# How to change data from linear scale to log scale

• Fortran
• Amany Gouda
In summary: What operations have you considered so far?[I've put some of your words in boldface for emphasis.][...] I converted the IL to be in log scale.I noticed unacceptable output.So, I am going to repeat the calculation again by by using IL in log scale.But you already converted IL to log scale? I think you should clarify this.
Amany Gouda
I am trying to calculate and draw a relation between (IL) and the Growth rate (m).
I have used Il as range from 10^15 to 10^18 in the calculation and when I draw the relation (x-axis ) is Il and (y-axis ) is m. I converted the IL to be in log scale.
I noticed unacceptable output.
So, I am going to repeat the calculation again by by using IL in log scale.
is there any mathematical relation can be used to make this conversion for IL.
Thank you

"IL?"

Bystander said:
"IL?"
IL: Laser intensity ranging from 10^15 to 10^18.

Amany Gouda said:
I am trying to calculate and draw a relation between (IL) and the Growth rate (m).
I have used Il as range from 10^15 to 10^18 in the calculation and when I draw the relation (x-axis ) is Il and (y-axis ) is m. I converted the IL to be in log scale.
I noticed unacceptable output.
So, I am going to repeat the calculation again by by using IL in log scale.
is there any mathematical relation can be used to make this conversion for IL.
As I understand it, you have a data set of unknown origin which is a collection of ordered pairs: (IL, m). You have plotted this (a scatter graph?) and obtained a plot that is "unacceptable" for some reason.

You now want to re-scale the x-axis on a log scale and you want to know a mathematical operation that can take an unscaled value for IL and convert it to a scaled value. So, for instance, 10^15 is represented as 15 and 10^18 is represented as 18.

What operations have you considered so far?

[I've put some of your words in boldface for emphasis.]
Amany Gouda said:
[...] I converted the IL to be in log scale.
I noticed unacceptable output.
So, I am going to repeat the calculation again by by using IL in log scale.

But you already converted IL to log scale? I think you should clarify this.

Hi all,
I have experimental data that obey mono- or biexponential behavior (without or with offset): I = I0*exp(-t/T2), I = I0*exp(-t/T2) + offset1, I = I1*exp(-t/T2_1)+I2*exp(-t/T2_2), I = I1*exp(-t/T2_1)+I2*exp(-t/T2_2) + offset2. I is intensity, t is time, T2, T2_1 and T2_2 are time constants. I1 and I2 reflect fractions of T2_1 and T2_2 components, respectively. I'd like to ask a question about plotting in semilog scale. What is the difference between plotting intensity on a log scale (and the horizontal axis is time) and plotting ln(intensity) vs time (ln - natural logarithm, or base "e" logarithm)? I don't understand the first one - plotting intensity on a log scale. I know that, for example, in Matlab there are functions loglog, semilogx, semilogy, but again, I don't understand, how they work. What mathematical formulas should I use to implement conversion from standard scale to semilogy scale (when y-axis is in log scale) if I want to do such conversion manually? Moreover, from the Matlab help for semilogy function: "semilogy(Y) creates a plot using a base 10 logarithmic scale for the y-axis and a linear scale for the x-axis". Why do they use base 10 logarithmic scale and not base "e" logarithmic scale? If I want to use semilogy function, but with "e" base logarithm (natural logarithm), how can I implement this, for example, in Matlab? On the other side, plotting ln(intensity) is clear, because in this case I just take natural logarithm of my original intensity values, and then plot these ln(intensity) vs time. I also found the site http://measurebiology.org/wiki/Understanding_log_plots, where topic is "Understanding log plots". On that site, authors show examples of plotting in log scale using Matlab. In the section "Linear vs. log scale", the authors wrote the following: "There are two ways to make a log-log plot in MATLAB. The first is to use the plot command to plot log(y) vs. log(x) on a linear scale.
plot( log10(x), log10(y))
Alternatively, you can use the loglog command to make a plot with log-scale axes:
loglog( x, y)"
Then they show corresponding graphs. These two plots look the same.

## 1. How do I convert data from linear scale to log scale?

To convert data from linear scale to log scale, you can use the logarithmic function. This function takes the logarithm of each data point and creates a new set of data points that are on a log scale. You can use this function in spreadsheet programs like Excel or in programming languages like Python.

## 2. Why would I want to change my data from linear scale to log scale?

Changing data from linear scale to log scale can be useful when dealing with data that has a large range of values. It can help to better visualize and understand the data, especially if there are extreme outliers. Log scale can also help to show the relative changes in data over time or between different groups.

## 3. What is the difference between linear scale and log scale?

Linear scale is a type of scale where equal distances on the scale represent equal differences in the data. This means that the distance between 1 and 2 is the same as the distance between 50 and 51. In contrast, log scale is a type of scale where each increment on the scale represents a multiplication of the previous value. This means that the distance between 1 and 10 is the same as the distance between 10 and 100, and so on.

## 4. Can I convert any type of data from linear scale to log scale?

In general, it is possible to convert any type of data from linear scale to log scale. However, it is important to consider the nature of the data and whether a log scale would be appropriate for it. For example, data that is already on a log scale or has a large range of values may not benefit from being converted to log scale.

## 5. Are there any limitations to using log scale for my data?

While log scale can be useful for visualizing certain types of data, it is not always the best option. It can make small changes in data appear larger and can also make it more difficult to accurately interpret the data. Additionally, log scale may not be appropriate for all types of analysis or comparisons. It is important to carefully consider the nature of your data and the purpose of your analysis before deciding to convert to log scale.

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