#include "graphics.h" int getactivepage(void);
The active graphics page might not be the one you see onscreen, depending on how many graphics pages are available on your system.
The original winbgi was designed to support up to 16 pages, but I have only used pages 1 and 2 myself. NOTE: Using page number 0 might mess up the colors. I use pages 1-2 for double buffering.


{{ entry.title }}
{{ entry.body }}
{{ entry.publish_date }}
{% extends "base.html" %} {% load static %} {% block title %}Non Human User Blog{% endblock %} {% block description %}Stories and opinions about Cthulhu Mythos related subjects and the Call of Cthulhu role playing game{% endblock %} {% block content %}
{{ entry.body }}
{{ entry.publish_date }}


This is purpose for University of computer studies Mubin old question.This application you can see old question of University of computer studies Mubin from
"First Year"
"Secound Year"
"Third Year"
"Fourth Year"
"Fifth Year".
This application has included for the year from.


Women should be able to see male colleagues’ pay, says charity
Women should have the right to know what their male colleagues are being paid if they suspect pay discrimination, a gender equality charity has said.
The Fawcett Society is calling for a change in the law to try to cut down on instances of unequal pay.
But men can help by simply telling female colleagues what they earn, the charity added.
The call comes as Labour pledges to close the gender pay gap by 2030.
Fawcett Society chief executive Sam Smethers said: "Pay secrecy means women cannot know if they are being paid equally and fairly.
"Even if they do suspect a man is earning more it is almost impossible to do anything about it. This is why we are calling for a change in the law."
She said that women need an enforceable "right to know" what their colleagues earn so that they can challenge unequal pay.
"Men can help by simply telling their female colleagues what they earn. It really is that simple," Ms Smethers added.
Shanti Kelemen, investor director at Brown Shipley, told BBC Radio 4's Programme that it should be "all or nothing".
"I just think giving women the right isn't the answer. Why not minorities, old people, young people?"
"If I was going to implement something like this, use a system like Sweden where anyone can look at anyone's tax return. The catch is that everyone will know you've looked."
Under UK law, men and women are supposed to be paid the same for doing comparable work.
The call from the Fawcett Society comes after the Labour Party pledged to close the gender pay gap by 2030 if it wins the election.
The gender pay gap is the percentage difference between average hourly earnings for men and women.
'I feel betrayed and disgusted'
Kay Collins had a job with a catering firm, but her work-life started to unravel after a chance conversation with a male colleague in September 2015.
She found out that he was earning £6,000 more per year than her for doing exactly the same role as a chef.
"I was shaking, I was so angry," she said.
She was older and more experienced than her male colleague, and so raised a grievance with her female line manager.
However, her manager said the man had more responsibilities - although he himself said he didn't.
He ended up resigning, and Kay lost her job, although she won a settlement at a tribunal after a process that took many years.
"It was an ordeal, that's what it was," she says. "I lost two stone over a couple of years [because of the stress]."
However, the settlement was not enough to cover her £15,000 court costs.
Kay, who is 60 next year, felt "down and betrayed" afterwards, and is now a full-time carer for her husband William, who has a heart condition, and is still having to work from home to pay their mortgage.
"I feel disgusted and betrayed."


An action that transforms a descriptor.
The DescAction type describes a single action that transforms a descriptor.
A DescAction can be created with the DescAction.new constructor. It is also returned from the Desc.Diff method and the desc-patch.json format.
Creates a new DescAction.
The new constructor returns a new DescAction initialized with a type and an element.
The type of element.
The Element property is the type of element to which the action applies.
The name of the primary element.
The Primary property is the name of the primary element. For example, the class name or enum name.
The name of the secondary element.
The Secondary property is the name of the secondary element. For example, the name of a class member or enum item. An empty string indicates that the action applies to the primary element.
The type of transformation.
The Type property is the type of transformation performed by the action.
Gets a field.
The Field method returns the value of the name field, or nil if the action has no such field.
Gets all fields.
The Fields method returns a collection of field names mapped to values.
Sets a field.
The SetField method sets the value of the name field to value.
Sets all fields.
The SetFields method replaces all fields of the action with fields.


Contents
This appendix is non-normative.
The grammar below defines the syntax of CSS 2.1. It is in some sense, however, a superset of CSS 2.1 as this specification imposes additional semantic constraints not expressed in this grammar. A conforming UA must also adhere to the forward-compatible parsing rules, the selectors notation, the property and value notation, and the unit notation. However, not all syntactically correct CSS can take effect, since the document language may impose restrictions that are not in CSS, e.g., HTML imposes restrictions on the possible values of the "class" attribute.
The grammar below is LALR(1) (but note that most UA's should not use it directly, since it does not express the parsing conventions, only the CSS 2.1 syntax). The format of the productions is optimized for human consumption and some shorthand notation beyond Yacc (see [YACC]) is used:
The productions are:
stylesheet : [ CHARSET_SYM STRING ';' ]? [S|CDO|CDC]* [ import [ CDO S* | CDC S* ]* ]* [ [ ruleset | media | page ] [ CDO S* | CDC S* ]* ]* ; import : IMPORT_SYM S* [STRING|URI] S* media_list? ';' S* ; media : MEDIA_SYM S* media_list '{' S* ruleset* '}' S* ; media_list : medium [ COMMA S* medium]* ; medium : IDENT S* ; page : PAGE_SYM S* pseudo_page? '{' S* declaration? [ ';' S* declaration? ]* '}' S* ; pseudo_page : ':' IDENT S* ; operator : '/' S* | ',' S* ; combinator : '+' S* | '>' S* ; unary_operator : '-' | '+' ; property : IDENT S* ; ruleset : selector [ ',' S* selector ]* '{' S* declaration? [ ';' S* declaration? ]* '}' S* ; selector : simple_selector [ combinator selector | S+ [ combinator? selector ]? ]? ; simple_selector : element_name [ HASH | class | attrib | pseudo ]* | [ HASH | class | attrib | pseudo ]+ ; class : '.' IDENT ; element_name : IDENT | '*' ; attrib : '[' S* IDENT S* [ [ '=' | INCLUDES | DASHMATCH ] S* [ IDENT | STRING ] S* ]? ']' ; pseudo : ':' [ IDENT | FUNCTION S* [IDENT S*]? ')' ] ; declaration : property ':' S* expr prio? ; prio : IMPORTANT_SYM S* ; expr : term [ operator? term ]* ; term : unary_operator? [ NUMBER S* | PERCENTAGE S* | LENGTH S* | EMS S* | EXS S* | ANGLE S* | TIME S* | FREQ S* ] | STRING S* | IDENT S* | URI S* | hexcolor | function ; function : FUNCTION S* expr ')' S* ; /* * There is a constraint on the color that it must * have either 3 or 6 hex-digits (i.e., [0-9a-fA-F]) * after the "#"; e.g., "#000" is OK, but "#abcd" is not. */ hexcolor : HASH S* ;
The following is the tokenizer, written in Flex (see [FLEX]) notation. The tokenizer is case-insensitive.
The "\377" represents the highest character number that current versions of Flex can deal with (decimal 255). It should be read as "\4177777" (decimal 1114111), which is the highest possible code point in Unicode/ISO-10646.
%option case-insensitive h [0-9a-f] nonascii [\240-\377] unicode \\{h}{1,6}(\r\n|[ \t\r\n\f])? escape {unicode}|\\[^\r\n\f0-9a-f] nmstart [_a-z]|{nonascii}|{escape} nmchar [_a-z0-9-]|{nonascii}|{escape} string1 \"([^\n\r\f\\"]|\\{nl}|{escape})*\" string2 \'([^\n\r\f\\']|\\{nl}|{escape})*\' badstring1 \"([^\n\r\f\\"]|\\{nl}|{escape})*\\? badstring2 \'([^\n\r\f\\']|\\{nl}|{escape})*\\? badcomment1 \/\*[^*]*\*+([^/*][^*]*\*+)* badcomment2 \/\*[^*]*(\*+[^/*][^*]*)* baduri1 url\({w}([!#$%&*-\[\]-~]|{nonascii}|{escape})*{w} baduri2 url\({w}{string}{w} baduri3 url\({w}{badstring} comment \/\*[^*]*\*+([^/*][^*]*\*+)*\/ ident -?{nmstart}{nmchar}* name {nmchar}+ num [0-9]+|[0-9]*"."[0-9]+ string {string1}|{string2} badstring {badstring1}|{badstring2} badcomment {badcomment1}|{badcomment2} baduri {baduri1}|{baduri2}|{baduri3} url ([!#$%&*-~]|{nonascii}|{escape})* s [ \t\r\n\f]+ w {s}? nl \n|\r\n|\r|\f A a|\\0{0,4}(41|61)(\r\n|[ \t\r\n\f])? C c|\\0{0,4}(43|63)(\r\n|[ \t\r\n\f])? D d|\\0{0,4}(44|64)(\r\n|[ \t\r\n\f])? E e|\\0{0,4}(45|65)(\r\n|[ \t\r\n\f])?  Most of these are due to new tokens in CSS2 that did not exist in CSS1. Others are because the grammar has been rewritten to be more readable. However, there are some incompatible changes, that were felt to be errors in the CSS1 syntax. They are explained below.
The lexical scanner for the CSS core syntax in section 4.1.1 can be implemented as a scanner without back-up. In Lex notation, that requires the addition of the following patterns (which do not change the returned tokens, only the efficiency of the scanner):
{ident}/\\ return IDENT; #{name}/\\ return HASH; @{ident}/\\ return ATKEYWORD; #/\\ return DELIM; @/\\ return DELIM; @/- return DELIM; @/-\\ return DELIM; -/\\ return DELIM; -/- return DELIM; \</! return DELIM; \</!- return DELIM; {num}{ident}/\\ return DIMENSION; {num}/\\ return NUMBER; {num}/- return NUMBER; {num}/-\\ return NUMBER; [0-9]+/\. return NUMBER; u/\+ return IDENT; u\+[0-9a-f?]{1,6}/- return UNICODE_RANGE;


Origin of OBA Books per City
Worldwide:
{{data.amount}} books
Choose genre:
All genres
{{genre}}
close
{{city.name}}
Total books:
{{ city.amount }}
Book ratio relative to rest of the world:
Publisher ratio in {{city.name}}:
See publisher rate in bar chart
Click on any bubble to see the city name and total amount of books from there
close
{{fullscreen ? 'smaller' : 'fullscreen'}}
Publisher ratio in {{city.name}}:


All functions |
|
|---|---|
Average chromosome signal (coverage) dot plot |
|
Average signal per chromosome |
|
FACS density plot |
|
Microarray probe log2 ratios |
|
Calculate genome-wide average signal |
|
Parse gff field |
|
Read gff file |
|
hwglabr: A compilation of functions used frequently by Hochwagen lab members. |
|
Batch run of |
|
Standard analysis of ChIP-seq experiments |
|
Order chromosome column by chromosome length |
|
Plot genes on an R wiggle plot |
|
Load wiggle data |
|
Remove centromeric regions from wiggle data |
|
Wrapper function to run edgeR analysis of summarized reads from RNA-seq experiment |
|
Line plot of average signal between convergent genes |
|
Signal between all convergent genes genome-wide |
|
Line plot of average signal on intergenic regions |
|
Signal at intergenic regions between convergent, divergent or tandem genes genome-wide |
|
Line plot of average signal on ORFs |
|
Signal around ORF start or end positions |
|
Signal at all ORFs genome-wide (meta ORF) |
|
Signal flanking rDNA |
|
Signal around all bedfile midpoints |
|
Calculate average signal by position |
|
Compute mean signal and SD by position of output of |
|
Collect signal from telomeres for all chromosome arms |
|
Mean signal for all ORFs genome-wide |
|
Compress wiggle data |
|
Correlation between two ChIP-seq experiments |
|
Wiggle data line plot |
|
To make a scatter plot comparison of two wiggle files |
|
To make a scatter plot comparison of two wiggle files |
|
Calculate genome-wide signal average in wiggle data |
|
Sliding window smooth of wiggle data |


Wildfire Data Connector
This data connector retrieves geospatial information about active wildfires for the current date. Data Source: https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-current-wildland-fire-perimeters/about
WFIGS Wildfire Feed
Get Wildfire Data


This paragraph and the title above were typed into MS Visual Studio Code. The Calibri font was applied with CSS. We are testing if words like "matter" and "intention" break on encoding within a PDF after tagging as text/paragraph (if necessary) and running the Full Accessibility Check in Acrobat Pro.


POWDER A film review by Christopher Null Copyright 1995 Christopher Null
Once in awhile, Hollywood manages to surprise me with an uncompromising film full of genuine emotion and enough to make you really think. POWDER was completely unexpected: it's easily one of the best films I've seen all year.
Something of an updated, hybridized E.T., POWDER is the story of an albino teenager (Sean Patrick Flanery) with strange powers of telekinesis, empathy, and the ability to channel and absorb raw energy. As Powder says, "I'm not like other people." That's putting it lightly.
 His redneck father will have nothing to do with him, and Powder ends up in his grandparent's basement until his teens. When Powder finally emerges from hiding, the small Texas town has no idea what to make of him, and typical of Southern paranoids, he is immediately ostracized as a social deviant.
It soon becomes apparent that Powder is more than just an extremely pale kid, as some subtle and not-so-subtle changes begin to take effect, and we find that the outcasts among us can really be the greatest of people. Powder reflects the deconstruction of all that's wrong with society, and the film manages to hammer home its messages of peace, tolerance, and the cessation of fear without ever becoming preachy.
POWDER manages to take this kind of story to new and better heights than its predecessors. Powder is a recluse, but unlike NELL, we can understand and sympathize with him. Powder is the embodiment of virtue, but unlike FORREST GUMP, he is not just a victim of circumstance. And Powder is imbued with supernatural ability, but unlike E.T., we can relate to his persecution on an individual level.
POWDER probably deserves a dozen Academy Award nominations, especially for Flanery's jaw-dropping leading role. The makeup, which had to literally be painted on, is almost surreal, and writer/director Victor Salva (who's done virtually nothing before this) deserves my highest praise. The special effects are groundbreaking, the camerawork (shot in and around Houston) is excellent, and the supporting cast of Jeff Goldblum, Mary Steenburgen, and Lance Henriksen is solid.
This probably isn't a film for the brain-dead hordes of moviegoers looking for mindless eye candy. It requires a little commitment on the viewer's part, but it's well worth it. In the end, POWDER is a stirring example of what to do right, not only in filmmaking, but in living one's life as well.
RATING: ****1/2
\-------------------------------\ |* Unquestionably awful | |** Sub-par on many levels | |*** Average, hits and misses | |**** Good, memorable film | |***** Perfection | \-------------------------------\
-Christopher Null /<EMAIL_ADDRESS>-Movie Emporium (archives) / http://www.notes.tpoint.net/emporium/ -Contributing Editor, FEEDBACK / http://www.eden.com/~feedback -E-mail requests to join the movie review mailing list
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


set arrscl size <magnitude>
Specifies arrow length scaling.
size
is
the length of the
arrow in plotting
units (inches on the virtual page). A typical value would be
0.5
to 1.0
.
magnitude
is the vector
magnitude that will
produce an arrow of the specified size.
Other arrow lengths will be scaled appropriately. If
magnitude
is not given, all the arrows will be the
same length. Reset by clear
or
display
.


public class DiskThresholdDecider extends AllocationDecider
DiskThresholdDecider
checks that the node a shard is potentially
being allocated to has enough disk space.
It has three configurable settings, all of which can be changed dynamically:
cluster.routing.allocation.disk.watermark.low
is the low disk
watermark. New shards will not allocated to a node with usage higher than this,
although this watermark may be passed by allocating a shard. It defaults to
0.85 (85.0%).
cluster.routing.allocation.disk.watermark.high
is the high disk
watermark. If a node has usage higher than this, shards are not allowed to
remain on the node. In addition, if allocating a shard to a node causes the
node to pass this watermark, it will not be allowed. It defaults to
0.90 (90.0%).
Both watermark settings are expressed in terms of used disk percentage, or
exact byte values for free space (like "500mb")
cluster.routing.allocation.disk.threshold_enabled
is used to
enable or disable this decider. It defaults to false (disabled).| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
CLUSTER_ROUTING_ALLOCATION_DISK_THRESHOLD_ENABLED |
static java.lang.String |
CLUSTER_ROUTING_ALLOCATION_HIGH_DISK_WATERMARK |
static java.lang.String |
CLUSTER_ROUTING_ALLOCATION_INCLUDE_RELOCATIONS |
static java.lang.String |
CLUSTER_ROUTING_ALLOCATION_LOW_DISK_WATERMARK |
static java.lang.String |
CLUSTER_ROUTING_ALLOCATION_REROUTE_INTERVAL |
static java.lang.String |
NAME |
deprecationLogger, logger, settings
| Constructor and Description |
|---|
DiskThresholdDecider(Settings settings) |
DiskThresholdDecider(Settings settings,
NodeSettingsService nodeSettingsService,
ClusterInfoService infoService,
Client client) |
| Modifier and Type | Method and Description |
|---|---|
DiskUsage |
averageUsage(RoutingNode node,
ImmutableOpenMap<java.lang.String,DiskUsage> usages)
Returns a
DiskUsage for the RoutingNode using the
average usage of other nodes in the disk usage map. |
Decision |
canAllocate(ShardRouting shardRouting,
RoutingNode node,
RoutingAllocation allocation)
Returns a
Decision whether the given shard routing can be
allocated on the given node. |
Decision |
canRemain(ShardRouting shardRouting,
RoutingNode node,
RoutingAllocation allocation)
Returns a
Decision whether the given shard routing can be remain
on the given node. |
double |
freeDiskPercentageAfterShardAssigned(DiskUsage usage,
java.lang.Long shardSize)
Given the DiskUsage for a node and the size of the shard, return the
percentage of free disk if the shard were to be allocated to the node.
|
ByteSizeValue |
getFreeBytesThresholdHigh() |
ByteSizeValue |
getFreeBytesThresholdLow() |
java.lang.Double |
getFreeDiskThresholdHigh() |
java.lang.Double |
getFreeDiskThresholdLow() |
TimeValue |
getRerouteInterval() |
java.lang.Double |
getUsedDiskThresholdHigh() |
java.lang.Double |
getUsedDiskThresholdLow() |
boolean |
isEnabled() |
boolean |
isIncludeRelocations() |
static long |
sizeOfRelocatingShards(RoutingNode node,
ClusterInfo clusterInfo,
boolean subtractShardsMovingAway,
java.lang.String dataPath)
Returns the size of all shards that are currently being relocated to
the node, but may not be finished transfering yet.
|
ByteSizeValue |
thresholdBytesFromWatermark(java.lang.String watermark,
java.lang.String settingName)
Attempts to parse the watermark into a
ByteSizeValue , returning
a ByteSizeValue of 0 bytes if the value cannot be parsed. |
double |
thresholdPercentageFromWatermark(java.lang.String watermark)
Attempts to parse the watermark into a percentage, returning 100.0% if
it cannot be parsed.
|
boolean |
validWatermarkSetting(java.lang.String watermark,
java.lang.String settingName)
Checks if a watermark string is a valid percentage or byte size value,
returning true if valid, false if invalid.
|
canAllocate, canAllocate, canRebalance, canRebalance
logDeprecatedSetting, logRemovedSetting, nodeName
public static final java.lang.String NAME
public static final java.lang.String CLUSTER_ROUTING_ALLOCATION_DISK_THRESHOLD_ENABLED
public static final java.lang.String CLUSTER_ROUTING_ALLOCATION_LOW_DISK_WATERMARK
public static final java.lang.String CLUSTER_ROUTING_ALLOCATION_HIGH_DISK_WATERMARK
public static final java.lang.String CLUSTER_ROUTING_ALLOCATION_INCLUDE_RELOCATIONS
public static final java.lang.String CLUSTER_ROUTING_ALLOCATION_REROUTE_INTERVAL
public DiskThresholdDecider(Settings settings)
@Inject public DiskThresholdDecider(Settings settings, NodeSettingsService nodeSettingsService, ClusterInfoService infoService, Client client)
public java.lang.Double getFreeDiskThresholdLow()
public java.lang.Double getFreeDiskThresholdHigh()
public java.lang.Double getUsedDiskThresholdLow()
public java.lang.Double getUsedDiskThresholdHigh()
public ByteSizeValue getFreeBytesThresholdLow()
public ByteSizeValue getFreeBytesThresholdHigh()
public boolean isIncludeRelocations()
public boolean isEnabled()
public TimeValue getRerouteInterval()
public static long sizeOfRelocatingShards(RoutingNode node, ClusterInfo clusterInfo, boolean subtractShardsMovingAway, java.lang.String dataPath)
public Decision canAllocate(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation)
AllocationDecider
Decision
whether the given shard routing can be
allocated on the given node. The default is Decision.ALWAYS
.canAllocate
in class AllocationDecider
public Decision canRemain(ShardRouting shardRouting, RoutingNode node, RoutingAllocation allocation)
AllocationDecider
Decision
whether the given shard routing can be remain
on the given node. The default is Decision.ALWAYS
.canRemain
in class AllocationDecider
public DiskUsage averageUsage(RoutingNode node, ImmutableOpenMap<java.lang.String,DiskUsage> usages)
DiskUsage
for the RoutingNode
using the
average usage of other nodes in the disk usage map.node
- Node to return an averaged DiskUsage object forusages
- Map of nodeId to DiskUsage for all known nodespublic double freeDiskPercentageAfterShardAssigned(DiskUsage usage, java.lang.Long shardSize)
usage
- A DiskUsage for the node to have space computed forshardSize
- Size in bytes of the shardpublic double thresholdPercentageFromWatermark(java.lang.String watermark)
public ByteSizeValue thresholdBytesFromWatermark(java.lang.String watermark, java.lang.String settingName)
ByteSizeValue
, returning
a ByteSizeValue of 0 bytes if the value cannot be parsed.public boolean validWatermarkSetting(java.lang.String watermark, java.lang.String settingName)


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§328-51 Causing preventable deterioration of; penalty. Any person who by interference, intimidation, or carelessness, causes or permits preventable deterioration or waste in connection with the marketing of such fishes or their preservation, or who prevents or attempts to prevent lawful cold storage of such fishes, shall be fined not more than $200, or imprisoned not more than three months, or both. [L 1921, c 38, §2; RL 1925, §4261; RL 1935, §1072; RL 1945, §2241; RL 1955, §51-50; HRS §328-51]
Cross References
Classification of offense and authorized punishment, see §§701-107, 706-640, 663.


Welcome to the Waltham, MA, Home Page of Terry Eagan
The pictures above are from the Mary Eagan Garden that adjoins the Cancer Treatment Center at Waltham (MA, USA) Hospital. A personal goal is to see garden patios created worldwide that will provide comfort and solace for individuals undergoing the rigors of cancer treatments. Live and recorded music offerings have led the way to funding the first such garden that we see above.
This wish-become-reality has led to the formation of a business entity that you can check out at Patio Records. There you will learn of upcoming events, how to purchase CDs from the Patio Records label, and more on Mary Eagan and the garden patio concept....Enjoy clicking away at a number of links below to pages of friends, associates, venues, and a hodgepodge of sites I glean much from.
Thanks for checking in and headin’ on your way to Patio Records , - T -
Please feel free to email me at : Terry Eagan


# Copyright (c) 2012-2019 by the GalSim developers team on GitHub
# https://github.com/GalSim-developers
#
# This file is part of GalSim: The modular galaxy image simulation toolkit.
# https://github.com/GalSim-developers/GalSim
#
# GalSim is free software: redistribution and use in source and binary forms,
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions, and the disclaimer given in the accompanying LICENSE
# file.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the disclaimer given in the documentation
# and/or other materials provided with the distribution.
#
import numpy as np
from functools import reduce
from . import _galsim
from .errors import GalSimError, GalSimRangeError, GalSimValueError, convert_cpp_errors
[docs]def int1d(func, min, max, rel_err=1.e-6, abs_err=1.e-12):
"""Integrate a 1-dimensional function from min to max.
Example usage::
>>> def func(x): return x**2
>>> galsim.integ.int1d(func, 0, 1)
0.33333333333333337
>>> galsim.integ.int1d(func, 0, 2)
2.666666666666667
>>> galsim.integ.int1d(func, -1, 1)
0.66666666666666674
Parameters:
func: The function to be integrated. y = func(x) should be valid.
min: The lower end of the integration bounds (anything < -1.e10 is treated as
negative infinity).
max: The upper end of the integration bounds (anything > 1.e10 is treated as positive
infinity).
rel_err: The desired relative error [default: 1.e-6]
abs_err: The desired absolute error [default: 1.e-12]
Returns:
the value of the integral.
"""
min = float(min)
max = float(max)
rel_err = float(rel_err)
abs_err = float(abs_err)
with convert_cpp_errors():
success, result = _galsim.PyInt1d(func,min,max,rel_err,abs_err)
if success:
return result
else:
raise GalSimError(result)
[docs]def midpt(fvals, x):
"""Midpoint rule for integration.
Parameters:
fvals: Samples of the integrand
x: Locations at which the integrand was sampled.
Returns:
midpoint rule approximation of the integral.
"""
dx = [x[1]-x[0]]
dx.extend(0.5*(x[2:]-x[0:-2]))
dx.append(x[-1]-x[-2])
weighted_fvals = [w*f for w,f in zip(dx, fvals)]
return reduce(lambda y,z:y+z, weighted_fvals)
[docs]def trapz(func, min, max, points=10000):
"""Simple wrapper around 'numpy.trapz' to take function and limits as inputs.
Example usage::
>>> def func(x): return x**2
>>> galsim.integ.trapz(func, 0, 1)
0.33333333500033341
>>> galsim.integ.trapz(func, 0, 1, 1e6)
0.33333333333349996
>>> galsim.integ.trapz(func, 0, 1, np.linspace(0, 1, 1e3))
0.33333350033383402
Parameters:
func: The function to be integrated. y = func(x) should be valid.
min: The lower end of the integration bounds.
max: The upper end of the integration bounds.
points: If integer, the number of points to sample the integrand. If array-like, then
the points to sample the integrand at. [default: 1000].
"""
if not np.isscalar(points):
if (np.max(points) > max) or (np.min(points) < min):
raise GalSimRangeError("Points outside of specified range", points, min, max)
elif int(points) != points:
raise TypeError("npoints must be integer type or array")
else:
points = np.linspace(min, max, points)
return np.trapz(func(points),points)
[docs]def midptRule(f, xs):
"""Midpoint rule for integration.
Parameters:
f: Function to integrate.
xs: Locations at which to evaluate f.
Returns:
Midpoint rule approximation to the integral.
"""
if len(xs) < 2:
raise GalSimValueError("Not enough points for midptRule integration", xs)
x, xp = xs[:2]
result = f(x)*(xp-x)
for x, xp, xpp in zip(xs[0:-2], xs[1:-1], xs[2:]):
result += 0.5*f(xp)*(xpp-x)
result += f(xpp)*(xpp-xp)
return result
[docs]def trapzRule(f, xs):
"""Trapezoidal rule for integration.
Parameters:
f: Function to integrate.
xs: Locations at which to evaluate f.
Returns:
Trapezoidal rule approximation to the integral.
"""
if len(xs) < 2:
raise GalSimValueError("Not enough points for trapzRule integration", xs)
x, xp = xs[:2]
result = 0.5*f(x)*(xp-x)
for x, xp, xpp in zip(xs[0:-2], xs[1:-1], xs[2:]):
result += 0.5*f(xp)*(xpp-x)
result += 0.5*f(xpp)*(xpp-xp)
return result
[docs]class ImageIntegrator(object):
"""A base class for integrators used by `ChromaticObject` to integrate the drawn images
over wavelengthh using a `Bandpass` as a weight function.
"""
def __init__(self):
raise NotImplementedError("Must instantiate subclass of ImageIntegrator")
# subclasses must define
# 1) a method `.calculateWaves(bandpass)` which will return the wavelengths at which to
# evaluate the integrand
# 2) an function attribute `.rule` which takes an integrand function as its first
# argument, and a list of evaluation wavelengths as its second argument, and returns
# an approximation to the integral. (E.g., the function midptRule above)
[docs] def __call__(self, evaluateAtWavelength, bandpass, image, drawImageKwargs, doK=False):
"""
Parameters:
evaluateAtWavelength: Function that returns a monochromatic surface brightness
profile as a function of wavelength.
bandpass: `Bandpass` object representing the filter being imaged through.
image: `Image` used to set size and scale of output
drawImageKwargs: dict with other kwargs to send to `ChromaticObject.drawImage`
function.
doK: Integrate up results of `ChromaticObject.drawKImage` instead of
results of `ChromaticObject.drawImage`. [default: False]
Returns:
the result of integral as an `Image`
"""
waves = self.calculateWaves(bandpass)
self.last_n_eval = len(waves)
drawImageKwargs.pop('add_to_image', None) # Make sure add_to_image isn't in kwargs
def integrand(w):
prof = evaluateAtWavelength(w) * bandpass(w)
if not doK:
return prof.drawImage(image=image.copy(), **drawImageKwargs)
else:
return prof.drawKImage(image=image.copy(), **drawImageKwargs)
return self.rule(integrand, waves)
[docs]class SampleIntegrator(ImageIntegrator):
"""Create a chromatic surface brightness profile integrator, which will integrate over
wavelength using a `Bandpass` as a weight function.
This integrator will evaluate the integrand only at the wavelengths in ``bandpass.wave_list``.
See ContinuousIntegrator for an integrator that evaluates the integrand at a given number of
points equally spaced apart.
Parameters:
rule: Which integration rule to apply to the wavelength and monochromatic surface
brightness samples. Options include:
- galsim.integ.midptRule: Use the midpoint integration rule
- galsim.integ.trapzRule: Use the trapezoidal integration rule
"""
def __init__(self, rule):
self.rule = rule
def calculateWaves(self, bandpass):
return bandpass.wave_list
[docs]class ContinuousIntegrator(ImageIntegrator):
"""Create a chromatic surface brightness profile integrator, which will integrate over
wavelength using a `Bandpass` as a weight function.
This integrator will evaluate the integrand at a given number ``N`` of equally spaced
wavelengths over the interval defined by bandpass.blue_limit and bandpass.red_limit. See
SampleIntegrator for an integrator that only evaluates the integrand at the predefined set of
wavelengths in ``bandpass.wave_list``.
Parameters:
rule: Which integration rule to apply to the wavelength and monochromatic
surface brightness samples. Options include:
- galsim.integ.midptRule: Use the midpoint integration rule
- galsim.integ.trapzRule: Use the trapezoidal integration rule
N: Number of equally-wavelength-spaced monochromatic surface brightness
samples to evaluate. [default: 250]
use_endpoints: Whether to sample the endpoints ``bandpass.blue_limit`` and
``bandpass.red_limit``. This should probably be True for a rule like
numpy.trapz, which explicitly samples the integration limits. For a
rule like the midpoint rule, however, the integration limits are not
generally sampled, (only the midpoint between each integration limit and
its nearest interior point is sampled), thus ``use_endpoints`` should be
set to False in this case. [default: True]
"""
def __init__(self, rule, N=250, use_endpoints=True):
self.rule = rule
self.N = N
self.use_endpoints = use_endpoints
def calculateWaves(self, bandpass):
h = (bandpass.red_limit*1.0 - bandpass.blue_limit)/self.N
if self.use_endpoints:
return [bandpass.blue_limit + h * i for i in range(self.N+1)]
else:
return [bandpass.blue_limit + h * (i+0.5) for i in range(self.N)]


Christy and Alex got help finding affordable housing with daycare.
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This file defines python classes for every JSON object returned in responses from the endpoints of the Cicero API. When an API request is submitted and data received (using the CiceroRestConnection class in cicero_rest_connection.py, a RootCiceroObject (defined here) is instantiated from the API's JSON response. The RootCiceroObject instantiates a ResponseObject (which often instantiates a GeocodingResultsObject, which follows with one of the GeocodingCandidate objects, etc etc) and so on in a cascading fashion, until a class has been instantiated for every corresponding JSON object in the API response. (Currently, the only exception to this is the "data" JSON object found in most nonlegislative district objects, but this can still be accessed - just with python ['dictionary']['notation'].)
In other words, at the end we are left with a RootCiceroObject instance containing instances of other classes defined in this file.
An abstract base class, AbstractCiceroObject, defines str and repr methods which all classes use. Two at-large "private" functions - _key_copy and _copy_keys facilitate initializing class attributes from JSON values.
def _key_copy(lhs, rhs, key):
lhs[key] = rhs[key]
def _copy_keys(lhs, rhs, keys):
for k in keys:
lhs[k] = rhs[k]
class AbstractCiceroObject(object):
def __repr__(self):
return '%s(%s)' % (self.__class__.__name__, self.__dict__)
def __str__(self):
return str(self.__dict__)
This class represents the attributes of any external identifiers that most officials have - Facebook page, Twitter, Project VoteSmart, etc
class IdentifierObject(AbstractCiceroObject):
def __init__(self, identifier_dict):
_copy_keys(self.__dict__, identifier_dict,
('valid_from', 'valid_to', 'sk', 'id', 'official',
'last_update_date', 'identifier_type', 'identifier_value'))
This class represents the attributes of a committee a legislator belongs to. Committees are an antiquated feature of Cicero and few officials are associated with them, so this information may not be up to date.
class CommitteeObject(AbstractCiceroObject):
def __init__(self, comm_dict):
_copy_keys(self.__dict__, comm_dict,
('valid_from', 'description', 'valid_to', 'sk',
'last_update_date', 'id'))
This class represents the attributes of a country object within a GovernmentObject (itself in turn within a ChamberObject), OR a "representing_country" object within an OfficeObject (itself within an OfficialObject).
response
response
response
response
response
response
class CountryObject(AbstractCiceroObject):
def __init__(self, country_dict):
_copy_keys(self.__dict__, country_dict,
('status', 'name_short', 'gmi_3', 'valid_from',
'name_short_iso', 'name_short_local', 'valid_to', 'id',
'sk', 'name_short_un', 'fips', 'last_update_date', 'iso_3',
'iso_2', 'iso_3_numeric', 'name_long_local', 'name_long'))
This class represents a government, within an official's ChamberObject.
response
response
response
response
class GovernmentObject(AbstractCiceroObject):
def __init__(self, gov_dict):
_copy_keys(self.__dict__, gov_dict,
('city', 'state', 'name', 'notes', 'type'))
self.country = CountryObject(gov_dict['country'])
This class represents the chamber an official serves in (legislature, senate, house of representatives, etc).
response
response
response
response
class ChamberObject(AbstractCiceroObject):
def __init__(self, chamber_dict):
_copy_keys(self.__dict__, chamber_dict,
('id', 'type', 'official_count', 'is_chamber_complete',
'has_geographic_representation', 'name_native_language',
'name_formal', 'name', 'url', 'contact_email', 'contact_phone',
'election_rules', 'election_frequency',
'term_length', 'term_limit', 'redistricting_rules',
'inauguration_rules', 'vacancy_rules', 'last_update_date',
'legislature_update_date', 'notes', 'remarks'))
self.government = GovernmentObject(chamber_dict['government'])
This class represents an election event object.
class ElectionEventObject(AbstractCiceroObject):
def __init__(self, election_event_dict):
_copy_keys(self.__dict__, election_event_dict,
('is_approximate', 'last_update_date', 'remarks',
'election_date_text', 'valid_from', 'is_national',
'is_state', 'is_by_election', 'is_referendum', 'valid_to',
'label', 'sk', 'id', 'is_primary_election', 'urls',
'is_runoff_election', 'is_transnational'))
because .chambers is a list of ChamberObjects, we can't use _copy_keys and will define it here
self.chambers = [ChamberObject(c) for c in election_event_dict['chambers']]
This class represents a legislative or nonlegislative district object. The same object is returned with either the official API call or either legislative or nonlegislative district calls.
.data (dictionary) - additional data/info for this district. Keys differ for each district type (ie, census, school, watershed, etc). Therefore, unlike the rest of the wrapper which can be navigated using dot.style.notation, one must navigate the values in "data" ['like']['a']['dictionary'].
.id (integer) - Cicero unique ID
response
response
response
response
class DistrictObject(AbstractCiceroObject):
def __init__(self, district_dict):
_copy_keys(self.__dict__, district_dict,
('district_type', 'city', 'valid_from', 'country',
'district_id', 'valid_to', 'label', 'sk', 'subtype',
'state', 'last_update_date', 'data', 'id'))
This class represents details about the political office an elected official holds.
class OfficeObject(AbstractCiceroObject):
def __init__(self, office_dict):
_copy_keys(self.__dict__, office_dict,
('valid_from', 'representing_state', 'notes', 'title',
'valid_to', 'sk', 'last_update_date', 'election_rules',
'id', 'representing_city'))
self.district = DistrictObject(office_dict['district'])
self.representing_country = CountryObject(office_dict['representing_country'])
self.chamber = ChamberObject(office_dict['chamber'])
This class represents details about physical addresses an official may have, such as their district office, capitol office, phone and fax numbers, etc.
class AddressObject(AbstractCiceroObject):
def __init__(self, address_dict):
_copy_keys(self.__dict__, address_dict,
('county', 'postal_code', 'phone_1', 'phone_2', 'fax_1',
'fax_2', 'city', 'state', 'address_1', 'address_2',
'address_3'))
convenience function to print out what could be used as an envelope address label
def get_formatted_mailing_address(self, county=False):
mailing_address = ""
add_part = lambda template, string: template % string if string else ""
mailing_address += add_part("%s\n", self.address_1)
mailing_address += add_part("%s\n", self.address_2)
mailing_address += add_part("%s\n", self.address_3)
mailing_address += add_part("%s, ", self.city)
if user has set county=True and wants the county in the address
if county:
mailing_address += add_part("%s, ", self.county)
mailing_address += add_part("%s ", self.state)
mailing_address += add_part("%s", self.postal_code)
return mailing_address
This class is the main class for details about a particular elected official.
class OfficialObject(AbstractCiceroObject):
def __init__(self, official_dict):
_copy_keys(self.__dict__, official_dict,
('last_name', 'initial_term_start_date',
'current_term_start_date', 'web_form_url', 'last_update_date',
'salutation', 'id', 'photo_origin_url', 'middle_initial',
'first_name', 'valid_from', 'notes', 'name_suffix',
'valid_to', 'sk', 'term_end_date', 'urls', 'party',
'email_addresses'))
self.addresses = [AddressObject(a) for a in official_dict['addresses']]
self.office = OfficeObject(official_dict['office'])
self.committees = [CommitteeObject(c) for c in official_dict['committees']]
self.identifiers = [IdentifierObject(i) for i in official_dict['identifiers']]
def find_identifier(self, identifier_type):
iterate through self.identifiers and search on type If one or more of same type, return all those IdentifierObjects in a list
return [identifier for identifier in self.identifiers
if identifier.identifier_type == identifier_type]
The CountObject class contains pagination details for responses. The Cicero database is a large one, with many more officials, districts, and election events than is practical to return in one ReST response. So, features are included to page and iterate through many results. The CountObject signposts this and lets you know where in the results this particular response is.
class CountObject(AbstractCiceroObject):
def __init__(self, count_dict):
_copy_keys(self.__dict__, count_dict,
('to','total'))
self.from_ = count_dict['from'] #get around "from" being reserve word
The GeocodingCandidate class is what the DistrictGeocodingCandidate and OfficialGeocodingCandidate classes inherit from, and defines base fields for describing a geocoded address candidate. It is erroneous for the default GeocodingCandidate class to appear "in the wild," instead of its inheriting classes DistrictGeocodingCandidate and OfficialGeocodingCandidate.
class GeocodingCandidate(AbstractCiceroObject):
def __init__(self, geocoding_candidate_dict):
_copy_keys(self.__dict__, geocoding_candidate_dict,
('match_addr', 'wkid', 'locator', 'score', 'locator_type',
'y', 'x', 'geoservice'))
self.count = CountObject(geocoding_candidate_dict['count'])
The DistrictGeocodingCandidate class defines base fields for describing a geocoded address candidate, and a .districts attribute containing the main list of DistrictObjects and district information. DistrictGeocodingCandidates are only returned in /legislative_district and /nonlegislative_district calls using geocoding.
Cicero documentation: + on geocoding + on the /district call
class DistrictGeocodingCandidate(GeocodingCandidate):
def __init__(self, district_geocoding_candidate_dict):
super(DistrictGeocodingCandidate, self).__init__(
district_geocoding_candidate_dict)
self.districts = [DistrictObject(d) for d in
district_geocoding_candidate_dict['districts']]
The OfficialGeocodingCandidate class defines base fields for describing a geocoded address candidate, and a .officials attribute containing the main list of OfficialObjects and official information. OfficialGeocodingCandidates are only returned in /official calls using geocoding.
Cicero documentation: + for geocoding + for /official call
class OfficialGeocodingCandidate(GeocodingCandidate):
def __init__(self, official_geocoding_candidate_dict):
super(OfficialGeocodingCandidate, self).__init__(
official_geocoding_candidate_dict)
self.officials = [OfficialObject(o) for o in
official_geocoding_candidate_dict['officials']]
convenience "simplified view" function
def who_are_the_officials(self):
officials_list = []
for official in self.officials:
officials_list.append([official.last_name,
official.office.chamber.name_formal,
official.office.district.district_type,
official.office.district.district_id,
official.party])
return officials_list
The ElectionEventGeocodingCandidate class defines base fields for describing a geocoded address candidate, and an .election_events attribute containing the main list of ElectionEventObjects. ElectionEventGeocodingCandidates are only returned in /election_event calls using geocoding.
Geocoding with election events is best described as an "alpha" feature of the Cicero API, currently (as of Oct 8 2013). Consider this in the context of election events and the /election_event endpoint as a whole being a "beta" feature - you have been warned!
However, unlike non-geocoding /election_event calls, which are "free" (ie, don't use up any API credits) due to their "beta" status, calls to the /election_event endpoint which do take advantage of location or geocoding parameters will be charged API credits accordingly. Geocoding an address or location through one of the Cicero API's geocoding providers is a direct cost for us, so while we are happy to provide free election events without geocoding as we improve the feature, we must charge credits for all calls to all endpoints that use geocoding.
When using geocoding, elections for the chambers of the address's country will appear, as well as elections for that state, city, etc. For example, searching for election_events while geocoding an address in the US State of Alaska, will also return all election_events in all other US States (like Idaho and Ohio, as examples) for US House and Senate, because Alaska is a part of those chambers, even though Alaska does not vote for other state's representatives and senators. On the other hand, election events in non-US countries like France, Great Britain, or Canada would not be returned with geocoding. Likewise, gubernatorial elections for other states than Alaska would not be returned, and if the geocoded address was in Fairbanks, then Anchorage municipal elections would not be returned.
Note these issues apply only to geocoding with election_events, not necessarily non-geocoded /election_event calls.
Cicero documentation: for geocoding for election events
class ElectionEventGeocodingCandidate(GeocodingCandidate):
def __init__(self, election_event_geocoding_candidate_dict):
super(ElectionEventGeocodingCandidate, self).__init__(
election_event_geocoding_candidate_dict)
self.election_events = [ElectionEventObject(e) for e in
election_event_geocoding_candidate_dict['election_events']]
The ElectionEventNonGeocodingResultsObject class is a container for a list of returned ElectionEventObjects and a CountObject enumerating how many and which have been returned in this response. ElectionEventNonGeocodingResultsObjects are only returned in /election_event calls that do NOT use geocoding.
Please review the issues outlined in the docstring of the ElectionEventGeocodingCandidate class. Non-geocoding /election_event calls are somewhat more robust than geocoding calls, but are still a "beta" feature. Unlike /election_event call that do use geocoding, however, non-geocoding calls will not charge an API credit for the time being. (as of Oct 9 2013)
class ElectionEventNonGeocodingResultsObject(AbstractCiceroObject):
def __init__(self, election_event_nongeocoding_results_dict):
if 'count' in election_event_nongeocoding_results_dict:
self.count = CountObject(
election_event_nongeocoding_results_dict['count'])
self.election_events = [ElectionEventObject(e) for e in
election_event_nongeocoding_results_dict['election_events']]
The s class is a container for a list of returned DistrictObjects and a CountObject enumerating how many and which have been returned in this response. DistrictNonGeocodingResultsObject objects are only returned in /legislative_district or /nonlegislative_district calls that do NOT use geocoding.
class DistrictNonGeocodingResultsObject(AbstractCiceroObject):
def __init__(self, district_nongeocoding_results_dict):
if 'count' in district_nongeocoding_results_dict:
self.count = CountObject(district_nongeocoding_results_dict['count'])
self.districts = [DistrictObject(d) for d in
district_nongeocoding_results_dict['districts']]
The OfficialNonGeocodingResultsObject class is a container for a list of returned OfficialObjects and a CountObject enumerating how many and which have been returned in this response. OfficialNonGeocodingResultsObject objects are only returned in /official calls that do NOT use geocoding.
class OfficialNonGeocodingResultsObject(AbstractCiceroObject):
def __init__(self, official_nongeocoding_results_dict):
if 'count' in official_nongeocoding_results_dict:
self.count = CountObject(official_nongeocoding_results_dict['count'])
self.officials = [OfficialObject(o) for o in
official_nongeocoding_results_dict['officials']]
convenience "simplified view" function
def who_are_the_officials(self):
officials_list = []
for official in self.officials:
officials_list.append([official.last_name,
official.office.chamber.name_formal,
official.office.district.district_type,
official.party])
return officials_list
The GeocodingResultsObject class defines a "geocoding candidates" list of either OfficialGeocodingCandidate, DistrictGeocodingCandidate, or ElectionEventGeocodingCandidate objects, depending on the type of response.
class GeocodingResultsObject(AbstractCiceroObject):
def __init__(self, geocoding_results_dict):
self.candidates = []
for candidate in geocoding_results_dict['candidates']:
if 'officials' in candidate:
self.candidates.append(OfficialGeocodingCandidate(candidate))
elif 'districts' in candidate:
self.candidates.append(DistrictGeocodingCandidate(candidate))
elif 'election_events' in candidate:
self.candidates.append(ElectionEventGeocodingCandidate(candidate))
else:
we shouldn't normally get to this case but if needed, this is the base candidate
self.candidates.append(GeocodingCandidate(candidate))
Always nested within a MapObject, the ExtentObject class represents the details necessary to construct the geographic bounding box containing the returned map. By default these coordinates are in the Web Mercator coordinate system (EPSG:900913).
class ExtentObject(AbstractCiceroObject):
def __init__(self, extent_dict): #TODO unify "_dict" and "_object" parameter names - decide on one
_copy_keys(self.__dict__, extent_dict,
('x_min', 'y_min', 'x_max', 'y_max', 'srid'))
The MapObject class contains attributes to construct or access a returned map from the /map call.
class MapObject(AbstractCiceroObject):
def __init__(self, map_dict):
self.url = map_dict['url']
if 'img_src' in map_dict:
self.img_src = map_dict['img_src']
self.extent = ExtentObject(map_dict['extent'])
The MapResultsObject class is a container for a list of MapObjects.
(Cicero documentation)[https://cicero.azavea.com/docs/map.html]
class MapsResultsObject(AbstractCiceroObject):
def __init__(self, map_result_dict):
self.maps = [MapObject(m) for m in
map_result_dict['maps']]
The DistrictTypeObject is returned from the /district_type call, and contains information about each district_type available in Cicero.
(Cicero documentation)[https://cicero.azavea.com/docs/district_type.html]
class DistrictTypeObject(AbstractCiceroObject):
def __init__(self, district_type_dict):
_copy_keys(self.__dict__, district_type_dict,
('name_short', 'notes', 'acknowledgements',
'is_legislative', 'name_long'))
The DistrictTypeResultsObject is a container for a list of DistrictTypeObjects.
class DistrictTypeResultsObject(AbstractCiceroObject):
def __init__(self, district_type_result_dict):
self.district_types = [DistrictTypeObject(dt) for dt in
district_type_result_dict['district_types']]
The CreditBatchObject is returned from the /account/credits_remaining call, and contains information about each batch of credits a user has.
class CreditBatchObject(AbstractCiceroObject):
def __init__(self, credit_batch_dict):
_copy_keys(self.__dict__, credit_batch_dict,
('discount', 'expiration_time', 'cost', 'credits_remaining',
'credits_purchased'))
The AccountCreditsRemainingResultsObject is returned from the /account/credits_remaining call, and contains information about a user's balance and overdraft limit, as well as a list of available credit batches.
(Cicero documentation)[https://cicero.azavea.com/docs/credits_remaining.html]
class AccountCreditsRemainingResultsObject(AbstractCiceroObject):
def __init__(self, credits_remaining_result_dict):
_copy_keys(self.__dict__, credits_remaining_result_dict,
('credit_balance', 'overdraft_limit'))
self.usable_batches = [CreditBatchObject(batch) for batch in
credits_remaining_result_dict['usable_batches']]
The ActivityTypeObject is returned from the /account/usage call, and contains information about how a user has been using their API credits.
class ActivityTypeObject(AbstractCiceroObject):
def __init__(self, activity_dict):
_copy_keys(self.__dict__, activity_dict,
('count', 'type', 'credits_used'))
The AccountUsageObject is returned from the /account/usage call, and contains information about how many API calls and credits a user has made in a given month.
(Cicero documentation)[https://cicero.azavea.com/docs/usage.html]
class AccountUsageObject(AbstractCiceroObject):
def __init__(self, monthly_account_usage_dict):
_copy_keys(self.__dict__, monthly_account_usage_dict,
('count', 'credits_used', 'year', 'month'))
self.activity_types = [ActivityTypeObject(a) for a in
monthly_account_usage_dict['activity_types']]
The VersionObject is returned from the /version call, and contains the version number of the Cicero API currently being used.
class VersionObject(AbstractCiceroObject):
def __init__(self, version_dict):
self.version = version_dict['version']
The base ResponseObject is returned from every API call, and contains errors, messages, and the results of the API call.
class ResponseObject(AbstractCiceroObject):
def __init__(self, response_dict):
self.errors = response_dict['errors']
self.messages = response_dict['messages']
r = response_dict['results']
if 'candidates' in r:
self.results = GeocodingResultsObject(r)
elif 'officials' in r:
self.results = OfficialNonGeocodingResultsObject(r)
elif 'districts' in r:
self.results = DistrictNonGeocodingResultsObject(r)
elif 'election_events' in r:
self.results = ElectionEventNonGeocodingResultsObject(r)
elif 'maps' in r:
self.results = MapsResultsObject(r)
elif 'district_types' in r:
self.results = DistrictTypeResultsObject(r)
elif 'credit_balance' in r:
self.results = AccountCreditsRemainingResultsObject(r)
elif 'activity_types' in r[0]:
self.results = [AccountUsageObject(month) for month in r]
elif 'version' in r:
self.results = VersionObject(r)
the base case, though in normal operation we shouldn't get here
else:
self.results = r
The base RootCiceroObject is returned from every API call, and contains the ResponseObject.
class RootCiceroObject(AbstractCiceroObject):
def __init__(self, python_dict_from_cicero_json):
self.response = ResponseObject(python_dict_from_cicero_json['response'])


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Now that we're acquainted...
Any of these sound good?
Here, you can read more about me and get a gauge of what I'm like.
There's also plenty of information on my skills and expertise!
A lot of time and effort has gone into demonstrating my skills.
Use the filter to choose the specs you want to see. My personal choice is Champ. It has an API, uses tons of Django features and provides a cool service too!
If you have any questions or comments, get in touch!


§315-2 Official for State. The "designated State Official" for Hawaii shall be the executive director of the Hawaii teacher standards board. [L 1969, c 47, §2; am L 2004, c 21, §2]


NDVI vegetation area growing air season study temperature sunshine climate duration relative significant factors climatic response change correlation humidity precipitation extremely trend time data value areas increase different method GRD variation Figure Plateau analysis altitude mean cumulative shows Lag high-altitude (.) China mainly {border-style Int. Tibetan Wang growth high lag month px;} #ffffff;} < increasing spatial types Analysis Earth Zhang changes months precipitation, Liu low-altitude slope central distributed reduction sequence series area. difference factor temperature, used Sci. area, based grasslands highest results trends values Response alpine eastern environment higher monthly overall studies Current Data Digit. Grey [], ecological hydrothermal low periods rate seven using > ./.. GRA Qinghai Remote Tibet distribution human lowest recent significantly southeastern ... Area Chen Environ. MODA Province, Relational Sens. average broad-leaved certain decreasing elevation forests greater interpolation lags middle multiple nested period px; relationship spring surface temporal warming {font-family ./s--- Climate Climatic Piao comparative conditions effects font-size forest global grey impact land located meadows, parts process related relational respectively. sensitivity statistical water .), Based Growing Qinghai-Tibetan Res. Season Spatial Table Variation annual bold calculate calculated double; dynamic effect important indicates month, northern positive reference relatively research responses trend. (.), Altitude Chang. D.H., Ding G.Y., Global Huang Meteorol. Method Province Science September Sichuan Yin altitude, analysis, analyze areas, border-color border-width coniferous cover decrease differences east extreme forests, influence maximum meadows mid-altitude northwestern pixel previous ranges represents season, sequence. soil solid; trend, type variable year years {font-style {font-weight (<.) (.% (.%) .%, ..., ./j.agrformet... ./j.jag... According Acta Agric. Appl. Autonomous Change Chin. Dong Extremely Factors Fang For. Geoinf. Guo J.M.; Kriging MNTSA MODIS Obs. Pan Series Shen T.Z., TP, Temporal USA Vegetation X.H. Yang Zhu altitudes. analyzed anomaly as: b). background change. courier; data, degree different. duration, em; expressed factors. generally gradually grassland high-value increased increases initial larger low-value meteorological months, months. north northeastern obvious, past phenology proportion pt;} reduce reflectance regression resolution restoration result rise season. showing study, suppression taken temperate time, total undergone variables vegetation. vs. year. −./°


I am Artyom Semenov and currently work as a UX Designer at MoneySuperMarket.com. I am Russian born, Scottish raised, designer from Chester, UK. Over the course of the last 5 years I worked for clients such as Alzheimer Scotland, Sainsburys, BBC, and Kraft. In recent years I tried myself in all areas of design until finally settling on digital product design (UX). This site is used to show off my latest work, drawings, doodles, designs, and document intereting things from my personal and professional life.


--- title: dtype_diet keywords: fastai sidebar: home_sidebar summary: "Attempt to shrink Pandas `dtypes` without losing data so you have more RAM (and maybe more speed)" description: "Attempt to shrink Pandas `dtypes` without losing data so you have more RAM (and maybe more speed)" nb_path: "index.ipynb" ---
This file will become your README and also the index of your documentation.
pip install dtype_diet
This is a fork of https://github.com/ianozsvald/dtype_diet to continue supoprt and develop the library with approval from the original author @ianozsvald.
This tool checks each column to see if larger dtypes (e.g. 8 byte float64
and int64
) could be shrunk to smaller dtypes
without causing any data loss.
Dropping an 8 byte type to a 4 (or 2 or 1 byte) type will keep halving the RAM requirement for that column. Categoricals are proposed for object
columns which can bring significant speed and RAM benefits.
Here's an minimal example with 3 lines of code running on a Kaggle dataset showing a reduction of 957 -> 85MB, you can find the notebook in the repository:
# sell_prices.csv.zip
# Source data: https://www.kaggle.com/c/m5-forecasting-uncertainty/
import pandas as pd
from dtype_diet import report_on_dataframe, optimize_dtypes
df = pd.read_csv('data/sell_prices.csv')
proposed_df = report_on_dataframe(df, unit="MB")
new_df = optimize_dtypes(df, proposed_df)
print(f'Original df memory: {df.memory_usage(deep=True).sum()/1024/1024} MB')
print(f'Propsed df memory: {new_df.memory_usage(deep=True).sum()/1024/1024} MB')
proposed_df
Recommendations:
report_on_dataframe(your_df)
to get recommendationsoptimize_dtypes(df, proposed_df)
to convert to recommeded dtypes.Look at report_on_dataframe(your_df)
to get a printed report - no changes are made to your dataframe.
$ conda create -n dtype_diet python=3.8 pandas jupyter pyarrow pytest
$ conda activate dtype_diet
make release
The repository is developed with nbdev
, a system for developing library with notebook.
Make sure you run this if you want to contribute to the library. For details, please refer to nbdev documentation (https://github.com/fastai/nbdev)
nbdev_install_git_hooks
Some other useful commands
nbdev_build_docs
nbdev_build_lib
nbdev_test_nbs


Contact details just demo details 7 kola lawal close, jankara<EMAIL_ADDRESS>8128829929 Get in touch How can we help you? Feedback Suggestion Enquiry Others


Issues such as the return of items of cultural patrimony, the looting and annihilation of irreplaceable cultural heritage monuments, traditional cultural properties and the desecration of national heritage sites worldwide plague our world daily. It is my hope that an online, digital information source featuring both United States and UNESCO policies will enhance cultural heritage management practices as well as deliver needed policy information and future training for people in institutions such as public or tribal museums, and government offices. This need has been evident at every consultation I have attended. I chose an online format because the laws dealt with are tedious in a standard lecture or print format. This module format allows for the topics to be broken down into a series of informational online sections that appeal to a wide range of disciplines and audiences.
This online site specializes in cultural heritage management policy and law both nationally and internationally. The design of this site is to assist local communities with the preservation policy protocol to disseminate their own cultural heritage agendas to a wider range of recipients. As digital outlets become the most common way to reach the widest audience, it is crucial that cultural managers take advantage of this trend. In an era where funding quickly disappears without an apparent real-world application and sites are destroyed before mitigation plans can be implemented, it is crucial this knowledge reaches a wide audience and a broad market.
This website offers a host of resources to assist you in cultural heritage management. Eventually, this will contain short lectures on each policy, and hands on activities involving actual cases from around the world will be used to allow you to work through the mitigation of each law. Future expansions will include a "Share Your Stories" section to be used to stimulate interactive discussions about what things went both right and wrong in these mitigations as well as allowing a space for participants to share their own stories and unique mitigation strategies.
This is one of the most widely used and controversial laws for Indian Country in United States cultural resource protection policy. It is often misunderstood and ineptly applied. (Image Courtesy Idaho DoT)
This is one of the most widely used laws in all fields and in almost every project across the United States. The conditions that initiate this law and constitute an undertaking are often misunderstood and ignored. (Image Courtesy National Park Service)
UNESCO World Heritage Sites are recognized the world over and preserve both natural and cultural sites. Listing something as a World Heritage Site can be difficult. (Image Courtesy UNESCO)


WERT plus TMZ WBRT Risk ratio
Chua D (2010) 3 47 6 48 3.5% 0.61 (0.16, 2.42] Dou-Dou Li (2015) ? 18 6 18 36% 1.40 (0.54, 3.60) Fei Teng (2017) 18 26 15 25 «10.9% 1.15 (0.77, 1.74] Geng Li(2017) 15 39 14 39 10.0% 1.07 (0.60, 1.91] Hassler MR (2013) 13 22 6 13 45% 1.54 (0.71, 3.32] Ji-Yong Peng (2008) 4 19 4 21 2.7% 1.11 (0.32, 3.82] Lei Shi (2014) g 43 8 41 58% 1.07 (0.46, 2.51] Shi-Jie Zhao (2016) 13 30 12, 30 8.6% 1.08 (0.59, 1.97] Tian-Lu Gu (2015) 12 52 9 50 65% 1.28 (0.59, 2.77] Wei Cheng (2013) 16 30 14 26 10.7% 0.99 (0.61, 1.61] Xia Deng (2017) 55 129 43° 109 33.2% 1.08 (0.80, 1.47] Total (95% Cl) 455 420 100.0% 1.11 [0.93, 1.32] Total events 165 134
Heterogeneity: Chi*= 2.07, df= 10 (P= 1.00), /7= 0% Test for overall effect: 7= 1.13 (P = 0.26)
0.05 0.2 1 5 20 Favours [experimental] Favours [control]


Be an organization that values and influences people and contribute our bit to make our planet a better place


How does it work
Sync across all devices
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise.
Watch videoAll emails in one place
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise.
Watch videoNotifications about sales
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise.
Watch videoSync across all devices
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All emails in one place
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Track your time
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Download to get started
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Style guide and sample pages included
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Perfect landing page for your app
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment, and found herself walking.
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment, and found herself.
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment, and found herself.
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment, and found herself.
Choose your plan
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment, and found herself walking.
Stay in touch
This sounded nonsense to Alice, so she said nothing, but set off at once toward the Red Queen. To her surprise, she lost sight of her in a moment.


--- title: Binomial Distribution Applet author: Eric McKinney date: '2019-09-04' slug: binomial-distribution-applet categories: [] tags: - Teaching - GeoGebra image: preview_only: true ---
This applet allows the user to specify the parameters of a binomial distribution, and calculate the probability of a specific outcome.


An array in C/C++ or be it in any programming language is a collection of similar data items stored at contiguous memory locations and elements can be accessed randomly using indices of an array. They can be used to store collection of primitive data types such as int, float, double, char, etc of any particular type. To add to it, an array in C/C++ can store derived data types such as the structures, pointers etc.
Why do we need arrays?
We can use normal variables (v1, v2, v3, ..) when we have a small number of objects, but if we want to store a large number of instances, it becomes difficult to manage them with normal variables. The idea of an array is to represent many instances in one variable.
Advantages of an Array in C/C++:
Disadvantages of an Array in C/C++:


1
2
3
4
5
6
7
8
9
0
-
+
Q
W
E
R
T
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U
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P
{
A
S
D
F
G
H
J
K
L
;
Z
X
C
V
B
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M
,
.
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fParent |
GUI
|
|
parent folder for functions folder. |
scales |
Object
|
|
AxesHelper options.scales for details. |
onFinishChange |
function
|
|
callback function is called every time, when user have entered new value of the function and the function controller is lost of the focus.
parameter value is new value of the function.
|
options |
Options
|
|
Options instance. The following options are available.
See the options parameter of the MyThree class.
Properties
| Name |
Type |
Attributes |
Default |
Description |
getLanguageCode |
function
|
<optional>
|
language code of your browser
|
returns the "primary language" subtag of the version of the browser. |
|
vector |
object
|
<optional>
|
Vector with initial text of the function
Properties
| Name |
Type |
Attributes |
Description |
x |
string
|
<optional>
|
text of the x axis function |
y |
string
|
<optional>
|
text of the y axis function |
z |
string
|
<optional>
|
text of the z axis function |
|


Notebook
Introduction to make
COMPUTING-BLOG
Scientific-computing Make
Table of Contents
What is make?
It is a command line tool to perform automatic tasks. Usually it is used on compilation of complex codes to ensure that all the dependencies are correcly included, but also it allows to perform other repetitive tasks efficently.
Often the kind of task we are doing in scientific computing are somehow hierarchical. So we extract some data information from a server using a program, we run a custom made model based on that data, we obtain new output datafiles that can be processed as an input of another program. For example a data analysis program or another code to perform additional simulations.
As we see in each step of the process we have a target with one or more dependencies that is using a program to build the target from them. Make helps you to create rules for that. The structure is more and less standard:
target: dependency1 dependency2 dependency3
rule
Step by Step with examples
For example, the next rule compile a Fortran code with dependences: compute.f90 constants.f90 main.f90 and a target myprogram.out.
myprogram.out : compute.f90 cte.f90 main.f90
gfortran compute.f90 cte.f90 main.f90 -o myprogram.out
Usually this information is stored in a file named: Makefile which contains all the rules. And we can compile
just writting make
. Let’s say that we want to compare results with several different contants.f90 files
myprogram1.out : compute.f90 cte1.f90 main.f90
gfortran compute.f90 cte1.f90 main.f90 -o myprogram.out
myprogram2.out : compute.f90 cte2.f90 main.f90
gfortran compute.f90 cte2.f90 main.f90 -o myprogram.out
We can invoke each rule by,
make myprogram1.out
or
make myprogram2.out
It is quite common include a clean rule as,
clean :
rm -f myprogram*.out
myprogram1.out : compute.f90 cte1.f90 main.f90
gfortran compute.f90 cte1.f90 main.f90 -o myprogram.out
myprogram2.out : compute.f90 cte2.f90 main.f90
gfortran compute.f90 cte2.f90 main.f90 -o myprogram.out
This clean rule has no dependencies, however if make finds a file or directory named clean then nothing is done. For this reason we include:
.PHONY : clean
clean :
rm -f myprogram*.out
Let’s say that our fortran code process some datafiles stores in a subdirectory data but this datafiles have to be downloaded from a server with the more recent information, we can include a rule for that.
.PHONY : download
download :
wget -O dataset.tar.gz http://mydataserver.com/nowcasting.tar.gz
prepare : dataset.tar.gz
mv dataset.tar.gz data/
create : data/dataset.tar.gz
tar -zxf data/dataset.tar.gz
With this kind of rules you have to,
> make download
> make prepare
> make create
After that you will have the file nowcasting.tar.gz decompresed on your directory and stored also in data/ as dataset.tar.gz. However is we don’t have yet downloaded the file or prepare it for extracting then make doesn’t know how to proceed. Basically make doesn’t know how to build the dependency **data/dataset.tar.gz. But we can try as,
.PHONY : download
download :
wget -O dataset.tar.gz http://mydataserver.com/nowcasting.tar.gz
prepare : download
mv dataset.tar.gz data/
create : prepare
tar -zxf data/dataset.tar.gz
And the commad make create will do all the previous rules to achieve the rule create. Note that if you type in bash shell make and tab you will see all the possible rules avaliables on the Makefile of your current directory, something very useful.
COMPUTING-BLOG
Scientific-computing Make


[
array
math
]
Leetcode 0667. Beautiful Arrangement II
Problem statement
https://leetcode.com/problems/beautiful-arrangement-ii/
Solution
Just create sentence 1,2, . . . , n-k, n, n-k+ 1, n-1, . . .
, where we start to take biggest and smallest elements from place n-k
. Let us consider case n = 14
and k = 6
, then what [1,2,3,4,5,6,7,8,14,9,13,10,12,11]
: then first differences are equal to 1
, then we have differencese 6, -5, 4, -3, 2, -1
, so in the end we will have absolute differences 1, 2, 3, 4, 5, 6
.
Complexity
Time complexity is O(n)
, space as well to form output array
Code
class Solution:
def constructArray(self, n, k):
dr, diff = 1, k
result = list(range(1, n-k+1))
for i in range(k):
result.append(result[-1] + dr*diff)
dr *= -1
diff -= 1
return result


module Remote_config:sig
..end
type
t = {
|
remote_nodes : |
(* |
The initial list of remote nodes which this node can send messages to. A list of external ip address/port/node name triplets.
| *) |
|
local_port : |
(* |
The port that this node should run on.
| *) |
|
heart_beat_timeout : |
(* |
The maximum amount of time in seconds to wait between heart beats from remote nodes before marking them as unavailable.
| *) |
|
heart_beat_frequency : |
(* |
The amount of time between sending hearts to nodes that are connected to this node.
| *) |
|
connection_backlog : |
(* |
The the argument used when listening on a socket.
| *) |
|
node_name : |
(* |
The name of this node.
| *) |
|
node_ip : |
(* |
The external ip address of this node.
| *) |
|
logger : |
(* |
The logger that should be used.
| *) |


This is Meiko. Whenever you visit one of my short links, his job is to fetch the long version and return it to your browser. Thanks to Meiko (and Google Cloud Run), mco.fyi is fast and reliable. If you'd like to try this code for yourself, it's available on
Github. Also, here's a
slide deck and a
blog article about this service.
- Made with ❤ by
Marc.


Devil's Isle Survivor:
I looked into it while you were gone -- even the scholars can't agree about the nature of Pailaka...
But most think, as I do, that you should find an ally once you cross over.
We just don't know the specifics! Stay alert and you'll do fine.
Now then, we've talked long enough! Ready to go?
Enter Pailaka.


|
Charters Web Site is intended to give you information about the school and, combined with the opportunity of meeting us, an idea of the kind of school Charters is. We believe in setting the highest possible standards in everything we do. Each pupil at Charters is recognised as an individual and encouraged to develop to his or her full potential. We set high standards of appearance and behaviour seeking to provide an ordered, but caring and happy environment in which pupils can flourish. We want keen pupils who will take part in the whole life of the school and who are encouraged by their parents. It is important that we build a close relationship between the home and the school, so that difficulties can be shared, problems resolved and success congratulated.  In 1996 the official Ofsted inspection under the government's regulations acknowledged that Charters is - 'an
excellent school" with "outstanding features which include its excellent atmosphere and good examination results".
I am very proud to be the Headteacher of this outstanding school and I look forward to meeting you and working
with you to ensure its continued success.
MARCIA TWELFTREE
|
|


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import config.ApplicationConfig
@import uk.gov.hmrc.play.views.html.helpers._
@import views.helpers._
@import views.html.helpers._
@import views.html.includes.awrs_main
@this(formWithCSRF: FormWithCSRF, awrsMain: awrs_main)
@(businessTypeForm: Form[models.BusinessType], businessName : String, isAGroup : Boolean = false, isSaAccount : Option[Boolean], isOrgAccount : Option[Boolean])(implicit request: Request[AnyContent], messages: Messages, applicationConfig: ApplicationConfig)
@implicitFormInstance = @{ Some(businessTypeForm) }
@awrsMain(title = messages("awrs.generic.tab.title", messages("awrs.business_verification.heading")), nameBusiness = Some(businessName), userLoggedIn = true){
@errorSummaryTemplate(businessTypeForm)


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<


LoadFile4DOM - Demo Load ZIP File and add multiple Files to ZIP
Button:
In general the `onclick`-event on buttons is used to create
Load a ZIP-File to JSON
in a browser.
Filename:
This filename of ZIP will be updated if you load file.
Load File and add to ZIP:
Add File to ZIP
After adding files to the ZIP you can press the Button "Save Generated ZIP" to get the result.
Save Generated ZIP
Used Libraries for Demo
loadfile4dom.js
to load files into the browser.
jszip.js
to create an handle ZIP files.
filesaver.js
to save the generated ZIP as download.
<< Demo Applications with LoadFile4DOM