Source code for watchdog.utils.dirsnapshot
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2011 Yesudeep Mangalapilly<EMAIL_ADDRESS># Copyright 2012 Google, Inc.
# Copyright 2014 Thomas Amland<EMAIL_ADDRESS>#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
:module: watchdog.utils.dirsnapshot
:synopsis: Directory snapshots and comparison.
:author<EMAIL_ADDRESS>(Yesudeep Mangalapilly)
.. ADMONITION:: Where are the moved events? They "disappeared"
This implementation does not take partition boundaries
into consideration. It will only work when the directory
tree is entirely on the same file system. More specifically,
any part of the code that depends on inode numbers can
break if partition boundaries are crossed. In these cases,
the snapshot diff will represent file/directory movement as
created and deleted events.
Classes
-------
.. autoclass:: DirectorySnapshot
:members:
:show-inheritance:
.. autoclass:: DirectorySnapshotDiff
:members:
:show-inheritance:
"""
import errno
import os
from stat import S_ISDIR
from watchdog.utils import platform
from watchdog.utils import stat as default_stat
[docs]class DirectorySnapshotDiff(object):
"""
Compares two directory snapshots and creates an object that represents
the difference between the two snapshots.
:param ref:
The reference directory snapshot.
:type ref:
:class:`DirectorySnapshot`
:param snapshot:
The directory snapshot which will be compared
with the reference snapshot.
:type snapshot:
:class:`DirectorySnapshot`
"""
def __init__(self, ref, snapshot):
created = snapshot.paths - ref.paths
deleted = ref.paths - snapshot.paths
# check that all unchanged paths have the same inode
for path in ref.paths & snapshot.paths:
if ref.inode(path) != snapshot.inode(path):
created.add(path)
deleted.add(path)
# find moved paths
moved = set()
for path in set(deleted):
inode = ref.inode(path)
new_path = snapshot.path(inode)
if new_path:
# file is not deleted but moved
deleted.remove(path)
moved.add((path, new_path))
for path in set(created):
inode = snapshot.inode(path)
old_path = ref.path(inode)
if old_path:
created.remove(path)
moved.add((old_path, path))
# find modified paths
# first check paths that have not moved
modified = set()
for path in ref.paths & snapshot.paths:
if ref.inode(path) == snapshot.inode(path):
if ref.mtime(path) != snapshot.mtime(path):
modified.add(path)
for (old_path, new_path) in moved:
if ref.mtime(old_path) != snapshot.mtime(new_path):
modified.add(old_path)
self._dirs_created = [path for path in created if snapshot.isdir(path)]
self._dirs_deleted = [path for path in deleted if ref.isdir(path)]
self._dirs_modified = [path for path in modified if ref.isdir(path)]
self._dirs_moved = [(frm, to) for (frm, to) in moved if ref.isdir(frm)]
self._files_created = list(created - set(self._dirs_created))
self._files_deleted = list(deleted - set(self._dirs_deleted))
self._files_modified = list(modified - set(self._dirs_modified))
self._files_moved = list(moved - set(self._dirs_moved))
@property
def files_created(self):
"""List of files that were created."""
return self._files_created
@property
def files_deleted(self):
"""List of files that were deleted."""
return self._files_deleted
@property
def files_modified(self):
"""List of files that were modified."""
return self._files_modified
@property
def files_moved(self):
"""
List of files that were moved.
Each event is a two-tuple the first item of which is the path
that has been renamed to the second item in the tuple.
"""
return self._files_moved
@property
def dirs_modified(self):
"""
List of directories that were modified.
"""
return self._dirs_modified
@property
def dirs_moved(self):
"""
List of directories that were moved.
Each event is a two-tuple the first item of which is the path
that has been renamed to the second item in the tuple.
"""
return self._dirs_moved
@property
def dirs_deleted(self):
"""
List of directories that were deleted.
"""
return self._dirs_deleted
@property
def dirs_created(self):
"""
List of directories that were created.
"""
return self._dirs_created
[docs]class DirectorySnapshot(object):
"""
A snapshot of stat information of files in a directory.
:param path:
The directory path for which a snapshot should be taken.
:type path:
``str``
:param recursive:
``True`` if the entire directory tree should be included in the
snapshot; ``False`` otherwise.
:type recursive:
``bool``
:param walker_callback:
.. deprecated:: 0.7.2
:param stat:
Use custom stat function that returns a stat structure for path.
Currently only st_dev, st_ino, st_mode and st_mtime are needed.
A function with the signature ``walker_callback(path, stat_info)``
which will be called for every entry in the directory tree.
:param listdir:
Use custom listdir function. See ``os.listdir`` for details.
"""
def __init__(self, path, recursive=True,
walker_callback=(lambda p, s: None),
stat=default_stat,
listdir=os.listdir):
self._stat_info = {}
self._inode_to_path = {}
st = stat(path)
self._stat_info[path] = st
self._inode_to_path[(st.st_ino, st.st_dev)] = path
def walk(root):
try:
paths = [os.path.join(root, name) for name in listdir(root)]
except OSError as e:
# Directory may have been deleted between finding it in the directory
# list of its parent and trying to delete its contents. If this
# happens we treat it as empty.
if e.errno == errno.ENOENT:
return
else:
raise
entries = []
for p in paths:
try:
entries.append((p, stat(p)))
except OSError:
continue
for _ in entries:
yield _
if recursive:
for path, st in entries:
if S_ISDIR(st.st_mode):
for _ in walk(path):
yield _
for p, st in walk(path):
i = (st.st_ino, st.st_dev)
self._inode_to_path[i] = p
self._stat_info[p] = st
walker_callback(p, st)
@property
def paths(self):
"""
Set of file/directory paths in the snapshot.
"""
return set(self._stat_info.keys())
[docs] def path(self, id):
"""
Returns path for id. None if id is unknown to this snapshot.
"""
return self._inode_to_path.get(id)
[docs] def inode(self, path):
""" Returns an id for path. """
st = self._stat_info[path]
return (st.st_ino, st.st_dev)
def isdir(self, path):
return S_ISDIR(self._stat_info[path].st_mode)
def mtime(self, path):
return self._stat_info[path].st_mtime
[docs] def stat_info(self, path):
"""
Returns a stat information object for the specified path from
the snapshot.
Attached information is subject to change. Do not use unless
you specify `stat` in constructor. Use :func:`inode`, :func:`mtime`,
:func:`isdir` instead.
:param path:
The path for which stat information should be obtained
from a snapshot.
"""
return self._stat_info[path]
def __sub__(self, previous_dirsnap):
"""Allow subtracting a DirectorySnapshot object instance from
another.
:returns:
A :class:`DirectorySnapshotDiff` object.
"""
return DirectorySnapshotDiff(previous_dirsnap, self)
def __str__(self):
return self.__repr__()
def __repr__(self):
return str(self._stat_info)


Module: Utopia::HTTP
- Defined in:
- lib/utopia/http.rb
Overview
HTTP protocol implementation.
Defined Under Namespace
Classes: Status
Constant Summary collapse
- Accept =
Pull in HTTP::Accept for parsing.
::HTTP::Accept
- STATUS_CODES =
A list of commonly used HTTP status codes. For help choosing the right status code, see http://racksburg.com/choosing-an-http-status-code/
{ :success => 200, :created => 201, :accepted => 202, :moved => 301, :found => 302, :see_other => 303, :not_modified => 304, :redirect => 307, :bad_request => 400, :unauthorized => 401, :forbidden => 403, :not_found => 404, :not_allowed => 405, :unsupported_method => 405, :gone => 410, :teapot => 418, :unprocessible => 422, # The best status code for a client-side ArgumentError. :error => 500, :unimplemented => 501, :unavailable => 503 }
- STATUS_DESCRIPTIONS =
A list of human readable descriptions for a given status code. For a more detailed description, see https://en.wikipedia.org/wiki/List_of_HTTP_status_codes
{ 200 => 'OK'.freeze, 201 => 'Created'.freeze, 202 => 'Accepted'.freeze, 203 => 'Non-Authoritive Information'.freeze, 204 => 'No Content'.freeze, 205 => 'Reset Content'.freeze, 206 => 'Partial Content'.freeze, 300 => 'Multiple Choices'.freeze, 301 => 'Moved Permanently'.freeze, 302 => 'Found'.freeze, 303 => 'See Other'.freeze, 304 => 'Not Modified'.freeze, 305 => 'Use Proxy'.freeze, 307 => 'Temporary Redirect'.freeze, 308 => 'Permanent Redirect'.freeze, 400 => 'Bad Request'.freeze, 401 => 'Permission Denied'.freeze, 402 => 'Payment Required'.freeze, 403 => 'Access Forbidden'.freeze, 404 => 'Resource Not Found'.freeze, 405 => 'Unsupported Method'.freeze, 406 => 'Not Acceptable'.freeze, 408 => 'Request Timeout'.freeze, 409 => 'Request Conflict'.freeze, 410 => 'Resource Removed'.freeze, 416 => 'Byte range unsatisfiable'.freeze, 422 => 'Unprocessible Entity'.freeze, 500 => 'Internal Server Error'.freeze, 501 => 'Not Implemented'.freeze, 503 => 'Service Unavailable'.freeze }.merge(Rack::Utils::HTTP_STATUS_CODES)
- CONTENT_TYPE =
'Content-Type'.freeze
- LOCATION =
'Location'.freeze
- CACHE_CONTROL =
'Cache-Control'.freeze


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| src/ | 98.54% | 675/685 | 88.22% | 292/331 | 99.03% | 102/103 | 98.61% | 639/648 | |
| src/baggage/ | 100% | 48/48 | 100% | 14/14 | 100% | 8/8 | 100% | 45/45 | |
| src/metrics/ | 100% | 55/55 | 100% | 11/11 | 100% | 9/9 | 100% | 53/53 | |
| src/metrics/noop/ | 92.86% | 13/14 | 100% | 0/0 | 50% | 3/6 | 90% | 9/10 | |
| src/propagators/ | 99.24% | 130/131 | 90.41% | 66/73 | 100% | 17/17 | 99.19% | 122/123 | |
| src/reporters/ | 98.09% | 154/157 | 93.75% | 60/64 | 94% | 47/50 | 97.99% | 146/149 | |
| src/samplers/ | 100% | 223/223 | 100% | 102/102 | 100% | 52/52 | 100% | 211/211 | |
| src/throttler/ | 97.33% | 73/75 | 94.44% | 34/36 | 100% | 17/17 | 97.22% | 70/72 |


It's great that you stopped by to learn some more about me today. Here's a little information about me to start getting acquainted. I'm sure we'll get to know each other better over the next few weeks!
Question 1: I was born in Washington state.
Answer: Nope! Originally I am from a suburb outside of Philadelphia. I spent my first 18 years in the same town.
Question 2: In college I majored in chemistry
Answer: Not quite...I started out as a chemistry major but found that I loved geology more during my sophomore year and switched majors.
Question 3: I spent five years studying German in school.
Answer: Yep! My grandmother grew up in Germany and I started learning the language in middle school to connect more with her.
Question 4: I went to Colgate University in central New York.
Answer: Yep! I spent four years in beautiful rural New York. First time I experienced windchills up to -20s.
Question 5: Scuba diving is one of my favorite activities.
Answer: Yep! I got scuba certified for my senior thesis project researching coral reefs and loved it. If I could I would li ve in the reefs.
Getting to Know Me, Getting to Know All About Me!
Professional Life
These are a few of my favorite...books!
Fun Facts


Using TensorRT to Optimize Caffe Models in Python¶
TensorRT 4.0 includes support for a Python API to load in and optimize Caffe models, which can then be executed and stored.
First, we import TensorRT.
In [1]:
import tensorrt as trt
We use PyCUDA to transfer data to/from the GPU and NumPy to store data.
In [2]:
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
For this example, we also want to import pillow
(an image processing
library) and randint
.
In [3]:
from random import randint
from PIL import Image
from matplotlib.pyplot import imshow #to show test case
Since we are converting a Caffe model, we also need to use the
caffeparser
located in tensorrt.parsers
.
In [4]:
from tensorrt import parsers
Typically, the first thing you will do is create a logger, which is used
extensively during the model conversion and inference process. We
provide a simple logger implementation in
tensorrt.infer.ConsoleLogger
, but you can define your own as well.
In [5]:
G_LOGGER = trt.infer.ConsoleLogger(trt.infer.LogSeverity.ERROR)
Now, we define some constants for our model, which we will use to classify digits from the MNIST dataset.
In [6]:
INPUT_LAYERS = ['data']
OUTPUT_LAYERS = ['prob']
INPUT_H = 28
INPUT_W = 28
OUTPUT_SIZE = 10
We now define data paths. Please change these to reflect where you placed the data included with the samples.
In [7]:
MODEL_PROTOTXT = './data/mnist/mnist.prototxt'
CAFFE_MODEL = './data/mnist/mnist.caffemodel'
DATA = './data/mnist/'
IMAGE_MEAN = './data/mnist/mnist_mean.binaryproto'
The first step is to create our engine. The Python API provides
utilities to make this simpler. Here, we use the caffe model converter
utility in tensorrt.utils
. We provide a logger, a path to the model
prototxt, the model file, the max batch size, the max workspace size,
the output layer(s) and the data type of the weights.
In [8]:
engine = trt.utils.caffe_to_trt_engine(G_LOGGER,
MODEL_PROTOTXT,
CAFFE_MODEL,
1,
1 << 20,
OUTPUT_LAYERS,
trt.infer.DataType.FLOAT)
Building Engine
Now let’s generate a test case for our engine.
In [9]:
rand_file = randint(0,9)
path = DATA + str(rand_file) + '.pgm'
im = Image.open(path)
%matplotlib inline
imshow(np.asarray(im))
arr = np.array(im)
img = arr.ravel()
print("Test Case: " + str(rand_file))
Test Case: 4
We now need to apply the mean to the input image; we have this stored in a .binaryproto file which can be read by the caffeparser.
In [10]:
parser = parsers.caffeparser.create_caffe_parser()
mean_blob = parser.parse_binary_proto(IMAGE_MEAN)
parser.destroy()
#NOTE: This is different than the C++ API, you must provide the size of the data
mean = mean_blob.get_data(INPUT_W ** 2)
data = np.empty([INPUT_W ** 2])
for i in range(INPUT_W ** 2):
data[i] = float(img[i]) - mean[i]
mean_blob.destroy()
Now we need to create a runtime for inference and create a context for our engine.
In [11]:
runtime = trt.infer.create_infer_runtime(G_LOGGER)
context = engine.create_execution_context()
Now we can run inference. We first make sure our data is in the correct datatype (FP32 for this model). Then, we allocate an empty array on the CPU to store our results from inference.
In [12]:
assert(engine.get_nb_bindings() == 2)
#convert input data to Float32
img = img.astype(np.float32)
#create output array to receive data
output = np.empty(OUTPUT_SIZE, dtype = np.float32)
Now, we allocate memory on the GPU with PyCUDA and register it with the engine. The size of the allocations is the size of the input/expected output * the batch size.
In [13]:
d_input = cuda.mem_alloc(1 * img.size * img.dtype.itemsize)
d_output = cuda.mem_alloc(1 * output.size * output.dtype.itemsize)
The engine needs bindings provided as pointers to the GPU memory. PyCUDA lets us do this for memory allocations by casting those allocations to ints.
In [14]:
bindings = [int(d_input), int(d_output)]
We then create a cuda stream to run inference.
In [15]:
stream = cuda.Stream()
Now we transfer the data to the GPU, run inference and the copy the results back.
In [16]:
#transfer input data to device
cuda.memcpy_htod_async(d_input, img, stream)
#execute model
context.enqueue(1, bindings, stream.handle, None)
#transfer predictions back
cuda.memcpy_dtoh_async(output, d_output, stream)
#syncronize threads
stream.synchronize()
We can run np.argmax
to get a prediction.
In [17]:
print("Test Case: " + str(rand_file))
print ("Prediction: " + str(np.argmax(output)))
Test Case: 4
Prediction: 4
We can also write our engine to a file to use later.
In [18]:
trt.utils.write_engine_to_file("./data/mnist/new_mnist.engine", engine.serialize())
Out[18]:
True
You can load this engine later by using tensorrt.utils.load_engine
.
In [19]:
new_engine = trt.utils.load_engine(G_LOGGER, "./data/mnist/new_mnist.engine")
Finally, we clean up our context, engine and runtime
In [20]:
context.destroy()
engine.destroy()
new_engine.destroy()
runtime.destroy()


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“He has constructed a pandaemonium in an upper story of his museum, in which he has congregated all the images of horror that his fertile fancy could devise; dwarfs that by machinery grow into giants before the eyes of the spectator; imps of ebony with eyes of flame; monstrous reptiles devouring youth and beauty; lakes of fire, and mountains of ice; in short, wax, paint and springs have done wonders.” — Fanny Trollope, Domestic Manners Of The Americans


The HOA DB is the administration web application for the Gander Road Homeowners Association. Users are expected to abide by the posted rules of the GRHA, and only use this website for the good of the Association. Data updates must be accurate and data should not be used for any purposes outside the administration of the HOA.


This small harbour is on the southern coast of Kauai.
Position 21°54.0'N, 159°35.6'W.
Although a designated 'Port of Entry', the office based at Lihue Airport has been reported as unable to clear yachts into Hawaii.
Last updated September 2013.
The small boat harbour is on the eastern side of the port.
It is best to call ahead for a berth as the harbourmaster, who looks after several harbours, may not be on site when you arrive.
Anchoring here is strongly discouraged due to the high chance of the boat dragging ashore.
A swell enters here in southerly winds.
Wahiawa Bay, just to the SE of the town, is a useful, though small, anchorage. The holding is good in mud and sand.
Last updated September 2014.


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Inspections Open source
Name
class
anonymous (javafx.event.EventHandler)
Location
method void playerDone(ActionEvent)
(application.MainController)
Problem synopsis
Anonymous class declaration context is not reachable from entry points. Class is never instantiated.
Instantiated from
Overrides library methods
- ">
void handle(ActionEvent)
(application.MainController.playerDone.anonymous
(javafx.event.EventHandler))
Not Reachable. 1 usage found in the project code.


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Gender:Action Year: 2000
characters
anthony
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anthony
Small paragraph of whta's the movie about
Becouse is abou cars anf action
target=


Dungeon Level Creation Class Class used as a master constructor for every dungeon levels, getting pre-written data and loading it when the game starts and when a transition occurs.
More...
|
| def | __init__ (self, spritelist, collidlist, updatelist) |
| | Dungeon Level Master Initializer The class initializer, taking empty lists, each for letting the make() function define what objects need to be drawn/collidable/updatable. More...
|
| |
| def | make (self, x, y) |
| | Dungeon Level Constructor The main function to load a given index of data from leveldata.py, and load it into the game environment. More...
|
| |
| def | transition (self, xchange, ychange) |
| | Dungeon level transition function, used to go to an adjacent level, clear all current data, and load new room data. More...
|
| |
|
def | open (self) |
| | Function to open all blocked doors in the current dungeon room (usually used when all enemies defeated, locked doors stay locked)
|
| |
|
def | endroom (self) |
| | Function to store current data, open all blocked doors, and possibly spawn a key/prize when all enemies defeated When all enemies in a room are killed, this function is called, changing the enemy array in leveldata.py for the current level to an empty array (so enemies don't reload once you enter again), running the self.open() function to open all blocked doors, and if the roomID is a predetermined key/rupy room, spawn a key/rupy in the middle of the room.
|
| |
|
def | clear (self) |
| | Clears the levelmanager's update lists Empty all sprite lists for the game (spritelist, collisionlist, and updatelist), usually used in transition from one room to another.
|
| |
|
|
| sl |
| | List of objects to be printed (ie player, not invisible walls)
|
| |
|
| cl |
| | List of objects that can be collided with (ie keese, not level background)
|
| |
|
| ul |
| | List of objects that need to be constantly updated (ie player, not walls/static objects)
|
| |
|
| x |
| | X-Coordinate of the level in leveldata's Room ID array (RID[y][x])
|
| |
|
| y |
| | Y-Coordinate of the level in leveldata's Room ID array (RID[y][x])
|
| |
|
| level |
| | Level background.
|
| |
|
| RID |
| | Misc room spawnings (ie shop room)
|
| |
|
| enarray |
| |
|
| boss |
| |
|
| doors |
| |
|
| killed |
| |
|
|
list | keyset = [3, 7, 17] |
| |
|
list | rupeset = [1, 16, 12] |
| |
|
| LD = None |
| | List of all rooms to load (to be altered)
|
| |
Dungeon Level Creation Class Class used as a master constructor for every dungeon levels, getting pre-written data and loading it when the game starts and when a transition occurs.
◆ __init__()
| def collision.levelmanager.LevelManager.__init__ |
( |
|
self, |
|
|
|
spritelist, |
|
|
|
collidlist, |
|
|
|
updatelist |
|
) |
| |
Dungeon Level Master Initializer The class initializer, taking empty lists, each for letting the make() function define what objects need to be drawn/collidable/updatable.
- Parameters
-
| spritelist | List of objects for the game to print (usually empty pygame.sprite.Group) |
| collidlist | List of objects which have a collision interaction with the player (usually empty pygame.sprite.Group) |
| updatelist | List of objects which need to be regularly updated (usually empty pygame.sprite.Group) |
◆ make()
| def collision.levelmanager.LevelManager.make |
( |
|
self, |
|
|
|
x, |
|
|
|
y |
|
) |
| |
Dungeon Level Constructor The main function to load a given index of data from leveldata.py, and load it into the game environment.
- Parameters
-
| i | Index of the room to be created |
◆ transition()
| def collision.levelmanager.LevelManager.transition |
( |
|
self, |
|
|
|
xchange, |
|
|
|
ychange |
|
) |
| |
Dungeon level transition function, used to go to an adjacent level, clear all current data, and load new room data.
- Parameters
-
| j | Value to add to the current index to get to new room |
The documentation for this class was generated from the following file:


| Home | Trees | Indices | Help |
|
|---|
|
|
object --+ | core.AbstractContainer --+ | core.AbstractNIContainer --+ | object --+ | | | AbstractEntity --+ | Chain
Represents a polymeric chain. Provides list-like and rank-based access to the residues in the chain:
>>> chain[0] <ProteinResidue [1]: SER None> >>> chain.residues[1] <ProteinResidue [1]: SER None>
You can also access residues by their PDB sequence number:
>>> chain.find(sequence_number=5, insertion_code='A') <ProteinResidue [1]: SER 5A>
| Nested Classes | |
|
Inherited from |
| Instance Methods | |||
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| SuperimposeInfo |
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| Chain |
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| Residue |
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| float |
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| Chain |
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| SuperimposeInfo |
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| float |
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| SuperimposeInfo |
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|
Inherited from Inherited from Inherited from Inherited from |
| Static Methods | |||
| Chain |
|
| Class Variables | |
__abstractmethods__ =
|
| Properties | |
| str |
accession Accession number |
| csb.core.enum |
alphabet Sequence alphabet corresponding to the current chain type |
| str |
entry_id Accession number + chain ID |
| bool |
has_torsion True if Chain.compute_torsion had been invoked
|
| str |
header FASTA header in PDB format |
| str |
id Chain's ID |
| iterator of AbstractEntity |
items Iterator over all immediate children of the entity |
| int |
length Number of residues |
| int |
molecule_id PDB MOL ID of this chain |
| str |
name Chain name |
| ChainResiduesCollection |
residues Rank-based access to Chain's Residues |
| SecondaryStructure |
secondary_structure Secondary structure (if available) |
| str |
sequence Chain sequence |
| TorsionAnglesCollection |
torsion Torsion angles |
| enum item |
type Chain type - any member of SequenceTypes |
|
Inherited from |
| Method Details |
x.__init__(...) initializes x; see help(type(x)) for signature
|
|
repr(x)
|
Align R, t = self.superimpose(other, what=['CA']) other.transform(R, t)
|
Make a deep copy of the chain. If this chain is part of a structure, detach from it.
|
Iterate over all residues in the chain, compute and set their torsion property.
|
Get a residue by its original Residue Sequence Number and Insertion Code.
|
Create a new chain from an existing sequence.
|
Compute the C-alpha RMSD against another chain (assuming equal length). Chains are superimposed with Least Squares Fit / Singular Value Decomposition.
|
Extract a subchain defined by [start, end]. If clone is True, this is a deep copy of the chain. Otherwise same as:
>>> chain.residues[start : end + 1]
but coordinates are checked and a Chain instance is returned.
|
Find the optimal fit between
>>> other.transform(rotation_matrix, translation_vector)
will result in
|
Compute the C-alpha TM-Score against another chain (assuming equal chain length and optimal configuration - no fitting is done).
|
Find the optimal fit between
>>> other.transform(rotation_matrix, translation_vector)
will result in
|
| Property Details |
accessionAccession number
|
alphabetSequence alphabet corresponding to the current chain type
|
entry_idAccession number + chain ID
|
has_torsionTrue if
|
headerFASTA header in PDB format
|
idChain's ID
|
itemsIterator over all immediate children of the entity
|
lengthNumber of residues
|
molecule_idPDB MOL ID of this chain
|
nameChain name
|
residuesRank-based access to Chain's Residues
|
secondary_structureSecondary structure (if available)
|
sequenceChain sequence
|
torsionTorsion angles
|
typeChain type - any member of SequenceTypes
|
| Home | Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0.1 on Tue Jul 4 20:19:05 2017 | http://epydoc.sourceforge.net |


Put words that have no meaning but look cool


So, our goal in this video is to take the antiderivative of this fairly crazy looking expression. Or another way of saying it is to find the indefinite integral of this crazy looking expression.  And the indefinite integral of the entire expression is going to be equal to the indefinite integral of each of the terms.
So this is going to be equal to, we could look at this term right over here and just take the indefinite, take the indefinite integral of that. x to the 3rd dx.
And then from that we can subtract the indefinite integral of this thing.
So we can say this is, and then minus. The indefinite integral of 5 times the square root of x d x.
And then we can look at this one right over here.
So then we can say plus the indefinite integral of 18 square root of x. Square roots of x over x to the third, dx and then finally, and I'm running out of colors here. Finally, I need more colors in my thing.
We can take the anti derivative of this. So plus the anti derivative of x to the negative 40th power dx. So, I've just rewritten this and color coded things. So, let's take the anti derivative of each of these and you'll see that'll we'll be able to do it. Using our whatever we wanna call the inverse of the power rule or the anti power rule. Whatever you might wanna call it. So lets look at the first one. So we have, we have, what I'm gonna do is I'm just gonna find the anti derivative without the constant and just add the constant at the end for the sake of this. So we just make sure we get the most general anti derivative. So here the exponent is a 3. So we can increase it by 1. So it's going to be x to the 4th. It's gonna be that same purple color.
Or that pink color. It's going to be x to the 4th. And we're gonna divide x to the 4th. So it's x to the 4th over 4 is the antiderivative of x to the 3rd, and you just had this scaling quantity. The 7 out front. So we can still just have 7 out front. So we get 7x to the 4th over 4. Fair enough. From that, we're going to subtract the antiderivative of this. Now, at first, this might not be obvious that this, you could use our inverse power rule or anti-power rule here. But then you just need to realize that 5 times the principal root of x is the same thing as.
So, this is the same thing as 5 x X to the 1 half power. And, so, once again, the exponent here is one half, we can increment it by 1.
This is going to be X to the 3 halves. And then, divide by the incremented exponent. So, divide by.
Three halves and of course we had this five out front so we still want to have the five out front. Now this next expression looks even wackier but once again we can simplify a little bit.
This is the same thing. Let me do it right over here.
This is the same thing as 18 times x to the one half times x to the negative three power. X to the third in the denominator is the same thing as x to the negative three. We have the same base, we can just add the exponent so this is going to be equal to 18 times x to the 2 and a half power, and a half power or another way of thinking about it, this is the same thing as 18 times x, 18 times x to the 5 halves power. Two the 5 halves power. Did I do that right? Yeah, negative 3, oh sorry, this is negative 2 and a half let me make this very clear and this is going to be the negative 5 half power. X to the negative 3 is the same thing as x to the negative 6 halves, negative six halves plus 1 half is negative 5 halves. So once again we just have to increment this exponent. So negative 5 halves, plus 1 is going to be negative 3 halves.
So you're going to have x, x to the negative 3 halves, and then you divide by what your exponent is when you increment, so divide it by negative, divide it by negative 3 halves. And then you have the 18 out front. And we obviously are going to have to simplify this.
And then finally, our exponent in this term, let me not use that purple any more, the exponent, in this term right over here is negative 40, if we increment we get x to the negative 39 power, all of that over negative 39. And now we can add our constant. Now we can add our constant C, and all we need to do is simplify all of this craziness.
So the first one is fairly simplified. We can write it as seven fourths. X to the 4th.
Now this term right over here is essentially 5 divided by 3 halves, so 5 over three halves is equal 5 times two thirds, which is equal to 10 over 3.
So this thing right over here simplifies to negative 10 over 3X to the three halves. And then we have all of this craziness.
Now 18 divided by negative three halves. divided by negative 3 halves is equal to 18 times. Negative two thirds, which is equal to, well, we can, we can simplify this a little bit. This is the same thing as 6. This is the same thing as 6 times negative 2 which is equal to negative 12.
So, this expression right over here, is negative 12 x to the negative 3 halves. And then finally this one right over here, we can just rewrite it as. If we want, we could, well, we could just write negative one thirty-ninths x to the negative 39. Plus, plus C, and we're done. We found the indefinite integral of all of this craziness. And I encourage you to take the derivative of this, and you can do it using really just the power rule, to take the derivative of this and verify that it does indeed equal this expression that we took, that we took the anti-derivative of.


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Data
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Parselmouth Vs TensorFlow Analysis
Enter File Name:
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Librosa sound wave
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Copyright UofM Data Bootcamp 2020


cat, type=lines,
input=
Aim: as in v0.11, default setting.
cat, type=lines, bufsize=32,
input=
Aim: Same input as Test 1, Line 2 is longer than bufsize, so server-side error occurred (bufio.Scanner: token too long).
cat, type=lines, bufsize=32,
input=
Aim: Line 2 ends with an incomplete UTF-8 sequence, so web browser drops connection automatically.
cat, type=text,
input=
Aim: Same input as Test 1, but use type=text. Web browser receives Line 2 in a single websocket message.
cat, type=text, bufsize=32,
input=
Aim: Same input as Test 2A, but use type=text. Server-side buffer can handle the case that UTF-8 character cut into 2 halves.
cat, type=binary,
input=
✅
Aim: Test type=binary.
cat, type=binary, bufsize=32,
input=
✅
Aim: Same input as Test 5, Web browser receives 2 packets when exceeding server-side buffer size.
curl, type=lines,
,
Aim: as in v0.11, default setting. Cannot display CURL progress messages at real-time. Also, whitespaces are trimmed.
curl, type=text,
,
Aim: Display real-time CURL progress messages. Do not trim whitespaces.


Overview
Cubicle is an open source model checker for verifying safety properties of array-based systems. This is a syntactically restricted class of parametrized transition systems with states represented as arrays indexed by an arbitrary number of processes. Cache coherence protocols and mutual exclusion algorithms are typical examples of such systems.
Cubicle model-checks by a symbolic backward reachability analysis on infinite sets of states represented by specific simple formulas, called cubes. Cubicle is based on ideas introduced by MCMT from which, in addition to revealing the implementation details, it differs in a more friendly input language and a concurrent architecture. Cubicle is written in OCaml. Its SMT solver is a tightly integrated, lightweight and enhanced version of Alt-Ergo; and its parallel implementation relies on the Functory library.
Download
Sources
Cubicle is distributed under
the Apache
licence.
You can download the sources of the latest version of
Cubicle here.
Documentation and source code can be browsed online.
Binaries
Installation instructions
Opam
If you use the package manager opam, the easiest way to install Cubicle is to do: $ opam install cubicle If you don't (want to) use opam, Cubicle can also be installed manually.
Manual installation
To compile Cubicle you will need OCaml version 4.00.0 (or newer) and the Ocaml Functory library version 0.5 (or newer) which can be downloaded here.
Note: You can still compile Cubicle without Functory but you will not be able to use its parallel features.
Uncompress the archive and do:
$ cd cubicle-#version#
$ ./configure
$ make
then with superuser rigths:
# make install
To run Cubicle with the BRAB algorithm on a
file file.cub simply do:
$ cubicle -brab nb file.cub
where nb is the number of different processes that will be
considered for the enumerative forward exploration.
For instance, doing cubicle -brab 2
german.cub will first perform an enumeration of the
states of the German protocol for an instance of the system
with only two processes. The BRAB algorithm will then be
initialized and run with the finite model it just constructed.
Publications
- Inférence d'invariants pour le model checking de systèmes paramétrés (in french)
- Vérification de programmes C concurrents avec Cubicle : Enfoncer les barrières (in French)
- Invariants for Finite Instances and Beyond
- Vérification de systèmes paramétrés avec Cubicle (in French)
-
Cubicle: A Parallel SMT-based Model Checker for Parameterized
Systems
Sylvain Conchon, Amit Goel, Sava Krstić, Alain Mebsout, and Fatiha Zaïdi.
PDF BibTeX
In Madhusudan Parthasarathy and Sanjit A. Seshia, editors, CAV 2012: Proceedings of the 24th International Conference on Computer Aided Verification, Lecture Notes in Computer Science, pages 718-724, Berkeley, California, USA, July 2012. Springer Verlag.
Experiments
All the following benchmarks have been executed on a 64 bits machine with a quad-core Intel ® Xeon ® processor @ 3.2 GHz and 24 GB of memory. The first four columns give details on the exploration of the finite instance. The last colum reports the total time of BRAB, i.e. the time needed for the forward exploration plus the time used by backward reachability. You can view more details about each result such as inferred invariants and search graphs by clicking on the name of the benchmark.
| Forward | Backward | Total time | ||||||
|---|---|---|---|---|---|---|---|---|
| # procs | depth lim. | # states | time | # nodes | # inv | # restarts | ||
| German-ish | 2 | ∞ | 23 | 0.01s | 4 | 3 | 0 | 0.03s |
| Szymanski_at | 2 | ∞ | 43 | 0.02s | 31 | 15 | 0 | 0.14s |
| Szymanski_na | 2 | ∞ | 75 | 0.02s | 38 | 16 | 0 | 0.20s |
| German_Baukus | 2 | ∞ | 1497 | 0.02s | 43 | 35 | 0 | 0.25s |
| German.CTC | 2 | ∞ | 1497 | 0.02s | 62 | 27 | 0 | 0.29s |
| German_pfs | 2 | ∞ | 1737 | 0.02s | 49 | 35 | 0 | 0.34s |
| Chandra-Toueg | 2 | ∞ | 148 | 0.02s | 3828 (770 + 1329 + 1729) | 5 | 2 | 2m17s |
| Flash_nodata | 2 | 6 | 439 | 0.02s | 37 | 30 | 0 | 0.36s |
| Flash_buggy | 2 | 6 | 445 | 0.02s | 228 | / | 0 | 2.97s |
| Flash | 3 | 14 | 452,523 | 8.54s | 1047 | 131 | 0 | 5m40s |
Documentation
Description language
Cubicle's input language contains some conventional programming constructs (arrays, enumerated and abstract types etc.). However, since we have designed our language with a particular set of protocols in mind, some of its aspects would need to be improved for describing other classes of algorithms.
In Cubicle, array-based systems are described by a set of type declarations, a set of array declarations, an initial configuration, and a transition relation given as a set of parameterized transitions.
The language has four built-in data types: integers
(int),
reals (real), boolean
(bool) and the type of process
identifiers (proc). It also supports
user-defined abstract
and enumerated data types. For instance,
type data
type msg = Empty | Req | Ack
defines an abstract type data, and an enumeration type
msg with the three given values.
The state of a system is described by a set of global variables and
arrays indexed by process identifiers. Thus,
var Memory : data
var Counter : int
array Channel[proc] : msg
describe the state consisting of two global
variables Memory
and Counter, respectively of
type data and
int, and an
array Channel containing values of
type msg.
The initial state is defined by a universal conjunction of
literals characterizing the values for some variables and arrays. For
example, the configuration
where Counter initially equals to
1 and Channel[z]
contains the value Empty
for any process z is expressed by:
init (z) { Counter = 1 && Channel[z] = Empty }
The execution of a parameterized system is defined by a set of
guarded transitions and consists of an infinite loop which
non-deterministically triggers at each iteration a transition whose
guard is true and whose action is to update state variables. Each
transition can take one or several process identifiers as arguments. A
guard is a conjunction of literals (equations, disequations or
inequations) and universal formulas. These formulas must be in
disjunctive normal form and are universally quantified by processes
different from the transition's arguments. Assignments of variables
can possibly be non-deterministic (denoted by := .). Arrays
updates are realized through a case-defined construct where each
condition is expressed as a conjunction of literals and the default
case is denoted by _.
transition send_req(i)
requires
{ Channel[i] = Empty && Counter < 10 &&
forall_other j. (j < i || Channel[j] <> Req) }
{
Counter := Counter + 1 ;
Memory := . ;
Channel[j] := case
| j = i : Req
| _ : Channel[j];
}
The safety property to be verified is expressed in its negated form as
a cube, existentially quantified by distinct processes, and
characterizes unsafe states. The user also has an option to
specify invariants of the system. For instance, the following formula
expresses that a state is unsafe when there exists two distinct
processes z1
and z2 such
that Channel
contains Req for both.
unsafe (z1 z2)
{ Channel[z1] = Req && Channel[z2] = Req }
Options
Cubicle can be run with different options that are shown below:
| -version | Print the version number on stdout and exit. |
| -quiet | Prevent Cubicle from printing the search trace while it performs backward reachability. |
| -depth n | Limit the depth of the search tree to n at maximum. If this limit si excedeed Cubicle will print reach bound on stderr. This value is set to 100 by default. |
| -nodes n | Limit the number of nodes to explore to n at maximum. If this limit si excedeed Cubicle will print reach bound on stderr. This value is set to 100000 by default. |
| -search s | Set the search strategy to s. s can be either breadth-first search (bfs) or depth-first search (dfs) or a variant of DFS (dfsl, dfsh, dfshl). By default, Cubicle explores the search space breadth-first (bfs). |
| -debug | When this flag is present, Cubicle will output cubes that were computed during the pre, and that are being explored. |
| -v | When this flag is present together with -debug, Cubicle will output the result of the pre. |
| -profiling | Tells cubicle to output profiling informations at the end of the search. Such as the time that was spent in computing pre-images, in the fixpoint checks, in computing relevant permutations, in applying substitutions, in the solver, etc. |
| -postpone n | Select the strategy to postpone
nodes. Values for n can be:
|
| -nodelete | Deactivate a posteriori deletion of nodes that become subsumed. |
| -j n | Run Cubicle in parallel on n cores. This option can only be used with the search strategy set to BFS. (Do not use in conjunction with -brab.) |
| -h, --help | Display a message with usage and the list of options. |
Options relevant to BRAB
| -brab n | Runs BRAB with a forward exploration with n processes. |
| -forward-depth dmax | Limits the forward exploration to states at depth dmax at maximum. |
| -only-forward | Stops after forward exploration. |
| -candheur nb | Select canditate invariants heuristic with nb (by default nb is the argument of -brab). |
| -v | Increases verbosity of debugging information. |
| -dot level | Displays search graph (dot must be installed) with detail level level (between 0 and 5). |
When running, Cubicle will produce traces on stdout for each
node that is explored. For example, a node with the following
trace
inv_2(#2) -> gnt_shared(#1) -> unsafe
will represent states that can reach the unsafe state by
first applying transition inv_2 in
process #2 and then applying
transition gnt_shared in
process #1.
If a system is proved unsafe, Cubicle will say so and output a couterexample trace from the inital state to the unsafe state in the same format.
Changelog
#CHANGELOG#
Examples
Below is an example of a simple mutal exclustion algorithm
Mutex:
var Turn : proc
array Want[proc] : bool
array Crit[proc] : bool
init (z) {
Want[z] = False && Crit[z] = False
}
unsafe (x y) {
Crit[x] = True && Crit[y] = True
}
transition req (i)
requires { Want[i] = False }
{
Want[j] := case
| i = j : True
| _ : Want[j]
}
transition enter (i)
requires {
Want[i] = True
&& Crit[i] = False
&& Turn = i }
{
Crit[j] := case
| i = j : True
| _ : Crit[j]
}
transition exit (i)
requires { Crit[i] = True }
{
Turn := . ;
Crit[j] := case
| i = j : False
| _ : Crit[j] ;
Want[j] := case
| i = j : False
| _ : Want[j]
}
Contact
You can contact us at our respective email addresses.


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| lqs {MASS} | R Documentation |
Fit a regression to the good points in the dataset, thereby
achieving a regression estimator with a high breakdown point.
lmsreg
and ltsreg
are compatibility wrappers.
lqs(x, ...) ## S3 method for class 'formula' lqs(formula, data, ..., method = c("lts", "lqs", "lms", "S", "model.frame"), subset, na.action, model = TRUE, x.ret = FALSE, y.ret = FALSE, contrasts = NULL) ## Default S3 method: lqs(x, y, intercept = TRUE, method = c("lts", "lqs", "lms", "S"), quantile, control = lqs.control(...), k0 = 1.548, seed, ...) lmsreg(...) ltsreg(...)
formula |
a formula of the form |
data |
data frame from which variables specified in
|
subset |
an index vector specifying the cases to be used in fitting. (NOTE: If given, this argument must be named exactly.) |
na.action |
function to specify the action to be taken if
|
model, x.ret, y.ret |
logical. If |
contrasts |
an optional list. See the |
x |
a matrix or data frame containing the explanatory variables. |
y |
the response: a vector of length the number of rows of |
intercept |
should the model include an intercept? |
method |
the method to be used. |
quantile |
the quantile to be used: see |
control |
additional control items: see |
k0 |
the cutoff / tuning constant used for chi()
and psi() functions when |
seed |
the seed to be used for random sampling: see |
... |
arguments to be passed to |
Suppose there are n
data points and p
regressors,
including any intercept.
The first three methods minimize some function of the sorted squared
residuals. For methods "lqs"
and "lms"
is the
quantile
squared residual, and for "lts"
it is the sum
of the quantile
smallest squared residuals. "lqs"
and
"lms"
differ in the defaults for quantile
, which are
floor((n+p+1)/2)
and floor((n+1)/2)
respectively.
For "lts"
the default is floor(n/2) + floor((p+1)/2)
.
The "S"
estimation method solves for the scale s
such that the average of a function chi of the residuals divided
by s
is equal to a given constant.
The control
argument is a list with components
psamp
:the size of each sample. Defaults to p
.
nsamp
:the number of samples or "best"
(the
default) or "exact"
or "sample"
.
If "sample"
the number chosen is min(5*p, 3000)
,
taken from Rousseeuw and Hubert (1997).
If "best"
exhaustive enumeration is done up to 5000 samples;
if "exact"
exhaustive enumeration will be attempted however
many samples are needed.
adjust
:should the intercept be optimized for each
sample? Defaults to TRUE
.
An object of class "lqs"
. This is a list with components
crit |
the value of the criterion for the best solution found, in
the case of |
sing |
character. A message about the number of samples which resulted in singular fits. |
coefficients |
of the fitted linear model |
bestone |
the indices of those points fitted by the best sample found (prior to adjustment of the intercept, if requested). |
fitted.values |
the fitted values. |
residuals |
the residuals. |
scale |
estimate(s) of the scale of the error. The first is based
on the fit criterion. The second (not present for |
There seems no reason other than historical to use the lms
and
lqs
options. LMS estimation is of low efficiency (converging
at rate n^{-1/3}) whereas LTS has the same asymptotic efficiency
as an M estimator with trimming at the quartiles (Marazzi, 1993, p.201).
LQS and LTS have the same maximal breakdown value of
(floor((n-p)/2) + 1)/n
attained if
floor((n+p)/2) <= quantile <= floor((n+p+1)/2)
.
The only drawback mentioned of LTS is greater computation, as a sort
was thought to be required (Marazzi, 1993, p.201) but this is not
true as a partial sort can be used (and is used in this implementation).
Adjusting the intercept for each trial fit does need the residuals to
be sorted, and may be significant extra computation if n
is large
and p
small.
Opinions differ over the choice of psamp
. Rousseeuw and Hubert
(1997) only consider p; Marazzi (1993) recommends p+1 and suggests
that more samples are better than adjustment for a given computational
limit.
The computations are exact for a model with just an intercept and adjustment, and for LQS for a model with an intercept plus one regressor and exhaustive search with adjustment. For all other cases the minimization is only known to be approximate.
P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection. Wiley.
A. Marazzi (1993) Algorithms, Routines and S Functions for Robust Statistics. Wadsworth and Brooks/Cole.
P. Rousseeuw and M. Hubert (1997) Recent developments in PROGRESS. In L1-Statistical Procedures and Related Topics, ed Y. Dodge, IMS Lecture Notes volume 31, pp. 201–214.
set.seed(123) # make reproducible lqs(stack.loss ~ ., data = stackloss) lqs(stack.loss ~ ., data = stackloss, method = "S", nsamp = "exact")


post_stack_tweets.Rd
Format and post StackOverflow questions as tweets, optionally returning them as a formatted list. This code is based on the tidyverse tweets bot by Jake Thompson.
post_stack_tweets(questions = NULL, hashtags = "rstats", post = TRUE)
| questions | A list of questions to tweet as produced by |
|---|---|
| hashtags | A character vector of hashtags to attach to the tweet.
See |
| post | Logical, defaults to |
Either nothing is returned or a formated list of tweets.
ggplot2_qs <- get_stack_questions(extracted_tags = "ggplot2", excluded_tags = "plotly", time_window = 60*24) post_stack_tweets(ggplot2_qs, hashtags = "rstats", post = FALSE)#> [[1]] #> ggplot2 - Correctly arrange odd number of plots into one figure (r,ggplot2,cowplot) #rstats https://stackoverflow.com/questions/52953569/ggplot2-correctly-arrange-odd-number-of-plots-into-one-figure #> #> [[2]] #> Plotting monthly percentages in shiny R (r,ggplot2,shiny,shinydashboard) #rstats https://stackoverflow.com/questions/52954681/plotting-monthly-percentages-in-shiny-r #> #> [[3]] #> ggplot order of geom_point and geom_line differ (r,ggplot2) #rstats https://stackoverflow.com/questions/52954846/ggplot-order-of-geom-point-and-geom-line-differ #> #> [[4]] #> How do I annotate a ggtree object with bootstrap values? (r,ggplot2,bioinformatics,phylogeny,ggtree) #rstats https://stackoverflow.com/questions/52956181/how-do-i-annotate-a-ggtree-object-with-bootstrap-values #> #> [[5]] #> How to annotate different values for each facet with dodged geom_boxplot on R? (r,ggplot2,text,boxplot) #rstats https://stackoverflow.com/questions/52958453/how-to-annotate-different-values-for-each-facet-with-dodged-geom-boxplot-on-r #> #> [[6]] #> How to fix the multiple error bar issue of ggplot in R? (ggplot2,errorbar) #rstats https://stackoverflow.com/questions/52960261/how-to-fix-the-multiple-error-bar-issue-of-ggplot-in-r #> #> [[7]] #> No legend in ggplot line graph (r,ggplot2,legend,linegraph) #rstats https://stackoverflow.com/questions/52960885/no-legend-in-ggplot-line-graph #> #> [[8]] #> Choropleth Plotting polygons with ggplot2 R on a map (r,ggplot2,gis,ggmap) #rstats https://stackoverflow.com/questions/52961807/choropleth-plotting-polygons-with-ggplot2-r-on-a-map #> #> [[9]] #> Why do I get the weird purple line in ggplot? (r,ggplot2) #rstats https://stackoverflow.com/questions/52961934/why-do-i-get-the-weird-purple-line-in-ggplot #> #> [[10]] #> R (ggplot2): line with data labels (r,ggplot2) #rstats https://stackoverflow.com/questions/52962517/r-ggplot2-line-with-data-labels #> #> [[11]] #> ggplot y axis adjustment and statistics (r,ggplot2,ggpubr) #rstats https://stackoverflow.com/questions/52966608/ggplot-y-axis-adjustment-and-statistics #> #> [[12]] #> How to install ggplot2 for R to a custom lib directory (r,ggplot2,installation,package) #rstats https://stackoverflow.com/questions/52967249/how-to-install-ggplot2-for-r-to-a-custom-lib-directory #> #> [[13]] #> ggplot2: can geom_point work in melted dataframes? (r,ggplot2,scatter-plot,dcast) #rstats https://stackoverflow.com/questions/52968996/ggplot2-can-geom-point-work-in-melted-dataframes #> #> [[14]] #> Framework for adding lines on existing plots: "points" and "plot" in ggplot (r,ggplot2) #rstats https://stackoverflow.com/questions/52970305/framework-for-adding-lines-on-existing-plots-points-and-plot-in-ggplot #> #> [[15]] #> grouped barplot from table (r,ggplot2,bar-chart) #rstats https://stackoverflow.com/questions/52970306/grouped-barplot-from-table #> #> [[16]] #> ggplot: Show 24-hour activity profiles for each day through 48 weeks (r,ggplot2,plot) #rstats https://stackoverflow.com/questions/52972439/ggplot-show-24-hour-activity-profiles-for-each-day-through-48-weeks #> #> [[17]] #> ggplot2: grouping a barplot by several instead of a single categorical variable (r,ggplot2) #rstats https://stackoverflow.com/questions/52973023/ggplot2-grouping-a-barplot-by-several-instead-of-a-single-categorical-variable #> #> [[18]] #> Plotting multiple densities in ggplot2: How to add an extra variable with a different scale? (r,ggplot2,scaling) #rstats https://stackoverflow.com/questions/52973132/plotting-multiple-densities-in-ggplot2-how-to-add-an-extra-variable-with-a-diff #> #> [[19]] #> How to pass a string argument to expression function in ggplot2? (ggplot2) #rstats https://stackoverflow.com/questions/52973186/how-to-pass-a-string-argument-to-expression-function-in-ggplot2 #>


A Git branch or tag is created. For more information, see the "Git data" REST API.
The event object includes properties that are common for all events. Each event object includes a payload
property and the value is unique to each event type. The payload
object for this event is described below.
payload
object| Key | Type | Description |
|---|---|---|
ref |
string |
The git ref resource. |
ref_type |
string |
The type of Git ref object created in the repository. Can be either branch or tag . |
master_branch |
string |
The name of the repository's default branch (usually master ). |
description |
string |
The repository's current description. |


Your browser fingerprint with default settings (canvas, screen resolutin, activeX disabled)
Your browser fingerprint with canvas enabled is:
Your browser fingerprint with ie_activex enabled is:
Your browser fingerprint with screen_resolution enabled is:


TemplateScript adds custom one-click templates and scripts to your sidebar when editing pages (or other forms). You can import libraries of scripts maintained by other users, or write your own scripts if you're feeling adventurous. See m:TemplateScript for more information.
These libraries have been imported for you (probably via your common.js or a gadget). You can choose which scripts will be shown. Your settings will be saved permanently to your user preferences; you can return to this page anytime to edit them. Changes are saved immediately.
{{#if libraries.length}} {{#each libraries}}{{{this.description}}}
(You haven't imported any libraries.)
{{/if}}


Email {{email}}
Address:{{address.street}} {{address.city}}
Hobbies:{{hobbies}}
Hobbies:
Posts Data
{{post.body}}


Connect the dots
by MutterQueen's birthday: The answer she gave to tourists asking if she knew the Queen | The Independent www.independent.co.uk
Even Queen Elizabeth II can occasionally walk among the public and avoid detection, at least according to an account from her former protection officer Richard Griffin.
Mr Griffin recalled a time when the monarch was approached by a group of American tourists while on a walk near the Balmoral Estate in Aberdeenshire at a recent talk, according to The Times. The group asked the Queen, who was wearing a headscarf and tweed coat, if she lived in the area, to which she responded simply by saying she had a house close–by.
“Have you ever met the Queen?” they asked.
“No,” she apparently replied, before pointing at the policeman and adding: “But he has.” The group were still none the wiser and left without realising whose company they had just shared.


--- layout: post status: publish published: true title: MC++ and IL Optimizations author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 654 wordpress_url: http://winterdom.com/2002/06/mcandiloptimizations date: '2002-06-20 03:14:12 +0000' date_gmt: '2002-06-20 03:14:12 +0000' categories: - ".NET" tags: [] comments: [] ---
Charles Cook on IL optmizations performed by the Managed C++ compiler: "I've not not seen any mention of this elsewhere. Maybe its just a rumour put about by the VC++ team :-)"
Not at all, actually. The MC++ compiler does indeed run the generated code thorugh the backend optimizer, doing some optimizations no other ms managed compiler does yet.
This has both an advantage and a disadvantage. The advantage, of course, is improved performance (altough I haven't seen any serious numbers on how better it is). The disadvantage is that it is one of the reasons (and an important one, at that) the MC++ compiler generates unverifiable code.


Florida county first in state to enact 'Second Amendment Sanctuary' in support of gun rights
by Robert GeartyA Florida county has become the first in the state to pass a resolution declaring itself a "Second Amendment Sanctuary" in support of gun rights.
The Lake County Commission in central Florida approved the measure last week in response to mandatory gun buyback proposals by Democrats running for president, the Orlando Sentinel reports.
The resolution, which passed by a 4-0 vote, says that Lake County’s law-abiding gun owners are safe from having their firearms taken away. The county's other commissioner was absent, but expressed support for it.
NEW MEXICO COUNTY BECOMES ‘SECOND AMENDMENT SANCTUARY’ IN PROTEST OF GUN CONTROL LEGISLATION
Commissioner Josh Blake was quoted by the paper and other local media as saying that the resolution draws a line in the sand.
“It doesn’t mince words, and I hope it sends a message to what can best be described as the authoritarian control freaks who see it as their job to forcibly disarm their fellow citizens.”
CALIFORNIA DESERT TOWN AIMS TO BE ‘SANCTUARY CITY’ FOR GUN OWNERS
But a Florida gun control group said the resolution, which Blake sponsored, was meaningless, according to the Sentinel.
CLICK HERE TO GET THE FOX NEWS APP
“I think it’s really just posturing by the gun lobby and their supporters,” said Andy Pelosi, who co-chairs The Florida Coalition to Prevent Gun Violence. “It really doesn’t have any teeth.”


Window in this demo have different value of "
maximizedDraggable
" option.
Maximize window and drag it by titlebar to feel the difference.


Hello World!
I'm simple!
Highly Customizable!
Easy to use!
And last but not the least, I'm Toggle-able!
toggle


CLI templating
You can influence the cli-generated output with templates. To do so, place a folder _template in your project. Basic
variable support works with double curly braces without spaces ( {{example}} ) The following files are
respected:
| Template File |
Entity |
Vairable |
Note |
|
api.php
|
component (API) |
name, frame |
|
|
route.php
|
component (Route) |
name, frame |
|
|
model.php
|
model |
name |
|
|
view.html
|
component (Route) |
name |
|
|
ce.php
|
Custom element |
name |
*PHP files for custom element is only generated if template is present |
|
ce.js
|
Custom element |
name |
|
|
ce.html
|
Custom element |
name |
|
|
*.json
|
Credential template |
|
It might sometimes be useful to provide a template for credentials to provide your team with
the possibility for an easy setup.
|
If the the cli-command `neoan3 new [component|model] <name>` finds the according template,
it generates files accordingly. Want to learn more about cli-templating?
github.com/neoan3/cli#templates
Asset folder
This folder is intended to hold static files of all sorts and neoan3 holds no opinion on the deeper structure.
Classically, you might want to have an image and/or upload folder within it.
Component folder
This folder holds hybrid, route, api and custom components. Components must have their own directory.
Note that neoan3's routing system performs a kebab-to-pascal-case transformation when evaluating routes. (as opposed to kebab-to-camel in versions <3.0)
This means that
your-app.com/my-component/
targets the component
/component/MyComponent
learn more about components
Frame folder
Frames are a unique and vital piece to a Neoan3.
Frames hold global information & functionality and hook into the framework's Serve class.
As such, a frame tends to provide project-specific settings like the HTML skeleton, used CSS, includes, apps etc.
Components can either extend a particular frame (default behavior of API-components),
construct them via the unicore singleton or use them as instance ( new MyFrameName() ).
learn more about frames
Model folder
Neoan3 follows an MVC-approach that isn't obvious when looking at the folder structure.
Models, however, do have their own directories within the model-folder.
While static use is encouraged due to some third party apps, Neoan3 does not dictate a certain structure.
learn more about models
Provider folder
In order to decouple components and models from the executing instance - and in order to use external testing/mocking -
you can program against interfaces that provide required instances as dependency injection.
The current version ships with a MySql provider for database transactions.
learn more about providers
default.php
This file is requested early in the initiation process and facilitates the possibility to hook into early events.
After installation, the default controller (usually a route controller) and 404-controller are already defined.
In most use-cases, you will want to change at least the value of default_ctrl to your "homepage" controller.
For example, if your application has a "home" route component with your start page, your default.php could look like this:
<?php
// default route (homepage)
const default_ctrl = 'home';
// or: define('default_ctrl','home');
// optional: custom 404
const default_404 = 'notFound';
// or: define('default_404','notFound');
// optional: react on early events
Event::listen('Core\\Route::notFound', function($call){
// redirect?
})
.htaccess & Apache
When running Neoan3 on Apache, the easiest way to set up intended behavior is using the provided
.htaccess. In most cases, you likely want to change the "RewriteBase" to "/" (assuming your web-root equals the application root).
Neoan3 requires mod_rewrite to be available.


First things first, after the initial install you need to give ubuntu a list of software packages and then this list will be reloaded any time you run the apt-get update command.
sudo apt-get update
There are some software that ubuntu isn’t allowed to include as part of the vanilla install but they do provide the location to download them. Among them audio/video codecs. Without them you won’t be able to play mp3, mp4 and other popular media files.
To install You need to use apt-get: sudo apt-get install ubuntu-restricted-extras
I find it annoying to have to open ubuntu software center any time I need to install a .deb package. You can set gdebi as the default .deb package installer.
sudo apt-get install gdebi
Through oh-my-zsh you can get better terminal auto completion, plugins and customization options.
sudo apt-get install zsh
set it as the default terminal shell chroot
Install zsh curl -L https://raw.github.com/robbyrussell/oh-my-zsh/master/tools/install.sh | sh
add update your .zshrc
References: Become A Command-Line Power User With Oh-My-ZSH And Z
It plays all the popular media formats, set it as the default file type for all your media files and change the settings to open only a single vlc instance.
sudo apt-get install vlc
When looking at a the directory structure of a folder ls may not be enough but tree and ranger are the best addons you could use.
sudo apt-get install ranger
& sudo apt-get install tree
Installing and Using Ranger, a Terminal File Manager, on a Ubuntu
You will have to download the .deb files for each installer but you can them up to update whenever you update the os chrome and opera
I find it better to work within the terminal and I can edit code using vim. The reason I chose vim-gnome is because it enables you to copy the the system clipboard “* and through tmux you can open many tabs or windows in one terminal window.
sudo apt-get install tmux vim-gnome
Then customize your .tmux.conf
and .vimrc
files as you see fit.


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Credits
CRC Algorithm from opensportident
MIT License - https://github.com/angexis/opensportident.js/ blob/master/LICENSE
Copyright
(c) 2016 Jan Vorwerk
(c) 2013 Simon Denier
Serialport
MIT License - https://github.com/node-serialport/node-serialport/ blob/master/LICENSE
Copyright (c) 2010 Christopher Williams. All rights reserved.
Electron
MIT License - https://github.com/electron/electron/ blob/master/LICENSE
Copyright (c) 2013-2017 GitHub Inc.
Material Design Icons
Apache 2.0 License - https://github.com/google/material-design-icons/ blob/master/LICENSE
LokiJS
MIT License - https://github.com/techfort/LokiJS/ blob/master/LICENSE.txt
Copyright (c) 2015 TechFort
License
MIT License
Copyright (c) 2017 Ben Brown
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the" Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


|
Perl Weekly
Issue #31 - 2012-02-27 - Delhi.pm is being reorganized and Cluj.pm created
latest | archive | by Gabor Szabo
|
|
Sorry for sending this late but I am a bit tense and busy with the Israeli Perl Workshop. It is tomorrow. We have over 120 people registered and we were just told that the allocated rooms are being renovated :).
Luckily we already got new rooms but I still have to print out the name-tags, the schedule and a few signs to direct people to the rooms. So I am sending this out. I hope I have not made too many typos.
Enjoy!
|
|
|
Announcements
|
DBIx::Class::Migration on CPAN
John Napiorkowski released the above module including a tutorial. I am really looking forward to trying it next week. The tutorial looks promising but it refers to a number of things you'll have to learn first, unless you already understand the difficulty in database schema upgrade and downgrade.
|
|
|
|
App::ArchiveDevelCover 1.000
Thomas Klausner (domm) likes checking test coverage and likes to see the progress he is making regarding to the coverage. So his new module allows you to save your previous test coverage report and create an index page for all the reports.
|
|
|
|
Articles
|
What's new with Marpa
Jeffrey Kegler has a new revision to the Marpa theory paper and provides some update on his plans and progress with Marpa::R2 and Milarepa.
|
|
Perl Mongers
|
|
|
Cluj.pm
There is a new Perl Monger group being organized in Cluj Napoca in Transylvania, Romania. Matt S. Trout and Chisel Wright were invited to the opening. Please refrain from invoking Dracula jokes -- Transylvania is beautiful!
|
|
Testing
|
Nagged by a Test Harness
I totally with chromatic that warnings surfacing from dependencies of dependencies are very disturbing and I applaud his sending a patch. I don't agree that this nagging is pointless. It would be nice if we could eliminate all the warnings generated during test execution. In some cases those warnings point at actual issues in the code or in the way the code is used.
|
|
Smoothing Selenium Use
Su-Shee describes how they built a DSL that fit their business language to express test cases of a complex ('rich' for people with positive attitude) web application. She shows some usage of the DSL (which is basically a set of method calls) and explains some of the issues they faced during the work.
|
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Code
|
Extracting data from a Japanese dictionary
Marcel Grunauer started a new blog and already published several interesting posts. In this one Hanekomu shows how he uses the XML based JMdict dictionary and creates JSon data structure that he can then use with JQuery. He also provides a couple of comments on how to deal with unicode that and how to speed up XML processing by a factor of 10. I hope he will stick to his frequent writings and I wish him success with his new company providing Perl development services!
|
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Think looping
Frequent updates to a database and frequent opening of a file can be very time consuming. Sebastian Willing (Sewi) shows how he made his code much faster by some caching and by trading accuracy in the database, for speed.
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Parrot
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Screenshots
|
Padre::Plugin::Moose 0.12
Ahmad M. Zawawi (azawawi) is cranking out new versions of his Moose and Padre integration at a wonderful speed. He also keeps showing us screenshots which makes it much easier to understand what he is doing. I am really looking forward to using this in teaching Moose.
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Perl 6
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Other
|
My English stinks, thank you for patching it!
Salvador Fandino wrote this, but I could have written as well. When reading documentation, blog post or comments in the Perl Weekly newsletter, it is important to remember that the author might not be a native English speaker. If you send us a nice comment on how to improve our grammar, we are usually grateful for that!
|
|
Be nice to me and I'll be nice to you (ebooks)
I did not know you can 'upgrade' your dead-tree O'Reilly books to eBooks for just $5. Claudio Ramirez (El-Che) has a point. If a corporation treats its customers with respect, those will do the same in return. Unfortunately many corporations don't do that.
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Training
|
Learning Perl Challenge: rhich
brian d foy started a new series of Newbie Perl Challenges that publishes on Monday. Unfortunately a few hours after I send out the Perl Weekly. So I am a week late announcing it.
|
|
The self promotion section
|
What is a Perl Monger group for?
What is the value your Perl Monger group provides you? What would provide you with value that would make it worth for you to join? What does it mean to 'join' a Perl Monger group?
|
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Events
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YAPC::NA
June 13-15, 2012, Madison, Wisconsin, USA
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You know, you could get the Perl Weekly right in your mailbox. Every Week. Free of charge!
|


Warning: Virtual Directory /mxgraph not found!
A virtual directory (/mxgraph
) that points to the top-level
directory of the mxGraph distribution is required in IIS for these examples
to work. This virtual directory must be manually created using the IIS Manager.


[gecode-users] next() in flatzinc
Guido Tack
tack at ps.uni-sb.de
Wed Oct 14 11:30:00 CEST 2009
Hi Fred.
There are currently no plans to add that functionality. Actually, the
run method may be in the wrong class, as FlatZincSpace is really just
meant to be a space that you can initialize from FlatZinc. In order
to perform incremental search, just use a DFS search engine as you
would do for any other space. The current run method is more like
what Driver provides (which doesn't have incremental search either,
currently).
Cheers,
Guido
Fred Spiessens wrote:
> Hi,
>
> I'm working with flatzinc in gecode 3.2.0.
> Is there support planned for incremental search in flatzinc? For
> instance by making an incremental version of:
>
> void FlatZincSpace::run(std::ostream& out, const Printer& p, const
> FlatZincOptions& opt, Support::Timer& t_total)
>
> something like this:
> bool FlatZincSpace::runInitial(std::ostream& out, const Printer& p,
> const FlatZincOptions& opt, Support::Timer& t_total)
> and:
> bool FlatZincSpace::runNext(std::ostream& out, const Printer& p,
> const FlatZincOptions& opt, Support::Timer& t_total)
> (possibly removing some arguments in the latter)
>
> I wanted to ask before making this available for myself.
> I could also help out with this if you want me to, and if you think
> it's useful for flatzinc.
>
> cheers,
> Fred.
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
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SWING KIDS A film review by James Berardinelli Copyright 1993 James Berardinelli
Date Released: 3/5/93 Running Length: 1:53 Rated: PG-13 (Mature themes, violence)
Starring: Robert Sean Leonard, Christian Bale, Frank Whaley, Barbara Hershey, Kenneth Branagh Director: Thomas Carter Producers: Mark Gordon and John Bard Manulis Screenplay: Jonathan Marc Feldman Music: James Horner Released by Hollywood Pictures
Hamburg. Nazi Germany. 1939. Peter (Robert Sean Leonard), Thomas (Christian Bale), and Arvid (Frank Whaley) are three "Swing Kids"--young rebel Germans who have fallen in love with the forbidden: "American movies, British fashion, and Swing music." When the evil specter of Nazism hovers over them in the form of a seemingly-benign member of the Gestapo (Kenneth Branagh), Peter and Thomas must begin to re-evaluate their priorities and values.
SWING KIDS is a movie with a multitude of problems, the biggest of which is its loose treatment of history and the Nazis. While it's undeniable that members of the Hitler Youth and the Gestapo were goons, SWING KIDS portrays them as such in a cartoonish manner. They are not chillingly menacing; simply nasty. Motivation is not a major concern. The kind of mentality that sent millions of Jews to their deaths is only weakly in evidence. In fact, the entire Holocaust is given only token screen time. The need to sanitize this film to make it palatable to mass audiences has robbed SWING KIDS of much of its power.
There's another, more subtle flaw in the script. Everyone in Germany who isn't a "Swing Kid" is regarded as a Nazi. This was simply not the case. Most Germans were not members of the National Socialist Party, and many citizens regarded Nazis with fear and distrust. The Luftwaffe was definitely not a "Nazi organization", yet SWING KIDS makes no distinction between Luftwaffe officers and the Gestapo.
Aside from the aforementioned problems, the plot is lacking when it comes to originality. That shouldn't be a surprise, however, coming from Hollywood Pictures, well-known for regurgitating formula movies. What is unusual is that some of the elements of this film do not allow it to fall into the "happily ever after" category. In that sense, it takes a little risk.
There are several scenes of high melodrama where the film comes close to parodying itself. While the two leads, Bale and Leonard, have been competent in previous roles, they are merely adequate here, and sometimes not even that. Barbara Hershey is completely wasted. Kenneth Branagh, in an uncredited role (it's more than just a cameo--he's in a number of scenes), is unsurprisingly one of the bright spots about this film. He manages to create a sympathetic Nazi without resorting to audience manipulation.
Undoubtedly, the best part of SWING KIDS is its musical numbers. The choreography and energy of these scenes is excellent. Unfortunately, they are too few and far between and by the second half of the movie, they've all but disappeared.
SWING KIDS will undoubtedly appeal to the masses--and especially the teen masses--who go to the movies simply to escape. In fact, some of those with little knowledge and less concern about the historical period portrayed may even find this movie "powerful." At its heart, however, SWING KIDS is seriously flawed, and, while it isn't a terrible movie, the best way to describe it may be "unfortunate."
Rating: 6.1 (C, **)
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Machine learning is an exciting field that has been gaining popularity recently due to some recent advancements that enhance our ability to compute solutions to problems that previously were not in reach.
Advances in computer hardware enable algorithms to run faster. This is not in the algorithmic abstract sense: an algorithm that takes O(n) runtime will still take O(n), regardless of hardware implementation. This is, rather, in the physical time. If an O(n) algorithm runs in 60 seconds on a computer, then Apple develops a computer that is twice as fast as the previous one, we get the algorithm running in 30 seconds. Computers in our pockets can do much more than desktops could 10 years ago, so this opens up the possibility of new methods of data collection and data analysis.
Theoretical advances are also cruical in the advancement of machine learning. Neural Networks are the hot new buzzword now, and everybody is scrambling to get their hands on experts that know how to implement these for their data. Although the concept was invented many years ago, it has seen a recent boom in popularity due to advances in the runtime of the training algorithm, as well as previously mentioned advances in hardware such as GPU accelleration that enable parallel training or the like.
The Bay Area
The San Francisco bay area is home to silicon valley, one of the technology advancement hubs in the world.  The location of the bay area is often overlooked as a central driver of change, but its California location is without a doubt a factor. The lovely weather allows us to make better machine learning advances.
Machine learning is the literacy of the future. The bay area is where it will happen.


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