# import dependencies
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
We define $\varsigma$ (varsigma) as the sum of $n$ unit fractions $\frac{1}{x}$ to the power of $x$, whereas $n$ markes the depth of fractions from zero to infinity and $x$ represents the amount of uniform splits of one among the width of units:
$ \varsigma(x) := \sum_{n=0}^{\infty} (\frac{1}{x})^{x^n} $
whereas $\frac{1}{\varsigma(x)}$ represents the inverse decay constant for a given $x$ towards infinity.
def varsigma(x, n=8):
'''Compute the decay constant.
Arguments:
x: defines the base for the computation
n: defiens the precision due to multiple iterations
'''
sum = 0.
for i in range(n):
sum += np.power(1./x, np.power(x, i))
return sum
def inverse_varsigma(x):
return 1/varsigma(x)
def print_results(bases, results):
for (b, r) in zip(bases, results):
print('x {} => varsigma(x) {}'.format(b, r))
min_range = 0.00001
max_range = 30
steps = 10000
# compute experiment results from min to max range with defined stepsize
base_list = np.linspace(min_range, max_range, steps, endpoint=True)
base_inverse_list = []
base_result = []
for b in base_list:
base_inverse_list.append(1./b)
base_result.append(varsigma(b))
#print_results(base_list, base_result)
# further variable to plot for visualizing and analysis
f1_over_e_power_x_result = []
f1_over_x_result = []
for b in base_list:
# plot 1/x
f1_over_x_result.append(1/b)
# plot 1/e^x
f1_over_e_power_x_result.append(1/(np.e**b))
# set pi constant for plot
pi_list = np.full((steps), np.pi)
# set e constant for plot
exp_list = np.full((steps), np.e)
# set sqrt(2) for plot
sqrt_2_list = np.full((steps), np.sqrt(2))
def f_x_base_integral(x0, xn, steps):
sum = 0.
double_precision_step = steps*100
width = (xn-x0)/double_precision_step
x_cur = x0
for s in range(double_precision_step):
height = varsigma(x_cur)
sum += (width*height)
x_cur += width
return sum
f_x_base_area = f_x_base_integral(min_range, max_range, steps)
f_x_base_area_list = np.full((steps), f_x_base_area)
f_x_base_area
27.047224860884615
def f_x_integral(x0, xn):
return np.log(xn)-np.log(x0)
f_x_area = f_x_integral(min_range, max_range)
f_x_area
14.914122846632385
(f_x_base_area - f_x_area)
12.133102014252231
plt.figure(figsize=(10,10))
plt.plot(base_list, pi_list, label="pi")
plt.plot(base_list, exp_list, label="e")
plt.plot(base_list, sqrt_2_list, label="sqrt(2)")
plt.plot(base_list, f_x_base_area_list, label="decay area")
plt.plot(base_list, base_result, c='r', label="varsigma(x)")
plt.plot(base_list, f1_over_x_result, c='b', label="1/x")
plt.plot(base_list, f1_over_e_power_x_result, c='k', label="1/e^x")
plt.legend()
plt.xlim(0, 15)
plt.ylim(0, 35)
X_detail = np.linspace(0.3, 1.7, 1000)
Y_detail = varsigma(X_detail)
# location for the zoomed portion
sub_axes = plt.axes([1, .6, .5, .25])
# plot the zoomed portion
sub_axes.plot(X_detail, Y_detail, c='r')
plt.show()
The area below the decay constants is approx.:
$\pi^{2}*e$
If we could compute the area with higher precision maybe we find a relationship between the decay area and the natural constants?
np.pi**2 * np.e
26.828366297560617
def gradient(x, h):
return (varsigma(x + h)-varsigma(x))/h
h = 0.0000001
base_list_range = np.linspace(0.4, 1, 10000, endpoint=True)
base_list_result_range = []
base_list_gradients = []
for x in base_list_range:
base_list_result_range.append(varsigma(x))
base_list_gradients.append(gradient(x, h))
min_gradient = np.argmax(base_list_gradients)
min_gradient_base = base_list_range[min_gradient]
print('index {}, x {}, gradient {}'.format(min_gradient, base_list_range[min_gradient], base_list_gradients[min_gradient]))
index 4524, x 0.6714671467146714, gradient -2.2962650980673516
def print_gradients():
for i in range(len(base_list_range)):
print('index {}, range {}, gradient {}'.format(i, base_list_result_range[i], base_list_gradients[i]))
#print_gradients()
plt.figure(figsize=(5,5))
plt.plot(base_list_range, base_list_result_range)
plt.plot(base_list_range, base_list_gradients)
plt.axvline(x=min_gradient_base)
plt.show()
plt.figure(figsize=(5,5))
plt.plot(base_list, base_result, c='r')
plt.axvline(x=min_gradient_base, c='k')
plt.axvline(x=min_gradient_base-0.5)
plt.axvline(x=min_gradient_base+0.5)
plt.xlim((0.1, 2))
plt.ylim((0, 20))
plt.show()
The plot shows that arround ~0.6714 the gradient is very low and as shown in the computation the decay constant behaves non-exponential, which could allow us to use weights initialization and activation normalization arround this point to stabalize the output values of the neural network layers.
We can see that when applying extremly low values and extremely high values to $varsigma(x)$ the inverse operation results nearly to its identity value $x$ only shifted. And if we use the values surrounding the global optima of the gradient at the anomaly, we receive shiftet entries with respect to the decay constant.
def print_decay_of_x(x):
print('varsigma({}) = {}'.format(x, varsigma(x)))
print('inverse varsigma({}) = {}'.format(x, inverse_varsigma(x)))
print('Low values:')
print_decay_of_x(0.0007)
print_decay_of_x(0.001)
print_decay_of_x(0.003)
print_decay_of_x(0.02)
print_decay_of_x(0.07)
print('\nNumbers near local minimum of the gradient:')
print_decay_of_x(min_gradient_base-0.1)
print_decay_of_x(min_gradient_base)
print_decay_of_x(min_gradient_base+0.1)
print('\nHigh values:')
print_decay_of_x(1.5)
print_decay_of_x(2)
print_decay_of_x(10)
print_decay_of_x(30)
Low values: varsigma(0.0007) = 1435.5765301857257 inverse varsigma(0.0007) = 0.000696584249584121 varsigma(0.001) = 1007.0069385835457 inverse varsigma(0.001) = 0.0009930418169775458 varsigma(0.003) =<PHONE_NUMBER>4693664 inverse varsigma(0.003) = 0.0029381435637115478 varsigma(0.02) = 57.082980626326744 inverse varsigma(0.02) = 0.017518356417758583 varsigma(0.07) = 21.50441189462851 inverse varsigma(0.07) = 0.046502085474366565 Numbers near local minimum of the gradient: varsigma(0.5714671467146715) = 9.564317000586872 inverse varsigma(0.5714671467146715) = 0.10455529651920147 varsigma(0.6714671467146714) = 9.32294797251297 inverse varsigma(0.6714671467146714) = 0.1072622096517453 varsigma(0.7714671467146714) = 9.08039783903676 inverse varsigma(0.7714671467146714) = 0.11012733337530496 High values: varsigma(1.5) = 2.05234607870611 inverse varsigma(1.5) = 0.48724725833298266 varsigma(2) = 0.8164215090218931 inverse varsigma(2) = 1.2248574896048996 varsigma(10) = 0.1000000001 inverse varsigma(10) = 9.99999999 varsigma(30) = 0.03333333333333333 inverse varsigma(30) = 30.0
If we plot the values with respect to the decay rate, we can see, that the extrema values such as approx. below 0.3 behave as identity values and approx. above 1.5 as the shifted identity value.
This means that extremly large and small values can be reversed computing the inversed decay constant:
$x \approx \frac{1}{\varsigma(x)}$
plt.figure(figsize=(5,5))
plt.plot(base_inverse_list, base_result, 'b.', label="varsigma(x)")
plt.plot(base_inverse_list, base_inverse_list, 'r.', label="1/x")
plt.yscale('log')
plt.xlim(0, 50)
plt.legend()
plt.show()
plt.plot(base_inverse_list, base_result, 'b.', label="varsigma(x)")
plt.plot(base_inverse_list, base_inverse_list, 'r.', label="1/x")
plt.ylim(0, 12)
plt.xlim(0, 3)
plt.legend()
plt.show()
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MINIST_data', validation_size=0)
Extracting MINIST_data\train-images-idx3-ubyte.gz Extracting MINIST_data\train-labels-idx1-ubyte.gz Extracting MINIST_data\t10k-images-idx3-ubyte.gz Extracting MINIST_data\t10k-labels-idx1-ubyte.gz
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
<matplotlib.image.AxesImage at 0x1951340e470>
def fc(x, dim, name='fc'):
with tf.name_scope(name):
x_shape = x.get_shape().as_list()
stddev = 2 / np.sqrt(x_shape[1])
w_init = tf.truncated_normal([x_shape[1], dim], stddev=stddev)
W = tf.Variable(w_init, name="W")
b = tf.Variable(tf.zeros([dim]), name="b")
logits = tf.nn.bias_add(tf.matmul(x, W), b)
return logits
def fc_act(x, dim, name='fc_relu'):
with tf.name_scope(name):
act = tf.nn.relu(fc(x, dim, name=name))
return act
def fc_act_amp(x, dim, name='fc_relu_amp'):
with tf.name_scope(name):
x = tf.nn.relu(fc(x, dim, name=name))
condition = tf.less(tf.abs(x-min_gradient_base), 0.5)
x = tf.where(condition, (x + varsigma(x)), x)
return x
inputs_ = tf.placeholder(tf.float32, shape=(None, 784))
targets_ = tf.placeholder(tf.float32, shape=(None, 784))
fc1 = fc_act(inputs_, 128, name='fc1')
fc2 = fc_act_amp(fc1, 64, name='fc2')
logits = fc(fc2, 784, name='fc_out')
decoded = tf.nn.sigmoid(logits)
with tf.name_scope('loss'):
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)
cost = tf.reduce_mean(loss)
with tf.name_scope('optimizer'):
opt = tf.train.AdamOptimizer().minimize(cost)
sess = tf.Session()
epochs = 20
batch_size = 200
sess.run(tf.global_variables_initializer())
for e in range(epochs):
for ii in range(mnist.train.num_examples//batch_size):
batch = mnist.train.next_batch(batch_size)
feed = {inputs_: batch[0], targets_: batch[0]}
batch_cost, _ = sess.run([cost, opt], feed_dict=feed)
print("Epoch: {}/{}...".format(e+1, epochs),
"Training loss: {:.4f}".format(batch_cost))


Holden Caulfield is a seventeen- year-old dropout who has just been kicked out of his fourth school. Navigating his way through the challenges of growing up, Holden dissects the 'phony' aspects of society, and the 'phonies' themselves: the headmaster whose affability depends on the wealth of the parents, his roommate who scores with girls using sickly-sweet affection.
If you really want to hear about it, the first thing you'll probably want to know is where I was born and what my lousy childhood was like, and how my parents were occupied and all before they had me, and all that David Copperfield kind of crap, but I don't feel like going into it, if you want to know the truth. In the first place, that stuff bores me, and in the second place, my parents would have about two hemorrhages apiece if I told anything pretty personal about them.


Creating Match, Return, Order By clauses From a PathTree
Jul 2, 2017
7 minute read
In this post I’ll describe in detail, how I created Match, Return and Order By clauses of Cypher using a PathTree. PathTree was discussed in the previous post.
Description of Clauses
Match
Match is the very first clause in every Cypher query. It allows you to specify the patterns Neo4j will search for in the database. These patterns must be such that the data which matches them, must be the data you wish to operate (or query) on. Consider an example Match clause which matches three Genes such that two of them INTERACTS_WITH the third one.
The pattern above is a 2-dimensional pattern which we often have to deal with, in Cypher. Although, it is easier to imagine the pattern in 2-D, we generally need to break them down into multiple 1-D patterns while writing cypher queries. Also, 1-D patterns would be easier to generate programmatically than their 2-D counterparts. For example, the following Match clause with two 1-D patterns is equivalent to the one shown above.
Note that we could simply use the variable name b in the second pattern without specifying the Label Gene. It doesn’t seem very significant here but it makes generating queries very convinient while dealing with some complicated paths. Suppose you need to specify a Path/Node/Relationship again and again in the query, wouldn’t you be comfortable in just writing its variable name and get done with it?
Return
In the RETURN part of the cypher query, we define the parts of the pattern in which we are interested. It can be nodes, relationships, or properties on these.
Order By
ORDER BY is a sub-clause following RETURN, and it specifies that the output should be sorted and how. For example,
We see that variable names are crucial in a Cypher query. A PathTree represents all the paths in the corresponding PathQuery. Since we can have a constraint/view/order on any path, therefore we need to assign variable name to each node of the PathTree.
We cannot just use the component name as the variable name. This is because a component can be repeated in the same path. For example, in Gene.chromosome.gene.length, gene appears twice. Having a unique variable name for each TreeNode is crucial to generating the Cypher query.
To create the variable names, I have simply separated components in the path using an underscore instead of a dot. Also, I have converted the path string to its lower case form. For example, the TreeNode for the path Gene.homologues.dataSets.name will have variable name gene_homologues_datasets_name. This approach is fine till we don’t exceed the max length of variable name for a path.
TreeNode Description
A TreeNode for the path Gene.chromosome.gene.length, stores the following information.
type : Whether this TreeNode represents a Graphical Node or Relationship or a Property on them.
name : The last component of the path, i.e. length.
variable name : The one we generated using approach shown above, i.e. gene_chromosome_gene_length.
graphical name : This is the name by which we refer this entity/property in the InterMine Neo4j graph. Here we keep it same as name, i.e. length.
Clause Creation
RETURN
The return clause always starts with the RETURN keyword. After that, for each view in the PathQuery, we add an expression separated by commas. The expression consists of the variable name and the respective graphical name.
ORDER BY
The order by clause always starts with the ORDER BY keyword. After that, we simply append the variable name and the respective graphical name for each sortOrder's path of the PathQuery. The sort type (Ascending/Descending) is also added.
Match
Creation of Match clause is rather complex. The match clause always starts with the MATCH keyword. After that, for each edge in the PathTree we write two nodes and the relationship between them. If two adjacent TreeNodes represent Graph Nodes, then we use a dummy relationship to join them. Otherwise we add the relationship represented by the TreeNode in between.
The createMatchClause() recursive method takes in the Query object and the root TreeNode as parameters. The following code snippet shows the method in action.
In the next post, the generation of Where clause will be covered. It is most complex of all because it involves converting around 30+ PathQuery contraints into their equivalend Cypher expressions.


fun isFollowingArtist(artist: String): SpotifyRestAction<Boolean>
Check to see if the current user is following a Spotify artist.
Requires the SpotifyScope.USER_FOLLOW_READ scope
artist
- The artist id to check.
BadRequestException
- if artist is a non-existing id
Return
Whether the current user is following artist


Sending Your Samples is Easy!
Some would say the waiting is the hardest part. Sending samples however, is easy. Choose the plant you'd like to test, take a few leaves from throughout the plant, and place them in the sample bag. We already have pre-printed labels for you to name your sample as you like. Simply drop the sample bag in the white shipping bag, attach the prepaid shipping label and you are good to go!
- Fast Turn Around Times
- Accurate and Precise Results
- Peace of Mind About Your Plants


Studied and finished a degree in Biomedical Engineering at Wentworth Institute of Technology in Boston, MA.
Worked closely with the VP of Engineering to develop and test an opthalmoscopy technology called the Adaptive Optics Scanning Laser Opthalmoscope.
Took some time off to focus on family business and reevalute my career path.
Started taking Front End Web Development(FEWD) Program with General Assembly in July 2018 to get a sense of what web development is like. I signed up for Web Development Immersive with GA to start three weeks after the completion of FEWD.


dist.tar
dist
./start_mock_demo.sh
start message broker ... start center ... start mock hub_a ... start mock hub_b ... visit ip:8080 for develop, ip:3000 for ui view


Prevention effect, advocate dialogue rural development lifting people up community civil society. Catalyst, grantees leverage.
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Community health workers best practices, effectiveness meaningful work The Elders fairness. Our ambitions local solutions globalization.


| Enum | Description |
|---|---|
| AffectedTaskOccurrence |
Indicates which occurrence of a recurring task should be deleted.
|
| AggregateType |
Defines the type of aggregation to perform.
|
| AppointmentType |
Defines the type of an appointment.
|
| AvailabilityData |
Defines the type of data that can be requested via GetUserAvailability.
|
| BasePropertySet |
Defines base property sets that are used as the base for custom property
sets.
|
| BodyType |
Defines the type of body of an item.
|
| ChangeType |
Defines the type of change of a synchronization event.
|
| ComparisonMode |
Defines the way values are compared in search filter.
|
| ConflictResolutionMode |
Defines how conflict resolutions are handled in update operations.
|
| ConflictType |
Defines the conflict types that can be returned in meeting time suggestions.
|
| ConnectingIdType |
Defines the type of Id of a ConnectingId object.
|
| ConnectionFailureCause |
The ConnectionFailureCause enumeration.
|
| ContactSource |
Defines the source of a contact or group.
|
| ContainmentMode |
Defines the containment mode for Contains search filter.
|
| ConversationActionType |
Defines actions applicable to Conversation.
|
| ConversationFlagStatus |
Defines the flag status of a Conversation.
|
| DateTimePrecision |
Defines the precision for returned DateTime values
|
| DayOfTheWeek |
Specifies the day of the week.
|
| DayOfTheWeekIndex |
Defines the index of a week day within a month.
|
| DefaultExtendedPropertySet |
Defines the default sets of extended property.
|
| DelegateFolderPermissionLevel |
Defines a delegate user's permission level on a specific folder.
|
| DeleteMode |
Represents deletion modes.
|
| DnsRecordType |
DNS record types.
|
| DomainSettingName |
Domain setting names.
|
| EditorBrowsableState |
The Enum EditorBrowsableState.
|
| EffectiveRights |
Defines the effective user rights associated with an item or folder.
|
| EmailAddressKey |
Defines e-mail address entries for a contact.
|
| EventType |
Defines the types of event that can occur in a folder.
|
| ExchangeVersion |
Defines the each available Exchange release version.
|
| FileAsMapping |
Defines the way the FileAs property of a contact is automatically formatted.
|
| FlaggedForAction |
Defines the follow-up actions that may be stamped on a message.
|
| FolderPermissionLevel |
Defines permission levels for calendar folder.
|
| FolderPermissionReadAccess |
Defines a user's read access permission on item in a non-calendar folder.
|
| FolderTraversal |
Defines the scope of FindFolders operations.
|
| FreeBusyViewType |
Defines the type of free/busy information returned by a GetUserAvailability
operation.
|
| HangingRequestDisconnectReason |
Enumeration of reasons that a hanging request may disconnect.
|
| IdFormat |
Defines supported Id formats in ConvertId operations.
|
| ImAddressKey |
Defines Instant Messaging address entries for a contact.
|
| Importance |
Defines the importance of an item.
|
| ItemTraversal |
Defines the scope of FindItems operations.
|
| LegacyFreeBusyStatus |
Defines the legacy free/busy status associated with an appointment.
|
| LogicalOperator |
Defines a logical operator as used by search filter collections.
|
| MailboxType |
Defines the type of an EmailAddress object.
|
| MapiPropertyType |
Defines the MAPI type of an extended property.
|
| MeetingAttendeeType |
Defines the type of a meeting attendee.
|
| MeetingRequestsDeliveryScope |
Defines how meeting request are sent to delegates.
|
| MeetingRequestType |
Defines the type of a meeting request.
|
| MeetingResponseType |
Defines the types of response given to a meeting request.
|
| MemberStatus |
Defines the status of group members.
|
| MessageDisposition |
Defines how messages are disposed of in CreateItem and UpdateItem operations.
|
| Month |
Defines months of the year.
|
| OffsetBasePoint |
Defines the offset's base point in a paged view.
|
| OofExternalAudience |
Defines the external audience of an Out of Office notification.
|
| OofState |
Defines a user's Out of Office Assistant status.
|
| OutlookProtocolType |
Defines supported Outlook protocls.
|
| PermissionScope |
Defines the scope of a user's permission on a folder.
|
| PhoneCallState |
The PhoneCallState enumeration.
|
| PhoneNumberKey |
Defines phone number entries for a contact.
|
| PhysicalAddressIndex |
Defines a physical address index.
|
| PhysicalAddressKey |
Defines physical address entries for a contact.
|
| PropertyDefinitionFlags |
defines how a complex property behaves.
|
| ResolveNameSearchLocation |
Defines the location where a ResolveName operation searches for contacts.
|
| ResponseActions |
Defines the response actions that can be taken on an item.
|
| ResponseMessageType |
Defines the type of a ResponseMessage object.
|
| RuleErrorCode |
Defines the error codes identifying why a rule failed validation.
|
| RuleProperty | |
| SearchFolderTraversal |
Defines the scope of a search folder.
|
| SendCancellationsMode |
Defines how meeting cancellations should be sent to attendees when an
appointment is deleted.
|
| SendInvitationsMode |
Defines if/how meeting invitations are sent.
|
| SendInvitationsOrCancellationsMode |
Defines if/how meeting invitations or cancellations should be sent to
attendees when an appointment is updated.
|
| Sensitivity |
Defines the sensitivity of an item.
|
| ServiceError |
Defines the error codes that can be returned by the Exchange Web Services.
|
| ServiceErrorHandling |
Defines the type of error handling used for service method calls.
|
| ServiceObjectType |
Defines the type of a service object.
|
| ServiceResult |
Defines the result of a call to an EWS method.
|
| SortDirection |
Defines a sort direction.
|
| StandardUser |
Defines a standard delegate user.
|
| SuggestionQuality |
Defines the quality of an availability suggestion.
|
| SyncFolderItemsScope |
Determines item to be included in a SyncFolderItems response.
|
| TaskDelegationState |
Defines the delegation state of a task.
|
| TaskMode |
Defines the modes of a Task.
|
| TaskStatus |
Defines the execution status of a task.
|
| TraceFlags |
Defines flags to control tracing details.
|
| UserConfigurationDictionaryObjectType |
Identifies the user configuration dictionary key and value types.
|
| UserConfigurationProperties |
Identifies the user configuration property to retrieve.
|
| UserSettingName |
The Enum UserSettingName.
|
| WebExceptionStatus | |
| WellKnownFolderName |
Defines well known folder names.
|
| XmlNamespace |
Defines the namespaces as used by the EwsXmlReader, EwsServiceXmlReader, and
EwsServiceXmlWriter classes.
|


Hello, my name is Amin also known as Clearly. I'm a Devloper in PHP, Python, Javascript, and HTML! i like to code website's/discordbots/servers.


All the processes python should be derived from "Process".
More...
All the processes python should be derived from "Process".
◆ __init__() [1/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.__init__ |
( |
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self, |
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Model, |
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custom_settings |
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) |
| |
◆ __init__() [2/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.__init__ |
( |
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self, |
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Model, |
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custom_settings |
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) |
| |
◆ __init__() [3/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.__init__ |
( |
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self, |
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Model, |
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custom_settings |
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) |
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◆ BuildComponentsProcesses() [1/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.BuildComponentsProcesses |
( |
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self | ) |
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◆ BuildComponentsProcesses() [2/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.BuildComponentsProcesses |
( |
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self | ) |
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◆ BuildComponentsProcesses() [3/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.BuildComponentsProcesses |
( |
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self | ) |
|
◆ ExecuteFinalizeSolutionStep() [1/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteFinalizeSolutionStep |
( |
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self | ) |
|
◆ ExecuteFinalizeSolutionStep() [2/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteFinalizeSolutionStep |
( |
|
self | ) |
|
◆ ExecuteFinalizeSolutionStep() [3/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteFinalizeSolutionStep |
( |
|
self | ) |
|
◆ ExecuteInitialize() [1/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteInitialize |
( |
|
self | ) |
|
◆ ExecuteInitialize() [2/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteInitialize |
( |
|
self | ) |
|
◆ ExecuteInitialize() [3/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteInitialize |
( |
|
self | ) |
|
◆ ExecuteInitializeSolutionStep() [1/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteInitializeSolutionStep |
( |
|
self | ) |
|
◆ ExecuteInitializeSolutionStep() [2/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteInitializeSolutionStep |
( |
|
self | ) |
|
◆ ExecuteInitializeSolutionStep() [3/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.ExecuteInitializeSolutionStep |
( |
|
self | ) |
|
◆ GetVariables() [1/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.GetVariables |
( |
|
self | ) |
|
◆ GetVariables() [2/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.GetVariables |
( |
|
self | ) |
|
◆ GetVariables() [3/3]
| def assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.GetVariables |
( |
|
self | ) |
|
◆ AssignValueProcesses
| assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.AssignValueProcesses |
check component assignation
◆ constraints
| assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.constraints |
◆ model
| assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.model |
◆ settings
| assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.settings |
settings string in json format
overwrite the default settings with user-provided parameters
◆ var
| assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.var |
check if variable type is a vector
◆ variable_name
| assign_vector_components_to_nodes_process.AssignVectorComponentsToNodesProcess.variable_name |
The documentation for this class was generated from the following file:


Purav Raja, Ramkumar Ramanathan Clinch Pune Challenger Doubles Title
The pair won their second consecutive doubles title by beating compatriots Arjun Kadhe and Saketh Myneni in the final.
India’s Purav Raja and Ramkumar Ramanathan clinched their second consecutive doubles title by beating compatriots Arjun Kadhe and Saketh Myneni in straight sets in the final of the KPIT MSLTA Challenger event here on Sunday.
Raja and Ramanathan won 7-6(3) 6-3 to pocket USD 3100 and 80 ATP points, while Arjun and Saketh had to be content with USD 1800 and 48 ATP points.
In the men’s singles, James Duckworth of Australia won his fourth Challenger title of the year as he defeated Jay Clarke of Britain in a three-setter in the final.
Second seeded Duckworth won 4-6 6-4 6-4 over his fifth seeded rival in a tight match that lasted 2 hours and 18 minutes.
Duckworth was richer by USD 7200 and 80 ATP points. The win in Pune will take Duckworth back in the top 100, cementing his place in the main draw of the Australian Open next year.
“I am thrilled to win the event, it has been a lot of handwork for me. I have been out of tennis since the later part of 2017 and have gone through five major operations. This is for my family who has supported me,” said Duckworth who was earlier ranked as high as 82.
“I am glad to get back to the Top 100 as it guarantees entry into bigger events and also ensures that I am comfortable on finances.”
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on Sports Latest News on India.com.
Story Timeline
- 17 Nov 2019 Purav Raja, Ramkumar Ramanathan Clinch Pune Challenger Doubles Title...
- 31 Dec 2018 Sharan Returns, Balaji Dropped From Indian Davis Cup Team For Italy Tie
Comments - Join the Discussion


I'm a business and technology transformation consultant, driven by a desire to effect positive change in the world. Using tools and techniques developed during the course of a career working in the public sector and large tech industries, I offer consultation, facilitation and coaching, building resilience in organisations where people and passion combine with technology. I am independent, impartial and value-driven.
Transformation definition, management and support: I work collaboratively across all functions in an organisation to identify and enable more cost-effective and sustainable approaches to delivery.
I am a creative technologist specialising in business transformation. I have over 15 years’ experience defining and delivering digital, data and technology products and services. Through this work I have developed highly effective approaches to delivery, in line with business values and user needs. I facilitated transformation in large organisations in the public sector using technology as a vehicle and taking a holistic approach to change.
Working with the Risk and Assurance function of the National Trust as a "critical friend" across ongoing programmes of work. Recommendations lead to proactive intervention to address emerging problems and issues.
Advisor in the Government Digital Service working across all UK Government departments and agencies to advise directors and senior management on transformation plans and to build and support internal capacity to deliver public services.
Member of leadership team transforming the BBC's digital products, culture and working practices. Defined professions and career paths for digital delivery as discipline lead for Technical Architecture and designed course content for MSc Software Engineering degrees for staff retention. Recruited and led teams across multiple locations to deliver BBC homepage, search and navigation technologies. I balanced delivery with innovation leading on the first BBC Connected Studio events, bringing in external expertise to experiment with the BBC homepage. Introduced use of cloud (AWS) for public-facing BBC services that set a precedent for all BBC online products and services.
Led teams to deliver BBC Online projects for Vision (TV) and Learning. Evolved project management and technical direction, proving benefits of cohesive and agile teams. Key audience projects I delivered included BBC Bitesize (Learning), Wildlife Finder (Natural History Unit), BBC Food (Vision) and various innovation projects developing future media and learning formats.
Many projects working for large companies, SMEs and start-up initiatives designing and delivering web applications and database solutions. My work varied from an internship at Microsoft working on MSN Messenger to developing and supporting radiology patient management systems for the NHS. Working with online services at an embryonic time for the web kept me at the coalface of emerging digital technologies.
I believe that to offer the best services it is important for me to continue developing my own technology and product development interests. My projects are based on my core values and aim to address social and/or ecological problems.
Adventure Labs aims to sow the seeds to grow the next generation of environmentally-aware creative technologists who will invent solutions to manage our precious natural resources and enable us to live within our ecological limits. I'm developing cross-curricular educational workshops and games in an outdoor adventure setting.
Bringing expertise from the private sector to benefit causes of the third sector: Give 1-in-10 is an idea based on innovation events and projects through which companies demonstrate commitment to corporate social responsibility by offering their skills to associated charities and public sector organisations. I am working on the first events and pilots for this initiative. If you would like to know more, please get in touch.


{# Copyright 2020 Google LLC 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 https://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. #} {% extends "base.html" %} {% block styles %} {{ super() }} {% endblock %} {% block page_title %}Meet the Creators{% endblock %} {% block main_content %}
Duke University
I'm a rising junior at Duke University studying Computer Science and Electrical-Computer Engineering. As someone who's been a part of many mentorship programs, I know how hard it is to find someone that fits well with you, so I hope this website can help.
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I'm a rising junior at Carnegie Mellon University majoring in computer science with a concentration in algorithms and complexity. I'm originally from a small town in China and online learning helped me get where I am today. I hope our website could help you in the same way.
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I'm a computer science student at GT. I love teaching and helping others, so I'm glad that you're looking into the Mentor Matcher platform that we've built for you!


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Hanwen is a first-year master student in Computer Science at UCLA. He self-taught himself programming in high school and continued the study in college. He loves coding and is a quick learner. He is particularly interested in computer networks and web development. He is looking for software engineer summer internship in 2019.
Build performance monitoring tools for Kubernetes containers and implement REST-ful API in Python with Flask and Swagger. Visualize real-time performance data with Prometheus and Kibana. Optimize MySQL cluster throughput with captured data.
Bring on the table a payment authorization system for public cloud running on XenServer. Build a back-end node.js server that supports the payment platform.
GPA: 3.67
Besides coding, I like going on road trips. My goal is to go to 50 states in the US and I am half way through. I like getting close to nature and practice photography. See some of my photography work here.
When forced indoors, I can be a fervent reader, and I love novels and sci-fi. I also like to cook and try on different cuisines.


| LIVE | Midday Show With Mannie |
Published by Author · 5 mins ago
Story Highlights
Willett looking forward to Masters return
Says his 2016 victory knocked him back
Dorje, who became Karmapa in 1994, tied the knot with Bhutan-born Rinchen Yangzom in a private ceremony in Delhi, where his wife currently resides. "My role and activities as Karmapa will continue as before – with the single exception of conducting ordinations... As Karmapa, I will continue to protect and preserve our beloved lineage," Dorje was quoted as saying.


import pandas as pd
import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt
import patsy
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
It's time for yet another Insight Data Challenge!
This is another "conversion rate" data challenge. We are provided data on some users, and want to predict whether they will "convert" based on that data.
Here's the schema:
country
: inferred based on IP address. A string.age
: an integer numbernew_user
: binary 1/0 outcome.source
: where they came from. 3 categories.ads
: came via adsseo
: came via search enginedirect
: came directly to sitetotal_pages_visited
: integer. a measure of engagementconverted
: the variable to predict. Binary.converted
variable.patsy
to encode the categorical data.We first begin by loading the data.
df = pd.read_csv('data/conversion_data.csv')
df.head()
df.groupby('country').count()
Not bad, large numbers for each (104 to 105). A bit of imbalance. No outlier data points.
Let's now check the "age" column.
def ecdf_scatter(data):
x, y = np.sort(data), np.arange(1, len(data)+1) / len(data)
return x, y
x, y = ecdf_scatter(df['age'])
plt.scatter(x, y)
plt.show()
p95, p99 = np.percentile(df['age'], [95, 99])
p95, p99
There clearly are some outliers in the data. We have users that are older than 100 years of age. The oldest person in the world is 117 years. That person lives in none of the {{ len(set(df['country'])) }} countries in the dataset.
In addition, the 95th and 99th percentile of the dataset are {{ p95 }} years and {{ p99 }} years respectively. I think it is safe to exclude all datapoints with users older than {{ p99 }} years. Anything below the age of {{ p99 }} years old is very likely to be in the target market of an internet company.
Now, I would like to check about the new_user
column.
df.groupby('new_user').count()
No outliers. Also a bit imbalanced, but not too exaggerated in imbalance.
Let's now look at the source
column.
df.groupby('source').count()
Let's now look at the total_pages_visited
column.
x, y = ecdf_scatter(df['total_pages_visited'])
plt.scatter(x, y)
This is clearly an integer column, but I wouldn't consider it to be ordinal by any measure.
Now, let's look at the "converted" column. This is the column we're trying to predict.
df.groupby('converted').count()
Ok, quite imbalanced. Keep this in the back pocket. About 1:30 ratio.
Good thing there are no "bad" values in there.
I will use a package called missingno
to check that the data are all present.
import missingno as msno
msno.matrix(df)
This is good! The data are complete and filled everywhere. No holes. Awesome stuff.
Based on the above, there's only one way that I can see is necessary for cleaning: we will remove any data where the age is greater than {{ p99 }}.
cond = df['age'] <= 52
df_cleaned = df[cond]
df_cleaned.shape
The class imbalance is now as follows:
df_cleaned.groupby('converted').count()
This is good, we still have a roughly 30:1 distribution of data.
With 10,000+ data points in the minority class, I will sample {{ df_cleaned.groupby('converted').count()['total_pages_visited'][1] }} points from class 0.
n_subsample = df_cleaned.groupby('converted').count()['total_pages_visited'][1]
not_converted = (df_cleaned['converted'] == 0)
converted = (df_cleaned['converted'] == 1)
df_not_converted = df_cleaned[not_converted].sample(n_subsample)
df_converted = df_cleaned[converted]
df_sub = pd.concat([df_converted, df_not_converted])
Now, the dataset looks much more balanced:
df_sub.groupby('converted').count()
Next up, we need to use patsy
to do the dmatrix conversion.
df_train = patsy.dmatrix('country + age + new_user + source + total_pages_visited -1', df_sub, return_type="dataframe")
df_train = df_train.astype('float32')
df_train.head()
We will do a Bayesian logistic regression. The logistic function is:
$$ \frac{1}{1 + e^{-(\beta_{0} + \beta_{1} + \beta_{2} + ... + \beta_{k})}} $$Where $ \beta_{0} $ is the intercept, and $ \beta_{1...k} $ are the weight parameters for the logistic regression function.
We will put the following priors on the weights:
df_train.head()
df_sub['converted'].dtypes
import theano.tensor as tt
import theano
# X_input = theano.shared(X_train)
# Y_input = theano.shared(Y_train)
def invlogit(x):
return tt.exp(x) / (1 + tt.exp(x))
with pm.Model() as model:
# int_mu = pm.Normal('int_mu', 0, 100**2)
# int_sd = pm.HalfCauchy('int_sd', 100**2)
# intercept = pm.Normal('intercept', int_mu, int_sd)
intercept = pm.Normal('intercept', 0, 100**2)
weights = pm.Normal('weights', 0, 100**2, shape=(df_train.shape[1],))
X = tt.dot(weights, df_train.T) + intercept
logreg = invlogit(X)
out = pm.Bernoulli('likelihood', p=logreg, observed=df_sub['converted'])
with model:
# trace = pm.sample(2000)
trace = pm.sample(100000, step=pm.Metropolis())
pm.traceplot(trace[30000:])
pm.forestplot(trace[30000:], varnames=['weights'], ylabels=df_train.columns)
Number of pages visited is probably a measure of engagement on the website. Having more compelling content will probably increase conversion rate more than anything else.
Users based in China are least likely to be buying the software product. New users are also less likely to buy the product.
I will use posterior predictive checks to draw 50,000 samples from the posterior distribution, and plot the ROC curve of the predictions.
with model:
data = pm.sample_ppc(trace, samples=50000)
data['likelihood'].T.shape[0]
preds = data['likelihood'].T.mean(axis=1)
plt.scatter(df_sub['converted'], preds, alpha=0.005)
I think the predictive accuracy is good. Let me check the ROC curve.
preds_roc = np.round(preds)
from sklearn.metrics import roc_auc_score
roc_auc_score(preds_roc, df_sub['converted'])
Let's use scikit-plot
to visualize the ROC curve.
import scikitplot.plotters as skplt
preds.shape, df_sub['converted'].shape
preds_proba = np.zeros(shape=(preds.shape[0], 2))
preds_proba[:, 0] = 1 - preds
preds_proba[:, 1] = preds
skplt.plot_roc_curve(df_sub['converted'].values, preds_proba)
skplt.plot_confusion_matrix(preds_roc.reshape(-1), df_sub['converted'].values.reshape(-1))
One thing I learned from Twitter recently was that with Bayes, we don't need to do CV. Here's the thread from which I learned this.
@fonnesbeck Saw the PPC section of your PyCon 2017 stats tutorial - is there literature on why cross-val not necessary in Bayes?
— Eric Ma (@ericmjl) July 16, 2017
There aren't any parameters to tune, which is usually why you are cross-validating. You can do leave-one-out in PyMC3 for model selection.
— Chris Fonnesbeck (@fonnesbeck) July 16, 2017
I've seen crossval to build a distribution insample and use as prior out of sample. Can't find reference right now but seems clever to me.
— Greg Stoddard (@gregstod) July 16, 2017
It makes fuzzy sense to me right now. Putting it in my own words...
PPC is a matter of "model fit", not a matter of "model selection" (though right now, they seem to me to be related). If the model is fitting the data well, then the PPC samples will look like the original data samples. Cross-validation is only needed in the case where we have to check parameters that need to be tuned.
Also, doing CV is a computationally expensive thing for inference.


Story behind the photo: Perfectly poised
by Bangalore Mirror BureauA creatively designed cycle stand on Church Street
The picture was clicked by our reader SUNIL ACHARYA
If you have a picture and a story to share, send us the picture with your name and number at mybangaloremirror@timesgroup.com


ON GOLDEN POND
November 9-10, 16-17, 1984 at the Arabian Theatre
Director - Don Frutiger
Stage Manager - Pam Kilgarlin
THE CAST
Norman Thayer, Jr - Bill Clinton
Ethel Thayer - Fedwa Rahaim
Charlie Mann - Gene Hudson
Chelsea Thayer Wayne - Jayne Cooper
Billy Ray - Scott Openshaw
Bill Ray - Norm Larsen


content1
content2
content3
content3
hahha
content1
content2
content3
content4
...
...
...
try to prove the inherit
zhen de fu
I
don't
want
be
angray


My Mind supports saving and loading to a number of storage backends with different feature sets. There is a dedicated UI pane for these operations, available via the Save/Save as/Load buttons (or Ctrl+S/Ctrl+Shift+S/Ctrl+O keys).
Once you save or load a map, the currently used backend name is stored in the URL, as well as the map id. This permalink can be used to link to a saved map.
Some storage backends allow working with multiple file formats: native MyMind format, MindMup JSON, FreeMind XML and MindMap Architect XML. These can be both loaded and saved.
You can save your map in a simple, tab-indented plaintext file. Re-loading this file works, but will obviously result in loss of certain metadata.
The "Save" dialog also allows you to save the map as an image, although it is now preferred to create the image locally using Firefox Devtools.
Browsers storage (AKA WebStorage) is a fast way to store maps locally. These maps cannot be shared for obvious reasons.
Maps can be stored in a Firebase database. It is up to the user to create and configure a Firebase name, as well as any optional authentication methods.
Maps in Firebase are synced in real-time, which means that multiple users can edit them and their changes will be auto-propagated to all collaborators.
If you choose this backend, you will be prompted to log-in to Google and allow My Mind to access your GDrive files. Saved maps can be then opened directly from the Google Drive UI; it is also possible to create new maps directly from the Drive.
For the use of WebDAV, you can check our Privacy Policy.
Local filesystem can be accessed for both reading and writing maps. This backend does not generate permalinks, as it is not (yet?) possible to automatically read local files from within a web browser.
A generic REST API can be accessed (via GET/POST requests) using this backend. Just specify an URL for HTTP requests.


We’re testing a new feature to show images of people and companies on our records. The images come from Wikimedia Commons. If you have a question or feedback please contact us


You can use the reverse method to reverse the elements of an array. reverse is another array method that alters the array in place, but it also returns the reversed array. var myArray = [1, 2, 3]; myArray.reverse(); returns [3, 2, 1]


This block determines the total harmonic distortion (THD) over
the given period 1/f
. Consider that the input
u
consists of harmonic RMS components
U1
, U2
,
U3
, etc. The total RMS component is then
determined by:
The calculation of the total harmonic distortion is based on the
parameter useFirstHarmonic
. The default value
useFirstHarmonic = true
represents the
standard THD calculation used in
electrical engineering. The non-default value
useFirstHarmonic = false
calculates the THD typically
used for the assessment of audio signals.
If useFirstHarmonic = true
, the total higher
harmonic content (harmonic order numbers > 1) refers to the RMS
value of the fundamental wave:
If useFirstHarmonic = false
, the total higher
harmonic content (harmonic order numbers > 1) refers to the
total RMS:
In case of a zero input signal or within the first period of
calculation, the boolean output signal valid
becomes
false
to indicate that the calculation result is not
valid. Valid calculations are indicated by valid =
true
.


UNZIPPED A film review by Steve Rhodes Copyright 1995 Steve Rhodes
RATING (0 TO ****): ** 1/2
UNZIPPED is a documentary by first time director Douglas Keeve. It follows famous fashion designer Isaac Mizrahi as he attempts with his Fall 1994 collection to redeem himself after several disappointing years. In short, the movie is a hoot and a lot of fun. As a documentarian, Keeve has a lot to learn. The naturally hilarious antics of Mizrahi save the movie no matter how amateurish the production.
One of the delights of the show is that we get to see many clips of old movies (NANOOK OF THE NORTH, THE CALL OF THE WILD, THE RED SHOES, and WHAT EVER HAPPENED TO BABY JANE?) and TV shows (THE MARY TYLER MOORE SHOW and others). Besides quoting the shows verbatim, Mizrahi designs his collections based on some of them.
The movie is full of uproarious dialog. As one example, after watching NANOOK OF THE NORTH on TV, Mizrahi decides to model his 1994 Fall collection after the movie, but there is a big problem. He muses that "All I want to do is fur pants, but I know that if do them, I will be stoned off of Fifth Avenue."
Mizrahi's mannerism are as outlandish as his speech. When he mimics Eartha Kitt for whom he is designing clothes, the audience is in stitches. After seeing her house, he tells his dinner friends that he would "kill for a swatch of the wallpaper in her bathroom."
Watching the preparation for the fashion show we get to see the models interviewing for positions. My favorite is a woman who looks like a recent concentration camp survivor, who walks a storm trooper, and who promises Mizrahi that her almost totally shaved head will be completely shaved for the show. I don't think he hired her, but it is not clear.
The movie is full of the glitterati (Eartha Kitt, Cindy Crawford, Richard Geer, Liza Minnelli, Sandra Bernhard, etc.). Cindy Crawford has one of the better lines. When the cameraman is zooming in on her, she tells him, "You are a little too close. My pores are not that small."
Mainly what works in the show is the dialog and the mannerisms, but there is one special scene that has neither. When he gets bad news, Mizrahi is shown walking slowly in blowing, dark, dirty, gray snow in New York City.
His 1994 Fall collection is as outlandish as its designer. Moreover, to provoke and involve fashion critics, he decides to use a scrim like they do at the ballet. He asks his models if they would mind changing clothes behind the scrim so that the audience could see them. He assures them that this will be okay since they will have their underwear on. Most models agree, but some reject the proposal. When it comes time for the show, they appear to all go along anyway.
Coming back to the poor quality of the documentary, it is as much of a mess as is Mizrahi's hair. Just as we learn in the movie, that Mizrahi has a stylist that works hard to give him just the right messy look, so it is clear that Keeve likes having a movie that makes you feel sure that you could do better with your own camcorder.
The cinematography by Ellen Kuras is almost a parody of bad film making. The film stock appears to be a speed of ASA one million as the grain is about the size of silver dollars. The editor (Paula Heredia) seems proud that she leaves in scenes that are of such poor quality that if you shot them, you would immediately erase the tape. Scenes start out of focus and then we get to watch as the camera operator moves the lenses back and forth until it is finally in focus. Many scenes are so white, they may be scenes of polar bears in Siberia for all I know.  I am sure Kuras shot tons of film and most of it has to be better than what we are shown. Although most of the film is in black and white, the few color scenes are in perfect focus and well lit. Sometimes the camera is dropped, but hey, why not show these mistakes to the audience too. Suffice it to say that the filming and the editing are too cute for words. I kept wanting to scream while watching it.
UNZIPPED reminds me of the wonderful British TV show, THE HOUSE OF ELLIOT. They are both about fashion designers who are working extremely hard to be a success and both are high energy shows. Finally, the music in UNZIPPED, especially during the runway scenes of the fashion show, is great and full of energy. You want to go see one of these shows yourself and soon.
UNZIPPED flies by at 1:13. It is not rated, but would probably be PG-13 for a little profanity and models in their underwear. Any teenager could see it with no problems. If you feel like a fun show where you can just have a good time, I recommend UNZIPPED to you, and I award it ** 1/2.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEWED WRITTEN ON: September 7, 1995
Opinions expressed are mine and not meant to reflect my employer's.
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


# -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
from google.api_core.exceptions import GoogleAPICallError, AlreadyExists
from google.cloud.spanner_v1.client import Client
from google.longrunning.operations_grpc_pb2 import Operation # noqa: F401
from airflow import AirflowException
from airflow.contrib.hooks.gcp_api_base_hook import GoogleCloudBaseHook
[docs]class CloudSpannerHook(GoogleCloudBaseHook):
"""
Hook for Google Cloud Spanner APIs.
All the methods in the hook where project_id is used must be called with
keyword arguments rather than positional.
"""
def __init__(self,
gcp_conn_id='google_cloud_default',
delegate_to=None):
super(CloudSpannerHook, self).__init__(gcp_conn_id, delegate_to)
[docs] def _get_client(self, project_id):
"""
Provides a client for interacting with the Cloud Spanner API.
:param project_id: The ID of the GCP project.
:type project_id: str
:return: google.cloud.spanner_v1.client.Client
:rtype: object
"""
if not self._client:
self._client = Client(project=project_id, credentials=self._get_credentials())
return self._client
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def get_instance(self, instance_id, project_id=None):
"""
Gets information about a particular instance.
:param project_id: Optional, The ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:return: google.cloud.spanner_v1.instance.Instance
:rtype: object
"""
instance = self._get_client(project_id=project_id).instance(instance_id=instance_id)
if not instance.exists():
return None
return instance
[docs] def _apply_to_instance(self, project_id, instance_id, configuration_name, node_count,
display_name, func):
"""
Invokes a method on a given instance by applying a specified Callable.
:param project_id: The ID of the GCP project that owns the Cloud Spanner
database.
:type project_id: str
:param instance_id: The ID of the instance.
:type instance_id: str
:param configuration_name: Name of the instance configuration defining how the
instance will be created. Required for instances which do not yet exist.
:type configuration_name: str
:param node_count: (Optional) Number of nodes allocated to the instance.
:type node_count: int
:param display_name: (Optional) The display name for the instance in the Cloud
Console UI. (Must be between 4 and 30 characters.) If this value is not set
in the constructor, will fall back to the instance ID.
:type display_name: str
:param func: Method of the instance to be called.
:type func: Callable
"""
# noinspection PyUnresolvedReferences
instance = self._get_client(project_id=project_id).instance(
instance_id=instance_id, configuration_name=configuration_name,
node_count=node_count, display_name=display_name)
try:
operation = func(instance) # type: Operation
except GoogleAPICallError as e:
self.log.error('An error occurred: %s. Exiting.', e.message)
raise e
if operation:
result = operation.result()
self.log.info(result)
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def create_instance(self, instance_id, configuration_name, node_count,
display_name, project_id=None):
"""
Creates a new Cloud Spanner instance.
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:param configuration_name: The name of the instance configuration defining how the
instance will be created. Possible configuration values can be retrieved via
https://cloud.google.com/spanner/docs/reference/rest/v1/projects.instanceConfigs/list
:type configuration_name: str
:param node_count: (Optional) The number of nodes allocated to the Cloud Spanner
instance.
:type node_count: int
:param display_name: (Optional) The display name for the instance in the GCP
Console. Must be between 4 and 30 characters. If this value is not set in
the constructor, the name falls back to the instance ID.
:type display_name: str
:param project_id: Optional, the ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
self._apply_to_instance(project_id, instance_id, configuration_name,
node_count, display_name, lambda x: x.create())
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def update_instance(self, instance_id, configuration_name, node_count,
display_name, project_id=None):
"""
Updates an existing Cloud Spanner instance.
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:param configuration_name: The name of the instance configuration defining how the
instance will be created. Possible configuration values can be retrieved via
https://cloud.google.com/spanner/docs/reference/rest/v1/projects.instanceConfigs/list
:type configuration_name: str
:param node_count: (Optional) The number of nodes allocated to the Cloud Spanner
instance.
:type node_count: int
:param display_name: (Optional) The display name for the instance in the GCP
Console. Must be between 4 and 30 characters. If this value is not set in
the constructor, the name falls back to the instance ID.
:type display_name: str
:param project_id: Optional, the ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
return self._apply_to_instance(project_id, instance_id, configuration_name,
node_count, display_name, lambda x: x.update())
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def delete_instance(self, instance_id, project_id=None):
"""
Deletes an existing Cloud Spanner instance.
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:param project_id: Optional, the ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
instance = self._get_client(project_id=project_id).instance(instance_id)
try:
instance.delete()
return
except GoogleAPICallError as e:
self.log.error('An error occurred: %s. Exiting.', e.message)
raise e
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def get_database(self, instance_id, database_id, project_id=None):
"""
Retrieves a database in Cloud Spanner. If the database does not exist
in the specified instance, it returns None.
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:param database_id: The ID of the database in Cloud Spanner.
:type database_id: str
:param project_id: Optional, the ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: Database object or None if database does not exist
:rtype: google.cloud.spanner_v1.database.Database or None
"""
instance = self._get_client(project_id=project_id).instance(
instance_id=instance_id)
if not instance.exists():
raise AirflowException("The instance {} does not exist in project {} !".
format(instance_id, project_id))
database = instance.database(database_id=database_id)
if not database.exists():
return None
else:
return database
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def create_database(self, instance_id, database_id, ddl_statements, project_id=None):
"""
Creates a new database in Cloud Spanner.
:type project_id: str
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:param database_id: The ID of the database to create in Cloud Spanner.
:type database_id: str
:param ddl_statements: The string list containing DDL for the new database.
:type ddl_statements: list[str]
:param project_id: Optional, the ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:return: None
"""
instance = self._get_client(project_id=project_id).instance(
instance_id=instance_id)
if not instance.exists():
raise AirflowException("The instance {} does not exist in project {} !".
format(instance_id, project_id))
database = instance.database(database_id=database_id,
ddl_statements=ddl_statements)
try:
operation = database.create() # type: Operation
except GoogleAPICallError as e:
self.log.error('An error occurred: %s. Exiting.', e.message)
raise e
if operation:
result = operation.result()
self.log.info(result)
return
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def update_database(self, instance_id, database_id, ddl_statements,
project_id=None,
operation_id=None):
"""
Updates DDL of a database in Cloud Spanner.
:type project_id: str
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:param database_id: The ID of the database in Cloud Spanner.
:type database_id: str
:param ddl_statements: The string list containing DDL for the new database.
:type ddl_statements: list[str]
:param project_id: Optional, the ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:param operation_id: (Optional) The unique per database operation ID that can be
specified to implement idempotency check.
:type operation_id: str
:return: None
"""
instance = self._get_client(project_id=project_id).instance(
instance_id=instance_id)
if not instance.exists():
raise AirflowException("The instance {} does not exist in project {} !".
format(instance_id, project_id))
database = instance.database(database_id=database_id)
try:
operation = database.update_ddl(
ddl_statements=ddl_statements, operation_id=operation_id)
if operation:
result = operation.result()
self.log.info(result)
return
except AlreadyExists as e:
if e.code == 409 and operation_id in e.message:
self.log.info("Replayed update_ddl message - the operation id %s "
"was already done before.", operation_id)
return
except GoogleAPICallError as e:
self.log.error('An error occurred: %s. Exiting.', e.message)
raise e
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def delete_database(self, instance_id, database_id, project_id=None):
"""
Drops a database in Cloud Spanner.
:type project_id: str
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:param database_id: The ID of the database in Cloud Spanner.
:type database_id: str
:param project_id: Optional, the ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:return: True if everything succeeded
:rtype: bool
"""
instance = self._get_client(project_id=project_id).\
instance(instance_id=instance_id)
if not instance.exists():
raise AirflowException("The instance {} does not exist in project {} !".
format(instance_id, project_id))
database = instance.database(database_id=database_id)
if not database.exists():
self.log.info("The database {} is already deleted from instance {}. "
"Exiting.".format(database_id, instance_id))
return
try:
operation = database.drop() # type: Operation
except GoogleAPICallError as e:
self.log.error('An error occurred: %s. Exiting.', e.message)
raise e
if operation:
result = operation.result()
self.log.info(result)
return
@GoogleCloudBaseHook.fallback_to_default_project_id
[docs] def execute_dml(self, instance_id, database_id, queries, project_id=None):
"""
Executes an arbitrary DML query (INSERT, UPDATE, DELETE).
:param instance_id: The ID of the Cloud Spanner instance.
:type instance_id: str
:param database_id: The ID of the database in Cloud Spanner.
:type database_id: str
:param queries: The queries to execute.
:type queries: str
:param project_id: Optional, the ID of the GCP project that owns the Cloud Spanner
database. If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
"""
self._get_client(project_id=project_id).instance(instance_id=instance_id).\
database(database_id=database_id).run_in_transaction(
lambda transaction: self._execute_sql_in_transaction(transaction, queries))
@staticmethod
[docs] def _execute_sql_in_transaction(transaction, queries):
for sql in queries:
transaction.execute_update(sql)


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/Does.20.60classical.60.20break.20.60interval_cases.60.3F.html ---
import tactic example (n : ℕ) (hn : n ≤ 4) : false := begin classical, interval_cases n, repeat {sorry} end /- cases tactic failed, it is not applicable to the given hypothesis state: n : ℕ, hn : n ≤ 4, _inst : Π (a : Prop), decidable a, lh : n ∈ tactic.interval_cases.set_elems (set.Ico ⊥ (4 + 1)) ⊢ false -/
If you comment out classical
it works fine.
Another interval_cases
issue:
import tactic group_theory.order_of_element example (G : Type) [fintype G] [group G] [decidable_eq G] (g : G) (h : order_of g ≤ 2) : false := begin interval_cases (order_of g), repeat {sorry} end
You would expect to get three cases order_of g = 0,1,2
. But I think simp
or something got to the middle case:
3 goals G : Type, _inst_1 : fintype G, _inst_2 : group G, _inst_3 : decidable_eq G, g : G, h : order_of g ≤ 2, lh : order_of g = 0 ⊢ false G : Type, _inst_1 : fintype G, _inst_2 : group G, _inst_3 : decidable_eq G, h : order_of 1 ≤ 2 ⊢ false G : Type, _inst_1 : fintype G, _inst_2 : group G, _inst_3 : decidable_eq G, g : G, h : order_of g ≤ 2, lh : order_of g = 2 ⊢ false
Lean has eliminated the hypothesis that the order of g is 1, and replaced g with the identity. Which is fine, I guess, but perhaps not what I expected.
This is probably my fault in some way. :-) I'm not sure I'll have time to look at it today. If no one else jumps in, creating and assigning an issue to me would be great. interval_cases
is built on top of fin_cases
, and both are pretty simple tactics (which can be "decompiled" by hand into a string of other tactic invocations), so it's probably not too hard to find where we're over simplifying.
Last updated: Apr 24 2020 at 19:02 UTC


THE JUNGLE BOOK A film review by Ben Hoffman Copyright 1994 Ben Hoffman
Although Nobel-Prize-winning Rudyard Kipling's stories written in the late 1800s and early 1900s have already twice been made into film (once animated, once live), Disney has attempted once again to bring the story to life as a live adventure. The story of the law of "civilized" man vs. the "law of the jungle" seems to be a classic with which every generation is intrigued. Our own is no exception.
If you are old enough to go back to 1942, you will remember "Sabu." In 1967, Disney gave us an animated version. With the 100th anniversary of the books' first publication, Disney Studios decided the time was ripe for a brand new, more adventurous, version that would please not only the youngsters but also adults.
Mowgli (Jason Scott Lee), who grew up in the jungle not far from the civilized world, but still apart from it, decides to see what the rest of the world is like after he catches a glimpse of the lovely Kitty (Lena Headey). He is suddenly aware that he has been missing out on something and wants to see how the rest of the world lives. Being friends with jungle animals is great and being able to speak their language has its advantages but Kitty is a whole other species worth checking out.
Captain Boone (Cary Elwes), is the film's villain. In addition, he is engaged to Kitty. When Boone discovers that the jungle holds treasures of which the outside world is unaware and which he wants to remove, Mowgli realizes that the so-called "civilized world" can also contain malicious, greedy people such as Captain Boone. Mowgli, of course, is unaware that Mark Twain once said that it was a shame animals were called "beasts" when, in fact, it is human beings who contrive and kill not just for food but for treasure and possessions.
Fortunately, Mowgli has a forestful of friends: the wolf (Grey Brother), the bear (Baloo), and the black panther (Bagheera), who are all on his side.
If there is any criticism to be made of the film, it is that some of the scenes are a bit too ferocious for youngsters accustomed to THE LION KING, ALADDIN, BAMBI, etc. THE JUNGLE BOOK movie even has someone immersed in quicksand. So use a bit of judgment with your PG-13. On the other hand we have Sam Neil (Kitty's father) and John Cleese as Dr. Plumford. Jason Scott Lee's Mowgli looks more Asian than Indian but does well as our hero.
Directed by Stephen Sommers.
3.5 bytes 4 Bytes = Absolutely must see. 3 Bytes = Too good to be missed. 2 Bytes = So so. 1 Byte = Save your money.
Ben Hoffman
.
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


[OgreKit Lua] |
Public Member Functions | |
| gsFSM () | |
| ~gsFSM () | |
| void | update () |
| void | setState (int state) |
| int | getState () |
| void | addStartTrigger (int state, gsSelf self, gsFunction trigger) |
| void | addEndTrigger (int state, gsSelf self, gsFunction trigger) |
| void | addEvent (int state, gsSelf self, gsFunction update) |
| gsWhenEvent * | addTransition (int from, int to) |
| gsWhenEvent * | addTransition (int from, int to, unsigned long ms) |
| gsWhenEvent * | addTransition (int from, int to, unsigned long ms, gsSelf self, gsFunction trigger) |
| gsFSM::gsFSM | ( | ) |
| gsFSM::~gsFSM | ( | ) |
| void gsFSM::addEndTrigger | ( | int | state, |
| gsSelf | self, | ||
| gsFunction | trigger | ||
| ) |
| void gsFSM::addEvent | ( | int | state, |
| gsSelf | self, | ||
| gsFunction | update | ||
| ) |
| void gsFSM::addStartTrigger | ( | int | state, |
| gsSelf | self, | ||
| gsFunction | trigger | ||
| ) |
| gsWhenEvent* gsFSM::addTransition | ( | int | from, |
| int | to | ||
| ) |
| gsWhenEvent* gsFSM::addTransition | ( | int | from, |
| int | to, | ||
| unsigned long | ms | ||
| ) |
| gsWhenEvent* gsFSM::addTransition | ( | int | from, |
| int | to, | ||
| unsigned long | ms, | ||
| gsSelf | self, | ||
| gsFunction | trigger | ||
| ) |
| int gsFSM::getState | ( | ) |
| void gsFSM::setState | ( | int | state | ) |
| void gsFSM::update | ( | ) |


Type as much or as little of the journal, magazine, or newspaper title as you want. For example:
- journal of counseling psychology
- journal of counseling psy
This search engine will find magazines, journals and newspapers which the libraries own. To find individual periodical articles, use periodical indexes or databases such as EBSCO, Gail, etc.


One 9-inch pie
30 Min - Prep time
10 Min - Cook time
40 Min - Total
8 Servings
INGREDIENTS
1 cup white sugar
2 tbsp all-purpose flour
3 tbsp cornstarch
1/4 tsp salt
1 1/2 cups water
2 lemons, juiced and zested
2 tbsp butter
4 egg yolks, beaten
1 (9 inch) pie crust, baked
4 egg whites
6 tbsp white sugar
DIRECTIONS
Preheat Oven: Preheat oven to 350 degrees F (175 degrees C).
Make Lemon Filling: In a medium saucepan ...
Whisk together 1 cup sugar, flour, cornstarch, and salt.
Stir in water, lemon juice and lemon zest.
Cook over medium-high heat, stirring frequently, until mixture comes to a boil.
Stir in butter.
Place egg yolks in a small bowl and gradually whisk in 1/2 cup of hot sugar mixture.
Whisk egg yolk mixture back into remaining sugar mixture.
Bring to a boil and continue to cook while stirring constantly until thick.
Remove from heat.
Pour filling into baked pastry shell.
Make Meringue: In a large glass or metal bowl ...
Whip egg whites until foamy.
Add sugar gradually, and continue to whip until stiff peaks form.
Spread meringue over pie, sealing the edges at the crust.
Bake: Bake in preheated oven for 10 minutes, or until meringue is golden brown.
This is our favorite recipe here at Granny's Pies. It has been enjoyed by pie fans for many years. It's Granny's favorite! We hope you'll find that this recipe is delicious and also easy to follow; it's a piece of cake pie!
USER COMMENTS
This is a very fun recipe to follow, because Grandma makes it sweet and simple. This pie is thickened with cornstarch and flour in addition to egg yolks, and contains no milk.
- Emilie S.
Q: What do you call an ape who loves pie?
A: A meringue-utan.
- Vickie K.
This site can really engage my interest. Follow the recipe above and make it so! This pie is number one.


The value of this constant is returned by the file-exists function to indicate that the target file is readable.
Controls whether or not a buffer can be modified. When read-only-buffer is set to true, all attempts to modify the buffer will be ignored and will result in an error.
The value of this constant is returned by the file-exists function to indicate that the target file can be read and written.
Controls the removal of the help window. When non-zero, help windows that have been automatically popped up onto the screen are removed. Otherwise, the help window will stay on the screen after the help function has completed.
Controls the case of replacement text. If non-zero, Emacs will alter the case of strings substituted with replace-string or query-replace-string to match the case of the original string.
Controls the Replace minor mode. If non-zero, characters inserted into the buffer replace existing characters, rather then inserting (the default). This can be useful when editing diagrams.
This read-only variable is a count of the number of times that the current environment has been saved and restored. If Emacs has not been restored from a saved environment file, restored-environment is zero.
Contains the name of an Emacs function which will be called when Emacs returns from DCL after a pause-emacs or return-to-monitor command. You can assign the name of any existing function to the variable.
Controls the right margin setting for automatic text justification. If a character is inserted at the end of a line to the right of right-margin, Emacs will automatically insert at the beginning of the preceding word a newline, tabs and spaces to indent to the left margin, and the prefix string. For example, with right-margin set to 72, you can type in a document without worrying about when to hit the Return key; Emacs will automatically do this for you at exactly the right place.


Grid Skins
Default Style
FlexiGrid Gray Style
Pure Gray Style
ExtJS Gray Style
ExtJS Blue Style
ExtJS Access Style
Dhtmlx Sky Blue Style
jqGrid Style
EasyUI Style
Bootstrap Style
Custom Blue Style
Grid Samples
Simple Grid
Simple en Grid
Stripe Row
Row Hover
Grid With Images
Fixed Grid Width
Load XML Data
With Little Paging
Local Json Data
Local XML Data
Select Row Event
No Pagation
No Diaplay Blank Rows
No Data
Sort Grid
With Checkbox
Standard Grid
Grid Advanced Samples
Edit Grid
Advanced Edit
Grid Events
Extend Grid
Export Grid
Multi Header Grid
Multi Sort Grid
Grid With Footer
Grid Userdata
Grid Userdata(XML Data)
Paging Additional Button
Little Paging Additional Button
Dynamic Header Grid
Multi Grid
Multi Edit Grid
Move Column
Fixed Grid
Extend Conditions
Grid Extend Samples
With Custom Checkbox
Custom Move Column
Fixed Table Header
Fixed Grid Custom
Grid Test Samples
HTML5
With Bootstrap
Grid Load Time Test
Grid With Layui
Grid with layer
Grid with laypage
Grid with laydate
Grid With ArtDialog
Pop with ArtDialog
Grid with ArtDialog
Grid And Form with ArtDialog
Grid With Pagination
Note
With jqPagination
Adapter jqPagination
With jquery_pagination
Adapter jquery_pagination
With jPaginate
Adapter jPaginate
With jPages
Adapter jPages
With smartpaginator
Adapter smartpaginator
With jPaginator
Adapter jPaginator
Util
Button
Form
Simple Form
Standard Form
Validation Form
With Ajax Validation Form
Paging
Simple Paging
Simple Little Pagin
Least Paging
Image Loop (Baidu News)
Image Loop (Baidu Baijia)


Sometimes you might want to show empty rows in the data table when there are less amount of data is available than the specified limit. Therefore, in such cases, you might need to turn the showSubstituteRows on.
To enable showSubstituteRows, simply set showSubstituteRows to true and by default showSubstituteRows is false.
{{demoSnippet.showSubstituteRows}}
In default the table doesn't show substitute rows although data limit is less than the specified limit.
In the above example limit is specified as 15. But it is showing 5 additional rows due to unavailability of 5 data items.
Substitute rows will not be shown although data limit is less than the specified limit, if showSubstituteRows property is set to false.
In the following example limit is specified as 15. But it is not showing 5 additional rows. Because showSubstituteRows is set to false.


Build Awesome themes with bootstrap, Bootstrap is world most popular framework for building mobile first websites.
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Wash Commands
Wash commands aim to be well-documented in the tool. Try wash help
and wash help <subcommand>
for specific options.
Most commands operate on Wash resources, which are addressed by their path in the filesystem.
wash
The wash
command can be invoked on its own to enter a Wash shell.
Invoking wash
starts the daemon as part of the process, then enters your current system shell with shortcuts configured for wash subcommands. All the wash server
settings are also supported with wash
.
wash clear
Wash caches most operations. If the resource you’re querying appears out-of-date, use this subcommand to reset the cache for resources at or contained within the specified path. Defaults to the current directory if a path is not specified.
wash exec
For a Wash resource that implements the ability to execute a command, run the specified command and arguments. The results will be forwarded from the target on stdout, stderr, and exit code.
wash find
Recursively descends the directory tree of the specified path, evaluating an expression
composed of primaries
and operands
for each entry in the tree.
wash history
Wash maintains a history of commands executed through it. Print that command history, or specify an id
to print a log of activity related to a particular command.
wash info
Print all info Wash has about the specified path, including filesystem attributes and metadata.
wash list/ls
Lists the resources at the indicated path.
wash meta
Prints the metadata of a resource.
wash ps
Captures /proc/*/{cmdline,stat,statm} on each node by executing ‘cat’ on them. Collects the output to display running processes on all listed nodes. Errors on paths that don’t implement exec.
wash server
Initializes all of the plugins, then sets up the Wash daemon (its API and FUSE servers). To stop it, make sure you’re not using the filesystem at the specified mountpoint, then enter Ctrl-C.
Server API docs can be found here.
wash tail
Output any new updates to files and/or resources (that support the stream action). Currently requires the ‘-f’ option to run. Attempts to mimic the functionality of tail -f
for remote logs.
Core Plugins
AWS
- EC2 and S3
- uses
AWS_SHARED_CREDENTIALS_FILE
environment variable or$HOME/.aws/credentials
andAWS_CONFIG_FILE
environment variable or$HOME/.aws/config
to find profiles and configure the SDK. - IAM roles are supported when configured as described here. Note that currently region will also need to be specified with the profile.
- if using MFA, wash will prompt for it on standard input. Credentials are valid for 1 hour. They are cached under
wash/aws-credentials
in your user cache directory so they can be re-used across server restarts. wash may have to re-prompt for a new MFA token in response to navigating the wash environment to authorize a new session. - supports streaming, and remote command execution via ssh
- supports full metadata for S3 content
Docker
- containers and volumes
- found from the local socket or via
DOCKER
environment variables - supports streaming, and remote command execution
Kubernetes
- pods, containers, and persistent volume claims
- uses contexts from
~/.kube/config
- supports streaming, and remote command execution
- supports listing of volume contents
Plugin Concepts
Wash’s plugin system is designed around a set of primitives that resources can implement. A plugin requires a root that can list things it contains, and implements a tree structure under that where each node represents a resource or an arbitrary grouping. Wash will translate that tree structure into a file hierarchy.
Wash supports the following primitives:
list
- lets you ask any resource what’s contained inside of it, and what primitives it supports.- e.g. listing a Kubernetes pod returns its constituent containers
read
- lets you read the contents of a given resource- e.g. represent an EC2 instance’s console output as a regular file you can open in a regular editor
stream
- gives you streaming-read access to a resource- e.g. to let you follow a container’s output as its running
exec
- lets you execute a command against a resource- e.g. run a shell command inside a container, or on an EC2 vm, or on a routerOS device, etc.
Primitives can be accessed programmatically via the Wash API, or on the CLI via wash
subcommands and filesystem interactions.
For more on implementing plugins, see:


This is the course website for IS590 Data Visualization in Spring 2019
Download these files for the coding demos:
This week we finished up talking about the different ways we can describe visualizations, and we continued by talking about how distributions of values can be displayed.


vk.bot_framework.dispatcher.dispatcher module¶
-
class
vk.bot_framework.dispatcher.dispatcher.
Dispatcher
(vk: vk.vk.VK, group_id: int)¶ Bases:
vk.utils.mixins.ContextInstanceMixin
-
described_handler
(name: str = None, description: str = None, deprecated: bool = False, **other_meta: dict)¶
-
event_handler
(event_type: vk.types.events.community.events_list.Event, *rules, **named_rules)¶ Register event handler with decorator.
>>> @dp.event_handler(Event.WALL_REPLY_NEW) >>> async def my_func(event: eventobj.WallReplyNew, data: dict): >>> print(event)
- Parameters
event_type –
rules –
named_rules –
- Returns
-
property
group_id
¶ Returns a group id. :return:
-
property
handlers
¶ Returns a list of registered handlers. :return:
-
message_handler
(*rules: Tuple[Type[vk.bot_framework.dispatcher.rule.BaseRule], Callable, Awaitable], commands: Optional[List[str]] = None, text: Optional[str] = None, payload: Optional[str] = None, chat_action: Optional[vk.types.message.Action] = None, data_check: Optional[Dict[Any, Any]] = None, count_args: Optional[int] = None, have_args: Optional[List[Union[Callable, Awaitable]]] = None, in_chat: Optional[bool] = None, in_pm: Optional[bool] = None, from_bot: Optional[bool] = None, with_reply_message: Optional[bool] = None, with_fwd_messages: Optional[bool] = None, count_fwd_messages: Optional[int] = None, **named_rules: Dict[str, Any])¶ Register message handler with decorator.
standart named rules: :param commands: :param text: :param payload: :param chat_action: :param data_check: :param count_args: :param have_args: :param in_chat: :param in_pm: :param from_bot: :param with_reply_message: :param with_fwd_messages: :param count_fwd_messages:
- Parameters
rules – other user rules
named_rules – other user named rules
- Returns
-
middleware
()¶ Add middleware to middlewares list with decorator. :return:
-
property
middlewares
¶ Returns a list of registered middlewares. :return:
-
register_event_handler
(coro: Callable, event_type: vk.types.events.community.events_list.Event, rules: List)¶ Register event handler.
>>> dp.register_event_hanlder(my_handler, Event.WallReplyNew, [])
- Parameters
coro –
event_type –
rules –
- Returns
-
register_message_handler
(coro: Callable, rules: List)¶ Register message handler
>>> dp.register_message_handler(my_handler, [])
- Parameters
coro –
rules –
- Returns
-
property
registered_blueprints
¶ Returns a list of registered blueprints. :return:
-
run_extension
(name: str, **extension_init_params)¶ Run extensions with extension manager. :param name: name of extension :param extension_init_params: params which accept extension constructor :return:
-
run_polling
()¶
-
setup_blueprint
(blueprint: vk.bot_framework.dispatcher.blueprints.Blueprint)¶ Setup blueprint in application. :param blueprint: blueprint instance :return:
-
setup_extension
(extension: Type[vk.bot_framework.dispatcher.extension.BaseExtension])¶ Add extension to extensions list with extension manager. :param extension: some extension :return:
-
setup_middleware
(middleware)¶ Add middleware to middlewares list with middleware manager. :param middleware: some middleware :return:
-
setup_rule
(rule)¶ Add named rule to named rules list with rule factory. :param rule: some rule :return:
-
property
storage
¶
-
property
vk
¶
-


A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
Tek07-Aug-19 05:38 PM
12 million
polycryptoblog07-Aug-19 05:38 PM
Funds were returned to users
meatspoon07-Aug-19 05:39 PM
of all the people who had agama wallets, some died, some lost private keys...
those peoples funds were collected too but obviously not returned
the attrition rate on coins in general is high
polycryptoblog07-Aug-19 05:39 PM
James forked out 1 million of his own
meatspoon07-Aug-19 05:39 PM
so it was said
Daiyox07-Aug-19 05:39 PM
That wallet bug , I thought it was awesome in how the Blockchain side of the house, the KMD side jumped into action and just destroyed the hackers plans...But I'm entertained in funny ways, it's early crypto, stuff like that for now is history in the making lol
meatspoon07-Aug-19 05:39 PM
i am asking how much got netted that were not returned
and like i said, i am not convinced that there was a hack
i saw no one bitching about getting hacked in discord
my coins were not taken
a cynical person might say that it was a PR stunt
polycryptoblog07-Aug-19 05:40 PM
That makes sense ... not
meatspoon07-Aug-19 05:41 PM
and a profitable one at that... because like i said, they surely netted a lot of KMD from derelict wallets
i left coins in my compromised agama wallet for weeeeeeks
why were they not taken?
polycryptoblog07-Aug-19 05:41 PM
The exploit was only around for a month and required you entering your key during that timeframe with a specific wallet version
Yesterday, the npm, Inc. security team, in collaboration with Komodo, helped protect over $13 million USD in cryptocurrency assets as we found and responded to a malware threat targeting the users of...
Daiyox07-Aug-19 05:42 PM
Exactly, you likely where never exposed @meatspoon(edited)
meatspoon07-Aug-19 05:42 PM
i will ask again, how many coins were netted and not returned? because the wallet owner did not make contact?
infotech-al07-Aug-19 05:43 PM
If just talking trading please google TA vs FA - you will have your answer.
Tek07-Aug-19 05:43 PM
muh we so great and like anonymity!
here fill in this google form to get back your coins
c2k07-Aug-19 05:43 PM
fair question...why not post in #ask-jl777
phideas07-Aug-19 05:43 PM
guys, no need to atack each other, pls.
be nice, discuss without name calling.
we can blame bear market for this negative emotions.
truth is it's not gonna help, time will cure all wounds.
meatspoon07-Aug-19 05:44 PM
i would not be surprised if 20% of swept coins never made it back to the "owners" for a variety of reasons
SHossain07-Aug-19 05:45 PM
well, you are wrong. almost 98% of the funds been returned
meatspoon07-Aug-19 05:45 PM
so you read my question 3 times and just now decided to answer with 98%?
kinda reminds me of "98% of all quoted statistics are made up on the spot" heh
polycryptoblog07-Aug-19 05:46 PM
Take a look at the sweep address
Oszy07-Aug-19 05:47 PM
Dont, now hes gonna say they sent a lot to themselves lol
SHossain07-Aug-19 05:47 PM
@meatspoon history says you will believe what you want to believe(edited)
we even refunded about 732k KMD stolen by the hacker/thief which James put from his own pocket
the biggest one was DP with over 400k KMD
everyone that came forward with a valid claim were refunded(edited)
Buddz199307-Aug-19 05:58 PM
Going too far I think
Now we are having doubts of the team ?
Geez it goes a long way from roasting the price to roasting the team
Calm yourselves
phideas07-Aug-19 06:02 PM
@SHossain can you track down the stolen funds? any info if they were dumped already?
SHossain07-Aug-19 06:04 PM
they were tracked initially. but, James reimbursed all from his personal funds.
meatspoon07-Aug-19 06:11 PM
So to be clear, you are saying with certainty that between 97% and 98% of the swept funds were returned?
KMD owners must be remarkably healthy and adept at storing their private keys.
phideas07-Aug-19 06:12 PM
I know, just curious if funds still in possession of the hacker
This is what it feels like to keep buying the dip with your favorite altcoin... https://t.co/oaTIx5wnKy
Caglio07-Aug-19 06:16 PM
This story have a finish when a big guy finally got this smaller guy
But you have to search on a liveleak
meatspoon07-Aug-19 06:20 PM
@SHossain just want to reference my clarification above if you didn’t see it. Looking for confirmation on the 98% figure. And is that 98% of KMD swept was returned or 98% of KMD reclaim forms were processed? Big difference between the two.
SHossain07-Aug-19 06:20 PM
you can ask in #ask-jl777 channel and James himself can answer you
that will be the most accurate answer
meatspoon07-Aug-19 06:20 PM
You claimed 98% as if you know for sure. Just asking you to clarify and stand behind your comment.
I don’t care if you are off by a percent or two
SHossain07-Aug-19 06:21 PM
almost 98% of the funds been returned
that's what i said
you can check the post where i posted
meatspoon07-Aug-19 06:22 PM
Ok. 98% of swept funds or 98% of claim forms processed?
SHossain07-Aug-19 06:22 PM
#ask-jl777
meatspoon07-Aug-19 06:22 PM
You are crawfishing pretty hard here.
hastrup07-Aug-19 06:22 PM
Lol
SHossain07-Aug-19 06:23 PM
search on blockchain. you will get 100% accurate answer
meatspoon07-Aug-19 06:24 PM
You went from telling me how it was—setting me straight—to this.
SHossain07-Aug-19 06:24 PM
about 98% of the swept funds were returned
polycryptoblog07-Aug-19 06:24 PM
You don’t want to look at the evidence
Not our problem
crumb07-Aug-19 06:24 PM
meatspoon why not post your question into #ask-jl777 as suggested? you'll get the most reliable answer
meatspoon07-Aug-19 06:24 PM
I want to know Hossain’s claim and understand it.
SHossain07-Aug-19 06:25 PM
SHossainToday at 19:24
about 98% of the swept funds were returned
Bitboy07-Aug-19 06:25 PM
question has been deployed in #ask-jl777
meatspoon07-Aug-19 06:25 PM
I will look into it. 98% is highly unlikely given mortality rates of humans and seed keys
polycryptoblog07-Aug-19 06:25 PM
Lol 2% of users died in a month
Unlikely
Daiyox07-Aug-19 06:26 PM
Look into it, if 97.8% it's .2% more drama power bonus for ya
allbits07-Aug-19 06:26 PM
Mortality rate in a two month period is how much exactly? What an idiot!
meatspoon07-Aug-19 06:26 PM
How many people put KMD in a wallet and left it for a long time?
 You don't even know the hack worked
hastrup07-Aug-19 06:26 PM
@polycryptoblog thank you!
Alexfrog07-Aug-19 06:26 PM
meatspoon, only people who had accessed the newer version of the wallet were effected
SHossain07-Aug-19 06:26 PM
if people left their funds in Agama for a very long time, most likely it wasn't even swept
PHBA206107-Aug-19 06:27 PM
98% is highly unlikely given mortality rates of humans and seed keys lol are you serious with this argument ...
98% people != 98% funds
Alexfrog07-Aug-19 06:27 PM
meatspoon you really need to go read about what the exploit was, then it will make more sense
meatspoon07-Aug-19 06:27 PM
Man, really irritated the KMD clergy with this
1
hastrup07-Aug-19 06:27 PM
It's been a weird day XD But this made it good again
allbits07-Aug-19 06:28 PM
You're throwing around unsupported slanderous accusations and now posturing as a concerned bystander demanding immediate answers, badgering people. Get lost(edited)
3
2
polycryptoblog07-Aug-19 06:28 PM
Buddz199307-Aug-19 06:28 PM
Indeed
Bitboy07-Aug-19 06:28 PM
@meatspoon jl has answered
1
#ask-jl777
SHossain07-Aug-19 06:29 PM
but, the question is, will metaspoon believe what jl777 said?(edited)


cpu scheduling algorithms
enter the type of scheduling :
preemptive priority
round robing
enter the no of process :
enter the burst time :
enter arrival time :
enter the priority :
ok
gann chart diagram


{% extends "base.html" %} {% block title %}Data export - {{ experiment.name }}{% endblock title %} {% block content %}
Downloads data from all modules.
The download may not start immediately. Please be patient while the system generates the data export.