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Copyright: Forever Less One Day
The state of copyright explained in 6 minutes
The state of copyright explained in 6 minutes
Never played portal. But, this is a pretty awesome short by Dan Trachtenberg
To the Apple Board of Directors and the Apple Community:
I have always said if there ever came a day when I could no longer meet my duties and expectations as Apple’s CEO, I would be the first to let you know. Unfortunately, that day has come.
I hereby resign as CEO of Apple. I would like to serve, if the Board sees fit, as Chairman of the Board, director and Apple employee.
As far as my successor goes, I strongly recommend that we execute our succession plan and name Tim Cook as CEO of Apple.
I believe Apple’s brightest and most innovative days are ahead of it. And I look forward to watching and contributing to its success in a new role.
I have made some of the best friends of my life at Apple, and I thank you all for the many years of being able to work alongside you.
Steve
Ironically, the conservative movement is now more unpopular than two often-marginalized groups it sometimes rails against — Muslims and atheists — and is the least popular of the 23 groups the poll asked about
Dieter Bohn from This is my next
Taking a look at the pinch-to-zoom action, it's very, very responsive. Really impressed with what they've managed to do here.
That choppy animation that can't track your fingers is responsive? How about those gestures that you couldn't even make work? The accompanying text at least seems more pragmatic.
I always thought of Ron Paul as the progenitor of the Tea Party. Although he's crazy (a doctor who doesn't believe in evolution), I always thought he did make valid points. However, if I had to vote for a Republican, I'd say we could do worse than another President Johnson.
I don't think he likes Lion.
Neil deGrasse Tyson:
How much would you pay for the universe?
2.78 kg; 52.07 cm
Apparently I'm an uncle.
Today, Amazon.com announced Kindle Cloud Reader, its latest Kindle reading application that leverages HTML5 and enables customers to read Kindle books instantly using only their web browser - online or offline - with no downloading or installation required. As with all Kindle apps, Kindle Cloud Reader automatically synchronizes your Kindle library, as well as your last page read, bookmarks, notes, and highlights for all of your Kindle books, no matter how you choose to read them. Kindle Cloud Reader with its integrated touch optimized Kindle Store is available starting today for Safari on iPad, Safari on desktop and Chrome
So? It's cool, but why would you use this over the iPad app?
The house of cards has already been built. There's no painless way to dismantle it now.
Lucas Mathias:
So let’s turn this around. Nobody has ever told me exactly why the patent system is doing any good.
Jojo are the new Toms. Shoes for a cause and you don't have to take someone's word for it. They allow you to monitor the progress of your contribution.
Anyone want to give me £1000 to buy some of this?
A fauxgo (fake logo) is a symbol or other small design created to represent a fictional company that exists only on film.
The implementation is in the source code. I can design a new engine for a car that does the same job. Maybe my design is better, maybe it's worse; but it doesn't infringe. Once you patented the shopping cart though. I can't implement one. I could have a better one, one that does it in half the lines of code; but you've patented the idea.
I'm not the most versed when it comes to patents and am sure in theory they're well and good, but don't understand them at all in practice. His argument makes a lot of sense.
A story which suggested that users of Internet Explorer have a lower IQ than people who chose other browsers appears to have been an elaborate hoax.
Turns out it wasn't true. But, probably still is.

Fifty and Fifty is a curated project which attempts to construct a handsome new way of looking at our country. Fifty designers, one per state, will illustrate their state motto, creating something steeped in history but completely modern and unique: a kind of designer's atlas.
Simply amazing.
Taken as a group, it feels like a remarkably solid case — Samsung can’t just up and countersue Apple with its own patents and hope to walk away with a handshake and a cross-license because of the various trademark, trade dress, and design patent claims. How the company decides to deal with those issues remains to be seen; there’s no question in my mind that Samsung designed TouchWiz to look and feel as much like iOS as possible, and then marketed it as such.
This is a pretty straightforward, dumbed down analysis of Apple's claims against Samsung. Doesn't seem to be much of an argument against most of it—of course, that's why I'm not a lawyer.
The ad-supported Kindle will cost $114 — $25 less than the cheapest Kindle currently available — and will be available on May 3.
$25 off? That's it? You'd think they could at least break the psychological $99 barrier. I'll stick to audiobooks on my iPod until there's a color e-ink display for comics.
This is design perfection. I'm going to start sending this with every gift.
Personally, I'd like to take it further and use only an iMac and an iPad.
New from 37Signals: Pow is a web server for Mac OS X that makes it easy to develop Rails and Rack applications without any configuration.
I'd love to use stuff like this, but I don't see any reason in my life for it.
[Garfield Minus Garfield] is a journey deep into the mind of an isolated young everyman as he fights a losing battle against loneliness and depression in a quiet American suburb.
Amazingly hilarious existential comic strip.
The joint statement did not specify an end date for Mr. Beck’s show...a spokeswoman referred back to the statement. Mr. Beck’s contract with Fox ends in December.
Should have been canceled two years ago.


This page makes some calls to the server for messages to render on screen. The message count display shows you how many requests are being processed and how many messages are left to be rendered. The application has some internal variables that you can synchronise on e.g. liveMessages, totalRequestsMade, renderingQueueCount,totalMessagesReceived. The application is 'finished' when some requests have been made and the renderingQueueCount equals 0. The display for the Message Count updates every second so there may be a delay between finishing the message processing and representing this on screen.
Message Count: 0


QuadTree
class QuadTree
a QuadTree implementation in JavaScript, a 2d spatial subdivision algorithm.
Constructor
new QuadTree(world: World, bounds: Bounds, max_objects: number, max_levels: number, level: number) → {}
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
| world | World |
the physic world this QuadTree belongs to |
||
| bounds | Bounds |
bounds of the node |
||
| max_objects | number |
<optional> |
4 |
max objects a node can hold before splitting into 4 subnodes |
| max_levels | number |
<optional> |
4 |
total max levels inside root Quadtree |
| level | number |
<optional> |
0 |
deepth level, required for subnodes |
Summary
Methods from QuadTree
|
|
|
| boolean |
|
|
|
|
|
|
|
| boolean |
|
| boolean |
|
| Array<object> |
|
Public Methods
clear(bounds: Bounds) → {}
clear the quadtree
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
| bounds | Bounds |
<optional> |
this.bounds |
the bounds to be cleared |
hasChildren() → {boolean}
return true if the node has any children
| Type | Description |
|---|---|
| boolean |
true if the node has any children |
insert(item: object) → {}
Insert the given object into the node. If the node exceeds the capacity, it will split and add all objects to their corresponding subnodes.
| Name | Type | Description |
|---|---|---|
| item | object |
object to be added |
insertContainer(container: Container) → {}
Insert the given object container into the node.
| Name | Type | Description |
|---|---|---|
| container | Container |
group of objects to be added |
isPrunable() → {boolean}
return true if the node is prunable
| Type | Description |
|---|---|
| boolean |
true if the node is prunable |
remove(item: object) → {boolean}
Remove the given item from the quadtree. (this function won't recalculate the impacted node)
| Name | Type | Description |
|---|---|---|
| item | object |
object to be removed |
| Type | Description |
|---|---|
| boolean |
true if the item was found and removed. |
retrieve(item: object, fn: object) → {Array<object>}
Return all objects that could collide with the given object
| Name | Type | Attributes | Description |
|---|---|---|---|
| item | object |
object to be checked against |
|
| fn | object |
<optional> |
a sorting function for the returned array |
| Type | Description |
|---|---|
| Array<object> |
array with all detected objects |


boost::svg::poly_path_point — polyline or polygon point coordinates (x, y)
// In header: <boost/svg_plot/detail/svg_elements.hpp> struct poly_path_point { // construct/copy/destruct poly_path_point(); poly_path_point(double, double); // public member functions void write(std::ostream &); // public data members double x; // polygon or polyline path point X SVG coordinate. double y; // polygon or polyline path point Y SVG coordinate. };
9.6 polyline & 9.7 The 'polygon' element.
poly_path_point
public member functionsvoid write(std::ostream & os);
Output poly_path_point
as SVG XML, Example: " 250,180" Leading space is redundant for 1st after "points= ", but others are separators, and arkward to know which is 1st.


@(params: Map[String, Any])@uk.gov.hmrc.hmrcemailrenderer.templates.helpers.html.template_main(params, "Your 2SV has been reset successfully") {
Dear @params("full_name"),
Your 2SV has been reset successfully.
From HMRC Government Gateway


MeteoLVProvider
public struct MeteoLVProvider
Meteo.lv observations provider
-
Initialize MeteoLVProvider
Declaration
Swift
public init()
-
Get observations from Latvian State Roads
Declaration
Swift
public func latvianRoadsObservations(completion: @escaping (Result<[LatvianRoadsStation]>) -> Void)
Parameters
completion
Completion block with Result type


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Latools Documentation¶
analyse (data_folder[, errorhunt, config, ...]) |
For processing and analysing whole LA - ICPMS datasets. |
D (data_file[, dataformat, errorhunt, cmap, ...]) |
Container for data from a single laser ablation analysis. |
filt (size, analytes) |
Container for creating, storing and selecting data filters. |
latools.analyse
¶
-
class
latools.
analyse
(data_folder, errorhunt=False, config='DEFAULT', dataformat=None, extension='.csv', srm_identifier='STD', cmap=None, time_format=None, internal_standard='Ca43', names='file_names')¶ Bases:
object
For processing and analysing whole LA - ICPMS datasets.
Parameters: data_folder : str
The path to a directory containing multiple data files.
errorhunt : bool
Whether or not to print each data file name before import. This is useful for tracing which data file is causing the import to fail.
config : str
The name of the configuration to use for the analysis. This determines which configuration set from the latools.cfg file is used, and overrides the default configuration setup. You might sepcify this if your lab routinely uses two different instruments.
dataformat : str or dict
Either a path to a data format file, or a dataformat dict. See documentation for more details.
extension : str
The file extension of your data files. Defaults to ‘.csv’.
srm_identifier : str
A string used to separate samples and standards. srm_identifier must be present in all standard measurements. Defaults to ‘STD’.
cmap : dict
A dictionary of {analyte: colour} pairs. Colour can be any valid matplotlib colour string, RGB or RGBA sequence, or hex string.
time_format : str
A regex string identifying the time format, used by pandas when created a universal time scale. If unspecified (None), pandas attempts to infer the time format, but in some cases this might not work.
internal_standard : str
The name of the analyte used as an internal standard throughout analysis.
names : str
‘file_names’ : use the file names as labels ‘metadata_names’ : used the ‘names’ attribute of metadata as the name anything else : use numbers.
Attributes
folder (str) Path to the directory containing the data files, as specified by data_folder. dirname (str) The name of the directory containing the data files, without the entire path. files (array_like) A list of all files in folder. param_dir (str) The directory where parameters are stored. report_dir (str) The directory where plots are saved. data (array_like) A list of latools.D data objects. data_dict (dict) A dict of latools.D data objects, labelled by sample name. samples (array_like) A list of samples. analytes (array_like) A list of analytes measured. stds (array_like) A list of the latools.D objects containing hte SRM data. These must contain srm_identifier in the file name. srm_identifier (str) A string present in the file names of all standards. cmaps (dict) An analyte - specific colour map, used for plotting. Methods
autorange
([analyte, gwin, win, smwin, conf, ...])Automatically separates signal and background data regions. bkg_calc_interp1d
([analytes, kind, n_min, ...])Background calculation using a 1D interpolation. bkg_calc_weightedmean
([analytes, ...])Background calculation using a gaussian weighted mean. bkg_subtract
([analytes, errtype])Subtract calculated background from data. calibrate
([poly_n, analytes, drift_correct, ...])Calibrates the data to measured SRM values. calibration_plot
([analytes, datarange, ...])Plot the calibration lines between measured and known SRM values. crossplot
([analytes, lognorm, bins, filt, ...])Plot analytes against each other as 2D histograms. despike
([expdecay_despiker, exponent, ...])Despikes data with exponential decay and noise filters. export_traces
([outdir, focus_stage, ...])Function to export raw data. filter_clear
([samples, subset])Clears (deletes) all data filters. filter_clustering
(analytes[, filt, ...])Applies an n - dimensional clustering filter to the data. filter_correlation
(x_analyte, y_analyte[, ...])Applies a correlation filter to the data. filter_distribution
(analyte[, binwidth, ...])Applies a distribution filter to the data. filter_off
([filt, analyte, samples, subset])Turns data filters off for particular analytes and samples. filter_on
([filt, analyte, samples, subset])Turns data filters on for particular analytes and samples. filter_reports
(analytes[, filt_str, ...])Plot filter reports for all filters that contain filt_str
in the name.filter_threshold
(analyte, threshold[, filt, ...])Applies a threshold filter to the data. find_expcoef
([nsd_below, analyte, plot, ...])Determines exponential decay coefficient for despike filter. get_background
([n_min, n_max, focus_stage, ...])Extract all background data from all samples on universal time scale. get_focus
([filt, samples, subset])Collect all data from all samples into a single array. get_starttimes
([time_format])getstats
([save, filename, samples, subset, ...])Return pandas dataframe of all sample statistics. make_subset
([samples, name])Creates a subset of samples, which can be treated independently. minimal_export
([target_analytes, override, path])Exports a analysis parameters, standard info and a minimal dataset, which can be imported by another user. ratio
([internal_standard, focus])Calculates the ratio of all analytes to a single analyte. sample_stats
([analytes, filt, stats, ...])Calculate sample statistics. trace_plots
([analytes, samples, ranges, ...])Plot analytes as a function of time. zeroscreen
([focus_stage])Remove all points containing data below zero (impossible) ablation_times bkg_plot filter_status set_focus srm_id_auto statplot -
ablation_times
(samples=None, subset=None)¶
-
apply_classifier
(name, samples=None, subset=None)¶ Apply a clustering classifier based on all samples, or a subset.
Parameters: name : str
The name of the classifier to apply.
subset : str
The subset of samples to apply the classifier to.
Returns
——-
name : str
-
autorange
(analyte=None, gwin=11, win=40, smwin=5, conf=0.01, on_mult=[1.0, 1.0], off_mult=None, d_mult=1.2, transform='log', thresh_n=None)¶ Automatically separates signal and background data regions.
Automatically detect signal and background regions in the laser data, based on the behaviour of a single analyte. The analyte used should be abundant and homogenous in the sample.
Step 1: Thresholding The background signal is determined using a gaussian kernel density estimator (kde) of all the data. Under normal circumstances, this kde should find two distinct data distributions, corresponding to ‘signal’ and ‘background’. The minima between these two distributions is taken as a rough threshold to identify signal and background regions. Any point where the trace crosses this thrshold is identified as a ‘transition’.
Step 2: Transition Removal The width of the transition regions between signal and background are then determined, and the transitions are excluded from analysis. The width of the transitions is determined by fitting a gaussian to the smoothed first derivative of the analyte trace, and determining its width at a point where the gaussian intensity is at at conf time the gaussian maximum. These gaussians are fit to subsets of the data centered around the transitions regions determined in Step 1, +/- win data points. The peak is further isolated by finding the minima and maxima of a second derivative within this window, and the gaussian is fit to the isolated peak.
Parameters: analyte : str
The analyte that autorange should consider. For best results, choose an analyte that is present homogeneously in high concentrations.
gwin : int
The smoothing window used for calculating the first derivative. Must be odd.
win : int
Determines the width (c +/- win) of the transition data subsets.
smwin : int
The smoothing window used for calculating the second derivative. Must be odd.
conf : float
The proportional intensity of the fitted gaussian tails that determines the transition width cutoff (lower = wider transition regions excluded).
trans_mult : array_like, len=2
Multiples of the peak FWHM to add to the transition cutoffs, e.g. if the transitions consistently leave some bad data proceeding the transition, set trans_mult to [0, 0.5] to ad 0.5 * the FWHM to the right hand side of the limit.
Returns: None
-
bkg_calc_interp1d
(analytes=None, kind=1, n_min=10, n_max=None, cstep=None, filter=False, f_win=7, f_n_lim=3)¶ Background calculation using a 1D interpolation.
scipy.interpolate.interp1D is used for interpolation.
Parameters: analytes : str or array - like
kind : str or int
Integer specifying the order of the spline interpolation used, or string specifying a type of interpolation. Passed to scipy.interpolate.interp1D
n_min : int
Background regions with fewer than n_min points will not be included in the fit.
cstep : float or None
The interval between calculated background points.
filter : bool
If true, apply a rolling filter to the isolated background regions to exclude regions with anomalously high values. If True, two parameters alter the filter’s behaviour:
- f_win : int
The size of the rolling window
- f_n_lim : float
The number of standard deviations above the rolling mean to set the threshold.
-
bkg_calc_weightedmean
(analytes=None, weight_fwhm=300.0, n_min=20, n_max=None, cstep=None, filter=False, f_win=7, f_n_lim=3)¶ Background calculation using a gaussian weighted mean.
Parameters: analytes : str or array - like
weight_fwhm : float
The full - width - at - half - maximum of the gaussian used to calculate the weighted average.
n_min : int
Background regions with fewer than n_min points will not be included in the fit.
cstep : float or None
The interval between calculated background points.
filter : bool
If true, apply a rolling filter to the isolated background regions to exclude regions with anomalously high values. If True, two parameters alter the filter’s behaviour:
- f_win : int
The size of the rolling window
- f_n_lim : float
The number of standard deviations above the rolling mean to set the threshold.
-
bkg_plot
(analytes=None, figsize=None, yscale='log', ylim=None, err='stderr', save=True)¶
-
bkg_subtract
(analytes=None, errtype='stderr')¶ Subtract calculated background from data.
Must run bkg_calc first!
-
calibrate
(poly_n=0, analytes=None, drift_correct=False, srm_errors=False, srms_used=['NIST610', 'NIST612', 'NIST614'], n_min=10)¶ Calibrates the data to measured SRM values.
Parameters: poly_n : int
Specifies the type of function used to map known SRM values to SRM measurements. 0: A linear function, forced through 0. 1 or more: An nth order polynomial.
focus : str
The focus stage of the data used to calculating the ratios.
srmfile : str or None
Path the the file containing the known SRM values. If None, the default file specified in the latools.cfg is used. Refer to the documentation for more information on the srmfile format.
Returns: None
-
calibration_plot
(analytes=None, datarange=True, loglog=False, save=True)¶ Plot the calibration lines between measured and known SRM values.
Parameters: analytes : optional, array_like or str
The analyte(s) to plot. Defaults to all analytes.
datarange : boolean
Whether or not to show the distribution of the measured data alongside the calibration curve.
loglog : boolean
Whether or not to plot the data on a log - log scale. This is useful if you have two low standards very close together, and want to check whether your data are between them, or below them.
Returns: (fig, axes)
matplotlib objects
-
crossplot
(analytes=None, lognorm=True, bins=25, filt=False, samples=None, subset=None, **kwargs)¶ Plot analytes against each other as 2D histograms.
Parameters: analytes : optional, array_like or str
The analyte(s) to plot. Defaults to all analytes.
lognorm : bool
Whether or not to log normalise the colour scale of the 2D histogram.
bins : int
The number of bins in the 2D histogram.
filt : str, dict or bool
Either logical filter expression contained in a str, a dict of expressions specifying the filter string to use for each analyte or a boolean. Passed to grab_filt.
Returns: None
-
crossplot_filters
(filter_string, analytes=None, samples=None, subset=None, filt=None)¶ Plot the results of a group of filters in a crossplot.
Parameters: filter_string : str
A string that identifies a group of filters. e.g. ‘test’ would plot all filters with ‘test’ in the name.
analytes : optional, array_like or str
The analyte(s) to plot. Defaults to all analytes.
Returns: fig, axes objects
-
despike
(expdecay_despiker=True, exponent=None, tstep=None, noise_despiker=True, win=3, nlim=12.0, exponentplot=False, autorange_kwargs={})¶ Despikes data with exponential decay and noise filters.
Parameters: expdecay_despiker : bool
Whether or not to apply the exponential decay filter.
exponent : None or float
The exponent for the exponential decay filter. If None, it is determined automatically using find_expocoef.
tstep : None or float
The timeinterval between measurements. If None, it is determined automatically from the Time variable.
noise_despiker : bool
Whether or not to apply the standard deviation spike filter.
win : int
The rolling window over which the spike filter calculates the trace statistics.
nlim : float
The number of standard deviations above the rolling mean that data are excluded.
exponentplot : bool
Whether or not to show a plot of the automatically determined exponential decay exponent.
Returns: None
-
export_traces
(outdir=None, focus_stage=None, analytes=None, samples=None, subset='All_Analyses', filt=False)¶ Function to export raw data.
Parameters: outdir : str
focus_stage : str
- The name of the analysis stage desired:
‘rawdata’: raw data, loaded from csv file. ‘despiked’: despiked data. ‘signal’/’background’: isolated signal and background data.
Created by self.separate, after signal and background regions have been identified by self.autorange.
- ‘bkgsub’: background subtracted data, created by
self.bkg_correct
‘ratios’: element ratio data, created by self.ratio. ‘calibrated’: ratio data calibrated to standards, created by
self.calibrate.
Defaults to the most recent stage of analysis.
analytes : str or array - like
Either a single analyte, or list of analytes to export. Defaults to all analytes.
samples : str or array - like
Either a single sample name, or list of samples to export. Defaults to all samples.
filt : str, dict or bool
Either logical filter expression contained in a str, a dict of expressions specifying the filter string to use for each analyte or a boolean. Passed to grab_filt.
-
filter_clear
(samples=None, subset=None)¶ Clears (deletes) all data filters.
-
filter_clustering
(analytes, filt=False, normalise=True, method='meanshift', include_time=False, samples=None, sort=True, subset=None, min_data=10, **kwargs)¶ Applies an n - dimensional clustering filter to the data.
Parameters: analytes : str
The analyte(s) that the filter applies to.
filt : bool
Whether or not to apply existing filters to the data before calculating this filter.
normalise : bool
Whether or not to normalise the data to zero mean and unit variance. Reccomended if clustering based on more than 1 analyte. Uses sklearn.preprocessing.scale.
method : str
- Which clustering algorithm to use. Can be:
- ‘meanshift’: The sklearn.cluster.MeanShift algorithm.
Automatically determines number of clusters in data based on the bandwidth of expected variation.
- ‘kmeans’: The sklearn.cluster.KMeans algorithm. Determines
the characteristics of a known number of clusters within the data. Must provide n_clusters to specify the expected number of clusters.
- ‘DBSCAN’: The sklearn.cluster.DBSCAN algorithm. Automatically
determines the number and characteristics of clusters within the data based on the ‘connectivity’ of the data (i.e. how far apart each data point is in a multi - dimensional parameter space). Requires you to set eps, the minimum distance point must be from another point to be considered in the same cluster, and min_samples, the minimum number of points that must be within the minimum distance for it to be considered a cluster. It may also be run in automatic mode by specifying n_clusters alongside min_samples, where eps is decreased until the desired number of clusters is obtained.
For more information on these algorithms, refer to the documentation.
include_time : bool
Whether or not to include the Time variable in the clustering analysis. Useful if you’re looking for spatially continuous clusters in your data, i.e. this will identify each spot in your analysis as an individual cluster.
samples : optional, array_like or None
Which samples to apply this filter to. If None, applies to all samples.
sort : bool
Whether or not you want the cluster labels to be sorted by the mean magnitude of the signals they are based on (0 = lowest)
min_data : int
The minimum number of data points that should be considered by the filter. Default = 10.
**kwargs
Parameters passed to the clustering algorithm specified by method.
Returns: None
-
filter_combine
(name, filt_str, samples=None, subset=None)¶ Make new filter from combination of other filters.
Parameters: name : str
The name of the new filter. Should be unique.
filt_str : str
A logical combination of partial strings which will create the new filter. For example, ‘Albelow & Mnbelow’ will combine all filters that partially match ‘Albelow’ with those that partially match ‘Mnbelow’ using the ‘AND’ logical operator.
Returns: None
-
filter_correlation
(x_analyte, y_analyte, window=None, r_threshold=0.9, p_threshold=0.05, filt=True, samples=None, subset=None)¶ Applies a correlation filter to the data.
Calculates a rolling correlation between every window points of two analytes, and excludes data where their Pearson’s R value is above r_threshold and statistically significant.
Data will be excluded where their absolute R value is greater than r_threshold AND the p - value associated with the correlation is less than p_threshold. i.e. only correlations that are statistically significant are considered.
Parameters: x_analyte, y_analyte : str
The names of the x and y analytes to correlate.
window : int, None
The rolling window used when calculating the correlation.
r_threshold : float
The correlation index above which to exclude data. Note: the absolute pearson R value is considered, so negative correlations below -r_threshold will also be excluded.
p_threshold : float
The significant level below which data are excluded.
filt : bool
Whether or not to apply existing filters to the data before calculating this filter.
Returns: None
-
filter_distribution
(analyte, binwidth='scott', filt=False, transform=None, samples=None, subset=None, min_data=10)¶ Applies a distribution filter to the data.
Parameters: analyte : str
The analyte that the filter applies to.
binwidth : str of float
Specify the bin width of the kernel density estimator. Passed to scipy.stats.gaussian_kde. str: The method used to automatically estimate bin width.
Can be ‘scott’ or ‘silverman’.
float: Manually specify the binwidth of the data.
filt : bool
Whether or not to apply existing filters to the data before calculating this filter.
transform : str
If ‘log’, applies a log transform to the data before calculating the distribution.
samples : array_like or None
Which samples to apply this filter to. If None, applies to all samples.
min_data : int
The minimum number of data points that should be considered by the filter. Default = 10.
Returns: None
-
filter_off
(filt=None, analyte=None, samples=None, subset=None)¶ Turns data filters off for particular analytes and samples.
Parameters: filt : optional, str or array_like
Name, partial name or list of names of filters. Supports partial matching. i.e. if ‘cluster’ is specified, all filters with ‘cluster’ in the name are activated. Defaults to all filters.
analyte : optional, str or array_like
Name or list of names of analytes. Defaults to all analytes.
samples : optional, array_like or None
Which samples to apply this filter to. If None, applies to all samples.
Returns: None
-
filter_on
(filt=None, analyte=None, samples=None, subset=None)¶ Turns data filters on for particular analytes and samples.
Parameters: filt : optional, str or array_like
Name, partial name or list of names of filters. Supports partial matching. i.e. if ‘cluster’ is specified, all filters with ‘cluster’ in the name are activated. Defaults to all filters.
analyte : optional, str or array_like
Name or list of names of analytes. Defaults to all analytes.
samples : optional, array_like or None
Which samples to apply this filter to. If None, applies to all samples.
Returns: None
-
filter_reports
(analytes, filt_str='all', samples=None, outdir=None, subset=None)¶ Plot filter reports for all filters that contain
filt_str
in the name.
-
filter_status
(sample=None, subset=None, stds=False)¶
-
filter_threshold
(analyte, threshold, filt=False, samples=None, subset=None)¶ Applies a threshold filter to the data.
Generates two filters above and below the threshold value for a given analyte.
Parameters: analyte : str
The analyte that the filter applies to.
threshold : float
The threshold value.
filt : bool
Whether or not to apply existing filters to the data before calculating this filter.
samples : array_like or None
Which samples to apply this filter to. If None, applies to all samples.
subset : str or number
The subset of samples (defined by make_subset) you want to apply the filter to.
Returns: None
-
find_expcoef
(nsd_below=0.0, analyte=None, plot=False, trimlim=None, autorange_kwargs={})¶ Determines exponential decay coefficient for despike filter.
Fits an exponential decay function to the washout phase of standards to determine the washout time of your laser cell. The exponential coefficient reported is nsd_below standard deviations below the fitted exponent, to ensure that no real data is removed.
Parameters: nsd_below : float
The number of standard deviations to subtract from the fitted coefficient when calculating the filter exponent.
analyte : str
The analyte to consider when determining the coefficient. Use high - concentration analyte for best estimates.
plot : bool or str
bool: Creates a plot of the fit if True. str: Creates a plot, and saves it to the location
specified in the str.
trimlim : float
A threshold limit used in determining the start of the exponential decay region of the washout. Defaults to half the increase in signal over background. If the data in the plot don’t fall on an exponential decay line, change this number. Normally you’ll need to increase it.
Returns: None
-
fit_classifier
(name, analytes, method, samples=None, subset=None, filt=True, sort_by=0, **kwargs)¶ Create a clustering classifier based on all samples, or a subset.
Parameters: name : str
The name of the classifier.
analytes : str or array-like
Which analytes the clustering algorithm should consider.
method : str
- Which clustering algorithm to use. Can be:
- ‘meanshift’: The sklearn.cluster.MeanShift algorithm.
Automatically determines number of clusters in data based on the bandwidth of expected variation.
- ‘kmeans’: The sklearn.cluster.KMeans algorithm. Determines
the characteristics of a known number of clusters within the data. Must provide n_clusters to specify the expected number of clusters.
samples : array-like
list of samples to consider. Overrides ‘subset’.
subset : str
The subset of samples used to fit the classifier. Ignored if ‘samples’ is specified.
sort_by : int
Which analyte the resulting clusters should be sorted by - defaults to 0, which is the first analyte.
**kwargs :
method-specific keyword parameters - see below.
Returns: name : str
-
get_background
(n_min=10, n_max=None, focus_stage='despiked', filter=False, f_win=5, f_n_lim=3)¶ Extract all background data from all samples on universal time scale. Used by both ‘polynomial’ and ‘weightedmean’ methods.
Parameters: n_min : int
The minimum number of points a background region must have to be included in calculation.
n_max : int
The maximum number of points a background region must have to be included in calculation.
filter : bool
If true, apply a rolling filter to the isolated background regions to exclude regions with anomalously high values. If True, two parameters alter the filter’s behaviour:
- f_win : int
The size of the rolling window
- f_n_lim : float
The number of standard deviations above the rolling mean to set the threshold.
Returns
——-
pandas.DataFrame object containing background data.
-
get_focus
(filt=False, samples=None, subset=None)¶ Collect all data from all samples into a single array. Data from standards is not collected.
Parameters: filt : str, dict or bool
Either logical filter expression contained in a str, a dict of expressions specifying the filter string to use for each analyte or a boolean. Passed to grab_filt.
samples : str or list
subset : str or int
Returns: None
-
get_starttimes
(time_format=None)¶
-
getstats
(save=True, filename=None, samples=None, subset=None, ablation_time=False)¶ Return pandas dataframe of all sample statistics.
-
make_subset
(samples=None, name=None)¶ Creates a subset of samples, which can be treated independently.
Parameters: samples : str or array - like
Name of sample, or list of sample names.
name : (optional) str or number
The name of the sample group. Defaults to n + 1, where n is the highest existing group number
-
minimal_export
(target_analytes=None, override=False, path=None)¶ Exports a analysis parameters, standard info and a minimal dataset, which can be imported by another user.
-
ratio
(internal_standard=None, focus='bkgsub')¶ Calculates the ratio of all analytes to a single analyte.
Parameters: internal_standard : str
The name of the analyte to divide all other analytes by.
focus : str
The focus stage of the data used to calculating the ratios.
Returns: None
-
sample_stats
(analytes=None, filt=True, stats=['mean', 'std'], eachtrace=True, csf_dict={})¶ Calculate sample statistics.
Returns samples, analytes, and arrays of statistics of shape (samples, analytes). Statistics are calculated from the ‘focus’ data variable, so output depends on how the data have been processed.
Parameters: analytes : optional, array_like or str
The analyte(s) to calculate statistics for. Defaults to all analytes.
filt : str, dict or bool
Either logical filter expression contained in a str, a dict of expressions specifying the filter string to use for each analyte or a boolean. Passed to grab_filt.
stats : array_like
list of functions or names of functions that take a single array_like input, and return a single statistic. Function should be able to cope with NaN values. Built-in functions:
‘mean’: arithmetic mean ‘std’: arithmetic standard deviation ‘se’: arithmetic standard error ‘H15_mean’: Huber mean (outlier removal) ‘H15_std’: Huber standard deviation (outlier removal) ‘H15_se’: Huber standard error (outlier removal)
eachtrace : bool
Whether to calculate the statistics for each analysis spot individually, or to produce per - sample means. Default is True.
Returns: None
Adds dict to analyse object containing samples, analytes and functions and data.
-
set_focus
(focus_stage=None, samples=None, subset=None)¶
-
srm_id_auto
(srms_used=['NIST610', 'NIST612', 'NIST614'], n_min=10)¶
-
statplot
(analytes=None, samples=None, figsize=None, stat='nanmean', err='nanstd', subset=None)¶
-
trace_plots
(analytes=None, samples=None, ranges=False, focus=None, outdir=None, filt=None, scale='log', figsize=[10, 4], stats=False, stat='nanmean', err='nanstd', subset='All_Analyses')¶ Plot analytes as a function of time.
Parameters: analytes : optional, array_like or str
The analyte(s) to plot. Defaults to all analytes.
samples: optional, array_like or str
The sample(s) to plot. Defaults to all samples.
ranges : bool
Whether or not to show the signal/backgroudn regions identified by ‘autorange’.
focus : str
The focus ‘stage’ of the analysis to plot. Can be ‘rawdata’, ‘despiked’:, ‘signal’, ‘background’, ‘bkgsub’, ‘ratios’ or ‘calibrated’.
outdir : str
Path to a directory where you’d like the plots to be saved. Defaults to ‘reports/[focus]’ in your data directory.
filt : str, dict or bool
Either logical filter expression contained in a str, a dict of expressions specifying the filter string to use for each analyte or a boolean. Passed to grab_filt.
scale : str
If ‘log’, plots the data on a log scale.
figsize : array_like
Array of length 2 specifying figure [width, height] in inches.
stats : bool
Whether or not to overlay the mean and standard deviations for each trace.
stat, err: str
The names of the statistic and error components to plot. Deafaults to ‘nanmean’ and ‘nanstd’.
Returns: None
-
zeroscreen
(focus_stage=None)¶ Remove all points containing data below zero (impossible)
-
latools.D
¶
-
class
latools.
D
(data_file, dataformat=None, errorhunt=False, cmap=None, internal_standard='Ca43', name='file_names')¶ Bases:
object
Container for data from a single laser ablation analysis.
Parameters: data_file : str
The path to a data file.
errorhunt : bool
Whether or not to print each data file name before import. This is useful for tracing which data file is causing the import to fail.
dataformat : str or dict
Either a path to a data format file, or a dataformat dict. See documentation for more details.
Attributes
sample (str) Sample name. meta (dict) Metadata extracted from the csv header. Contents varies, depending on your dataformat. analytes (array_like) A list of analytes measured. data (dict) A dictionary containing the raw data, and modified data from each processing stage. Entries can be: rawdata: created during initialisation. despiked: created by despike signal: created by autorange background: created by autorange bkgsub: created by bkg_correct ratios: created by ratio calibrated: created by calibrate focus (dict) A dictionary containing one item from data. This is the currently ‘active’ data that processing functions will work on. This data is also directly available as class attributes with the same names as the items in focus. focus_stage (str) Identifies which item in data is currently assigned to focus. cmap (dict) A dictionary containing hex colour strings corresponding to each measured analyte. bkg, sig, trn (array_like, bool) Boolean arrays identifying signal, background and transition regions. Created by autorange. bkgrng, sigrng, trnrng (array_like) An array of shape (n, 2) containing pairs of values that describe the Time limits of background, signal and transition regions. ns (array_like) An integer array the same length as the data, where each analysis spot is labelled with a unique number. Used for separating analysys spots when calculating sample statistics. filt (filt object) An object for storing, selecting and creating data filters. Methods
ablation_times
()Function for calculating the ablation time for each ablation. autorange
([analyte, gwin, win, smwin, conf, ...])Automatically separates signal and background data regions. bkg_subtract
(analyte, bkg[, ind])Subtract provided background from signal (focus stage). calibrate
(calib_fns, calib_params[, ...])Apply calibration to data. cluster_DBSCAN
(data[, eps, min_samples, ...])Identify clusters using DBSCAN algorithm. cluster_kmeans
(data, n_clusters)Identify clusters using K - Means algorithm. cluster_meanshift
(data[, bandwidth, bin_seeding])Identify clusters using Meanshift algorithm. crossplot
([analytes, bins, lognorm, filt])Plot analytes against each other. despike
([expdecay_despiker, exponent, ...])Applies expdecay_despiker and noise_despiker to data. expdecay_despiker
([exponent, tstep])Apply exponential decay filter to remove unrealistically low values. filt_report
([filt, analytes, savedir])Visualise effect of data filters. filter_clustering
(analytes[, filt, ...])Applies an n - dimensional clustering filter to the data. filter_correlation
(x_analyte, y_analyte[, ...])Apply correlation filter. filter_distribution
(analytes[, binwidth, ...])Apply a Distribution Filter filter_threshold
(analyte, threshold[, filt])Apply threshold filter. findmins
(x, y)Function to find local minima. get_params
()Returns paramters used to process data. mkrngs
()Transform boolean arrays into list of limit pairs. noise_despiker
([win, nlim])Apply standard deviation filter to remove anomalous high values. ratio
([internal_standard, focus])Divide all analytes by a specified internal_standard analyte. sample_stats
([analytes, filt, stat_fns, ...])TODO: WORK OUT HOW TO HANDLE ERRORS PROPERLY! setfocus
(focus)Set the ‘focus’ attribute of the data file. tplot
([analytes, figsize, scale, filt, ...])Plot analytes as a function of Time. drift_params -
ablation_times
()¶ Function for calculating the ablation time for each ablation.
Returns: dict of times for each ablation.
-
autorange
(analyte=None, gwin=11, win=40, smwin=5, conf=0.01, on_mult=[1.0, 1.0], off_mult=None, d_mult=1.2, transform='log', bkg_thresh=None)¶ - Automatically separates signal and background data regions.
Automatically detect signal and background regions in the laser data, based on the behaviour of a single analyte. The analyte used should be abundant and homogenous in the sample.
Step 1: Thresholding The background signal is determined using a gaussian kernel density estimator (kde) of all the data. Under normal circumstances, this kde should find two distinct data distributions, corresponding to ‘signal’ and ‘background’. The minima between these two distributions is taken as a rough threshold to identify signal and background regions. Any point where the trace crosses this thrshold is identified as a ‘transition’.
Step 2: Transition Removal The width of the transition regions between signal and background are then determined, and the transitions are excluded from analysis. The width of the transitions is determined by fitting a gaussian to the smoothed first derivative of the analyte trace, and determining its width at a point where the gaussian intensity is at at conf time the gaussian maximum. These gaussians are fit to subsets of the data centered around the transitions regions determined in Step 1, +/- win data points. The peak is further isolated by finding the minima and maxima of a second derivative within this window, and the gaussian is fit to the isolated peak.
Parameters: analyte : str
The analyte that autorange should consider. For best results, choose an analyte that is present homogeneously in high concentrations.
gwin : int
The smoothing window used for calculating the first derivative. Must be odd.
win : int
Determines the width (c +/- win) of the transition data subsets.
smwin : int
The smoothing window used for calculating the second derivative. Must be odd.
on_mult and off_mult : tuple, len=2
Factors to control the width of the excluded transition regions. A region n times the full - width - half - maximum of the transition gradient will be removed either side of the transition center. on_mult and off_mult refer to the laser - on and laser - off transitions, respectively. See manual for full explanation. Defaults to (1.5, 1) and (1, 1.5).
Returns: None
-
bkg_subtract
(analyte, bkg, ind=None)¶ Subtract provided background from signal (focus stage).
Results is saved in new ‘bkgsub’ focus stage
Returns: None
-
calibrate
(calib_fns, calib_params, analytes=None, drift_correct=False)¶ Apply calibration to data.
The calib_dict must be calculated at the analyse level, and passed to this calibrate function.
Parameters: calib_dict : dict
A dict of calibration values to apply to each analyte.
Returns: None
-
cluster_DBSCAN
(data, eps=None, min_samples=None, n_clusters=None, maxiter=200)¶ Identify clusters using DBSCAN algorithm.
Parameters: data : array_like
array of size [n_samples, n_features].
eps : float
The minimum ‘distance’ points must be apart for them to be in the same cluster. Defaults to 0.3. Note: If the data are normalised (they should be for DBSCAN) this is in terms of total sample variance. Normalised data have a mean of 0 and a variance of 1.
min_samples : int
The minimum number of samples within distance eps required to be considered as an independent cluster.
n_clusters : int
The number of clusters expected. If specified, eps will be incrementally reduced until the expected number of clusters is found.
maxiter : int
The maximum number of iterations DBSCAN will run.
Returns: dict
boolean array for each identified cluster and core samples.
-
cluster_kmeans
(data, n_clusters)¶ Identify clusters using K - Means algorithm.
Parameters: data : array_like
array of size [n_samples, n_features].
n_clusters : int
The number of clusters expected in the data.
Returns: dict
boolean array for each identified cluster.
-
cluster_meanshift
(data, bandwidth=None, bin_seeding=False)¶ Identify clusters using Meanshift algorithm.
Parameters: data : array_like
array of size [n_samples, n_features].
bandwidth : float or None
If None, bandwidth is estimated automatically using sklean.cluster.estimate_bandwidth
bin_seeding : bool
Setting this option to True will speed up the algorithm. See sklearn documentation for full description.
Returns: dict
boolean array for each identified cluster.
-
crossplot
(analytes=None, bins=25, lognorm=True, filt=True)¶ Plot analytes against each other.
Parameters: analytes : optional, array_like or str
The analyte(s) to plot. Defaults to all analytes.
lognorm : bool
Whether or not to log normalise the colour scale of the 2D histogram.
bins : int
The number of bins in the 2D histogram.
filt : str, dict or bool
Either logical filter expression contained in a str, a dict of expressions specifying the filter string to use for each analyte or a boolean. Passed to grab_filt.
Returns: (fig, axes)
-
crossplot_filters
(filter_string, analytes=None)¶ Plot the results of a group of filters in a crossplot.
Parameters: filter_string : str
A string that identifies a group of filters. e.g. ‘test’ would plot all filters with ‘test’ in the name.
analytes : optional, array_like or str
The analyte(s) to plot. Defaults to all analytes.
Returns: fig, axes objects
-
despike
(expdecay_despiker=True, exponent=None, tstep=None, noise_despiker=True, win=3, nlim=12.0)¶ Applies expdecay_despiker and noise_despiker to data.
Parameters: expdecay_despiker : bool
Whether or not to apply the exponential decay filter.
exponent : None or float
The exponent for the exponential decay filter. If None, it is determined automatically using find_expocoef.
tstep : None or float
The timeinterval between measurements. If None, it is determined automatically from the Time variable.
noise_despiker : bool
Whether or not to apply the standard deviation spike filter.
win : int
The rolling window over which the spike filter calculates the trace statistics.
nlim : float
The number of standard deviations above the rolling mean that data are excluded.
Returns: None
-
drift_params
(pout, a)¶
-
expdecay_despiker
(exponent=None, tstep=None)¶ Apply exponential decay filter to remove unrealistically low values.
Parameters: exponent : float
Expinent used in filter
tstep : float
The time increment between data points. Calculated from Time variable if None.
Returns: None
-
filt_report
(filt=None, analytes=None, savedir=None)¶ Visualise effect of data filters.
Parameters: filt : str
Exact or partial name of filter to plot. Supports partial matching. i.e. if ‘cluster’ is specified, all filters with ‘cluster’ in the name will be plotted. Defaults to all filters.
analyte : str
Name of analyte to plot.
save : str
file path to save the plot
Returns: (fig, axes)
-
filter_clustering
(analytes, filt=False, normalise=True, method='meanshift', include_time=False, sort=None, min_data=10, **kwargs)¶ Applies an n - dimensional clustering filter to the data.
Parameters: analytes : str
The analyte(s) that the filter applies to.
filt : bool
Whether or not to apply existing filters to the data before calculating this filter.
normalise : bool
Whether or not to normalise the data to zero mean and unit variance. Reccomended if clustering based on more than 1 analyte. Uses sklearn.preprocessing.scale.
method : str
- Which clustering algorithm to use. Can be:
- ‘meanshift’: The sklearn.cluster.MeanShift algorithm.
Automatically determines number of clusters in data based on the bandwidth of expected variation.
- ‘kmeans’: The sklearn.cluster.KMeans algorithm. Determines
the characteristics of a known number of clusters within the data. Must provide n_clusters to specify the expected number of clusters.
- ‘DBSCAN’: The sklearn.cluster.DBSCAN algorithm. Automatically
determines the number and characteristics of clusters within the data based on the ‘connectivity’ of the data (i.e. how far apart each data point is in a multi - dimensional parameter space). Requires you to set eps, the minimum distance point must be from another point to be considered in the same cluster, and min_samples, the minimum number of points that must be within the minimum distance for it to be considered a cluster. It may also be run in automatic mode by specifying n_clusters alongside min_samples, where eps is decreased until the desired number of clusters is obtained.
For more information on these algorithms, refer to the documentation.
include_time : bool
Whether or not to include the Time variable in the clustering analysis. Useful if you’re looking for spatially continuous clusters in your data, i.e. this will identify each spot in your analysis as an individual cluster.
sort : bool, str or array-like
Whether or not to label the resulting clusters according to their contents. If used, the cluster with the lowest values will be labelled from 0, in order of increasing cluster mean value.analytes
True: Sort by all analytes used to generate the cluster. str: Sort by a single specified analyte array-like: Sort by a number of specified analytes.
min_data : int
The minimum number of data points that should be considered by the filter. Default = 10.
**kwargs
Parameters passed to the clustering algorithm specified by method.
Returns: None
-
filter_correlation
(x_analyte, y_analyte, window=None, r_threshold=0.9, p_threshold=0.05, filt=True)¶ Apply correlation filter.
Parameters: x_analyte, y_analyte : str
The names of the x and y analytes to correlate.
window : int, None
The rolling window used when calculating the correlation.
r_threshold : float
The correlation index above which to exclude data. Note: the absolute pearson R value is considered, so negative correlations below -r_threshold will also be excluded.
p_threshold : float
The significant level below which data are excluded.
filt : bool
Whether or not to apply existing filters to the data before calculating this filter.
Returns: None
-
filter_distribution
(analytes, binwidth='scott', filt=False, transform=None, min_data=10)¶ Apply a Distribution Filter
Parameters: analytes : str or list
The analyte that the filter applies to.
binwidth : str of float
Specify the bin width of the kernel density estimator. Passed to scipy.stats.gaussian_kde. str: The method used to automatically estimate bin width.
Can be ‘scott’ or ‘silverman’.
float: Manually specify the binwidth of the data.
filt : bool
Whether or not to apply existing filters to the data before calculating this filter.
transform : str
If ‘log’, applies a log transform to the data before calculating the distribution.
min_data : int
The minimum number of data points that should be considered by the filter. Default = 10.
Returns: None
-
filter_new
(name, filt_str)¶ Make new filter from combination of other filters.
Parameters: name : str
The name of the new filter. Should be unique.
filt_str : str
A logical combination of partial strings which will create the new filter. For example, ‘Albelow & Mnbelow’ will combine all filters that partially match ‘Albelow’ with those that partially match ‘Mnbelow’ using the ‘AND’ logical operator.
Returns: None
-
filter_threshold
(analyte, threshold, filt=False)¶ Apply threshold filter.
Generates threshold filters for the given analytes above and below the specified threshold.
- Two filters are created with prefixes ‘_above’ and ‘_below’.
- ‘_above’ keeps all the data above the threshold. ‘_below’ keeps all the data below the threshold.
i.e. to select data below the threshold value, you should turn the ‘_above’ filter off.
Parameters: analyte : TYPE
Description of analyte.
threshold : TYPE
Description of threshold.
filt : TYPE
Description of filt.
Returns: None
-
findmins
(x, y)¶ Function to find local minima.
Parameters: x, y : array_like
1D arrays of the independent (x) and dependent (y) variables.
Returns: array_like
Array of points in x where y has a local minimum.
-
get_params
()¶ Returns paramters used to process data.
Returns: dict
dict of analysis parameters
-
mkrngs
()¶ Transform boolean arrays into list of limit pairs.
Gets Time limits of signal/background boolean arrays and stores them as sigrng and bkgrng arrays. These arrays can be saved by ‘save_ranges’ in the analyse object.
-
noise_despiker
(win=3, nlim=12.0)¶ Apply standard deviation filter to remove anomalous high values.
Parameters: win : int
The window used to calculate rolling statistics.
nlim : float
The number of standard deviations above the rolling mean above which data are considered outliers.
Returns: None
-
ratio
(internal_standard=None, focus='bkgsub')¶ Divide all analytes by a specified internal_standard analyte.
Parameters: internal_standard : str
The analyte used as the internal_standard.
focus : str
The analysis stage to perform the ratio calculation on. Defaults to ‘signal’.
Returns: None
-
sample_stats
(analytes=None, filt=True, stat_fns={}, eachtrace=True)¶ TODO: WORK OUT HOW TO HANDLE ERRORS PROPERLY!
Calculate sample statistics
Returns samples, analytes, and arrays of statistics of shape (samples, analytes). Statistics are calculated from the ‘focus’ data variable, so output depends on how the data have been processed.
Parameters: analytes : array_like
List of analytes to calculate the statistic on
filt : bool or str
- The filter to apply to the data when calculating sample statistics.
bool: True applies filter specified in filt.switches. str: logical string specifying a partucular filter
stat_fns : dict
Dict of {name: function} pairs. Functions that take a single array_like input, and return a single statistic. Function should be able to cope with NaN values.
eachtrace : bool
True: per - ablation statistics False: whole sample statistics
Returns: None
-
setfocus
(focus)¶ Set the ‘focus’ attribute of the data file.
The ‘focus’ attribute of the object points towards data from a particular stage of analysis. It is used to identify the ‘working stage’ of the data. Processing functions operate on the ‘focus’ stage, so if steps are done out of sequence, things will break.
Parameters: focus : str
- The name of the analysis stage desired:
- ‘rawdata’: raw data, loaded from csv file when object
is initialised.
‘despiked’: despiked data. ‘signal’/’background’: isolated signal and background data,
padded with np.nan. Created by self.separate, after signal and background regions have been identified by self.autorange.
- ‘bkgsub’: background subtracted data, created by
self.bkg_correct
‘ratios’: element ratio data, created by self.ratio. ‘calibrated’: ratio data calibrated to standards, created by
self.calibrate.
Returns: None
-
tplot
(analytes=None, figsize=[10, 4], scale='log', filt=None, ranges=False, stats=False, stat='nanmean', err='nanstd', interactive=False, focus_stage=None, err_envelope=False)¶ Plot analytes as a function of Time.
Parameters: analytes : array_like
list of strings containing names of analytes to plot. None = all analytes.
figsize : tuple
size of final figure.
scale : str or None
‘log’ = plot data on log scale
filt : bool, str or dict
False: plot unfiltered data. True: plot filtered data over unfiltered data. str: apply filter key to all analytes dict: apply key to each analyte in dict. Must contain all analytes plotted. Can use self.filt.keydict.
ranges : bool
show signal/background regions.
stats : bool
plot average and error of each trace, as specified by stat and err.
stat : str
average statistic to plot.
err : str
error statistic to plot.
interactive : bool
Make the plot interactive.
Returns: figure
-
latools.filt
¶
-
class
latools.
filt
(size, analytes)¶ Bases:
object
Container for creating, storing and selecting data filters.
Parameters: size : int
The length that the filters need to be (should be the same as your data).
analytes : array_like
A list of the analytes measured in your data.
Attributes
size (int) The length that the filters need to be (should be the same as your data). analytes (array_like) A list of the analytes measured in your data. components (dict) A dict containing each individual filter that has been created. info (dict) A dict containing descriptive information about each filter in components. params (dict) A dict containing the parameters used to create each filter, which can be passed directly to the corresponding filter function to recreate the filter. switches (dict) A dict of boolean switches specifying which filters are active for each analyte. keys (dict) A dict of logical strings specifying which filters are applied to each analyte. sequence (dict) A numbered dict specifying what order the filters were applied in (for some filters, order matters). n (int) The number of filters applied to the data. Methods
add
(name, filt[, info, params, setn])Add filter. clean
()Remove unused filters. clear
()Clear all filters. get_components
(key[, analyte])Extract filter components for specific analyte(s). get_info
()Get info for all filters. grab_filt
(filt[, analyte])Flexible access to specific filter using any key format. make
(analyte)Make filter for specified analyte(s). make_fromkey
(key)Make filter from logical expression. make_keydict
([analyte])Make logical expressions describing the filter(s) for specified analyte(s). off
([analyte, filt])Turn off specified filter(s) for specified analyte(s). on
([analyte, filt])Turn on specified filter(s) for specified analyte(s). remove
([name, setn])Remove filter. -
add
(name, filt, info='', params=(), setn=None)¶ Add filter.
Parameters: name : str
filter name
filt : array_like
boolean filter array
info : str
informative description of the filter
params : tuple
parameters used to make the filter
Returns: None
-
clean
()¶ Remove unused filters.
-
clear
()¶ Clear all filters.
-
fuzzmatch
(fuzzkey, multi=False)¶ Identify a filter by fuzzy string matching.
Partial (‘fuzzy’) matching performed by fuzzywuzzy.fuzzy.ratio
Parameters: fuzzkey : str
A string that partially matches one filter name more than the others.
Returns: The name of the most closely matched filter.
-
get_components
(key, analyte=None)¶ Extract filter components for specific analyte(s).
Parameters: key : str
string present in one or more filter names. e.g. ‘Al27’ will return all filters with ‘Al27’ in their names.
analyte : str
name of analyte the filter is for
Returns: array_like
boolean filter
-
get_info
()¶ Get info for all filters.
-
grab_filt
(filt, analyte=None)¶ Flexible access to specific filter using any key format.
Parameters: f : str, dict or bool
either logical filter expression, dict of expressions, or a boolean
analyte : str
name of analyte the filter is for.
Returns: array_like
boolean filter
-
make
(analyte)¶ Make filter for specified analyte(s).
Filter specified in filt.switches.
Parameters: analyte : str or array_like
Name or list of names of analytes.
Returns: array_like
boolean filter
-
make_fromkey
(key)¶ Make filter from logical expression.
Takes a logical expression as an input, and returns a filter. Used for advanced filtering, where combinations of nested and/or filters are desired. Filter names must exactly match the names listed by print(filt).
- Example:
- key = ‘(Filter_1 | Filter_2) & Filter_3’
- is equivalent to:
- (Filter_1 OR Filter_2) AND Filter_3
statements in parentheses are evaluated first.
Parameters: key : str
logical expression describing filter construction.
Returns: array_like
boolean filter
-
make_keydict
(analyte=None)¶ Make logical expressions describing the filter(s) for specified analyte(s).
Parameters: analyte : optional, str or array_like
Name or list of names of analytes. Defaults to all analytes.
Returns: dict
containing the logical filter expression for each analyte.
-
off
(analyte=None, filt=None)¶ Turn off specified filter(s) for specified analyte(s).
Parameters: analyte : optional, str or array_like
Name or list of names of analytes. Defaults to all analytes.
filt : optional. int, list of int or str
Number(s) or partial string that corresponds to filter name(s).
Returns: None
-
on
(analyte=None, filt=None)¶ Turn on specified filter(s) for specified analyte(s).
Parameters: analyte : optional, str or array_like
Name or list of names of analytes. Defaults to all analytes.
filt : optional. int, str or array_like
Name/number or iterable names/numbers of filters.
Returns: None
-
remove
(name=None, setn=None)¶ Remove filter.
Parameters: name : str
name of the filter to remove
setn : int or True
int: number of set to remove True: remove all filters in set that ‘name’ belongs to
Returns: None
-
Other Functions¶
-
latools.
R2calc
(meas, model, force_zero=False)¶
-
latools.
add_config
(config_name, params, config_file=None, make_default=True)¶ Adds a new configuration to latools.cfg.
Parameters: config_name : str
The name of the new configuration. This should be descriptive (e.g. UC Davis Foram Group)
params : dict
A (parameter, value) dict defining non - default parameters associated with the new configuration. Possible parameters include: srmfile : str
Path to srm file used in calibration. Defaults to GeoRem values for NIST610, NIST612 and NIST614 provided with latools.
- dataformat : dict (as str)
See dataformat documentation.
config_file : str
Path to the configuration file that will be modified. Defaults to latools.cfg in package install location.
make_default : bool
Whether or not to make the new configuration the default for future analyses. Default = True.
Returns: None
-
latools.
bool_2_indices
(bool_array)¶ Get list of limit tuples from boolean array.
Parameters: bool_array : array_like
boolean array
Returns: array_like
[2, n] array of (start, end) values describing True parts of bool_array
-
latools.
collate_data
(in_dir, extension='.csv', out_dir=None)¶ Copy all csvs in nested directroy to single directory.
Function to copy all csvs from a directory, and place them in a new directory.
Parameters: in_dir : str
Input directory containing csv files in subfolders
extension : str
The extension that identifies your data files. Defaults to ‘.csv’.
out_dir : str
Destination directory
Returns: None
-
latools.
config_locator
()¶ Prints the location of the latools.cfg file.
-
latools.
enumerate_bool
(bool_array, nstart=0)¶ Consecutively numbers contiguous booleans in array.
i.e. a boolean sequence, and resulting numbering T F T T T F T F F F T T F 0-1 1 1 - 2 —3 3 -
where ‘ - ‘
Parameters: bool_array : array_like
Array of booleans.
nstart : int
The number of the first boolean group.
-
latools.
fastgrad
(a, win=11)¶ Returns rolling - window gradient of a.
Function to efficiently calculate the rolling gradient of a numpy array using ‘stride_tricks’ to split up a 1D array into an ndarray of sub - sections of the original array, of dimensions [len(a) - win, win].
Parameters: a : array_like
The 1D array to calculate the rolling gradient of.
win : int
The width of the rolling window.
Returns: array_like
Gradient of a, assuming as constant integer x - scale.
-
latools.
fastsmooth
(a, win=11)¶ Returns rolling - window smooth of a.
Function to efficiently calculate the rolling mean of a numpy array using ‘stride_tricks’ to split up a 1D array into an ndarray of sub - sections of the original array, of dimensions [len(a) - win, win].
Parameters: a : array_like
The 1D array to calculate the rolling gradient of.
win : int
The width of the rolling window.
Returns: array_like
Gradient of a, assuming as constant integer x - scale.
-
latools.
gauss
(x, *p)¶ Gaussian function.
Parameters: x : array_like
Independent variable.
*p : parameters unpacked to A, mu, sigma
A: area mu: centre sigma: width
-
latools.
get_example_data
(destination_dir)¶
-
latools.
intial_configuration
()¶ Convenience function for configuring latools.
Run this function when you first use latools to specify the location of you SRM data file and your data format file.
See documentation for full details.
-
latools.
nominal_values
(a)¶
-
latools.
pretty_element
(s)¶ Returns formatted element name.
Parameters: s : str
of format [A-Z][a-z]?[0-9]+
Returns: str
LaTeX formatted string with superscript numbers.
-
latools.
rangecalc
(xs, ys, pad=0.05)¶
-
latools.
reproduce
(log_file, plotting=False, data_folder=None, srm_table=None, custom_stat_functions=None)¶ Reproduce a previous analysis exported with latools.minimal_export
-
latools.
rolling_window
(a, window, pad=None)¶ Returns (win, len(a)) rolling - window array of data.
Parameters: a : array_like
Array to calculate the rolling window of
window : int
Description of window.
pad : same as dtype(a)
Description of pad.
Returns: array_like
An array of shape (n, window), where n is either len(a) - window if pad is None, or len(a) if pad is not None.
-
latools.
std_devs
(a)¶
-
latools.
stderr
(a)¶ Calculate the standard error of a.
-
latools.
tuples_2_bool
(tuples, x)¶ Generate boolean array from list of limit tuples.
Parameters: tuples : array_like
[2, n] array of (start, end) values
x : array_like
x scale the tuples are mapped to
Returns: array_like
boolean array, True where x is between each pair of tuples.
-
latools.
unitpicker
(a, llim=0.1)¶ Determines the most appropriate plotting unit for data.
Parameters: a : array_like
raw data array
llim : float
minimum allowable value in scaled data.
Returns: (float, str)
(multiplier, unit)
-
latools.
unpack_uncertainties
(uarray)¶ Convenience function to unpack nominal values and uncertainties from an
uncertainties.uarray
.- Returns:
- (nominal_values, std_devs)
-
latools.
weighted_average
(x, y, x_new, fwhm=300)¶ Calculate gaussian weigted moving mean, SD and SE.
Parameters: x, y : array - like
The x and y data to smooth
x_new : array - like
The new x - scale to interpolate the data


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Please select one of the bus-stops below, to set it as your departure location.


Example setup for rig¶
To give a realistic example I will explain my own setup. I build a portable silver box, which is the housing for the DC / DC converters, the power supply, the ethernet switch, the mount computer and all the switches and connectors needed for attaching the silver box to the mount.
All other components are located directly on the telescope, so the is only an ethernet connection and two 12 V power supply wires to the telescope.


Reporting date:
January 12, 2020
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May 11, 2020
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Content Statistics
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--- layout: post title: gfxCardStatus is officially open-source. published: true comments: true ---
You heard right! I have open-sourced the code for gfxCardStatus. It can be found on GitHub at http://github.com/codykrieger/gfxCardStatus. Create a fork, go nuts, and feel free to send the comments and suggestions my way!
v1.7 is almost upon us, which will support toggling automatic switching between GPUs, or manually locking the machine on one or the other GPU. Cheers!
Oh, and I made Engadget and AnandTech, as well as various other sweet tech news sites today. :)
ck


Booking me to perform at your wedding or event is a simple, straightforward process.
I have performed at world class venues across the globe. My stunning repertoire, harp, and presentation are sure to impress.
I love what I do. I'm flexible and would love to help make your event unforgettable.
Music is my lifelong passion. I began studying harp, piano, and voice at six and first performed professionally at eleven. I have performed extensively at notable venues including the Edgewater Hotel in Seattle, the Avalon Theater in Hollywood, and the Kahala Resort in Honolulu.
Recently I have traveled internationally as the principal harpist aboard Cunard's luxury cruise ship The Queen Elizabeth. I toured Scandanavia and the Mediterranean while entertaining cruise passengers as a solo artist and as part of the onboard orchestra. When I am not traveling or performing, I teach harp, piano, and voice. Passing on the gift of music to others, helping them achieve their goals and build self esteem is incredibly rewarding.


Score Page
Your score on the easy page was: {{EasyScore}}
Your number of incorrect guesses on the easy page was: {{incorrectEasyScore}}
Your score on the Medium page was: {{MediumScore}}
Your number of incorrect guesses on the Medium page was: {{incorrectMediumScore}}
Your score on the Hard page was: {{HardScore}}
Your number of incorrect guesses on the Hard page was: {{incorrectHardScore}}


Edward Willems is a perpetual student and sometimes an artist. He studied Studio Art at the University of Guelph for five years before beginning his journey of coding at Fanshawe. He no longer has time for hobbies, but tries to make time for reading fantasy novels and painting miniatures. He is determined to make fun user-friendly websites and programs, making interaction intuitive and interesting.
Micah Berbleschpe is a feral and vicious coder. He first got into Python thinking it would involve fighting oversized snakes, and instead took to the coding language faster than English. His fevered grunts and growls from over his keyboard have made him a frighteningly productive member of the team. He was last seen with a lot of copper wiring under his arm, but his current whereabouts are unknown.
Mikhail Gorbachev is the former leader of the Soviet Union. He had worked under multiple hats, including First Party Secretary of the Stavropol Regional Committee and General Secretary of state. His extensive experience and hardworking attitude makes him the ideal front end web designer. His focus on efficiency and usability is contagious, and he keeps the whole team productive and effective. Where would we be without him?


--- layout: post title: Rough storyboard sample date: '2017-11-08T13:27:40+01:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/167276681906/rough-Storyboards-sample ---


Cammie Love
Chicago, IL 60628 ● LinkedIn ● LinkedIn GitHub ● GitHubA work in process: Full Stack Web and Hybrid Mobile Applications Developer
Full stack web and hybrid mobile applications developer specializing in full stack JavaScript application and architectures. Experienced in all stages of the development life cycle, well versed in numerous programming languages.
- Hands-on experience leading all stages of system development efforts, including requirements in definition design, architecture, testing and support.
- Outstanding leadership abilities; able to coordinate and direct all phases of project-based efforts.
Anticipated Core Competencies
- Full Stack Development
- Front End Development
Full Stack Web and Hybrid Mobile Applications Developer
Full stack web and hybrid mobile applications developer specializing in full stack JavaScript application and architectures. Experienced in all stages of the development life cycle, well versed in numerous programming languages.
- Building hands-on experience leading all stages of system development efforts, including requirements in definition design, architecture, testing and support.
- Outstanding leadership abilities; able to coordinate and direct all phases of project-based efforts.
Core Competencies
- Full Stack Development
- Front End Development
- Hybrid Mobile Development
- Server Side Development
Certifications and Technical Proficiencies
- Certifications: Agile Certified Scrum Master
- Platforms: Linux, MEAN, NodeJS, Angular, Ionic
- Databases: MongoDB
- Tools: VS Code, SSH, Gulp, Git
- Languages: HTML, CSS, SASS, JavaScript, ES6, SQL, TypeScript
Professional Experience
Bob's Custom Websites - Chicago, IL \2015 - Present\
Bob's Custom Websites builds custom web applications for clients across a large number of industries.
Web Developer
- Work with ES6, NodeJS, HTML, JavaScript, CSS, MongoDB to build customized web applications for a diverse set of customers.
- Designed the application to meet the users’ requirements document.
- Ensured corporate and department objectives were accomplished in accordance with outlined objectives and mission statements.
Key Contributions
- Full stack LAMP development(Linux, Apache, MEAN, Ajax).
- Recognized for the development of excellent business relationships, providing exemplary customer service.
Multiple Movie Houses - Hollywood, CA \1969 - 2019\
Directed a number of pictures no one else was willing to take credit for.
Director
- With with writers to realize a common vision.
- Manages multiple departments and teams in unison to achieve a common goal.
Key Contributions
- Released the un-releasable.
- No-one wanted them, but they had to be made. That's where I come in.
Professional Experience
Bob's Custom Websites - Chicago, IL \2015 - Present\
Bob's Custom Websites builds custom web applications for clients across a large number of industries.
Web Developer
- Work with ES6, NodeJS, HTML, JavaScript, CSS, MongoDB to build customized web applications for a diverse set of customers.
- Designed the application to meet the users’ requirements document.
- Ensured corporate and department objectives were accomplished in accordance with outlined objectives and mission statements.
Key Contributions
- Full stack LAMP development(Linux, Apache, MEAN, Ajax).
- Recognized for the development of excellent business relationships, providing exemplary customer service.
Multiple Movie Houses - Hollywood, CA \1969 - 2019\
Directed a number of pictures no one else was willing to take credit for.
Director
- With with writers to realize a common vision.
- Manages multiple departments and teams in unison to achieve a common goal.
Key Contributions
- Released the un-releasable.
- No-one wanted them, but they had to be made. That's where I come in.


A SageMakerEstimator that runs a K-Means Clustering training job on Amazon SageMaker upon a call to fit() on a DataFrame and returns a SageMakerModel that can be used to transform a DataFrame using the hosted K-Means model.
A SageMakerEstimator that runs a Linear Learner training job in "binary classifier" mode in SageMaker and returns a SageMakerModel that can be used to transform a DataFrame using the hosted Linear Learner model.
A SageMakerEstimator that runs a Linear Learner training job in "binary classifier" mode in SageMaker and returns a SageMakerModel that can be used to transform a DataFrame using the hosted Linear Learner model. The Linear Learner Binary Classifier is useful for classifying examples into one of two classes.
Amazon SageMaker Linear Learner trains on RecordIO-encoded Amazon Record protobuf data. SageMaker Spark writes a DataFrame to S3 by selecting a column of Vectors named "features" and, if present, a column of Doubles named "label". These names are configurable by passing a map with entries in trainingSparkDataFormatOptions with key "labelColumnName" or "featuresColumnName", with values corresponding to the desired label and features columns.
Inferences made against an Endpoint hosting a Linear Learner Binary classifier model contain a "score" field and a "predicted_label" field, both appended to the input DataFrame as Doubles.
A SageMakerEstimator that runs a Linear Learner training job in "regressor" mode in SageMaker and returns a SageMakerModel that can be used to transform a DataFrame using the hosted Linear Learner model.
A SageMakerEstimator that runs a Linear Learner training job in "regressor" mode in SageMaker and returns a SageMakerModel that can be used to transform a DataFrame using the hosted Linear Learner model. The Linear Learner Regressor is useful for predicting a real-valued label from training examples.
Amazon SageMaker Linear Learner trains on RecordIO-encoded Amazon Record protobuf data. SageMaker Spark writes a DataFrame to S3 by selecting a column of Vectors named "features" and, if present, a column of Doubles named "label". These names are configurable by passing a map with entries in trainingSparkDataFormatOptions with key "labelColumnName" or "featuresColumnName", with values corresponding to the desired label and features columns.
For inference against a hosted Endpoint, the SageMakerModel returned by fit() by Linear Learner uses ProtobufRequestRowSerializer to serialize Rows into RecordIO-encoded Amazon Record protobuf messages, by default selecting the column named "features" expected to contain a Vector of Doubles.
Inferences made against an Endpoint hosting a Linear Learner Regressor model contain a "score" field appended to the input DataFrame as a Double
A SageMakerEstimator that runs a PCA training job in SageMaker and returns a SageMakerModel that can be used to transform a DataFrame using the hosted PCA model.
A SageMakerEstimator that runs a PCA training job in SageMaker and returns a SageMakerModel that can be used to transform a DataFrame using the hosted PCA model. PCA, or Principal Component Analysis, is useful for reducing the dimensionality of data before training with another algorithm.
Amazon SageMaker PCA trains on RecordIO-encoded Amazon Record protobuf data. SageMaker Spark writes a DataFrame to S3 by selecting a column of Vectors named "features" and, if present, a column of Doubles named "label". These names are configurable by passing a map with entries in trainingSparkDataFormatOptions with key "labelColumnName" or "featuresColumnName", with values corresponding to the desired label and features columns.
PCASageMakerEstimator uses ProtobufRequestRowSerializer to serialize Rows into RecordIO-encoded Amazon Record protobuf messages for inference, by default selecting the column named "features" expected to contain a Vector of Doubles.
Inferences made against an Endpoint hosting a PCA model contain a "projection" field appended to the input DataFrame as a Dense Vector of Doubles.
A SageMakerEstimator that runs an XGBoost training job in SageMaker and returns a SageMakerModel that can be used to transform a DataFrame using the hosted XGBoost model.
A SageMakerEstimator that runs an XGBoost training job in SageMaker and returns a SageMakerModel that can be used to transform a DataFrame using the hosted XGBoost model. XGBoost is an open-source distributed gradient boosting library that Amazon SageMaker has adapted to run on Amazon SageMaker.
XGBoost trains and infers on LibSVM-formatted data. XGBoostSageMakerEstimator uses Spark's LibSVMFileFormat to write the training DataFrame to S3, and serializes Rows to LibSVM for inference, selecting the column named "features" by default, expected to contain a Vector of Doubles.
Inferences made against an Endpoint hosting an XGBoost model contain a "prediction" field appended to the input DataFrame as a column of Doubles, containing the prediction corresponding to the given Vector of features.
https://github.com/dmlc/xgboost for more on XGBoost.
A SageMakerEstimator that runs a K-Means Clustering training job on Amazon SageMaker upon a call to fit() on a DataFrame and returns a SageMakerModel that can be used to transform a DataFrame using the hosted K-Means model. K-Means Clustering is useful for grouping similar examples in your dataset.
Amazon SageMaker K-Means clustering trains on RecordIO-encoded Amazon Record protobuf data. SageMaker Spark writes a DataFrame to S3 by selecting a column of Vectors named "features" and, if present, a column of Doubles named "label". These names are configurable by passing a map with entries in trainingSparkDataFormatOptions with key "labelColumnName" or "featuresColumnName", with values corresponding to the desired label and features columns.
For inference, the SageMakerModel returned by fit() by the KMeansSageMakerEstimator uses ProtobufRequestRowSerializer to serialize Rows into RecordIO-encoded Amazon Record protobuf messages for inference, by default selecting the column named "features" expected to contain a Vector of Doubles.
Inferences made against an Endpoint hosting a K-Means model contain a "closest_cluster" field and a "distance_to_cluster" field, both appended to the input DataFrame as columns of Double.


| THE GERMAN 'GROUP OF EXPERTS |
The German 'Group of Experts' has now - as far as possible - completed its investigation of the foundering of the M. V. ESTONIA, a disaster that has taken the greatest toll of human life in the Baltic Sea in times of peace. The German 'Group of Experts' has during the past five years performed a difficult investigation in a partly hostile and reluctant environment. The work was carried out during the first two years - 1995/1996 - in close co-operation with the Swedish leader of the Technical Group of the Joint Accident Investigation Commission of Estonia, Finland and Sweden (JAIC), Börje Stenström († February 1997), and also with the Finnish member of the Technical Group, Dr. Tuomo Karppinen and the expert Dr. Klaus Rahka. All the investigation results of this 'Group of Experts' were submitted either verbally in joint meetings with the members and the experts from Estonia, Finland and Sweden attending or in letters with attached photos and/or video film documentation to the JAIC. This documentation and information concerned the design, construction and building of M. V. ESTONIA in general and the locking devices of the bow visor in particular, including the load calculations, the co-operation with the Classification Society Bureau Veritas, and the Owners. Furthermore, detailed evaluations of all survivors' statements, statements and other documentation such as photos and videos showing the condition of safety relevant parts of the ferry before the casualty obtained from previous passengers and crew members were submitted. In response, the JAIC submitted information and documentation, and up until the end of 1996 a relatively intense exchange of views and technical discussions took place which died away with Börje Stenström. A very detailed evaluation of the condition of the visor performed by metallurgical experts, of the objects recovered from the wreck or cut off from the visor, of the video films from the wreck made by ROVs and divers, of the testimony of shipyard employees having participated in the design, construction and building of VIKING SALLY, of previous passengers and crew members of the ferry as well as of survivors of the disaster form the backbone of this Investigation Report and are the basis for its conclusions. Breaking tests with mock-ups of the Atlantic lock were carried out by the Institute for Shipbuilding of the Hamburg University in co-operation with a member and an expert from the Finnish part of the JAIC. During the first half of 1997 it became evident that the JAIC would not change its first technical findings and conclusions published in the so-called Part-Report in April 1995 in spite of all the evidence submitted by this 'Group of Experts' and others which indicate a totally different chain of causation and sequence of events. Therefore it was decided to demonstrate the own investigation results to the Swedish public in two Exhibitions in the heart of the City of Stockholm, because it was Sweden who had paid the highest death toll in the disaster and consequently the Swedish public followed our investigations very closely. As from the Exhibitions - especially after the second - considerable support was received from Swedish citizens and further contact was made with a number of very important witnesses of fact. This Investigation Report covers all the evidence available to this 'Group of Experts' until 27.11.1999. Whenever considered relevant the attention of the reader will be drawn to the findings of the JAIC in comparison to the findings of this 'Group of Experts'. During the course of the investigation and, in particular, during the two exhibitions in Stockholm, many meetings with survivors and relatives of the casualty victims took place, which left deep impressions. Many of the relatives and survivors pleaded to assist them in the finding of the truth. This authors of this Report have attempted to comply with this plea.
In summary this investigation reveals that,
- M.V. ESTONIA was unseaworthy when she left Tallinn on the evening of the 27.9.1994 for her last voyage and
- this condition was due to reckless operation and careless inspections and
- the report of the JAIC misleads its readers, whether purposely or due to the incompetence of its authors, is left to the decision of the readers of this report.


This package contains several test scripts and classes aimed at testing the functionality of the bitstream package.


This directory contains the Microsoft Visual C++ 6.00 Console application.
OPERATION OF 'SWVC32'
This version of 'SWVC32' is text instead of graphics oriented. It displays
the information about the DS2406 Dual Addressable Switch Plus Memory such
as: TMEX Driver Version, Type Version, ROM number (1-Wire network address)
and the status of the Activity Latch and the open drain outputs inside the
switch. The Open drain outputs for each DS2406 on the 1-Wire network can be
independently switched on or off whether there is one or many devices sharing
the same 1-wire port. The numbers are given in a numbered list. A number
from that list can be chosen to update the value of the Sensed Level and
to set the Flip Flop ON/OFF on switch or reset the value of the Activity Latch.
README.TXT - This file.
SWVC32.C - Main C module for SWVC32.EXE.
SWVC32.DSP - Microsoft Visual C++ project file.
SWVC32.DSW - Microsoft Visual C++ workspace file.
SWVC32.NCB - Microsoft Visual C++ generated file.
SWVC32.OPT - Microsoft Visual C++ generated file.
SWVC32.PLG - Microsoft Visual C++ generated build log file (HTML).
iBTMEXCW.H - Include file that has the function prototypes and constants.
IBFS32.LIB - Library file to link to the TMEX DLL functions.
SWVC32.EXE - Program executable.


Bayes rule is one the most fundamental thing we learn in probabilistic theory, yet the application of this simple rule seems a daunting effort. One of the reason being the simplification of the problem to the coin and dice example. There is a quote by Einstein which can be paraphased as this > “Everything needs to be made simple but not simpler”. The above quote suits exact to our problem here, we are so used to the coin and the dice problem that we have lost interest in the field of probability. In this blog I will try to capture some interesting problems to which bayesian rule can be applied to make proper insight from the data.
\[ P(A \mid B) = \frac{P(B \mid A) \, P(A)}{P(B)} \]
We all are very much familiar with this theorem, we have prooved this in our
schools but the usage still reamins a question mark who is A
and B
. How
does this theorem help me solving real world problems. But to the matter of
fact the whole world of Bayesian inference stands on the strong shoulders of
this very simple formula. So lets make an effort to understand everything about
the A and B and in my case through examples (Emperical learning : Learning from
real world data and not by metaphysical proofs)
So a lot of insights can be gained by renaming the variable A and B in the
theorem. From literature the variable is named in many ways, from my
prespective the most useful name which can be used in almost all examples is
Evidence
and Fact
.
So lets rename the variables and rewrite the Baye’s theorem
\[ P(fact \mid evidence) = \frac{P(evidence \mid fact) \, P(fact)}{P(evidence)} \]
So lets define both :
Fact
: The latent variable we are trying to learn
Evidence
: The observations we make during trials from which we have to
learn.
Baye’s Theorem can be re read as follows based on the new definitions > Bayes theorem is about updating our belief of the fact based on evidences.
From the definition we have made the assumption that is predominant in naming the variables as fact and evidence, which is that all the examples explained here will have some hidden variable and some observations. Baye’s theorem is not limited to only such pattern recognition problems but cab be applied in many different places. But since these kind of problems form the major chunk in the machine learning problem set.
Lets find examples which can be formulated as the evidence, fact pairs.
There is a website which you are supposed to market. After 1 week of initial marketing the results was - 833 unique visitors out of which 18 created an account. Now the question is if we do more marketing and increase the unique visitors count to 10000 then how many people will register? How confident are you about the results?
Evidence
: 18 out of 833 unique visitor made account.
Fact
: Probability that an unique visitor will create an account.


ZinJS is a new javascript and css based library which create art from you website.
How it works? It is simple. Just download library from Github and link zinjs.js into your source code.
It is free? Yes, it's completely free. You can use, redistribuate or change library even in commercial projects. ZinJS is distribuated under new BSD licence. You must only keep copyright marks inside source code of library.
What I can do with library? The only limit is your imagination. You can create website based on "zooming" of content or rotating or something else...
Documentation We will add documentation on this site as soon as possible. In a meanwhile you can follow us on Github. Github also provide bug report form.
Prototype Please note that ZinJS is still prototype so it's still under development. Some features may be altered.