I made a very simple Octave script
a = [10e6, 11e6, 12e6];
b = [10, 11, 12];
plot(a, b, 'rd-')
which outputs the following graph.
Graph
Is it possible to set the numbering on the x-axis to engineering notation, rather than scientific, and have it display "10.5e+6, 11e+6, 11.5e+6" instead of "1.05e+7, 1.1e+7, 1.15+e7"?
While octave provides a 'short eng' formatting option, which does what you're asking for in terms of printing to the terminal, it does not appear to provide this functionality in plots or when formatting strings via sprintf.
Therefore you'll have to find a way to do this by yourself, with some creative string processing of the initial xticks, and substituting the plot's ticklabels accordingly. Thankfully it's not that hard :)
Using your example:
a = [10e6, 11e6, 12e6];
b = [10, 11, 12];
plot(a, b, 'rd-')
format short eng % display stdout in engineering format
TickLabels = disp( xticks ) % collect string as it would be displayed on the stdout
TickLabels = strsplit( TickLabels ) % tokenize at spaces
TickLabels = TickLabels( 2 : end - 1 ) % discard start and end empty tokens
TickLabels = regexprep( TickLabels, '\.0+e', 'e' ) % remove purely zero decimals using a regular expression
TickLabels = regexprep( TickLabels, '(\.[1-9]*)0+e', '$1e' ) % remove non-significant zeros in non-zero decimals using a regular expression
xticklabels( TickLabels ) % set the new ticklabels to the plot
format % reset short eng format back to default, if necessary
Related
I've got a little MatLab script, which I try to understand. It doesn't do very much. It only reads a text from a file and encode and decode it with the Huffman-functions.
But it throws an error while decoding:
"error: out of memory or dimension too large for Octave's index type
error: called from huffmandeco>dict2tree at line 95 column 19"
I don't know why, because I debugged it and don't see a large index type.
I added the part which calculates p from the input text.
%text is a random input text file in ASCII
%calculate the relative frequency of every Symbol
for i=0:127
nlet=length(find(text==i));
p(i+1)=nlet/length(text);
end
symb = 0:127;
dict = huffmandict(symb,p); % Create dictionary
compdata = huffmanenco(fdata,dict); % Encode the data
dsig = huffmandeco(compdata,dict); % Decode the Huffman code
I can oly use octave instead of MatLab. I don't know, if there is an unexpected error. I use the Octave Version 6.2.0 on Win10. I tried the version for large data, it didn't change anything.
Maybe anyone knows the error in this context?
EDIT:
I debugged the code again. In the function huffmandeco I found the following function:
function tree = dict2tree (dict)
L = length (dict);
lengths = zeros (1, L);
## the depth of the tree is limited by the maximum word length.
for i = 1:L
lengths(i) = length (dict{i});
endfor
m = max (lengths);
tree = zeros (1, 2^(m+1)-1)-1;
for i = 1:L
pointer = 1;
word = dict{i};
for bit = word
pointer = 2 * pointer + bit;
endfor
tree(pointer) = i;
endfor
endfunction
The maximum length m in this case is 82. So the function calculates:
tree = zeros (1, 2^(82+1)-1)-1.
So it's obvious why the error called a too large index type.
But there must be a solution or another error, because the code is tested before.
I haven't weeded through the code enough to know why yet, but huffmandict is not ignoring zero-probability symbols the way it claims to. Nor have I been able to find a bug report on Savannah, but again I haven't searched thoroughly.
A workaround is to limit the symbol list and their probabilities to only the symbols that actually occur. Using containers.Map would be ideal, but in Octave you can do that with a couple of the outputs from unique:
% Create a symbol table of the unique characters in the input string
% and the indices into the table for each character in the string.
[symbols, ~, inds] = unique(textstr);
inds = inds.'; % just make it easier to read
For the string
textstr = 'Random String Input.';
the result is:
>> symbols
symbols = .IRSadgimnoprtu
>> inds
inds =
Columns 1 through 19:
4 6 11 7 12 10 1 5 15 14 9 11 8 1 3 11 13 16 15
Column 20:
2
So the first symbol in the input string is symbols(4), the second is symbols(6), and so on.
From there, you just use symbols and inds to create the dictionary and encode/decode the signal. Here's a quick demo script:
textstr = 'Random String Input.';
fprintf("Starting string: %s\n", textstr);
% Create a symbol table of the unique characters in the input string
% and the indices into the table for each character in the string.
[symbols, ~, inds] = unique(textstr);
inds = inds.'; % just make it easier to read
% Calculate the frequency of each symbol in table
% max(inds) == numel(symbols)
p = histc(inds, 1:max(inds))/numel(inds);
dict = huffmandict(symbols, p);
compdata = huffmanenco(inds, dict);
dsig = huffmandeco(compdata, dict);
fprintf("Decoded string: %s\n", symbols(dsig));
And the output:
Starting string: Random String Input.
Decoded string: Random String Input.
To encode strings other than the original input string, you would have to map the characters to symbol indices (ensuring that all symbols in the string are actually present in the symbol table, obviously):
>> [~, s_idx] = ismember('trogdor', symbols)
s_idx =
15 14 12 8 7 12 14
>> compdata = huffmanenco(s_idx, dict);
>> dsig = huffmandeco(compdata, dict);
>> fprintf("Decoded string: %s\n", symbols(dsig));
Decoded string: trogdor
I'm trying to plot values from my geothermal heat pump log files to analyse it's performance. I tried with excel but it was to slow and not possible to get the plot type I wanted so I'm trying Octave instead. I have absolutely no experience with octave so please forgive my incompetence!
I've processed the .log files with open office calc to get into a decent delimited format. The first column is datetime with the format MM/DD/YY HH:MM:SS, in total there is 21 columns (but I only need 5) and one header line with a label, coma delimiter is '.' and delimiter is ','. The file can be downloaded here and the first 7 columns look like this:
02/19/2018 23:07:00,-0.7,47.5,42,47.3,52.1,1.5
I'm currently trying to plot this with demonstration 3 plotyy from here. Column 2, 3, 5 and 8 imports correctly so I'm figuring it's a problem with the datetime column 1. How can I get Octave to import column 1 correctly and use it as x axis in this plot?:
data=csvread('heatpump.csv');
clf;
hold on
t=data(:,1);
x=data(:,3);
y=data(:,5);
z=data(:,2);
o=data(:,8);
[hax, h1, h2] = plotyy (t, x, t, y);
[~, h3, h4] = plotyy (t, z, t, o);
set ([h3, h4], "linestyle", "--");
xlabel (hax(1), "Time");
title (hax(2), 'Heat pump analysis');
ylabel (hax(1), "Radiator and hot water temp");
ylabel (hax(2), "Outdoor temp and brine out");
There are many, many ways. Here I show you how to read the csv using csv2cell from the io package. I've tried to modify your existing code as less as sane. The first columns is used verbatim (well, I inserted a linebreak) to the plot. There is also a commented version which actually does the conversion and you could then use datetick. Btw, If you add google drive links it would be cool if you add direct links so someone can easily grab the csv or insert the url in the code as I've done, see below.
set (0, "defaultlinelinewidth", 2);
url = "https://drive.google.com/uc?export=download&id=1K_czefz-Wz4HPdvc7YqIqIupPwMi8a7r";
fn = "heatpump.csv";
if (! exist (fn, "file"))
urlwrite (url, fn);
endif
pkg load io
d = csv2cell (fn);
# convert to serial date
# (but you don't have if you want to keep the old format)
#t = datenum (d(2:end,1), "mm/dd/yyyy HH:MM:SS");
data = cell2mat (d(2:end,2:end));
clf;
hold on
t = 1:rows (data);
# Attention: the date/time column time was removed above, so the indizes are shifted
x = data(:,2);
y = data(:,4);
z = data(:,1);
o = data(:,7);
[hax, h1, h2] = plotyy (t, x, t, y);
[hax2, h3, h4] = plotyy (t, z, t, o);
grid on
#set ([h3, h4], "linestyle", "--");
xlabel (hax(1), "Time");
title (hax(2), 'Heat pump analysis');
ylabel (hax(1), "Radiator and hot water temp");
ylabel (hax(2), "Outdoor temp and brine out");
# use date as xtick
# extract them
date_time = d (get(hax2(1), "xtick"), 1);
# break them after the date part
date_time = strrep (date_time, " ", "\n");
# feed them back
set (hax, "xticklabel", date_time)
set (hax2, "xticklabel", date_time)
print ("-S1200,1000", "-F:10", "out.png")
I have a self-defined function in Mathematica, which has the following syntax:
outputval = myfunc[r, sigma, S, K, T, lambda, eta1, eta2, p]
When the function is called as above with numeric input values, it outputs a single output value.
For each input variable I have 5 different values. I want to input all combinations of all 5 values of the 9 input variables in my function and export a CSV file containing the 9 input values and their respective output value in the 10th column.
I am very new to Mathematica and I have no clue how to do so. Any help is appreciated:)
A small example will illustrate how to get what you want:
xvals = {1, 2}
yvals = {3, 4}
{Sequence ## #, f ## #} & /# Tuples[{xvals, yvals}]
Warning: 5^9==1953125. So you may with to use a Do loop and write directly to file instead of creating these lists. To illustrate:
fmt = StringTemplate["``,``,``"];
Do[Print[fmt[x, y, f[x, y]]], {x, xvals}, {y, yvals}]
You'll want to replace Print with WriteLine.
I'm just starting to learn how to code in Sagemath, I know it's similar to python but I don't have much experience with that either.
I'm trying to add two binary numbers representing fractions. That is, something like
a = '110'
b = '011'
bin(int(a,2) + int(b,2))
But using values representing fractions, such as '1.1'.
Thanks in advance!
If you want to do this in vanilla Python, parsing the binary fractions by hand isn't too bad (the first part being from this answer);
def binstr_to_float(s):
t = s.split('.')
return int(t[0], 2) + int(t[1], 2) / 2.**len(t[1])
def float_to_binstr(f):
i = 0
while int(f) != f:
f *= 2
i += 1
as_str = str(bin(int(f)))
if i == 0:
return as_str[2:]
return as_str[2:-i] + '.' + as_str[-i:]
float_to_binstr(parse_bin('11.1') + parse_bin('0.111')) # is '100.011'
In python you can use the Binary fractions package. With this package you can convert binary-fraction strings into floats and vice-versa. Then, you can perform operations on them.
Example:
>>> from binary_fractions import Binary
>>> sum = Binary("1.1") + Binary("10.01")
>>> str(sum)
'0b11.11'
>>> float(sum)
3.75
>>>
It has many more helper functions to manipulate binary strings such as: shift, add, fill, to_exponential, invert...
PS: Shameless plug, I'm the author of this package.
I'm trying to use PyAlogoTrade's event profiler
However I don't want to use data from yahoo!finance, I want to use my own but can't figure out how to parse in the CSV, it is in the format:
Timestamp Low Open Close High BTC_vol USD_vol [8] [9]
2013-11-23 00 800 860 847.666666 886.876543 853.833333 6195.334452 5248330 0
2013-11-24 00 745 847.5 815.01 860 831.255 10785.94131 8680720 0
The complete CSV is here
I want to do something like:
def main(plot):
instruments = ["AA", "AES", "AIG"]
feed = yahoofinance.build_feed(instruments, 2008, 2009, ".")
Then replace yahoofinance.build_feed(instruments, 2008, 2009, ".") with my CSV
I tried:
import csv
with open( 'FinexBTCDaily.csv', 'rb' ) as csvfile:
data = csv.reader( csvfile )
def main( plot ):
feed = data
But it throws an attribute error. Any ideas how to do this?
I suggest to create your own Rowparser and Feed, which is much easier than it sounds, have a look here: yahoofeed
This also allows you to work with intraday data and cleanup the data if needed, like your timestamp.
Another possibility, of course, would be to parse your file and save it, so it looks like a yahoo feed. In your case, you would have to adapt the columns and the Timestamp.
Step A: follow PyAlgoTrade doc on GenericBarFeed class
On this link see the addBarsFromCSV() in CSV section of the BarFeed class in v0.16
On this link see the addBarsFromCSV() in CSV section of the BarFeed class in v0.17
Note
- The CSV file must have the column names in the first row.
- It is ok if the Adj Close column is empty.
- When working with multiple instruments:
--- If all the instruments loaded are in the same timezone, then the timezone parameter may not be specified.
--- If any of the instruments loaded are in different timezones, then the timezone parameter should be set.
addBarsFromCSV( instrument, path, timezone = None )
Loads bars for a given instrument from a CSV formatted file. The instrument gets registered in the bar feed.
Parameters:
(string) instrument – Instrument identifier.
(string) path – The path to the CSV file.
(pytz) timezone – The timezone to use to localize bars.Check pyalgotrade.marketsession.
Next:
A BarFeed loads bars from CSV files that have the following format:
Date Time, Open, High, Low, Close, Volume, Adj Close
2013-01-01 13:59:00,13.51001,13.56,13.51,13.56789,273.88014126,13.51001
Step B: implement a documented CSV-file pre-formatting
Your CSV data will need a bit of sanity ( before will be able to be used in PyAlgoTrade methods ),however it is doable and you can create an easy transformator either by hand or with a use of a powerful numpy.genfromtxt() lambda-based converters facilities.
This sample code is intended for an illustration purpose, to see immediately the powers of converters for your own transformations, as CSV-structure differs.
with open( getCsvFileNAME( ... ), "r" ) as aFH:
numpy.genfromtxt( aFH,
skip_header = 1, # Ref. pyalgotrade
delimiter = ",",
# v v v v v v
# 2011.08.30,12:00,1791.20,1792.60,1787.60,1789.60,835
# 2011.08.30,13:00,1789.70,1794.30,1788.70,1792.60,550
# 2011.08.30,14:00,1792.70,1816.70,1790.20,1812.10,1222
# 2011.08.30,15:00,1812.20,1831.50,1811.90,1824.70,2373
# 2011.08.30,16:00,1824.80,1828.10,1813.70,1817.90,2215
converters = { 0: lambda aString: mPlotDATEs.date2num( datetime.datetime.strptime( aString, "%Y.%m.%d" ) ), #_______________________________________asFloat ( 1.0, +++ )
1: lambda aString: ( ( int( aString[0:2] ) * 60 + int( aString[3:] ) ) / 60. / 24. ) # ( 15*60 + 00 ) / 60. / 24.__asFloat < 0.0, 1.0 )
# HH: :MM HH MM
}
)
You can use pyalgotrade.barfeed.addBarsFromSequence with list comprehension to feed in data from CSV row by row/bar by bar. Basically you create a bar from each row, pass OHLCV as init parameters and extra columns with additional data in a dictionary. You can try something like this (with all the required imports):
data = pd.DataFrame(index=pd.date_range(start='2021-11-01', end='2021-11-05'), columns=['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', 'ExtraCol1', 'ExtraCol3', 'ExtraCol4', 'ExtraCol5'], data=np.random.rand(5, 10))
feed = yahoofeed.Feed()
feed.addBarsFromSequence('instrumentID', data.index.map(lambda i:
BasicBar(
i,
data.loc[i, 'Open'],
data.loc[i, 'High'],
data.loc[i, 'Low'],
data.loc[i, 'Close'],
data.loc[i, 'Volume'],
data.loc[i, 'Adj Close'],
Frequency.DAY,
data.loc[i, 'ExtraCol1':].to_dict())
).values)
The input data frame was created with random values to make this example easier to reproduce, but the part where the bars are added to the feed should work the same for data frames from CSVs given that the valid column names are used.