i'm creating some BIRT-Reports with Eclipse. Now i got the following problem.
I've got two datasets (Set one named diag, set two named risk). In my report i produce fpr every data in diag a region with an diag_id. Now i tried to use this diag_id as input parameter for the second dataset (risk). Is this possible, and how is this possible?
To link one dataset to another in BIRT, you can either:
Create a subreport within your report that links one dataset to another via an input parameter - see this Eclipse tutorial.
or:
Create a joint dataset that explicitly links the two datasets together - see the answer to this StackOverflow question.
Alternatively, if both datasets come from the same relational database, you could simply combine the two queries into a single query.
If you are using scripted data sources, you could use variables.
Add a variable through the Eclipse UI called "diag_id".
In the fetch script of diag, set diag_id:
vars["diag_id"] = ...; // store value in Variable.
Then, in the open script of risk, use the diag_id however you need to.
diag_id = vars["diag_id"];
This implies that placement of risk report elements are nested inside the diag repeating element so that diag.fetch will happen before each risk.open.
Related
Dear Anylogic Community,
I am struggling with finding the right approach for storing my simulation results. I have datasets created that keep track of every value I am interested in. They live in Main (see below)
My aim is to do a parameter variation experiment. In every run, I change the value for p_nDrones (see below)
After the experiment, I would like to store all the datasets in one excel sheet.
However, when I do the parameter variation experiment and afterwards check the log of the dataset (datasets_log), the changed values do not even show up (2 is the value I did set up in the normal simulation).
Now my question. Do I need to create another type of dataset if I want to track the values that are produced in the experiments? Why are they not stored after executing the experiment?
I really would appreciate if someone could share the best way to set up this export of experiment results. I would like to store the whole time series for every dataset.
Thank you!
Best option would be to write the outputs to some external file at the end of each model run.
If you want to use Excel, which I personally would not advise, even though it has a nice excelFile.writeDataSet() function, you can.
I would rather write the data to a text file as you will have much for control over the writing, the file itself, it is thread-safe, and useable in many many more platforms than Microsoft Excel.
See my example below:
Setup parameters in your model that you will write the data to at the end of the model of type TextFile. Here I used the model on destroy code to write out the data from the data sets.
Here you can immediately see the benefit of using the text file! You can add the number of drones we are simulating (or scenario name or any other parameter) in a column, whereas with Excel this would be a pain...
Now you can pass your specific text file to the model to use by adding it to the parameter variation page, providing it to the model through the parameters.
You will see that I also set up some headers for the text file in the Initial Experiment setup part, and then at the very end of the experiment, I close the text files in the After experiment section so that the text files can be used.
Here is the result if you simply right-click on the text files and open them in Excel. (Excel will always have a purpose, even if it is just to open text files ;-) )
I am trying to use parameter variation in AnyLogic. My inputs are 3 parameters, each varying 5 times. My output is water demand. What I need from parameter variation is the way in which demand changes according to the different combinations of the three parameters. I imagine something like: there are 10,950 rows (one for each day), the first column is time (in days), the second column are the values for the first combination, the second column is the second combination, and so on and so forth. What would be the best way to track this metadata to then be able to export it to excel? I have added a "dataset" to my main to track demand through each simulation, but I am not sure what to add to the parameter variation experiment interface to track the output across the different iterations. It would also be helpful to have a way to know which combination of inputs produced a given output (for example, have the combination be the name for each column). I see that there are Java Actions, but I haven't been able to figure out the code to do what I need. I appreciate any help with this matter.
The easiest approach is just to track this in output database tables which are then exported to Excel at the end of your run. As long as these tables include outputs from multiple runs (and are, for example, only cleared at the start of the experiment not the run), your Parameter Variation experiment will end up with an Excel file having outcomes from all the runs. (You will probably need to turn off parallel execution in the PV experiment so you don't run into issues trying to write to the same Excel file in parallel.)
So, for example, you might have tables:
run_details with columns id, parm1, parm2 and parm3 (with proper column names given your actual parameters and some unique ID generated for each run)
output_demand with columns run_id, sim_time_hrs and demand_value (if, say, you're storing some demand value each hour of simulated time) where run_id cross-references the run's ID in run_details
(There is extra complexity in how you could allocate a unique run ID and how and when you write to/clear those tables, but I'm just presenting the core design. You can also get round the need-serial-execution point by programmatically controlling when you export to Excel, rather than using the built-in "Export tables at the end of model execution" capability, but that's also more complicated.)
I built a custom CHAID tree in SPSS modeler. I would like to assign the particular terminal nodes to all of the records in the dataset. How would I go about doing this from within the software?
Assuming that you used the regular node called CHAID, if you select inside the diamond icon (created chaid model) in the tab configurations the rule identifyer, the output will add another variable called $RI-XXX that will classify all the records within the terminal nodes. Just check that option and then put a table node after that and all the records will be classified.
You just need to apply the algorithm to whatever data set you need, and you only need to inputs to be the same (type and eventually storage).
The diamond contains the algo and you can disconnect it and connects to whatever you want.
http://beyondthearc.com/blog/wp-content/uploads/2015/02/spss.png
using Talend Open Studio, I have a data-processing component, for which I'd appreciate your advice on how to make this possible (a) in a single component and (b) without a dirty workaround - thanks.
Relating part (a):
I have two different inputs:
One Input (with exactly one row) defines some kind of metadata for my processing.
One Input (with 1...n rows) defines the core data to process.
Currently, I solved this first requirement using two components and passing my metadata to the second component using the globalMap. But it would be nice, if I could integrate both connections into one component.
Relating part (b): After I have read all my input rows, I need to process them all at once. So far, so easy, I could use the end-section - my problem comes here: After that processing, I need to create a number of output-rows for a single output connection. Problem is, that Output-rows can only be created in the main-part and there I don't know when the last row was read...
Currently, I solved this counting the input-rows in advance and then, after that number is reached, I create that output. But this seems a really dirty workaround to me, so maybe someone has a solution for that, too?
Thank you for any useful tips!
I'm using Eclipse (Neon.3 Release (4.6.3)) with PyDev plugin for Python.
The code I'm debugging has a large number of variables, many of which are nested within other variables. I'd like to select a subset of these variables to be included in a separate view so I can bypass having to drill down into the variables at each step, which is often a tedious process.
The primary data structure being used is a pandas DataFrame containing numerous columns, and I typically need to see only a small portion of the values from a few of the DataFrame's columns.
For example, let's say I have a DataFrame 'df' with a column named 'X'. Whenever I debug this code I want to see the values of df.X between indices i and j (i.e. df.X[i:j+1]). i and j may change from time to time as they're also variables in the code but not in 'df'. So how can I create a streamlined tab/view of variables that only includes df.X._values[i:j+1], preferably separate from the standard Variables view?
Thanks in advance for any suggestions or feedback in general.
This can be done by using the 'Expressions' view within the Debug perspective.
For the example in the question above I can add the following expression to see only what I want:
list(df.X._values[i:j+1])