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Best customer service. I really appreciate all the efforts that Tech Assistant have. From customer service department he put on solving my issue. I was pleasantly surprised. Great work!
A fantastic organisation! Great cutomer support from beginning to end of the process. The team are really informed and go the extra mile at every stage. I would recommend them unreservedly.
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Customer service is obviously their strategy and it shows. I have referred others already.


# File vendor/plugins/rails_sql_views/test/view_test.rb, line 17 def test_drop_view assert_nothing_raised do ActiveRecord::Base.connection.create_view(:v_place, 'select * from places', :force => true) do |v| v.column :v_address v.column :v_city v.column :v_state v.column :v_country end ActiveRecord::Base.connection.drop_view(:v_place) end assert_raises(ActiveRecord::StatementInvalid) do ActiveRecord::Base.connection.execute "SELECT * FROM v_place" end end


>From the creative team that invented the "why don't we all just whip it out and see whose got the biggest dick" film, Armageddon surpasses its predecessors in budget, effects and pure testosterone output. I'm talking about Jerry Bruckheimer (ConAir, Crimson Tide) and Michael Bay (The Rock, Bad Boys), the guys that seem to annually discharge the type of "sword-fight" picture that features an all male cast and relies on big explosions to keep their date-raping homophobe frat-boy legions glued to the screen. You just know if these two were involved in the music business, they would probably be responsible for the horrific big-haired cock-rock that came into popularity in the mid 80's.
With that said, Armageddon is still good. Well, maybe good isn't the right word. It is very entertaining and even features (gulp) a female in a sort of important role. Not peripheral characters like Nicolas Cage's wife in ConAir or Sean Connery's daughter in The Rock, but a real honest to goodness woman who…OK…maybe I'm exaggerating. She's still just eye candy.
The following is an excerpt of the PLANET SICK-BOY review for Deep Impact. There really isn't any point in me thinking up a fresh way to say what I can just cut and paste from somewhere else. It's pretty much the same story, so everybody follow along:
The premise is simple – an asteroid the size of New York City is headed toward Earth, threatening global destruction and the eventual extinction of all its inhabitants. And no, the world isn't saved by an Apple PowerBook or Slim Whitman records.
No doubt aided by a story culled from recent headlines, Deep Impact is also set apart from the rest of the big budget schlock by tapping into real human emotion. Some of the heartstring tugging is a little too much to handle, but if you're used to the weekly emotion manipulation hour every Thursday night at 10:00 PM, it won't seem quite so painful. I wonder if you can get an emotion callous? I wonder how many times I can use the word "emotion" in this paragraph?
Just replace the title and the part about "tapping into real human emotion". Not that the typical "Bruce saves the world" movie usually disappoints in that department. While Deep Impact spent more time developing characters, Armageddon spent that time (and 30 additional minutes) blowing stuff up. In fact, it cost more to market Armageddon ($80,000,000.00) than it did to make Deep Impact. Them explosions ain't cheap, but I'm sure that they'll payoff when the dust settles and the final numbers come in. Armageddon will likely outgross its rival within two weeks.
The other main difference in Armageddon is that the ragtag bunch sent off into space to take care of the asteroid isn't an experienced group of astronauts, but a crusty team of oil drillers. Join us, as we meet the main players:
Harry Stamper (Willis, The Fifth Element) - the best oil rigger in the world, Harry owns runs the company that employs the other cast members. He is also the father of...
Grace Stamper (Tyler, Stealing Beauty) - the beanpole that came of age on the big rig and, thusly, has fallen for one of her father's workers named...
A.J. Frost (Affleck, Good Will Hunting) - the cocky young-turk driller who, when he isn't penetrating the earth for black gold, penetrates the doe-eyed Grace behind his boss's back
Rockhound (Buscemi, ConAir) - the crazy kooky guy with the messed up teeth
Jayotis "Bear" Kurleen (Michael Duncan) - the black guy
Oscar Choi (Owen Wilson, Bottle Rocket) - the cowboy
Chick (Will Patton, The Postman) - the other guy
Like I said, not much character development and that's kind of surprising in a film with a 2 1/2 runtime. Even more unexpected is the fact that it was born of a writing team that, individually, was responsible for Jumanji and The Devil's Advocate. Rumor has it that the studio brought in some heavy hitting script doctors to punch up the story, but it doesn't really show. For the record, they are Paul Attanasio (Donnie Brasco), Ann Biderman (Primal Fear) and Robert Towne (Chinatown).
But, who needs a script when size is the only thing that matters? Armageddon is probably the slickest looking film this side of Titanic. While the actual events are completely preposterous, they look very authentic. Credit Titanic King of the World and his effect company, Digital Domain, for the realism. And bored viewers can look for Digital Domain's creepy metal skull (from T2) trademark, which appears a few times in the film.
So, basically, don't go to see a great movie. Go to have a great time.
PLANET SICK-BOY: http://home.eznet.net/~jpopick
The review above was posted to the
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Related links: index of all rec.arts.movies.reviews reviews


quantify.engine¶
-
class
quantipy.
Quantity
(link, weight=None, base_all=False, ignore_flags=False)¶ The Quantity object is the main Quantipy aggregation engine.
Consists of a link’s data matrix representation and sectional defintion of weight vector (wv), x-codes section (xsect) and y-codes section (ysect). The instance methods handle creation, retrieval and manipulation of the data input matrices and section definitions as well as the majority of statistical calculations.
-
calc
(expression, axis='x', result_only=False)¶ Compute (simple) aggregation level arithmetics.
-
count
(axis=None, raw_sum=False, cum_sum=False, effective=False, margin=True, as_df=True)¶ Count entries over all cells or per axis margin.
Parameters: - axis ({None, 'x', 'y'}, deafult None) – When axis is None, the frequency of all cells from the uni- or multivariate distribution is presented. If the axis is specified to be either ‘x’ or ‘y’ the margin per axis becomes the resulting aggregation.
- raw_sum (bool, default False) – If True will perform a simple summation over the cells given the axis parameter. This ignores net counting of qualifying answers in favour of summing over all answers given when considering margins.
- cum_sum (bool, default False) – If True a cumulative sum of the elements along the given axis is returned.
- effective (bool, default False) – If True, compute effective counts instead of traditional (weighted) counts.
- margin (bool, deafult True) – Controls whether the margins of the aggregation result are shown. This also applies to margin aggregations themselves, since they contain a margin in (form of the total number of cases) as well.
- as_df (bool, default True) – Controls whether the aggregation is transformed into a Quantipy- multiindexed (following the Question/Values convention) pandas.DataFrame or will be left in its numpy.array format.
Returns: Passes a pandas.DataFrame or numpy.array of cell or margin counts to the
result
property.Return type: self
-
exclude
(codes, axis='x')¶ Wrapper for _missingfy(…keep_codes=False, …, keep_base=False, …) Excludes specified codes from aggregation.
-
filter
(condition, keep_base=True, inplace=False)¶ Use a Quantipy conditional expression to filter the data matrix entires.
-
group
(groups, axis='x', expand=None, complete=False)¶ Build simple or logical net vectors, optionally keeping orginating codes.
Parameters: - groups (list, dict of lists or logic expression) –
The group/net code defintion(s) in form of…
- a simple list:
[1, 2, 3]
- a dict of list:
{'grp A': [1, 2, 3], 'grp B': [4, 5, 6]}
- a logical expression:
not_any([1, 2])
- a simple list:
- axis ({
'x'
,'y'
}, default'x'
) – The axis to group codes on. - expand ({None,
'before'
,'after'
}, defaultNone
) – If'before'
, the codes that are grouped will be kept and placed before the grouped aggregation; vice versa for'after'
. Ignored on logical expressions found ingroups
. - complete (bool, default False) – If True, codes that define the Link on the given
axis
but are not present in thegroups
defintion(s) will be placed in their natural position within the aggregation, respecting the value ofexpand
.
Returns: Return type: None
- groups (list, dict of lists or logic expression) –
-
limit
(codes, axis='x')¶ Wrapper for _missingfy(…keep_codes=True, …, keep_base=True, …) Restrict the data matrix entires to contain the specified codes only.
-
normalize
(on='y', per_cell=False)¶ Convert a raw cell count result to its percentage representation.
Parameters: - on ({'y', 'x', 'counts_sum', str}, default 'y') – Defines the base to normalize the result on.
'y'
will produce column percentages,'x'
will produce row percentages. It is also possible to use another question’s frequencies to compute rebased percentages providing its name instead. - per_cell (bool, default False) – Compute percentages on a cell-per-cell basis, effectively treating
each categorical row as a base figure on its own. Only possible if the
on
argument does not indidcate an axis result ('x'
,'y'
,'counts_sum'
), but instead another variable’s name. The relatedxdef
codes collection length must be identical for this for work, otherwise aValueError
is raised.
Returns: Updates a count-based aggregation in the
result
property.Return type: self
- on ({'y', 'x', 'counts_sum', str}, default 'y') – Defines the base to normalize the result on.
-
rescale
(scaling, drop=False)¶ Modify the object’s
xdef
property reflecting new value defintions.Parameters: - scaling (dict) – Mapping of old_code: new_code, given as of type int or float.
- drop (bool, default False) – If True, codes not included in the scaling dict will be excluded.
Returns: Return type: self
-
summarize
(stat='summary', axis='x', margin=True, as_df=True)¶ Calculate distribution statistics across the given axis.
Parameters: - stat ({'summary', 'mean', 'median', 'var', 'stddev', 'sem', varcoeff',) – ‘min’, ‘lower_q’, ‘upper_q’, ‘max’}, default ‘summary’ The measure to calculate. Defaults to a summary output of the most important sample statistics.
- axis ({'x', 'y'}, default 'x') – The axis which is reduced in the aggregation, e.g. column vs. row means.
- margin (bool, default True) – Controls whether statistic(s) of the marginal distribution are shown.
- as_df (bool, default True) – Controls whether the aggregation is transformed into a Quantipy- multiindexed (following the Question/Values convention) pandas.DataFrame or will be left in its numpy.array format.
Returns: Passes a pandas.DataFrame or numpy.array of the descriptive (summary) statistic(s) to the
result
property.Return type: self
-
swap
(var, axis='x', update_axis_def=True, inplace=True)¶ Change the Quantity’s x- or y-axis keeping filter and weight setup.
All edits and aggregation results will be removed during the swap.
Parameters: - var (str) – New variable’s name used in axis swap.
- axis ({‘x’, ‘y’}, default
'x'
) – The axis to swap. - update_axis_def (bool, default False) – If self is of type
'array'
, the name and item definitions (that are e.g. used in theto_df()
method) can be updated to reflect the swapped axis variable or kept to show the original’s ones. - inplace (bool, default True) – Whether to modify the Quantity inplace or return a new instance.
Returns: swapped
Return type: New Quantity instance with exchanged x- or y-axis.
-
unweight
()¶ Remove any weighting by dividing the matrix by itself.
-
weight
()¶ Weight by multiplying the indicator entries with the weight vector.
-
-
class
quantipy.
Test
(link, view_name_notation, test_total=False)¶ The Quantipy Test object is a defined by a Link and the view name notation string of a counts or means view. All auxiliary figures needed to arrive at the test results are computed inside the instance of the object.
-
get_se
()¶ Compute the standard error (se) estimate of the tested metric.
The calculation of the se is defined by the parameters of the setup. The main difference is the handling of variances. unpooled implicitly assumes variance inhomogenity between the column pairing’s samples. pooled treats variances effectively as equal.
-
get_sig
()¶ TODO: implement returning tstats only.
-
get_statistic
()¶ Returns the test statistic of the algorithm.
-
run
()¶ Performs the testing algorithm and creates an output pd.DataFrame.
The output is indexed according to Quantipy’s Questions->Values convention. Significant results between columns are presented as lists of integer y-axis codes where the column with the higher value is holding the codes of the columns with the lower values. NaN is indicating that a cell is not holding any sig. higher values compared to the others.
-
set_params
(test_total=False, level='mid', mimic='Dim', testtype='pooled', use_ebase=True, ovlp_correc=True, cwi_filter=False, flag_bases=None)¶ Sets the test algorithm parameters and defines the type of test.
This method sets the test’s global parameters and derives the necessary measures for the computation of the test statistic. The default values correspond to the SPSS Dimensions Column Tests algorithms that control for bias introduced by weighting and overlapping samples in the column pairs of multi-coded questions.
Note
The Dimensions implementation uses variance pooling.
Parameters: - test_total (bool, default False) – If set to True, the test algorithms will also include an existent total (@-) version of the original link and test against the unconditial data distribution.
- level (str or float, default 'mid') – The level of significance given either as per ‘low’ = 0.1, ‘mid’ = 0.05, ‘high’ = 0.01 or as specific float, e.g. 0.15.
- mimic ({'askia', 'Dim'} default='Dim') – Will instruct the mimicking of a software specific test.
- testtype (str, default 'pooled') – Global definition of the tests.
- use_ebase (bool, default True) – If True, will use the effective sample sizes instead of the the simple weighted ones when testing a weighted aggregation.
- ovlp_correc (bool, default True) – If True, will consider and correct for respondent overlap when testing between multi-coded column pairs.
- cwi_filter (bool, default False) – If True, will check an incoming count aggregation for cells that fall below a treshhold comparison aggregation that assumes counts to be independent.
- flag_bases (list of two int, default None) – If provided, the output dataframe will replace results that have
been calculated on (eff.) bases below the first int with
'**'
and mark results in columns with bases below the second int with'*'
Returns: Return type: self
-


--- layout: layouts/default title: All Tags - Mark's Blog subtitle: Here's every tag I've ever tagged. permalink: /blog/tags/ ---


This is my first IP in Moringa school prep class. I am glad that I am learning new things each and everyday.


Once you sign up, you can join our Google Community. The Google Community will be the place where you can watch streaming hangouts live, discuss with your peers, and post your projects. The “Questions” section on the Creative Learning Community page will be the place to post any questions that come up. Either the Creative Learning Team or your fellow peers will be able to provide answers.
No. There will be no certification. But you’ll learn a lot, and hopefully have a great time working with other smart people.
When you first sign up, we will group you with other participants to ask questions and help each other out. However, how much work you do within your group is up to you. We think working in small groups will make this a much better experience.
Slacker! But so are we. That's why we made sure you can watch it from the video section of this site (http://learn.media.mit.edu) the week after it streamed or from the weekly community page. Find the link to the week on the Course page.


longdesc attribute
The longdesc attribute specifies a hyperlink to a detailed description of an image.


2020-02-10 寻找现象后面的本质：（二）别人给予的尊敬 Looking for the Essence Behind the Phenomenon: (1) Respect Accorded by Others wherein
2020-02-09 寻找现象后面的本质： （一）官场中的互捧与艺术圈中的互贬 Looking for the Essence Behind the Phenomenon: (1) Log-rolling in the Officialdom and Belittling in the Art Circle wherein


groupBarplot.Rd
Sort taxa by summed abundance across all samples and plot sorted taxon composition with a bar per sample
groupBarplot( abundances, groups = c(), aggregate = "none", taxon.color.map = NULL, group.color.map = NULL, topTaxa = 10, sortGroupwise = TRUE, sumGroupwise = FALSE, group.order = c(), hide.taxa = c(), randSampleNum = NA, summedTaxonColor = "#a9a9a9", extendTaxonColorMap = FALSE, legend = TRUE, legend.shift = 1, legend.hidegroups = FALSE, ... )
| abundances | a matrix with taxa as rows and samples as columns |
|---|---|
| groups | group membership vector with as many entries as samples |
| aggregate | if groups are given, plot the aggregate across the group (or its selected samples if randSampleNum is true); possible values: none, median and mean |
| taxon.color.map | map of taxon-specific colors, should match row names; taxa not present in the color map will be colored in summedTaxonColor |
| group.color.map | map of group-specific colors, should match group names |
| topTaxa | number of top taxa to be plotted |
| sortGroupwise | if true, samples are sorted according to groups |
| sumGroupwise | if true, taxa are summed and sorted separately across samples within each group (if true, samples are always sorted group-wise) |
| group.order | if a vector with group names (one for each group) is given, group samples will be sorted in the order indicated; can also be used to only plot selected groups |
| hide.taxa | do not consider these taxa as top-abundant taxa, but keep them among Others |
| randSampleNum | if larger 0, sortGroupwise is set to true and the indicated sample number is randomly selected for each group |
| summedTaxonColor | the color of the summed taxa, by default gray |
| extendTaxonColorMap | if true, taxa not in the taxon color map are added there and the extended color map is returned |
| legend | add a legend with the color code |
| legend.shift | increase/decrease this parameter to shift the color legend further to the right/left |
| legend.hidegroups | do not show the group memberships in the legend |
| \dots | Additional arguments passed to plot() |
if extendTaxonColorMap is true, the taxon color map is returned
Note that taxa are always summed across all samples, also in the presence of a group membership vector, unless sumGroupwise is true.
data(ibd_taxa) data(ibd_metadata) groups=as.vector(ibd_metadata$Diagnosis) # taxon abundances were prefiltered and therefore do not add up to 1 groupBarplot(ibd_taxa,groups=groups,randSampleNum=10)#> [1] "Number of samples in group CD : 68" #> [1] "Number of samples in group UC : 53" #> [1] "Number of samples in group Control : 34"# sum taxa group-wise for sorting instead across all samples groupBarplot(ibd_taxa,groups=groups,sumGroupwise=TRUE, legend.hidegroups=TRUE)#> [1] "Number of samples in group CD : 68" #> [1] "Number of samples in group UC : 53" #> [1] "Number of samples in group Control : 34"#> [1] "Number of higher-level taxa: 71" #> [1] 71 7groupBarplot(ibd_genera,groups=groups,randSampleNum=10)#> [1] "Number of samples in group CD : 68" #> [1] "Number of samples in group UC : 53" #> [1] "Number of samples in group Control : 34"


rotten
>
Library
> Death
Human Sacrifice
Maladies
Lupus
Syphilis
Accidents
Drowning
Airplane Crashes
Automobile Accidents
Helicopter Accidents
AIDS / HIV
Stroke
Pneumonia
Tuberculosis
Emphysema
Alzheimer's
Heart Failure
Cancer
Lymphoma
Leukemia
Colon Cancer
Stomach Cancer
Skin Cancer
Kidney Cancer
Brain Cancer
Ovarian Cancer
Bone Cancer
Prostate Cancer
Breast Cancer
Lung Cancer
Pancreatic Cancer
Liver Cancer
Throat Cancer
Celebrity Deaths
Abortion
Strange Deaths
Gallery of Unusual Pathology
Styrofoam Balls
Strange Celebrity Deaths
Strange Burials
Cremation
Suicide
Suicide by Cop
Suicide Notes
Famous Suicides
Execution
Crucifixion
Last Meals
Plastic Shredder
Lethal Injection
Gas Chamber
Electric Chair
Hanging
Firing Squad
Guillotine
Last Words
Ossuaries
Sedlec
Kamikaze
Embalming
Cannibalism
Swift's Modest Proposal
Catacombs
Mummies
Pornopolis
|
Rotten
|
Faces of Death
|
Famous Nudes


Mobile Travel App
Trip 9 is a travel app for Chinese tourist who is traveling in Southeast Asia. It helps users find local retailers, bars, restaurants and other serviceshelps. Users are able to search by GPS to experience more local play, and check the shop address, map and reviews.
On landing page, choosing which city in Southeast Asia you would like to explore. Trip 9 provides a list of popular places, e.g. bars, restaurtants, spa, regarding to different categories for you to explore. You can view the list based on their instant location as well.
Searching local experience by different categories. All local places is listed and sorted by the distance for you to review.
Hot places lists the most popuplar local plances for you to explore. You can check the details of each places, e.g. address, map, phone number and review.