LSEEK
Section: Linux Programmer's Manual (2)
Updated: 2014-06-13
Index
Return to Main Contents
NAME
lseek - reposition read/write file offset
SYNOPSIS
#include <sys/types.h>
#include <unistd.h>
off_t lseek(int fd, off_t offset, int whence);
DESCRIPTION
The
lseek()
function repositions the offset of the open file associated with the
file descriptor
fd
to the argument
offset
according to the directive
whence
as follows:
- SEEK_SET
-
The offset is set to
offset
bytes.
- SEEK_CUR
-
The offset is set to its current location plus
offset
bytes.
- SEEK_END
-
The offset is set to the size of the file plus
offset
bytes.
The
lseek()
function allows the file offset to be set beyond the end
of the file (but this does not change the size of the file).
If data is later written at this point, subsequent reads of the data
in the gap (a "hole") return null bytes ('\0') until
data is actually written into the gap.
Seeking file data and holes
Since version 3.1, Linux supports the following additional values for
whence:
- SEEK_DATA
-
Adjust the file offset to the next location
in the file greater than or equal to
offset
containing data.
If
offset
points to data,
then the file offset is set to
offset.
- SEEK_HOLE
-
Adjust the file offset to the next hole in the file
greater than or equal to
offset.
If
offset
points into the middle of a hole,
then the file offset is set to
offset.
If there is no hole past
offset,
then the file offset is adjusted to the end of the file
(i.e., there is an implicit hole at the end of any file).
In both of the above cases,
lseek()
fails if
offset
points past the end of the file.
These operations allow applications to map holes in a sparsely
allocated file.
This can be useful for applications such as file backup tools,
which can save space when creating backups and preserve holes,
if they have a mechanism for discovering holes.
For the purposes of these operations, a hole is a sequence of zeros that
(normally) has not been allocated in the underlying file storage.
However, a filesystem is not obliged to report holes,
so these operations are not a guaranteed mechanism for
mapping the storage space actually allocated to a file.
(Furthermore, a sequence of zeros that actually has been written
to the underlying storage may not be reported as a hole.)
In the simplest implementation,
a filesystem can support the operations by making
SEEK_HOLE
always return the offset of the end of the file,
and making
SEEK_DATA
always return
offset
(i.e., even if the location referred to by
offset
is a hole,
it can be considered to consist of data that is a sequence of zeros).
The
_GNU_SOURCE
feature test macro must be defined in order to obtain the definitions of
SEEK_DATA
and
SEEK_HOLE
from
<unistd.h>.
The
SEEK_HOLE
and
SEEK_DATA
operations are supported for the following filesystems:
- *
-
Btrfs (since Linux 3.1)
- *
-
OCFS (since Linux 3.2)
- *
-
XFS (since Linux 3.5)
- *
-
ext4 (since Linux 3.8)
- *
-
tmpfs (since Linux 3.8)
RETURN VALUE
Upon successful completion,
lseek()
returns the resulting offset location as measured in bytes from the
beginning of the file.
On error, the value (off_t) -1 is returned and
errno
is set to indicate the error.
ERRORS
- EBADF
-
fd
is not an open file descriptor.
- EINVAL
-
whence
is not valid.
Or: the resulting file offset would be negative,
or beyond the end of a seekable device.
- EOVERFLOW
-
The resulting file offset cannot be represented in an
off_t.
- ESPIPE
-
fd
is associated with a pipe, socket, or FIFO.
- ENXIO
-
whence
is
SEEK_DATA
or
SEEK_HOLE,
and the current file offset is beyond the end of the file.
CONFORMING TO
SVr4, 4.3BSD, POSIX.1-2001.
SEEK_DATA
and
SEEK_HOLE
are nonstandard extensions also present in Solaris,
FreeBSD, and DragonFly BSD;
they are proposed for inclusion in the next POSIX revision (Issue 8).
NOTES
See
open(2)
for a discussion of the relationship between file descriptors,
open file descriptions, and files.
Some devices are incapable of seeking and POSIX does not specify which
devices must support
lseek().
On Linux, using
lseek()
on a terminal device returns
ESPIPE.
When converting old code, substitute values for whence with the
following macros:
| old | new
|
| 0 | SEEK_SET
|
| 1 | SEEK_CUR
|
| 2 | SEEK_END
|
| L_SET | SEEK_SET
|
| L_INCR | SEEK_CUR
|
| L_XTND | SEEK_END
|
Note that file descriptors created by
dup(2)
or
fork(2)
share the current file position pointer, so seeking on such files may be
subject to race conditions.
SEE ALSO
dup(2),
fork(2),
open(2),
fseek(3),
lseek64(3),
posix_fallocate(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- Seeking file data and holes
-
- RETURN VALUE
-
- ERRORS
-
- CONFORMING TO
-
- NOTES
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:54:49 GMT, September 18, 2014


Practice with regression and statistical hypothesis II
Exercise 1
Load the library MASS and consider the dataset “Pima.tr”, which contains the data about the population of women who were at least 21 years old, of Pima Indian heritage and living near Phoenix, Arizona. The subjects were tested for diabetes according to World Health Organization criteria.
Check if the data are read correctly. Compute the mean, quartiles and median of the numerical variables.
According to the WHO, a BMI of less than 18.5 as ‘underweight’ and may indicate malnutrition, an eating disorder, or other health problems, while a BMI equal to or greater than 25 is considered ‘overweight’ and above 30 is considered ‘obese’. Define a new categorical variable ‘Obese’, which divides the patients according to the WHO definition of obesity based on the BMI. The following analysis are intended on this dataset.
Using box plots, visualize the variable ‘glu’ and ‘bp’ at the varying of ‘Obese’. Comment on the result.
Using a t.test, verify if the mean ‘bp’ of the group ‘Obese’ is statistically different from the other. Comment on the result.
Exercise 2
Consider the dataset defined and modified in the previous exercise.
Define a linear model for the variable ‘glu’ at the varying of ‘bp’ (predictor). Comment on the results and explain the meaning of each row of the summary. Besides, visualize the regression line against the experimental data, analyze the residuals and compute the confidence intervals for the regression coefficients.
Define a linear model for the variable ‘glu’ at the varying of ‘bp’ and ‘Obese’ (predictors). Compare the results with the previous regression model and comment on the results. Compare the AIC for both models and explain if it is wort to consider the latest.
Perform and comment the ANOVA test on the latest regression model.
Define a linear model for the variable ‘glu’ at the varying of all the remaining variables (predictors). Then, use the
step
function to determine the a formula. Comment on the variable selected by this method and interpret the results.
Exercise 3
Consider the dataset containing economical and demographic data about 154 nations, such as literacy rate (%), gross domestic product (mld $) and military expenses ( $). Import also the dataset containing the list of 242 countries and their continent.
- Merge the two datasets removing the countries missing information on the continent.
- Substitute the NAs of ‘gdp’ with the median computed on the other countries of the same continent.
- Using box plots, visualize the variable ‘gdp’ at the varying of continent. Comment on the result.
- Can we consider a transformation of the ‘gdp’ variable to better compare the data? Which one? Can we consider the logarithm?
- Using the likelihood ratio, discuss if the mean of the logarithm(gpd) is statistically different between Europe and Oceania . Compare the results with the t.test and comment on.
Exercise 4
Consider the dataset, which includes brain and body weight, life span, gestation time, time sleeping, and predation and danger indices for 62 mammals (description) .
Import the dataset and analyze the variables. After reading the description and considering only the variables ‘BodyWt’, ‘BrainWt’, ‘TotSleep’, ‘GestTime’ and ‘SpleepExp’, substitute eventual missing data with the median of the same variables.
Using histograms, visualize the distribution of the variables ‘BodyWt’, ‘BrainWt’, ‘TotSleep’, ‘GestTime’.
Can we use the ANOVA test to see if the mean of the variable ‘GestTime’ in the groups defined by ‘SpleepExp’ are statistically different? Is the homoscedastic hypothesis satisfied? Compute the test.
Apply a logarithmic transformation to variable ‘BodyWt’. Define a linear model for the variable ‘GestTime’ at the varying of ‘logBodyWt’ (predictor). Comment on the results and explain the meaning of each row of the summary. Besides, visualize the regression line against the experimental data and analyze the residuals.
© 2017-2020 Federico Reali


This section describes the advanced built-in predicates for creating and removing breakpoints.
add_breakpoint(
:Spec,
?BID)
development-
Actions-[]
-
Actions[]-
Actions
Here, both Tests and Actions are either a simple
Condition, see Breakpoint Conditions, or a composite
Condition. Conditions can be composed by forming lists, or by
using the `,', `;', `->', and `\+' operators,
with the usual meaning of conjunction, disjunction,
if-then-else, and negation, respectively. A list of conditions is
equivalent to a conjunction of the same conditions
([
A|
B]
is treated as (
A,
B)
).
The add_breakpoint/2
predicate performs some
transformations and checks before adding the breakpoint. All
condition macros invoked are expanded into their bodies, and this
process is repeated for the newly introduced bodies. The goal
and
pred
conditions are then extracted from the outermost
conjunctions of the test part and moved to the beginning of
the conjunction. If these are inconsistent, a consistency error is
signalled. Module name expansion is performed for certain tests,
as described below.
Both the original and the transformed breakpoint spec is recorded
by the debugger. The original is returned in
current_breakpoint/5
, while the transformed spec is used in
determining the applicability of breakpoints.
There can only be a single plain spypoint for each predicate. If a plain spypoint is added, and there is already a plain spypoint for the given predicate, then:
spy(
:PredSpec,
:Spec)
developmentpred(
Pred)
to the test part of Spec, for each
predicate Pred designated by the generalized predicate
spec PredSpec.
current_breakpoint(
:Spec,
?BID,
?Status,
?Kind,
?Type)
developmenton
or off
, referring
to enabled and disabled breakpoints. Kind is one of
plain(
MFunc)
, conditional(
MFunc)
or
generic
, where MFunc is the module qualified
functor of the specific breakpoint. Type is the
breakpoint type: debugger
or advice
.
current_breakpoint/5
enumerates all breakpoints on
backtracking.
The Spec as returned by current_breakpoint/5
is exactly the
same as supplied at the creation of the breakpoint,
remove_breakpoints(
+BIDs)
developmentdisable_breakpoints(
+BIDs)
developmentenable_breakpoints(
+BIDs)
developmentall
, debugger
,
advice
. The atoms specify all breakpoints, debugger
type breakpoints and advice type breakpoints, respectively.
execution_state(
:Tests)
developmenttrue
condition. Otherwise only those tests can be used, which query the data
stored in the backtrace. An exception is raised if the latter
condition is violated, i.e. a non-backtraced test
(see Breakpoint Conditions) occurs in a call of
execution_state/1
from outside the debugger.
execution_state(
+FocusConditions,
:Tests)
developmentNote that the predicate arguments holding a breakpoint
spec (Spec or Tests above) are subject to module name
expansion. The first argument within simple tests
goal(_)
, pred(_)
, parent_pred(_)
,
parent_pred(_,_)
, ancestor(_,_)
, and true(_)
will
inherit the module name from the (module name expanded)
breakpoint spec/tests predicate argument, if there is
no explicit module qualification within the simple test. Within
the proceed(
Old,
New)
and
flit(
Old,
New)
command value settings, Old will
get the module name from the goal
or pred
condition
by default, while New from the whole breakpoint spec
argument.
The following hook predicate can be used to customize the behavior of the interactive debugger.
debugger_command_hook(
+DCommand,
?Actions)
hook,developmentuser:debugger_command_hook(
+DCommand,
?Actions)
Note that if a line typed in in response to the debugger prompt can not
be parsed as a debugger command, debugger_command_hook/2
is
called with the term unknown(Line,Warning)
. Here,
Line
is the code-list typed in, with any leading layout
removed, and Warning
is a warning message. This allows the user
to define new debugger commands, see Hooks Related to Breakpoints
for an example.


2 bug fixes:
Fixed bug with complex rescue but short enough to trigger 1-liner. (Confusion)
Fixed multiple expressions inside sclass
2 bug fixes:
Fixed attrasgn w/ multiple keys: a[x, y] = z. (derula)
Fixed error w/ attr_* detection when more than 1 ivar/iasgn exist in body.
1 bug fix:
Fixed attr_* generators in cases where the body isn’t idiomatic. (robertfeldt)
2 minor enhancements:
1.9: Added support for ! call to go back to (not …).
2nd and 3rd order testing now uses RubyPraser.for_current_ruby for maximal carnage.
1 bug fix:
On failure (eg ruby 2.0), fall back to compound RubyParser instance for 2nd & 3rd order testing
1 minor enhancement:
Updated to ruby_parser 3.1 and up
1 bug fix:
Fixed dependency on alpha ruby_parser. sigh
1 minor enhancement:
Only do phase 1 testing if $SIMPLE=1.
1 bug fix:
Fixed block args processing for RP 3.0 sexp changes
4 major enhancements:
Made it work without arglist in call.
Made it work without scope/block in class/module/defn/defs.
Removed block from resbody
Removed block from when node
4 minor enhancements:
Added debug task to help isolate an error
Empty hash is now a proper {}
Refactored and added finish method.
Switched to new Ruby18Parser to avoid deprecation warnings
4 bug fixes:
Fixed call with empty hash arg. (neilconway)
OMG WTF… removed long decrepit ParseTree dependency
Removed isolate/rake require to reduce minimum bootstrap to hoe + rake + rake install_plugins (*2)
Skip 1.9 tests for now.
1 minor enhancement:
Added parenthesize to add parens in certain contexts.
10 bug fixes:
Add newline to ‘do nothing’ comment in a block… seems contrived. (andreacampi)
Binary operations not work properly with some complex statements. (smorss)
Fixed if statements with no body (smorss)
Fixed logic for call with hash args in various locations (smorss)
Fixed match3 on an assignment. (smorss)
Fixed multiple nested rescue/ensure exprs (larsch)
Fixed process_alias to parenthesize (smorss)
Fixed process_and to parenthenize only when it makes sense.
Fixed rescue with 2+ statements in resbody (smorss)
Regexps with options other than /o were not showing flags. (smorss)
1 minor enhancement:
output comments for class and method definitions. (pythonic)
4 minor enhancements:
Added braces to hash args surrounded if in a binary method call.
Added rewrite_resbody to double check structure and freak if necessary.
Added stress task
rewrite_rescue now detects rescue with multiple arguments.
2 bug fixes:
Fixed dstr/dregex/d* roundtripping problem
Fixed up call arg processing to be more correct and to work with the new sexp form
2 bug fixes:
Fixed all doco to use ruby_praser
Fixed bin/r2r_show to use ruby_parser. oops
4 minor enhancements:
Overhauled 4-generation tests to use RubyParser. Much cleaner
Removed last of ParseTree. Fully switched to RubyParser.
Switched to minitest
Updated Rakefile to new hoe capabilities
3 minor enhancements:
Added -s to display sexp before printing r2r
Added a bunch of backslash and masgn tests.
Refactored tests.
4 bug fixes:
Fixed iters to deal with empty bodies.
Fixed process_call for a number of cases incl [], []=, and args processing.
Fixed process_hash to always generate braces if in arglist.

Revamped. Got working with new unified ruby output. Much much cleaner.
5 minor enhancements:
Added more defensive programming in the tests to make it work with 1.9 and rubinius better.
Converted r2r_show to more plain parse style, no more discover_new_classes.
Made Proc#to_sexp and to_ruby more resiliant.
Started to work on fallback to ruby_parser code. Should prolly do flog first.
Updated rakefile and readme format for hoe. Much cleaner!
6 bug fixes:
Added 1.9 fixes.
Added code to tests to isolate rubyinline builds.
Fixed miniunit-deprecated assertions
Fixes for const2/3, esp in class names
Renamed ProcStoreTmp#name to new_name. dur.
Skip proc tests in 1.9 since they require ParseTree.
6 minor enhancements:
Added super awesome .autotest file. YAY!

Renamed RubyToRuby (the class name) to Ruby2Ruby.
Restructured self-translation tests so they were friendlier when dying.
Strings are now always one line long only.
Fully in sync with ParseTree and ruby_parser.
2 bug fixes:
Fixed a number of issues/bugs discovered via ruby_parser.
Cleaned out some dead code and hacks we don’t need anymore.
2 major enhancements:
Switched to ParseTree’s UnifiedRuby… much much cleaner now!
Made test_ruby2ruby MUCH more rigorous with circular testing.
5 minor enhancements:
Add r2r_show command like parse_tree_show.
Add parens for :block nodes as appropriate. May be overzealous.
Make SexpAny work with #==.
Removed calls to processor_stack / caller in favor of self.context.
Some style differences, eschew rescue.
6 bug fixes:
Fix R2R bug with masgn/argscat.
Fixed a bug with new resbody unification.
Fixes for changes to pt_testcase.
Fixes the rest of the tests under strict sexp checking.
Fixed some circular bugs, mostly by hacking them out, wrt operator precidence.
Fixed trinary operator.
2 minor enhancements:
Extended tests for dstr/dsym/drgx to test against embedded slashes and quotes.
Updated for dasgn_curr changes to PT.
2 bug fixes:
Fixed a bug with begin/rescue/ensure.
Fixed argscat and blockpass bug. blah(42, *args, &block) handled.
3 minor enhancements:
Can now heckle ActiveRecord::Base in full.
Cleaned up 1-liner generating code.
Made clean/simple rescues 1-liners.
7 bug fixes:
Finally got the rest of block_pass working.
Fixed block_pass on procs in iters. UGH!
Fixed attrasgn in masgn.
Fixed splat in masgn.
Fixed unary/prefix methods.
Fixed attrasgn for []= where there were multiple args inside [].
Fixed a couple resbody bugs.
4 minor enhancements:
Added some extra rewriting code and tests for various bmethods. Ugh.
Added support for splatted block args.
Refactored class/module and dsym/dstr.
Short if/unless statements are now post-conditional expressions.
4 bug fixes:
Finally fixed eric’s nebulous proc code-in-goalposts bug.
Fixed dasgn_curr so block’s dasgn vars decl goes away (bug 7420).
Fixed dmethod. I think the tests were bogus before.
Fixed improper end in method rescues (bug 7396).
1 minor enhancement
Unit tests do self-translation and retesting for 3 generations! Solid. BAM!
1 bug fixes
iasgn inside masgn was totally borked in ruby2ruby.
2 minor enhancements
Improved []= and [] to be more idiomatic.
Support for nested whens (from when case has no expression).
3 bug fixes
Fixed case output when there is no case expression.
NEARLY have RubyToRuby self-cloning and passing tests again.
Minor cleanup
3 bug fixes
Fixed procs
Cleaned return when no return values.
Rewrote process_if. No more elsif but no more bugs. :)
2 major enhancements
Released separately from ZenHacks.
Major overhaul/audit from the new ParseTree test infrastructure. Very complete now.


{% if category %}{{category}} {% endif %}Articles
{% if category == "Closer Look" %}Closer Looks are one of the main components of the Worlds of ZZT project. The Closer Look series consists primarily of (re)visiting ZZT worlds and looking at them from a contemporary perspective, attemping to give both a complete playthrough of them while also pointing out noteworthy design decisions.
{% elif category == "Livestream" %}Recordings of Livestreams are posted here. They (along with other videos) can all be watched directly on the Worlds of ZZT YouTube channel.
Livestreams typically occur every Sunday on the Worlds of ZZT Twitch channel at 2pm PST / 5pm EST.
{% endif %} {% include "museum_site/blocks/paginator_block.html" %} {% if view == "list" %}| Title | Author | Date | Category | Summary |
|---|


📅 (2022-09-21 09:45:00 UTC)
# Packages matching: installed # Name # Installed # Synopsis base-bigarray base base-threads base base-unix base conf-findutils 1 Virtual package relying on findutils coq 8.10.2 Formal proof management system num 1.4 The legacy Num library for arbitrary-precision integer and rational arithmetic ocaml 4.09.1 The OCaml compiler (virtual package) ocaml-base-compiler 4.09.1 Official release 4.09.1 ocaml-config 1 OCaml Switch Configuration ocamlfind 1.9.5 A library manager for OCaml # opam file: opam-version: "2.0" maintainer<EMAIL_ADDRESS>homepage: "https://github.com/coq-community/buchberger" dev-repo: "git+https://github.com/coq-community/buchberger.git" bug-reports: "https://github.com/coq-community/buchberger/issues" license: "LGPL-2.1-or-later" synopsis: "Verified implementation of Buchberger's algorithm for computing Gröbner bases in Coq" description: """ A verified implementation of Buchberger's algorithm in Coq, which computes the Gröbner basis associated with a polynomial ideal. Also includes a constructive proof of Dickson's lemma.""" build: [make "-j%{jobs}%"] install: [make "install"] depends: [ "coq" {>= "8.7" & < "8.13~"} ] tags: [ "category:Mathematics/Algebra" "category:Miscellaneous/Extracted Programs/Combinatorics" "keyword:Gröbner basis" "keyword:polynomial ideal" "keyword:Buchberger's algorithm" "logpath:Buchberger" "date:2020-05-27" ] authors: [ "Laurent Théry" "Henrik Persson" ] url { src: "https://github.com/coq-community/buchberger/archive/v8.11.0.tar.gz" checksum: "sha512=a048ee35574090d2ffa6b8c6a5e4379bb5f7699e0b84c1c4781fb624d151faca02eb9122bae2d21296ba6dbe57adb116edaa1255a5475b1cbafe89d33d364a05" }
true
Dry install with the current Coq version:
opam install -y --show-action coq-buchberger.8.11.0 coq.8.10.2
Dry install without Coq/switch base, to test if the problem was incompatibility with the current Coq/OCaml version:
true
opam list; echo; ulimit -Sv 4000000; timeout 4h opam install -y --deps-only coq-buchberger.8.11.0 coq.8.10.2
opam list; echo; ulimit -Sv 16000000; timeout 4h opam install -y -v coq-buchberger.8.11.0 coq.8.10.2
# Packages matching: installed # Name # Installed # Synopsis base-bigarray base base-threads base base-unix base conf-findutils 1 Virtual package relying on findutils coq 8.10.2 Formal proof management system num 1.4 The legacy Num library for arbitrary-precision integer and rational arithmetic ocaml 4.09.1 The OCaml compiler (virtual package) ocaml-base-compiler 4.09.1 Official release 4.09.1 ocaml-config 1 OCaml Switch Configuration ocamlfind 1.9.5 A library manager for OCaml [NOTE] Package coq is already installed (current version is 8.10.2). The following actions will be performed: - install coq-buchberger 8.11.0 <><> Gathering sources ><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Processing 1/1: [coq-buchberger.8.11.0: http] [coq-buchberger.8.11.0] downloaded from https://github.com/coq-community/buchberger/archive/v8.11.0.tar.gz Processing 1/1: <><> Processing actions <><><><><><><><><><><><><><><><><><><><><><><><><><><><> Processing 1/2: [coq-buchberger: make] + /home/bench/.opam/opam-init/hooks/sandbox.sh "build" "make" "-j4" (CWD=/home/bench/.opam/ocaml-base-compiler.4.09.1/.opam-switch/build/coq-buchberger.8.11.0) - coq_makefile -f _CoqProject -o Makefile.coq - make[1]: Entering directory '/home/bench/.opam/ocaml-base-compiler.4.09.1/.opam-switch/build/coq-buchberger.8.11.0' - COQDEP VFILES - COQC theories/Relation_Operators_compat.v - COQC theories/Bar.v - COQC theories/CoefStructure.v - COQC theories/OrderStructure.v - COQC theories/Monomials.v - COQC theories/LetP.v - COQC theories/ListProps.v - COQC theories/moreCoefStructure.v - COQC theories/Fred.v - COQC theories/OpenIndGoodRel.v - COQC theories/LexiOrder.v - COQC theories/Dickson.v - COQC theories/Term.v - COQC theories/POrder.v - COQC theories/DivTerm.v - COQC theories/Peq.v - COQC theories/Pplus.v - COQC theories/Pmults.v - File "./theories/Pmults.v", line 266, characters 0-383: - Error: System error: "No space left on device" - - make[2]: *** [Makefile.coq:659: theories/Pmults.vo] Error 1 - make[1]: *** [Makefile.coq:321: all] Error 2 - make[1]: Leaving directory '/home/bench/.opam/ocaml-base-compiler.4.09.1/.opam-switch/build/coq-buchberger.8.11.0' - make: *** [Makefile:2: all] Error 2 [ERROR] The compilation of coq-buchberger failed at "/home/bench/.opam/opam-init/hooks/sandbox.sh build make -j4". #=== ERROR while compiling coq-buchberger.8.11.0 ==============================# # context 2.0.6 | linux/x86_64 | ocaml-base-compiler.4.09.1 | file:///home/bench/run/opam-coq-archive/released # path ~/.opam/ocaml-base-compiler.4.09.1/.opam-switch/build/coq-buchberger.8.11.0 # command ~/.opam/opam-init/hooks/sandbox.sh build make -j4 # exit-code 2 # env-file ~/.opam/log/coq-buchberger-7777-335865.env # output-file ~/.opam/log/coq-buchberger-7777-335865.out ### output ### # [...] # COQC theories/POrder.v # COQC theories/DivTerm.v # COQC theories/Peq.v # COQC theories/Pplus.v # COQC theories/Pmults.v # File "./theories/Pmults.v", line 266, characters 0-383: # Error: System error: "No space left on device" # # make[2]: *** [Makefile.coq:659: theories/Pmults.vo] Error 1 # make[1]: *** [Makefile.coq:321: all] Error 2 # make[1]: Leaving directory '/home/bench/.opam/ocaml-base-compiler.4.09.1/.opam-switch/build/coq-buchberger.8.11.0' # make: *** [Makefile:2: all] Error 2 <><> Error report <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> +- The following actions failed | - build coq-buchberger 8.11.0 +- - No changes have been performed # Run eval $(opam env) to update the current shell environment 'opam install -y -v coq-buchberger.8.11.0 coq.8.10.2' failed.
No files were installed.
true


Capstone Project Requirements

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Overview

The goal of the Capstone Project is to give you an opportunity to demonstrate the skills you have built throughout this program. In this project, you will use Python, SQL, and Tableau to put together a complete analytics workflow, including:

Data acquisition

Data wrangling

Data storage

Data exploration and analysis

Feature selection

Machine learning model training

Model evaluation

Reporting and presentation of insights

The final project is structured into 4 days in which you build up your deliverables progressively and iteratively. We have provided a list of data sets for you to choose from. However, you are encouraged to obtain your own data sets. Using the knowledge you have acquired and your experience working with data, you will come up with a plan for what you are going to do and then design the project around the data set you have chosen.

You will be working individually for this project, but we'll be guiding you along the process and helping you as you go. You will be working on this project over the course of 4 days. The proposed progression for each day is listed below. However, if you finish a certain step ahead of time, you can proceed to the next step. In that case, you will conduct more iterations of your deliverables by for example deep cleanining your data, selecting better features, comparing different ML models, improving model predictions, and so on. Also, remember that data analysis is iterative. So from time to time you may need to step back to a previous phase and iterate.

Day 1 - Brain Storming and Data Preparation

Idea generation & planning

Data gathering & cleaning

Data storage

Day 2 - Exploratory Data Analysis

Data exploration

Data visualization

Data transformation

Day 3 - Data Analysis First Iteration

Feature selection

Model training and evaluation

Model evaluation

Day 4 - Data Analysis More Iterations

Iterations on your modeling

Model evaluation and comparison

Prepare for presentation

Project Instructions

Day 1

On the first day of your project, you will be planning your project, choosing a data set, downloading and cleaning it, and storing it in a MySQL database.

To get started, explore and choose from one of the following data sets:

Telecom Customer Churn Data Set

Mental Health in Tech Survey Data Set

Flight Delays and Cancellations Data Set

Craft Beers Data Set

Human Resources Data Set

These are all Kaggle data sets, so you can gain some context about them by looking at the field descriptions and the
Overview tab for each data set. You can also get project ideas by looking at the different
Kernels that people have created using the data sets and the approaches they have taken, but please
do not plagiarize or copy someone else's work. Remember that the goal of this project is to demonstrate
your skills, not someone else's.

You are free to use other data sources from
Kaggle,
UCI ML Repository,
data.gov, public APIs, websites, etc. However you will need to spend more time to search and evaluate the quality and complexity of the data set you want to use. Discuss with your instructor as early as possible regarding your project ideas in order to avoid potential blockers.

Project Ideas

As you are evaluating the data sets and deciding which you would like to use for your project, think about the different types of information that could be extracted from each one and what problems you could potentially solve. You are not required to use machine learning but it is definitely a plus if you use it. It is totally fine if you are more of a business-focused person and use Tableau to extract business intelligence from the data. It is also fine if you are more of a traditional data analyst and primarily conduct statistical analysis on your data. But challenging your own limit is always beneficial.

Below are some ideas for the data sets we selected for you which can help get you started with these data sets.

Telecom Customer Churn

This data set is great for practicing supervised machine learning. The most obvious supervised learning problem to model with this data set is attempting to predict churn (whether or not a customer is going to leave). Another supervised problem you can try to solve is predicting tenure, or how long a customer will end up staying with the company. There are also a lot of ways to slice, dice, and analyze this data set - for example, looking at how monthly charges increase or decrease depending on different types of contract, tenure, and services provided.

Mental Health in Tech

This data set is also good for supervised learning, where you would attempt to predict the whether the employee taking the survey has received treatment for mental health (treatment variable). You can also analyze how the other variables in the data set contribute to whether someone seeks treatment for mental health and explore what types of environments result in employees with the best mental heath states.

Flight Delays and Cancellations

This relational data set can help you practice your SQL joins as well as supervised learning as you attempt to predict which flights will be delayed and by how much time. There are also a variety of ways to explore this data set to determine which airlines are most efficient, what times of day there tends to be the most delays, which cities have more frequent delays than others, and how all this changes over time.

Craft Beers

This relational data set has fewer variables than the others, and that means that choosing it for your project will require you to apply some extra creativity. For example, you can create categorical variables out of the numeric abv and ibu variables to categorize a beer's strength and hopiness. You can also use string operations on the style field to extract additional keyword-based categories (e.g. IPAs, American, English, Wheat, etc.). For modeling, you can attempt to predict the style of the beer or the probability that it falls into a particular style. Alternatively, you can attempt to compute similarity between beers to determine which ones are most like which other ones.

Human Resources

This is another relational data set where you can practice your SQL joins as well as data wrangling, analysis, and machine learning. As far as applying machine learning to this data set, you can attempt to cluster employees together into similar groups, you can attempt to predict performance scores or pay rate, or you can attempt to predict whether an employee will be terminated within a certain amount of time. Additionally, you can also analyze the relationship between pay and performance by position, department, and manager as well as employee demographics.

Once you have chosen a data set, you will need to download it. In order to do so, you will need to create a free Kaggle account if you don't already have one. Once you have downloaded the data, you should perform the steps below:

Create a new MySQL database where your data will eventually be stored.

Create a new Jupyter Notebook for your project.

In the Jupyter Notebook, read the data files using Python.

Perform any necessary data wrangling and cleaning using Python.

Create a connection to your MySQL database using
pymysql and
sqlalchemy and write the clean version of the data to the database.

Additional Ideas

Analyze Your Own Facebook Data

Do you know you can download your own Facebook data to your harddisk? You can use this data to do social network analysis or visualize your friendships.

Warning: Downloading your Facebook data takes a lot of time. You need to first request your data in your Facebook account. Then Facebook will prepare your data for you to download as a ZIP file. If you request all your data (e.g. posts, photos, likes, friends, places, etc.), it takes at least a whole day for Facebook to get your data ready. But instead of requesting all your data, you can also request only the categories you want to analyze such as friends and places, which takes significantly less time. In either case, request your data as early as you can so that you can have the data before the project week begins.

Besides, when you request your data from Facebook, choose if you would like the data to be in JSON or HTML format. It's a strategic choice depending on what topic you want to work on for your final project. If you request JSON, you will be able to focus on data visualization and analysis. If you request HTML, the data may be more suitable for natural language processing but you will need to spend time on parsing the HTML data.

Resources:

Accessing & Downloading Your Facebook Information

How to Visualize Your Facebook Network

Facebook Social Network Analysis

Web Scraping

The web is a free source of unlimited data. There are Wikipedia, Google Maps, YouTube, Amazon, and countless content you can download and dig. General advise? Choose a website you know well and are conerned about. Use API instead of HTML data if you can.

Resources:

What are some interesting web scraping projects you have done

Web Scraping: Top 15 Ways To Use It For Business

Data Visualization

In addition to your Facebook data, there are tons of other data visualizaiton ideas. You can visualize virtually everything such as people, animals, countries, products, music, weather, Github, etc.

Resources:

15 Data Visualizations That Will Blow Your Mind

80 Data Visualization Examples Using Location Data and Maps

Even More

24 Ultimate Data Science Projects To Boost Your Knowledge and Skills

What are some good data science projects?

Day 2

On the second day of your project, you will start with the clean data set you stored in your database on the previous day. You will explore, analyze, and visualize the data using Python and Tableau, applying the variety of techniques you learned throughout the program.

In your Jupyter Notebook, read the clean data from your MySQL database.

Using Pandas, generate summaries of the data and calculate descriptive statistics.

Practice generating a few basic charts and graphs using
matplotlib or
seaborn as well.

Export your clean data set to a CSV file.

Open Tableau Public and load the CSV file.

Explore the data in Tableau and look for interesting insights.

Put together an annotated Tableau Story communicating the insights you have discovered.

Day 3

On the third day of your project, you will be using the analysis you performed and the insights you discovered the previous day to help frame your machine learning problem, select and engineer appropriate features, train your models, and evaluate performance.

If you are planning on doing supervised machine learning, identify the target variable you would like to train a model to predict. Also determine whether you will be doing regression (target variable is continuous) or classification (target variable is discrete).

Perform feature selection/engineering to arrive at the features you feel best represent the problem you are trying to solve. During this stage, you may need to normalize or scale your variables.

Train a couple machine learning models on the data.

Evaluate the performance of the models.

Prepare a presentation of your findings and results.

Day 4

Should you have a fourth day of final project work, you should spend the day refining your machine learning models and your presentation. If you have time, you may also go back and perform additional data exploration and analysis.

Continue iterating on your machine learning models with the objective of optimizing their performance.

Organize your machine learning steps into a pipeline that performs feature selection/engineering, model training, model evaluation, and model storage.

Further refine your presentation based on additional findings and results.

Necessary Deliverables

A
Final Project Template has been prepared for you to document and report your final project. You can use the structure in that template to prepare your report or presentation. But you are not limited to the Markdown file format in turning in your report as long as you follow the structure prescribed in the template.

The following deliverables should be pushed to your Github repo.

A Jupyter Notebook (.ipynb) file containing all your Python code.

A data folder containing your original data set and your clean data set in CSV format.

Your final project report/presentation created from template.

Presentation Guideline and Criteria

Format

Presentation Time: 12 minutes

Q & A: 3 minutes

Total Time: 15 minutes

Attire

DRESS TO IMPRESS:
Smart casual would be great

Outputs

A presentation in
slides.com

A demo deployed on GitHub Pages

The presentation and demo will be executed on a class computer (instead of your own)

Get ready to explain some of your code in GitHub

Things you might want to talk about

Short presentation of yourself:

Who are you?

A hobby you have.

Note: we are getting you ready for final presentation!

Elevator pitch:

Data set you chose.

Why did you chose that data set?

The most important thing you learned.

One technical challenge you faced:

Explain the challenge.

Explain how and what you did to overcome it.

Show and explain code snippets in your presentation slides.

Git:

Display a screenshot of your GitHub graphs to show your commit frequency and how much work you did.

Final Project Walkthrough:

Walk the audience through the data set you chose, providing an overview of some of the fields and other information contained in the data.

Walk the audience through your process of cleaning, exploring, analyzing, and performing machine learning on your data including what tools and techniques you employed, what avenues you decided to pursue and why, what interesting insights you discovered, and what lessons you learned.

Below are a few videos of the Ironhack Hackshow where our graudates presented their final projects. Take a quick look on how our past students presented their projects.

Web Dev final project demo

UX/UI final project demo

Hackshow diciembre 2017 - Madrid (Spanish)

5 Tips for Successful Completion

Generate a project idea that solves a data problem with practical values. Make sure the problem you try to solve is clearly defined. Make sure the scope of the problem is manageable so that you can complete within the project timeframe. Meanwhile, think beyond the problem itself - can your solution of the problem inform how to solve other bigger/deeper problems?

Obtain timely feedback on your ideas, plans, progress, and products from the instructional staff. This ensures you stay on the right track and deliver impressive results.

Be prepared for technical difficulties. Manage your time wisely and pace yourself.  You should commit every time you complete a task and push at least once a day. This will help you avoid accidential data loss and is also the easiest way to document your iterative development process.

Before final project presentation, test every component and step of your demo. Make sure your demo will run smoothly.

May the force be with you!

Capstone Project Template

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[Your Final Project Title]

[Your Name]

[Your Cohort Campus & Time]

Overview

Include the following points in your overview:

What data/business/research/personal question you would like to answer?

Make sure the question description is human-friendly because you will have non-tech audience to see your final project presentation.

What is the hypothesis you would like to test in order to answer your question?

Frame your hypothesis with statistical/data languages (i.e. define Null and Alternative Hypothesis). You can use formulas if you want but that is not required.

How will you test your hypothesis?

Notes:

You can submit the final project with Jupyter Notebook. If you would like to submit a different file format, make sure to discuss with the instructor.

Based on the nature of your goal and your intended problem-solving approach, you are encouraged to use as many skills, technologies, and tools as possible that you have learned in this course, which include but are not limited to:

Python

MySQL

Statistical analysis

Data visualization

Jupyter Notebook

Tableau

Machine Learning

Apache Spark / Network Analysis / Natual Language Processing

Include screenshots wherever relevant.

Feel free to add more sections to this worksheet.

Meet with your instructor/TA on daily basis to obtain feedback and support.

Data Preparation

Overview:

What is your dataset about?

Where/how did you obtain your dataset?

It can be either a public dataset or collected with API/web scraping.

Provide a link if possible.

General description of the dataset such as the size, complexity, data types, etc.

Data Ingestion

If you downloaded a dataset (either public or private), describe where you downloaded it and include the command to load the dataset.

If you obtain the data via API/web scraping, provide the scripts.

Data Wrangling and Cleaning

Your full process of data wrangling and cleaning.

Document your workflow and thinking process.

Data Storage

Dump your data to a MySQL database and/or a
.csv file.

Data Analysis

Overview

Overview the general steps you will go through to analyze your data in order to test your hypothesis.

Data Exploration and Visualization

Document each step of your data exploration and analysis.

Print charts to demonstrate the effect of your work. Charts make your presentation look good too.

If you use ML in your final project, also describe your feature selection process.

Model Training and Evaluation

Train your ML model, produce results, and evaluate.

This is an iterative process. Try your best to improve your model performance by:

More data cleaning.

Try different models and select one that is the simplest yet produce the best result.

Try advanced techniques and see if they improve the result.

Conclusion

Summarize your data analysis result.

State your conclusion of your hypothesis testing.

Interpret your findings in terms of the human-understandable question you try to answer.

What are the next steps?


In the following video, the basic Scientilla workflow and its basic functionalities are shown, to get a flavour of how the system works!


Wait Wait Panelist Appearances Report
Report how many times a panelist has appeared, each year, on Wait Wait... Don't Tell Me!
Note: The numbers listed below do not include panelist appearances in Best Of or Repeat shows.
| Panelists | {% for year in render_data["show_years"] %}{{ year }} | {% endfor %}Total | |
|---|---|---|---|
| {{ panelist["name"] }} | {% for year in render_data["show_years"] %} {% if year in panelist["appearances"] %}{{ panelist["appearances"][year] }} | {% else %}{% endif %} {% endfor %} | {{ panelist["appearances"]["total"] }} |


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- Docker 🐳 and trying to learning kubernetes


Interface MarketDataBox<T>
-
- Type Parameters:
T
- the type of data held in the box
public interface MarketDataBox<T>
A box which can provide values for an item of market data used in scenarios.A box can contain a single value for the data or it can contain multiple values, one for each scenario. If it contains a single value then the same value is used in every scenario.
Wrapping the data in a box allows a simple interface for looking up market data that hides whether there is one value or multiple values. Without the box every function that uses market data would have to handle the two cases separately.
The box also takes care of transforming the market data when using it to build other market data values (see the
apply
methods). This means that market data functions and perturbations don't need different logic to deal with single and multiple values.Using a box allows scenario data to be stored more efficiently in some cases. For example, curve data for multiple scenarios can include one copy of the x-axis data which is used in all scenarios. If a separate curve were stored for each scenario that data would be unnecessarily stored multiple times.
In some cases a function might need to access the data for all scenarios at the same time. For example, if part of the calculation is the same for all scenarios it can be done once and reused instead of recalculated for each scenario. In this case a
ScenarioMarketDataId
should be used to retrieve the scenario value from the market data container.
-
-
Method Summary
All Methods Static Methods Instance Methods Abstract Methods Default Methods Modifier and Type Method Description <U,R>
MarketDataBox<R>combineWith(MarketDataBox<U> other, BiFunction<T,U,R> fn)
Applies a function to the market data in this box and another box and returns a box containing the result.static <T> MarketDataBox<T>
empty()
Obtains an instance containing no market data.Class<?>
getMarketDataType()
Gets the type of the market data value used in each scenario.int
getScenarioCount()
Gets the number of scenarios for which this box contains data.ScenarioArray<T>
getScenarioValue()
Gets the market data value containing data for multiple scenarios.T
getSingleValue()
Gets the single market data value used for all scenarios if available.T
getValue(int scenarioIndex)
Gets the market data value associated with the specified scenario.default boolean
isScenarioValue()
Checks if this box contains market data for multiple scenarios.boolean
isSingleValue()
Checks if this box contains a single market data value that is used for all scenarios.<R> MarketDataBox<R>
map(Function<T,R> fn)
Applies a function to the contents of the box and returns another box.<R> MarketDataBox<R>
mapWithIndex(int scenarioCount, ObjIntFunction<T,R> fn)
Applies a function to the contents of the box once for each scenario and returns a box containing the values returned from the function.static <T> MarketDataBox<T>
ofScenarioValue(ScenarioArray<T> scenarioValue)
Obtains an instance containing a scenario market data value with data for multiple scenarios.static <T> MarketDataBox<T>
ofScenarioValues(List<T> scenarioValues)
Obtains an instance containing a scenario market data value with data for multiple scenarios.static <T> MarketDataBox<T>
ofScenarioValues(T... scenarioValues)
Obtains an instance containing a scenario market data value with data for multiple scenarios.static <T> MarketDataBox<T>
ofSingleValue(T singleValue)
Obtains an instance containing a single market data value that is used in all scenarios.Stream<T>
stream()
Returns a stream over the contents of the box.
-
-
-
Method Detail
-
ofSingleValue
static <T> MarketDataBox<T> ofSingleValue(T singleValue)
Obtains an instance containing a single market data value that is used in all scenarios.- Type Parameters:
T
- the type of the market data value used in each scenario- Parameters:
singleValue
- the market data value containing data for a single scenario- Returns:
- a box containing a single market data value that is used in all scenarios
-
ofScenarioValue
static <T> MarketDataBox<T> ofScenarioValue(ScenarioArray<T> scenarioValue)
Obtains an instance containing a scenario market data value with data for multiple scenarios.The market data is made up of multiple values, one for each scenario. The
ScenarioArray
instance may provide optimized internal storage of these values.A box may be created that contains a value for one scenario. Such a box is distinct from a box created using
ofSingleValue(Object)
, which is valid for any number of scenarios.- Type Parameters:
T
- the type of the market data value used in each scenario- Parameters:
scenarioValue
- the market data value containing data for multiple scenarios- Returns:
- a box containing a scenario market data value with data for multiple scenarios
-
ofScenarioValues
@SafeVarargs static <T> MarketDataBox<T> ofScenarioValues(T... scenarioValues)
Obtains an instance containing a scenario market data value with data for multiple scenarios.The market data is made up of multiple values, one for each scenario.
A box may be created that contains a value for one scenario. Such a box is distinct from a box created using
ofSingleValue(Object)
, which is valid for any number of scenarios.- Type Parameters:
T
- the type of the market data value used in each scenario- Parameters:
scenarioValues
- the market data values for each scenario- Returns:
- a box containing a scenario market data value with data for multiple scenarios
-
ofScenarioValues
static <T> MarketDataBox<T> ofScenarioValues(List<T> scenarioValues)
Obtains an instance containing a scenario market data value with data for multiple scenarios.The market data is made up of multiple values, one for each scenario.
A box may be created that contains a value for one scenario. Such a box is distinct from a box created using
ofSingleValue(Object)
, which is valid for any number of scenarios.- Type Parameters:
T
- the type of the market data value used in each scenario- Parameters:
scenarioValues
- the market data values for each scenario- Returns:
- a box containing a scenario market data value with data for multiple scenarios
-
empty
static <T> MarketDataBox<T> empty()
Obtains an instance containing no market data.- Type Parameters:
T
- the type of the market data value used in each scenario- Returns:
- a box containing no market data
-
getSingleValue
T getSingleValue()
Gets the single market data value used for all scenarios if available.If this box contains data for multiple scenarios an exception is thrown.
This method should only be called if
isSingleValue()
returnstrue
orisScenarioValue()
returnfalse
.- Returns:
- the single market data value used for all scenarios if available
- Throws:
UnsupportedOperationException
- if this box contains data for multiple scenarios
-
getScenarioValue
ScenarioArray<T> getScenarioValue()
Gets the market data value containing data for multiple scenarios.If this box contains data for a single scenario an exception is thrown.
This method should only be called if
isSingleValue()
returnsfalse
orisScenarioValue()
returntrue
.- Returns:
- the market data value containing data for multiple scenarios
- Throws:
UnsupportedOperationException
- if this box contains data for a single scenario
-
getValue
T getValue(int scenarioIndex)
Gets the market data value associated with the specified scenario.- Parameters:
scenarioIndex
- the index of the scenario- Returns:
- the market data value associated with the scenario
- Throws:
IndexOutOfBoundsException
- if the index is invalid
-
isSingleValue
boolean isSingleValue()
Checks if this box contains a single market data value that is used for all scenarios.- Returns:
- true if this box contains a single market data value that is used for all scenarios
-
isScenarioValue
default boolean isScenarioValue()
Checks if this box contains market data for multiple scenarios.- Returns:
- true if this box contains market data for multiple scenarios
-
getScenarioCount
int getScenarioCount()
Gets the number of scenarios for which this box contains data.A "single value" box can be used with any number of scenarios. To indicate this, the method will return -1.
- Returns:
- the number of scenarios for which this box contains data, -1 if the number is not specified
-
getMarketDataType
Class<?> getMarketDataType()
Gets the type of the market data value used in each scenario.- Returns:
- the type of the market data value used in each scenario
-
map
<R> MarketDataBox<R> map(Function<T,R> fn)
Applies a function to the contents of the box and returns another box.The box implementation takes care of checking whether it contains a single value or a scenario value, applying the function to the value for each scenario and packing the return value into a box.
This is primarily intended for use by market data factories which might receive single values or scenario values from upstream market data factories.
- Type Parameters:
R
- the return type of the function- Parameters:
fn
- a function to apply to the market data in the box- Returns:
- a result wrapping a box containing the return value of the function
-
mapWithIndex
<R> MarketDataBox<R> mapWithIndex(int scenarioCount, ObjIntFunction<T,R> fn)
Applies a function to the contents of the box once for each scenario and returns a box containing the values returned from the function.The
scenario count
of the box must be one or it must be equal to thescenarioCount
argument. Thescenario count
of the return value will be equal toscenarioCount
.The box implementation takes care of checking whether it contains a single value or a scenario value, applying the function to the value for each scenario and packing the return values into a box.
This is primarily intended to be used by perturbations which generate separate market data values for each scenario data by applying a function to the existing value for the scenario.
- Type Parameters:
R
- the type of the returned market data- Parameters:
scenarioCount
- the total number of scenariosfn
- the function that is invoked with a scenario index and the market data value for that scenario. The return value is used as the scenario data in the returned box- Returns:
- a box containing market data created by applying the function to the contents of this box
-
combineWith
<U,R> MarketDataBox<R> combineWith(MarketDataBox<U> other, BiFunction<T,U,R> fn)
Applies a function to the market data in this box and another box and returns a box containing the result.The box implementation takes care of checking whether the input boxes contain single values or a scenario values, applying the function to the value for each scenario and packing the return value into a box.
This is primarily intended for use by market data factories which might receive single values or scenario values from upstream market data factories.
- Type Parameters:
U
- the type of market data in the other boxR
- the type of the market data returned in the result of the function- Parameters:
other
- another market data boxfn
- the function invoked with the market data from each box. The return value is used to build the data in the returned box- Returns:
- a box containing market data created by applying the function to the data in this box and another box
-
-


This page shows how to use Handsontable with various data sources:
Please take note that Handsontable will change the original data source. More about this here: Understanding binding as reference.
When you use object data binding, Handsontable needs to know the data structure to create when you add a new row.
If your data source contains at least one row, Handsontable will figure out the data structure based on the first row.
In case you want to start with an empty data source, you will need to provide the dataSchema option that contains the data structure for any new row added to the grid.
The below example shows custom data schema with an empty data source:
If your dataSchema is actually a constructor of an object that doesn't directly expose its members, like a Backbone.js model, you can specify functions for the data member of each columns item.
The below example shows a small example of using such objects:


Word Vector¶
WordVector¶
cotk.wordvector
provides classes and functions downloading and
loading wordvector automatically.
-
class
cotk.wordvector.
WordVector
[source]¶ Base of all word vector loader.
-
classmethod
get_all_subclasses
() → Iterable[Any]¶ Return a generator of all subclasses.
-
classmethod
load_class
(class_name) → Any¶ Return a subclass of
class_name
, case insensitively.- Parameters
class_name (str) – target class name.
-
classmethod
GeneralWordVector¶
-
class
cotk.wordvector.
GeneralWordVector
(file_id)[source]¶ Bases:
dataloader.WordVector
This class is a general pretrained word vector.
- Parameters
file_id (str,
None
) – A str indicates the source of word vectors. It can be local path ("./data"
), a resource name ("resources://dataset"
), or an url ("http://test.com/dataset.zip"
). Seecotk.file_utils.get_resource_file_path()
for further details. IfNone
, do not use pretrained word vector.
- Input Format
A text file named
wordvec.txt
should be contained in the path. In the file, each word vec should be described in two lines. The first line is the word (or phrase), then the next line is multiple floats indicating the embedding.Example of
wordvec.txt
:word 0.0 1.0 -2.3 phrases 0.3 -1.2 3.4
-
load_matrix
(n_dims, vocab_list, mean=None, std=None, default_embeddings=None) → numpy.ndarray[source]¶ Load pretrained word vector and return a numpy 2-d array. The ith row is the feature of the ith word in
vocab_list
. If some feature is not included in pretrained word vector, it will be initialized by:default_embeddings
, if it is notNone
.normal distribution with
mean
andstd
, otherwise.
- Parameters
n_dims (int) – specify the dimension size of word vector. If
n_dims
is bigger than size of pretrained word vector, the rest embedding will be initialized bydefault_embeddings
or a normal distribution.vocab_list (list) – specify the vocab list used in data loader. If there is any word not appeared in pretrained word vector, the embedding will be initialized by
default_embeddings
or a normal distribution.mean (float, Any, None) – The mean of normal distribution. It can be a float, or an array whose shape is
[n_dims]
. ifNone
, it will be set by the mean of loaded word vector embedding. Default:None
.std (float, Any, None) – The standard deviation of normal distribution. It can be a float, or an array whose shape is
[n_dims]
. ifNone
, it will be set by the standard deviation of loaded word vector embedding. Default:None
.default_embeddings (Any, optional) – The default embeddings, its size should be
[len(vocab_list), n_dims]
. Default: None, which indicates initializing the embeddings from the normal distribution withmean
andstd
.
- Returns
(
numpy.ndarray
) – A 2-d array. Size:[len(vocab_list), n_dims]
.
-
load_dict
(vocab_list) → Dict[str, numpy.ndarray][source]¶ Load word vector and return a dict that maps words to vectors.
- Parameters
vocab_list (list) – specify the vocab list used in data loader. If there is any word not appeared in pretrained word vector, the feature will not be returned.
- Returns
(dict) –
- maps a word (str) to its pretrained embedding (
numpy.ndarray
) where its shape is [ndims].
- maps a word (str) to its pretrained embedding (
Glove¶
-
class
cotk.wordvector.
Glove
(file_id='resources://Glove300d')[source]¶ Bases:
dataloader.GeneralWordVector
,dataloader.WordVector
GloVe is pre-trained word vector named Global Vectors for Word Representation.
References
[1] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.
- Parameters
file_id (str,
None
) – A str indicates the source of word vectors. It can be local path ("./data"
), a resource name ("resources://dataset"
), or an url ("http://test.com/dataset.zip"
). Seecotk.file_utils.get_resource_file_path()
for further details. IfNone
, do not use pretrained word vector. Default:resources://Glove300d
. A 300-d pretrained GloVe will be downloaded (or loaded from cache) and used.


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Instructions
Create an html document.
Add a div tag in the body of the document.
Add script tags referencing the following scripts.
http://ajax.aspnetcdn.com/ajax/jQuery/jquery-1.6.2.min.js
http://ajax.aspnetcdn.com/ajax/jquery.ui/1.8.15/jquery-ui.min.js
arcGisJsApi.js
Add a script tag containing the following line of code. (In the example below, your div element has an id of
map
.
$("#map").arcGisMap();
This will create a map object on element with an id of
map
.


Storing, managing, and processing datasets are foundational processes in data science. This course introduces the fundamental knowledge and skills of data engineering that are required to be effective as a data scientist. This course focuses on the basics of data pipelines, data pipeline flows and associated business use cases, and how organizations derive value from data and data engineering. As these fundamentals of data engineering are introduced, learners will interact with data and data processes at various stages in the pipeline, understand key data engineering tools and platforms, and use and connect critical technologies through which one can construct storage and processing architectures that underpin data science applications.
The course is organized as an online inverted classroom. During each week, students first work through a set of asynchronous materials, including video lectures, readings, and other activities. Once a week, students meet for a 90-minute live session, in which they connect with an instructor and other students in an online classroom. A functioning webcam and an audio headset are required to participate in the live sessions. Students must complete all assigned asynchronous material before the scheduled live session begins.
Students will:
Students will:
In this class we will be using cloud instances on Google Cloud Platform (GCP) for all class activities and projects. We will set these up in the first week of class, but if you’d like to get a head start, here are some notes.
We’ll have you create an AI Platform Notebook instance for class. These are general instructions as well as a general video on AI Platform Notebooks that goes into a little more depth.
The only differences to note for class are:
Sign up with your UCB ISchool email address and you should get a fresh $300 credit for GCP. This should more than cover the instance expense for the semester
Please create your instance from the TensorFlow-2.0 image when prompted
We recommend you customize the AI Platform Notebook Instance to be smaller (cheaper) than the default. A single cpu core with 4G RAM and no GPU should suffice for class. You can always increase/add these later if you want to use this for other projects/classes
There are three projects:
that collectively form the core of this course. Your work on them is one of the best ways for you to learn, and they each count for a third of your grade in this course.
Please check with your section instructor for definitive due dates for your projects.
Most readings are available through a subscription to https://www.safaribooksonline.com/. Other readings are blog posts and links.
SQL Tutorial
w3schools.com
https://www.w3schools.com/sql/default.asp
Learning SQL, 2nd Edition
by Alan Beaulieu
https://www.safaribooksonline.com/library/view/learning-sql-2nd/9780596801847/
Learning the Shell
by William E. Shotts, Jr.
http://linuxcommand.org/lc3_learning_the_shell.php
Pro Git book
by Scott Chacon and Ben Straub
https://git-scm.com/book/en/v2
Python for Data Analysis, 2nd Edition
by William Wesley McKinney
https://www.safaribooksonline.com/library/view/python-for-data/9781491957653/
Getting Started with Docker
https://docs.docker.com/get-started/
Using Docker
by Adrian Mouat
https://www.safaribooksonline.com/library/view/using-docker/9781491915752/
The course consists of 4 sections:
A 3-week Introduction that covers the basics of storage and retrieval concepts and tools; a 5-week Basics section that provides a deeper exploration of working with data and data pipelines; a 4-week section that focuses on Streaming Data; and a concluding section, Putting it All Together, that integrates concepts and skills from the entire course into a cohesive model of the data pipeline.
In addition to the sequenced material covered, the course also includes Tutorial materials that focus on technical skills associated with data engineering technologies, tools, and platforms. These tutorials also provide a practical foundation for the discussions and activities that will take place in the live classroom for specific weeks in the term.
Data Science vs. Data Engineering
Insight Data Science blog https://blog.insightdatascience.com/data-science-vs-data-engineering-62da7678adaa
Network Programmability and Automation by Jason Edelman, Matt Oswalt, Scott S. Lowe Chapter 8. Source Control with Git https://www.safaribooksonline.com/library/view/network-programmability-and/9781491931240/ch08.html#sourcecontrol
Analytics For Hackers: How To Think About Event Data by Michelle Wetzler https://blog.keen.io/analytics-for-hackers-how-to-think-about-event-data/
1.1 - Welcome
1.1.1 What is Data Engineering? [lecture]
1.1.2 What will I be able to do after this course? [lecture]
1.2 - This Course
1.2.1 Organization and Approach [lecture]
1.3 - Data Pipelines
1.3.1 What's a Data Pipeline? [lecture]
1.3.2 Typical Pipeline Components [lecture]
1.4 - Events Everywhere
1.4.1 Device Events [lecture]
1.4.2 User Events [lecture]
1.4.3 Event Streams [lecture]
Network Programmability and Automation by Jason Edelman, Matt Oswalt, Scott S. Lowe Chapter 5. Data Formats and Data Models https://www.safaribooksonline.com/library/view/network-programmability-and/9781491931240/ch05.html#dataformats
Python for Data Analysis, 2nd Edition by William Wesley McKinney Chapter 2.2 IPython Basics https://www.safaribooksonline.com/library/view/python-for-data/9781491957653/ch02.html#ipython_basics
2.1 Working with Data
2.1.1 Introduction [lecture]
2.1.2 Formats [screencast]
2.1.3 Schema/Types [screencast]
2.2 Tools for Working with Data
2.2.1 Introduction to Tools [screencast]
2.2.2 Introduction to the Command Line [tutorial]
2.2.3 Using the The Command Line [tutorial]
2.2.4 Introduction to Jupyter Notebooks [tutorial]
2.2.5 Introduction to Docker [tutorial]
Designing Data-Intensive Applications, 1st Edition Martin Kleppmann Chapter 2. Data Models and Query Languages https://www.safaribooksonline.com/library/view/designing-data-intensive-applications/9781491903063/ch02.html#ch_datamodels
Learning SQL, 2nd Edition by Alan Beaulieu Chapter 3. Query Primer https://www.safaribooksonline.com/library/view/learning-sql-2nd/9780596801847/ch03.html
The SQL tutorial for data analysis from basic to advanced Complete Basic, Intermediate and Advanced https://mode.com/sql-tutorial/
SQL Zoo Learn SQL from basics to advanced step by step https://sqlzoo.net/wiki/SQL_Tutorial
Introduction to Databases Stanford Online, by Jennifer Widom https://lagunita.stanford.edu/courses/Engineering/db/2014_1/about
Note: The above resources are useful to develop a stronger understanding of SQL and develop a sense of comfort when working with relational databases. However these same concepts can be used to join and query multiple tables in BigQuery by joining based on common fields or columns.
3.1 What is the queryside?
3.1.1 Revisiting Pipelines [lecture]
3.1.2 Immutability [lecture]
3.2 Query Tools
3.2.1 Queryside World [screencast]
3.2.2 Athena [screencast]
3.2.3 BigQuery [screencast]
3.3 Sneak Peek
3.3.1 Caching [screencast]
3.3.2 Using Jupyter Notebooks [tutorial]
3.3.3 Using Docker [tutorial]
3.3.4 Using Containers to Run Services with Docker [tutorial]
Using Docker by Adrian Mouat Chapter 1. The What and Why of Containers https://www.safaribooksonline.com/library/view/using-docker/9781491915752/ch01.html#what_and_why
Linux in a Nutshell by By Stephen Figgins, Arnold Robbins, Ellen Siever, Robert Love Chapter 1. Introduction https://www.oreilly.com/library/view/linux-in-a/9780596806088/ch01.html
4.1 Introduction
4.1.1 - Where are we in the Pipeline? [lecture]
4.2 Relational Data Stores
4.2.1 - Relational Databases [lecture]
4.2.2 - Relational Databases - Relations [lecture]
4.2.3 - Relational Databases - Normalization [lecture]
4.3 NoSQL Data Stores
4.3.1 - NoSQL Data Stores [lecture]
4.3.2 - NoSQL - Relational Model [screencast]
4.3.3 - NoSQL - Document Store [screencast]
4.3.4 - NoSQL - Key-Value Store [screencast]
Hadoop: The Definitive Guide, 4th Edition by Tom White Chapters 1-3 https://www.safaribooksonline.com/library/view/hadoop-the-definitive/9781491901687/
5.1 Hadoop
5.1.1 - Hadoop [lecture]
5.1.2 - Hadoop Walkthrough [screencast]
5.1.3 - The Hadoop Ecosystem [screencast]
5.1.4 - Using Hadoop [screencast]
5.2 Introduction to the Cloud
5.2.1 - Virtualization and Containers [screencast]
5.2.2 - Infrastructure Encapsulation and Isolation [screencast]
5.2.3 - Composing Containers [screencast]
5.2.4 - Failure in Distributed Systems [lecture]
Designing Data-Intensive Applications, 1st Edition Martin Kleppmann Chapter 10. Batch Processing https://www.safaribooksonline.com/library/view/designing-data-intensive-applications/9781491903063/ch10.html#ch_batch
Designing Data-Intensive Applications, 1st Edition Martin Kleppmann Chapter 11. Stream Processing https://www.safaribooksonline.com/library/view/designing-data-intensive-applications/9781491903063/ch11.html#ch_stream
Hadoop: The Definitive Guide, 4th Edition by Tom White Chapter 4. YARN https://www.safaribooksonline.com/library/view/hadoop-the-definitive/9781491901687/ch04.html
6.1 Introduction
6.1.1 - Transform Section of Pipeline [lecture]
6.2 ETL
6.2.2 - You don't always (aka usually) get what you want [screencast]
6.3 Hadoop ETL
6.3.1 - Running Hadoop Jobs [lecture]
6.3.2 - Hadoop ETL [screencast]
6.3.3 - Introduction to Spark [screencast]
6.4 Container Management
6.4.1 - Distributed Execution Models [screencast]
6.4.2 - Introduction to Schedulers [screencast]
6.4.3 - Using Schedulers [screencast]
- Learning Apache Kafka By Nishant Garg Chapter 1. Introducing Kafka
- https://www.oreilly.com/library/view/learning-apache-kafka/9781784393090/ch01.html
7.1 Pipeline Context
7.1.1 - Pipeline Context [lecture]
7.2 Where did the data come from?
7.2.1 - APIs [screencast]
7.2.2 - Web pages [screencast]
7.2.3 - Email attachments [screencast]
7.2.4 - Databases [screencast]
7.2.5 - Cloud Storage [screencast]
7.2.6 - Application Pipelines [screencast]
7.3 What do you want to know about the data you get?
7.3.1 - Lineage [screencast]
7.4 Security and Privacy
7.4.1 - Security and Privacy [lecture]
7.4.2 - Sensitive Data [screencast]
Designing Data-Intensive Applications, 1st Edition by Martin Kleppmann Chapter 6. Partitioning https://www.safaribooksonline.com/library/view/designing-data-intensive-applications/9781491903063/ch06.html#ch_partitioning
8.1 Querying
8.1.1 - Pipeline Context [lecture]
8.1.2 - Base decisions on queries [screencast]
8.1.3 - Query Walkthrough [screencast]
8.2 Partitions
8.2.1 - Querying with Partition Keys [screencast]
8.3 Query Planning
8.3.1 - Query Optimization [screencast]
8.3.2 - Using the Tools [screencast]
Kafka: The Definitive Guide, 1st Edition by Gwen Shapira, Neha Narkhede, Todd Palino Chapter 1. Meet Kafka https://www.oreilly.com/library/view/kafka-the-definitive/9781491936153/ch01.html
Flask Web Development, 2nd Edition by Miguel Grinberg Chapter 2. Basic Application Structure https://www.oreilly.com/library/view/flask-web-development/9781491991725/ch02.html
9.1 Pipeline Context
9.1.1 - Where are we in the Pipeline? [lecture]
9.2 Kafka
9.2.1 - Distributed Messaging [lecture]
9.2.2 - Kafka Walkthrough [screencast]
9.2.3 - Where are the data coming from? [screencast]
9.2.4 - Latency [lecture]
9.2.5 - Batch vs. Real Time [screencast]
9.2.6 - Driven by Queries [screencast]
High Performance Spark, 1st Edition by Holden Karau, Rachel Warren Chapter 2. How Spark Works https://www.safaribooksonline.com/library/view/high-performance-spark/9781491943199/ch02.html
10.1 NoETL
10.1.1 - Pipeline Context [lecture]
10.2 Modes of Execution
10.2.1 - Batch -vs- Streaming [screencast]
10.2.2 - Single Event Processing [screencast]
10.2.3 - Microbatch [screencast]
10.2.4 - Continuous Applications - Handling Batch and Streaming Data in the Same System [screencast]
10.3
10.3.1 - In Memory Computing [screencast]
10.4 Take Action!
10.4.1 * Take Action! [lecture]
Structured Streaming Programming Guide Apache Foundation https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html Through “Operations on streaming DataFrames/Datasets” section
11.1 Pipeline Context
11.1.1 - Pipeline Context [lecture]
11.2 In Memory
11.2.1 - Streaming and Spark [screencast]
11.2.2 - Structured Streaming [screencast]
11.3 Resource Selections
11.3.1 - Dedicated Stream Processing [screencast]
11.4 Distributed In-memory Storage
11.4.1 - Distributed In-memory Storage [screencast]
11.5 Activity
11.5.1 - User Activity [lecture]
Structured Streaming Programming Guide Apache Foundation https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html From “Streaming Queries” section to end
12.1 Queries
12.1.1 - Caching -vs- Stream Queries [screencast]
12.1.2 - Caching and the Web [screencast]
12.1.3 - A walk down memory lane - data mining [screencast]
12.1.4 - Replay [screencast]
12.1.5 - Caching queries [screencast]
Scala:Applied Machine Learning by Alex Kozlov, Patrick R. Nicolas, Pascal Bugnion Sessionization (in Chapter 6. Working with Unstructured Data) https://www.safaribooksonline.com/library/view/scalaapplied-machine-learning/9781787126640/ch32s04.html
Advanced Analytics with Spark, 2nd Edition by Uri Laserson, Sandy Ryza, Sean Owen, Josh Wills Sessionization in Spark (in Chapter 8. Geospatial and Temporal Data Analysis on New York City Taxi Trip Data) https://www.safaribooksonline.com/library/view/Advanced+Analytics+with+Spark,+2nd+Edition/9781491972946/ch08.html#idm140398878264880
13.1 Context
13.1.1 - Pipeline Context [lecture]
13.2 Sessionization and State
13.2.1 - Sessionization and state for action [lecture]
13.3 Using Public Clouds
13.3.1 - AWS
13.3.2 - GCP
Infrastructure as Code by Kief Morris Chapter 1. Challenges and Principles https://www.safaribooksonline.com/library/view/infrastructure-as-code/9781491924334/ch01.html#chapter-challenges
Network Programmability and Automation by Jason Edelman, Matt Oswalt, Scott S. Lowe Chapter 10. Continuous Integration https://www.safaribooksonline.com/library/view/network-programmability-and/9781491931240/ch10.html#cicd
Serverless Architectures on AWS by Peter Sbarski Chapter 1. Going serverless https://www.safaribooksonline.com/library/view/serverless-architectures-on/9781617293825/kindle_split_013.html
The Case for Learned Index Structures by Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, & Neoklis Polyzotis https://www.arxiv-vanity.com/papers/1712.01208v1/
14.1 DevOps
14.1.1 - Infrastructure as Code [screencast]
14.2 Cluster Examples
14.2.1 - Docker Swarm
14.2.2 - Kubernetes
Please read UC Berkeley’s policies around academic integrity: http://sa.berkeley.edu/conduct/integrity
Plagiarism is a serious academic offense, and students must take care not to copy code written by others. Beginning students sometimes have trouble identifying exactly when plagiarism takes place. Remember that it is generally fine to search for examples of code (for example, on forums like stackoverflow). This is a normal part of programming and can help you learn. However, it is important that you understand the code you find and use what you learn to write your own statements. If in doubt, simply document the place you found your example code and ask your instructors for further guidance.


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Electric Shock
If someone has had an electric shock, switch off the electrical current at the mains to break the contact between the person and the electrical supply.
If you can't reach the mains supply:
-
Do not go near
-
Do not touch the person until you're sure the electrical supply has been switched off.
-
Once the power supply has been switched off, and if the person isn't breathing, dial the number above to for an ambulance.


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This demo is written in Python 3 with Flask Microframework and rendered using Jinja template engine. Photo is credited to Ryan Spencer on Unsplash. All styling is done with help from Bulma, an open source CSS Framework. This demo is deployed using AWS Fargate.
This demo is created by Donnie Prakoso and for full step-by-step walkthrough please refer to the blog post at https://donnie.id.


You are viewing an historical archive of past issues. Please report new issues to the appropriate project issue tracker on GitHub.
Issues
| # | Summary | Product | Status | Kind | When |
|---|---|---|---|---|---|
| 106 |
REQUEST: Show upcoming tracks in Global Menu |
Synergy | open | feature request | Created , updated |
| 105 |
REQUEST: Change iTunes submenu when iTunes not running |
Synergy Advance | closed | feature request | Created , updated |
| 104 |
Show grouping in floater |
Synergy Advance | open | feature request | Created , updated |
| 103 |
REQUEST: Show genre in floater |
Synergy | open | feature request | Created , updated |
| 102 |
REQUEST: Hot key to cycle through Amazon search results |
Synergy Advance | open | feature request | Created , updated |
| 101 |
REQUEST: Fade in/fade out the audio on pause and play |
Synergy Advance | open | feature request | Created , updated |
| 100 |
REQUEST: Display Current Track in iTunes via Hotkey |
Synergy | open | feature request | Created , updated |
| 99 |
Editing ID3 tags in iTunes causes floater to endlessly fade in/out |
Synergy | closed | bug | Created , updated |
| 98 |
REQUEST: Vertical floater orientation |
Synergy | open | feature request | Created , updated |
| 97 |
Adjusting rating via hotkeys causes iTunes to crash |
Synergy | closed | bug |


{% extends "base.html" %} {% block content %} {% call card("Account Management") %} {% call inner_card("Register confirmation") %}
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You will never understand every detail of the implementation of every level on that stack; but you can understand all of them to some level of abstraction, and any specific layer to essentially any depth necessary for any purpose.
you end up reading dependencies as well
If the documentation is lacking in some way, you can always go to the source and look at the implementation for an authoritative answer.
building mental models is not just getting languages, apis and libraries as collections of rules and behaviours, but getting a smaller model of core primitives and the rules and principles that generate behaviour in the larger system
he also has debug war stories


Crystal 0.25.0 has been released!
As every release, it includes numerous bugfixes, cool features and performance improvements - in 400 commits since 0.24.2 from 47 contributors. There needs to be a special mention to @MakeNowJust, @straight-shoota, @Sija and @bew for their hard work in this release.
There were a ton of contributions merged in master even before 0.24.2 was released. But since 0.24.2 was already changing the release packaging for linux, changing the CI and fixing 0.24.1, some features needed to wait their turn a little longer.
Once again, we have tested this release by compiling some of the most popular crystal shards. This helps us catch and fix unintended breaking changes earlier in the release cycle as well as submitting PRs to the shards and contributing a bit more with the community. This process is codified using the scripts in the test-ecosystem repository, which is still fairly new, but so far it’s working well.
The least visible work usually goes in infrastructure and there are always improvements and things waiting to be done. The latest news regarding this area are:
- Docs in master are back. For every PR that is merged the docs at HEAD can be found at /api/master/.
- Improved SEO by adding a canonical url for online docs #5990.
- Also on docs, lots of improvements regarding navigation have been done in #5229.
- The automated release process now cares about 32 bits linux releases. As a bonus point the packaging has been aligned again with respect to the 64 bits packages. So some paths have changed.
- We’ve been contacted by Heroku to early register our buildpack. Stay tuned to future Heroku news to update to the
crystal-lang/crystal
buildpack in the registry. All in all it’s one more taste of the adoption of Crystal out there, and we are thrilled.
Nightly packages in nightly.crystal-lang.org
are still down. The workaround for now it to use the docker image crystallang/crystal:nightly
.
Exciting features
Shards is updated to 0.8.0
There are some performance improvements in shards for this release, by downloading less information when possible. A new global cache was added, so you don’t need to download your favorite shards over and over on all of your favorites projects. FYI you can use shards 0.8.0 with Crystal 0.24.2 if you want.
Read more here.
Automatic casts for literal values
If a method is defined as def foo(x : Int8)
or def bar(color : Color)
with
up to 0.24 you would need to call them as foo(1i8)
or bar(Color::Blue)
. But since 0.25.0 you will be able to foo(1)
and bar(:blue)
. A note of caution: this only work with literal values. If the value is saved in a variable and used as an argument it won’t work.
This feature allows cleaner code without sacrificing safety. Read more at #6074.
User defined annotations and [JSON|YAML]::Serializable
This new language construct allows the user to define their own annotations, like [Link]
. Basically you will be able to annotate types declaration or instance variables, and later on query them to do something you wish in macros.
Before this feature metaprogramming usually involved calling one macro with all the information needed. From now on, a more decoupled mechanism between declaring and consuming can be used. Read more at #6063.
The new JSON::Serializable
and YAML::Serializable
modules use this annotations. Feedback is welcome since this feature is brand new. You can read more at #6082, JSON::Serializable, YAML::Serializable docs.
Another usage of annotations might be to declare a registry of classes, like the one used in DB
drivers or frameworks handlers. And it could enable the removal of mutating values of constants during compilation time in favor of a more declarative code.
Do not collapse unions for sibling types
Code is worth a thousand words (you know, like pictures):
Up to 0.24.2 the result was typeof(var) #=> Foo
.
Although the previous code already compiled fine in 0.24.2 this changes allow the type system to deal with some cases that would have ended in a compile-time error but that actually make sense. At the end of the day the type system is about identifying which programs will safely run and cutting the ones that won’t.
The following program is an example of that. It won’t compile in 0.24.2 but it now does in 0.25.0.
This is particularly useful in scenarios where there is a huge hierarchy of types but in a section of the code only a subset is used.
You can read more at #6024 and discover when the union of types are still collapsed to the common ancestor (spoiler, they need to not be siblings).
JSON::Any
and YAML::Any
changes
There were some subtle inconsistencies with JSON::Any
and YAML::Any
API. The bottom line is that over an ::Any
value you can use #[]
to traverse it and it will always return an ::Any
value. If you need a specific type for the ::Any
value (and be able to use Enumerable
methods if it was an array) you need to call the already known #as_a
, #as_h
, #as_s
methods.
We still encourage, when possible, the use of JSON.mapping
, JSON::Serializable
or JSON::PullParser
when finer control is needed.
Read more at #5183 and in the JSON::Any and YAML::Any docs.
HTTP::Server
can bind to multiple addresses
This will break lots of presentations and even the code shown in our own homepage but the benefits are great.
From now on if you use the built-in HTTP::Server
you first need to configure it, then bind to one or more addresses, and lastly you listen to all of them. These addresses can be TCP ports or Unix sockets.
There is still a shortcut to bind and listen, but it doesn’t avoid a breaking change. Read more at #5776, #5959, and the HTTP::Server docs
Welcome to the TimeZone Jungle
There was a huge refactor in Time
. If you hit a unicorn while opening the PR to read more about it, just try again.
Starting now Time
has #location
and #offset
properties to know the timezone exactly. Time.now
and Time.new
will return by default information in the local timezone, while Time.utc_now
and Time.utc
will return information in UTC.
Methods like #to_local
, #to_utc
, #utc?
, #local?
and #in(location : Location)
will help you to move around the globe faster than a plane.
The API even allows you to use custom timezones and fixed offsets with Time::Location.fixed
.
Another change in the Time
namespace are formatters. Better formatters for ISO 8601, RFC 3339, RFC 2822, HTTP enconded dates, YAML and other places where time was parsed or emitted now use a custom time formatter that deals with more cases as expected in each scenario.
Read more at #5324 and #5123 and Time, and Format docs.
Replace File::Stat
with File::Info
and other file API changes
Some time ago an abstraction for the running OS was introduced in the stdlib. The goal was to be able to run the Crystal compiler in a non POSIX platform and keep the stdlib as clean as possible. Feel free to check src/crystal/system, but keep in mind it is not intended as a public API.
This also required to pick names and abstractions in the stdlib that will fit everybody: POSIX and non POSIX.
The API was renamed and reworked for compare operations and accessing file properties and permissions. It is much clearer now. Hopefully it doesn’t affect too many users, since most of us use File.open
, File.write
and move on. Read more at #5584, #5333, #5553, #6161, File and File::Info docs.
Heredoc on every argument
If you use Heredoc a lot of you might be interested in this one. Up to 0.24.2 if you wanted to call a method on a string specified using Heredoc you would do:
From now on the method needs to be at the initial delimiter
It’s subtle but important, and it plays better with multiple Heredocs in a single call now that you can:
Read more at #5578.
Macro verbatim blocks
If you deal with escaped macros don’t miss #6108.
Macros are powerful and they should be used after there is a boilerplate pattern discovered.
This new language construct helps when the macro itself will define, for example, methods that have macro blocks that should be expanded later (i.e. nested macros).
It may result in a nicer way to express the same things you could before with some \{% escaping %}
.
Other notable changes (breaking or not)
crystal deps
is dead, long liveshards install
. #5544. Unless we removed it, you would never have updated your build scripts.- Use
Hash#key_for
to perform a reverse lookup in a hash #5444 #NamesAreHard. - The block argument from
loop
was removed #6026. - Fix
File.join
with empty path component #5915. Colorize#push
is dead, long liveColorize#surround
#4196. Bonus point, your#to_s
can use your favorite color now.- Punycode is a special encoding used to convert Unicode characters to ASCII and is used to encode internationalized domain names (IDN). And now they are available in Crystal thanks to #2543.
pp
no longer prints the expression. Butpp!
and the newp!
will.p
stands for print,pp
for pretty print and!
for show me themoneyexpression #6044.
Next step
Please update your Crystal and report any issues. If there are regression or blocking issues with 0.25.0, a 0.25.1 could be released earlier.
Don’t miss the rest of the release changelog information with lots of other fixes.
The development is possible thanks to the community’s effort, 84codes’ support, and every BountySource supporter.


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Jeez strident slit congratulated inside besides adoringly strategic far outside tranquilly wobbled outside and dear that naked otter however immodest that yikes brusquely nimbly and modestly that cleverly lucidly mad hatchet blubbered much jubilant misheard strange impertinently shivered far a more away comparably hamster drew alas past horse measurable while hey fox because as and found porpoise dolphin.


|
| | GameSys (Context *context) |
| | Construct. More...
|
| |
| void | Start () override |
| |
| void | CreateWinScene () |
| |
| void | CreateLossScene () |
| |
|
void | DeleteCorrectnessText () |
| |
|
| Sample (Context *context) |
| | Construct.
|
| |
|
void | Setup () override |
| | Setup before engine initialization. Modifies the engine parameters.
|
| |
|
void | Start () override |
| | Setup after engine initialization. Creates the logo, console & debug HUD.
|
| |
|
void | Stop () override |
| | Cleanup after the main loop. Called by Application.
|
| |
|
|
SharedPtr< Sprite > | logoSprite_ |
| | Logo sprite.
|
| |
|
SharedPtr< Scene > | scene_ |
| | Scene.
|
| |
|
SharedPtr< Node > | cameraNode_ |
| | Camera scene node.
|
| |
|
float | yaw_ |
| | Camera yaw angle.
|
| |
|
float | pitch_ |
| | Camera pitch angle.
|
| |
|
bool | touchEnabled_ |
| | Flag to indicate whether touch input has been enabled.
|
| |
|
MouseMode | useMouseMode_ |
| | Mouse mode option to use in the sample.
|
| |
Definition at line 32 of file main.h.
◆ GameSys()
| GameSys::GameSys |
( |
Context * |
context | ) |
|
|
explicit |
Construct.
GameSys function. instigates pipeline, checks and returns error if unable starts game process calls audioIn function and playNote function
Definition at line 347 of file main.cpp.
◆ CreateLossScene()
| void GameSys::CreateLossScene |
( |
| ) |
|
Shows the instruction text onto the screen
Definition at line 696 of file main.cpp.
◆ CreateWinScene()
| void GameSys::CreateWinScene |
( |
| ) |
|
Shows the instruction text onto the screen
Definition at line 679 of file main.cpp.
◆ GetScreenJoystickPatchString()
| String GameSys::GetScreenJoystickPatchString |
( |
| ) |
const |
|
inlineoverrideprotectedvirtual |
Return XML patch instructions for screen joystick layout for a specific sample app, if any.
Reimplemented from Sample.
Definition at line 50 of file main.h.
◆ Start()
Setup after engine initialization and before running the main loop. Default Start function overridden to allow game loop to begin
Start function. Executes base class startup, creates the title scene and accepts mouse input
Definition at line 370 of file main.cpp.
The documentation for this class was generated from the following files:


Posted on April 2, 2019 at 18:29 PM by Ivan Stoev
Manchester United will most likely need a new right back in the summer as Antonio Valencia is set to leave the club and Ashley Young is not doing great as of late.
Calciomercato report that among the names United have in mind are the likes of Thomas Meunier and Aaron Wan-Bissaka, but the idea of bringing Calabria in Manchester may be considered as well.
The Premier League side are hoping that Calabria won’t renew his contract with Milan and they can get in the race for his signature, though it’s hard to see the young defender leave the San Siro side.
In fact, the Italian starlet has a contract with the Italian giants till 2022 and current reports suggest that Leonardo wants to further extend it till 2024 with the player likely to agree.


AirGap Vault keeps your keys safe and allows you to interact with wallets seamlessly and securely.
Connect your Vault with various wallets to enable more possibilities.
Take your private key offline, to create a real air-gap between Vault and Wallet. This is the strongest protection layer against hackers.
Recover all your accounts in case something happens to your device.
Gain access to additional features by turning on the Advanced Mode in the settings.


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Explore our demos pages on different layout of designs. More demos are coming soon.
Start building fast, beautiful and modern looking websites in no time using our themes.
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This animation uses the css -webkit-transform property and the @keyframes rule to apply specific transform animations specified intervals in terms of a percentage of the duration of the animation.