#! /usr/bin/python
# -*- coding: utf8 -*-
import numpy as np
from six.moves import xrange
[docs]def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
"""
Generate a generator that input a group of example in 2D numpy.array and
their labels, return the examples and labels by the given batchsize.
Parameters
----------
inputs : numpy.array
(X) The input features, every row is a example.
targets : numpy.array
(y) The labels of inputs, every row is a example.
batch_size : int
The batch size.
shuffle : boolean
Indicating whether to use a shuffling queue, shuffle the dataset before return.
Examples
--------
>>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
>>> y = np.asarray([0,1,2,3,4,5])
>>> for batch in tl.iterate.minibatches(inputs=X, targets=y, batch_size=2, shuffle=False):
>>> print(batch)
... (array([['a', 'a'],
... ['b', 'b']],
... dtype='<U1'), array([0, 1]))
... (array([['c', 'c'],
... ['d', 'd']],
... dtype='<U1'), array([2, 3]))
... (array([['e', 'e'],
... ['f', 'f']],
... dtype='<U1'), array([4, 5]))
"""
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
if shuffle:
excerpt = indices[start_idx:start_idx + batch_size]
else:
excerpt = slice(start_idx, start_idx + batch_size)
yield inputs[excerpt], targets[excerpt]
[docs]def seq_minibatches(inputs, targets, batch_size, seq_length, stride=1):
"""
Generate a generator that return a batch of sequence inputs and targets.
It is for the "Synced sequence input and output" as
`Karpathy RNN Blog <http://karpathy.github.io/2015/05/21/rnn-effectiveness/>`_
shows.
If batch_size = 100, seq_length = 5, a return input will have 500 rows (examples).
Examples
--------
>>> Synced sequence input and output.
>>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
>>> y = np.asarray([0, 1, 2, 3, 4, 5])
>>> for batch in tl.iterate.seq_minibatches(inputs=X, targets=y, batch_size=2, seq_length=2, stride=1):
>>> print(batch)
... (array([['a', 'a'],
... ['b', 'b'],
... ['b', 'b'],
... ['c', 'c']],
... dtype='<U1'), array([0, 1, 1, 2]))
... (array([['c', 'c'],
... ['d', 'd'],
... ['d', 'd'],
... ['e', 'e']],
... dtype='<U1'), array([2, 3, 3, 4]))
...
...
>>> Many to One
>>> return_last = True
>>> num_steps = 2
>>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
>>> Y = np.asarray([0,1,2,3,4,5])
>>> for batch in tl.iterate.seq_minibatches(inputs=X, targets=Y, batch_size=2, seq_length=num_steps, stride=1):
>>> x, y = batch
>>> if return_last:
>>> tmp_y = y.reshape((-1, num_steps) + y.shape[1:])
>>> y = tmp_y[:, -1]
>>> print(x, y)
... [['a' 'a']
... ['b' 'b']
... ['b' 'b']
... ['c' 'c']] [1 2]
... [['c' 'c']
... ['d' 'd']
... ['d' 'd']
... ['e' 'e']] [3 4]
"""
assert len(inputs) == len(targets)
n_loads = (batch_size * stride) + (seq_length - stride)
for start_idx in range(0, len(inputs) - n_loads + 1, (batch_size * stride)):
seq_inputs = np.zeros((batch_size, seq_length) + inputs.shape[1:],
dtype=inputs.dtype)
seq_targets = np.zeros((batch_size, seq_length) + targets.shape[1:],
dtype=targets.dtype)
for b_idx in xrange(batch_size):
start_seq_idx = start_idx + (b_idx * stride)
end_seq_idx = start_seq_idx + seq_length
seq_inputs[b_idx] = inputs[start_seq_idx:end_seq_idx]
seq_targets[b_idx] = targets[start_seq_idx:end_seq_idx]
flatten_inputs = seq_inputs.reshape((-1,) + inputs.shape[1:])
flatten_targets = seq_targets.reshape((-1,) + targets.shape[1:])
yield flatten_inputs, flatten_targets
[docs]def ptb_iterator(raw_data, batch_size, num_steps):
"""Iterates on a list of words. Yields (Returns) the source contexts and
the target context by the given batch_size and num_steps (sequence_length).\n
see ``PTB tutorial``.
e.g. x = [0, 1, 2] y = [1, 2, 3] , when batch_size = 1, num_steps = 3,
raw_data = [i for i in range(100)]
In TensorFlow's tutorial, this generates batch_size pointers into the raw
PTB data, and allows minibatch iteration along these pointers.
Parameters
----------
raw_data : a list
the context in list format; note that context usually be
represented by splitting by space, and then convert to unique
word IDs.
batch_size : int
the batch size.
num_steps : int
the number of unrolls. i.e. sequence_length
Yields
------
Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
The second element of the tuple is the same data time-shifted to the
right by one.
Raises
------
ValueError : if batch_size or num_steps are too high.
Examples
--------
>>> train_data = [i for i in range(20)]
>>> for batch in ptb_iterator(train_data, batch_size=2, num_steps=3):
>>> x, y = batch
>>> print(x, '\n',y)
... [[ 0 1 2] <---x 1st subset/ iteration
... [10 11 12]]
... [[ 1 2 3] <---y
... [11 12 13]]
...
... [[ 3 4 5] <--- 1st batch input 2nd subset/ iteration
... [13 14 15]] <--- 2nd batch input
... [[ 4 5 6] <--- 1st batch target
... [14 15 16]] <--- 2nd batch target
...
... [[ 6 7 8] 3rd subset/ iteration
... [16 17 18]]
... [[ 7 8 9]
... [17 18 19]]
Code Reference
--------------
tensorflow/models/rnn/ptb/reader.py
"""
raw_data = np.array(raw_data, dtype=np.int32)
data_len = len(raw_data)
batch_len = data_len // batch_size
data = np.zeros([batch_size, batch_len], dtype=np.int32)
for i in range(batch_size):
data[i] = raw_data[batch_len * i:batch_len * (i + 1)]
epoch_size = (batch_len - 1) // num_steps
if epoch_size == 0:
raise ValueError("epoch_size == 0, decrease batch_size or num_steps")
for i in range(epoch_size):
x = data[:, i*num_steps:(i+1)*num_steps]
y = data[:, i*num_steps+1:(i+1)*num_steps+1]
yield (x, y)
# def minibatches_for_sequence2D(inputs, targets, batch_size, sequence_length, stride=1):
# """
# Input a group of example in 2D numpy.array and their labels.
# Return the examples and labels by the given batchsize, sequence_length.
# Use for RNN.
#
# Parameters
# ----------
# inputs : numpy.array
# (X) The input features, every row is a example.
# targets : numpy.array
# (y) The labels of inputs, every row is a example.
# batchsize : int
# The batch size must be a multiple of sequence_length: int(batch_size % sequence_length) == 0
# sequence_length : int
# The sequence length
# stride : int
# The stride step
#
# Examples
# --------
# >>> sequence_length = 2
# >>> batch_size = 4
# >>> stride = 1
# >>> X_train = np.asarray([[1,2,3],[4,5,6],[7,8,9],[10,11,12],[13,14,15],[16,17,18],[19,20,21],[22,23,24]])
# >>> y_train = np.asarray(['0','1','2','3','4','5','6','7'])
# >>> print('X_train = %s' % X_train)
# >>> print('y_train = %s' % y_train)
# >>> for batch in minibatches_for_sequence2D(X_train, y_train, batch_size=batch_size, sequence_length=sequence_length, stride=stride):
# >>> inputs, targets = batch
# >>> print(inputs)
# >>> print(targets)
# ... [[ 1. 2. 3.]
# ... [ 4. 5. 6.]
# ... [ 4. 5. 6.]
# ... [ 7. 8. 9.]]
# ... [1 2]
# ... [[ 4. 5. 6.]
# ... [ 7. 8. 9.]
# ... [ 7. 8. 9.]
# ... [ 10. 11. 12.]]
# ... [2 3]
# ... ...
# ... [[ 16. 17. 18.]
# ... [ 19. 20. 21.]
# ... [ 19. 20. 21.]
# ... [ 22. 23. 24.]]
# ... [6 7]
# """
# print('len(targets)=%d batch_size=%d sequence_length=%d stride=%d' % (len(targets), batch_size, sequence_length, stride))
# assert len(inputs) == len(targets), '1 feature vector have 1 target vector/value' #* sequence_length
# # assert int(batch_size % sequence_length) == 0, 'batch_size % sequence_length must == 0\
# # batch_size is number of examples rather than number of targets'
#
# # print(inputs.shape, len(inputs), len(inputs[0]))
#
# n_targets = int(batch_size/sequence_length)
# # n_targets = int(np.ceil(batch_size/sequence_length))
# X = np.empty(shape=(0,len(inputs[0])), dtype=np.float32)
# y = np.zeros(shape=(1, n_targets), dtype=np.int32)
#
# for idx in range(sequence_length, len(inputs), stride): # go through all example during 1 epoch
# for n in range(n_targets): # for num of target
# X = np.concatenate((X, inputs[idx-sequence_length+n:idx+n]))
# y[0][n] = targets[idx-1+n]
# # y = np.vstack((y, targets[idx-1+n]))
# yield X, y[0]
# X = np.empty(shape=(0,len(inputs[0])))
# # y = np.empty(shape=(1,0))
#
#
# def minibatches_for_sequence4D(inputs, targets, batch_size, sequence_length, stride=1): #
# """
# Input a group of example in 4D numpy.array and their labels.
# Return the examples and labels by the given batchsize, sequence_length.
# Use for RNN.
#
# Parameters
# ----------
# inputs : numpy.array
# (X) The input features, every row is a example.
# targets : numpy.array
# (y) The labels of inputs, every row is a example.
# batchsize : int
# The batch size must be a multiple of sequence_length: int(batch_size % sequence_length) == 0
# sequence_length : int
# The sequence length
# stride : int
# The stride step
#
# Examples
# --------
# >>> sequence_length = 2
# >>> batch_size = 2
# >>> stride = 1
# >>> X_train = np.asarray([[1,2,3],[4,5,6],[7,8,9],[10,11,12],[13,14,15],[16,17,18],[19,20,21],[22,23,24]])
# >>> y_train = np.asarray(['0','1','2','3','4','5','6','7'])
# >>> X_train = np.expand_dims(X_train, axis=1)
# >>> X_train = np.expand_dims(X_train, axis=3)
# >>> for batch in minibatches_for_sequence4D(X_train, y_train, batch_size=batch_size, sequence_length=sequence_length, stride=stride):
# >>> inputs, targets = batch
# >>> print(inputs)
# >>> print(targets)
# ... [[[[ 1.]
# ... [ 2.]
# ... [ 3.]]]
# ... [[[ 4.]
# ... [ 5.]
# ... [ 6.]]]]
# ... [1]
# ... [[[[ 4.]
# ... [ 5.]
# ... [ 6.]]]
# ... [[[ 7.]
# ... [ 8.]
# ... [ 9.]]]]
# ... [2]
# ... ...
# ... [[[[ 19.]
# ... [ 20.]
# ... [ 21.]]]
# ... [[[ 22.]
# ... [ 23.]
# ... [ 24.]]]]
# ... [7]
# """
# print('len(targets)=%d batch_size=%d sequence_length=%d stride=%d' % (len(targets), batch_size, sequence_length, stride))
# assert len(inputs) == len(targets), '1 feature vector have 1 target vector/value' #* sequence_length
# # assert int(batch_size % sequence_length) == 0, 'in LSTM, batch_size % sequence_length must == 0\
# # batch_size is number of X_train rather than number of targets'
# assert stride >= 1, 'stride must be >=1, at least move 1 step for each iternation'
#
# n_example, n_channels, width, height = inputs.shape
# print('n_example=%d n_channels=%d width=%d height=%d' % (n_example, n_channels, width, height))
#
# n_targets = int(np.ceil(batch_size/sequence_length)) # 实际为 batchsize/sequence_length + 1
# print(n_targets)
# X = np.zeros(shape=(batch_size, n_channels, width, height), dtype=np.float32)
# # X = np.zeros(shape=(n_targets, sequence_length, n_channels, width, height), dtype=np.float32)
# y = np.zeros(shape=(1,n_targets), dtype=np.int32)
# # y = np.empty(shape=(0,1), dtype=np.float32)
# # time.sleep(2)
# for idx in range(sequence_length, n_example-n_targets+2, stride): # go through all example during 1 epoch
# for n in range(n_targets): # for num of target
# # print(idx+n, inputs[idx-sequence_length+n : idx+n].shape)
# X[n*sequence_length : (n+1)*sequence_length] = inputs[idx+n-sequence_length : idx+n]
# # X[n] = inputs[idx-sequence_length+n:idx+n]
# y[0][n] = targets[idx+n-1]
# # y = np.vstack((y, targets[idx-1+n]))
# # y = targets[idx: idx+n_targets]
# yield X, y[0]


Andy York
Music Collection - Bands and Solo Work
Contents:
Powerhead
Spoody Goon
Suburban Funeral
Misc.
Powerhead
That Freak
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Footer Section


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---
layout: collage
category: collages
excerpt: Noto is Google’s font family was designed to achieve visual harmonization across the world's languages and is vailable in both serif and sans-serif flavours. Unfortunately, both are available in only two weights, somewhat limiting their versatility.
font-1-name: Noto Sans
font-1-hex-1: 373736
font-1-source: Google Fonts
font-2-name: Noto Serif
font-2-hex-1: 373736
font-2-source: Google Fonts
swatch-hex-1: 3c3c3b
swatch-hex-2: 27405f
swatch-hex-3: 7f98b9
swatch-hex-4: e4f1fa
swatch-hex-5: c8403d
---
{% include sample-logo.html %}
{% include sample-swatch.html %}
{% include sample-typography-1.html %}
{% include sample-blockquote.html %}
{% include sample-image-header.html %}
{% include sample-images.html %}
{% include sample-buttons.html %}
{% include sample-adjectives.html %}


Vector2¶
The Vector2 module.
Vector2¶
-
Const_UnityEngineVector2
(UnityEngine.Vector2 Vector2)¶
outputs: unnamed (Vector2
)
flow: none
Vector2¶
-
Variable_UnityEngineVector2
(UnityEngine.Vector2 Vector2System.String nameSystem.Boolean publicSystem.Boolean syncedSystem.String syncMode)¶
outputs: none
flow: none
op_Addition¶
-
UnityEngineVector2.
__op_Addition__UnityEngineVector2_UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 UnityEngine.Vector2)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 Angle¶
-
UnityEngineVector2.
__Angle__UnityEngineVector2_UnityEngineVector2__SystemSingle
(UnityEngine.Vector2 fromUnityEngine.Vector2 to)¶
outputs: unnamed (Single
)
flow: none
Vector2 ClampMagnitude¶
-
UnityEngineVector2.
__ClampMagnitude__UnityEngineVector2_SystemSingle__UnityEngineVector2
(UnityEngine.Vector2 vectorSystem.Single maxLength)¶
outputs: unnamed (Vector2
)
flow: none
ctor¶
-
UnityEngineVector2.
__ctor__SystemSingle_SystemSingle__UnityEngineVector2
(System.Single System.Single)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 Distance¶
-
UnityEngineVector2.
__Distance__UnityEngineVector2_UnityEngineVector2__SystemSingle
(UnityEngine.Vector2 aUnityEngine.Vector2 b)¶
outputs: unnamed (Single
)
flow: none
op_Division¶
-
UnityEngineVector2.
__op_Division__UnityEngineVector2_UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 UnityEngine.Vector2)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 Dot¶
-
UnityEngineVector2.
__Dot__UnityEngineVector2_UnityEngineVector2__SystemSingle
(UnityEngine.Vector2 lhsUnityEngine.Vector2 rhs)¶
outputs: unnamed (Single
)
flow: none
op_Equality¶
-
UnityEngineVector2.
__op_Equality__UnityEngineVector2_UnityEngineVector2__SystemBoolean
(UnityEngine.Vector2 UnityEngine.Vector2)¶
outputs: unnamed (Boolean
)
flow: none
Vector2 Equals¶
-
UnityEngineVector2.
__Equals__SystemObject__SystemBoolean
(UnityEngine.Vector2 instanceSystem.Object other)¶
outputs: unnamed (Boolean
)
flow: none
Vector2 get_down¶
-
UnityEngineVector2.
__get_down__UnityEngineVector2
()¶
outputs: unnamed (Vector2
)
flow: none
Vector2 get_Item¶
-
UnityEngineVector2.
__get_Item__SystemInt32__SystemSingle
(UnityEngine.Vector2 instanceSystem.Int32 index)¶
outputs: unnamed (Single
)
flow: none
Vector2 get_kEpsilon¶
-
UnityEngineVector2.
__get_kEpsilon__SystemSingle
()¶
outputs: unnamed (Single
)
flow: none
Vector2 get_kEpsilonNormalSqrt¶
-
UnityEngineVector2.
__get_kEpsilonNormalSqrt__SystemSingle
()¶
outputs: unnamed (Single
)
flow: none
Vector2 get_left¶
-
UnityEngineVector2.
__get_left__UnityEngineVector2
()¶
outputs: unnamed (Vector2
)
flow: none
Vector2 get_magnitude¶
-
UnityEngineVector2.
__get_magnitude__SystemSingle
(UnityEngine.Vector2 instance)¶
outputs: unnamed (Single
)
flow: none
Vector2 get_negativeInfinity¶
-
UnityEngineVector2.
__get_negativeInfinity__UnityEngineVector2
()¶
outputs: unnamed (Vector2
)
flow: none
Vector2 get_normalized¶
-
UnityEngineVector2.
__get_normalized__UnityEngineVector2
(UnityEngine.Vector2 instance)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 get_one¶
-
UnityEngineVector2.
__get_one__UnityEngineVector2
()¶
outputs: unnamed (Vector2
)
flow: none
Vector2 get_positiveInfinity¶
-
UnityEngineVector2.
__get_positiveInfinity__UnityEngineVector2
()¶
outputs: unnamed (Vector2
)
flow: none
Vector2 get_right¶
-
UnityEngineVector2.
__get_right__UnityEngineVector2
()¶
outputs: unnamed (Vector2
)
flow: none
Vector2 get_sqrMagnitude¶
-
UnityEngineVector2.
__get_sqrMagnitude__SystemSingle
(UnityEngine.Vector2 instance)¶
outputs: unnamed (Single
)
flow: none
Vector2 get_up¶
-
UnityEngineVector2.
__get_up__UnityEngineVector2
()¶
outputs: unnamed (Vector2
)
flow: none
Vector2 get_x¶
-
UnityEngineVector2.
__get_x__SystemSingle
(UnityEngine.Vector2)¶
outputs: unnamed (Single
)
flow: none
Vector2 get_y¶
-
UnityEngineVector2.
__get_y__SystemSingle
(UnityEngine.Vector2)¶
outputs: unnamed (Single
)
flow: none
Vector2 get_zero¶
-
UnityEngineVector2.
__get_zero__UnityEngineVector2
()¶
outputs: unnamed (Vector2
)
flow: none
Vector2 GetHashCode¶
-
UnityEngineVector2.
__GetHashCode__SystemInt32
(UnityEngine.Vector2 instance)¶
outputs: unnamed (Int32
)
flow: none
Vector2 GetType¶
-
UnityEngineVector2.
__GetType__SystemType
(UnityEngine.Vector2 instance)¶
outputs: unnamed (Type
)
flow: none
op_Implicit¶
-
UnityEngineVector2.
__op_Implicit__UnityEngineVector3__UnityEngineVector2
(UnityEngine.Vector3)¶
outputs: unnamed (Vector2
)
flow: none
op_Inequality¶
-
UnityEngineVector2.
__op_Inequality__UnityEngineVector2_UnityEngineVector2__SystemBoolean
(UnityEngine.Vector2 UnityEngine.Vector2)¶
outputs: unnamed (Boolean
)
flow: none
Vector2 Lerp¶
-
UnityEngineVector2.
__Lerp__UnityEngineVector2_UnityEngineVector2_SystemSingle__UnityEngineVector2
(UnityEngine.Vector2 aUnityEngine.Vector2 bSystem.Single t)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 LerpUnclamped¶
-
UnityEngineVector2.
__LerpUnclamped__UnityEngineVector2_UnityEngineVector2_SystemSingle__UnityEngineVector2
(UnityEngine.Vector2 aUnityEngine.Vector2 bSystem.Single t)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 Max¶
-
UnityEngineVector2.
__Max__UnityEngineVector2_UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 lhsUnityEngine.Vector2 rhs)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 Min¶
-
UnityEngineVector2.
__Min__UnityEngineVector2_UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 lhsUnityEngine.Vector2 rhs)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 MoveTowards¶
-
UnityEngineVector2.
__MoveTowards__UnityEngineVector2_UnityEngineVector2_SystemSingle__UnityEngineVector2
(UnityEngine.Vector2 currentUnityEngine.Vector2 targetSystem.Single maxDistanceDelta)¶
outputs: unnamed (Vector2
)
flow: none
op_Multiply¶
-
UnityEngineVector2.
__op_Multiply__UnityEngineVector2_UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 UnityEngine.Vector2)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 Normalize¶
-
UnityEngineVector2.
__Normalize__SystemVoid
(UnityEngine.Vector2 instance)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Vector2 Perpendicular¶
-
UnityEngineVector2.
__Perpendicular__UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 inDirection)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 Reflect¶
-
UnityEngineVector2.
__Reflect__UnityEngineVector2_UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 inDirectionUnityEngine.Vector2 inNormal)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 Scale¶
-
UnityEngineVector2.
__Scale__UnityEngineVector2_UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 aUnityEngine.Vector2 b)¶
outputs: unnamed (Vector2
)
flow: none
Vector2 set_Item¶
-
UnityEngineVector2.
__set_Item__SystemInt32_SystemSingle__SystemVoid
(UnityEngine.Vector2 instanceSystem.Int32 indexSystem.Single value)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Vector2 set_x¶
-
UnityEngineVector2.
__set_x__SystemSingle
(UnityEngine.Vector2 System.Single)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Vector2 set_y¶
-
UnityEngineVector2.
__set_y__SystemSingle
(UnityEngine.Vector2 System.Single)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Vector2 Set¶
-
UnityEngineVector2.
__Set__SystemSingle_SystemSingle__SystemVoid
(UnityEngine.Vector2 instanceSystem.Single newXSystem.Single newY)¶
outputs: none
flow inputs: unnamed
flow outputs: unnamed
Vector2 SignedAngle¶
-
UnityEngineVector2.
__SignedAngle__UnityEngineVector2_UnityEngineVector2__SystemSingle
(UnityEngine.Vector2 fromUnityEngine.Vector2 to)¶
outputs: unnamed (Single
)
flow: none
Vector2 SmoothDamp¶
-
UnityEngineVector2.
__SmoothDamp__UnityEngineVector2_UnityEngineVector2_UnityEngineVector2Ref_SystemSingle_SystemSingle_SystemSingle__UnityEngineVector2
(UnityEngine.Vector2 currentUnityEngine.Vector2 targetSystem.Single smoothTimeSystem.Single maxSpeedSystem.Single deltaTime)¶
outputs: currentVelocity (Vector2
), unnamed (Vector2
)
flow: none
Vector2 SqrMagnitude¶
-
UnityEngineVector2.
__SqrMagnitude__UnityEngineVector2__SystemSingle
(UnityEngine.Vector2 a)¶
outputs: unnamed (Single
)
flow: none
op_Subtraction¶
-
UnityEngineVector2.
__op_Subtraction__UnityEngineVector2_UnityEngineVector2__UnityEngineVector2
(UnityEngine.Vector2 UnityEngine.Vector2)¶
outputs: unnamed (Vector2
)
flow: none


Welcome to Ray's Blog
Happiness is a journey, not a destination. So work like you don't need the money, Love like you have never been hurt, and dance like no one's watching.
Read MoreHappiness is a journey, not a destination. So work like you don't need the money, Love like you have never been hurt, and dance like no one's watching.
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Miller
This page contains an index of all the individuals in the database with the surname of Miller. Selecting the person’s name will take you to that person’s individual page.
| Given Name | Birth | Death | Partner | Parents |
|---|---|---|---|---|
| Annie | about 1881 | 1927-12-03 | Thornton, Frederick William | Miller, [Living] |
| Charles James, B.Sc. | 1929-10-03 | 1963-06-29 | Good, [Living] | Miller, James Winton, Alice May |
| Eva Clarice | 1887-08-24 | 1975-04-11 | Young, John Henry | Miller, Thomas William (Surveyor) Irish, Sarah Jane |
| Harold C. | 1930 | Orton- Welch, Mabel | Miller, [Living] | |
| James | 1897 | 1961-10-28 | Winton, Alice May | |
| Mary | Lock, Nicholas Parsons | |||
| Mary Jane | Vote, Thomas | |||
| Rothwell Charles | 1973/1975 | England, Rita | Miller, Thomas "Charles" Teague, Charlotte E | |
| Thomas “Charles” | 1886-01-16 | 1978-08-07 | Teague, Charlotte E, Barder, Olive B.F. | Miller, Thomas William (Surveyor) Irish, Sarah Jane |
| Thomas William, (Surveyor) | 1863-07-30 | 1899-03-13 | Irish, Sarah Jane | |
| [Living] | Howe, Graydon Raymond, Garnsworthy, Clarence | Miller, Thomas "Charles" Teague, Charlotte E | ||
| [Living] | McLennan, [Living] | Miller, Thomas "Charles" Teague, Charlotte E | ||
| [Living] | Miller, [Living] Flanagan, [Living] | |||
| [Living] | Miller, [Living] Flanagan, [Living] | |||
| [Living] | Miller, [Living] McLennan, [Living] | |||
| [Living] | Miller, Rothwell Charles England, Rita | |||
| [Living] | Miller, [Living] McLennan, [Living] | |||
| [Living] | Flanagan, [Living] | Miller, [Living] McLennan, [Living] | ||
| [Living] | Miller, Rothwell Charles England, Rita | |||
| [Living] | Miller, Rothwell Charles England, Rita | |||
| [Living] | Braithwaite, [Living] | |||
| [Living] | Miller, [Living] Braithwaite, [Living] | |||
| [Living] | Miller, [Living] Braithwaite, [Living] | |||
| [Living] | Bennett, Dorothy Meryl | Miller, James Winton, Alice May | ||
| [Living] | Miller, Charles James B.Sc. Good, [Living] | |||
| [Living] | ||||
| [Living] | Lakin, [Living] | |||
| [Living] | Miller, Harold C. Orton- Welch, Mabel | |||
| [Living] | Miller, Harold C. Orton- Welch, Mabel | |||
| [Living] |


Turn On Compiler Warnings!
{% include toc %}
This post originally appeared on DevCafe
Compiler warnings can be a nuisance, clogging the output with unnecessarily verbose information. This is especially true of C++, even more so when the code uses template magic. However, turning them off can be rather harmful.
TL;DR
Set -Wall -Wextra
, or equivalent, as your basic compiler flags, both in debug and in release mode. If you are using CMake, the Autocmake project does it by default
An example from the cnpy
library
Consider the following C++11 example, but it applies equally well to earlier standards. The cnpy
library for saving/loading C++ arrays to/from NumPy binary format has a basic data structure, called NpyArray
. An object of this type is constructed by supplying:
- the shape of the array, i.e. a
std::vector<size_t>
. For example, a 2-dimensional array with dimensions 10 and 11 will have:std::vector<size_t> shape({10, 11});
- the word size of the data type to be dumped to NumPy array. This is either read from the
.npy
when loading the array, or determined by the result ofsizeof(T)
, whereT
is the data type, when saving the array.
The constructor will then compute how large a memory buffer is needed and allocate a std::vector<char>
. The number of values in the array is computed from the shape array:
or more compactly using std::accumulate
:
The type information is encoded in the .npy
file format header. When loading the array the user will have to perform a reinterpret_cast
to get the correct data type.
Runtime error!
Stripped to its barebones, the NpyArray
class looks like this:
#include <algorithm>
#include <cassert>
#include <iostream>
#include <numeric>
#include <vector>
struct VectorWtf
{
VectorWtf(const std::vector<size_t> & s, size_t w) :
nValues_(std::accumulate(s.begin(),
s.end(),
1,
std::multiplies<size_t>())),
buffer_(nValues_ * w, char(0)) {
std::cout << "nValues_ " << nValues_ << std::endl;
std::cout << "w " << w << std::endl;
std::cout << "nValues_ * w " << nValues_ * w << std::endl;
assert(buffer_.size() == nValues_ * w);
for (size_t i = 0; i < nValues_ * w; ++i) {
std::cout << buffer_[i] << std::endl;
}
std::cout << "buffer_.size() " << buffer_.size() << std::endl;
}
std::vector<char> buffer_;
size_t nValues_;
};
int main()
{
std::vector<size_t> shape({10, 11});
size_t w = 16;
VectorWtf(shape, w);
return 0;
}
Let’s now try to compile it:
The live example on Coliru shows that we get a runtime error because the assertion in the constructor fails.
nValues_ 110
w 16
nValues_ * w 1760
a.out: main.cpp:18: VectorWtf::VectorWtf(const std::vector<long unsigned int>&, size_t): Assertion `buffer_.size() == nValues_ * w' failed.
bash: line 7: 13432 Aborted (core dumped) ./a.out
What’s happening?  The buffer_
data member is initialized using the nValues_
data member This shouldn’t be a problem, since it’s initialized first in the constructor, right? Wrong! According to the standard, 12.6.2 Section 13.3 data members are initialized in the order they were declared in the class. Thus buffer_
gets initialized first, using an undefined value for nValues_
.
Fixing it
The correct struct
declaration is thus:
#include <algorithm>
#include <cassert>
#include <iostream>
#include <numeric>
#include <vector>
struct VectorWtf
{
VectorWtf(const std::vector<size_t> & s, size_t w) :
nValues_(std::accumulate(s.begin(),
s.end(),
1,
std::multiplies<size_t>())),
buffer_(nValues_ * w, char(0)) {
std::cout << "nValues_ " << nValues_ << std::endl;
std::cout << "w " << w << std::endl;
std::cout << "nValues_ * w " << nValues_ * w << std::endl;
assert(buffer_.size() == nValues_ * w);
for (size_t i = 0; i < nValues_ * w; ++i) {
std::cout << buffer_[i] << std::endl;
}
std::cout << "buffer_.size() " << buffer_.size() << std::endl;
}
size_t nValues_;
std::vector<char> buffer_;
};
int main()
{
std::vector<size_t> shape({10, 11});
size_t w = 16;
VectorWtf(shape, w);
return 0;
}
which also honors the tenet of ordering data members in your classes and structs by their size in memory. However, this is something very easily forgotten. How to avoid these kinds of errors?
- Do not initialize data members based on other data members. This is, in my opinion, overly restrictive.
- Insert assertions in the constructors. Very useful, but assertions only work when
-DNDEBUG
is not given to the compiler. Most of the times this is not the case when compiling with optimization. - Turn on compiler warnings.
-Wall
catches this mistake and many others. For an extra layer of warnings, I also turn on-Wextra
. This is the output on Coliru
main.cpp: In constructor 'VectorWtf::VectorWtf(const std::vector<long unsigned int>&, size_t)':
main.cpp:27:10: warning: 'VectorWtf::nValues_' will be initialized after [-Wreorder]
size_t nValues_;
^~~~~~~~
main.cpp:26:21: warning: 'std::vector<char> VectorWtf::buffer_' [-Wreorder]
std::vector<char> buffer_;
^~~~~~~
main.cpp:9:3: warning: when initialized here [-Wreorder]
VectorWtf(const std::vector<size_t> & s, size_t w) :
^~~~~~~~~


An example overview for the challenge should include a description of the problem.
Teams wishing to participate in the challenge should download the data. The teams should then evaluate their algorithm performance on the data. Users can submit results to their personal result set and then choose what result to submit to the leaderboard.
A FAQ with more details can be found here.


This is a general life cycle that happens when HAXcms "boots up". When web components are unpacking the contents of what to do it generally will work this way.
- page load, should it be no-js or a web component delivered, preflight
- haxcms-site-builder loads site.json, reads JSON Outline Schema and starts to bootstrap that into a theme, items, title, etc
- Appends a haxcms-editor-builder tag which checks for "context"
- Context is established based on global values for php, nodejs, beaker, etc
- Uses a login end point to check if there's a JWT in the user's local data
- When it finds a backend, it injects a tag to bridge that backend
- HAXeditor gets injected into the correct place in the theme which then all of that unpacks and does its thing
HAXcms allows for jumping in at different core operational steps and reacting to these changes based on an event driven design pattern. This means that an event "fires" in PHP, much like it does in JavaScript and code written to react to it can make changes at that point in the operation.
Here is an example from HAXiam which implements custom configuration event listeners
// file included which has the class in question
include_once str_replace('_iamConfig/HAXcmsConfig.php', '', __FILE__) . '/system/lib/IAM.php';
// in the configuration file HAXcmsConfig.php
// this adds an event listener for the haxcms-jwt-get event
// and then says to pass the data in question to class $IAM->getJwtUser();
$HAXCMS->addEventListener('haxcms-jwt-get', array($IAM, 'getJwtUser'));
// later on in additionally included code
class IAM {
/**
* Callback for event: haxcms-jwt-get
*/
public function getJwtUser(&$token) {
if ($this->enterprise->userVar) {
$token['user'] = $this->enterprise->userVar;
}
}
}
// this ensures that there is a global $IAM object
// it also ensures there's an instance so that when our above
// listener fires it'll correctly associate to this code
global $IAM;
$IAM = new IAM();
Core listeners to influence how the data is constructed. You can add these to your _config/config.php file
- haxcms-init
- just after HAXcms bootstraps its initial configuration
- haxcms-site-metadata
- when constructing metadata block at the top of the document
- haxcms-connection-settings
- for authorized users, this is the block of end points for connecting to and modifying HAXcms logic as far as where data flows.
The following hooks allow for supplanting the HAXcms (intentionally) simplistic login system. This allows you to do custom integrations for your organization. HAXiam implements many of these
if you need specific examples.
- haxcms-login-test
- when a login didn't pass our simplistic built in checks. Useful for enterprise integrations and other SSO methods of access
- haxcms-validate-user
- same as login-test except this fires to validate every transaction to ensure the user name matches when decoding the JWT
- haxcms-jwt-get
- when we ask for the JWT. This allows SSO to supply the token manually via a different methodology than our core one
- haxcms-refresh-token-get
- when our connection is deemed needing to refresh, this supplies the refresh token for when and what that looks like


About Hugo
Hugo is a static site engine written in Go.
It makes use of a variety of open source projects including:
Learn more and contribute on GitHub.
Hugo is a static site engine written in Go.
It makes use of a variety of open source projects including:
Learn more and contribute on GitHub.


Other administrators on the wallet will receive an email asking them to re-invite you to the wallet, which will enable you to recover your key and reset your password.


--- title: About ---
42°2'16"N 70°12'47"W
potts.shawnee[at]gmail.com
You've reached the personal Jekyll page of Shawnee. I built this web page using a combination of Javascript, HTML, CSS, and YAML.


Actions you can perform on Notes
Creates NoteManager instance
MainActivity. Shows previews for already saved notes
Note entity
This class parses some of actions that can be done with Notes
Such as: ADD, REMOVE, SHOW
Observer interface for NoteActionManager
Observer manager
Manages Notes and NoteObservers
Standard observer interface for Notes
Observer manager
RecyclerView adapter for notes


Delete version {{activeVersionId}}?
Are you sure you want to delete the profiles {{getSelectedProfileIds().join(", ")}}


A simple template for building a fast, pre-generated HTML website using the Eleventy static site generator. With Netlify CMS baked-in, ready to deploy to Netlify in a couple of clicks.
Use it as a starter for your own JAMstack projects, or as an easy way to get started building websites with Eleventy.
Based on the Eleventy Base Blog repo (see there for additional info on Eleventy usage).
🔥 This project is featured on Netlify's official template showcase 🔥
After deploying this project, Netlify Identity will add you as a CMS user and will email you an invite. It is not necessary to accept this invite if you wish to use an OAuth provider (e.g. Github) to manage authentication for your CMS. It is recommended to use this method of authentication as it removes the need for an email & password to log in to the CMS and is generally more secure. You will need to add an OAuth provider in your Netlify app settings under "Settings" > "Identity" > "External providers".
Once you've added an OAuth provider, navigate to /admin
on your site, select your provider from the
list, and you should then be logged into your CMS. Cool huh?
Now you're all set, and you can start editing content!
If you change the repo that was created at deploy time from public to private, you'll need to regenerate your token, as the token generated using the deploy to Netlify button can only access public repositories. To regenerate your token, head to "Settings" in your Netlify site dashboard, go to the "Identity" section, then scroll to "Services" where you'll see an "Edit settings" button. Click that and you'll see a text link to "Generate access token in GitHub".
If you need any help with setting up Netlify CMS, you can reach out to the Netlify team in the Netlify CMS Gitter.
git clone https://github.com/danurbanowicz/eleventy-netlify-boilerplate.git my-blog-name
cd my-blog-name
Specifically have a look at .eleventy.js
to see if you want to configure any Eleventy options differently.
npm install @11ty/eleventy
This file contains your site title and author details, and can be used to store any other commonly used site data.
npx @11ty/eleventy
Or build automatically when a template changes:
npx @11ty/eleventy --watch
Or build and host locally for local development:
npx @11ty/eleventy --serve
Or in debug mode:
DEBUG=* npx @11ty/eleventy
This is an ongoing project and I welcome contributions and suggestions! Feel free to submit a PR.
If you need any help with setting up Netlify CMS, you can reach out to the Netlify team in the Netlify CMS Gitter.


An ogg_packet and its position in the stream. More...
#include <oggz_packet.h>
An ogg_packet and its position in the stream.


Using "Cycle" In Ruby
How To Use This Enumerable Method
3/20/15
You can't get far in learning how to code without coming across a basic loop structure. One of the major benefits of programming a computer to do the work for you is that when you need to repeat a step several times, rather than manually write out the steps over and over, you can tell the computer to loop over one set of the code multiple times. Going a step further, if you want to change a piece of information between each run of the code, like say you give your code input with multiple data points and you want to perform the code for each data point given, you can set up an iteration to run your code (which keep in mind could contain loops within it!) for each data point.
 But what if you want to take it a step further and repeat this iteration multiple times? You could type out your iteration several times, but by now you know that is not how an efficient coder thinks. Instead, you can use a single command to tell your computer to repeat the iteration a ceratin number of times. In this case, you can use the Ruby enumerable method, "cycle" to repeat an iteration a given number of times.
Here are some examples of #cycle at work:
example_array = ["There's", "no", "place", "like", "home!"]
example_array.cycle(3) {|x| print x + " "}
Prints out, "There's no place like home! There's no place like home! There's no place like home! "


| 009:011 Khan | : |
But if they repent, perform As-Salat (Iqamat- as-Salat) and give Zakat, then they are your brethren in religion. (In this way) We explain the Ayat (proofs, evidences, verses, lessons, signs, revelations, etc.) in detail for a people who know. |
| 009:011 Maulana | : |
But if they repent and keep up prayer and pay the poor-rate, they are your brethren in faith. And We make the messages clear for a people who know. |
| 009:011 Pickthal | : |
But if they repent and establish worship and pay the poor-due, then are they your brethren in religion. We detail Our revelations for a people who have knowledge. |
| 009:011 Rashad | : |
If they repent and observe the Contact Prayers (Salat) and give the obligatory charity (Zakat), then they are your brethren in religion. We thus explain the revelations for people who know. |
| 009:011 Sarwar | : |
If they repent, perform their prayers, pay religious tax, they would be your brothers in the religion. We explain Our revelations to people of knowledge. |
| 009:011 Shakir | : |
But if they repent and keep up prayer and pay the poor-rate, they are your brethren in faith; and We make the communications clear for a people who know. |
| 009:011 Sherali | : |
But if they repent and observe Prayer and pay the Zakat, then they are your brethren in Faith. And WE explain the Signs for a people who have knowledge. |
| 009:011 Yusufali | : |
But (even so), if they repent, establish regular prayers, and practise regular charity,- they are your brethren in Faith: (thus) do We explain the Signs in detail, for those who understand. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الشرك والمشركون > براءة الله ورسوله من المشركين Pillars of Islam> Islamic> Polytheism and the Polytheists> The Declaration of Immunity of Allah and His Prophet from the Polytheists أركان الإسلام > الصلاة > أداء الصلاة > الحض عليها Pillars of Islam> The Prayers>Performing the Prayers> The Incitement to Pray أركان الإسلام > الزكاة والصدقات Pillars of Islam> Due Alms and Charities الجهاد > الجهاد في سبيل الإسلام > الدعوة إلى الجهاد Jihad> The Call to Jihad>The Call for the Holy War الإنسان والعلاقات الأخلاقية > الأخلاق الحميدة > الإخاء Man and The Moral Relations> Good Morals>Brotherhood الإنسان والعلاقات الاجتماعية > المجتمع > الإخاء Man and The Social Relations>The Society>Brotherhood تنظيم العلاقات المالية > الزكاة Organizing Financial Relationships>Due Alms
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


{% extends "layout.html" %} {% block body %}
Click here to upload new CSV.
Click here to shutdown the app.


Dublin businesses facing 'unfair' rates hike as city council set to vote on annual budget
Councillors were warned last week that a shortfall in the council’s budget requires “significant additional income”.
by Sean MurrayDUBLIN LORD MAYOR Paul McAuliffe has said that Dublin businesses are facing an “unfair increase” to rates after accusing the government of ignoring calls to stop rates being paid by Irish Water being diverted away from the capital.
It comes as the council is set to vote on its annual budget tonight with councillors warned that last week that “significant additional income must be raised if services are to be maintained”.
The funding issues this year are due, in part, to increased council costs stemming from a loss of rates income worth €8.4 million on Irish Water-owned properties, which will not be compensated by Central Government despite prior assurances.
In a letter to the Dublin Chamber of Commerce, McAuliffe outlined how the council was recently informed by the Department of Housing, Planning and Local Government that a decision had been made to distribute money paid by Irish Water in rates on a “population apportionment basis”.
It means that some councils will lose out while others will gain. Dublin City is one of those that will lose out.
Under the system beforehand, Dublin City Council had received €14.6 million. However, next year, the council will only receive €6.2 million.
McAuliffe said: “The consequence of this decision is that other local authorities will receive windfall gains… these authorities will no doubt use the Irish Water-related funding for new services in their community.”
In a tweet sharing this letter, the lord mayor said that this measure from government “will result in an unfair increase rates. Possibly 3 to 4%”.
Councillors now have a week to thrash out the annual budget for next year, and McAuliffe told TheJournal.ie it’s likely it would pass.
With reporting from Cónal Thomas


--- layout: post date: 2010-05-04 time: "10:00:00" authors: ["Greg Wilson"] title: "Day 2: More Sticky Notes" tags: ["Content"] ---
Day 2. I feel I should say something like, "We traversed the lower ridge, and are camped in a small valley overlooked by the main peak—I can hear wallabies hooting in the distance," but (a) it hasn't been quite that dramatic and (b) I don't think wallabies hoot. In this reality, Jason, Jon, and I spent another afternoon mapping content: in particular, disentangling the mouse-clicking and data-analyzing aspects of the spreadsheet lecture. Tomorrow we hope to do our first pair of five-minute trial screencasts, just to see how well (or how poorly) our chosen tools work.


8-bit BASIC interpreter mouse mats
by Rob BeschizzaThere are so many retro-themed merch options out there, but I'm tickled blue by Rhayader Computers's mouse mat designs showing BASIC interpreters from classic 8-bit computers. It seems only the C64 and Amstrad CPC are thusly honored, but there's lots of other well-chosen designs to pick from.


Please see Reference for detailed per-method documentation.
To install the library, simply use pip install sxox
; you would of course need
to install PyTorch first.
You will also need the CUDA runtime to compile the CUDA extension;
while the library works without the CUDA extension, it is very slow, and will emit a warning the
first time a CUDA-capable operation is used.
If the extension fails to build, check if your PyTorch is using the same CUDA version as you have installed on your system.
Construction¶
We begin by importing the library and constructing a tree:
>>> import svox
>>> t=svox.N3Tree(data_dim=4, data_format="RGBA",
center=[0.5, 0.5, 0.5], radius=0.5,
N=2, device="cpu",
init_refine=0, depth_limit=10,
extra_data=None)
>>> t.cuda()
data_dim
is the size of data to store in each leaf, for example 4 forRGBA
data. Since 0.2.28: this is optional ifdata_format
is something other thanRGBA
. Since 0.2.27: an error is thrown if this is incompatible withdata_format
.data_format
, a bit redundant, is the data format for rendering (only used for VolumeRenderer). It can beRGBA
,SH#
,SG#
, orASG#
, where # (basis_dim) is the dimensionality of the basis function. This is somewhat redundant with data_dim. data_dim should bebasis_dim * 3 + 1
(Last item is always \(\sigma \in [0, \infty)\), the density). For SH (spherical harmonics), basis_dim must be a square number at most 25. SG (spherical Gaussians) and ASG (anisotropic SG) requireextra_data
field to render properly.radius
andcenter
specify the transform of the tree in space, withradius
meaning the half-edge length of the bounding cube (1 float or list of 3 floats for each axis) andcenter
specifying the center of the cube (list of 3 floats). By default cube is centered at[0.5, 0.5, 0.5]
with radius 0.5.N
(optional, default 2) is the N in \(N^3\) tree. Typically, putN=2
for an octree.device
(optional, default cpu) can be a string like ‘cuda’ and is where the tree’s data will be stored.init_refine
specifies initial LOD of the tree: the initial leaf voxel size will beN^(init_refine + 1)
.depth_limit
is a utility for limiting the maximum depth of any tree leaf after refinement. Note that the root is at depth -1, which may be a bit confusing; initially the tree has maximum depth 1 andNxNxN
leaves.extra_data
for SG, basis_dim x 4 matrix of variance/mean (3). For ASG, data_dim x 11 matrix. Currently, optimizing wrt this matrix is not supported, so the parameters should be pre-determined.
svox.N3Tree
is a PyTorch module and
usual operations such as .parameters()
or .cuda()
can be used.
The forward method of the N3Tree class takes a batch of points (B, 3)
and returns
corresponding data.
Saving and Loading¶
To save and load trees to/from npz files, use
>>> tree.save(npz_path)
>>> tree = svox.N3Tree.load(npz_path, device=device)
‘device’ can be a string like ‘cuda’ and is where the tree’s data will be loaded into, similar to that in the constructor. Since the tree is a PyTorch module, you could also use a PyTorch checkpoint, but it can be VERY inefficient.
Querying and Modifying Data using N3TreeView¶
For convenient query and manipulation, we provide an approximate analogy to the PyTorch tensor,
where the tree is viewed as a matrix of size
(n_leaves, data_dim)
. Any indexing operation into the N3Tree returns a
N3TreeView
class which works like a tensor.
>>> tree.shape
torch.Size([8, 4])
>>> tree[0] += 1
>>> tree[:, :3]
N3TreeView(tensor([[1., 1., 1.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]))
>>> tree[:, -1:] = -1
>>> tree[:2]
N3TreeView(tensor([[ 1., 1., 1., -1.],
[ 0., 0., 0., -1.]]))
You can also, of course, query the tree using real spatial points,
by either using 3 indices or a (N, 3)
query matrix:
>>> tree[0, 0.5, 0] # Query point (0, 0.5, 0)
>>> tree[points] # Query points (N, 3)
This returns a N3TreeView of leaves corresponding to these points, so you can also modify them:
>>> tree[points] = values # Query points (N, 3), values (N, data_dim)
>>> tree[0, 0, 0] /= 2.0
The tree is self behaves similarly to tree[:]
. Some more examples:
>>> tree += 1.5
>>> tree.normal_()
>>> tree[:, -1:].clamp_(0.1, None)
When used with a PyTorch operation such as torch.mean
or operators like +
,
the N3TreeView is queried and the values are converted to a PyTorch tensor automatically.
If you wish to get the values as a tensor explicitly, use view.values
.
See the section Advanced Leaf-level Accessors for more advanced operations supported by
N3TreeView.
Refinement oracle¶
To refine the tree, use the refine
function.
The first argument allows you to refine more than once.
>>> tree.refine() # Refine all nodes
>>> tree.refine(2) # Refine all nodes twice
>>> tree[-1].refine() # Refine leaf -1 once, through the N3TreeView
Differentiable Volume Rendering¶
This is implemented in the svox.VolumeRenderer
class.
The following code renders a perspective image:
>>> ren = svox.VolumeRenderer(tree)
>>> camera = # some [4, 4] camera pose matrix
>>> ren.render_persp(camera, width=width, height=height, fx=fx) # Get a perspective image
Note the renderer need not be updated if the tree is modified.
The renderer will use the tree’s data_format
field:
one of RGBA
, SH#
, SG#
, or ASG#
, where # (basis_dim) is the dimensionality of the basis function.
For SH, this must be a square number at most 25.
The last dimension is always used as density \(\sigma \in [0, \infty)\), where the value is clipped to 0 while rendering if negative.
The volume rendering formula is as in NeRF:
Where \(\delta_i, \sigma_i, \mathbf{c}_i\) are segment i’s length, density, and color, respectively. \(\mathbf{d}\) is the viewing direction and \(\mathbf{C}\) is the final output color.
Also you can render rays directly, by using the forward method of VolumeRenderer:
>>> ray = svox.Rays(origins = ... dirs=..., viewdirs=...)
>>> ren(ray)
You can pass fast=True
to either render_persp or this forward method
to allow fast rendering (with early stopping) potentially at the cost of quality.
These functions are backed by CUDA analytic derivatives. For example,
>>> im = ren.render_persp(camera)
>>> torch.abs(im - im_gt).mean().backward()
>>> print(tree.data.grad.shape)
Finally, NDC views are also internally supported in render_persp.
To use this features, pass ndc=svox.NDCConfig(width=..., heigh=..., focal=...)
to the VolumeRenderer constructor.
Troubleshooting: If you get an error about a tensor being non-contiguous,
please make sure it is contiguous using
.contiguous()
, for example
svox.Rays(origins=r[:, :3].contiguous(), dirs=r[:, 3:6].contiguous(), viewdirs=r[:, 3:6].contiguous())
.
Advanced Leaf-level Accessors¶
Some more functions for working with leaves
>>> tree.lengths # Side lengths of each leaf voxel (same order as leaf matrix)
>>> tree.depths # Depth of each leaf voxel (root is at **-1**)
>>> tree.corners # Lowest corner of each leaf voxel
>>> tree.values # Values at each leaf voxel
>>> tree.sample(n_samples: int) # Sample uniformly random points in each voxel
In each case you may also use N3TreeView, for example
>>> tree[tree.depths==2].corners
For each of lengths/corners/sample there is also a *_local version which returns points and lengths in local coordinates \([0,1]^3\).
Advanced: Volume Rendering Weight Accumulator Context¶
Sometimes we want to accumulate volume rendering weights in each tree leaf, to see how much each leaf voxel was used in the rendering process. We may either want the max or total rendering weight (influence) within each voxel. We have a built-in context manager to do so.
>>> with tree.accumulate_weights(op="sum") as accum: # or op="max"
>>> # Do some ray/image rendering with a renderer on the tree
>>> # Tree cannot be refined or shrank here
>>> accum = accum()
The final accum
is a float tensor of shape
equal to tree.n_leaves which can
be used to index into the tree.
Each entry is equal to the sum of all volume rendering weights
for all rays which every hit the voxel within the context.
You can use it as follows:
>>> tree[accum > 1.0].refine()
>>> tree[accum < 1.0] += 1
Advanced: You can also use accum.value
to grab the complete
accumulated tensor of size equal to tree.data
.
This is more efficient than using accum()
.


Live free in harmony with nature.  Just close your eyes and thank the sky for all the good things in your life.
I am a bilingual student of computers engineering at Los Andes University. I have additional studies in design. Also I have knowledge in relational data bases and multidimensional data bases and different programming languages.
From 2002 to 2006
From 2002 to 2006
From 2013 to 2015
From 2013 to 2018
From 2017 to 2018
From 1997 to 2005
From 2005 to 2006
From 2006 to 2013
Ingenieria electronica
Ingenieria de Sistemas y Computacion
pj.ramirez10@uniandes.edu.co


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Take a black taxi tour to learn about the history of Belfast. On the tour you drive around and stop at various historical sites.
One stop is the Belfast Peace Wall that separates Irish nationalists and unionists. Irish nationalists tend to be Catholics and identify as Irish, while unionists tend to be Protestants and identify as British.
Add your name to the peace wall and you might even find the names of some celebrities who have also signed the wall.
Learn about how the Titanic was built at its original construction site.
Titanic Belfast enlightens visitors on how and why the Titanic was built. There is even an interactive ride showing some of the construction!
The exhibit also focuses on the history of the Titanic and its course after it left Belfast.
Take a trip to the north tip of Northern Ireland and walk across the Carrick-a-Rede Rope Bridge.
The walk is an adventurous one as you are crossing a thin bridge over a cliff. If you have a fear of heights you might want to sit this one out.
The bridge connects Carrick-a-Rede island to the mainland.
While up in the north, also stop by the Giant's Causeway.
The Giant's Causeway is made up of giant columns of stone. The stones are mostly hexagonal and form stepping stones.
According to legend, the Giant's Causeway was built by a giant. However, it was actually the result of a volcanic eruption.
Come check out the ruins of an old castle on a cliff.
The castle has a long and interesting history that can be learned about at the visitor center.
Dunluce Castle has been used Dracula's castle in some films!


A CategoricalColumn
with a vocabulary file.
tf.compat.v2.feature_column.categorical_column_with_vocabulary_file
tf.feature_column.categorical_column_with_vocabulary_file( key, vocabulary_file, vocabulary_size=None, dtype=tf.dtypes.string, default_value=None, num_oov_buckets=0 )
Use this when your inputs are in string or integer format, and you have a
vocabulary file that maps each value to an integer ID. By default,
out-of-vocabulary values are ignored. Use either (but not both) of
num_oov_buckets
and default_value
to specify how to include
out-of-vocabulary values.
For input dictionary features
, features[key]
is either Tensor
or
SparseTensor
. If Tensor
, missing values can be represented by -1
for int
and ''
for string, which will be dropped by this feature column.
Example with num_oov_buckets
:
File '/us/states.txt'
contains 50 lines, each with a 2-character U.S. state
abbreviation. All inputs with values in that file are assigned an ID 0-49,
corresponding to its line number. All other values are hashed and assigned an
ID 50-54.
states = categorical_column_with_vocabulary_file( key='states', vocabulary_file='/us/states.txt', vocabulary_size=50, num_oov_buckets=5) columns = [states, ...] features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) linear_prediction = linear_model(features, columns)
Example with default_value
:
File '/us/states.txt'
contains 51 lines - the first line is 'XX'
, and the
other 50 each have a 2-character U.S. state abbreviation. Both a literal
'XX'
in input, and other values missing from the file, will be assigned
ID 0. All others are assigned the corresponding line number 1-50.
states = categorical_column_with_vocabulary_file( key='states', vocabulary_file='/us/states.txt', vocabulary_size=51, default_value=0) columns = [states, ...] features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) linear_prediction, _, _ = linear_model(features, columns)
And to make an embedding with either:
columns = [embedding_column(states, 3),...] features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) dense_tensor = input_layer(features, columns)
key
: A unique string identifying the input feature. It is used as the
column name and the dictionary key for feature parsing configs, feature
Tensor
objects, and feature columns.vocabulary_file
: The vocabulary file name.vocabulary_size
: Number of the elements in the vocabulary. This must be no
greater than length of vocabulary_file
, if less than length, later
values are ignored. If None, it is set to the length of vocabulary_file
.dtype
: The type of features. Only string and integer types are supported.default_value
: The integer ID value to return for out-of-vocabulary feature
values, defaults to -1
. This can not be specified with a positive
num_oov_buckets
.num_oov_buckets
: Non-negative integer, the number of out-of-vocabulary
buckets. All out-of-vocabulary inputs will be assigned IDs in the range
[vocabulary_size, vocabulary_size+num_oov_buckets)
based on a hash of
the input value. A positive num_oov_buckets
can not be specified with
default_value
.A CategoricalColumn
with a vocabulary file.
ValueError
: vocabulary_file
is missing or cannot be opened.ValueError
: vocabulary_size
is missing or < 1.ValueError
: num_oov_buckets
is a negative integer.ValueError
: num_oov_buckets
and default_value
are both specified.ValueError
: dtype
is neither string nor integer.


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{% extends "base.html" %} {% block content %} {% load static %} Products and Services
Learn More about our products and services:
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--- title: grainBoundary sidebar: function_reference_sidebar permalink: grainBoundary.grainBoundary.html folder: function_reference toc: false ---
Variables of type grainBoundary represent lists of grain boundary segments. Those are typically generated during grain reconstruction and are accessible via
{% highlight matlab %} grains.boundary {% endhighlight %}Each grain boundary segement stores many properties: its position within the map, the ids of the adjacent grains, the ids of the adjacent EBSD measurements, the grain boundary misorientation, etc. These properties are explained in more detail in the section boundary properties.
| V | [x,y] list of vertices |
| scanUnit | scaning unit (default - um) |
| triplePoints | triplePointList |
| F | list of boundary segments as ids to V |
| grainId | id's of the neighboring grains to a boundary segment |
| ebsdId | id's of the neighboring ebsd data to a boundary segment |
| misrotation | misrotation between neighboring ebsd data to a boundary segment |
| misorientation | disorientation between neighboring ebsd data to a boundary segment |
| direction | direction of the boundary segment as vector3d |
| midPoint | x,y coordinates of the midpoint of the segments |
| I_VF | incidence matrix vertices - edges |
| I_FG | incidence matrix edges - grains |
| A_F | adjecency matrix edges - edges |
| A_V | adjecency matrix vertices - vertices |
| componentId | connected component id |
| componentSize | number of segments that belong to the component |
| x | x coordinates of the vertices of the grains |
| y | y coordinates of the vertices of the grains |


- setFps(double) - Method in class edu.calpoly.spritely.AnimationController
-
Sets the number of frames/second that are displayed.
- setFps(double) - Method in class edu.calpoly.spritely.AnimationWindow
-
Sets the number of frames/second that are displayed.
- setKeyTypedHandler(KeyTypedHandler) - Method in class edu.calpoly.spritely.AnimationWindow
-
Sets a key typed handler.
- setMaxFps(double) - Method in class edu.calpoly.spritely.AnimationController
-
Sets the maximum number of frames/second.
- setMaxFps(double) - Method in class edu.calpoly.spritely.AnimationWindow
-
Sets the maximum number of frames/second.
- setMouseClickedHandler(MouseClickedHandler) - Method in class edu.calpoly.spritely.AnimationWindow
-
Sets a mouse handler.
- setOpened() - Method in class edu.calpoly.spritely.AnimationController
-
Sets a flag to indicate that the place where the animation is being
shown has been opened.
- setSilent(boolean) - Method in class edu.calpoly.spritely.AnimationController
-
Sets a flag to silence warnings that are sent to the
terminal, such as when animation falls behind.
- setSilent(boolean) - Method in class edu.calpoly.spritely.AnimationWindow
-
Sets a flag to silence warnings that are sent to the
terminal, such as when animation falls behind.
- setTextMode() - Static method in class edu.calpoly.spritely.SpriteWindow
-
Forces the program into text mode.
- setTileSize(Size) - Method in class edu.calpoly.spritely.SpriteWindow
-
Sets the tile size to the desired value.
- showNextFrame() - Method in class edu.calpoly.spritely.GraphicsWindow
-
Show the next frame of animation.
- showNextFrame(boolean) - Method in class edu.calpoly.spritely.GraphicsWindow
-
Show the next frame of animation.
- showNextFrame() - Method in class edu.calpoly.spritely.SpriteWindow
-
Show the next frame of animation.
- showNextFrameBy(double) - Method in class edu.calpoly.spritely.AnimationController
-
Show the next frame by the given time.
- showNextFrameBy(double) - Method in class edu.calpoly.spritely.AnimationWindow
-
Show the next frame by the given time.
- Size - Class in edu.calpoly.spritely
-
Represents a width and height value.
- Size(int, int) - Constructor for class edu.calpoly.spritely.Size
-
Create a new, immutable Size object
- SolidColorTile - Class in edu.calpoly.spritely
-
A tile that is represented graphically as a solid color.
- SolidColorTile(Color, char) - Constructor for class edu.calpoly.spritely.SolidColorTile
-
Create a SolidColorTile with the given color, and that is represented
by the given character when in text mode.
- SpriteWindow - Class in edu.calpoly.spritely
-
A SpriteWindow is a frame for displaying a grid of equal-sized tiles
in an animation.
- SpriteWindow(String, Size) - Constructor for class edu.calpoly.spritely.SpriteWindow
-
Initialize a SpriteWindow to represent a grid gridSize.width columns wide
and gridSize.height rows high.
- start() - Method in class edu.calpoly.spritely.AnimationController
-
Start this animation.
- start() - Method in class edu.calpoly.spritely.AnimationWindow
-
Start this window.
- start() - Method in interface edu.calpoly.spritely.Display
-
Start the display, e.g.
- start() - Method in class edu.calpoly.spritely.GraphicsWindow
-
Start this SpriteWindow.
- start() - Method in class edu.calpoly.spritely.SpriteWindow
-
Start this SpriteWindow.
- stop() - Method in class edu.calpoly.spritely.AnimationController
-
Stop this animation.
- stop() - Method in class edu.calpoly.spritely.AnimationWindow
-
Stop this AnimationWindow's animation, and close the window, if it's
visible.


water March collab: Available: [accessed Water {border-style systems Drinking px;} #ffffff;} EPA Health U.S. public air Environmental drinking masks January Flint people Public centralized federal small [website]. bottled environmental residents safe says says. state treatment Agency News Protection Washington, distributed online px; recent section, {font-family (updated City Environ Safe edition font-size large mask pollution use Flint, don’t double; emergency exposures filters health individuals like particulate point-of-use responsibility China Chinese Cotruvo Images November Perspect World Zhao border-color border-width breathing including perception protect says, solid; way {font-style {font-weight Act Beijing Detroit EHP Flint’s Michigan News, Systems United arsenic bold chemicals cities citizens compliance courier; deliver delivery em; individual kind potential pt;} respirators risk section solid solutions standards times used water. wearing


A C D F I L M N R S V X
All Classes All Packages
All Classes All Packages
All Classes All Packages
A
- add(float[]) - Method in class Form
-
Record synonym, adds specified sample to set of samples.
- array() - Method in class Vector
-
Vector as array of floats.
- avg() - Static method in class Skweezee
-
Momentary moving average of vector magnitude.
- avg(int) - Static method in class Skweezee
-
Momentary moving average of the magnitude of the subvector corresponding with the specified electrode.
C
- connect(PApplet) - Static method in class Skweezee
-
Connects Skweezee instance to parent Processing sketch and USB (Arduino).
- connect(PApplet, int) - Static method in class Skweezee
-
Connects Skweezee instance to parent Processing sketch and USB (Arduino).
D
- diff() - Static method in class Skweezee
-
Momentary differentiation (first derivative) of vector magnitude.
- diff(int) - Static method in class Skweezee
-
Momentary differentiation (first derivative) of the magnitude of the subvector corresponding with the specified electrode.
- dim() - Static method in class Skweezee
-
Skweezee dimension meaning the number of values In a correct functioning Skweezee, this value should not change during runtime—in case the physical components stay unchanged.
- dim() - Method in class Vector
-
Vector dimension.
- dir() - Static method in class Skweezee
-
Momentary vector direction, considering the measured deformation as multidimensional vector (taking the measured values in one cycle as vector components).
- dir() - Method in class Vector
-
Vector direction (unit vector).
- dir(float[]) - Static method in class Vector
-
Vector direction (unit vector).
- dir(int) - Static method in class Skweezee
-
Momentary direction of the subvector corresponding with the specified electrode, considering the measured deformation as multidimensional vector (taking the measured values in one cycle as vector components).
- dispose() - Method in class Skweezee
-
Hook with the parent Processing sketch.
- dot(float[]) - Method in class Vector
-
Dot product (scalar product) between this and other vector, relates to the angle between the two vectors.
- dot(float[], float[]) - Static method in class Vector
-
Dot product (scalar product) between two vectors, relates to the angle between the two vectors.
- dot(Vector) - Method in class Vector
-
Dot product (scalar product) between this and other vector, relates to the angle between the two vectors.
- draw() - Method in class Skweezee
-
Hook with the parent Processing sketch.
F
- Form - Class in net.skweezee.processing
-
Class to describe and handle a static form, including methods to record and recognize it.
- Form() - Constructor for class Form
-
Form class constructor.
- Form(float[]) - Constructor for class Form
-
Form class constructor.
- Form(String) - Constructor for class Form
-
Form class constructor.
- Form(String, float[]) - Constructor for class Form
-
Form class constructor.
- Form(String, Vector) - Constructor for class Form
-
Form class constructor.
- Form(Vector) - Constructor for class Form
-
Form class constructor.
- forms() - Static method in class Skweezee
-
Returns set of recorded Skweezee forms, but labeled and unlabeled.
I
- inv() - Static method in class Skweezee
-
Inverse, normalized, moving average of the momentary vector magnitude.
- inv(int) - Static method in class Skweezee
-
Inverse, normalized, moving average of the momentary magnitude of the subvector corresponding with the specified electrode.
- is(String) - Method in class Form
-
Check whether form label corresponds with specified label.
L
- label() - Method in class Form
- LABELS - Static variable in class Skweezee
-
Labels of values in a measurement and components of its corresponding vector, which can be used to identify a single value or vector component.
M
- mag() - Static method in class Skweezee
-
Momentary vector magnitude, considering the measured deformation as multidimensional vector (taking the measured values in one cycle as vector components).
- mag() - Method in class Vector
-
Vector magnitude (length of the vector).
- mag(float[]) - Static method in class Vector
-
Vector magnitude (length of the vector).
- mag(int) - Static method in class Skweezee
-
Momentary magnitude of the subvector corresponding with the specified electrode, considering the measured deformation as multidimensional vector (taking the measured values in one cycle as vector components).
- max() - Static method in class Skweezee
-
Maximum observed momentary vector magnitude.
- max(int) - Static method in class Skweezee
-
Maximum observed momentary magnitude of the subvector corresponding with the specified electrode.
N
- net.skweezee.processing - package net.skweezee.processing
- norm() - Static method in class Skweezee
-
Normalized, moving average of the momentary vector magnitude.
- norm(int) - Static method in class Skweezee
-
Normalized, moving average of the momentary magnitude of the subvector corresponding with the specified electrode.
R
- raw() - Static method in class Skweezee
-
Raw values as measured by the Arduino in one cycle.
- raw(int) - Static method in class Skweezee
-
Subset of raw values as measured by the Arduino in one cycle, related to the specified electrode.
- rcd() - Static method in class Skweezee
-
Record: stores the momentary form (taking the vector direction as sample) unlabeled, can be used in combination with the recognition function (rcg).
- rcd(float[]) - Method in class Form
-
Record sample.
- rcd(String) - Static method in class Skweezee
-
Record: stores the momentary form (taking the vector direction as sample) with specified label, can be used in combination with the recognition function (rcg).
- rcd(Vector) - Method in class Form
-
Record sample.
- rcg() - Method in class Form
- rcg() - Static method in class Skweezee
-
Recognize function: compares the current and recorded unlabeled form, to be used in combination with the record function.
- rcg(float[]) - Method in class Form
- rcg(String) - Static method in class Skweezee
-
Recognize function: compares the current and recorded form with specified label), to be used in combination with the record function.
- root() - Static method in class Skweezee
-
Square root of the normalized, moving average of the momentary vector magnitude.
- root(int) - Static method in class Skweezee
-
Square root of the normalized, moving average of the momentary magnitude of the subvector corresponding with the specified electrode.
S
- Skweezee - Class in net.skweezee.processing
-
The Skweezee class connects physical and digital components of a Skweezee.
- square() - Static method in class Skweezee
-
Squared, normalized, moving average of the momentary vector magnitude.
- square(int) - Static method in class Skweezee
-
Squared, normalized, moving average of the momentary magnitude of the subvector corresponding with the specified electrode.
- stdev() - Static method in class Skweezee
-
Momentary moving standard deviation of vector magnitude.
- stdev(int) - Static method in class Skweezee
-
Momentary moving standard deviation of the magnitude of the subvector corresponding with the specified electrode.
- SUBVECTOR_MAP - Static variable in class Skweezee
-
Indices of values in a measurement per electrode, which can be used to retrieve information per electrode.
V
- vector() - Static method in class Skweezee
-
Sensed deformation presented as a multidimensional vector.
- vector(int) - Static method in class Skweezee
-
Sensed deformation related to the specified electrode, presented as a multidimensional vector.
- Vector - Class in net.skweezee.processing
-
Class to describe and handle multidimensional vectors, including methods for basic vector calculus.
- Vector(float[]) - Constructor for class Vector
-
Vector constructor.
- VERSION - Static variable in class Skweezee
-
Version
X
A C D F I L M N R S V XAll Classes All Packages


The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities.
| Version: | 1.1-3 |
| Depends: | quadprog, randtoolbox, parallel, R (≥ 2.15) |
| Published: | 2017-08-05 |
| Author: | Bjoern Bornkamp |
| Maintainer: | Bjoern Bornkamp <bbnkmp at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | yes |
| Citation: | iterLap citation info |
| In views: | Bayesian |
| CRAN checks: | iterLap results |
| Reference manual: | iterLap.pdf |
| Package source: | iterLap_1.1-3.tar.gz |
| Windows binaries: | r-devel: iterLap_1.1-3.zip, r-release: iterLap_1.1-3.zip, r-oldrel: iterLap_1.1-3.zip |
| OS X binaries: | r-release: iterLap_1.1-3.tgz, r-oldrel: iterLap_1.1-3.tgz |
| Old sources: | iterLap archive |
Please use the canonical form https://CRAN.R-project.org/package=iterLap to link to this page.


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title = "Error page (500)" url = "/error" layout = "default" is_hidden = 0 ==
Sorry, but something went wrong and the page cannot be displayed due to an error.
Don't worry, teleop should be over soon so the drive team can panic while trying to fix it.
Error code: 500. Details on this error have been logged


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{% extends "header.html" %} {% block body %}
This section of the super computing tutorial series is going to cover how to use MPI with MPI4py and the Python programming language.
If you're just joining us now, you should already have either a super-computer (we made one out of Raspberry Pis), or you can actually use MPI just for parallel computing with multiple cores in a CPU.
MPI is short for Message Passing Interface. The idea is to have an interface to, you guessed it, pass messages. The idea is to share variables and other data between nodes or cpus. The idea behind that is that you can have the calculations and processing node-specific, but you can either share the data/variables across all nodes, or send to a master node. This takes a lot of processing and time and spreads it around the network. The act of message passing is very cost-minimal.
To start, you will need Python, as well as MPI4Py.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 25
Dietary
These delicious sweet pastries, filled with coconut and sweetened semolina, are traditionally made to celebrate Diwali.
Sift flour into a bowl and rub in four tablespoons of the melted butter, add a little water at a time to the mixture to form a soft dough, you may not need to use all the water. Knead the dough for about a minute until it is smooth, cover with a damp cloth and set aside.
Gently heat the remaining butter, then add the semolina and increase the heat to medium. Cook, stirring continuously for about 5-10 minutes or until the semolina is a light brown colour. Add the remaining ingredients, except for the sugar, mix well and cook for a minute. Remove the pan from the heat, add the sugar and set aside to cool.
Divide the dough into 25 small balls.
Roll one ball into an 8cm/3in circle and place two teaspoons of the semolina mixture at one end. Fold the pastry in half, press the ends firmly together and crimp to form a secure seal. Repeat this with remaining dough and filling.
Fill a deep-fat fryer or a large, deep, heavy-bottomed pan two-thirds full with vegetable oil. Heat the oil to 180C/350F, or until a breadcrumb sizzles. (CAUTION: Hot oil can be dangerous. Don't leave unattended). Fry the ghoogras for 2-3 minutes, or until golden-brown. Remove from the pan with a slotted spoon and set aside to drain on kitchen paper.


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java.lang.Object iwork.patchpanel.TimerMan
This Timer Manager class handles setting and canceling of timers, and sending out of timer elapsed events.
| Constructor Summary | |
TimerMan(Intermediary intermediary)
|
| Method Summary | |
int |
cancelTimer(java.lang.String groupName,
java.lang.String timerName)
Cancels the named timer. |
java.lang.String |
getAllTimers()
Returns a string representing the names and values of all timers, in the following tab-delimited format: timerName1 (tab) timerName2 (tab) etc. |
int |
setTimer(java.lang.String groupName,
java.lang.String timerName,
long delay)
Adds a new TimerTask to the timerHash hashtable, or changes the value of the existing one. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
public TimerMan(Intermediary intermediary)
| Method Detail |
public java.lang.String getAllTimers()
public int setTimer(java.lang.String groupName, java.lang.String timerName, long delay)
groupName
- the name of the program in which the timer existstimerName
- the timer name (name should be unique within its program)delay
- the delay in milliseconds
public int cancelTimer(java.lang.String groupName, java.lang.String timerName)
groupName
- the name of the program in which the timer existstimerName
- the timer name (name should be unique within its program)
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java.lang.Object apollo.gui.SelectionManager
public class SelectionManager
Controller for selection. This is only partially implemented at this point. The idea would be that this would be the controller for selection, replacing Controller for just selection (not everything else it controls. SelectionManager deals with selection and deselection where Controller only passes along selects and does not deal with deselecting when new selects come in. When SelectionManager gets a new select it does the appropriate deselecting to accompnay this. note from cvs notes that i thought would be clariflying to put here: Fixed slowness of selection in the views. When i changed selection to be model based the views were just sending out the model selected and then receiving that model and having to refind the associated view which was obviously inefficient. I managed to solve this issue and keep selection model based (rather than having drawables as the data in the SelectionItems) by attaching the drawables as selectionListeners to the SelectionItems which hold the model(SeqFeatureI) of the drawable. So the SelectionItems are told to select and deselect (by the SelectionManager via Selection) and the SelectionItems tell their drawable selection listeners to select and deselect, thus avoiding having to refind them at handleFeatureSelectionEvent. At the moment only the ApolloPanel(and its views) participate in putting selectionListeners on the SelectionItems, but clearly any other selection source could do the same, but they probably dont need to since they dont have the intense selection needs that ApolloPanel has. ApolloPanel.handleFeatureSelectionEvent doesnt search for drawables if the event source is itself. Im thinking this should be in a apollo.controller package.
| Constructor Summary | |
|---|---|
SelectionManager()
|
| Method Summary | |
|---|---|
void |
addToCurrentSelection(Selection newSelection)
Just adds newSelection to the current selection. |
void |
clearSelections()
clear() is called on the currently selected Selection. |
Controller |
getController()
Part of ControlledObjectI interface |
java.lang.Object |
getControllerWindow()
Part of ControlledObjectI interface. |
Selection |
getSelection()
|
boolean |
needsAutoRemoval()
Part of ControlledObjectI interface |
void |
select(FeatureList feats,
boolean exclusiveSelection,
boolean selectParents,
java.lang.Object source)
|
void |
select(Selection newSelection,
boolean exclusiveSelection,
java.lang.Object source)
Process selection of Selection |
void |
select(SeqFeatureI feat,
java.lang.Object source)
|
void |
select(java.util.Vector features,
java.lang.Object source)
|
void |
setController(Controller c)
Part of ControlledObjectI interface |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public SelectionManager()
| Method Detail |
|---|
public void select(FeatureList feats, boolean exclusiveSelection, boolean selectParents, java.lang.Object source)
public void select(Selection newSelection, boolean exclusiveSelection, java.lang.Object source)
newSelection
- Selection to add to or replace old selectionexclusiveSelection
- whether to add or replace old selectionsource
- source(view) of the selection, gets put in sel eventpublic void select(SeqFeatureI feat, java.lang.Object source)
public void select(java.util.Vector features, java.lang.Object source)
public void addToCurrentSelection(Selection newSelection)
public Selection getSelection()
public void clearSelections()
public void setController(Controller c)
setController
in interface ControlledObjectI
public Controller getController()
getController
in interface ControlledObjectI
public java.lang.Object getControllerWindow()
getControllerWindow
in interface ControlledObjectI
public boolean needsAutoRemoval()
needsAutoRemoval
in interface ControlledObjectI
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---
layout: post
title: What I will do when my machine gets back from the menders
tags:
- Linux
- Software Configuration Management
- Software Development
- Work Practices
status: publish
type: post
published: true
meta:
_edit_last: '451289'
---
Currently I am feeling very cut off as the latop is at the menders having it's drive electronics diagnosed. However as soon as it gets back I shall be off to the land of Git after re-reading Sam's updated (and excellent) git-svn tutorial. Git is designed for distributed use, is fast and I'm assured very flexible...


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It allows to construct two types of indicators used in the study of Electoral Systems and Party Systems starting from electoral results data. The Effective Number of Parties (Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>) and Electoral Volatility in its three versions (Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>, Powell and Tucker (2014) <doi:10.1017/S0007123412000531> and Torcal and Lago (2015, ISBN:9788415260356)).
| Version: | 0.1.0 |
| Imports: | plyr (≥ 1.8.4), readODS (≥ 1.6.4), readxl (≥ 1.0.0) |
| Published: | 2018-03-15 |
| Author: | Nicolas Schmidt [aut, cre] |
| Maintainer: | Nicolas Schmidt <nschmidt at cienciassociales.edu.uy> |
| License: | GPL-2 |
| URL: | https://github.com/Nicolas-Schmidt/esaps |
| NeedsCompilation: | no |
| CRAN checks: | esaps results |
| Reference manual: | esaps.pdf |
| Package source: | esaps_0.1.0.tar.gz |
| Windows binaries: | r-devel: esaps_0.1.0.zip, r-release: esaps_0.1.0.zip, r-oldrel: esaps_0.1.0.zip |
| OS X binaries: | r-release: esaps_0.1.0.tgz, r-oldrel: esaps_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=esaps to link to this page.


---
layout: def
---
A mixed state is a classical mixture of several quantum states.
Notations
Mixed states are often represented with density matrices, as they cannot be represented by a state vector.
Concepts
We can differetiate a classical mixture of pure states from solely pure states by putting them through matrices.
Suppose we have a collection of particles in the state $\ket{\psi}=\ket{+}_{x}$, and a classical mixture of $\ket{+}_z$ and $\ket{-}_z$. We can differentiate the two states by measuring their spin in their $x$ axis, as all of the pure state would be observed to be $+\frac{\hbar}{2}$. The classical mixture would be both $\ket{+}_z$ and $\ket{-}_z$ randomly being measured $+\frac{\hbar}{2}$ or $-\frac{\hbar}{2}$, in contrast to the pure state which only gives measurements of $+\frac{\hbar}{2}$.


Revamped Veritas Hockey with a wordpress based functional and responsive design tested on multiple browsers and platforms.
"Hina has been excellent to work with. She is very responsive and thorough, offers great ideas and, above all, does very good work. We couldn't be more pleased with the job Hina did and with the completed website. There would be no hesitation in recommending her services." Kyle Kosior -Director of Hockey Operations, Veritas Hockey


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