Eligible, the next step is to file an application to participate in a LEAP examination. A copy of the LEAP Certification form must also be submitted with the standard State application form when filing for a LEAP examination. If a copy isn't attached, the application will be returned to the applicant. The individual should always keep his. Accompanying the map is a podcast series that will help guide and shape your journey. (Stay tuned for the level 3 podcast.) For on-the-go listening to the podcast, try using fai.gov on your mobile phone! For specific questions or information on this 1102 Career Map, please contact one of the LEAP team members: Jeffrey Maciejewski; Noemi Edwards. Find and compare top Legal Case Management software on Capterra, with our free and interactive tool. Quickly browse through hundreds of Legal Case Management tools and systems and narrow down your top choices. Filter by popular features, pricing options, number of users, and read reviews from real users and find a tool that fits your needs. LEAP is an integrated legal practice and case management software for solo & small law firms wishing to become more efficient, flexible and profitable. It allows everyone in a small law firm to work collaboratively using a single integrated system that enables. Simple and secure file delivery Trusted by businesses around the world to send any file of any size. Sign up for a free trial! Plans starting at just $10/month.
It's hard to find anyone in the 21st century, who doesn't know what a file is. When we say file, we mean of course, a file on a computer. There may be people who don't know anymore the 'container', like a cabinet or a folder, for keeping papers archived in a convenient order. A file on a computer is the modern counterpart of this. It is a collection of information, which can be accessed and used by a computer program. Usually, a file resides on a durable storage. Durable means that the data is persistent, i.e. it can be used by other programs after the program which has created or manipulated it, has terminated.
The term file management in the context of computers refers to the manipulation of data in a file or files and documents on a computer. Though everybody has an understanding of the term file, we present a formal definition anyway:
Pixatool 1 54 – create 8bit pixel style images short. A file or a computer file is a chunk of logically related data or information which can be used by computer programs. Usually a file is kept on a permanent storage media, e.g. a hard drive disk. A unique name and path is used by human users or in programs or scripts to access a file for reading and modification purposes.
The term 'file' - as we have described it in the previous paragraph - appeared in the history of computers very early. Usage can be tracked down to the year 1952, when punch cards where used.
A programming language without the capability to store and retrieve previously stored information would be hardly useful.
The most basic tasks involved in file manipulation are reading data from files and writing or appending data to files.
The syntax for reading and writing files in Python is similar to programming languages like C, C++, Java, Perl, and others but a lot easier to handle.
In our first example we want to show how to read data from a file. The way of telling Python that we want to read from a file is to use the open function. The first parameter is the name of the file we want to read and with the second parameter, assigned to the value 'r', we state that we want to read from the file:
The 'r' is optional. An open() command with just a file name is opened for reading per default. The open() function returns a file object, which offers attributes and methods.
After we have finished working with a file, we have to close it again by using the file object method close():
Now we want to finally open and read a file. The method rstrip() in the following example is used to strip off whitespaces (newlines included) h the right side of the string 'line':
fobj = open('ad_lesbiam.txt')for line in fobj: print(line.rstrip())fobj.close()
If we save this script and call it, we get the following output, provided that the text file 'ad_lesbiam.txt' is available:
By the way, the poem above is a love poem of the ancient Roman poet Catull, who was hopelessly in love with a woman called Lesbia.
Writing to a file is as easy as reading from a file. To open a file for writing we set the second parameter to 'w' instead of 'r'. To actually write the data into this file, we use the method write() of the file handle object.
Let's start with a very simple and straightforward example:
Especially if you are writing to a file, you should never forget to close the file handle again. Otherwise you will risk your data ending up in a non consistent state.
You will often find the with statement for reading and writing files. The advantage is that the file will be automatically closed after the indented block after the with has finished execution:
Our first example can also be rewritten like this with the with statement:
Example for simultaneously reading and writing:
Every line of the input text file is prefixed by its line number. So, the result looks like this:
There is one possible problem, which we have to point out. What happens if we open a file for writing, and this file already exists? You can consider yourself fortunate, if the content of this file was of no importance, or if you have a backup of it. Otherwise you have a problem, because as soon as an open() with a 'w' has been executed the file will be removed. This is often what you want, but sometimes you just want to append to the file, like it's the case with logfiles.
If you want to append something to an existing file, you have to use 'a' instead of 'w'.
So far we worked on files line by line by using a for loop. Very often, especially if the file is not too large, it's more convenient to read the file into a complete data structure, e.g. a string or a list. The file can be closed after reading and the work is accomplished on this data structure:
In the above example, the complete poem is read into the list poem. We can acces e.g. the 3rd line with poem[2].
Another convenient way to read in a file might be the method read() of open. With this method we can read the complete file into a string, as we can see in the next example:
This string contains the complete content of the file, which includes the carriage returns and line feeds.
It's possible to set - or reset - a file's position to a certain position, also called the offset. To do this, we use the method seek. It has only one parameter in Python3 (no 'whence' is available as in Python2). The parameter of seek determines the offset which we want to set the current position to. To work with seek, we will often need the method tell, which 'tells' us the current position. When we have just opened a file, it will be zero. We will demonstrate the way of working with both seek and tell in the following example. You have to create a file called 'buck_mulligan.txt' with the content 'Stately, plump Buck Mulligan came from the stairhead, bearing a bowl of lather on which a mirror and a razor lay crossed.':
It's also possible to set the file position relative to the current position by using tell correspondingly: Page booth 2 3 – website screenshot tool shortcut.
In the following example we will open a file for reading and writing at the same time. If the file doesn't exist, it will be created. If you want to open an existing file for read and write, you should better use 'r+', because this will not delete the content of the file.
We don't really mean what the header says. On the contrary, we want to prevent any nasty situation, like losing the data, which your Python program has calculated. So, we will show you, how you can save your data in an easy way that you or better your program can reread them at a later date again. We are 'pickling' the data, so that nothing gets lost.
Python offers a module for this purpose, which is called 'pickle'. With the algorithms of the pickle module we can serialize and de-serialize Python object structures. 'Pickling' denotes the process which converts a Python object hierarchy into a byte stream, and 'unpickling' on the other hand is the inverse operation, i.e. the byte stream is converted back into an object hierarchy. What we call pickling (and unpickling) is also known as 'serialization' or 'flattening' a data structure.
An object can be dumped with the dump method of the pickle module:
dump() writes a pickled representation of obj to the open file object file. The optional protocol argument tells the pickler to use the given protocol:
The default protocol of Python3 is 3.
If fix_imports is True and protocol is less than 3, pickle will try to map the new Python3 names to the old module names used in Python2, so that the pickle data stream is readable with Python 2.
Objects which have been dumped to a file with pickle.dump can be reread into a program by using the method pickle.load(file). pickle.load recognizes automatically, which format had been used for writing the data.A simple example:
The file data.pkl can be read in again by Python in the same or another session or by a different program:
Only the objects and not their names are saved. That's why we use the assignment to villes in the previous example, i.e. data = pickle.load(f).
In our previous example, we had pickled only one object, i.e. a list of French cities. But what about pickling multiple objects? The solution is easy: We pack the objects into another object, so we will only have to pickle one object again. We will pack two lists 'programming_languages' and 'python_dialects' into a list pickle_objects in the following example:
The pickled data from the previous example, - i.e. the data which we have written to the file data.pkl, - can be separated into two lists again, when we reread the data:
</pre>import picklef = open('data.pkl','rb')languages, dialects) = pickle.load(f)print(languages, dialects)['Python', 'Perl', 'C++', 'Java', 'Lisp'] ['Jython', 'IronPython', 'CPython']</pre>
One drawback of the pickle module is that it is only capable of pickling one object at the time, which has to be unpickled in one go. Let's imagine this data object is a dictionary. It may be desirable that we don't have to save and load every time the whole dictionary, but save and load just a single value corresponding to just one key. The shelve module is the solution to this request. A 'shelf' - as used in the shelve module - is a persistent, dictionary-like object. The difference with dbm databases is that the values (not the keys!) in a shelf can be essentially arbitrary Python objects -- anything that the 'pickle' module can handle. This includes most class instances, recursive data types, and objects containing lots of shared sub-objects. The keys have to be strings.
The shelve module can be easily used. Actually, it is as easy as using a dictionary in Python. Before we can use a shelf object, we have to import the module. After this, we have to open a shelve object with the shelve method open. The open method opens a special shelf file for reading and writing:
</pre>import shelves = shelve.open('MyShelve')</pre>
If the file 'MyShelve' already exists, the open method will try to open it. If it isn't a shelf file, - i.e. a file which has been created with the shelve module, - we will get an error message. If the file doesn't exist, it will be created.
We can use s like an ordinary dictionary, if we use strings as keys:
A shelf object has to be closed with the close method:
We can use the previously created shelf file in another program or in an interactive Python session:
It is also possible to cast a shelf object into an 'ordinary' dictionary with the dict function:
The following example uses more complex values for our shelf object:
The data is persistent!
To demonstrate this once more, we reopen our MyPhoneBook:
Write a function which reads in a text from file and returns a list of the paragraphs.You may use one of the following books:
Save the following text containing city names and times as 'cities_and_times.txt'.
Each line contains the name of the city, followed by the name of the day ('Sun') and the time in the form hh:mm. Read in the file and create an alphabetically ordered list of the form
Finally, the list should be dumped for later usage with the pickle module. We will use this list in our chapter on Numpy dtype.
City names can consist of multiple words like 'Salt Lake City'. That is why we have to use the asterisk in the line, in which we split a line. So city will be a list with the words of the city, e.g. ['Salt', 'Lake', 'City']. ' '.join(city) turns such a list into a 'proper' string with the city name, i.e. in our example 'Salt Lake City'.
Studies: Learning Early About Peanut allergy (LEAP); Persistence of Oral Tolerance to Peanut (LEAP-On)
Sponsor: NIH-National Institute of Allergy and Infectious Diseases (NIAID)
Testing online. Collaborator: Immune Tolerance Network (ITN)
Prime: Rho
Project Status: Completed
Start and End Dates: LEAP: December 2006–May 2014; LEAP-On: May 2011–May 2015
Disease: Peanut allergy is a food allergy that produces mild to severe reactions, including hives, coughing, choking, trouble breathing, and anaphylaxis, which can be fatal if not treated immediately. Peanut allergy prevalence has doubled over the past 10 years in countries that encourage avoiding peanuts during pregnancy, lactation, and infancy. Currently, no cure exists for peanut allergy, which affects about 1.5% of young children.
Objective: The LEAP study tested the hypothesis that early introduction of peanut in the diets of children at high risk for developing peanut allergy could lead to a reduction in peanut allergy at age 5. The LEAP-On follow-up study sought to determine whether the reduction in peanut allergy among those in the peanut-consuming group would be maintained if they avoided eating peanut for 12 months.
Study Information: The LEAP study enrolled 640 infants, 4 to 11 months of age, who were considered to be at high risk for developing peanut allergy due to the presence of severe eczema and/or egg allergy at screening. They were randomized to either avoid peanut completely, as per the pediatric guidelines in place at the time, or to consume approximately 6 grams of peanut protein each week given over the course of 3 meals until the age of 5. The LEAP-On follow-up study followed 556 of the original 640 children in LEAP (both peanut eaters and avoiders) for a 12-month period of peanut avoidance.
Rho served as the statistical and data coordinating center for the ITN, providing support for statistical analysis, safety monitoring, and data management, as well as supporting study protocol and manuscript development. Rho also supported the next phase of the study, LEAP-On, which sought to determine whether the reduction in peanut allergy among those in the peanut-consuming group would be maintained if they avoided eating peanut.
Results: For the LEAP study, at 5 years of age, the group randomized to consume peanut had a rate of peanut allergy 81% lower than that of the group that avoided peanut. In the LEAP-On follow-up study, after 12 months of avoiding peanuts, only 4.8% of the original peanut consumers were discovered to be allergic, compared with 18.6% of the original peanut avoiders.
Full details of the studies and results were published in The New England Journal of Medicine and can be found in the following articles:
Public Health Impact: LEAP study results indicate that high-risk infants who consumed a snack containing peanut prevents them from developing the allergy. The LEAP study was the first randomized trial that prevented food allergy in a large cohort of high-risk infants. The LEAP-On follow-up study demonstrated that peanut consumption could be stopped for at least one year without impacting a child’s ability to tolerate peanut.
Five years prior to the publication of the LEAP results, an expert panel convened by NIAID of the NIH issued a report entitled Guidelines for the Diagnosis and Management of Food Allergy in the United States. Because of a lack of definitive studies at the time, the report did not offer strategies for the prevention of peanut allergy. However, in light of the results of the LEAP trial, another expert panel was convened in 2015 to review new evidence. After reviewing the evidence, the expert panel concluded that the “early introduction of peanut will result in the prevention of peanut allergy in a large number of infants.” The expert panel developed 3 addendum guidelines to the earlier report that provide guidance to healthcare providers on the early introduction of peanuts into the diets of infants. The final document entitled Addendum Guidelines for the Prevention of Peanut Allergy in the United States: Report of the NIAID–Sponsored Expert Panel was published in January 2017. The entire report may be found at https://www.niaid.nih.gov/sites/default/files/addendum-peanut-allergy-prevention-guidelines.pdf.
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