Is Mathematica capable of producing 2d contour plots from .txt files with the contour levels being in logarithmic scale?

Question

I have a text file that contains five columns of data in which columns 1 and 2 correspond to projected distance along dimension 1 and 2 from the center of the dark halo and then columns 3, 4 and 5 are values of the neutral hydrogen column density in projections 0 "z", 1 "x" and 2 "y" respectively. I would like to transform this .txt table into actual 2d maps in which the color strength of each pixel would correspond to the magnitude of the function in that pixel. This means that I would have three planes xy, yz and xz in which each pixel is filled with a color.

My values for columns 3,4 and 5 are mostly zero with few of them on the order of $10^{14}$ to $10^{21}$. I am interested in having three contour levels (less than $10^{17}$, between $10^{17}$ and $10^{20.3}$ and larger than $10^{20.3}$). Let's say if the function value is smaller than $10^{17}$, the color should be LightRed, if the function value is between $10^{17}$ and $10^{20.3}$, the color should be Red, and if the function value is larger than $10^{20.3}$, the color should be DarkRed. In other words, my 2d map is in fact a set of 3 contour plots that are produced out of .txt table. (Here is the link of a portion of my data: MyData)

Is there anyway to translate this into the maps by a color bar and apply it to the maps?

Based on the examples shown in ListContourPlot, I could not find something similar to my case. But, here is what I have for the three maps (thanks to Conor Consett for solving the original problem of importing a .txt file into mathematica notebook) including the modifications I made in the original code (considering ALL data and not the abridged version uploaded here):

datam10q = 
 Import["/Users/U of A/Desktop/covering_fractions/m10q.txt", "Table"]
xy10q = datam10q[[All, {1, 2, 3}]];
yz10q = datam10q[[All, {1, 2, 4}]];
xz10q = datam10q[[All, {1, 2, 5}]];
{ListContourPlot[xy10q, 
  Contours -> {{10^14, Thick}, {10^15.5, Thick}, {10^17, 
     Thick}, {10^20.3, Thick}}, 
  ContourShading -> {Yellow, Orange, Red, Brown, Black}, 
  ContourStyle -> None, InterpolationOrder -> 5, 
  BoundaryStyle -> Black, PerformanceGoal -> "Quality"], 
 ListContourPlot[yz10q, 
  Contours -> {{10^14, Thick}, {10^15.5, Thick}, {10^17, 
     Thick}, {10^20.3, Thick}}, 
  ContourShading -> {Yellow, Orange, Red, Brown, Black}, 
  ContourStyle -> None, InterpolationOrder -> 5, 
  BoundaryStyle -> Black, PerformanceGoal -> "Quality"], 
 ListContourPlot[xz10q, 
  Contours -> {{10^14, Thick}, {10^15.5, Thick}, {10^17, 
     Thick}, {10^20.3, Thick}}, 
  ContourShading -> {Yellow, Orange, Red, Brown, Black}, 
  ContourStyle -> None, InterpolationOrder -> 5, 
  BoundaryStyle -> Black, PerformanceGoal -> "Quality"]}

Answer 1

I would approach this by experimenting with a tiny version of the problem.

Here is a 5 column .txt file I made: http://pastebin.com/wCPxdvQB

Download it and Import it into your session with:

data = Import["/Users/johncosnett/Downloads/xy123data.txt", "Table"]

xy = data[[All, {1, 2, 3}]];
yz = data[[All, {1, 2, 4}]];
xz = data[[All, {1, 2, 5}]];

{ListContourPlot[xy, PlotTheme -> {"Monochrome", Red}], 
 ListContourPlot[yz, PlotTheme -> {"Monochrome", Red}], 
 ListContourPlot[xy, PlotTheme -> {"Monochrome", Red}]}

Answer 2

(Not an answer, just an extended comment.)

Your dataset contains a lot of zeros in the $z$-coordinate, so it isn't exactly clear what you mean by logarithmic plot. Assuming that zero values must be simply ignored, we can log-transform the data:

data = Import["http://pastebin.com/raw/6Tj9LHvH", "Table"];
xy10q = DeleteCases[data[[All, {1, 2, 3}]], {_, _, 0.}];
xy10q[[All, 3]] = Log10[xy10q[[All, 3]]];
ListPlot3D[xy10q, InterpolationOrder -> 1, ViewPoint -> {Infinity, 0, 0}]

From the above plot it is clear that the dataset isn't suited for producing a contour plot because it contains a lot of noise. Here is a slice from the middle:

ListPlot[Cases[xy10q, p : {0.254`, _, _} :> Rest@p]]

So you should define your goal more precisely: what information you actually wish to extract from your very noisy dataset containing a lot of zeros in the $z$-coordinate? Also it might help if you explain what this data mean.