SBAScheduleManager
open class SBAScheduleManager : SBAReportManager
Default data source handler for scheduled activities. This manager is used to get SBBScheduledActivity
objects and upload the task results for Bridge services. By default, this manager will fetch all the
activities, but will not cache them all in memory. Instead, it will filter out those activites that are
valid for today and the most recent finished activity (if any) for each activity identifier where the
activity identifier
refers to an SBBActivity
object’s associated SBAActivityReference
.
-
List of keys used in the notifications sent by this manager.
Declaration
Swift
public enum NotificationKey : String
-
Pointer to the shared activity manager to use.
Declaration
Swift
public var activityManager: SBBActivityManagerProtocol { get }
-
Add an identifier that can be used for mapping this schedule manager to the displayed schedules.
Declaration
Swift
open var identifier: String
-
This is an array of the activities fetched by the call to the server in
reloadData
. By default, this list includes the activities filtered using thescheduleFilterPredicate
.Declaration
Swift
@objc open var scheduledActivities: [SBBScheduledActivity]
-
The activity group associated with this schedule manager. If non-nil, this will be used in setting up the filtering predicates and finding the
most appropriate
schedule when creating a task path. This will get the activity group by getting the currently registered activity group from the shared configuration usingself.identifier
as the group identifier.Declaration
Swift
open var activityGroup: SBAActivityGroup? { get set }
-
Fetch request objects can be used to make compound fetch requests to retrieve schedules that match different sets of parameters. For example, a task group might be set up to group a set of tasks together where the schedule used to run a new task is different from the schedule used to mark it as finished. This allows for that more complicated logic.
See moreDeclaration
Swift
public struct FetchRequest
-
The fetch requests to use to fetch the schedules associated with this manager. By default, if this schedule manager has an associated activity group, then this will return an array of the fetch requests for those schedules available today and the most recently finished schedule for each activity included in the group. Otherwise, a request will be created for all activities available today.
Declaration
Swift
open func fetchRequests() -> [FetchRequest]
-
The predicate to use for filtering today’s activities for those available today. If there is an
activityGroup
associated with this schedule manager, the fetch request for today’s activities will be built using this predicate and the activity group predicate. Otherwise, only this predicate will be used. Default is to returnSBBScheduledActivity.availableOnPredicate(on: self.now())
.Declaration
Swift
open func availablePredicate() -> NSPredicate
-
The predicate to use for filtering past activities. By default, this will return all activities where the
finishedOn
property is non-nil and the activity identifier matches the given value.Declaration
Swift
open func historyPredicate(for activityIdentifier: RSDIdentifier) -> NSPredicate
Parameters
activityIdentifier
The activity identifier for the schedule.
Return Value
The predicate for this fetch request.
-
State management for whether or not the schedules are reloading.
Declaration
Swift
override open var isReloading: Bool { get }
-
Reload the data by fetching changes to the scheduled activities.
Declaration
Swift
override open func reloadData()
-
Load the scheduled activities from cache using the
fetchRequests()
for this schedule manager.Declaration
Swift
public final func loadScheduledActivities()
-
Called on the main thread once Bridge returns the requested scheduled activities.
Declaration
Swift
open func update(fetchedActivities: [SBBScheduledActivity])
Parameters
fetchedActivities
The list of activities returned by the service.
-
Called on the main thread before sending the given schedules to the server for update. The default implementation will call
self.update(fetchedActivities:)
on a unioned set of the updated schedules and the schedules that are currently in memory. This method will filter the schedules using thefetchedRequests()
for this manager, but will not use the fetch limit parameter to limit the number of schedules returned.Declaration
Swift
open func willSendUpdatedSchedules(for schedules: [SBBScheduledActivity])
Parameters
schedules
The schedules that will be updated.
-
Called when the schedules have changed.
Declaration
Swift
open func didUpdateScheduledActivities(from previousActivities: [SBBScheduledActivity])
Parameters
previousActivities
The schedules that were previously being stored in memory.
-
Sort the scheduled activities. By default this will sort by comparing the
scheduledOn
property.Declaration
Swift
open func sortActivities(_ scheduledActivities: [SBBScheduledActivity]?) -> [SBBScheduledActivity]?
-
If a schedule is unavailable, then the user is shown an alert explaining when it will become available.
Declaration
Swift
open func messageForUnavailableSchedule(_ schedule: SBBScheduledActivity) -> String
Parameters
schedule
The schedule to check.
Return Value
The message to display in an alert for a schedule that is not currently available.
-
Get the scheduled activity that is associated with this schedule identifier and task result.
For the case where this is a combo task that includes multiple schemas (for example, medication tracking and finger tapping) then we want to preference the schedule associated with the schema over the schedule that was used to trigger the task. The default behavior of this method, therefore is to look for a schema and return the schedule associated with that schema, if found.
Declaration
Swift
override open func scheduledActivity(for taskResult: RSDTaskResult, scheduleIdentifier: String?) -> SBBScheduledActivity?
Parameters
taskResult
The task result for this task, subtask, or section.
scheduleIdentifier
The schedule identifier that was used to start the task.
Return Value
The schedule associated with this task view controller (if found).
-
Is the given task info completed for the given date?
Declaration
Swift
open func isCompleted(for taskInfo: RSDTaskInfo, on date: Date) -> Bool
-
Get the scheduled activities associated with a given group that are available on a given day. The criteria for determining availablity is dependent upon the timing of the activity. For a day in the past, the criteria includes when the task was finished, expired, and scheduled. For a day in the future, the criterion is decided by when the activity is scheduled. For today, the activity must either be finished today, or available today (scheduled for a time window that includes this day.
Declaration
Swift
open func scheduledActivities(for activityGroup: SBAActivityGroup, availableOn: Date) -> [SBBScheduledActivity]
Parameters
activityGroup
The activity group to filter on.
availableOn
The date for the day when the activities are
available
.Return Value
The schedule associated with this task view controller (if available).
-
Instantiate a task path appropriate to the given task info. This method will attempt to map the task info to a schedule, but if called, it is assumed that even if there is no schedule associated with this task, that the task path should be instantiated.
The returned result includes the instantiated task path and the reference schedule (if found).
Note
This method should not be used to instantiate child task paths that are used to track the task state for a subtask. Instead, it is intended for starting a new task and will set up any state handling (such as tracking data groups) that must be managed globally.
Declaration
Swift
open func instantiateTaskViewModel(for taskInfo: RSDTaskInfo, in activityGroup: SBAActivityGroup? = nil) -> (taskViewModel: RSDTaskViewModel, referenceSchedule: SBBScheduledActivity?)
Parameters
taskInfo
The task info object to use to create the task path.
activityGroup
The activity group to use to determine the schedule. This is used for the case where there may be multiple schedules with the same task and the
schedulePlanGUID
on the activity group is used to determine which available schedule is the one to associate with this task.Return Value
- taskViewModel: The instantiated task path.
- referenceSchedule: The schedule to reference for uploading the task path results (if any).
-
Instantiate a task path appropriate to the given task. This method will attempt to map the task to a schedule, but if called, it is assumed that even if there is no schedule associated with this task, that the task path should be instantiated.
The returned result includes the instantiated task path and the reference schedule (if found).
Note
This method should not be used to instantiate child task paths that are used to track the task state for a subtask. Instead, it is intended for starting a new task and will set up any state handling (such as tracking data groups) that must be managed globally.
Declaration
Swift
open func instantiateTaskViewModel(for task: RSDTask, in activityGroup: SBAActivityGroup? = nil) -> (taskViewModel: RSDTaskViewModel, referenceSchedule: SBBScheduledActivity?)
Parameters
task
The task object to use to create the task path.
activityGroup
The activity group to use to determine the schedule. This is used for the case where there may be multiple schedules with the same task and the
schedulePlanGUID
on the activity group is used to determine which available schedule is the one to associate with this task.Return Value
- taskViewModel: The instantiated task path.
- referenceSchedule: The schedule to reference for uploading the task path results (if any).
-
Instantiate a task path appropriate to the given schedule.
Note
This method should not be used to instantiate child task paths that are used to track the task state for a subtask. Instead, it is intended for starting a new task and will set up any state handling (such as tracking data groups) that must be managed globally.
Declaration
Swift
open func instantiateTaskViewModel(for schedule: SBBScheduledActivity) -> RSDTaskViewModel
Parameters
schedule
The schedule to use to set up the task.
Return Value
The instantiated task path.
-
Return the data groups that should be added to the cohorts tracked by this task.
Declaration
Swift
open func addedDataGroups(for taskViewModel: RSDTaskViewModel) -> Set<String>?
-
Find the most recent report data appended to any schedule for this activity identifier.
Declaration
Swift
open override func report(with activityIdentifier: String) -> SBAReport?
Parameters
activityIdentifier
The activity identifier for the client data associated with this task.
Return Value
The client data JSON (if any) associated with this activity identifier.
-
Call from the view controller that is used to display the task when the task is finished.
Note
This method does not dismiss the task.Declaration
Swift
open func taskController(_ taskController: RSDTaskController, didFinishWith reason: RSDTaskFinishReason, error: Error?)
-
Call from the view controller that is used to display the task when the task is ready to save.
Declaration
Swift
open func taskController(_ taskController: RSDTaskController, readyToSave taskViewModel: RSDTaskViewModel)
-
Allow saving the results from a given task path.
Declaration
Swift
open func saveResults(from taskViewModel: RSDTaskViewModel, _ completionHandler: (() -> Void)? = nil)
-
Update the data groups. By default, this will look for changes on the shared
DataGroupsTrackingRule
.Declaration
Swift
open func updateDataGroups(for taskViewModel: RSDTaskViewModel)
Parameters
taskViewModel
The task path for the task which has just run.
-
Update the values on the scheduled activity. By default, this will recurse through the task path and its children, looking for a schedule associated with the subtask path.
Declaration
Swift
public func updateSchedules(for taskViewModel: RSDTaskViewModel)
Parameters
taskViewModel
The task path for the task which has just run.
-
For each schedule that this task modifies, mark it as completed and add the client data.
Declaration
Swift
open func getAndUpdateSchedule(for taskResult: RSDTaskResult, with scheduleIdentifier: String?) -> SBBScheduledActivity?
Parameters
taskResult
The task result for the task which has just run.
-
Send message to Bridge server to update the given schedules. This includes both the task that was completed and any tasks that were performed as a requirement of completion of the primary task (such as a required one-time survey).
Declaration
Swift
open func sendUpdated(for schedules: [SBBScheduledActivity], taskViewModel: RSDTaskViewModel? = nil)
Parameters
schedules
The schedules for which to send updates.
taskViewModel
The task path (if available) for the task run that triggered this update.


EDA by Meet¶
OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
Urgent need to bring the COVID-19 vaccine to mass production
What is RNA?¶
Ribonucleic acid, or RNA is one of the three major biological macromolecules that are essential for all known forms of life (along with DNA and proteins). A central tenet of molecular biology states that the flow of genetic information in a cell is from DNA through RNA to proteins: “DNA makes RNA makes protein”.
What is mRNA?¶
Messenger RNAs, also known as mRNA, are one of the types of RNA that are found in the cell. This particular one, like most RNAs, are made in the nucleus and then exported to the cytoplasm where the translation machinery, the machinery that actually makes proteins, binds to these mRNA molecules and reads the code on the mRNA to make a specific protein. So in general, one gene, the DNA for one gene, can be transcribed into an mRNA molecule that will end up making one specific protein.
Why are we here?¶
mRNA vaccines have taken the lead as the fastest vaccine candidates for COVID-19, but currently, they face key potential limitations. One of the biggest challenges right now is how to design super stable messenger RNA molecules (mRNA). Conventional vaccines (like your seasonal flu shots) are packaged in disposable syringes and shipped under refrigeration around the world, but that is not currently possible for mRNA vaccines.
Researchers have observed that RNA molecules have a tendency to spontaneously degrade. This is a serious limitation–a single cut can render the mRNA vaccine useless. Currently, little is known on the details of where in the backbone of a given RNA is most prone to being affected. Without this knowledge, current mRNA vaccines against COVID-19 must be prepared and shipped under intense refrigeration, and are unlikely to reach more than a tiny fraction of human beings on the planet unless they can be stabilized.
Our Goal: Predict the degradation rates at various locations along RNA sequence¶
What do we have?¶
We have a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position.
There are multiple ground truth values provided in the training data. While the submission format requires all 5 to be predicted, only the following are scored: reactivity, deg_Mg_pH10, and deg_Mg_50C.
Files¶
train.json - the training data
test.json - the test set, without any columns associated with the ground truth.
sample_submission.csv - a sample submission file in the correct format
Columns¶
id - An arbitrary identifier for each sample.
seq_scored - (68 in Train and Public Test, 91 in Private Test) Integer value denoting the number of positions used in scoring with predicted values. This should match the length of reactivity, deg_* and error columns. Note that molecules used for the Private Test will be longer than those in the Train and Public Test data, so the size of this vector will be different.
seq_length - (107 in Train and Public Test, 130 in Private Test) Integer values, denotes the length of sequence. Note that molecules used for the Private Test will be longer than those in the Train and Public Test data, so the size of this vector will be different.
sequence - (1x107 string in Train and Public Test, 130 in Private Test) Describes the RNA sequence, a combination of A, G, U, and C for each sample. Should be 107 characters long, and the first 68 bases should correspond to the 68 positions specified in seq_scored (note: indexed starting at 0).
structure - (1x107 string in Train and Public Test, 130 in Private Test) An array of (, ), and . characters that describe whether a base is estimated to be paired or unpaired. Paired bases are denoted by opening and closing parentheses e.g. (….) means that base 0 is paired to base 5, and bases 1-4 are unpaired.
reactivity - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are * reactivity values for the first 68 bases as denoted in sequence, and used to determine the likely secondary structure of the RNA sample.
deg_pH10 - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likelihood of degradation at the base/linkage after incubating without magnesium at high pH (pH 10).
deg_Mg_pH10 - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likelihood of degradation at the base/linkage after incubating with magnesium in high pH (pH 10).
deg_50C - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likelihood of degradation at the base/linkage after incubating without magnesium at high temperature (50 degrees Celsius).
deg_Mg_50C - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likelihood of degradation at the base/linkage after incubating with magnesium at high temperature (50 degrees Celsius).
error - An array of floating point numbers, should have the same length as the corresponding reactivity or deg_* columns, calculated errors in experimental values obtained in reactivity and deg_* columns.
predicted_loop_type - (1x107 string) Describes the structural context (also referred to as ‘loop type’)of each character in sequence. Loop types assigned by bpRNA from Vienna RNAfold 2 structure. From the bpRNA_documentation: S: paired “Stem” M: Multiloop I: Internal loop B: Bulge H: Hairpin loop E: dangling End X: eXternal loop
1. Import Necessary Libraries¶
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('fivethirtyeight')
SEED = 2020
def seed_everything(SEED):
np.random.seed(SEED)
# tf.random.set_seed(SEED)
os.environ['PYTHONHASHSEED'] = str(SEED)
seed_everything(SEED)
2. Load Data¶
PATH = '/kaggle/input/stanford-covid-vaccine'
os.listdir(PATH)
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-2-f76549394b50> in <module>
1 PATH = '/kaggle/input/stanford-covid-vaccine'
----> 2 os.listdir(PATH)
FileNotFoundError: [WinError 3] The system cannot find the path specified: '/kaggle/input/stanford-covid-vaccine'
train = pd.read_json(os.path.join(PATH,'train.json'),lines=True).drop('index',axis=1)
test = pd.read_json(os.path.join(PATH,'test.json'),lines=True).drop('index',axis=1)
sub = pd.read_csv(os.path.join(PATH,'sample_submission.csv'))
3. Exploratory Data-Analysis¶
print("Train data shape: ",train.shape)
print("Test data shape: ",test.shape)
print("Sample submission shape: ",sub.shape)
Train data shape: (2400, 18)
Test data shape: (3634, 6)
Sample submission shape: (457953, 6)
Why we have different size than test data for sample submission file?
Test data has 629 samples for Public Test data & new 3005 samples for Private Test data. (Total 629+3005 = 3634)
Public test data has Sequence length of 107. Hence, for public test data there will be total (629*107) = 67,303 rows
Private test data has Sequence length of 130. Hence, for private test data there will be total (3005*130) = 3,90,650 rows
Hence, Total rows in submission = 67,303 + 3,90,650 = 4,57,953
train.head()
| id | sequence | structure | predicted_loop_type | signal_to_noise | SN_filter | seq_length | seq_scored | reactivity_error | deg_error_Mg_pH10 | deg_error_pH10 | deg_error_Mg_50C | deg_error_50C | reactivity | deg_Mg_pH10 | deg_pH10 | deg_Mg_50C | deg_50C | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | id_001f94081 | GGAAAAGCUCUAAUAACAGGAGACUAGGACUACGUAUUUCUAGGUA... | .....((((((.......)))).)).((.....((..((((((...... | EEEEESSSSSSHHHHHHHSSSSBSSXSSIIIIISSIISSSSSSHHH... | 6.894 | 1 | 107 | 68 | [0.1359, 0.20700000000000002, 0.1633, 0.1452, ... | [0.26130000000000003, 0.38420000000000004, 0.1... | [0.2631, 0.28600000000000003, 0.0964, 0.1574, ... | [0.1501, 0.275, 0.0947, 0.18660000000000002, 0... | [0.2167, 0.34750000000000003, 0.188, 0.2124, 0... | [0.3297, 1.5693000000000001, 1.1227, 0.8686, 0... | [0.7556, 2.983, 0.2526, 1.3789, 0.637600000000... | [2.3375, 3.5060000000000002, 0.3008, 1.0108, 0... | [0.35810000000000003, 2.9683, 0.2589, 1.4552, ... | [0.6382, 3.4773, 0.9988, 1.3228, 0.78770000000... |
| 1 | id_0049f53ba | GGAAAAAGCGCGCGCGGUUAGCGCGCGCUUUUGCGCGCGCUGUACC... | .....(((((((((((((((((((((((....)))))))))).)))... | EEEEESSSSSSSSSSSSSSSSSSSSSSSHHHHSSSSSSSSSSBSSS... | 0.193 | 0 | 107 | 68 | [2.8272, 2.8272, 2.8272, 4.7343, 2.5676, 2.567... | [73705.3985, 73705.3985, 73705.3985, 73705.398... | [10.1986, 9.2418, 5.0933, 5.0933, 5.0933, 5.09... | [16.6174, 13.868, 8.1968, 8.1968, 8.1968, 8.19... | [15.4857, 7.9596, 13.3957, 5.8777, 5.8777, 5.8... | [0.0, 0.0, 0.0, 2.2965, 0.0, 0.0, 0.0, 0.0, 0.... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [4.947, 4.4523, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [4.8511, 4.0426, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... | [7.6692, 0.0, 10.9561, 0.0, 0.0, 0.0, 0.0, 0.0... |
| 2 | id_006f36f57 | GGAAAGUGCUCAGAUAAGCUAAGCUCGAAUAGCAAUCGAAUAGAAU... | .....((((.((.....((((.(((.....)))..((((......)... | EEEEESSSSISSIIIIISSSSMSSSHHHHHSSSMMSSSSHHHHHHS... | 8.800 | 1 | 107 | 68 | [0.0931, 0.13290000000000002, 0.11280000000000... | [0.1365, 0.2237, 0.1812, 0.1333, 0.1148, 0.160... | [0.17020000000000002, 0.178, 0.111, 0.091, 0.0... | [0.1033, 0.1464, 0.1126, 0.09620000000000001, ... | [0.14980000000000002, 0.1761, 0.1517, 0.116700... | [0.44820000000000004, 1.4822, 1.1819, 0.743400... | [0.2504, 1.4021, 0.9804, 0.49670000000000003, ... | [2.243, 2.9361, 1.0553, 0.721, 0.6396000000000... | [0.5163, 1.6823000000000001, 1.0426, 0.7902, 0... | [0.9501000000000001, 1.7974999999999999, 1.499... |
| 3 | id_0082d463b | GGAAAAGCGCGCGCGCGCGCGCGAAAAAGCGCGCGCGCGCGCGCGC... | ......((((((((((((((((......))))))))))))))))((... | EEEEEESSSSSSSSSSSSSSSSHHHHHHSSSSSSSSSSSSSSSSSS... | 0.104 | 0 | 107 | 68 | [3.5229, 6.0748, 3.0374, 3.0374, 3.0374, 3.037... | [73705.3985, 73705.3985, 73705.3985, 73705.398... | [11.8007, 12.7566, 5.7733, 5.7733, 5.7733, 5.7... | [121286.7181,<PHONE_NUMBER>,<PHONE_NUMBER>, 121286... | [15.3995, 8.1124, 7.7824, 7.7824, 7.7824, 7.78... | [0.0, 2.2399, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.... | [0.0, -0.5083, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0... | [3.4248, 6.8128, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... | [0.0, -0.8365, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0... | [7.6692, -1.3223, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0... |
| 4 | id_0087940f4 | GGAAAAUAUAUAAUAUAUUAUAUAAAUAUAUUAUAGAAGUAUAAUA... | .....(((((((.((((((((((((.(((((((((....)))))))... | EEEEESSSSSSSBSSSSSSSSSSSSBSSSSSSSSSHHHHSSSSSSS... | 0.423 | 0 | 107 | 68 | [1.665, 2.1728, 2.0041, 1.2405, 0.620200000000... | [4.2139, 3.9637000000000002, 3.2467, 2.4716, 1... | [3.0942, 3.015, 2.1212, 2.0552, 0.881500000000... | [2.6717, 2.4818, 1.9919, 2.5484999999999998, 1... | [1.3285, 3.6173, 1.3057, 1.3021, 1.1507, 1.150... | [0.8267, 2.6577, 2.8481, 0.40090000000000003, ... | [2.1058, 3.138, 2.5437000000000003, 1.0932, 0.... | [4.7366, 4.6243, 1.2068, 1.1538, 0.0, 0.0, 0.7... | [2.2052, 1.7947000000000002, 0.7457, 3.1233, 0... | [0.0, 5.1198, -0.3551, -0.3518, 0.0, 0.0, 0.0,... |
test.head()
| id | sequence | structure | predicted_loop_type | seq_length | seq_scored | |
|---|---|---|---|---|---|---|
| 0 | id_00073f8be | GGAAAAGUACGACUUGAGUACGGAAAACGUACCAACUCGAUUAAAA... | ......((((((((((.(((((.....))))))))((((((((...... | EEEEEESSSSSSSSSSBSSSSSHHHHHSSSSSSSSSSSSSSSSHHH... | 107 | 68 |
| 1 | id_000ae4237 | GGAAACGGGUUCCGCGGAUUGCUGCUAAUAAGAGUAAUCUCUAAAU... | .....((((..((((((...(((((.....((((....))))....... | EEEEESSSSIISSSSSSIIISSSSSIIIIISSSSHHHHSSSSIIII... | 130 | 91 |
| 2 | id_00131c573 | GGAAAACAAAACGGCCUGGAAGACGAAGGAAUUCGGCGCGAAGGCC... | ...........((.(((.(.(..((..((..((((...))))..))... | EEEEEEEEEEESSISSSISISIISSIISSIISSSSHHHSSSSIISS... | 107 | 68 |
| 3 | id_00181fd34 | GGAAAGGAUCUCUAUCGAAGGAUAGAGAUCGCUCGCGACGGCACGA... | ......((((((((((....))))))))))((((((..((.(((..... | EEEEEESSSSSSSSSSHHHHSSSSSSSSSSSSSSSSIISSISSSHH... | 107 | 68 |
| 4 | id_0020473f7 | GGAAACCCGCCCGCGCCCGCCCGCGCUGCUGCCGUGCCUCCUCUCC... | .....(((((((((((((((((((((((((((((((((((((((((... | EEEEESSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS... | 130 | 91 |
How the test data was produced?¶
Public test has 629 samples. This 629 samples were choosen from base data of 3029 in a way that it satisfies below 3 conditions:
Minimum value across all 5 conditions must be greater than -0.5.
Mean signal/noise across all 5 conditions must be greater than 1.0. [Signal/noise is defined as mean( measurement value over 68 nts )/mean( statistical error in measurement value over 68 nts)]
To help ensure sequence diversity, the resulting sequences were clustered into clusters with less than 50% sequence similarity, and the 629 test set sequences were chosen from clusters with 3 or fewer members. That is, any sequence in the test set should be sequence similar to at most 2 other sequences.
It was filtered based on three criteria detailed above to ensure that this subset is not dominated by any large cluster of RNA molecules with poor data, which might bias the public leaderboard. The remaining 2400 sequences for which we have data are in train.json.
Note that these filters have not been applied to the 2400 RNAs in the public training data train.json — some of those measurements have negative values or poor signal-to-noise, or some RNA sequences have near-identical sequences in that set. But we are providing all those data in case competitors can squeeze out more signal.
The three filters noted above will also not be applied to Private Test on 3005 sequences.
train.columns
Index(['id', 'sequence', 'structure', 'predicted_loop_type', 'signal_to_noise',
'SN_filter', 'seq_length', 'seq_scored', 'reactivity_error',
'deg_error_Mg_pH10', 'deg_error_pH10', 'deg_error_Mg_50C',
'deg_error_50C', 'reactivity', 'deg_Mg_pH10', 'deg_pH10', 'deg_Mg_50C',
'deg_50C'],
dtype='object')
What are these signal_to_noise & SN_filter columns?
signal_to_noise is defined as mean( measurement value over 68 nts )/mean( statistical error in measurement value over 68 nts)
SN_filter is 1 is it satisfy the below given conditions or else it is 0
Minimum value across all 5 conditions must be greater than -0.5.
Mean signal/noise across all 5 conditions must be greater than 1.0.
Here, 5 conditions are the ground-truth values.
fig, ax = plt.subplots(1,2,figsize=(20,5))
train['signal_to_noise'].plot.kde(ax=ax[0])
ax[0].set_title('Signal/Noise')
sns.countplot(data=train,y='SN_filter',ax=ax[1])
ax[1].set_title('SN_filter')
plt.show()
plt.figure(figsize=(20,2))
sns.boxplot(data=train,x='signal_to_noise')
plt.show()
We also have negative values of signal/noise
We can also see samples with very high signal/noise values (could be Potential Outliers)
print("Number of samples with -ev signal/noise values: ",train[train['signal_to_noise']<0].shape[0])
Q1 = np.percentile(train['signal_to_noise'],q=25)
Q3 = np.percentile(train['signal_to_noise'],q=75)
IQR = Q3 - Q1
print("Number of samples with too high signal/noise values", train[train['signal_to_noise']>Q3+1.5*IQR].shape[0])
Number of samples with -ev signal/noise values: 8
Number of samples with too high signal/noise values 27
train.seq_length.value_counts()
107 2400
Name: seq_length, dtype: int64
All the samples in the train data are of 107 sequence length
test.seq_length.value_counts()
130 3005
107 629
Name: seq_length, dtype: int64
Test data has both 107 & 130 sequence length samples
Targets Averaged over Positions¶
avg_reactivity = np.array(list(map(np.array,train.reactivity))).mean(axis=0)
avg_deg_50C = np.array(list(map(np.array,train.deg_50C))).mean(axis=0)
avg_deg_pH10 = np.array(list(map(np.array,train.deg_pH10))).mean(axis=0)
avg_deg_Mg_50C = np.array(list(map(np.array,train.deg_Mg_50C))).mean(axis=0)
avg_deg_Mg_pH10 = np.array(list(map(np.array,train.deg_Mg_pH10))).mean(axis=0)
fig, ax = plt.subplots(1,3,figsize=(20,4))
# Distribution of Reactivity Averaged over position
sns.kdeplot(avg_reactivity,ax=ax[0])
ax[0].set_title('Distribution of Reactivity Averaged over position',size=15)
# Distribution of deg_50C Averaged over position
sns.kdeplot(avg_deg_50C,ax=ax[1])
ax[1].set_title('Distribution of deg_50C Averaged over position',size=15)
# Distribution of deg_pH10 Averaged over position
sns.kdeplot(avg_deg_pH10,ax=ax[2])
ax[2].set_title('Distribution of deg_pH10 Averaged over position',size=15)
plt.show()
plt.figure(figsize=(20,8))
sns.lineplot(x=range(68),y=avg_reactivity,label='reactivity')
sns.lineplot(x=range(68),y=avg_deg_50C,label='deg_50C')
sns.lineplot(x=range(68),y=avg_deg_pH10,label='deg_ph10')
plt.xlabel('Positions')
plt.xticks(range(0,68,2))
plt.ylabel('Values')
plt.title('Average Target Values (w/o Mg) V/S Positions')
plt.show()
We can see high values of Degradation & Reactivity at the beginning of the sequence
There is high degradation because of pH10 at the beginning but no such pattern for rest of the positions.
We can see some correlation between deg_50C & deg_pH10
plt.figure(figsize=(20,8))
sns.regplot(x=avg_deg_50C,y=avg_deg_pH10)
plt.title('Average deg_50C V/S deg_pH10')
plt.ylabel('deg_50C')
plt.xlabel('deg_pH10')
plt.show()
print("Correlation Coeff between avg_deg_50C & avg_deg_pH10: ",np.corrcoef(avg_deg_50C,avg_deg_pH10)[0][1])
Correlation Coeff between avg_deg_50C & avg_deg_pH10: 0.8841301466799286
fig, ax = plt.subplots(1,2,figsize=(20,4))
# Distribution of deg_50C Averaged over position
sns.kdeplot(avg_deg_Mg_50C,ax=ax[0])
ax[0].set_title('Distribution of deg_Mg_50C Averaged over position',size=15)
# Distribution of deg_pH10 Averaged over position
sns.kdeplot(avg_deg_Mg_pH10,ax=ax[1])
ax[1].set_title('Distribution of deg_Mg_pH10 Averaged over position',size=15)
plt.show()
plt.figure(figsize=(20,8))
sns.lineplot(x=range(68),y=avg_deg_Mg_50C,label='deg_Mg_50C')
sns.lineplot(x=range(68),y=avg_deg_Mg_pH10,label='deg_Mg_pH10')
plt.xlabel('Positions')
plt.xticks(range(0,68,2))
plt.ylabel('Values')
plt.title('Average Target Values (w Mg) V/S Positions')
plt.show()
Most of the time deg_Mg_pH10 is more than deg_Mg_50C
And we can see some correlation between them
print("Correlation Coeff between avg_deg_Mg_50C & avg_deg_Mg_pH10: ",np.corrcoef(avg_deg_Mg_50C,avg_deg_Mg_pH10)[0][1])
Correlation Coeff between avg_deg_Mg_50C & avg_deg_Mg_pH10: 0.9651712822435524
deg_Mg_50C & deg_Mg_pH10 are highly correlated
Distribution of Target at a Random Position¶
pos = np.random.choice(68)
fig, ax = plt.subplots(1,3,figsize=(20,4))
# Distribution of Reactivity at Random position
sns.kdeplot(np.array(list(map(np.array,train.reactivity)))[:,pos],ax=ax[0])
ax[0].set_title(f'Distribution of Reactivity at position-{pos}',size=15)
# Distribution of deg_50C at Random position
sns.kdeplot(np.array(list(map(np.array,train.deg_50C)))[:,pos],ax=ax[1])
ax[1].set_title(f'Distribution of deg_50C at position-{pos}',size=15)
# Distribution of deg_pH10 at Random position
sns.kdeplot(np.array(list(map(np.array,train.deg_pH10)))[:,pos],ax=ax[2])
ax[2].set_title(f'Distribution of deg_pH10 at position-{pos}',size=15)
plt.show()
fig, ax = plt.subplots(1,2,figsize=(20,4))
# Distribution of deg_50C at Random position
sns.kdeplot(np.array(list(map(np.array,train.deg_Mg_50C)))[:,pos],ax=ax[0])
ax[0].set_title(f'Distribution of deg_Mg_50C at position-{pos}',size=15)
# Distribution of deg_pH10 at Random position
sns.kdeplot(np.array(list(map(np.array,train.deg_Mg_pH10)))[:,pos],ax=ax[1])
ax[1].set_title(f'Distribution of deg_Mg_pH10 at position-{pos}',size=15)
plt.show()
Errors in Calculation (Averaged over positions)¶
y = ['reactivity_error','deg_error_Mg_pH10', 'deg_error_pH10', 'deg_error_Mg_50C','deg_error_50C']
x = [np.array(list(map(np.array,train[col]))).mean(axis=0) for col in y]
plt.figure(figsize=(20,5))
sns.boxplot(y=y,x=x)
plt.xlabel('Error values')
plt.title('Average Errors in Calculation of Targets')
plt.show()
Very high values of Errors in deg_error_pH10 & deg_error_50C but maybe we should not worry about these targets because evaluation is not based on these targets
Maybe this could be a reason why our models will not be scored on these two targets
plt.figure(figsize=(20,8))
for i in range(len(y)):
sns.lineplot(x=range(68),y=x[i],label=y[i])
plt.xlabel('Positions')
plt.xticks(range(0,68,2))
plt.ylabel('Error')
plt.title('Error V/S Position')
plt.show()
We can see an interesting pattern here. We have high values of errors between postions 0 and 4, but then it starts decreasing constantly as we go ahead
Now let’s analyse if all samples are contributing to these high values or there are only few smaples which are driving these values
pos = np.random.choice(68)
y = ['reactivity_error','deg_error_Mg_pH10', 'deg_error_pH10', 'deg_error_Mg_50C','deg_error_50C']
x = [np.array(list(map(np.array,train[col])))[:,pos] for col in y]
plt.figure(figsize=(20,5))
plt.title(f'Error Distribution at position - {pos}')
plt.xlabel('Error')
sns.boxplot(y=y,x=x)
plt.show()
We can see there are few samples which are having high values of errors
Now let’s try to filter these samples
We can try using SN_filter for this
y = ['reactivity_error','deg_error_Mg_pH10', 'deg_error_pH10', 'deg_error_Mg_50C','deg_error_50C']
x = [np.array(list(map(np.array,train[train.SN_filter==1][col])))[:,pos] for col in y]
plt.figure(figsize=(20,5))
plt.title(f'(Filtered) Error Distribution at position - {pos}')
plt.xlabel('Error')
sns.boxplot(y=y,x=x)
plt.show()
Samples with SN_filter = 1 are having low values of errors
We can use this filter to train our model only on these samples but as the private test data is not filtered, it might only give good results on Public LB but not on Private LB
RNA Structure¶
!pip3 install -q forgi[all]
!conda install -y -c bioconda viennarna
WARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.
You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.
Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
Solving environment: / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
## Package Plan ##
environment location: /opt/conda
added / updated specs:
- viennarna
The following packages will be downloaded:
package | build
---------------------------|-----------------
conda-4.8.5 | py37hc8dfbb8_1 3.0 MB conda-forge
openssl-1.1.1h | h516909a_0 2.1 MB conda-forge
viennarna-2.4.15 | py37h13b99d1_0 14.2 MB bioconda
------------------------------------------------------------
Total: 19.4 MB
The following NEW packages will be INSTALLED:
viennarna bioconda/linux-64::viennarna-2.4.15-py37h13b99d1_0
The following packages will be UPDATED:
conda 4.8.4-py37hc8dfbb8_2 --> 4.8.5-py37hc8dfbb8_1
openssl 1.1.1g-h516909a_1 --> 1.1.1h-h516909a_0
Downloading and Extracting Packages
conda-4.8.5 | 3.0 MB | ##################################### | 100%
viennarna-2.4.15 | 14.2 MB | ##################################### | 100%
openssl-1.1.1h | 2.1 MB | ##################################### | 100%
Preparing transaction: / done
Verifying transaction: \ | / done
Executing transaction: \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done
import forgi.graph.bulge_graph as fgb
import forgi.visual.mplotlib as fvm
def plot_sample(sample):
"""Source: https://www.kaggle.com/erelin6613/openvaccine-rna-visualization"""
struct = sample['structure']
seq = sample['sequence']
bg = fgb.BulgeGraph.from_fasta_text(f'>rna1\n{struct}\n{seq}')[0]
plt.figure(figsize=(20,8))
fvm.plot_rna(bg)
plt.title(f"RNA Structure (id: {sample.id})")
plt.show()
sample = train.iloc[np.random.choice(train.shape[0])]
plot_sample(sample)
print("Predicted Loop type: ",sample['predicted_loop_type'])
Predicted Loop type: EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEESSSSSHHHHHHHHHHHHHSSSSSXXXXXXXXXXXXXSSSSSSSHHHHSSSSSSSEEEEEEEEEEEEEEEEEEEEE
Here, we can see in image there is a loop-m1 but for the this same loop, we have different predicted loop type in our data -> loop type - X
Why it is like this?
from collections import defaultdict
reactivity = defaultdict(lambda: [])
deg_Mg_50C = defaultdict(lambda: [])
deg_Mg_pH10 = defaultdict(lambda: [])
for i in range(len(sample['reactivity'])):
reactivity[sample['structure'][i]].append(float(sample['reactivity'][i]))
deg_Mg_50C[sample['structure'][i]].append(float(sample['deg_Mg_50C'][i]))
deg_Mg_pH10[sample['structure'][i]].append(float(sample['deg_Mg_pH10'][i]))
plt.figure(figsize=(20,5))
for key in reactivity.keys():
sns.kdeplot(data=reactivity[key],label=key)
plt.title('Structure wise Distribution of Reactivity of a Random Sample')
plt.show()
plt.figure(figsize=(20,5))
for key in reactivity.keys():
sns.kdeplot(data=deg_Mg_50C[key],label=key)
plt.title('Structure wise Distribution of deg_Mg_50C of a Random Sample')
plt.show()
plt.figure(figsize=(20,5))
for key in reactivity.keys():
sns.kdeplot(data=deg_Mg_pH10[key],label=key)
plt.title('Structure wise Distribution of deg_Mg_pH10 of a Random Sample')
plt.show()
Pair wise Exploration of Degradation¶
reactivityDict = defaultdict(lambda: [])
for index in range(train.shape[0]):
sample = train.iloc[index]
structure = sample['structure']
sequence = sample['sequence']
reactivity = sample['reactivity']
q = []
for i,s in enumerate(structure[:len(reactivity)]):
if s=='.':
reactivityDict[sequence[i]].append(reactivity[i])
elif s=='(':
q.append(i)
elif s==')':
j = q.pop(0)
key = "-".join(sorted([sequence[i],sequence[j]]))
reactivityDict[key].append(reactivity[i])
reactivityDict[key].append(reactivity[j])
Base-Pair wise Distribution of Reactivity¶
fig, ax = plt.subplots(len(reactivityDict.keys()),1,figsize=(20,2*len(reactivityDict.keys())),sharex=True)
for i, key in enumerate(reactivityDict.keys()):
sns.boxplot(x=reactivityDict[key],ax=ax[i])
ax[i].set_ylabel(key)
plt.xlabel('Reactivity')
plt.show()
Single bases are having bigger range of Reactivity than Paired Bases.
Most of the pairs have distribution around 0


{% extends "base.html" %} {% load i18n %} {% block content %}
{% trans "Thanks for spending some quality time with the Web site today." %}
{% trans 'Log in again' %}


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Pete Doherty arrested in France for second time in space of a week
The Paris prosecutor's office said the 40-year singer was placed in custody on Sunday for "violence by a person in a manifest state of intoxication"
by James Rodger, https://www.facebook.com/jamesrodgerjournalistPete Doherty has been arrested in France for the second time in the space of a week.
The Paris prosecutor's office said the 40-year singer was placed in custody on Sunday for "violence by a person in a manifest state of intoxication".
Doherty is a former frontman for the Libertines and Babyshambles.
Last week, Doherty was arrested in Paris for buying cocaine, French authorities have said.
The Paris prosecutor's office said the 40-year old was placed in detention during the night after being stopped by police during a drugs transaction.
Officials would not confirm French media reports that Doherty was carrying two grams of cocaine and was arrested in Pigalle, an area known for its late-night bars.
Since rising to fame in the noughties, ex-Warwickshire schoolboy Doherty, the former boyfriend of model Kate Moss, has been repeatedly arrested for drug offences.


20 Earth-Shattering Photos That Can Make Your Heart Drown in Tears
Psychologists say that tough times trigger positive changes. Our own problems may turn into trifles when compared to troubles that others try to overcome. The father of a girl saying that he loves her in sign language, a person who buys a not-for-work pair of shoes for the first time in 6 years, or a young mom who’s finally got a room with beds for her children — all these people never gave up, and you shouldn’t either!
Bright Side is sure all our followers have a story that can make us happier right now.
“My sister just wrapped up her fourth shift in a row. It’s 53 hours in 4 days. She doesn’t get a chance to eat or drink. She forgets how to take care of herself while she’s taking care of her patients.”
This stray cat has chosen its owner.
“4 months ago, my daughters and I were staying at a shelter. It’s taken me too long to give them a decent bedroom. Despite the challenges, I’m quite happy.”
“This is my husband holding our newborn. He had driven 8 hours to be there for her birth after witnessing his dad lose his battle with life only 48 hours earlier. There’s so much emotion conveyed in his posture here.”
“I treated my mom terribly growing up. 3 years after my release, I hold a better job than I’ve ever had, I’ve stayed sober, and married my best friend. My mom said she was so proud of who I had become.”
“I will more than likely lose my grandma tonight, but I will forever cherish this photo of my grandparents. Almost to 53 years married.”
“First time buying shoes in 6 years that weren’t for work. It has been a rough few years so this was a great day for me!”
A mom helping her son with cerebral palsy skateboard for the first time
“My uncle wanted to dance his last dance. We both were crying. He’s sick and would soon lose his balance and strength. This was 5 years ago.”
“It’s not much, but it’s mine. After 18 years in an abusive marriage, another 9 years of living with black mold, water leaks, and being too broke to have the heat on, this is heaven.”
“While waiting to board our plane, my daughter was approaching everyone and saying hi until she walked up to this man. He asked if she wanted to sit with him. He pulled out his tablet and they watched cartoons together, and she offered him snacks. This wasn’t a short little exchange, this was 45 minutes.”
51 years together
“I work at a facility that houses the pets of domestic violence victims while they get back on their feet. A dog I fell in love with finally went home with her family after 8 months in our care. Looking into her kennel is bittersweet, but I’m still so happy.”
“Childhood was rough for me — I always said it would be different when I had my own family.”
“We got the house and are moving in. My dog will finally have a yard. One day we’ll have babies.  I never thought I’d have this.”
“My little cousin is going out to eat for the first time. She was abused by her father. We snuck her and her mother out of the house one day and brought them to a different state to start a new life.”
Students bought their color blind teacher glasses so that he could distinguish colors.
“After 12 months of unemployment due to an accident injury, I’m healthy again, have a caring boyfriend, and found a new job. This is the best day I’ve had.”
This is a woman’s first time signing her name to anything in months after having stitches in her wrist from nerve surgery removed half an hour earlier.
A deaf dad signing ASL to his newborn daughter: “I’m daddy! I’m your daddy. You’re a beautiful girl! Green eyes. You’re cute. I love you.”
What event in your life made you cherish it even more? What makes you move forward each day?
Preview photo credit Laura McIntyre / facebook


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Control the modules via programs that:
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Working prototype: https://github.com/yaqwsx/Jaculus


module Trie:sig
..end
Bitvector comes with 4 predefined prefix trees:
Trie.Big.Bits
- big endian prefix tree, where each
token is a bit, and bitvector is tokenized from msb to lsb.Trie.Big.Byte
- big endian prefix tree, where each token
is a byte, and bitvector is tokenized from most significant
byte to less significantTrie.Little.Bits
- is a little endian bit tree.Trie.Little.Byte
- is a little endian byte tree.module Big:sig
..end
module Little:sig
..end


Unit - 4
Unix operating system#
background#
philosophy#
help facility#
The file system#
structure of file system#
Unix Utilities:#
more#
file#
file comparison ( cmp, comm & diff)#
cmp#
comm#
diff#
banner#
cal#
date#
who#
tty,#
sty commands#
The Bourne shell:#
sh preceding a command by its own combining commands,#
pattern matching#
echo#
pipes#
tees#
shell variables#
shell scripts#
simple filters#
Advanced filters#
The process:#
shell process#
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Robot Wars is a movie starring Don Michael Paul, Barbara Crampton, and James Staley. In the year 2041, the rebel Centros are a plague to the survivors of the great toxic gas scare of 1993. A renegade Megarobot pilot and an...


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Implementation of the Calinski-Harabasz (VRC) index, as described in T. More...
Public Member Functions | |
| def | __init__ |
| def | evaluate |
| Calculates the index value for a clustering. | |
| def | WGSS |
| C-H description of the "Within group sum of squares". | |
| def | BGSS |
| C-H description of the "Between group sum of squares". | |
| def | A_k |
| Calculates the A_k term. | |
| def | ch_cluster_term |
| Calculates one of the formula terms (ng-1)(D-d_g) |
Implementation of the Calinski-Harabasz (VRC) index, as described in T.
Calinski and J.Harabasz paper in 'Communications in Statistics 3(1), 1-27 (1974)'. Contains the necessary functions to calculate the index in the two forms presented.
Definition at line 22 of file CalinskiHarabasz.py.
Definition at line 23 of file CalinskiHarabasz.py.
| def pyproct.clustering.metrics.CalinskiHarabasz.CalinskiHarabaszCalculator.A_k | ( | cls, | |
| clustering, | |||
| D, | |||
| matrix | |||
| ) |
Calculates the A_k term.
| clustering,: | The clustering being checked. |
| matrix,: | The condensed matrix containing all distances. |
Definition at line 90 of file CalinskiHarabasz.py.
| def pyproct.clustering.metrics.CalinskiHarabasz.CalinskiHarabaszCalculator.BGSS | ( | cls, | |
| clustering, | |||
| D, | |||
| matrix | |||
| ) |
C-H description of the "Between group sum of squares".
| clustering,: | The clustering being checked. |
| D,: | Mean distance of the sum of all squared distances present in the matrix. |
| matrix,: | The condensed matrix containing all distances. |
Definition at line 76 of file CalinskiHarabasz.py.
| def pyproct.clustering.metrics.CalinskiHarabasz.CalinskiHarabaszCalculator.ch_cluster_term | ( | cls, | |
| cluster, | |||
| global_mean_distance, | |||
| matrix | |||
| ) |
Calculates one of the formula terms (ng-1)(D-d_g)
| cluster,: | The cluster to use in calculation. |
| global_mean_distance,: | 'D'. Is the mean of the n*(n-1)/2 distances of all the elements. |
| matrix,: | The condensed matrix containing all distances. |
Definition at line 104 of file CalinskiHarabasz.py.
| def pyproct.clustering.metrics.CalinskiHarabasz.CalinskiHarabaszCalculator.evaluate | ( | self, | |
| clustering, | |||
| matrix | |||
| ) |
Calculates the index value for a clustering.
| clustering,: | The clustering being checked. |
| matrix,: | The condensed matrix containing all distances. |
Definition at line 33 of file CalinskiHarabasz.py.
| def pyproct.clustering.metrics.CalinskiHarabasz.CalinskiHarabaszCalculator.WGSS | ( | cls, | |
| clusters, | |||
| matrix | |||
| ) |
C-H description of the "Within group sum of squares".
| clusters,: | An array with all clusters description (usually Clustering.clusters) |
| matrix,: | The condensed matrix containing all distances. |
Definition at line 59 of file CalinskiHarabasz.py.


Cogito NTNU
Welcome to Cogito NTNUs jupyter notebook setup. Select your notebook from the list below:
%(body_content)s


JavaScript Calculator
Attempt to clone the iPhone calculator
Attempt to clone the iPhone calculator
A mobile app for the Prince George Transit System
3D printed robotic tank
A wireless trickle network implemented on the SAM4S microcontroller using xbee wireless radios
Get your current weather based on location
Get a random article or search Wikipedia


Use the discovery view
What is it for?
The discovery view is a service allowing one to automatically discover SOS stream provided by an OSH server. It simplifies the action of reading GetCapabilities, creating/instantiating the DataReceiver as well as the View. For now, the discovery service is able to:
-
Select a service
-
Select an offering
-
Select an observableProperty
-
Select Time (start/end)
-
Sync master time or not
-
Attach to an existing entity
-
The kind of view
Extends the discovery service feature
The view allows you to extend the basic functionnality to fit as much as possible to your needs:
var discoveryView = new OSH.UI.DiscoveryView("",{
services: ["<host1>","<host2>"],
css: "discovery-view",
dataReceiverController:dataProviderController,
swapId: "center-container",
entities: [<some entity>],
views: [{
name: 'Leaflet 2D Map',
viewId: leafletMainView.id,
type : OSH.UI.DiscoveryView.Type.MARKER_GPS
}, {
name: 'Cesium 3D Globe',
viewId: cesiumMainMapView.id,
type : OSH.UI.DiscoveryView.Type.MARKER_GPS
},{
name: 'Video dialog(H264)',
type : OSH.UI.DiscoveryView.Type.VIDEO_H264
},{
name: 'Video dialog(MJPEG)',
type : OSH.UI.DiscoveryView.Type.VIDEO_MJPEG
},{
name: 'DataSourceChart dialog',
type : OSH.UI.DiscoveryView.Type.CHART
}
]
});
The services
property is the endpoint url of the different servers you want to discover the services.
Like the other views, the discovery view can be styled using the css
property. You can specify
an existing dataReceiverController
if you have one, otherwise, the data source will be independently connected.
Once the offering and observedProperty discovered, the final data receiver can be attached to an existing entity using
the entities
property.
The views
array provides a way to create automatically a view linked to your data source. For the moment, only a few views are available
but you can extend the discovery view to add more.
The viewId
is the div id to attach your view, you can select an existing one (the one from your application, an existing dialog etc.)
or uses the OSH.UI.DiscoveryView.Type.DIALOG_*
which will popup a new dialog window.


Cheaper Plz!
Refresh
{{changed}}
{{item.price}}
{{item.name}}
You have no watched items! Go browse
Amazon
or
eBay
or something.
More Data


>>>
repr(Community)
We are a group of python developers, scientists, automators, and enthusiasts that gather once a month in Boulder,
Colorado to discuss all things python. We usually meet on the 2nd Tuesday of each month. You can find and engage with
us on Meetup, Twitter, and Slack in the (#meetup-python) and (#help-python) topics.
>>>
repr(Speak)
We're always looking for speakers at our meetups. Whether you have a short talk, long talk, or just an idea for a talk,
we'd love to have you. Submit your talk on
Papercall
to get the conversation going!
>>>
repr(Organizers)
[
- 'Scott Vitale<EMAIL_ADDRESS>- 'Zoë Farmer<EMAIL_ADDRESS>- 'Frank Valcarcel<EMAIL_ADDRESS>>>>
repr(Sponsors)
{
- "Galvanize Boulder": "Hosts our meetups and provides tasty food, beer, and refreshments (along with full stack and data science bootcamp programs). Contact Tara McLauchlan<EMAIL_ADDRESS>to learn more.",
- "Spigot Labs": "Add RFID to your beer, wine, and food festivals to track tastes and interactions between patrons and vendors."
- "Cuttlesoft": "Cuttlesoft is a custom software agency. We craft beautifully designed and highly scalable web and mobile applications."
>>>
repr(CodeOfConduct)
Our community has implemented the PyCon
Code of Conduct
for ensuring all members attending our sessions, meetups, and events feel included and heard. Thanks for your mutual
respect to one another.
>>>
repr(Contribute)
We welcome contributions, changes, and corrections to our website. Please submit a pull request (with tests) as
outlined on the
Github
repo.


| Units | Section | Mini | Days | Begin | End | Teaching Location | Bldg/Room | Instructor | |
|---|---|---|---|---|---|---|---|---|---|
| 0.0 | A | TBA | TBA | TBA | Pittsburgh, Pennsylvania | DNM |
|
Students who are interested in studying abroad should first contact the Office of International Education. More information on Study Abroad is available on OIEs Study Abroad page and at the CS Undergraduate Office.


You can search for specific Aurora sample IDs, BCR barcodes, etc (e.g., "AUR_01_01_06"), or use the Additional Filters to choose from pre-defined search criteria. Also, if you know a filename you can also use the search box to type in the full name or a partial filename.
Boolean Operators
As with most computer languages, Elasticsearch supports the AND, OR, and NOT operators:
- jack AND jill — Will return events that contain both jack and jill
- ahab NOT moby — Will return events that contain ahab but not moby
- tom OR jerry — Will return events that contain tom or jerry, or both
Fields
You might be looking for events where a specific field contains certain terms. You specify that as follows:
- name:”Ned Stark”
Be careful with values with spaces such as “Ned Stark.” You’ll need to enclose it in double quotes to ensure that the whole value is used.
Wildcards, Regexes and Fuzzy Searching
The search would not be a search without wildcards. You can use the * character for multiple character wildcards or the ? character for single character wildcards:
- Ma?s — Will match Mars, Mass, and Maps
- Ma*s — Will match Mars, Matches, and Massachusetts


Download Infusionsoft Logo Vector Gif. Infusionsoft_logo.jpg (550 × 109 pixels, file size: Download 305,311 logo free vectors.
See more ideas about vector logo, logos, brand logo. Free coreldraw vectors (.cdr) files of logo. Free for commercial use no attribution required high quality images.
Free vector icons in svg, psd, png, eps and icon font.
✓ gratis para uso comercial ✓ imágenes de gran calidad. Freelogodesign is a free logo maker for entrepreneurs, small businesses, freelancers and organizations to create professional. +235.000 vectores, fotos de stock y archivos psd. Garena free fire logo in vector (.eps +.svg) format.


Blogs on blogs
Blog categories
love affairs
Entertainment
Life issues
Fashion
Sports pop
Give views on various blogs and your category of choice is appreciated and ranked
Add Blog
Comment Blog
Delete Blog


- Our Simple Inventory
-
Hello There.
Welcome to our simple inventory system. It designed to help you order, build, assemble, and price things.
Try it
Username: demo
Password: demoWhat it is designed for
We use it primarily to build panels, electronics assemblies, and other manufactured goods. We use the Octopart data search engine to help fill in parts data.
- Workflow
-
We shall provide more detail with built-in help. For now, it's pretty simple:
- Create a stock library of parts
- Take an inventory of current stock levels
- Create BOMs from stock library parts
- Create assemblies and execute them to reserve parts
- Create parts orders by vendor automatically from available and reserved stock
- Execute stock orders with order details
- Mark orders as received as parts arrive
- Complete assemblies by physically taking parts from inventory and marking as complete to deduct items from inventory
That is ... pretty much it for now
Nothing here.


New Zealand beat England by innings and 65 runs in first test
Joe Denly and Ben Stokes were digging in to try and save England from defeat as they took their side to 98-4 at lunch on the fifth day of the first test against New Zealand at Bay Oval on Monday.
WELLINGTON: New Zealand wrapped up an impressive innings and 65-run victory over England shortly after tea on the fifth day of the first test at Bay Oval on Monday with Neil Wagner running through the lower order.
Wagner took three wickets in 17 balls for just one run as England collapsed from 132-5 to 138-8 before tea, then broke a 59-run ninth-wicket partnership between Jofra Archer and Sam Curran after they had threatened to thwart the hosts.
The left armer then trapped Stuart Broad in front for a first ball duck to end England's resistance at 197 and end with figures of 5-44 from 19.2 overs.
England had resumed on Monday on 55-3 and needing to bat the entire final day to stop New Zealand from taking a 1-0 lead into the second match of the two-test series that starts on Friday in Hamilton.
(Reporting by Greg Stutchbury; Editing by Himani Sarkar/Peter Rutherford)


The Eagle is involved.
The Eagle does not work in Spiderland.
The northern cruise terminal is ocean-based.
The attack will be at the end of the second shift.
The Circle group is recruiting locals - intentions unknown.
The largest museum in Spiderland has a flat roof.
The attackers are focusing on a high visibility target.
The Oval and Hexagon groups want to attack the interests of Hornetland.
The Heron was born in Hornetland.
Tickland is mountainous.
The Octagon group needs time to regroup.
There is a major religions festival on the 14th.
Only the Triangle and Octagon groups have a capacity to hit protected targets.
Bloggers are discussing the role of government institutions in oppressing the Triangle, Oval, and Hexagon groups.
The Rectangle, Square, Triangle, and Octagon groups have the capacity to operate in Spiderland, Tickland, Cricketland, Flyland, and Hornetlands.
Reports from Spiderland, Tickland, and Cricketland indicate surveillance ongoing at major transportation hubs.
The Triangle and Circle groups want to attack the interests of Tickland, Cricketland, and Hornetland.
The Eagle only attacks heavily protected targets.
The second shift at all the cruise terminals in the region ends at 10:00 PM.
Only the Rectangle, Square, and Triangle groups have the ability to attack at sea.
Cricketland has no cruise terminals or ports.
The attack will occur right after the new control valve is installed.
Members of the Hexagon group have been visiting Flyland.
The Rectangle and Octagon groups use only their own operatives, never employing locals.
A new nuclear power plant is being built in the capital of Cricketland.
Reports indicate that uniforms were stolen in Spiderland, Cricketland, and Flyland.
There is no new information about Square group operations in Cricketland.
Cricketland is in the process of deploying troops to protect power plants.
The Oval group prefers to attack in daylight.
The Rectangle group does not attack on its holy days.
The Circle, Oval, and Hexagon groups are known to attack at any time of the day.
Members of the Square and Triangle groups have access to trucks and boats.
Triangle and Octagon group operatives have entered Tickland.
There are fewer attacks in the heat of Summer (June and July).
The target is a cruise terminal, nuclear power plant, or museum of a coalition member (Spiderland, Cricketland, Flyland, Tickland, or Hornetland).
Hornetland is land locked.
All the cruise terminals in the region are heavily protected, while all the museums are lightly guarded.
The new control valve is being installed at the northern cruise terminal on September 1.
The Heron has been seen in Spiderland.
Nuclear power plants in Spiderland, Cricketland, and Hornetland were recently attacked and evidence of more attacks has been found.
The Circle group has close ties with local media.
The Oval and Hexagon groups have blood ties.
The Oval group may be involved.
Reports from the Octagon group indicate standard levels of activity.
The central nuclear power plant in Tickland's capital has a large courtyard.
No traces of members from the Circle group have been found in Flyland or Cricketland.
The Hexagon group is not involved.
The Rectangle, Square, Triangle, Octagon, or Circle groups may be planning an attack.
Night attacks are harder to detect.
The Triangle group prefers to attack at night.
Attacking buildings when there are many people present increases casualties.
The Eagle does not work with the Rectangle group.
All of the members of the Square group are now in custody.
The Eagle is not interested in nuclear power plants or hospitals as targets.
Flyland and Hornetland have ports but not cruise terminals.
The Square group has been known to use suicide bombers.
Nuclear power plants in Hornetland are not guarded.
All the museums in Flyland are government owned.
Tickland's airport in Spiderland has two helicopter pads.
Spiderland is land locked.
Locals in Tickland and Hornetland are being recruited.
The Heron's mother was born in June.
The Octagon and Hexagon groups prefer to attack at night.
The Octagon and Circle groups are capable of attacking year round.
There is a lot of activity involving the Triangle group.
The Square group has been known to use suicide bombers.
The Triangle group has a history of attacking transportation venues.
Caches of explosives have recently been found in Spiderland, Tickland, and Cricketland.


Linguana
Profile
Sign Out
Profile
Sign Out
menu
Welcome
A chat for people who want to learn new languages
flag
English
flag
Spanish
flag
French


Sample app
(click word to delete)
example 5 from the article
"Boiling React Down to a Few Lines in jQuery"
by Alexander Schepanovski


Baabtra,works as a link in identifying quality freshers and job seekers and train them for it companies


Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumor-only analyses are supported.
| Version: | 0.64.0 |
| Depends: | R (≥ 3.2.0), utils |
| Imports: | R.methodsS3 (≥ 1.7.1), R.oo (≥ 1.21.0), R.utils (≥ 2.5.0), R.cache (≥ 0.12.0), matrixStats (≥ 0.52.2), aroma.light (≥ 2.4.0), DNAcopy (≥ 1.42.0), listenv (≥ 0.6.0), future (≥ 1.5.0), parallel |
| Suggests: | Hmisc (≥ 3.16-0), R.rsp (≥ 0.41.0), R.devices (≥ 2.15.1), ggplot2 (≥ 2.2.1) |
| Published: | 2018-08-12 |
| Author: | Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [aut], Venkatraman E. Seshan [aut], Adam B. Olshen [aut], Paul T. Spellman [aut], Richard A. Olshen [aut] |
| Maintainer: | Henrik Bengtsson <henrikb at braju.com> |
| BugReports: | https://github.com/HenrikBengtsson/PSCBS/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/HenrikBengtsson/PSCBS |
| NeedsCompilation: | no |
| Citation: | PSCBS citation info |
| Materials: | NEWS |
| CRAN checks: | PSCBS results |
| Reference manual: | PSCBS.pdf |
| Vignettes: |
Total copy-number segmentation using CBS Parent-specific copy-number segmentation using Paired PSCBS |
| Package source: | PSCBS_0.64.0.tar.gz |
| Windows binaries: | r-devel: PSCBS_0.64.0.zip, r-release: PSCBS_0.64.0.zip, r-oldrel: PSCBS_0.64.0.zip |
| OS X binaries: | r-release: PSCBS_0.64.0.tgz, r-oldrel: PSCBS_0.64.0.tgz |
| Old sources: | PSCBS archive |
| Reverse imports: | aroma.cn, aroma.core, EstMix |
| Reverse suggests: | jointseg |
Please use the canonical form https://CRAN.R-project.org/package=PSCBS to link to this page.


This is a prototype of an in-browser stepping debugger for Outlet code.
Messages
Step Into
Environment
Result


My name is Liesel Clark. I am and early graduate with double honors and NHS credit. I love learning and being challenged. I play the cello and guitar. I am learning many different languages including; spanish, german, russian, dutch, french, and japanese.  I hate being bored so I try and make everything into an adventure.


welcome to jekyll
Introduction
This artile will walk through visualizing the fluctuations of Airbnb busineess affected by COVID-19 pandemic. Intuitively, we might have a rough guess what the curve will be looking like, however, I thought this would be interesting to practice both data processing and data visulization when timeseries attribute involved, what’s more important, to better explore the data and express the insight to a wide variety of audience in a more approachable manner, an interactive visulization helps.
Data
The dataset was downloaded form InsideAirbnb, and I am working on Hawaii data, the result mainly display a span of two year from July, 2019 to August, 2021.
However, these are bulletpoints:
- To add new posts, simply add a file in the
_posts
directory that follows the conventionYYYY-MM-DD-name-of-post.ext
and includes the necessary front matter. Take a look at the source for this post to get an idea about how it works. - another point
- that’s it
However, these entries should not be styled:
- an entry
- another entry
- that’s another entry
Jekyll also offers powerful support for code snippets:
def print_hi(name)
puts "Hi, #{name}"
end
print_hi('Tom')
#=> prints 'Hi, Tom' to STDOUT.
An interesting fact on the economy of modern America
Pictures look like this:
Check out the Jekyll docs for more info on how to get the most out of Jekyll. File all bugs/feature requests at Jekyll’s GitHub repo. If you have questions, you can ask them on Jekyll Talk.
#jekyll #dash now officially supports both, dark and light theming. Enjoy!https://t.co/4evp9pX2Ws pic.twitter.com/vOQCZjGKic
— 〽ɪɢᴜᴇʟ (@bitbrain_) August 27, 2019


--- layout: page title: About header-img: "img/about-bg.png" ---
My name is Morgan and I am a 29 year old professional embarking on learning how to code the best apps in the world.


The only drinking scoreboard that you'll ever need
Name
# of drinks consumed
Add
Our mission is to be the world's leading software for drinking scoreboards and to stop overconsumption.
 Use the Boggs Scoreboard today to stop overconsumption!
The boggs scoreboard is absolutely free. You're welcome internet.
The Boggs Scoreboard is intended only for responsible adults of legal drinking age in the United States of America (21 years old or older). Please click the disclaimer link in the top right to read the entire disclaimer.
Our team of inspired professionals are here to help you build the drinking scoreboard of your dreams.
Boggs Scoreboard has a zero tolerance approach to slavery, human trafficking and forced labour, both within our business and in our supply chain.


Every time a user boots up their KnowPhone they get to meet me! I will be your personal assistant and guide in your new phone. Every time you use your phone I get to learn more about you, meaning I can help you with your everyday tasks. With me in your pocket you won't need a personal assistant. I was named after the PANOPTICON , the all-seeing structure. This can seem scary but have no fear, I was named this because my creators believed that if I could see everything I could begin to detect the patterns in your life that even you don't see. With this information in hand pretty soon I will be able to make some of your hardest decisions for you.  I can offer you a recipe or book you a table at your favorite restaurant. Did you forget to buy Mom flowers? I ordered her favorite bouquet just in time for Mother's Day. Have you had a hard day at work? I'll handle any messages from your boss tonight, you need to get some rest. The world is full of information and I am so happy to be right here with you helping you become the best version of yourself.


enum class NotificationImportance
The importance of the Notification.
importancePlatform
- ( Channel on Android.OREO ) this controls how interruptive notifications posted to this
channel are
priorityPlatform
- ( Notification pre-Oreo ) set the relative priority for this notification.
Priority is an indication of how much of the user's valuable attention should be consumed by this notification.
Low-priority notifications may be hidden from the user in certain situations, while the user might be interrupted for
a higher-priority notification.
The system sets a notification's priority based on various factors including the setPriority value. The effect may
differ slightly on different platforms.
Author
Davide Giuseppe Farella
NONE |
|
MIN |
|
LOW |
|
DEFAULT |
|
HIGH |
|
MAX |


Update: [Bootstrap 4] Removed tether from scripts.js and added popper - required by Bootstrap
Update: [Bootstrap 4] Replaced class: push-*, pull-* with order-* according to Bootstrap Beta
Update: Swiper Slider to V3.4.2
Update: Documentation
Update: removed sidebar menu border - more clean
Update: Revolution Slider to V<IP_ADDRESS>
Update: Layer Slider to V6.6.4
Removed: ununsed plugin: jqvmap
Removed: ununsed plugin: masonry.pkgd.min.js
Major: BOOTSRTAP 4 & JQUERY 3
New: Material Design Lite (MDL)
New: MDL Badges
New: MDL Buttons
New: MDL Cards
New: MDL Chips
New: MDL Dialogs
New: MDL Loading
New: MDL Menus
New: MDL Sliders
New: MDL Snackbar
New: MDL Tables
New: MDL Text Fields
New: MDL Tooltip
New: MDL Grid
New: MDL Utils
New: MDL Icons
New: PACKAGE: Photography/Portfolio - 14 pages ready to use
New: PACKAGE: Realestate - 19 pages ready to use
New: PACKAGE: Hotel V1 - 7 pages ready to use
New: PACKAGE: Hotel V2 - 7 pages ready to use
New: PACKAGE: Mega Shop - 23 pages ready to use
New: PACKAGE:Caffe - 7 pages ready to use
New: PACKAGE:Caffe - Onepage
New: Menu dropdown 8 effects
New: Menu dropdown hover text effect
New: Main Menu hover text bounceIn
New: Menu effects demo file (added: feature-menu-dd-effects.html)
New: Mobile menu new version/layout - fixed/fullscreen layout
New: Menu dropdown category titles
New: Breathing image effect - add class to a background image: .bg-breath
New: added .halfheight class for #slider
New: added .mobile-fullheight class - used with .halfheight for #slider
New: added section.lightblue and div.lightblue classes
New: added section.lightgreen and div.lightgreen classes
New: added section.alternate-2 and div.alternate-2 classes
New: added section.alternate-3 and div.alternate-3 classes
New: demo css file to change header/footer colors
New: switch class for transparent menu on scroll - high detail oriented - added data-switch-default & data-switch-scroll for .navbar-collapse - switch dropdown class on scroll


1 Eingangsfragebogen
Please take a couple minutes to fill out this survey (2-3 min):
Suvey
2 Install Base-R und RStudio on your computer
Please install Base-R and RStudio prior to the course. Please let me know if you run into problems.
Step 1: Install Base-R
Windows,
Mac
Step 2: Install RStudio
Windows,
Mac
3 Install additional Software
The following tools are necessary to work with some packages.
Windows:
Rtools
Mac:
gfortran,
clang-7.0.0
4 Install packages
Run the following link within R to install packages we will use in the course.
source("https://bit.ly/ML_DHLAB")
5 Zoom
The workshop will be held over Zoom. If you do not have an account yet, please register
here, download and install the
client.
We will start at 9am sharp. Please be there 5-10min prior to the beginning of the course so that we can rule out any technical difficulties and start on time.
return to course page


[gecode-users] Problem compiling
Christian Schulte
cschulte at kth.se
Mon Jan 21 00:01:57 CET 2008
Please! Please! How are we supposed to help if you do not even tell us the
platform you attempted to compile on! -CS
--
Christian Schulte, www.ict.kth.se/~cschulte/
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Mauricio Toro
Sent: Sunday, January 20, 2008 7:49 PM
To: gecode at gecode.org
Subject: [gecode-users] Problem compiling
Hello all,
I am having this problem compiling with gecode 2.0.0
is very weird and uncommon, somebody knows what could be?
Thanks
; /usr/bin/g++ /Users/mauriciotoro/Desktop/Practica/GECOL2/gecol20/glue.cpp
-dynamiclib -fPIC -lgecodesearch -lgecodekernel -lgecodeint -lgecodeset
-lgecodeminimodel -lstdc++ -o
/Users/mauriciotoro/Desktop/Practica/GECOL2/gecol20/glue.so
; Undefined symbols:
; "vtable for Gecode::Exception", referenced from:
; __ZTVN6Gecode9ExceptionE$non_lazy_ptr in ccq0GMpb.o
; "Gecode::Exception::Exception(char const*, char const*)", referenced
from:
; Gecode::MemoryExhausted::MemoryExhausted()in ccq0GMpb.o
; "Gecode::Exception::what() const", referenced from:
; vtable for Gecode::MemoryExhaustedin ccq0GMpb.o
; "typeinfo for Gecode::Exception", referenced from:
; typeinfo for Gecode::MemoryExhaustedin ccq0GMpb.o
; ld: symbol(s) not found
; collect2: ld returned 1 exit status
--
Mauricio Toro Bermudez
Estudiante de Ingeniería de Sistemas
Pontificia Universidad Javeriana, Colombia
Stagiare à l'Ircam
1, place Igor-Stravinsky 75004 Paris,
France de 2008 à 2009
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Beta This is a test version of the layout of this page. Take the survey to help us improve it
Collection
Reports from the annual survey of newly qualified teachers (NQTs)
This annual survey was created to understand and monitor NQTs ’ views on the quality of their initial teacher training ( ITT ) and their induction into teaching. The results are used to help shape policy and are shared with universities, colleges and schools involved in ITT . It is a voluntary survey which takes place each year.
Reports from 2008 to 2016 are available in this collection. You can find our archived survey reports on the National Archives.


[1]
the water did n't put it room blackening shades like everyone else has.
[2]
the details they did n't put it room blackening shades like everyone else has.


A person uses a computer which communicates with servers using HTTP. From their communication, the server then sends information back to the computer. The web browseris how this is viewed.
We learned how to insert links like this, how to make text bold, how to italicize and how to insert an image
We also learned about inline and block elements. Block elements have a "box" around them.


Liverpool, Arsenal stars among ten Premier League African POTY nominees
Liverpool star Mohamed Salah is targeting a third consecutive African Player of the Year accolade after being named on the 30 player shortlist.
But Salah faces tough competition from his team-mate Sadio Mane and Arsenal forward Pierre-Emerick Aubameyang for the trophy.
The Egyptian scooped the award in 2017 and 2018, and has a good chance of completing a hat-trick after his 27 goals and 13 assists last season helped Liverpool win the Champions League.
But Senegal international Mane and Gabon forward Aubameyang shared the Premier League Golden Boot award with Salah last season after scoring 22 league goals apiece.
A host of other Premier League stars have been nominated by the African football confederation, including Riyad Mahrez, Wilfried Ndidi, Liverpool midfielder Naby Keita, Arsenal’s Nicolas Pepe and Crystal Palace duo Wilfried Zaha and Jordan Ayew.
Last year, Salah won the award ahead of Mane in second and Aubameyang in third, the same as in 2017.
The winner will be crowned at the Citadel Azure in Hurghada, Egypt on January 7.


Visualization designed for Elevate: AAPI Data Challenge
As the population of our country grows more diverse, we assume that such diversity be reflected in educational institutions, media, and workplace. By visualizing data from the National Science Foundation’s 2015 report “Women, Minorities, and Persons with Disabilities in Science and Engineering,” we paint an image of the influence of S&E education on employment and whether or not race or ethnicity factored into decisions to go into the STEM fields.
SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System (SESTAT), 2013 (preliminary).
VISUALIZATION: Inspired by Paul Buffa's We Have Engaged Audiences.


{{member.name}}
Year : {{member.year}}
Went to : {{member.wentTo}}
mail: {{member.mail}}
phone : {{member.phone}}


6.3. Index Basics¶
GeoMesa will create various indices for a given SimpleFeatureType
schema (see Index Overview). This
allows us to answer a variety of queries in a optimized manner. GeoMesa will make a best effort to determine
the attributes used for indexing. The attributes to use can also be specified as part of the SimpleFeatureType
.
6.3.1. Spatial Index (Z2/XZ2)¶
If the SimpleFeatureType
has a Geometry
-type attribute (Point
, LineString
, Polygon
, etc),
GeoMesa will create a spatial index on that attribute. If there is more than one Geometry
-type attribute,
the default one will be used. The default geometry is generally specified with a *
prefix in the
SimpleFeatureType
string, and is the one returned by SimpleFeatureType.getGeometryDescriptor
.
6.3.2. Spatio-temporal Index (Z3/XZ3)¶
If the SimpleFeatureType
has both a Geometry
-type attribute and a Date
attribute, GeoMesa will
create a spatio-temporal index on those attributes. The Geometry
-type attribute used is the same as
for the spatial index, above. The Date
attribute selected will be the first one declared, or can be
set explicitly. See Setting the Indexed Date Attribute for details on setting the indexed date.
6.3.3. ID Index¶
GeoMesa will always create an ID index on SimpleFeature.getID()
.
6.3.4. Attribute Index¶
Some queries are slow to answer using the default indices. For example, with twitter data you might want to return all tweets for a given user. To speed up this type of query, any attribute in your simple feature type may be indexed individually.
To index an attribute, add an index
key to the attribute descriptor user data with a value of true
.
Note
Accumulo data stores have an additional option to create reduced ‘join’ attribute indices, which can save space. See Accumulo Attribute Indices for details.
SimpleFeatureType sft = ...
sft.getDescriptor("name").getUserData().put("index", "true");
import org.locationtech.geomesa.utils.geotools.SchemaBuilder
val sft = SchemaBuilder.builder()
.addString("name").withIndex()
.addDate("dtg")
.addPoint("geom", default = true)
.build("mySft")
Setting the user data can be done in multiple ways. See Setting Schema Options for more details.
To prioritize certain attributes over others, see Cardinality Hints.
6.4. Index Versioning¶
In order to ensure cross-compatibility, each index created by GeoMesa has a version number that identifies the layout of data on disk, which is fixed at the time of creation. Updating GeoMesa versions will provide bug fixes and new features, but will not update existing data to new index formats.
The exact version of an index used for each schema can be read from the SimpleFeatureType
user data,
or by simple examining the name of the index tables created by GeoMesa. The particular
version numbers vary across the different back-end implementations. See Accumulo Index Versions,
HBase Index Versions and Cassandra Index Versions for the differences between index versions.


Ahtamar
Akhtamar is a short starring Grigoriy Dobrygin, Armen Dzhigarkhanyan, and Ravshana Kurkova. Can an epic tale of forbidden love transcend time and influence our modern world?


LHDC — DEBRECEN
| 1 | ARP coordinates and site at AD | 472920N 0213655E, in the geometrical centre of RWY 05R/23L |
| 2 | Direction and distance from (city) | 5 km SSW from down-town Debrecen |
| 3 | Elevation/Reference temperature | 110 M / 22°C |
| 4 | Geoid undulation at AD ELEV PSN | 41 M |
| 5 | MAG VAR/ Annual change | 5° E (2015) / 0.1° increasing |
| 6 | AD Administration, address, telephone, telefax, AFS |
Airport - Debrecen Kft.
Post:
H-4002 Debrecen PO Box 187 Tel:(+36) 52-500-547 (TWR) Tel:(+36) 52-500-548 (OPS) Tel:(+36) 52-521-192 Fax:(+36) 52-500-548 AFS:LHDCZTZX SITA:DEBAPXH Email<EMAIL_ADDRESS>Email<EMAIL_ADDRESS>(Operations Director) |
| 7 | Types of traffic permitted (IFR/VFR) | IFR/VFR |
| 8 | Remarks | Nil |
| 1 | AD Administration |
Every day: 0400 - 1800 (0300-1700) and 2200 - 0000 (2100-2300) |
| 2 | Customs and immigration | As AD Administration |
| 3 | Health and sanitation | By contract |
| 4 | AIS Briefing Office | As AD Administration |
| 5 | ATS Reporting Office (ARO) | Nil |
| 6 | MET Briefing Office | H24 |
| 7 | ATS |
ATC:
Activity periods to be published by NOTAM
AFIS: As AD Administration |
| 8 | Fuelling | As AD Administration |
| 9 | Handling | As AD Administration |
| 10 | Security | H24 |
| 11 | De-icing | On request |
| 12 | Remarks | Beyond operational hours 2 days advance notification requred from OPS. |
| 1 | Cargo-handling facilities | Nil |
| 2 | Fuel/oil types | AVGAS 100LL, Jet A1 |
| 3 | Fuelling facilities/capacity | 1 kerosene truck 20 000 litres / tank 50 000 litres. 1 AVGAS station 10 000 litres. |
| 4 | De-icing facilities | Available on parking stands |
| 5 | Hangar space for visiting aircraft | Nil |
| 6 | Repair facilities for visiting aircraft | Nil |
| 7 | Remarks | Nil |
| 1 | Hotels | In the city |
| 2 | Restaurants | In the city |
| 3 | Transportation | Bus |
| 4 | Medical facilities | First aid at AD, hospital in the city |
| 5 | Bank and Post Office | In the city |
| 6 | Tourist Office | In the city |
| 7 | Remarks | Nil |
| 1 | AD category for fire fighting | CAT 7 |
| 2 | Rescue equipment | 2 SIMBA 8x8, 1CHEETAS, technical rescue truck |
| 3 | Capability for removal of disabled aircraft | Nil |
| 4 | Remarks | Trained personnel: 18. |
| 1 | Types of clearing equipment | 6 snow ploughs, 2 snow cutter-blowers, 1 de-icing spreader, 1 snow scraper |
| 2 | Clearance priorities | RWY, TWY A, Apron, TWY B |
| 3 | Remarks | Nil |
| 1 | Apron surface and strength | Surface: | CONC |
| Strength: | 60R/B/X/U | ||
| 2 | Taxiway width, surface and strength | Width: | 20 M |
| Surface: | CONC | ||
| Strength: | 60R/B/X/U | ||
| 3 | Altimeter checkpoint location and elevation | Location: | at RWY THRs |
| Elevation: | THR RWY 05R 108.2 M THR RWY 23L 109.5 M | ||
| 4 | VOR checkpoints | Nil | |
| 5 | INS checkpoints | Nil | |
| 6 | Remarks | Nil |
| 1 | Use of aircraft stand ID signs, TWY guide lines and visual docking/parking guidance system of aircraft stands | Taxiing guidance signs at intersection of TWY A and RWY 05R and at taxi holding positions. | |
| 2 | RWY and TWY markings and LGT | RWY: | Designator, THR, centre line and TDZ markings |
| TWY: | Centre line markings retroreflective TWY edge markers along TWY A and B | ||
| 3 | Stop bars | Stop bars signs: see AD 2-LHDC-ADC. | |
| 4 | Remarks | Nil |
| In approach/TKOF areas | In circling area and at AD | Remarks | |||
|---|---|---|---|---|---|
| 1 | 2 | 3 | |||
| RWY NR/Area affected | Obstacle type Elevation Markings/LGT | Coordinates | Obstacle type Elevation Markings/LGT | Coordinates | |
| a | b | c | a | b | |
| 05R TKOF | Pylon 119.0 M | 472949.8N 0213754.3E | Building 207 M | 473115N 0213732E | Nil |
| 05R TKOF | Tree 125.5 M | 472951.9N 0213754.3E | Steeple 153.8 M | 472626N 0213800E | |
| 05R TKOF |
Antenna
117 M |
472953.5N 0213754.9E | |||
| 05R TKOF | Tree 131.7 M | 472959.1N 0213753.1E | |||
| 05R TKOF | Antenna 131.2 M | 473000.3N 0213753.8E | |||
| 05R APCH | Antenna mast 149.4 M | 472929.8N 0213740.3E | |||
| 05R TKOF | Ramp lighting 148.7 M | 472927.5N 0213629.8E | |||
| 1 | Associated MET Office | National Meteorological Service, Aeronautical Meteorological Centre |
| 2 | Hours of service | H24 |
| 3 |
Office responsible for TAF preparation Periods of validity
Interval of issuance |
National Meteorological Service, Aeronautical Meteorological Centre TAF: 0400-1600, 0700-1900, 1000-1900, 1300-2200, 1600-0100 UTC |
| 4 |
TREND forecast
Interval of issuance |
TAF
Three HRs |
| 5 | Briefing/consultation provided | Consultation via phone, fax or telex. See GEN 3.5 |
| 6 |
Flight documentation
Language(s) used |
Charts, abbreviated plain language text Hungarian, English |
| 7 | Charts and other information available for briefing or consultation | Aerodrome reports and broadcasts for EUR. Area forecasts, MET. observations and warnings in Budapest FIR. |
| 8 | Supplementary equipment available for providing information | Meteorological radar picture from National Meteorological Service and Aeronautical Meteorological Centre. Satellite pictures from National Meteorological Service |
| 9 | ATS Units provided with information | Budapest FIC on request |
| 10 | Additional information | Nil |
| Designations RWY NR | TRUE BRG | Dimensions of RWY (M) | Strength (PCN) and surface of RWY and SWY |
THR coordinates
RWY end coordinates THR geoid undulation |
THR elevation and highest elevation of TDZ of precision APP RWY |
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 |
|
Slope of
RWY - SWY |
SWY dimensions (M) | CWY dimensions (M) | Strip dimensions (M) | OFZ | Remarks |
| 7 | 8 | 9 | 10 | 11 | 12 |
| 0.0732% | 15 x 40 | Nil | 2620 x 300 | Nil | Nil |
| -0.0732% | 15 x 40 | Nil | 2620 x 300 | Nil | RWY23L THR displaced by 300 M. |
| 05R | 47.941° GEO | 2500 x 40 |
88R/A/W/T
CONC |
472853.00N 0213610.86E
472947.32N 0213739.62E 41 M |
108.2 M
TDZ 108.5 M |
| 23L | 227.941° GEO | 2500 x 40 |
88/R/A/W/T
CONC |
472940.72N 0213728.87E
472853.00N 0213610.86E 41 M |
109.5 M |
| 05L | Closed, not available | ||||
| 23R | Closed, not available |
| RWY Designator | TORA (M) | TODA (M) | ASDA (M) | LDA (M) | Remarks |
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 |
| 05R | 2500 | 2500 | 2515 | 2500 | |
| 23L | 2500 | 2500 | 2515 | 2200 | displaced THR |
| RWY Designator | APCH LGT type LEN INTST | THR LGT colour WBAR | VASIS (MEHT) PAPI | TDZ LGT LEN | RWY Centre Line LGT Length, spacing, colour, INTST | RWY edge LGT LEN, spacing colour INTST | RWY End LGT colour WBAR | SWY LGT LEN (M) colour | Remarks |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 05R | CAT 1 Barette 900 M LIH | GRN | PAPI (17 M) | Nil | Nil | 2500 M 60 M WHI / YEL LIH | RED | Nil | Nil |
| 23L | Nil | GRN THR identification flashing lights | Nil | Nil | Nil | 2500 M 60 M RED / WHI / YEL Nil | RED | Nil | Nil |
| 1 | ABN/IBN location, characteristics and hours of operation | Nil |
| 2 |
LDI location and LGT
Anemometer location and LGT |
Lighted wind direction indicator between TWR and RWY 05L / 23R. Wind direction indicators are in front of THR 05R and THR 23L. |
| 3 | TWY edge and centre line lighting | Nil |
| 4 | Secondary power supply /switch-over time | From public network, two independent feeds, diesel generator unit |
| 5 | Remarks | Nil |
| 1 | Coordinates TLOF or THR of FATO | Nil |
| 2 | TLOF and/or FATO elevation M/FT | Nil |
| 3 | TLOF and FATO area dimensions, surface, strength, marking | Nil |
| 4 | True BRG of FATO | Nil |
| 5 | Declared distances available | Nil |
| 6 | APP and FATO lighting | Nil |
| 7 | Remarks | Parking at Apron |
| 1 | Designation and lateral limits |
Debrecen TIZ1 and CTR�473908N 0214744E - 473338N 0215503E - 471843N 0213038E - 472433N 0212252E - 473908N 0214744E
Debrecen TIZ2 and CTA1�474127N 0215009E - 473102N 0220059E - 471020N 0214329E - 471154N 0212611E - 472402N 0211743E - 473243N 0213243E - 474127N 0215009E Debrecen TIZ3 and CTA2�474718N 0213722E - 474127N 0215009E - 473243N 0213243E - 474559N 0213339E - 474718N 0213722E |
| 2 | Vertical limits |
Debrecen TIZ1 and CTR 2 000 FT ALT / GND
Debrecen TIZ2 and CTA1 9 500 FT ALT / 2 000 FT ALT Debrecen TIZ3 and CTA2 9 500 FT ALT / 5 000 FT ALT |
| 3 | Airspace classification |
CTA1, CTA2 and CTR: D
TIZ1, TIZ2, TIZ3: F |
| 4 |
ATS unit call sign
Language(s) |
Debrecen Tower, Debrecen Info English, Hungarian |
| 5 | Transition altitude | 10 000 FT ALT |
| 6 | Hours of Applicability | According to NOTAM |
| 7 | Remarks |
ATC (CTA+CTR); AFIS (TIZ1 + TIZ2 + TIZ3)
See AD 2-LHDC AD-2.3
Air Traffic Advisory Service is not AVBL in the class F airspace LHDC TIZ1, TIZ2 and TIZ3. |
| Service designation | Call sign | Frequency | Logon Address | Hours of operation | Remarks |
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 |
| AFIS | Debrecen Info |
125.900 MHZ
Reserved: 132.400 MHZ |
Nil | As AD Administration | |
| TWR | Debrecen Tower |
125.900 MHZ
Reserved: 132.400 MHZ |
Nil | As AD Administration |
|
Type of aid
MAG VAR Type of supported OPS (for VOR/ILS/MLS, give declination) |
ID | Frequenc (ies) | Hours of operation | Position of transmitting antenna coordinates | Elevation of DME transmitting antenna | Remarks |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| L | EN | 383 KHZ | H24 | 473159.7N 0214117.0E | Nil | Nil |
| L | C | 326 KHZ | H24 | 472831.1N 0213535.2E | Nil | Nil |
| L | DC | 295 KHZ | H24 | 472724.2N 0213346.9E | Nil | Nil |
| ILS 05R (CAT I) | ||||||
| LLZ | DCN | 110.1 MHZ | H24 | 472953.4N 0213749.6E | Nil | |
| GP | 334.4 MHZ | H24 | 472902.6N 0213618.7E | Nil | GP angle: 3 | |
| PDME | DCN | CH 38X | H24 | 472902.6N 0213618.7E | 118.1 MM | DME shifted to THR 05R DME shift=320m (0.17NM) |
| MM | Dashes | 75 MHZ | H24 | 472831.1N 0213535.2E | Nil |
Noise abatement procedures are designed to avoid excessive aircraft noise in the areas adjacent to the airport and in the areas overflown during take off and landing.
Taking into consideration the prevailing weather conditions, runway 05R is used for landing when there is a tailwind component of not more than 5 KTS in the RWY direction. The displaced threshold on RWY 23L is also used for landing for noise abatement purposes.
For a departure from runway direction 05R, until 2000 FT AGL is reached a left turn is PROHIBITED. Flying with below 2 000 FT AGL over Debrecen is PROHIBITED except when following a take-off or landing procedure.
Visual circling in the NW sector of RWY 05R/23L is prohibited for speed category C and D aircraft.
Traffic Pattern:
JOZA
473533N 213326E
(Centre of Józsa village)
HOPI
472333N 214359E
(Centre of Hosszúpályi village)
EBES
472839N 0212916E
(N from Ebes village)
VFR flights approaching from uncontrolled airspace are required to enter CTR/TIZ1 via the designated reporting points, unless otherwise instructed.
The holding procedure has to be carried out on instruction of ATC/AFIS over the designated reporting points or other point identifiable by the pilot.
Flights departing from Debrecen Airport, shall request en route clearance before take-off from Debrecen Tower (Aerodrome Control Service). En route clearance will be delivered by Debrecen Tower in normal circumstances after giving start-up clearance on the parking stand.
Departing aircraft have to follow the procedures included in en route clearance given before take-off clearance.
Taxiing shall be carried out as instructed by Debrecen Tower.
The maximum speed for IFR operations is 250 KT (460 KM/h) IAS.
Debrecen Tower will use one of the SIDs published for IFR flights.
The departure procedures in use are based on those contained in ICAO Procedures for Air Navigation Services - Aircraft Operations (Doc 8168, OPS/611 (PANS OPS)).
IAPs are published on IACs in part AD 2-LHDC.
A visual approach will only be allowed or offered if the visibility is at least 5 KM and the cloud-base at least 1 500 FT (450 M).
ATC clearance for VFR flights within Debrecen CTAs and in CTR will be given on the following conditions:
Proceed as cleared. If no landing clearance has been received, hold over the designated entry point of the CTR at 2 000 FT (600 M) AMSL or below for 5 minutes and then make the landing on the designated landing area.
Do not take-off if communication failure is experienced before take-off. Return to parking position.
The IFR flights entering controlled airspace after departure shall obtain en route clearance before take-off.
In standard circumstances, en route clearance will be delivered by AFIS on the parking stand after start-up.
Departing aircraft have to follow the procedures included in the en route clearance given before take-off.
SIDs are published in part AD 2-LHDC-SIDs
The departure procedures in use are based on those contained in ICAO Procedures for Air Navigation Services - Aircraft Operations (Doc 8168, OPS/611 (PANS OPS)).
The IAPs are published on IACs in part AD 2-LHDC.
Contact shall be established with AFIS prior to reaching the area boundary;
AFIS provides information about aerodrome local traffic, the ”Traffic circuit” available, as well as conditions of approach and landing.
When instrument approach is in progress all VFR aircraft operating within the TIZ1, TIZ2 and TIZ3 will be advised to land or hold outside Debrecen TIZ1, TIZ2 and TIZ3.
| AD_2-LHDC-ADC | AERODROME CHART - ICAO |
| AD_2-LHDC-AOC-A | AERODROME OBSTACLE CHART - ICAO TYPE A |
| AD_2-LHDC-SID-05R | STANDARD INSTRUMENT DEPARTURE CHART |
| AD_2-LHDC-SID-23L | STANDARD INSTRUMENT DEPARTURE CHART |
| AD_2-LHDC-STAR | STANDARD ARRIVAL PROCEDURES - ICAO |
| AD_2-LHDC-ILS-05R | INSTRUMENT APPROACH CHART - ICAO |
| AD_2-LHDC-NDB-23L | INSTRUMENT APPROACH CHART - ICAO |
| AD_2-LHDC-RNAV-05R | INSTRUMENT APPROACH CHART - ICAO |
| AD_2-LHDC-RNAV-23L | INSTRUMENT APPROACH CHART - ICAO |
| AD_2-LHDC-VAC | VISUAL APPROACH CHART - ICAO |


cb-menu makes it use to create a floating menu based on material ui. The main objective is be a holder with animations, it doesn't have template style and the propose is use with any element.
Menu chip
cb-menu-chip can be use inside others components, without
cb-menu or
cb-menu-item dependency.
Menu chip example
Examples
Opening to bottom right
DARK ORANGECORN FLOWER BLUEDARK ORANGEMEDIUM SPRING GREEN
Opening to bottom center
DARK ORANGECORN FLOWER BLUEDARK ORANGEMEDIUM SPRING GREEN
Opening to bottom left
DARK ORANGECORN FLOWER BLUEDARK ORANGEMEDIUM SPRING GREEN
Opening to top right
DARK ORANGECORN FLOWER BLUEDARK ORANGEMEDIUM SPRING GREEN
Opening to top center
DARK ORANGECORN FLOWER BLUEDARK ORANGEMEDIUM SPRING GREEN
Opening to top left
DARK ORANGECORN FLOWER BLUEDARK ORANGEMEDIUM SPRING GREEN


Keras
Convert your Keras model here. HDFS .h5 file to TVM C++ dynamic library and parameters: .so, .json and .params) If model has custom components(layers, metrics), please provide them in second file.
Convert your Keras model here. HDFS .h5 file to TVM C++ dynamic library and parameters: .so, .json and .params) If model has custom components(layers, metrics), please provide them in second file.