I am currently learning to use MPI and trying to scatter blocks of my array to the rest of my processors.
My root processor is the last one (nproc-1) and I am generating the array in that processor. In my next iteration of my code it will be a random array.
For all my processors I am allocating contiguous memory using calloc both for 'array' and 'grain'.
Grain stores the data to process and since I need the above and below rows from the original array, I made it of size grain_length+2.
My issue is that I get the correct data from the original array except for the last two values (see output example below).
int main(int argc, char** argv)
{
int i, j, m;
int array_size, grain_length;
int rc, rank, nproc;
MPI_Status status;
rc = MPI_Init(&argc, &argv);
if (rc != MPI_SUCCESS)
{
printf("Error starting MPI Program.\n");
MPI_Abort(MPI_COMM_WORLD, rc);
}
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &nproc);
array_size = 8;
grain_length = array_size / nproc;
double **array= (double **) calloc(array_size, sizeof (double *));
for (i = 0; i < array_size; i++)
array[i] = (double *) calloc(array_size, sizeof (double));
double **grain = (double **) calloc(grain_length+2, sizeof (double *));
for (i = 0; i < grain_length + 2; i++)
grain[i] = (double *) calloc(array_size, sizeof (double));
if (array == NULL || grain == NULL)
{
printf("Memory could not be allocated for the arrays.");
exit(EXIT_FAILURE);
}
if (rank == nproc-1)
{
for (i = 0; i < array_size; i++)
{
for (j = 0; j < array_size; j++)
{
//array[i][j] = rand() % 10;
array[i][j] = i+j;
}
}
}
MPI_Scatter(
&array[0][0], grain_length*array_size, MPI_DOUBLE,
&grain[1][0], grain_length*array_size, MPI_DOUBLE,
nproc-1, MPI_COMM_WORLD);
for (m = 0; m < nproc; m++)
{
if (rank == m)
{
printf("Grain from processor %d:\n", rank);
for (i = 0; i < grain_length+2; i++)
{
for (j = 0; j < array_size; j++)
{
printf("%f\t", grain[i][j]);
}
printf("\n");
}
printf("\n");
}
MPI_Barrier(MPI_COMM_WORLD);
}
if (rank == nproc-1)
{
printf("Array from processor %d:\n", rank);
for (i = 0; i < array_size; i++)
{
for (j = 0; j < array_size; j++)
{
printf("%f\t", array[i][j]);
}
printf("\n");
}
printf("\n");
}
MPI_Finalize();
return 0;
}
Here is the output. In Grain 0, the first and last row are 0s as expected since the above and below rows will be sent and placed there. Then the second row is correct but the third row is missing the 7 and 8 values which are the first values in Grain 1.
Are the two 0s in Grain 0 the array's two pointers addresses? I don't understand why I am getting incomplete data when the array in memory is stored contiguously.
I tried to use scatterv with the displacement but I am not sure I understand how it works.
I also tried to create an MPI Type but didn't get far away with that either.
What I managed to do is to broadcast the each row of the array to all the others processors. But it is quite inefficient I think. This is how I did it.
for (i=0; i < array_size; i++)
MPI_Bcast(&array[i][0], array_size, MPI_DOUBLE, nproc-1, MPI_COMM_WORLD);
Many thanks in advance for your help!!
Grain from processor 0:
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
0.000000 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000
1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 0.000000 0.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Grain from processor 1:
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
7.000000 8.000000 0.000000 0.000000 2.000000 3.000000 4.000000 5.000000
8.000000 9.000000 0.000000 0.000000 3.000000 4.000000 0.000000 0.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Grain from processor 2:
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 0.000000 0.000000
6.000000 7.000000 8.000000 9.000000 10.000000 11.000000 0.000000 0.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Grain from processor 3:
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
0.000000 0.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000
0.000000 0.000000 6.000000 7.000000 8.000000 9.000000 0.000000 0.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Array from processor 3:
0.000000 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000
1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.000000
2.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.000000
3.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000
4.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 11.000000
5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 11.000000 12.000000
6.000000 7.000000 8.000000 9.000000 10.000000 11.000000 12.000000 13.000000
7.000000 8.000000 9.000000 10.000000 11.000000 12.000000 13.000000 14.000000
I was able to achieve the result you want by changing the length of sent information to each individual process from:
MPI_Scatter(
&array[0][0], grain_length*array_size, MPI_DOUBLE,
&grain[1][0], grain_length*array_size, MPI_DOUBLE,
nproc-1, MPI_COMM_WORLD);
To:
MPI_Scatter(
&array[0][0], 4+grain_length*array_size, MPI_DOUBLE,
&grain[1][0], 4+grain_length*array_size, MPI_DOUBLE,
nproc-1, MPI_COMM_WORLD);
The result:
Grain from processor 0:
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
0.000000 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000
1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Grain from processor 1:
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.000000
3.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Grain from processor 2:
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
4.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 11.000000
5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 11.000000 12.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Grain from processor 3:
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
6.000000 7.000000 8.000000 9.000000 10.000000 11.000000 12.000000 13.000000
7.000000 8.000000 9.000000 10.000000 11.000000 12.000000 13.000000 14.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Array from processor 3:
0.000000 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000
1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.000000
2.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.000000
3.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000
4.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 11.000000
5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 11.000000 12.000000
6.000000 7.000000 8.000000 9.000000 10.000000 11.000000 12.000000 13.000000
7.000000 8.000000 9.000000 10.000000 11.000000 12.000000 13.000000 14.000000
I hope it will help you.
Related
I have a dataframe with 152 rows and 300 columns.
Showing you first 3 rows and all 300 columns
genea 2500 2691 genea 191.0 + 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.380752 -0.380752 -0.531231 -0.681710 -0.681710 -0.681710 -0.681710 -0.340855 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.190376 -0.380752 -0.380752 -0.380752 -0.380752 -0.380752 -0.380752 -0.380752 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.451626 0.903252 0.903252 0.654369 0.654369 0.778811 0.903252 0.903252 -0.681710 -0.681710 -0.340855 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.809625 1.619250 1.619250 1.257220 1.257220 1.057214 0.857208 0.857208 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.190376 -0.380752 -0.380752 0.672255 0.672255 0.672255 0.672255 0.672255 0.903252 0.903252 1.422216 1.941180 1.941180 1.508340 1.508340 1.912020 2.315700 2.315700 3.317330 3.317330 3.840005 4.362680 4.362680 3.508340 3.508340 3.128800 2.749260 2.749260 3.531090 3.531090 2.982865 2.434640 2.434640 1.975690 1.975690 2.516920 3.058150 3.058150 5.556610 5.556610 5.922590 6.288570 6.288570 2.056200 2.056200 2.563420 3.070640 3.070640 3.577700 3.577700 4.076065 4.574430 4.574430 4.008980 4.008980 4.648165 5.287350 5.287350 5.550990 5.550990 3.810200 2.069410 2.069410 0.000000 0.000000 1.584965 3.169930 3.169930 3.169930 3.169930 3.243285 3.316640 3.316640 4.766030 4.766030 4.925570 5.085110 5.085110 6.746300 6.746300 6.693390 6.640480 6.640480 5.850710 5.850710 5.628100 5.405490 5.405490 4.830740 4.830740 5.017090 5.203440 5.203440 6.095880 6.095880 6.392065 6.688250 6.688250 6.337030 6.337030 5.835895 5.334760 5.334760 4.836420 4.836420 4.736225 4.636030 4.636030 3.659990 3.659990 4.325255 4.990520 4.990520 4.756270 4.756270 2.378135 0.000000 0.000000 3.700440 3.700440 3.921625 4.142810 4.142810 4.318290 4.318290 4.490965 4.663640 4.663640 4.643860 4.643860 3.706855 2.769850 2.769850 2.878250 2.878250 3.156445 3.434640 3.434640 3.676790 3.676790 3.867180 4.057570 4.057570 4.192870 4.192870 4.521820 4.850770 4.850770 4.602990 4.602990 4.119790 3.636590 3.636590 3.899620 3.899620 4.155710 4.411800 4.411800
chr11 62841618 62841809 geneb 191.0 - -0.613539 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.228451 -0.380752 -0.380752 -0.380752 -0.380752 -0.152301 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.038075 -0.380752 -0.380752 -0.380752 -0.380752 -0.342677 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.228451 -0.380752 -0.380752 -0.380752 -0.380752 -0.152301 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.228451 -0.380752 -0.380752 -0.380752 -0.380752 -0.152301 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.068171 -0.681710 -0.681710 0.269267 0.903252 0.857144 0.442170 0.442170 0.718819 0.903252 0.812927 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.068171 -0.681710 -0.681710 -0.681710 -0.681710 -0.651614 -0.380752 -0.380752 0.447699 1.000000 1.000000 1.000000 1.000000 1.350976 1.584960 1.626464 2.000000 2.000000 1.400000 1.000000 0.900000 0.000000 0.000000 1.550976 2.584960 2.584960 2.584960 2.584960 0.805533 -0.380752 -0.180752 1.619250 1.619250 2.198676 2.584960 2.743457 4.169930 4.169930 4.612106 4.906890 4.874697 4.584960 4.584960 3.633984 3.000000 2.858496 1.584960 1.584960 3.348120 4.523560 4.523560 4.523560 4.523560 1.809424 0.000000 0.370044 3.700440 3.700440 3.824310 3.906890 3.862144 3.459430 3.459430 3.427222 3.405750 3.561390 4.962150 4.962150 5.458362 5.789170 5.720218 5.099650 5.099650 4.866226 4.710610 4.661025 4.214760 4.214760 3.676302 3.317330 3.349619 3.640220 3.640220 4.456088 5.000000 5.032193 5.321930 5.321930 5.101292 4.954200 4.852898 3.941180 3.941180 4.168286 4.319690 4.374439 4.867180 4.867180 4.999348 5.087460 4.978714 4.000000 4.000000 2.550976 1.584960 1.688389 2.619250 2.619250 2.619250 2.619250 2.357325 0.000000 0.000000 0.000000 0.000000 0.332193 3.321930 3.321930 3.321930 3.321930 3.335680 3.459430 3.459430 3.068182 2.807350 2.807350 2.807350 2.807350 2.922940 3.000000 2.800000 1.000000 1.000000 0.400000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.038075 -0.380752 -0.380752 -0.380752 -0.380752 -0.380752 -0.380752 -0.380752 0.447699 1.000000
chr17 43367899 43368087 genec 188.0 - 0.000000 1.600000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 1.400000 0.000000 0.000000 0.000000 0.000000 -0.204513 -0.681710 -0.681710 -0.681710 -0.681710 -0.681710 -0.681710 -0.681710 -0.136342 0.000000 -0.114226 -0.380752 -0.380752 -0.380752 -0.380752 -0.266526 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.204513 -0.681710 -0.681710 -0.681710 -0.681710 -0.477197 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.114226 -0.380752 -0.380752 0.419248 0.619248 0.619248 0.619248 0.619248 0.923850 1.000000 1.000000 1.000000 1.000000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.267968 1.584960 1.584960 1.584960 1.584960 0.316992 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.800000 1.000000 1.000000 1.000000 1.000000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.300000 1.000000 1.000000 1.000000 1.000000 0.700000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.800000 1.000000 1.000000 1.000000 1.000000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.267968 1.584960 1.584960 1.584960 1.584960 0.316992 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.300000 1.000000 1.000000 1.000000 1.000000 0.700000 0.000000 0.000000 1.600000 2.000000 2.974379 5.247930 5.247930 5.674674 5.781360 5.694507 5.491850 5.491850 3.623866 3.156870 3.505008 4.317330 4.317330 5.117330 5.317330 5.650009 6.426260 6.426260 6.155220 6.087460 6.087460 6.087460 6.087460 5.180852 4.954200 4.464519 3.321930 3.321930 3.934354 4.087460 3.936710 3.584960 3.584960 3.252936 3.169930 2.994439 2.584960 2.584960 4.030848 4.392320 4.371203 4.321930 4.321930 4.532354 4.584960 4.722789 5.044390 5.044390 5.044390 5.044390 4.413427 2.941180 2.941180 2.141180 1.941180 2.320089 3.204210 3.204210 3.040842 3.000000 3.000000 3.000000 3.000000 0.600000 0.000000 0.996579 3.321930 3.321930 3.431930 3.459430 3.459430 3.459430 3.459430 0.387284 -0.380752 -0.380752 -0.380752 -0.380752 -0.076150 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.204513 -0.681710 -0.681710 -0.681710 -0.681710 -0.477197 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
I am plotting the values in this dataframe using pheatmap function as follows:
pheatmap(dmat,
scale="none",
cluster_rows = FALSE,
cluster_cols = FALSE,
annotation_names_col = FALSE,
show_colnames= FALSE,
color = colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(500),
main = "figure1",
border_color = NA
)
I want to add a label at 200th column "TSS" on this pheatmap generated above. What code should I use for that? "TSS" should appear in the last row and 200th column
Thanks in advance
Here is a solution based on grobs.
library(pheatmap)
library(RColorBrewer)
library(grid)
# Generate data
nc <- 300
nr <- 152
ref <- 200 # The column where you need to add a label
dmat <- matrix(runif(nr*nc), ncol=nc)
dmat[,ref] <- 0
q <- pheatmap(dmat,
scale="none",
cluster_rows = FALSE,
cluster_cols = FALSE,
annotation_names_col = FALSE,
show_colnames= FALSE,
color = colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(500),
main = "Figure1",
border_color = NA
)
downViewport("matrix.4-3-4-3")
grid.text("TSS", x=ref/nc, y=1, vjust=-0.5, gp=gpar(col="red", fontface=2, fontsize=12))
popViewport()
The list of available viewports generated by pheatmap can be retrieved using
grid.draw(q)
current.vpTree()
# viewport[ROOT]->(viewport[layout]->(viewport[layout]->
# (viewport[main.1-3-1-3], viewport[legend.4-5-5-5], viewport[matrix.4-3-4-3])))
My vector a <- c(2,0,0,1,0, 3, 0, 0,0,0,5,6)
The zeros represent the missing values
I want to interpolate missing values only if the succession of 0 is shorter than 4
I am looking for a function which allows me to perform this
The wished output is [1] 2.00 1.67 1.33 1.00 2.00 3.00 0.00 0.00 0.00 0.00 5.00 6.00
library(zoo)
temp1 = na.approx(replace(a, a == 0, NA))
temp2 = inverse.rle(with(rle(a), list(values = replace(values, values == 0 & lengths < 4, NA),
lengths = lengths)))
replace(temp2, is.na(temp2), temp1[is.na(temp2)])
# [1] 2.000000 1.666667 1.333333 1.000000 2.000000 3.000000 0.000000 0.000000
# [9] 0.000000 0.000000 5.000000 6.000000
The following (as commented by G.Grothendieck) is better
temp = na.approx(object = replace(a, a == 0, NA), maxgap = 3)
replace(temp, is.na(temp), 0)
# [1] 2.000000 1.666667 1.333333 1.000000 2.000000 3.000000 0.000000 0.000000
# [9] 0.000000 0.000000 5.000000 6.000000
I have this data frame in R and I need to select only rows that match at least two of the following conditions :
A >= 5
B >= 5
C >= 5
D >= 5
A B C D
1 0.000000 48.936170 0.000000 29.787234
2 0.000000 72.340426 0.000000 6.382979
3 0.000000 78.723404 0.000000 2.127660
4 2.127660 78.723404 0.000000 0.000000
5 0.000000 43.617021 0.000000 35.106383
6 0.000000 79.787234 0.000000 1.063830
7 3.191489 0.000000 77.659574 0.000000
8 77.659574 0.000000 2.127660 0.000000
9 46.808511 0.000000 0.000000 31.914894
10 35.106383 0.000000 27.659574 0.000000
The only solution I found is to use "if"...
if ( ((data$A >=5) + (data$B >=5) + (data$C >=5) + (data$D >=5)) >=2 ) {
#result }
...but I cannot find how to combine the if selection with my data frame.
I tried like this but I doesn't seem to be the solution for this problem :
Selection = data[if ( ((data$A >=5) + (data$B >=5) + (data$C >=5) + (data$D >=5)) >=2 ),]
Thanking you in advance for your help,
You could also do
df <- read.table(header=T, text=" A B C D
1 0.000000 48.936170 0.000000 29.787234
2 0.000000 72.340426 0.000000 6.382979
3 0.000000 78.723404 0.000000 2.127660
4 2.127660 78.723404 0.000000 0.000000
5 0.000000 43.617021 0.000000 35.106383
6 0.000000 79.787234 0.000000 1.063830
7 3.191489 0.000000 77.659574 0.000000
8 77.659574 0.000000 2.127660 0.000000
9 46.808511 0.000000 0.000000 31.914894
10 35.106383 0.000000 27.659574 0.000000")
df[rowSums(df >= 5) >= 2, ]
# A B C D
# 1 0.00000 48.93617 0.00000 29.787234
# 2 0.00000 72.34043 0.00000 6.382979
# 5 0.00000 43.61702 0.00000 35.106383
# 9 46.80851 0.00000 0.00000 31.914894
# 10 35.10638 0.00000 27.65957 0.000000
Something wrong in my code. I want to obtain the function e(p) = p * L'(p) / L(p) using some fitting technique.
For instance, if L(p) = p^4, then e(p)=4, but when I try to do this using splinefun(), something wrong hapens:
p <- 1:50/50
lp <- p^4
spl <- splinefun(p,lp)
dlp <- spl(p, deriv = 1)
p*dlp/lp
and the result
[1] 9.464102 3.816987 4.014530 3.998358 4.000225 3.999965 4.000006 3.999999
[9] 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000
[17] 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000
[25] 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000
[33] 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000
[41] 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000001 3.999996
[49] 4.000012 3.999956 `
Why do the numbers 9.464102 3.816987 appear? Is it possible to obtain 4 in all the cases?
I'm trying to calculate the cross-correlation scores between a series of vectors.
My code for doing so is:
smusF<-array(ccf(arrMat[[r,1]],arrMat[[h,1]],lag.max=(length(arrMat[[r,1]])+length(arrMat[[h,1]])))[[1]])
Where smusF is the vector of all ccf scores, for each overlap position, and the r and h variables assign differing vectors for comparison.
What I'm finding is sometimes this code has no errors and works correctly, but sometimes it produces the error message:
Error in plot.window(...) : need finite 'ylim' values
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
I'm really stuck as to why this happens in some instances and not others, and what I can do to fix it. Any help much appreciated.
An example of two vectors that produce this error message when compared are..
> arrMat[[r,1]]
[1] 0.011688 0.014871 0.015314 0.013446 0.008538 0.006948 0.006514 0.004343
[9] 0.002171 0.000000 0.002196 0.006899 0.012790 0.014289 0.015993 0.015321
[17] 0.016845 0.010438 0.005219 0.003040 0.007235 0.011430 0.009546 0.005351
[25] 0.004531 0.006752 0.011283 0.009062 0.006842 0.002311 0.002311 0.003614
[33] 0.006072 0.008825 0.009742 0.012397 0.013938 0.019788 0.022163 0.025293
[41] 0.022794 0.024204 0.020493 0.017092 0.010652 0.009013 0.007760 0.008768
[49] 0.008235 0.008858 0.005392 0.002696 0.000000 0.000000 0.000869 0.001737
[57] 0.003474 0.003474 0.003474 0.002212 0.009292 0.016371 0.027220 0.023992
[65] 0.023172 0.015995 0.015421 0.010799 0.011676 0.012986 0.017361 0.018033
[73] 0.018508 0.022458 0.027989 0.034674 0.030668 0.024449 0.013905 0.009333
[81] 0.005809 0.005219 0.003441 0.002433 0.001425 0.000950 0.002138 0.003326
[89] 0.004989 0.003326 0.001663 0.000000 0.000000 0.000000 0.000000 0.000000
[97] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[105] 0.000000 0.000000 0.000000 0.000770 0.001540 0.002311 0.001540 0.000770
[113] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[121] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[129] 0.000000 0.000000 0.000000 0.001188 0.002376 0.004719 0.004687 0.004654
[137] 0.002311 0.001155 0.000000 0.000000 0.000713 0.001425 0.002138 0.001425
[145] 0.000713 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[153] 0.000000 0.000000
And..
> arrMat[[h,1]]
[1] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[9] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[17] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[25] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[33] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[41] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[49] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[57] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[65] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[73] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[81] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[89] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[97] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[105] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[113] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[121] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[129] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[137] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[145] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[153] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[161] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[169] 0.000000 0.000000 0.012729 0.025457 0.045459 0.043641 0.063643 0.089100
[177] 0.125907 0.119074 0.079510 0.040635 0.023581 0.031983 0.034051 0.036118
[185] 0.030912 0.023639 0.016365 0.007273 0.003637 0.007273 0.018184 0.029094
[193] 0.030912 0.025457 0.020002 0.010910 0.005455 0.000000 0.000000 0.000000
[201] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[209] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[217] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[225] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[233] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[241] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[249] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[257] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[265] 0.000000 0.000000 0.000000 0.000000
For future reference it seems ccf is not happy comparing un-fluctuating vectors, such as a vector of only '1's against a vector of only '2's, or in the above example, overlaps to '0's against '0's