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Python Distilled, © 2022
Chapters
1 Python Basics
When the interpreter starts (by running python
or python3
), a prompt for the REPL (Read-Eval-Print-Loop) appears.
In the REPL, the _
variable holds the result of the last operation. This feature only works interactively.
A variable is a name (also called identifier) that refers to a value. A value represents an object of some type.
A type hint may be attached to a name:
x: int = 42
Warning: Type hints are merely for code readability, and can be used by third-party code-checking tools. It does not change the execution of code in any way, nor does it prevent you from assigning the variable to a different type later.
An expression is a combination of primitives, names, and operators that produce a value.
An f-string allows expressions to be embedded inside it. For example: f'{year:>4d} {total:0.2f}'
1.14 Exceptions
A try-finally
block is appropriate whenever certain actions must be performed no matter what happens. For example, releasing a lock to avoid deadlock:
import threading lock = threading.Lock() ... lock.acquire() try: ... # statements finally: lock.release()
Alternatively, if the resource can be used using the with
statement, the cleanup will happen regardless of what happens inside the with
block.
with lock: ... # statements ...
1.15 Program Termination
To force exit, write: raise SystemExit()
. A message can be passed to SystemExit()
.
To ensure cleanup actions, register them with the atexit
module.
import atexit connection = open_connection("abc.com") def cleanup(): close_connection(connection) atexit.register(cleanup)
1.16 Objects and Classes
All values used in a program are objects.
Example of a class:
class Stack: def __init__(self): self._items = [] def push(self, item): self._items.append(item) def pop(self): return self._items.pop() def __repr__(self): return f'<{type(self).__name__} at 0x{id(self):x}, size={len(self)}>' def __len__(self): return len(self._items)
Using the class:
s = Stack() s.push('Dave') s.push(42) x = s.pop() y = s.pop()
Inheritance:
class MyStack(Stack): def swap(self): a = self.pop() b = self.pop() self.push(a) self.push(b)
Using super
:
class NumericStack(Stack): def push(self, item): if not isinstance(item, (int, float)): raise TypeError('Expected a number.') super().push(item)
1.20 Structuring an Application
A typical project may be organized as:
tutorial-project/ tutorial/ __init__.py readport.py pcost.py stack.py tests/ test_stack.py test_pcost.py examples/ sample.py doc/ tutorial.txt
1.21 Managing Third-Party Packages
Create a virtualenv:
python3 -m venv myproject_env # activate the virtualenv (depends on the operating system) python -m pip install somepackage
2 Operators, Expressions, and Data Manipulation
An expression represents a computation that evaluates to a concrete value.
The left-hand side of an assignment represents a location where a reference to an object is stored. It may be:
- An identifier such as
value
- An attribute of an object
- An index within a container
The assignment expression operator :=
can be used to perform the combined action of expression evaluation and assignment. It is generally used in if
and while
statements:
while (line := file.readline()): print(line)
Comparisons to None
should be done with is
and is not
(PEP 8).
Use None
as the default keyword argument for optional arguments. In the function, check if y is None
.
def f(x, items=None): if items is None: items = [] items.append(x) return items
2.8 Conditional expressions
A common pattern is assigning a value conditionally:
if a <= b: minvalue = a else: minvalue = b
This may be written as:
minvalue = a if a <= b else b
One way to copy nested lists is to make a copy of each sublist:
a = [3, 4, 5] c = [list(a) for _ in range(4)]


CURLOPT_QUOTE explained
NAME
CURLOPT_QUOTE - (S)FTP commands to run before transfer
SYNOPSIS
#include <curl/curl.h>
CURLcode curl_easy_setopt(CURL *handle, CURLOPT_QUOTE, struct curl_slist *cmds);
DESCRIPTION
Pass a pointer to a linked list of FTP or SFTP commands to pass to the server prior to your request. This will be done before any other commands are issued (even before the CWD command for FTP). The linked list should be a fully valid list of 'struct curl_slist' structs properly filled in with text strings. Use curl_slist_append to append strings (commands) to the list, and clear the entire list afterwards with curl_slist_free_all. Disable this operation again by setting a NULL to this option. When speaking to an FTP server, prefix the command with an asterisk (*) to make libcurl continue even if the command fails as by default libcurl will stop at first failure.
The set of valid FTP commands depends on the server (see RFC 959 for a list of mandatory commands).
The valid SFTP commands are:
The chgrp command sets the group ID of the file named by the file operand to the group ID specified by the group operand. The group operand is a decimal integer group ID.
The chmod command modifies the file mode bits of the specified file. The mode operand is an octal integer mode number.
The chown command sets the owner of the file named by the file operand to the user ID specified by the user operand. The user operand is a decimal integer user ID.
The ln and symlink commands create a symbolic link at the target_file location pointing to the source_file location.
The mkdir command creates the directory named by the directory_name operand.
The pwd command returns the absolute pathname of the current working directory.
The rename command renames the file or directory named by the source operand to the destination path named by the target operand.
The rm command removes the file specified by the file operand.
The rmdir command removes the directory entry specified by the directory operand, provided it is empty.
The statvfs command returns statistics on the file system in which specified file resides. (Added in 7.49.0)
symlink source_file target_file
See ln.
DEFAULT
PROTOCOLS
EXAMPLE
struct curl_slist *h = NULL; h = curl_slist_append(h, "RNFR source-name"); h = curl_slist_append(h, "RNTO new-name"); curl = curl_easy_init(); if(curl) { curl_easy_setopt(curl, CURLOPT_URL, "ftp://example.com/foo.bin"); /* pass in the FTP commands to run before the transfer */ curl_easy_setopt(curl, CURLOPT_QUOTE, headerlist); ret = curl_easy_perform(curl); curl_easy_cleanup(curl); }
AVAILABILITY
SFTP support added in 7.16.3. *-prefix for SFTP added in 7.24.0
RETURN VALUE
Returns CURLE_OK
SEE ALSO
CURLOPT_POSTQUOTE, CURLOPT_PREQUOTE
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--- layout: post status: publish published: true title: Anakrino author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 884 wordpress_url: http://winterdom.com/2003/04/anakrino date: '2003-04-19 07:44:43 +0000' date_gmt: '2003-04-19 07:44:43 +0000' categories: - ".NET" tags: [] comments: - id: 603 author: Neil Cowburn [MVP] author_email<EMAIL_ADDRESS>author_url: '' date: '2003-04-22 08:30:33 +0000' date_gmt: '2003-04-22 08:30:33 +0000' content: |
I have the very same experience with Windows Server 2003.
FWIW, I've managed to replicate the same error msg I get in Win03 in Win XP... twice... using differing methods.
The first method consisted of manually adding an entry to the assemblies node in the anakrino.xml file. The entry pointed to an assembly Anakrino would have no idea where it was kept. Saving the file and launching Anakrino caused it to crash immediately.
Secondly, I've found that the order in which the .NET Frameworks are installed seems to be paramount. After blasting my Win XP system a couple of months ago, I installed the Everett beta first (with dotnetfx v1.1), followed by VS .NET 2002 (with dotnetfx v1.0). Anakrino always crashed whenever I tried to launch it. I fixed this solution by uninstalling both frameworks and installing them in numerical order (v1.0 and then v1.1). After installation, Anakrino worked without a hitch.
Interestingly, I blasted my Win XP system again this morning. I've just installed VS .NET 2003 RTM with dotnetfx v1.1. There is no dotnetfx v1.0 on this system. Anakrino crashes.
I wonder what Jay makes of this...
---Jeff Saurik [1] talks about Anakrino release 10 and it working with .NET v1.1. I can say it sure works with v1.1; that's not the problem, as I ran it for a long time on my WinXP box with both VS.NET 2002 and the Everett betas and RCs without a hitch.
What does happen is that in my experience, it will always crash on Windows Server 2003. I've never managed to run it here, fwiw.
Update: for what it's worth, exemplar still works when run from the command line directly. No nice GUI, but it's better than nothing :)
[1] Great all-around guy; met him a couple years back at a Microsoft


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Epidemic Simulator
Hi! This is a simple, cohort-based epidemic simulation. More details below.
*changing this value will cause a restart of the simulation.
Day Total sick Total healthy Total cured Total deceased
Sick People
Healthy People
New Infections
Cured People
Deceased People
Age Cohorts: Sick People
Age Cohorts: Deceased People
Relative Values
Cohort Mortality Rate
Age Cohorts
Simulation Details
The simulation starts with a given pool of infected people, which fall
sick for a given amount of days and have a given chance of dying during
their sickness, with a mortality that depends on their age
(see above). A sick person will infect a given number of other people
while sick, the total number of infections over the
course of the sickness is given as the infection rate
(you can tune this parameter). Only healthy people can be infected,
i.e. once someone is cured we assume that person cannot be infected again.
Also, we assume that the infection rate is based on the number of interactions
with other people from the population, hence when the number of healthy
vs. cured and infected people decreases in a given age cohort, the chance of
infecting someone from that cohort also decreases.
We use the age distribution of the German population to evaluate
the effect of the infection on different age cohorts.
By varying the infection rate you can get a feeling for how
different rates affect the number of deceased people, the
speed of the spreading of the infection and the maximum number
of infected people.
Feel free to suggest improvements and contribute to this project on Github.
This is not a realistic simulation, it only provides a
(hopefully) intuitive way
to visualize the relationship between infection rate and the
spreading of the infection through the population.


--- layout: home author_profile: true title: Who am I? excerpt: System administrator,Web developer,App developer,And a bit of a designer. header: overlay_image: /assets/images/header.jpg ---


You must log into the database as Bob through trusted context 'trCtx' before proceeding with this page.
Bob is an employee of customer service department. Check if Bob can access 'credit_card' table data using trusted connection.
Bob acquires SELECT privilege through the 'custService' role.
The query results in the right pane show that user Bob is able to execute SELECT statement successfully. ALL other statements on the right pane will fail as Bob does not have privileges.


skyline.ionosphere package¶
Submodules¶
skyline.ionosphere.agent module¶
skyline.ionosphere.common_functions module¶
-
get_metrics_db_object
(base_name)[source]¶ Returns the data of a metric from metrics table as an object and populates memcached with a dict of the object
- Parameters
base_name (str) – the metric base_name
- Returns
metrics_db_object
- Return type
object
-
get_calculated_features
(calculated_feature_file)[source]¶ Called by
spin_process
to create a list of feature_names and their calc_values.- Parameters
calculated_feature_file (str) – the full path and file name of the features.transposed.csv file
- Returns
calculated_features
- Return type
list
-
minmax_scale_check
(fp_id_metric_ts, anomalous_timeseries, range_tolerance, range_tolerance_percentage, fp_id, base_name, metric_timestamp, features_percentage_diff)[source]¶ Called by nothing yet. Used to run a minmax scaling check and determine if the features_sum of the 2 minmax scaled timeseries match.
- Parameters
fp_id_metric_ts –
anomalous_timeseries –
range_tolerance –
:param range_tolerance_percentage :param fp_id: :param base_name: :param metric_timestamp: :param features_percentage_diff: :type fp_id_metric_ts: int :return: (minmax_not_anomalous, minmax_fp_features_sum, minmax_fp_features_count, minmax_anomalous_features_sum, minmax_anomalous_features_count) :rtype: tuple
skyline.ionosphere.echo module¶
skyline.ionosphere.ionosphere module¶
-
class
Ionosphere
(parent_pid)[source]¶ Bases:
threading.Thread
The Ionosphere class which controls the ionosphere thread and spawned processes.
-
mysql_insert
(insert)[source]¶ Insert data into mysql table
- Parameters
insert (str) – the insert string
- Returns
int
- Return type
int or boolean
Example usage:
query = 'insert into host (host) VALUES ('this_host')' result = self.mysql_insert(query)
Note
- If the MySQL query fails a boolean will be returned not a tuple
False
None
-
manage_ionosphere_unique_metrics
()[source]¶ Create a Redis set of all Ionosphere enabled metrics.
Manage the ionosphere.untrainable_metrics set, removing items when they ‘expire’
- Parameters
i – python process id
- Returns
returns True
-
new_load_metric_vars
(metric_vars_file)[source]¶ Load the metric variables for a check from a metric check variables file
- Parameters
metric_vars_file (str) – the path and filename to the metric variables files
- Returns
the metric_vars list or
False
- Return type
list
-
spawn_learn_process
(i, timestamp)[source]¶ Spawn a process to learn.
This is used for Ionosphere to learn if anomalous metrics remain anomalous over time, as the resolution decreases. It follows the multiprocessing methodology the was introduced in Analyzer and Mirage in the context of the process objects being cleared down and the learn processes cannot create memory leaks as the process always terminates or is terminated this prevents any memory leaks in the parent.
-
process_ionosphere_echo
(i, metric_check_file)[source]¶ Spawn a process_ionosphere_echo check to create features profiles at settings.FULL_DURATION for Mirage metrics
- Parameters
i (object) – python process id
metric_check_file (str) – full path to the metric check file
- Returns
boolean
- Return type
boolean
-
spin_process
(i, metric_check_file, ionosphere_busy)[source]¶ Assign an anomalous metric to check against features profiles.
- Parameters
i (object) – python process id
metric_check_file (str) – full path to the metric check file
ionosphere_busy (boolen) – whether to Ionosphere manage and alternate between normal Ionosphere and echo analysis
- Returns
int
- Return type
int or boolean
-
skyline.ionosphere.layers module¶
-
run_layer_algorithms
(base_name, layers_id, timeseries, layers_count, layers_checked)[source]¶ Called by
spin_process
to evaluate anomalies against a custom layers boundary algorithm.- Parameters
metric (str) – the metric base_name
layers_id – the layer id
timeseries (list) – the time series list
layers_count (int) – the number of layers for the metric
layers_checked (int) – the number of layers that have been checked
- Returns
True or False
- Return type
boolean
skyline.ionosphere.learn module¶
-
learn_load_metric_vars
(metric_vars_file)[source]¶ Load the metric variables for a check from a metric check variables file
- Parameters
metric_vars_file (str) – the path and filename to the metric variables files
- Returns
the metric_vars list or
False
- Return type
list
-
get_learn_json
(learn_json_file, base_name, use_full_duration, metric_timestamp, learn_full_duration_days)[source]¶ Called by
learn()
to surface a use_full_duration timeseries for the metric from Graphite and save as json.
-
get_metric_from_metrics
(base_name, engine)[source]¶ Called by
learn()
and returns the metric id and metric db object- Parameters
timestamp (int) – timestamp at which learn was called
- Returns
tuple
- Return type
(int, object)
-
get_ionosphere_fp_ids
(base_name, metrics_id, engine)[source]¶ Called by
learn()
and returns the fp_ids list
-
remove_work_list_from_redis_set
(learn_metric_list)[source]¶ Called by
learn()
to remove a work item from the Redis set
-
ionosphere_learn
(timestamp)[source]¶ Called by
spawn_learn_process
to re-evaluate anomalies and such, like creating learning features profiles, when a human makes a features profile and the automated creation of learnt features profiles and learn features profiles.- Parameters
timestamp (int) – timestamp at which learn was called
- Returns
True or False
- Return type
boolean
learn uses a Redis set as a work queue. This set is populated with the learn jobs, jobs being pieces of work that learn needs to do. The learn Redis work set list item has the following elements:
[str(deadline_type), str(job_type), int(metric_timestamp), str(base_name), int(parent_id), int(generation)]
Each job_type has a deadline_time that learn calculates from the job_type
ionosphere.learn.work deadlines - deadline_types
Hard - missing a deadline is a total system failure.
Firm - infrequent deadline misses are tolerable, but may degrade the system’s quality of service. The usefulness of a result is zero after its deadline.
Soft - the usefulness of a result degrades after its deadline, thereby degrading the system’s quality of service.
references:
Brian L. Troutwine @bltroutwine - seminal Belgium 2014 devopsdays presentation - Automation with Humans in Mind: Making Complex Systems Predictable, Reliable and Humane - https://legacy.devopsdays.org/events/2014-belgium/proposals/automation-with-humans-in-mind/ - video - http://www.ustream.tv/recorded/54703629
https://en.wikipedia.org/wiki/Real-time_computing#Criteria_for_real-time_computing
learn work types:
learn_fp_human - Create a features profile for the human created features profile after
LEARN_VALID_TIMESERIES_OLDER_THAN_SECONDS
atLEARN_FULL_DURATION_DAYS
whatever those maybe it, will find out. Copy the training_data dir tosettings.IONOSPHERE_LEARN_FOLDER
and the metric_check_file is rewritten with the determined (LEARN_FULL_DURATION_DAYS
* 86400) as the new full_duration. Then this features profile is created as a generation 0 features profile at the learn use_full_duration seconds. This jobs is only ever add via the Ionosphere UI ~skyline.ionosphere_functions.create_features_profile TODO: now_timestamp/ where the now_timestamp relaces the metric_timestamp context and as the metric_check_file has been replaced and this features profile was created later. This prevents the pollution of metric_timestamp training data features profile. This is a new features profile. Is it generation 0 or generation 1? The more I think I am edging to generation 1, however as long as the first automated full_duration profiles that they create are generation 1 as well… hmm the generation game… TDB - deadline: ‘Soft’learn_fp_automatic - Create a features profile for the automatic created features profile as per the learn_fp_human above. - deadline: ‘Soft’
learn_fp_generation - Create a features profile for the Ionosphere training_data metric after
LEARN_VALID_TIMESERIES_OLDER_THAN_SECONDS
atLEARN_FULL_DURATION_DAYS
have passed and compare to other knownLEARN_FULL_DURATION_DAYS
features profiles. If it matches any, then the metric training_data is added as features profile incremented by 1 generation ifMAX_GENERATIONS
is not breached. - deadline: ‘Soft’learn_fp_learnt - Create a features profile for the Ionosphere learnt features profile after
LEARN_VALID_TIMESERIES_OLDER_THAN_SECONDS
atLEARN_FULL_DURATION_DAYS
whatever those maybe it, will find out. Copies the training_data dir tosettings.IONOSPHERE_LEARN_FOLDER
/now_timestamp/ where the now_timestamp relaces the metric_timestamp context and the metric_check_file has the full_duration replace by the relevant use_full_duration. Then this features profile is created as an incremented generation features profile at use_full_duration via learn. - deadline: ‘Soft’


{% extends "base.html" %} {% block content %}
Expensely is a tool for tracking expenses between folks.
Add an expense »


|
| | ADGenICam (const char *portName, size_t maxMemory, int priority, int stackSize) |
| | Constructor for the ADGenICam class. More...
|
| |
| virtual asynStatus | writeInt32 (asynUser *pasynUser, epicsInt32 value) |
| | Sets an int32 parameter. More...
|
| |
| virtual asynStatus | writeInt64 (asynUser *pasynUser, epicsInt64 value) |
| | Sets an int64 parameter. More...
|
| |
| virtual asynStatus | writeFloat64 (asynUser *pasynUser, epicsFloat64 value) |
| | Sets an float64 parameter. More...
|
| |
| virtual asynStatus | readEnum (asynUser *pasynUser, char *strings[], int values[], int severities[], size_t nElements, size_t *nIn) |
| |
| void | report (FILE *fp, int details) |
| | Print out a report; calls ADDriver::report to get base class report as well. More...
|
| |
| virtual asynStatus | drvUserCreate (asynUser *pasynUser, const char *drvInfo, const char **pptypeName, size_t *psize) |
| |
| virtual asynStatus | readStatus () |
| |
| virtual asynStatus | setImageParams () |
| |
| virtual asynStatus | pauseAcquisition () |
| |
| virtual asynStatus | resumeAcquisition () |
| |
| virtual void | decompressMono12p (int numPixels, bool leftShift, epicsUInt8 *input, epicsUInt16 *output) |
| | Decompresses Mono12p to epicsUInt16. More...
|
| |
| virtual void | decompressMono12Packed (int numPixels, bool leftShift, epicsUInt8 *input, epicsUInt16 *output) |
| | Decompresses Mono12Packed to epicsUInt16. More...
|
| |
| virtual GenICamFeature * | createFeature (GenICamFeatureSet *set, std::string const &asynName, asynParamType asynType, int asynIndex, std::string const &featureName, GCFeatureType_t featureType)=0 |
| |
| virtual asynStatus | addADDriverFeatures () |
| |
| virtual asynStatus | startCapture ()=0 |
| |
| virtual asynStatus | stopCapture ()=0 |
| |
| | ADDriver (const char *portName, int maxAddr, int numParams, int maxBuffers, size_t maxMemory, int interfaceMask, int interruptMask, int asynFlags, int autoConnect, int priority, int stackSize) |
| | All of the arguments are simply passed to the constructor for the asynNDArrayDriver base class, except numParams. More...
|
| |
| virtual asynStatus | connect (asynUser *pasynUser) |
| | Connects driver to device; This method is called when the driver's pasynCommon->connect() function is called. More...
|
| |
| virtual void | setShutter (int open) |
| | Set the shutter position. More...
|
| |
| | asynNDArrayDriver (const char *portName, int maxAddr, int maxBuffers, size_t maxMemory, int interfaceMask, int interruptMask, int asynFlags, int autoConnect, int priority, int stackSize) |
| | This is the constructor for the asynNDArrayDriver class. More...
|
| |
| virtual | ~asynNDArrayDriver () |
| |
| virtual asynStatus | writeOctet (asynUser *pasynUser, const char *value, size_t maxChars, size_t *nActual) |
| | Called when asyn clients call pasynOctet->write(). More...
|
| |
| virtual asynStatus | readGenericPointer (asynUser *pasynUser, void *genericPointer) |
| | This method copies an NDArray object from the asynNDArrayDriver to an NDArray pointer passed in by the caller. More...
|
| |
| virtual asynStatus | writeGenericPointer (asynUser *pasynUser, void *genericPointer) |
| | This method currently does nothing, but it should be implemented in this base class. More...
|
| |
| virtual asynStatus | setIntegerParam (int index, int value) |
| | Sets the value for an integer in the parameter library. More...
|
| |
| virtual asynStatus | setIntegerParam (int list, int index, int value) |
| | Sets the value for an integer in the parameter library. More...
|
| |
| virtual asynStatus | readInt32 (asynUser *pasynUser, epicsInt32 *value) |
| |
| virtual asynStatus | readFloat64 (asynUser *pasynUser, epicsFloat64 *value) |
| |
| virtual asynStatus | createFilePath (const char *path, int pathDepth) |
| | Function to create a directory path for a file. More...
|
| |
| virtual asynStatus | checkPath () |
| | Checks whether the directory specified NDFilePath parameter exists. More...
|
| |
| virtual bool | checkPath (std::string &filePath) |
| | Checks whether the directory specified exists. More...
|
| |
| virtual asynStatus | createFileName (int maxChars, char *fullFileName) |
| | Build a file name from component parts. More...
|
| |
| virtual asynStatus | createFileName (int maxChars, char *filePath, char *fileName) |
| | Build a file name from component parts. More...
|
| |
| virtual asynStatus | readNDAttributesFile () |
| | Create this driver's NDAttributeList (pAttributeList) by reading an XML file This clears any existing attributes from this drivers' NDAttributeList and then creates a new list based on the XML file. More...
|
| |
| virtual asynStatus | getAttributes (NDAttributeList *pAttributeList) |
| | Get the current values of attributes from this driver and appends them to an output attribute list. More...
|
| |
| asynStatus | incrementQueuedArrayCount () |
| |
| asynStatus | decrementQueuedArrayCount () |
| |
| int | getQueuedArrayCount () |
| |
| void | updateQueuedArrayCount () |
| |


Have a look at "apg-api.html" & "apg-api.js" to see how it is used.
Click on "generate" to generate a grammar object from the SABNF grammar.
(Errors are not reported in detail.)
Then click the "parse" button to use the generated grammar object to parse the input string.
SABNF grammar:
SABNF input string:
output:


Located in /PEAR/PackageProjector/Packager.php (line 37)
PEAR_Packager | --PEAR_PackageProjector_Packager
Logging method.
Documentation generated on Sun, 24 May 2009 15:10:00 +0900 by phpDocumentor 1.4.2


Neil Harris tips Cardiff City 'maverick' to shine under him but questions team mindset after sloppy defending at Charlton
The Bluebirds came back from 2-0 down to earn a draw against Charlton at The Valley
by Glen WilliamsNeil Harris believes he can coax the very best out of Lee Tomlin after the playmaker produced another touch of magic to snatch a draw for Cardiff City away at Charlton Athletic.
The Bluebirds showed great character to come back from two goals down to earn a point at The Valley and it was Tomlin's introduction from the bench which proved a masterstroke.
Fans have been calling for Tomlin to have greater and more consistent opportunities this season and once again he put his hand up for selection against Stoke City on Tuesday.
Harris said that Tomlin 'showed his class' when he rifled home City's second of the afternoon and when asked if he is the type of player who could really shine under his tutelage, he replied: "Without a shadow of a doubt.
"I like goalscorers and players who have got some ability.
"Every player has to work hard in my changing room and have to have discipline, but you have to have some mavericks and match-winners.
"That's Tomlin and the wide players, like Josh Murphy, who will have his chances, too.
"Nathaniel Mendez-Laing, Junior Hoilett, they are your mavericks, you need goals from them."
Harris, in his first game since taking charge of the Bluebirds, was, however, disappointed with his team at the other end.
City were carved open by two counter attacks in the first half with ease, much to the displeasure of the travelling Cardiff City supporters.
The manager shared their disappointment after the match, questioning whether his side's mindset needs to change in order to stop leaking goals on the road.
"I wouldn't say concerned," he said, "I would say I was disappointed with the goals today.
"We defend as a team and attack as a team. So we'll have to look at the mindset of the group, why haven't we been keeping clean sheets over the course of the season?
"Is it an individual mindset or a group mindset?
"We gave the ball away and have been countered on and not defended properly, that's the mindset that has got to change straight away."
It is certainly something that needs to be put right quickly if the Bluebirds are to begin a charge up the table anytime soon.


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/Cotactic.html ---
Is there a tactic which can prove the dual of a categorical statement? If not, does anybody know prior work (e.g. did someone do this in Coq)?
No, not yet. We discussed this a long time ago, but there were hardly any examples to apply it to then.
I'm thinking of doing some abelian category stuff, and this feels like it would be important for quality of life there. I might look into implementing it but it seems tricky
It probably is tricky. Are you familiar with to_additive
? It may make sense to do something similar to that
One limit of to_additive
is that, if I understand correctly, there's no capability for changing the order of inputs. In particular moving from x^n
to n \bub x
can't be done automatically. This will be much worse in the limit/colimits situation so a baby problem would be to make to_additive
handle powers.
Last updated: Apr 24 2020 at 19:02 UTC


Copyright © 2011 Mark R. Greenstreet
Authors: Mark R. Greenstreet (mrg@cs.ubc.ca).
accum
function for accumulating samples,
and functions for computing the mean and standard deviation from
such accumulations. I might add more statistical and data analysis
functions at a later date, but I don't have any plans to do so
immediately.
| accum/1 | Create a new accumulator using Data
If Data is a number, an accumulator is created with
that number as it's one sample. |
| accum/2 | Add the sample from Data to the accumulator
Acc . |
| mean/1 | Compute the mean of the samples in X . |
| std/1 | Equivalent to std(X, sample). |
| std/2 | Compute the standard deviation of the samples in X . |
accum(T::Data) -> accumulator()
Create a new accumulator using Data
If Data
is a number, an accumulator is created with
that number as it's one sample. If Data
is a list
of numbers, then an accumulator is created with all of the samples
from the list.
accum(T::Data, Acc) -> accumulator()
Add the sample from Data
to the accumulator
Acc
.
Return the resulting accumulator.
mean(Stat_acc::X) -> number() | undefined
Compute the mean of the samples in X
.
If X
an accumulator, then the mean is computed for
the samples that were used to generate X
.
If X
is a number or a list of numbers, it is used
to create an accumulator (see accum/1
) and then the
mean is calculated.
If there are no samples in the accumulator of the list, then the
atom 'undefined'
is returned.
std(X) -> number() | undefined
Equivalent to std(X, sample).
std(Stat_acc::X, P_or_S::SampleOrPopulation) -> number() | undefined
Compute the standard deviation of the samples in X
.
If X
an accumulator, then the standard deviation is
computed for the samples that were used to generate X
.
If X
is a number or a list of numbers, it is used
to create an accumulator (see accum/1
) and then the
standard deviation is calculated.
The parameter SampleOrPopulation
specifies whether
to compute a
sample
or population standard deviation. If you're not sure which
you want, you probably want 'sample'
.
For a 'sample'
standard-deviation, there must be at
least two samples, or the result is the atom 'undefined'.
For a 'population'
standard-deviation, there must be at
least one sample, or the result is the atom 'undefined'.
Generated by EDoc, Jan 25 2017, 12:13:15.


Program
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Minima maxime quam architecto quo inventore harum ex magni, dicta impedit.
What?
What?
{{site.title}} is a program for ......
How?
Mentors and mentees
Bring together mentors and mentees. Evaluate and cluster mentees for the course.
When?
May - Aug
Three months practical course


Chapter 3 Getting Started
3.1 Starting SPSS
We want to appreciate different users are using different operating systems. The procedure to startup the SPSS may differ slightly. This part uses screenshots from the window version of SPSS. Other window versions such as Unix and/or MacOS will have the same functionality, but could appear differently than what is being depiced herein.
When you start the SPSS, you will be presented with the above dialog box. This depends with the system administrator options that is working with your version program. On the dialog box, click Type in data and OK. Or just (Close). This will present a blank data window.
The provided blank window have basic interface for SPSS.
## Entering Data
Before you get to know the operability of SPSS, you must understnd the key to success with SPSS. You should understand how SPSS stores data and uses the data. In order to illustrate the hasics data entry with SPSS, we will use an example 2.1.1
Example 2.1.1
A survey was given to different students from a college who were studying different courses (Enginnering, Economics, Computer Science, and Agriculture). The students were asked whether or not they revise extensively in the morning or late in the evening. This survey also asked for their first semester grade in the class (100% being the highest grade possible for all the courses). The response sheets fro the first 5 students are presented below
The main goal is to enter the data from the five college students into SPSS for the analysis. We first need to determine the variables that will be entered. A variable is any informtion that will keep on varying among participants. The variables we need from example 2.1.1 are:
StudetID:
Course:
Revion Time:
Grade %
Recall the blank SPSS window, there is two button (Data View and Variable View) on the bottom-left of the window. In the data view, we have columns and rows, the columns represents variables, and rows represent participants. Since the variables we have defined from example 2.1.1 have only four variables, therefore, we will create a data file with four columns, and five rows, each representing an entry from individual respondent/student.
3.2 Defining Variables
Before we can enter any data, we must first enter some basic information about each variable into SPSS. For instance, variables must first be given names that
– begin with a letter, and – do not contain a space.
Thus, the variable name “Q7” is acceptable, while the variable name “7Q” is not. Similarly, the variable name “PRE_TEST” is acceptable, but the variable name “PRE TEST” is not. Capitalization does not matter, but variable names are capitalized in this text to make it clear when we are referring to a variable name, even if the variable name is not necessarily capitalized in screenshots.
To define a variable, click on the Variable View tab at the bottom of the main screen (refer to the SPSS Blank Window). This will show you the Variable View window. To return to the Data View window, click on the Data View tab.
From the Variable View screen, SPSS allows you to create and edit all of the variables in your data file. Each column represents some property of a variable, and each row represents a variable. All variables must be given a name. To do that, click on the first empty cell in the Name column and type a valid SPSS variable name. The program will then fill in default values for most of the other properties.
One useful function of SPSS is the ability to define variable and value labels. Variable labels allow you to associate a description with each variable.
Value labels allow you to associate a description with each value of a variable. For instance, for most procedures, SPSS requires numerical values. Thus, for data such as the course (i.e., Engineering, Economic, Computer Science, and Agriculture), we need to first code the values as numbers. We can assign the number 1 to Engineering, 2 to Economic, 3 to Computer Science, and 4 to Agriculture. To help us keep track of the numbers we have assigned to the values, we use value labels.
To assign value labels, click in the cell you want to assign values to in the Values column (in this case, for Variable 2 i.e., the course). This will bring up a small gray button (shown below). Click on that button to bring up the Value Labels dialog box. The revision time should also be coded as 1 = Morning, and 2 = Evening.
When you enter a value label, you must click Add after each entry. This will move the value and its associated label into the bottom section of the window. When all labels have been added, click OK to return to the Variable View window.
In addition to naming and labeling the variable, you have the option of defining the variable type. To do so, simply click on the Type, Width, or Decimals columns in the Variable View window. The default value is a numeric field that is eight digits wide with two decimal places displayed. If your data are more than eight digits to the left of the decimal place, they will be displayed in scientific notation (e.g., the number 2,000,000,000 will be displayed as 2.00E+09). SPSS maintains accuracy beyond two decimal places, but all output will be rounded to two decimal places unless otherwise indicated in the Decimals column.
There are several other options available in this screen, which are beyond the scope of this text. In our example, we will be using numeric variables with all the default values.
After you have coded the book, click on the Data View tab to open the data-entry screen. Enter data horizontally, beginning with the first student’s ID number. Enter the code for each variable in the appropriate column. To enter the GRADE variable value, enter the student’s class/semester grade.
The previous data window can be changed to look like the screenshot on the next page by clicking on the Value Labels icon (see below). In this case, the cells display value labels rather than the corresponding codes. If data are entered in this mode, it is not necessary to enter codes, as clicking the button that appears in each cell as the cell is selected will present a drop-down list of the predefined labels. You may use whichever method you prefer.
3.3 Loading and Saving Data Files
Once you have entered your data, you will need to save it with a unique name so that you can retrieve it when necessary for later use.
Loading and saving SPSS data files works in the same way as most Windows-based software. Under the File menu, there are Open, Save, and Save As commands. SPSS data files have a “.sav” extension, which is added by default to the end of the filename (that is, do not type “.sav” after the filename; SPSS will add it automatically). This tells Windows that the file is an SPSS data file. Other SPSS extensions include “.spv” for saved output files and “.sps” for saved syntax files.
When you save your data file (by clicking File, then clicking Save or Save As to specify a unique name), pay special attention to where you save it. You will probably want to save your data on a removable USB drive so that you can take the file with you.
3.4 Load Your Data
When you load your data (by clicking File, then clicking Open, then Data, or by clicking the open file folder icon), you get a similar window. This window lists all files with the “.sav” extension. If you have trouble locating your saved file, make sure you are looking in the right directory.
3.5 Running Your First Analysis
Any time you open a data window, you can run any of the analyses available. To get started, we will calculate the students’ average grade. (With only five students, you can easily check your answer by hand, but imagine a data file with 50,000 student records.)
The majority of the available statistical tests are under the Analyze menu. This menu displays all the options available for your version of the SPSS program (the menus in this book were created with SPSS Statistics Version 25). Other versions may have slightly different sets of options.
To calculate a mean (average), we are asking the computer to summarize our dataset. Therefore, we run the command by clicking Analyze, then Descriptive Statistics, then Descriptives.
This brings up the Descriptives dialog box. Note that the left side of the box contains a list of all the variables in our data file. On the right is an area labeled Variable(s), where we can specify the variables we would like to use in this particular analysis.
We want to compute the mean for the variable called GRADE. Thus, we need to select the variable name in the left window (by clicking on it). To transfer it to the right window, click on the right arrow between the two windows. The arrow always points to the window opposite the highlighted item and can be used to transfer selected variables in either direction. Note that double-clicking on the variable name will also transfer the variable to the opposite window. Standard Windows conventions of “Shift” clicking or “Ctrl” clicking to select multiple variables can be used as well. Note: Some configurations of SPSS show the variable names, and others show the variable labels (if any). This can be changed under Edit → Options → General.
When we click on the OK button, the analysis will be conducted, and we will be ready to examine our output.
3.6 Examining and Printing Output Files
After an analysis is performed, the output is placed in the output window, and the output window becomes the active window. If this is the first analysis you have conducted since starting SPSS, then a new output window will be created. If you have run previous analyses and saved them, your output is added to the end of your previous output.
To switch back and forth between the data window and the output window, select the desired window from the Window menu bar. Alternately, you can select the window using the taskbar at the bottom of the screen.
The output window is split into two sections. The left section is an outline of the output (SPSS refers to this as the outline view). The right section is the output itself.
The section on the left of the output window provides an outline of the entire output window. All of the analyses are listed in the order in which they were conducted. Note that this outline can be used to quickly locate a section of the output. Simply click on the section you would like to see, and the right window will jump to the appropriate place.
Clicking on a statistical procedure also selects all of the output for that command. By pressing the Delete key, that output can be deleted from the output window. This is a quick way to be sure that the output window contains only the desired output. Output can also be selected and pasted into a word processor or spreadsheet by clicking Edit, then Copy to copy the output. You can then switch to your word processor and click Edit, then Paste.
To print your output, simply click File, then Print, or click on the printer icon on the toolbar. You will have the option of printing all of your output or just the currently selected section. Be careful when printing! Each time you run a command, the output is added to the end of your previous output. Thus, you could be printing a very large output file containing information you may not want or need.
One way to ensure that your output window contains only the results of the current command is to create a new output window just before running the command. To do this, click File, then New, then Output. All your subsequent commands will go into your new output window.
You can also save your output files as SPSS format files (.spv extension). Note that SPSS saves whatever window you have open.  If you are on an output window it will save your output.
Practice Exercise
Load the sample data file you created earlier (SAMPLE.sav). Run the Descriptives command for the variable GRADE, and print the output. Next, select the data window and print it.
3.7 Modifying Data Files
Once you have created a data file, it is really quite simple to add additional cases (rows/participants) or additional variables (columns).
To add the data, simply place two additional rows in the Data View window (after loading your sample data). Notice that as new participants are added, the row numbers become bold. When done, the screen should look like the screenshot above.
New variables can also be added. For example, if the first three participants were given special training on time management, and the four new participants were not, the data file can be changed to reflect this additional information. The new variable could be called TRAINING (whether or not the participant received training), and it would be coded so that 0 = No and 1 = Yes. Thus, the first three participants would be assigned a “1” and the last four participants a “0.” To do this, switch to the Variable View window, then add the TRAINING variable to the bottom of the list. Then switch back to the Data View window to update the data.
Adding data and variables are logical extensions of the procedures we used to originally create the data file. Save this new data file. We will be using it again later in this book.
Practice Exercise
Follow the previous example (where TRAINING is the new variable). Make the modifications to your SAMPLE.sav data file and save it. Good!


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