| < Day Day Up > |
2.2. Deleting TextBefore you start practicing deletion commands, you might want to know the undo command, which is discussed fully later in this chapter. Typing C-_ or C-x u undoes your last edit; typing undo again undoes the edit before that one, and so on. Emacs provides many ways to delete text. The simplest way to delete text is to press the Del key, which deletes the character immediately to the left of the cursor. See Figure 2-4 for possible locations of the Del key on your keyboard. It is sometimes referred to as the Backspace key. Del is easiest to define by what it does: it deletes the previous character.  That's the key Emacs refers to as Del. Emacs provides a number of other deletion commands—perhaps too many for your taste, although you'll eventually find a reason to use most of them. For example, C-d (for delete-character) deletes the character under the cursor. The command for deleting the next word is M-d (for kill-word). Once again, note how the Meta key augments the command: C-d operates on a character, and M-d operates on a word. Emacs has commands to delete the next or previous word, sentence, and paragraph. By their names, you can guess what they do when you're between words, sentences, or paragraphs. If you're in the middle of an entity, however, they do something a little surprising: they delete a portion of the current word, sentence, or paragraph, backward or forward depending on whether the command deletes previous or next. For example, here's how M-d acts differently depending on where the cursor is.
Similarly, if you are in the middle of a word and ask Emacs to delete the previous word (M-Del, for backward-kill-word), it deletes from the cursor position back to the beginning of the current word. If you want to delete an entire line or part of a line, use the command C-k (for kill-line). This command deletes everything from the cursor to the end of the line.  So, it usually takes two C-k's to delete a line: one to delete the text and one to delete the resulting blank line. If you want to delete everything from the beginning of the line up to the cursor, try the more complex incantation Meta - C-k (i.e., hold down Meta, followed by a hyphen, and then C-k). You can also use C-k to join two lines. If you're at the end of a line, C-k deletes the newline character, effectively making two lines into one long line. 2.2.1 The Kill RingBy now you may have noticed that some deletion commands in Emacs are called kill commands, such as kill-region, kill-word, and the like. In Emacs, killing is not fatal, but in fact, quite the opposite. Text that has been killed is not gone forever but is hidden in an area called the kill ring. The kill ring, though it sounds somewhat like a violent gang, is an internal storage area where Emacs puts things you've copied or deleted. Do not confuse the kill ring with the system clipboard, which allows for copying and pasting between applications. We'll cover how Emacs relates to the system clipboard later in this chapter. You can get back what you've deleted by typing C-y (for yank).[3]  In the following example, we'll use C-k four times to delete the first two lines of A Tale of Two Cities. (Remember: the first C-k deletes the text; the second C-k deletes the remaining blank line.) Then we'll use a single C-y to bring everything back.
What exactly goes into the kill ring? Everything you delete with C-k in addition to everything you delete with C-w and everything you copy with M-w (two commands that you'll learn shortly) go into the kill ring. Words, sentences, and paragraphs that you delete with M-d, M-Del, and their relatives also go into the kill ring. In addition, text that you delete with C-u followed by either Del or C-d goes into the kill ring. About the only thing that Emacs doesn't save in the kill ring is single characters, deleted with Del or C-d. (If you need to, you can get this type of deletion back using the undo command, bound to both C- _ and C-x u.) Emacs is clever about what it puts into the kill ring: when it is assembling a big block of text from a group of deletions, it always assembles the text correctly. For example, you can type a few M-d's, followed by some M-Del's, with a couple of C-k's thrown in. When you type C-y, Emacs yanks all the text that you've deleted in the proper order. However, there's one thing you have to watch out for.  For example, if you type C-k, then delete a single character with C-d, then type another C-k, you've broken the chain. Emacs doesn't consider deletion of a single character with C-d a "kill" command; it's just a deletion and it isn't stored. In this case, you haven't made a single chain of kill commands; you've made two chains. Later, we'll see how to get the older killed text back. Table 2-3 summarizes the commands for deleting, killing, and yanking text, including options from the Edit menu.
|
| < Day Day Up > |


This place was opened in 1943 and is set to have the best Masala dosas in the city. It is always crowded, and you cannot ever get in there without waiting. Their Masala dosas are very unique and the restaurant serves a total of only 3 or 4 dishes and coffee. Bangalore is know for its Idlis and dosas (South Indian food).
This place was opened in the 1960s and is set to have the best Idli, Vada Chutni in the city. Their Chutney is very unique and very tasty. The place is also known for good coffee.
This hotel was set up in 1989 and is known famously for its Andhra cuisine/ Andhra meals. The food is very unique, spicy and delicious.
This was initially started in 1954 and then expanded. It is very well known for its chaats like pani puri, dahi puri etc. It is also very well known for its sweets.
This place came into being in the 1990s. It is very well known for its South and North Indian food. I personally love the gobi manchurian and paneer butter masala the best.
This is a street filled with stalls of food on the whole street and has South Indian cuisine, chaats, street food etc. The food here is just amazing and this street is so crowded on weekends, that you won't get a chance to step a foot inside.
This is an Indo-Chinese restaurant which is very famous for its Indo-Chinese food and has a lot of vegetarian options too.
This is a South Indian restaurant whose origin is from Tamil Nadu. It's very famous for its Sambar and Vada and many other dishes.
This is a very high-class mall and has an open setting on its roof with a lot of high-end restaurants and bars (all kinds of cuisines) and live music.
This is a very nice punjabi restaurant with spicy and delicious Punjabi dishes. They have added a lot of innovation into some of their dishes.
This is a classy restaurant which serves barbeque and a lot of other dishes along with a north Indian buffet. They have a wide variety of innovative appetizers and there is a wide variety even for vegetarians.
This place is very well known for its sweets and chaats. I love the pani puri the best over here.


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Twitter's project is incredibly well-documented and developed. Read it if you haven't already.
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smtp - Client-side tcl implementation of the smtp protocol
The smtp library package provides the client side of the Simple Mail Transfer Protocol (SMTP) (1) (2).
This command sends the MIME part (see package mime) represented by token to an SMTP server. options is a list of options and their associated values. The recognized options are:
A list of SMTP servers. The default is localhost.
If multiple servers are specified they are tried in sequence. Note that the -ports are iterated over in tandem with the servers. If there are not enough ports for the number of servers the default port (see below) is used. If there are more ports than servers the superfluous ports are ignored.
A list of SMTP ports. The default is 25.
See option -servers above regardig the behaviour for then multiple servers and ports are specified.
The name to use as our hostname when connecting to the server. By default this is either localhost if one of the servers is localhost, or is set to the string returned by info hostname.
Indicates that the SMTP server should be asked to queue the message for later processing. A boolean value.
Indicates that the SMTP server must find at least one recipient acceptable for the message to be sent. A boolean value.
A string containing an 822-style address specification. If present the header isn't examined for an originator address.
A string containing one or more 822-style address specifications. If present the header isn't examined for recipient addresses). If the string contains more than one address they will be separated by commas.
A list containing two elements, an smtp header and its associated value (the -header option may occur zero or more times).
This package supports the RFC 3207 TLS extension (3) by default provided the tls package is available. You can turn this off with this boolean option.
This boolean flag is false by default. When this flag is set the package will import TLS on a sucessfully opened channel. This is needed for connections using native TLS negotiation instead of STARTTLS. The tls package is automatically required when needed.
This option lets you specify a command to be called if an error occurs during TLS setup. The command is called with the SMTP code and diagnostic message appended. The command should return 'secure' or 'insecure' where insecure will cause the package to continue on the unencrypted channel. Returning 'secure' will cause the socket to be closed and the next server in the -servers list to be tried.
If your SMTP server requires authentication (RFC 2554 (4)) before accepting mail you can use -username and -password to provide your authentication details to the server. Currently this package supports DIGEST-MD5, CRAM-MD5, LOGIN and PLAIN authentication methods. The most secure method will be tried first and each method tried in turn until we are either authorized or we run out of methods. Note that if the server permits a TLS connection, then the authorization will occur after we begin using the secure channel.
Please also read the section on Authentication, it details the necessary prequisites, i.e. packages needed to support these options and authentication.
If the -originator option is not present, the originator address is taken from From (or Resent-From); similarly, if the -recipients option is not present, recipient addresses are taken from To, cc, and Bcc (or Resent-To, and so on). Note that the header key/values supplied by the -header option (not those present in the MIME part) are consulted. Regardless, header key/values are added to the outgoing message as necessary to ensure that a valid 822-style message is sent.
The command returns a list indicating which recipients were unacceptable to the SMTP server. Each element of the list is another list, containing the address, an SMTP error code, and a textual diagnostic. Depending on the -atleastone option and the intended recipients, a non-empty list may still indicate that the message was accepted by the server.
Beware. SMTP authentication uses SASL. I.e. if the user has to authenticate a connection, i.e. use the options -user and -password (see above) it is necessary to have the sasl package available so that smtp can load it.
This is a soft dependency because not everybody requires authentication, and sasl depends on a lot of the cryptographic (secure) hashes, i.e. all of md5, otp, md4, sha1, and ripemd160.
proc send_simple_message {recipient email_server subject body} { package require smtp package require mime set token [mime::initialize -canonical text/plain \ -string $body] mime::setheader $token Subject $subject smtp::sendmessage $token \ -recipients $recipient -servers $email_server mime::finalize $token } send_simple_message<EMAIL_ADDRESS>localhost \ "This is the subject." "This is the message."
This package uses the TLS package to handle the security for https urls and other socket connections.
Policy decisions like the set of protocols to support and what ciphers to use are not the responsibility of TLS, nor of this package itself however. Such decisions are the responsibility of whichever application is using the package, and are likely influenced by the set of servers the application will talk to as well.
For example, in light of the recent POODLE attack discovered by Google many servers will disable support for the SSLv3 protocol. To handle this change the applications using TLS must be patched, and not this package, nor TLS itself. Such a patch may be as simple as generally activating tls1 support, as shown in the example below.
package require tls tls::init -tls1 1 ;# forcibly activate support for the TLS1 protocol ... your own application code ...
Jonathan B. Postel, "SIMPLE MAIL TRANSFER PROTOCOL", RFC 821, August 1982. (http://www.rfc-editor.org/rfc/rfc821.txt)
J. Klensin, "Simple Mail Transfer Protocol", RFC 2821, April 2001. (http://www.rfc-editor.org/rfc/rfc2821.txt)
P. Hoffman, "SMTP Service Extension for Secure SMTP over Transport Layer Security", RFC 3207, February 2002. (http://www.rfc-editor.org/rfc/rfc3207.txt)
J. Myers, "SMTP Service Extension for Authentication", RFC 2554, March 1999. (http://www.rfc-editor.org/rfc/rfc2554.txt)
This document, and the package it describes, will undoubtedly contain bugs and other problems. Please report such in the category smtp of the Tcllib Trackers. Please also report any ideas for enhancements you may have for either package and/or documentation.
When proposing code changes, please provide unified diffs, i.e the output of diff -u.
Note further that attachments are strongly preferred over inlined patches. Attachments can be made by going to the Edit form of the ticket immediately after its creation, and then using the left-most button in the secondary navigation bar.
email, internet, mail, mime, net, rfc 2554, rfc 2821, rfc 3207, rfc 821, rfc 822, smtp, tls
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Copyright © 1999-2000 Marshall T. Rose and others


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As large amount of data is being generated and stored in the database, performance and data manageability is a concern. The key challenges when dealing with such data include :
Index is a database object created on frequently accessed columns of a table. Indexes contain keys that point to the physical location of where the data is being stored. DB2 Indexes work just like an index in a book to provide a quick and easy reference to data within the table.
A composite index is one index made up of combined values from multiple columns in a table. If multiple columns in a table will often be accessed together a composite index will be efficient.
A clustered index is one whose order of the rows in the data pages correspond to the order of the rows in the index. For example, if the clustering index is defined on a date key, then the DB2 database manager will attempt to rearrange data physically in ascending or descending date sequence.
This tutorial demonstrates indexing over tables and highlights the best practices to do the same. The need for creating indexes and their advantages are analysed. A graph comparing the query execution time against the tables with and without indexes is generated and the steps to create an explain snapshot displaying the use of indexes during query processing are indicated.


This tests passing a dom node as the first argument to createWidget(). Note that djConfig.parseWidgets==false, so that the widget markup in this file won't be parsed until createWidget() is told to explicitly do so.
HTML before
HTML before
HTML after
HTML after
HTML after


Configuration Parameters¶
This section describes the various configuration parameters for controlling
what the corr2
and corr3
scripts (or functions) do:
Parameters about the input file(s)¶
- file_name
(str or list) The file(s) with the data to be correlated.
For an auto-correlation, like cosmic shear, this will be the only file name you need to specify. This parameter is always required, and depending on what kind of correlation you are doing, you may need to specify others below.
Normally, there would only be a single file name here, but sometimes the galaxy data comes in multiple files. To treat them all as though they were a single large catalog, you may specify a list of file names here:
file_name : [ file1.dat, file2.dat, file3.dat ]
If you are specifying this on the command line, you’ll need to put quotes around the names, or it won’t be parsed correctly:
file_name="[file1.dat,file2.dat,file3.dat]"
- file_name2
(str or list) The file(s) to use for the second field for a cross-correlation.
If you want to cross-correlate one file (or set of files) with another, then
file_name2
is used to specify the second thing being correlated. e.g. for galaxy-galaxy lensing,file_name
should be the catalog of lenses, andfile_name2
should be the catalog of source shear values.- file_name3
(str or list) The file(s) to use for the third field for a three-point cross-correlation.
- rand_file_name
(str or list) If necessary, a list of random files with the same masking as the
file_name
catalog.- rand_file_name2
(str or list) If necessary, a list of random files with the same masking as the
file_name2
catalog.- rand_file_name3
(str or list) If necessary, a list of random files with the same masking as the
file_name3
catalog.When doing NN and NNN correlations, you need to account for masks and variable depth by providing a file or list of files that correspond to a uniform- density field as observed with the same masking and other observational details. For cross-correlations, you need to provide both of the above values to separately calibrate the first and second fields.
rand_file_name
may also be used for NG and NK correlations, but it is not required in those cases.- file_list
(str) A text file with file names in lieu of
file_name
.- file_list2
(str) A text file with file names in lieu of
file_name2
.- file_list3
(str) A text file with file names in lieu of
file_name3
.- rand_file_list
(str) A text file with file names in lieu of
rand_file_name
.- rand_file_list2
(str) A text file with file names in lieu of
rand_file_name2
.- rand_file_list3
(str) A text file with file names in lieu of
rand_file_name3
.If you have a list of file names, it may be cumbersome to list them all in the
file_name
(etc) parameter. It may be easier to do something likels *.cat > catlist
and then usefile_list=catlist
as the list of file names to use. Of course, it is an error to specify bothfile_list
andfile_name
(or any of the other corresponding pairs).- file_type
(ASCII, FITS, HDF5, or Parquet) The file type of the input files.
- delimiter
(str, default = ‘0’) The delimeter between input values in an ASCII catalog.
- comment_marker
(str, default = ‘#’) The first (non-whitespace) character of comment lines in an input ASCII catalog.
The default file type is normally ASCII. However, if the file name includes “.fit” in it, then a fits binary table is assumed. You can override this behavior using
file_type
.Furthermore, you may specify a delimiter for ASCII catalogs if desired. e.g. delimiter=’,’ for a comma-separated value file. Similarly, comment lines usually begin with ‘#’, but you may specify something different if necessary.
- ext
(int/str, default=1 for FITS or root for HDF5) The extension (fits) or group (hdf) to read from
Normally if you are using a fits file, the binary fits table is taken from the first extension, HDU 1. If you want to read from a different HDU, you can specify which one to use here. For HDF files, the default is to read from the root of the file, but you can also specify group names like “/data/cat1”
- first_row
(int, default=1)
- last_row
(int, default=-1)
- every_nth
(int, default=1)
You can optionally not use all the rows in the input file. You may specify
first_row
,last_row
, or both to limit the rows being used. The rows are numbered starting with 1. Iflast_row
is not positive, it means to use to the end of the file. Ifevery_nth
is set, it will skip rows, selecting only 1 out of every n rows.- npatch
(int, default=1)
How many patches to split the catalog into (using kmeans if no other patch information is provided) for the purpose of jackknife variance or other options that involve running via patches. (default: 1)
Note
If the catalog has ra,dec,r positions, the patches will be made using just ra,dec.
- kmeans_init
(str, default=’tree’)
- kmeans_alt
(bool, default=False)
If using kmeans to make patches, these two parameters specify which init method to use and whether to use the alternate kmeans algorithm. cf.
Field.run_kmeans
- patch_centers
(str)
Alternative to setting patch by hand or using kmeans, you may instead give patch_centers either as a file name or an array from which the patches will be determined.
- x_col
(int/str) Which column to use for x.
- y_col
(int/str) Which column to use for y.
- ra_col
(int/str) Which column to use for ra.
- dec_col
(int/str) Which column to use for dec.
For the positions of the objects, you can specify either x,y values, which imply a flat-sky approximation has already been performed (or ignored), or ra,dec values, which are of course positions on the curved sky.
For ASCII files, the columns are specified by number, starting with 1 being the first column (not 0!). For FITS files, the columns are specified by name, not number.
- x_units
(str, default=None) The units of x values.
- y_units
(str, default=None) The units of y values.
- ra_units
(str) The units of ra values.
- dec_units
(str) The units of dec values.
All distances on the sky include a “units” parameter to specify what in units the values are specified. Options for units are radians, hours, degrees, arcmin, arcsec. For ra, dec the units field is required. But for x,y, you can ignore all the unit issues, in which case the output distances will be in the same units as the input positions.
- r_col
(int/str) Which column to use for r.
When using spherical coordinates, ra,dec, you can optionally provide a distance to the object. In this case, the calculation will be done in three dimensional distances rather than angular distances. The distances between objects will be the 3-D Euclidean distance, so you should define your r values appropriately, given whatever cosmology you are assuming.
r_col
is invalid in conjunction withx_col
,y_col
.- z_col
(int/str) Which column to use for z.
Rather than specifying 3-D coordinates as (ra, dec, r), you may instead specify them as (x, y, z).
z_col
is invalid in conjunction withra_col
,dec_col
.- g1_col
(int/str) Which column to use for g1.
- g2_col
(int/str) Which column to use for g2.
If you are doing one of the shear correlation functions (i.e. NG, KG, GG), then you need to specify the shear estimates of the corresponding galaxies. The g1,g2 values are taken to be reduced shear values. They should be unbiases estimators of g1,g2, so they are allowed to exceed \(|g| = 1\). (This is required for some methods to produce unbiased estimates.
- k_col
(int/str) Which column to use for kappa.
If you are doing one of the kappa correlation functions (i.e. NK, KG, KK), then you need to specify the column to use for kappa. While kappa is nominally the lensing convergence, it could really be any scalar quantity, like temperature, size, etc.
- patch_col
(int/str) Which column to use for patch.
Use precalculated patch numbers to split the catalog into patches.
- w_col
(int/str) Which column to use for the weight (if any).
- wpos_col
(int/str) Which column to use for the position weight (if any).
The weight column is optional. If omitted, all weights are taken to be 1.
- flag_col
(int/str) Which column to use for the weight (if any).
- ignore_flag
(int) What flag(s) should be ignored.
- ok_flag
(int) What flag(s) are ok to use.
The code can be set to ignore objects with a particular flag value if desired. Some codes output a flag along with the shear value. Typically any flag != 0 should be ignored, but you can optionally have the code ignore only particular flags, treating the flag value as a bit mask. If
ignore_flag
is set to something, then objects with(flag & ignore_flag != 0)
will be ignored. Ifok_flag
is set, then objects with(flag & ~ok_flag != 0)
will be ignored. The default is equivalent took_flag = 0
, which ignores any flag != 0.- x_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
x_col
.- y_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
y_col
.- z_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
z_col
.- ra_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
ra_col
.- dec_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
dec_col
.- r_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
r_col
.- g1_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
g1_col
.- g2_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
g2_col
.- k_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
k_col
.- patch_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
patch_col
.- w_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
w_col
.- wpos_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
wpos_col
.- flag_ext
(int/str) Which HDU (fits) or group (HDF) to use for the
flag_col
.If you want to use an extension other than the first one, normally you would specify which fits extension or HDF5 group to use with the
ext
parameter. However, if different columns need to come from different HDUs, then you can override the default (given byext
, or ‘1’ (fits), or ‘/’ (HDF) if there is noext
parameter) for each column separately.- allow_xyz
(bool, default=False)
Whether to allow x,y,z columns in conjunction with ra, dec.
- flip_g1
(bool, default=False) Whether to flip the sign of g1.
- flip_g2
(bool, default=False) Whether to flip the sign of g2.
Sometimes there are issues with the sign conventions of gamma. If you need to flip the sign of g1 or g2, you may do that with
flip_g1
orflip_g2
(or both).- keep_zero_weight
(bool, default=False)
Whether to keep objects with wpos=0 in the catalog (including any objects that indirectly get wpos=0 due to NaN or flags), so they would be included in ntot and also in npairs calculations that use this Catalog, although of course not contribute to the accumulated weight of pairs.
Note
If you are cross-correlating two files with different formats, you may set any of the above items from
file_type
toflip_g2
as a two element list (i.e. two values separated by a space). In this case, the first item refers to the file(s) infile_name
, and the second item refers to the file(s) in files_name2.You may not mix (x,y) columns with (ra,dec) columns, since its meaning would be ambiguous.
If you don’t need a particular column for one of the files, you may use 0 to indicate not to read that column. This is true for any format of input catalog.
Also, if the given column only applies to one of the two input files (e.g. k_col for an n-kappa cross-correlation) then you may specify just the column name or number for the file to which it does apply.
Parameters about the binned correlation function to be calculated¶
- bin_type
(str, default=’Log’) Which type of binning should be used.
See Metrics for details.
- min_sep
(float) The minimum separation to include in the output.
- max_sep
(float) The maximum separation to include in the output.
- nbins
(int) The number of output bins to use.
- bin_size
(float) The size of the output bins in log(sep).
The bins for the histogram may be defined by setting any 3 of the above 4 parameters. The fourth one is automatically calculated from the values of the other three.
See Binning for details about how these parameters are used for the different choice of
bin_type
.- sep_units
(str, default=None) The units to use for
min_sep
andmax_sep
.sep_units
is also the units of R in the output file. For ra, dec values, you should always specifysep_units
explicitly to indicate what angular units you want to use for the separations. But if your catalogs use x,y, or if you specify 3-d correlations with r, then the output separations are in the same units as the input positions.See sep_units for more discussion about this parameter.
- bin_slop
(float, default=1) The fraction of a bin width by which it is ok to let the pairs miss the correct bin.
The code normally determines when to stop traversing the tree when all of the distance pairs for the two nodes have a spread in distance that is less than the bin size. i.e. the error in the tree traversal is less than the uncertainty induced by just binning the results into a histogram. This factor can be changed by the parameter
bin_slop
. It is probably best to keep it at 1, but if you want to make the code more conservative, you can decrease it, in which case the error from using the tree nodes will be less than the error in the histogram binning. (In practice, if you are going to do this, you are probably better off just decreasing thebin_size
instead and leavingbin_slop=1
.)See bin_slop for more discussion about this parameter.
- brute
(bool/int, default=False) Whether to do the “brute force” algorithm, where the tree traversal always goes to the leaf cells.
In addition to True or False, whose meanings are obvious, you may also set
brute
to 1 or 2, which means to go to the leaves for cat1 or cat2, respectively, but stop traversing the other catalog according to the normalbin_slop
criterion.See brute for more discussion about this parameter.
- min_u
(float) The minimum u=d3/d2 to include for three-point functions.
- max_u
(float) The maximum u=d3/d2 to include for three-point functions.
- nubins
(int) The number of output bins to use for u.
- ubin_size
(float) The size of the output bins for u.
- min_v
(float) The minimum positive v=(d1-d2)/d3 to include for three-point functions.
- max_v
(float) The maximum positive v=(d1-d2)/d3 to include for three-point functions.
- nvbins
(int) The number of output bins to use for positive v. The total number of bins in the v direction will be twice this number.
- vbin_size
(float) The size of the output bins for v.
- metric
(str, default=’Euclidean’) Which metric to use for distance measurements.
See Metrics for details.
- min_rpar
(float) If the metric supports it, the minimum Rparallel to allow for pairs to be included in the correlation function.
- max_rpar
(float) If the metric supports it, the maximum Rparallel to allow for pairs to be included in the correlation function.
- period
(float) For the ‘Periodic’ metric, the period to use in all directions.
- xperiod
(float) For the ‘Periodic’ metric, the period to use in the x directions.
- yperiod
(float) For the ‘Periodic’ metric, the period to use in the y directions.
- zperiod
(float) For the ‘Periodic’ metric, the period to use in the z directions.
Parameters about the output file(s)¶
The kind of correlation function that the code will calculate is based on which output file(s) you specify. It will do the calculation(s) relevant for each output file you set. For each output file, the first line of the output says what the columns are. See the descriptions below for more information about the output columns.
- nn_file_name
(str) The output filename for count-count correlation function.
This is the normal density two-point correlation function.
The output columns are:
R_nom
= The center of the binmeanR
= The mean separation of the points that went into the bin.meanlogR
= The mean log(R) of the points that went into the bin.xi
= The correlation function.sigma_xi
= The 1-sigma error bar for xi.DD
,RR
= The raw numbers of pairs for the data and randomsDR
(ifnn_statistic=compensated
) = The cross terms between data and random.RD
(ifnn_statistic=compensated
cross-correlation) = The cross term between random and data, which for a cross-correlation is not equivalent toDR
.
- nn_statistic
(str, default=’compensated’) Which statistic to use for xi as the estimator of the NN correlation function.
Options are (D = data catalog, R = random catalog)
‘compensated’ is the now-normal Landy-Szalay statistic: xi = (DD-2DR+RR)/RR, or for cross-correlations, xi = (DD-DR-RD+RR)/RR
‘simple’ is the older version: xi = (DD/RR - 1)
- ng_file_name
(str) The output filename for count-shear correlation function.
This is the count-shear correlation function, often called galaxy-galaxy lensing.
The output columns are:
R_nom
= The center of the binmeanR
= The mean separation of the points that went into the bin.meanlogR
= The mean log(R) of the points that went into the bin.gamT
= The mean tangential shear with respect to the point in question.gamX
= The shear component 45 degrees from the tangential direction.sigma
= The 1-sigma error bar forgamT
andgamX
.weight
= The total weight of the pairs in each bin.npairs
= The total number of pairs in each bin.
- ng_statistic
(str, default=’compensated’ if
rand_files
is given, otherwise ‘simple’) Which statistic to use for the mean shear as the estimator of the NG correlation function.Options are:
‘compensated’ is simiar to the Landy-Szalay statistic: Define:
NG = Sum(gamma around data points)
RG = Sum(gamma around random points), scaled to be equivalent in effective number as the number of pairs in NG.
npairs = number of pairs in NG.
Then this statistic is gamT = (NG-RG)/npairs
‘simple’ is the normal version: gamT = NG/npairs
- gg_file_name
(str) The output filename for shear-shear correlation function.
This is the shear-shear correlation function, used for cosmic shear.
The output columns are:
R_nom
= The center of the binmeanR
= The mean separation of the points that went into the bin.meanlogR
= The mean log(R) of the points that went into the bin.xip
= <g1 g1 + g2 g2> where g1 and g2 are measured with respect to the line joining the two galaxies.xim
= <g1 g1 - g2 g2> where g1 and g2 are measured with respect to the line joining the two galaxies.xip_im
= <g2 g1 - g1 g2>.In the formulation of xi+ using complex numbers, this is the imaginary component. It should normally be consistent with zero, especially for an auto-correlation, because if every pair were counted twice to get each galaxy in both positions, then this would come out exactly zero.
xim_im
= <g2 g1 + g1 g2>.In the formulation of xi- using complex numbers, this is the imaginary component. It should be consistent with zero for parity invariant shear fields.
sigma_xi
= The 1-sigma error bar for xi+ and xi-.weight
= The total weight of the pairs in each bin.npairs
= The total number of pairs in each bin.
- nk_file_name
(str) The output filename for count-kappa correlation function.
This is nominally the kappa version of the ne calculation. However, k is really any scalar quantity, so it can be used for temperature, size, etc.
The output columns are:
R_nom
= The center of the binmeanR
= The mean separation of the points that went into the bin.meanlogR
= The mean log(R) of the points that went into the bin.kappa
= The mean kappa this distance from the foreground points.sigma
= The 1-sigma error bar for <kappa>.weight
= The total weight of the pairs in each bin.npairs
= The total number of pairs in each bin.
- nk_statistic
(str, default=’compensated’ if
rand_files
is given, otherwise ‘simple’) Which statistic to use for the mean shear as the estimator of the NK correlation function.Options are:
‘compensated’ is simiar to the Landy-Szalay statistic: Define:
NK = Sum(kappa around data points)
RK = Sum(kappa around random points), scaled to be equivalent in effective number as the number of pairs in NK.
npairs = number of pairs in NK.
Then this statistic is
<kappa>
= (NK-RK)/npairs‘simple’ is the normal version:
<kappa>
= NK/npairs
- kk_file_name
(str) The output filename for kappa-kappa correlation function.
This is the kappa-kappa correlation function. However, k is really any scalar quantity, so it can be used for temperature, size, etc.
The output columns are:
R_nom
= The center of the binmeanR
= The mean separation of the points that went into the bin.meanlogR
= The mean log(R) of the points that went into the bin.xi
= The correlation function <k k>sigma_xi
= The 1-sigma error bar for xi.weight
= The total weight of the pairs in each bin.npairs
= The total number of pairs in each bin.
- kg_file_name
(str) The output filename for kappa-shear correlation function.
This is the kappa-shear correlation function. Essentially, this is just galaxy-galaxy lensing, weighting the tangential shears by the foreground kappa values.
The output columns are:
R_nom
= The center of the binmeanR
= The mean separation of the points that went into the bin.meanlogR
= The mean log(R) of the points that went into the bin.kgamT
= The kappa-weighted mean tangential shear.kgamX
= The kappa-weighted shear component 45 degrees from the tangential direction.sigma
= The 1-sigma error bar forkgamT
andkgamX
.weight
= The total weight of the pairs in each bin.npairs
= The total number of pairs in each bin.
- nnn_file_name
(str) The output filename for count-count-count correlation function.
This is three-point correlation function of number counts.
The output columns are:
R_nom
= The center of the bin in R = d2 where d1 > d2 > d3u_nom
= The center of the bin in u = d3/d2v_nom
= The center of the bin in v = +-(d1-d2)/d3meand1
= The mean value of d1 for the triangles in each binmeanlogd1
= The mean value of log(d1) for the triangles in each binmeand2
= The mean value of d2 for the triangles in each binmeanlogd2
= The mean value of log(d2) for the triangles in each binmeand3
= The mean value of d3 for the triangles in each binmeanlogd3
= The mean value of log(d3) for the triangles in each binzeta
= The correlation function.sigma_zeta
= The 1-sigma error bar for zeta.DDD
,RRR
= The raw numbers of triangles for the data and randomsDDR
,DRD
,RDD
,DRR
,RDR
,RRD
(ifnn_statistic=compensated
) = The cross terms between data and random.
- nnn_statistic
(str, default=’compensated’) Which statistic to use for xi as the estimator of the NNN correlation function.
Options are:
‘compensated’ is the Szapudi & Szalay (1998) estimator: zeta = (DDD-DDR-DRD-RDD+DRR+RDR+RRD-RRR)/RRR
‘simple’ is the older version: zeta = (DDD/RRR - 1), although this is not actually an estimator of zeta. Rather, it estimates zeta(d1,d2,d3) + xi(d1) + xi(d2) + xi(d3).
- ggg_file_name
(str) The output filename for shear-shear-shear correlation function.
This is the shear three-point correlation function. We use the “natural components” as suggested by Schenider & Lombardi (2003): Gamma_0, Gamma_1, Gamma_2, Gamma_3. All are complex-valued functions of (d1,d2,d3). The offer several options for the projection direction. We choose to use the triangle centroid as the reference point.
The output columns are:
R_nom
= The center of the bin in R = d2 where d1 > d2 > d3u_nom
= The center of the bin in u = d3/d2v_nom
= The center of the bin in v = +-(d1-d2)/d3meand1
= The mean value of d1 for the triangles in each binmeanlogd1
= The mean value of log(d1) for the triangles in each binmeand2
= The mean value of d2 for the triangles in each binmeanlogd2
= The mean value of log(d2) for the triangles in each binmeand3
= The mean value of d3 for the triangles in each binmeanlogd3
= The mean value of log(d3) for the triangles in each bingam0r
= The real part of Gamma_0.gam0i
= The imag part of Gamma_0.gam1r
= The real part of Gamma_1.gam1i
= The imag part of Gamma_1.gam2r
= The real part of Gamma_2.gam2i
= The imag part of Gamma_2.gam3r
= The real part of Gamma_3.gam3i
= The imag part of Gamma_3.sigma_gam
= The 1-sigma error bar for the Gamma values.weight
= The total weight of the triangles in each bin.ntri
= The total number of triangles in each bin.
- kkk_file_name
(str) The output filename for kappa-kappa-kappa correlation function.
This is the three-point correlation function of a scalar field.
The output columns are:
R_nom
= The center of the bin in R = d2 where d1 > d2 > d3u_nom
= The center of the bin in u = d3/d2v_nom
= The center of the bin in v = +-(d1-d2)/d3meand1
= The mean value of d1 for the triangles in each binmeanlogd1
= The mean value of log(d1) for the triangles in each binmeand2
= The mean value of d2 for the triangles in each binmeanlogd2
= The mean value of log(d2) for the triangles in each binmeand3
= The mean value of d3 for the triangles in each binmeanlogd3
= The mean value of log(d3) for the triangles in each binzeta
= The correlation function.sigma_zeta
= The 1-sigma error bar for zeta.weight
= The total weight of the triangles in each bin.ntri
= The total number of triangles in each bin.
- precision
(int) The number of digits after the decimal in the output.
All output quantities are printed using scientific notation, so this sets the number of digits output for all values. The default precision is 4. So if you want more (or less) precise values, you can set this to something else.
Derived output quantities¶
The rest of these output files are calculated based on one or more correlation functions.
- m2_file_name
(str) The output filename for the aperture mass statistics.
This file outputs the aperture mass variance and related quantities, derived from the shear-shear correlation function.
The output columns are:
R
= The radius of the aperture. (Spaced the same way asR_nom
is in the correlation function output files.Mapsq
= The E-mode aperture mass variance for each radius R.Mxsq
= The B-mode aperture mass variance.MMxa
,MMxb
= Two semi-independent estimate for the E-B cross term. (Both should be consistent with zero for parity invariance shear fields.)sig_map
= The 1-sigma error bar for these values.Gamsq
= The variance of the top-hat weighted mean shear in apertures of the given radius R.sig_gam
= The 1-sigma error bar forGamsq
.
- m2_uform
(str, default=’Crittenden’) The function form of the aperture
The form of the aperture mass statistic popularized by Schneider is
U = 9/Pi (1-r^2) (1/3-r^2) Q = 6/Pi r^2 (1-r^2)
However, in many ways the form used by Crittenden:
U = 1/2Pi (1-r^2) exp(-r^2/2) Q = 1/4Pi r^2 exp(-r^2/2)
is easier to use. For example, the skewness of the aperture mass has a closed form solution in terms of the 3-point function for the Crittenden form, but no such formula is known for the Schneider form.
The
m2_uform
parameter allows you to switch between the two forms, at least for 2-point applications. (You will get an error if you try to use ‘Schneider’ with the m3 output.)- nm_file_name
(str) The output filename for <N Map> and related values.
This file outputs the correlation of the aperture mass with the aperture-smoothed density field, derived from the count-shear correlation function.
The output columns are:
R
= The radius of the aperture. (Spaced the same way asR_nom
is in the correlation function output files.NMap
= The E-mode aperture mass correlated with the density smoothed with the same aperture profile as the aperture mass statistic uses.NMx
= The corresponding B-mode statistic.sig_nmap
= The 1-sigma error bar for these values.
- norm_file_name
(str) The output filename for <Nap Map>^2/<Nap^2><Map^2> and related values.
This file outputs the <Nap Map> values normalized by <Nap^2><Map^2>. This provides an estimate of the correlation coefficient, r.
The output columns are:
R
= The radius of the aperture. (Spaced the same way asR_nom
is in the correlation function output files.NMap
= The E-mode aperture mass correlated with the density smoothed with the same aperture profile as the aperture mass statistic uses.NMx
= The corresponding B-mode statistic.sig_nmap
= The 1-sigma error bar for these values.Napsq
= The variance of the aperture-weighted galaxy density.sig_napsq
= The 1-sigma error bar for <Nap^2>.Mapsq
= The aperture mass variance.sig_mapsq
= The 1-sigma error bar for <Map^2>.NMap_norm
= <Nap Map>^2 / (<Nap^2> <Map^2>)sig_norm
= The 1-sigma error bar for this value.Nsq_Mapsq
= <Nap^2> / <Map^2>sig_nn_mm
= The 1-sigma error bar for this value.
Miscellaneous parameters¶
- verbose
(int, default=1) How verbose the code should be during processing.
0 = no output unless there is an error
1 = output warnings
2 = output progress information
3 = output extra debugging lines
This is overridden by the
-v
command line argument for thecorr2
executable.- log_file
(str, default=None) Where to write the logging information.
The default is to write lines to the screen, but this option allows you to write them to a file instead. With the
corr2
executable, this can also be specified with the-l
command line argument.- output_dots
(bool, default=(
verbose
>=2)) Whether to output progress dots during the calculation of the correlation function.- split_method
(str, default=’mean’) Which method to use for splitting cells.
When building the tree, there are three obvious choices for how to split a set of points into two chld cells. The direction is always taken to be the coordinate direction with the largest extent. Then, in that direction, you can split at the mean value, the median value, or the “middle” = (xmin+xmax)/2. To select among these,
split_method
may be given as “mean”, “median”, or “middle” respectively.In addition, sometimes it may be useful to inject some randomness into the tree construction to study how much the results depend on the specific splitting used. For that purpose, there is also the option to set
split_method
= ‘random’, which will choose a random point in the middle two quartiles of the range.- min_top
(int, default=3) The minimum number of top layers to use when setting up the field.
The OpenMP parallelization happens over the top level cells, so setting this > 0 ensures that there will be multiple jobs to be run in parallel. For systems with very many cores, it may be helpful to set this larger than the default value of 3.
- max_top
(int, default=10) The maximum number of top layers to use when setting up the field.
The top-level cells are the cells where each calculation job starts. There will typically be of order 2^max_top top-level cells.
- num_threads
(int, default=0) How many (OpenMP) threads should be used.
The default is to try to determine the number of cpu cores your system has and use that many threads.


There are several tools provided by the Redline package which are useful when working with RPM files. This includes a scanner for examining existing RPM files, a programmatic interface for generating RPM packages, and an Ant task that can be imported and invoked from build scripts in order to package deployment files.
The scanner tool can be invoked from the command line using the main class
org.redline_rpm.Scanner
. From the command line
this class accepts as it's single argument a path to a valid RPM file. If the file specified by the providing path does
not exist, or if the file in not a valid RPM archive an exception will be thrown. And example of invoking the scanner
follows:
java -cp redline.jar org.redline_rpm.Scanner test.rpm
The scanner is most useful for debugging RPM files and tools as it generates a significant amount of output. For more generalized RPM information the standard RPM tools are recommended.
The builder is the main entry point for constructing RPM files programmatically. The tool is used by
constructing an instance of the org.redline_rpm.Builder
class and adding files and headers to the archive. When the builder is configured a final call will generate the RPM
file to a given java.nio.channels.FileChannel
.
Redline can be used to package projects that use the Ant build system. Included in the source and binary distributions
is an Ant task that can be used within an Ant build script to construct an RPM distributable. The task can be defined
using the antlib namespace convention by binding the namespace antlib:org.redline_rpm
and using the namespace prefix to reference the rpm
task type. An example of using the build
in Ant task follows:
<project name="test" default="rpm" xmlns:redline="antlib:org.redline_rpm"> <target name="rpm"> <mkdir dir="rpms"/> <redline:rpm group="Java Development" name="test" version="1.2.3" destination="rpms"> <zipfileset prefix="/usr/share/java" file="test-1.2.3.jar"/> <link path="/usr/share/java/test.jar" target="/usr/share/java/test-1.2.3.jar"/> <depends name="test-lib" version="1.2.3"/> </redline:rpm> </target> </project>
Another example, which includes setting the source package property:
<redline group="Java Development" name="test" version="1.2.6" destination="rpms" sourcePackage="test.rpm"> <tarfileset prefix="/tmp/rpmtest/bin" file="bin/*" filemode="744" username="none" group="none"/> <rpmfileset prefix="/tmp/rpmtest/conf" config="true" noreplace="true" file="conf/test1.conf" filemode="644" username="none" group="none"/> <rpmfileset prefix="/tmp/rpmtest/conf" config="true" file="conf/test2.conf" filemode="644" username="none" group="none"/> <depends name="test-lib" version="1.2.3"/> <redline>
This build script creates a new RPM file named test-1.2.3-1.noarch.rpm
in the rpms
directory
containing the local file test-1.2.3.jar
to be installed into /use/share/java
on the installation
machine. In addition a symbolic link is included from the packaged file to /usr/share/java/test.jar
.
The resulting RPM has a dependency on an RPM named test-lib
.
Redlone can also be used from within Maven using 'maven-antrun-plugin'. This is a sample configuration for building an RPM from Maven using Redline instead of the native system RPM tools:
<plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-antrun-plugin</artifactId> <version>1.7</version> <executions> <execution> <id>generateSources</id> <phase>package</phase> <goals> <goal>run</goal> </goals> <configuration> <tasks> <ant antfile="${basedir}/src/main/scripts/buildrpm.xml"> <target name="rpm"/> </ant> </tasks> </configuration> </execution> </executions> <dependencies> <dependency> <groupId>org.redline-rpm</groupId> <artifactId>redline</artifactId> <version>1.1.16</version> </dependency> </dependencies> </plugin>


Thought: My girl friend hasn't texted since an hour, she must have died!
Hypothesis: She is dead.
Evidence: She hasn't texted since in an hour.
$P(E) = $ Probability of her not texting given that she has died = 100%
$P(H) = $ Probability of suddenly dying = 0.01%
$P(E/-H) = $ Probability of her not texting (regardless of her well being) = 50%
Here in order to find the posterior $P(H/E)$ (Probability of her dying given that she did not text) Bayes theorem is used which can be written as,
$P(H/E) = \frac{P(E)P(H)}{(P(E)+P(H)) + (P(-H)P(E/-H))}$
[1] https://www.intechopen.com/books/kalman-filters-theory-for-advanced-applications/kalman-filter-for-moving-object-tracking-performance-analysis-and-filter-design
[2] https://towardsdatascience.com/kalman-filter-interview-bdc39f3e6cf3
[3] https://www.youtube.com/playlist?list=PLX2gX-ftPVXU3oUFNATxGXY90AULiqnWT
[4] http://ais.informatik.uni-freiburg.de/teaching/ws13/mapping/
[5] https://www.youtube.com/playlist?list=PLzCl0zqNaIBuo957MnuuDyNF2YqOYfaDP


***I am able to ship same day if ordered by 3pm EST***
We ship cornhole board sets via insured FedEx Home Delivery, FedEx Ground, or UPS Ground. Delivery for each service is 1-5 business days.
If your boards arrive damaged, please contact us within 72 hours so we can send a replacement order.
Have a blast!
Conditions for returns are on a case by case basis. Customer is responsible for all return shipping charges.
Please send us an e-mail with your request/concerns: YardSportsLLC@gmail.com.


| Week | Date | Lecture | Lab/Assignment | Reading |
|---|---|---|---|---|
| 1 |
Tue 1/17 |
Why Data Science? (Video, Slides) |
Lab 1: Python and Jupyter
|
Chapter 1: Why Data Science |
| Thu 1/19 | Causality and Experiments (Video, Slides) |
Homework 1 (due Wed 9/2) (Solutions) | Chapter 1: Causality And Experiments | |
| 2 | Tue 1/24 | Expressions (Video, Slides) |
Chapter 1: Expressions | |
| Thu 1/26 | Sequences (Video, Slides) |
Lab 2: Sequences | Chapter 1: Expressions | |
| 3 | Tue 1/31 | No Lecture: Labor Day | ||
| Thu 2/2 | Visualizations (Video, Slides) |
Lab 3: Tables | Chapter 2: Visualizations | |
| 4 | Tue 2/7 | Functions (Video, Slides) |
Chapter 2: Functions | |
| Thu 2/9 | Sampling (Video, Slides) |
Lab 4: Histograms | Chapter 2: Sampling | |
| 5 | Tue 2/14 | Iteration (Video, Slides) |
Chapter 3: Iteration Chapter 2: Appendix Statements |
|
| Thu 2/16 | Distributions I (Video, Slides) |
Lab 5: Sampling | Chapter 3: Distributions | |
| 6 | Tue 2/21 | Total Variation Distance (Video, Slides) |
Chapter 3: Total Variation Distance | |
| Thu 2/16 | Hypothesis Testing (Video, Slides) |
Lab 6: Distributions | Chapter 3: Are The Panels Representative Of The Population? | |
| 7 | Tue 2/28 | Permutation Tests II (Video, Slides) |
Chapter 3: Comparing Two Groups | |
| Thu 3/2 | Statistics (Video, Slides) |
Lab 7: Project 1 Part 1 Project 1 (due Mon 11/2) |
Chapter 3: Further Generalization: New Test Statistics | |
| 8 | Tue 3/7 | Bell Curves (Video, Slides) |
Chapter 3: The Normal Distribution | |
| Thu 3/9 | Central Limit Theorem (Video, Slides) |
Lab 8: Project 1 Part 2 | Chapter 3: Approximately Normal Probability Histograms | |
| 9 | Tue 3/14 | No Lecture: Midterm | ||
| Thu 3/16 | Confidence Intervals (Video, Slides) |
Lab 9: Project 1 Part 2 (again) | Chapter 3: Inference About The Mean Of An Unknown Population | |
| 10 | Tue 3/21 | The Bootstrap (Video, Slides) |
Chapter 3: The Bootstrap | |
| Thu 3/23 | A/B Testing (Video, Slides) |
Lab 10: Cumulative Distribution Functions | Chapter 3: Using The Bootstrap Method To Test Hypotheses | |
| -- | Tue 3/28 | Spring Break | Chapter 4: Regression | |
| Thu 3/30 | Spring Break | Lab 11: Bootstrap and Regression | Chapter 4: Bootstrap For Regression | |
| 11 | Tue 4/4 | Classifying Song Lyrics (Video, Slides) |
Chapter 4: Classification | |
| Thu 4/6 | Classifying Song Lyrics (Video, Slides) |
|||
| 12 | Tue 4/11 | Classifying Song Lyrics (Video, Slides) |
||
| Thu 4/13 | Multiple Regression (Video, Slides) |
Lab 12: Project 2 Project 2 (due Fri 12/4) |
Chapter 4: Multiple Regression | |
| 13 | Tue 4/18 | Chance Models (Video, Slides) |
||
| Thu 4/20 | No Lecture: Thanksgiving | |||
| 14 | Tue 4/25 | Conditional Probability (Video, Slides) |
||
| Th 4/27 | Bayes' Theorem (Video, Slides) |
Lab 13: Project 2 (again) |


Welcome {{request.user.first_name}},
My Postholds
{% if my_postholds %}
Create A Position {% endif %}
-
{% for my_posthold in my_postholds %}
- {{my_posthold.organization.organization_details}} {{my_posthold.get_role_display}} {% endfor %}
You currently have no approved positions
Kindly communicate to your company admin to do so
{%endif%} {% if my_role == 'managerial' %}Create A Position {% endif %}
Flight Logs
-
{% for all_company_log in all_company_logs %}
- {{all_company_log}} by {{all_company_log.reserve_airspace.created_by}} {{all_company_log.reserve_airspace.get_status_display}} {% endfor %}
Employees
{% for org in organization_and_users %}
{{org.organization_details}}
-
{% for user in org.users.all%}
- {{user}} {% endfor %}


The conversation seemed fairly ordinary.
I grew up with a lot of audio books (and now podcasts)
so paying attention while listening has been
something that is natural. The other person thought
it was weird; that is expected when you ask someone
to speak on the spot for 3 minutes.
Listening is the easiest for practicing so I will
go with that to sustain my practice. People at
work like to talk so there will be ample opportunity.


Joined
Profile Settings
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My Wishlist
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Account Settings


{% extends "base2.html" %} {% block title %}Home{% endblock title %} {% load cms_tags i18n story organization project storybase_featured sekizai_tags menu_tags verbatim storybase_tags compress %} {% block content %}
Floodlight is a place where people can tell and share inspiring stories from the Metro Denver community. This easy tool allows users to create, post and share digital stories that include photo, video, data, and narrative, calling readers to action. It’s also a place to uncover connections between stories, and reach even higher levels of community impact.