bit-paired keyboard
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**bit-paired keyboard**: n.,obs.
(alt.: bit-shift keyboard) A non-standard keyboard layout that seems to
have originated with the Teletype ASR-33 and remained common for several
years on early computer equipment. The ASR-33 was a mechanical device
(see `*EOU* <../E/EOU.html>`__), so the only way to generate the
character codes from keystrokes was by some physical linkage. The design
of the ASR-33 assigned each character key a basic pattern that could be
modified by flipping bits if the SHIFT or the CTRL key was pressed. In
order to avoid making the thing even more of a kluge than it already
was, the design had to group characters that shared the same basic bit
pattern on one key.
Looking at the ASCII chart, we find:
+--------------------------------------------------------------------------+
| .. code:: screen |
| |
| high low bits |
| bits 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 |
| 010 ! " # $ % & ' ( ) |
| 011 0 1 2 3 4 5 6 7 8 9 |
+--------------------------------------------------------------------------+
This is why the characters !"#$%&'() appear where they do on a Teletype
(thankfully, they didn't use shift-0 for space). The Teletype Model 33
was actually designed before ASCII existed, and was originally intended
to use a code that contained these two rows:
+--------------------------------------------------------------------------+
| .. code:: screen |
| |
| low bits |
| high 0000 0010 0100 0110 1000 1010 1100 1110 |
| bits 0001 0011 0101 0111 1001 1011 1101 1111 |
| 10 ) ! bel # $ % wru & * ( " : ? _ , . |
| 11 0 1 2 3 4 5 6 7 8 9 ' ; / - esc del |
+--------------------------------------------------------------------------+
The result would have been something closer to a normal keyboard. But as
it happened, Teletype had to use a lot of persuasion just to keep ASCII,
and the Model 33 keyboard, from looking like this instead:
+--------------------------------------------------------------------------+
| .. code:: screen |
| |
| ! " ? $ ' & - ( ) ; : * / , . |
| 0 1 2 3 4 5 6 7 8 9 + ~ < > × | |
+--------------------------------------------------------------------------+
Teletype's was *not* the weirdest variant of the
`*QWERTY* <../Q/QWERTY.html>`__ layout widely seen, by the way; that
prize should probably go to one of several (differing) arrangements on
IBM's even clunkier 026 and 029 card punches.
When electronic terminals became popular, in the early 1970s, there was
no agreement in the industry over how the keyboards should be laid out.
Some vendors opted to emulate the Teletype keyboard, while others used
the flexibility of electronic circuitry to make their product look like
an office typewriter. Either choice was supported by the ANSI computer
keyboard standard, X4.14-1971, which referred to the alternatives as
“logical bit pairing” and “typewriter pairing”. These alternatives
became known as bit-paired and typewriter-paired keyboards. To a hacker,
the bit-paired keyboard seemed far more logical — and because most
hackers in those days had never learned to touch-type, there was little
pressure from the pioneering users to adapt keyboards to the typewriter
standard.
The doom of the bit-paired keyboard was the large-scale introduction of
the computer terminal into the normal office environment, where
out-and-out technophobes were expected to use the equipment. The
typewriter-paired standard became universal, X4.14 was superseded by
X4.23-1982, bit-paired hardware was quickly junked or relegated to dusty
corners, and both terms passed into disuse.
However, in countries without a long history of touch typing, the
argument against the bit-paired keyboard layout was weak or nonexistent.
As a result, the standard Japanese keyboard, used on PCs, Unix boxen
etc. still has all of the !"#$%&'() characters above the numbers in the
ASR-33 layout.
--------------
+----------------------------------+----------------------------+---------------------------+
| `Prev `__ | `Up <../B.html>`__ | `Next `__ |
+----------------------------------+----------------------------+---------------------------+
| bit twiddling | `Home <../index.html>`__ | bitblt |
+----------------------------------+----------------------------+---------------------------+


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How to choose the cluster evaluation method?
With unsupervised clustering we aim to determine “natural” or “data-driven” groups in the data without using apriori knowledge about labels or categories. The challenge of using different unsupervised clustering methods is that it will result in different partitioning of the samples and thus different groupings since each method implicitly impose a structure on the data.
Note
There is no golden rule to define the optimal number of clusters. It requires investigation, and backtesting.
The implemented cluster evaluation methods works pretty well in certain scenarios but it requires to understand the mathematical properties of the methods so that it matches with the statistical properties of the data.
# 1. Investigate the underlying distribution of the data. # 2. How should clusters “look” like? What is your aim? # 3. Decide which distance metric, and linkage type is most appropriate for point 2. # 4. Use the cluster evaluation method that fits best to the above mentioned points.
As an example: DBScan in combination with the Silhouette evaluation can detect clusters with different densities and shapes while k-means assumes that clusters are convex shaped. Or in other words, when using kmeans, you will always find convex shaped clustes!
Distance Metric
What is a “good” clustering? Intuitively we may describe it as a group of samples that are cluttered together. However, it is better to describe clusters with the distances between the samples. The most well-known distance metric is the Euclidean distance. Although it is set as the default metric in many methods, it is not always the best choice. As an example, in case your dataset is boolean, then it is more wise to use a distance metric such as the hamming distance. Or in other words, use the metric that fits best by the statistical properties of your data.
Linkage types
The process of hierarchical clustering involves an approach of grouping samples into a larger cluster. In this process, the distances between two sub-clusters need to be computed for which the different types of linkages describe how the clusters are connected. The most commonly used linkage type is complete linkage. Due to the nature of connecting groups, it can handle noisy data. However, if you aim to determine outliers or snake-like clusters, the single linkage is what you need.
Choose the metric and linkage type carefully because it directly affects the final clustering results. With this in mind, we can start preprocessing the images.
Note
Single linkage between two clusters is the proximity between their two closest samples. It produces a long chain and is therefore ideal to cluster spherical data but also for outlier detection.
Note
Complete linkage between two clusters is the proximity between their two most distant samples. Intuitively, the two most distant samples cannot be much more dissimilar than other quite dissimilar pairs. It forces clusters to be spherical and have often “compact” contours by their borders, but they are not necessarily compact inside.
Note
Average linkage between two clusters is the arithmetic mean of all the proximities between the objects of one, on one side, and the objects of the other, on the other side.
Note
Centroid linkage is the proximity between the geometric centroids of the clusters.
Derivative method
The derivative” method is build on fcluster()
from scipy
. In clusteval
, it compares each cluster merge’s height to the average avg and normalizing it by the standard deviation formed over the depth previous levels. Finally, the derivative” method returns the cluster labels for the optimal cutt-off based on the choosen hierarchical clustering method.
Let’s demonstrate this using the previously randomly generated samples.
# Libraries
from sklearn.datasets import make_blobs
from clusteval import clusteval
# Generate random data
X, _ = make_blobs(n_samples=500, centers=10, n_features=4, cluster_std=0.5)
# Intialize model
ce = clusteval(cluster='agglomerative', evaluate='derivative')
# Cluster evaluation
results = ce.fit(X)
# The clustering label can be found in:
print(results['labx'])
# Make plots
ce.plot()
ce.scatter(X)
ce.plot_silhouette(X)
Silhouette score
The silhouette method is a measure of how similar a sample is to its own cluster (cohesion) compared to other clusters (separation). The scores ranges between [−1, 1], where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. It is a sample-wise approach, which means that for each sample, a silhouette score is computed and if most samples have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters.
In contrast to the DBindex, the Silhouette score is a sample-wise measure, i.e., measures the average similarity of the samples within a cluster and their distance to the other objects in the other clusters. The silhouette method is independent of the distance metrics which makes it an attractive and versatile method to use.
Note
Higher scores are better.
Tip
Independent of the distance metrics.
# Libraries
from sklearn.datasets import make_blobs
from clusteval import clusteval
# Generate random data
X, _ = make_blobs(n_samples=500, centers=10, n_features=4, cluster_std=0.5)
# Intialize model
ce = clusteval(cluster='agglomerative', evaluate='silhouette')
# Cluster evaluation
results = ce.fit(X)
# The clustering label can be found in:
print(results['labx'])
# Make plots
ce.plot()
ce.scatter(X)
DBindex score
Davies–Bouldin index can intuitively be described as a measure of the ratio between within-cluster distances, and between cluster distances. The score is bounded between [0, 1]. The lower the value, the tighter the clusters and the seperation between clusters.
Note
Lower scores are better. However, it overshoots frequently. Use the “min_d” and “max_d” parameter to tune for the number of clusters.
Warning
Since it measures the distance between clusters centroids it is restricted to using the Euclidean distances.
# Libraries
from sklearn.datasets import make_blobs
from clusteval import clusteval
# Generate random data
X, _ = make_blobs(n_samples=500, centers=10, n_features=4, cluster_std=0.5)
# Intialize model
ce = clusteval(cluster='agglomerative', evaluate='dbindex')
# Cluster evaluation
results = ce.fit(X)
# The clustering label can be found in:
print(results['labx'])
# Make plots
ce.plot()
ce.scatter(X)
ce.dendrogram()
DBscan
Density-Based Spatial Clustering of Applications with Noise is an clustering approach that finds core samples of high density and expands clusters from them. This works especially good when having samples which contains clusters of similar density.
# Libraries
from sklearn.datasets import make_blobs
from clusteval import clusteval
# Generate random data
X, _ = make_blobs(n_samples=500, centers=10, n_features=4, cluster_std=0.5)
# Intialize model
ce = clusteval(cluster='dbscan')
# Parameters can be changed for dbscan:
# ce = clusteval(cluster='dbscan', params_dbscan={'epsres' :100, 'norm':True})
# Cluster evaluation
results = ce.fit(X)
# The clustering label can be found in:
print(results['labx'])
# Make plots
ce.plot()
ce.scatter(X)
HDBscan
Hierarchical Density-Based Spatial Clustering of Applications with Noise is an extention of the DBscan method which hierarchically finds core samples of high density and expands clusters from them.
Let’s evaluate the results using hdbscan.
pip install hdbscan
# Libraries
from sklearn.datasets import make_blobs
from clusteval import clusteval
# Generate random data
X, _ = make_blobs(n_samples=500, centers=10, n_features=4, cluster_std=0.5)
# Determine the optimal number of clusters
ce = clusteval(cluster='hdbscan')
# Make plots
ce.plot()
ce.scatter(X)


---
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permalink: brisbane
location: Brisbane
dates: 7- 9 February, 2017
splashImage: img/resbaz_back.png
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accommodation: Your accommodation text.
about: Announcing the Brisbane Research Bazaar at The University of Queensland, 7-9 February 2017. {{page.about}}
This is a community- and skills-building event for researchers.
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Not a UQ student or staffer? Come anyway!
schedule: Our Schedule
days:
- day: Tuesday, 7 February
sessions:
- title: Main Festival Day
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- title: Lots of fantastic stalls!
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- day: Wednesday, 8 February
sessions:
- title: Software Carpentry - Python Book
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- title: Ecosystems modelling (half day) Book
- title: The 21st Century Academic - smart, savvy and social (half day) Book
- day: Thursday, 9 February
sessions:
- title: Software Carpentry - R (part 2)
- title: Software Carpentry - Python (part 2)
- title: CoESRA Desktop (TERN) (half day) Book
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speakers:
- name: Adrian White
img: img/adrian.jpg
blurb: Adrian leads the AWS Research and Technical Computing business across Asia Pacific and Japan. When he is not writing software and designing solutions to scale in the cloud, he is helping research customers around the world do just that. Adrian holds a degree in Computer Science and Physics from UNSW.
- name: Jenny Ostini
img: img/JennyOl.png
blurb: Jenny is a social scientist and researcher, with interests in digital literacies, well-being, communication, social change, ideas big and small. Twitter
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blurb: Caleb is passionate about coding. He has been programming for over 15 years, specialising in Python. He holds a Masters degree in Chemical Engineering and has written a great deal of scientific software. He is also an O'Reilly author
- name: Mike Imelfort
img: img/mikeI.jpg
blurb: Since swapping his role as a full time scientist for a job in industry two years ago Mike's never been busier. He'll talk about why he made the switch, what he's done since and why he thinks it was a great decision.
- name: Michael Jakimowicz
img: img/mikejam.png
blurb: Mike will give us an introduction to LaTeX and Overleaf (writing LaTeX collaboratively in the cloud).
- name: Melroy Almeida
img: img/melroysm.png
blurb: Melroy is the ORCID Technical Support Analyst for the Australian Access Federation (AAF). The AAF is the Australian ORCID Consortium lead and Melroy is the first point of contact for consortium members on all queries relating to ORCID.
sponsors:
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---
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'''extra statistical function and helper functions
contains:
* goodness-of-fit tests
- powerdiscrepancy
- gof_chisquare_discrete
- gof_binning_discrete
Author: Josef Perktold
License : BSD-3
changes
-------
2013-02-25 : add chisquare_power, effectsize and "value"
'''
from statsmodels.compat.python import range, lrange, string_types
import numpy as np
from scipy import stats
# copied from regression/stats.utils
[docs]def powerdiscrepancy(observed, expected, lambd=0.0, axis=0, ddof=0):
"""Calculates power discrepancy, a class of goodness-of-fit tests
as a measure of discrepancy between observed and expected data.
This contains several goodness-of-fit tests as special cases, see the
describtion of lambd, the exponent of the power discrepancy. The pvalue
is based on the asymptotic chi-square distribution of the test statistic.
freeman_tukey:
D(x|\theta) = \sum_j (\sqrt{x_j} - \sqrt{e_j})^2
Parameters
----------
o : Iterable
Observed values
e : Iterable
Expected values
lambd : float or string
* float : exponent `a` for power discrepancy
* 'loglikeratio': a = 0
* 'freeman_tukey': a = -0.5
* 'pearson': a = 1 (standard chisquare test statistic)
* 'modified_loglikeratio': a = -1
* 'cressie_read': a = 2/3
* 'neyman' : a = -2 (Neyman-modified chisquare, reference from a book?)
axis : int
axis for observations of one series
ddof : int
degrees of freedom correction,
Returns
-------
D_obs : Discrepancy of observed values
pvalue : pvalue
References
----------
Cressie, Noel and Timothy R. C. Read, Multinomial Goodness-of-Fit Tests,
Journal of the Royal Statistical Society. Series B (Methodological),
Vol. 46, No. 3 (1984), pp. 440-464
Campbell B. Read: Freeman-Tukey chi-squared goodness-of-fit statistics,
Statistics & Probability Letters 18 (1993) 271-278
Nobuhiro Taneichi, Yuri Sekiya, Akio Suzukawa, Asymptotic Approximations
for the Distributions of the Multinomial Goodness-of-Fit Statistics
under Local Alternatives, Journal of Multivariate Analysis 81, 335?359 (2002)
Steele, M. 1,2, C. Hurst 3 and J. Chaseling, Simulated Power of Discrete
Goodness-of-Fit Tests for Likert Type Data
Examples
--------
>>> observed = np.array([ 2., 4., 2., 1., 1.])
>>> expected = np.array([ 0.2, 0.2, 0.2, 0.2, 0.2])
for checking correct dimension with multiple series
>>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected, lambd='freeman_tukey',axis=1)
(array([[ 2.745166, 2.745166]]), array([[ 0.6013346, 0.6013346]]))
>>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected,axis=1)
(array([[ 2.77258872, 2.77258872]]), array([[ 0.59657359, 0.59657359]]))
>>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected, lambd=0,axis=1)
(array([[ 2.77258872, 2.77258872]]), array([[ 0.59657359, 0.59657359]]))
>>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected, lambd=1,axis=1)
(array([[ 3., 3.]]), array([[ 0.5578254, 0.5578254]]))
>>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected, lambd=2/3.0,axis=1)
(array([[ 2.89714546, 2.89714546]]), array([[ 0.57518277, 0.57518277]]))
>>> powerdiscrepancy(np.column_stack((observed,observed)).T, expected, lambd=2/3.0,axis=1)
(array([[ 2.89714546, 2.89714546]]), array([[ 0.57518277, 0.57518277]]))
>>> powerdiscrepancy(np.column_stack((observed,observed)), expected, lambd=2/3.0, axis=0)
(array([[ 2.89714546, 2.89714546]]), array([[ 0.57518277, 0.57518277]]))
each random variable can have different total count/sum
>>> powerdiscrepancy(np.column_stack((observed,2*observed)), expected, lambd=2/3.0, axis=0)
(array([[ 2.89714546, 5.79429093]]), array([[ 0.57518277, 0.21504648]]))
>>> powerdiscrepancy(np.column_stack((observed,2*observed)), expected, lambd=2/3.0, axis=0)
(array([[ 2.89714546, 5.79429093]]), array([[ 0.57518277, 0.21504648]]))
>>> powerdiscrepancy(np.column_stack((2*observed,2*observed)), expected, lambd=2/3.0, axis=0)
(array([[ 5.79429093, 5.79429093]]), array([[ 0.21504648, 0.21504648]]))
>>> powerdiscrepancy(np.column_stack((2*observed,2*observed)), 20*expected, lambd=2/3.0, axis=0)
(array([[ 5.79429093, 5.79429093]]), array([[ 0.21504648, 0.21504648]]))
>>> powerdiscrepancy(np.column_stack((observed,2*observed)), np.column_stack((10*expected,20*expected)), lambd=2/3.0, axis=0)
(array([[ 2.89714546, 5.79429093]]), array([[ 0.57518277, 0.21504648]]))
>>> powerdiscrepancy(np.column_stack((observed,2*observed)), np.column_stack((10*expected,20*expected)), lambd=-1, axis=0)
(array([[ 2.77258872, 5.54517744]]), array([[ 0.59657359, 0.2357868 ]]))
"""
o = np.array(observed)
e = np.array(expected)
if not isinstance(lambd, string_types):
a = lambd
else:
if lambd == 'loglikeratio': a = 0
elif lambd == 'freeman_tukey': a = -0.5
elif lambd == 'pearson': a = 1
elif lambd == 'modified_loglikeratio': a = -1
elif lambd == 'cressie_read': a = 2/3.0
else:
raise ValueError('lambd has to be a number or one of ' + \
'loglikeratio, freeman_tukey, pearson, ' +\
'modified_loglikeratio or cressie_read')
n = np.sum(o, axis=axis)
nt = n
if n.size>1:
n = np.atleast_2d(n)
if axis == 1:
nt = n.T # need both for 2d, n and nt for broadcasting
if e.ndim == 1:
e = np.atleast_2d(e)
if axis == 0:
e = e.T
if np.all(np.sum(e, axis=axis) == n):
p = e/(1.0*nt)
elif np.all(np.sum(e, axis=axis) == 1):
p = e
e = nt * e
else:
raise ValueError('observed and expected need to have the same ' +\
'number of observations, or e needs to add to 1')
k = o.shape[axis]
if e.shape[axis] != k:
raise ValueError('observed and expected need to have the same ' +\
'number of bins')
# Note: taken from formulas, to simplify cancel n
if a == 0: # log likelihood ratio
D_obs = 2*n * np.sum(o/(1.0*nt) * np.log(o/e), axis=axis)
elif a == -1: # modified log likelihood ratio
D_obs = 2*n * np.sum(e/(1.0*nt) * np.log(e/o), axis=axis)
else:
D_obs = 2*n/a/(a+1) * np.sum(o/(1.0*nt) * ((o/e)**a - 1), axis=axis)
return D_obs, stats.chi2.sf(D_obs,k-1-ddof)
#todo: need also binning for continuous distribution
# and separated binning function to be used for powerdiscrepancy
[docs]def gof_chisquare_discrete(distfn, arg, rvs, alpha, msg):
'''perform chisquare test for random sample of a discrete distribution
Parameters
----------
distname : string
name of distribution function
arg : sequence
parameters of distribution
alpha : float
significance level, threshold for p-value
Returns
-------
result : bool
0 if test passes, 1 if test fails
Notes
-----
originally written for scipy.stats test suite,
still needs to be checked for standalone usage, insufficient input checking
may not run yet (after copy/paste)
refactor: maybe a class, check returns, or separate binning from
test results
'''
# define parameters for test
## n=2000
n = len(rvs)
nsupp = 20
wsupp = 1.0/nsupp
## distfn = getattr(stats, distname)
## np.random.seed(9765456)
## rvs = distfn.rvs(size=n,*arg)
# construct intervals with minimum mass 1/nsupp
# intervalls are left-half-open as in a cdf difference
distsupport = lrange(max(distfn.a, -1000), min(distfn.b, 1000) + 1)
last = 0
distsupp = [max(distfn.a, -1000)]
distmass = []
for ii in distsupport:
current = distfn.cdf(ii,*arg)
if current - last >= wsupp-1e-14:
distsupp.append(ii)
distmass.append(current - last)
last = current
if current > (1-wsupp):
break
if distsupp[-1] < distfn.b:
distsupp.append(distfn.b)
distmass.append(1-last)
distsupp = np.array(distsupp)
distmass = np.array(distmass)
# convert intervals to right-half-open as required by histogram
histsupp = distsupp+1e-8
histsupp[0] = distfn.a
# find sample frequencies and perform chisquare test
#TODO: move to compatibility.py
freq, hsupp = np.histogram(rvs,histsupp)
cdfs = distfn.cdf(distsupp,*arg)
(chis,pval) = stats.chisquare(np.array(freq),n*distmass)
return chis, pval, (pval > alpha), 'chisquare - test for %s' \
'at arg = %s with pval = %s' % (msg,str(arg),str(pval))
# copy/paste, remove code duplication when it works
[docs]def gof_binning_discrete(rvs, distfn, arg, nsupp=20):
'''get bins for chisquare type gof tests for a discrete distribution
Parameters
----------
rvs : array
sample data
distname : string
name of distribution function
arg : sequence
parameters of distribution
nsupp : integer
number of bins. The algorithm tries to find bins with equal weights.
depending on the distribution, the actual number of bins can be smaller.
Returns
-------
freq : array
empirical frequencies for sample; not normalized, adds up to sample size
expfreq : array
theoretical frequencies according to distribution
histsupp : array
bin boundaries for histogram, (added 1e-8 for numerical robustness)
Notes
-----
The results can be used for a chisquare test ::
(chis,pval) = stats.chisquare(freq, expfreq)
originally written for scipy.stats test suite,
still needs to be checked for standalone usage, insufficient input checking
may not run yet (after copy/paste)
refactor: maybe a class, check returns, or separate binning from
test results
todo :
optimal number of bins ? (check easyfit),
recommendation in literature at least 5 expected observations in each bin
'''
# define parameters for test
## n=2000
n = len(rvs)
wsupp = 1.0/nsupp
## distfn = getattr(stats, distname)
## np.random.seed(9765456)
## rvs = distfn.rvs(size=n,*arg)
# construct intervals with minimum mass 1/nsupp
# intervalls are left-half-open as in a cdf difference
distsupport = lrange(max(distfn.a, -1000), min(distfn.b, 1000) + 1)
last = 0
distsupp = [max(distfn.a, -1000)]
distmass = []
for ii in distsupport:
current = distfn.cdf(ii,*arg)
if current - last >= wsupp-1e-14:
distsupp.append(ii)
distmass.append(current - last)
last = current
if current > (1-wsupp):
break
if distsupp[-1] < distfn.b:
distsupp.append(distfn.b)
distmass.append(1-last)
distsupp = np.array(distsupp)
distmass = np.array(distmass)
# convert intervals to right-half-open as required by histogram
histsupp = distsupp+1e-8
histsupp[0] = distfn.a
# find sample frequencies and perform chisquare test
freq,hsupp = np.histogram(rvs,histsupp)
#freq,hsupp = np.histogram(rvs,histsupp,new=True)
cdfs = distfn.cdf(distsupp,*arg)
return np.array(freq), n*distmass, histsupp
# -*- coding: utf-8 -*-
"""Extension to chisquare goodness-of-fit test
Created on Mon Feb 25 13:46:53 2013
Author: Josef Perktold
License: BSD-3
"""
def chisquare(f_obs, f_exp=None, value=0, ddof=0, return_basic=True):
'''chisquare goodness-of-fit test
The null hypothesis is that the distance between the expected distribution
and the observed frequencies is ``value``. The alternative hypothesis is
that the distance is larger than ``value``. ``value`` is normalized in
terms of effect size.
The standard chisquare test has the null hypothesis that ``value=0``, that
is the distributions are the same.
Notes
-----
The case with value greater than zero is similar to an equivalence test,
that the exact null hypothesis is replaced by an approximate hypothesis.
However, TOST "reverses" null and alternative hypothesis, while here the
alternative hypothesis is that the distance (divergence) is larger than a
threshold.
References
----------
McLaren, ...
Drost,...
See Also
--------
powerdiscrepancy
scipy.stats.chisquare
'''
f_obs = np.asarray(f_obs)
n_bins = len(f_obs)
nobs = f_obs.sum(0)
if f_exp is None:
# uniform distribution
f_exp = np.empty(n_bins, float)
f_exp.fill(nobs / float(n_bins))
f_exp = np.asarray(f_exp, float)
chisq = ((f_obs - f_exp)**2 / f_exp).sum(0)
if value == 0:
pvalue = stats.chi2.sf(chisq, n_bins - 1 - ddof)
else:
pvalue = stats.ncx2.sf(chisq, n_bins - 1 - ddof, value**2 * nobs)
if return_basic:
return chisq, pvalue
else:
return chisq, pvalue #TODO: replace with TestResults
def chisquare_power(effect_size, nobs, n_bins, alpha=0.05, ddof=0):
'''power of chisquare goodness of fit test
effect size is sqrt of chisquare statistic divided by nobs
Parameters
----------
effect_size : float
This is the deviation from the Null of the normalized chi_square
statistic. This follows Cohen's definition (sqrt).
nobs : int or float
number of observations
n_bins : int (or float)
number of bins, or points in the discrete distribution
alpha : float in (0,1)
significance level of the test, default alpha=0.05
Returns
-------
power : float
power of the test at given significance level at effect size
Notes
-----
This function also works vectorized if all arguments broadcast.
This can also be used to calculate the power for power divergence test.
However, for the range of more extreme values of the power divergence
parameter, this power is not a very good approximation for samples of
small to medium size (Drost et al. 1989)
References
----------
Drost, ...
See Also
--------
chisquare_effectsize
statsmodels.stats.GofChisquarePower
'''
crit = stats.chi2.isf(alpha, n_bins - 1 - ddof)
power = stats.ncx2.sf(crit, n_bins - 1 - ddof, effect_size**2 * nobs)
return power
[docs]def chisquare_effectsize(probs0, probs1, correction=None, cohen=True, axis=0):
'''effect size for a chisquare goodness-of-fit test
Parameters
----------
probs0 : array_like
probabilities or cell frequencies under the Null hypothesis
probs1 : array_like
probabilities or cell frequencies under the Alternative hypothesis
probs0 and probs1 need to have the same length in the ``axis`` dimension.
and broadcast in the other dimensions
Both probs0 and probs1 are normalized to add to one (in the ``axis``
dimension).
correction : None or tuple (nobs, df)
If None, then the effect size is the chisquare statistic divide by
the number of observations.
If the correction is a tuple (nobs, df), then the effectsize is
corrected to have less bias and a smaller variance. However, the
correction can make the effectsize negative. In that case, the
effectsize is set to zero.
Pederson and Johnson (1990) as referenced in McLaren et all. (1994)
cohen : bool
If True, then the square root is returned as in the definition of the
effect size by Cohen (1977), If False, then the original effect size
is returned.
axis : int
If the probability arrays broadcast to more than 1 dimension, then
this is the axis over which the sums are taken.
Returns
-------
effectsize : float
effect size of chisquare test
'''
probs0 = np.asarray(probs0, float)
probs1 = np.asarray(probs1, float)
probs0 = probs0 / probs0.sum(axis)
probs1 = probs1 / probs1.sum(axis)
d2 = ((probs1 - probs0)**2 / probs0).sum(axis)
if correction is not None:
nobs, df = correction
diff = ((probs1 - probs0) / probs0).sum(axis)
d2 = np.maximum((d2 * nobs - diff - df) / (nobs - 1.), 0)
if cohen:
return np.sqrt(d2)
else:
return d2


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 6
Sorrel has a sharp, crisp taste and is delicious with spinach. The base for the sauce can be made a day ahead; just reheat gently when ready to serve and add the spinach and sorrel.
Preheat the oven to 180C/350F/Gas 4. Place each salmon fillet on top of a piece of greaseproof paper, place a knob of butter and slice of lemon on top of the fish. Draw the edges of the paper together to make a parcel, leaving enough room inside for the steam to circulate during cooking, then fold over the edges of the paper to seal. Cook in the oven for 20 minutes.
For the sauce, place the melted butter, lemon juice, flour, crème frâiche and egg yolk into a food processor or blender and pulse until smooth. Season with sea salt and freshly ground black pepper then blend again.
Pour the blended mixture into a heatproof bowl and set the bowl over a pan of barely simmering water. Whisk continuously over a low heat for about 10 minutes until thick and fluffy then add the sorrel and spinach and heat gently for a few minutes. Check the seasoning and serve hot with the cooked salmon.


======================================
>Keywords : Computer Supported Creativity ; Artificial Creativity. (Secretan, 2011)
| ZotWeb | paper-conference | |
| Src Url | [[Secretan]] (2011) | |
Creativity support tools are important to professionals and amateurs alike. Typically, these tools abstract away the mundane details of creative design to allow the user to focus on her creative process. However, emerging techniques in data mining, machine learning, and artificial intelligence all have the potential to enhance the efficiency, productivity, accessibility and usability of these tools. Leveraging these techniques, creativity support tools are poised to move from the role of an assistant carrying out chores for the user to a role as a creative collaborator.
tools abstract away the mundane details of creative design to allow the user to focus on her creative process.
Shneiderman, B., “Creativity Support Tools: Accelerating Discovery and Innovation,” Communications of the ACM , Vol. 50, No. 12, December 2007.
Finke, R.; Ward, T. B. & Smith, S. M. (1992). Creative cognition: Theory, research, and applications. MIT Press.
emerging techniques in data mining, machine learning, and artificial intelligence all have the potential to enhance the efficiency, productivity, accessibility and usability of these tools.
General Terms: Design, Human Factors.
Keywords : Computer Supported Creativity ; Artificial Creativity.
Citer: (Secretan, 2011)
FTag: Secretan-2011
APA7: Secretan, J. (2011). Semi-automated creativity: Software as a creative collaborator. Proceedings of the 8th ACM Conference on Creativity and Cognition, 447–448. https://doi.org/10.1145/2069618.2069743


Surprisingly serious Aphro professor
Professor
Skarmory
Mantine
Mega Charizard Y
Thundurus
Landorus
Dragonite
"Decem the type of nigga to search for anal tutorials on youtube"


"The show today is totally different, I thought we needed a momement here to take it all in, recharge before we soldier on again, 2016 wasn't easy for a lot of people, yes, there were a lot of highs, but equally a lot of lows, let's take a minute
here to relax, to think about what we are set out to do in this world and this is mainly to understand each other, even with all our differences, because at the end of the day this is what makes us special and unique, we must learn to accept one
another and lift others along the way, I will let the music do the talking, it says it all, until next time, close your eyes and fly!"
-Nicole Moudaber - ITM Radio 142, Jan 10th, 2017


Find best deals at MakeMyTrip for Flight Tickets, Hotels, Holiday Packages, Bus and Train / Railway Reservations for India & International travel. Book cheap air tickets online for Domestic & International airlines, customized holiday packages and special deals on Hotel Bookings.


--- layout: post title: Episode 113 | Jeffrey Richter - Software Business Family link: http://traffic.libsyn.com/developeronfire/DeveloperOnFire-113-JeffreyRichter.mp3 length: 43778008 speaker: Dave Rael duration: '00:45:23' subtitle: 'Developer On Fire: Jeffrey Richter' guest_name: Jeffrey Richter image: /assets/images/JeffreyRichter.png guest_blog: http://www.wintellect.com/devcenter/author/jeffreyr guest_twitter: JeffRichter episode_number: 113 book_recommendations: - { url: "http://www.amazon.com/dp/1572315482/?tag=devonfir-20", title: "Advanced Windows", author: "Jeffrey Richter"} tags: - { url: "http://www.amazon.com/dp/1572315482/?tag=devonfir-20", title: "Advanced Windows", author: "Jeffrey Richter"} redirect_from: - "/113/" - "/113/" - "/113/" - "/0113/" - "/jeffrey-richter-software-business-family/" - "/JeffreyRichter/" - "/Jeffrey-Richter/" - "/Podcast/Episodes/113/" - "/Podcast/Episodes/113/" - "/Podcast/Episodes/113/" - "/Podcast/Episodes/0113/" - "/Podcast/Episodes/jeffrey-richter-software-business-family/" - "/Podcast/Episodes/JeffreyRichter/" - "/Podcast/Episodes/Jeffrey-Richter/" ---
Jeffrey Richter is a co-founder of Wintellect and the author of several best-selling .NET Framework and Win32 programming books including CLR via C#, 4th Edition, Windows via C/C++, 5th Edition, Programming Server-Side Applications for Microsoft Windows 2000, and Windows 95: A Developer’s Guide. Jeffrey has also been a contributing editor to MSDN Magazine where he authored many feature articles and columns. Jeff has consulted with Microsoft's .NET Framework and Windows teams for many years. Jeffrey holds both helicopter and airplane pilot licenses and is a member of the International Brotherhood of Magicians. He also enjoys playing drums and keyboards. He attends concerts regularly to indulge his passion for jazz bands. He also loves to travel and explore new places. Jeffrey can usually be found tinkering with some new technology living his life on the bleeding edge. His lot in life is to always want to purchase something that should be shipping any day now. Jeff lives in Kirkland, WA.


Finances overview
Finances overview
| Project duration | Total costs | Funding level (%) | Funding sought (£) |
|
Contribution to project (£) |
|---|---|---|---|---|---|
| 15 months | £ |
20% | 60,271 | 2,468 | 138,164 |


The design and
implementation of new security features in Java 2 SDK is the work
of primarily members of the JavaSoft security group. Other (past
and present) members of the JavaSoft community provided invaluable
insight, detailed reviews, and much needed technical assistance.
Significant contributors, in alphabetical order, include but are
not limited to: Gigi Ankeny, Josh Bloch, Satya Dodda, Charlie Lai,
Sheng Liang, Jan Luehe, Marianne Mueller, Jeff Nisewanger, Hemma
Prafullchandra, Roger Riggs, Nakul Saraiya, Bill Shannon, Roland
Schemers, and Vijay Srinivasan.
This work is not
possible without strong support from JavaSoft management (our
thanks go to Dick Neiss, Jon Kannegaard, and Alan Baratz), and the
testing and documentation groups (especially Mary Dageforde). We
are grateful for technical guidance from James Gosling, Graham
Hamilton, and Jim Mitchell.
We received numerous
suggestions from our corporate partners and licensees, whom we
could not fully list here.
CONTENTS | PREV | NEXT


28 November 2005
As of 22 November, WHO has received reports of 53 suspected cases and 23 deaths in the Bafoulabé district of Kayes region.
The mass vaccination campaign in this district has been completed, and 186 553 persons have been immunized, resulting in 95 % vaccination coverage. The 2.2 million doses of yellow fever vaccine provided from the Global Alliance for Vaccines and Immunization (GAVI) emergency stockpile, through the International Coordinating Group (ICG) on Vaccine Provision for Yellow fever Control, are scheduled to arrive on 29 November. These doses will be used to target the population of the 6 other surrounding high risk districts (Kangaba, Kati, Kayes, Kéniéba, Kita and Yanfolila).
The team from the Global Outbreak Alert and Response Network (see previous report) with the addition of an entomologist, has arrived in Mali and will assist the Ministry of Health in responding to the outbreak.


Crow beach, consumption council
Crow beach is located on the beautiful hillside of Formosa. Here we value our individual consumption keeping the common consumption low.
Contact:
Crow beach CC


{% extends 'email/base.html' %}
{% block header %}Welcome to
Drink Stash!{% endblock %}
{% block leadtext %}
Your username is {{ user.username }}, although you can use your
email to log in too. Get started by following the link below!
{% endblock %}
{% block linkref %}{{ protocol}}://{{ domain }}{% url 'password_reset_confirm' uidb64=uid token=token %}{% endblock %}
{% block linktext %}Set your password{% endblock %}
{% block followtext %}Happy Drinking!{% endblock %}


Alcohol is not the answer, it just makes you forget the question.
Homepage
My Saved Breweries
(current)
Post New Brewery
Login
email:
password:
login
Sign up
name:
email:
password:
I am over 21!
signup


Flood alert near Shawbury as county prepares for more rainfall
by Nick HumphreysA flood alert has been put out in Shropshire as the county prepares for more heavy downpours.
The alert has been put in place at the Tern and Perry Catchments near Shawbury.
The Flood Information Service said: "River levels are high but steady at the Tern - Walcot river gauge as a result of recent rainfall in an already saturated catchment.
"Consequently, flooding of roads and farmland continues. Flooding may affect low lying land and roads adjacent to the river from Wolverley to Newport.
"Other locations that may be affected include Market Drayton, Wem and Rodington.
"Further rainfall is forecast over the next 24 hours. We expect river levels to remain high over the next few days. We are closely monitoring the situation.
"Please avoid using low lying footpaths near local watercourses and plan driving routes to avoid low lying roads near rivers, which may be flooded."
There are no flood warnings or alerts for anywhere else in Shropshire, with the county expected to avoid the brunt of the latest flood problems.
Most of the warnings and severe warnings are in the north-east and the south of England but people have still been warned to take care.
Alveley Community First Responders were out at calls last night and warned of persistent rain and flooding.
The Met Office has said today will be: "A dry but murky day for many, with bright spells extremely limited. The cloud will be thick enough to produce light rain locally, particularly over high ground. Slightly milder than average for the time of year. Maximum temperature 10 °C."
It has said tonight will be: "A largely cloudy but mild night with a few spots of rain around. More frequent outbreaks of rain will spread from the south towards dawn. Minimum temperature 6 °C."


Robin Elahi, PhD |
|
Research |
Mushroom corals (Fungia spp.) are an enigmatic group of tropical reef corals. Unlike most corals, Fungia species are not colonial, but solitary organisms. A juvenile coral looks like a mushroom (hence the common name) and grows in the protected crevices of the reef framework. At some point, the coral detaches from its stalk, and actually moves (by contracting and expanding its tissue) out onto the reef for further growth and eventual reproduction. Often, Fungia will move away from the natal reef, thereby expanding the size of reefs laterally. In Moorea, French Polynesia, I tested whether photosynthetic energy intake limits the maximum size of these corals by applying a model developed originally for anemones (Anthopleura xanthogrammica). Unlike the anemones, growth in Fungia concinna is not limited strictly be energetic constraints, and maximum size must instead be determined by alternative physiological or ecological constraints. Often, mushroom corals occur in heterospecific aggregations, but visible signs of direct competition are uncommon. Although negative interactions between fungiids are generally though to be negligible, I used a simple field experiment in Okinawa, Japan to show that calcification is reduced in groups. The reduction in growth did not depend on the identity of the neighbor (the same species or a different species). Surprisingly, neighbor identity did affect coral behavior - mucus production and movement occurred more frequently in heterospecific than conspecific groups. These results suggest that there is a cost to group living, but there may be (unmeasured) benefits to living in conspecific aggregations. |


Welcome to my About the project page you. This is the Final Project for Software Engineering. As of now, the website is still not up yet. Due to being in still in the planning phase. However, as we go throughtout the sprint, this page will be updated. It will contain information regarding how to use it, the capacity, and other pages linked to the main page. So. Stay tuned and we will see how this goes.


This is the in-memory, non-persistent implementation of the Science Program Interfaces. For most of the implementation classes, the method documentation is left to the interface definition.


The value of design
For thirty years Beazley has been refining an approach to the design of insurance that aims to give clients the maximum benefit with the minimum of hassle.
Innovation is a popular word in the specialist insurance market. We are proud to have produced a wide array of innovative insurance products that combine risk management, financial indemnity and incident response services. But we know that our clients are not looking for innovation per se. They want insurance that works, insurance that addresses their most pressing needs.
Drawing on the diverse talents of our people and working closely with brokers, we focus on markets where customised products, backed by expert service, are truly valued - often involving complex or challenging risks that other insurers shun. We design our products, from the ground up, around our client's precise needs.
Andrew Horton
Awards
27 October 2016
Beazley wins two Lloyds Market Innovation Awards
Cyber / Data breach
12 October 2016
Beazley increases technology and data breach underwriting team for large US risks
Environmental
23 August 2016
Beazley doubles environmental risk capacity to $50m with new Beazley Elements consortium at Lloyd’s
Straight answers
Beautifully designed insurance involves swift and decisive underwriting. A Lloyd's broker does not bring a risk to the Beazley box at Lloyd's for a "maybe". He or she expects, and receives, a straight answer - can you write my client's risk or not?
Similarly brokers around the world expect, and receive, the same service. We hire highly experienced underwriters and give them the authority they need to give brokers straight answers.
Neil Maidment
The moment of truth: claims service
Insurance is a promise. In the specialist lines of business on which we focus, fulfilling that promise requires well designed claims service delivered by experts from diverse backgrounds who are every bit as knowledgeable as our underwriters.
Our approach to claimsDependable, high quality claims service has been at the heart of Beazley's business for three decades. We focus on our expertise, responsiveness, partnership, fairness and accountability knowing that it matters greatly to brokers and clients. In each of these areas we aim to provide a service that will exceed our clients' expectations and endorse the broker's good judgement in having recommended Beazley to their clients.


DWP
Page 19
Because of the complexity of this structure build and since I kind of left the build
hanging in the air, I am devoting another entry, (at least) to the DWP depot at Virginia,
Minnesota. I alluded on page 18 that we are now using a laser cutter. We bought a
small desktop machine in December 2012 to aid in these custom structure builds. As
I am dedicated to getting the appearance of building like this depot as close to
prototype as possible, things like windows and doors can be a real slog if you build
them up by hand. The curved top windows in the second story would have been a real
pain to fabricate. The laser made all this super easy for us. We are continually
finding more ways we can use it. As time permits the doors and windows that you see
in the custom built structures will be offered as parts to you scratch-
In this photo the sub-
This shot shows several things; the paper figure in the dispatcher’s office on the upper level, the complexity of the roof edge where it wraps around the upper story window and some of the detail that has been added such as window shades. Interior lighted is installed and a degree of interior detail is also included.
In this overall shot much is now completed. The shingle, however, are only halfway to completion. All of the stone trim and corner trim is in place as is all of the wood trim except around the base of the peaked roof. The shingles are laser cut strips. Some are visible to the lower left. They are cut from textured charcoal paper with an adhesive peel and stick backing. Even so, it takes a great deal of time to apply the strips and get them lined up properly. So far there are 200 10” long strips on the completed roofs. We figure another 200 to finish. There is still a lot of touch up painting to do as well as weathering. The platform has been started but is not yet ready for the depot to sit on it.
This is partly why the shingles take so long. There is a lot of cutting in and around, but it sure looks nice. This is the upper story window again. It was know as the Crow’s Nest and was home to the dispatcher. He had a swell lake view and the towers gave him a pretty fair view of the tracks coming and going. The ornate sandstone ornament above the window is speculative. We could not really make it out, but it sure looks like a shock of wheat, so that is what we made it to be on the model.
A down low photo shows the roof brackets and roof edge trim. There is a peek inside the open freight door showing some of the freight waiting to be moved. The curled up paper on this end is from the adhesive backing. Not much longer and the depot will be moved to it’s home on the DWP layout. The trestle is on it’s way out to be installed later this week along with a number of other pieces.


Enabling Find My Firefox Device
March 22, 2015
There is an interesting option in Firefox OS based Mobile device which is running Firefox OS 2.0+. We can find our device where ever it is present if it theft. We have some simple steps to enable this option in our Mobile devices.
[embed]https://www.flickr.com/photos/95790166@N06/16890119942/[/embed]The first step is to Enable Geolocation. When Geolocation is enabled, then it has the ability to get the coordinates like latitude and longitude and find our device in Maps. So we can easily find where our device is.
[embed]https://www.flickr.com/photos/95790166@N06/16890333621/[/embed]
Then the Next step is to create a Firefox Accounts. Creating Firefox Accounts is very easier and it is very simple too. We have also Previously discussed how to create Firefox Accounts in Firefox OS. The main reason for creating the Firefox accounts is to find our device from Online, we have seen lot of benefits about Firefox accounts like Sync of bookmarks from system based browser to Mobile browser.
After Signing up to Firefox Accounts We can easily Enable the Find my Device option. If you have not enabled Firefox Accounts you will can also do when you go inside settings.
[embed]https://www.flickr.com/photos/95790166@N06/16865425746/[/embed]
Once if you have done with creating the accounts, the next step is to either enable or disable the option.
[embed]https://www.flickr.com/photos/95790166@N06/16891433775/[/embed]
Advantages of Creating Firefox Accounts
- You can remotely sit in a browser and find your device.
- You Can ring the device if u have lost it in your home.
- You can lock it with a passcode from browser itself.
- You can also erase the data in the mobile.


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CommentsCan’t wait to try by StellaI can’t wait for my daughter to try this
she has such … by susan


Year Four
Year Four
I haven’t even written a handful of words yet, but I already feel like, without some significant divergence from the norm, this year’s customary reflection will ring contrived.
It’s 12:21 PM EST, NYC time. I’m sitting in an empty Manhattan apartment in an emptier Manhattan. The city is quiet — far quieter than the internet flowing around and through it may suggest.
LinkedIn and Medium, the only cables I’ve left plugged into the failed social network that powered much of the last year’s chaos, are strewn with articles aplenty and gratuitous displays of gratitude and pithy platitudes and affluent influencer aplomb.
Befitting those digital surroundings, I sink further into my armchair and grunt into my $8 third-wave, Brooklyn-born latté. I’m a New Yorker now, after all. I earned that title this year, even if I’m not willing to play the part of staff writer for their rag.
After all, what can I write about 2020 that hasn’t already been written?
How can I, as a white heterosexual man who falls so crystal clearly after this year of working from home in Manhattan into the world’s top 10% with scant effort to have earned it, write on hardships that I haven’t known and that I’m not equipped to observe and document?
How can I pontificate on the poor of soul and the poor of heart and the poor of wallet that suffered this year?
To do so would be both tone deaf and color blind. I can’t. And so I won’t.
In simplest terms, and with the harshest of synopses, this was a year deluged with death. Some unavoidable, many unnecessary, and all consequential.
But alas, for fear of achieving the aforementioned if not implied self righteousness, my mind this year toddles towards self indulgence instead.
For me, this year, among the most consequential deaths was a quiet one: one that came not with the bang befitting of the year but with the wimpy whisper of my Wonder Years.
I’m talking, of course, about the death of Flash and the immortality project that went along with it.
Of Mice And Macromedia
I am not generally a sentimental person.
Sure, I may tear up at the satisfied grin of Ralphie’s father as he sees his son’s face flush with Red Rider happiness; or when I hear the delicate tip toe of tiny fingers across the ivories as Gosling’s quiet whisper-singing opens one of the musical’s marquee tracks; or when Kate Hudson starts singing Tiny Dancer to bring the band back together; or when the camera pans around Pink, his stoned friends splayed across the football field, as Don jokes, “I just wanna be able to look back and say… I had as much fun as I could when I was stuck in this place” — you know, the seminal scenes that succinctly describe my humanity in simplest terms.
But if I’m being honest, my sensitivity is generally only truly aroused upon the arrival of a new muse. It’s some sort of weird Darwinian reflex, one that has earned me the eternally-degrading title of the Bamboozler.
Yet, as I sat in a cab at 2:35 A.M. this morning driving through Times Square from Jersey home to the Upper East Side, hearing the shrill silence borne by the vast emptiness of activity and of noise in the city’s center that so perfectly punctuated the pandemic-produced loneliness I enjoyed and Vanessa endured this year, I did feel sentimental — and I mourned for the death of my first true muse.
On December 31 at 11:59 EST, Adobe end-of-life’d Flash.
It seems fitting that this happened after we watched Pixar’s latest, Soul. I won’t spoil the film, but its’ primary preoccupation is with the notion that our passions aren’t, in fact, our purposes.
Yet, with that clear thesis still fresh in mind, I couldn’t help but feel the death of Flash so distinctly. Its life, alongside my own, flashed before my eyes as I stared blankly out the light-streaking portlet of that cab early this morning.
I found Flash in the fall of 1997.
There was a boy in elementary school that I was particularly envious of; a girl I had a crush on liked him. He was one of those sort of effortlessly-cool kids: he was good at art, he played guitar, and he did well in school.
One day, I was at his house, and his older sister, as I remember it, had turned him on to this radical animation tool on the computer. Coming from Kid Pix just years early, this application was immensely more powerful. With simple tools, you could create complex animations, and those complex animations could then be shared as videos that you could run on your computer — or a friend’s.
He’d begun to tinker with it, and it was at that point — with my own computer fascination — that I set out to beat him at just one thing: this Windows application from a company named Macromedia titled “Flash 2”.
Over the next eight years, I would develop an obsession with Macromedia’s magic. I read books. I scoured the young internet. And I experimented. I learned, in earnest, to animate with it, and later to code with it as the tools for front-end animation turned into tools for back-end software development.
Suddenly, my simple animations could actually be used to create complex programs, and to take input and process server-side actions. Suddenly, my websites went from being static visual “pictures” to fascinating, interactive movies and games. It was, in no uncertain terms, a revelation.
Newgrounds followed. Shockwave followed. Albino Black Sheep followed. And soon so too did hundreds and then thousands of dollars in teenage earnings.
I started making websites for people and for small businesses. A pizza place. A tattoo parlor. An air conditioning supplier. I started making popular films, including a 1:1 remake of that famous Nike commercial — with stickmen, of course, which was the style at the time in ought-2.
And then, it got serious. I was hired by the Toronto District School Board to take the entire co-op program for high school and turn it into an interactive Flash course. And I did.
My work won awards, and I was seen as the wizard of this then-fledgling technology. I was wonderstruck. The sheer possibilities were endless — I could create web sites, and web applications, and full on games. The world was moving in this direction — my direction.
This was my future. I was born to be The Flash. This was my destiny.
In 2005, Adobe bought Macromedia — just in time to profit for a year or so before the web would then be fundamentally changed forever after.
Computers were evolving faster than web tech was at that time, and mobile was emerging as the future of how we would compute. And like all technologies, it wasn’t long before that the sprint towards novel innovation had turned to a trudge towards obsolesce for Flash and to a lesser degree, at the time, for Adobe as a whole.
The web grew up, and so did I, and we both outgrew the toys that made us.
I didn’t miss it in the years that followed. I’d long since move towards JavaScript and PHP and the more modern web tools upon which our current Rome is built. My career progressed from web dev to tech writer to product manager. And I progressed from the fat funny guy to less-fat but still fun lover boy to the pretty-normal professional pleb.
I rarely felt sentimental towards the past, because I felt I was moving well towards a better tomorrow with each step further away from that beginning.
But this year, something changed. I found myself careening into the holiday season on the verge of burnout. I was miserable, stressed, and disengaged — and I couldn’t in good conscience place the blame on COVID.
In candid truth, this year was relatively easy for Vanessa and I . She missed her family, of course, but we remained isolated, and healthy, and comfortably employed and likely positioned to benefit from this coming year’s rising tides.
Sure, we paid way too much to rent what amounts to a hotel room in a closed amusement park, but that aside, we had no right to be stressed. Two well-to-do DINKS on the Upper East Side with a killer healthcare plan and walking distance to one of the best hospitals in the world?
That’s what made feeling burnt out so frustrating — and why the death of Flash was so moving to me.
Like the great toys and places and people and memories of our youth, Flash for me represented the better times — the times in the Before Covid (B.C.) era when things were a little bit simpler, and a little more romantic and a lot more fun.
There’s nothing romantic about the year behind us. There’s nothing worth remembering, aside from perhaps the many mistakes and lies and deaths that this country is solely responsible for and for the crumbling globalism around it which its institutions encourage in a sick game of cat’s cradle it plays with its international bedfellows.
That’s why I cried for the death of Flash this morning. It, like all the deaths this year, was one of many demarcated the turning point from once A.D. now B.C. to A.C., the last epoch of my lifetime.
Impressions Of An Immortality Project
Perhaps it’s likewise fitting that 2020 and that the death of Flash happened as I approach my 40th birthday.
This age offers me enough hindsight to draw deeper observations from, and to allow myself the due time to digest, our entire collective existence — not just the petty illusions of my immediate, isolated manufacture like that next girl chase or that next exam or that go-live date.
Then again, perhaps it was the move from Product to Sales, or perhaps reading the ramblings of our modern monarch thumbing tweets that would taint the times, or a thousand other things that led me to latch onto our futile immortality projects.
If you haven’t read The Denial Of Death, I strongly recommend it, but I can summarize it quite succinctly, if you’ll forgive me the tangent, to make clear the salient takeaways of today’s scribing.
We have reached an apex in humanity. The death of religion and the rebirth of tribalism have accelerated a path we were already traveling rapidly down as a collective.
Our intelligence, and our ability to index it and curate it and access it and digest it, has put us in an impossible position. If you’re not a nihilist or a practiced philistine of professional medicine, you’ve likely faced the fear of death at least once and many times more questioned what this is really all for.
This is our universal truth. We as humans don’t just genetically “fear” death and work subconsciously to avoid it, as do literally all of our known biological peers, but we uniquely and consciously fear it. We don’t sit up late at night with one eye open to avoid physical danger; most of us do so out of mentally, and entirely, manufactured danger.
To avoid this constant paralysis, then, we create ways to rationalize our existence. Our subconscious then wonderfully and without fail manufactures “projects” that keep us thinking we are each the exception to the rule of Death.
These “immortality projects”, as the book’s author describes, are our invented pursuits that give us the freedom of blissful ignorance and the obsession of impassioned pursuits that lead to all kinds of crazy things — from real wars to Twitter wars to Flash war games to the war for a young girl’s heart that drove a young boy to spend an entire lifetime going down one road with great purpose purely by random happenstance.
The theory is simple: the stronger our self-psychoses, the grander our immortality projects.
And whether it was my fumbling belief that my destiny lie in Flash, or Trump’s belief that Tweets could overturn an election, or Middle America’s belief that all lives matter — these beliefs are all manufactured as our immortality projects to help us nestle into our beds at night feeling like we’re the heroes of not just our, but, the story.
But 2020, if nothing else, was a year when these immortality projects were so clearly perverted, so devastatingly interrupted, by the complete and utter lack of control we truly have on our realities at any given time.
And for fear of sounding too nihilistic, I leave 2020 feeling, if not hoping to feel, that there’s some grand peace of mind to draw from that.
The End Game
Alas, as my stamina for writing this annual recap wanes, I find myself remiss to not mention the myriad of moments that Manhattan opened up its maw to allow us during the pandemic:
- Riding Revel scooter’s through Times Square as it was empty, like starring characters in some weird Woody Allen and Wes Anderson collab
- Sitting in an empty Central Park nearly every single day to the soundtrack of birds chirping and the laughable “I jog now” locals wheezing
- Having run of the house to walk nearly 100,000 blocks (~9M steps) worth of Manhattan over the course of the year
- Becoming a godparent to a beautiful black baby from a beautiful mixed family to the backdrop of Black Lives Matter
- Realizing that the decisions Vanessa and I made years ago to walk the path we now walk was justly done, as we walk together now and stronger for it
- Finding flow at work to collaborate on closing nearly $40M in revenue as an individual contributor who’d come down this path to learn sales
These are but a few of the moments that I will remember and cherish from this year past alongside the many affected lives and lost causes that I will mourn, if for no other reason than human and humane respect for The Other.
Yet, I will mourn no further Flash and my lost childhood innocence where the joy of learning, and of competition, was enough to get me through the days and to keep me from the trivial turmoil of my white-collar work.
Like the protagonist’s piano-playing in Soul, my passion was never Flash and the illusion that the immortality project I’d created for myself was my purpose, or that the stress I impose on myself now to “do a good job” and to be “better, better, [if] never best” is worth it because it’ll get me more money and that will give me more freedom and then I’ll be happy.
2020 was a reminder not to travel as soon as you can or do what you love or stop smoking or lose weight. It wasn’t a reminder that this shit’s made up and the points don’t matter.
Rather, 2020 was a stark reminder that these immortality projects we create for ourselves are more prisons than passions — and the sooner we can all understand that, the sooner we can start changing our behaviors to be softer on each other and perhaps more importantly softer on ourselves.
 Rather, we need only take a breath, take a break, have a smoke or a bourbon or a burger, and try to remember that you can’t control much beyond how you feel about your present moment and the people who make you who you are.
I struggle with that every day, and I’ll continue to do so, but that’s okay: I just need to say “I’m sorry” to myself.
I don’t know what else to take from 2020, but if that’s all I can accomplish in 2021, I will be better for it — and so will those around me. And if we all just did the same, that would be a damn good start to what I hope will be a better back half of this decade.
For fear of avoiding that foreshadowed self righteousness, though, let me simply say this: I want to congratulate you on surviving the year and I want to say I’m sorry for whatever grief you went through or pain you suffered. I hope the year ahead is better for us both — for us all.
And if you need a quick pick me up, don’t forget to remember all the great art our species can create before it, as our lives, is gone in a Flash.


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Antclipse Task
Description
UNSTABLE CODE, some parameters are supposed to change
This task creates classpaths or filesets based on your
current .classpath
file generated by Eclipse
Classpath creation is simple, it just produces a classpath
that you can subsequently retrieve by its refid. The
filesets are a little trickier, because the task is
producing a fileset per directory in the case of sources and
a separate fileset for the output. Which is not necessarily
bad, since the content of each directory usually serves a
different purpose. Now, in order to avoid conflicting refids
each fileset has a name composed by the idcontainer,
followed by a dash and postfixed by the path. Supposing that
your output path is bin/classes
and the
idcontainer is default, the task will create a fileset with
refid "antclipse-bin-classes
" (since Ant
substitutes "-
" for "/
"). The
fileset will include all the files contained in your output
directory, but without the trailing path
bin/classes
(as you usually strip it when
creating the distribution jar).
If you have two source directories, called src
and test
, you'll be provided with two filesets,
with refids like antclipse-src
and
antclipse-test
. However, you don't have to code
the path manually since some properties are created as a
"byproduct" each time you execute the task. Their names are
"idref" postfixed by "outpath" and "srcpath" (in the case of
the source, you'll find the location of the first source
directory).
Parameters
| Attribute | Description | Required |
| produce | This parameter tells the task whether to produce a "classpath" or a "fileset" (multiple filesets, as a matter of fact). | Yes |
| idcontainer | The refid which will serve to identify the deliverables. When multiple filesets are produced, their refid is a concatenation between this value and something else (usually obtained from a path). Default "antclipse". | No |
| includelibs | Boolean, whether to include or not the project libraries. Default is "true". | No |
| includesource | Boolean, whether to include or not the project source directories. Default is "false". | No |
| includeoutput | Boolean, whether to include or not the project output directories. Default is "false". | No |
| verbose | Boolean, telling the app to throw some info during each step. Default is false. | No |
| includes | A regexp for files to include. It is taken into account only when producing a classpath, doesn't work on source or output files. It is a real regexp, not a "*". expression. | No |
| excludes | A regexp for files to exclude. It is taken into account only when producing a classpath, doesn't work on source or output files. It is a real regexp, not a "*". expression. | No |
Parameters specified as nested elements
None at the moment.
TODOS
- make "includes" and "excludes" to work on the source and output filesets
- maybe find an elegant solution to this multiple fileset/directories issues
- work with files referenced in other projects
Example
This is a pretty self-explanatory Ant script, just follow the comments.
<?xml version="1.0"?> <project default="compile" name="test" basedir="."> <taskdef name="antclipse" classname="net.sf.antcontrib.antclipse.ClassPathTask"/> <target name="make.fs.output"> <!-- creates a fileset including all the files from the output directory, called ecl1-bin if your binary directory is bin/ --> <antclipse produce="fileset" idcontainer="ecl1" includeoutput="true" includesource="false" includelibs="false" verbose="true"/> </target> <target name="make.fs.sources"> <!-- creates a fileset for each source directory, called ecl2-*source-dir-name*/ --> <antclipse produce="fileset" idcontainer="ecl2" includeoutput="false" includesource="true" includelibs="false" verbose="true"/> </target> <target name="make.fs.libs"> <!-- creates a fileset containing all your project libs called ecl3/ --> <antclipse produce="fileset" idcontainer="ecl3" verbose="true"/> </target> <target name="make.cp"> <!-- creates a fileset containing all your project libs called ecl3/ --> <antclipse produce="classpath" idcontainer="eclp" verbose="true" includeoutput="true"/> </target> <target name="compile" depends="make.fs.libs, make.fs.output, make.fs.sources, make.cp"> <echo message="The output path is ${ecl1outpath}"/> <echo message="The source path is ${ecl2srcpath}"/> <!-- makes a jar file with the content of the output directory --> <zip destfile="out.jar"><fileset refid="ecl1-${ecl1outpath}"/></zip> <!-- makes a zip file with all your sources (supposing you have only source directory) --> <zip destfile="src.zip"><fileset refid="ecl2-${ecl2srcpath}"/></zip> <!-- makes a big zip file with all your project libraries --> <zip destfile="libs.zip"><fileset refid="ecl3"/></zip> <!-- imports the classpath into a property then echoes the property --> <property name="cpcontent" refid="eclp"/> <echo>The newly created classpath is ${cpcontent}</echo> </target> </project>


struct att_ctrl_t { double dt; att_ctrl_t(); att_ctrl_t(const vec3_t &roll_pid, const vec3_t &pitch_pid, const vec3_t &yaw_pid); ~att_ctrl_t(); };
Attitude controller


Scenario: ParallelData resolves XHRs after app requests are made
Assertions:
Correct number of requests
Properties reflect parallel load result
All events fired as expected
onParallelDataResponse fired as expected


--- layout: post title: Lemon-Rosemary Split Pea Soup date: 2015-02-11 23:21:11.000000000 -05:00 type: post published: true status: publish categories: - recipes - soup tags: - quick and easy - soup - split pea - vegan meta: _edit_last: '1' author: login: sophie email<EMAIL_ADDRESS>display_name: Pika first_name: '' last_name: '' ---
Take everything you know and love (or hate!) about split pea soup and throw it out the window. Far, far, away, into that 5-foot snowbank formerly known as the sidewalk.
This split pea soup is a game changer. It’s everything that classic split pea soup is not: al-dente instead of mushy; vegetarian, instead of ham and bacon-infused; a pretty photogenic yellow instead of grey-green; lemony and bright, but still hearty enough to combat the cold. Best of all, it takes all of 30 minutes, instead of the usual hours of slow-and-low cooking. Don’t get me wrong-- I love classic split pea soup: the mushiness, the heaviness, the homeliness. But sometimes, after the third snowstorm in a row, you need a little pick-me-up.
Lemon-Rosemary Split Pea Soup
Serves 4
This soup is the perfect antidote to yet another snowstorm.
Thanks to 101 Cookbooks for the al-dente inspiration!
2 cups yellow split peas, rinsed and picked over
1 large onion, sliced
1 tbs extra virgin olive oil
1 dash dry vermouth (or other dry white wine; I rarely buy white wine but always have vermouth for perfect Manhattans)
a pinch of salt
5 cups water
½ tsp dried rosemary
½ fresh lemon
for serving: sea salt, black pepper, olive oil
Rinse and clean split peas.
Meanwhile, in a large, deep saucepan or pot, sautee onion in the olive oil with a pinch of salt for 2-3 minutes, or just till nicely browned. Add a glug of vermouth, sautee a minute more.
Add the peas, rosemary, and 4-5 cups of water (add more if too much evaporates while cooking).
Bring to a boil and continue on a boil for 20-30 mins, lid propped but half covered so not too much broth evaporates, until peas are al dente. Add a pinch more of salt to taste if needed.
Squeeze lemon into soup and throw it in.
Serve warm bowls of soup, drizzled with olive oil and a crack of fresh pepper and a sprinkle of coarse sea salt.


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|
mxGraph
|
Implements a canvas that draws onto an image. More...
Public Member Functions | |
| mxImageCanvas (mxGdiCanvas canvas, int width, int height, Color? background, bool antiAlias) | |
| Object | DrawCell (mxCellState state) |
| see com.mxgraph.mxICanvas.DrawCell() More... | |
| Object | DrawLabel (string text, mxCellState state, bool html) |
| see com.mxgraph.mxICanvas.DrawLabel() More... | |
| Image | Destroy () |
| Destroys this canvas and all allocated resources. More... | |
Protected Attributes | |
| mxGdiCanvas | canvas |
| Inner canvas used for the actual rendering. More... | |
| Graphics | previousGraphics |
| Stores the previous graphics reference of the inner canvas. More... | |
| Image | image |
| Stores the image that holds the graphics. More... | |
Properties | |
| mxGdiCanvas | GdiCanvas [get] |
| Returns the inner canvas. More... | |
| Image | Image [get] |
| Returns the image that hold the graphics. More... | |
| Point | Translate [get, set] |
| see com.mxgraph.mxICanvas.Translate More... | |
| double | Scale [get, set] |
| see com.mxgraph.mxICanvas.Translate More... | |
| Properties inherited from com.mxgraph.mxICanvas | |
| Point | Translate [get, set] |
| Sets or returns the user object of the cell. More... | |
| double | Scale [get, set] |
| Sets or returns the user object of the cell. More... | |
Implements a canvas that draws onto an image.
|
inline |
| canvas | |
| width | |
| height | |
| background | |
| antiAlias |
References com.mxgraph.mxUtils.CreateImage(), and com.mxgraph.mxGdiCanvas.Graphics.
|
inline |
Destroys this canvas and all allocated resources.
References com.mxgraph.mxGdiCanvas.Graphics.
Referenced by com.mxgraph.mxCellRenderer.CreateImage().
|
inline |
see com.mxgraph.mxICanvas.DrawCell()
Implements com.mxgraph.mxICanvas.
References com.mxgraph.mxGdiCanvas.DrawCell().
|
inline |
see com.mxgraph.mxICanvas.DrawLabel()
Implements com.mxgraph.mxICanvas.
References com.mxgraph.mxGdiCanvas.DrawLabel().
|
protected |
Inner canvas used for the actual rendering.
|
protected |
Stores the image that holds the graphics.
|
protected |
Stores the previous graphics reference of the inner canvas.
|
get |
Returns the inner canvas.
|
get |
Returns the image that hold the graphics.
|
getset |
|
getset |
see com.mxgraph.mxICanvas.Translate
Referenced by com.mxgraph.mxGraphViewImageReader.CreateCanvas().


The cryptocurrencies market might not look very sharp these days, as the last period the market experienced another significant downtrend. However, there are a few cryptos you should keep an eye on this week, and among them, there are Cardano (ADA) and Komodo (KMD). Cardano (...


Subsetting objects¶
When retrieving objects from the WSS, the user may specify which parts of the object to retrieve. This is useful for quickly retrieving small portions of large objects (to a webpage, for example) rather than having to fetch the entire object which might be hundreds of megabytes.
Note that performing subsetting on small objects may provide little to no benefit, and in some cases may be slower, since the WSS has to parse the serialized object rather than directly returning the serialized form to the client.
As usual, it is assumed that a functional client is available (see Build and initialize the workspace client). The examples use the Python client, but translating to other clients is trivial. Only the most common cases are covered - see the Workspace API documentation for complete coverage.
For the examples, the following spec was used:
In [16]: print ws.get_type_info("SubSetExample.SubSetExample")['spec_def']
typedef structure {
mapping<string, mapping<string, string>> map;
list<mapping<string, string>> array;
} SubSetExample;
The object in question:
In [20]: data = {'map': {'mid1': {'id': 'id1', 'stuff': 'foo'},
....: 'mid2': {'id': 'id2', 'stuff': 'bar'}
....: },
....: 'array': [{'id': 'id1', 'stuff': 'foo'},
....: {'id': 'id2', 'stuff': 'bar'},
....: {'id': 'id3', 'stuff': 'baz'}
....: ]
....: }
In [24]: ws.save_objects(
{'workspace': 'MyWorkspace',
'objects': [{'name': 'subsetexample',
'type': u'SubSetExample.SubSetExample',
'data': data,
}
]
})
Out[24]:
[[1,
u'subsetexample',
u'SubSetExample.SubSetExample-1.0',
u'2015-12-16T03:57:03+0000',
1,
u'kbasetest',
13,
u'MyWorkspace',
u'f9449880abc5722c7add56e773544719',
168,
{}]]
Get the contents of a single key of the mapping:
In [11]: ws.get_objects2({'objects':
[{'workspace': 'MyWorkspace',
'name': 'subsetexample',
'included': ['/map/mid1']
}
]})['data']
Out[25]:
[{u'copy_source_inaccessible': 0,
u'created': u'2015-12-16T03:57:03+0000',
u'creator': u'kbasetest',
u'data': {u'map': {u'mid1': {u'id': u'id1', u'stuff': u'foo'}}},
u'extracted_ids': {},
u'info': [1,
u'subsetexample',
u'SubSetExample.SubSetExample-1.0',
u'2015-12-16T03:57:03+0000',
1,
u'kbasetest',
13,
u'MyWorkspace',
u'f9449880abc5722c7add56e773544719',
168,
{}],
u'provenance': [],
u'refs': []}]
Get all the stuff
fields from the mapping:
In [39]: ws.get_objects2({'objects':
[{'workspace': 'MyWorkspace',
'name': 'subsetexample',
'included': ['/map/*/stuff']
}
]})['data']
Out[39]:
[{u'copy_source_inaccessible': 0,
u'created': u'2015-12-16T04:04:59+0000',
u'creator': u'kbasetest',
u'data': {u'map': {u'mid1': {u'stuff': u'foo'},
u'mid2': {u'stuff': u'bar'}}},
u'extracted_ids': {},
u'info': [1,
u'subsetexample',
u'SubSetExample.SubSetExample-1.0',
u'2015-12-16T04:04:59+0000',
2,
u'kbasetest',
13,
u'MyWorkspace',
u'24cd918528461efcb9d6f6a02c3a7965',
168,
{}],
u'provenance': [],
u'refs': []}]
Get all the id
fields from the array:
In [33]: ws.get_objects2({'objects':
[{'workspace': 'MyWorkspace',
'name': 'subsetexample',
'included': ['/array/[*]/id']
}
]})['data']
Out[33]:
[{u'copy_source_inaccessible': 0,
u'created': u'2015-12-16T04:04:59+0000',
u'creator': u'kbasetest',
u'data': {u'array': [{u'id': u'id1'}, {u'id': u'id2'}, {u'id': u'id3'}]},
u'extracted_ids': {},
u'info': [1,
u'subsetexample',
u'SubSetExample.SubSetExample-1.0',
u'2015-12-16T04:04:59+0000',
2,
u'kbasetest',
13,
u'MyWorkspace',
u'24cd918528461efcb9d6f6a02c3a7965',
168,
{}],
u'provenance': [],
u'refs': []}]
Get the first and third elements of the array (note that the returned array is compressed to only 2 cells, but the ordering of the source array is maintained):
In [35]: ws.get_objects2({'objects':
[{'workspace': 'MyWorkspace',
'name': 'subsetexample',
'included': ['/array/2', '/array/0']
}
]})['data']
Out[35]:
[{u'copy_source_inaccessible': 0,
u'created': u'2015-12-16T04:04:59+0000',
u'creator': u'kbasetest',
u'data': {u'array': [{u'id': u'id1', u'stuff': u'foo'},
{u'id': u'id3', u'stuff': u'baz'}]},
u'extracted_ids': {},
u'info': [1,
u'subsetexample',
u'SubSetExample.SubSetExample-1.0',
u'2015-12-16T04:04:59+0000',
2,
u'kbasetest',
13,
u'MyWorkspace',
u'24cd918528461efcb9d6f6a02c3a7965',
168,
{}],
u'provenance': [],
u'refs': []}]
The previous two calls can be used to find and fetch portions of an array. First fetch the parts of the subdocuments to be used to determine which portions of the array are desired, and next fetch the array subdocuments of interest based on processing the first query. This approach may or may not be faster than fetching the entire array, so the user should test their particular use case.


Toys & Games Etc in Columbia, SC - Information and Directions
Toys & Games Etc in Columbia, SC. Come to Citysearch® to get information, directions, and reviews on Toys & Games Etc and other Party Supplies in Columbia .
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Esco Import Toys is a major wholesale toy and novelty distributor to toy stores, specialty retail shops, church and school fundraisers, kid's doctors and dentists and more. . Music Toys, Flutes, etc. Balls, Hi-Bounce, etc. Putty, Slime, Sitcky Stuff .
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Detailed Seller Information · America Games & Toys Etc Storefront. Search for other products from this seller: Go Back to standard item view. Loading... Loading .
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Toys and Games - Walmart.com
Shop toys and games including video games, learning toys, riding toys, bikes, outdoor play toys and more. Save money and live better at Walmart.
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To connect with Toys & Games Etc, sign up for Facebook today. . Swivel, Auto Timer, and Remote Control Function By Equinox i m looking for some info on this .
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Inflatable Bouncers, Toys, Games, etc. Policy - Willamette University
. Events and Conferences; Inflatable Bouncers, Toys, and Games, etc. . toys, games, etc, the contracting company must provide Willamette University with a .
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git-apply(1) Manual Page
NAME
git-apply - Apply a patch to files and/or to the index
SYNOPSIS
git apply [--stat] [--numstat] [--summary] [--check] [--index] [--3way] [--apply] [--no-add] [--build-fake-ancestor=<file>] [-R | --reverse] [--allow-binary-replacement | --binary] [--reject] [-z] [-p<n>] [-C<n>] [--inaccurate-eof] [--recount] [--cached] [--ignore-space-change | --ignore-whitespace ] [--whitespace=(nowarn|warn|fix|error|error-all)] [--exclude=<path>] [--include=<path>] [--directory=<root>] [--verbose] [<patch>...]
DESCRIPTION
Reads the supplied diff output (i.e. "a patch") and applies it to files. With the --index option the patch is also applied to the index, and with the --cached option the patch is only applied to the index. Without these options, the command applies the patch only to files, and does not require them to be in a Git repository.
This command applies the patch but does not create a commit. Use git-am(1) to create commits from patches generated by git-format-patch(1) and/or received by email.
OPTIONS
- <patch>...
The files to read the patch from. - can be used to read from the standard input.
- --stat
Instead of applying the patch, output diffstat for the input. Turns off "apply".
- --numstat
Similar to --stat, but shows the number of added and deleted lines in decimal notation and the pathname without abbreviation, to make it more machine friendly. For binary files, outputs two - instead of saying 0 0. Turns off "apply".
- --summary
Instead of applying the patch, output a condensed summary of information obtained from git diff extended headers, such as creations, renames and mode changes. Turns off "apply".
- --check
Instead of applying the patch, see if the patch is applicable to the current working tree and/or the index file and detects errors. Turns off "apply".
- --index
When --check is in effect, or when applying the patch (which is the default when none of the options that disables it is in effect), make sure the patch is applicable to what the current index file records. If the file to be patched in the working tree is not up-to-date, it is flagged as an error. This flag also causes the index file to be updated.
- --cached
Apply a patch without touching the working tree. Instead take the cached data, apply the patch, and store the result in the index without using the working tree. This implies --index.
- -3
- --3way
When the patch does not apply cleanly, fall back on 3-way merge if the patch records the identity of blobs it is supposed to apply to, and we have those blobs available locally, possibly leaving the conflict markers in the files in the working tree for the user to resolve. This option implies the --index option, and is incompatible with the --reject and the --cached options.
- --build-fake-ancestor=<file>
Newer git diff output has embedded index information for each blob to help identify the original version that the patch applies to. When this flag is given, and if the original versions of the blobs are available locally, builds a temporary index containing those blobs.
When a pure mode change is encountered (which has no index information), the information is read from the current index instead.
- -R
- --reverse
Apply the patch in reverse.
- --reject
For atomicity, git apply by default fails the whole patch and does not touch the working tree when some of the hunks do not apply. This option makes it apply the parts of the patch that are applicable, and leave the rejected hunks in corresponding *.rej files.
- -z
When --numstat has been given, do not munge pathnames, but use a NUL-terminated machine-readable format.
Without this option, each pathname output will have TAB, LF, double quotes, and backslash characters replaced with \t, \n, \", and \\, respectively, and the pathname will be enclosed in double quotes if any of those replacements occurred.
- -p<n>
Remove <n> leading slashes from traditional diff paths. The default is 1.
- -C<n>
Ensure at least <n> lines of surrounding context match before and after each change. When fewer lines of surrounding context exist they all must match. By default no context is ever ignored.
- --unidiff-zero
By default, git apply expects that the patch being applied is a unified diff with at least one line of context. This provides good safety measures, but breaks down when applying a diff generated with --unified=0. To bypass these checks use --unidiff-zero.
Note, for the reasons stated above usage of context-free patches is discouraged.
- --apply
If you use any of the options marked "Turns off apply" above, git apply reads and outputs the requested information without actually applying the patch. Give this flag after those flags to also apply the patch.
- --no-add
When applying a patch, ignore additions made by the patch. This can be used to extract the common part between two files by first running diff on them and applying the result with this option, which would apply the deletion part but not the addition part.
- --allow-binary-replacement
- --binary
Historically we did not allow binary patch applied without an explicit permission from the user, and this flag was the way to do so. Currently we always allow binary patch application, so this is a no-op.
- --exclude=<path-pattern>
Don't apply changes to files matching the given path pattern. This can be useful when importing patchsets, where you want to exclude certain files or directories.
- --include=<path-pattern>
Apply changes to files matching the given path pattern. This can be useful when importing patchsets, where you want to include certain files or directories.
When --exclude and --include patterns are used, they are examined in the order they appear on the command line, and the first match determines if a patch to each path is used. A patch to a path that does not match any include/exclude pattern is used by default if there is no include pattern on the command line, and ignored if there is any include pattern.
- --ignore-space-change
- --ignore-whitespace
When applying a patch, ignore changes in whitespace in context lines if necessary. Context lines will preserve their whitespace, and they will not undergo whitespace fixing regardless of the value of the --whitespace option. New lines will still be fixed, though.
- --whitespace=<action>
When applying a patch, detect a new or modified line that has whitespace errors. What are considered whitespace errors is controlled by core.whitespace configuration. By default, trailing whitespaces (including lines that solely consist of whitespaces) and a space character that is immediately followed by a tab character inside the initial indent of the line are considered whitespace errors.
By default, the command outputs warning messages but applies the patch. When git-apply is used for statistics and not applying a patch, it defaults to nowarn.
You can use different <action> values to control this behavior:
nowarn turns off the trailing whitespace warning.
warn outputs warnings for a few such errors, but applies the patch as-is (default).
fix outputs warnings for a few such errors, and applies the patch after fixing them (strip is a synonym --- the tool used to consider only trailing whitespace characters as errors, and the fix involved stripping them, but modern Gits do more).
error outputs warnings for a few such errors, and refuses to apply the patch.
error-all is similar to error but shows all errors.
- --inaccurate-eof
Under certain circumstances, some versions of diff do not correctly detect a missing new-line at the end of the file. As a result, patches created by such diff programs do not record incomplete lines correctly. This option adds support for applying such patches by working around this bug.
- -v
- --verbose
Report progress to stderr. By default, only a message about the current patch being applied will be printed. This option will cause additional information to be reported.
- --recount
Do not trust the line counts in the hunk headers, but infer them by inspecting the patch (e.g. after editing the patch without adjusting the hunk headers appropriately).
- --directory=<root>
Prepend <root> to all filenames. If a "-p" argument was also passed, it is applied before prepending the new root.
For example, a patch that talks about updating a/git-gui.sh to b/git-gui.sh can be applied to the file in the working tree modules/git-gui/git-gui.sh by running git apply --directory=modules/git-gui.
Configuration
- apply.ignorewhitespace
Set to change if you want changes in whitespace to be ignored by default. Set to one of: no, none, never, false if you want changes in whitespace to be significant.
- apply.whitespace
When no --whitespace flag is given from the command line, this configuration item is used as the default.
Submodules
If the patch contains any changes to submodules then git apply treats these changes as follows.
If --index is specified (explicitly or implicitly), then the submodule commits must match the index exactly for the patch to apply. If any of the submodules are checked-out, then these check-outs are completely ignored, i.e., they are not required to be up-to-date or clean and they are not updated.
If --index is not specified, then the submodule commits in the patch are ignored and only the absence or presence of the corresponding subdirectory is checked and (if possible) updated.
SEE ALSO
GIT
Part of the git(1) suite


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Getting VDJdb in plain text format
The latest version of the VDJdb database in tab-delimited format is available
here.
Contributing
Users interested in contributing TCR sequences of known antigen specificity to the database should read the instruction at
the following
link. To submit the
contribution use the
issue system of the VDJdb repository. If you are not
familiar with GitHub you can file your submission by
creating a new issue and attaching your files. Advanced users can use the pull-request system.
Citing
Please cite the database as
Authors: Mikhail Goncharov, Dmitry Bagaev, Dmitrii Shcherbinin, Ivan Zvyagin,
Dmitry Bolotin, Paul G. Thomas, Anastasia A. Minervina, Mikhail V. Pogorelyy, Kristin Ladell, James E. McLaren,
David A. Price, Thi H. O. Nguyen, Louise C. Rowntree, E. Bridie Clemens, Katherine Kedzierska, Garry Dolton,
Cristina Rafael Rius, Andrew Sewell, Jerome Samir, Fabio Luciani, Ksenia V. Zornikova, Alexandra A. Khmelevskaya,
Saveliy A. Sheetikov, Grigory A. Efimov, Dmitry Chudakov & Mikhail Shugay
Contacts
For reporting database inconsistencies please use our
VDJdb-db issue tracker. Bug reports regarding this web
portal can be posted at
VDJdb-web issue tracker. For general
questions/enquiries regarding VDJdb please contact
mikhail (dоt) shugay (аt) gmail (dоt) com.


--- layout: data_model title: ActionRelationshipType this_version: 1.1 ---
The ActionRelationshipType characterizes a relationship between a specified cyber observable action and another cyber observable action.
| Field Name | Type | Description |
|---|---|---|
| Type0..1 | ControlledVocabularyStringType |
The Type field utilizes a standardized controlled vocabulary to describe the nature of the relationship between this Action and the related Action. This field is implemented through the xsi:type controlled vocabulary extension mechanism. The default vocabulary type is ActionRelationshipTypeVocab in the http://cybox.mitre.org/default_vocabularies-2 namespace. This type is defined in the cybox_default_vocabularies.xsd file or at the URL http://cybox.mitre.org/XMLSchema/default_vocabularies/2.0.1/cybox_default_vocabularies.xsd. Users may also define their own vocabulary using the type extension mechanism (by specifying a vocabulary name and/or reference using the vocab_name and vocab_reference attributes, respectively) or simply use this as a string field. |
| Action_Reference1..n | ActionReferenceType |
The Action_Reference construct captures references to other Actions. |