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Easily deploy Dask on job queuing systems like PBS, Slurm, MOAB, and SGE.

The Dask-jobqueue project makes it easy to deploy Dask on common job queuing systems typically found in high performance supercomputers, academic research institutions, and other clusters. It provides a convenient interface that is accessible from interactive systems like Jupyter notebooks, or batch jobs.


Dask jobqueue can also adapt the cluster size dynamically based on current load. This helps to scale up the cluster when necessary but scale it down and save resources when not actively computing.

Dask-jobqueue should be configured for your cluster so that it knows how many resources to request of each job and how to break up those resources. You can specify configuration either with keyword arguments when creating a Cluster object, or with a configuration file.

You can pass keywords to the Cluster objects to define how Dask-jobqueue should define a single job:

Note that the cores and memory keywords above correspond not to your full desired deployment, but rather to the size of a single job which should be no larger than the size of a single machine in your cluster. Separately you will specify how many jobs to deploy using the scale method.

Specifying all parameters to the Cluster constructor every time can be error prone, especially when sharing this workflow with new users. Instead, we recommend using a configuration file like the following:

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for real-world examples.

If you place this in your ~/.config/dask/ directory then Dask-jobqueue will use these values by default. You can then construct a cluster object without keyword arguments and these parameters will be used by default.

You can still override configuration values with keyword arguments

If you have imported dask_jobqueue then a blank jobqueue.yaml will be added automatically to ~/.config/dask/jobqueue.yaml . You should use the section of that configuation file that corresponds to your job scheduler. Above we used PBS, but other job schedulers operate the same way. You should be able to share these with colleagues. If you can convince your IT staff you can also place such a file in /etc/dask/ and it will affect all people on the cluster automatically.

For more information about configuring Dask, see the Carolbar Womens Cute Sweet Flat Bows Lace Assorted Colors Loafer Shoes Light Brown Z0LTvFk

Dask-jobqueue creates a Dask Scheduler in the Python process where the cluster object is instantiated:

You then ask for more workers using the scale command:

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The Event Segmentation chartallows you to accomplish deeper segmentation on your events and the users who perform them. You can use this tab to compare or segment your events by user properties or event properties, over a selected time frame. In addition, you can also view your data in real-time (only available on Enterprise plans), hourly (only available on Enterprise plans), daily, weekly, or monthly granularity. Like any other chart in Amplitude, you can name it, save it, archive it, add it to a dashboard, and export it in various different file formats. You can also create KHSKXThe New Winter Boots With Thick Solid Round Female AllMatch Elastic And Bare Boots Shoes Brown McID09
outof Event Segmentation charts.

Table of Contents

Chart Control Panel

The chart control panel is made of up three modules to help you setup your chart: the Events module (left module), the Segmentation module (right module), and the Custom module (bottom module, this module always varies by the chart).

Events (Left) Module

Use the left module to select up to 10 events to query on. After you select an event, hover over the event to add conditional clauses to that specific event:

In addition to the events you send to Amplitude, the event dropdown menu also offers four Amplitude events (prefixed by '[Amplitude]') to query on.

From top to bottom:

Segmentation (Right) Module: Compare User Segments

Use the right module to create and analyze segments of users or groups. There are many configurations in this module, and this section will detail how to create and modify segments.

Modify a Segment By default, the first segment is called "All Users," indicating that no filters are applied to the segment. To apply a filter, click on the "Select user property..." dropdown menu to filter by a user property or behavioral cohort . Then, select the operator you would like the user property to follow: is, is not, contains, does not contain, less/greater than (or equal to), set is, set is not, set contains, set does not contain, and glob match.

If you wish to include a list of property values in the dropdown, you can copy and paste a list of comma-separated values. For example, if you've implemented "email" as a user property, you can paste the following string instead of selecting each value one at a time:


Using an 'OR' Clause To filter on multiple values of the same property, simply add more values in the "Select value(s)..." box. Adding a property value creates an 'OR' clause in the affected segment definition. As seen below, the segment now includes users who performed an event in the United States OR Canada OR the United Kingdom.

Using an 'AND' Clause To add additional filters, click on the "+where" button. Adding a filter creates an 'AND' clause to your segment definition. As seen below, the segment definition now includes users who performed an event in the United States AND using Spanish.

Add a Segment To add additional(not filters), click on the "+Segment" button. As seen below, we created two segments to compare: one of the users in the United States and one of the users in Canada.

Segmentation Module: Group By User Property

The right module also contains the "Group By" function that allows you to quickly filter your segments by certain user properties.

After you select the property you want to use to group your users by, by default, the chart displays the top five segments by the measurement selected. You can select more or deselect segments via the data table below the chart. In the example below, we grouped by '[Amplitude] Device family' to graph the number of daily active users in the last 30 days, grouped by the device family they used.

You can group your data by a second property. In the example below, we created a segment that includes only users who have performed an event in the United States in the last 30 days. Then, with the group by function, we group the users by region and by service carrier. This graphs the number of daily active users in the last 30 days, who performed an event in the United States, grouped by region and by service carrier.

Segmentation Module: Saving User Segments

The right module also has the ability to save user segments for quick accessibility to common configurations of the right module across. Saved user segments can then be applied to any Amplitude chart and are global for other team members to use.

To do save user segments, click the "<->Shortcuts..." button and a separate window will appear to let you create segments and save them. For example, let's say we want to save a segment of users who are in the United States and are on iOS devices. Since this is a segment we want to use throughout the platform, it is much more efficient to save it instead of having to recreate it for every single chart. Once you create your segment and press the "Save" button, you will be prompted to rename it in the left sidebar.

You can use the left sidebar to search for previously saved segments created by yourself or others in your organization. For example, let's say another employee hasalready created a configuration that groups users in the United States by platform. So, instead of having to recreate all the parameters, you can simply search for it and apply it to your chart. You can then click the "Apply" button to apply it to any chart with the Segmentation module.

Segmentation Module: Custom Segment Labels

If you have a very complicated segment, then sometimes the default chart labels may be difficult to read. You can customize the names of your segments to reflect the user segment it represents. For example, the following chart has 2 segments where each segment is segmented by 4 different things. The default name is hard to read.

You can change the name by hovering over the segment name and clicking on it. Changing the segment name will also change the chart labels.

Segmentation Module: Inline Behavioral Cohorts

You can create simple behavioral cohorts inline directly within the right module of the chart control panel. This will allow you to create a behavioral cohort in the context of a chart without having to navigate away into the Kingko® Women Spring Autumn Slipon Lowheeled Fleece Boots Brown TJl3NW
. To do this, select the "+perform" button in the right module. For example, the following chart would allow me to see outof the users who purchased a concert ticket in the last 30 days, how many also downloaded a song or video. This feature is only available to customers on our Enterprise plan.

For more information on the clauses available in the dropdowns to create cohorts, see our cohort documentation .

Metrics (Bottom) Module

The bottom module allows you to specify which metric you want to query. Below is a description of the different metrics you can query in the Event Segmentation chart. You can select between these by using the "..measured by" dropdown in the bottom module. Depending on if you are viewing the chart as a line chart, bar chart, or stacked area chart, the metrics may display different things. For example, here is a bar chart visualization.

On the other hand, a stacked area chart visualization may be more useful if there are a lot of segments on your chart with similar values. This visualization stacks segments on top of each other where the height of each colored area is the value of each data point. The value represented on the Y-axis of the chart will vary depending on which metric you have selected. The Y-axis will show the sum of the values of the data points for each corresponding X-axis label. Stacked area charts make it easier to get a sense of the total size of each individual segment whereas it would be difficult to see that with a line chart. It is also useful in showing how the inflection of a certain segment affected the overall value on that day. For example,in the following chart Marco Tozzi Women’s 25101 Combat Boots Blue Azure fLoPaFAW
shows that on June 22nd, there were 33,934 users in the United States. However, that data point corresponds to about 60,000on the Y-axis because the Y-axis is showing the sum of all the segments.

Event Segmentation also has a fourth visualization that is useful if you have multiple group bys applied to your chart. You can find more information about it below this table. The following table describes what each of the first three visualizations mean with respect to the metric selected in the bottom module.

Group By Visualization

If you have multiple group bys applied, then often times the default visualizations can become confusing and hard to interpret data. The group by visualization offers a clearer way to analyze your data. For example, the following chart control panel groups 'Play Song or Video' by 'Genre_Type' and also by country and platform.

The group by visualization will generate a table view that displays the values in separate columns. This makes it easier to digest the data. For example, we can see that for users in the United States who played pop songs, most of them were on an Android platform.

Custom Formulas

In an Event Segmentation chart, you can write formulas to perform or calculate specific analyses and metrics on events. You can plot up to four formulas on the same chart, separated by semicolons (;).You can also perform the following arithmetic operations: parenthesis, addition (+), subtraction (-), multiplication (*), and division (/). There are several functions you can use to create custom formulas to apply to your Event Segmentation chart. The text box will autocomplete with suggestions for formulas to use, and you can also press ctrl+space to display all formulas. To read more about each formula and see some examples of use cases, see our Custom Formulas article .

Rolling Averages

Rolling averages will display the unweighted mean and therefore "smooth out" a chart. This functionality is useful if you have cyclical users such as people who use your product Monday-Friday, but not on Saturday-Sunday. To apply a rolling average to your chart, select the "More" button in the right-hand side of the bottommodule. Rolling averages are not supported for the Histogram, Property Histogram, and Custom Formula measurement options.

Example 1: Measured by Event Totals

The belowline chart displays the daily Event Totals between February5th and March 7th, without a rolling average.

The below line chart displays the daily Event Totals, with a rolling average of7 days. In this chart, the February28th data point is an average of the numbers between February22nd and February28th from the first chart shown above.

You can read further data points as follows:

Example 2: Measured by Uniques

Similar to Example 1, suppose we create a line chart of daily Uniques as a rolling average of 30 days between February 9th and May 9th.

You can then read certain data points as follows:


When Rolling Average is active, Microscope's "View Users" functionality will continue to show users in the current data point chosen and not over the whole interval being averaged on.

Rolling Windows

Rolling windows will display the aggregate last N days of information in a single data point. This functionality is useful if you want to see metrics such as your 7-day active user count on a daily basis. To apply a rolling window to your chart, select the "Advanced" button in the right-hand side of the bottom module. Rolling windows are not supported for the Property Histogram and Custom Formula metrics.

The following chart displays daily Uniques between April 5th and May 5th without a rolling window. With Microscope, we can see that on April 21st, we had 173,144 users.

The below line chart displays the daily Uniques with a rolling window of 7 days. In this chart, the April 21st data point is the number of unique users between April 15thand April 21st. So, while the above chart is showing you the unique users on each day, the rolling window chart will allow you to look at unique users over 7 dayson a daily basis.

You can then read certain data points as follows:

The same logic applies for all other metrics in the bottom module that allow rolling windows.


When RollingWindow is active, Microscope's "View Users" functionality will continue to show users in the current data point chosen and not over the whole interval being aggregatedon.

Chart Interpretation

You can set up and interpret any Event Segmentation chart easily as the UI allows you to read the parameters like a sentence. For example, the following chart shows you the number of 'PlaySong' events grouped by 'Type' performed by 'Users' in the United States and measured by Event Totals daily for the last 30 days.

You can choose to view the chart as a line chart or as a bar chart via the buttons in the upper right-hand corner of the chart. Depending on the metric you have selected, each visualization may represent information in a slightly different way. Please see this table for more information on the difference. You can also use the date picker to choose a more specific time range to analyze your data within and can switch between "Last", "Between", and "Since".

Additionally, you have the option to view data in real-time, hourly, daily, weekly, or monthly units by toggling between the different options in the dropdown menu next to the chart visual options. If you are looking at multiple segments in your chart, you can manually select and deselect each segment by hovering over the segment name in the bar below the chart and removing it or by clicking the "+" button to add it back. Finally, you can click on any data point in the chart and inspect the users that make up that data point by using Microscope .

Real-time Hourly Segmentation

Please note the following limitations when you view data on a real-time or hourly basis.

Period-Over-Period Comparison

In Event Segmentation, you the ability to perform a period-over-period comparison by clicking the checkbox at the bottom of the date picker. You will then be able to select if you want to compare each data point to the previous interval or the same interval from the previous day, week, month, or year.

For example, let's say you want to compare the daily active users for the current week compared to last week.

The blue segment shows you the current period and the green segment shows you your data for last week. Microscope is currently shown on the data point for Sep 19, and the corresponding green data point would be for the day before, Sep 18:

Since the period-over-period comparison interval is configurable, you can choose what dates you actually want to compare. For example, here we're now comparing the current date with the same day from the previous week. Here, the corresponding green data point is now the same day the week before, so the chart is comparing Sep 19 to Sep 12:

Breakdown Data Table

Belowthe chart is a table of the data displayed. The following screenshotis an example data table of an event segmented with a group by applied. You can select or deselect which segments you see in the graph by clicking on the segment name in the data table.Furthermore, the data table can display some simple calculations for you depending on the metric you are viewing the chart in.The default purple selection will select all top values/events. These values/events will update automatically if new top values/events are sent to the platform. To turn off this functionality, deselect the segments and explicitly select the values/events you want to preserve on the chart. This will be a green selection. If you have multiple events selected, the data table below will show the event name. You can also click on the event name to include or exclude it from the chart. To sort by column, click the column name (i.e. Sum, May 21). Lastly, you can download the data table by clicking the "Export CSV" button.

Video Walkthrough

[Amplitude] Top Events:

From the Specification: The value of can be one of the following strings. , , , , , , , , , , , .

From the Specification:

ES5 Note: For convenience the return value of for both and was changed from to and in ECMAScript 5.


The operator compares the constructors of its two operands. It is only useful when comparing custom made objects. Used on built-in types, it is nearly as useless as the Brinley Co Women’s Mike Pump Nude YAzDY

Comparing Custom Objects

Using with Native Types

One important thing to note here is that does not work on objects that originate from different JavaScript contexts (e.g. different documents in a web browser), since their constructors will not be the exact same object.

In Conclusion

The operator should only be used when dealing with custom made objects that originate from the same JavaScript context. Just like the operator, every other use of it should be avoided .

JavaScript is a language, so it will apply wherever possible.

To avoid the issues above, use of the strict equal operator is highly recommended. Although this avoids a lot of common pitfalls, there are still many further issues that arise from JavaScript's weak typing system.

Constructors of Built-In Types

The constructors of the built in types like and behave differently when being used with the keyword and without it.

Using a built-in type like as a constructor will create a new object, but leaving out the keyword will make the function behave like a converter.

In addition, passing literals or non-object values will result in even more type coercion.

The best option is to cast to one of the three possible types explicitly .

Casting to a String

By prepending an empty string, a value can easily be cast to a string.

Casting to a Number

Using the unary plus operator, it is possible to cast to a number.

Casting to a Boolean

By using the not operator twice, a value can be converted to a boolean.

ES5 Note: Number literals that start with a are interpreted as octal (Base 8). Octal support for these has been removed in ECMAScript 5 strict mode.


The function will execute a string of JavaScript code in the local scope.

However, only executes in the local scope when it is being called directly when the name of the called function is actually .

The use of should be avoided. 99.9% of its "uses" can be achieved without it.

in Disguise

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and can both take a string as their first argument. This string will always get executed in the global scope since is not being called directly in that case.

Security Issues

also is a security problem, because it executes any code given to it. It should never be used with strings of unknown or untrusted origins.

In Conclusion

should never be used. Any code that makes use of it should be questioned in its workings, performance and security. If something requires in order to work, it should not be used in the first place. A should be used, that does not require the use of .

JavaScript has two distinct values for nothing, and , with the latter being more useful.

The Value

is a type with exactly one value: .

The language also defines a global variable that has the value of ; this variable is also called . However, this variable is neither a constant nor a keyword of the language. This means that its can be easily overwritten.

Here are some examples of when the value is returned:

Handling Changes to the Value of

Since the global variable only holds a copy of the actual of , assigning a new value to it does not change the value of the .

Still, in order to compare something against the value of , it is necessary to retrieve the value of first.

To protect code against a possible overwritten variable, a common technique used is to add an additional parameter to an TPulling Women’s Ballet Brown boWNEIwW0
that gets no argument passed to it.

Another way to achieve the same effect would be to use a declaration inside the wrapper.

The only difference here is that this version results in 4 more bytes being used in case it is minified, and there is no other statement inside the anonymous wrapper.

Uses of

While in the context of the JavaScript language is mostly used in the sense of a traditional , the actual (both a literal and a type) is more or less just another data type.

It is used in some JavaScript internals (like declaring the end of the prototype chain by setting ), but in almost all cases, it can be replaced by .

ES5 Note: in ECMAScript 5 is no longer in strict mode, but its name can still be shadowed by for example a function with the name .


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