-pilled / blue-pilled axis.
Check out Carl's article to learn more!


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Town Biz 349 15th St. Lovers of the Town. Representing Oakland through art & apparel. M-F 11am-7:00p, Sat 11-5, Closed Sun #OaklandDoingBusinessWithOakland www.facebook.com/townbizoak/shop


• In a couple of sentences, how would you describe the command line in plain English?
Can you think of an analogy for it?
Command line is an interface in which you can access and operate your computers system through. Where you can for example create files and remove files, check your system status and Trouble shoot.
• Did you stick to the timebox guidelines? If not, what change would you make next time?
Yes I have just managed to stick to the time. It was a bit of a stress though as I find it super hard to just stick to the need to know stuff, and to not go off on wild tangents.
• Name 5 commands you used, and what they do
•Did you learn anything unexpected?
Yes I was surprised how everything could be accessed and operated via the Command line.
•Whats the difference between git and GitHub?
Git is the version control sofware that is installed on your computer and GitHub is a opensource webservice, where people can collaberate on projects together.
•Can you think of an analogy to describe them?
It's like if two people are making a salad recipe together. One is working on the dressing and the other is working on the base of the salad. Once they have finished creating on their workbenches they meet in the middle bench (or hub) where they test out combined the two when they are happy.
•Do you think you would still remember the difference a week from now if you didn't revisit the material? Yes, infact i'm pretty sure I will have an even deeper understanding (or I'm hopeing I will anyway)
•Did you stick to the timebox suggestions? If not, why not?
• What is a GitHub work flow?
Creating branches for projects, then adding commits to the branch, then creating a pull request to be reviewed. You can then deploy your project for final testing before merging it with the master branch.
• What did you notice about your own learning? What did you do when you were confused or blocked?
I did seem to take a lot longer than I think is expected to get through the work. When I got confused or blocked I would go back and see if I had missed something, making sure I read every word (this may explain how long it took me).
• Is there anything you'd do differently if you were to repeat the learning exploration again?
I would probably read the descriptions a bit more thoroughly to start with, however I seem to be struggling with getting stuff done within the timebox, so I keep skimming then missing shit, then having to back track, creating even more of a time hole.
• How would you describe stage and commit to your non-tech-savvy friend?
The stage is basically where you would put the files in order to put into your repository, so to commit them is to make a copy of the file as it is in the staging area, into the repository.
• What is Master?
The master is the reviewed and final result of projects
• Why create a Branch?
We create branches so that we can make changes to a clone of the main branch without changing it inevitably
• Do the concepts feel intuitive or difficult to understand?
I must say I am struggling to gasp it fully keeping within the timeboxes
• What are some examples of when you would fork?
I might want to fork if I want to start a project using another repo as a base, or if I want to perhaps try something that could be risky and not want to stuff with the original repo.
• Write your own step-by-step fork and clone instructions. Describe what you did.
•Did you have any moments where it all clicked? What clicked?
I'm getting there
•What is the difference between cloning from a forked or non-forked repo. When you clone from a forked repo the edits you do and commit get pushed up into the copy on your platform. When you clone from a non-forked repo your commits go directly to the original repo to be pulled.
•Can you think of an analogy to describe forking?  Then if you are happy with that then you can copy that step into the plain icing.
•What is a remote?
A remote is a repository that is not directly on your computer.
•How are remotes set up? By using git remote add followed by name and the URL.
•How would you explain a remote to a non-technical person using an analogy?
A remote is a website repository, by adding a remote you can keep a track of changes added to an original document online and add them to your copy if needed. (so no, not really)
•How many times did you feel like "oh God I don't understand this"? Almost every step of the way
•Reflect on this activity. When did you feel frustrated?
I hardly felt frustrated, it was rather straight forward and I found it quite fun.
•If you didn't already know that this is the way websites are made; was it what you pictured? How does the reality of this process differ from your preconceptions?
It was somewhat what I had pictured. I really never thought that I would be able to create something like this myself.
•Describe the process for solving problems
Make sure that you fully understand the problem, and plan your approach. Breaking up the problem into sub categories to solve then solve each sub-problem starting from the easiest through to the more difficult problems. then put all together and attempt to solve it. If you get stuck there is always google, try debugging, and talk it through (I now have my equivalent of a rubber ducky, in the form of a sand filled fox I was using to weigh my papers down... and my wife).
•Will you follow that process? How?
Yes most likely, It is a proven proccess and it makes sense.
I have a note pad next to me at all times which I will use to break down my problems, then using the timebox and pomodoro technique I will make my way through each one. And as I explained the google, fox and Wife method will be used when I get stuck.
•What did you learn about flipped classroom?
Flipped classroom is where you are given course materials before class to study up on. The class time is then used to get more in depth and collaborate with peers and facilitators. Then after class is used to reflect and practice.
• What was it like trying to summarise yourself to a group of strangers? Could you feel your ego? Were you self conscious?
It did feel a bit odd to talk about what seems quite personal, and exposing. But I get it.
Figure 1 In the first image it appears that they have checked their status and there are a few things to note. One is that the branch is ahead of origin/master by two commits. which means that to be up to date they would have to push up the commits to be up to date. Two is that there is a file that has been modified and not commited, Three is that there have been two deleted files that are not staged for commit, and the command line suggests to add/remove file or to checkout file.
Figure 2 In this image they have asked for the remote location which shows that the place that any updates get pushed to is the same location that they get fetched from. They then attempt to pull any udates that have been made to the origin but have hit a conflict message which appears to be in regards to a file that was deleted in branch but modified in the HEAD. Then when run git status this gives them a run down on the changes to be commited: 1 modified and 1 new. The unmerged paths: before mentioned file and changes not staged for commit: a deleted md file.
They then run a git log possibly to check what moderations were done to the file from HEAD, to decide what step to take next (whether to delete or add changes and commit).
I hadent come across the CONFLICT scenerio before, it was great to learn why this is and how to rectify.
•In addition to the challenge specific reflections, answer the following questions in your reflection:
• What parts of your learning strategy worked? What problems did you face?
Making a plan of attack in the beginning of the day using Google calenders, then using the timeboxes, putting my tasks into my pomodoro app has really helped my time managment.
Taking a breather when I am feeling ovewhelmed has helped too, if I just walk away for a couple of minutes and do something completly un-related, I usually find that when I look back on the task, it becomes alot clearer.
I already had an office set up, however I think that having a study dedicated space has really helped. I was thinking that maybe on my core days I could find a spot in the library that I could use as my second space (somehow being in a public space whilst doing core seems appropriate, and a change of scenery wouldn't hurt).
• How confident are you with each of the Learning Competencies? I'm fairly confident now, I feel as though i'm a bit behind or missing bits but then somehow it all comes together.
• Which parts of the work did you enjoy?
I enjoyed most of it, it's all very new and I like new. It's very satisfying when you don't feel like you are getting a task then it all clicks.
• Which parts of the work did you find tedious?

• Did you hear a self critical voice in your head throughout the process of learning during this sprint? What did it say to you? Do you believe what it said?
Yes I usually do with most things. There are many points of self doubt, but I figure that the imporatant thing is that you try getting the self confident voice out to put in a good debate on your behalf, and prove that voice wrong by simply doing.


The department of Electronics & Communication Engineering started right from the inception of the college in the year 1997. The primary objective of the department has been to impart quality education, training and research at the undergraduate and postgraduate in various areas of Electronics and Communication Engineering with broad emphasis on design aspects of electronic systems. The Department presently offers UG Programme B.Tech with an intake of 300 and two PG programmes VLSI and Embedded Systems with an intake of 18 each. The Department has been accredited by NBA in 2006, 2009, 2014 and 2017 for excellence in engineering education.
Over the span of 20 years since inception, the department has been successful in attracting some of the best minds both in the student and teaching community.
The Department has several well-equipped laboratories, namely, Microwave Engineering, Digital Communications, Analog Communications ,VLSI, Embedded Systems, Digital Signal Processing, Signals and Systems, Digital Image Processing, Digital Electronics, Analog Electronics, Microcontroller, Communication Protocol, Electronic Circuit Analysis, OOPs through JAVA and Advanced English Communication Skills. The Department motivates students to take up innovative projects through participation in an active in-house product development Lab. Industry interaction is being promoted through several projects.
"To be recognized globally for high caliber engineers in electronics and communication,capable of adapting to the changing needs of industry and society."
"To impart the students design skills of high order, promote research aptitude and inculcate awareness of professional and societal needs."


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The Killers. (Universal; 1964) (95 mins.)
It has been reported in a number of Biographies that Ernest Hemingway believed Mark Hellinger's 1946 Production of his short story "The Killers" (Directed by Robert Siodmak) was the finest of all Screen adaptations of his work (a relative status considering the many abysmal movies it contended with for that honor). In "Papa Hemingway", however, a 1967 memoir of the years he spent playing Straight-man to the Great Novelist, A.E. Hotchner has Hemingway falling asleep at a screening of the 1946 film five minutes into the picture! "Never saw him last past the first reel", said Miss Mary (Papa's widow). That Hemingway could fill a theatre with his snoring while a movie that he claimed to admire unfurled before him says more about the Nobel Prizewinner -- ultimately, Hemingway knew as much about Cinema as I know about Deep-Sea Fishing -- than it does the film itself. Siodmak's "The Killers" is a genuinely great film; one of the landmarks of Film-Noir and, for the first fifteen minutes or so, it IS the best Hemingway adaptation there is. In fact, such is the reputation of the '46 film, that when it was "remade" in 1964 by Don Siegel, Puritanical Cineastes leapt to defend the first movie's honor with such haste that through their cries of "cheap", "tawdry" and "lurid" they neglected to note that the Siegel film is nothing less than a subversive Masterpiece.
There is more Mickey Spillane than there is Hemingway in Universal's 1964 "The Killers". It's two protagonists, Charlie and Lee (Lee Marvin and Clu Gulager), are a pair of Professional Hit-men who are at their most eloquent only when they're brutalizing someone needlessly. On a routine assignment, the two are sent to a School for the Blind to whack-out one of the instructors. They discover their prey teaching Automotive Repair to a bunch of Middle-aged Blind guys (think about that one for a second), and, even though he's been tipped-off by a janitor about their arrival, he doesn't budge. Having dispatched their target, it should be Miller Time for our "Heroes", but it isn't. On the train headed for home, Charlie is troubled. Why did their guy just stand there and take it? He knew they were coming; and Charlie'd recognized the sap, too. Sure. Used to be a big Small-Time Race car Driver named Johnny North (John Cassavettes). Bad business. Cracked himself up in a race some years back; end of a great career. Charlie has it on good authority that ol' Johnny got himself mixed up in an Armored Car Heist and (so the story goes) made off with the cash. So what's a guy with seven-figures squirreled away somewhere doing teaching Auto-repair to the Blind? Charlie's mind races and Lee is way ahead of him. If HE doesn't have the dough, somebody does. How hard, Charlie reasons, can it be to find it and take it away from whoever's got it?   So much for Hemingway.
As Charlie, the hit man approaching retirement, Lee Marvin gives one of his most indelible performances. It's almost a dry run for the semi-psychotic near-ghost he would play three years later in John Boorman's "Point Blank". Like that film (and unlike Siodmak's film), we are invited by the Director to take a kind of vicarious pleasure in the wanton behavior before us. To make it "worse", Charlie and Lee's search for the Armored Car boodle isn't even invested with the almost-Mystical force Robert Aldrich gave Mike Hammer's quest for The Great Whatzit in "Kiss Me Deadly". There's no angle by which anyone in this picture is in anyway redeemed, hence there's nowhere for a traditional moviegoer to turn. Siegel has us where he wants us. Don Siegel was always very good at laying traps for those in the audience who sought some kind of identification with those up there on the screen. Manny Farber once observed that everyone in Siegel's movies is compromised by something. Including the audience, he might well have added. Ultimately, Siegel is saying, we don't care about Johnny North anymore than the men who killed him. If we in the audience must insist on "rooting" for someone, the only choice we are granted are two Murderous thugs. That Siegel forces us to see moral twilight from the point of view of Professional Hitmen is finally what makes "The Killers" unique. No crime drama before it so subverted the audience's desire for Heroes so boldly. No one is redeemed. Papa would've hated it, but he'd have stayed awake.
H. Quinlan (23 November, 1998)
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Spending Analysis of G20 Countries
The G20 countries includes the world's major economies, comprising 19 countries and the European Union. In this project, we displayed the expenditure information of 13 G20 countries. The time span ranges from 2011 to 2015. We analyzed spending of educatoin, healthcare and military of these countries. We also compare these spending with the GDP for countries and per capita, respectively.
The data source is from the World Bank Open Data. These data are quried by searching expenditure (of GDP), expenditure per capita and population of these countries. However, the education spending data for many countries is missing such as USA and China. We retained these missing values without manipulating. After data preprocessing and calculating, cleaned data has been uploaded to google sheet and then use Google Chart API generate the charts. You can find it from here.