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Dear Groovy specialists,
I stumbled upon a phenomenon in Groovy which I would describe as follows:
Given a list of maps that share some common keys, it is possible to access the Map values directly via the List.
Example:
ArrayList people = [
["height": 172, "age": 42],
["height": 180, "age": 66],
["height": 180, "age": null],
["height": 180],
["age": 10]
]
println "people.height: " + people.height
println "people.age: " + people.age
Output:
people.height: [172, 180, 180, 180, null]
people.age: [42, 66, null, null, 10]
Does this syntax (e.g. people.height / people.age) have a name?
Thanks in advance!
PS: This answer would be another example of the mentioned syntax
Does this syntax (e.g. people.height / people.age) have a name?
That is Groovy's syntax for property access.
(Note that a property is a different thing than a field and Groovy has a bunch of special dynamic stuff that happens during property access.)
Let me explain, I'm working in a bank and I'm trying to make a short python script that calculates the percentage of different shareholders.
In my example EnterpriseA is owned by different Shareholders directly and indirectly I stored it as it follows :
EnterpriseA = {'Shareholder0': {'Shareholder1': 25, 'Shareholder2': 31, 'Shareholder3': 17, 'Shareholder4': 27},
'Shareholder3': {'Shareholder1': 34, 'Shareholder4': 66}}
I want to calculate how much each shareholders have of EntrepriseA, but I can't figure how to check if a shareholder appears multiple times in all my dictionaries.
What I'm thinking is checking if Shareholder1 appears multiple times if so calculate how many percentage he owns of EnterpriseA like this :
percentage = EnterpriseA['Shareholder0']['Shareholder1'] + (EnterpriseA['Shareholder0']['Shareholder3']*EnterperiseA['Shareholder3']['Shareholder1']/100)
I've made a quick drawing for better understanding
If the maximum depth is only ever singly nested then you can just write a little helper function.
Edit:
From what you've explained, 'Shareholder0' is basically a list of direct enterprise shares.
I've modified the helper function and included a constant reflecting that.
ENTERPRISE_SHARES = 'Shareholder0'
EnterpriseA = {
'Shareholder0': {
'Shareholder1': 25,
'Shareholder2': 31,
'Shareholder3': 17,
'Shareholder4': 27
},
'Shareholder3': {
'Shareholder1': 34,
'Shareholder4': 66
}
}
def calc_percent(enterprise, name):
parent_percents = enterprise[ENTERPRISE_SHARES]
total_percent = parent_percents.get(name, 0)
for shareholder, shares in enterprise.items():
if shareholder != ENTERPRISE_SHARES and shareholder != name:
total_percent += parent_percents[shareholder] / 100 * shares.get(name, 0)
return total_percent
print(calc_percent(EnterpriseA, 'Shareholder1'))
print(calc_percent(EnterpriseA, 'Shareholder2'))
print(calc_percent(EnterpriseA, 'Shareholder4'))
I need to import an excel document into mathematica which has 2000 compounds in it, with each compound have 6 numerical constants assigned to it. The end goal is to type a compound name into mathematica and have the 6 numerical constants be outputted. So far my code is:
t = Import["Titles.txt.", {"Text", "Lines"}] (imports compound names)
n = Import["NA.txt.", "List"] (imports the 6 values for each compound)
n[[2]] (outputs the second compounds 6 values)
Instead of n[[#]] i would like to know how to type in a compound from the imported compound names and have the 6 values be outputted .
I'm not sure if I understand your question - you have two text files, rather than an Excel file, for example, and it's not clear what the data looks like. But there are probably plenty of ways to do this. Here's a suggestion (it might not be the best way):
Let's assume that you've got all your data into a table (a list of lists):
pt = {
{"Hydrogen", "H", 1, 1.0079, -259, -253, 0.09, 0.14, 1776, 1, 13.5984},
{"Helium", "He", 2, 4.0026, -272, -269, 0, 0, 1895, 18, 24.5874},
{"Lithium" , "Li", 3, 6.941, 180, 1347, 0.53, 0, 1817, 1, 5.3917}
}
To find the information associated with a particular string:
Cases[pt, {"Helium", rest__} -> rest]
{"He", 2, 4.0026, -272, -269, 0, 0, 1895, 18, 24.5874}
where the pattern rest__ holds everything that was found after "Helium".
To look for the second item:
Cases[pt, {_, "Li", rest__} -> rest]
{2, 4.0026, -272, -269, 0, 0, 1895, 18, 24.5874}
If you add more information to the patterns, you have more flexibility in how you choose elements from the table:
Cases[pt, {name_, symbol_, aNumber_, aWeight_, mp_, bp_, density_,
crust_, discovered_, rest__}
/; discovered > 1850 -> {name, symbol, discovered}]
{{"Helium", "He", 1895}}
For something interactive, you could knock up a Manipulate:
elements = pt[[All, 1]];
headings = {"symbol", "aNumber", "aWeight", "mp", "bp", "density", "crust", "discovered", "group", "ion"};
Manipulate[
Column[{
elements[[x]],
TableForm[{
headings, Cases[pt, {elements[[x]], rest__} -> rest]}]}],
{x, 1, Length[elements], 1}]
I have a database with documents that are roughly of the form:
{"created_at": some_datetime, "deleted_at": another_datetime, "foo": "bar"}
It is trivial to get a count of non-deleted documents in the DB, assuming that we don't need to handle "deleted_at" in the future. It's also trivial to create a view that reduces to something like the following (using UTC):
[
{"key": ["created", 2012, 7, 30], "value": 39},
{"key": ["deleted", 2012, 7, 31], "value": 12}
{"key": ["created", 2012, 8, 2], "value": 6}
]
...which means that 39 documents were marked as created on 2012-07-30, 12 were marked as deleted on 2012-07-31, and so on. What I want is an efficient mechanism for getting the snapshot of how many documents "existed" on 2012-08-01 (0+39-12 == 27). Ideally, I'd like to be able to query a view or a DB (e.g. something that's been precomputed and saved to disk) with the date as the key or index, and get the count as the value or document. e.g.:
[
{"key": [2012, 7, 30], "value": 39},
{"key": [2012, 7, 31], "value": 27},
{"key": [2012, 8, 1], "value": 27},
{"key": [2012, 8, 2], "value": 33}
]
This can be computed easily enough by iterating through all of the rows in the view, keeping a running counter and summing up each day as I go, but that approach slows down as the data set grows larger, unless I'm smart about caching or storing the results. Is there a smarter way to tackle this?
Just for the sake of comparison (I'm hoping someone has a better solution), here's (more or less) how I'm currently solving it (in untested ruby pseudocode):
require 'date'
def date_snapshots(rows)
current_date = nil
current_count = 0
rows.inject({}) {|hash, reduced_row|
type, *ymd = reduced_row["key"]
this_date = Date.new(*ymd)
if current_date
# deal with the days where nothing changed
(current_date.succ ... this_date).each do |date|
key = date.strftime("%Y-%m-%d")
hash[key] = current_count
end
end
# update the counter and deal with the current day
current_date = this_date
current_count += reduced_row["value"] if type == "created_at"
current_count -= reduced_row["value"] if type == "deleted_at"
key = current_date.strftime("%Y-%m-%d")
hash[key] = current_count
hash
}
end
Which can then be used like so:
rows = couch_server.db(foo).design(bar).view(baz).reduce.group_level(3).rows
date_snapshots(rows)["2012-08-01"]
Obvious small improvement would be to add a caching layer, although it isn't quite as trivial to make that caching layer play nicely incremental updates (e.g. the changes feed).
I found an approach that seems much better than my original one, assuming that you only care about a single date:
def size_at(date=Time.now.to_date)
ymd = [date.year, date.month, date.day]
added = view.reduce.
startkey(["created_at"]).
endkey( ["created_at", *ymd, {}]).rows.first || {}
deleted = view.reduce.
startkey(["deleted_at"]).
endkey( ["deleted_at", *ymd, {}]).rows.first || {}
added.fetch("value", 0) - deleted.fetch("value", 0)
end
Basically, let CouchDB do the reduction for you. I didn't originally realize that you could mix and match reduce with startkey/endkey.
Unfortunately, this approach requires two hits to the DB (although those could be parallelized or pipelined). And it doesn't work as well when you want to get a lot of these sizes at once (e.g. view the whole history, rather than just look at one date).
Say I have a matrix that looks something like this:
{{foobar, 77},{faabar, 81},{foobur, 22},{faabaa, 8},
{faabian, 88},{foobar, 27}, {fiijii, 52}}
and a list like this:
{foo, faa}
Now I would like to add up the numbers for each line in the matrix based on the partial match of the strings in the list so that I end up with this:
{{foo, 126},{faa, 177}}
I assume I need to map a Select command, but I am not quite sure how to do that and match only the partial string. Can anybody help me? Now my real matrix is around 1.5 million lines so something that isn't too slow would be of added value.
Here is a starting point:
data={{"foobar",77},{"faabar",81},{"foobur",22},{"faabaa",8},{"faabian",88},{"foobar",27},{"fiijii",52}};
{str,vals}=Transpose[data];
vals=Developer`ToPackedArray[vals];
findValPos[str_List,strPat_String]:=
Flatten[Developer`ToPackedArray[
Position[StringPosition[str,strPat],Except[{}],{1},Heads->False]]]
Total[vals[[findValPos[str,"faa"]]]]
Here is yet another approach. It is reasonably fast, and also concise.
data =
{{"foobar", 77},
{"faabar", 81},
{"foobur", 22},
{"faabaa", 8},
{"faabian", 88},
{"foobar", 27},
{"fiijii", 52}};
match = {"foo", "faa"};
f = {#2, Tr # Pick[#[[All, 2]], StringMatchQ[#[[All, 1]], #2 <> "*"]]} &;
f[data, #]& /# match
{{"foo", 126}, {"faa", 177}}
You can use ruebenko's pre-processing for greater speed.
This is about twice as fast as his method on my system:
{str, vals} = Transpose[data];
vals = Developer`ToPackedArray[vals];
f2 = {#, Tr # Pick[vals, StringMatchQ[str, "*" <> # <> "*"]]} &;
f2 /# match
Notice that in this version I test substrings that are not at the beginning, to match ruebenko's output. If you want to only match at the beginning of strings, which is what I assumed in the first function, it will be faster still.
make data
mat = {{"foobar", 77},
{"faabar", 81},
{"foobur", 22},
{"faabaa", 8},
{"faabian", 88},
{"foobar", 27},
{"fiijii", 52}};
lst = {"foo", "faa"};
now select
r1 = Select[mat, StringMatchQ[lst[[1]], StringTake[#[[1]], 3]] &];
r2 = Select[mat, StringMatchQ[lst[[2]], StringTake[#[[1]], 3]] &];
{{lst[[1]], Total#r1[[All, 2]]}, {lst[[2]], Total#r2[[All, 2]]}}
gives
{{"foo", 126}, {"faa", 177}}
I'll try to make it more functional/general if I can...
edit(1)
This below makes it more general. (using same data as above):
foo[mat_, lst_] := Select[mat, StringMatchQ[lst, StringTake[#[[1]], 3]] &]
r = Map[foo[mat, #] &, lst];
MapThread[ {#1, Total[#2[[All, 2]]]} &, {lst, r}]
gives
{{"foo", 126}, {"faa", 177}}
So now same code above will work if lst was changed to 3 items instead of 2:
lst = {"foo", "faa", "fii"};
How about:
list = {{"foobar", 77}, {"faabar", 81}, {"foobur", 22}, {"faabaa",
8}, {"faabian", 88}, {"foobar", 27}, {"fiijii", 52}};
t = StringTake[#[[1]], 3] &;
{t[#[[1]]], Total[#[[All, 2]]]} & /# SplitBy[SortBy[list, t], t]
{{"faa", 177}, {"fii", 52}, {"foo", 126}}
I am sure I have read a post, maybe here, in which someone described a function that effectively combined sorting and splitting but I cannot remember it. Maybe someone else can add a comment if they know of it.
Edit
ok must be bedtime -- how could I forget Gatherby
{t[#[[1]]], Total[#[[All, 2]]]} & /# GatherBy[list, t]
{{"foo", 126}, {"faa", 177}, {"fii", 52}}
Note that for a dummy list of 1.4 million pairs this took a couple of seconds so not exactly a super fast method.