text:
# sklearn.linear_model.Perceptron¶

class sklearn.linear_model.Perceptron(penalty=None, alpha=0.0001, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, eta0=1.0, n_jobs=None, random_state=0, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight=None, warm_start=False)[source]

Read more in the User Guide.

Parameters
penalty{‘l2’,’l1’,’elasticnet’}, default=None

The penalty (aka regularization term) to be used.

alphafloat, default=0.0001

Constant that multiplies the regularization term if regularization is used.

fit_interceptbool, default=True

Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.

max_iterint, default=1000

The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method.

New in version 0.19.

tolfloat, default=1e-3

The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol).

New in version 0.19.

shufflebool, default=True

Whether or not the training data should be shuffled after each epoch.

verboseint, default=0

The verbosity level

eta0double, default=1

Constant by which the updates are multiplied.

n_jobsint, default=None

The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

random_stateint, RandomState instance, default=None

Used to shuffle the training data, when shuffle is set to True. Pass an int for reproducible output across multiple function calls. See Glossary.

early_stoppingbool, default=False

Whether to use early stopping to terminate training when validation. score is not improving. If set to True, it will automatically set aside a stratified fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs.

New in version 0.20.

validation_fractionfloat, default=0.1

The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.

New in version 0.20.

n_iter_no_changeint, default=5

Number of iterations with no improvement to wait before early stopping.

New in version 0.20.

class_weightdict, {class_label: weight} or “balanced”, default=None

Preset for the class_weight fit parameter.

Weights associated with classes. If not given, all classes are supposed to have weight one.

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

warm_startbool, default=False

When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.

Attributes
coef_ndarray of shape = [1, n_features] if n_classes == 2 else [n_classes, n_features]

Weights assigned to the features.

intercept_ndarray of shape = [1] if n_classes == 2 else [n_classes]

Constants in decision function.

n_iter_int

The actual number of iterations to reach the stopping criterion. For multiclass fits, it is the maximum over every binary fit.

classes_ndarray of shape (n_classes,)

The unique classes labels.

t_int

Number of weight updates performed during training. Same as (n_iter_ * n_samples).

Notes

Perceptron is a classification algorithm which shares the same underlying implementation with SGDClassifier. In fact, Perceptron() is equivalent to SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant", penalty=None).

References

https://en.wikipedia.org/wiki/Perceptron and references therein.

Examples

>>> from sklearn.datasets import load_digits
>>> from sklearn.linear_model import Perceptron
>>> clf = Perceptron(tol=1e-3, random_state=0)
>>> clf.fit(X, y)
Perceptron()
>>> clf.score(X, y)
0.939...

Methods

 decision_function(self, X) Predict confidence scores for samples. densify(self) Convert coefficient matrix to dense array format. fit(self, X, y[, coef_init, intercept_init, …]) Fit linear model with Stochastic Gradient Descent. get_params(self[, deep]) Get parameters for this estimator. partial_fit(self, X, y[, classes, sample_weight]) Perform one epoch of stochastic gradient descent on given samples. predict(self, X) Predict class labels for samples in X. score(self, X, y[, sample_weight]) Return the mean accuracy on the given test data and labels. set_params(self, \*\*kwargs) Set and validate the parameters of estimator. sparsify(self) Convert coefficient matrix to sparse format.
__init__(self, penalty=None, alpha=0.0001, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, eta0=1.0, n_jobs=None, random_state=0, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight=None, warm_start=False)[source]

Initialize self. See help(type(self)) for accurate signature.

decision_function(self, X)[source]

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters
Xarray_like or sparse matrix, shape (n_samples, n_features)

Samples.

Returns
array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)

Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

densify(self)[source]

Convert coefficient matrix to dense array format.

Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

Returns
self

Fitted estimator.

fit(self, X, y, coef_init=None, intercept_init=None, sample_weight=None)[source]

Fit linear model with Stochastic Gradient Descent.

Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)

Training data.

yndarray of shape (n_samples,)

Target values.

coef_initndarray of shape (n_classes, n_features), default=None

The initial coefficients to warm-start the optimization.

intercept_initndarray of shape (n_classes,), default=None

The initial intercept to warm-start the optimization.

sample_weightarray-like, shape (n_samples,), default=None

Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.

Returns
self :

Returns an instance of self.

get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

partial_fit(self, X, y, classes=None, sample_weight=None)[source]

Perform one epoch of stochastic gradient descent on given samples.

Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.

Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)

Subset of the training data.

yndarray of shape (n_samples,)

Subset of the target values.

classesndarray of shape (n_classes,), default=None

Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.

sample_weightarray-like, shape (n_samples,), default=None

Weights applied to individual samples. If not provided, uniform weights are assumed.

Returns
self :

Returns an instance of self.

predict(self, X)[source]

Predict class labels for samples in X.

Parameters
Xarray_like or sparse matrix, shape (n_samples, n_features)

Samples.

Returns
Carray, shape [n_samples]

Predicted class label per sample.

score(self, X, y, sample_weight=None)[source]

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters
Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns
scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_params(self, **kwargs)[source]

Set and validate the parameters of estimator.

Parameters
**kwargsdict

Estimator parameters.

Returns
selfobject

Estimator instance.

sparsify(self)[source]

Convert coefficient matrix to sparse format.

Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

The intercept_ member is not converted.

Returns
self

Fitted estimator.

Notes

For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.

After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.


text:
# Windows project long horn

Discussion in 'Computer Science & Culture' started by RickyH, Jan 5, 2006.

Not open for further replies.
1. ### RickyHValued Senior Member

Messages:
1,317
I've been hearing on the new microsoft OS. As a project they called LongHorn, form this i heard they are making a new OS that will completely alter the way they have been making OS's by making it faster more reliable and even simplier to use. So in other words Windows might have a decent OS to use in the future

3. ### LightRegistered Senior Member

Messages:
2,258
Unless I'm badly mistaken, I believe "Longhorn" was the code name they used for XP while it was under development. The latest thing from Microsoft is called Vista which is scheduled for release later this year.

5. ### Avatarsmoking revolverValued Senior Member

Messages:
19,083
You are badly mistaken, the development codename for Vista (means "chicken" in my native language) was Longhorn.
bwhahahaha!

Besides I already use and have been using such an OS for 2 years now. It's called Linux.

7. ### RickyHValued Senior Member

Messages:
1,317
ehh google microsoft longhorn and mostly what will come up is somthing about microsoft vista.... i can only assume thats what there basic code name is for a new OS.

8. ### RickyHValued Senior Member

Messages:
1,317

lol notice where i said microsoft is making a good OS. Sorry to say but Linux although is better is irrelavent

9. ### RickyHValued Senior Member

Messages:
1,317
Stephan told conference attendees that Longhorn will:

# launch applications 15 percent faster than Windows XP does
# boot PCs 50 percent faster than they boot currently and will allow PCs to resume from standby in two seconds
# allow users to patch systems with 50 percent fewer reboots required
# reduce the number of system images required by 50 percent
# enable companies to migrate users 75 percent faster than they can with existing versions of Windows.

http://www.microsoft-watch.com/article2/0,2180,1838263,00.asp

10. ### Avatarsmoking revolverValued Senior Member

Messages:
19,083
Actually it is Windows that is irrelevant to me, even if it's not to you.

11. ### LightRegistered Senior Member

Messages:
2,258
Thanks. That's what happens when someone is going strictly from memory - thus my disclaimer.

12. ### RickyHValued Senior Member

Messages:
1,317

Well regardless if it is irrelevant to you or not. It is irrelavent to this thread. Well I suppose it's not anymore, seeing as were talking about it now.

Messages:
19,083

format: plain


text:
# Synopsis: Fluid Invasion

A phenomenon called capillary fracturing determines how fluids drain into tightly packed porous granular media.

The cracks that form in drying soil or paint are evidence of fracture in wet granular materials. Similar fracturing also plays a role in environmentally sensitive scenarios such as extracting petroleum from the deep seas, ${\text{CO}}_{2}$ sequestration, and fracking, where pressurized fluid is injected into rocks to release natural gas.

These are all examples of one fluid displacing another in porous media. While work in recent years has contributed to a clearer understanding of this wide-ranging phenomenon, most of it focused on either rigid porous media or porous materials with a low packing density. In Physical Review Letters, Ran Holtzman and colleagues at the Massachusetts Institute of Technology, Cambridge, address how the fluid-fluid displacement takes place in a frictional granular material at high packing fraction. They injected air into a cylinder containing tightly packed water-saturated glass beads, while varying the injection rate of the air, the size of the beads, and the confinement stress from weights placed on a rigid disk that rests on top of the beads. They observed that there are three ways—distinguished by the pattern of the air’s path through the beads—in which one fluid can invade another: capillary fingering, viscous fingering, and capillary fracturing, which result from competition between the viscous and capillary forces on the one hand and pressure forces and internal friction on the other. The authors’ experiment and scaling analysis show that capillary fracturing is a consequence of the interfacial tension between the fluids and can take place at vanishingly small injection rates, unlike, say, fracking. – Sami Mitra

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### Focus: The Difference Between Round and Square Pipes

Calculations of the motion of particles carried by a fluid flowing through a pipe find a surprising effect of the pipe's shape. Read More »

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Ring-shaped polymers have been used to detect a theoretical tube that restricts the motion of molten polymer chains. Read More »

Fluid Dynamics

### Focus: Explaining the Periodicity of Snow Spikes

A new model explains the fixed periodicity of a rare snow formation that appears at high altitudes. Read More »


text:
ROOT   Reference Guide
ROOT::Minuit2::MnLineSearch Class Reference

Implements a 1-dimensional minimization along a given direction (i.e.

quadratic interpolation) It is independent of the algorithm that generates the direction vector. It brackets the 1-dimensional Minimum and iterates to approach the real Minimum of the n-dimensional function.

Definition at line 40 of file MnLineSearch.h.

## Public Member Functions

MnLineSearch ()

~MnLineSearch ()

MnParabolaPoint operator() (const MnFcn &, const MinimumParameters &, const MnAlgebraicVector &, double, const MnMachinePrecision &) const
Perform a line search from position defined by the vector st along the direction step, where the length of vector step gives the expected position of Minimum. More...

#include <Minuit2/MnLineSearch.h>

## ◆ MnLineSearch()

 ROOT::Minuit2::MnLineSearch::MnLineSearch ( )
inline

Definition at line 43 of file MnLineSearch.h.

## ◆ ~MnLineSearch()

 ROOT::Minuit2::MnLineSearch::~MnLineSearch ( )
inline

Definition at line 45 of file MnLineSearch.h.

## ◆ operator()()

 MnParabolaPoint ROOT::Minuit2::MnLineSearch::operator() ( const MnFcn & fcn, const MinimumParameters & st, const MnAlgebraicVector & step, double gdel, const MnMachinePrecision & prec ) const

Perform a line search from position defined by the vector st along the direction step, where the length of vector step gives the expected position of Minimum.

fcn is Value of function at the starting position , gdel (if non-zero) is df/dx along step at st. Return a parabola point containing Minimum x position and y (function Value)

• add a falg to control the debug

Definition at line 46 of file MnLineSearch.cxx.

The documentation for this class was generated from the following files:


text:
Chapter 561

According to the World Health Organization, status epilepticus (SE) is defined as seizures that are sufficiently frequent or prolonged to produce an “enduring epileptic condition.”1 Implicit in this definition is the concept that the event’s duration renders it physiologically distinct from other seizure types and, therefore, it requires specialized workup and management.

Although there is no well-determined absolute duration beyond which a prolonged seizure is definitively distinct from shorter ones, in practice SE is defined as continuous seizures, or a cluster of seizures without return to consciousness, lasting 30 minutes or more. With this definition, the incidence of SE in children approximates 18 to 38 per 100,000 persons/year.2 More than 40% of SE cases occur in children younger than 2 years of age.3

A reasonable and more inclusive definition of SE takes into account observations that individual seizures rarely last longer than 5 minutes, and that in clinical practice seizures longer than 5 minutes are often treated acutely. Accordingly, there is a trend toward reducing the duration required for diagnosis of SE, with some reports defining minimal SE duration as 5, 10, or 20 minutes; incidence of SE by these definitions has not been extensively studied.4

The recent trend toward reducing seizure duration in the definition of SE is supported by clinical and experimental data. For instance, Shinnar and colleagues found a bimodal distribution of new-onset seizure durations in children clustered around 2 means: 3.6 minutes (76% of cases), and 31 minutes (24% of cases).5 This corroborates the observation that seizures lasting longer than 5 minutes are likely to continue for at least several minutes longer, and fits well with the widely accepted practice to administer anticonvulsants acutely for seizures lasting 5 minutes or more. The rationale for earlier treatment of SE is also supported by data from animal seizure models that show loss and dysfunction of GABAA receptors and reduced sensitivity to benzodiazepines after prolonged seizures in rats.6-9

### Classification

Numerous seizure types, if prolonged, can comprise SE. With some overlap, these can be divided into convulsive and nonconvulsive subgroups. Prolonged primary or secondary generalized tonic-clonic seizures, including febrile seizures, are the most common form of convulsive SE in children.

Nonconvulsive SE (NCSE) is a relatively heterogenous group of disorders.11 The clinical picture is often that of depressed consciousness ranging from confusion to coma. Particularly in the intensive care unit (ICU), NCSE is now becoming more widely recognized, in part due to increased use of electroencephalogram (EEG) monitoring. In a recent report, EEG ordered to evaluate unresponsiveness in the pediatric ICU showed a pattern consistent with NCSE in 33% of 178 cases.12 Prolonged absence seizures (absence status) are also a form of NCSE characterized by altered consciousness, at times lasting hours until the diagnosis is made by EEG. Another form of NCSE, one in which consciousness is typically preserved, ...

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# Evidence for Transient Atmospheres during Eruptive Outgassing on the Moon

Abstract : Events following the giant impact formation of the Moon are thought to have led to volatile depletion and concurrent mass-dependent fractionation of the isotopes of moderately volatile elements (MVE). The detailed processes and conditions surrounding this episode remain obscured and are not unified by a single model for all volatile elements and compounds. Using available data, including new Zn isotope data for eight lunar samples, we demonstrate that the isotopic fractionation of MVE in the Moon is best expressed by nonideal Rayleigh distillation, approaching the fractionation factor α using the reduced masses of the evaporated isotopologs. With these calculations, a best fit for the data is obtained when the lunar MVE isotope data are normalized to ordinary or enstatite chondrites ( ${{\rm{\Delta }}}_{\mathrm{Moon}-\mathrm{OC},\mathrm{EC}}$ ), rather than a bulk silicate Earth composition. This analysis further indicates that the parent body from which the Moon formed cannot have partitioned S into its core based on S isotope compositions of lunar rocks. The best fit between ${{\rm{\Delta }}}_{\mathrm{Moon}-\mathrm{OC},\mathrm{EC}}$ and modeled nonideal Rayleigh fractionation is defined by a slope that corresponds to a saturation index of 90% ± 4%. In contrast, the older Highland suite is defined by a saturation index of 75% ± 2%, suggesting that the vapor phase pressure was higher during mare basalt eruptions. This provides the first tangible evidence that the Moon was veiled by a thin atmosphere during mare basalt eruption events spanning at least from 3.8 to 3 billion years ago and implies that MVE isotope fractionation dominantly occurred after the Moon had accreted.
Keywords :
Document type :
Journal articles

https://hal-insu.archives-ouvertes.fr/insu-03748792
Contributor : Nathalie POTHIER Connect in order to contact the contributor
Submitted on : Wednesday, August 10, 2022 - 4:06:02 PM
Last modification on : Thursday, August 11, 2022 - 3:41:31 AM

### File

van_Kooten_2020_Planet._Sci._J...
Publisher files allowed on an open archive

### Citation

Elishevah M. M. E. van Kooten, Frédéric Moynier, James M. D. Day. Evidence for Transient Atmospheres during Eruptive Outgassing on the Moon. The Planetary Science Journal, 2020, 1, ⟨10.3847/PSJ/abbe13⟩. ⟨insu-03748792⟩

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text:
## Geometry: Common Core (15th Edition)

72 $m^{2}$
First, find the base length of the triangle pictured. $a^{2}$+$b^{2}$=$c^{2}$ $8^{2}$+$b^{2}$=$10^{2}$ 64+$b^{2}$=100 $b^{2}$=36 b=6 Add this to the portion of the base given: 6+6=12. This is now your $b_{1}$. The figure shows you that $b_{2}$=6. We use the formula for the area of a trapezoid to obtain: A=$\frac{1}{2}$h($b_{1}$+$b_{2}$) A=$\frac{1}{2}$(8)(12+6) A=$\frac{1}{2}$(8)(18) A=72


text:
# Common Emitter. Why and how to

Ok, we more or less know meaning of Bipolar, Junction and Transistor.
And Common Emitter (or Base or Collector)?

Every electrical circuit wants a voltage reference; as you personally need certainty in life, electrical current is for a voltage one; otherwise it simply doesn't flow.
This reference could potentially be any circuital point at any voltage: for our goals ground (zero voltage) is enough. But this is for the whole circuit

Common Emitter means that E is used as reference for the other two terminals, whose B is input and C output.
In example schemes you'll usually find (for a NPN BJT) C to the most positive battery, B to the lesser one and E to the ground (eventually with resistors, capacitors and inductors in the middle).
In picture you see $V_{+}$ and $V_{in}$ respectively circuit and polarization power supplies: look at transistor symbol, indicating a NPN with arrow going out from B. PNP has it ingoing, so that NPN has arrow Not Penetrating and PNP has it Penetrating.
As usual resistors limit the amount of current, that could run transistor over leading it to burning.

We've introduced a new term: polarization (or voltage bias). In few words it means the way we put transistor into working condition.
Basically a BJT can be applied as amplifier or switcher (current ON or OFF): in both cases polarization called active sets the transistor to make the current run.
How to do it?

Talking about BJT you must know that on junction zones naturally exists a voltage you can increase or decrease according to your necessities. Its value is around 0.5 or 0.6 V (exceptions excluded).
Well... if you want a NPN BJT active put $V_{in}>0.6V$, so that current can - conventionally - flow from B to E (that is electrons from E to B); you arithmetically add $V_{in}$ to $V_{BE}$ decreasing BE junction voltage and so easing the passage (direct polarization).
On the other side $V_{+}>V_{in}$; this way you make the opposite (note the continuous symmetry!).
$V_{CB}$ is increased so that current cannot go from B to C (or electrons from C to B), but only vice versa (inverse polarization).

With a PNP same talk applies, just with roles inverted.

Polarization leads transistor to the so called quiescent point or Q-point.
Consider a graph showing the C's current based on $V_{CE}$, it indicates the current value "actually" flowing through the transistor, being the matching point between current curve and CE resistance's expression.
[Remember Ohm's law: $V=R\cdot I$ or also $R=\frac{V}{I}$].
R is a line (load line) connecting max voltage to max current, while current's curves depend by B's current for a factor commonly indicated with β, variable from 40 to 400 because of structural features and working temperature $I_{C}=\beta I_{B}$.
Picture above shows exactly and quickly what just described.
With one curve set and load line known, Q-point automatically comes out.

This picture shows instead a more common configuration, with a resistance placed between E and ground; equations system returns an interesting result: output gain decreases but stability is guaranteed, while on previous scheme wasn't, so that BJT were possibly exposed to overheating for exceeding current.
[Mathematical explanation is out of our goals, but if you want to know more deeply search for common emitter with degeneration].


text:
'; Elasticity Of Demand Function Example
• Best Scar Removal Cream For Face In Pakistan
• Sister Schuberts Dinner Yeast Rolls Reviews
• Olay Night Eye Cream Before And After
• # Elasticity Of Demand Function Example

### In economics the price elasticity of demand refers to the elasticity of a demand function qp and can be expressed as dqdpqpp or the ratio of the value of the marginal function dqdp to the value of the average function qpp.

Elasticity of demand function example. This relationship provides an easy way of determining whether a demand curve is elastic or inelastic. Noting that dqdp 10 we get p qp dq dp p 500 10p 10 p p50. Determine an original and new price point for this example. This is the situation when the percentage change in quantity demand increases or decreases more than a percentage change in price decrease or increase.

As an example of perfectly elastic demand imagine that two stores sell identical ounces of gold. Instead all consumers would buy gold from the dealer that sells it for less. Using our result from a we get 30 30 50 15. Now at this price consumers buy 4000 bottles per week.

B what is the price elasticity of demand when the price is 30. P sub 1 and p sub 2. Now the calculation of price elasticity of demand can be done as below. The real life examples could be luxurious products like ac tv smartphones.

With perfectly elastic demand no one would buy the more expensive gold. We saw that we can calculate any elasticity by the formula. Follow these steps to determine the elasticity of demand via arc elasticity. Example of arc elasticity of demand.

Price elasticity 25 20 050. One sells it for 1800 an ounce while the other one sells it for 1799 an ounce. If the negative sign is not ignored the cheese demand will be analyzed as more elastic in india 05 than that in england 20. A compute the price elasticity of this demand function.

In order to increase sales it has been decided to decrease the price to 250 which will increase sales to 5000 bottles. Here this situation is called elastic demand. What is the income elasticityof demand when income is 20000 and price is 5. The price elasticity of demand in the above mentioned example of cheese demand in india and england is estimated as 05 in case of india but 20 in case of england.

Demand is q 110p 032i where p is the price of the good and i is the consumers income. Example 1 suppose the demand curve for opads is given by q 500 10p.

How To Calculate The Price Elasticity Of Demand 5 Terrific

Solved The Acise Elasticity Of Demand The Price Elastici

The Price Elasticity Of Demand

Price Elasticity Of Demand Wikipedia

Measuring Income Elasticity Of Demand Percentage Point And Arc

Do you need anything, like a medicine maybe?
Take Offers


text:
# Convert PDF 2 sides per page to 1 side per page

How can I convert a PDF with 2 sides per page to 1 side per page?

-
This is absolutely crazy! Is there no way to change page settings in a PDF to be one-sided instead of two-sided? –  Nathan Fellman Aug 31 '10 at 9:23
@Nathan Fellman: Crazy, yes. But crazy things sometimes give you most of the fun ;-) –  Kurt Pfeifle Sep 15 '10 at 21:07

OK, the problem was already solved with the help of Acrobat (full version, not Reader). But what to do if you don't have access to Acrobat? Could this be done with Ghostscript and pdftk as well?

# How to solve this with the help of Ghostscript...

...and for the fun of it, let's not use an input file with "double-up" pages, but one with "treble-up". Actually, I received one such PDF today by email. It was a flyer, folded in Leporello's Scheme. The sheet size was A4 landscape (842pt x 595pt), and it was folded and layed-out like this:

Front side to be printed, page 1 of PDF
+--------+--------+--------+   ^
|        |        |        |   |
|   5    |   6    |   1    |   |
|        |        |        | 595 pt
|        |        |        |   |
|        |        |        |   |
|        |        |        |   |
+--------+--------+--------+   v
^        ^
fold     fold
v        v
+--------+--------+--------+   ^
|        |        |        |   |
|   2    |   3    |   4    |   |
|        |        |        | 595 pt
|        |        |        |   |
|        |        |        |   |
|        |        |        |   |
+--------+--------+--------+   v
Back side to be printed, page 2 of PDF
<---------- 842 pt -------->

I want to create 1 PDF with 6 pages, each of which has the unusual size of 280.67pt x 595 pt.

## First Step

Let's first extract the left sections from each of the input pages:

gswin32c.exe ^
-o left-sections.pdf ^
-sDEVICE=pdfwrite ^
-g2807x5950 ^
-c "<</PageOffset [0 0]>> setpagedevice" ^
-f myflyer.pdf

What did these parameters do?

• -o ...............: Names output file. Implicitely also uses -dBATCH -dNOPAUSE -dSAFER.
• -sDEVICE=pdfwrite : we want PDF as output format.
• -g................: sets output media size in pixels. pdfwrite's default resolution is 720 dpi. Hence multiply by 10 to get a match for PageOffset.
• -c "..............: asks Ghostscript to process the given PostScript code snippet just before the main input file (which needs to follow with -f).
• <</PageOffset ....: sets shifting of page image on the medium. (Of course, for left pages the shift by [0 0] has no real effect.)
• -f ...............: process this input file.

Which result did the last command achieve?

This one:

Output file: left-sections.pdf, page 1
+--------+  ^
|        |  |
|   5    |  |
|        |595 pt
|        |  |
|        |  |
|        |  |
+--------+  v

Output file: right-sections.pdf, page 2
+--------+  ^
|        |  |
|   2    |  |
|        |595 pt
|        |  |
|        |  |
|        |  |
+--------+  v
< 280 pt >

## Second Step

Now let's do the analogous thing for the center sections:

gswin32c.exe ^
-o center-sections.pdf ^
-sDEVICE=pdfwrite ^
-g2807x5950 ^
-c "<</PageOffset [280.67 0]>> setpagedevice" ^
-f myflyer.pdf

Result:

Output file: center-sections.pdf, page 1
+--------+  ^
|        |  |
|   6    |  |
|        |595 pt
|        |  |
|        |  |
|        |  |
+--------+  v

Output file: center-sections.pdf, page 2
+--------+  ^
|        |  |
|   3    |  |
|        |595 pt
|        |  |
|        |  |
|        |  |
+--------+  v
< 280 pt >

## Third Step

Last, the right sections:

gswin32c.exe ^
-o right-sections.pdf ^
-sDEVICE=pdfwrite ^
-g2807x5950 ^
-c "<</PageOffset [561.34 0]>> setpagedevice" ^
-f myflyer.pdf

Result:

Output file: right-sections.pdf, page 1
+--------+  ^
|        |  |
|   1    |  |
|        |595 pt
|        |  |
|        |  |
|        |  |
+--------+  v

Output file: right-sections.pdf, page 2
+--------+  ^
|        |  |
|   4    |  |
|        |595 pt
|        |  |
|        |  |
|        |  |
+--------+  v
< 280 pt >

## Last Step

Now we combine the pages into one file:

pdftk.exe ^
A=right-sections.pdf ^
B=center-sections.pdf ^
C=left-sections.pdf ^
cat A1 B2 C2 A2 B1 C1 ^
output single-files-input.pdf
verbose

Done. Here is the desired result. 6 different pages, sized 280.67x595.

Result:

+--------+  +--------+  +--------+  +--------+  +--------+  +--------+   ^
|        |  |        |  |        |  |        |  |        |  |        |   |
|   1    |  |   2    |  |   3    |  |   4    |  |   5    |  |   6    |   |
|        |  |        |  |        |  |        |  |        |  |        | 595 pt
|        |  |        |  |        |  |        |  |        |  |        |   |
|        |  |        |  |        |  |        |  |        |  |        |   |
|        |  |        |  |        |  |        |  |        |  |        |   |
+--------+  +--------+  +--------+  +--------+  +--------+  +--------+   v
< 280 pt >  < 280 pt >  < 280 pt >  < 280 pt >  < 280 pt >  < 280 pt >

-
Holy crap, awesome art right here! –  Ivo Flipse Mar 1 '11 at 22:28
This worked well for me, but I had to set the PageOffset to a negative number in order to get the other sections. –  jonchang Jan 8 '13 at 19:09

You could duplicate the document, then crop the pages so that only the even page numbers show in one file and only the odd page numbers in the other. Then split the files into single pages and recombine to make one document with single sides to a page...

You can do this using a number of methods, for example:

1. Use the Adobe Acrobat crop tool to crop out one side of the double page, and apply the crop to all pages.
2. Split the files to individual pages using the the 'burst' command in pdftoolkit
3. Rename the files sequential using a file renaming tool (e.g. ReNamer)
4. Recombine the pages using the 'cat' command in pdftoolkit
-

@peims, thanks. Here's the step-by-step version of your method. I tried it on a file I wanted to convert for my Kindle DX, and it works perfectly:

• Use the full version of Acrobat v9 to crop the left hand side of the page, and save it as "left.pdf":
• Use the crop tool to mark the left hand side of the page.
• Right click, and select "Set Cropbox".
• Select "Document..Crop Pages", and apply the crop to the whole document.
• Repeat with the right hand pages, save as "right.pdf".
• At this stage, you have two documents: "left.pdf" with the left hand pages, and "right.pdf" with the right hand pages.

Next, use pdftk.exe (from http://www.pdflabs.com/tools/pdftk-the-pdf-toolkit/) to interleave the results into a single file. Copy "pdftk.exe", "left.pdf" and "right.pdf" to to "D:\", and execute:

• D:>pdftk D:\left.pdf burst output %05d_A.pdf
• D:>pdftk D:\right.pdf burst output %05d_B.pdf
• D:>pdftk *_?.pdf cat output combined.pdf

Note: if you copy the files to "C:\", it won't work under Win 7 due to security permissions. If you don't have a D:\, then create a directory "C:\x" to complete the operation.

These results would normally be good enough. However, there's two more optional steps to improve the output.

• (optional final step 1) At this stage, the document is huge (my doc ballooned from 7MB to 80MB), so you can reduce the file size by using either:
• "Advanced..Preflight" with the "Compatible with Acrobat 5" setting.
• (optional final step 2) The pages are all different sizes. Repeat the crop on all pages, so everything is a uniform size.
-

I use the following script to process scanned books on Mac and Linux. This can get quite memory intensive.

#!/bin/bash
#
# This script processes scanned books. After scanning the books have been cropped with
# Preview. This does kind of a "soft crop" that we need to make a bit "harder".
#
# The second step is to take each page of the PDF and split this into two two pages,
# because each page of the scanned document actually contains two pages of the book.
#
#

FILE=mktemp.pdf
FILE2=mktemp.pdf
FILE3=mktemp.pdf

echo "Making a temporary copy of the input file."

cp $1$FILE

#
# Start cropping
#

echo "Cropping the PDF"

# The first regex removes all boxes but CropBox. The second regex renames the  CropBox as MediaBox

perl -pi.bak -e 's/\/(Media|Bleed|Art|Trim)Box[\n\l\f\s]*$(.+?)$//msg;' $FILE perl -pi.bak -e 's/CropBox/MediaBox/g;'$FILE

echo "Validating the PDF"

#Run PDFTK to ensure that the file is OK

cat $FILE | pdftk - output$FILE2

#
# Done cropping, start splitting the pages
#

echo "Splitting the pages in two and changing to 200 dpi with imagemagick. Output goes to $FILE3" convert -density 200$FILE2 -crop 50%x0 +repage $FILE3 # # Done spliting, copy the result in a new file # mv$FILE3 \$1.pages.pdf

-

Just had the same problem. I stumbled upon briss, an open source java GUI tool for separating and cropping pdf pages:

http://sourceforge.net/projects/briss/

It worked very well for me, on Linux, even though the user interface isn't completely trivial. It even worked with a pdf with some differently sized pages!

-