I have a Gnuplot data file broken into sections (2 blank lines). I wish to capture the indices I've selected to plot into the key of the plot, "plot 'datafile' index 5:10:1 us 7:8 ti XXX", where XXX is the index. I understand the the pseudocolumn, column(-2), contains the index value.
How do I capture the index and 'sprint' it into the title? The index is, of course, and integer.
What do you want to see as XXX? The index? Do you actually mean the indices from 5 to 10 as several key entries? As far as I know the title is only evaluated once per plot command. Hence, you have to put the plot command into a for loop.
Script:
### plotting several blocks with index as title
reset session
# create some random test data
set table $Data
set samples 10
do for [i=1:12] {
plot '+' u 1:(i+rand(0)) w table, \
'+' u ("") every ::::1 w table # two empty lines
}
unset table
set key out
plot for [i=5:10] $Data u 1:2 index i w l ti sprintf("index %d",i)
### end of script
Result:
Here is a relevant plot from the on-line demo collection:
plot #2 of 'iterate.dem'
The plot command that generated it is
splot for [i=1:*] "whale.dat" index i title sprintf("scan %d",i) with lines
If you do not want to extract all the available data segments you can adjust the iteration limits to match which set of index values you want to select.
Related
I have a file "a_test.dat" with two data blocks that I can select via the corresponding index.
# first
x1 y1
3 1
6 2
9 8
# second
x2 y2
4 5
8 2
2 7
Now I want to connect the data points of both indices with an arrow.
set arrow from (x1,y1) to (x2,y2).
I can plot both blocks with one plot statement. But I cannot get the points to set the arrows.
plot "a_test.dat" index "first" u 1:2, "" index "second" u 1:2
From version 5.2 you can use gnuplot arrays:
stats "a_test.dat" nooutput
array xx[STATS_records]
array yy[STATS_records]
# save all data into two arrays
i = 1
fnset(x,y) = (xx[i]=x, yy[i]=y, i=i+1)
# parse data ignoring output
set table $dummy
plot "" using (fnset($1,$2)) with table
unset table
# x2,y2 data starts at midpoint in array
numi = int((i-1)/2)
plot for [i=1:numi] $dummy using (xx[i]):(yy[i]):(xx[numi+i]-xx[i]):(yy[numi+i]-yy[i]) with vectors
Use stats to count the number of lines in the file, so that the array can
be large enough. Create an array xx and another yy to hold the data.
Use plot ... with table to read the file again, calling your function
fnset() for each data line with the x and y column values. The function
saves them at the current index i, which it increments. It was
initialised to 1.
For 3+3 data lines, i ends up at 7, so we set numi to (i-1)/2 i.e. 3.
Use plot for ... vectors to draw the arrows. Each arrow needs 4 data
items from the array. Note that the second x,y must be a relative delta,
not an absolute position.
I am trying to get my head around the Pandas module and started learning about the Series data structure.
I have created the following Series in Spyder :-
songs = pd.Series(data = [145,142,38,13], name = "Count")
I can obtain information about the Series index using the code:-
songs.index
The output of the above code is as follows:-
My question is where it states Start = 0 and Stop = 4, what are these referring to?
I have interpreted start = 0 as the first element in the Series is in row 0.
But i am not sure what Stop value refers to as there are no elements in row 4 of the Series?
Can some one explain?
Thank you.
This concept as already explained adequately in the comments (indexing is at minus one the count of items) is prevalent in many places.
For instance, take the list data structure-
z = songs.to_list()
[145, 142, 38, 13]
len(z)
4 # length is four
# however indexing stops at i-1 position 'i' being the length/count of items in the list.
z[4] # this will raise an IndexError
# you will have to start at index 0 going till only index 3 (i.e. 4 items)
z[0], z[1], z[2], z[-1] # notice how -1 can be used to directly access the last element
I was looking for an answer everywhere, but I just couldn't find one to this problem (maybe I was just too stupid to use other answers, because I'm new to R).
I have two data frames with different numbers of rows. I want to create a plot containing a single bar per data frame. Both should have the same length and the count of different variables should be stacked over each other. For example: I want to compare the proportions of gender in those to data sets.
t1<-data.frame(cbind(c(1:6), factor(c(1,2,2,1,2,2))))
t2<-data.frame(cbind(c(1:4), factor(c(1,2,2,1))))
1 represents male, 2 represents female
I want to create two barplots next to each other that represent, that the proportions of gender in the first data frame is 2:4 and in the second one 2:2.
My attempt looked like this:
ggplot() + geom_bar(aes(1, t1$X2, position = "fill")) + geom_bar(aes(1, t2$X2, position = "fill"))
That leads to the error: "Error: stat_count() must not be used with a y aesthetic."
First I should merge the two dataframes. You need to add a variable that will identify the origin of the data, add in both dataframes a column with an ID (like t1 and t2). Keep in mind that your columnames are the same in both frames so you will be able to use the function rbind.
t1$data <- "t1"
t2$data <- "t2"
t <- (rbind(t1,t2))
Now you can make the plot:
ggplot(t[order(t$X2),], aes(data, X2, fill=factor(X2))) +
geom_bar(stat="identity", position="stack")
I am writing code for a Naive Bayes model(I know there's a standard implementation in Sklearn, but I want to code it anyway) - For this I have say upwards of 30 features, against all of which I have the corresponding click & impression counts (Treat them as True/False flags)
What I need then, is to calculate
P(Click/F1, F2.. F30) = (P(Click)*P(F1/Click)*P(F2|click) ..*P(F30|Click))/(P(F1, F2...F30), and
P(NoClick/F1, F2.. F30) = (P(NoClick)*P(F1/NoClick)*P(F2|Noclick) ..*P(F30|NOClick))/(P(F1, F2...F30)
Where I will disregard the denominator as it will affect both Click & Non click behaviour similarly.
Example, for two features, day_custom & is_tablet_phone, I have
is_tablet_phone click impression
FALSE 375417 28291280
TRUE 17743 4220980
day_custom click impression
Fri 77592 7029703
Mon 43576 3773571
Sat 65950 5447976
Sun 66460 5031271
Thu 74329 6971541
Tue 55282 4575114
Wed 51555 4737712
My approach to the Problem : Assuming I read the individual files in data frame, one after another, I want the abilty to calculate & store the corresponding Probablities back in a file, that I will then use for real time prediction of Probabilty to click vs no click.
One possible structure of "processed file" thus would be -:
Here's my entire code -:
In the full blown example, I am traversing the entire directory structure(of 30 txt files, one at a time, from the base path) - which is why I need the ability to create "names" at runtime.
for base_path in base_paths:
for root, dirs, files in os.walk(base_path):
for file in files:
file_paths.append(os.path.join(root, file))
For reasons of tractability, follow from here, by taking the 2 txt files as sample input
file_paths=['/home/ekta/Desktop/NB/day_custom.txt','/home/ekta/Desktop/NB/is_tablet_phone.txt']
flag=0
for filehandle in file_paths:
feature_name=filehandle.split("/")[-1].split(".")[0]
df= pd.read_csv(filehandle,skiprows=0, encoding='utf-8',sep='\t',index_col=False,dtype={feature_name: object,'click': int,'impression': int})
df2=df[(df.impression-df.click>0) & (df.click >0)]
if flag ==0:
MySumC,MySumNC,Mydict=0,0,collections.defaultdict(dict)
MySumC=sum(df2['click'])
MySumNC=sum(df2['impression'])
P_C=float(MySumC)/float(MySumC+MySumNC)
P_NC=1-P_C
for feature_value in df2[feature_name]:
Mydict[feature_name+'_'+feature_value]={'P_'+feature_name+'_'+feature_value+'_C':(df2[df2[feature_name]==feature_value]['click']*float(P_C))/MySumC, \
'P_'+feature_name+'_'+feature_value+'_NC':(df2[df2[feature_name]==feature_value]['impression']*float(P_NC))/MySumNC}
flag=1 %Set the flag as "1" because we don't need to compute the MySumC,MySumNC, P_C & P_NC again
Question :
It looks like THIS loop is the killer here.Also, intutively, looping on a dataframe is a BAD practice. How can I rewrite this, perhaps using Map/Apply ?
for feature_value in df2[feature_name]:
Mydict[feature_name+'_'+feature_value]={'P_'+feature_name+'_'+feature_value+'_C':(df2[df2[feature_name]==feature_value]['click']*float(P_C))/MySumC, \
'P_'+feature_name+'_'+feature_value+'_NC':(df2[df2[feature_name]==feature_value]['impression']*float(P_NC))/MySumNC}
What I need in Mydict , which is a hash to store each feature name and each feature value in it
{'day_custom_Mon':{'P_day_custom_Mon_C':.787,'P_day_custom_Mon_NC': 0.556},
'day_custom_Tue':{'P_day_custom_Tue_C':0.887,'P_day_custom_Tue_NC': 0.156},
'day_custom_Wed':{'P_day_custom_Tue_C':0.087,'P_day_custom_Tue_NC': 0.167}
'day_custom_Thu':{'P_day_custom_Tue_C':0.947,'P_day_custom_Tue_NC': 0.196},
'is_tablet_phone_True':{'P_is_tablet_phone_True_C':.787,'P_is_tablet_phone_True_NC': 0.066},
'is_tablet_phone_False':{'P_is_tablet_phone_False_C':.787,'P_is_tablet_phone_False_NC': 0.077},
.. and so on..
%PPS: I just made up those float numbers, but you get the point
Also because I will later serialize this file & pass to Redis directly, for other systems to feed on it, in an cron-job manner, so I need to preserve some sort of Dynamic naming .
What I tried -:
Since I am reading feature_name as
feature_name=filehandle.split("/")[-1].split(".")[0]` # thereby abstracting & creating variables dynamically
def funct1(row):
return row[feature_name]
def funct2(row):
return row['click']
def funct3(row):
return row['impression']
then..
df2.apply(funct2,axis=1)df2.apply(funct,axis=1)*float(P_C))/MySumC, df2.apply(funct3,axis=1)*float(P_NC))/MySumNC Gives me both the values I need for a feature_value(say Mon, Tue, Wed, and so on..) for a feature_name (say,day_custom)
I also know that df2.apply(funct1, axis=1) contains part of mycustom "names"(ie feature values), how would I then build these names using map/apply ?
Ie. I will have the values, but how would I create the "key" 'P_'+feature_name+'_'+feature_value+'_C' , since feature value post apply is returned as a series object.
check out the following recipe which does exactly what you want, only using data frame manipulations. I also simplified the actual frequency calculation a bit ;)
#set the feature name values as the index of
df2.set_index(feature_name, inplace=True)
#This is what df2.set_index() looks like:
# click impression
#day_custom
#Fri 9917 3163
#Mon 2566 3818
#Sat 8725 7753
#Sun 6938 8642
#Thu 6136 2556
#Tue 5234 2356
#Wed 9463 9433
#rename the index of your data frame
df2.rename(index=lambda x:"%s_%s"%('day_custom', x), inplace=True)
#compute the total sum of your data frame entries
totsum = float(df2.values.sum())
#use apply to multiply every data frame element by the total sum
df2 = df2.applymap(lambda x:x/totsum)
#transpose the data frame to have the following shape
#day_custom day_custom_Fri day_custom_Mon ...
#click 0.102019 0.037468 ...
#impression 0.087661 0.045886 ...
#
#
dftranspose = df2.T
# template kw for formatting
templatekw = {'click':"P_%s_C", 'impression':"P_%s_NC"}
# build a list of small data frames with correct index names P_%s_NC etc
dflist = [dftranspose[[col]].rename(lambda x:templatekw[x]%col) for col in dftranspose]
#use the concatenate function to produce a sparse dictionary
MyDict= pd.concat(dflist).to_dict()
Instead of assigning to MyDict at the end, you can use the update-method during the loop.
For understanding the comments below, see here my
Original answer:
Try to use a pivot_table:
def clickfunc(x):
return np.sum(x) * P_C / MySumC
def impressionfunc(x):
return np.sum(x) * P_NC / MySumNC
newtable = df2.pivot_table(['click', 'impression'], 'feature_name', \
aggfunc=[clickfunc, impressionfunc])
#transpose the table for the dictionary to have the right form
newtable = newtable.T
#to_dict functionality already gives the correct result
MyDict = newtable.to_dict()
#rename by copying
for feature_value, subdict in MyDict.items():
word = feature_name +"_"+ feature_value
copydict[word] = {'P_' + word + '_C':subdict['click'],\
'P_' + word + '_NC':subdict['impression'] }
This gives you the result you want in copydict
itertuples() is what worked for me(worked at lightspeed) - though It is still not using the map/apply approach that I so much wanted to see. Itertuples on a pandas dataframe returns the whole row, so I no longer have to do df2[df2[feature_name]==feature_value]['click'] - be aware that this matching by value is not only expensive, but also undesired, since it may return a series, if there were duplicate rows. itertuples solves that problem were elegantly, though I need to then access the individual objects/columns by integer indexes , which means less re-usable code. I could abstract this, but It wont be like accessing by column names, the status-quo.
for row in df2.itertuples():
Mydict[feature_name+'_'+str(row[1])]={'P_'+feature_name+'_'+str(row[1])+'_C':(row[2]*float(P_C))/MySumC, \
'P_'+feature_name+'_'+str(row[1])+'_NC':(row[3]*float(P_NC))/MySumNC}
Note that I am accesing each column in the row by row[1] , row[2] and like. For example, row has (0, u'Fri', 77592, 7029703)
Post this I get
dict(Mydict)
{'day_custom_Thu': {'P_day_custom_Thu_NC': 0.18345372640838162, 'P_day_custom_Thu_C': 0.0019559423132143377}, 'day_custom_Mon': {'P_day_custom_Mon_C': 0.0011466875948906617, 'P_day_custom_Mon_NC': 0.099300235316209587}, 'day_custom_Sat': {'P_day_custom_Sat_NC': 0.14336163246883712, 'P_day_custom_Sat_C': 0.0017354517827023852}, 'day_custom_Tue': {'P_day_custom_Tue_C': 0.001454726996987919, 'P_day_custom_Tue_NC': 0.1203925662982053}, 'day_custom_Sun': {'P_day_custom_Sun_NC': 0.13239618235343156, 'P_day_custom_Sun_C': 0.0017488722589598259}, 'is_tablet_phone_TRUE': {'P_is_tablet_phone_TRUE_NC': 0.11107365073163174, 'P_is_tablet_phone_TRUE_C': 0.00046690100046229593}, 'day_custom_Wed': {'P_day_custom_Wed_NC': 0.12467127727567069, 'P_day_custom_Wed_C': 0.0013566522616712882}, 'day_custom_Fri': {'P_day_custom_Fri_NC': 0.1849842396242351, 'P_day_custom_Fri_C': 0.0020418070466026303}, 'is_tablet_phone_FALSE': {'P_is_tablet_phone_FALSE_NC': 0.74447539516197614, 'P_is_tablet_phone_FALSE_C': 0.0098789704610580936}}
OSX v10.6.8 and Gnuplot v4.4
I have a data file with 8 columns. I would like to take the first value from the 6th column and make it the title. Here's what I have so far:
#m1 m2 q taua taue K avgPeriodRatio time
#1 2 3 4 5 6 7 8
K = #read in data here
graph(n) = sprintf("K=%.2e",n)
set term aqua enhanced font "Times-Roman,18"
plot file using 1:3 title graph(K)
And here is what the first few rows of my data file looks like:
1.00e-07 1.00e-07 1.00e+00 1.00e+05 1.00e+04 1.00e+01 1.310 12070.00
1.11e-06 1.00e-07 9.02e-02 1.00e+05 1.00e+04 1.00e+01 1.310 12070.00
2.12e-06 1.00e-07 4.72e-02 1.00e+05 1.00e+04 1.00e+01 1.310 12070.00
3.13e-06 1.00e-07 3.20e-02 1.00e+05 1.00e+04 1.00e+01 1.310 12090.00
I don't know how to correctly read in the data or if this is even the right way to go about this.
EDIT #1
Ok, thanks to mgilson I now have
#m1 m2 q taua taue K avgPeriodRatio time
#1 2 3 4 5 6 7 8
set term aqua enhanced font "Times-Roman,18"
K = "`head -1 datafile | awk '{print $6}'`"
print K+0
graph(n) = sprintf("K=%.2e",n)
plot file using 1:3 title graph(K)
but I get the error: Non-numeric string found where a numeric expression was expected
EDIT #2
file = "testPlot.txt"
K = "`head -1 file | awk '{print $6}'`"
K=K+0 #Cast K to a floating point number #this is line 9
graph(n) = sprintf("K=%.2e",n)
plot file using 1:3 title graph(K)
This gives the error--> head: file: No such file or directory
"testPlot.gnu", line 9: Non-numeric string found where a numeric expression was expected
You have a few options...
FIRST OPTION:
use columnheader
plot file using 1:3 title columnheader(6)
I haven't tested it, but this may prevent the first row from actually being plotted.
SECOND OPTION:
use an external utility to get the title:
TITLE="`head -1 datafile | awk '{print $6}'`"
plot 'datafile' using 1:3 title TITLE
If the variable is numeric, and you want to reformat it, in gnuplot, you can cast strings to a numeric type (integer/float) by adding 0 to them (e.g).
print "36.5"+0
Then you can format it with sprintf or gprintf as you're already doing.
It's weird that there is no float function. (int will work if you want to cast to an integer).
EDIT
The script below worked for me (when I pasted your example data into a file called "datafile"):
K = "`head -1 datafile | awk '{print $6}'`"
K=K+0 #Cast K to a floating point number
graph(n) = sprintf("K=%.2e",n)
plot "datafile" using 1:3 title graph(K)
EDIT 2 (addresses comments below)
To expand a variable in backtics, you'll need macros:
set macro
file="mydatafile.txt"
#THE ORDER OF QUOTES (' and ") IS CRUCIAL HERE.
cmd='"`head -1 ' . file . ' | awk ''{print $6}''`"'
# . is string concatenation. (this string has 3 pieces)
# to get a single quote inside a single quoted string
# you need to double. e.g. 'a''b' yields the string a'b
data=#cmd
To address your question 2, it is a good idea to familiarize yourself with shell utilities -- sed and awk can both do it. I'll show a combination of head/tail:
cmd='"`head -2 ' . file . ' | tail -1 | awk ''{print $6}''`"'
should work.
EDIT 3
I recently learned that in gnuplot, system is a function as well as a command. To do the above without all the backtic gymnastics,
data=system("head -1 " . file . " | awk '{print $6}'")
Wow, much better.
This is a very old question, but here's a nice way to get access to a single value anywhere in your data file and save it as a gnuplot-accessible variable:
set term unknown #This terminal will not attempt to plot anything
plot 'myfile.dat' index 0 every 1:1:0:0:0:0 u (var=$1):1
The index number allows you to address a particular dataset (separated by two carriage returns), while every allows you to specify a particular line.
The colon-separated numbers after every should be of the form 1:1:<line_number>:<block_number>:<line_number>:<block_number>, where the line number is the line with the the block (starting from 0), and the block number is the number of the block (separated by a single carriage return, again starting from 0). The first and second numbers say plot every 1 lines and every one data block, and the third and fourth say start from line <line_number> and block <block_number>. The fifth and sixth say where to stop. This allows you to select a single line anywhere in your data file.
The last part of the plot command assigns the value in a particular column (in this case, column 1) to your variable (var). There needs to be two values to a plot command, so I chose column 1 to plot against my variable assignment statement.
Here is a less 'awk'-ward solution which assigns the value from the first row and 6th column of the file 'Data.txt' to the variable x16.
set table
# Syntax: u 0:($0==RowIndex?(VariableName=$ColumnIndex):$ColumnIndex)
# RowIndex starts with 0, ColumnIndex starts with 1
# 'u' is an abbreviation for the 'using' modifier
plot 'Data.txt' u 0:($0==0?(x16=$6):$6)
unset table
A more general example for storing several values is given below:
# Load data from file to variable
# Gnuplot can only access the data via the "plot" command
set table
# Syntax: u 0:($0==RowIndex?(VariableName=$ColumnIndex):$ColumnIndex)
# RowIndex starts with 0, ColumnIndex starts with 1
# 'u' is an abbreviation for the 'using' modifier
# Example: Assign all values according to: xij = Data33[i,j]; i,j = 1,2,3
plot 'Data33.txt' u 0:($0==0?(x11=$1):$1),\
'' u 0:($0==0?(x12=$2):$2),\
'' u 0:($0==0?(x13=$3):$3),\
'' u 0:($0==1?(x21=$1):$1),\
'' u 0:($0==1?(x22=$2):$2),\
'' u 0:($0==1?(x23=$3):$3),\
'' u 0:($0==2?(x31=$1):$1),\
'' u 0:($0==2?(x32=$2):$2),\
'' u 0:($0==2?(x33=$3):$3)
unset table
print x11, x12, x13 # Data from first row
print x21, x22, x23 # Data from second row
print x31, x32, x33 # Data from third row