```It seems like none of the answers I see up to now are actually producing heat maps. The difference between a heat map and a `ListDensityPlot` is important. In _Mathematica_ vocabulary, the heat map is a `SmoothDensityHistogram`.

First of all, I tried to _directly_ use the 'heatMap' function in [my answer][1]. I just tried it with the data in that post:

data = RandomReal[1, {100, 2}];

Show[heatMap[data, "Points" -> 300, "Radius" -> {10, .02},
PlotRange -> {{0, 1}, {0, 1}},
ColorFunction -> ColorData["Rainbow"]], Graphics[Point@data],
PlotRange -> {{0, 1}, {0, 1}}]

![smearedsquare][2]

All I did is to specify a _tuple_ `{10, .02}` for the `"Radius"` option. Its first entry is the radius in the vertical direction, and with a choice of `10` this smears all the data out over the entire vertical image range.

This shows it works without modifying the code. But of course I have to tweak the function in order to make it look more "one-dimensional":

heatMap[data_, opts : OptionsPattern[]] :=
Module[{n, size, xRange, pr},
n = "Points" /. {opts} /. {"Points" -> 100};
pr = PlotRange /. {opts} /. {PlotRange :>
Map[{Min[#], Max[#]} &, Transpose[data]]};
xRange = -Subtract @@ pr[[1]];
size = Floor[
Graphics[
{Inset[
ArrayPlot[
Rescale@GaussianFilter[
ImageData@ColorNegate@ColorConvert[
Rasterize[
Graphics[
Point[data],
Background -> White,
ImageMargins -> 0,
PlotRange -> pr
],
"Image",
ImageSize -> n
],
"GrayScale"
],
{3 size, size},
],
ColorFunction -> (ColorFunction /. {opts} /. {ColorFunction ->
ColorData["LakeColors"]}),
Frame -> False
],
pr[[All, 1]],
{0, 0}, xRange]},
PlotRange -> pr,
Frame -> True,
FrameTicks -> {Automatic, None}
]
]

So here I removed the `PlotRangePadding` and the `FrameTicks` on the left side, as well as the `PlotRangePadding`. I think that's all you need to change. Having collapsed the `data` onto a single axis, the vertical smearing for `GaussianFilter` needs to be only of order `1` (in relation to the horizontal axis) - so that's what I used. Then I set the `PlotRange` appropriately and get this:

data = RandomReal[{0, 1}, 100];
data = {#, 0} & /@ data;

heatMap[data, "Points" -> 300, "Radius" -> {1, .02},
PlotRange -> {{0, 1}, {0, .04}}, PlotRangePadding -> 0,
FrameLabel -> None]

![heatmapThin][3]

The meaning of the option `"Points"` (number of horizontal sampling points) is the same as described in the linked post.

**Edit**

As I mentioned, a heat map is also realizable using built-in smoothed histogram techniques. I think the easiest way to do that in the present case would be as follows:

DensityPlot[
Evaluate@PDF[SmoothKernelDistribution[data, {1, .02}], {y, x}], {x,
0, 1}, {y, 0, .2}, AspectRatio -> Automatic, PlotPoints -> {200, 2},
FrameTicks -> {Automatic, None}, PlotRangePadding -> None]

![heatmapDensityPlot][4]

The unequal smearing is now achieved by specifying a _tuple_ for the "bandwidth" parameter in `SmoothKernelDistribution`: in `{1, .02}`, the `1` is again chosen to be large compared to the plot range in the `y` direction (from `0` to `.2`) so that you get vertical bands. It's of also necessary to adjust the number of `PlotPoints` to be large enough in the horizontal direction so as to capture all the details of the distribution. The vertical number of `PlotPoints` (the second number in `{200, 2}`) can be set to the smallest possible value, `2`.

**Edit 2**

Of course, we can also backtrack even further and go to the original data set you started with -  which was purely one-dimensional. In that situation, you can simply do something like this:

data = RandomReal[1, 100];

DensityPlot[
Evaluate[{PDF[SmoothKernelDistribution[data, .02], x], 0}], {x, 0,
1}, {y, 0, .04}, AspectRatio -> Automatic, PlotPoints -> {200, 2},
FrameTicks -> {Automatic, None}, PlotRangePadding -> None]

![No Smearing][5]

This involves _no_ need for smearing in the vertical direction because it calculates the density function one-dimensionally in the first place. The methods above have their justification too, when the `data` list does have two-dimensional points that you want to project onto a single axis.

[1]: http://mathematica.stackexchange.com/a/6082/245
[2]: http://i.stack.imgur.com/fIo5b.png
[3]: http://i.stack.imgur.com/phRlQ.png
[4]: http://i.stack.imgur.com/GSiVz.png
[5]: http://i.stack.imgur.com/sZHM3.png```