Source code for classes.InputEvent
"""Contains a class that represent an input event to DYNAP-se
"""
[docs]class InputEvent:
"""Class that represent an input event in DYNAP-se
"""
def __init__(self, virtualSourceCoreId = None, neuronAddress = None, coreDest = None, address = None, time = None):
"""Return a new InputEvent object
Parameters:
virtualSourceCoreId (int): id of the core where is located the virtual neuron
neuronAddress (int): id of virtual source neuron
coreDest (int, hot coded): id of the destination cores where the spike is delivered
address (int): address of the event (for manual insertions of events)
time (float): time delay associated with the event
Note:
coreDest is a 16-bit hot coded number. This means that every coded bit can activate or
deactivate a destination core, for example::
- coreDest = 0001 -> to core 0
- coreDest = 0010 -> to core 1
- coreDest = 0100 -> to core 2
- coreDest = 1000 -> to core 3
Every combination is accepted, for example::
- coreDest = 0011 -> to core 0 and 1
- coreDest = 0110 -> to core 2 and 3
- coreDest = 1111 -> to all cores
"""
if (virtualSourceCoreId != None) & (neuronAddress != None) & (coreDest != None) & (time != None):
self.virtualSourceCoreId = virtualSourceCoreId
self.neuronAddress = neuronAddress
self.coreDest = coreDest
self.create_address_event()
self.time = time
elif (address != None) & (time != None):
self.address = address
self.time = time
self.decode_address_event()
### ===========================================================================
[docs] def create_address_event(self):
"""Create an address and time for an event in the spike generator
Parameters:
None
Note:
The final address is composed in the following way::
Bits:
13 12 11 10 9 8 7 6 5 4 3 2 1 0
|-----------------| neuronAddress
|-| virtualSourceCoreId
|-----| coreDest
"""
self.address = (self.neuronAddress << 6) & 0xffff | self.coreDest & 0xf | (self.virtualSourceCoreId << 4) & 0x30
### ===========================================================================
[docs] def decode_address_event(self):
"""Decode event characteristics starting from time and address
Parameters:
None
Note:
The final address is composed in the following way::
Bits:
13 12 11 10 9 8 7 6 5 4 3 2 1 0
|-----------------| neuronAddress
|-| virtualSourceCoreId
|-----| coreDest
"""
self.virtualSourceCoreId = (self.address >> 4) & 0x3
self.coreDest = self.address & 0xf
self.neuronAddress = (self.address >> 6) & 0xff


The PyObjC package provides the glue needed to interface the Python interpreter
with the Objective-C language. At its core is the objc
module makes
Objective-C objects and classes available as first-class Python citizens. It is
not only possible to use Objective-C objects but you can also subclass
Objective-C classes.
To make it easier to use the core objc
module, this package also provides
a number of modules that are wrappers for the Cocoa frameworks, such as
Foundation and AppKit. These modules not only give you access to the classes in
these frameworks but also to the functions and constants in those modules.
More information on PyObjC can be found on our website.
To install this package double-click on PyObjC.pkg
. This will install the
PyObjC python package and some Project Builder templates. The documentation
and examples are found in the folder PyObjC Documentation and Examples
.
Install these by dragging this folder to you disk, /Library/Documentation
is a suitable location.
NOTE: The installer package is for MacOS X 10.2 only and is for use with the Apple supplied Python 2.2 installation. If you use MacPython 2.3 you should use the Package Manager application to install PyObjC. If you use other versions of MacOS X you should install from the sources, see the source archive for details.


from __future__ import print_function, division
from sympy.core.basic import Basic
from sympy.core.compatibility import as_int, with_metaclass
from sympy.core.sets import Set, Interval, Intersection
from sympy.core.singleton import Singleton, S
from sympy.core.symbol import symbols
from sympy.core.sympify import sympify
from sympy.core.decorators import deprecated
[docs]class Naturals(with_metaclass(Singleton, Set)):
"""
Represents the natural numbers (or counting numbers) which are all
positive integers starting from 1. This set is also available as
the Singleton, S.Naturals.
Examples
========
>>> from sympy import S, Interval, pprint
>>> 5 in S.Naturals
True
>>> iterable = iter(S.Naturals)
>>> next(iterable)
1
>>> next(iterable)
2
>>> next(iterable)
3
>>> pprint(S.Naturals.intersect(Interval(0, 10)))
{1, 2, ..., 10}
See Also
========
Naturals0 : non-negative integers (i.e. includes 0, too)
Integers : also includes negative integers
"""
is_iterable = True
_inf = S.One
_sup = S.Infinity
def _intersect(self, other):
if other.is_Interval:
return Intersection(
S.Integers, other, Interval(self._inf, S.Infinity))
return None
def _contains(self, other):
from sympy.assumptions.ask import ask, Q
if ask(Q.positive(other)) and ask(Q.integer(other)):
return True
return False
def __iter__(self):
i = self._inf
while True:
yield i
i = i + 1
@property
def _boundary(self):
return self
[docs]class Naturals0(Naturals):
"""Represents the whole numbers which are all the non-negative integers,
inclusive of zero.
See Also
========
Naturals : positive integers; does not include 0
Integers : also includes the negative integers
"""
_inf = S.Zero
def _contains(self, other):
from sympy.assumptions.ask import ask, Q
if ask(Q.negative(other)) == False and ask(Q.integer(other)):
return True
return False
[docs]class Integers(with_metaclass(Singleton, Set)):
"""
Represents all integers: positive, negative and zero. This set is also
available as the Singleton, S.Integers.
Examples
========
>>> from sympy import S, Interval, pprint
>>> 5 in S.Naturals
True
>>> iterable = iter(S.Integers)
>>> next(iterable)
0
>>> next(iterable)
1
>>> next(iterable)
-1
>>> next(iterable)
2
>>> pprint(S.Integers.intersect(Interval(-4, 4)))
{-4, -3, ..., 4}
See Also
========
Naturals0 : non-negative integers
Integers : positive and negative integers and zero
"""
is_iterable = True
def _intersect(self, other):
from sympy.functions.elementary.integers import floor, ceiling
if other.is_Interval and other.measure < S.Infinity:
s = Range(ceiling(other.left), floor(other.right) + 1)
return s.intersect(other) # take out endpoints if open interval
return None
def _contains(self, other):
from sympy.assumptions.ask import ask, Q
if ask(Q.integer(other)):
return True
return False
def __iter__(self):
yield S.Zero
i = S(1)
while True:
yield i
yield -i
i = i + 1
@property
def _inf(self):
return -S.Infinity
@property
def _sup(self):
return S.Infinity
@property
def _boundary(self):
return self
class Reals(with_metaclass(Singleton, Interval)):
def __new__(cls):
return Interval.__new__(cls, -S.Infinity, S.Infinity)
[docs]class ImageSet(Set):
"""
Image of a set under a mathematical function
Examples
========
>>> from sympy import Symbol, S, ImageSet, FiniteSet, Lambda
>>> x = Symbol('x')
>>> N = S.Naturals
>>> squares = ImageSet(Lambda(x, x**2), N) # {x**2 for x in N}
>>> 4 in squares
True
>>> 5 in squares
False
>>> FiniteSet(0, 1, 2, 3, 4, 5, 6, 7, 9, 10).intersect(squares)
{1, 4, 9}
>>> square_iterable = iter(squares)
>>> for i in range(4):
... next(square_iterable)
1
4
9
16
"""
def __new__(cls, lamda, base_set):
return Basic.__new__(cls, lamda, base_set)
lamda = property(lambda self: self.args[0])
base_set = property(lambda self: self.args[1])
def __iter__(self):
already_seen = set()
for i in self.base_set:
val = self.lamda(i)
if val in already_seen:
continue
else:
already_seen.add(val)
yield val
def _is_multivariate(self):
return len(self.lamda.variables) > 1
def _contains(self, other):
from sympy.solvers import solve
L = self.lamda
if self._is_multivariate():
solns = solve([expr - val for val, expr in zip(other, L.expr)],
L.variables)
else:
solns = solve(L.expr - other, L.variables[0])
for soln in solns:
try:
if soln in self.base_set:
return True
except TypeError:
if soln.evalf() in self.base_set:
return True
return False
@property
def is_iterable(self):
return self.base_set.is_iterable
@deprecated(useinstead="ImageSet", issue=3958, deprecated_since_version="0.7.4")
def TransformationSet(*args, **kwargs):
"""Deprecated alias for the ImageSet constructor."""
return ImageSet(*args, **kwargs)
class Range(Set):
"""
Represents a range of integers.
Examples
========
>>> from sympy import Range
>>> list(Range(5)) # 0 to 5
[0, 1, 2, 3, 4]
>>> list(Range(10, 15)) # 10 to 15
[10, 11, 12, 13, 14]
>>> list(Range(10, 20, 2)) # 10 to 20 in steps of 2
[10, 12, 14, 16, 18]
>>> list(Range(20, 10, -2)) # 20 to 10 backward in steps of 2
[12, 14, 16, 18, 20]
"""
is_iterable = True
def __new__(cls, *args):
from sympy.functions.elementary.integers import ceiling
# expand range
slc = slice(*args)
start, stop, step = slc.start or 0, slc.stop, slc.step or 1
try:
start, stop, step = [S(as_int(w)) for w in (start, stop, step)]
except ValueError:
raise ValueError("Inputs to Range must be Integer Valued\n" +
"Use ImageSets of Ranges for other cases")
n = ceiling((stop - start)/step)
if n <= 0:
return S.EmptySet
# normalize args: regardless of how they are entered they will show
# canonically as Range(inf, sup, step) with step > 0
start, stop = sorted((start, start + (n - 1)*step))
step = abs(step)
return Basic.__new__(cls, start, stop + step, step)
start = property(lambda self: self.args[0])
stop = property(lambda self: self.args[1])
step = property(lambda self: self.args[2])
def _intersect(self, other):
from sympy.functions.elementary.integers import floor, ceiling
from sympy.functions.elementary.miscellaneous import Min, Max
if other.is_Interval:
osup = other.sup
oinf = other.inf
# if other is [0, 10) we can only go up to 9
if osup.is_integer and other.right_open:
osup -= 1
if oinf.is_integer and other.left_open:
oinf += 1
# Take the most restrictive of the bounds set by the two sets
# round inwards
inf = ceiling(Max(self.inf, oinf))
sup = floor(Min(self.sup, osup))
# if we are off the sequence, get back on
off = (inf - self.inf) % self.step
if off:
inf += self.step - off
return Range(inf, sup + 1, self.step)
if other == S.Naturals:
return self._intersect(Interval(1, S.Infinity))
if other == S.Integers:
return self
return None
def _contains(self, other):
from sympy.assumptions.ask import ask, Q
return (other >= self.inf and other <= self.sup and
ask(Q.integer((self.start - other)/self.step)))
def __iter__(self):
i = self.start
while(i < self.stop):
yield i
i = i + self.step
def __len__(self):
return ((self.stop - self.start)//self.step)
def _ith_element(self, i):
return self.start + i*self.step
@property
def _last_element(self):
return self._ith_element(len(self) - 1)
@property
def _inf(self):
return self.start
@property
def _sup(self):
return self.stop - self.step
@property
def _boundary(self):
return self


oncoEnrichR is an R package for functional interrogation of human genesets in the context of cancer. The package is intended for exploratory analysis and prioritization of a gene list (referred to here as query set) from high-throughput cancer biology experiments, e.g. genetic screens (siRNA/CRISPR), protein proximity labeling, or transcriptomics (differential expression). The tool queries a number of high-quality data resources in order to assemble useful gene annotations and analyses in an interactive report (examples from the report shown below).
The contents of the final report attempts to shed light on the following questions:
July 8th 2021: 1.0.4 release
June 29th 2021: 1.0.3 release
June 8th 2021: 1.0.0 release
Data harvested from the following resources form the backbone of oncoEnrichR:
oncoEnrichR performs its operations through the following procedures/methods:
Consists of two main processing steps:
1) Takes an input/query list of human gene/protein identifiers (e.g. UniProt accession, RefSeq/Ensembl transcript identifer etc.) as input and conducts uniform identifier conversion
2) Performs extensive annotation, enrichment and membership analyses of the query set against underlying data sources on cancer-relevant properties of human genes and their interrelationships.
Technically, the method returns a list object with all contents of the analyses performed. The specific arguments/options and default values are outlined below:
onco_enrich(
query,
query_id_type = "symbol",
ignore_id_err = TRUE,
project_title = "Project title",
project_owner = "Project owner",
project_description = "Project description",
bgset = NULL,
bgset_id_type = "symbol",
bgset_description = "All protein-coding genes",
p_value_cutoff_enrichment = 0.05,
p_value_adjustment_method = "BH",
q_value_cutoff_enrichment = 0.2,
min_geneset_size = 10,
max_geneset_size = 500,
min_subcellcomp_confidence = 1,
subcellcomp_show_cytosol = FALSE,
simplify_go = TRUE,
ppi_add_nodes = 50,
ppi_score_threshold = 900,
show_ppi = TRUE,
show_drugs_in_ppi = TRUE,
show_disease = TRUE,
show_top_diseases_only = TRUE,
show_cancer_hallmarks = TRUE,
show_drug = TRUE,
show_enrichment = TRUE,
show_tcga_aberration = TRUE,
show_tcga_coexpression = TRUE,
show_subcell_comp = TRUE,
show_crispr_lof = TRUE,
show_cell_tissue = FALSE,
show_prognostic_cancer_assoc = TRUE,
show_complex = TRUE)
| Argument | Description |
|---|---|
query |
character vector with gene/query identifiers |
query_id_type |
character indicating type of identifier used for query set (one of “uniprot_acc”, “symbol”, “entrezgene”, “ensembl_gene”, “refseq_mrna”, “refseq_protein”, “ensembl_protein”, or “ensembl_mrna”) |
ignore_id_err |
logical indicating if analysis should continue when erroneous/unmatched query identifiers are encountered in query or background gene set |
project_title |
project title (report title) |
project_owner |
project owner (e.g. lab/PI) |
project_description |
brief description of project, how target list was derived |
bgset |
character vector with gene identifiers, used as reference/background for enrichment/over-representation analysis |
bgset_id_type |
character indicating type of identifier used for background set (one of “uniprot_acc”, “symbol”, “entrezgene”, “ensembl_gene”, “refseq_mrna”, “refseq_protein”, “ensembl_protein”, or “ensembl_mrna”) |
bgset_description |
character with description of background gene set (e.g. ‘All lipid-binding proteins (n = 200)’) |
p_value_cutoff_enrichment |
cutoff p-value for enrichment/over-representation analysis |
p_value_adjustment_method |
one of “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none” |
q_value_cutoff_enrichment |
cutoff q-value for enrichment/over-representation analysis |
min_geneset_size |
minimal size of geneset annotated by term for testing in enrichment/over-representation analysis |
max_geneset_size |
maximal size of geneset annotated by term for testing in enrichment/over-representation analysis |
min_subcellcomp_confidence |
minimum confidence level of subcellular compartment annotations (range from 1 to 6, 6 is strongest) |
subcellcomp_show_cytosol |
logical indicating if subcellular heatmap should highlight cytosol as a subcellular protein location or not |
simplify_go |
remove highly similar GO terms in results from GO enrichment/over-representation analysis (recommended) |
ppi_add_nodes |
number of neighbouring nodes to add to query set when computing the protein-protein interaction network (STRING) |
ppi_score_threshold |
minimum significance score (0-1000) for protein-protein interactions to be included in network (STRING) |
show_ppi |
logical indicating if report should contain protein-protein interaction data of query set and their closely interacting partners (STRING), local network communities, and rank of query set based on network centrality |
show_drugs_in_ppi |
logical indicating if targeted drugs (>= phase 3) should be displayed in protein-protein interaction network (Open Targets Platform) |
show_disease |
logical indicating if report should contain ranked associations to cancer phenotypes (overall), as well as tumor-type specific rankings (association score >= 0.4, minimum number of sources contributing to association >= 2, (Open Targets Platform)) |
show_top_diseases_only |
logical indicating if report should only show top (20) cancer phenotypes/disease associations from Open Targets Platform |
show_cancer_hallmarks |
logical indicating if report should show evidence that relates query member genes to cancer hallmarks |
show_drug |
logical indicating if report should contain cancer drugs targeted towards proteins in the query list (early and late development phase) and tractability/druggability data for all query entries, from Open Targets Platform) |
show_enrichment |
logical indicating if report should perform and list functional enrichment/over-representation analysis of query set (MSigDB, GO, KEGG, REACTOME, WikiPathways, NetPath) |
show_tcga_aberration |
logical indicating if report should contain TCGA aberration plots (amplifications/deletions, SNVs/InDels (oncoplots)) |
show_tcga_coexpression |
logical indicating if report should list oncogenes/tumor suppressor genes that significantly correlate with entries in query set in terms of expression (across TCGA cohorts) |
show_subcell_comp |
logical indicating if report should list subcellular compartment annotations (ComPPI) |
show_crispr_lof |
logical indicating if report should list results from CRISPR/Cas9 loss-of-fitness screens and associated target priority scores (Project Score) |
show_cell_tissue |
logical indicating if report should list results from tissue (GTex)- and cell-type (HPA) specific gene expression patterns in query set |
show_prognostic_cancer_assoc |
logical indicating if report should list results from significant associations between gene expression, mutation, methylation and survival (Human Protein Atlas - Pathology Atlas, and survival.cshl.edu) |
show_complex |
logical indicating if report should show membership of target proteins in known protein complexes (CORUM) |
Consists of two main processing steps:
1) Transform the contents of the analyses returned by oncoEnrichR::onco_enrich() into various visualizations and interactive tables
2) Assemble and write the final analysis report through
A target list of n = 134 high-confidence interacting proteins with the c-MYC oncoprotein were previously identified through BioID protein proximity assay in standard cell culture and in tumor xenografts (Dingar et al., J Proteomics, 2015). We ran this target list through the oncoEnrichR analysis workflow using the following configurations for the onco_enrich
method:
project_title = "cMYC_BioID_screen"
project_owner = "Raught et al."
and produced the following HTML report with results.
Below are R commands provided to reproduce the example output. NOTE: Replace “LOCAL_FOLDER” with a directory on your local computer:
library(oncoEnrichR)
myc_interact_targets <- read.csv(system.file("extdata","myc_data.csv", package = "oncoEnrichR"), stringsAsFactors = F)
myc_report <- oncoEnrichR::onco_enrich(query = myc_interact_targets$symbol, show_cell_tissue = T, project_title = "cMYC_BioID_screen", project_owner = "Raught et al.")
oncoEnrichR::write(report = myc_report, file = "LOCAL_FOLDER/myc_report_oncoenrichr.html", format = "html")
oncoEnrichR::write(report = myc_report, file = "LOCAL_FOLDER/myc_report_oncoenrichr.xlsx", format = "excel")
OncoEnrichR is supported by the Centre for Cancer Cell Reprogramming at the University of Oslo/Oslo University Hospital, and Elixir Norway (Oslo node).
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.


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DetectionKit
DetectionKit is a media detection library for macOS for use in Objective-C and Swift projects.
- Detects running media players and web browsers
- Retrieves information about the currently playing video or stream
Documentation can be viewed here.
Dependencies
To use DetectionKit, you need to include the following frameworks with your application:
- anitomy-osx.framework
- CocoaOniguruma.framework
- AFNetworking.framework
- GBPing.framework - (included, Apache Licensed)
- PingNotifier.framework - (included)
License
Unless noted, DetectionKit is licensed under the BSD License.


About Me
Projects
Resume
MY
WORKS.
Hi there, I'm Albert.
PPfix App
I am responsible for wireframe and UI of the server app 2.0 revision, which is based on smartphone and computer repair.
SunnyBaby
A cool and cute weather app,whose wireframe,UI and icons made by me.
Yitu App
I designed the lite UI to help students find their favourite books.
EDEN App
I designed a cool style for the technology news app,which includes smartphone and computer.


Personal Radar
Michael Watson
So much to learn and evaluate
What is a tech radar?
A start
The tech
AngularJs
SVG
Bindings
Hello there {{name}}
Hello there {{name}}
Basic svg
Circle
Path
Binding a model to angular
Increase width
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Questions


| I l@ve RuBoard |
9.5 PyView: An Image and Notes SlideshowA picture may be worth a thousand words, but it takes considerably fewer to display one with Python. The next program, PyView, implements a simple photo slideshow program in portable Python/Tkinter code. 9.5.1 Running PyViewPyView pulls together many of the topics we studied in the last chapter: it uses after events to sequence a slideshow, displays image objects in an automatically sized canvas, and so on. Its main window displays a photo on a canvas; users can either open and view a photo directly or start a slideshow mode that picks and displays a random photo from a directory, at regular intervals specified with a scale widget.  To view other sets of photos, either pass a directory name in as a first command-line argument or change the default directory name in the script itself. I can't show you a slideshow in action here, but I can show you the main window in general. Figure 9-13 shows the main PyView window's default display. Figure 9-13. PyView without notesThough it's not obvious as rendered in this book, the black-on-red label at the top gives the pathname of the photo file displayed. For a good time, move the slider at the bottom all the way over to "0" to specify no delay between photo changes, and click Start to begin a very fast slideshow. If your computer is at least as fast as mine, photos flip by much too fast to be useful for anything but subliminal advertising. Slideshow photos are loaded on startup to retain references to them (remember, you must hold on to image objects). But the speed with which large GIFs can be thrown up in a window in Python is impressive, if not downright exhilarating. The GUI's Start button changes to a Stop button during a slideshow (its text attribute is reset with the widget config method). Figure 9-14 shows the scene after pressing Stop at an opportune moment. Figure 9-14. PyView after stopping a slideshowIn addition, each photo can have an associated "notes" text file which is automatically opened along with the image. You can use this feature to record basic information about the photo. Press the Note button to open an additional set of widgets that let you view and change the note file associated with the currently displayed photo. This additional set of widgets should look familiar -- the PyEdit text editor of the previous section is attached to PyView to serve as a display and editing widget for photo notes. Figure 9-15 shows PyView with the attached PyEdit note-editing component opened. Figure 9-15. PyView with notesThis makes for a very big window, usually best view maximized (taking up the entire screen). The main thing to notice, though, is the lower right corner of this display above the scale -- it's simply an attached PyEdit object, running the very same code listed in the prior section. Because PyEdit is implemented as a GUI class, it can be reused like this in any GUI that needs a text editing interface. When embedded like this, PyEdit's menus are based on a frame (it doesn't own the window at large), text content is stored and fetched directly, and some stand-alone options are omitted (e.g, the File pull-down is gone). The note file viewer only appears if you press the Note button, and it is erased if you press it again; PyView uses widget pack and pack_forget methods introduced at the end of the last chapter to show and hide the note viewer frame. The window automatically expands to accommodate the note viewer when it is packed and displayed. It is also possible to open the note file in a PyEdit pop-up window, but PyView embeds the editor to retain a direct visual association. Watch for PyEdit to show up embedded within another GUI like this when we meet PyMail in Chapter 11. A caveat here: out of the box, PyView supports as many photo formats as Tkinter's PhotoImage object does; that's why it looks for GIF files by default. You can improve this by installing the PIL extension to view JPEGs (and many others). But because PIL is an optional extension today, it's not incorporated into this PyView release. See the end of Chapter 7 for more on PIL and image formats. 9.5.2 PyView Source CodeBecause the PyView program was implemented in stages, you need to study the union of two files and classes to understand how it truly works. One file implements a class that provides core slideshow functionality; the other implements a class the extends the original class, to add additional features on top of the core behavior. Let's start with the extension class: Example 9-16 adds a set of features to an imported slideshow base class -- note editing, a delay scale and file label, etc. This is the file that is actually run to start PyView. Example 9-16. PP2E\Gui\SlideShow\slideShowPlus.py################################################################### # SlideShowPlus: add note files with an attached PyEdit object, # a scale for setting the slideshow delay interval, and a label # that gives the name of the image file currently being displayed; ################################################################### import os, string from Tkinter import * from PP2E.Gui.TextEditor.textEditor import * from slideShow import SlideShow #from slideShow_threads import SlideShow class SlideShowPlus(SlideShow): def __init__(self, parent, picdir, editclass, msecs=2000): self.msecs = msecs self.editclass = editclass SlideShow.__init__(self, parent=parent, picdir=picdir, msecs=msecs) def makeWidgets(self): self.name = Label(self, text='None', bg='red', relief=RIDGE) self.name.pack(fill=X) SlideShow.makeWidgets(self) Button(self, text='Note', command=self.onNote).pack(fill=X) Button(self, text='Help', command=self.onHelp).pack(fill=X) s = Scale(label='Speed: msec delay', command=self.onScale, from_=0, to=3000, resolution=50, showvalue=YES, length=400, tickinterval=250, orient='horizontal') s.pack(side=BOTTOM, fill=X) s.set(self.msecs) if self.editclass == TextEditorMain: # make editor now self.editor = self.editclass(self.master) # need root for menu else: self.editor = self.editclass(self) # embedded or popup self.editor.pack_forget( ) # hide editor initially self.editorUp = self.image = None def onStart(self): SlideShow.onStart(self) self.config(cursor='watch') def onStop(self): SlideShow.onStop(self) self.config(cursor='hand2') def onOpen(self): SlideShow.onOpen(self) if self.image: self.name.config(text=os.path.split(self.image[0])[1]) self.config(cursor='crosshair') self.switchNote( ) def quit(self): self.saveNote( ) SlideShow.quit(self) def drawNext(self): SlideShow.drawNext(self) if self.image: self.name.config(text=os.path.split(self.image[0])[1]) self.loadNote( ) def onScale(self, value): self.msecs = string.atoi(value) def onNote(self): if self.editorUp: # if editor already open #self.saveNote( ) # save text, hide editor self.editor.pack_forget( ) self.editorUp = 0 else: self.editor.pack(side=TOP) # else unhide/pack editor self.editorUp = 1 # and load image note text self.loadNote( ) def switchNote(self): if self.editorUp: self.saveNote( ) # save current image's note self.loadNote( ) # load note for new image def saveNote(self): if self.editorUp: currfile = self.editor.getFileName() # or self.editor.onSave( ) currtext = self.editor.getAllText( ) # but text may be empty if currfile and currtext: try: open(currfile, 'w').write(currtext) except: pass # this may be normal if run off cd def loadNote(self): if self.image and self.editorUp: root, ext = os.path.splitext(self.image[0]) notefile = root + '.note' self.editor.setFileName(notefile) try: self.editor.setAllText(open(notefile).read( )) except: self.editor.clearAllText( ) def onHelp(self): showinfo('About PyView', 'PyView version 1.1\nJuly, 1999\n' 'An image slide show\nProgramming Python 2E') if __name__ == '__main__': import sys picdir = '../gifs' if len(sys.argv) >= 2: picdir = sys.argv[1] editstyle = TextEditorComponentMinimal if len(sys.argv) == 3: try: editstyle = [TextEditorMain, TextEditorMainPopup, TextEditorComponent, TextEditorComponentMinimal][string.atoi(sys.argv[2])] except: pass root = Tk( ) root.title('PyView 1.1 - plus text notes') Label(root, text="Slide show subclass").pack( ) SlideShowPlus(parent=root, picdir=picdir, editclass=editstyle) root.mainloop( ) The core functionality extended by SlideShowPlus lives in Example 9-17. This was the initial slideshow implementation; it opens images, displays photos, and cycles through a slideshow. You can run it by itself, but you won't get advanced features like notes and sliders added by the SlideShowPlus subclass. Example 9-17. PP2E\Gui\SlideShow\slideShow.py######################################################################## # SlideShow: a simple photo image slideshow in Python/Tkinter; # the base feature set coded here can be extended in subclasses; ######################################################################## from Tkinter import * from glob import glob from tkMessageBox import askyesno from tkFileDialog import askopenfilename import random Width, Height = 450, 450 imageTypes = [('Gif files', '.gif'), # for file open dialog ('Ppm files', '.ppm'), # plus jpg with a Tk patch, ('Pgm files', '.pgm'), # plus bitmaps with BitmapImage ('All files', '*')] class SlideShow(Frame): def __init__(self, parent=None, picdir='.', msecs=3000, **args): Frame.__init__(self, parent, args) self.makeWidgets( ) self.pack(expand=YES, fill=BOTH) self.opens = picdir files = [] for label, ext in imageTypes[:-1]: files = files + glob('%s/*%s' % (picdir, ext)) self.images = map(lambda x: (x, PhotoImage(file=x)), files) self.msecs = msecs self.beep = 1 self.drawn = None def makeWidgets(self): self.canvas = Canvas(self, bg='white', height=Height, width=Width) self.canvas.pack(side=LEFT, fill=BOTH, expand=YES) self.onoff = Button(self, text='Start', command=self.onStart) self.onoff.pack(fill=X) Button(self, text='Open', command=self.onOpen).pack(fill=X) Button(self, text='Beep', command=self.onBeep).pack(fill=X) Button(self, text='Quit', command=self.onQuit).pack(fill=X) def onStart(self): self.loop = 1 self.onoff.config(text='Stop', command=self.onStop) self.canvas.config(height=Height, width=Width) self.onTimer( ) def onStop(self): self.loop = 0 self.onoff.config(text='Start', command=self.onStart) def onOpen(self): self.onStop( ) name = askopenfilename(initialdir=self.opens, filetypes=imageTypes) if name: if self.drawn: self.canvas.delete(self.drawn) img = PhotoImage(file=name) self.canvas.config(height=img.height(), width=img.width( )) self.drawn = self.canvas.create_image(2, 2, image=img, anchor=NW) self.image = name, img def onQuit(self): self.onStop( ) self.update( ) if askyesno('PyView', 'Really quit now?'): self.quit( ) def onBeep(self): self.beep = self.beep ^ 1 def onTimer(self): if self.loop: self.drawNext( ) self.after(self.msecs, self.onTimer) def drawNext(self): if self.drawn: self.canvas.delete(self.drawn) name, img = random.choice(self.images) self.drawn = self.canvas.create_image(2, 2, image=img, anchor=NW) self.image = name, img if self.beep: self.bell( ) self.canvas.update( ) if __name__ == '__main__': import sys if len(sys.argv) == 2: picdir = sys.argv[1] else: picdir = '../gifs' root = Tk( ) root.title('PyView 1.0') root.iconname('PyView') Label(root, text="Python Slide Show Viewer").pack( ) SlideShow(root, picdir=picdir, bd=3, relief=SUNKEN) root.mainloop( ) To give you a better idea of what this core base class implements, Figure 9-16 shows what it looks like if run by itself (actually, two copies run by themselves) by a script called slideShow_frames, which is on this book's CD. Figure 9-16. Two attached SlideShow objectsThe simple slideShow_frames scripts attach two instances of SlideShow to a single window -- a feat possible only because state information is recorded in class instance variables, not globals. The slideShow_toplevels script also on the CD attaches two SlideShows to two top-level pop-up windows instead. In both cases, the slideshows run independently, but are based on after events fired from the same single event loop in a single process. |
| I l@ve RuBoard |


View Admin data
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It is using the data for the form captured in PageData.js.


Particle Size Models User’s Guide¶
- Release
2.1
- Date
Jun 05, 2020
Scripts¶
The class objects and method functions defined in the particle_size_models
module provide a flexible to compute bubble and droplet size distributions
for breakup of immiscible gases or liquids into seawater. . The examples
detailed below show how to set up and run simulations using either the
ModelBase or Model classes. Further examples are presented in the
following scripts distributed in the ./bin/psm
directory with the source
code.
Examples¶
This example demonstrates the tasks necessary to compute particle size
distributions either from specified fluid properties (e.g., using the
ModelBase or PureJet classes) or from fluid properties computed by
objects in the dbm module (e.g., using the Model class). Before
running these examples, be sure to install the TAMOC
package and run
all of the tests in the ./test
directory. The commands below should
be executed in an IPython session. Start IPython by executing:
ipython --pylab
at the command prompt. The --pylab
flag is needed to get the correct
behavior of the output plots.
Using Specified Fluid Properties¶
Sometimes, especially when comparing breakup models to measured data, we would like to specify all of the fluid properties to use in the breakup calculation. We may do this using the ModelBase (which can handle releases of both oil and gas) or the PureJet (which handles single-phase releases) classes. In this example, we will use the ModelBase class.
Start by importing the particle_size_models module and then defining the properties of the fluids at the release:
>>> from tamoc import particle_size_models as psm
>>> # Gas Properties ----------
>>> rho_gas = 131.8
>>> mu_gas = 0.00002
>>> sigma_gas = 0.06
>>> # Oil Properties ----------
>>> rho_oil = 599.3
>>> mu_oil = 0.0002
>>> sigma_oil = 0.015
>>> # Seawater Properties -----
>>> rho = 1037.1
>>> mu = 0.002
Next, we create a ModelBase object that contains these fluid property data:
>>> spill = psm.ModelBase(rho_gas, mu_gas, sigma_gas, rho_oil, mu_oil,
sigma_oil, rho, mu)
We can now use this object to compute size distributions for a variety of situations as long as these fluid properties do not change. Generally, these properties would change is the release temperature or the release depth were to change.
As an example, let’s compute the characteristic values of the particle size distributions for a 0.30 m diameter orifice with specified gas and oil mass flow rates of 7.4 kg/s and 34.5 kg/s, respectively:
>>> m_gas = 7.4
>>> m_oil = 34.5
>>> spill.simulate(0.30, m_gas, m_oil)
The .simulate() method does not return any output. Rather, the results are stored in the object attributes. We may view these attribute values either by printing them directly or using the .get-methods. For example, the median particle sizes are:
>>> spill.d50_gas
0.01134713688939418
>>> spill.get_d50(0) # 0 = gas, 1 = liquid
spill.get_d50(0)
>>> spill.d50_oil
0.0033149657926870454
>>> spill.get_d50(1)
0.0033149657926870454
The .simulate() method also computed the characteristic width of the particle size distributions. To compute the distributions, we use the get_distributions() method, which returns the sizes and volume fractions of the gas bubble and oil droplet size distributions, as follows:
>>> de_gas, vf_gas, de_oil, vf_oil = spill.get_distributions(10, 15)
>>> de_gas
array([0.0057077 , 0.00655033, 0.00751736, 0.00862716, 0.0099008 ,
0.01136247, 0.01303992, 0.01496502, 0.01717432, 0.01970979])
>>> vf_gas
array([0.01545088, 0.0432876 , 0.09350044, 0.15570546, 0.19990978,
0.19788106, 0.15101303, 0.08885147, 0.04030462, 0.01409565])
>>> de_oil
array([0.00035434, 0.00044692, 0.00056369, 0.00071098, 0.00089675,
0.00113105, 0.00142658, 0.00179932, 0.00226946, 0.00286243,
0.00361034, 0.00455367, 0.00574348, 0.00724417, 0.00913696])
>>> vf_oil
array([0.00522565, 0.00788413, 0.01185467, 0.01773296, 0.02631885,
0.03859967, 0.05559785, 0.07791868, 0.10476347, 0.13228731,
0.15193437, 0.15128424, 0.12160947, 0.0710618 , 0.02592687])
It is easy to interpret these distributions after they are plotted. Use the plot_psd() method to see a default presentation of the data:
>>> spill.plot_psd(1)
Using Fluid Properties from the dbm Module¶
When using the plume models in TAMOC
(e.g., the bent_plume_model or
the stratified_plume_model), it is important that the fluid properties
used to compute the model initial conditions matches that used in the plume
simulations. The Model class is designed to provide this functionality.
As an example, let’s consider a natural gas pipeline leak. As with most
models in TAMOC
, we need ambient CTD data before we can start with any
of the other calculations. The ambient module now provides default
world-ocean average CTD data when no other data source is available. To
create an ambient.Profile object using this built-in data, do the
following:
>>> from tamoc import ambient
>>> profile = ambient.Profile(None, current=np.array([0.05, 0.1, 0]),
current_units = 'm/s')
We can test this data by requesting the properties at 50 m water depth:
>>> T, S, u, v = profile.get_values(50., ['temperature', 'salinity',
'ua', 'va'])
>>> T
288.23999999999995
>>> S
35.01
>>> u
0.05
>>> v
0.1
We also need to create a natural gas dbm.FluidMixture object and specify a mass flux of 5.5 kg/s of gas:
>>> from tamoc import dbm, dbm_utilities
>>> gas_comp, gas_frac = dbm_utilities.natural_gas()
>>> gas = dbm.FluidMixture(gas_comp)
>>> m_gas = 5.5 * gas_frac
With the profile and dbm object created, we can now create the particle_size_model.Model object:
>>> leak = psm.Model(profile, gas, m_gas, 50.)
Once we create the Model object, it can be used similarly to the ModelBase object, but without having to specify the mass flux anymore. If we want to change the mass flux, we need to use the update_m_mixture() method. For instance, we can compute the characteristic particle sizes through a 5 cm diameter hole as follows:
>>> leak.simulate(0.05)
>>> leak.d50_gas
>>> 0.005861081233586573
>>> leak.get_distributions(15, 0)
>>> leak.plot_psd(2, 0)
If we want to change the orifice size, then we would do the following:
>>> leak.simulate(0.1)
>>> leak.get_distributions(15, 0)
>>> leak.plot_psd(3,0)
Or, if we wanted to reduce the mass flux to 2.3 kg/s, then we would do the following:
>>> m_gas = 2.3 * gas_frac
>>> leak.update_m_mixture(m_gas)
>>> leak.simulate(0.1)
>>> leak.get_distributions(15, 0)
>>> leak.plot_psd(4,0)
This example demonstrated a pure gas plume. Since the Model class takes a dbm.FluidMixture object as input, it can automatically also consider a release of oil and gas. As a quick example, consider the following:
>>> oil = dbm.FluidMixture(['methane', 'decane'])
>>> m_mix = np.array([0.3, 0.7]) * 18.5
>>> blowout = psm.Model(profile, oil, m_mix, 1000.)
>>> blowout.simulate(0.15)
>>> blowout.get_distributions(15, 15)
>>> blowout.plot_psd(5)


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--- layout: archive title: Zulip Chat Archive permalink: /stream/144837-PR-reviews/topic/.23854.20pointwise.20operations.20for.20sets.html ---
It's very exciting to see the avalanche of merges. I guess it took an April Fools Day for everyone to recover from their ITP deadline... Is there anyone who wants to take a look at #854? It only adds a file to mathlib. I purposely didn't make things instances, because "there is more than one way to do it". (You can have several reasonable multiplications of sets.)
I think I'll let the people assigned to it review it
btw, I think you merged the same three branches a bunches of time today. I'm thinking it might have been excessive.
My apologies. I was just trying to make sure they were up to date when a reviewer passed by... But I agree that I might have gone overboard...
After Mario's comment we wanted to maximise the chance that the PR was up to date with master when someone looks at it. This PR and 856 are holding up development on the perfectoid project because of a diamond
Okay, I want to merge this. How do I mergify?
Not yet
I mean mergify isn't running yet
Just do it the old way
Thanks a lot @Mario Carneiro
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


--- layout: archive title: Projects ---
Project 1 description and links
Project 2 description and links


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DOWN PERISCOPE A film review by Andrew Hicks Copyright 1996 Andrew Hicks / Fatboy Productions
(1996) *1/2 (out of four)
Even the best comic actor is at the mercy of his or her material, as this subpar submarine comedy proves. DOWN PERISCOPE stars Kelsey "Frasier" Grammer as an inept Navy captain who is given command of his own ship as part of a corrupt officer's (James Coburn) plan to get him out of the Navy.
Then comes the transparent plotline. First Grammer has to fix up the delapidated Korean War ship the Navy has given him, and that comes courtesy a sad sight gag montage that includes a guy with a mop knocking Schneider overboard. After the ship is fixed up come the early drills, where everyone seems completely inept, like the electrician who can't connect wires so instead lets the current run through himself every time Grammer gets on the phone.
Finally, and this covers the final hour of the movie, Grammer has to lead his men (and Holly) to overcome the other ships in a wargames exercise. And he has to be a clever rebel to do it, in scenes that stretch all levels of believability.  The whole while, Evil Captain Coburn is overacting underwater, cursing Grammer (as most people watching the movie probably are too).
It's all predictable without any original humor to redeem it. The jokes are right out of "McHale's Navy" and other cut-rate sitcoms of the past. There's one scene where Schneider looks through the ship's pantry in disgust, holds one food can up and announces, "This expired in 1966!" I yelled back at the screen, "So did these jokes." I watched this movie with the whole family and everyone hated it. My mom was so bored she brought out the coupon book midway through and, around the start of the second hour, I started writing my will.
The blame can't really be placed on the stars, though. Kelsey Grammer is good in this, he just doesn't have anything to work with.  Grammer probably did something even worse -- got really drunk and agreed to appear in a movie he knew nothing about.  As for Schneider (whose character is downright annoying), there was nowhere to go but up after SURF NINJAS, although he's still in the depths of the comedy ocean after jumping the "SNL" ship.
My theory is that DOWN PERISCOPE is one of those movies that got the green light before the script was even written as a formula comedy ("Okay, it's POLICE ACADEMY in the Navy with Frasier at the helm and the chick from DUMB AND DUMBER.") and after everyone was signed on turned into a disaster. Judging from the video box, most other movie critics agree with me. The only quote the DOWN PERISCOPE copy writers could dig up came from the Prevue channel's Jim Ferguson. I guess even Jeff Craig from "60 Second Preview" (who said TANK GIRL "kicks butt!" without ever seeing it) didn't like this.
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|
Standard Reference Data Program |
Data Gateway |
Chemistry WebBook |
Go To: Top, Constants of diatomic molecules, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data compiled by: H.M. Rosenstock, K. Draxl, B.W. Steiner, and J.T. Herron
View reactions leading to FLa+ (ion structure unspecified)
| IE (eV) | Method | Reference |
|---|---|---|
| 6.3 ± 0.3 | EI | Zmbov and Margrave, 1968 |
Go To: Top, Gas phase ion energetics data, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data compiled by: K.P. Huber and G. Herzberg
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Emission | Emission spectra attributed to LaF: three systems of V shaded bands (P and Q heads) | |||||||||||
| νe = 30188.9, ω'e = 674.7, ω'ex'e = 2.35, ω"e = 603.8, ω"ex"e = 1.96 | ||||||||||||
| Shenyavskaya, Gurvich, et al., 1968 | ||||||||||||
| νe = 30076.4, ω'e = 682.4, ω'ex'e = 2.28, ω"e = 607.1, ω"ex"e = 1.88 | ||||||||||||
| Shenyavskaya, Gurvich, et al., 1965 | ||||||||||||
| νe = 28468, ω'e = 657, ω'ex'e = 1.1, ω"e = 607, ω"ex"e = 1.8 | ||||||||||||
| Shenyavskaya, Gurvich, et al., 1965 | ||||||||||||
| one system of R shaded bands in the region 27500 - 28200 cm-1. | ||||||||||||
| Shenyavskaya, Gurvich, et al., 1965 | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| e 3Φ4 | [0.2302] | [1.7E-7] | [2.098] | e ← a R | 22340.20 Z | |||||||
| Barrow, Lee, et al., 1970 | ||||||||||||
| e 3Φ3 | [0.2294] | [2.0E-7] | [2.098] | e ← a R | 22149.20 Z | |||||||
| Barrow, Lee, et al., 1970 | ||||||||||||
| e 3Φ2 | [0.2277] | [1.7E-7] | [2.098] | e ← a R | 21834.46 Z | |||||||
| Barrow, Lee, et al., 1970 | ||||||||||||
| d 3Φ4 | [483] | [0.2336] | [2.2E-7] | [2.082] | d ← a R | 18809.40 Z | ||||||
| Barrow, Lee, et al., 1970; Hauge, 1970 | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| d 3Φ3 | [473.2] | [0.2321] | [2.3E-7] | [2.082] | d ← a R | 18519.90 Z | ||||||
| Barrow, Lee, et al., 1970; Hauge, 1970 | ||||||||||||
| d 3Φ2 | [484.6] | [0.2322] | [1.9E-7] | [2.082] | d ← a R | 18295.80 Z | ||||||
| Barrow, Lee, et al., 1970; Hauge, 1970 | ||||||||||||
| c2 3Δ2 | [0.2191] | [1.9E-7] | [2.146] | c2 ← a2 R | 13597.27 Z | |||||||
| Barrow, Bastin, et al., 1967; Barrow, Lee, et al., 1970 | ||||||||||||
| c1 3Δ1 | [0.2189] | [2.0E-7] | [2.147] | c1 ← a1 R | 13567.00 Z | |||||||
| Barrow, Bastin, et al., 1967; Barrow, Lee, et al., 1970 | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| b2 3Δ2 | [0.2216] | [2.9E-7] | [2.133] | b2 ← a2 R | 13316.35 Z | |||||||
| Barrow, Lee, et al., 1970 | ||||||||||||
| a3 3Δ3 | 1 | [537] | [0.23875] | [1.87E-7] | 2.0548 | |||||||
| a2 3Δ2 | 1 | [537.65] | 0.23902 | 0.00121 | [1.88E-7] | 2.0548 | ||||||
| a1 3Δ1 | 1 | [537.14] | 0.23831 | 0.00120 | [1.79E-7] | 2.0548 | ||||||
| E 1Σ+ | [(421)] | [0.229] 2 | [2.10] | E ← X R | 22574.22 H | |||||||
| Barrow, Lee, et al., 1970 | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| D 1Σ+ | [0.242] 2 | [2.04] | D ← X R | 22485.35 H | ||||||||
| Barrow, Lee, et al., 1970 | ||||||||||||
| C 1Π | [549] | [0.23745] 3 | [1.65E-7] | [2.0611] | C ← X R | 20959.83 Z | ||||||
| Barrow, Lee, et al., 1970; Hauge, 1970 | ||||||||||||
| B 1Π | [474] | [0.22936] 3 | [2.7E-7] | [2.0971] | B ← X R | 16184.00 Z | ||||||
| Barrow, Lee, et al., 1970; Hauge, 1970 | ||||||||||||
| A 1Σ+ | [489.4] Z | 0.2278 | 0.0011 | [2.1E-7] | 2.104 | A ← X R | 11661.9 Z | |||||
| Barrow, Bastin, et al., 1967; Barrow, Lee, et al., 1970 | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| X 1Σ+ | 0 1 | [570] | [0.24562] | [1.78E-7] | [2.0265] |
| 1 | Not certain whether X 1Σ or a 3Δ the ground state. |
| 2 | Strong interaction between the two 1Σ states. |
| 3 | Λ-type doubling. |
Go To: Top, Gas phase ion energetics data, Constants of diatomic molecules, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Zmbov and Margrave, 1968
Zmbov, K.F.; Margrave, J.L.,
Mass spectrometric studies of scandium, yttrium, lanthanum, and rare-earth fluorides,
Advan. Chem. Ser., 1968, 72, 267. [all data]
Shenyavskaya, Gurvich, et al., 1968
Shenyavskaya, E.A.; Gurvich, L.V.; Maltsev, A.A.,
On a band system in the discharge spectrum in lanthanum trifluorode vapors,
Opt. Spectrosc. Engl. Transl., 1968, 24, 556, In original 1025. [all data]
Shenyavskaya, Gurvich, et al., 1965
Shenyavskaya, E.A.; Gurvich, L.V.; Maltsev, A.A.,
Electron spectra of the LaF molecule,
Vestn. Mosk. Univ., 1965, 20, 10. [all data]
Barrow, Lee, et al., 1970
Barrow; Lee; Partridge,
Unpublished quoted in Rosen, 1970 cited in Huber and Herzberg, 1979, 1970, 371. [all data]
Hauge, 1970
Hauge,
Unpublished quoted in Rosen, 1970 cited in Huber and Herzberg, 1979, 1970, 371. [all data]
Barrow, Bastin, et al., 1967
Barrow, R.F.; Bastin, M.W.; Moore, D.L.G.; Pott, C.J.,
Electronic states of gaseous fluorides of scandium, yttrium and lanthanum,
Nature (London), 1967, 215, 1072. [all data]
Rosen, 1970
Rosen, B.,
International tables of selected constants. 17. Spectroscopic data relative to diatomic molecules, Pub. Pergamon Press, Oxford, 1970, 0. [all data]
Huber and Herzberg, 1979
Huber, K.P.; Herzberg, G.,
Molecular Spectra and Molecular Structure. IV. Constants of Diatomic Molecules, Van Nostrand Reinhold Company, New York, 1979, 716. [all data]
Go To: Top, Gas phase ion energetics data, Constants of diatomic molecules, References
Copyright for NIST Standard Reference Data is governed by the Standard Reference Data Act.
|
Standard Reference Data Program |
Data Gateway |
Chemistry WebBook |
If you have comments or questions about this site, please contact us.


There will be two queues : 1. grammer-pool-queue 2. grammer-feeder-queue
grammer-pool-queue : This queue will be responsible for handling requests for grammar pool. The request and response dto will be more like key and value pair.
Currently both of these key value pairs are of string type. But value can be of custom type. like key can be birthday and its value can be in pool as xxx date
2. grammar-feeder-queue : This queue will be responsible for feeding grammar inside the pool. The dto will be having key, value and customDTO. The customDTO will be having name and date.
The Pool will be divided into two main parts:
1. common pool 2. private pool
the objetcive is to first feed grammar into the common pool, but some of the words will remain into the private pool, like keyword birthday will remain in common pool but its value will be '/' i.e an indication that its value will be in the private pool.
Strategy to find a word:
always try to first find the word inside the common pool, if not found then try to find it out in the private pool.
below are the cases which will come into picture:
1. keyword found in common pool return value. 2. keyword found in the common pool but the value is the pointer to the private pool.3. keyword not found in the common pool, try to find out in the private pool, if found then return. if not then some message.
keyword which will remian in common pool : before, after, hour, min, day, days, months, month, year, years


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Collector Yumi:
I gave you the key already, didn't I? Good, good. Use it and I am sure it will open the book. Come back here after you have finished reading it.