Handbook Get in Shape Fast 2

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If you're altar-bound right now, odds are that as you're playing with your soon-to-be new signature and agonizing over centerpieces, you're also considering what you'll look like to your nearest and dearest as they stare at your backside on your way down the aisle. To "Canon in D. And since you're reading this, well, we can only guess that you're one of them.

But why, in the midst of the centerpiece brainstorming and DJ haggling and bouquet choosing, should you add another to-do in the weeks leading up to your I Do's? Of course losing your love handles isn't going to make you a better bride — or even a happier one, as anyone who's watched a carb-free wife-to-be lose her mind over place settings at the rehearsal dinner knows.

No, getting yourself in shape before your wedding really can be summed up by two words: Okay, and two more: Being engaged is an awesome time of relationship transition. You're starting a new life with the person you're mad for and you're about to spend the next couple of months getting used to what the rest of your lives will be like together.

If that's not heavy enough, the chaos that comes with planning a wedding can quickly zap your feel-good mojo.

Squabbles about seating, dimwit caterers, getting intimately acquainted with your soon-to-be mother-in-law — both your mind and your body are going to be running on overdrive until you've hugged your last guest goodbye. But research shows that getting your heart rate up, working your muscles to capacity, and eating healthily can kick your brain into "I'm a superstar! So even if you won't have eyes staring at your bare biceps on your wedding day, fitting in regular pre-matrimonial workouts can keep you focused and feeling great — and what better way to start your new life?

We asked Lynn Bode, a personal trainer with over 13 years of experience and the owner of the online fitness program WorkoutsForBrides. Her advice featured on the following pages can help you look and — more important — feel your best from "Yes, I Will" to "Yes, I Do. At the same time columns 0 and 2 should be selected with an advanced integer index.

These are some detailed notes, which are not of importance for day to day indexing in no particular order:. Data type objects dtype , Scalars.

10-Minute Workout to Sculpt Your Body in 2 Weeks

If the ndarray object is a structured array the fields of the array can be accessed by indexing the array with strings, dictionary-like. Indexing x['field-name'] returns a new view to the array, which is of the same shape as x except when the field is a sub-array but of data type x.

Indexing into a structured array can also be done with a list of field names, e. Currently this returns a new array containing a copy of the values in the fields specified in the list.

As of NumPy 1. A copy will continue to be returned for now, but a FutureWarning will be issued when writing to the copy. If you depend on the current behavior, then we suggest copying the returned array explicitly, i. This will work with both past and future versions of NumPy.

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If the accessed field is a sub-array, the dimensions of the sub-array are appended to the shape of the result. This iterator object can also be indexed using basic slicing or advanced indexing as long as the selection object is not a tuple. This should be clear from the fact that x. It can be used for integer indexing with 1-dimensional C-style-flat indices.

The shape of any returned array is therefore the shape of the integer indexing object. Note In Python, x[ exp1, exp2, Warning The above is not true for advanced indexing. Note Remember that a slicing tuple can always be constructed as obj and used in the x[obj] notation. Warning The definition of advanced indexing means that x[ 1,2,3 ,] is fundamentally different than x[ 1,2,3 ]. Advanced indexes always are broadcast and iterated as one: Example From each row, a specific element should be selected.

Using both together the task can be solved using advanced indexing: Example From a 4x3 array the corner elements should be selected using advanced indexing.

Using the method explained previously one could write: It is like concatenating the indexing result for each advanced index element In the simplest case, there is only a single advanced index. Example A common use case for this is filtering for desired element values. For example one may wish to select all entries from an array which are not NaN: Example From an array, select all rows which sum up to less or equal two: Example Use boolean indexing to select all rows adding up to an even number.

The native NumPy indexing type is intp and may differ from the default integer array type. For advanced assignments, there is in general no guarantee for the iteration order. This means that if an element is set more than once, it is not possible to predict the final result. An empty tuple index is a full scalar index into a zero dimensional array. On the other hand x[ If a zero dimensional array is present in the index and it is a full integer index the result will be a scalar and not a zero dimensional array.

Advanced indexing is not triggered. A view if no advanced index is present, otherwise a copy. When the result of an advanced indexing operation has no elements but an individual index is out of bounds, whether or not an IndexError is raised is undefined e. When a casting error occurs during assignment for example updating a numerical array using a sequence of strings , the array being assigned to may end up in an unpredictable partially updated state. However, if any other error such as an out of bounds index occurs, the array will remain unchanged.

Indexing — NumPy v Manual

The memory layout of an advanced indexing result is optimized for each indexing operation and no particular memory order can be assumed. When using a subclass especially one which manipulates its shape , the default ndarray.

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    The Buff Bride's Handbook: Get in Shape for Your Wedding Day

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