Maple 2018 Questions and Posts

These are Posts and Questions associated with the product, Maple 2018

Hello Friends

I have a critical problem that I wish to solve it with maple

suppose we have a list like following: y_obs=(2,4,8,7,9,52,35,478,52) and corresponding variance σy=(.2,.3,.5,.87,.1.2,.22,.78,.99,1.5)
we know y as the function of x described such as y_theoric=x+p and minimizing X is

X=Sigma [(y_theoric-y_obs)^2]/σy which includes the sum of nine numbers...

the question is:

How we can find p from likelihood function and plot general behavior of y versus of x through two above series?

for example this solution used in article under the names Hubble parameter data constraints on dark energy by Yun Chen and Bhatra Ratra (Physics Letters B)

Thank you


I mean 

 plots:-implicitplot(sqrt(b)*sqrt(1-4*p/b)-2*arctan(sqrt((9*p/b-22201/10000)/(9/4-9*p/b))) = 0, b = 0 .. 5,
 p = 0 .. 5, gridrefine = 2, rational);

I find the above result unsatisfactory.


     I have a very large expression I'm tring to average (by doing limit(1/L*int(<expr>,t=-L..L),L=infinty) ). However, given that this expression is so large (sum of 11k terms), I'm was looking for ways to speed up the calculation and use less RAM.

     I found that applying the lim@int seperately to each term of the sum helps. Also, since my integrand has various functions of other variables (eg. f(x), g(y), etc), Maple seems to go faster if I freeze those functions using frontend.

     However, the problem I'm running into is that, as I'm running through my for-loop to apply lim@int to each term, Maple starts running slower and slower, as well as taking more and more RAM. Additionally, it doesn't go to completion because it runs out of stack space.

     Is there an optimization trick to avoid this problem? I've tried garbage collecting after each iteration; that helps the RAM problem (at a cost of speed), but it still slows down over time.

      I've attached my code: MWE.maple. Fair warning: on a non-server computer, it is liable to run very slow.

Is it possible to convert E1(x) to Ei(x) explicitly?

In particular I have this expression which is real, but imaginary numbers appear due to the definition of Ei1 for negative arguments.

(-exp(j*z)*Ei(1, j*z)+I*Pi*exp(-j*z)+exp(-j*z)*Ei(1, -j*z))/(2*j)




Thank you

The procedure F computes the smallest prime factor of  ithprime(n)-2

F := n -> ifactors((ithprime(n)-2))[2][1][1]:

Strangely, print(F)  must be interrupted!
Warning,  computation interrupted

F := n -> ifactors((ithprime(n)-2))[2][1][1];

Warning,  computation interrupted
Warning,  computation interrupted

(F is defined, only the display fails).
This happens only for interface(typesetting=extended).
In Maple 2017 it was OK.


how I can remove this error.

Error, numeric exception: division by zero


      I was trying to apply some assumptions to a pdsolve command and noticed a strange error. Here's a minimal working example.


pdsolve(diff(f(t,x),t) = 0, {f(t,x)}, ivars = {x, t}) assuming x::real:


Error, (in simpl/relopsum) invalid terms in sum: diff(f(t,x),t) = 0

Is this indeed a bug, or is it expected behavior?

We have released an update to Maple, Maple 2018.1. This release provides enhancements to the mathematical computation engine, including physics and DEs.  It also provides substantial improvements to the command line version, easier access to group management tools in the MapleCloud, and a few other interface improvements.

This update is available through Tools>Check for Updates in Maple, and is also available from our website on the Maple 2018.1 download page, where you can also find more details.

please see maple attachment below.

btw, what i'm really trying to do is represent spin 1/2 states, along with the usual spin operators, and be able to take tensor products of the spin states. any hints on how to do that would be greatly appreciated. 

btw, the physics package is amazing! 




One of the frustrating things I am finding in using Maple debugger is that I can't set breakpoint using stopat() at some line number in some inner module, if the inner module is local module. It has to be an exported module.

Here is an example

   export main_entry;
   local foo1; #inner module. But if local, Can't set breakpoint from outside

     export main_entry;
     local foo2;

     foo2:=proc() #or breakpoint in first line here
        local s,x;
        s:=1;     #suppose I want to set break point here
     end proc;
     end proc;    
  end module;

  main_entry :=proc()
    foo1:-main_entry(); #call inner module's proc
  end proc;

end module;

Since there is inner module, local to outside module, I can't set break into the inner module procs anywhere.

Error, module does not export `foo1`

A workaround for now is to change "local" to "export" on the inner modules declarations I want to debug, so I can setbreak points inside them, then when done, make them local again. So changed "local foo1" to "export foo1" above, and now



This was also a little strange to me, since foo2() is a LOCAL proc to module foo1, yet Maple did not complain like it did when module foo1 was a LOCAL module to foo. I would have expected Maple to complain again for same reason.

So I am using the above workaround for now. But it is just a little annoying becuase I have to keep changing module from LOCAL to EXPORT in order to set breakpoints, then remember to make them local again.

Is there a better workaround?

A product suggestion: make stopat() ignore the local module setting and treat it as export for the purpose of setting a break point. This way one does not have to change the code just to set a breakpoint. Or if not to change current behavior, add a new option, as in

          stopat(........, option= ignore_local_setting)

So the default remain the same as now, but with the new option, it will set breakpoint anywhere, even in local modules.


Thank you



Hello, everyone! My name’s Sophie and I’m an intern at Maplesoft. @Samir Khan asked me to develop a couple of demonstration applications using the DeepLearning package - my work is featured on the Application Center

I thought I’d describe two critical commands used in the applications – DNNClassifier() and DNNRegressor().

The DNNClassifier calls tf.estimator.DNNClassifier from the Tensorflow Python API. This command builds a feedforward multilayer neural network that is trained with a set of labeled data in order to perform classification on similar, unlabeled data.

Dataset used for training and validating the classifier has the type DataFrame in Maple. In the Prediction of malignant/benign of breast mass example, the training set is a DataFrame with 32 columns in total, with column labels: “ID Number”, “Diagnosis”, “radius”, “texture”, etc. Note that labeling the columns of the dataset is mandatory, as later the neural network needs to identify which feature column corresponds to which list of values.

Feature columns are what come between the raw input data and the classifier model; they are required by Tensorflow to specify how the input data should be transformed before given to the model. Maple now supports three types of Feature Columns, including:

  • NumericColumn that represents real, numerical figure,
  • CategoricalColumn that denotes categorical(ordinal) data
  • BucketizedColumn that organizes continuous data into a discrete number buckets with specified boundaries.

In this application, the input data consists of 30 real, numeric values that represents physical traits of a cell nucleus computed from a digitized image of the breast mass. We create a list of NumericColumns by calling

fc := [seq(NumericColumn(u,shape=[1]), u in cols[3..])]:

where cols is a list of column labels and shape[1] indicates that each data input is just a single numeric value.

When we create a DNNClassifier, we need to specify the feature columns (input layer), the architecture of the neural network (hidden layers) and the number of classes (output layer). Recall that the DNNClassifier builds a feedforward multilayer neural network, hence when we call the function, we need to indicate how many hidden layers we want and how many nodes there should be on each of the layer. This is done by passing a list of non-negative integers as the parameter hidden_units when we call the function. In the example, we did:

classifier := DNNClassifier(fc, hidden_units=[20,40,20],num_classes=2):

where we set 3 hidden layer each with 20, 40, 20 nodes respectively. In addition, there are 30 input nodes (i.e. the number of feature columns) and 1 output node (i.e. binary classification). The diagram below illustrates a simpler example with an input layer with 3 nodes, 2 hidden layers with 7, 5 nodes and an output layer with 1 node.

(Created using NN-SVG by

After we built the model, we can train it by calling

classifier:-Train(train_data[3..32], train_data[2], steps = 256, num_epochs = 3, shuffle = true):

where we

  1. Give the training data (train_data[3..32]) and the corresponding labels (train_data[2]) to the model.
  2. Specified that the entire dataset will be passed to the model for three times and each iteration has 256 steps.
  3. Specified that data batches for training will be created by randomly shuffling the tensors.

Now the training process is complete, we can use the validation set to evaluate the effectiveness of our model.

classifier:-Evaluate(test_data[3..32],test_data[2], steps = 32);

The output indicates an accuracy of ~92.11% in this case. There are more indices like accuracy_basline, auc, average_loss that help us decide if we need to modify the architecture for better performance.

We then build a predictor function that takes an arbitrary set of measurements as a DataSeries and returns a prediction generated by the trained DNN classifier.

predictor := proc (ds) classifier:-Predict(Transpose(DataFrame(ds)), num_epochs = 1, shuffle = false)[1] end proc;

Now we can pass a DataSeries with 30 labeled rows to the predictor: (Recall the cols is a list of the column names)

ds := DataSeries([11.49, 14.59, 73.99, 404.9, 0.1046, 8.23E-02, 5.31E-02, 1.97E-02, 0.1779, 6.57E-02, 0.2034, 1.166, 1.567, 14.34, 4.96E-03, 2.11E-02, 4.16E-02, 8.04E-03, 1.84E-02, 3.61E-03, 12.4, 21.9, 82.04, 467.6, 0.1352, 0.201, 0.2596, 7.43E-02, 0.2941, 9.18E-02], labels = cols[3..]); 

The output indicates that the probability of this data being a class _id [0] is ~90.79%. In other words, according to our model, the probability of this breast mass cell being benign is ~90.79%.

The use of the DNNRegressor is very similar (almost identical) to that of the Classifier, the only significant difference is that while the Classifier predicts discrete labels as classes, the Regressor predicts a continuous qualitative result with the provided data (Note that CategoricalColumn is still applicable). For more details about the basic usage of the DNNRegressor, please refer to Predicting the burnt area of a forest fires with DNN Regressor.


From help it says

"For any variable used within a procedure without being explicitly
mentioned in a local localSequence; or global globalSequence; the
following rules are used to determine whether it is local or global:

The variable is searched for amongst the locals and globals (explicit or implicit)
in surrounding procedures
, starting with the innermost.  If the name is
encountered as a parameter, local variable, or global variable of such
a surrounding procedure, that is what it refers to."


So it seems if I do not use explicit "global" on a variable, Maple can figure
if it is global or not using the above rules. But when I use explicit "global"
on a variable, Maple did not seem to do the same thing. Here is an example

  local a, inner_proc;

  inner_proc := proc()
     local b;
     b   := ithprime(10);
     a   := b; #this assiged the global (to this proc)
               #variable, which is "a" correctly
  end proc;


end proc;

calling foo() gives 29.

But since the variable "a" in foo() is a global with respect to the inner_proc() (based on what the above help page seems to say), then why this does not work

  local a, inner_proc;

  inner_proc := proc()
     local b;
     global a;
     b   := ithprime(10);
     a   := b;                
  end proc;


end proc;

Now foo() returns "a". So Maple did not assign 29 to the global "a", (global to the inner_proc).

This for me makes little sense. Is global in Maple means the outermost scope only, skipping everything in between?

I thought global means any variable outside the proc itself. So if the proc() was inside another proc(), then the variables in the outer proc are global to the inner proc, even if they are declared local to the outer proc.

Why did Maple not do the assignment when I explicitly declare "a" to be global in the inner proc?



     I'm having trouble simplifying this square root:


I expect it to give just p but instead it returns the full, unsimplifed expression.

This appears as part of a larger expression, and if it fails to simplify, terms won't cancel and the expression is much longer than it needs to be.


Why does Maple dsolve give this strange error from dsolve? I do not see that the input is wrong

F := x * ( y(x) + x*sqrt(x*y(x)) + sqrt(x^3*y(x)) );
ode:= diff(y(x),x) = F;

I do not see where the input is invalid. I stared at it for 5 minutes.

Mathematica can solve this as follows

ode = y'[x] == x ( y[x] + x Sqrt[x y[x]] + Sqrt[x^3 y[x]]  )
DSolve[ode, y[x], x]

Did I type something wrong in Maple?

    2018, June 12, 1:40 hours, MapleCloud version: 60



I want z  to becomes abs(z) then later on, remove abs() and obtain z back in the same form it was.

I found that Maple changes z when I put it inside abs. Like this


So instead of 1-y^2, I end up now with  y^2-1. I want to keep the same expression I started with.

Mathematica does not do this:

expr = 1 - y^2

Is there a way or option to tell Maple not change the expression when I put it inside abs()?

This complicates what I am trying to do in Maple.

thank you

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