The aim of this project is to use an already developed Monte Carlo program (either the ising Model or a variational Monte Carlo code) to produce, in case of the Ising model, the energy as function of temperature. For the variational Monte Carlo calculation of interacting electrons in an oscilaltor trap, the data are represented by the ground state energies as functions of the variational parameters.
In its simplest form the energy of the Ising model is expressed as, without an externally applied magnetic field, $$ E=-J\sum_{< kl >}^{N}s_ks_l $$ with \( s_k=\pm 1 \). The quantity \( N \) represents the total number of spins and \( J \) is a coupling constant expressing the strength of the interaction between neighboring spins. The symbol \( < kl> \) indicates that we sum over nearest neighbors only. We will assume that we have a ferromagnetic ordering, viz \( J> 0 \). We will use periodic boundary conditions and the Metropolis algorithm only. Alternatively we can use the supplied variational Monte Carlo program which solves Schroedinger's equation for two interacting electrons in a harmonic oscillator trap. Both codes can be found at the webpage of the course under programs.
If we opt for the Ising model code, we need to generate for every span over the lattice and output of the total energy and the magnetic moment (if we wish to study this quantity as well), that is compute and write to file as function of temperature \( \langle E\rangle \) and \( \langle \vert M\vert \rangle \). We will use a fixed lattice siste of \( L\times L = 40 \times 40 \). Make sure the calculations have been equilibrated and compute these expectation values for for \( T\in [1.0,3.0] \) with a step in temperature \( \Delta T=0.1 \) or smaller. This are the data you will use to estimate the standard deviation in the next part of the project. You should keep a file for the different temperatures as you will need to compute the standard deviation for each temperature.
Alternatively you can run the variational Monte Carlo program for two interacting electrons confined to move in a harmonic oscillator trap. Here you will produce the variational expectaction value of the energy as a function of the variational parameters \( \alpha \) and \( \beta \). You could single out only a set of such parameters after you have found the minimum energy.
These two cases form then our training data which we will use for our estimates of the standard deviation.
This part deals with widely used resampling methods to find the standard deviation on our data sets. The methods we can use are the Blocking method, the Bootstrap method and the Jackknife method. The latter two are examples of so-called resampling methods. The functions provided under the program folder encode all three methods. The typical situation is to generate data using a C++ or Fortan code, as in part a) above. These data are then used by a post-analysis program in order to perform a statistical analysis.
In case of the Ising model, the final product will be a table of the expectation values of the energy (or the magnetic moment) as function of temperature and with a proper standard deviation. These data will then enter our analysis in parts c-e). We will call these data the training data.
If you opt for the variational Monte Carlo program, you need to make a table of the ground state energy as function of various variational parameters (you can limit yourself to \( \alpha \) only) with proper standard deviation estimations.
Your task here is thus to use the data from part a) and generate the standard deviation using one (or more) of the methods discussed above. In general, the Bootstrap method is the most widely used one.
These data will then be used in our next step, where we will try to obtain a functional approximation to our data sets.
With the data from part b), with a proper determination of the standard deviation, the task here is to find a fit to the data using regression methods. Here we will focus on the least square method with a straight line fit first and thereafter using singular value decomposition to fit a function that reproduces the data.
Since obtaining these data points may not be trivial, we want to use these data to fit a function which can allow us to make predictions for values of \( y \) which are not in the present set. The perhaps simplest approach is to assume we can parametrize our function in terms of a polynomial of degree \( n-1 \) with \( n \) points, that is $$ y=y(x) \rightarrow y(x_i)=\tilde{y}_i+\epsilon_i=\sum_{j=0}^{n-1} \beta_i x_i^j+\epsilon_i, $$ where \( \epsilon_i \) is the error in our approximation.
In order to find the optimal parameters \( \beta_i \) instead of solving the above linear algebra problem, we define a function which gives a measure of the spread between the values \( y_i \) (which represent hopefully the exact values) and the parametrized values \( \tilde{y}_i \), namely $$ Q(\hat{\beta})=\sum_{i=0}^{n-1}\left(y_i-\tilde{y}_i\right)^2=\left(\hat{y}-\hat{\tilde{y}}\right)^T\left(\hat{y}-\hat{\tilde{y}}\right), $$ or using the matrix \( \hat{X} \) as $$ Q(\hat{\beta})=\left(\hat{y}-\hat{X}\hat{\beta}\right)^T\left(\hat{y}-\hat{X}\hat{\beta}\right). $$
The function $$ Q(\hat{\beta})=\left(\hat{y}-\hat{X}\hat{\beta}\right)^T\left(\hat{y}-\hat{X}\hat{\beta}\right), $$ can be linked to the variance of the quantity \( y_i \) if we interpret the latter as the mean value of for example a numerical experiment. When linking below with the maximum likelihood approach below, we will indeed interpret \( y_i \) as a mean value $$ y_{i}=\langle y_i \rangle = \beta_0x_{i,0}+\beta_1x_{i,1}+\beta_2x_{i,2}+\dots+\beta_{n-1}x_{i,n-1}+\epsilon_i, $$ where \( \langle y_i \rangle \) is the mean value. Keep in mind also that till now we have treated \( y_i \) as the exact value. Normally, the response (dependent or outcome) variable \( y_i \) the outcome of a numerical experiment or another type of experiment and is thus only an approximation to the true value. It is then always accompanied by an error estimate, often limited to a statistical error estimate given by the standard deviation discussed earlier. In the discussion here we will treat \( y_i \) as our exact value for the response variable.
Introducing the standard deviation \( \sigma_i \) for each measurement \( y_i \), we define now the \( \chi^2 \) function as $$ \chi^2(\hat{\beta})=\sum_{i=0}^{n-1}\frac{\left(y_i-\tilde{y}_i\right)^2}{\sigma_i^2}=\left(\hat{y}-\hat{\tilde{y}}\right)^T\frac{1}{\hat{\Sigma^2}}\left(\hat{y}-\hat{\tilde{y}}\right), $$ where the matrix \( \hat{\Sigma} \) is a diagonal matrix with \( \sigma_i \) as matrix elements.
In order to find the parameters \( \beta_i \) we will then minimize the spread of \( \chi^2(\hat{\beta}) \) by requiring $$ \frac{\partial \chi^2(\hat{\beta})}{\partial \beta_j} = \frac{\partial }{\partial \beta_j}\left[ \sum_{i=0}^{n-1}\left(\frac{y_i-\beta_0x_{i,0}-\beta_1x_{i,1}-\beta_2x_{i,2}-\dots-\beta_{n-1}x_{i,n-1}}{\sigma_i}\right)^2\right]=0, $$ which results in $$ \frac{\partial \chi^2(\hat{\beta})}{\partial \beta_j} = -2\left[ \sum_{i=0}^{n-1}\frac{x_{ij}}{\sigma_i}\left(\frac{y_i-\beta_0x_{i,0}-\beta_1x_{i,1}-\beta_2x_{i,2}-\dots-\beta_{n-1}x_{i,n-1}}{\sigma_i}\right)\right]=0, $$ or in a matrix-vector form as $$ \frac{\partial \chi^2(\hat{\beta})}{\partial \hat{\beta}} = 0 = \hat{A}^T\left( \hat{b}-\hat{A}\hat{\beta}\right). $$ where we have defined the matrix \( \hat{A} =\hat{X}/\hat{\Sigma} \) with matrix elements \( a_{ij} = x_{ij}/\sigma_i \) and the vector \( \hat{b} \) with elements \( b_i = y_i/\sigma_i \).
We can rewrite $$ \frac{\partial \chi^2(\hat{\beta})}{\partial \hat{\beta}} = 0 = \hat{A}^T\left( \hat{b}-\hat{A}\hat{\beta}\right), $$ as $$ \hat{A}^T\hat{b} = \hat{A}^T\hat{A}\hat{\beta}, $$ and if the matrix \( \hat{A}^T\hat{A} \) is invertible we have the solution $$ \hat{\beta} =\left(\hat{A}^T\hat{A}\right)^{-1}\hat{A}^T\hat{b}. $$ If we then introduce the matrix $$ \hat{H} = \hat{A}^T\hat{A}, $$ we have then the following expression for the parameters \( \beta_j \) (the matrix elements of \( \hat{H} \) are \( h_{ij} \)) $$ \beta_j = \sum_{k=0}^{p-1}h_{jk}\sum_{i=0}^{n-1}\frac{y_i}{\sigma_i}\frac{x_{ik}}{\sigma_i} = \sum_{k=0}^{p-1}h_{jk}\sum_{i=0}^{n-1}b_ia_{ik} $$ We state without proof the expression for the uncertainty in the parameters \( \beta_j \) as $$ \sigma^2(\beta_j) = \sum_{i=0}^{n-1}\sigma_i^2\left( \frac{\partial \beta_j}{\partial y_i}\right)^2, $$ resulting in $$ \sigma^2(\beta_j) = \left(\sum_{k=0}^{p-1}h_{jk}\sum_{i=0}^{n-1}a_{ik}\right)\left(\sum_{l=0}^{p-1}h_{jl}\sum_{m=0}^{n-1}a_{ml}\right) = h_{jj}! $$
The first step here is to approximate the function \( y \) with a first-order polynomial, that is we write $$ y=y(x) \rightarrow y(x_i) \approx \beta_0+\beta_1 x_i. $$ By computing the derivatives of \( \chi^2 \) with respect to \( \beta_0 \) and \( \beta_1 \) show that these are given by $$ \frac{\partial \chi^2(\hat{\beta})}{\partial \beta_0} = -2\left[ \sum_{i=0}^{1}\left(\frac{y_i-\beta_0-\beta_1x_{i}}{\sigma_i^2}\right)\right]=0, $$ and $$ \frac{\partial \chi^2(\hat{\beta})}{\partial \beta_0} = -2\left[ \sum_{i=0}^{1}x_i\left(\frac{y_i-\beta_0-\beta_1x_{i}}{\sigma_i^2}\right)\right]=0. $$ Define then $$ \gamma = \sum_{i=0}^{1}\frac{1}{\sigma_i^2}, $$ $$ \gamma_x = \sum_{i=0}^{1}\frac{x_{i}}{\sigma_i^2}, $$ $$ \gamma_y = \sum_{i=0}^{1}\left(\frac{y_i}{\sigma_i^2}\right), $$ $$ \gamma_{xx} = \sum_{i=0}^{1}\frac{x_ix_{i}}{\sigma_i^2}, $$ $$ \gamma_{xy} = \sum_{i=0}^{1}\frac{y_ix_{i}}{\sigma_i^2}, $$ and show that $$ \beta_0 = \frac{\gamma_{xx}\gamma_y-\gamma_x\gamma_y}{\gamma\gamma_{xx}-\gamma_x^2}, $$ $$ \beta_1 = \frac{\gamma_{xy}\gamma-\gamma_x\gamma_y}{\gamma\gamma_{xx}-\gamma_x^2}. $$ Find these parameters for your data set and compare the fitted function \( y=y(x) \rightarrow y(x_i) \approx \beta_0+\beta_1 x_i \) and find the error in the parameters \( \beta_0 \) and \( \beta_1 \) as well. How does your parametrization compare with the input data \( y_i \)? Give a critical discussion of the method.
Develop now a program where you extract the parameters \( \beta_i \) and their corresponding errors using the general Least Square method (LSM), with polynomials of degree \( p=2 \) and \( p=3 \). Which polyomials gives the best fit to your data? Here you should prepare an input file with the number of data points in your training data set, the corresponding standard deviation \( \sigma_i \) as well as the degree \( p \) of the polynomial you want to fit.
The LSM suffers often from both being underdetermined and overdetermined in the unknown coefficients \( \beta_i \). A better approach is to use the Singular Value Decomposition (SVD) method discussed in the lecture notes on regression analysis. Here you should use the SVD programs and use the same input file you used for the general LSM. In the main functions you should specifiy which function you wish to approximate the date with. The example programs discussed above show you examples on how to use these codes as well as how to write the functional form of the polynomial or fitting function.
If you wish to read more about the Ising model and statistical physics here are three suggestions.
Here follows a brief recipe and recommendation on how to write a report for each project.


class Solution: def minOperations(self, nums1: List[int], nums2: List[int]) -> int: s1 = sum(nums1) s2 = sum(nums2) if s1 > s2: return self.minOperations(nums2, nums1) n1 = len(nums1) n2 = len(nums2) refund1 = [6 - x for x in nums1] refund2 = [x - 1 for x in nums2] r = refund1 + refund2 r.sort(reverse = True) index = 0 count = 0 diff = s2 - s1 while diff > 0 and index < len(r): diff -= r[index] index += 1 count += 1 if diff > 0: return -1 return count


The 5-Course R-Track is like completing years of university education but actually getting skills that organizations need right now (no fluff) and you can apply your new skills immediately as you make progress in the courses.
Each course gets you one step closer to becoming a data scientist.
R-Track | Step 1
Learn data science foundations with 10+ tidyverse
packages in one integrated course.
R-Track | Step 2
Learn H2O
, recipes
, lime
, while you follow a real-world consulting-style business project.
R-Track | Step 3
Learn state-of-the-art forecasting using modeltime
, timetk
, and GluonTS
machine learning.
R-Track | Step 4
Learn Shiny
for building machine learning-powered web applications that are production-ready.
R-Track | Step 5
Build and Deploy Scalable Data Science Applications using R
, Shiny
, and AWS
Cloud Technology.


Lotterywd is an independent service website offering online sale of lotto tickets and is not connected nor supervised by National Lottery, any other provider of the products available on this website.
change language:
Lotterywd is an independent service website offering online sale of lotto tickets and is not connected nor supervised by National Lottery, any other provider of the products available on this website.


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|
Cola Vending Machine 5
GUI expanded version of CVM-4
|
#include "WriteToScreenBuffer.h"
Go to the source code of this file.
Functions | |
| void | WTSB_StaticInMachine (sim_t *pSim) |
| These functions write to the money display sections. More... | |
| void | WTSB_StaticChange (sim_t *pSim) |
| void | WTSB_StaticWallet (sim_t *pSim) |
| void | WTSB_StaticAccount (sim_t *pSim) |
| void | WTSB_Redraw (sim_t *pSim) |
| void WTSB_Redraw | ( | sim_t * | pSim | ) |
Redraws every buffer
Definition at line 39 of file WriteToScreenBuffer.c.
| void WTSB_StaticAccount | ( | sim_t * | pSim | ) |
Display amount of money you have to Account buffer
Definition at line 30 of file WriteToScreenBuffer.c.
| void WTSB_StaticChange | ( | sim_t * | pSim | ) |
Display amount of money you have to Change buffer
Definition at line 12 of file WriteToScreenBuffer.c.
| void WTSB_StaticInMachine | ( | sim_t * | pSim | ) |
These functions write to the money display sections.
Display amount of money you have to InMachine buffer
Definition at line 3 of file WriteToScreenBuffer.c.
| void WTSB_StaticWallet | ( | sim_t * | pSim | ) |
Display amount of money you have to Wallet buffer
Definition at line 21 of file WriteToScreenBuffer.c.


To get the Space ID, go to web.ciscospark.com, select the space you want to message and get the ID from the URL.
Example: https://web.ciscospark.com/#/rooms/f2fbc1f0-317c-11e6-8308-f3bee39e57c5
The Space ID would be: f2fbc1f0-317c-11e6-8308-f3bee39e57c5
You can also use the room ID representation acquired from GET https://api.ciscospark.com/v1/rooms
See https://developer.ciscospark.com/endpoint-rooms-get.html for more information


| 394 | (A) | witħ ilke a wape of the wynde that his wynges touchede |
| 400 | (C) | At yche a blaste of þe wynde . þat þe wynges towched |
| 401 | (D) | with a eche a weef of þe wynde þat þe wynge touchid |
| (E) | ---- no text ---- | |
| (Ex) | ---- no text ---- | |
| 408 | (L) | with eche wynde of a wap þat to þe wynges sprongyn |
| (P) | ---- no text ---- | |
| 394 | (U) | At ilk a waif of the wynd / that to the wenge touches |
| (V) | ---- no text ---- |


-- Alexis de Tocqueville --
It's been four days since the initial call for a referendum on Lee Ward's Exon Inspiration and my vCity mailbox overfloweth with an assortment of diatribes, flames, incoherencies, and ramblings in a dozen different languages. This is to be expected. With the vPopuli now approaching sixty million registered citizens, the average daily intake is something like six thousand messages. On hot issues, it doubles to twelve thousand. And that is just to First Citizen. I have no idea how many messages the same people are posting to each other.


acl acl2013 acl2013-243 acl2013-243-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Aobo Wang ; Min-Yen Kan
Abstract: We address the problem of informal word recognition in Chinese microblogs. A key problem is the lack of word delimiters in Chinese. We exploit this reliance as an opportunity: recognizing the relation between informal word recognition and Chinese word segmentation, we propose to model the two tasks jointly. Our joint inference method significantly outperforms baseline systems that conduct the tasks individually or sequentially.


Hexagon iMatch is a powerful reconciliation system that transforms the way reconciliation processes are automated today. The product combines unparalleled preprocessing automation, the ability to handle large volumes of data and intelligent rule building to match & reconcile records.
This industry agnostic solution is flexible enough to cater to multiple types of reconciliations like Bank Reconciliation, collections and disbursements, across verticals. Being highly configurable, it provides greater control, automation and process standardization which enables organizations to focus their resources on value accretive activities.
iMatch is a tool that can evolve with changing business operations through its in-built, user-defined rule engine that uses data driven learning. The system can ensure that reconciliation is over 90% automated and is designed to function in a ‘non-intrusive’ mode that automatically detects the arrival of new data. With the elimination of a manually extensive process, iMatch ensures that business records, financial & accounting records and bank transactions are in sync.
Key Benefits of iMatch
Auto reconciliation reduces manual labor and costs by enabling audit programs,
Reduces revenue leakages and measures service provider deliverables.
A Streamlined process increases operational efficiency by over 33%.
Machine learning provides for continuous improvement of automation percentage.
High speed of processing enables reconciliation of 18 bank accounts in 20 minutes.
Modular by Design
automatically detects the arrival of new data & facilitates data extraction, collation and transformation. Data cleansing is done by applying validations
Hexagon mPower Front Office supports decision making, trade origination and trade flow confirmation through a set of blotters. It also provides for personalization of these blotters. Pre-trade compliance is weaved throughout the trade flow at appropriate trade points. A scenario analyzer to verifies the impact of trades on compliance and exposure. Portfolio metrics is a crucial part of the Product.
Hexagon mPower Front Office supports decision making, trade origination and trade flow confirmation through a set of blotters. It also provides for personalization of these blotters. Pre-trade compliance is weaved throughout the trade flow at appropriate trade points. A scenario analyzer to verifies the impact of trades on compliance and exposure. Portfolio metrics is a crucial part of the Product.
Hexagon mPower Front Office supports decision making, trade origination and trade flow confirmation through a set of blotters. It also provides for personalization of these blotters. Pre-trade compliance is weaved throughout the trade flow at appropriate trade points. A scenario analyzer to verifies the impact of trades on compliance and exposure. Portfolio metrics is a crucial part of the Product.
Hexagon mPower Front Office supports decision making, trade origination and trade flow confirmation through a set of blotters. It also provides for personalization of these blotters. Pre-trade compliance is weaved throughout the trade flow at appropriate trade points. A scenario analyzer to verifies the impact of trades on compliance and exposure. Portfolio metrics is a crucial part of the Product.
Hexagon mPower Front Office supports decision making, trade origination and trade flow confirmation through a set of blotters. It also provides for personalization of these blotters. Pre-trade compliance is weaved throughout the trade flow at appropriate trade points. A scenario analyzer to verifies the impact of trades on compliance and exposure. Portfolio metrics is a crucial part of the Product.
Hexagon mPower Front Office supports decision making, trade origination and trade flow confirmation through a set of blotters. It also provides for personalization of these blotters. Pre-trade compliance is weaved throughout the trade flow at appropriate trade points. A scenario analyzer to verifies the impact of trades on compliance and exposure. Portfolio metrics is a crucial part of the Product.
Enquire about Hexagon mPower


---
layout: single
header:
image: page_outlining_header.jpg
author_profile: true
---
Hello,
I'm Hang GAO.
I am currently a M.S. student in Computer Science at Columbia, targeted in pursuing a Ph.D. degree in the field of Machine Learning. I am most interested in deep generative models and its transfer learning application, whether it be in their development, inference, or more generally their foundations for computational and statistical analysis.
- To briefly define myself, I'd like to go for:
If you want to get in touch with me, email could be the best way.


1 write to ExperimentName
System.Management.Automation (1)
engine\Attributes.cs (1)
590
ExperimentName
= experimentName;
1 reference to ExperimentName
System.Management.Automation (1)
engine\Attributes.cs (1)
625
_effectiveAction = ExperimentalFeature.GetActionToTake(
ExperimentName
, ExperimentAction);


Briana Wong
Project Portfolio
Briana Wong is a super-senior at UCSD in Computer Science.
Projects
SD Hacks
Quest Mind and Body Hackathon
iClicker


level
title
short description
description
coin Num
green percentage
%
yellow percentage
%
red percentage
%
sum of percentage must be 100


Beef Goulash
Although it takes some time to make - about 1hr 30 minutes, it is actually quite
easy to make, and it is a really hearty and delicious recipe, if I do say so
myself.
Ingredients
The ingredients here are for about 2-3 portions, depending on your appetite:
- 500g beef
- 300-400g potatoes
- 1 carrot
- 1 medium onion
- 12 tablespoons tomato paste
- 500ml water
- 3-4 garlic cloves
- 3-4 bay leaves
- Curcuma
- Paprika
- Oregano
- Parsley
- Caraway
- Basil (optional)
- Cilantro (optional)
- 2-3 champignon mushrooms (optional)
Directions
- Heat up the frying pan and add some oil.
- Cut the potatoes into small pieces and fry them on the frying pan over
medium-heat, add some salt.
- Chop the onion.
- Cut the beef into small enough pieces and dust with flour.
- After about 15 minutes of frying the potatoes, add the onions and beef to the
pan and fry for another 10-15 minutes, or until brown.
- While the beef, potatoes and onions are frying, cut the carrot and mushrooms,
and chop the parsley and cilantro.
- Once the beef is brown, add water to the pan, and add the rest of the
ingredients, including salt, black pepper and herbs/spices to taste.
- Mix thoroughly, cover the pan with a lid and stew for about 60-80 minutes
over low-heat. While it is stewing, you can get back to doing some other
things, like watching memes.
- Remove from stove, serve hot and enjoy with some beer or cider (or your
favorite beverage).
Originally published at https://www.yaroslavps.com/food/beef-goulash/
Contribution
Recipe tags:
hungarian
beef
stew


The following settings apply across all filter types.
This name is the name used to identify a filter among other filters in your FilterZen Filter Web Part. It is also the filter name sent to connected Web Parts.
Different kinds of Web Parts may vary in their requirements for filter names. When connected to a List View Web Part (or a Data View Web Part whose data source is a SharePoint list or document library), those out-of-the-box, filter-receiving SharePoint Web Parts require the FilterZen Filter Web Part to send filter names that correspond exactly to internal Column names (which never change once a Column has been created) rather than their current display names (Column titles, which can be changed by end-users at any time).
FilterZen cannot change this requirement (to send filter names corresponding exactly to internal Colum names) — it was built right into SharePoint. You therefore need to specify filter names corresponding to the internal Column names.
To make this as easy as possible for you, we built a useful tool right into the Web Part's configuration tool pane that empowers you to instantly figure out the proper filter name to use for the Column you want to filter, that is, its internal Column name. Once you expand this tool by clicking the Filtering List Data link, you can immediately select the list you are filtering to see all its Columns. Now select the Column and you will see its internal name. Just copy and paste it into the Column / filter name text box, or click the Use as filter name link:
If you are filtering a Data View Web Part sourcing its data from a list in another web site or site collection, just enter the web site's URL in the tool's text box and click OK to be able to locate the list in the drop-down box.
The FilterZen Filter Web Part only ever sends those of your filters that are marked enabled. Tick or untick the This filter is enabled check box to enable or disable a filter.
All filter types that optionally support an interactive mode (i.e. all filter types except Page Column Filter and Page Request / URL Filter) also have a setting to set a default filter value, even though they vary in their semantics and user interface depending on the filter type. The default filter value is the filter value initially sent, and in interactive mode, also the one initially presented to end users, which they may then change.
For each filter you create, you need to decide which approach to dealing with empty filter values is optimal given the particular filtering context:
Every filter type provides the Send filter value even if empty check box to control this setting.
All filter types, including the non-interactive ones, let you specify an optional fallback filter value that will only ever be sent if the filter value is empty.
All filters except Page Column Filters and Page Request / URL Filters offer an optional interactive mode that you can turn on or off via the Allow users to enter/pick a filter value check box. In interactive mode, the FilterZen Filter Web Part will render into the Web Part Page a visible, interactive user interface control for changing the filter value of each interactive filter. This user interface control will be a single-line text box for Text Filters, a date picker for Date Filters, and a drop-down list for Choice Filters, List Lookup Filters, User Filters, SQL Data Filters and BDC Filters. You can optionally specify a label text in the filter label text box, which will then be prepended to the filtering user interface control by the FilterZen Filter Web Part, to let your users know what this control is actually for. There are also additional Web Part settings for further customizing the appearance and behavior of all interactive filtering user controls.
Tick the Pre-filtering — only offer pickable choices that also exist in a List column check box in a Choice Filter, List Lookup Filter, SQL Data Filter, BDC Filter or User Filter editor to be able to enter a List View URL and an internal Column name both used to eliminate from the drop-down list of available choices all those that do not exist as exact values in the specified List Column.
In the drop-down list labelled Always suppress certain filter values, select an item other than Don't suppress any filter values to show a multi-line text box allowing you to enter specific filter values. Your drop-down choice then controls whether the filter values entered are the only ones allowed to be sent (Suppress all filter values except...), or the only ones that should never be sent (Suppress no filter values except...).


<bit-note bitType="info" bitTitle="Informational Note">
Content of the note.
</bit-note>
<bit-note bitType="warning" bitTitle="Warning Note">
Content of the note.
</bit-note>
<bit-note bitType="error" bitTitle="Error Note">
Content of the note.
</bit-note>
<bit-note bitType="success" bitTitle="Success Note">
Content of the note.
</bit-note>


from __future__ import print_function, division
from sympy.core import S, sympify, diff, oo
from sympy.core.function import Function, ArgumentIndexError
from sympy.core.relational import Eq
from sympy.core.logic import fuzzy_not
from sympy.polys.polyerrors import PolynomialError
from sympy.functions.elementary.complexes import im, sign, Abs
from sympy.functions.elementary.piecewise import Piecewise
from sympy.core.decorators import deprecated
from sympy.utilities import filldedent
###############################################################################
################################ DELTA FUNCTION ###############################
###############################################################################
[docs]class DiracDelta(Function):
"""
The DiracDelta function and its derivatives.
DiracDelta is not an ordinary function. It can be rigorously defined either
as a distribution or as a measure.
DiracDelta only makes sense in definite integrals, and in particular, integrals
of the form ``Integral(f(x)*DiracDelta(x - x0), (x, a, b))``, where it equals
``f(x0)`` if ``a <= x0 <= b`` and ``0`` otherwise. Formally, DiracDelta acts
in some ways like a function that is ``0`` everywhere except at ``0``,
but in many ways it also does not. It can often be useful to treat DiracDelta
in formal ways, building up and manipulating expressions with delta functions
(which may eventually be integrated), but care must be taken to not treat it
as a real function.
SymPy's ``oo`` is similar. It only truly makes sense formally in certain contexts
(such as integration limits), but SymPy allows its use everywhere, and it tries to be
consistent with operations on it (like ``1/oo``), but it is easy to get into trouble
and get wrong results if ``oo`` is treated too much like a number.
Similarly, if DiracDelta is treated too much like a function, it is easy to get wrong
or nonsensical results.
DiracDelta function has the following properties:
1) ``diff(Heaviside(x),x) = DiracDelta(x)``
2) ``integrate(DiracDelta(x-a)*f(x),(x,-oo,oo)) = f(a)`` and
``integrate(DiracDelta(x-a)*f(x),(x,a-e,a+e)) = f(a)``
3) ``DiracDelta(x) = 0`` for all ``x != 0``
4) ``DiracDelta(g(x)) = Sum_i(DiracDelta(x-x_i)/abs(g'(x_i)))``
Where ``x_i``-s are the roots of ``g``
Derivatives of ``k``-th order of DiracDelta have the following property:
5) ``DiracDelta(x,k) = 0``, for all ``x != 0``
Examples
========
>>> from sympy import DiracDelta, diff, pi, Piecewise
>>> from sympy.abc import x, y
>>> DiracDelta(x)
DiracDelta(x)
>>> DiracDelta(1)
0
>>> DiracDelta(-1)
0
>>> DiracDelta(pi)
0
>>> DiracDelta(x - 4).subs(x, 4)
DiracDelta(0)
>>> diff(DiracDelta(x))
DiracDelta(x, 1)
>>> diff(DiracDelta(x - 1),x,2)
DiracDelta(x - 1, 2)
>>> diff(DiracDelta(x**2 - 1),x,2)
2*(2*x**2*DiracDelta(x**2 - 1, 2) + DiracDelta(x**2 - 1, 1))
>>> DiracDelta(3*x).is_simple(x)
True
>>> DiracDelta(x**2).is_simple(x)
False
>>> DiracDelta((x**2 - 1)*y).expand(diracdelta=True, wrt=x)
DiracDelta(x - 1)/(2*Abs(y)) + DiracDelta(x + 1)/(2*Abs(y))
See Also
========
Heaviside
simplify, is_simple
sympy.functions.special.tensor_functions.KroneckerDelta
References
==========
.. [1] http://mathworld.wolfram.com/DeltaFunction.html
"""
is_real = True
[docs] def fdiff(self, argindex=1):
"""
Returns the first derivative of a DiracDelta Function.
The difference between ``diff()`` and ``fdiff()`` is:-
``diff()`` is the user-level function and ``fdiff()`` is an object method.
``fdiff()`` is just a convenience method available in the ``Function`` class.
It returns the derivative of the function without considering the chain rule.
``diff(function, x)`` calls ``Function._eval_derivative`` which in turn calls
``fdiff()`` internally to compute the derivative of the function.
Examples
========
>>> from sympy import DiracDelta, diff
>>> from sympy.abc import x
>>> DiracDelta(x).fdiff()
DiracDelta(x, 1)
>>> DiracDelta(x, 1).fdiff()
DiracDelta(x, 2)
>>> DiracDelta(x**2 - 1).fdiff()
DiracDelta(x**2 - 1, 1)
>>> diff(DiracDelta(x, 1)).fdiff()
DiracDelta(x, 3)
"""
if argindex == 1:
#I didn't know if there is a better way to handle default arguments
k = 0
if len(self.args) > 1:
k = self.args[1]
return self.func(self.args[0], k + 1)
else:
raise ArgumentIndexError(self, argindex)
[docs] @classmethod
def eval(cls, arg, k=0):
"""
Returns a simplified form or a value of DiracDelta depending on the
argument passed by the DiracDelta object.
The ``eval()`` method is automatically called when the ``DiracDelta`` class
is about to be instantiated and it returns either some simplified instance
or the unevaluated instance depending on the argument passed. In other words,
``eval()`` method is not needed to be called explicitly, it is being called
and evaluated once the object is called.
Examples
========
>>> from sympy import DiracDelta, S, Subs
>>> from sympy.abc import x
>>> DiracDelta(x)
DiracDelta(x)
>>> DiracDelta(x,1)
DiracDelta(x, 1)
>>> DiracDelta(1)
0
>>> DiracDelta(5,1)
0
>>> DiracDelta(0)
DiracDelta(0)
>>> DiracDelta(-1)
0
>>> DiracDelta(S.NaN)
nan
>>> DiracDelta(x).eval(1)
0
>>> DiracDelta(x - 100).subs(x, 5)
0
>>> DiracDelta(x - 100).subs(x, 100)
DiracDelta(0)
"""
k = sympify(k)
if not k.is_Integer or k.is_negative:
raise ValueError("Error: the second argument of DiracDelta must be \
a non-negative integer, %s given instead." % (k,))
arg = sympify(arg)
if arg is S.NaN:
return S.NaN
if arg.is_nonzero:
return S.Zero
if fuzzy_not(im(arg).is_zero):
raise ValueError("Function defined only for Real Values. Complex part: %s found in %s ." % (repr(im(arg)), repr(arg)) )
@deprecated(useinstead="expand(diracdelta=True, wrt=x)", issue=12859, deprecated_since_version="1.1")
def simplify(self, x):
return self.expand(diracdelta=True, wrt=x)
def _eval_expand_diracdelta(self, **hints):
"""Compute a simplified representation of the function using
property number 4. Pass wrt as a hint to expand the expression
with respect to a particular variable.
wrt is:
- a variable with respect to which a DiracDelta expression will
get expanded.
Examples
========
>>> from sympy import DiracDelta
>>> from sympy.abc import x, y
>>> DiracDelta(x*y).expand(diracdelta=True, wrt=x)
DiracDelta(x)/Abs(y)
>>> DiracDelta(x*y).expand(diracdelta=True, wrt=y)
DiracDelta(y)/Abs(x)
>>> DiracDelta(x**2 + x - 2).expand(diracdelta=True, wrt=x)
DiracDelta(x - 1)/3 + DiracDelta(x + 2)/3
See Also
========
is_simple, Diracdelta
"""
from sympy.polys.polyroots import roots
wrt = hints.get('wrt', None)
if wrt is None:
free = self.free_symbols
if len(free) == 1:
wrt = free.pop()
else:
raise TypeError(filldedent('''
When there is more than 1 free symbol or variable in the expression,
the 'wrt' keyword is required as a hint to expand when using the
DiracDelta hint.'''))
if not self.args[0].has(wrt) or (len(self.args) > 1 and self.args[1] != 0 ):
return self
try:
argroots = roots(self.args[0], wrt)
result = 0
valid = True
darg = abs(diff(self.args[0], wrt))
for r, m in argroots.items():
if r.is_real is not False and m == 1:
result += self.func(wrt - r)/darg.subs(wrt, r)
else:
# don't handle non-real and if m != 1 then
# a polynomial will have a zero in the derivative (darg)
# at r
valid = False
break
if valid:
return result
except PolynomialError:
pass
return self
[docs] def is_simple(self, x):
"""is_simple(self, x)
Tells whether the argument(args[0]) of DiracDelta is a linear
expression in x.
x can be:
- a symbol
Examples
========
>>> from sympy import DiracDelta, cos
>>> from sympy.abc import x, y
>>> DiracDelta(x*y).is_simple(x)
True
>>> DiracDelta(x*y).is_simple(y)
True
>>> DiracDelta(x**2 + x - 2).is_simple(x)
False
>>> DiracDelta(cos(x)).is_simple(x)
False
See Also
========
simplify, Diracdelta
"""
p = self.args[0].as_poly(x)
if p:
return p.degree() == 1
return False
def _eval_rewrite_as_Piecewise(self, *args):
"""Represents DiracDelta in a Piecewise form
Examples
========
>>> from sympy import DiracDelta, Piecewise, Symbol, SingularityFunction
>>> x = Symbol('x')
>>> DiracDelta(x).rewrite(Piecewise)
Piecewise((DiracDelta(0), Eq(x, 0)), (0, True))
>>> DiracDelta(x - 5).rewrite(Piecewise)
Piecewise((DiracDelta(0), Eq(x - 5, 0)), (0, True))
>>> DiracDelta(x**2 - 5).rewrite(Piecewise)
Piecewise((DiracDelta(0), Eq(x**2 - 5, 0)), (0, True))
>>> DiracDelta(x - 5, 4).rewrite(Piecewise)
DiracDelta(x - 5, 4)
"""
if len(args) == 1:
return Piecewise((DiracDelta(0), Eq(args[0], 0)), (0, True))
def _eval_rewrite_as_SingularityFunction(self, *args):
"""
Returns the DiracDelta expression written in the form of Singularity Functions.
"""
from sympy.solvers import solve
from sympy.functions import SingularityFunction
if self == DiracDelta(0):
return SingularityFunction(0, 0, -1)
if self == DiracDelta(0, 1):
return SingularityFunction(0, 0, -2)
free = self.free_symbols
if len(free) == 1:
x = (free.pop())
if len(args) == 1:
return SingularityFunction(x, solve(args[0], x)[0], -1)
return SingularityFunction(x, solve(args[0], x)[0], -args[1] - 1)
else:
# I dont know how to handle the case for DiracDelta expressions
# having arguments with more than one variable.
raise TypeError(filldedent('''
rewrite(SingularityFunction) doesn't support arguments with more that 1 variable.'''))
@staticmethod
def _latex_no_arg(printer):
return r'\delta'
def _sage_(self):
import sage.all as sage
return sage.dirac_delta(self.args[0]._sage_())
###############################################################################
############################## HEAVISIDE FUNCTION #############################
###############################################################################
[docs]class Heaviside(Function):
"""Heaviside Piecewise function
Heaviside function has the following properties [1]_:
1) ``diff(Heaviside(x),x) = DiracDelta(x)``
``( 0, if x < 0``
2) ``Heaviside(x) = < ( undefined if x==0 [1]``
``( 1, if x > 0``
3) ``Max(0,x).diff(x) = Heaviside(x)``
.. [1] Regarding to the value at 0, Mathematica defines ``H(0) = 1``,
but Maple uses ``H(0) = undefined``. Different application areas
may have specific conventions. For example, in control theory, it
is common practice to assume ``H(0) == 0`` to match the Laplace
transform of a DiracDelta distribution.
To specify the value of Heaviside at x=0, a second argument can be given.
Omit this 2nd argument or pass ``None`` to recover the default behavior.
>>> from sympy import Heaviside, S
>>> from sympy.abc import x
>>> Heaviside(9)
1
>>> Heaviside(-9)
0
>>> Heaviside(0)
Heaviside(0)
>>> Heaviside(0, S.Half)
1/2
>>> (Heaviside(x) + 1).replace(Heaviside(x), Heaviside(x, 1))
Heaviside(x, 1) + 1
See Also
========
DiracDelta
References
==========
.. [2] http://mathworld.wolfram.com/HeavisideStepFunction.html
.. [3] http://dlmf.nist.gov/1.16#iv
"""
is_real = True
[docs] def fdiff(self, argindex=1):
"""
Returns the first derivative of a Heaviside Function.
Examples
========
>>> from sympy import Heaviside, diff
>>> from sympy.abc import x
>>> Heaviside(x).fdiff()
DiracDelta(x)
>>> Heaviside(x**2 - 1).fdiff()
DiracDelta(x**2 - 1)
>>> diff(Heaviside(x)).fdiff()
DiracDelta(x, 1)
"""
if argindex == 1:
# property number 1
return DiracDelta(self.args[0])
else:
raise ArgumentIndexError(self, argindex)
def __new__(cls, arg, H0=None, **options):
if H0 is None:
return super(cls, cls).__new__(cls, arg, **options)
else:
return super(cls, cls).__new__(cls, arg, H0, **options)
[docs] @classmethod
def eval(cls, arg, H0=None):
"""
Returns a simplified form or a value of Heaviside depending on the
argument passed by the Heaviside object.
The ``eval()`` method is automatically called when the ``Heaviside`` class
is about to be instantiated and it returns either some simplified instance
or the unevaluated instance depending on the argument passed. In other words,
``eval()`` method is not needed to be called explicitly, it is being called
and evaluated once the object is called.
Examples
========
>>> from sympy import Heaviside, S
>>> from sympy.abc import x
>>> Heaviside(x)
Heaviside(x)
>>> Heaviside(19)
1
>>> Heaviside(0)
Heaviside(0)
>>> Heaviside(0, 1)
1
>>> Heaviside(-5)
0
>>> Heaviside(S.NaN)
nan
>>> Heaviside(x).eval(100)
1
>>> Heaviside(x - 100).subs(x, 5)
0
>>> Heaviside(x - 100).subs(x, 105)
1
"""
H0 = sympify(H0)
arg = sympify(arg)
if arg.is_negative:
return S.Zero
elif arg.is_positive:
return S.One
elif arg.is_zero:
return H0
elif arg is S.NaN:
return S.NaN
elif fuzzy_not(im(arg).is_zero):
raise ValueError("Function defined only for Real Values. Complex part: %s found in %s ." % (repr(im(arg)), repr(arg)) )
def _eval_rewrite_as_Piecewise(self, arg, H0=None):
"""Represents Heaviside in a Piecewise form
Examples
========
>>> from sympy import Heaviside, Piecewise, Symbol, pprint
>>> x = Symbol('x')
>>> Heaviside(x).rewrite(Piecewise)
Piecewise((0, x < 0), (Heaviside(0), Eq(x, 0)), (1, x > 0))
>>> Heaviside(x - 5).rewrite(Piecewise)
Piecewise((0, x - 5 < 0), (Heaviside(0), Eq(x - 5, 0)), (1, x - 5 > 0))
>>> Heaviside(x**2 - 1).rewrite(Piecewise)
Piecewise((0, x**2 - 1 < 0), (Heaviside(0), Eq(x**2 - 1, 0)), (1, x**2 - 1 > 0))
"""
if H0 is None:
return Piecewise((0, arg < 0), (Heaviside(0), Eq(arg, 0)), (1, arg > 0))
if H0 == 0:
return Piecewise((0, arg <= 0), (1, arg > 0))
if H0 == 1:
return Piecewise((0, arg < 0), (1, arg >= 0))
return Piecewise((0, arg < 0), (H0, Eq(arg, 0)), (1, arg > 0))
def _eval_rewrite_as_sign(self, arg, H0=None):
"""Represents the Heaviside function in the form of sign function.
The value of the second argument of Heaviside must specify Heaviside(0)
= 1/2 for rewritting as sign to be strictly equivalent. For easier
usage, we also allow this rewriting when Heaviside(0) is undefined.
Examples
========
>>> from sympy import Heaviside, Symbol, sign
>>> x = Symbol('x', real=True)
>>> Heaviside(x).rewrite(sign)
sign(x)/2 + 1/2
>>> Heaviside(x, 0).rewrite(sign)
Heaviside(x, 0)
>>> Heaviside(x - 2).rewrite(sign)
sign(x - 2)/2 + 1/2
>>> Heaviside(x**2 - 2*x + 1).rewrite(sign)
sign(x**2 - 2*x + 1)/2 + 1/2
>>> y = Symbol('y')
>>> Heaviside(y).rewrite(sign)
Heaviside(y)
>>> Heaviside(y**2 - 2*y + 1).rewrite(sign)
Heaviside(y**2 - 2*y + 1)
See Also
========
sign
"""
if arg.is_real:
if H0 is None or H0 == S.Half:
return (sign(arg)+1)/2
def _eval_rewrite_as_SingularityFunction(self, args):
"""
Returns the Heaviside expression written in the form of Singularity Functions.
"""
from sympy.solvers import solve
from sympy.functions import SingularityFunction
if self == Heaviside(0):
return SingularityFunction(0, 0, 0)
free = self.free_symbols
if len(free) == 1:
x = (free.pop())
return SingularityFunction(x, solve(args, x)[0], 0)
# TODO
# ((x - 5)**3*Heaviside(x - 5)).rewrite(SingularityFunction) should output
# SingularityFunction(x, 5, 0) instead of (x - 5)**3*SingularityFunction(x, 5, 0)
else:
# I dont know how to handle the case for Heaviside expressions
# having arguments with more than one variable.
raise TypeError(filldedent('''
rewrite(SingularityFunction) doesn't support arguments with more that 1 variable.'''))
def _sage_(self):
import sage.all as sage
return sage.heaviside(self.args[0]._sage_())


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I (we) request that the above named students be permitted to participate
in the Chinese Language School of Southern California (CLSSC) activities.
He/She is in good physical condition. In case of illness or accident, the
CLSSC has my (our) authorization to secure necessary medical attention. I
(we) will not hold CLSSC or its officers or teachers liable for medical aid
rendered and will reimburse CLSSC for medical or other expenses incurred in
his/her care. I (we) am (are) hereby waiving all claims against CLSSC and
Los Angeles Unified School District, which provides campus for school
activities for injury, accident, illness or death occurring during school hours.
Translation Here
Express Assumption of Risk and Responsibility
Translation Here
As a member of CLSSC, I (we) understand and concur that purchasing liability
insurance coverage other than for enrolled students would result in undue
financial burden on CLSSC, and it is not necessary. However, I (we) also
recognize the fact that there is possibility of accident, injury or medical
emergency occurring to uninsured members, family, relatives or friends. I
(we) also realize that personal property may be lost or damaged, that certain
foreseeable and unforeseeable events could contribute to the unpredictability
of the risks and danger of any activity that I (we) may choose to participate.
In recognition of the aforementioned potential risks, I (we) assume the risks
of personal injury, accidents and property loss or damage of the uninsured
members, family, relatives, friends, and hereby release CLSSC and Los Angeles
Unified School District from any financial or legal responsibility regardless
of the cause.
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address in a school-wide roster. This roster is for school purpose only and
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Testing environment: I have two hight spec laptop connecting to one wireless router, they all have internet access. Install process: step 1 install RHEL6 for management server; step 2 install CloudStack software on management server; step 3 install XenServer; step 4 CloudStack initialization and configure. Install RHEL6 64bit OS notes:
-
User FQDN as hostname, for example cloudstack.xen.cn; use this command to confirm: hostname –fqdn
-
Use 0~3.xenserver.pool.ntp.org as ntp server
-
Install KDE desktop, with MySQL/Clent
-
Configure static ip address, for sure both DNS and GW are all right, for internet download.
-
Shutdown iptables firewall, by this way you could ignore port settings of iptables, this is not secure, but you can speed up a little bit
Setting up local DVD as yum source, in order to install dependance packages of Cladestack, mount DVD to /media folder, you have to install following rpm packages before create a new yum source. [shell] mount /dev/cdrom /media yum install deltarpm-3.5-0.5.20090913git.el6.x86_64.rpm yum install python-deltarpm-3.5-0.5.20090913git.el6.x86_64.rpm yum install createrepo-0.9.8-4.el6.noarch.rpm [/shell]
Crete a new yum source file, see below: [shell] [root@cloudstack 1] cat /etc/yum.repos.d/rhel6.repo [rhel6] name=rhel6 baseurl=file:///media/ enabled=1 gpgcheck=0 [/shell]
Checking if new source file is loaded correctly: [shell] yum list [/shell]
Download CloudStack install package for RHEL 6, unzip it and execute install command [shell] tar xzvf CloudStack-oss-3.0.2-1-rhel6.2.tar.gz ./install Select m Select y [/shell]
You will see install script need yum to install all dependence rpm packages; you should get a different list as below:
[shell] tomcat6-lib noarch 6.0.24-33.el6 rhel6 2.9 M tomcat6-servlet-2.5-api noarch 6.0.24-33.el6 rhel6 93 k ws-commons-util noarch 1.0.1-13.el6 rhel6 37 k wsdl4j noarch 1.5.2-7.8.el6 rhel6 157 k xml-commons-apis x86_64 1.3.04-3.6.el6 rhel6 439 k xml-commons-resolver x86_64 1.1-4.18.el6 rhel6 145 k
Transaction Summary
Install 38 Package(s)
Total download size: 91 M Installed size: 172 M Is this ok [y/N]: y Downloading Packages: ———————————————————————————————————— Total 115 MB/s | 91 MB 00:00 Running rpm_check_debug Running Transaction Test Transaction Test Succeeded Running Transaction Installing : libgcj-4.4.6-3.el6.x86_64 1/38 Installing : jakarta-commons-logging-1.0.4-10.el6.noarch 2/38 Installing : cloud-deps-3.0.2-1.el6.x86_64 3/38 Installing : cloud-utils-3.0.2-1.el6.x86_64 4/38
Installed: cloud-client.x86_64 0:3.0.2-1.el6
Dependency Installed: axis.noarch 0:1.2.1-7.2.el6 bcel.x86_64 0:5.2-7.2.el6 classpathx-jaf.x86_64 0:1.0-15.4.el6 classpathx-mail.noarch 0:1.1.1-9.4.el6 cloud-agent-scripts.x86_64 0:3.0.2-1.el6 cloud-client-ui.x86_64 0:3.0.2-1.el6 cloud-core.x86_64 0:3.0.2-1.el6 cloud-deps.x86_64 0:3.0.2-1.el6 cloud-python.x86_64 0:3.0.2-1.el6 cloud-server.x86_64 0:3.0.2-1.el6 cloud-setup.x86_64 0:3.0.2-1.el6 cloud-utils.x86_64 0:3.0.2-1.el6 ecj.x86_64 1:3.4.2-6.el6 ipmitool.x86_64 0:1.8.11-12.el6 jakarta-commons-collections.noarch 0:3.2.1-3.4.el6 jakarta-commons-daemon.x86_64 1:1.0.1-8.9.el6 jakarta-commons-dbcp.noarch 0:1.2.1-13.8.el6 jakarta-commons-discovery.noarch 1:0.4-5.4.el6 jakarta-commons-httpclient.x86_64 1:3.1-0.6.el6 jakarta-commons-logging.noarch 0:1.0.4-10.el6 jakarta-commons-pool.x86_64 0:1.3-12.7.el6 java-1.5.0-gcj.x86_64 0:<IP_ADDRESS>-29.1.el6 java_cup.x86_64 1:0.10k-5.el6 libgcj.x86_64 0:4.4.6-3.el6 log4j.x86_64 0:1.2.14-6.4.el6 mx4j.noarch 1:3.0.1-9.13.el6 regexp.x86_64 0:1.5-4.4.el6 sinjdoc.x86_64 0:0.5-9.1.el6 tomcat6.noarch 0:6.0.24-33.el6 tomcat6-el-2.1-api.noarch 0:6.0.24-33.el6 tomcat6-jsp-2.1-api.noarch 0:6.0.24-33.el6 tomcat6-lib.noarch 0:6.0.24-33.el6 tomcat6-servlet-2.5-api.noarch 0:6.0.24-33.el6 ws-commons-util.noarch 0:1.0.1-13.el6 wsdl4j.noarch 0:1.5.2-7.8.el6 xml-commons-apis.x86_64 0:1.3.04-3.6.el6 xml-commons-resolver.x86_64 0:1.1-4.18.el6
Complete! Done [/shell] At this point, management server has been installed successfully on my 8GB laptop.
Install MySQL server from CloudStack install script: [shell] ./install > select d Installing the MySQL server… Loaded plugins: product-id, refresh-packagekit, security, subscription-manager Updating certificate-based repositories. cloud-temp | 2.6 kB 00:00 … rhel6 | 4.0 kB 00:00 … Setting up Install Process Package mysql-server-5.1.52-1.el6_0.1.x86_64 already installed and latest version Nothing to do Starting the MySQL server… Initializing MySQL database： Installing MySQL system tables… OK Filling help tables… OK
To start mysqld at boot time you have to copy support-files/mysql.server to the right place for your system
PLEASE REMEMBER TO SET A PASSWORD FOR THE MySQL root USER ! To do so, start the server, then issue the following commands:
/usr/bin/mysqladmin -u root password ‘new-password’ /usr/bin/mysqladmin -u root -h cloudstack.xen.cn password ‘new-password’
Alternatively you can run: /usr/bin/mysql_secure_installation
which will also give you the option of removing the test databases and anonymous user created by default. This is strongly recommended for production servers.
See the manual for more instructions.
You can start the MySQL daemon with: cd /usr ; /usr/bin/mysqld_safe &
You can test the MySQL daemon with mysql-test-run.pl cd /usr/mysql-test ; perl mysql-test-run.pl
Please report any problems with the /usr/bin/mysqlbug script!
[OK] Starting mysqld： [OK] Done [/shell]
We had installed MySQL server successfully; some parameters must be changed from default value. I show my configure file as a simple bellow:
[shell] cat /etc/my.cfn [mysqld] datadir=/var/lib/mysql #Added for CloudStack innodb_rollback_on_timeout=1 innodb_lock_wait_timeout=600 max_connections=350 log-bin=mysql-bin binlog-format = ‘ROW’ #Above are for CloudStack socket=/var/lib/mysql/mysql.sock user=mysql # Disabling symbolic-links is recommended to prevent assorted security risks symbolic-links=0
[mysqld_safe] log-error=/var/log/mysqld.log pid-file=/var/run/mysqld/mysqld.pid [/shell]
Restart MySQL server.
[shell] [root@cloudstack CloudStack-oss-3.0.2-1-rhel6.2] service mysqld restart Stop mysqld： [OK] Starting mysqld： [OK] [/shell]
Set MySQL root password: [shell] [root@cloudstack home] mysql -u root Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 2 Server version: 5.1.52-log Source distribution
Copyright (c) 2000, 2010, Oracle and/or its affiliates. All rights reserved. This software comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to modify and redistribute it under the GPL v2 license
Type ‘help;’ or ‘\h’ for help. Type ‘\c’ to clear the current input statement.
mysql> set password = password(‘citrix’); Query OK, 0 rows affected (0.00 sec)
mysql> exit Bye [/shell]
Initialisation Cloudtack management server database. [shell] [root@cloudstack home] cloud-setup-databases cloud:citrix@localhost –deploy-as=root:citrix Mysql user name:cloud [ OK ] Mysql user password:citrix [ OK ] Mysql server ip:localhost [ OK ] Mysql server port:3306 [ OK ] Mysql root user name:root [ OK ] Mysql root user password:citrix [ OK ] Checking Cloud database files … [ OK ] Checking local machine hostname … [ OK ] Checking SELinux setup … [ OK ] Detected local IP address as <IP_ADDRESS>, will use as cluster management server node IP[ OK ] Preparing /etc/cloud/management/db.properties [ OK ] Applying /usr/share/cloud/setup/create-database.sql [ OK ] Applying /usr/share/cloud/setup/create-schema.sql [ OK ] Applying /usr/share/cloud/setup/create-database-premium.sql [ OK ] Applying /usr/share/cloud/setup/create-schema-premium.sql [ OK ] Applying /usr/share/cloud/setup/server-setup.sql [ OK ] Applying /usr/share/cloud/setup/templates.sql [ OK ] Applying /usr/share/cloud/setup/create-index-fk.sql [ OK ] Processing encryption … [ OK ] Finalizing setup … [ OK ]
CloudStack has successfully initialized database, you can check your database configuration in /etc/cloud/management/db.properties [/shell]
Database initialization successful, then we need to run CloudStack setup command. [shell] [root@cloudstack home] cloud-setup-management Starting to configure CloudStack Management Server: Configure sudoers … [OK] Configure Firewall … [OK] Configure CloudStack Management Server …[OK] CloudStack Management Server setup is Done! [/shell]
After we have installed CloudStack management server. We are going to create storage. I use my management server for setting up NFS share. It’s convenient and easy enough. But according CloudStack document, you’d better attached a 16 TB NFS storage on management server. If you have pls use it. For me 250 GB HD is good for my testing purpose. You may have one more Linux machine for NFS server, then you can follow my steps.
[shell]
[root@cloudstack ~] service rpcbind start
[root@cloudstack ~] service nfs start
Start NFS server： [OK]
Stop NFS quota： [OK]
Start NFS service： [OK]
Start NFS mountd： [OK]
[root@cloudstack ~] chkconfig nfs on
[root@cloudstack ~] chkconfig rpcbind on
[root@cloudstack home] cd /home
[root@cloudstack home] mkdir primary
[root@cloudstack home] mkdir secondary
[root@cloudstack home] vi /etc/exports #set NFS path
[root@cloudstack home] cat /etc/exports
/home *(rw,async,no_root_squash)
[root@cloudstack home] exportfs -a
[root@cloudstack home] exportfs
/home
Edit /etc/sysconfig/nfs file, for sure it has follow parameter. [shell] MOUNTD_NFS_V3=”yes” LOCKD_TCPPORT=32803 LOCKD_UDPPORT=32769 MOUNTD_PORT=892 RQUOTAD_PORT=875 STATD_PORT=662 STATD_OUTGOING_PORT=2020 [/shell]
Restart NFS server, you’d better test it from a XenServer host. To for sure XenServer can read and write NFS share: [shell] [root@xs root]mkdir test [root@xs root]mount <IP_ADDRESS>:/home /root/test [root@xs root]ls test [root@xs root]touch testfile #this file must be showed by ls on both management server and XenServer host. [/shell]
Test your intenet connection from management server, We need to download system vm template and install it on management server. [shell] /usr/lib64/cloud/agent/scripts/storage/secondary/cloud-install-sys-tmplt -m /home/secondary -u http://download.cloud.com/templates/acton/acton-systemvm-02062012.vhd.bz2 -h xenserver -F 正在解析主机 download.cloud.com… <IP_ADDRESS> 正在连接 download.cloud.com|<IP_ADDRESS>|:80… 已连接。 已发出 HTTP 请求，正在等待回应… 200 OK 长度：140616708 (134M) [binary/octet-stream]
99% [===================================================================================================> ] 140,512,160 149K/s eta(英国中部时 99% [===================================================================================================> ] 140,551,600 145K/s eta(英国中部时 100%[====================================================================================================>] 140,616,708 155K/s in 11m 27s 2012-06-29 20:03:57 (200 KB/s) - 已保存 “/usr/lib64/cloud/agent/scripts/storage/secondary/a5fc3577-c5e7-49ff-8fbf-ed88dc8aaff5.vhd” [140616708/140616708])
Uncompressing to /usr/lib64/cloud/agent/scripts/storage/secondary/a5fc3577-c5e7-49ff-8fbf-ed88dc8aaff5.vhd.tmp (type bz2)…could take a long time Moving to /home/secondary/template/tmpl/1/1///a5fc3577-c5e7-49ff-8fbf-ed88dc8aaff5.vhd…could take a while Successfully installed system VM template to /home/secondary/template/tmpl/1/1/ [/shell] Go into secondary storage folder to verify template file, check the file size.
I use a 32GB HP latop for XenServer, it’s in same network subnet with management server, and it also can access internet.
XenServer installation note:
-
Download XenServer 6.0.201 iso image, it can work with CloudStack 3.0.201 well
-
Internet access is convenient for download CloudStack support package.
-
Using same 0~3.xenserver.pool.ntp.org NTP server
Access XenServer host from my management server. There are some post-install steps need to be done for CloudStack. [shell] [root@cloudstack .ssh]# ssh <IP_ADDRESS> #Connect to XenServer host ip address The authenticity of host ‘<IP_ADDRESS> (<IP_ADDRESS>)’ can’t be established. RSA key fingerprint is ae:5b:8a:dc:2a:c8:6c:56:f6:7a:48:02:e6:db:78:dd. Are you sure you want to continue connecting (yes/no)? yes Warning: Permanently added ‘<IP_ADDRESS>’ (RSA) to the list of known hosts<EMAIL_ADDRESS>password: Type “xsconsole” for access to the management console. [root@hpxs1 ~] pwd /root [root@hpxs1 ~] mkdir tmp [root@hpxs1 ~] cd tmp [root@hpxs1 tmp] wget http://download.cloud.com/releases/3.0.1/XS-6.0.2/xenserver-cloud-supp.tgz –2012-06-29 21:55:08– http://download.cloud.com/releases/3.0.1/XS-6.0.2/xenserver-cloud-supp.tgz Resolving download.cloud.com… <IP_ADDRESS> Connecting to download.cloud.com|<IP_ADDRESS>|:80… connected. HTTP request sent, awaiting response… 200 OK Length: 3342001 (3.2M) [application/x-tar] Saving to: `xenserver-cloud-supp.tgz’
100%[================================================================>] 3,342,001 217K/s in 15s
2012-06-29 21:55:49 (214 KB/s) - `xenserver-cloud-supp.tgz’ saved [3342001/3342001] [root@hpxs1 tmp] tar xf xenserver-cloud-supp.tgz [root@hpxs1 tmp] ls example-answerfile post-install.sh src xenserver-cloud-supp.tgz packages.cloud-supp README xenserver-cloud-supp.iso [root@hpxs1 tmp] xe-install-supplemental-pack xenserver-cloud-supp.iso Installing ‘XenServer Cloud Supp Pack’…
Preparing… ########################################### [100%] 1:iptables ########################################### [ 10%] 2:iptables-ipv6 ########################################### [ 20%] 3:arptables ########################################### [ 30%] 4:iptables-debuginfo ########################################### [ 40%] 5:ipset-modules-kdump-2.6########################################### [ 50%] 6:ipset-modules-xen-2.6.3########################################### [ 60%] 7:csp-pack ########################################### [ 70%] 8:ebtables ########################################### [ 80%] 9:ipset ########################################### [ 90%] 10:iptables-devel ########################################### [100%] Update sysctl to enable tables checking Memory required by all installed packages: 894697472 Truncating to static-max: 789839872 Setting VM.memory_target: 789839872 Pack installation successful. [root@hpxs1 tmp] [root@hpxs1 tmp] xe-switch-network-backend bridge Cleaning up old ifcfg files Remove… ifcfg-xenbr0 Disabling openvswitch daemon Configure system for bridge networking You *MUST now reboot your system [root@hpxs1 tmp] reboot [/shell]
Test if NFS storage is up and running for my XenServer host. [shell] [root@hpxs1 ~] mkdir nfs [root@hpxs1 ~] mount <IP_ADDRESS>:/home/primary /root/nfs [root@hpxs1 ~] cd nfs [root@hpxs1 nfs] touch testfile [root@hpxs1 nfs] ls testfile #Check this file inside NFS folder. [/shell]
Now, it’s time to initialize CloudStack on management server. It has a very nice wizard for helping you lunching your zone. [nggallery id=14]
I had followed above to installed CloudStack twice within less 3 hours. If you get other tips/issue, pls drop me a comment, I will try my best to response you.


{% extends 'base/navbar-page.html' %} {% load static %} {% block page_title %}Saratoga History{% endblock %} {% block content %} {# Book #} You can make an online reservation in Cubaism.com for more info or make a reservation: book now {# History #} history {% for i in history_text %} {{ i }} {% endfor %} {{ last_paragraph }} {% endblock %}
You can make an online reservation in Cubaism.com for more info or make a reservation:
book now
history
{{ i }}
{{ last_paragraph }}


| Food | ||
|---|---|---|
| Freki und Geri | Last Month's Catch | The Raider's Reward |
| Two delicious sausages made from a mix of goat, beef mutton, pork
and whatever offal our thralls found too terrible to eat. Served with
plundered potatoes and a medley of seasonal veggies $12.99 |
Greenland shark, just like mamma used to bury! Our prime fillets are
cut from sharks meat aged and fermented to perfection each batch is tested
by our scullury thralls to ensure that it is safe. $25.99 |
Our house specialty! A porterhouse steak cooked to order, served with
pillaged potatoes, pickled herring, and a generous helping of mead-glazed carrots $29.99 |
| MEAT! | Seafarer's plate | Salad* |
| It is meat! What more do you want? Just a heap of meat cooked over an open fire, rotated by our
scullury thralls. If it is not cooked to perfection, they don't eat for the day! $22.99 |
Fresh fish of whatever variety gets hauled in by our nets. We brave trecherous waters to
provide the finest finned foods! Choose fried, grilled, or pickled. $18.99 |
I didn't realize we offered this. I guess it is picked fresh from the weeds behind the thralls'
huts. It is probably safe. Management does not guarantee the safety of the salad $6.99 to prevent us from serving it. |


This release is all about bootstrapping a Spincast application. This process has been greatly improved and the creation of the Guice context associated with an application is now easier. A new component, the Bootstrapper, is at the core of this process.
Here's a fully working "Hello World!" Spincast application :
public class App { public static void main(String[] args) { Spincast.init(args); } @Inject protected void init(DefaultRouter router, Server server) { router.GET("/").handle(context -> context.response().sendHtml("
Hello World!
")); server.start(); } }
Speaking of "Hello world!" applications, you can now find three of them in the "Demos / Tutorials" section! :
Finally, we introduced a new section in the documentation dedicated to plugins : what they really are, how to install them, etc.


Junyan Mao
Home
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Color Game
Key Beats
Algorithm Visualizer
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Search
Press A-Z and turn up the speaker!
Inspired by:
Patatap
Sound credit:
Neuronal Synchrony


K-Means is a clustering algorithm that groups n observations into k groups. It works well for well-clustered data, and is a great choice if you know the number of groups beforehand. Be aware that k-means solves for local optima (instead of global), so different iterations may yield different clusters. Also, because the algorithm clusters based on mean points with euclidian distance, it roughlyexpects clusters to be roughly spherical and of even sizes (see Cirlces and Moons below for examples where these expecations aren't met).


Time Module
The Python time
module provides many ways of representing time in code, such as objects, numbers, and strings. It also provides functionality other than representing time, like waiting during code execution and measuring the efficiency of your code.
This article will walk you through the most commonly used functions and objects in time
.
By the end of this article, you’ll be able to:
- Understand core concepts at the heart of working with dates and times, such as epochs, time zones, and daylight savings time
- Represent time in code using floats, tuples, and
struct_time
- Convert between different time representations
- Suspend thread execution
- Measure code performance using
perf_counter()
You’ll start by learning how you can use a floating point number to represent time.
Free Bonus: Click here to get our free Python Cheat Sheet that shows you the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.
Dealing With Python Time Using Seconds
One of the ways you can manage the concept of Python time in your application is by using a floating point number that represents the number of seconds that have passed since the beginning of an era—that is, since a certain starting point.
Let’s dive deeper into what that means, why it’s useful, and how you can use it to implement logic, based on Python time, in your application.
The Epoch
You learned in the previous section that you can manage Python time with a floating point number representing elapsed time since the beginning of an era.
Merriam-Webster defines an era as:
- A fixed point in time from which a series of years is reckoned
- A system of chronological notation computed from a given date as basis
The important concept to grasp here is that, when dealing with Python time, you’re considering a period of time identified by a starting point. In computing, you call this starting point the epoch.
The epoch, then, is the starting point against which you can measure the passage of time.
For example, if you define the epoch to be midnight on January 1, 1970 UTC—the epoch as defined on Windows and most UNIX systems—then you can represent midnight on January 2, 1970 UTC as 86400
seconds since the epoch.
This is because there are 60 seconds in a minute, 60 minutes in an hour, and 24 hours in a day. January 2, 1970 UTC is only one day after the epoch, so you can apply basic math to arrive at that result:
60 * 60 * 24
# 86400
It is also important to note that you can still represent time before the epoch. The number of seconds would just be negative.
For example, you would represent midnight on December 31, 1969 UTC (using an epoch of January 1, 1970) as -86400
seconds.
While January 1, 1970 UTC is a common epoch, it is not the only epoch used in computing. In fact, different operating systems, filesystems, and APIs sometimes use different epochs.
As you saw before, UNIX systems define the epoch as January 1, 1970. The Win32 API, on the other hand, defines the epoch as January 1, 1601.
You can use time.gmtime()
to determine your system’s epoch:
import time
time.gmtime(0)
## output
time.struct_time(tm_year=1970, tm_mon=1, tm_mday=1, tm_hour=0,
tm_min=0, tm_sec=0, tm_wday=3, tm_yday=1, tm_isdst=0)
You’ll learn about gmtime()
and struct_time
throughout the course of this article. For now, just know that you can use time
to discover the epoch using this function.
Now that you understand more about how to measure time in seconds using an epoch, let’s take a look at Python’s time
module to see what functions it offers that help you do so.
Python Time in Seconds as a Floating Point Number
First, time.time()
returns the number of seconds that have passed since the epoch. The return value is a floating point number to account for fractional seconds:
from time import time
time()
#<PHONE_NUMBER>.9323719`
The number you get on your machine may be very different because the reference point considered to be the epoch may be very different.
Further Reading: Python 3.7 introduced time_ns()
, which returns an integer value representing the same elapsed time since the epoch, but in nanoseconds rather than seconds.
Measuring time in seconds is useful for a number of reasons:
- You can use a float to calculate the difference between two points in time.
- A float is easily serializable, meaning that it can be stored for data transfer and come out intact on the other side.
Sometimes, however, you may want to see the current time represented as a string. To do so, you can pass the number of seconds you get from time()
into time.ctime()
.
Python Time in Seconds as a String Representing Local Time
As you saw before, you may want to convert the Python time, represented as the number of elapsed seconds since the epoch, to a string. You can do so using ctime()
:
from time import time, ctime
t = time()
ctime(t)
# 'Mon Feb 25 19:11:59 2019'
Here, you’ve recorded the current time in seconds into the variable t
, then passed t
as an argument to ctime()
, which returns a string representation of that same time.
Technical Detail: The argument, representing seconds since the epoch, is optional according to the ctime()
definition. If you don’t pass an argument, then ctime()
uses the return value of time()
by default. So, you could simplify the example above:
from time import ctime
ctime()
## 'Mon Feb 25 19:11:59 2019'
The string representation of time, also known as a timestamp, returned by ctime()
is formatted with the following structure:
- Day of the week:
Mon
(Monday
) - Month of the year:
Feb
(February
) - Day of the month:
25
- Hours, minutes, and seconds using the 24-hour clock notation:
19:11:59
- Year:
2019
The previous example displays the timestamp of a particular moment captured from a computer in the South Central region of the United States. But, let’s say you live in Sydney, Australia, and you executed the same command at the same instant.
Instead of the above output, you’d see the following:
from time import time, ctime
t = time()
ctime(t)
## 'Tue Feb 26 12:11:59 2019'
Notice that the day of week
, day of month
, and hour
portions of the timestamp are different than the first example.
These outputs are different because the timestamp returned by ctime()
depends on your geographical location.
Note: While the concept of time zones is relative to your physical location, you can modify this in your computer’s settings without actually relocating.
The representation of time dependent on your physical location is called local time and makes use of a concept called time zones.
Note: Since local time is related to your locale, timestamps often account for locale-specific details such as the order of the elements in the string and translations of the day and month abbreviations. ctime()
ignores these details.
Let’s dig a little deeper into the notion of time zones so that you can better understand Python time representations.
Understanding Time Zones
A time zone is a region of the world that conforms to a standardized time. Time zones are defined by their offset from Coordinated Universal Time (UTC) and, potentially, the inclusion of daylight savings time (which we’ll cover in more detail later in this article).
Fun Fact: If you’re a native English speaker, you might be wondering why the abbreviation for “Coordinated Universal Time” is UTC rather than the more obvious CUT. However, if you’re a native French speaker, you would call it “Temps Universel Coordonné,” which suggests a different abbreviation: TUC.
Ultimately, the International Telecommunication Union and the International Astronomical Union compromised on UTC as the official abbreviation so that, regardless of language, the abbreviation would be the same.
UTC and Time Zones
UTC is the time standard against which all the world’s timekeeping is synchronized (or coordinated). It is not, itself, a time zone but rather a transcendent standard that defines what time zones are.
UTC time is precisely measured using astronomical time, referring to the Earth’s rotation, and atomic clocks.
Time zones are then defined by their offset from UTC. For example, in North and South America, the Central Time Zone (CT) is behind UTC by five or six hours and, therefore, uses the notation UTC-5:00 or UTC-6:00.
Sydney, Australia, on the other hand, belongs to the Australian Eastern Time Zone (AET), which is ten or eleven hours ahead of UTC (UTC+10:00 or UTC+11:00).
This difference (UTC-6:00 to UTC+10:00) is the reason for the variance you observed in the two outputs from ctime()
in the previous examples:
- Central Time (CT):
'Mon Feb 25 19:11:59 2019'
- Australian Eastern Time (AET):
'Tue Feb 26 12:11:59 2019'
These times are exactly sixteen hours apart, which is consistent with the time zone offsets mentioned above.
You may be wondering why CT can be either five or six hours behind UTC or why AET can be ten or eleven hours ahead. The reason for this is that some areas around the world, including parts of these time zones, observe daylight savings time.
Daylight Savings Time
Summer months generally experience more daylight hours than winter months. Because of this, some areas observe daylight savings time (DST) during the spring and summer to make better use of those daylight hours.
For places that observe DST, their clocks will jump ahead one hour at the beginning of spring (effectively losing an hour). Then, in the fall, the clocks will be reset to standard time.
The letters S and D represent standard time and daylight savings time in time zone notation:
- Central Standard Time (CST)
- Australian Eastern Daylight Time (AEDT)
When you represent times as timestamps in local time, it is always important to consider whether DST is applicable or not.
ctime()
accounts for daylight savings time. So, the output difference listed previously would be more accurate as the following:
- Central Standard Time (CST):
'Mon Feb 25 19:11:59 2019'
- Australian Eastern Daylight Time (AEDT):
'Tue Feb 26 12:11:59 2019'
Dealing With Python Time Using Data Structures
Now that you have a firm grasp on many fundamental concepts of time including epochs, time zones, and UTC, let’s take a look at more ways to represent time using the Python time
module.
Python Time as a Tuple
Instead of using a number to represent Python time, you can use another primitive data structure: a tuple.
The tuple allows you to manage time a little more easily by abstracting some of the data and making it more readable.
When you represent time as a tuple, each element in your tuple corresponds to a specific element of time:
- Year
- Month as an integer, ranging between 1 (January) and 12 (December)
- Day of the month
- Hour as an integer, ranging between 0 (12 A.M.) and 23 (11 P.M.)
- Minute
- Second
- Day of the week as an integer, ranging between 0 (Monday) and 6 (Sunday)
- Day of the year
- Daylight savings time as an integer with the following values:
1
is daylight savings time.0
is standard time.-1
is unknown.
Using the methods you’ve already learned, you can represent the same Python time in two different ways:
from time import time, ctime
t = time()
t
#<PHONE_NUMBER>.360564
ctime(t)
# 'Tue Feb 26 07:06:55 2019'
time_tuple = (2019, 2, 26, 7, 6, 55, 1, 57, 0)
In this case, both t
and time_tuple
represent the same time, but the tuple provides a more readable interface for working with time components.
Technical Detail: Actually, if you look at the Python time represented by time_tuple
in seconds (which you’ll see how to do later in this article), you’ll see that it resolves to<PHONE_NUMBER>.0
rather than<PHONE_NUMBER>.360564
.
This is because the tuple doesn’t have a way to represent fractional seconds.
While the tuple provides a more manageable interface for working with Python time, there is an even better object: struct_time
.
Python Time as an Object
The problem with the tuple construct is that it still looks like a bunch of numbers, even though it’s better organized than a single, seconds-based number.
struct_time
provides a solution to this by utilizing NamedTuple
, from Python’s collections
module, to associate the tuple’s sequence of numbers with useful identifiers:
from time import struct_time
time_tuple = (2019, 2, 26, 7, 6, 55, 1, 57, 0)
time_obj = struct_time(time_tuple)
time_obj
## output
time.struct_time(tm_year=2019, tm_mon=2, tm_mday=26, tm_hour=7, tm_min=6, tm_sec=55, tm_wday=1, tm_yday=57, tm_isdst=0)`
Technical Detail: If you’re coming from another language, the terms struct
and object
might be in opposition to one another.
In Python, there is no data type called struct
. Instead, everything is an object.
However, the name struct_time
is derived from the C-based time library where the data type is actually a struct
.
In fact, Python’s time
module, which is implemented in C, uses this struct
directly by including the header file times.h
.
Now, you can access specific elements of time_obj
using the attribute’s name rather than an index:
day_of_year = time_obj.tm_yday
day_of_year
# 57
day_of_month = time_obj.tm_mday
day_of_month
# 26
Beyond the readability and usability of struct_time
, it is also important to know because it is the return type of many of the functions in the Python time
module.
Converting Python Time in Seconds to an Object
Now that you’ve seen the three primary ways of working with Python time, you’ll learn how to convert between the different time data types.
Converting between time data types is dependent on whether the time is in UTC or local time.
Coordinated Universal Time (UTC)
The epoch uses UTC for its definition rather than a time zone. Therefore, the seconds elapsed since the epoch is not variable depending on your geographical location.
However, the same cannot be said of struct_time
. The object representation of Python time may or may not take your time zone into account.
There are two ways to convert a float representing seconds to a struct_time
:
- UTC
- Local time
To convert a Python time float to a UTC-based struct_time
, the Python time
module provides a function called gmtime()
.
You’ve seen gmtime()
used once before in this article:
import time
time.gmtime(0)
## output
time.struct_time(tm_year=1970, tm_mon=1, tm_mday=1,
tm_hour=0, tm_min=0, tm_sec=0, tm_wday=3, tm_yday=1,
tm_isdst=0)
You used this call to discover your system’s epoch. Now, you have a better foundation for understanding what’s actually happening here.
gmtime()
converts the number of elapsed seconds since the epoch to a struct_time
in UTC. In this case, you’ve passed 0
as the number of seconds, meaning you’re trying to find the epoch, itself, in UTC.
Note: Notice the attribute tm_isdst
is set to 0
. This attribute represents whether the time zone is using daylight savings time. UTC never subscribes to DST, so that flag will always be 0
when using gmtime()
.
As you saw before, struct_time
cannot represent fractional seconds, so gmtime()
ignores the fractional seconds in the argument:
import time
time.gmtime(1.99)
## output
time.struct_time(tm_year=1970, tm_mon=1, tm_mday=1,
tm_hour=0, tm_min=0, tm_sec=1, tm_wday=3, tm_yday=1,
tm_isdst=0)
Notice that even though the number of seconds you passed was very close to 2
, the .99
fractional seconds were simply ignored, as shown by tm_sec=1
.
The secs
parameter for gmtime()
is optional, meaning you can call gmtime()
with no arguments. Doing so will provide the current time in UTC:
import time
time.gmtime()
time.struct_time(tm_year=2019, tm_mon=2, tm_mday=28, tm_hour=12, tm_min=57, tm_sec=24, tm_wday=3, tm_yday=59, tm_isdst=0)`
Interestingly, there is no inverse for this function within time
. Instead, you’ll have to look in Python’s calendar
module for a function named timegm()
:
import calendar
import time
time.gmtime()
time.struct_time(tm_year=2019, tm_mon=2, tm_mday=28, tm_hour=13, tm_min=23, tm_sec=12, tm_wday=3, tm_yday=59, tm_isdst=0)
calendar.timegm(time.gmtime())
#<PHONE_NUMBER>
timegm()
takes a tuple (or struct_time
, since it is a subclass of tuple) and returns the corresponding number of seconds since the epoch.
Historical Context: If you’re interested in why timegm()
is not in time
, you can view the discussion in Python Issue 6280.
In short, it was originally added to calendar
because time
closely follows C’s time library (defined in time.h
), which contains no matching function. The above-mentioned issue proposed the idea of moving or copying timegm()
into time
.
However, with advances to the datetime
library, inconsistencies in the patched implementation of time.timegm()
, and a question of how to then handle calendar.timegm()
, the maintainers declined the patch, encouraging the use of datetime
instead.
Working with UTC is valuable in programming because it’s a standard. You don’t have to worry about DST, time zone, or locale information.
That said, there are plenty of cases when you’d want to use local time. Next, you’ll see how to convert from seconds to local time so that you can do just that.
Local Time
In your application, you may need to work with local time rather than UTC. Python’s time
module provides a function for getting local time from the number of seconds elapsed since the epoch called localtime()
.
The signature of localtime()
is similar to gmtime()
in that it takes an optional secs
argument, which it uses to build a struct_time
using your local time zone:
import time
time.time()
#<PHONE_NUMBER>.86196
time.localtime(1551448206.86196)
# time.struct_time(tm_year=2019, tm_mon=3, tm_mday=1, tm_hour=7, tm_min=50, tm_sec=6, tm_wday=4, tm_yday=60, tm_isdst=0)`
Notice that tm_isdst=0
. Since DST matters with local time, tm_isdst
will change between 0
and 1
depending on whether or not DST is applicable for the given time. Since tm_isdst=0
, DST is not applicable for March 1, 2019.
In the United States in 2019, daylight savings time begins on March 10. So, to test if the DST flag will change correctly, you need to add 9 days’ worth of seconds to the secs
argument.
To compute this, you take the number of seconds in a day (86,400) and multiply that by 9 days:
new_secs =<PHONE_NUMBER>.86196 + (86400 * 9)
time.localtime(new_secs)
# time.struct_time(tm_year=2019, tm_mon=3, tm_mday=10, tm_hour=8, tm_min=50, tm_sec=6, tm_wday=6, tm_yday=69, tm_isdst=1)`
Now, you’ll see that the struct_time
shows the date to be March 10, 2019 with tm_isdst=1
. Also, notice that tm_hour
has also jumped ahead, to 8
instead of 7
in the previous example, because of daylight savings time.
Since Python 3.3, struct_time
has also included two attributes that are useful in determining the time zone of the struct_time
:
tm_zone
tm_gmtoff
At first, these attributes were platform dependent, but they have been available on all platforms since Python 3.6.
First, tm_zone
stores the local time zone:
import time
current_local = time.localtime()
current_local.tm_zone
# 'CST'
Here, you can see that localtime()
returns a struct_time
with the time zone set to CST
(Central Standard Time).
As you saw before, you can also tell the time zone based on two pieces of information, the UTC offset and DST (if applicable):
import time
current_local = time.localtime()
current_local.tm_gmtoff
# -21600
current_local.tm_isdst
# 0
In this case, you can see that current_local
is 21600
seconds behind GMT, which stands for Greenwich Mean Time. GMT is the time zone with no UTC offset: UTC±00:00.
21600
seconds divided by seconds per hour (3,600) means that current_local
time is GMT-06:00
(or UTC-06:00
).
You can use the GMT offset plus the DST status to deduce that current_local
is UTC-06:00
at standard time, which corresponds to the Central standard time zone.
Like gmtime()
, you can ignore the secs
argument when calling localtime()
, and it will return the current local time in a struct_time
:
import time
time.localtime()
time.struct_time(tm_year=2019, tm_mon=3, tm_mday=1, tm_hour=8, tm_min=34, tm_sec=28, tm_wday=4, tm_yday=60, tm_isdst=0)`
Unlike gmtime()
, the inverse function of localtime()
does exist in the Python time
module. Let’s take a look at how that works.
Converting a Local Time Object to Seconds
You’ve already seen how to convert a UTC time object to seconds using calendar.timegm()
. To convert local time to seconds, you’ll use mktime()
.
mktime()
requires you to pass a parameter called t
that takes the form of either a normal 9-tuple or a struct_time
object representing local time:
import time
time_tuple = (2019, 3, 10, 8, 50, 6, 6, 69, 1)
time.mktime(time_tuple)
#<PHONE_NUMBER>.0
time_struct = time.struct_time(time_tuple)
time.mktime(time_struct)
#<PHONE_NUMBER>.0`
It’s important to keep in mind that t
must be a tuple representing local time, not UTC:
from time import gmtime, mktime
# 1
current_utc = time.gmtime()
current_utc
time.struct_time(tm_year=2019, tm_mon=3, tm_mday=1, tm_hour=14, tm_min=51, tm_sec=19, tm_wday=4, tm_yday=60, tm_isdst=0)
# 2
current_utc_secs = mktime(current_utc)
current_utc_secs
#<PHONE_NUMBER>.0
# 3
time.gmtime(current_utc_secs)
time.struct_time(tm_year=2019, tm_mon=3, tm_mday=1, tm_hour=20, tm_min=51, tm_sec=19, tm_wday=4, tm_yday=60, tm_isdst=0)`
Note: For this example, assume that the local time is March 1, 2019 08:51:19 CST
.
This example shows why it’s important to use mktime()
with local time, rather than UTC:
-
gmtime()
with no argument returns astruct_time
using UTC.current_utc
showsMarch 1, 2019 14:51:19 UTC
. This is accurate becauseCST is UTC-06:00
, so UTC should be 6 hours ahead of local time. -
mktime()
tries to return the number of seconds, expecting local time, but you passedcurrent_utc
instead. So, instead of understanding thatcurrent_utc
is UTC time, it assumes you meantMarch 1, 2019 14:51:19 CST
. -
gmtime()
is then used to convert those seconds back into UTC, which results in an inconsistency. The time is nowMarch 1, 2019 20:51:19 UTC
. The reason for this discrepancy is the fact thatmktime()
expected local time. So, the conversion back to UTC adds another 6 hours to local time.
Working with time zones is notoriously difficult, so it’s important to set yourself up for success by understanding the differences between UTC and local time and the Python time functions that deal with each.
Converting a Python Time Object to a String
While working with tuples is fun and all, sometimes it’s best to work with strings.
String representations of time, also known as timestamps, help make times more readable and can be especially useful for building intuitive user interfaces.
There are two Python time
functions that you use for converting a time.struct_time
object to a string:
asctime()
strftime()
You’ll begin by learning aboutasctime()
.
asctime()
You use asctime()
for converting a time tuple or struct_time
to a timestamp:
import time
time.asctime(time.gmtime())
# 'Fri Mar 1 18:42:08 2019'
time.asctime(time.localtime())
# 'Fri Mar 1 12:42:15 2019'`
Both gmtime()
and localtime()
return struct_time
instances, for UTC and local time respectively.
You can use asctime()
to convert either struct_time
to a timestamp. asctime()
works similarly to ctime()
, which you learned about earlier in this article, except instead of passing a floating point number, you pass a tuple. Even the timestamp format is the same between the two functions.
As with ctime()
, the parameter for asctime()
is optional. If you do not pass a time object to asctime()
, then it will use the current local time:
import time
time.asctime()
# 'Fri Mar 1 12:56:07 2019'`
As with ctime()
, it also ignores locale information.
One of the biggest drawbacks of asctime()
is its format inflexibility. strftime()
solves this problem by allowing you to format your timestamps.
strftime()
You may find yourself in a position where the string format from ctime()
and asctime()
isn’t satisfactory for your application. Instead, you may want to format your strings in a way that’s more meaningful to your users.
One example of this is if you would like to display your time in a string that takes locale information into account.
To format strings, given a struct_time
or Python time tuple, you use strftime()
, which stands for “string format time.”
strftime()
takes two arguments:
format
specifies the order and form of the time elements in your string.t
is an optional time tuple.
To format a string, you use directives. Directives are character sequences that begin with a %
that specify a particular time element, such as:
%d
: Day of the month%m
: Month of the year%Y
: Year
For example, you can output the date in your local time using the ISO 8601 standard like this:
import time
time.strftime('%Y-%m-%d', time.localtime())
# '2019-03-01'`
Further Reading: While representing dates using Python time is completely valid and acceptable, you should also consider using Python’s datetime
module, which provides shortcuts and a more robust framework for working with dates and times together.
For example, you can simplify outputting a date in the ISO 8601 format using datetime
:
from datetime import date
date(year=2019, month=3, day=1).isoformat()
#'2019-03-01'`
As you saw before, a great benefit of using strftime()
over asctime()
is its ability to render timestamps that make use of locale-specific information.
For example, if you want to represent the date and time in a locale-sensitive way, you can’t use asctime()
:
from time import asctime
asctime()
# 'Sat Mar 2 15:21:14 2019'
import locale
locale.setlocale(locale.LC_TIME, 'zh_HK') # Chinese - Hong Kong
# 'zh_HK'
asctime()
# 'Sat Mar 2 15:58:49 2019'`
Notice that even after programmatically changing your locale, asctime()
still returns the date and time in the same format as before.
Technical Detail: LC_TIME
is the locale category for date and time formatting. The locale
argument 'zh_HK'
may be different, depending on your system.
When you use strftime()
, however, you’ll see that it accounts for locale:
from time import strftime, localtime
strftime('%c', localtime())
# 'Sat Mar 2 15:23:20 2019'
import locale
locale.setlocale(locale.LC_TIME, 'zh_HK') # Chinese - Hong Kong
# 'zh_HK'
strftime('%c', localtime())
# '六 3/ 2 15:58:12 2019' 2019'
Here, you have successfully utilized the locale information because you used strftime()
.
Note: %c
is the directive for locale-appropriate date and time.
If the time tuple is not passed to the parameter t
, then strftime()
will use the result of localtime()
by default. So, you could simplify the examples above by removing the optional second argument:
from time import strftime strftime(‘The current local datetime is: %c’) ‘The current local datetime is: Fri Mar 1 23:18:32 2019'`
Here, you’ve used the default time instead of passing your own as an argument. Also, notice that the format
argument can consist of text other than formatting directives.
Further Reading: Check out this thorough list of directives available to strftime()
.
The Python time
module also includes the inverse operation of converting a timestamp back into a struct_time
object.
Converting a Python Time String to an Object
When you’re working with date and time related strings, it can be very valuable to convert the timestamp to a time object.
To convert a time string to a struct_time
, you use strptime()
, which stands for “string parse time”:
from time import strptime
strptime('2019-03-01', '%Y-%m-%d')
time.struct_time(tm_year=2019, tm_mon=3, tm_mday=1, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=4, tm_yday=60, tm_isdst=-1)`
The first argument to strptime()
must be the timestamp you wish to convert. The second argument is the format
that the timestamp is in.
The format
parameter is optional and defaults to '%a %b %d %H:%M:%S %Y'
. Therefore, if you have a timestamp in that format, you don’t need to pass it as an argument:
strptime('Fri Mar 01 23:38:40 2019')
time.struct_time(tm_year=2019, tm_mon=3, tm_mday=1, tm_hour=23, tm_min=38, tm_sec=40, tm_wday=4, tm_yday=60, tm_isdst=-1)`
Since a struct_time
has 9 key date and time components, strptime()
must provide reasonable defaults for values for those components it can’t parse from string
.
In the previous examples, tm_isdst=-1
. This means that strptime()
can’t determine by the timestamp whether it represents daylight savings time or not.
Now you know how to work with Python times and dates using the time
module in a variety of ways. However, there are other uses for time
outside of simply creating time objects, getting Python time strings, and using seconds elapsed since the epoch.
Suspending Execution
One really useful Python time function is sleep()
, which suspends the thread’s execution for a specified amount of time.
For example, you can suspend your program’s execution for 10 seconds like this:
from time import sleep, strftime
strftime('%c')
# 'Fri Mar 1 23:49:26 2019'
sleep(10)
strftime('%c')
# 'Fri Mar 1 23:49:36 2019'`
Your program will print the first formatted datetime
string, then pause for 10 seconds, and finally print the second formatted datetime
string.
You can also pass fractional seconds to sleep()
:
from time import sleep
sleep(0.5)`
sleep()
is useful for testing or making your program wait for any reason, but you must be careful not to halt your production code unless you have good reason to do so.
Before Python 3.5, a signal sent to your process could interrupt sleep()
. However, in 3.5 and later, sleep()
will always suspend execution for at least the amount of specified time, even if the process receives a signal.
sleep()
is just one Python time function that can help you test your programs and make them more robust.
Measuring Performance
You can use time
to measure the performance of your program.
The way you do this is to use perf_counter()
which, as the name suggests, provides a performance counter with a high resolution to measure short distances of time.
To use perf_counter()
, you place a counter before your code begins execution as well as after your code’s execution completes:
from time import perf_counter
def longrunning_function():
... for i in range(1, 11):
... time.sleep(i / i ** 2)
...
start = perf_counter()
longrunning_function()
end = perf_counter()
execution_time = (end - start)
execution_time
# 8.201258441999926`
First, start
captures the moment before you call the function. end
captures the moment after the function returns. The function’s total execution time took (end - start)
seconds.
Technical Detail: Python 3.7 introduced perf_counter_ns()
, which works the same as perf_counter()
, but uses nanoseconds instead of seconds.
perf_counter()
(or perf_counter_ns()
) is the most precise way to measure the performance of your code using one execution. However, if you’re trying to accurately gauge the performance of a code snippet, I recommend using the Python timeit
module.
timeit
specializes in running code many times to get a more accurate performance analysis and helps you to avoid oversimplifying your time measurement as well as other common pitfalls.
Conclusion
Congratulations! You now have a great foundation for working with dates and times in Python.
Now, you’re able to:
- Use a floating point number, representing seconds elapsed since the epoch, to deal with time
- Manage time using tuples and
struct_time
objects - Convert between seconds, tuples, and timestamp strings
- Suspend the execution of a Python thread
- Measure performance using
perf_counter()
On top of all that, you’ve learned some fundamental concepts surrounding date and time, such as:
- Epochs
- UTC
- Time zones
- Daylight savings time
Now, it’s time for you to apply your newfound knowledge of Python time in your real world applications!
Further Reading
If you want to continue learning more about using dates and times in Python, take a look at the following modules:
datetime
: A more robust date and time module in Python’s standard librarytimeit
: A module for measuring the performance of code snippetsastropy
: Higher precision datetimes used in astronomy
Source : .