I would like to use ggplot that can show all observations (all points) and a line (mean of all observation) over multiple times points post treatment for two groups (treated and untreated) with differnt colors between groups. I have attached my example data set and a sample graph from previous publication
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Assume I have two datasets, each one containing 5000 samples, and each sample has three dimensions. I am looking for a way to "reorder" the samples in one (or probably both) dataset such that the sum of least square error (error: Euclidean distance between points) between these two data sets be minimum.
Please see the attached photo for clarification.
Thanks.
See this image for more clarification:
https://www.mathworks.com/matlabcentral/answers/uploaded_files/1017640/image.png
Originally I had a dataframe containing power consumption of some devices like this:
and I wanted to plot power consumption vs time for different devices, one plot per one of 6 possible dates. After grouping by date I got plots like this one (for each group = date):
Then I tried to create similar plot, but switch date and device roles so that it is grouped by device and colored by date. In order to do it I prepared this dataframe:
It is similar to the previous one, but has many NaN values due to differing measurement times. I thought it won't be a problem, but then after grouping by device, subplots look like this one (ex is just a name of sub-dataframe extracted from loop going through groups = devices):
This is the ex dataframe (mean lag between observations is around 20 seconds)
Question: What should I do to make plot grouped by device look like ones grouped by date? (I'd like to use ex dataframe but handle NaNs somehow.)
I found solution in answer to similar question: ex.interpolate(method='linear').plot(). This line will fill gaps between data points via interpolation between plotting. This is the result:
Another thing that can help is adding .plot(marker='o', ms = 3) which won't fill gaps between points, but at least will make points visible (previously some points, mainly the peaks in energy consumption were too small in scale of whole plot). This is the result:
I want to plot 5 different data frames in 1 plot. Containing the same measurement but done at different times. The plot should share the x-Axis for all measurement.
First thing i did was to calculate the time between the measurement points. It differs between 5-10 ms but sometimes also big gaps of 200 ms.
Then i calculated the running sum over this column. Then i set this column as the index (dtype "timedelta64[ns]")
Now i want to plot those 5 times.series in one plot which share the x-Axis (as time in ms)
But i don´t now how because they have almost no common index together. The plot should have one common x-Axis from 0-3 seconds containing the 5 measurements.
Thank you!
2 Example DataFrames:
example for measuremt01
example for measuremt02
when two series intersect, the lines are not getting plotted. In the below the red marked data points belong to one series but since a data point from "Actual rating" series is intersecting the line is not plotted. similarly, the green marked data points belong to "Rating" series. But since a data point from Actual rating is intersecting the line is not plotted. How to solve this?
I'm using Core Plot to draw graphs in my app.
I just encountered a problem:
I have dates on the X-Axis and I use a custom labeling policy.
If I only have a few records everything works fine
If I have many records all the labels are near and not useful :-(
So the question is: How can I decide which values display and which not to always have 10 labels, separated one from the other.
Divide the number of points by the number of labels you want and round up. For example, if you have 25 data points and want roughly 10 labels, label every third data point. You'll end up with 9 evenly spaced labels.