Replacing part of string while leaving the rest intact with Notepad++ - regex

I'm trying to edit a huge file with lines like:
1 9416 0 0 10 10 0 dropitems.drop_MFighter_m012_t91_u_m00 mfighter.MFighter_m201_t201_u 0 0 0 0 0 1 0 0 icon.armor_t91_u_i00 -1 7570 46 1 2531A298 0 1 21 1 Fighter.MFighter_m201_u 1 mfighter.MFighter_m201_t201_u 2 Fighter.MFighter_m201_hrh_ad00 104 114 Fighter.MFighter_m201_hrs_ad00 115 114 2 MFighter.MFighter_m201_HRR_ad00_t201_a MFighter.MFighter_m201_HRR_ad00_t201_a 1 Fighter.FFighter_m012_u 1 FFighter.FFighter_m012_t201_u 2 Fighter.FFighter_m012_hrr_ad00 114 114 Fighter.FFighter_m012_hra_ad00 97 114 2 MFighter.MFighter_m201_HRR_ad00_t201_a MFighter.MFighter_m201_HRR_ad00_t201_a 1 DarkElf.MDarkElf_m010_u 1 MDarkElf.MDarkElf_m201_t201_u 2 DarkElf.MDarkElf_m010_Hrr_ad00 114 114 DarkElf.MDarkElf_m010_Hra_ad00 97 114 2 MDarkElf.MDarkElf_m010_HRR_ad00_t201_a MDarkElf.MDarkElf_m010_HRR_ad00_t201_a 1 DarkElf.FDarkElf_m006_u 1 FDarkElf.FDarkElf_m006_t201_u 2 DarkElf.FDarkElf_m006_hrr_ad00 114 114 DarkElf.FDarkElf_m006_hra_ad00 97 114 2 MDarkElf.MDarkElf_m010_HRR_ad00_t201_a MDarkElf.MDarkElf_m010_HRR_ad00_t201_a 1 Dwarf.MDwarf_m008_u 1 MDwarf.MDwarf_m201_t201_u 2 Dwarf.MDwarf_m008_Hrr_ad00 114 114 Dwarf.MDwarf_m008_Hra_ad00 97 114 2 MDwarf.MDwarf_m008_HRR_ad00_t201_a MDwarf.MDwarf_m008_HRR_ad00_t201_a 1 Dwarf.FDwarf_m008_u 1 FDwarf.FDwarf_m008_t201_u 2 Dwarf.FDwarf_m008_hrr_ad00 114 114 Dwarf.FDwarf_m008_hra_ad00 97 114 2 MDwarf.MDwarf_m008_HRR_ad00_t201_a MDwarf.MDwarf_m008_HRR_ad00_t201_a 1 Elf.MElf_m011_u 1 MElf.MElf_m011_t201_u 2 Elf.MElf_m011_Hrr_ad00 114 114 Elf.MElf_m011_Hra_ad00 97 114 2 MElf.MElf_m011_HRR_ad00_t201_a MElf.MElf_m011_HRR_ad00_t201_a 1 Elf.FElf_m011_u 1 FElf.FElf_m011_t201_u 2 Elf.FElf_m011_hrr_ad00 114 114 Elf.FElf_m011_hra_ad00 97 114 2 MElf.MElf_m011_HRR_ad00_t201_a MElf.MElf_m011_HRR_ad00_t201_a 1 Magic.MMagic_m011_u 1 mMagic.MMagic_m011_t301_u 2 0 255 Magic.MMagic_m011_Rra_ad00 97 114 2 Mmagic.Mmagic_m011_Rra_ad00_t301_x 1 Magic.FMagic_m013_u 1 FMagic.FMagic_m013_t301_u 2 0 255 Magic.FMagic_m013_Rra_ad00 97 114 2 Mmagic.Mmagic_m011_Rra_ad00_t301_x 1 Orc.MOrc_m007_u 1 MOrc.MOrc_m201_t201_u 2 Orc.MOrc_m007_hrh_ad00 104 114 Orc.MOrc_m007_hrs_ad00 115 114 2 MOrc.MOrc_m007_HRR_ad00_t201_a MOrc.MOrc_m007_HRR_ad00_t201_a 1 Orc.FOrc_m007_u 1 FOrc.FOrc_m007_t201_u 2 Orc.FOrc_m007_hrr_ad00 114 114 Orc.FOrc_m007_hra_ad00 97 114 2 MOrc.MOrc_m007_HRR_ad00_t201_a MOrc.MOrc_m007_HRR_ad00_t201_a 1 Shaman.MShaman_m007_u 1 MShaman.MShaman_m007_t301_u 2 0 255 Shaman.MShaman_m007_Rra_ad00 97 114 2 MShaman.MShaman_m007_Rra_ad00_t301_x 1 Shaman.FShaman_m007_u 1 FShaman.FShaman_m007_t301_u 2 0 255 Shaman.FShaman_m007_Rra_ad00 97 114 2 MShaman.MShaman_m007_Rra_ad00_t301_x 1 Kamael.mkamael_m005_u 2 MKamael.MKamael_m005_t101_u Mkamael.Mkamael_m005_t101_ut 3 Kamael.MKamael_m005_Lrr_ad00 114 114 Kamael.Mkamael_m000_w_ad00 119 95 Kamael.mkamael_m005_l_ad00 108 95 3 MKamael.MKamael_m005_Lrr_ad00_t101_a Mkamael.Mkamael_m000_t00_w Mkamael.Mkamael_m005_t101_ut 1 Kamael.fkamael_m009_u 2 FKamael.FKamael_m009_t101_u FKamael.FKamael_m009_t101_ut 3 Kamael.FKamael_m009_Lrr_ad00 114 114 Kamael.Fkamael_m000_w_ad00 119 95 Kamael.Fkamael_m009_l_ad00 108 95 3 MKamael.MKamael_m005_Lrr_ad00_t101_a Fkamael.Fkamael_m000_t00_w FKamael.FKamael_m009_t101_ut 1 1 0 0 LineageEffect.p_u002_a 4 ItemSound.armor_metal_alt_6 ItemSound.public_armor_04 ItemSound.shield_steel_1 ItemSound.shield_steel_8 ItemSound.itemdrop_armor_lightmetal ItemSound.itemequip_armor_lightmetal 1 0 2 5 0 226 0 0 36
I have come up with the following regular expression to find similar lines:
^.*?(_t91(.*?)1 0 2 5).*?$
Basically I have to change number 5 to number 6 and I'm trying to do so with the following regular expression:
\1t91\21 0 2 6
The result of that is a line starting in 't91' and ending as it should with the number replaced, but then it repeats itself 2-3 times (i.e: 2 6 0 226 0 0 36t91 etc.)
Do you guys have any ideas about this? Regular expressions are still a mystery to me.
Thanks in advance.


From what I see you are trying to replace 5 as a whole word in a specific environment.
To match a whole word, you'd need \b (a word boundary) so as not to change a value like 55.
To replace it just once on a whole line, you can use the regex
^(.*?_t91.*?1\s+0\s+2\s+)5(\b.*?)$
|-----first group------| | 2|
And replace with ${1}6${2} (although \16\2 works here, too).
If there are multiple values like this on a single line, use
(_t91.*?1\s+0\s+2\s+)5\b
And replace with ${1}6.

Related

Assign groups based on variables

In SAS, I have a dataset(have) as below, I need to add a group variable based on test and visitnum. When visitnum is 101 and 108, they need to be in the same group. The desired as shown as data want.
data have:
test visitnum ord seq
aa 101 0 0
aa 101 0 1
aa 108 1 0
aa 108 1 1
aa 108 2 0
aa 108 2 1
aa 115 1 0
aa 115 1 1
aa 115 2 0
aa 115 2 1
bb 101 0 0
bb 101 0 1
bb 108 1 0
bb 108 1 1
bb 108 2 0
bb 108 2 1
bb 115 1 0
bb 115 1 1
bb 115 2 0
bb 115 2 1
data want:
test visitnum ord seq group
aa 101 0 0 1
aa 101 0 1 1
aa 108 1 0 1
aa 108 1 1 1
aa 108 2 0 1
aa 108 2 1 1
aa 115 1 0 2
aa 115 1 1 2
aa 115 2 0 2
aa 115 2 1 2
bb 101 0 0 3
bb 101 0 1 3
bb 108 1 0 3
bb 108 1 1 3
bb 108 2 0 3
bb 108 2 1 3
bb 115 1 0 4
bb 115 1 1 4
bb 115 2 0 4
bb 115 2 1 4

First sort your data by test and visitnum. There are two cases when we want to increment the group number:
When it's the start of a test group and the visitnum is 101 or 108
When it's the start of a visitnum group and it's not 101 or 108
Here's how this looks:
proc sort data=have;
by test visitnum;
run;
data want;
set have;
by test visitnum;
if( first.test AND visitnum IN(101, 108)
OR (first.visitnum AND visitnum NOT IN(101, 108) )
)
then group+1;
run;
Output:
test visitnum ord seq group
aa 101 0 0 1
aa 101 0 1 1
aa 108 1 0 1
aa 108 1 1 1
aa 108 2 0 1
aa 108 2 1 1
aa 115 1 0 2
aa 115 1 1 2
aa 115 2 0 2
.. ... .. .. ..
bb 115 2 0 4
bb 115 2 1 4

How to loop rows and columns in pandas while replacing values with a constant increment

I am trying to replace values in a dataframe by 0. the first column I need to replace the 1st 3 values, the next column the 1st 6 values so on so forth increasing by 3 every time
a=np.array([133,124,156,189,132,176,189,192,100,120,130,140,150,50,70,133,124,156,189,132])
b = pd.DataFrame(a.reshape(10,2), columns= ['s','t'])
for columns in b:
yy = 3
for i in xrange(yy):
b[columns][i] = 0
yy += 3
print b
the outcome is the following
s t
0 0 0
1 0 0
2 0 0
3 189 189
4 132 132
5 176 176
6 189 189
7 192 192
8 100 100
9 120 120
I am clearly missing something really simple, to make the loop replace 6 values instead of only 3 in column t, any ideas?

i would do it this way:
i = 1
for c in b.columns:
b.ix[0 : 3*i-1, c] = 0
i += 1
Demo:
In [86]: b = pd.DataFrame(np.random.randint(0, 100, size=(20, 4)), columns=list('abcd'))
In [87]: %paste
i = 1
for c in b.columns:
b.ix[0 : 3*i-1, c] = 0
i += 1
## -- End pasted text --
In [88]: b
Out[88]:
a b c d
0 0 0 0 0
1 0 0 0 0
2 0 0 0 0
3 10 0 0 0
4 8 0 0 0
5 49 0 0 0
6 55 48 0 0
7 99 43 0 0
8 63 29 0 0
9 61 65 74 0
10 15 29 41 0
11 79 88 3 0
12 91 74 11 4
13 56 71 6 79
14 15 65 46 81
15 81 42 60 24
16 71 57 95 18
17 53 4 80 15
18 42 55 84 11
19 26 80 67 59

You need inicialize yy=3 before loop:
yy = 3
for columns in b:
for i in xrange(yy):
b[columns][i] = 0
yy += 3
print b
Python 3 solution:
yy = 3
for columns in b:
for i in range(yy):
b[columns][i] = 0
yy += 3
print (b)
s t
0 0 0
1 0 0
2 0 0
3 189 0
4 100 0
5 130 0
6 150 50
7 70 133
8 124 156
9 189 132
Another solution:
yy= 3
for i, col in enumerate(b.columns):
b.ix[:i*yy+yy-1, col] = 0
print (b)
s t
0 0 0
1 0 0
2 0 0
3 189 0
4 100 0
5 130 0
6 150 50
7 70 133
8 124 156
9 189 132

In R, Train/update model with multiple datasets

In R, I'm trying to train a neural network on multiple files. I have preformed the multinom function on a single dataset but I cannot find how to train my model with another dataset.
So I want to apply a model from a previous call to new data without re-estimating the model.
So first you build a model as in Sam Thomas's answer is explained.
#load libraries
library(nnet)
library(MASS)
#Define data
example(birthwt)
# Define training and test data
set.seed(321)
index <- sample(seq_len(nrow(bwt)), 130)
bwt_train <- bwt[index, ]
bwt_test <- bwt[-index, ]
# Build model
bwt.mu <- multinom(low ~ ., data=bwt_train)
Then I have another similar dataset I want to train/update the earlier created model with. So I want to update the model with new data to improve my model.
# New data set (for example resampled bwt)
bwt2=sapply(bwt, sample)
head(bwt2,3)
low age lwt race smoke ptd ht ui ftv
[1,] 1 31 115 3 1 1 0 0 2
[2,] 1 20 95 1 0 1 0 0 3
[3,] 2 25 95 2 0 1 0 1 1
# Define training and test data with new dataset
set.seed(321)
index <- sample(seq_len(nrow(bwt2)), 130)
bwt2_train <- bwt2[index, ]
bwt2_test <- bwt2[-index, ]
Now with this new dataset I want to optimze the model. I cannot merge the two datasets because the model should update over time when new data is available. This also because it is not preferable to recalculate everytime we have new data availble.
Thanks in advance,
Adam

Borrowed from an example in ?nnet::multinom
library(nnet)
library(MASS)
example(birthwt)
head(bwt, 2)
low age lwt race smoke ptd ht ui ftv
1 0 19 182 black FALSE FALSE FALSE TRUE 0
2 0 33 155 other FALSE FALSE FALSE FALSE 2+
set.seed(321)
index <- sample(seq_len(nrow(bwt)), 130)
bwt_train <- bwt[index, ]
bwt_test <- bwt[-index, ]
bwt.mu <- multinom(low ~ ., bwt_train)
(pred <- predict(bwt.mu, newdata=bwt_test))
[1] 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0
[39] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0
Levels: 0 1
Or if you want the probabilities
(pred <- predict(bwt.mu, newdata=bwt_test, type="probs"))
1 5 6 16 19 23 24
0.43672841 0.65881933 0.21958026 0.39061949 0.51970665 0.01627479 0.17210620
26 27 28 29 30 37 40
0.06133368 0.31568117 0.05665126 0.26507476 0.37419673 0.18475433 0.14946268
44 46 47 51 56 58 60
0.09670367 0.72178459 0.06541529 0.37448908 0.31883809 0.09532218 0.27515734
61 64 67 69 72 74 76
0.27515734 0.09456443 0.16829037 0.62285841 0.12026718 0.47417711 0.09603950
78 87 94 99 100 106 114
0.34588019 0.30327432 0.87688323 0.21177276 0.06576210 0.19741587 0.22418653
115 117 118 120 125 126 130
0.14592195 0.19340994 0.14874536 0.30176632 0.09513698 0.08334515 0.03886775
133 134 139 140 145 147 148
0.41216817 0.85046516 0.46344537 0.34219775 0.33673304 0.26894886 0.43778705
152 163 164 165 168 174 180
0.19044485 0.27800125 0.17865143 0.86783149 0.25969355 0.60623964 0.34931986
182 183 185
0.22944657 0.08066599 0.22863967

Trying to get rid of commas in numbers with regex in R gives strange output

I'm new to R. This is my data (using dplyr):
> withCommas
Source: local data frame [326 x 1]
NA
1 16,244,600
2 8,227,103
3 5,959,718
4 3,428,131
5 2,612,878
6 2,471,784
7 2,252,664
8 2,014,775
9 2,014,670
10 1,841,710
.. ...
Classes ‘tbl_df’ and 'data.frame': 326 obs. of 1 variable:
$ : Factor w/ 207 levels ""," 1,008 "," 1,129 ",..: 40 178 143 100 66 63 61 58 57 16 ...
I'm trying to get rid of the commas (so the first row should be 16244600). So I tried the following:
#1st try
noCommas <- gsub("([0-9]+)\\,([0-9])", "\\1\\2", withCommas)
#2nd try
noCommas <- gsub(",", "", withCommas)
In all cases, I got this output:
[1] "c(40 178 143 100 66 63 61 58 57 16 14 11 9 6 4 182 176 174 170 161 148 147 139 137 136 134 118 117 116 114 113 109 107 105 95 93 92 90 89 88 87 84 83 78 75 74 73 72 71 70 56 55 49 47 43 42 39 28 25 24 23 190 188 181 172 165 163 162 160 153 152 151 150 149 146 145 144 138 132 131 130 129 128 127 126 125 124 115 112 111 110 106 98 97 96 94 86 85 82 81 80 77 76 69 68 54 52 51 50 46 45 44 41 \n38 37 36 35 34 33 32 31 30 29 27 26 22 21 20 19 18 17 187 186 185 184 183 179 177 169 168 167 166 159 158 157 156 155 142 141 140 122 121 120 119 104 103 102 101 99 67 65 64 62 60 59 15 13 12 10 8 7 5 3 2 189 180 175 173 173 171 164 154 135 133 108 91 79 53 48 123 1 191 191 191 191 191 1 191 191 191 191 191 191 191 191 191 191 191 191 191 191 191 191 191 191 1 206 1 205 200 202 198 201 196 \n195 204 194 199 193 203 197 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1)"
This seems very strange to me as I don't understand where the numbers are coming from. Any help appreciated.
Edit:
Only the first 225 rows of the variable withCommas have values. After that, the values of the column are empty.
Source: http://data.worldbank.org/data-catalog/GDP-ranking-table
CSV: https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FGDP.csv

What about this solution? I think that main problems arise because a data frame is a list and gsub is expecting a character string and so passing it to that function cause to apply the function to the lists and not the elements that are part of the lists themselves. That's the reason for an apply function. Or of course, if the columns is one, passing just that column as a vector with ddf$column_with_commas as the solution provided by other users.
as.data.frame(apply(ddf, 2, function(x) as.numeric(gsub(",", "", x))))
NA.
1 16244600
2 8227103
3 5959718
4 3428131
5 2612878
6 2471784
7 2252664
8 2014775
9 2014670
10 1841710
Data
ddf <- structure(list(NA. = structure(c(2L, 10L, 9L, 8L, 7L, 6L, 5L,
4L, 3L, 1L), .Label = c("1,841,710", "16,244,600", "2,014,670",
"2,014,775", "2,252,664", "2,471,784", "2,612,878", "3,428,131",
"5,959,718", "8,227,103"), class = "factor")), .Names = "NA.", class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"))

data <- read.table(header=F, text="1 16,244,600
2 8,227,103
3 5,959,718
4 3,428,131
5 2,612,878
6 2,471,784
7 2,252,664
8 2,014,775
9 2,014,670
10 1,841,710 ")
colnames(data) <- c("a","b")
data$b <- as.numeric(gsub(",", "", data$b))
Output:
a b
1 1 16244600
2 2 8227103
3 3 5959718
4 4 3428131
5 5 2612878
6 6 2471784
7 7 2252664
8 8 2014775
9 9 2014670
10 10 1841710

Histogram image maching using opencv

I need to perform a matching between an image and an histogram I receive as a text.
I do the cdf for both of them:
//Calculating cumulative histogram of src
double total = src.rows*src.cols;
double probSrc[255];
int newValuesSrc[255];
double cuml = 0;
for(int j = 0; j < 256; j++)
{
probSrc[j] = imageHistogram[j]/total; // Probability of each value in image
cuml = cuml + probSrc[j]; // Cumulative probability of current and all previous values
double cdfmax = cuml * 255; // Cumulative probability * max value
newValuesSrc[j] = (int) round(cdfmax);
cout << imageHistogram[j] << " "<< probSrc[j] << " " << newValuesSrc[j] << endl;
}
readHistogramFromFile();
//Calculating cumulative histogram from file
double probDst[255];
int newValuesDst[255];
cuml = 0;
for(int j = 0; j < 256; j++)
{
probDst[j] = receivedHistogram[j]/total; // Probability of each value in image
cuml = cuml + probDst[j]; // Cumulative probability of current and all previous values
double cdfmax = cuml * 255; // Cumulative probability * max value
newValuesDst[j] = (int) round(cdfmax);
cout << receivedHistogram[j] << " "<< probDst[j] << " " << newValuesDst[j] << endl;
}
and I get this values:
For the src image:
207677 0.0901376 23
37615 0.016326 27
19098 0.00828906 29
11955 0.0051888 31
8744 0.00379514 32
7386 0.00320573 32
6546 0.00284115 33
6178 0.00268142 34
5967 0.00258984 34
5437 0.00235981 35
5280 0.00229167 36
5127 0.00222526 36
5002 0.00217101 37
4839 0.00210026 37
4754 0.00206337 38
4676 0.00202951 38
4547 0.00197352 39
4517 0.0019605 39
4484 0.00194618 40
4290 0.00186198 40
4197 0.00182161 41
4188 0.00181771 41
4265 0.00185113 42
4229 0.0018355 42
4233 0.00183724 43
4245 0.00184245 43
4358 0.00189149 44
4330 0.00187934 44
4400 0.00190972 45
4474 0.00194184 45
4519 0.00196137 46
4415 0.00191623 46
4477 0.00194314 47
4468 0.00193924 47
4580 0.00198785 48
4416 0.00191667 48
4558 0.0019783 49
4674 0.00202865 49
4705 0.0020421 50
4998 0.00216927 50
4848 0.00210417 51
4782 0.00207552 51
4883 0.00211936 52
4989 0.00216536 52
4957 0.00215148 53
4987 0.0021645 53
5133 0.00222786 54
4967 0.00215582 54
5217 0.00226432 55
5185 0.00225043 56
5140 0.0022309 56
5236 0.00227257 57
5291 0.00229644 57
5458 0.00236892 58
5473 0.00237543 59
5464 0.00237153 59
5495 0.00238498 60
5439 0.00236068 60
5458 0.00236892 61
5557 0.00241189 62
5881 0.00255252 62
5900 0.00256076 63
5935 0.00257595 64
5902 0.00256163 64
6040 0.00262153 65
6203 0.00269227 66
6146 0.00266753 66
6140 0.00266493 67
6075 0.00263672 68
6054 0.0026276 68
6238 0.00270747 69
6060 0.00263021 70
6153 0.00267057 70
6303 0.00273568 71
6231 0.00270443 72
6278 0.00272483 72
6360 0.00276042 73
6359 0.00275998 74
6368 0.00276389 75
6438 0.00279427 75
6329 0.00274696 76
6408 0.00278125 77
6360 0.00276042 77
6378 0.00276823 78
6329 0.00274696 79
6394 0.00277517 79
6517 0.00282856 80
6521 0.0028303 81
6707 0.00291102 82
6788 0.00294618 82
6761 0.00293446 83
6878 0.00298524 84
7004 0.00303993 85
6963 0.00302214 85
7050 0.0030599 86
6940 0.00301215 87
6875 0.00298394 88
7073 0.00306988 89
7035 0.00305339 89
7146 0.00310156 90
7007 0.00304123 91
7159 0.0031072 92
7089 0.00307682 92
7185 0.00311849 93
7410 0.00321615 94
7237 0.00314106 95
7334 0.00318316 96
7364 0.00319618 97
7452 0.00323437 97
7760 0.00336806 98
7839 0.00340234 99
7882 0.00342101 100
7885 0.00342231 101
8055 0.00349609 102
7923 0.0034388 103
8165 0.00354384 103
8306 0.00360503 104
8271 0.00358984 105
8275 0.00359158 106
8634 0.0037474 107
8684 0.0037691 108
8752 0.00379861 109
9080 0.00394097 110
8958 0.00388802 111
9094 0.00394705 112
9279 0.00402734 113
9234 0.00400781 114
9348 0.00405729 115
9440 0.00409722 116
9431 0.00409332 117
9662 0.00419358 118
9842 0.0042717 119
9816 0.00426042 121
9957 0.00432161 122
10353 0.00449349 123
10626 0.00461198 124
10764 0.00467187 125
10832 0.00470139 126
10767 0.00467318 128
11222 0.00487066 129
11469 0.00497786 130
11661 0.0050612 131
11731 0.00509158 133
12023 0.00521832 134
12086 0.00524566 135
12094 0.00524913 137
12362 0.00536545 138
12364 0.00536632 139
12659 0.00549436 141
12587 0.00546311 142
12776 0.00554514 144
13037 0.00565842 145
13252 0.00575174 147
13425 0.00582682 148
13595 0.00590061 150
13795 0.00598741 151
14308 0.00621007 153
14232 0.00617708 154
14657 0.00636155 156
14966 0.00649566 157
14867 0.00645269 159
15051 0.00653255 161
15510 0.00673177 162
15357 0.00666536 164
15326 0.00665191 166
15308 0.0066441 168
15316 0.00664757 169
15321 0.00664974 171
15298 0.00663976 173
15435 0.00669922 174
15496 0.00672569 176
15307 0.00664366 178
15343 0.00665929 179
15356 0.00666493 181
15315 0.00664714 183
15444 0.00670312 185
15346 0.00666059 186
15583 0.00676345 188
15429 0.00669661 190
15641 0.00678863 191
15661 0.00679731 193
15638 0.00678733 195
15689 0.00680946 197
15866 0.00688628 198
15552 0.00675 200
15150 0.00657552 202
15185 0.00659071 203
14941 0.00648481 205
14989 0.00650564 207
14585 0.0063303 208
14718 0.00638802 210
14553 0.00631641 212
14612 0.00634201 213
14520 0.00630208 215
14358 0.00623177 216
13931 0.00604644 218
13580 0.0058941 220
13370 0.00580295 221
13281 0.00576432 222
13053 0.00566536 224
12711 0.00551693 225
12556 0.00544965 227
12556 0.00544965 228
12125 0.00526259 229
12184 0.00528819 231
11975 0.00519748 232
12198 0.00529427 233
11919 0.00517318 235
11898 0.00516406 236
11589 0.00502995 237
11348 0.00492535 239
11011 0.00477908 240
10523 0.00456727 241
10388 0.00450868 242
9795 0.0042513 243
9251 0.00401519 244
9014 0.00391233 245
8436 0.00366146 246
8266 0.00358767 247
7851 0.00340755 248
7299 0.00316797 249
6996 0.00303646 250
6303 0.00273568 250
5625 0.00244141 251
5375 0.0023329 251
5102 0.00221441 252
4747 0.00206033 253
4313 0.00187196 253
3809 0.00165321 253
3307 0.00143533 254
2756 0.00119618 254
2276 0.000987847 254
1935 0.000839844 255
1617 0.000701823 255
1087 0.000471788 255
547 0.000237413 255
217 9.4184e-05 255
31 1.34549e-05 255
4 1.73611e-06 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
And for the histogram I receive:
10 4.34028e-06 0
11 4.77431e-06 0
12 5.20833e-06 0
13 5.64236e-06 0
14 6.07639e-06 0
15 6.51042e-06 0
16 6.94444e-06 0
17 7.37847e-06 0
18 7.8125e-06 0
19 8.24653e-06 0
20 8.68056e-06 0
22 9.54861e-06 0
24 1.04167e-05 0
26 1.12847e-05 0
28 1.21528e-05 0
30 1.30208e-05 0
34 1.47569e-05 0
38 1.64931e-05 0
42 1.82292e-05 0
50 2.17014e-05 0
60 2.60417e-05 0
70 3.03819e-05 0
80 3.47222e-05 0
90 3.90625e-05 0
100 4.34028e-05 0
120 5.20833e-05 0
140 6.07639e-05 0
160 6.94444e-05 0
160 6.94444e-05 0
150 6.51042e-05 0
140 6.07639e-05 0
130 5.64236e-05 0
120 5.20833e-05 0
110 4.77431e-05 0
100 4.34028e-05 0
90 3.90625e-05 0
80 3.47222e-05 0
70 3.03819e-05 0
60 2.60417e-05 0
50 2.17014e-05 0
40 1.73611e-05 0
30 1.30208e-05 0
20 8.68056e-06 0
10 4.34028e-06 0
10 4.34028e-06 0
10 4.34028e-06 0
10 4.34028e-06 0
11 4.77431e-06 0
12 5.20833e-06 0
13 5.64236e-06 0
14 6.07639e-06 0
15 6.51042e-06 0
16 6.94444e-06 0
17 7.37847e-06 0
18 7.8125e-06 0
19 8.24653e-06 0
20 8.68056e-06 0
22 9.54861e-06 0
24 1.04167e-05 0
26 1.12847e-05 0
28 1.21528e-05 0
30 1.30208e-05 0
34 1.47569e-05 0
38 1.64931e-05 0
42 1.82292e-05 0
50 2.17014e-05 0
60 2.60417e-05 0
70 3.03819e-05 0
80 3.47222e-05 0
90 3.90625e-05 0
100 4.34028e-05 0
120 5.20833e-05 0
140 6.07639e-05 0
160 6.94444e-05 0
160 6.94444e-05 0
150 6.51042e-05 0
140 6.07639e-05 0
130 5.64236e-05 1
120 5.20833e-05 1
110 4.77431e-05 1
100 4.34028e-05 1
90 3.90625e-05 1
80 3.47222e-05 1
70 3.03819e-05 1
60 2.60417e-05 1
50 2.17014e-05 1
40 1.73611e-05 1
30 1.30208e-05 1
20 8.68056e-06 1
10 4.34028e-06 1
10 4.34028e-06 1
11 4.77431e-06 1
12 5.20833e-06 1
13 5.64236e-06 1
14 6.07639e-06 1
15 6.51042e-06 1
16 6.94444e-06 1
17 7.37847e-06 1
18 7.8125e-06 1
19 8.24653e-06 1
20 8.68056e-06 1
22 9.54861e-06 1
24 1.04167e-05 1
26 1.12847e-05 1
28 1.21528e-05 1
30 1.30208e-05 1
34 1.47569e-05 1
38 1.64931e-05 1
42 1.82292e-05 1
50 2.17014e-05 1
60 2.60417e-05 1
70 3.03819e-05 1
80 3.47222e-05 1
90 3.90625e-05 1
100 4.34028e-05 1
120 5.20833e-05 1
140 6.07639e-05 1
160 6.94444e-05 1
160 6.94444e-05 1
150 6.51042e-05 1
140 6.07639e-05 1
130 5.64236e-05 1
120 5.20833e-05 1
110 4.77431e-05 1
100 4.34028e-05 1
90 3.90625e-05 1
80 3.47222e-05 1
70 3.03819e-05 1
60 2.60417e-05 1
50 2.17014e-05 1
40 1.73611e-05 1
30 1.30208e-05 1
20 8.68056e-06 1
10 4.34028e-06 1
10 4.34028e-06 1
10 4.34028e-06 1
10 4.34028e-06 1
11 4.77431e-06 1
12 5.20833e-06 1
13 5.64236e-06 1
14 6.07639e-06 1
15 6.51042e-06 1
16 6.94444e-06 1
17 7.37847e-06 1
18 7.8125e-06 1
19 8.24653e-06 1
20 8.68056e-06 1
22 9.54861e-06 1
24 1.04167e-05 1
26 1.12847e-05 1
28 1.21528e-05 1
30 1.30208e-05 1
34 1.47569e-05 1
38 1.64931e-05 1
42 1.82292e-05 1
50 2.17014e-05 1
60 2.60417e-05 1
70 3.03819e-05 1
80 3.47222e-05 1
90 3.90625e-05 1
100 4.34028e-05 1
120 5.20833e-05 1
140 6.07639e-05 1
160 6.94444e-05 1
160 6.94444e-05 1
150 6.51042e-05 1
140 6.07639e-05 1
130 5.64236e-05 1
120 5.20833e-05 1
110 4.77431e-05 1
100 4.34028e-05 1
90 3.90625e-05 1
80 3.47222e-05 1
70 3.03819e-05 1
60 2.60417e-05 1
50 2.17014e-05 1
40 1.73611e-05 1
30 1.30208e-05 1
20 8.68056e-06 1
10 4.34028e-06 1
10 4.34028e-06 1
10 4.34028e-06 1
20 8.68056e-06 1
30 1.30208e-05 1
40 1.73611e-05 1
50 2.17014e-05 1
60 2.60417e-05 1
70 3.03819e-05 1
80 3.47222e-05 1
90 3.90625e-05 1
100 4.34028e-05 1
120 5.20833e-05 1
140 6.07639e-05 1
160 6.94444e-05 1
160 6.94444e-05 1
150 6.51042e-05 1
140 6.07639e-05 1
130 5.64236e-05 1
120 5.20833e-05 1
110 4.77431e-05 1
100 4.34028e-05 1
90 3.90625e-05 1
80 3.47222e-05 1
70 3.03819e-05 1
60 2.60417e-05 1
50 2.17014e-05 1
40 1.73611e-05 1
30 1.30208e-05 1
20 8.68056e-06 1
10 4.34028e-06 1
10 4.34028e-06 1
10 4.34028e-06 1
20 8.68056e-06 1
30 1.30208e-05 1
40 1.73611e-05 1
40 1.73611e-05 1
50 2.17014e-05 1
55 2.38715e-05 1
60 2.60417e-05 1
65 2.82118e-05 1
70 3.03819e-05 1
75 3.25521e-05 1
80 3.47222e-05 1
85 3.68924e-05 2
90 3.90625e-05 2
95 4.12326e-05 2
90 3.90625e-05 2
80 3.47222e-05 2
70 3.03819e-05 2
60 2.60417e-05 2
50 2.17014e-05 2
40 1.73611e-05 2
30 1.30208e-05 2
20 8.68056e-06 2
10 4.34028e-06 2
10 4.34028e-06 2
10 4.34028e-06 2
20 8.68056e-06 2
30 1.30208e-05 2
40 1.73611e-05 2
40 1.73611e-05 2
50 2.17014e-05 2
55 2.38715e-05 2
60 2.60417e-05 2
65 2.82118e-05 2
70 3.03819e-05 2
75 3.25521e-05 2
80 3.47222e-05 2
85 3.68924e-05 2
90 3.90625e-05 2
95 4.12326e-05 2
100 4.34028e-05 2
105 4.55729e-05 2
110 4.77431e-05 2
115 4.99132e-05 2
120 5.20833e-05 2
As you can see, the src histogram are a lot more distributed than the histrogram I receive ([0-255] against [0-2]).
My question is, what do I do now? How do I match them?

Why don't you scale [0-2] histogram to [0-255]? oldValue * 255 / 2.